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

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/python/046_Fastq_PE_Filter_by_ID.py

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hkkenneth/lihs

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# Author: Kenneth Lui <hkkenneth@gmail.com> # Last Updated on: 20-02-2013 ## Usage: python ~/code/python/046_Fastq_PE_Filter_by_ID.py <ID LIST> <IN LIST OUTPUT FASTQ> <OUT LIST OUTPUT FASTQ> <INPUT FASTQ> <READ ID SEPARATER (DEFAULT SPACE)> ## This is PE in the sense that it tries to parse the paired ID!! ## ID in the list are only the "common part" (e.g. no /1 , /2) of the IDs ## Take a fastq file, can split the sequences into 2 files according to whether the id exists in the list ## for the in list output fastq file, the order is the same as the id list ## if the output fastq is not needed, use "-" import sys if len(sys.argv) < 4: raise SystemExit, 'use grep "##" ~/code/python/019f_Fastq_PE_Remove_Duplicates_With_Stat.py to get usage' def linesToFile(lines, f): for line in lines: f.write(line) read_id_sep = " " if len(sys.argv) > 5: read_id_sep = sys.argv[5] id_dict = {} id_list = [] for line in open(sys.argv[1], 'r'): id_list.append(line[:-1]) id_dict[line[:-1]] = [] if sys.argv[2] == "-": f1 = None else: f1 = open(sys.argv[2], 'w') if sys.argv[3] == "-": f2 = None else: f2 = open(sys.argv[3], 'w') f1in = open(sys.argv[4], 'r') f1lines = f1in.readlines() i = 0 while i < len(f1lines): id = f1lines[i][1:f1lines[i].find(read_id_sep)] if id in id_dict: if f1 is not None: id_dict[id] = f1lines[i:(i+4)] elif f2 is not None: linesToFile(f1lines[i:(i+4)], f2) i += 4 if f1 is not None: for id in id_list: if id in id_dict: linesToFile(id_dict[id], f1) if f1 is not None: f1.close() if f2 is not None: f2.close()

[ "hkkenneth@gmail.com" ]

hkkenneth@gmail.com

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/fengzhuang/page/dzh_page.py

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pivosxbmc/ALL_project

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#coding=utf-8 '下一个页面handle' import sys sys.path.append('D:\\jx\\fengzhuang') from base.find_element import FindElement import time class H_multi_Page(object): """H5界面""" def __init__(self, driver): self.elements = FindElement(driver,'Multi') def get_multi_H5_button_element(self): return self.elements.get_multi_element('H_home_click') def get_multi_home_msgbox_button_element(self): return self.elements.get_multi_element('H_home_msgbox_button')

[ "1094491399@qq.com" ]

1094491399@qq.com

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/backend/tstcr2020102701_dev_14091/settings.py

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crowdbotics-apps/tstcr2020102701-dev-14091

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2023-01-03T05:09:02.457778

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""" Django settings for tstcr2020102701_dev_14091 project. Generated by 'django-admin startproject' using Django 2.2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os import environ import logging env = environ.Env() # SECURITY WARNING: don't run with debug turned on in production! DEBUG = env.bool("DEBUG", default=False) # 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 = env.str("SECRET_KEY") ALLOWED_HOSTS = env.list("HOST", default=["*"]) SITE_ID = 1 SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https") SECURE_SSL_REDIRECT = env.bool("SECURE_REDIRECT", default=False) # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.sites' ] LOCAL_APPS = [ 'home', 'users.apps.UsersConfig', ] THIRD_PARTY_APPS = [ 'rest_framework', 'rest_framework.authtoken', 'rest_auth', 'rest_auth.registration', 'bootstrap4', 'allauth', 'allauth.account', 'allauth.socialaccount', 'allauth.socialaccount.providers.google', 'django_extensions', 'drf_yasg', # start fcm_django push notifications 'fcm_django', # end fcm_django push notifications ] INSTALLED_APPS += LOCAL_APPS + THIRD_PARTY_APPS MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'tstcr2020102701_dev_14091.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'tstcr2020102701_dev_14091.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } if env.str("DATABASE_URL", default=None): DATABASES = { 'default': env.db() } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' MIDDLEWARE += ['whitenoise.middleware.WhiteNoiseMiddleware'] AUTHENTICATION_BACKENDS = ( 'django.contrib.auth.backends.ModelBackend', 'allauth.account.auth_backends.AuthenticationBackend' ) STATIC_ROOT = os.path.join(BASE_DIR, "staticfiles") STATICFILES_DIRS = [ os.path.join(BASE_DIR, 'static') ] STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage' # allauth / users ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_AUTHENTICATION_METHOD = 'email' ACCOUNT_USERNAME_REQUIRED = False ACCOUNT_EMAIL_VERIFICATION = "optional" ACCOUNT_CONFIRM_EMAIL_ON_GET = True ACCOUNT_LOGIN_ON_EMAIL_CONFIRMATION = True ACCOUNT_UNIQUE_EMAIL = True LOGIN_REDIRECT_URL = "users:redirect" ACCOUNT_ADAPTER = "users.adapters.AccountAdapter" SOCIALACCOUNT_ADAPTER = "users.adapters.SocialAccountAdapter" ACCOUNT_ALLOW_REGISTRATION = env.bool("ACCOUNT_ALLOW_REGISTRATION", True) SOCIALACCOUNT_ALLOW_REGISTRATION = env.bool("SOCIALACCOUNT_ALLOW_REGISTRATION", True) REST_AUTH_SERIALIZERS = { # Replace password reset serializer to fix 500 error "PASSWORD_RESET_SERIALIZER": "home.api.v1.serializers.PasswordSerializer", } REST_AUTH_REGISTER_SERIALIZERS = { # Use custom serializer that has no username and matches web signup "REGISTER_SERIALIZER": "home.api.v1.serializers.SignupSerializer", } # Custom user model AUTH_USER_MODEL = "users.User" EMAIL_HOST = env.str("EMAIL_HOST", "smtp.sendgrid.net") EMAIL_HOST_USER = env.str("SENDGRID_USERNAME", "") EMAIL_HOST_PASSWORD = env.str("SENDGRID_PASSWORD", "") EMAIL_PORT = 587 EMAIL_USE_TLS = True # start fcm_django push notifications FCM_DJANGO_SETTINGS = { "FCM_SERVER_KEY": env.str("FCM_SERVER_KEY", "") } # end fcm_django push notifications # Swagger settings for api docs SWAGGER_SETTINGS = { "DEFAULT_INFO": f"{ROOT_URLCONF}.api_info", } if DEBUG or not (EMAIL_HOST_USER and EMAIL_HOST_PASSWORD): # output email to console instead of sending if not DEBUG: logging.warning("You should setup `SENDGRID_USERNAME` and `SENDGRID_PASSWORD` env vars to send emails.") EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend"

[ "team@crowdbotics.com" ]

team@crowdbotics.com

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/cloud/quantum/quantum/openstack/common/lockutils.py

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virt2x/folsomCloud

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2021-01-01T17:26:28.405651

2013-10-17T12:36:04

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# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright 2011 OpenStack LLC. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import errno import functools import os import shutil import tempfile import time import weakref from eventlet import semaphore from quantum.openstack.common import cfg from quantum.openstack.common import fileutils from quantum.openstack.common import log as logging LOG = logging.getLogger(__name__) util_opts = [ cfg.BoolOpt('disable_process_locking', default=False, help='Whether to disable inter-process locks'), cfg.StrOpt('lock_path', default=os.path.abspath(os.path.join(os.path.dirname(__file__), '../')), help='Directory to use for lock files') ] CONF = cfg.CONF CONF.register_opts(util_opts) class _InterProcessLock(object): """Lock implementation which allows multiple locks, working around issues like bugs.debian.org/cgi-bin/bugreport.cgi?bug=632857 and does not require any cleanup. Since the lock is always held on a file descriptor rather than outside of the process, the lock gets dropped automatically if the process crashes, even if __exit__ is not executed. There are no guarantees regarding usage by multiple green threads in a single process here. This lock works only between processes. Exclusive access between local threads should be achieved using the semaphores in the @synchronized decorator. Note these locks are released when the descriptor is closed, so it's not safe to close the file descriptor while another green thread holds the lock. Just opening and closing the lock file can break synchronisation, so lock files must be accessed only using this abstraction. """ def __init__(self, name): self.lockfile = None self.fname = name def __enter__(self): self.lockfile = open(self.fname, 'w') while True: try: # Using non-blocking locks since green threads are not # patched to deal with blocking locking calls. # Also upon reading the MSDN docs for locking(), it seems # to have a laughable 10 attempts "blocking" mechanism. self.trylock() return self except IOError, e: if e.errno in (errno.EACCES, errno.EAGAIN): # external locks synchronise things like iptables # updates - give it some time to prevent busy spinning time.sleep(0.01) else: raise def __exit__(self, exc_type, exc_val, exc_tb): try: self.unlock() self.lockfile.close() except IOError: LOG.exception(_("Could not release the acquired lock `%s`"), self.fname) def trylock(self): raise NotImplementedError() def unlock(self): raise NotImplementedError() class _WindowsLock(_InterProcessLock): def trylock(self): msvcrt.locking(self.lockfile, msvcrt.LK_NBLCK, 1) def unlock(self): msvcrt.locking(self.lockfile, msvcrt.LK_UNLCK, 1) class _PosixLock(_InterProcessLock): def trylock(self): fcntl.lockf(self.lockfile, fcntl.LOCK_EX | fcntl.LOCK_NB) def unlock(self): fcntl.lockf(self.lockfile, fcntl.LOCK_UN) if os.name == 'nt': import msvcrt InterProcessLock = _WindowsLock else: import fcntl InterProcessLock = _PosixLock _semaphores = weakref.WeakValueDictionary() def synchronized(name, lock_file_prefix, external=False, lock_path=None): """Synchronization decorator. Decorating a method like so:: @synchronized('mylock') def foo(self, *args): ... ensures that only one thread will execute the bar method at a time. Different methods can share the same lock:: @synchronized('mylock') def foo(self, *args): ... @synchronized('mylock') def bar(self, *args): ... This way only one of either foo or bar can be executing at a time. The lock_file_prefix argument is used to provide lock files on disk with a meaningful prefix. The prefix should end with a hyphen ('-') if specified. The external keyword argument denotes whether this lock should work across multiple processes. This means that if two different workers both run a a method decorated with @synchronized('mylock', external=True), only one of them will execute at a time. The lock_path keyword argument is used to specify a special location for external lock files to live. If nothing is set, then CONF.lock_path is used as a default. """ def wrap(f): @functools.wraps(f) def inner(*args, **kwargs): # NOTE(soren): If we ever go natively threaded, this will be racy. # See http://stackoverflow.com/questions/5390569/dyn # amically-allocating-and-destroying-mutexes sem = _semaphores.get(name, semaphore.Semaphore()) if name not in _semaphores: # this check is not racy - we're already holding ref locally # so GC won't remove the item and there was no IO switch # (only valid in greenthreads) _semaphores[name] = sem with sem: LOG.debug(_('Got semaphore "%(lock)s" for method ' '"%(method)s"...'), {'lock': name, 'method': f.__name__}) if external and not CONF.disable_process_locking: LOG.debug(_('Attempting to grab file lock "%(lock)s" for ' 'method "%(method)s"...'), {'lock': name, 'method': f.__name__}) cleanup_dir = False # We need a copy of lock_path because it is non-local local_lock_path = lock_path if not local_lock_path: local_lock_path = CONF.lock_path if not local_lock_path: cleanup_dir = True local_lock_path = tempfile.mkdtemp() if not os.path.exists(local_lock_path): cleanup_dir = True fileutils.ensure_tree(local_lock_path) # NOTE(mikal): the lock name cannot contain directory # separators safe_name = name.replace(os.sep, '_') lock_file_name = '%s%s' % (lock_file_prefix, safe_name) lock_file_path = os.path.join(local_lock_path, lock_file_name) try: lock = InterProcessLock(lock_file_path) with lock: LOG.debug(_('Got file lock "%(lock)s" at %(path)s ' 'for method "%(method)s"...'), {'lock': name, 'path': lock_file_path, 'method': f.__name__}) retval = f(*args, **kwargs) finally: # NOTE(vish): This removes the tempdir if we needed # to create one. This is used to cleanup # the locks left behind by unit tests. if cleanup_dir: shutil.rmtree(local_lock_path) else: retval = f(*args, **kwargs) return retval return inner return wrap

[ "quan.xu@intel.com" ]

quan.xu@intel.com

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/hq_extracter.py

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vc2309/Blue-sky-tools

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import pandas as pd ifiles=['sjs_all_feb_report_m.csv'] floors=['HQ - G/F' ,'HQ - 2S' ,'HQ - 2N' ,'HQ - 3S' ,'HQ - 3N' ,'HQ - 4N' ,'HQ - 5S' ,'HQ - 5N' ,'HQ - 6S' ,'HQ - 6N' ,'HQ - 7S' ,'HQ - 7N' ,'HQ - 8S' ,'HQ - 8N' ,'HQ - 9S' ,'HQ - 9N' ,'HQ - AC' ,'HQ - 11' ,'HQ - 12' ,'HQ - 13' ,'HQ - Lift' ,'HQ - 10'] def extract_hq(file): print("here") df=pd.read_csv(file) hq_df=pd.DataFrame() floor=[] for f in floors: floor.append(df[df['location']==f]) hq_df=pd.concat(floor) print(hq_df.head()) hq_df.to_csv('hq_jan.csv') def main(): for file in ifiles: extract_hq(file) if __name__=='__main__' : print("ok") main()

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easyopsapis/easyops-api-python

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# @generated by generate_proto_mypy_stubs.py. Do not edit! import sys from google.protobuf.descriptor import ( Descriptor as google___protobuf___descriptor___Descriptor, ) from google.protobuf.internal.containers import ( RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer, ) from google.protobuf.message import ( Message as google___protobuf___message___Message, ) from tuna_service_sdk.model.pipeline.build_status_pb2 import ( BuildStatus as tuna_service_sdk___model___pipeline___build_status_pb2___BuildStatus, ) from tuna_service_sdk.model.pipeline.git_meta_pb2 import ( GitMeta as tuna_service_sdk___model___pipeline___git_meta_pb2___GitMeta, ) from typing import ( Iterable as typing___Iterable, Optional as typing___Optional, Text as typing___Text, Union as typing___Union, ) from typing_extensions import ( Literal as typing_extensions___Literal, ) builtin___bool = bool builtin___bytes = bytes builtin___float = float builtin___int = int if sys.version_info < (3,): builtin___buffer = buffer builtin___unicode = unicode class Build(google___protobuf___message___Message): DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... class Artifact(google___protobuf___message___Message): DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... packageName = ... # type: typing___Text versionName = ... # type: typing___Text ctime = ... # type: typing___Text packageId = ... # type: typing___Text versionId = ... # type: typing___Text def __init__(self, *, packageName : typing___Optional[typing___Text] = None, versionName : typing___Optional[typing___Text] = None, ctime : typing___Optional[typing___Text] = None, packageId : typing___Optional[typing___Text] = None, versionId : typing___Optional[typing___Text] = None, ) -> None: ... if sys.version_info >= (3,): @classmethod def FromString(cls, s: builtin___bytes) -> Build.Artifact: ... else: @classmethod def FromString(cls, s: typing___Union[builtin___bytes, builtin___buffer, builtin___unicode]) -> Build.Artifact: ... def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ... def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ... def ClearField(self, field_name: typing_extensions___Literal[u"ctime",b"ctime",u"packageId",b"packageId",u"packageName",b"packageName",u"versionId",b"versionId",u"versionName",b"versionName"]) -> None: ... id = ... # type: typing___Text sender = ... # type: typing___Text created = ... # type: builtin___int yaml_string = ... # type: typing___Text number = ... # type: typing___Text events = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text] @property def git_meta(self) -> tuna_service_sdk___model___pipeline___git_meta_pb2___GitMeta: ... @property def artifact(self) -> Build.Artifact: ... @property def status(self) -> tuna_service_sdk___model___pipeline___build_status_pb2___BuildStatus: ... def __init__(self, *, id : typing___Optional[typing___Text] = None, git_meta : typing___Optional[tuna_service_sdk___model___pipeline___git_meta_pb2___GitMeta] = None, sender : typing___Optional[typing___Text] = None, artifact : typing___Optional[Build.Artifact] = None, created : typing___Optional[builtin___int] = None, yaml_string : typing___Optional[typing___Text] = None, status : typing___Optional[tuna_service_sdk___model___pipeline___build_status_pb2___BuildStatus] = None, number : typing___Optional[typing___Text] = None, events : typing___Optional[typing___Iterable[typing___Text]] = None, ) -> None: ... if sys.version_info >= (3,): @classmethod def FromString(cls, s: builtin___bytes) -> Build: ... else: @classmethod def FromString(cls, s: typing___Union[builtin___bytes, builtin___buffer, builtin___unicode]) -> Build: ... def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ... def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ... def HasField(self, field_name: typing_extensions___Literal[u"artifact",b"artifact",u"git_meta",b"git_meta",u"status",b"status"]) -> builtin___bool: ... def ClearField(self, field_name: typing_extensions___Literal[u"artifact",b"artifact",u"created",b"created",u"events",b"events",u"git_meta",b"git_meta",u"id",b"id",u"number",b"number",u"sender",b"sender",u"status",b"status",u"yaml_string",b"yaml_string"]) -> None: ...

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service@easyops.cn

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rsoorya/basic-banking-system

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2023-03-20T08:01:08.227325

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"""bankingsystem 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('banktransfers.urls')) ]

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sooryaramarao18@gmail.com

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mudassir-cm/djangoprojects

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2023-06-22T00:32:36.381689

2021-07-15T16:05:20

373,089,093

0

null

UTF-8

Python

false

889

py

from django.urls import path from myapp import views urlpatterns = [ path('dashboard/', views.dashboard, name='dashboard'), path('usersignup/', views.usersignup, name='usersignup'), path('userlogin/', views.userlogin, name='userlogin'), path('userlogout/', views.userlogout, name='userlogout'), path('addpatient/', views.addpatient, name='addpatient'), path('updatepatient/<int:id>/', views.updatepatient, name='updatepatient'), path('deletepatient/<int:id>/', views.deletepatient, name='deletepatient'), path('patienthistory/<int:id>/', views.patienthistory, name='patienthistory'), path('addpatienthistory/<int:id>/', views.addpatienthistory, name='addpatienthistory'), path('deletepatienthistory/<int:id>/', views.deletepatienthistory, name='deletepatienthistory'), path('updatepatienthistory/<int:id>', views.updatepatienthistory, name='updatepatienthistory'), ]

[ "mudasir2021@gmail.com" ]

mudasir2021@gmail.com

9ac476d6b34148a27108c3759f98e25aad93b6dd

5cd7b7ffc787587faa3bbd1dae6f2c9255961ca2

/graph_traversals.py

fbbfe6397fe31b98fefda607de0f28536d39cd28

[]

no_license

caroljunq/data-structure-exercises

af5d71c4ec07282f4dadcbf86e09e78ffa72eae6

b884b1c88220381dfa5c36d954f4761a35f990df

refs/heads/master

2020-03-08T01:38:31.973299

2019-04-02T12:26:08

127,836,434

0

null

UTF-8

Python

false

1,343

py

def BFS1(graph,start): queue = [start] visited = [start] while queue: v = queue.pop(0) print(v) for u in graph[v]: if u not in visited: visited.append(u) queue.append(u) def BFS_distance(graph,start): queue = [start] visited = [start] distances = {k: 0 for k in graph.keys()} while queue: v = queue.pop(0) for u,d in graph[v]: if u not in visited: visited.append(u) queue.append(u) distances[u] += distances[v] + d print(distances) def dfs(graph, start, visited): visited.append(start) for v in graph[start]: if v not in visited: dfs(graph,v,visited) return visited graph1 = { 'A': ['B','S'], 'B': ['A'], 'C': ['S','D','E','F'], 'D': ['C'], 'E': ['C','H'], 'F': ['C','G'], 'G': ['F','H','S'], 'H': ['E','G'], 'S':['A','C','G'] } # BFS1(graph1,'A') graph2 = { 'A': [('C',5),('B',5),('E',5)], 'B': [('A',5),('D',3)], 'C': [('A',5),('E',6),('D',4)], 'D': [('B',3),('C',4)], 'E': [('A',5),('C',6)], } graph3 = { 'A': [('B',3)], 'B': [('C',6),('D',1),('E',5)], 'C': [('E',6)], 'D': [('E',7)], 'E': [], } BFS_distance(graph3,'A') print(dfs(graph1,'A',[]))

[ "junqueiracarolina@live.com" ]

junqueiracarolina@live.com

8321df0e4633da445505ba2fe5a951ef62ef9419

00b8dff516dde0bb5b05fe82ee9bed20b80ce410

/PythonCode Meilenstein 6/venv/Lib/site-packages/tensorflow_core/_api/v2/compat/v1/ragged/__init__.py

8c32616f901edd904041b1ccb18863f89b4bc128

[]

no_license

georgerich/MyCo-Gruppe-3

2f6ef26f9eb7ff0745c045fc84f8300d31e51d03

fae4426f8b1e56c01906762686c6eb287073fc5b

refs/heads/master

2020-08-27T13:45:06.672132

2020-01-30T03:36:49

217,384,271

5

1

null

2020-01-29T23:18:03

2019-10-24T19:55:31

Python

UTF-8

Python

false

8,415

py

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """Ragged Tensors. This package defines ops for manipulating ragged tensors (`tf.RaggedTensor`), which are tensors with non-uniform shapes. In particular, each `RaggedTensor` has one or more *ragged dimensions*, which are dimensions whose slices may have different lengths. For example, the inner (column) dimension of `rt=[[3, 1, 4, 1], [], [5, 9, 2], [6], []]` is ragged, since the column slices (`rt[0, :]`, ..., `rt[4, :]`) have different lengths. For a more detailed description of ragged tensors, see the `tf.RaggedTensor` class documentation and the [Ragged Tensor Guide](/guide/ragged_tensors). ### Additional ops that support `RaggedTensor` Arguments that accept `RaggedTensor`s are marked in **bold**. * `tf.batch_gather`(**params**, **indices**, name=`None`) * `tf.bitwise.bitwise_and`(**x**, **y**, name=`None`) * `tf.bitwise.bitwise_or`(**x**, **y**, name=`None`) * `tf.bitwise.bitwise_xor`(**x**, **y**, name=`None`) * `tf.bitwise.invert`(**x**, name=`None`) * `tf.bitwise.left_shift`(**x**, **y**, name=`None`) * `tf.bitwise.right_shift`(**x**, **y**, name=`None`) * `tf.clip_by_value`(**t**, clip_value_min, clip_value_max, name=`None`) * `tf.concat`(**values**, axis, name=`'concat'`) * `tf.debugging.check_numerics`(**tensor**, message, name=`None`) * `tf.dtypes.cast`(**x**, dtype, name=`None`) * `tf.dtypes.complex`(**real**, **imag**, name=`None`) * `tf.dtypes.saturate_cast`(**value**, dtype, name=`None`) * `tf.dynamic_partition`(**data**, **partitions**, num_partitions, name=`None`) * `tf.expand_dims`(**input**, axis=`None`, name=`None`, dim=`None`) * `tf.gather_nd`(**params**, **indices**, name=`None`, batch_dims=`0`) * `tf.gather`(**params**, **indices**, validate_indices=`None`, name=`None`, axis=`None`, batch_dims=`0`) * `tf.identity`(**input**, name=`None`) * `tf.io.decode_base64`(**input**, name=`None`) * `tf.io.decode_compressed`(**bytes**, compression_type=`''`, name=`None`) * `tf.io.encode_base64`(**input**, pad=`False`, name=`None`) * `tf.math.abs`(**x**, name=`None`) * `tf.math.acos`(**x**, name=`None`) * `tf.math.acosh`(**x**, name=`None`) * `tf.math.add_n`(**inputs**, name=`None`) * `tf.math.add`(**x**, **y**, name=`None`) * `tf.math.angle`(**input**, name=`None`) * `tf.math.asin`(**x**, name=`None`) * `tf.math.asinh`(**x**, name=`None`) * `tf.math.atan2`(**y**, **x**, name=`None`) * `tf.math.atan`(**x**, name=`None`) * `tf.math.atanh`(**x**, name=`None`) * `tf.math.ceil`(**x**, name=`None`) * `tf.math.conj`(**x**, name=`None`) * `tf.math.cos`(**x**, name=`None`) * `tf.math.cosh`(**x**, name=`None`) * `tf.math.digamma`(**x**, name=`None`) * `tf.math.divide_no_nan`(**x**, **y**, name=`None`) * `tf.math.divide`(**x**, **y**, name=`None`) * `tf.math.equal`(**x**, **y**, name=`None`) * `tf.math.erf`(**x**, name=`None`) * `tf.math.erfc`(**x**, name=`None`) * `tf.math.exp`(**x**, name=`None`) * `tf.math.expm1`(**x**, name=`None`) * `tf.math.floor`(**x**, name=`None`) * `tf.math.floordiv`(**x**, **y**, name=`None`) * `tf.math.floormod`(**x**, **y**, name=`None`) * `tf.math.greater_equal`(**x**, **y**, name=`None`) * `tf.math.greater`(**x**, **y**, name=`None`) * `tf.math.imag`(**input**, name=`None`) * `tf.math.is_finite`(**x**, name=`None`) * `tf.math.is_inf`(**x**, name=`None`) * `tf.math.is_nan`(**x**, name=`None`) * `tf.math.less_equal`(**x**, **y**, name=`None`) * `tf.math.less`(**x**, **y**, name=`None`) * `tf.math.lgamma`(**x**, name=`None`) * `tf.math.log1p`(**x**, name=`None`) * `tf.math.log_sigmoid`(**x**, name=`None`) * `tf.math.log`(**x**, name=`None`) * `tf.math.logical_and`(**x**, **y**, name=`None`) * `tf.math.logical_not`(**x**, name=`None`) * `tf.math.logical_or`(**x**, **y**, name=`None`) * `tf.math.logical_xor`(**x**, **y**, name=`'LogicalXor'`) * `tf.math.maximum`(**x**, **y**, name=`None`) * `tf.math.minimum`(**x**, **y**, name=`None`) * `tf.math.multiply`(**x**, **y**, name=`None`) * `tf.math.negative`(**x**, name=`None`) * `tf.math.not_equal`(**x**, **y**, name=`None`) * `tf.math.pow`(**x**, **y**, name=`None`) * `tf.math.real`(**input**, name=`None`) * `tf.math.reciprocal`(**x**, name=`None`) * `tf.math.reduce_any`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_max`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_mean`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_min`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_prod`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_sum`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.rint`(**x**, name=`None`) * `tf.math.round`(**x**, name=`None`) * `tf.math.rsqrt`(**x**, name=`None`) * `tf.math.sign`(**x**, name=`None`) * `tf.math.sin`(**x**, name=`None`) * `tf.math.sinh`(**x**, name=`None`) * `tf.math.sqrt`(**x**, name=`None`) * `tf.math.square`(**x**, name=`None`) * `tf.math.squared_difference`(**x**, **y**, name=`None`) * `tf.math.subtract`(**x**, **y**, name=`None`) * `tf.math.tan`(**x**, name=`None`) * `tf.math.truediv`(**x**, **y**, name=`None`) * `tf.math.unsorted_segment_max`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.math.unsorted_segment_mean`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.math.unsorted_segment_min`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.math.unsorted_segment_prod`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.math.unsorted_segment_sqrt_n`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.math.unsorted_segment_sum`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.ones_like`(**tensor**, dtype=`None`, name=`None`, optimize=`True`) * `tf.rank`(**input**, name=`None`) * `tf.realdiv`(**x**, **y**, name=`None`) * `tf.reduce_all`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.size`(**input**, name=`None`, out_type=`tf.int32`) * `tf.squeeze`(**input**, axis=`None`, name=`None`, squeeze_dims=`None`) * `tf.stack`(**values**, axis=`0`, name=`'stack'`) * `tf.strings.as_string`(**input**, precision=`-1`, scientific=`False`, shortest=`False`, width=`-1`, fill=`''`, name=`None`) * `tf.strings.join`(**inputs**, separator=`''`, name=`None`) * `tf.strings.length`(**input**, name=`None`, unit=`'BYTE'`) * `tf.strings.reduce_join`(**inputs**, axis=`None`, keepdims=`False`, separator=`''`, name=`None`) * `tf.strings.regex_full_match`(**input**, pattern, name=`None`) * `tf.strings.regex_replace`(**input**, pattern, rewrite, replace_global=`True`, name=`None`) * `tf.strings.strip`(**input**, name=`None`) * `tf.strings.substr`(**input**, pos, len, name=`None`, unit=`'BYTE'`) * `tf.strings.to_hash_bucket_fast`(**input**, num_buckets, name=`None`) * `tf.strings.to_hash_bucket_strong`(**input**, num_buckets, key, name=`None`) * `tf.strings.to_hash_bucket`(**input**, num_buckets, name=`None`) * `tf.strings.to_hash_bucket`(**input**, num_buckets, name=`None`) * `tf.strings.to_number`(**input**, out_type=`tf.float32`, name=`None`) * `tf.strings.unicode_script`(**input**, name=`None`) * `tf.tile`(**input**, multiples, name=`None`) * `tf.truncatediv`(**x**, **y**, name=`None`) * `tf.truncatemod`(**x**, **y**, name=`None`) * `tf.where`(**condition**, **x**=`None`, **y**=`None`, name=`None`) * `tf.zeros_like`(**tensor**, dtype=`None`, name=`None`, optimize=`True`)n """ from __future__ import print_function as _print_function import sys as _sys from tensorflow.python.ops.ragged.ragged_array_ops import boolean_mask from tensorflow.python.ops.ragged.ragged_array_ops import stack_dynamic_partitions from tensorflow.python.ops.ragged.ragged_concat_ops import stack from tensorflow.python.ops.ragged.ragged_factory_ops import constant from tensorflow.python.ops.ragged.ragged_factory_ops import constant_value from tensorflow.python.ops.ragged.ragged_factory_ops import placeholder from tensorflow.python.ops.ragged.ragged_functional_ops import map_flat_values from tensorflow.python.ops.ragged.ragged_math_ops import range from tensorflow.python.ops.ragged.ragged_tensor_value import RaggedTensorValue from tensorflow.python.ops.ragged.segment_id_ops import row_splits_to_segment_ids from tensorflow.python.ops.ragged.segment_id_ops import segment_ids_to_row_splits del _print_function

[ "chzeit02@hs-esslingen.de" ]

chzeit02@hs-esslingen.de

709471af2874d6df8eb12fa07ee2cbbce98113aa

cba0d86ea6e8f21a2616a0ebcdf53a96449d3ccb

/python/codestat.py

ce8de83199feb2bf70eedc13625344a2e8fc2bbb

[]

no_license

zaopuppy/base

1bfb4b6bddc60c0138872f4a62ea0518aa4054df

6bf09bb02120a8edeb1160da0332d5465e6dd556

refs/heads/master

2020-04-15T09:58:47.744981

2015-01-17T08:20:15

8,814,879

0

null

UTF-8

Python

false

7,911

py

#!/usr/bin/env python # -*- coding: utf-8 -*- import sys import os import os.path from functools import reduce if sys.version_info.major != 3: raise Exception("Wrong python major version") class FileInfo(): def __init__(self): pass class TextStatisticHandler(): def __init__(self): self.last_byte = None self.line_no = 0 def handle(self, buf): self.last_byte = buf[-1] self.line_no = reduce(lambda x, _: x + 1, filter(lambda x: x == ord('\r'), buf), self.line_no) def end(self): if self.last_byte is None: return if self.last_byte not in b'\r\n': self.line_no += 1 def get(self): info = FileInfo() info.line_no = self.line_no return info def dump(self): return self.line_no class XmlStatisticHandler(): def __init__(self): self.last_byte = None self.line_no = 0 def handle(self, buf): self.last_byte = buf[-1] def end(self): if self.last_byte is None: return def get(self): info = FileInfo() info.line_no = self.line_no return info def dump(self): return self.line_no class PythonStatisticHandler(): def __init__(self): self.line_no = 0 self.begin_of_line = True self.ignore_to_end = False def handle(self, buf): for b in buf: if b == ord('#'): if self.begin_of_line: self.ignore_to_end = True self.begin_of_line = False elif b == ord('\n'): if not self.ignore_to_end and not self.begin_of_line: self.line_no += 1 self.ignore_to_end = False self.begin_of_line = True elif b in b' \r\t': # begin_of_line = False pass else: self.begin_of_line = False def end(self): if not self.begin_of_line and not self.ignore_to_end: self.line_no += 1 def get(self): info = FileInfo() info.line_no = self.line_no return info def dump(self): return self.line_no class CppStatisticHandler(): COMMENT_NONE = 0 # "//" COMMENT_LINE = 1 # "/" --+--> "//" # | # +--> "/*" COMMENT_PRE = 2 # "/* " COMMENT_BLOCK = 3 # "*" --> "*/" COMMENT_POST_BLOCK = 4 def __init__(self): self.line_no = 0 self.comment_type = self.COMMENT_NONE # for skipping blank line self.has_code = False def handle(self, buf): for b in buf: # print("type: {}, b: {}".format(self.comment_type, chr(b))) if self.comment_type == self.COMMENT_NONE: if b == ord('/'): self.comment_type = self.COMMENT_PRE elif b in b' \r\t': # ignore pass elif b == ord('\n'): if self.has_code: self.line_no += 1 self.has_code = False else: self.has_code = True elif self.comment_type == self.COMMENT_LINE: if b == ord('\n'): self.comment_type = self.COMMENT_NONE self.has_code = False elif self.comment_type == self.COMMENT_PRE: if b == ord('/'): self.comment_type = self.COMMENT_LINE elif b == ord('*'): self.comment_type = self.COMMENT_BLOCK else: if b == ord('\n'): self.line_no += 1 self.has_code = False else: self.has_code = True self.comment_type = self.COMMENT_NONE elif self.comment_type == self.COMMENT_BLOCK: if b == ord('*'): self.comment_type = self.COMMENT_POST_BLOCK elif b == ord('\n'): if self.has_code: self.line_no += 1 self.has_code = False elif self.comment_type == self.COMMENT_POST_BLOCK: if b == ord('/'): self.comment_type = self.COMMENT_NONE elif b == ord('\n'): self.has_code = False else: raise Exception("Unknown comment type, something was wrong, tell me: zhaoyi.zero@gmail.com") def end(self): if self.has_code: self.line_no += 1 def get(self): info = FileInfo() info.line_no = self.line_no return info def dump(self): return self.line_no def statistic_text(f): handler = TextStatisticHandler() with open(f, "rb") as fp: for buf in iter(lambda: fp.read(1024), b''): handler.handle(buf) handler.end() print("{}: {}".format(f, handler.dump())) return handler.get() def statistic_xml(f): handler = XmlStatisticHandler() with open(f, "rb") as fp: for buf in iter(lambda: fp.read(1024), b''): handler.handle(buf) handler.end() handler.end() print("{}: {}".format(f, handler.dump())) return handler.get() # doesn't support unicode file yet def statistic_python(f): handler = PythonStatisticHandler() with open(f, "rb") as fp: for buf in iter(lambda: fp.read(1024), b''): handler.handle(buf) handler.end() print("{}: {}".format(f, handler.dump())) return handler.get() def statistic_cpp(f): handler = CppStatisticHandler() with open(f, "rb") as fp: for buf in iter(lambda: fp.read(1024), b''): handler.handle(buf) handler.end() print("{}: {}".format(f, handler.dump())) return handler.get() STATISTIC_HANDLERS = { ".py": statistic_python, ".cc": statistic_cpp, ".java": statistic_cpp, ".txt": statistic_text, } def get_type_by_file_name(file_name): """ all in lower cause """ file_name = file_name.lower() idx = file_name.rfind(".") if idx >= 0: return file_name[idx:] else: return None def statistic_dir(d): for dir_path, dir_names, file_names in os.walk(d): for f in file_names: yield statistic_file(os.path.join(dir_path, f)) def statistic_file(f): file_names = os.path.basename(f) file_type = get_type_by_file_name(file_names) handler = STATISTIC_HANDLERS.get(file_type, None) if handler is None: print(type(f)) print("file [{}] (as type {}) doesn't support".format(f, file_type)) return None info = handler(f) info.type = file_type return info def statistic(f): stat_info = {} if os.path.isdir(f): for info in statistic_dir(f): if info is None: continue if info.type not in stat_info.keys(): stat_info[info.type] = 0 stat_info[info.type] += info.line_no else: stat_info["XX"] = statistic_file(f).line_no for item in stat_info.items(): print("{}: {}".format(item[0], item[1])) def main(): args = sys.argv[1:] file_list = args # file_list = filter(lambda x: not x.startswith("--"), args) # opt_list = filter(lambda x: x.startswith("--"), args) for f in file_list: statistic(f) if __name__ == "__main__": main()

[ "zhaoyi.zero@gmail.com" ]

zhaoyi.zero@gmail.com

0b3c1a3a47cc1a7f7fa584af3bf3fc47ba2f9311

8a8082e184835f051ba5369a028b33ef7841a4c9

/src/slacken/__init__.py

b0c75ad6836aa3dd5ece6746cc4ef667459cabd0

[ "MIT" ]

permissive

alistair-broomhead/slacken

f49d32830e2d92dc8f0c2a1c1eb86f7366f46406

d8c2a6ae35b2ae982e97fb1782e8a5a6340c5605

refs/heads/master

2021-03-12T21:42:15.696327

2013-05-30T16:38:05

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Python

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py

""" Tools for working with restful apis """ __author__ = 'Alistair Broomhead' from slacken.dicttypes import AttrDict from slacken.rest_access import RESTaccess from slacken.xml_accessor import XMLAccessor

[ "alistair.broomhead@mindcandy.com" ]

alistair.broomhead@mindcandy.com

d6975104cb7ecc5f7e92a88667057e945d1059b0

2016f22390c4d91d848a142805411085e90935fa

/core/forms.py

9a06ef4295d643b1215e10cf219f5ceff9a90ebd

[]

no_license

matheuslins/SearchSystem

c4de76fbca0e5833deff9f22b7f1e5d5fcb623fc

37f3c36ddbb078991dc1cd2d68051109c99ed520

refs/heads/master

2021-01-12T11:40:43.454847

2016-11-07T14:02:00

72,256,977

0

null

UTF-8

Python

false

253

py

from django import forms from .models import Box, BoxLog class BoxForm(forms.ModelForm): class Meta: model = Box fields = ['name', 'number', 'content'] class BoxLogForm(forms.ModelForm): class Meta: model = BoxLog fields = ['box']

[ "msl@cin.ufpe.br" ]

msl@cin.ufpe.br

e62326a6ccb2c56f1fd73a7e059c705feebf05ab

78632e8b5a9e2193ad313731d1adbf769fa989b3

/day4/puzzle.py

87d4e5d9e6c11e957d472684c4438e9fdb846726

[]

no_license

SudheerBabuGitHub/AOC2020

0a1677175b8bf89f6b2e9b2784074291bee4edbe

dfd40e9f4f3a04aba8f4f9fabb2b63676bdd2671

refs/heads/master

2023-02-09T05:13:20.727016

2020-12-25T02:25:18

318,072,695

0

null

UTF-8

Python

false

8,029

py

def get_byr (record): #byr (Birth Year) - four digits; at least 1920 and at most 2002. chararray = list(record) recordlen = len(record)-1 foundbyr = False byr = 0 for idx,c in enumerate(chararray): if idx==recordlen-3-4: break elif chararray[idx]+chararray[idx+1]+chararray[idx+2] == "byr": idx += 4 valid = True for i in range(4): if chararray[idx+i] < '0' or chararray[idx+i] > '9': valid = False break if not valid: break if chararray[idx+4] != ' ' and chararray[idx+4] != '\n': break byr = int(chararray[idx] + chararray[idx+1] + chararray[idx+2] + chararray[idx+3]) if byr >= 1920 and byr <= 2002: foundbyr = True break else: continue return foundbyr def get_iyr (record): #iyr (Issue Year) - four digits; at least 2010 and at most 2020. chararray = list(record) recordlen = len(record)-1 foundbyr = False iyr = 0 for idx,c in enumerate(chararray): if idx==recordlen-3-4: break elif chararray[idx]+chararray[idx+1]+chararray[idx+2] == "iyr": idx += 4 valid = True for i in range(4): if chararray[idx+i] < '0' or chararray[idx+i] > '9': valid = False break if not valid: break if chararray[idx+4] != ' ' and chararray[idx+4] != '\n': break iyr = int(chararray[idx] + chararray[idx+1] + chararray[idx+2] + chararray[idx+3]) if iyr >= 2010 and iyr <= 2020: foundbyr = True break else: continue return foundbyr def get_eyr (record): #eyr (Expiration Year) - four digits; at least 2020 and at most 2030. chararray = list(record) recordlen = len(record)-1 foundbyr = False eyr = 0 for idx,c in enumerate(chararray): if idx==recordlen-3-4: break elif chararray[idx]+chararray[idx+1]+chararray[idx+2] == "eyr": idx += 4 valid = True for i in range(4): if chararray[idx+i] < '0' or chararray[idx+i] > '9': valid = False break if not valid: break if chararray[idx+4] != ' ' and chararray[idx+4] != '\n': break eyr = int(chararray[idx] + chararray[idx+1] + chararray[idx+2] + chararray[idx+3]) if eyr >= 2020 and eyr <= 2030: foundbyr = True break else: continue return foundbyr def get_hgt (record): """" hgt (Height) - a number followed by either cm or in: If cm, the number must be at least 150 and at most 193. If in, the number must be at least 59 and at most 76. """ chararray = list(record) recordlen = len(record)-1 foundbyr = False unit = "" value = "" hgt = 0 for idx,c in enumerate(chararray): if idx==recordlen-3-4: break elif chararray[idx]+chararray[idx+1]+chararray[idx+2] == "hgt": idx += 4 i = 0 while chararray[idx+i] != ' ' and chararray[idx+i] != '\n': value += chararray[idx+i] i += 1 unit = chararray[idx+i-2]+chararray[idx+i-1] if unit != "cm" and unit != "in": break if chararray[idx+i-2] == ':': break hgt = int(value[0:-2]) if unit == "cm" and (hgt >= 150 and hgt<=193): foundbyr = True elif unit == "in" and (hgt >= 59 and hgt<=76): foundbyr = True break else: continue return foundbyr def get_hcl (record): #hcl(HairColor) - a # followed by exactly six characters 0-9 or a-f. chararray = list(record) recordlen = len(record)-1 foundbyr = False for idx,c in enumerate(chararray): if idx==recordlen-3-7: break elif chararray[idx]+chararray[idx+1]+chararray[idx+2] == "hcl": idx += 4 if chararray[idx] != '#': break idx += 1 valid = True for i in range(6): if chararray[idx + i] < '0' or chararray[idx + i] > 'f': valid = False break if chararray[idx + i] > '9' and chararray[idx + i] < 'a': valid = False break if not valid: break if chararray[idx+6] != ' ' and chararray[idx+6] != '\n': break foundbyr = True break else: continue return foundbyr def get_ecl (record): #ecl (Eye Color) - exactly one of: amb blu brn gry grn hzl oth. chararray = list(record) recordlen = len(record)-1 foundbyr = False ecl = "" colours = ["amb","blu","brn","gry","grn","hzl","oth"] for idx,c in enumerate(chararray): if idx==recordlen-3-3: break elif chararray[idx]+chararray[idx+1]+chararray[idx+2] == "ecl": idx += 4 ecl = chararray[idx] + chararray[idx + 1] + chararray[idx + 2] if chararray[idx+3] != ' ' and chararray[idx+3] != '\n': break for colour in colours: if colour == ecl: foundbyr = True break break else: continue return foundbyr def get_pid (record): #pid (Passport ID) - a nine-digit number, including leading zeroes. chararray = list(record) recordlen = len(record)-1 foundbyr = False for idx,c in enumerate(chararray): if idx==recordlen-3-9: break elif chararray[idx]+chararray[idx+1]+chararray[idx+2] == "pid": idx += 4 valid = True for i in range(9): if chararray[idx+i] <'0' or chararray[idx+i] > '9': valid = False break if not valid: break if chararray[idx+9] != ' ' and chararray[idx+9] != '\n': break foundbyr = True break else: continue return foundbyr def get_cid (record): #cid (Country ID) - ignored, missing or not. chararray = list(record) recordlen = len(record)-1 foundbyr = False for idx,c in enumerate(chararray): if idx==recordlen-3: break elif chararray[idx]+chararray[idx+1]+chararray[idx+2] == "cid": foundbyr = True break else: continue return foundbyr file = open("input.txt","r") lines = file.readlines() validcnt = 0 record = "" for line in lines: if line != "\n": record += line continue else: if get_byr(record) == False: #print("missing byr") record = "" continue if get_iyr(record) == False: #print("missing iyr") record = "" continue if get_eyr(record) == False: #print("missing eyr") record = "" continue if get_hgt(record) == False: #print("missing hgt") record = "" continue if get_hcl(record) == False: #print("missing hcl") record = "" continue if get_ecl(record) == False: #print("missing ecl") record = "" continue if get_pid(record) == False: #print("missing pid") record = "" continue #get_cid(record) record = "" validcnt += 1 print(validcnt)

[ "75408000+SudheerBabuGitHub@users.noreply.github.com" ]

75408000+SudheerBabuGitHub@users.noreply.github.com

04f5d0325e7a0300279d870a928bcd03205e6b62

bcf84ea70de9f49bb8506a34af3fcafa35421f4a

/wlb_app_dev.py

1997776e7c825b02ddae4b40bc3d4211c8bb6868

[]

no_license

awoltman/App_Dev

44b2c46e85869471a59641a13e9815b09819ebc3

710ca49188c42fdd7920fd96d9fd44a0b304fc07

refs/heads/master

2020-06-10T21:29:47.103560

2019-07-01T19:31:48

193,755,173

0

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UTF-8

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4,263

py

from pymodbus.client.sync import ModbusTcpClient as ModbusClient import matplotlib.pyplot as plt import xlwt from datetime import datetime import sqlite3 import time #def init(): conn = sqlite3.connect('test_data.db') client = ModbusClient(mesthod = 'tcp', host = '10.81.7.195', port = 8899) UNIT = 0x01 c = conn.cursor() time_temp = () ''' ## Initialized the database(.db) file ## c.execute("CREATE TABLE TEMPS ('Time', 'T1', 'T2', 'T3', 'T4')") c.execute("CREATE TABLE FREEZE_TIMES ('Time', 'Freeze_Time_1', 'Freeze_Time 2', 'Freeze Time 3', 'Freeze Time 4', 'Freeze Time 5', 'Freeze Time 6', 'Freeze Time 7','Freeze Time 8', 'Freeze Time 9', 'Freeze Time 10',\ 'Freeze Time 11', 'Freeze Time 12', 'Freeze Time 13', 'Freeze Time 14', 'Freeze Time 15', 'Freeze Time 16', 'Freeze Time 17','Freeze Time 18', 'Freeze Time 19', 'Freeze Time 20')") ''' ''' ## Setting up styles for Excel ## style0 = xlwt.easyxf('font: name Times New Roman, color-index red, bold on',num_format_str='#,##0.00') style1 = xlwt.easyxf(num_format_str='D-MMM-YY') wb = xlwt.Workbook() ws = wb.add_sheet('Tempurature Data') ws.write(0, 1, 'T1', style0) ws.write(0, 2, 'T2', style0) ws.write(0, 3, 'T3', style0) ws.write(0, 4, 'T4', style0) ws.write(0, 4, 'Time', style0) ''' i = 0 def record_temps(): try: while True: #named_tuple = time.localtime() # get struct_time time_string = time.strftime("%m/%d/%Y %H:%M.%S") Freezetime_temp = client.read_holding_registers(574,20,unit = UNIT) f_one = Freezetime_temp.registers[0] f_two = Freezetime_temp.registers[1] f_three = Freezetime_temp.registers[2] f_four = Freezetime_temp.registers[3] f_five = Freezetime_temp.registers[4] f_six = Freezetime_temp.registers[5] f_seven = Freezetime_temp.registers[6] f_eight = Freezetime_temp.registers[7] f_nine = Freezetime_temp.registers[8] f_ten = Freezetime_temp.registers[9] f_eleven = Freezetime_temp.registers[10] f_twelve = Freezetime_temp.registers[11] f_thirteen = Freezetime_temp.registers[12] f_fourteen = Freezetime_temp.registers[13] f_fifteen = Freezetime_temp.registers[14] f_sixteen = Freezetime_temp.registers[15] f_seventeen = Freezetime_temp.registers[16] f_eighteen = Freezetime_temp.registers[17] f_nineteen = Freezetime_temp.registers[18] f_twenty = Freezetime_temp.registers[19] time_temp = [time_string,f_one,f_two,f_three,f_four,f_five,f_six,f_seven,f_eight,f_nine,f_ten,f_eleven,f_twelve,f_thirteen,f_fourteen,f_fifteen,f_sixteen,f_seventeen,f_eighteen,f_nineteen,f_twenty] c.execute("INSERT INTO FREEZE_TIMES values (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)", time_temp) Temps_store = client.read_holding_registers(6,4,unit =UNIT) temp_temp = (time_string, Temps_store.registers[0],Temps_store.registers[1],Temps_store.registers[2],Temps_store.registers[3]) c.execute("INSERT INTO TEMPS values (?,?,?,?,?)", temp_temp) conn.commit() ''' ##This section is for writing to Excel## ws.write(ex, 0, time_string, style1) ws.write(ex, 1, Temps_temp.registers[0], style0) ws.write(ex, 2, Temps_temp.registers[1], style0) ws.write(ex, 3, Temps_temp.registers[2], style0) ws.write(ex, 4, Temps_temp.registers[3], style0) ''' except KeyboardInterrupt: ''' ## used to save EXCEL file once done collecting data ## wb.save('temp.xls') ''' conn.close() select() def reset_default(): client.write_registers(451,1,unit =UNIT) pass def select(): print('C for collect') print('D for done') print('R for reset defaults') g = input('Enter what you would like to do:') if(g == 'C'): record_temps() elif(g == 'D'): client.close() elif(g == 'R'): reset_default() else: select() def login(): pass def new_user(): pass def fogot_id(): pass def diplay_rt(): pass select()

[ "awoltman@hawk.iit.edu" ]

awoltman@hawk.iit.edu

52d086cf4cdc8386facda306ca5a7b87da497e67

dfdceb3a1893cc52c33dc31c69170a6a60840d7d

/logAnalysis.py

2e7bff57a89bb2049b60ce599d0fd18b70c55083

[]

no_license

psk84/Python-Practice

6bb8aa10da8186610cf83f040f3c72d002424a4a

e5f66d7998109759f96a59d5afab66059fb3adec

refs/heads/master

2021-01-23T03:28:32.301392

2015-01-19T09:48:24

29,351,897

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UTF-8

Python

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929

py

import sys import pymongo # file insert #f = open("C:/Dev/test.txt", 'w') #for i in range(1, 11): # data = "[%d send]success.\n" % i # f.write(data) #for j in range(12, 23): # data = "[%d send]fail.\n" % j # f.write(data) #f.close() success_count = 0; fail_count = 0; f = open("C:/Dev/test.txt", 'r') while 1: line = f.readline() if not line: break print(line) if "success" in line: success_count = success_count + 1 else : fail_count = fail_count + 1 f.close() print("success=%d" % success_count) print("fail=%d" % fail_count) # mongo db insert connection = pymongo.MongoClient("mongodb://localhost") db = connection.terrydb users = db.users # db data delete users.remove() doc = {'_id':'myid', 'successCount': success_count,'failCount': fail_count} try: users.insert(doc) except: print("fail")

[ "seungkyupark84@gmail.com" ]

seungkyupark84@gmail.com

6fead26c5691ec527b0a25f5b1bb51407b45423b

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p03433/s234156745.py

8af8f4093c0e131eec273745a1b4cdfd8539bffb

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

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UTF-8

Python

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py

n = int(input().rstrip()) a = int(input().rstrip()) if n % 500 <= a: print('Yes') else: print('No')

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

3ef5e4152c3be2e2571bcf1fd3c78b7e282b5057

bf874dd4c151a097616cb48710862d352f82b8ee

/learning_logs/migrations/0002_entry.py

bfe7f6179815e9fa1f1f9d7def65014e759d771b

[]

no_license

whjwhjwhj/PythonLearningOffice

0fd2763b17ab8bd35640d794916c566774ce31c2

a828a621c7da468507db0185801f92b22e85a531

refs/heads/master

2020-04-03T13:55:53.547106

2018-11-26T03:10:34

155,303,061

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UTF-8

Python

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py

# Generated by Django 2.1.2 on 2018-10-29 06:18 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('learning_logs', '0001_initial'), ] operations = [ migrations.CreateModel( name='Entry', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('text', models.TextField()), ('data_added', models.DateTimeField(auto_now_add=True)), ('topic', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='learning_logs.Topic')), ], options={ 'verbose_name_plural': 'entries', }, ), ]

[ "wei_hj@aliyun.com" ]

wei_hj@aliyun.com

87416760e8d527e89eda7274e938fa35d0f5862c

ec551303265c269bf1855fe1a30fdffe9bc894b6

/topic12_backtrack/T37_solveSudoku/interview.py

aa39e66a9273588c348549634ece2fa51180ca9a

[]

no_license

GongFuXiong/leetcode

27dbda7a5ced630ae2ae65e19d418ebbc65ae167

f831fd9603592ae5bee3679924f962a3ebce381c

refs/heads/master

2023-06-25T01:05:45.683510

2021-07-26T10:05:25

null

0

null

UTF-8

Python

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2,380

py

''' 37. 解数独编写一个程序，通过已填充的空格来解决数独问题。一个数独的解法需遵循如下规则：数字 1-9 在每一行只能出现一次。数字 1-9 在每一列只能出现一次。数字 1-9 在每一个以粗实线分隔的 3x3 宫内只能出现一次。空白格用 '.' 表示。 ''' class Solution: def solveSudoku(self, board): """ Do not return anything, modify board in-place instead. """ # 把所有没填数字的位置找到 all_points = [] for i in range(9): for j in range(9): if board[i][j] == ".": all_points.append([i, j]) # check函数是为了检查是否在point位置k是合适的 def check(point, k): row_i = point[0] col_j = point[1] for i in range(9): # 检查行 if i != row_i and board[i][col_j] == k: return False # 检查列 if i != col_j and board[row_i][i] == k: return False # 检查块 for i in range(row_i//3*3 , row_i//3*3+3): for j in range(col_j//3*3, col_j//3*3+3): if i != row_i and j != col_j and board[i][j] == k: return False return True def backtrack(i): # 回溯终止条件 if i == len(all_points): return True for j in range(1, 10): # 检查是否合适 if check(all_points[i],str(j)): # 合适就把位置改过来 board[all_points[i][0]][all_points[i][1]] = str(j) if backtrack(i+1): # 回溯下一个点 return True board[all_points[i][0]][all_points[i][1]] = "."# 不成功把原来改回来 return False backtrack(0) print(f"board:{board}") if __name__ == "__main__": solution = Solution() while 1: str1 = input() if str1 != "": nums = [[c for c in s.split(",")] for s in str1.split(";")] print(f"nums:{nums}") res = solution.permute(nums) print(res) else: break

[ "958747457@qq.com" ]

958747457@qq.com

96e741b71f0fdae9ef6c8ea3f4c3df4f559e42b5

09deb7c2156929e8a65dca55d2142ced87f55bb0

/Exam5.py

fe9d27fc80599987e3b72355971118978307332f

[]

no_license

nyirweb/python-four

9495218a01f5ea8e7a3aba7617a2b61db262f2af

185172d57760a3ce9f4ef56da626f8269d1d3e09

refs/heads/master

2020-04-16T11:07:47.173221

2019-01-13T15:58:35

165,524,610

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null

UTF-8

Python

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750

py

# Kérj be egy egész óra értéket. Ha a szám nem 0 és 24 óra között van, # akkor adjon hibaüzenetet, egyébként köszönjön el a program a napszaknak megfelelően! # 4-9: Jó reggelt!, 10-17: Jó napot!, 18-21: Jó estét!, 22-3: Jó éjszakát! ido = int(input("Add meg az időt!")) elkoszones = ["Jó reggelt!","Jó napot!","Jó estét!","Jó éjszakát!"] if(ido >= 0 and ido <=24): if(ido>=4 and ido<=9): print(elkoszones[0]) if(ido>=10 and ido<=17): print(elkoszones[1]) if(ido>=18 and ido<=21): print(elkoszones[2]) if(ido>=22): print(elkoszones[3]) if(ido<=3): print(elkoszones[3]) else: print("Sajnos nem egész óra értéket adtál meg!")

[ "noreply@github.com" ]

nyirweb.noreply@github.com

8d8d34714355ffbcb9dc8397fb7b9605238dd8de

bc1c69344fe601eec08a9f411205b436da84a7cf

/1_python/0730/project/problem_d.py

ffa8f7075dfafb0466982031c30c50dbabdc2174

[]

no_license

mooncs/TIL

774f01f1ccb2b827060fa0fd8d574bfb560b5443

0919d8c894fe8bf9644d72db6a0e474191c9d719

refs/heads/main

2023-08-15T07:35:05.055894

2021-09-16T05:34:51

386,134,362

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UTF-8

Python

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1,509

py

import requests from tmdb import TMDBHelper from pprint import pprint def recommendation(title): """ 제목에 해당하는 영화가 있으면 해당 영화의 id를 기반으로 추천 영화 목록을 출력. 추천 영화가 없을 경우 [] 출력. 영화 id검색에 실패할 경우 None 출력. """ tmdb_helper = TMDBHelper('83b326269660ac3171fddfc110d21cc7') # 추천작을 검색하는 url을 완성하기 위해서는 영화의 id가 필요하다. # get_movie_id를 이용하여 영화의 id를 우선적으로 가져온다. movie_title = tmdb_helper.get_movie_id(title) # 영화의 id가 None이면 None을 반환 if movie_title == None: return movie_title # 영화의 id가 있으면 get_request_url와 f-string을 활용하여 url을 가져온다. else: url = tmdb_helper.get_request_url(f'/movie/{movie_title}/recommendations', language='ko') data = requests.get(url) reco_data = data.json() reco_lst = reco_data.get('results') # 추천작의 영화 제목을 담을 빈리스트를 생성하고, 반복문을 통해 추천작의 정보를 모아서 반환한다. reco_title = [] for i in range( len(reco_lst) ): reco_title.append( reco_lst[i].get('title') ) return reco_title if __name__ == '__main__': pprint(recommendation('기생충')) pprint(recommendation('그래비티')) pprint(recommendation('검색할 수 없는 영화'))

[ "csmoong@naver.com" ]

csmoong@naver.com

283c023713ac72b6528ac5f38534be1c28255c27

34f425d7e511fa19220f77f0dbb6c6585451ab14

/bin/generate_rules.py

3acaeb921652be97e7639346b7a51772c5707f2b

[ "CC-BY-4.0" ]

permissive

stamhe/YousList

409d2703005a0152d73af2cf0418f2ebb4d17d7e

0fbc3cf6db069ea0162babd8a3aed05bf8d0e9be

refs/heads/master

2021-08-23T03:04:09.268088

2017-12-02T17:54:58

2017-12-02T20:01:04

null

0

null

UTF-8

Python

false

6,808

py

#!/usr/bin/env python import os import sys import fileinput import re import uuid import six if sys.version_info[:2] >= (2, 7): import json from collections import OrderedDict else: import simplejson as json from ordereddict import OrderedDict pwd = os.path.dirname(os.path.abspath(__file__)) root = os.path.dirname(pwd) class FilterParser: # For scheme, see Appendix A of http://www.ietf.org/rfc/rfc2396.txt DOMAIN_PREFIX = '^[a-z0-9+_.]+:/+(?:[^/]+\\.)?' def __init__(self, name='Generated Package', basepkg=None): self.pkg = OrderedDict() self.id_dict = {} self.rules = [] if basepkg: try: f = open(basepkg) obj = json.load(f, object_pairs_hook=OrderedDict) orig_pkg = obj[0] self.pkg['id'] = orig_pkg['id'] self.pkg['name'] = orig_pkg['name'] for rule in orig_pkg['rules']: self.id_dict[rule['name']] = rule['id'] finally: f.close() if 'id' not in self.pkg: self.pkg['id'] = str(uuid.uuid4()) if 'name' not in self.pkg: self.pkg['name'] = name def parse(self): for line in fileinput.input(): self._parse_rule(line) self.pkg['rules'] = self.rules if six.PY2: sys.stdout.write( json.dumps([self.pkg], ensure_ascii=False, indent=4, separators=(',', ': ')) \ .encode('utf-8')) else: sys.stdout.write( json.dumps([self.pkg], ensure_ascii=False, indent=4, separators=(',', ': '))) def _parse_rule(self, line): if six.PY2: line = line.strip().decode('utf-8') else: line = line.strip() if not line or line.startswith('!') or re.match('\[Adblock.*\]', line): return if '##' in line: # Element hiding rule self._parse_hiding_rule(line) elif line.startswith('#@#'): sys.stderr.write('Skipping this rule: ' + line + '\n') elif '#@#' in line: # Element hiding exception rule raise Exception('Cannot handle this rule: ' + line) else: # Blocking rule self._parse_blocking_rule(line) def _parse_hiding_rule(self, line): rule = OrderedDict() name = line if name in self.id_dict: rule['id'] = self.id_dict[name] else: rule['id'] = str(uuid.uuid4()) rule['name'] = name urls, css = line.split('##', 2) if ',' in urls: url_list = urls.split(',') for url in url_list: self._parse_hiding_rule(url + '##' + css) return url = urls trigger = OrderedDict() if url: trigger['url-filter'] = self.DOMAIN_PREFIX + url.replace('.', '\\.') else: trigger['url-filter'] = '.*' action = OrderedDict() action['type'] = 'css-display-none' action['selector'] = css content = OrderedDict() content['trigger'] = trigger content['action'] = action rule['content'] = content self.rules.append(rule) def _parse_blocking_rule(self, line): rule = OrderedDict() splits = line.split('$', 2) if len(splits) < 2: splits.append('') url, options = splits name = url.lstrip('||').rstrip('^') url = url.rstrip('^').strip('*') if options: name += '$' + options if name in self.id_dict: rule['id'] = self.id_dict[name] else: rule['id'] = str(uuid.uuid4()) rule['name'] = name trigger = {} # * Adblock Plus' filterToRegExp: # https://github.com/adblockplus/adblockpluscore/blob/master/lib/common.js # * uBlock Origin's strToRegex: # https://github.com/gorhill/uBlock/blob/master/src/js/static-net-filtering.js url_regex = url for search, replace in [[r'\*+', '*'], [r'\^\|$', '^'], [r'[.+?${}()|[\]\\]', r'\\\g<0>'], ['\*', '.*'], [r'^\\\|\\\|', self.DOMAIN_PREFIX], [r'^\\\|', '^'], [r'\\\|$', '$']]: url_regex = re.sub(search, replace, url_regex) trigger['url-filter'] = url_regex opt_dict = self._parse_options(options) trigger.update(opt_dict) trigger_ordered_keys = ['url-filter', 'resource-type', 'load-type', 'if-domain', 'unless-domain'] trigger_ordered_dict = OrderedDict() for key in trigger_ordered_keys: if key in trigger: trigger_ordered_dict[key] = trigger[key] action = OrderedDict() action['type'] = 'block' content = OrderedDict() content['trigger'] = trigger_ordered_dict content['action'] = action rule['content'] = content self.rules.append(rule) def _parse_options(self, options): opt_dict = {} if options: options = options.split(',') else: options = [] for option in options: splits = option.split('=', 2) if len(splits) < 2: splits.append('') opt_key, opt_val = splits if opt_key == 'domain': domains = opt_val.split('|') if_domain = [] unless_domain = [] for domain in domains: if domain.startswith('~'): unless_domain.append(domain.lstrip('~')) else: if_domain.append(domain) if len(if_domain) and len(unless_domain): raise Exception('Cannot handle these domains: ' + opt_val) elif len(if_domain): opt_dict['if-domain'] = if_domain elif len(unless_domain): opt_dict['unless-domain'] = unless_domain elif opt_key == 'script': opt_dict['resource-type'] = ['script'] elif opt_key == 'third-party': opt_dict['load-type'] = ['third-party'] else: raise Exception('Cannot handle this option: ' + opt_key) return opt_dict orig_pkg = os.path.join(root, 'Rules.1blockpkg') parser = FilterParser(basepkg=orig_pkg) parser.parse()

[ "yousbe@gmail.com" ]

yousbe@gmail.com

fc62026ad385c261dc340d5914e1490389de7b69

16abd82b9523f0fc7ae6df0aac11fd03e2e3d9f3

/boards/tests/test_views.py

c6631a2dcbefbde8dc9659cd11ccf5750f89b5e0

[]

no_license

msm3858/projektforum

cf5255a5781f3536db56cf1b680557ca876f8221

c6a0abda9f147d3578e430012780bda3eb4f20b5

refs/heads/master

2021-09-10T10:03:32.962523

2018-03-24T06:26:18

124,791,248

0

null

UTF-8

Python

false

5,560

py

from django.test import TestCase from django.urls import reverse, resolve from ..views import home, board_topics, new_topic from ..models import Board, Topic, Post, User from ..forms import NewTopicForm # Create your tests here. ######################### # TEST HOME ######################### class HomeTests(TestCase): def setUp(self): self.board = Board.objects.create( name='Django', description='Django board.') url = reverse('boards:home') self.response = self.client.get(url) def test_home_view_status_code(self): self.assertEquals(self.response.status_code, 200) def test_home_url_resolves_home_view(self): view = resolve('/') self.assertEquals(view.func, home) def test_home_view_contains_link_to_topics_page(self): board_topics_url = reverse( 'boards:board_topics', kwargs={'pk': self.board.pk}) self.assertContains( self.response, 'href="{0}"'.format(board_topics_url)) ######################### # TEST BOARD ######################### class BoardTopicsTests(TestCase): def setUp(self): Board.objects.create( name='Django', description='Django board.') def test_board_topics_view_success_status_code(self): url = reverse('boards:board_topics', kwargs={'pk': 1}) response = self.client.get(url) self.assertEquals(response.status_code, 200) def test_board_topics_view_not_found_status_code(self): url = reverse('boards:board_topics', kwargs={'pk': 99}) response = self.client.get(url) self.assertEquals(response.status_code, 404) def test_board_topics_url_resolves_board_topics_view(self): view = resolve('/boards/1/') self.assertEquals(view.func, board_topics) def test_board_topics_view_contains_link_back_to_homepage(self): board_topics_url = reverse('boards:board_topics', kwargs={'pk': 1}) response = self.client.get(board_topics_url) homepage_url = reverse('boards:home') self.assertContains(response, 'href="{0}"'.format(homepage_url)) def test_board_topics_view_contains_navigation_links(self): board_topics_url = reverse('boards:board_topics', kwargs={'pk': 1}) homepage_url = reverse('boards:home') new_topic_url = reverse('boards:new_topic', kwargs={'pk': 1}) response = self.client.get(board_topics_url) self.assertContains(response, 'href="{0}"'.format(homepage_url)) self.assertContains(response, 'href="{0}"'.format(new_topic_url)) ######################### # TEST NEW TOPIC ######################### class NewTopicTests(TestCase): def setUp(self): Board.objects.create(name='Django', description='Django board.') User.objects.create_user( username='marcin', email='msm@msm.com', password='123') def test_new_topic_view_success_status_code(self): url = reverse('boards:new_topic', kwargs={'pk': 1}) response = self.client.get(url) self.assertEquals(response.status_code, 200) def test_new_topic_view_not_fount_status_code(self): url = reverse('boards:new_topic', kwargs={'pk': 99}) response = self.client.get(url) self.assertEquals(response.status_code, 404) def test_new_topic_view_reselves_board_topics_view(self): view = resolve('/boards/1/new/') self.assertEquals(view.func, new_topic) def test_new_topic_view_contains_link_back_to_board_topics_view(self): new_topic_url = reverse('boards:new_topic', kwargs={'pk': 1}) board_topics_url = reverse('boards:board_topics', kwargs={'pk': 1}) response = self.client.get(new_topic_url) self.assertContains(response, 'href="{0}"'.format(board_topics_url)) def test_csrf(self): url = reverse('boards:new_topic', kwargs={'pk': 1}) response = self.client.get(url) self.assertContains(response, 'csrfmiddlewaretoken') def test_new_topic_valid_post_data(self): url = reverse('boards:new_topic', kwargs={'pk': 1}) data = { 'subject': 'Test title', 'message': 'Lorem ipsum dolor sit amet' } response = self.client.post(url, data) self.assertTrue(Topic.objects.exists()) self.assertTrue(Post.objects.exists()) def test_new_topic_invalid_post_data(self): ''' Invalid post data should not redirect The expected behaviour is to show the form again with validation errors ''' url = reverse('boards:new_topic', kwargs={'pk': 1}) response = self.client.post(url, {}) form = response.context.get('form') self.assertEquals(response.status_code, 200) self.assertTrue(form.errors) def test_new_topic_invalid_post_data_empty_fields(self): ''' Invalid post data should not redirect The expected behaviour is to show the form again with validation errors ''' url = reverse('boards:new_topic', kwargs={'pk': 1}) data = { 'subject': '', 'message': '' } response = self.client.post(url, data) self.assertEquals(response.status_code, 200) self.assertFalse(Topic.objects.exists()) self.assertFalse(Post.objects.exists()) def test_contains_form(self): url = reverse('boards:new_topic', kwargs={'pk': 1}) response = self.client.get(url) form = response.context.get('form') self.assertIsInstance(form, NewTopicForm)

[ "=" ]

=

d38c04f002d4092d4b95e17d4581f523d0a76782

6de1ea8c6840f714af12edde76395e21854b0214

/app.py

f3e1b07d7f37d7d5020ced8f70852fa72f315b43

[]

no_license

VNgit/Youtube_Downloader

97bbff4b3ecc060b2f0456042aa7b83e0717117c

0688cd38992c843d194d5eb9bd4d9029c78bbfb6

refs/heads/master

2020-08-28T17:44:03.517436

2019-10-26T11:39:37

217,773,126

1

0

null

2019-10-26T21:46:51

null

UTF-8

Python

false

1,315

py

from flask import Flask,redirect,render_template,request,url_for from flask_sqlalchemy import SQLAlchemy from pytube import YouTube import os app = Flask(__name__) # creatimg configs app.config['SQLALCHEMY_DATABASE_URI']='postgresql://postgres:morgan8514@127.0.0.1:5432/YD' # dialect+driver://username:password@host:port/database app.config['SECRET_KEY'] = 'secret_key' db=SQLAlchemy(app) from models.download_info import YD @app.before_first_request def create(): db.create_all @app.route('/', methods=['POST','GET']) def home(): if request.method=='POST': recieved_url= request.form['users_input_url'] print('url recieved') # python function to download video def download_yt(url): print('getting video...') yt = YouTube(url) print('getting streams') streams=yt.streams.first() print('getting video title...') # print(yt.title) print('downloading video...') yt.streams.download() print('#####Download complete#####') # calling function to download download_video = download_yt(recieved_url) return render_template('home.html') return render_template('home.html') if __name__ == "__main__": app.run(debug=True)

[ "morgangicheha4@gmail.com" ]

morgangicheha4@gmail.com

8dbb60941dda40d486d7ee5f240f7fbb4e73da62

227654cd915b560b14f49f388d4256a0ce968b16

/agent/models.py

f8f5cca2af918c3cf49ac20911596ad878b08d19

[]

no_license

chitharanjanpraveen/insurance_rdbms_project

cb7d976def7ce3b1a962c4703c53518bcacebb9a

7a41e9a688efdd216001bf100ae59ac2653a15eb

refs/heads/master

2020-03-09T15:37:40.403717

2018-04-25T05:06:05

128,864,014

0

2

null

2018-04-25T05:06:06

2018-04-10T02:48:11

Python

UTF-8

Python

false

918

py

from django.db import models # Create your models here. class office(models.Model): office_name = models.CharField(max_length=50, primary_key=True) adress = models.CharField(max_length=120) phone_no = models.IntegerField() manager_name = models.CharField(max_length=40) def __str__(self): return str(self.office_name) class agent(models.Model): fname = models.CharField(max_length=80) lname = models.CharField(max_length=60) address=models.CharField(max_length=120) phone_no = models.CharField(max_length=20) sex = models.CharField(max_length=1) age = models.IntegerField() dob = models.DateField() pass_word = models.CharField(max_length=50) agentid = models.AutoField(primary_key=True) agent_office_name = models.ForeignKey(office, on_delete=models.CASCADE) def __str__(self): return str(self.agentid)

[ "chitharanjancharan@gmail.com" ]

chitharanjancharan@gmail.com

d0e06b60a764d60c8b89feca2c614b4bed4c4f35

a9d93637a75bf2d074a06897dbf8404657ff2606

/app.py

37ddb0fc7ebc9e176753fc7f2e0ae527472080e5

[]

no_license

Blackscure/flsk-wolf

76f82ca190a0656e0403f03bb2c2d7bba490b0ed

ef84adcac76f40239848020e0da5dca68b7a5f0f

refs/heads/master

2020-03-30T12:48:28.492523

2018-10-02T13:29:25

151,241,709

0

null

2018-10-02T13:27:48

2018-10-02T11:11:22

HTML

UTF-8

Python

false

1,184

py

from flask import Flask, render_template app = Flask(__name__) @app.route('/') def index(): movie_names = ['Avator', 'Pirate of Carribbean', 'Spectre', 'The dark Knight Rises', 'John Carter', 'Spiderman', 'Tangled'] movies = { 'Avator': {'critical_review': 732, 'duration': 170, 'indb_score':7.9}, 'Pirate of Carribbean': {'critical_review': 303, 'duration': 230, 'indb_score':8.9}, 'Spectre': {'critical_review': 702, 'duration': 180, 'indb_score':7.9}, 'The dark Knight Rises': {'critical_review': 172, 'duration': 150, 'indb_score':7.3}, 'John Carter': {'critical_review': 422, 'duration': 130, 'indb_score':4.9}, 'Spiderman': {'critical_review': 832, 'duration': 120, 'indb_score':3.9}, 'Tangled': {'critical_review': 392, 'duration': 110, 'indb_score':7.2}, } return render_template('index.html', movie_names=movie_names, movies=movies) @app.errorhandler(404) def page_not_found(error): return render_template('404.html'),404 if __name__ == '__main__': app.run(debug=True)

[ "Wekesabuyahi@gmail.com" ]

Wekesabuyahi@gmail.com

e0025fafdb890a62fc827f31bb52fdb01549d239

183741e464b55916e042fce7467e1318470d3aca

/exercises/rosalind-python/ini3.py

988e7d5194ae8bd1b9828275ca0d484922b05973

[]

no_license

phss/playground

418cda5116a7f47f12dc2d8474e7f8f29be03ab8

6e1072d81fc6d06b3ad62eedbde3342ea0044e23

refs/heads/master

2023-07-19T22:59:04.272038

2021-12-31T12:46:08

74,849

2

0

null

2023-07-06T21:08:18

2008-11-12T12:49:46

CSS

UTF-8

Python

false

244

py

s = "SP0M7gw0bYZbOQfPTVGPCZ8PlethodondDFgwZc0DqjmMtII8qC0Xu05qnUxqT46YJqh44YRNNPs0ZGlQmHvaXGAEr1dCNayFD47rY5U1YMXqomiiNKnBN7BlEbeYZaHeqHs8L0T8znplagopuslnG0iuTK77I6ex5T." a, b, c, d = map(int, "23 31 140 146".split()) print s[a:b+1], s[c:d+1]

[ "paulo.schneider@gmail.com" ]

paulo.schneider@gmail.com

300c13b7d14b8eeb64fe0620787ba963d4b4a22d

3c03ecb8e066f2d4eac73a469a75e5906734c66c

/_2019_2020/Classworks/_21_08_02_2020/_4.py

bb2dfe36b4e2eba4992e43a24a81bc1310665095

[]

no_license

waldisjr/JuniorIT

af1648095ec36535cc52770b114539444db4cd0b

6a67e713708622ae13db6d17b48e43e3d10611f2

refs/heads/master

2023-03-26T06:29:06.423163

2021-03-27T06:27:34

null

0

null

UTF-8

Python

false

87

py

file = open('1(4.py)', 'w') for i in range(1000): file.write(f"{i}\n") file.close()

[ "waldis_jr@outlook.com" ]

waldis_jr@outlook.com

733a2ad7b12dde095d29b9fa83d43bb259c76031

dee4bd3227686c6c1e115287b17b879def3c445f

/django_us_markets/app.py

9324b0f3b50a8288d90036e1b6e433bf8bee6b4f

[ "MIT" ]

permissive

TabbedOut/django_us_markets

6f24cd8a22f1d1573f949b53f6f871ffee48389e

2acb3001a4969fcf5f49217bc7bc677c823301cb

refs/heads/master

2021-01-10T15:08:18.950544

2016-04-08T20:52:08

55,809,081

0

null

UTF-8

Python

false

137

py

from django.apps import AppConfig class USMarketsAppConfig(AppConfig): name = 'django_us_markets' verbose_name = 'US Markets'

[ "nosamanuel@gmail.com" ]

nosamanuel@gmail.com

8bed32b1d24b6e065eedde91cef18790224a81ef

f757fc2a0f70e7cb25e390f603b8580eb8fe5cfd

/Week_2-Simple_Programs/4.-Functions/Exercise_gcd_recur.py

545cf01012a3ae71c3d12fffe52ee3f85532162c

[]

no_license

Raj-Yadav/MITx-6.00.1x-Introduction-to-Computer-Science-and-Programming-Using-Python

03a9659f6d6a1234d60a2f6b9d315dc782ba6b2d

b394681f478fedf877183eb16be55a883531eea4

refs/heads/master

2020-09-04T06:41:28.469232

2018-04-29T09:42:40

null

0

null

UTF-8

Python

false

860

py

# Exercise: gcd recur # (5/5 points) # ESTIMATED TIME TO COMPLETE: 6 minutes # The greatest common divisor of two positive integers is the largest integer that divides each of them without remainder. For example, # gcd(2, 12) = 2 # gcd(6, 12) = 6 # gcd(9, 12) = 3 # gcd(17, 12) = 1 # A clever mathematical trick (due to Euclid) makes it easy to find greatest common divisors. Suppose that a and b are two positive # integers: # If b = 0, then the answer is a # Otherwise, gcd(a, b) is the same as gcd(b, a % b) # Write a function gcdRecur(a, b) that implements this idea recursively. This function takes in two positive integers and returns one # integer. def gcdRecur(a, b): ''' a, b: positive integers returns: a positive integer, the greatest common divisor of a & b. ''' if b == 0: return a return gcdRecur(b, a % b)

[ "dlujanschi2@gmail.com" ]

dlujanschi2@gmail.com

11b308168ba28877a5c958de927939f0d6578a0b

868d1bd002a66bce3f86054b00a69c49f285126f

/books/01.DeepLearningScratch/chapter02/02.ArrayAND/AND.py

f9a2f0072f5a98a98a74d7b66abc9f98a71a7f43

[]

no_license

doukheeWon-gmail/DeepLearningStudy

cf81ac5867373c8028519133a1cca80024f8f0ff

d346d0572c45e2f2229bd14e5aadeb077074ffa9

refs/heads/master

2023-03-16T19:05:49.594092

2021-03-08T09:03:46

null

0

null

UTF-8

Python

false

209

py

#coding: utf-8 import numpy as np def AND(x1, x2): x = np.array([x1,x2]) w = np.array([0.5,0.5]) b = -0.7 tmp = np.sum(w*x) + b if tmp <= 0: return 0 else: return 1

[ "fain9301@yahoo.com" ]

fain9301@yahoo.com

52f8d22f90a6a6870ff064d288a72be4c6ab50de

7d78a18fcb8f34cc84e9439bd19cf491e3e0ec49

/Code/Particle_Identification/msc-hpc/hpc-mini-1/model8.py

7fca90d0b9552dd533fb15cee80aeff0c4a24a33

[]

no_license

PsycheShaman/MSc-thesis

62767951b67b922ce5a21cad5bdb258998b7d2ea

34504499df64c7d6cc7c89af9618cd58d6378e8e

refs/heads/master

2022-03-12T07:17:57.309357

2019-12-10T21:17:39

151,471,442

4

0

null

UTF-8

Python

false

6,794

py

# -*- coding: utf-8 -*- """ Created on Sun Jun 16 18:47:05 2019 @author: gerhard """ import glob import numpy as np #P_files = glob.glob("C:/Users/gerhard/Documents/msc-thesis-data/P_*.pkl", recursive=True) x_files = glob.glob("/scratch/vljchr004/1_8_to_2_2_GeV/x_*.pkl") y_files = glob.glob("/scratch/vljchr004/1_8_to_2_2_GeV/y_*.pkl") #x_files = glob.glob("C:\\Users\\gerhard\\Documents\\msc-thesis-data\\cnn\\x_*.pkl") #y_files = glob.glob("C:\\Users\\gerhard\\Documents\\msc-thesis-data\\cnn\\y_*.pkl") import pickle print("loading first x pickle........................................................................................") with open(x_files[0], 'rb') as x_file0: x = pickle.load(x_file0) print("loading first y pickle........................................................................................") with open(y_files[0], 'rb') as y_file0: y = pickle.load(y_file0) #with open(P_files[0], 'rb') as P_file0: # P = pickle.load(P_file0) x.shape = (x.shape[1],x.shape[2],x.shape[3]) print("x.shape") print(x.shape) print("recursively adding x pickles........................................................................................") for i in x_files[1:]: with open(i,'rb') as x_file: print(i) xi = pickle.load(x_file) xi.shape = (xi.shape[1],xi.shape[2],xi.shape[3]) print("xi.shape") print(xi.shape) x = np.concatenate((x,xi),axis=0) print("recursively adding y pickles........................................................................................") for i in y_files[1:]: with open(i,'rb') as y_file: yi = pickle.load(y_file) y = np.concatenate((y,yi),axis=None) #for i in P_files[1:]: # with open(i,'rb') as P_file: # Pi = pickle.load(P_file) # P = np.concatenate((P,Pi),axis=None) #x_files = glob.glob("/scratch/vljchr004/data/msc-thesis-data/cnn/x_*.npy") #y_files = glob.glob("/scratch/vljchr004/data/msc-thesis-data/cnn/y_*.npy") # #print("recursively adding x numpys........................................................................................") # #for i in x_files[0:]: # with open(i,'rb') as x_file: # print(i) # xi = np.load(x_file) # x = np.concatenate((x,xi),axis=0) # #print("recursively adding y numpys........................................................................................") # #for i in y_files[0:]: # with open(i,'rb') as y_file: # yi = np.load(y_file) # y = np.concatenate((y,yi),axis=None) nz = np.array([np.count_nonzero(i) for i in x]) zeros = np.where(nz==0) x = np.delete(x,zeros,axis=0) y = np.delete(y,zeros) #P = np.delete(P,zeros) x.shape = (x.shape[0],x.shape[1],x.shape[2],1) #x.shape = (x.shape[0],x.shape[2],x.shape[1]) print("x.shape after reshape for lstm") print(x.shape) #GeV_range2 = np.where(P>=1.8 and P<=2.2) # #x = x[GeV_range2,:,:,:] #y = y[GeV_range2] electrons = np.where(y==1) electrons = electrons[0] pions = np.where(y==0) pions = pions[0] pions = pions[0:electrons.shape[0]] x_1 = x[electrons,:,:] x_2 = x[pions,:,:] x = np.vstack((x_1,x_2)) y_1 = y[electrons] y_2 = y[pions] y = np.concatenate((y_1,y_2),axis=None) ma = np.max(x) x = x/ma #ma = np.amax(x,axis=2) # #x = np.divide(x,ma) #check the division above before running!!!!!!!!!!!1 from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2,random_state=123456) from tensorflow.keras.utils import to_categorical y_train = to_categorical(y_train) y_test = to_categorical(y_test) import tensorflow from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten from tensorflow.keras.layers import Conv2D, MaxPooling2D, LSTM, Bidirectional, TimeDistributed model = Sequential() model.add(Conv2D(32,(6,6),input_shape=(17,24,1),padding="same",activation="relu")) model.add(Conv2D(64,(6,6),padding="same",activation="relu")) model.add(MaxPooling2D((2,2))) model.add(Conv2D(64,(4,4),padding="same",activation="relu")) model.add(Conv2D(128,(4,4),padding="same",activation="relu")) model.add(MaxPooling2D((2,2))) model.add(Conv2D(128,(3,3),padding="same",activation="relu")) model.add(Conv2D(256,(3,3),padding="same",activation="relu")) model.add(MaxPooling2D((2,2))) model.add(Conv2D(256,(3,3),padding="same",activation="relu")) model.add(Conv2D(512,(3,3),padding="same",activation="relu")) model.add(MaxPooling2D((2,2))) model.add(Flatten()) model.add(Dense(1024,activation="relu")) model.add(Dense(1024,activation="relu")) model.add(Dense(512,activation="relu")) model.add(Dense(512,activation="relu")) model.add(Dense(256,activation="relu")) model.add(Dense(256,activation="relu")) model.add(Dense(128,activation="relu")) model.add(Dense(128,activation="relu")) model.add(Dense(64,activation="relu")) model.add(Dense(32,activation="relu")) model.add(Dense(2,activation="softmax")) adam = tensorflow.keras.optimizers.Adam() # Let's train the model using RMSprop model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy']) batch_size=32 epochs=50 history=model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2, shuffle=True)#, #class_weight=class_weights) import matplotlib.pyplot as plt # summarize history for accuracy plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.savefig('/home/vljchr004/hpc-mini/model8_history1.png', bbox_inches='tight') # summarize history for loss plt.close() plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.savefig('/home/vljchr004/hpc-mini/model8_history2.png', bbox_inches='tight') model.probs = model.predict_proba(x_test) import numpy as np np.savetxt("/home/vljchr004/hpc-mini/model8_results.csv", np.array(model.probs), fmt="%s") np.savetxt("/home/vljchr004/hpc-mini/model8_y_test.csv", np.array(y_test), fmt="%s") model.save('/home/vljchr004/hpc-mini/model8_.h5') # creates a HDF5 file 'my_model.h5' del model print("<-----------------------------done------------------------------------------>")

[ "christiaan.viljoen@cern.ch" ]

christiaan.viljoen@cern.ch

089e4ed108db3fef009b0d7feab3fd86866630e7

61ff23ae86e6a4bc74b0893e7f3b9600416f9dd7

/mipt/acm.mipt.ru/042/test1.py

1a9c52180c01920df8a0e559b4c3c0fac59c2fca

[]

no_license

sergia-ch/inf

ee99c560300310cfadac01ff95a2d42869efcf31

3c73d6efc9e1c107d720680f6e4865edbb6fb185

refs/heads/master

2023-01-30T18:08:52.866094

2020-12-07T19:13:24

15,929,207

0

null

UTF-8

Python

false

33

py

print 'ABCD'*249 print 'ABC'*331

[ "etoestja@yandex.ru" ]

etoestja@yandex.ru

0c1bb1c57fb00f0ac5712f22c6993c18079bff76

10c3eb5229186bb24b2ed64a7054e36aacd94931

/submit_sunnybrook_unet_lstm_multi.py

60a485c674940beb056630b92284ee469446f060

[]

no_license

alexliyang/cardiac-segmentation-cc

d493bfa66ee2802632f04c5f298e35ee510a39a1

c78b0a39600467060531c98f4207df0c4240abd4

refs/heads/master

2021-04-06T11:22:32.746009

2017-12-29T02:33:31

null

0

null

UTF-8

Python

false

11,413

py

#!/usr/bin/env python2.7 import re, sys, os import shutil, cv2 import numpy as np from train_sunnybrook_unet_lstm_mul import read_contour, map_all_contours, export_all_contours, map_endo_contours from helpers import reshape, get_SAX_SERIES, draw_result from unet_lstm_multi_model import unet_lstm_multi_model, dice_coef_endo_each, dice_coef_myo_each SAX_SERIES = get_SAX_SERIES() SUNNYBROOK_ROOT_PATH = 'D:\cardiac_data\Sunnybrook' VAL_CONTOUR_PATH = os.path.join(SUNNYBROOK_ROOT_PATH, 'Sunnybrook Cardiac MR Database ContoursPart2', 'ValidationDataContours') VAL_IMG_PATH = os.path.join(SUNNYBROOK_ROOT_PATH, 'Sunnybrook Cardiac MR Database DICOMPart2', 'ValidationDataDICOM') VAL_OVERLAY_PATH = os.path.join(SUNNYBROOK_ROOT_PATH, 'Sunnybrook Cardiac MR Database OverlayPart2', 'ValidationDataOverlay') ONLINE_CONTOUR_PATH = os.path.join(SUNNYBROOK_ROOT_PATH, 'Sunnybrook Cardiac MR Database ContoursPart1', 'OnlineDataContours') ONLINE_IMG_PATH = os.path.join(SUNNYBROOK_ROOT_PATH, 'Sunnybrook Cardiac MR Database DICOMPart1', 'OnlineDataDICOM') ONLINE_OVERLAY_PATH = os.path.join(SUNNYBROOK_ROOT_PATH, 'Sunnybrook Cardiac MR Database OverlayPart1', 'OnlineDataOverlay') SAVE_VAL_PATH = os.path.join(SUNNYBROOK_ROOT_PATH, 'Sunnybrook_val_submission') SAVE_ONLINE_PATH = os.path.join(SUNNYBROOK_ROOT_PATH, 'Sunnybrook_online_submission') def create_submission(contours, data_path, output_path, contour_type = 'a'): if contour_type == 'a': weights = 'model_logs/temp_weights.hdf5' else: sys.exit('\ncontour type "%s" not recognized\n' % contour_type) num_phases = 5 crop_size = 128 input_shape = (num_phases, crop_size, crop_size, 1) num_classes = 3 images, masks = export_all_contours(contours, data_path, output_path, crop_size, num_classes=num_classes) model = unet_lstm_multi_model(input_shape, num_classes, weights=weights, contour_type=contour_type, transfer=True) pred_masks = model.predict(images, batch_size=32, verbose=1) print('\nEvaluating dev set ...') result = model.evaluate(images, masks, batch_size=32) result = np.round(result, decimals=10) print('\nDev set result {:s}:\n{:s}'.format(str(model.metrics_names), str(result))) num = 0 for c_type in ['i', 'm']: for idx, ctr in enumerate(contours): img, mask = read_contour(ctr, data_path, num_classes) h, w, d = img.shape if c_type == 'i': tmp = pred_masks[idx,...,2] elif c_type == 'm': tmp = pred_masks[idx,...,1] tmp = tmp[..., np.newaxis] tmp = reshape(tmp, to_shape=(h, w, d)) tmp = np.where(tmp > 0.5, 255, 0).astype('uint8') tmp2, coords, hierarchy = cv2.findContours(tmp.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) if not coords: print('\nNo detection in case: {:s}; image: {:d}'.format(ctr.case, ctr.img_no)) coords = np.ones((1, 1, 1, 2), dtype='int') if c_type == 'i': man_filename = ctr.ctr_endo_path[ctr.ctr_endo_path.rfind('\\')+1:] elif c_type == 'm': man_filename = ctr.ctr_epi_path[ctr.ctr_epi_path.rfind('\\')+1:] auto_filename = man_filename.replace('manual', 'auto') img_filename = re.sub(r'-[io]contour-manual.txt', '.dcm', man_filename) man_full_path = os.path.join(save_dir, ctr.case, 'contours-manual', 'IRCCI-expert') auto_full_path = os.path.join(save_dir, ctr.case, 'contours-auto', 'FCN') img_full_path = os.path.join(save_dir, ctr.case, 'DICOM') dcm = 'IM-0001-%04d.dcm' % (ctr.img_no) #dcm = 'IM-%s-%04d.dcm' % (SAX_SERIES[ctr.case], ctr.img_no) dcm_path = os.path.join(data_path, ctr.case, 'DICOM', dcm) overlay_full_path = os.path.join(save_dir, ctr.case, 'Overlay') for dirpath in [man_full_path, auto_full_path, img_full_path, overlay_full_path]: if not os.path.exists(dirpath): os.makedirs(dirpath) if 'DICOM' in dirpath: src = dcm_path dst = os.path.join(dirpath, img_filename) shutil.copyfile(src, dst) elif 'Overlay' in dirpath: draw_result(ctr, data_path, overlay_full_path, c_type, coords) else: dst = os.path.join(auto_full_path, auto_filename) if not os.path.exists(auto_full_path): os.makedirs(auto_full_path) with open(dst, 'wb') as f: for cd in coords: cd = np.squeeze(cd) if cd.ndim == 1: np.savetxt(f, cd, fmt='%d', delimiter=' ') else: for coord in cd: np.savetxt(f, coord, fmt='%d', delimiter=' ') print('\nNumber of multiple detections: {:d}'.format(num)) dst_eval = os.path.join(save_dir, 'evaluation_{:s}.txt'.format(c_type)) with open(dst_eval, 'wb') as f: f.write(('Dev set result {:s}:\n{:s}'.format(str(model.metrics_names), str(result))).encode('utf-8')) f.close() # Detailed evaluation: detail_eval = os.path.join(save_dir, 'evaluation_detail_{:s}.csv'.format(c_type)) evalEndoArr = dice_coef_endo_each(masks, pred_masks) evalMyoArr = dice_coef_myo_each(masks, pred_masks) caseArr = [ctr.case for ctr in contours] imgArr = [ctr.img_no for ctr in contours] resArr = np.transpose([caseArr, imgArr, evalEndoArr, evalMyoArr]) np.savetxt(detail_eval, resArr, fmt='%s', delimiter=',') #np.savetxt(f, '\nDev set result {:s}:\n{:s}'.format(str(model.metrics_names), str(result))) def create_endo_submission(endos, data_path, output_path, contour_type = 'a'): if contour_type == 'a': weights = 'model_logs/temp_weights.hdf5' else: sys.exit('\ncontour type "%s" not recognized\n' % contour_type) num_phases = 5 crop_size = 128 input_shape = (num_phases, crop_size, crop_size, 1) num_classes = 3 images, masks = export_all_contours(endos, data_path, output_path, crop_size, num_classes=num_classes) model = unet_lstm_multi_model(input_shape, num_classes, weights=weights, contour_type=contour_type, transfer=True) pred_masks = model.predict(images, batch_size=8, verbose=1) print('\nEvaluating dev set ...') result = model.evaluate(images, masks, batch_size=8) result = np.round(result, decimals=10) print('\nDev set result {:s}:\n{:s}'.format(str(model.metrics_names), str(result))) num = 0 c_type = 'i' for idx, ctr in enumerate(endos): img, mask = read_contour(ctr, data_path, num_classes) h, w, d = img.shape if c_type == 'i': tmp = pred_masks[idx, ..., 2] elif c_type == 'm': tmp = pred_masks[idx, ..., 1] tmp = tmp[..., np.newaxis] tmp = reshape(tmp, to_shape=(h, w, d)) tmp = np.where(tmp > 0.5, 255, 0).astype('uint8') tmp2, coords, hierarchy = cv2.findContours(tmp.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) if not coords: print('\nNo detection in case: {:s}; image: {:d}'.format(ctr.case, ctr.img_no)) coords = np.ones((1, 1, 1, 2), dtype='int') if c_type == 'i': man_filename = ctr.ctr_endo_path[ctr.ctr_endo_path.rfind('\\') + 1:] elif c_type == 'm': man_filename = ctr.ctr_epi_path[ctr.ctr_epi_path.rfind('\\') + 1:] auto_filename = man_filename.replace('manual', 'auto') img_filename = re.sub(r'-[io]contour-manual.txt', '.dcm', man_filename) man_full_path = os.path.join(save_dir, ctr.case, 'contours-manual', 'IRCCI-expert') auto_full_path = os.path.join(save_dir, ctr.case, 'contours-auto', 'FCN') img_full_path = os.path.join(save_dir, ctr.case, 'DICOM') dcm = 'IM-0001-%04d.dcm' % (ctr.img_no) # dcm = 'IM-%s-%04d.dcm' % (SAX_SERIES[ctr.case], ctr.img_no) dcm_path = os.path.join(data_path, ctr.case, 'DICOM', dcm) overlay_full_path = os.path.join(save_dir, ctr.case, 'Overlay') for dirpath in [man_full_path, auto_full_path, img_full_path, overlay_full_path]: if not os.path.exists(dirpath): os.makedirs(dirpath) if 'DICOM' in dirpath: src = dcm_path dst = os.path.join(dirpath, img_filename) shutil.copyfile(src, dst) elif 'Overlay' in dirpath: draw_result(ctr, data_path, overlay_full_path, c_type, coords) else: dst = os.path.join(auto_full_path, auto_filename) if not os.path.exists(auto_full_path): os.makedirs(auto_full_path) with open(dst, 'wb') as f: for cd in coords: cd = np.squeeze(cd) if cd.ndim == 1: np.savetxt(f, cd, fmt='%d', delimiter=' ') else: for coord in cd: np.savetxt(f, coord, fmt='%d', delimiter=' ') print('\nNumber of multiple detections: {:d}'.format(num)) dst_eval = os.path.join(save_dir, 'evaluation_{:s}.txt'.format(c_type)) with open(dst_eval, 'wb') as f: f.write(('Dev set result {:s}:\n{:s}'.format(str(model.metrics_names), str(result))).encode('utf-8')) f.close() # Detailed evaluation: detail_eval = os.path.join(save_dir, 'evaluation_detail_{:s}.csv'.format(c_type)) evalEndoArr = dice_coef_endo_each(masks, pred_masks) evalMyoArr = dice_coef_myo_each(masks, pred_masks) caseArr = [ctr.case for ctr in endos] imgArr = [ctr.img_no for ctr in endos] resArr = np.transpose([caseArr, imgArr, evalEndoArr, evalMyoArr]) np.savetxt(detail_eval, resArr, fmt='%s', delimiter=',') if __name__== '__main__': contour_type = 'a' os.environ['CUDA_VISIBLE_DEVICES'] = '0' save_dir = 'D:\cardiac_data\Sunnybrook\Sunnybrook_online_submission_unet_multi' print('\nProcessing online '+contour_type+' contours...') online_ctrs = list(map_all_contours(ONLINE_CONTOUR_PATH)) online_endos = list(map_endo_contours(ONLINE_CONTOUR_PATH)) create_submission(online_ctrs, ONLINE_IMG_PATH, ONLINE_OVERLAY_PATH, contour_type) create_endo_submission(online_endos, ONLINE_IMG_PATH, ONLINE_OVERLAY_PATH, contour_type) save_dir = 'D:\cardiac_data\Sunnybrook\Sunnybrook_val_submission_unet_multi' print('\nProcessing val '+contour_type+' contours...') val_ctrs = list(map_all_contours(VAL_CONTOUR_PATH)) val_endos = list(map_endo_contours(VAL_CONTOUR_PATH)) create_submission(val_ctrs, VAL_IMG_PATH, VAL_OVERLAY_PATH, contour_type) create_endo_submission(val_endos, VAL_IMG_PATH, VAL_OVERLAY_PATH, contour_type) print('\nAll done.')

[ "congchao120@163.com" ]

congchao120@163.com

cfb6ce942daaff03042e09cdb5aa421640b2b65e

48b449a147004ff9b01228376376b455a17a1905

/Hello World/Scripts/easy_install-3.7-script.py

93ba8682f27e62c1ce8fd5dce61f78296ff4bb7f

[]

no_license

Sanzid-Imran/PyShop

1df0b2989114f3e56519857399bb6ae7c5835247

74bb53e3bc06e927ff03f8936f1272d1d863aafb

refs/heads/master

2020-07-11T13:54:09.382190

2019-08-26T21:16:31

204,559,312

0

null

UTF-8

Python

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461

py

#!"E:\Python\Python Projects\PyShop\Hello World\Scripts\python.exe" # EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install-3.7' __requires__ = 'setuptools==40.8.0' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('setuptools==40.8.0', 'console_scripts', 'easy_install-3.7')() )

[ "sanzidimran@gmail.com" ]

sanzidimran@gmail.com

e2665c86a8e4a5ab790a047591db59cf721964c1

fcd8f4935dac3b0b89fa21ace968cb872663a11f

/Python/JewelsAndStones.py

a11bcaf9c7ab3609654741d8575cf5eb3068895d

[]

no_license

peterer0625/Portfolio

48dab8829455ecff19da9532f23fa42c1d3652f7

274d52b571b53ada51551fcbf1872642823cf5af

refs/heads/master

2022-10-28T10:22:06.908252

2022-10-23T14:35:53

117,188,061

0

null

UTF-8

Python

false

1,011

py

# coding: utf-8 # In[23]: #You're given strings J representing the types of stones that are jewels, and S representing the stones you have. Each character in S is a type of stone you have. You want to know how many of the stones you have are also jewels. #The letters in J are guaranteed distinct, and all characters in J and S are letters. Letters are case sensitive, so "a" is considered a different type of stone from "A". #Example 1: #Input: J = "aA", S = "aAAbbbb" #Output: 3 #Example 2: #Input: J = "z", S = "ZZ" #Output: 0 #Note: #S and J will consist of letters and have length at most 50. #The characters in J are distinct. class Solution: def numJewelsInStones(self, J, S): """ :type J: str :type S: str :rtype: int """ result = 0 for letter in S: if letter in J: result = result + 1 return result J = "aA" S = "aAAbbbb" ret = Solution().numJewelsInStones(J, S) print(ret)

[ "peterer0625@gmail.com" ]

peterer0625@gmail.com

dd54ff3664693406afaf577c7eb07363b7f5de25

47c7e2a674c393d4c50766b783e247d01d37fec2

/Frontend/src/Server/API/python/offer_writing.py

eba824ed3cba86f464189bbe1c5e3a7c1d428eea

[]

no_license

WendyMin/LearningSystem

cdfc8c9cf151d600640dca5f20fe620565aff456

b2bf05ab42472feed989f11672274826ae8f6947

refs/heads/master

2021-04-03T04:59:11.531568

2020-06-08T12:49:06

124,355,444

0

null

UTF-8

Python

false

1,355

py

# coding=utf-8 from __future__ import division import json import MySQLdb import time import datetime import random import urllib import json from urllib import urlencode from urllib import quote import sys reload(sys) sys.setdefaultencoding('utf-8') def get_sentence(): conn = MySQLdb.Connect(host = '127.0.0.1',user = 'root', passwd = '123456', db = 'gyc_f_e', \ port = 3306,charset='utf8') cur = conn.cursor() select_num = [] while True: a = random.randint(1,300) if a not in select_num: select_num.append(int(a)) if len(select_num) == 3: break # print select_num result_list = [] for j in range(len(select_num)): re_dict = {} sql = 'select en_sentence,ch_sentence from writing_sentence WHERE id = "%s"' % (select_num[j]) cur.execute(sql) result = cur.fetchall() re_dict['id'] = str(select_num[j]) re_dict['english'] = str(result[0][0]).decode('utf-8') re_dict['chinese'] = str(result[0][1]).decode('utf-8') if j == 2: re_dict['type'] = '1' else: re_dict['type'] = '0' result_list.append(re_dict) # print result_list jsondata = json.dumps(result_list,ensure_ascii=False) print jsondata if __name__ == '__main__': get_sentence()

[ "min711s9d31@126.com" ]

min711s9d31@126.com

cc6ad7fc575cee1468c6abf40ef02c2fae92bd9d

06cde6aef06a8d7a71862a339d2c68716f1ed792

/venv/bin/f2py

ef04aa82ada83c2c82a232a016bea0c21608f3dc

[]

no_license

MagnusAFyhr/CryptoTensor

fc6a0942bea443bb0b966a977bd4ab168b5e568e

2ab3856f67c87fca9112a05984db43adfdf09fe0

refs/heads/master

2022-12-20T05:48:45.111232

2020-10-06T01:15:25

301,040,774

0

null

UTF-8

Python

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#!/Users/magnusfyhr/PycharmProjects/CryptoTensor/venv/bin/python # -*- coding: utf-8 -*- import re import sys from numpy.f2py.f2py2e import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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magnus.fyhr@marquette.edu

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# Approach 3 - Solution 2 from string import ascii_lowercase as lowercase_letters def is_pangram(sentence): actual_bits = 0 expected_bits = 0b11111111111111111111111111 for i, char in enumerate(sentence): if char.isalpha(): letter_index = ord(char.lower()) - ord("a") bit_shift = 1 << letter_index actual_bits = actual_bits | bit_shift return expected_bits == actual_bits # Approach 3 - Solution 2 intentionally doesn't contain any comments. # As discussed in the course, this is a practice problem for you: apply Approach 3 - study the code of others -- to this solution.

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/gluon2pytorch/gluon2pytorch.py

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chipper1/gluon2pytorch

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import json import torch import torch.nn as nn import torch.nn.functional as F import mxnet as mx import numpy as np # Import comverters from .layers import CONVERTERS # Import PyTorch model template from .pytorch_model_template import pytorch_model_template def eval_model(pytorch_source, pytorch_dict, module_name): # Tricky code torch nn F exec(pytorch_source) globals()[module_name] = locals()[module_name] pytorch_model = locals()[module_name]() pytorch_model.load_state_dict(pytorch_dict) return pytorch_model def render_module(inits, calls, inputs, outputs, dst_dir, pytorch_dict, pytorch_module_name): """ Render model. """ inits = [i for i in inits if len(i) > 0] output = pytorch_model_template.format(**{ 'module_name': pytorch_module_name, 'module_name_lower': pytorch_module_name.lower(), 'inits': '\n'.join(inits), 'inputs': ', '.join(['x' + str(i) for i in inputs]), 'calls': '\n'.join(calls), 'outputs': ', '.join(['x' + str(i) for i in outputs]), }) if dst_dir is not None: import os import errno try: os.makedirs(dst_dir) except OSError as e: if e.errno != errno.EEXIST: raise with open(os.path.join(dst_dir, pytorch_module_name.lower() + '.py'), 'w+') as f: f.write(output) f.close() torch.save(pytorch_dict, os.path.join(dst_dir, pytorch_module_name.lower() + '.pt')) return output def gluon2pytorch(net, args, dst_dir, pytorch_module_name, debug=True): """ Function to convert a model. """ x = [mx.nd.array(np.ones(i)) for i in args] x = net(*x) # Get network params params = net.collect_params() # Create a symbol to trace net # x = mx.sym.var('data') x = [mx.sym.var('__input__' + str(i)) for i in range(len(args))] sym = net(*x) if len(sym) > 1: group = mx.sym.Group(sym) else: group = sym # Get JSON-definition of the model json_model = json.loads(group.tojson())['nodes'] # Create empty accumulators nodes = [] is_skipped = [] pytorch_dict = {} inits = [] calls = [] inputs = [] outputs = [i[0] for i in json.loads(group.tojson())['heads']] last = 0 # Trace model for i, node in enumerate(json_model): # If the node has 'null' op, it means, that it's not a real op, but only parameter # TODO: convert constants if node['op'] == 'null': if node['name'].find('__input__') == 0: inputs.append(int(node['name'][9:])) is_skipped.append(1) continue # It's not 'null' is_skipped.append(0) # Create dict with necessary node parameters op = { 'name': node['name'][:-4], 'type': node['op'], } print(op, node) if len(node['inputs']) > 0: orginal_inputs = [i for i in np.array(node['inputs'])[:, 0] if i in inputs] op['inputs'] = [i for i in np.array(node['inputs'])[:, 0] if is_skipped[i] != 1 or i in orginal_inputs] else: print(json_model) op['inputs'] = [] try: # Not all nodes have 'attrs' op['attrs'] = node['attrs'] except KeyError: op['attrs'] = {} # Debug output if debug: print(op) print('__') # Append new node to list nodes.append(op) # If operation is in available convertors, convert it if op['type'] in CONVERTERS: init_str, call_str = CONVERTERS[op['type']](i, op, nodes, params, pytorch_dict) inits.append(init_str) calls.append(call_str) else: raise AttributeError('Layer isn\'t supported') pytorch_source = render_module(inits, calls, inputs, outputs, dst_dir, pytorch_dict, pytorch_module_name) return eval_model(pytorch_source, pytorch_dict, pytorch_module_name)

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srikanthmadhu30/dgex1

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""" Django settings for exproject1 project. Generated by 'django-admin startproject' using Django 2.0.13. For more information on this file, see https://docs.djangoproject.com/en/2.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.0/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/2.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'j@*@p8q12ch8xu1jij1hk@t5a-gf*8bs!@(&-l4gdg-t&bss&s' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'myapp' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'exproject1.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'exproject1.wsgi.application' # Database # https://docs.djangoproject.com/en/2.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.0/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/2.0/howto/static-files/ STATIC_URL = '/static/'

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/Programs/HackerRank/NumPy/Floor, Ceil and Rint.py

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MZen2610/TrainingPython

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# floor # The tool floor returns the floor of the input element-wise. # The floor of is the largest integer where . # # import numpy # # my_array = numpy.array([1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9]) # print numpy.floor(my_array) #[ 1. 2. 3. 4. 5. 6. 7. 8. 9.] # ceil # The tool ceil returns the ceiling of the input element-wise. # The ceiling of is the smallest integer where . # # import numpy # # my_array = numpy.array([1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9]) # print numpy.ceil(my_array) #[ 2. 3. 4. 5. 6. 7. 8. 9. 10.] # rint # The rint tool rounds to the nearest integer of input element-wise. # # import numpy # # my_array = numpy.array([1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9]) # print numpy.rint(my_array) #[ 1. 2. 3. 4. 6. 7. 8. 9. 10.] # Sample Input # # 1.1 2.2 3.3 4.4 5.5 6.6 7.7 8.8 9.9 # Sample Output # # [ 1. 2. 3. 4. 5. 6. 7. 8. 9.] # [ 2. 3. 4. 5. 6. 7. 8. 9. 10.] # [ 1. 2. 3. 4. 6. 7. 8. 9. 10.] import numpy as np a = input().strip().split(' ') # my_array = np.array(a, dtype= np.float64) # преобразовать тип можно так или my_array = np.array(a).astype(float) # так # print(my_array) print(str(np.floor(my_array)).replace('.', '. ').replace('[', '[ ').replace(' ]', ']')) print(str(np.ceil(my_array)).replace('.', '. ').replace('[', '[ ').replace(' ]', ']')) print(str(np.rint(my_array)).replace('.', '. ').replace('[', '[ ').replace(' ]', ']'))

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/student/migrations/0002_auto_20200320_1058.py

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[]

no_license

brightcomputers/brightcomputers

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# Generated by Django 3.0.2 on 2020-03-20 05:28 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('student', '0001_initial'), ] operations = [ migrations.AlterField( model_name='studentdetails', name='phoneno', field=models.CharField(max_length=12, null=True), ), ]

[ "brightcomputersinfo@gmail.com" ]

brightcomputersinfo@gmail.com

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/TwitterAnalysis/explore/login.py

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[]

no_license

claudianorscini/Project

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refs/heads/master

2021-01-22T13:47:59.777917

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import tweepy #consumer key,access token, access secret ckey = "U7A6sqsqmeQHvipknVGmdjzwT" csecret = "zTDbhjrqJcGLsUn3S9V4SYV5BQi61C1XbrxObSLUbFFxJKTnFV" atoken = "891974438585081857-3Uw4GCUWC7FJGLRQumzYeLnio5COWT9" asecret = "S0QriqKzg1JFCHehOvwqhf4ICXfNlNVjJr3r5yNd7k9hG" def authentication(): auth = tweepy.OAuthHandler(ckey, csecret) auth.set_access_token(atoken, asecret) return auth

[ "claudia.norscini@gmail.com" ]

claudia.norscini@gmail.com

ff205523f1fbaee9e28ed6e8585cb6a773b6de04

850f599b0afb3ad83893e5f3c037c1738c7ebd7e

/cryptocurrency/crypto/urls.py

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[]

no_license

sparshk/crypto_news

f782dfc298bef7d2c65ce558325d1e6c812a7055

f1808bbad8a1f8e1477dd02aff7de0abd63560b4

refs/heads/master

2020-03-25T04:33:20.042227

2018-08-03T08:55:21

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"""cryptocurrency URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.0/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.urls import path from . import views urlpatterns = [ path('', views.home, name="home"), path('prices/', views.prices, name="prices") ]

[ "noreply@github.com" ]

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4267a7d9a4abdcec70ee181694c1027b497830dc

f8d026eb1cf6bb114e14b1c0f4c285d4f8175212

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[]

no_license

PanYubug/mysite

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from django.urls import path from . import views app_name = 'polls' urlpatterns = [ path('', views.index, name='index'), # ex: /polls/5/ path('<int:question_id>/', views.detail, name='detail'), # path('specifics/<int:question_id>/', views.detail, name='detail'), # ex: /polls/5/results/ path('<int:question_id>/results/', views.results, name='results'), # ex: /polls/5/vote/ path('<int:question_id>/vote/', views.vote, name='vote'), ]

[ "344146473@qq.com" ]

344146473@qq.com

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Lee-hsien-sen/python-excel-unittest

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import re import requests import json # # # 在Cookie Version 0中规定空格、方括号、圆括号、等于号、逗号、双引号、斜杠、问号、@，冒号，分号等特殊符号都不能作为Cookie的内容。 # def get_ieng_teacher_pro_headers(openid): # 以下数据获取《IENG公众号》的MELEDEV method = 'POST' url = 'https://wxteacher.tope365.com/teacher/login' headers = {} # headers = {'content-type': 'application/json'} headers["Content-Type"] = "application/json; charset=UTF-8" data = json.dumps(openid) print(data) if openid == '': # headers["Content-Type"] = "application/json; charset=UTF-8" wx_cookie = "" print( "url_openid为空的数据------------------------------------------------------------=========================================================================================") else: session = ieng_wx_session(method, url, data, headers) # 判断接口方法函数 wx_cookie = 'JSESSIONID_COOKIE=' + session print(wx_cookie) return wx_cookie def ieng_wx_session(method, url, data, headers): try: print("每请求一次getmeiledevsessions接口，则打印一次-----------------------------------------") if method =='POST': r = requests.post(url,data=data,headers=headers) elif method == 'GET': #get的data用params来传递#get的data用params来传递#get的data用params来传递#get的data用params来传递 r = requests.get(url, params=data, headers=headers) #get的data用params来传递#get的data用params来传递#get的data用params来传递 elif method == 'PUT': r = requests.put(url,data=data,headers=headers) elif method == 'DELETE': r = requests.delete(url) else: print("--------------------请求方法"+method+"不支持-----------------") r = '接口请求出现异常' except: print(u'接口请求35345失败') r = '接口请求出现异常' try: headers4 = dict(r.headers) # 因r.headers返回的不是dict类型，所以dict转化 print(headers4) if 'Set-Cookie' in str(headers4): a = headers4['Set-Cookie'] # print(type(a),a) if 'JSESSIONID_COOKIE=' in a: b = (re.findall('JSESSIONID_COOKIE=([\w,\-]+);', a))[0] print("获取JSESSIONID_COOKIE成功") else: b='' print('获取JSESSIONID_COOKIE失败，返回headers中Set-Cookie中未找到JSESSIONID_COOKIE') else: b = '' print('获取失败，返回headers中未找到存放ieng_wx的Set-Cookie') print("获取到接口返回的ieng_wx_cookie的值为： ", b) except: b = '' print(u'当前请求，无法直接获取返回的header信息，或出现无法预料的错误') return b # url = 'http://meiledev.soyoung.com/v1/user/testlogin' # headers = {'User-Agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_2_2 like Mac OS X) AppleWebKit/604.4.7 (KHTML, like Gecko) Mobile/15C202 MicroMessenger/6.6.1 NetType/WIFI Language/zh_CN'} # # data = {'openid': 'oESYd1cJy0UIVlFVQ8HnXvt4AMw0'} # data = {'openid': 'oeUP30MdsPv2xwxyqXZnNXWqhlYU'} # # method ='GET' # getMELEDEV = getMELEDEVsession(method, url, data,headers) # 判断接口方法函数 # MELEDEV = getMELEDEV.getMELEDEVsession() if __name__ == "__main__": data = {"loginName":"zhangfeng1","password":"000000"} ieng_token = get_ieng_teacher_pro_headers(data) print(ieng_token)

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KevinKang1211/Ticket_Bot

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#!/usr/bin/env python3 # -*- coding:utf-8 -*- """ Loki module for query_time Input: inputSTR str, utterance str, args str[], resultDICT dict Output: resultDICT dict """ DEBUG_query_time = True userDefinedDICT = {"大": ["大人", "成人"], "小": ["小孩", "孩童"]} # 將符合句型的參數列表印出。這是 debug 或是開發用的。 def debugInfo(inputSTR, utterance): if DEBUG_query_time: print("[query_time] {} ===> {}".format(inputSTR, utterance)) def getResult(inputSTR, utterance, args, resultDICT): debugInfo(inputSTR, utterance) if utterance == "[19]:[47]": # 待處理 pass return resultDICT

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/TOC-Project-2017/fsm2.py

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[ "MIT" ]

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C14036227/TOC-Project-2017-ver2

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refs/heads/master

2021-01-23T10:34:48.404606

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from transitions.extensions import GraphMachine class TocMachine(GraphMachine): def __init__(self, **machine_configs): self.machine = GraphMachine( model = self, **machine_configs ) def is_going_to_state1(self, update): text = update.message.text return text.lower() == 'go to state1' def is_going_to_state2(self, update): text = update.message.text return text.lower() == 'go to state2' def on_enter_state1(self, update): update.message.reply_text("I'm entering state1") self.go_back(update) def on_exit_state1(self, update): print('Leaving state1') def on_enter_state2(self, update): update.message.reply_text("I'm entering state2") self.go_back(update) def on_exit_state2(self, update): print('Leaving state2')

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[]

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""" Django settings for mxonline project. Generated by 'django-admin startproject' using Django 2.0.3. For more information on this file, see https://docs.djangoproject.com/en/2.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.0/ref/settings/ """ import sys 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__))) sys.path.insert(0, os.path.join(BASE_DIR, 'apps')) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'hw$ull9#yd)%((n32%_jx_cy+!kcr@u8-ywc_r4pg6kjmzx(f6' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'users', 'organization', 'operation', 'courses', ] # 此处重载使UserProfile生效 AUTH_USER_MODEL = "users.UserProfile" MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mxonline.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mxonline.wsgi.application' # Database # https://docs.djangoproject.com/en/2.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': 'mxonline3', 'USER': 'root', 'PASSWORD': '123456', 'HOST': '127.0.0.1', } } # Password validation # https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.0/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/2.0/howto/static-files/ STATIC_URL = '/static/' TEMPLATE_DIRS = ( os.path.join(BASE_DIR, 'templates'), )

[ "1816635208@qq.com" ]

1816635208@qq.com

a0343c09cb651f68bbb731f118efdaf292f4c557

61cbd965173d4fe7f5c1bcea7ba2fa660460b227

/apps/evaluation/apis.py

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[]

no_license

iLaus/flask_web

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8456880f3db289aa3019bebdac6b43a9f026638d

refs/heads/dev

2020-04-10T00:45:25.434280

2018-12-12T16:50:10

160,695,310

1

null

2018-12-11T16:40:15

2018-12-06T15:37:29

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# -*- coding: utf-8 -*- #!/usr/bin/python from flask_restful import Api, Resource def hello(): return 'hello world!' class HelloWorld(Resource): todos = {} def get(self, todo_id): return {"result": todo_id}

[ "hogon.wang@gmail.com" ]

hogon.wang@gmail.com

52ea82d92a9babeebea7a0dd0d767f59a4c54501

11fe80e8eb954f89a0e3b9e3961f6c055d993f9a

/receiver.py

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[]

no_license

mehakismail/res-websokt

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711d1314c9c1a041ed682aff52968ab3d4771c3d

refs/heads/main

2023-03-24T18:42:48.081992

2021-03-18T21:46:41

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import time import json import socketio import time sio = socketio.Client() sio.connect('http://192.168.42.105:2494') @sio.on('connect') def connect(): print('connection established') @sio.on('__TEST') def message(data): print(data)

[ "noreply@github.com" ]

mehakismail.noreply@github.com

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/backend/migrations/0022_auto_20210103_1409.py

93c867f257dafa18296b9f8be217709e1f226e4c

[]

no_license

omgorganica/wh_backend

6e362bced1021beee5c910e754491300a9474ccc

bab97a3970464605e6ac57d9bec193e3843f985e

refs/heads/master

2023-03-13T13:55:41.096608

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# Generated by Django 3.1.3 on 2021-01-03 09:09 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('backend', '0021_auto_20210102_2034'), ] operations = [ migrations.AlterModelOptions( name='user', options={'ordering': ['-current_balance']}, ), ]

[ "omgorganica@yandex.ru" ]

omgorganica@yandex.ru

9e5ddb8b8df7a4a0133cd8e979a21ba7c73dec76

a47f8facbd8ee621999ad16736f6aa059440e137

/water_film_imaging2.py

4e1417a00ae68920184e9ec7bf03b2648702912a

[]

no_license

wethug/tES-fmri

e14bb207048174e404729196c4036d168317bf1d

2f18a050a901a8412f6240a771259e7c8bd3d66f

refs/heads/main

2023-03-19T21:44:37.916345

2021-03-13T12:05:46

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# 水膜成像 import matplotlib import matplotlib.pyplot as plt bar_width = 0.15 y1 = [809.09, 877.94, 867.09, 883.01, 932.71] y2 = [743.34, 805.86, 808.02, 842.59, 899.36] y3 = [742.76, 806.66, 806.97, 843.01, 897.21] y4 = [745.04, 807.94, 806.09, 841.32, 900.14] y5 = [745.32, 808.32, 809.76, 844.23, 897.98] y6 = [745.76, 805.89, 804.98, 845.72, 899.34] rate1 = [8.13, 8.21, 6.81, 4.58, 3.58] rate2 = [8.20, 8.12, 6.93, 4.53, 3.81] rate3 = [7.92, 7.97, 7.04, 4.72, 3.49] rate4 = [7.88, 7.93, 6.61, 4.39, 3.72] rate5 = [7.83, 8.21, 7.16, 4.22, 3.58] x_text = ['slice1', 'slice2', 'slice3', 'slice4', 'slice5'] x_list = range(len(x_text)) # 定义中文格式 font = {'family': 'MicroSoft Yahei', 'size': 24} matplotlib.rc('font', **font) _, ax1 = plt.subplots(figsize=(15.84, 10.8)) ax2 = ax1.twinx() b1 = ax1.bar([x - 2.5 * bar_width for x in x_list], y1, bar_width) b2 = ax1.bar([x - 1.5 * bar_width for x in x_list], y2, bar_width) b3 = ax1.bar([x - 0.5 * bar_width for x in x_list], y3, bar_width) b4 = ax1.bar([x + 0.5 * bar_width for x in x_list], y4, bar_width) b5 = ax1.bar([x + 1.5 * bar_width for x in x_list], y5, bar_width) b6 = ax1.bar([x + 2.5 * bar_width for x in x_list], y5, bar_width) l1, = ax2.plot(x_list, rate1) l2, = ax2.plot(x_list, rate2) l3, = ax2.plot(x_list, rate3) l4, = ax2.plot(x_list, rate4) l5, = ax2.plot(x_list, rate5) label_font = {'family': 'MicroSoft Yahei', 'size': 26} plt.xticks(range(len(x_list)), x_text) plt.xlabel('水膜成像层数', font=label_font) ax1.set_ylabel('SNR', font=label_font) ax1.set_ylim(700, 950) ax2.set_ylabel('相比于对照组下降占比/%', font=label_font) legend_font = {'family': 'MicroSoft Yahei', 'size': 15} plt.title('不同刺激电流对水膜成像的影响') plt.legend(handles=[b1, b2, b3, b4, b5, b6, l1, l2, l3, l4, l5], labels=['对照组', 'A组', 'B组', 'C组', 'D组', 'E组', 'line1', 'line2', 'line3', 'line4', 'line5'], prop=legend_font, loc=2, bbox_to_anchor=(1.08, 1.0), borderaxespad=0.) plt.show()

[ "v-runli@microsoft.com" ]

v-runli@microsoft.com

c169e6fcf2692886e4ac0f87de10d9fe39168f51

a0e50cec23451a53d16952d4c55ee924f900ef15

/Wa_Tor.pyde

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[]

no_license

Freidelf/FMFN05-ChaosProject

33b8d4c869dcc8bf13291464f9a167fca83148cb

d0e8859c23459b6143794e3bc7095da97d0ca48f

refs/heads/master

2020-03-17T06:29:00.646910

2018-05-24T08:13:14

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2018-05-21T09:37:32

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pyde

import random import copy #strogatz nonlinear dynamics and chaos class Water: xpos = X ypos = Y moved = 0 def __init__(self, X, Y): self.xpos = X self.ypos = Y self.moved = 0 def move(self): return def isFish(self): return False def isShark(self): return False def isWater(self): return True cols, rows = 200,200; Dim = 1000; SET_INIT_COND = 0; Fishes = 0 Sharks = 0 SAVEDfishes = 0 SAVEDsharks = 0 PLOT_LENGTH = 500 SHOW_PLOT = 0 FishArray = [0]*PLOT_LENGTH SharkArray = [0]*PLOT_LENGTH counter = 0 file = open('nbr_of_animals_run_1.txt', "w") CURRENTmatrix = [[Water(x,y) for x in range(cols)] for y in range(rows)]; SAVEDmatrix = [[Water(x,y) for x in range(cols)] for y in range(rows)]; def setup(): size(Dim, Dim) background(255) def draw(): global Sharks global Fishes global counter global FishArray global SharkArray counter = (counter + 1)%PLOT_LENGTH print("antal hajar: " + str(Sharks) + "antal fiskar: " + str(Fishes)) file.writelines(str(Sharks) + "," + str(Fishes) + "\n" ) if SET_INIT_COND == 0: initCondition() else: frameRate(60) t = random.randint(1,4) for i in range(cols): for j in range(rows): if t == 1: i = -i if t == 2: j = -j if t == 3: i = -i j = -j chronon(i,j) for i in range(cols): for j in range(rows): temp = CURRENTmatrix[i][j] CURRENTmatrix[i][j].moved = 0 if temp.isWater(): fill(0) elif temp.isFish(): fill(0,0,255) elif temp.isShark(): fill(0,255,0) rectMode(CORNER) rect(i*Dim/cols,j*Dim/rows,Dim/cols,Dim/rows) FishArray[counter] = Fishes SharkArray[counter] = Sharks if SHOW_PLOT == 1: # fill(255) # rect(Dim/2,2*Dim/4,Dim/2,Dim/4) # for i in range(PLOT_LENGTH): # fill(255,0,0) # rect(Dim/2 + i, 3*Dim/4 - FishArray[i]/(Dim/5),1,1) # fill(0) fill(255) rect(Dim/2,2*Dim/4,Dim/2,Dim/4) for i in range(PLOT_LENGTH): fill(0) rect(Dim/2 + 10 + FishArray[i]/(Dim/40), 3*Dim/4 - 10 - SharkArray[i]/(Dim/40),1,1) stroke(0) line(Dim/2 + 10, 3*Dim/4, Dim/2 + 10, Dim/2) line(Dim/2, 3*Dim/4 - 10, Dim, 3*Dim/4 - 10) noStroke() def initCondition(): noStroke() for i in range(cols): for j in range(rows): temp = CURRENTmatrix[i][j] if temp.isWater(): fill(0) elif temp.isFish(): fill(0,0,255) elif temp.isShark(): fill(0,255,0) rectMode(CORNER) rect(i*Dim/cols,j*Dim/rows,Dim/cols,Dim/rows) def chronon(x,y): if (CURRENTmatrix[x][y].moved == 0): CURRENTmatrix[x][y].moved = 1 CURRENTmatrix[x][y].move() def keyPressed(): counter = 1 global SET_INIT_COND global SHOW_PLOT global Fishes global Sharks global SAVEDmatrix global SAVEDfishes global SAVEDsharks global file if key == DELETE: # SAVEDmatrix = copy.deepcopy(CURRENTmatrix) SAVEDfishes = Fishes SAVEDsharks = Sharks for i in range(cols): for j in range(rows): if CURRENTmatrix [i][j].isWater(): SAVEDmatrix[i][j] = Water(i,j) elif CURRENTmatrix[i][j].isFish(): SAVEDmatrix[i][j] = Fish(i,j) SAVEDmatrix[i][j].ReprodTimer = CURRENTmatrix[i][j].ReprodTimer else: SAVEDmatrix[i][j] = Shark(i,j) SAVEDmatrix[i][j].ReprodTimer = CURRENTmatrix[i][j].ReprodTimer SAVEDmatrix[i][j].Energy = CURRENTmatrix[i][j].Energy if key == BACKSPACE: counter += 1 file = open("nbr_of_animals_run_" + str(counter) + ".txt", "w") Fishes = SAVEDfishes Sharks = SAVEDsharks for i in range(cols): for j in range(rows): if SAVEDmatrix [i][j].isWater(): CURRENTmatrix[i][j] = Water(i,j) elif SAVEDmatrix[i][j].isFish(): CURRENTmatrix[i][j] = Fish(i,j) CURRENTmatrix[i][j].ReprodTimer = SAVEDmatrix[i][j].ReprodTimer else: CURRENTmatrix[i][j] = Shark(i,j) CURRENTmatrix[i][j].ReprodTimer = SAVEDmatrix[i][j].ReprodTimer CURRENTmatrix[i][j].Energy = SAVEDmatrix[i][j].Energy if key == ENTER: if SET_INIT_COND == 0: SET_INIT_COND = 1 else: SET_INIT_COND = 0 if key == TAB: Fishes = 0 Sharks = 0 for i in range(cols): for j in range(rows): d = random.randint(0,500) if d < 400: CURRENTmatrix[i][j] = Water(i,j) elif d < 494: CURRENTmatrix[i][j] = Fish(i,j) else: CURRENTmatrix[i][j] = Shark(i,j) def keyReleased(): global SHOW_PLOT if key == BACKSPACE: SHOW_PLOT = 0 # def mousePressed(): # global Fishes # global Sharks # if mouseButton == LEFT: # temp = CURRENTmatrix[floor(mouseX/(Dim/rows))][floor(mouseY/(Dim/cols))] # if temp.isWater(): # CURRENTmatrix[floor(mouseX/(Dim/rows))][floor(mouseY/(Dim/cols))] = Shark(floor(mouseX/(Dim/rows)), floor(mouseY/(Dim/cols))) # elif temp.isShark(): # Sharks -= 1 # CURRENTmatrix[floor(mouseX/(Dim/rows))][floor(mouseY/(Dim/cols))] = Fish(floor(mouseX/(Dim/rows)), floor(mouseY/(Dim/cols))) # elif temp.isFish: # Fishes -=1 # CURRENTmatrix[floor(mouseX/(Dim/rows))][floor(mouseY/(Dim/cols))] = Water(floor(mouseX/(Dim/rows)), floor(mouseY/(Dim/cols))) class Fish: xpos = X ypos = Y ReprodTimer = 0.0 moved = 0 def __init__(self, X, Y): self.xpos = X self.ypos = Y global Fishes Fishes += 1 def move(self): self.xpos = self.xpos%cols self.ypos = self.ypos%rows surrounding = [CURRENTmatrix[self.xpos%cols][(self.ypos + 1)%rows], CURRENTmatrix[(self.xpos + 1)%cols][self.ypos%rows], CURRENTmatrix[self.xpos%cols][(self.ypos - 1)%rows], CURRENTmatrix[(self.xpos - 1)%cols][self.ypos%rows]] p = ["down","right","up","left"] possibilities = [] oldx = self.xpos%rows oldy = self.ypos%cols for i in range(4): if surrounding[i].isWater(): possibilities.append(p[i]) if possibilities: decision = random.choice(possibilities) if decision == "up": self.ypos -= 1 elif decision == "right": self.xpos += 1 elif decision == "down": self.ypos += 1 elif decision == "left": self.xpos -= 1 global toDraw # toDraw.append((oldx%rows,oldy%cols)) # toDraw.append((self.xpos%rows,self.ypos%cols)) CURRENTmatrix[self.xpos%rows][self.ypos%cols] = self if self.ReprodTimer > 1: CURRENTmatrix[oldx][oldy] = Fish(oldx,oldy) self.ReprodTimer = 0.0 else: CURRENTmatrix[oldx][oldy] = Water(oldx,oldy) self.ReprodTimer += 0.08 def isFish(self): return True def isShark(self): return False def isWater(self): return False class Shark: moved = 0 xpos = X ypos = Y ReprodTimer = 0.0 Energy = 0 global Fishes global Sharks def __init__(self, X, Y): self.xpos = X self.ypos = Y global Sharks Sharks += 1 self.Energy = 0.7 def move(self): if self.Energy < 0.0: # toDraw.append((self.xpos%rows,self.ypos%cols)) CURRENTmatrix[self.xpos%cols][self.ypos%rows] = Water(self.xpos%cols, self.ypos%rows) global Sharks Sharks -= 1 return self.xpos = self.xpos%cols self.ypos = self.ypos%rows surrounding = [CURRENTmatrix[self.xpos%cols][(self.ypos + 1)%rows], CURRENTmatrix[(self.xpos + 1)%cols][self.ypos%rows], CURRENTmatrix[self.xpos%cols][(self.ypos - 1)%rows], CURRENTmatrix[(self.xpos - 1)%cols][self.ypos%rows]] p = ["down","right","up","left"] possibilitiesWater = [] possibilitiesFish = [] oldx = self.xpos oldy = self.ypos for i in range(4): if surrounding[i].isWater(): possibilitiesWater.append(p[i]) if surrounding[i].isFish(): possibilitiesFish.append(p[i]) if not possibilitiesFish: if possibilitiesWater: decision = random.choice(possibilitiesWater) if decision == "up": self.ypos -= 1 elif decision == "right": self.xpos += 1 elif decision == "down": self.ypos += 1 elif decision == "left": self.xpos -= 1 CURRENTmatrix[self.xpos%cols][self.ypos%rows] = self # toDraw.append((oldx%rows,oldy%cols)) # toDraw.append((self.xpos%rows,self.ypos%cols)) if self.Energy > 0.95: CURRENTmatrix[oldx][oldy] = Shark(oldx,oldy) else: CURRENTmatrix[oldx][oldy] = Water(oldx,oldy) else: decision = random.choice(possibilitiesFish) global Fishes Fishes -= 1 if self.Energy < 0.85: self.Energy +=0.15 if decision == "up": self.ypos -= 1 elif decision == "right": self.xpos += 1 elif decision == "down": self.ypos += 1 elif decision == "left": self.xpos -= 1 CURRENTmatrix[self.xpos%cols][self.ypos%rows] = self # toDraw.append((oldx%rows,oldy%cols)) # toDraw.append((self.xpos%rows,self.ypos%cols)) if self.Energy > 0.98: CURRENTmatrix[oldx][oldy] = Shark(oldx,oldy) else: CURRENTmatrix[oldx][oldy] = Water(oldx,oldy) self.Energy -= 0.04 def isFish(self): return False def isShark(self): return True def isWater(self): return False

[ "noreply@github.com" ]

Freidelf.noreply@github.com

6afd366e3327f8166973d2de937fb4955ff5dd05

e08319a0eee6ef40eb544b1c694233ac26388de0

/clue/adafruit-circuitpython-bundle-6.x-mpy-20210209/examples/ssd1608_simpletest.py

05880b48d5ca02f48517ceed4535faf61876954e

[]

no_license

rwhiffen/circuit-python

363b873b2429521296bcab5d5c88271eb5270073

23b3d577c9d1ed6cb48aa7f48c77911bf62cd5c3

refs/heads/master

2021-12-04T11:04:29.368458

2021-11-30T00:54:29

229,330,747

0

null

UTF-8

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# SPDX-FileCopyrightText: 2021 ladyada for Adafruit Industries # SPDX-License-Identifier: MIT """Simple test script for 1.54" 200x200 monochrome display. Supported products: * Adafruit 1.54" Monochrome ePaper Display Breakout * https://www.adafruit.com/product/4196 """ import time import board import displayio import adafruit_ssd1608 displayio.release_displays() # This pinout works on a Feather M4 and may need to be altered for other boards. spi = board.SPI() # Uses SCK and MOSI epd_cs = board.D9 epd_dc = board.D10 epd_reset = board.D5 epd_busy = board.D6 display_bus = displayio.FourWire( spi, command=epd_dc, chip_select=epd_cs, reset=epd_reset, baudrate=1000000 ) time.sleep(1) display = adafruit_ssd1608.SSD1608( display_bus, width=200, height=200, busy_pin=epd_busy ) g = displayio.Group() f = open("/display-ruler.bmp", "rb") pic = displayio.OnDiskBitmap(f) t = displayio.TileGrid(pic, pixel_shader=displayio.ColorConverter()) g.append(t) display.show(g) display.refresh() print("refreshed") time.sleep(120)

[ "rich@whiffen.orgw" ]

rich@whiffen.orgw

baac9827d91c255af7cab59e278e6eb5be8f1fe0

e1db584567a8b8cc135d9beec7c4997685494dd8

/other/sensor_magnet/tests/test_actuators.py

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[ "MIT" ]

permissive

gurbain/tigrillo2

459d4b8d950736faea7af77256a7ce11f1338fba

66ad26c0aff39da74ca76f712b6f01b40d383f34

refs/heads/master

2021-08-16T12:02:39.863300

2018-06-08T22:44:40

113,203,747

2

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py

import serial import sys import time c = serial.Serial("/dev/ttyACM0", 9600) if not c.isOpen(): c.open() i = 0 while i < 1000: c.write("A90,90,90,90") time.sleep(0.01) while i < 1000: for read in c.read(): sys.stdout.write(read) i += 1

[ "gabrielurbain@gmail.com" ]

gabrielurbain@gmail.com

6f51ba0a831c70bc2b9a14c70a2d156a6a454c37

76c4c6965ca962422408fe3d35b7c0dbd00af68b

/Python/search3_non_recursive.py

e386b50749881c706ca200bceedb6b858af9a78d

[]

no_license

AnandDhanalakota/Vin-Pro

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4ff62d476ce8bb30ffe89b9735907e128d02b2e9

refs/heads/master

2020-03-21T10:06:15.553553

2017-10-13T21:15:14

null

0

null

UTF-8

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py

import os """ __________________d:\___________________ / / \ \ suneeta softs vin-pro songs_____ / \ | / \ / \ sunc++,cellsoft .... cs, asm,... sainikudu, newyork, .... ========================================================================= ===!!!=====!!!==!!!==!!!!=====!!!==!!!!!!!!!!==!!!!!!!!!!==!!!=========== ===!!!=====!!!==!!!==!!!!!====!!!==!!!!!!!!====!!!!!!!!====!!!=========== ===!!!=====!!!==!!!==!!!=!!===!!!==!!!=========!!!=========!!!=========== ====!!!===!!!===!!!==!!!==!!==!!!==!!!!!!!=====!!!!!!!=====!!!=========== ====!!!===!!!===!!!==!!!==!!==!!!==!!!!!!!=====!!!!!!!=====!!!=========== ====!!!===!!!===!!!==!!!===!!=!!!==!!!=========!!!=========!!!=========== =====!!!=!!!====!!!==!!!====!!!!!==!!!!!!!!====!!!!!!!!====!!!!!!!!!===== ======!!!!!=====!!!==!!!=====!!!!==!!!!!!!!!!==!!!!!!!!!!==!!!!!!!!!!!=== ========================================================================= """ paths=["d:\\"] temp=[] while 1: for xyz in paths: for dir in os.listdir(xyz): path = xyz+'\\'+dir if os.path.isdir(path): temp+=[path] else: print path if not temp : break paths = temp temp = []

[ "vineelko@microsoft.com" ]

vineelko@microsoft.com

b9235f24b706833d32ec5e0f7f82633c4ab895c0

2aeab3fbd7a8778760a6036557f422fc4356f761

/pycrescolib/globalcontroller.py

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[]

no_license

rynsy/pycrescolib-demo

8658cdcb950cbcbad1a5206317d4566af66a1e70

3a6091247a691888dadd0038a50fc32d49b67a80

refs/heads/master

2023-08-22T20:37:27.229250

2021-10-20T17:35:15

null

0

null

UTF-8

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6,008

py

import json from pycrescolib.utils import decompress_param, get_jar_info, compress_param, encode_data class globalcontroller(object): def __init__(self, messaging): self.messaging = messaging def submit_pipeline(self, cadl): message_event_type = 'CONFIG' message_payload = dict() message_payload['action'] = 'gpipelinesubmit' message_payload['action_gpipeline'] = compress_param(json.dumps(cadl)) message_payload['action_tenantid'] = '0' retry = self.messaging.global_controller_msgevent(True, message_event_type, message_payload) # returns status and gpipeline_id return retry def remove_pipeline(self, pipeline_id): message_event_type = 'CONFIG' message_payload = dict() message_payload['action'] = 'gpipelineremove' message_payload['action_pipelineid'] = pipeline_id retry = self.messaging.global_controller_msgevent(True, message_event_type, message_payload) return retry def get_pipeline_list(self): message_event_type = 'EXEC' message_payload = dict() message_payload['action'] = 'getgpipelinestatus' reply = self.messaging.global_controller_msgevent(True, message_event_type, message_payload) reply = json.loads(decompress_param(reply['pipelineinfo']))['pipelines'] return reply def get_pipeline_info(self, pipeline_id): message_event_type = 'EXEC' message_payload = dict() message_payload['action'] = 'getgpipeline' message_payload['action_pipelineid'] = pipeline_id reply = self.messaging.global_controller_msgevent(True, message_event_type, message_payload) reply = json.loads(decompress_param(reply['gpipeline'])) return reply def get_pipeline_status(self, pipeline_id): reply = self.get_pipeline_info(pipeline_id) status_code = int(reply['status_code']) return status_code def get_agent_list(self, dst_region=None): message_event_type = 'EXEC' message_payload = dict() message_payload['action'] = 'listagents' if dst_region is not None: message_payload['action_region'] = dst_region reply = self.messaging.global_controller_msgevent(True, message_event_type, message_payload) reply = json.loads(decompress_param(reply['agentslist']))['agents'] ''' for agent in reply: dst_agent = agent['name'] dst_region = agent['region'] r = self.get_agent_resources(dst_region,dst_agent) print(r) ''' return reply def get_agent_resources(self, dst_region, dst_agent): message_event_type = 'EXEC' message_payload = dict() message_payload['action'] = 'resourceinfo' message_payload['action_region'] = dst_region message_payload['action_agent'] = dst_agent reply = self.messaging.global_controller_msgevent(True, message_event_type, message_payload) reply = json.loads(json.loads(decompress_param(reply['resourceinfo']))['agentresourceinfo'][0]['perf']) return reply def get_plugin_list(self): # this code makes use of a global message to find a specific plugin type, then send a message to that plugin message_event_type = 'EXEC' message_payload = dict() message_payload['action'] = 'listplugins' result = self.messaging.global_controller_msgevent(message_event_type, message_payload) pluginslist = json.loads(decompress_param(result['pluginslist'])) plugin_name = 'io.cresco.repo' pluginlist = pluginslist['plugins'] for plugin in pluginlist: if plugin['pluginname'] == plugin_name: break; message_payload['action'] = 'repolist' for i in range(10): result = self.messaging.global_plugin_msgevent(True, message_event_type, message_payload, plugin['region'], plugin['agent'], plugin['name']) print(result) def upload_plugin_global(self, jar_file_path): #get data from jar configparams = get_jar_info(jar_file_path) # "configparams" ''' configparams = dict() configparams['pluginname'] = 'io.cresco.cepdemo' configparams['version'] = '1.0.0.SNAPSHOT-2020-09-01T203900Z' configparams['md5'] = '34de550afdac3bcabbbac99ea5a1519c' ''' #read input file in_file = open(jar_file_path, "rb") # opening for [r]eading as [b]inary jar_data = in_file.read() # if you only wanted to read 512 bytes, do .read(512) in_file.close() message_event_type = 'CONFIG' message_payload = dict() message_payload['action'] = 'savetorepo' message_payload['configparams'] = compress_param(json.dumps(configparams)) message_payload['jardata'] = encode_data(jar_data) reply = self.messaging.global_controller_msgevent(True, message_event_type, message_payload) # returns reply with status and pluginid return reply def get_region_resources(self, dst_region): message_event_type = 'EXEC' message_payload = dict() message_payload['action'] = 'resourceinfo' message_payload['action_region'] = dst_region reply = self.messaging.global_controller_msgevent(True, message_event_type, message_payload) #reply = json.loads(json.loads(decompress_param(reply['resourceinfo']))['agentresourceinfo'][0]['perf']) reply = json.loads(decompress_param(reply['resourceinfo'])) return reply def get_region_list(self): message_event_type = 'EXEC' message_payload = dict() message_payload['action'] = 'listregions' reply = self.messaging.global_controller_msgevent(True, message_event_type, message_payload) reply = json.loads(decompress_param(reply['regionslist']))['regions'] return reply

[ "cbumgardner@gmail.com" ]

cbumgardner@gmail.com

d485cfa23c7f446ebfa1be31d86428513cf3a031

711756b796d68035dc6a39060515200d1d37a274

/output_cog/optimized_38775.py

aa5ba685bb6ffb0c7e77e41ce4af889ae20a5bd0

[]

no_license

batxes/exocyst_scripts

8b109c279c93dd68c1d55ed64ad3cca93e3c95ca

a6c487d5053b9b67db22c59865e4ef2417e53030

refs/heads/master

2020-06-16T20:16:24.840725

2016-11-30T16:23:16

75,075,164

0

null

UTF-8

Python

false

10,853

py

import _surface import chimera try: import chimera.runCommand except: pass from VolumePath import markerset as ms try: from VolumePath import Marker_Set, Link new_marker_set=Marker_Set except: from VolumePath import volume_path_dialog d= volume_path_dialog(True) new_marker_set= d.new_marker_set marker_sets={} surf_sets={} if "Cog2_GFPN" not in marker_sets: s=new_marker_set('Cog2_GFPN') marker_sets["Cog2_GFPN"]=s s= marker_sets["Cog2_GFPN"] mark=s.place_marker((576.874, 540.822, 485.573), (0.89, 0.1, 0.1), 18.4716) if "Cog2_0" not in marker_sets: s=new_marker_set('Cog2_0') marker_sets["Cog2_0"]=s s= marker_sets["Cog2_0"] mark=s.place_marker((598.091, 565.823, 546.833), (0.89, 0.1, 0.1), 17.1475) if "Cog2_1" not in marker_sets: s=new_marker_set('Cog2_1') marker_sets["Cog2_1"]=s s= marker_sets["Cog2_1"] mark=s.place_marker((623.318, 587.057, 622.151), (0.89, 0.1, 0.1), 17.1475) if "Cog2_GFPC" not in marker_sets: s=new_marker_set('Cog2_GFPC') marker_sets["Cog2_GFPC"]=s s= marker_sets["Cog2_GFPC"] mark=s.place_marker((611.306, 453.071, 585.762), (0.89, 0.1, 0.1), 18.4716) if "Cog2_Anch" not in marker_sets: s=new_marker_set('Cog2_Anch') marker_sets["Cog2_Anch"]=s s= marker_sets["Cog2_Anch"] mark=s.place_marker((686.143, 680.092, 779.238), (0.89, 0.1, 0.1), 18.4716) if "Cog3_GFPN" not in marker_sets: s=new_marker_set('Cog3_GFPN') marker_sets["Cog3_GFPN"]=s s= marker_sets["Cog3_GFPN"] mark=s.place_marker((583.516, 558.339, 528.007), (1, 1, 0), 18.4716) if "Cog3_0" not in marker_sets: s=new_marker_set('Cog3_0') marker_sets["Cog3_0"]=s s= marker_sets["Cog3_0"] mark=s.place_marker((582.521, 557.977, 526.767), (1, 1, 0.2), 17.1475) if "Cog3_1" not in marker_sets: s=new_marker_set('Cog3_1') marker_sets["Cog3_1"]=s s= marker_sets["Cog3_1"] mark=s.place_marker((563.861, 574.065, 540.474), (1, 1, 0.2), 17.1475) if "Cog3_2" not in marker_sets: s=new_marker_set('Cog3_2') marker_sets["Cog3_2"]=s s= marker_sets["Cog3_2"] mark=s.place_marker((550.902, 557.517, 559.175), (1, 1, 0.2), 17.1475) if "Cog3_3" not in marker_sets: s=new_marker_set('Cog3_3') marker_sets["Cog3_3"]=s s= marker_sets["Cog3_3"] mark=s.place_marker((542.229, 538.937, 539.879), (1, 1, 0.2), 17.1475) if "Cog3_4" not in marker_sets: s=new_marker_set('Cog3_4') marker_sets["Cog3_4"]=s s= marker_sets["Cog3_4"] mark=s.place_marker((514.423, 537.738, 535.439), (1, 1, 0.2), 17.1475) if "Cog3_5" not in marker_sets: s=new_marker_set('Cog3_5') marker_sets["Cog3_5"]=s s= marker_sets["Cog3_5"] mark=s.place_marker((493.423, 550.371, 549.158), (1, 1, 0.2), 17.1475) if "Cog3_GFPC" not in marker_sets: s=new_marker_set('Cog3_GFPC') marker_sets["Cog3_GFPC"]=s s= marker_sets["Cog3_GFPC"] mark=s.place_marker((588.124, 560.504, 499.948), (1, 1, 0.4), 18.4716) if "Cog3_Anch" not in marker_sets: s=new_marker_set('Cog3_Anch') marker_sets["Cog3_Anch"]=s s= marker_sets["Cog3_Anch"] mark=s.place_marker((397.241, 545.752, 593.753), (1, 1, 0.4), 18.4716) if "Cog4_GFPN" not in marker_sets: s=new_marker_set('Cog4_GFPN') marker_sets["Cog4_GFPN"]=s s= marker_sets["Cog4_GFPN"] mark=s.place_marker((511.625, 649.589, 724.084), (0, 0, 0.8), 18.4716) if "Cog4_0" not in marker_sets: s=new_marker_set('Cog4_0') marker_sets["Cog4_0"]=s s= marker_sets["Cog4_0"] mark=s.place_marker((511.625, 649.589, 724.084), (0, 0, 0.8), 17.1475) if "Cog4_1" not in marker_sets: s=new_marker_set('Cog4_1') marker_sets["Cog4_1"]=s s= marker_sets["Cog4_1"] mark=s.place_marker((523.133, 647.98, 698.582), (0, 0, 0.8), 17.1475) if "Cog4_2" not in marker_sets: s=new_marker_set('Cog4_2') marker_sets["Cog4_2"]=s s= marker_sets["Cog4_2"] mark=s.place_marker((525.208, 637.211, 672.704), (0, 0, 0.8), 17.1475) if "Cog4_3" not in marker_sets: s=new_marker_set('Cog4_3') marker_sets["Cog4_3"]=s s= marker_sets["Cog4_3"] mark=s.place_marker((532.957, 627.817, 647.087), (0, 0, 0.8), 17.1475) if "Cog4_4" not in marker_sets: s=new_marker_set('Cog4_4') marker_sets["Cog4_4"]=s s= marker_sets["Cog4_4"] mark=s.place_marker((546.267, 619.066, 623.377), (0, 0, 0.8), 17.1475) if "Cog4_5" not in marker_sets: s=new_marker_set('Cog4_5') marker_sets["Cog4_5"]=s s= marker_sets["Cog4_5"] mark=s.place_marker((562.643, 609.598, 601.974), (0, 0, 0.8), 17.1475) if "Cog4_6" not in marker_sets: s=new_marker_set('Cog4_6') marker_sets["Cog4_6"]=s s= marker_sets["Cog4_6"] mark=s.place_marker((572.556, 598.08, 577.717), (0, 0, 0.8), 17.1475) if "Cog4_GFPC" not in marker_sets: s=new_marker_set('Cog4_GFPC') marker_sets["Cog4_GFPC"]=s s= marker_sets["Cog4_GFPC"] mark=s.place_marker((386.539, 560.102, 751.952), (0, 0, 0.8), 18.4716) if "Cog4_Anch" not in marker_sets: s=new_marker_set('Cog4_Anch') marker_sets["Cog4_Anch"]=s s= marker_sets["Cog4_Anch"] mark=s.place_marker((758.573, 634.065, 399.435), (0, 0, 0.8), 18.4716) if "Cog5_GFPN" not in marker_sets: s=new_marker_set('Cog5_GFPN') marker_sets["Cog5_GFPN"]=s s= marker_sets["Cog5_GFPN"] mark=s.place_marker((605.732, 621.954, 593.966), (0.3, 0.3, 0.3), 18.4716) if "Cog5_0" not in marker_sets: s=new_marker_set('Cog5_0') marker_sets["Cog5_0"]=s s= marker_sets["Cog5_0"] mark=s.place_marker((605.732, 621.954, 593.966), (0.3, 0.3, 0.3), 17.1475) if "Cog5_1" not in marker_sets: s=new_marker_set('Cog5_1') marker_sets["Cog5_1"]=s s= marker_sets["Cog5_1"] mark=s.place_marker((624.194, 600.594, 591.035), (0.3, 0.3, 0.3), 17.1475) if "Cog5_2" not in marker_sets: s=new_marker_set('Cog5_2') marker_sets["Cog5_2"]=s s= marker_sets["Cog5_2"] mark=s.place_marker((642.628, 579.779, 597.448), (0.3, 0.3, 0.3), 17.1475) if "Cog5_3" not in marker_sets: s=new_marker_set('Cog5_3') marker_sets["Cog5_3"]=s s= marker_sets["Cog5_3"] mark=s.place_marker((636.927, 556.397, 613.563), (0.3, 0.3, 0.3), 17.1475) if "Cog5_GFPC" not in marker_sets: s=new_marker_set('Cog5_GFPC') marker_sets["Cog5_GFPC"]=s s= marker_sets["Cog5_GFPC"] mark=s.place_marker((608.734, 497.014, 507.327), (0.3, 0.3, 0.3), 18.4716) if "Cog5_Anch" not in marker_sets: s=new_marker_set('Cog5_Anch') marker_sets["Cog5_Anch"]=s s= marker_sets["Cog5_Anch"] mark=s.place_marker((661.84, 608.133, 724.55), (0.3, 0.3, 0.3), 18.4716) if "Cog6_GFPN" not in marker_sets: s=new_marker_set('Cog6_GFPN') marker_sets["Cog6_GFPN"]=s s= marker_sets["Cog6_GFPN"] mark=s.place_marker((606.707, 539.554, 539.754), (0.21, 0.49, 0.72), 18.4716) if "Cog6_0" not in marker_sets: s=new_marker_set('Cog6_0') marker_sets["Cog6_0"]=s s= marker_sets["Cog6_0"] mark=s.place_marker((606.921, 539.122, 539.763), (0.21, 0.49, 0.72), 17.1475) if "Cog6_1" not in marker_sets: s=new_marker_set('Cog6_1') marker_sets["Cog6_1"]=s s= marker_sets["Cog6_1"] mark=s.place_marker((602.149, 532.701, 513.098), (0.21, 0.49, 0.72), 17.1475) if "Cog6_2" not in marker_sets: s=new_marker_set('Cog6_2') marker_sets["Cog6_2"]=s s= marker_sets["Cog6_2"] mark=s.place_marker((585.556, 539.87, 491.818), (0.21, 0.49, 0.72), 17.1475) if "Cog6_3" not in marker_sets: s=new_marker_set('Cog6_3') marker_sets["Cog6_3"]=s s= marker_sets["Cog6_3"] mark=s.place_marker((559.419, 549.009, 495.526), (0.21, 0.49, 0.72), 17.1475) if "Cog6_4" not in marker_sets: s=new_marker_set('Cog6_4') marker_sets["Cog6_4"]=s s= marker_sets["Cog6_4"] mark=s.place_marker((544.538, 565.047, 513.163), (0.21, 0.49, 0.72), 17.1475) if "Cog6_5" not in marker_sets: s=new_marker_set('Cog6_5') marker_sets["Cog6_5"]=s s= marker_sets["Cog6_5"] mark=s.place_marker((523.277, 578.335, 526.055), (0.21, 0.49, 0.72), 17.1475) if "Cog6_6" not in marker_sets: s=new_marker_set('Cog6_6') marker_sets["Cog6_6"]=s s= marker_sets["Cog6_6"] mark=s.place_marker((497.067, 580.776, 536.171), (0.21, 0.49, 0.72), 17.1475) if "Cog6_GFPC" not in marker_sets: s=new_marker_set('Cog6_GFPC') marker_sets["Cog6_GFPC"]=s s= marker_sets["Cog6_GFPC"] mark=s.place_marker((565.717, 630.616, 525.687), (0.21, 0.49, 0.72), 18.4716) if "Cog6_Anch" not in marker_sets: s=new_marker_set('Cog6_Anch') marker_sets["Cog6_Anch"]=s s= marker_sets["Cog6_Anch"] mark=s.place_marker((429.722, 525.801, 547.642), (0.21, 0.49, 0.72), 18.4716) if "Cog7_GFPN" not in marker_sets: s=new_marker_set('Cog7_GFPN') marker_sets["Cog7_GFPN"]=s s= marker_sets["Cog7_GFPN"] mark=s.place_marker((611.519, 627.195, 530.498), (0.7, 0.7, 0.7), 18.4716) if "Cog7_0" not in marker_sets: s=new_marker_set('Cog7_0') marker_sets["Cog7_0"]=s s= marker_sets["Cog7_0"] mark=s.place_marker((614.858, 606.49, 544.807), (0.7, 0.7, 0.7), 17.1475) if "Cog7_1" not in marker_sets: s=new_marker_set('Cog7_1') marker_sets["Cog7_1"]=s s= marker_sets["Cog7_1"] mark=s.place_marker((624.743, 560.967, 576.711), (0.7, 0.7, 0.7), 17.1475) if "Cog7_2" not in marker_sets: s=new_marker_set('Cog7_2') marker_sets["Cog7_2"]=s s= marker_sets["Cog7_2"] mark=s.place_marker((637.906, 513.521, 608.447), (0.7, 0.7, 0.7), 17.1475) if "Cog7_GFPC" not in marker_sets: s=new_marker_set('Cog7_GFPC') marker_sets["Cog7_GFPC"]=s s= marker_sets["Cog7_GFPC"] mark=s.place_marker((663.455, 481.934, 537.566), (0.7, 0.7, 0.7), 18.4716) if "Cog7_Anch" not in marker_sets: s=new_marker_set('Cog7_Anch') marker_sets["Cog7_Anch"]=s s= marker_sets["Cog7_Anch"] mark=s.place_marker((637.242, 473.186, 705.227), (0.7, 0.7, 0.7), 18.4716) if "Cog8_0" not in marker_sets: s=new_marker_set('Cog8_0') marker_sets["Cog8_0"]=s s= marker_sets["Cog8_0"] mark=s.place_marker((579.913, 519.906, 554.457), (1, 0.5, 0), 17.1475) if "Cog8_1" not in marker_sets: s=new_marker_set('Cog8_1') marker_sets["Cog8_1"]=s s= marker_sets["Cog8_1"] mark=s.place_marker((602.611, 526.699, 570.855), (1, 0.5, 0), 17.1475) if "Cog8_2" not in marker_sets: s=new_marker_set('Cog8_2') marker_sets["Cog8_2"]=s s= marker_sets["Cog8_2"] mark=s.place_marker((631.482, 529.241, 573.589), (1, 0.5, 0), 17.1475) if "Cog8_3" not in marker_sets: s=new_marker_set('Cog8_3') marker_sets["Cog8_3"]=s s= marker_sets["Cog8_3"] mark=s.place_marker((653.721, 545.788, 584.54), (1, 0.5, 0), 17.1475) if "Cog8_4" not in marker_sets: s=new_marker_set('Cog8_4') marker_sets["Cog8_4"]=s s= marker_sets["Cog8_4"] mark=s.place_marker((672.791, 567.689, 591.808), (1, 0.5, 0), 17.1475) if "Cog8_5" not in marker_sets: s=new_marker_set('Cog8_5') marker_sets["Cog8_5"]=s s= marker_sets["Cog8_5"] mark=s.place_marker((675.915, 594.142, 605.603), (1, 0.5, 0), 17.1475) if "Cog8_GFPC" not in marker_sets: s=new_marker_set('Cog8_GFPC') marker_sets["Cog8_GFPC"]=s s= marker_sets["Cog8_GFPC"] mark=s.place_marker((625.733, 581.142, 545.283), (1, 0.6, 0.1), 18.4716) if "Cog8_Anch" not in marker_sets: s=new_marker_set('Cog8_Anch') marker_sets["Cog8_Anch"]=s s= marker_sets["Cog8_Anch"] mark=s.place_marker((726.761, 616.193, 667.856), (1, 0.6, 0.1), 18.4716) for k in surf_sets.keys(): chimera.openModels.add([surf_sets[k]])

[ "batxes@gmail.com" ]

batxes@gmail.com

cafd06c3fd03d5ead92c2a9dbd0bb3a1e82d9bb7

29585537e2e96c169ae83cd660070ba3af0a43a9

/admin_confirm/file_cache.py

7baaefffd6d41d2e7817da142ddfd47e3bb6e475

[ "Apache-2.0" ]

permissive

ballke-dev/django-admin-confirm

4c400e0d6cb3799e7d9901731db99b4a579ec06e

21f5a37c5ecf1fee30f95d8a2ce01207916a22f8

refs/heads/main

2023-06-23T03:54:50.326670

2021-07-22T17:04:13

386,659,834

0

NOASSERTION

2021-07-16T14:11:48

2021-07-16T14:11:47

null

UTF-8

Python

false

3,633

py

""" FileCache - caches files for ModelAdmins with confirmations. Code modified from: https://github.com/MaistrenkoAnton/filefield-cache/blob/master/filefield_cache/cache.py Original copy date: April 22, 2021 --- Modified to be compatible with more versions of Django/Python --- MIT License Copyright (c) 2020 Maistrenko Anton 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. """ from django.core.files.uploadedfile import InMemoryUploadedFile try: from cStringIO import StringIO as BytesIO # noqa: WPS433 except ImportError: from io import BytesIO # noqa: WPS433, WPS440 from django.core.cache import cache from admin_confirm.constants import CACHE_TIMEOUT from admin_confirm.utils import log class FileCache(object): "Cache file data and retain the file upon confirmation." timeout = CACHE_TIMEOUT def __init__(self): self.cache = cache self.cached_keys = [] def set(self, key, upload): """ Set file data to cache for 1000s :param key: cache key :param upload: file data """ try: # noqa: WPS229 state = { "name": upload.name, "size": upload.size, "content_type": upload.content_type, "charset": upload.charset, "content": upload.file.read(), } upload.file.seek(0) self.cache.set(key, state, self.timeout) log(f"Setting file cache with {key}") self.cached_keys.append(key) except AttributeError: # pragma: no cover pass # noqa: WPS420 def get(self, key): """ Get the file data from cache using specific cache key :param key: cache key :return: File data """ upload = None state = self.cache.get(key) if state: file = BytesIO() file.write(state["content"]) upload = InMemoryUploadedFile( file=file, field_name="file", name=state["name"], content_type=state["content_type"], size=state["size"], charset=state["charset"], ) upload.file.seek(0) log(f"Getting file cache with {key}") return upload def delete(self, key): """ Delete file data from cache :param key: cache key """ self.cache.delete(key) self.cached_keys.remove(key) def delete_all(self): "Delete all cached file data from cache." self.cache.delete_many(self.cached_keys) self.cached_keys = []

[ "noreply@github.com" ]

ballke-dev.noreply@github.com

a4b4a0a3244cac402cda7f3b4ed5278efc2fa651

c4b47ba53d40e861571c82f8a968a989974dc433

/fireball/blobs/admin.py

454a72b4217a2e674b995a6f5a635ca10bde368e

[]

no_license

underlost/fireball

4be3e441a82f6a0fbb603b33be8493f03019392e

3cf312fa88860e9f2e9f34479b5b1962dae09f55

refs/heads/master

2016-09-01T18:45:18.059628

2013-06-03T16:26:12

null

0

null

UTF-8

Python

false

250

py

from django.contrib import admin from fireball.blobs.models import Blob class BlobAdmin(admin.ModelAdmin): list_filter = ('user',) search_fields = ['description','url',] list_display = ('user', 'url',) admin.site.register(Blob,BlobAdmin)

[ "underlost@gmail.com" ]

underlost@gmail.com

f789665473eb4f3fe85e39c56c286c518c116c7a

895f5581d12379c507018f36c58b63920190f287

/ShoppingCart/urls.py

db7a92f20ab5b5ff56278559a6c793a21f336dda

[]

no_license

Feras-1998/graduation-project

c1a4d65449b573f5c10c4059d78b423f13ad9be8

b93e736ecc710d7ec1f31e4db30c3c5288a7bcf5

refs/heads/master

2023-03-19T12:06:53.199292

2021-03-21T17:56:55

350,102,451

0

null

UTF-8

Python

false

777

py

from django.urls import path from ShoppingCart import ShoppingCartView as view app_name = "ShoppingCart" urlpatterns = [ path('viwe', view.view_shopping_cart, name='view_shopping_cart'), path('add/product/<int:product_id>/quantity/<int:quantity>', view.add_product_to_cart, name='add_product_to_cart'), path('edit/product/<int:product_id>/quantity/<int:quantity>', view.edit_product_quantity_cart, name='edit_product_quantity_cart'), path('add/product/<int:product_id>/quantity/<int:quantity>', view.add_offer_to_cart, name='add_offer_to_cart'), path('edit/product/<int:CartOffer_id>/quantity/<int:quantity>', view.edit_offer_quantity_cart, name='edit_offer_quantity_cart'), path('test/', view.mainUseCase, name='mainUseCase') ]

[ "feras@LAPTOP-LNAH19LL" ]

feras@LAPTOP-LNAH19LL

9606fc118d763b54512c8278ba1755a594d973cb

61e98b0302a43ab685be4c255b4ecf2979db55b6

/sdks/python/.tox/docs/lib/python2.7/site-packages/sphinx/environment/__init__.py

f760583ece5807e4a028e2fb675ec70d4f9836db

[ "BSD-3-Clause", "EPL-2.0", "CDDL-1.0", "Apache-2.0", "WTFPL", "GPL-2.0-only", "BSD-2-Clause", "MIT", "LicenseRef-scancode-unknown-license-reference", "CDDL-1.1", "Classpath-exception-2.0" ]

permissive

dzenyu/kafka

5631c05a6de6e288baeb8955bdddf2ff60ec2a0e

d69a24bce8d108f43376271f89ecc3b81c7b6622

refs/heads/master

2021-07-16T12:31:09.623509

2021-06-28T18:22:16

198,724,535

0

Apache-2.0

2019-07-24T23:51:47

2019-07-24T23:51:46

null

UTF-8

Python

false

50,863

py

# -*- coding: utf-8 -*- """ sphinx.environment ~~~~~~~~~~~~~~~~~~ Global creation environment. :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ import re import os import sys import time import types import codecs import fnmatch from os import path from glob import glob from six import iteritems, itervalues, class_types, next from six.moves import cPickle as pickle from docutils import nodes from docutils.io import NullOutput from docutils.core import Publisher from docutils.utils import Reporter, relative_path, get_source_line from docutils.parsers.rst import roles from docutils.parsers.rst.languages import en as english from docutils.frontend import OptionParser from sphinx import addnodes from sphinx.io import SphinxStandaloneReader, SphinxDummyWriter, SphinxFileInput from sphinx.util import get_matching_docs, docname_join, FilenameUniqDict from sphinx.util.nodes import clean_astext, WarningStream, is_translatable, \ process_only_nodes from sphinx.util.osutil import SEP, getcwd, fs_encoding, ensuredir from sphinx.util.images import guess_mimetype from sphinx.util.i18n import find_catalog_files, get_image_filename_for_language, \ search_image_for_language from sphinx.util.console import bold, purple from sphinx.util.docutils import sphinx_domains from sphinx.util.matching import compile_matchers from sphinx.util.parallel import ParallelTasks, parallel_available, make_chunks from sphinx.util.websupport import is_commentable from sphinx.errors import SphinxError, ExtensionError from sphinx.versioning import add_uids, merge_doctrees from sphinx.transforms import SphinxContentsFilter from sphinx.environment.managers.indexentries import IndexEntries from sphinx.environment.managers.toctree import Toctree default_settings = { 'embed_stylesheet': False, 'cloak_email_addresses': True, 'pep_base_url': 'https://www.python.org/dev/peps/', 'rfc_base_url': 'https://tools.ietf.org/html/', 'input_encoding': 'utf-8-sig', 'doctitle_xform': False, 'sectsubtitle_xform': False, 'halt_level': 5, 'file_insertion_enabled': True, } # This is increased every time an environment attribute is added # or changed to properly invalidate pickle files. # # NOTE: increase base version by 2 to have distinct numbers for Py2 and 3 ENV_VERSION = 50 + (sys.version_info[0] - 2) dummy_reporter = Reporter('', 4, 4) versioning_conditions = { 'none': False, 'text': is_translatable, 'commentable': is_commentable, } class NoUri(Exception): """Raised by get_relative_uri if there is no URI available.""" pass class BuildEnvironment(object): """ The environment in which the ReST files are translated. Stores an inventory of cross-file targets and provides doctree transformations to resolve links to them. """ # --------- ENVIRONMENT PERSISTENCE ---------------------------------------- @staticmethod def frompickle(srcdir, config, filename): with open(filename, 'rb') as picklefile: env = pickle.load(picklefile) if env.version != ENV_VERSION: raise IOError('build environment version not current') if env.srcdir != srcdir: raise IOError('source directory has changed') env.config.values = config.values return env def topickle(self, filename): # remove unpicklable attributes warnfunc = self._warnfunc self.set_warnfunc(None) values = self.config.values del self.config.values domains = self.domains del self.domains managers = self.detach_managers() # remove potentially pickling-problematic values from config for key, val in list(vars(self.config).items()): if key.startswith('_') or \ isinstance(val, types.ModuleType) or \ isinstance(val, types.FunctionType) or \ isinstance(val, class_types): del self.config[key] with open(filename, 'wb') as picklefile: pickle.dump(self, picklefile, pickle.HIGHEST_PROTOCOL) # reset attributes self.attach_managers(managers) self.domains = domains self.config.values = values self.set_warnfunc(warnfunc) # --------- ENVIRONMENT INITIALIZATION ------------------------------------- def __init__(self, srcdir, doctreedir, config): self.doctreedir = doctreedir self.srcdir = srcdir self.config = config # the method of doctree versioning; see set_versioning_method self.versioning_condition = None self.versioning_compare = None # the application object; only set while update() runs self.app = None # all the registered domains, set by the application self.domains = {} # the docutils settings for building self.settings = default_settings.copy() self.settings['env'] = self # the function to write warning messages with self._warnfunc = None # this is to invalidate old pickles self.version = ENV_VERSION # All "docnames" here are /-separated and relative and exclude # the source suffix. self.found_docs = set() # contains all existing docnames self.all_docs = {} # docname -> mtime at the time of reading # contains all read docnames self.dependencies = {} # docname -> set of dependent file # names, relative to documentation root self.included = set() # docnames included from other documents self.reread_always = set() # docnames to re-read unconditionally on # next build # File metadata self.metadata = {} # docname -> dict of metadata items # TOC inventory self.titles = {} # docname -> title node self.longtitles = {} # docname -> title node; only different if # set differently with title directive self.tocs = {} # docname -> table of contents nodetree self.toc_num_entries = {} # docname -> number of real entries # used to determine when to show the TOC # in a sidebar (don't show if it's only one item) self.toc_secnumbers = {} # docname -> dict of sectionid -> number self.toc_fignumbers = {} # docname -> dict of figtype -> # dict of figureid -> number self.toctree_includes = {} # docname -> list of toctree includefiles self.files_to_rebuild = {} # docname -> set of files # (containing its TOCs) to rebuild too self.glob_toctrees = set() # docnames that have :glob: toctrees self.numbered_toctrees = set() # docnames that have :numbered: toctrees # domain-specific inventories, here to be pickled self.domaindata = {} # domainname -> domain-specific dict # Other inventories self.indexentries = {} # docname -> list of # (type, string, target, aliasname) self.versionchanges = {} # version -> list of (type, docname, # lineno, module, descname, content) # these map absolute path -> (docnames, unique filename) self.images = FilenameUniqDict() self.dlfiles = FilenameUniqDict() # temporary data storage while reading a document self.temp_data = {} # context for cross-references (e.g. current module or class) # this is similar to temp_data, but will for example be copied to # attributes of "any" cross references self.ref_context = {} self.managers = {} self.init_managers() def init_managers(self): managers = {} for manager_class in [IndexEntries, Toctree]: managers[manager_class.name] = manager_class(self) self.attach_managers(managers) def attach_managers(self, managers): for name, manager in iteritems(managers): self.managers[name] = manager manager.attach(self) def detach_managers(self): managers = self.managers self.managers = {} for _, manager in iteritems(managers): manager.detach(self) return managers def set_warnfunc(self, func): self._warnfunc = func self.settings['warning_stream'] = WarningStream(func) def set_versioning_method(self, method, compare): """This sets the doctree versioning method for this environment. Versioning methods are a builder property; only builders with the same versioning method can share the same doctree directory. Therefore, we raise an exception if the user tries to use an environment with an incompatible versioning method. """ if method not in versioning_conditions: raise ValueError('invalid versioning method: %r' % method) condition = versioning_conditions[method] if self.versioning_condition not in (None, condition): raise SphinxError('This environment is incompatible with the ' 'selected builder, please choose another ' 'doctree directory.') self.versioning_condition = condition self.versioning_compare = compare def warn(self, docname, msg, lineno=None, **kwargs): """Emit a warning. This differs from using ``app.warn()`` in that the warning may not be emitted instantly, but collected for emitting all warnings after the update of the environment. """ # strange argument order is due to backwards compatibility self._warnfunc(msg, (docname, lineno), **kwargs) def warn_node(self, msg, node, **kwargs): """Like :meth:`warn`, but with source information taken from *node*.""" self._warnfunc(msg, '%s:%s' % get_source_line(node), **kwargs) def clear_doc(self, docname): """Remove all traces of a source file in the inventory.""" if docname in self.all_docs: self.all_docs.pop(docname, None) self.reread_always.discard(docname) self.metadata.pop(docname, None) self.dependencies.pop(docname, None) self.titles.pop(docname, None) self.longtitles.pop(docname, None) self.images.purge_doc(docname) self.dlfiles.purge_doc(docname) for version, changes in self.versionchanges.items(): new = [change for change in changes if change[1] != docname] changes[:] = new for manager in itervalues(self.managers): manager.clear_doc(docname) for domain in self.domains.values(): domain.clear_doc(docname) def merge_info_from(self, docnames, other, app): """Merge global information gathered about *docnames* while reading them from the *other* environment. This possibly comes from a parallel build process. """ docnames = set(docnames) for docname in docnames: self.all_docs[docname] = other.all_docs[docname] if docname in other.reread_always: self.reread_always.add(docname) self.metadata[docname] = other.metadata[docname] if docname in other.dependencies: self.dependencies[docname] = other.dependencies[docname] self.titles[docname] = other.titles[docname] self.longtitles[docname] = other.longtitles[docname] self.images.merge_other(docnames, other.images) self.dlfiles.merge_other(docnames, other.dlfiles) for version, changes in other.versionchanges.items(): self.versionchanges.setdefault(version, []).extend( change for change in changes if change[1] in docnames) for manager in itervalues(self.managers): manager.merge_other(docnames, other) for domainname, domain in self.domains.items(): domain.merge_domaindata(docnames, other.domaindata[domainname]) app.emit('env-merge-info', self, docnames, other) def path2doc(self, filename): """Return the docname for the filename if the file is document. *filename* should be absolute or relative to the source directory. """ if filename.startswith(self.srcdir): filename = filename[len(self.srcdir) + 1:] for suffix in self.config.source_suffix: if fnmatch.fnmatch(filename, '*' + suffix): return filename[:-len(suffix)] else: # the file does not have docname return None def doc2path(self, docname, base=True, suffix=None): """Return the filename for the document name. If *base* is True, return absolute path under self.srcdir. If *base* is None, return relative path to self.srcdir. If *base* is a path string, return absolute path under that. If *suffix* is not None, add it instead of config.source_suffix. """ docname = docname.replace(SEP, path.sep) if suffix is None: for candidate_suffix in self.config.source_suffix: if path.isfile(path.join(self.srcdir, docname) + candidate_suffix): suffix = candidate_suffix break else: # document does not exist suffix = self.config.source_suffix[0] if base is True: return path.join(self.srcdir, docname) + suffix elif base is None: return docname + suffix else: return path.join(base, docname) + suffix def relfn2path(self, filename, docname=None): """Return paths to a file referenced from a document, relative to documentation root and absolute. In the input "filename", absolute filenames are taken as relative to the source dir, while relative filenames are relative to the dir of the containing document. """ if filename.startswith('/') or filename.startswith(os.sep): rel_fn = filename[1:] else: docdir = path.dirname(self.doc2path(docname or self.docname, base=None)) rel_fn = path.join(docdir, filename) try: # the path.abspath() might seem redundant, but otherwise artifacts # such as ".." will remain in the path return rel_fn, path.abspath(path.join(self.srcdir, rel_fn)) except UnicodeDecodeError: # the source directory is a bytestring with non-ASCII characters; # let's try to encode the rel_fn in the file system encoding enc_rel_fn = rel_fn.encode(sys.getfilesystemencoding()) return rel_fn, path.abspath(path.join(self.srcdir, enc_rel_fn)) def find_files(self, config, buildername=None): """Find all source files in the source dir and put them in self.found_docs. """ matchers = compile_matchers( config.exclude_patterns[:] + config.templates_path + config.html_extra_path + ['**/_sources', '.#*', '**/.#*', '*.lproj/**'] ) self.found_docs = set() for docname in get_matching_docs(self.srcdir, config.source_suffix, exclude_matchers=matchers): if os.access(self.doc2path(docname), os.R_OK): self.found_docs.add(docname) else: self.warn(docname, "document not readable. Ignored.") # Current implementation is applying translated messages in the reading # phase.Therefore, in order to apply the updated message catalog, it is # necessary to re-process from the reading phase. Here, if dependency # is set for the doc source and the mo file, it is processed again from # the reading phase when mo is updated. In the future, we would like to # move i18n process into the writing phase, and remove these lines. if buildername != 'gettext': # add catalog mo file dependency for docname in self.found_docs: catalog_files = find_catalog_files( docname, self.srcdir, self.config.locale_dirs, self.config.language, self.config.gettext_compact) for filename in catalog_files: self.dependencies.setdefault(docname, set()).add(filename) def get_outdated_files(self, config_changed): """Return (added, changed, removed) sets.""" # clear all files no longer present removed = set(self.all_docs) - self.found_docs added = set() changed = set() if config_changed: # config values affect e.g. substitutions added = self.found_docs else: for docname in self.found_docs: if docname not in self.all_docs: added.add(docname) continue # if the doctree file is not there, rebuild if not path.isfile(self.doc2path(docname, self.doctreedir, '.doctree')): changed.add(docname) continue # check the "reread always" list if docname in self.reread_always: changed.add(docname) continue # check the mtime of the document mtime = self.all_docs[docname] newmtime = path.getmtime(self.doc2path(docname)) if newmtime > mtime: changed.add(docname) continue # finally, check the mtime of dependencies for dep in self.dependencies.get(docname, ()): try: # this will do the right thing when dep is absolute too deppath = path.join(self.srcdir, dep) if not path.isfile(deppath): changed.add(docname) break depmtime = path.getmtime(deppath) if depmtime > mtime: changed.add(docname) break except EnvironmentError: # give it another chance changed.add(docname) break return added, changed, removed def update(self, config, srcdir, doctreedir, app): """(Re-)read all files new or changed since last update. Store all environment docnames in the canonical format (ie using SEP as a separator in place of os.path.sep). """ config_changed = False if self.config is None: msg = '[new config] ' config_changed = True else: # check if a config value was changed that affects how # doctrees are read for key, descr in iteritems(config.values): if descr[1] != 'env': continue if self.config[key] != config[key]: msg = '[config changed] ' config_changed = True break else: msg = '' # this value is not covered by the above loop because it is handled # specially by the config class if self.config.extensions != config.extensions: msg = '[extensions changed] ' config_changed = True # the source and doctree directories may have been relocated self.srcdir = srcdir self.doctreedir = doctreedir self.find_files(config, app.buildername) self.config = config # this cache also needs to be updated every time self._nitpick_ignore = set(self.config.nitpick_ignore) app.info(bold('updating environment: '), nonl=True) added, changed, removed = self.get_outdated_files(config_changed) # allow user intervention as well for docs in app.emit('env-get-outdated', self, added, changed, removed): changed.update(set(docs) & self.found_docs) # if files were added or removed, all documents with globbed toctrees # must be reread if added or removed: # ... but not those that already were removed changed.update(self.glob_toctrees & self.found_docs) msg += '%s added, %s changed, %s removed' % (len(added), len(changed), len(removed)) app.info(msg) self.app = app # clear all files no longer present for docname in removed: app.emit('env-purge-doc', self, docname) self.clear_doc(docname) # read all new and changed files docnames = sorted(added | changed) # allow changing and reordering the list of docs to read app.emit('env-before-read-docs', self, docnames) # check if we should do parallel or serial read par_ok = False if parallel_available and len(docnames) > 5 and app.parallel > 1: par_ok = True for extname, md in app._extension_metadata.items(): ext_ok = md.get('parallel_read_safe') if ext_ok: continue if ext_ok is None: app.warn('the %s extension does not declare if it ' 'is safe for parallel reading, assuming it ' 'isn\'t - please ask the extension author to ' 'check and make it explicit' % extname) app.warn('doing serial read') else: app.warn('the %s extension is not safe for parallel ' 'reading, doing serial read' % extname) par_ok = False break if par_ok: self._read_parallel(docnames, app, nproc=app.parallel) else: self._read_serial(docnames, app) if config.master_doc not in self.all_docs: raise SphinxError('master file %s not found' % self.doc2path(config.master_doc)) self.app = None for retval in app.emit('env-updated', self): if retval is not None: docnames.extend(retval) return sorted(docnames) def _read_serial(self, docnames, app): for docname in app.status_iterator(docnames, 'reading sources... ', purple, len(docnames)): # remove all inventory entries for that file app.emit('env-purge-doc', self, docname) self.clear_doc(docname) self.read_doc(docname, app) def _read_parallel(self, docnames, app, nproc): # clear all outdated docs at once for docname in docnames: app.emit('env-purge-doc', self, docname) self.clear_doc(docname) def read_process(docs): self.app = app self.warnings = [] self.set_warnfunc(lambda *args, **kwargs: self.warnings.append((args, kwargs))) for docname in docs: self.read_doc(docname, app) # allow pickling self to send it back self.set_warnfunc(None) del self.app del self.domains del self.config.values del self.config return self def merge(docs, otherenv): warnings.extend(otherenv.warnings) self.merge_info_from(docs, otherenv, app) tasks = ParallelTasks(nproc) chunks = make_chunks(docnames, nproc) warnings = [] for chunk in app.status_iterator( chunks, 'reading sources... ', purple, len(chunks)): tasks.add_task(read_process, chunk, merge) # make sure all threads have finished app.info(bold('waiting for workers...')) tasks.join() for warning, kwargs in warnings: self._warnfunc(*warning, **kwargs) def check_dependents(self, already): to_rewrite = (self.toctree.assign_section_numbers() + self.toctree.assign_figure_numbers()) for docname in set(to_rewrite): if docname not in already: yield docname # --------- SINGLE FILE READING -------------------------------------------- def warn_and_replace(self, error): """Custom decoding error handler that warns and replaces.""" linestart = error.object.rfind(b'\n', 0, error.start) lineend = error.object.find(b'\n', error.start) if lineend == -1: lineend = len(error.object) lineno = error.object.count(b'\n', 0, error.start) + 1 self.warn(self.docname, 'undecodable source characters, ' 'replacing with "?": %r' % (error.object[linestart + 1:error.start] + b'>>>' + error.object[error.start:error.end] + b'<<<' + error.object[error.end:lineend]), lineno) return (u'?', error.end) def read_doc(self, docname, app=None): """Parse a file and add/update inventory entries for the doctree.""" self.temp_data['docname'] = docname # defaults to the global default, but can be re-set in a document self.temp_data['default_domain'] = \ self.domains.get(self.config.primary_domain) self.settings['input_encoding'] = self.config.source_encoding self.settings['trim_footnote_reference_space'] = \ self.config.trim_footnote_reference_space self.settings['gettext_compact'] = self.config.gettext_compact docutilsconf = path.join(self.srcdir, 'docutils.conf') # read docutils.conf from source dir, not from current dir OptionParser.standard_config_files[1] = docutilsconf if path.isfile(docutilsconf): self.note_dependency(docutilsconf) with sphinx_domains(self): if self.config.default_role: role_fn, messages = roles.role(self.config.default_role, english, 0, dummy_reporter) if role_fn: roles._roles[''] = role_fn else: self.warn(docname, 'default role %s not found' % self.config.default_role) codecs.register_error('sphinx', self.warn_and_replace) # publish manually reader = SphinxStandaloneReader(self.app, parsers=self.config.source_parsers) pub = Publisher(reader=reader, writer=SphinxDummyWriter(), destination_class=NullOutput) pub.set_components(None, 'restructuredtext', None) pub.process_programmatic_settings(None, self.settings, None) src_path = self.doc2path(docname) source = SphinxFileInput(app, self, source=None, source_path=src_path, encoding=self.config.source_encoding) pub.source = source pub.settings._source = src_path pub.set_destination(None, None) pub.publish() doctree = pub.document # post-processing self.process_dependencies(docname, doctree) self.process_images(docname, doctree) self.process_downloads(docname, doctree) self.process_metadata(docname, doctree) self.create_title_from(docname, doctree) for manager in itervalues(self.managers): manager.process_doc(docname, doctree) for domain in itervalues(self.domains): domain.process_doc(self, docname, doctree) # allow extension-specific post-processing if app: app.emit('doctree-read', doctree) # store time of reading, for outdated files detection # (Some filesystems have coarse timestamp resolution; # therefore time.time() can be older than filesystem's timestamp. # For example, FAT32 has 2sec timestamp resolution.) self.all_docs[docname] = max( time.time(), path.getmtime(self.doc2path(docname))) if self.versioning_condition: old_doctree = None if self.versioning_compare: # get old doctree try: with open(self.doc2path(docname, self.doctreedir, '.doctree'), 'rb') as f: old_doctree = pickle.load(f) except EnvironmentError: pass # add uids for versioning if not self.versioning_compare or old_doctree is None: list(add_uids(doctree, self.versioning_condition)) else: list(merge_doctrees( old_doctree, doctree, self.versioning_condition)) # make it picklable doctree.reporter = None doctree.transformer = None doctree.settings.warning_stream = None doctree.settings.env = None doctree.settings.record_dependencies = None # cleanup self.temp_data.clear() self.ref_context.clear() roles._roles.pop('', None) # if a document has set a local default role # save the parsed doctree doctree_filename = self.doc2path(docname, self.doctreedir, '.doctree') ensuredir(path.dirname(doctree_filename)) with open(doctree_filename, 'wb') as f: pickle.dump(doctree, f, pickle.HIGHEST_PROTOCOL) # utilities to use while reading a document @property def docname(self): """Returns the docname of the document currently being parsed.""" return self.temp_data['docname'] @property def currmodule(self): """Backwards compatible alias. Will be removed.""" self.warn(self.docname, 'env.currmodule is being referenced by an ' 'extension; this API will be removed in the future') return self.ref_context.get('py:module') @property def currclass(self): """Backwards compatible alias. Will be removed.""" self.warn(self.docname, 'env.currclass is being referenced by an ' 'extension; this API will be removed in the future') return self.ref_context.get('py:class') def new_serialno(self, category=''): """Return a serial number, e.g. for index entry targets. The number is guaranteed to be unique in the current document. """ key = category + 'serialno' cur = self.temp_data.get(key, 0) self.temp_data[key] = cur + 1 return cur def note_dependency(self, filename): """Add *filename* as a dependency of the current document. This means that the document will be rebuilt if this file changes. *filename* should be absolute or relative to the source directory. """ self.dependencies.setdefault(self.docname, set()).add(filename) def note_included(self, filename): """Add *filename* as a included from other document. This means the document is not orphaned. *filename* should be absolute or relative to the source directory. """ self.included.add(self.path2doc(filename)) def note_reread(self): """Add the current document to the list of documents that will automatically be re-read at the next build. """ self.reread_always.add(self.docname) def note_versionchange(self, type, version, node, lineno): self.versionchanges.setdefault(version, []).append( (type, self.temp_data['docname'], lineno, self.ref_context.get('py:module'), self.temp_data.get('object'), node.astext())) # post-processing of read doctrees def process_dependencies(self, docname, doctree): """Process docutils-generated dependency info.""" cwd = getcwd() frompath = path.join(path.normpath(self.srcdir), 'dummy') deps = doctree.settings.record_dependencies if not deps: return for dep in deps.list: # the dependency path is relative to the working dir, so get # one relative to the srcdir if isinstance(dep, bytes): dep = dep.decode(fs_encoding) relpath = relative_path(frompath, path.normpath(path.join(cwd, dep))) self.dependencies.setdefault(docname, set()).add(relpath) def process_downloads(self, docname, doctree): """Process downloadable file paths. """ for node in doctree.traverse(addnodes.download_reference): targetname = node['reftarget'] rel_filename, filename = self.relfn2path(targetname, docname) self.dependencies.setdefault(docname, set()).add(rel_filename) if not os.access(filename, os.R_OK): self.warn_node('download file not readable: %s' % filename, node) continue uniquename = self.dlfiles.add_file(docname, filename) node['filename'] = uniquename def process_images(self, docname, doctree): """Process and rewrite image URIs.""" def collect_candidates(imgpath, candidates): globbed = {} for filename in glob(imgpath): new_imgpath = relative_path(path.join(self.srcdir, 'dummy'), filename) try: mimetype = guess_mimetype(filename) if mimetype not in candidates: globbed.setdefault(mimetype, []).append(new_imgpath) except (OSError, IOError) as err: self.warn_node('image file %s not readable: %s' % (filename, err), node) for key, files in iteritems(globbed): candidates[key] = sorted(files, key=len)[0] # select by similarity for node in doctree.traverse(nodes.image): # Map the mimetype to the corresponding image. The writer may # choose the best image from these candidates. The special key * is # set if there is only single candidate to be used by a writer. # The special key ? is set for nonlocal URIs. node['candidates'] = candidates = {} imguri = node['uri'] if imguri.startswith('data:'): self.warn_node('image data URI found. some builders might not support', node, type='image', subtype='data_uri') candidates['?'] = imguri continue elif imguri.find('://') != -1: self.warn_node('nonlocal image URI found: %s' % imguri, node, type='image', subtype='nonlocal_uri') candidates['?'] = imguri continue rel_imgpath, full_imgpath = self.relfn2path(imguri, docname) if self.config.language: # substitute figures (ex. foo.png -> foo.en.png) i18n_full_imgpath = search_image_for_language(full_imgpath, self) if i18n_full_imgpath != full_imgpath: full_imgpath = i18n_full_imgpath rel_imgpath = relative_path(path.join(self.srcdir, 'dummy'), i18n_full_imgpath) # set imgpath as default URI node['uri'] = rel_imgpath if rel_imgpath.endswith(os.extsep + '*'): if self.config.language: # Search language-specific figures at first i18n_imguri = get_image_filename_for_language(imguri, self) _, full_i18n_imgpath = self.relfn2path(i18n_imguri, docname) collect_candidates(full_i18n_imgpath, candidates) collect_candidates(full_imgpath, candidates) else: candidates['*'] = rel_imgpath # map image paths to unique image names (so that they can be put # into a single directory) for imgpath in itervalues(candidates): self.dependencies.setdefault(docname, set()).add(imgpath) if not os.access(path.join(self.srcdir, imgpath), os.R_OK): self.warn_node('image file not readable: %s' % imgpath, node) continue self.images.add_file(docname, imgpath) def process_metadata(self, docname, doctree): """Process the docinfo part of the doctree as metadata. Keep processing minimal -- just return what docutils says. """ self.metadata[docname] = md = {} try: docinfo = doctree[0] except IndexError: # probably an empty document return if docinfo.__class__ is not nodes.docinfo: # nothing to see here return for node in docinfo: # nodes are multiply inherited... if isinstance(node, nodes.authors): md['authors'] = [author.astext() for author in node] elif isinstance(node, nodes.TextElement): # e.g. author md[node.__class__.__name__] = node.astext() else: name, body = node md[name.astext()] = body.astext() for name, value in md.items(): if name in ('tocdepth',): try: value = int(value) except ValueError: value = 0 md[name] = value del doctree[0] def create_title_from(self, docname, document): """Add a title node to the document (just copy the first section title), and store that title in the environment. """ titlenode = nodes.title() longtitlenode = titlenode # explicit title set with title directive; use this only for # the <title> tag in HTML output if 'title' in document: longtitlenode = nodes.title() longtitlenode += nodes.Text(document['title']) # look for first section title and use that as the title for node in document.traverse(nodes.section): visitor = SphinxContentsFilter(document) node[0].walkabout(visitor) titlenode += visitor.get_entry_text() break else: # document has no title titlenode += nodes.Text('<no title>') self.titles[docname] = titlenode self.longtitles[docname] = longtitlenode def note_toctree(self, docname, toctreenode): """Note a TOC tree directive in a document and gather information about file relations from it. """ self.toctree.note_toctree(docname, toctreenode) def get_toc_for(self, docname, builder): """Return a TOC nodetree -- for use on the same page only!""" return self.toctree.get_toc_for(docname, builder) def get_toctree_for(self, docname, builder, collapse, **kwds): """Return the global TOC nodetree.""" return self.toctree.get_toctree_for(docname, builder, collapse, **kwds) def get_domain(self, domainname): """Return the domain instance with the specified name. Raises an ExtensionError if the domain is not registered. """ try: return self.domains[domainname] except KeyError: raise ExtensionError('Domain %r is not registered' % domainname) # --------- RESOLVING REFERENCES AND TOCTREES ------------------------------ def get_doctree(self, docname): """Read the doctree for a file from the pickle and return it.""" doctree_filename = self.doc2path(docname, self.doctreedir, '.doctree') with open(doctree_filename, 'rb') as f: doctree = pickle.load(f) doctree.settings.env = self doctree.reporter = Reporter(self.doc2path(docname), 2, 5, stream=WarningStream(self._warnfunc)) return doctree def get_and_resolve_doctree(self, docname, builder, doctree=None, prune_toctrees=True, includehidden=False): """Read the doctree from the pickle, resolve cross-references and toctrees and return it. """ if doctree is None: doctree = self.get_doctree(docname) # resolve all pending cross-references self.resolve_references(doctree, docname, builder) # now, resolve all toctree nodes for toctreenode in doctree.traverse(addnodes.toctree): result = self.resolve_toctree(docname, builder, toctreenode, prune=prune_toctrees, includehidden=includehidden) if result is None: toctreenode.replace_self([]) else: toctreenode.replace_self(result) return doctree def resolve_toctree(self, docname, builder, toctree, prune=True, maxdepth=0, titles_only=False, collapse=False, includehidden=False): """Resolve a *toctree* node into individual bullet lists with titles as items, returning None (if no containing titles are found) or a new node. If *prune* is True, the tree is pruned to *maxdepth*, or if that is 0, to the value of the *maxdepth* option on the *toctree* node. If *titles_only* is True, only toplevel document titles will be in the resulting tree. If *collapse* is True, all branches not containing docname will be collapsed. """ return self.toctree.resolve_toctree(docname, builder, toctree, prune, maxdepth, titles_only, collapse, includehidden) def resolve_references(self, doctree, fromdocname, builder): for node in doctree.traverse(addnodes.pending_xref): contnode = node[0].deepcopy() newnode = None typ = node['reftype'] target = node['reftarget'] refdoc = node.get('refdoc', fromdocname) domain = None try: if 'refdomain' in node and node['refdomain']: # let the domain try to resolve the reference try: domain = self.domains[node['refdomain']] except KeyError: raise NoUri newnode = domain.resolve_xref(self, refdoc, builder, typ, target, node, contnode) # really hardwired reference types elif typ == 'any': newnode = self._resolve_any_reference(builder, refdoc, node, contnode) elif typ == 'doc': newnode = self._resolve_doc_reference(builder, refdoc, node, contnode) # no new node found? try the missing-reference event if newnode is None: newnode = builder.app.emit_firstresult( 'missing-reference', self, node, contnode) # still not found? warn if node wishes to be warned about or # we are in nit-picky mode if newnode is None: self._warn_missing_reference(refdoc, typ, target, node, domain) except NoUri: newnode = contnode node.replace_self(newnode or contnode) # remove only-nodes that do not belong to our builder process_only_nodes(doctree, builder.tags, warn_node=self.warn_node) # allow custom references to be resolved builder.app.emit('doctree-resolved', doctree, fromdocname) def _warn_missing_reference(self, refdoc, typ, target, node, domain): warn = node.get('refwarn') if self.config.nitpicky: warn = True if self._nitpick_ignore: dtype = domain and '%s:%s' % (domain.name, typ) or typ if (dtype, target) in self._nitpick_ignore: warn = False # for "std" types also try without domain name if (not domain or domain.name == 'std') and \ (typ, target) in self._nitpick_ignore: warn = False if not warn: return if domain and typ in domain.dangling_warnings: msg = domain.dangling_warnings[typ] elif typ == 'doc': msg = 'unknown document: %(target)s' elif node.get('refdomain', 'std') not in ('', 'std'): msg = '%s:%s reference target not found: %%(target)s' % \ (node['refdomain'], typ) else: msg = '%r reference target not found: %%(target)s' % typ self.warn_node(msg % {'target': target}, node, type='ref', subtype=typ) def _resolve_doc_reference(self, builder, refdoc, node, contnode): # directly reference to document by source name; # can be absolute or relative docname = docname_join(refdoc, node['reftarget']) if docname in self.all_docs: if node['refexplicit']: # reference with explicit title caption = node.astext() else: caption = clean_astext(self.titles[docname]) innernode = nodes.inline(caption, caption) innernode['classes'].append('doc') newnode = nodes.reference('', '', internal=True) newnode['refuri'] = builder.get_relative_uri(refdoc, docname) newnode.append(innernode) return newnode def _resolve_any_reference(self, builder, refdoc, node, contnode): """Resolve reference generated by the "any" role.""" target = node['reftarget'] results = [] # first, try resolving as :doc: doc_ref = self._resolve_doc_reference(builder, refdoc, node, contnode) if doc_ref: results.append(('doc', doc_ref)) # next, do the standard domain (makes this a priority) results.extend(self.domains['std'].resolve_any_xref( self, refdoc, builder, target, node, contnode)) for domain in self.domains.values(): if domain.name == 'std': continue # we did this one already try: results.extend(domain.resolve_any_xref(self, refdoc, builder, target, node, contnode)) except NotImplementedError: # the domain doesn't yet support the new interface # we have to manually collect possible references (SLOW) for role in domain.roles: res = domain.resolve_xref(self, refdoc, builder, role, target, node, contnode) if res and isinstance(res[0], nodes.Element): results.append(('%s:%s' % (domain.name, role), res)) # now, see how many matches we got... if not results: return None if len(results) > 1: nice_results = ' or '.join(':%s:' % r[0] for r in results) self.warn_node('more than one target found for \'any\' cross-' 'reference %r: could be %s' % (target, nice_results), node) res_role, newnode = results[0] # Override "any" class with the actual role type to get the styling # approximately correct. res_domain = res_role.split(':')[0] if newnode and newnode[0].get('classes'): newnode[0]['classes'].append(res_domain) newnode[0]['classes'].append(res_role.replace(':', '-')) return newnode def create_index(self, builder, group_entries=True, _fixre=re.compile(r'(.*) ([(][^()]*[)])')): return self.indices.create_index(builder, group_entries=group_entries, _fixre=_fixre) def collect_relations(self): traversed = set() def traverse_toctree(parent, docname): if parent == docname: self.warn(docname, 'self referenced toctree found. Ignored.') return # traverse toctree by pre-order yield parent, docname traversed.add(docname) for child in (self.toctree_includes.get(docname) or []): for subparent, subdocname in traverse_toctree(docname, child): if subdocname not in traversed: yield subparent, subdocname traversed.add(subdocname) relations = {} docnames = traverse_toctree(None, self.config.master_doc) prevdoc = None parent, docname = next(docnames) for nextparent, nextdoc in docnames: relations[docname] = [parent, prevdoc, nextdoc] prevdoc = docname docname = nextdoc parent = nextparent relations[docname] = [parent, prevdoc, None] return relations def check_consistency(self): """Do consistency checks.""" for docname in sorted(self.all_docs): if docname not in self.files_to_rebuild: if docname == self.config.master_doc: # the master file is not included anywhere ;) continue if docname in self.included: # the document is included from other documents continue if 'orphan' in self.metadata[docname]: continue self.warn(docname, 'document isn\'t included in any toctree')

[ "alex.barreto@databricks.com" ]

alex.barreto@databricks.com

fc67a4b14f117567e54950ce6518e138da60cd34

11b7a20881ef60d8de74885ca3de9e549eb9ca41

/temp.py

7eca941e0b829654d9fcf88cfb71b715a7b640ff

[]

no_license

Buvi14/BIlling-app

030d6c3f65cd69a3890f5c392e9b6763313f219a

c9b0f8ea798ff89a6082f9436b43e0c489a0633c

refs/heads/master

2020-03-22T11:02:55.381723

2018-07-08T11:41:24

139,943,011

1

3

null

UTF-8

Python

false

659

py

# -*- coding: utf-8 -*- """ Spyder Editor This is a temporary script file. """ #Plotting the graph for given data import jason from sklearn.externals import joblib import matplotlib.pyplot as mp #plotting the graph import pandas as pd #for import the data mydata=pd.read_csv('example.csv') #import the data x=input("Enter the product_id/product_value/price:") #x-axis values y=input("Enter the product_id/product_value/price:") #y-axis values mydata.plot.bar(x,y) mp.show() #showing the histogram

[ "noreply@github.com" ]

Buvi14.noreply@github.com

596411c05f2c94b4b357beb48a6cac370bb39083

82fce9aae9e855a73f4e92d750e6a8df2ef877a5

/Lab/venv/lib/python3.8/site-packages/OpenGL/GL/ARB/seamless_cube_map.py

25e63a7d3f689d0ff11cc0c81f81b889b4c44394

[]

no_license

BartoszRudnik/GK

1294f7708902e867dacd7da591b9f2e741bfe9e5

6dc09184a3af07143b9729e42a6f62f13da50128

refs/heads/main

2023-02-20T19:02:12.408974

2021-01-22T10:51:14

307,847,589

0

null

UTF-8

Python

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1,689

py

'''OpenGL extension ARB.seamless_cube_map This module customises the behaviour of the OpenGL.raw.GL.ARB.seamless_cube_map to provide a more Python-friendly API Overview (from the spec) When sampling from cube map textures, a three-dimensional texture coordinate is used to select one of the cube map faces and generate a two dimensional texture coordinate ( s t ), at which a texel is sampled from the determined face of the cube map texture. Each face of the texture is treated as an independent two-dimensional texture, and the generated ( s t ) coordinate is subjected to the same clamping and wrapping rules as for any other two dimensional texture fetch. Although it is unlikely that the generated ( s t ) coordinate lies significantly outside the determined cube map face, it is often the case that the locations of the individual elements required during a linear sampling do not lie within the determined face, and their coordinates will therefore be modified by the selected clamping and wrapping rules. This often has the effect of producing seams or other discontinuities in the sampled texture. This extension allows implementations to take samples from adjacent cube map faces, providing the ability to create seamless cube maps. The official definition of this extension is available here: http://www.opengl.org/registry/specs/ARB/seamless_cube_map.txt ''' from OpenGL.raw.GL.ARB.seamless_cube_map import _EXTENSION_NAME def glInitSeamlessCubeMapARB(): '''Return boolean indicating whether this extension is available''' from OpenGL import extensions return extensions.hasGLExtension( _EXTENSION_NAME ) ### END AUTOGENERATED SECTION

[ "rudnik49@gmail.com" ]

rudnik49@gmail.com

de5077e4e7e2995c4bcfdc1792f7dab4aef7c588

d1105ac79b89138a9dd6233b881c541041ee8512

/chap3_case2_radiationLineConstraint/heatEq_code_generator.py

e5a672d6d9eda59b5b3c797d0ba229bf0ff2c59e

[]

no_license

bowenfan96/mpc-rom

4db2a165118b07926e5988a92155e740e5d4f18b

a5ab132ea58ea5a4f2229f53eaee1bd180cc6e7b

refs/heads/master

2023-07-27T12:25:28.804007

2021-09-07T13:27:03

378,615,015

0

null

UTF-8

Python

false

2,184

py

# This file exists to parse A and B matrices into pyomo entries # While it seems silly, this is necessary because pyomo's simulator (which calls casadi) does not support matrices # Namely, it cannot simulate variables indexed by more than 1 set (so each variable can only be indexed by time) # It also doesn't support if statements within the model, so this seems to be the only way import numpy as np N = 20 # ----- GENERATE THE MODEL MATRICES ----- # Apply the method of lines on the heat equation to generate the A matrix # Length of the rod = 1 m # Number of segments = number of discretization points - 1 (as 2 ends take up 2 points) length = 1 num_segments = N - 1 # Thermal diffusivity alpha alpha = 0.1 segment_length = length / num_segments # Constant c = alpha / (segment_length ** 2) # Generate A matrix A_mat = np.zeros(shape=(N, N)) for row in range(A_mat.shape[0]): for col in range(A_mat.shape[1]): if row == col: A_mat[row][col] = -2 elif abs(row - col) == 1: A_mat[row][col] = 1 else: A_mat[row][col] = 0 # Multiply constant to all elements in A A = c * A_mat # Generate B matrix # Two sources of heat at each end of the rod num_heaters = 2 B_mat = np.zeros(shape=(N, num_heaters)) # First heater on the left B_mat[0][0] = 1 # Second heater on the right B_mat[N - 1][num_heaters - 1] = 1 # Multiply constant to all elements in B B = c * B_mat for i in range(1, 19): print("self.model.x{} = Var(self.model.time)".format(i)) print("self.model.x{}_dot = DerivativeVar(self.model.x{}, wrt=self.model.time)".format(i, i)) print("self.model.x{}[0].fix(x_init[{}])".format(i, i)) for i in range(1, 19): print( ''' def _ode_x{}(m, _t): return m.x{}_dot[_t] == self.A[{}][{}-1] * m.x{}[_t] + self.A[{}][{}] * m.x{}[_t] + self.A[{}][{}+1] * m.x{}[_t] self.model.x{}_ode = Constraint(self.model.time, rule=_ode_x{})\n '''.format(i, i, i, i, i-1, i, i, i, i, i, i+1, i, i) ) for i in range(1, 19): print("temp_x.append(value(self.model.x{}[time]))".format(i)) for i in range(20): print(" + (m.x{}[_t] ** 4 - env_temp ** 4)".format(i))

[ "bowen@users.noreply.github.com" ]

bowen@users.noreply.github.com

9cbc8919bbd13651a03f5b1e0bce41ea11c7531a

66b72447770c0848f8f9b89c2d4c2473fb9a0581

/0938.range_sum_of_bst_E.py

ede7558859e324f7364560fd9a3387c53e70503c

[]

no_license

zvant/LeetCodeSolutions

5d27a25dbc0ec522bde61d436f88e81c67e1367a

ca2c7959c3ea8912abc0be5575272479a5ef3cb0

refs/heads/master

2022-09-21T06:26:17.520429

2022-09-08T02:40:16

140,584,965

1

0

null

UTF-8

Python

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1,134

py

# https://leetcode.com/problems/range-sum-of-bst/ # 2021/10 # 288 ms # Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right # class Solution: # def BFS(self, root, L, R): # if root is None: # return # if root.val > L: # self.BFS(root.left, L, R) # if root.val < R: # self.BFS(root.right, L, R) # if root.val >= L and root.val <= R: # self.summary += root.val # def rangeSumBST(self, root: TreeNode, L: int, R: int) -> int: # self.summary = 0 # self.BFS(root, L, R) # return self.summary class Solution: def rangeSumBST(self, root: TreeNode, L: int, R: int) -> int: total = 0 S = [root] while len(S) > 0: node = S.pop() if not node is None: x = node.val if x >= L and x <= R: total += x S.append(node.left) S.append(node.right) return total

[ "kzz1994@gmail.com" ]

kzz1994@gmail.com

a7e02fbd7355c82ec1aa61752bec20b5cb11b8ab

0d381e2cb2d66f7ef92c3efff5e6f93aa803963b

/solved/firstBadVersion.py

61e638086b85708f08f2c839bd9e734efb372d0d

[]

no_license

shannonmlance/leetcode

2242ce3b1b636d89728860e9bfa7b266d6a2fd6b

f63e7c1df256b5524f7e891d40620946a618fee2

refs/heads/master

2021-06-27T22:02:09.346343

2021-04-29T00:44:03

225,467,340

0

null

UTF-8

Python

false

2,426

py

# You are a product manager and currently leading a team to develop a new product. Unfortunately, the latest version of your product fails the quality check. Since each version is developed based on the previous version, all the versions after a bad version are also bad. # Suppose you have n versions [1, 2, ..., n] and you want to find out the first bad one, which causes all the following ones to be bad. # You are given an API bool isBadVersion(version) which will return whether version is bad. Implement a function to find the first bad version. You should minimize the number of calls to the API. # Example: # Given n = 5, and version = 4 is the first bad version. # call isBadVersion(3) -> false # call isBadVersion(5) -> true # call isBadVersion(4) -> true # Then 4 is the first bad version. class Solution: def firstBadVersion(self, n): # make recursive call, passing in 1 and the given number of versions as the starting parameters c = self.rFindBadVersion(1, n) # return the number from the recursive call return c # recursive binary search method def rFindBadVersion(self, s, e): # if the given start number is equal to the given end number, return the end number, as this is the first bad version if s == e: return e # find the middle by subtracting the start from the end and dividing the difference, then add the start to the quotient m = (e - s)//2 + s # make the "api" call # if the response is false if not self.isBadVersion(m): # change the start number to equal the middle number, plus one s = m + 1 # if the response is true else: # change the end number to equal the middle number e = m # repeat the recursive call, passing in the updated start and end numbers return self.rFindBadVersion(s, e) # boolean "api" call that returns whether the given version is the first bad version def isBadVersion(self, v): # define the first bad version's number firstBadVersion = 46 # if the given version is less than the first bad version, return false if v < firstBadVersion: return False # if the given version is not less than the first bad version, return true else: return True n = 45 s = Solution() a = s.firstBadVersion(n) print(a)

[ "shannon.lance@sap.com" ]

shannon.lance@sap.com

fcd0b3996dcc8bf3891d3ed563e44c660b62677b

3d19e1a316de4d6d96471c64332fff7acfaf1308

/Users/D/dmsilv/facebook_fans.py

3fc1f0e56bfce614a8af5c9b37936e98b95a0c94

[]

no_license

BerilBBJ/scraperwiki-scraper-vault

4e98837ac3b1cc3a3edb01b8954ed00f341c8fcc

65ea6a943cc348a9caf3782b900b36446f7e137d

refs/heads/master

2021-12-02T23:55:58.481210

2013-09-30T17:02:59

null

0

null

UTF-8

Python

false

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py

# Blank Python import scraperwiki from BeautifulSoup import BeautifulSoup #define the order our columns are displayed in the datastore scraperwiki.metadata.save('data_columns', ['Page Name', 'Fans']) #scrape the fan section def scrape_fans(soup): data_table = soup.find("table",{ "class" : "uiGrid"}) #find the pages with most fans section rows= data_table.findAll("tr") #find all the table rows for row in rows: #loop through the rows cells = row.findAll("td") #find all the cells for cell in cells: #loop through the cells #setup the data record record={} print cell #table_cells=cell.findAll("p") #find all the p items if table_cells: #if the item exists store it record['Page Name'] = table_cells[0].text record['Fans'] = table_cells[1].text[:-5] scraperwiki.datastore.save(["Page Name"], record) def scrape_page(url): html = scraperwiki.scrape(url) soup = BeautifulSoup(html) #print soup.prettify() link_table=soup.find("div", {"class" : "alphabet_list clearfix"}) #next_link=soup.findAll("a") for link in link_table: next_url=link['href'] #print next_url html1 = scraperwiki.scrape(next_url) soup1 = BeautifulSoup(html1) scrape_fans(soup1) #setup the base url base_url = 'http://facebook.com/directory/pages/' #setup the startup url #call the scraping function scrape_page(base_url) # Blank Python import scraperwiki from BeautifulSoup import BeautifulSoup #define the order our columns are displayed in the datastore scraperwiki.metadata.save('data_columns', ['Page Name', 'Fans']) #scrape the fan section def scrape_fans(soup): data_table = soup.find("table",{ "class" : "uiGrid"}) #find the pages with most fans section rows= data_table.findAll("tr") #find all the table rows for row in rows: #loop through the rows cells = row.findAll("td") #find all the cells for cell in cells: #loop through the cells #setup the data record record={} print cell #table_cells=cell.findAll("p") #find all the p items if table_cells: #if the item exists store it record['Page Name'] = table_cells[0].text record['Fans'] = table_cells[1].text[:-5] scraperwiki.datastore.save(["Page Name"], record) def scrape_page(url): html = scraperwiki.scrape(url) soup = BeautifulSoup(html) #print soup.prettify() link_table=soup.find("div", {"class" : "alphabet_list clearfix"}) #next_link=soup.findAll("a") for link in link_table: next_url=link['href'] #print next_url html1 = scraperwiki.scrape(next_url) soup1 = BeautifulSoup(html1) scrape_fans(soup1) #setup the base url base_url = 'http://facebook.com/directory/pages/' #setup the startup url #call the scraping function scrape_page(base_url)

[ "pallih@kaninka.net" ]

pallih@kaninka.net

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9f90cf2f09729a3f71b0c8308c72f733906536f3

/seriesTaylor.py

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[]

no_license

bayronortiz/MetodosNumericos

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2016-09-06T02:40:23.028216

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# -*- coding: utf-8 -*- # Fecha: 08-Sept-2015 from math import * # Calcula la serie Taylor de la funcion fx=Cos (x) # Param xi:Xi -- xii: Xi+1 def fxN(xi,xii,n): h= xii-xi valor= 0 # Guarda el valor de la funcion temp= 0 # Guarda el valor temporal de la funcion negativo= False signo=(1,-1,-1, 1) cs= 0 #Controlador de signo for i in range(n+1): if i%2==0: temp= (signo[cs]*cos(xi) * h**i)/factorial(i) else: temp= (signo[cs]*sin(xi) * h**i)/factorial(i) cs+=1 if cs == 4: #Reiniciamos controlador de signo cs= 0 valor+= temp #Sumamos a valor serie taylor return valor n= input("Ingrese el Orden (n)--> ") print "Orden | f(xi+1)" #Falta hallar el error aproximado for i in range(n+1): print "%d | %.9f" % (i, fxN(pi/4,pi/3,i))

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danilo@linux-vpl0.site

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d4f9d104479b6f9a64175a3fe8554860bf0d62b2

/supply_line2.py

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[]

no_license

pohily/checkio

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2020-05-16T03:18:18.068186

2019-07-06T13:22:20

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coord = {'A':0, 'B':1, 'C':2, 'D':3, 'E':4, 'F':5, 'G':6, 'H':7, 'I':8, 'J':9, 'K':10, 'L':11} back = {0:'A', 1:'B', 2:'C', 3:'D', 4:'E', 5:'F', 6:'G', 7:'H', 8:'I', 9:'J', 10:'K', 11:'L'} checks_odd = [[-1, 0], [0, 1], [1, 1], [1, 0], [1, -1], [0, -1]] checks_even = [[-1, 0], [-1, 1], [0, 1], [1, 0], [0, -1], [-1, -1]] def decode_coord(point): return (int(point[1]) - 1, coord[point[0]]) def encode(point): return back[point[1]] + str(point[0]+1) def supply_line(you, depots, enemies): #dup of our map tmp_map = [['', '', '', '', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', '', '', '', '']] # find enemy ZOC ZOC = set() for enemy in enemies: row, col = decode_coord(enemy) ZOC.add((row, col)) tmp_map[row][col] = 'z' if col % 2 == 0: checks = checks_even else: checks = checks_odd for check in checks: if 0 <= row + check[0] <= 8 and 0 <= col + check[1] <= 11: ZOC.add((row + check[0], col + check[1])) tmp_map[row + check[0]][col + check[1]] = 'z' # just to see tmp = [] for p in ZOC: tmp.append(encode(p)) print('ZOC', tmp) # check if depots in/out of ZOC tmp = [] for depot in depots: row, col = decode_coord(depot) if (row, col) not in ZOC: tmp.append((row, col)) tmp_map[row][col] = 'D' depots = tmp[:] if not depots: return None # just to see tmp = [] for p in depots: tmp.append(encode(p)) #print('depots', tmp) # find number of steps from start for every cell in map result = [] for depot in depots: if depot in ZOC: continue row, col = decode_coord(you) start = (row, col) visited = [start] count = 0 #number of steps from map tmp_map[row][col] = count ok = False # flag point = depot while True: change = False for i, r in enumerate(tmp_map): for j, c in enumerate(r): if c == count: if j % 2 == 0: checks = checks_even else: checks = checks_odd for check in checks: x = i + check[0] y = j + check[1] if 0 <= x <= 8 and 0 <= y <= 11: point = (x, y) if point == depot: visited.append(point) #tmp_map[x][y] = count + 1 result.append(count + 1) ok = True break if point not in visited and point not in ZOC: tmp_map[x][y] = count + 1 visited.append(point) change = True if not change: if not result: return None # depots can't be reached else: break # nothing during cycle if not ok: count += 1 ''' for i in tmp_map: # print our map step by step print(i) print()''' else: break for i in tmp_map: # print our map print(i) print() print(result) return min(result) print(supply_line("B7",["C2"],["E3","E4","L1","H2","C3","E8"])) ''' if __name__ == '__main__': assert supply_line("B4", {"F4"}, {"D4"}) == 6, 'simple' assert supply_line("A3", {"A9", "F5", "G8"}, {"B3", "G6"}) == 11, 'multiple' assert supply_line("C2", {"B9", "F6"}, {"B7", "E8", "E5", "H6"}) is None, 'None' assert supply_line("E5", {"C2", "B7", "F8"}, set()) == 4, 'no enemies' assert supply_line("A5", {"A2", "B9"}, {"B3", "B7", "E3", "E7"}) == 13, '2 depots' print('"Run" is good. How is "Check"?') '''

[ "noreply@github.com" ]

pohily.noreply@github.com

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/pyblp/utilities/basics.py

bd2060c78e628c77393492763db5863b5c7a861b

[ "MIT" ]

permissive

markpham/pyblp

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refs/heads/master

2020-07-16T04:14:00.834907

2019-08-31T23:05:00

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"""Basic functionality.""" import contextlib import functools import inspect import multiprocessing.pool import re import time import traceback from typing import ( Any, Callable, Container, Dict, Hashable, Iterable, Iterator, List, Mapping, Optional, Set, Sequence, Type, Tuple, Union ) import numpy as np from .. import options # define common types Array = Any RecArray = Any Data = Dict[str, Array] Options = Dict[str, Any] Bounds = Tuple[Array, Array] # define a pool managed by parallel and used by generate_items pool = None @contextlib.contextmanager def parallel(processes: int) -> Iterator[None]: r"""Context manager used for parallel processing in a ``with`` statement context. This manager creates a context in which a pool of Python processes will be used by any method that requires market-by-market computation. These methods will distribute their work among the processes. After the context created by the ``with`` statement ends, all worker processes in the pool will be terminated. Outside of this context, such methods will not use multiprocessing. Importantly, multiprocessing will only improve speed if gains from parallelization outweigh overhead from serializing and passing data between processes. For example, if computation for a single market is very fast and there is a lot of data in each market that must be serialized and passed between processes, using multiprocessing may reduce overall speed. Arguments --------- processes : `int` Number of Python processes that will be created and used by any method that supports parallel processing. Examples -------- .. raw:: latex \begin{examplenotebook} .. toctree:: /_notebooks/api/parallel.ipynb .. raw:: latex \end{examplenotebook} """ # validate the number of processes if not isinstance(processes, int): raise TypeError("processes must be an int.") if processes < 2: raise ValueError("processes must be at least 2.") # start the process pool, wait for work to be done, and then terminate it output(f"Starting a pool of {processes} processes ...") start_time = time.time() global pool try: with multiprocessing.pool.Pool(processes) as pool: output(f"Started the process pool after {format_seconds(time.time() - start_time)}.") yield output(f"Terminating the pool of {processes} processes ...") terminate_time = time.time() finally: pool = None output(f"Terminated the process pool after {format_seconds(time.time() - terminate_time)}.") def generate_items(keys: Iterable, factory: Callable[[Any], tuple], method: Callable) -> Iterator: """Generate (key, method(*factory(key))) tuples for each key. The first element returned by factory is an instance of the class to which method is attached. If a process pool has been initialized, use multiprocessing; otherwise, use serial processing. """ if pool is None: return (generate_items_worker((k, factory(k), method)) for k in keys) return pool.imap_unordered(generate_items_worker, ((k, factory(k), method) for k in keys)) def generate_items_worker(args: Tuple[Any, tuple, Callable]) -> Tuple[Any, Any]: """Call the the specified method of a class instance with any additional arguments. Return the associated key along with the returned object. """ key, (instance, *method_args), method = args return key, method(instance, *method_args) def structure_matrices(mapping: Mapping) -> RecArray: """Structure a mapping of keys to (array or None, type) tuples as a record array in which each sub-array is guaranteed to be at least two-dimensional. """ # determine the number of rows in all matrices size = next(a.shape[0] for a, _ in mapping.values() if a is not None) # collect matrices and data types matrices: List[Array] = [] dtypes: List[Tuple[Union[str, Tuple[Hashable, str]], Any, Tuple[int]]] = [] for key, (array, dtype) in mapping.items(): matrix = np.zeros((size, 0)) if array is None else np.c_[array] dtypes.append((key, dtype, (matrix.shape[1],))) matrices.append(matrix) # build the record array structured = np.recarray(size, dtypes) for dtype, matrix in zip(dtypes, matrices): structured[dtype[0] if isinstance(dtype[0], str) else dtype[0][1]] = matrix return structured def update_matrices(matrices: RecArray, update_mapping: Dict) -> RecArray: """Update fields in a record array created by structure_matrices by re-structuring the matrices.""" mapping = update_mapping.copy() for key in matrices.dtype.names: if key not in mapping: if len(matrices.dtype.fields[key]) > 2: mapping[(matrices.dtype.fields[key][2], key)] = (matrices[key], matrices[key].dtype) else: mapping[key] = (matrices[key], matrices[key].dtype) return structure_matrices(mapping) def extract_matrix(structured_array_like: Mapping, key: Any) -> Optional[Array]: """Attempt to extract a field from a structured array-like object or horizontally stack field0, field1, and so on, into a full matrix. The extracted array will have at least two dimensions. """ try: matrix = np.c_[structured_array_like[key]] return matrix if matrix.size > 0 else None except Exception: index = 0 parts: List[Array] = [] while True: try: part = np.c_[structured_array_like[f'{key}{index}']] except Exception: break index += 1 if part.size > 0: parts.append(part) return np.hstack(parts) if parts else None def extract_size(structured_array_like: Mapping) -> int: """Attempt to extract the number of rows from a structured array-like object.""" size = 0 getters = [ lambda m: m.shape[0], lambda m: next(iter(structured_array_like.values())).shape[0], lambda m: len(next(iter(structured_array_like.values()))), lambda m: len(m) ] for get in getters: try: size = get(structured_array_like) break except Exception: pass if size > 0: return size raise TypeError( f"Failed to get the number of rows in the structured array-like object of type {type(structured_array_like)}. " f"Try using a dictionary, a NumPy structured array, a Pandas DataFrame, or any other standard type." ) def interact_ids(*columns: Array) -> Array: """Create interactions of ID columns.""" interacted = columns[0].flatten().astype(np.object) if len(columns) > 1: interacted[:] = list(zip(*columns)) return interacted def output(message: Any) -> None: """Print a message if verbosity is turned on.""" if options.verbose: if not callable(options.verbose_output): raise TypeError("options.verbose_output should be callable.") options.verbose_output(str(message)) def output_progress(iterable: Iterable, length: int, start_time: float) -> Iterator: """Yield results from an iterable while outputting progress updates at most every minute.""" elapsed = time.time() - start_time next_minute = int(elapsed / 60) + 1 for index, iterated in enumerate(iterable): yield iterated elapsed = time.time() - start_time if elapsed > 60 * next_minute: output(f"Finished {index + 1} out of {length} after {format_seconds(elapsed)}.") next_minute = int(elapsed / 60) + 1 def format_seconds(seconds: float) -> str: """Prepare a number of seconds to be displayed as a string.""" hours, remainder = divmod(int(round(seconds)), 60**2) minutes, seconds = divmod(remainder, 60) return f'{hours:02}:{minutes:02}:{seconds:02}' def format_number(number: Any) -> str: """Prepare a number to be displayed as a string.""" if not isinstance(options.digits, int): raise TypeError("options.digits must be an int.") template = f"{{:^+{options.digits + 6}.{options.digits - 1}E}}" formatted = template.format(float(number)) if "NAN" in formatted: formatted = formatted.replace("+", " ") return formatted def format_se(se: Any) -> str: """Prepare a standard error to be displayed as a string.""" formatted = format_number(se) for string in ["NAN", "-INF", "+INF"]: if string in formatted: return formatted.replace(string, f"({string})") return f"({formatted})" def format_options(mapping: Options) -> str: """Prepare a mapping of options to be displayed as a string.""" strings: List[str] = [] for key, value in mapping.items(): if callable(value): value = f'{value.__module__}.{value.__qualname__}' elif isinstance(value, float): value = format_number(value) strings.append(f'{key}: {value}') joined = ', '.join(strings) return f'{{{joined}}}' def format_table( header: Sequence[Union[str, Sequence[str]]], *data: Sequence, title: Optional[str] = None, include_border: bool = True, include_header: bool = True, line_indices: Container[int] = ()) -> str: """Format table information as a string, which has fixed widths, vertical lines after any specified indices, and optionally a title, border, and header. """ # construct the header rows row_index = -1 header_rows: List[List[str]] = [] header = [[c] if isinstance(c, str) else c for c in header] while True: header_row = ["" if len(c) < -row_index else c[row_index] for c in header] if not any(header_row): break header_rows.insert(0, header_row) row_index -= 1 # construct the data rows data_rows = [[str(c) for c in r] + [""] * (len(header) - len(r)) for r in data] # compute column widths widths = [] for column_index in range(len(header)): widths.append(max(len(r[column_index]) for r in header_rows + data_rows)) # build the template template = " " .join("{{:^{}}}{}".format(w, " |" if i in line_indices else "") for i, w in enumerate(widths)) # build the table lines = [] if title is not None: lines.append(f"{title}:") if include_border: lines.append("=" * len(template.format(*[""] * len(widths)))) if include_header: lines.extend([template.format(*r) for r in header_rows]) lines.append(template.format(*("-" * w for w in widths))) lines.extend([template.format(*r) for r in data_rows]) if include_border: lines.append("=" * len(template.format(*[""] * len(widths)))) return "\n".join(lines) def get_indices(ids: Array) -> Dict[Hashable, Array]: """get_indices takes a one-dimensional array input and returns a dictionary such that the keys are the unique values of the array and the values are the indices where the key appears in the array. Examples -------- >>> ids = np.array([1, 2, 1, 2, 3, 3, 1, 2]) >>> get_indices(ids) {1: array([0, 2, 6]), 2: array([1, 3, 7]), 3: array([4, 5])} """ flat = ids.flatten() sort_indices = flat.argsort(kind='mergesort') sorted_ids = flat[sort_indices] changes = np.ones(flat.shape, np.bool) changes[1:] = sorted_ids[1:] != sorted_ids[:-1] reduce_indices = np.nonzero(changes)[0] return dict(zip(sorted_ids[reduce_indices], np.split(sort_indices, reduce_indices)[1:])) class SolverStats(object): """Structured statistics returned by a generic numerical solver.""" converged: bool iterations: int evaluations: int def __init__(self, converged: bool = True, iterations: int = 0, evaluations: int = 0) -> None: """Structure the statistics.""" self.converged = converged self.iterations = iterations self.evaluations = evaluations class StringRepresentation(object): """Object that defers to its string representation.""" def __repr__(self) -> str: """Defer to the string representation.""" return str(self) class Groups(object): """Computation of grouped statistics.""" sort_indices: Array reduce_indices: Array unique: Array codes: Array counts: Array group_count: int def __init__(self, ids: Array) -> None: """Sort and index IDs that define groups.""" # sort the IDs flat = ids.flatten() self.sort_indices = flat.argsort() sorted_ids = flat[self.sort_indices] # identify groups changes = np.ones(flat.shape, np.bool) changes[1:] = sorted_ids[1:] != sorted_ids[:-1] self.reduce_indices = np.nonzero(changes)[0] self.unique = sorted_ids[self.reduce_indices] # encode the groups sorted_codes = np.cumsum(changes) - 1 self.codes = sorted_codes[self.sort_indices.argsort()] # compute counts self.group_count = self.reduce_indices.size self.counts = np.diff(np.append(self.reduce_indices, self.codes.size)) def sum(self, matrix: Array) -> Array: """Compute the sum of each group.""" return np.add.reduceat(matrix[self.sort_indices], self.reduce_indices) def mean(self, matrix: Array) -> Array: """Compute the mean of each group.""" return self.sum(matrix) / self.counts[:, None] def expand(self, statistics: Array) -> Array: """Expand statistics for each group to the size of the original matrix.""" return statistics[self.codes] class Error(Exception): """Errors that are indistinguishable from others with the same message, which is parsed from the docstring.""" stack: Optional[str] def __init__(self) -> None: """Optionally store the full current traceback for debugging purposes.""" if options.verbose_tracebacks: self.stack = ''.join(traceback.format_stack()) else: self.stack = None def __eq__(self, other: Any) -> bool: """Defer to hashes.""" return hash(self) == hash(other) def __hash__(self) -> int: """Hash this instance such that in collections it is indistinguishable from others with the same message.""" return hash((type(self).__name__, str(self))) def __repr__(self) -> str: """Defer to the string representation.""" return str(self) def __str__(self) -> str: """Replace docstring markdown with simple text.""" doc = inspect.getdoc(self) # normalize LaTeX while True: match = re.search(r':math:`([^`]+)`', doc) if match is None: break start, end = match.span() doc = doc[:start] + re.sub(r'\s+', ' ', re.sub(r'[\\{}]', ' ', match.group(1))).lower() + doc[end:] # normalize references while True: match = re.search(r':ref:`[a-zA-Z0-9]+:([^`]+)`', doc) if match is None: break start, end = match.span() doc = doc[:start] + re.sub(r'<[^>]+>', '', match.group(1)) + doc[end:] # remove all remaining domains and compress whitespace doc = re.sub(r'[\s\n]+', ' ', re.sub(r':[a-z\-]+:|`', '', doc)) # optionally add the full traceback if self.stack is not None: doc = f"{doc} Traceback:\n\n{self.stack}\n" return doc class NumericalError(Error): """Floating point issues.""" _messages: Set[str] def __init__(self) -> None: super().__init__() self._messages: Set[str] = set() def __str__(self) -> str: """Supplement the error with the messages.""" combined = ", ".join(sorted(self._messages)) return f"{super().__str__()} Errors encountered: {combined}." class MultipleReversionError(Error): """Reversion of problematic elements.""" _bad: int _total: int def __init__(self, bad_indices: Array) -> None: """Store element counts.""" super().__init__() self._bad = bad_indices.sum() self._total = bad_indices.size def __str__(self) -> str: """Supplement the error with the counts.""" return f"{super().__str__()} Number of reverted elements: {self._bad} out of {self._total}." class InversionError(Error): """Problems with inverting a matrix.""" _condition: float def __init__(self, matrix: Array) -> None: """Compute condition number of the matrix.""" super().__init__() from .algebra import compute_condition_number self._condition = compute_condition_number(matrix) def __str__(self) -> str: """Supplement the error with the condition number.""" return f"{super().__str__()} Condition number: {format_number(self._condition)}." class InversionReplacementError(InversionError): """Problems with inverting a matrix led to the use of a replacement such as an approximation.""" _replacement: str def __init__(self, matrix: Array, replacement: str) -> None: """Store the replacement description.""" super().__init__(matrix) self._replacement = replacement def __str__(self) -> str: """Supplement the error with the description.""" return f"{super().__str__()} The inverse was replaced with {self._replacement}." class NumericalErrorHandler(object): """Decorator that appends errors to a function's returned list when numerical errors are encountered.""" error: Type[NumericalError] def __init__(self, error: Type[NumericalError]) -> None: """Store the error class.""" self.error = error def __call__(self, decorated: Callable) -> Callable: """Decorate the function.""" @functools.wraps(decorated) def wrapper(*args: Any, **kwargs: Any) -> Any: """Configure NumPy to detect numerical errors.""" detector = NumericalErrorDetector(self.error) with np.errstate(divide='call', over='call', under='ignore', invalid='call'): np.seterrcall(detector) returned = decorated(*args, **kwargs) if detector.detected is not None: returned[-1].append(detector.detected) return returned return wrapper class NumericalErrorDetector(object): """Error detector to be passed to NumPy's error call function.""" error: Type[NumericalError] detected: Optional[NumericalError] def __init__(self, error: Type[NumericalError]) -> None: """By default no error is detected.""" self.error = error self.detected = None def __call__(self, message: str, _: int) -> None: """Initialize the error and store the error message.""" if self.detected is None: self.detected = self.error() self.detected._messages.add(message)

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jeff@jeffgortmaker.com

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740f54d593e3b808c093ca8da2142c5785393df9

/nets/VGG16.py

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sinianyutian/ssd-keras

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refs/heads/master

2023-04-27T09:55:55.961775

2021-05-09T17:02:26

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import keras.backend as K from keras.layers import (Activation, Conv2D, Dense, Flatten, GlobalAveragePooling2D, Input, MaxPooling2D, Reshape, ZeroPadding2D, concatenate, merge) from keras.models import Model def VGG16(input_tensor): #----------------------------主干特征提取网络开始---------------------------# # SSD结构,net字典 net = {} # Block 1 net['input'] = input_tensor # 300,300,3 -> 150,150,64 net['conv1_1'] = Conv2D(64, kernel_size=(3,3), activation='relu', padding='same', name='conv1_1')(net['input']) net['conv1_2'] = Conv2D(64, kernel_size=(3,3), activation='relu', padding='same', name='conv1_2')(net['conv1_1']) net['pool1'] = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool1')(net['conv1_2']) # Block 2 # 150,150,64 -> 75,75,128 net['conv2_1'] = Conv2D(128, kernel_size=(3,3), activation='relu', padding='same', name='conv2_1')(net['pool1']) net['conv2_2'] = Conv2D(128, kernel_size=(3,3), activation='relu', padding='same', name='conv2_2')(net['conv2_1']) net['pool2'] = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool2')(net['conv2_2']) # Block 3 # 75,75,128 -> 38,38,256 net['conv3_1'] = Conv2D(256, kernel_size=(3,3), activation='relu', padding='same', name='conv3_1')(net['pool2']) net['conv3_2'] = Conv2D(256, kernel_size=(3,3), activation='relu', padding='same', name='conv3_2')(net['conv3_1']) net['conv3_3'] = Conv2D(256, kernel_size=(3,3), activation='relu', padding='same', name='conv3_3')(net['conv3_2']) net['pool3'] = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool3')(net['conv3_3']) # Block 4 # 38,38,256 -> 19,19,512 net['conv4_1'] = Conv2D(512, kernel_size=(3,3), activation='relu', padding='same', name='conv4_1')(net['pool3']) net['conv4_2'] = Conv2D(512, kernel_size=(3,3), activation='relu', padding='same', name='conv4_2')(net['conv4_1']) net['conv4_3'] = Conv2D(512, kernel_size=(3,3), activation='relu', padding='same', name='conv4_3')(net['conv4_2']) net['pool4'] = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool4')(net['conv4_3']) # Block 5 # 19,19,512 -> 19,19,512 net['conv5_1'] = Conv2D(512, kernel_size=(3,3), activation='relu', padding='same', name='conv5_1')(net['pool4']) net['conv5_2'] = Conv2D(512, kernel_size=(3,3), activation='relu', padding='same', name='conv5_2')(net['conv5_1']) net['conv5_3'] = Conv2D(512, kernel_size=(3,3), activation='relu', padding='same', name='conv5_3')(net['conv5_2']) net['pool5'] = MaxPooling2D((3, 3), strides=(1, 1), padding='same', name='pool5')(net['conv5_3']) # FC6 # 19,19,512 -> 19,19,1024 net['fc6'] = Conv2D(1024, kernel_size=(3,3), dilation_rate=(6, 6), activation='relu', padding='same', name='fc6')(net['pool5']) # x = Dropout(0.5, name='drop6')(x) # FC7 # 19,19,1024 -> 19,19,1024 net['fc7'] = Conv2D(1024, kernel_size=(1,1), activation='relu', padding='same', name='fc7')(net['fc6']) # x = Dropout(0.5, name='drop7')(x) # Block 6 # 19,19,512 -> 10,10,512 net['conv6_1'] = Conv2D(256, kernel_size=(1,1), activation='relu', padding='same', name='conv6_1')(net['fc7']) net['conv6_2'] = ZeroPadding2D(padding=((1, 1), (1, 1)), name='conv6_padding')(net['conv6_1']) net['conv6_2'] = Conv2D(512, kernel_size=(3,3), strides=(2, 2), activation='relu', name='conv6_2')(net['conv6_2']) # Block 7 # 10,10,512 -> 5,5,256 net['conv7_1'] = Conv2D(128, kernel_size=(1,1), activation='relu', padding='same', name='conv7_1')(net['conv6_2']) net['conv7_2'] = ZeroPadding2D(padding=((1, 1), (1, 1)), name='conv7_padding')(net['conv7_1']) net['conv7_2'] = Conv2D(256, kernel_size=(3,3), strides=(2, 2), activation='relu', padding='valid', name='conv7_2')(net['conv7_2']) # Block 8 # 5,5,256 -> 3,3,256 net['conv8_1'] = Conv2D(128, kernel_size=(1,1), activation='relu', padding='same', name='conv8_1')(net['conv7_2']) net['conv8_2'] = Conv2D(256, kernel_size=(3,3), strides=(1, 1), activation='relu', padding='valid', name='conv8_2')(net['conv8_1']) # Block 9 # 3,3,256 -> 1,1,256 net['conv9_1'] = Conv2D(128, kernel_size=(1,1), activation='relu', padding='same', name='conv9_1')(net['conv8_2']) net['conv9_2'] = Conv2D(256, kernel_size=(3,3), strides=(1, 1), activation='relu', padding='valid', name='conv9_2')(net['conv9_1']) #----------------------------主干特征提取网络结束---------------------------# return net

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sinianyutian.noreply@github.com

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import csv with open('lunch.csv','r',encoding='utf8') as f: # lines = f.readlines() items = csv.reader(f) print(items) for item in items: print(item)

[ "bearsgod@gmail.com" ]

bearsgod@gmail.com

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2020-03-31T12:15:46.727011

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# -*- coding: utf-8 -*- """ Miscellaneous utilities and tools """ from __future__ import absolute_import, print_function import functools import keyword import os import re import sys from contextlib import contextmanager from operator import itemgetter from distutils.filelist import FileList from six import PY2 from .templates import licenses from .contrib import scm_setuptools_too_old @contextmanager def chdir(path): """Contextmanager to change into a directory Args: path (str): path to change current working directory to """ curr_dir = os.getcwd() os.chdir(path) try: yield finally: os.chdir(curr_dir) def is_valid_identifier(string): """Check if string is a valid package name Args: string (str): package name Returns: bool: True if string is valid package name else False """ if not re.match("[_A-Za-z][_a-zA-Z0-9]*$", string): return False if keyword.iskeyword(string): return False return True def make_valid_identifier(string): """Try to make a valid package name identifier from a string Args: string (str): invalid package name Returns: str: valid package name as string or :obj:`RuntimeError` Raises: :obj:`RuntimeError`: raised if identifier can not be converted """ string = string.strip() string = string.replace("-", "_") string = string.replace(" ", "_") string = re.sub('[^_a-zA-Z0-9]', '', string) string = string.lower() if is_valid_identifier(string): return string else: raise RuntimeError("String cannot be converted to a valid identifier.") def list2str(lst, indent=0, brackets=True, quotes=True, sep=','): """Generate a Python syntax list string with an indention Args: lst ([str]): list of strings indent (int): indention brackets (bool): surround the list expression by brackets quotes (bool): surround each item with quotes sep (str): separator for each item Returns: str: string representation of the list """ if quotes: lst_str = str(lst) if not brackets: lst_str = lst_str[1:-1] else: lst_str = ', '.join(lst) if brackets: lst_str = '[' + lst_str + ']' lb = '{}\n'.format(sep) + indent*' ' return lst_str.replace(', ', lb) def exceptions2exit(exception_list): """Decorator to convert given exceptions to exit messages This avoids displaying nasty stack traces to end-users Args: exception_list [Exception]: list of exceptions to convert """ def exceptions2exit_decorator(func): @functools.wraps(func) def func_wrapper(*args, **kwargs): try: func(*args, **kwargs) except tuple(exception_list) as e: print("ERROR: {}".format(e)) sys.exit(1) return func_wrapper return exceptions2exit_decorator # from http://en.wikibooks.org/, Creative Commons Attribution-ShareAlike 3.0 def levenshtein(s1, s2): """Calculate the Levenshtein distance between two strings Args: s1 (str): first string s2 (str): second string Returns: int: distance between s1 and s2 """ if len(s1) < len(s2): return levenshtein(s2, s1) # len(s1) >= len(s2) if len(s2) == 0: return len(s1) previous_row = range(len(s2) + 1) for i, c1 in enumerate(s1): current_row = [i + 1] for j, c2 in enumerate(s2): insertions = previous_row[j + 1] + 1 deletions = current_row[j] + 1 substitutions = previous_row[j] + (c1 != c2) current_row.append(min(insertions, deletions, substitutions)) previous_row = current_row return previous_row[-1] def best_fit_license(txt): """Finds proper license name for the license defined in txt Args: txt (str): license name Returns: str: license name """ ratings = {lic: levenshtein(txt, lic.lower()) for lic in licenses} return min(ratings.items(), key=itemgetter(1))[0] def utf8_encode(string): """Encode a Python 2 unicode object to str for compatibility with Python 3 Args: string (str): Python 2 unicode object or Python 3 str object Returns: str: Python 2 str object or Python 3 str object """ return string.encode(encoding='utf8') if PY2 else string def utf8_decode(string): """Decode a Python 2 str object to unicode for compatibility with Python 3 Args: string (str): Python 2 str object or Python 3 str object Returns: str: Python 2 unicode object or Python 3 str object """ return string.decode(encoding='utf8') if PY2 else string def get_files(pattern): """Retrieve all files in the current directory by a pattern. Use ** as greedy wildcard and * as non-greedy wildcard. Args: pattern (str): pattern as used by :obj:`distutils.filelist.Filelist` Returns: [str]: list of files """ filelist = FileList() if '**' in pattern: pattern = pattern.replace('**', '*') anchor = False else: anchor = True filelist.include_pattern(pattern, anchor) return filelist.files def prepare_namespace(namespace_str): """Check the validity of namespace_str and split it up into a list Args: namespace_str (str): namespace, e.g. "com.blue_yonder" Returns: [str]: list of namespaces, e.g. ["com", "com.blue_yonder"] Raises: :obj:`RuntimeError` : raised if namespace is not valid """ namespaces = namespace_str.split('.') if namespace_str else list() for namespace in namespaces: if not is_valid_identifier(namespace): raise RuntimeError( "{} is not a valid namespace package.".format(namespace)) return ['.'.join(namespaces[:i+1]) for i in range(len(namespaces))] def check_setuptools_version(): """Check that setuptools has all necessary capabilities for setuptools_scm Raises: :obj:`RuntimeError` : raised if necessary capabilities are not met """ if scm_setuptools_too_old: raise RuntimeError( "Your setuptools version is too old (<12). " "Use `pip install -U setuptools` to upgrade.\n" "If you have the deprecated `distribute` package installed " "remove it or update to version 0.7.3.")

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tehbitardcity@gmail.com

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/xitou.py

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neko2048/wrf_analysis_Xitou

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from matplotlib.pyplot import * import numpy as np from netCDF4 import Dataset import pandas as pd #from wrf import getvar, interplevel def findArgmin(data, value): IdxMinflat = np.argmin(abs(data - value)) idxMin = np.unravel_index(IdxMinflat, data.shape) return idxMin class WRFData: def __init__(self, fileDir): self.filedir = fileDir self.wrfdata = Dataset(fileDir) self.xlon = self.getVar('XLONG')#self.wrfdata["XLONG"] self.xlat = self.getVar('XLAT')#self.wrfdata["XLAT"] self.pdTimes = self.constructLocalTime() def delSpinUpTime(self, var): """remove the spin up (first 24 hours)""" var = var[13:] return var def getVar(self, varName): var = np.array(self.wrfdata[varName]) var = self.delSpinUpTime(var) return var def UTCtoLocal(self, UTCTimes): LocalTimes = UTCTimes + pd.Timedelta(8, 'hour') return LocalTimes def constructLocalTime(self): oriTimes = self.getVar('Times') newTimes = [] for i in range(len(oriTimes)): newTimes.append(pd.to_datetime("".join(char.decode("utf-8") for char in oriTimes[i]), format="%Y-%m-%d_%H:%M:%S")) newTimes = pd.DatetimeIndex(newTimes) LocalTimes = self.UTCtoLocal(newTimes) return LocalTimes class XitouData: def __init__(self, fileDir): self.wrfdata = WRFData(fileDir) self.times = self.wrfdata.pdTimes self.xitouLon = 120.7838246 self.xitouLat = 23.6759616 self.idxLon, self.idxLat = self.getXitouIdx() self.T2 = self.getVarValue("T2") # K self.Q2 = self.getVarValue("Q2") # kg / kg self.Psrf = self.getVarValue("PSFC") # hPa self.ev = self.Qv2Ev() # hPa self.evs = self.getEvs() # hPa self.RH = self.getRH() def getXitouIdx(self): idxGridLon = findArgmin(self.wrfdata.xlon, self.xitouLon)[2] idxGridLat = np.argmin(abs(self.wrfdata.xlat[:, :, idxGridLon] - self.xitouLat)) return idxGridLon, idxGridLat def getVarValue(self, varName): VarField = self.wrfdata.getVar(varName) VarValue = np.array(VarField)[:, self.idxLat, self.idxLon] return VarValue def Qv2Ev(self): ev = self.Psrf/100 * self.Q2 / (self.Q2 + 0.622) # hPa return ev def getEvs(self): """ use Goff-Gratch, 1946. input: T (K) output: es (hPa) """ T = self.T2 Tst = 373.15 # boiling point (K) ln_es = -7.90298 * (Tst / T - 1) + \ 5.02808 * np.log10(Tst / T) - \ 1.3816e-7 * (10 ** (11.344 * (1 - T / Tst)) -1) + \ 8.1328e-3 * (10 ** (-3.49149 * (Tst / T - 1)) - 1) + \ np.log10(1013.25) # saturated vapor pressure (hPa) es = 10 ** (ln_es) return es def getRH(self): RH = self.ev / self.evs return RH class ModeCollector: def __init__(self): self.times = [] self.T2s = [] self.Q2s = [] self.RHs = [] def collectTimes(self, value): self.times.append(value) def collectT2(self, value): self.T2s.append(value) def collectQ2(self, value): self.Q2s.append(value) def collectRH(self, value): self.RHs.append(value) def collectData(self, PlaceData): self.collectTimes(PlaceData.times) self.collectT2(PlaceData.T2) self.collectQ2(PlaceData.Q2) self.collectRH(PlaceData.RH) def squeezeList(self): self.times = pd.DatetimeIndex(np.hstack(self.times)) self.T2s = np.hstack(self.T2s) self.Q2s = np.hstack(self.Q2s) self.RHs = np.hstack(self.RHs) class DrawSys: def __init__(self, ModeName, Mode): self.ModeName = ModeName self.Mode = Mode def drawT2(self): figure(figsize=(20, 8)) grid(True) for i, mode in enumerate(self.ModeName): plot(self.Mode[i].times, self.Mode[i].T2s-273, label=self.ModeName[i]) xticks(self.Mode[0].times[::12], ["{MONTH}-{DAY}-{HOUR}".format(MONTH=x.month, DAY=x.day, HOUR=x.hour) for x in self.Mode[0].times[::12]]) legend() ylim(10, 25) ylabel(r"[$\degree C$]") title("T2 from {T1} to {T2}".format(T1=self.Mode[0].times[0], T2=self.Mode[0].times[-1])) savefig("{MODE}_T2.jpg".format(MODE=self.ModeName[0][0]), dpi=300) def drawQ2(self): figure(figsize=(20, 8)) grid(True) for i, mode in enumerate(self.ModeName): plot(self.Mode[i].times, self.Mode[i].Q2s, label=self.ModeName[i]) xticks(self.Mode[0].times[::12], ["{MONTH}-{DAY}-{HOUR}".format(MONTH=x.month, DAY=x.day, HOUR=x.hour) for x in self.Mode[0].times[::12]]) legend() ylim(0.005, 0.020) ylabel(r"[$kg / kg$]") title("Q2 from {T1} to {T2}".format(T1=self.Mode[0].times[0], T2=self.Mode[0].times[-1])) savefig("{MODE}_Q2.jpg".format(MODE=self.ModeName[0][0]), dpi=300) def drawRH2(self): figure(figsize=(20, 8)) grid(True) for i, mode in enumerate(self.ModeName): plot(self.Mode[i].times, self.Mode[i].RHs*100, label=self.ModeName[i]) xticks(self.Mode[0].times[::12], ["{MONTH}-{DAY}-{HOUR}".format(MONTH=x.month, DAY=x.day, HOUR=x.hour) for x in self.Mode[0].times[::12]]) legend() ylim(50, 110) ylabel("%") title("T2 from {T1} to {T2}".format(T1=self.Mode[0].times[0], T2=self.Mode[0].times[-1])) savefig("{MODE}_RH.jpg".format(MODE=self.ModeName[0][0]), dpi=300) if __name__ == "__main__": #dateList = [x for x in range(15, 25)] dateList = pd.date_range("20210415T12", periods=9, freq="D") NC = ModeCollector() NM = ModeCollector() NU = ModeCollector() WC = ModeCollector() WM = ModeCollector() WU = ModeCollector() NModeName = ['NC', 'NM', 'NU'] WModeName = ['WC', 'WM', 'WU'] NMode = [NC, NM, NU] WMode = [WC, WM, WU] for i, mode in enumerate(NModeName): print(mode) for j, date in enumerate(dateList): wrf_dir = "/home/twsand/fskao/wrfOUT43v1/{MODE}202104{DATE}/wrfout_d04_2021-04-{DATE}_12:00:00".format(MODE=mode, DATE=date.day) NMode[i].collectData(XitouData(wrf_dir)) NMode[i].squeezeList() Ndraw = DrawSys(NModeName, NMode) Ndraw.drawT2() Ndraw.drawQ2() Ndraw.drawRH2() for i, mode in enumerate(WModeName): print(mode) for j, date in enumerate(dateList): wrf_dir = "/home/twsand/fskao/wrfOUT43v1/{MODE}202104{DATE}/wrfout_d04_2021-04-{DATE}_12:00:00".format(MODE=mode, DATE=date.day) WMode[i].collectData(XitouData(wrf_dir)) WMode[i].squeezeList() Wdraw = DrawSys(WModeName, WMode) Wdraw.drawT2() Wdraw.drawQ2() Wdraw.drawRH2()

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uc@UCdeiMac.local

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JFCeron/IBIO4490

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# manipulacion de imagenes from PIL import Image import cv2 import matplotlib.pyplot as plt # matematicas import numpy as np # imagenes para mezclar dante = Image.open("dante.jpg") pato = Image.open("whiteduck.jpg") # alinear imagenes pato = pato.rotate(-2).crop((100,80,pato.size[0]-75,pato.size[1]-30)) # ajustar tamanos tamano = (int(np.mean([dante.size[0],pato.size[0]])),int(np.mean([dante.size[1],pato.size[1]]))) dante = dante.resize(tamano) pato = pato.resize(tamano) # almacenar foto editada del pato plt.imshow(pato).write_png('pato_modificado.jpg') # convertir a matriz dante = np.array(dante) pato = np.array(pato) # filtar imagenes blurrpato = 100 blurrdante = 10 lowpass = cv2.GaussianBlur(dante, ksize=(51,51), sigmaX=blurrdante, sigmaY=blurrdante).astype('int') highpass = pato - cv2.GaussianBlur(pato, ksize=(51,51), sigmaX=blurrpato, sigmaY=blurrpato).astype('int') highpass[highpass < 0] = 0 # imagen hibrida hibrida = highpass+lowpass hibrida[hibrida > 255] = 255 hibrida = hibrida.astype('uint8') plt.imshow(hibrida).write_png('danteduck.jpg') # piramide altura = 5 espacio = 10 piramide = np.zeros((2*hibrida.shape[0] + espacio*altura,hibrida.shape[1],3)).astype('uint8')+255 piramide[0:hibrida.shape[0],:,:] = hibrida zoom_actual = hibrida y_actual = hibrida.shape[0]+espacio for i in range(1,altura): zoom_actual = cv2.pyrDown(zoom_actual) piramide[y_actual:(y_actual+zoom_actual.shape[0]), 0:zoom_actual.shape[1],:] = zoom_actual y_actual = y_actual+zoom_actual.shape[0]+espacio plt.imshow(piramide).write_png('piramide.jpg') # blended: construccion de piramides gaussianas y laplacianas G_dante = [] L_dante = [] G_pato = [] L_pato = [] dante_actual = dante pato_actual = pato for i in range(5): G_dante.append(dante_actual) G_pato.append(pato_actual) dante_actual = cv2.pyrDown(dante_actual) pato_actual = cv2.pyrDown(pato_actual) L_i_dante = G_dante[i].astype('int') - cv2.pyrUp(dante_actual)[0:G_dante[i].shape[0],0:G_dante[i].shape[1],:].astype('int') L_i_pato = G_pato[i].astype('int') - cv2.pyrUp(pato_actual)[0:G_pato[i].shape[0],0:G_pato[i].shape[1],:].astype('int') L_i_dante[L_i_dante < 0] = 0 L_i_pato[L_i_pato < 0] = 0 L_dante.append(L_i_dante.astype('uint8')) L_pato.append(L_i_pato.astype('uint8')) # concatenacion de laplacianas concat = [] for i in range(5): concat_i = L_dante[i] concat_i[:,0:int(concat_i.shape[1]/2),:] = L_pato[i][:,0:int(concat_i.shape[1]/2),:] concat.append(concat_i) # reconstruccion de imagen blended blended = concat[4] for i in range(4): blended = cv2.pyrUp(blended) if concat[3-i].shape[1]%2 == 1: blended = cv2.add(blended[:,0:(blended.shape[1]-1),:],concat[3-i]) else: blended = cv2.add(blended,concat[3-i]) cv2.imwrite('blended.png',blended)

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# -*- coding: utf-8 -*- from __future__ import division import numpy as np def somaDiagonalPrincipal(a): soma=0 for i in range(0,a.shape[0],1): soma=soma+a[i,i] return soma def somaDiagonalSecundaria(a): soma=0 for i in range(0,a.shape[0],1): soma=soma+a[i,a.shape[0]-i-1] return soma def somaLinhas(a): s=[] for i in range(0,a.shape[0],1): soma=0 for j in range(0,a.shape[1],1): soma=soma+a[i,j] s.append(soma) return s def somaColunas(a): r=[] for j in range(0,a.shape[1],1): soma=0 for i in range(0,a.shape[0],1): soma=soma+a[i,j] r.append(soma) return r def quadradoMagico(a): sdP=somaDiagonalPrincipal(a) sdS=somaDiagonalSecundaria(a) somaL=somaLinhas(a) somaC=somaColunas(a) contador=0 for i in range(0,len(somaL),1): if sdP==sdS==somaL[i]==somaC[i]: contador=contador+1 if contador==len(somaL): return True else: return False #programa principal n=input('digite o numero de linhas da matriz:') #n=input('digite o numero de colunas da matriz:') matriz=np.zeros((n,n)) for i in range(0,matriz.shape[0],1): for j in range(0,matriz.shape[1],1): matriz[i,j]=input('digite um elemento da matriz:') if quadradoMagico(matriz): print('S') else: print('N')

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import os from datetime import datetime from time import sleep from random import choice import requests from agents import AGENTS url = 'http://www.ziroom.com/detail/info' params = { 'id': '61155405', 'house_id': '60185997', } headers = { 'User-Agent': choice(AGENTS), } while True: resp = requests.get(url, params=params, headers=headers) now = datetime.now().strftime('%Y-%m-%d %H:%M:%S') if resp.status_code != 200: print(now, 'Failed') sleep(5) continue try: data = resp.json()['data'] status = data['status'] price = data['price'] print(now, status, price) if status != 'tzpzz': break except Exception: print(data) sleep(10) cmd = os.system('zsh -c "while true;do;afplay /System/Library/Sounds/Ping.aiff -v 30;done"')

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seselinfeng/pms_scrapy

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# Define here the models for your spider middleware # # See documentation in: # https://docs.scrapy.org/en/latest/topics/spider-middleware.html import hashlib from urllib import parse from scrapy import signals, Request # useful for handling different item types with a single interface from itemadapter import is_item, ItemAdapter from scrapy.http import HtmlResponse class WfpmsCouponSpiderMiddleware: def process_request(self, request, spider): if spider.name == 'wfpms_coupon': # 获取爬取网站地址 spider.driver.get(request.url) iframe = spider.driver.find_elemnt_by_xpath('') spider.driver.switch_to.frame(iframe) body = spider.driver.page_source return HtmlResponse(url=spider.driver.current_url, body=body, encoding='utf-8', request=request) else: return None # # class SignSpiderMiddleware: # def process_start_requests(self, start_requests, spider): # # Called with the start requests of the spider, and works # # similarly to the process_spider_output() method, except # # that it doesn’t have a response associated. # # # Must return only requests (not items). # for r in start_requests: # yield r # # def process_request(self, request, spider): # if spider.name == 'wfpms_test': # if "http://test.wfpms.com:9000/api/GetDiscoups" in request.url: # # 解析url # params = parse.parse_qs(parse.urlparse(request.url.lower()).query) # print(params) # # 排序 # str_list = Sign.para_filter(params) # # 拼接请求 # params_str = Sign.create_link_string(str_list) + '&token=d5b9fedec0b3ad976842e83313cb2c75d616cafa' # # 生成签名 # sign = Sign.encryption( # "login_chainid=440135&login_shift=a&_=1607680643688&token=d5b9fedec0b3ad976842e83313cb2c75d616cafa") # url = "http://test.wfpms.com:9000/api/GetMebType?" + 'login_chainid=440135&login_shift=A&_=1607680643688&token=145_4239_d197b0ac6cbafe4b680aa3227ddab0411111' + f'&sign={sign}' # request.replace(url=url) # print(f"request.url{request.url}") # return None # else: # return None class WfpmsSpiderMiddleware: # Not all methods need to be defined. If a method is not defined, # scrapy acts as if the spider middleware does not modify the # passed objects. @classmethod def from_crawler(cls, crawler): # This method is used by Scrapy to create your spiders. s = cls() crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) return s def process_spider_input(self, response, spider): # Called for each response that goes through the spider # middleware and into the spider. # Should return None or raise an exception. return None def process_spider_output(self, response, result, spider): # Called with the results returned from the Spider, after # it has processed the response. # Must return an iterable of Request, or item objects. for i in result: yield i def process_spider_exception(self, response, exception, spider): # Called when a spider or process_spider_input() method # (from other spider middleware) raises an exception. # Should return either None or an iterable of Request or item objects. pass def process_start_requests(self, start_requests, spider): # Called with the start requests of the spider, and works # similarly to the process_spider_output() method, except # that it doesn’t have a response associated. # Must return only requests (not items). for r in start_requests: yield r def spider_opened(self, spider): spider.logger.info('Spider opened: %s' % spider.name) class WfpmsDownloaderMiddleware: # Not all methods need to be defined. If a method is not defined, # scrapy acts as if the downloader middleware does not modify the # passed objects. @classmethod def from_crawler(cls, crawler): # This method is used by Scrapy to create your spiders. s = cls() crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) return s def process_request(self, request, spider): # Called for each request that goes through the downloader # middleware. # Must either: # - return None: continue processing this request # - or return a Response object # - or return a Request object # - or raise IgnoreRequest: process_exception() methods of # installed downloader middleware will be called return None def process_response(self, request, response, spider): # Called with the response returned from the downloader. # Must either; # - return a Response object # - return a Request object # - or raise IgnoreRequest return response def process_exception(self, request, exception, spider): # Called when a download handler or a process_request() # (from other downloader middleware) raises an exception. # Must either: # - return None: continue processing this exception # - return a Response object: stops process_exception() chain # - return a Request object: stops process_exception() chain pass def spider_opened(self, spider): spider.logger.info('Spider opened: %s' % spider.name)

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shashankaddagarla/lstm

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# 10-16-18 # # currently a two-layer lstm model with dropouts (default .25 dropout) after each layer and a dense layer at the end to produce output import os import time import h5py import numpy as np import tensorflow as tf from keras.layers import Activation, Dense from keras.layers import LSTM from keras.layers import Dropout from keras.models import Sequential from keras.models import load_model from keras import optimizers activation_function = 'tanh' loss = 'mae' optimizer = optimizers.Adam(clipnorm = 1) dropout = .25 def build_model(layers, activ_func=activation_function, dropout=dropout, optimizer=optimizer): model = Sequential() model.add(LSTM(input_shape = (layers[0], layers[1]), return_sequences = True, units= layers[2])) # first layer so required input_shape model.add(Dropout(dropout)) model.add(LSTM(layers[3], return_sequences = False, activation = activ_func)) model.add(Dropout(dropout)) model.add(Dense(units=layers[4])) model.add(Activation(activ_func)) start = time.time() model.compile(loss = loss, optimizer = optimizer) print('>>> Model compiled! Took {} seconds.'.format(time.time() - start)) return model def save_model(model, name='my_model'): model.save(filename+'.h5') del model def load_model(name): if(os.path.isfile(name+'.h5')): return load_model(name+'.h5') else: print('>>> The specified model cannot be found.') return None

[ "shshnktj@stanford.edu" ]

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# https://www.acmicpc.net/problem/15649 from sys import stdout from itertools import permutations print = stdout.write n, m = map(int, input().split()) for k in permutations([i for i in range(1, n+1)], m): print(' '.join(map(str, k))+'\n')

[ "happytime870616@gmail.com" ]

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from couchdbkit.ext.django.schema import Document, DictProperty from mmda.engine.cache import CachedDocument class CachedTag(Document, CachedDocument): """ Contains tag related meta-data fetched from various sources. """ cache_state = DictProperty(default={})

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2022-06-01T08:33:53.034821

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#!/usr/bin/env python from distutils.core import setup from catkin_pkg.python_setup import generate_distutils_setup d = generate_distutils_setup( packages=[ 'rviz_tools_py', ], package_dir={'': 'src'}, ) setup(**d)

[ "2022087641@qq.com" ]

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PyDataWorkshop/IntroPython

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from math import sqrt print("Quadratic function : (a * x^2) + b*x + c") a = float(input("a: ")) b = float(input("b: ")) c = float(input("c: ")) r = b**2 - 4*a*c if r > 0: num_roots = 2 x1 = (((-b) + sqrt(r))/(2*a)) x2 = (((-b) - sqrt(r))/(2*a)) print("There are 2 roots: %f and %f" % (x1, x2)) elif r == 0: num_roots = 1 x = (-b) / 2*a print("There is one root: ", x) else: num_roots = 0 print("No roots, discriminant < 0.") exit() - See more at: http://www.w3resource.com/python-exercises/math/python-math-exercise-30.php#sthash.gypCq4M0.dpuf

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ha9011/django_drf_react_gabom

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2023-04-03T02:37:59.311406

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# Generated by Django 3.0.11 on 2021-03-25 13:15 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('accounts', '0006_auto_20210306_0104'), ] operations = [ migrations.AlterField( model_name='profile', name='user', field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='profile', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='user', name='email', field=models.EmailField(blank=True, default='test@naver.com', max_length=254), ), ]

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#!/Users/kwon/PycharmProjects/digital_fabrication_studio/venv/bin/python # EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==39.1.0','console_scripts','easy_install' __requires__ = 'setuptools==39.1.0' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('setuptools==39.1.0', 'console_scripts', 'easy_install')() )

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tpanthera/testpyblock

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2020-03-27T17:10:19.983182

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# Python advance concepts used # while solving questions in hacker earth this was common to use : # below code will take input from user , strip will remove white spaces , split() will return list after spliting # the given separator by spaces , here its , map will convert the every elements in list to integer and return map object. # list constructor will convert the map object to list object. test = list(map(int,input().strip().split())) # Publisher/subscriber design pattern used during connected with IOT devices with mqtt protocol.

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2021-01-20T18:08:21.393092

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# generate data for a histogram example import numpy as np N = 100 a = 10*np.random.randn(N) for i in range(N): print("{} {}".format(i, a[i]))

[ "michael.zingale@stonybrook.edu" ]

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2021-07-21T19:23:32.860444

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import os f = open('y.txt', 'wb+') # os.linesep 代表当前操作系统上的换行符 f.write(('我爱Python' + os.linesep).encode('utf-8')) f.writelines((('土门壁甚坚，' + os.linesep).encode('utf-8'), ('杏园度亦难。' + os.linesep).encode('utf-8'), ('势异邺城下，' + os.linesep).encode('utf-8'), ('纵死时犹宽。' + os.linesep).encode('utf-8')))

[ "baidongbin@thunisoft.com" ]

baidongbin@thunisoft.com

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2022-08-01T21:36:17.475108

2020-05-29T11:57:38

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import sys import time from datetime import datetime start_time=datetime.now() import pandas as pd import numpy as np import math import os import csv cwd =os.getcwd() version=cwd.split("/")[-1] program_name=cwd.split("/")[-2].split("_")[0] print(cwd) str_cwd=cwd.replace("/"+program_name+"/"+version,"") print(str_cwd) f_l=0 start_time=datetime.now() with open('faultyLine.txt') as f: f_l = f.readline() print("**************") print(f_l) print("**************") f_l=int(f_l) ############Original############## st1 = datetime.now() df_train=pd.read_csv('statementResult.csv') #training output dataset y = np.array([df_train['Result']]).T y=y.tolist() #print y #training input dataset df_train.drop(['Result'],1 , inplace=True) t_in = df_train.values.tolist() x = np.array(t_in) x=x.tolist() #print len(y[0]) total_failed=np.count_nonzero(y) total_passed=len(y)-total_failed suspicious=[] #print len(y) #print len(x[0]) #print total_passed,total_failed f = total_failed p = total_passed for i in range(0,len(x[0])): nsuccess=0 nfailure=0 for j in range(0,len(y)): #print x[j][i],y[j][0] if x[j][i]==1 and y[j][0]==0: nsuccess=nsuccess+1 elif x[j][i]==1 and y[j][0]==1: nfailure=nfailure+1 try: #nfailure=Ncf... nsuccess=Ncs #Nf=total_failed.... Ns=total_passed #print nfailure,nsuccess ep = nsuccess ef = nfailure np1 = p - ep nf = f - ef sus_score = float((f+p)*ef)/float(f*(ef + ep)) suspicious.append(sus_score) print(str(i)+" "+str(sus_score)) except ZeroDivisionError: suspicious.append(0) d = {} for i in range(0,len(suspicious)): key = float(suspicious[i]) #print key if key !=0: if key not in d: d[key] = [] d[key].append(i) ct1=0 ct2=0 ct3=0 fct=0 print("Faulty line:"+str(f_l)) for x in sorted(d): print (x,len(d[x])) if f_l not in d[x] and fct==0: ct1=ct1+len(d[x]) elif f_l not in d[x] and fct==1: ct3=ct3+len(d[x]) else: fct=1 ct2=len(d[x]) print("We have to search "+str(ct3+1)+" to "+str(ct3+ct2)) nwt1= (datetime.now() -st1) o1=ct3+1 o2=ct3+ct2 ############Original with uniqueness############## st2 = datetime.now() df_train=pd.read_csv('uniqueResult.csv') #training output dataset y = np.array([df_train['Result']]).T y=y.tolist() #print y #training input dataset df_train.drop(['Result'],1 , inplace=True) t_in = df_train.values.tolist() x = np.array(t_in) x=x.tolist() #print len(y[0]) total_failed=np.count_nonzero(y) total_passed=len(y)-total_failed suspicious=[] #print len(y) #print len(x[0]) #print total_passed,total_failed f = total_failed p = total_passed for i in range(0,len(x[0])): nsuccess=0 nfailure=0 for j in range(0,len(y)): #print x[j][i],y[j][0] if x[j][i]==1 and y[j][0]==0: nsuccess=nsuccess+1 elif x[j][i]==1 and y[j][0]==1: nfailure=nfailure+1 try: #nfailure=Ncf... nsuccess=Ncs #Nf=total_failed.... Ns=total_passed #print nfailure,nsuccess ep = nsuccess ef = nfailure np1 = p - ep nf = f - ef sus_score = float((f+p)*ef)/float(f*(ef + ep)) suspicious.append(sus_score) print(str(i)+" "+str(sus_score)) except ZeroDivisionError: suspicious.append(0) d = {} for i in range(0,len(suspicious)): key = float(suspicious[i]) #print key if key !=0: if key not in d: d[key] = [] d[key].append(i) ct1=0 ct2=0 ct3=0 fct=0 print("Faulty line:"+str(f_l)) for x in sorted(d): print (x,len(d[x])) if f_l not in d[x] and fct==0: ct1=ct1+len(d[x]) elif f_l not in d[x] and fct==1: ct3=ct3+len(d[x]) else: fct=1 ct2=len(d[x]) print("We have to search "+str(ct3+1)+" to "+str(ct3+ct2)) nwt2= (datetime.now() -st2) o3=ct3+1 o4=ct3+ct2 ############Original with slicing############## st3=datetime.now() #code for retriving the sliced data sdf=pd.read_csv('slice1.csv') ys=np.array([sdf['In_Slice']]).T ys=ys.tolist() df_train=pd.read_csv('statementResult.csv') #training output dataset y = np.array([df_train['Result']]).T y=y.tolist() #print y #training input dataset df_train.drop(['Result'],1 , inplace=True) t_in = df_train.values.tolist() x = np.array(t_in) x=x.tolist() #print len(y[0]) total_failed=np.count_nonzero(y) total_passed=len(y)-total_failed suspicious=[] #print len(y) #print len(x[0]) #print total_passed,total_failed f = total_failed p = total_passed for i in range(0,len(x[0])): nsuccess=0 nfailure=0 for j in range(0,len(y)): #print x[j][i],y[j][0] if x[j][i]==1 and y[j][0]==0: nsuccess=nsuccess+1 elif x[j][i]==1 and y[j][0]==1: nfailure=nfailure+1 try: #nfailure=Ncf... nsuccess=Ncs #Nf=total_failed.... Ns=total_passed #print nfailure,nsuccess ep = nsuccess ef = nfailure np1 = p - ep nf = f - ef if ys[i][0]==0: sus_score=-999 else: sus_score = float((f+p)*ef)/float(f*(ef + ep)) suspicious.append(sus_score) print(str(i)+" "+str(sus_score)) except ZeroDivisionError: suspicious.append(0) d = {} for i in range(0,len(suspicious)): key = float(suspicious[i]) #print key if key !=0: if key not in d: d[key] = [] d[key].append(i) ct1=0 ct2=0 ct3=0 fct=0 print("Faulty line:"+str(f_l)) for x in sorted(d): print (x,len(d[x])) if f_l not in d[x] and fct==0: ct1=ct1+len(d[x]) elif f_l not in d[x] and fct==1: ct3=ct3+len(d[x]) else: fct=1 ct2=len(d[x]) print("We have to search "+str(ct3+1)+" to "+str(ct3+ct2)) nwt3= (datetime.now() -st3) o5=ct3+1 o6=ct3+ct2 ############Original with slicing and uniqueness############## st4=datetime.now() #code for retriving the sliced data sdf=pd.read_csv('slice1.csv') ys=np.array([sdf['In_Slice']]).T ys=ys.tolist() df_train=pd.read_csv('uniqueResult.csv') #training output dataset y = np.array([df_train['Result']]).T y=y.tolist() #print y #training input dataset df_train.drop(['Result'],1 , inplace=True) t_in = df_train.values.tolist() x = np.array(t_in) x=x.tolist() #print len(y[0]) total_failed=np.count_nonzero(y) total_passed=len(y)-total_failed suspicious=[] #print len(y) #print len(x[0]) #print total_passed,total_failed f = total_failed p = total_passed for i in range(0,len(x[0])): nsuccess=0 nfailure=0 for j in range(0,len(y)): #print x[j][i],y[j][0] if x[j][i]==1 and y[j][0]==0: nsuccess=nsuccess+1 elif x[j][i]==1 and y[j][0]==1: nfailure=nfailure+1 try: #nfailure=Ncf... nsuccess=Ncs #Nf=total_failed.... Ns=total_passed #print nfailure,nsuccess ep = nsuccess ef = nfailure np1 = p - ep nf = f - ef if ys[i][0]==0: sus_score=-999 else: sus_score = float((f+p)*ef)/float(f*(ef + ep)) suspicious.append(sus_score) print(str(i)+" "+str(sus_score)) except ZeroDivisionError: suspicious.append(0) d = {} for i in range(0,len(suspicious)): key = float(suspicious[i]) #print key if key !=0: if key not in d: d[key] = [] d[key].append(i) ct1=0 ct2=0 ct3=0 fct=0 print("Faulty line:"+str(f_l)) for x in sorted(d): print (x,len(d[x])) if f_l not in d[x] and fct==0: ct1=ct1+len(d[x]) elif f_l not in d[x] and fct==1: ct3=ct3+len(d[x]) else: fct=1 ct2=len(d[x]) print("We have to search "+str(ct3+1)+" to "+str(ct3+ct2)) nwt4= (datetime.now() -st4) o7=ct3+1 o8=ct3+ct2 end_time=datetime.now() csvfile=open(str_cwd+"/forbes.csv", "a+") spamwriter1 = csv.writer(csvfile, delimiter=',') stmt_complex=[] stmt_complex.append(program_name); stmt_complex.append(str(version)); #stmt_complex.append(str(sys.argv[1])); stmt_complex.append(f_l); stmt_complex.append(o1); stmt_complex.append(o2); stmt_complex.append(nwt1); stmt_complex.append(o3); stmt_complex.append(o4); stmt_complex.append(nwt2); stmt_complex.append(o5); stmt_complex.append(o6); stmt_complex.append(nwt3); stmt_complex.append(o7); stmt_complex.append(o8); stmt_complex.append(nwt4); spamwriter1.writerow(stmt_complex);

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import timeit setup_mktout = """ def mktout(mean_mu_alpha, errors, par_gamma): mu10 = errors[:, 0] * math.exp(mean_mu_alpha[0]) mu11 = math.exp(par_gamma) * mu10 # mu with gamma mu20 = errors[:, 1] * math.exp(mean_mu_alpha[1]) mu21 = math.exp(par_gamma) * mu20 alpha1 = errors[:, 2] * math.exp(mean_mu_alpha[2]) alpha2 = errors[:, 3] * math.exp(mean_mu_alpha[3]) j_is_larger = (mu10 > mu20) threshold2 = (1 + mu10 * alpha1) / (168 + alpha1) j_is_smaller = ~j_is_larger threshold3 = (1 + mu20 * alpha2) / (168 + alpha2) case1 = j_is_larger * (mu10 < 1 / 168) case2 = j_is_larger * (mu21 >= threshold2) case3 = j_is_larger ^ (case1 | case2) case4 = j_is_smaller * (mu20 < 1 / 168) case5 = j_is_smaller * (mu11 >= threshold3) case6 = j_is_smaller ^ (case4 | case5) t0 = ne.evaluate("case1*168+case2 * (168 + alpha1 + alpha2) / (1 + mu11 * alpha1 + mu21 * alpha2) +case3 / threshold2 +case4 * 168 +case5 * (168 + alpha1 + alpha2) / (1 + mu11 * alpha1 + mu21 * alpha2) + case6 / threshold3") t1 = ne.evaluate("case2 * (t0 * alpha1 * mu11 - alpha1) +case3 * (t0 * alpha1 * mu10 - alpha1) +case5 * (t0 * alpha1 * mu11 - alpha1)") t2 = 168 - t0 - t1 p12 = case2 + case5 p1 = case3 + p12 p2 = case6 + p12 return t1.sum()/10000, t2.sum()/10000, p1.sum()/10000, p2.sum()/10000 """ setup_code = """ import integrate import integrate_alt import integrate_full import numpy as np import numexpr as ne import math par_mu_rho = 0.8 par_alpha_rho = 0.7 cov_epsilon = [[1, par_mu_rho], [par_mu_rho, 1]] cov_nu = [[1, par_alpha_rho], [par_alpha_rho, 1]] nrows = 10000 np.random.seed(123) epsilon_sim = np.random.multivariate_normal([0, 0], cov_epsilon, nrows) nu_sim = np.random.multivariate_normal([0, 0], cov_nu, nrows) errors = np.concatenate((epsilon_sim, nu_sim), axis=1) errors = np.exp(errors) """ setup_mean_mu_alpha = """ out = np.zeros(5, dtype=np.float64) mean_mu_alpha = np.array([-6,-6,-1,-1], dtype=np.float64) """ n = 10000 out = timeit.timeit( stmt="integrate.outer_loop_if([-6,-6,-1,-1], errors, -0.7, n)", setup=setup_code + "n = {0}".format(n), number=1, ) print("outer_loop_if:", out) out = timeit.timeit( stmt="integrate.outer_loop([-6,-6,-1,-1], errors, -0.7, n)", setup=setup_code + "n = {0}".format(n), number=1, ) print("outer_loop:", out) out = timeit.timeit( stmt="integrate.mktout_if([-6,-6,-1,-1], errors, -0.7)", setup=setup_code, number=n, ) print("mktout_if:", out) out = timeit.timeit( stmt="integrate.mktout([-6,-6,-1,-1], errors, -0.7)", setup=setup_code, number=n, ) print("mktout:", out) out = timeit.timeit( stmt="integrate_alt.outer_loop_alt([-6,-6,-1,-1], errors, -0.7, n)", setup=setup_code + "n = {0}".format(n), number=1, ) print("outer_loop_alt(mktout2):", out) out = timeit.timeit( stmt="integrate_alt.mktout_alt([-6,-6,-1,-1], errors, -0.7)", setup=setup_code, number=n, ) print("mktout2:", out) out = timeit.timeit( stmt="integrate_full.mktout_full(mean_mu_alpha, errors, -0.7)", setup=setup_code + setup_mean_mu_alpha, number=n, ) print("mktout_full:", out) out = timeit.timeit( stmt="integrate_full.outer_loop_full(out, mean_mu_alpha, errors, -0.7, n)", setup=setup_code + setup_mean_mu_alpha + "n = {0}".format(n), number=1, ) print("outer_loop_full:", out) out = timeit.timeit( stmt="mktout([-6,-6,-1,-1], errors, -0.7)", setup=setup_code + setup_mktout, number=n, ) print("python:", out)

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class Mem: VBLANK_HANDLER = 0x40 LCD_HANDLER = 0x48 TIMER_HANDLER = 0x50 SERIAL_HANDLER = 0x58 JOYPAD_HANDLER = 0x60 VRAM_BASE = 0x8000 TILE_DATA_TABLE_0 = 0x8800 TILE_DATA_TABLE_1 = 0x8000 BACKGROUND_MAP_0 = 0x9800 BACKGROUND_MAP_1 = 0x9C00 WINDOW_MAP_0 = 0x9800 WINDOW_MAP_1 = 0x9C00 OAM_BASE = 0xFE00 JOYP = 0xFF00 SB = 0xFF01 # Serial Data SC = 0xFF02 # Serial Control DIV = 0xFF04 TIMA = 0xFF05 TMA = 0xFF06 TAC = 0xFF07 IF = 0xFF0F NR10 = 0xFF10 NR11 = 0xFF11 NR12 = 0xFF12 NR13 = 0xFF13 NR14 = 0xFF14 NR20 = 0xFF15 NR21 = 0xFF16 NR22 = 0xFF17 NR23 = 0xFF18 NR24 = 0xFF19 NR30 = 0xFF1A NR31 = 0xFF1B NR32 = 0xFF1C NR33 = 0xFF1D NR34 = 0xFF1E NR40 = 0xFF1F NR41 = 0xFF20 NR42 = 0xFF21 NR43 = 0xFF22 NR44 = 0xFF23 NR50 = 0xFF24 NR51 = 0xFF25 NR52 = 0xFF26 LCDC = 0xFF40 STAT = 0xFF41 SCY = 0xFF42 # SCROLL_Y SCX = 0xFF43 # SCROLL_X LY = 0xFF44 # LY aka currently drawn line 0-153 >144 = vblank LCY = 0xFF45 DMA = 0xFF46 BGP = 0xFF47 OBP0 = 0xFF48 OBP1 = 0xFF49 WY = 0xFF4A WX = 0xFF4B BOOT = 0xFF50 IE = 0xFFFF class Interrupt: VBLANK = 1 << 0 STAT = 1 << 1 TIMER = 1 << 2 SERIAL = 1 << 3 JOYPAD = 1 << 4

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# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import copy import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from ... import iam as _iam __all__ = ['RepositoryWorkspaceIamMemberArgs', 'RepositoryWorkspaceIamMember'] @pulumi.input_type class RepositoryWorkspaceIamMemberArgs: def __init__(__self__, *, member: pulumi.Input[str], name: pulumi.Input[str], role: pulumi.Input[str], condition: Optional[pulumi.Input['_iam.v1.ConditionArgs']] = None): """ The set of arguments for constructing a RepositoryWorkspaceIamMember resource. :param pulumi.Input[str] member: Identity that will be granted the privilege in role. The entry can have one of the following values: * user:{emailid}: An email address that represents a specific Google account. For example, alice@gmail.com or joe@example.com. * serviceAccount:{emailid}: An email address that represents a service account. For example, my-other-app@appspot.gserviceaccount.com. * group:{emailid}: An email address that represents a Google group. For example, admins@example.com. * domain:{domain}: A G Suite domain (primary, instead of alias) name that represents all the users of that domain. For example, google.com or example.com. :param pulumi.Input[str] name: The name of the resource to manage IAM policies for. :param pulumi.Input[str] role: The role that should be applied. :param pulumi.Input['_iam.v1.ConditionArgs'] condition: An IAM Condition for a given binding. """ pulumi.set(__self__, "member", member) pulumi.set(__self__, "name", name) pulumi.set(__self__, "role", role) if condition is not None: pulumi.set(__self__, "condition", condition) @property @pulumi.getter def member(self) -> pulumi.Input[str]: """ Identity that will be granted the privilege in role. The entry can have one of the following values: * user:{emailid}: An email address that represents a specific Google account. For example, alice@gmail.com or joe@example.com. * serviceAccount:{emailid}: An email address that represents a service account. For example, my-other-app@appspot.gserviceaccount.com. * group:{emailid}: An email address that represents a Google group. For example, admins@example.com. * domain:{domain}: A G Suite domain (primary, instead of alias) name that represents all the users of that domain. For example, google.com or example.com. """ return pulumi.get(self, "member") @member.setter def member(self, value: pulumi.Input[str]): pulumi.set(self, "member", value) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ The name of the resource to manage IAM policies for. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter def role(self) -> pulumi.Input[str]: """ The role that should be applied. """ return pulumi.get(self, "role") @role.setter def role(self, value: pulumi.Input[str]): pulumi.set(self, "role", value) @property @pulumi.getter def condition(self) -> Optional[pulumi.Input['_iam.v1.ConditionArgs']]: """ An IAM Condition for a given binding. """ return pulumi.get(self, "condition") @condition.setter def condition(self, value: Optional[pulumi.Input['_iam.v1.ConditionArgs']]): pulumi.set(self, "condition", value) class RepositoryWorkspaceIamMember(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, condition: Optional[pulumi.Input[pulumi.InputType['_iam.v1.ConditionArgs']]] = None, member: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, role: Optional[pulumi.Input[str]] = None, __props__=None): """ Sets the access control policy on the specified resource. Replaces any existing policy. Can return `NOT_FOUND`, `INVALID_ARGUMENT`, and `PERMISSION_DENIED` errors. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[pulumi.InputType['_iam.v1.ConditionArgs']] condition: An IAM Condition for a given binding. :param pulumi.Input[str] member: Identity that will be granted the privilege in role. The entry can have one of the following values: * user:{emailid}: An email address that represents a specific Google account. For example, alice@gmail.com or joe@example.com. * serviceAccount:{emailid}: An email address that represents a service account. For example, my-other-app@appspot.gserviceaccount.com. * group:{emailid}: An email address that represents a Google group. For example, admins@example.com. * domain:{domain}: A G Suite domain (primary, instead of alias) name that represents all the users of that domain. For example, google.com or example.com. :param pulumi.Input[str] name: The name of the resource to manage IAM policies for. :param pulumi.Input[str] role: The role that should be applied. """ ... @overload def __init__(__self__, resource_name: str, args: RepositoryWorkspaceIamMemberArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Sets the access control policy on the specified resource. Replaces any existing policy. Can return `NOT_FOUND`, `INVALID_ARGUMENT`, and `PERMISSION_DENIED` errors. :param str resource_name: The name of the resource. :param RepositoryWorkspaceIamMemberArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(RepositoryWorkspaceIamMemberArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, condition: Optional[pulumi.Input[pulumi.InputType['_iam.v1.ConditionArgs']]] = None, member: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, role: Optional[pulumi.Input[str]] = None, __props__=None): opts = pulumi.ResourceOptions.merge(_utilities.get_resource_opts_defaults(), opts) if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = RepositoryWorkspaceIamMemberArgs.__new__(RepositoryWorkspaceIamMemberArgs) __props__.__dict__["condition"] = condition if member is None and not opts.urn: raise TypeError("Missing required property 'member'") __props__.__dict__["member"] = member if name is None and not opts.urn: raise TypeError("Missing required property 'name'") __props__.__dict__["name"] = name if role is None and not opts.urn: raise TypeError("Missing required property 'role'") __props__.__dict__["role"] = role __props__.__dict__["etag"] = None __props__.__dict__["project"] = None super(RepositoryWorkspaceIamMember, __self__).__init__( 'google-native:dataform/v1beta1:RepositoryWorkspaceIamMember', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'RepositoryWorkspaceIamMember': """ Get an existing RepositoryWorkspaceIamMember resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = RepositoryWorkspaceIamMemberArgs.__new__(RepositoryWorkspaceIamMemberArgs) __props__.__dict__["condition"] = None __props__.__dict__["etag"] = None __props__.__dict__["member"] = None __props__.__dict__["name"] = None __props__.__dict__["project"] = None __props__.__dict__["role"] = None return RepositoryWorkspaceIamMember(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def condition(self) -> pulumi.Output[Optional['_iam.v1.outputs.Condition']]: """ An IAM Condition for a given binding. See https://cloud.google.com/iam/docs/conditions-overview for additional details. """ return pulumi.get(self, "condition") @property @pulumi.getter def etag(self) -> pulumi.Output[str]: """ The etag of the resource's IAM policy. """ return pulumi.get(self, "etag") @property @pulumi.getter def member(self) -> pulumi.Output[str]: """ Specifies the principals requesting access for a Google Cloud resource. `members` can have the following values: * `allUsers`: A special identifier that represents anyone who is on the internet; with or without a Google account. * `allAuthenticatedUsers`: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. * `user:{emailid}`: An email address that represents a specific Google account. For example, `alice@example.com` . * `serviceAccount:{emailid}`: An email address that represents a Google service account. For example, `my-other-app@appspot.gserviceaccount.com`. * `serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]`: An identifier for a [Kubernetes service account](https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, `my-project.svc.id.goog[my-namespace/my-kubernetes-sa]`. * `group:{emailid}`: An email address that represents a Google group. For example, `admins@example.com`. * `domain:{domain}`: The G Suite domain (primary) that represents all the users of that domain. For example, `google.com` or `example.com`. * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a user that has been recently deleted. For example, `alice@example.com?uid=123456789012345678901`. If the user is recovered, this value reverts to `user:{emailid}` and the recovered user retains the role in the binding. * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`. If the service account is undeleted, this value reverts to `serviceAccount:{emailid}` and the undeleted service account retains the role in the binding. * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, `admins@example.com?uid=123456789012345678901`. If the group is recovered, this value reverts to `group:{emailid}` and the recovered group retains the role in the binding. """ return pulumi.get(self, "member") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the resource to manage IAM policies for. """ return pulumi.get(self, "name") @property @pulumi.getter def project(self) -> pulumi.Output[str]: """ The project in which the resource belongs. If it is not provided, a default will be supplied. """ return pulumi.get(self, "project") @property @pulumi.getter def role(self) -> pulumi.Output[str]: """ Role that is assigned to the list of `members`, or principals. For example, `roles/viewer`, `roles/editor`, or `roles/owner`. """ return pulumi.get(self, "role")

[ "noreply@github.com" ]

pulumi.noreply@github.com

2b68130fbff02ea4e15ff6aca8bd248f9a3d3f79

a0d0dafc26ebfb84ff72c61cb85c587d93257a6c

/KIDs/analyze_single_tone.py

09573a4659654e3073eecb6f0bce454336a2c07b

[]

no_license

rmcgeehan0/submm_python_routines

0486d5f874b169629378374058f0fe666ebbb440

d37e676dc1946807edaec7143780307e2894c6c6

refs/heads/master

2020-03-27T09:32:51.519444

2018-08-25T20:23:47

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0

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Python

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py

import numpy as np import matplotlib.pyplot as plt from KIDs import resonance_fitting from KIDs import calibrate from scipy import interpolate import pickle from scipy.stats import binned_statistic def calibrate_single_tone(fine_f,fine_z,gain_f,gain_z,stream_f,stream_z,plot_period = 1,interp = "quadratic"): fig = plt.figure(3,figsize = (16,10)) plt.subplot(241,aspect = 'equal') plt.title("Raw data") plt.plot(np.real(stream_z[::plot_period]),np.real(stream_z[::plot_period]),'.') plt.plot(np.real(fine_z),np.imag(fine_z),'o') plt.plot(np.real(gain_z),np.imag(gain_z),'o') plt.subplot(242) plt.title("Raw data") plt.plot(np.real(stream_z[::plot_period]),np.imag(stream_z[::plot_period]),'.') plt.plot(np.real(fine_z),np.imag(fine_z),'o') # normalize amplistude variation in the gain scan amp_norm_dict = resonance_fitting.amplitude_normalization_sep(gain_f, gain_z, fine_f, fine_z, stream_f, stream_z) plt.subplot(243) plt.title("Gain amplitude variation fit") plt.plot(gain_f,10*np.log10(np.abs(gain_z)**2),'o') plt.plot(gain_f,10*np.log10(np.abs(amp_norm_dict['normalized_gain'])**2),'o') plt.plot(fine_f,10*np.log10(np.abs(amp_norm_dict['normalized_fine'])**2),'o') plt.plot(gain_f,10*np.log10(np.abs(amp_norm_dict['poly_data'])**2)) plt.subplot(244) plt.title("Data nomalized for gain amplitude variation") plt.plot(np.real(amp_norm_dict['normalized_fine']),np.imag(amp_norm_dict['normalized_fine']),'o') #plt.plot(gain_dict['freqs'][:,k]*10**6,np.log10(np.abs(amp_norm_dict['poly_data'])**2)) plt.plot(np.real(amp_norm_dict['normalized_stream'][::plot_period]),np.imag(amp_norm_dict['normalized_stream'][::plot_period]),'.') #fit the gain gain_phase = np.arctan2(np.real(amp_norm_dict['normalized_gain']),np.imag(amp_norm_dict['normalized_gain'])) tau,fit_data_phase,gain_phase_rot = calibrate.fit_cable_delay(gain_f,gain_phase) plt.subplot(245) plt.title("Gain phase fit") plt.plot(gain_f,gain_phase_rot,'o') plt.plot(gain_f,fit_data_phase) plt.xlabel("Frequency (MHz)") plt.ylabel("Phase") #remove cable delay gain_corr = calibrate.remove_cable_delay(gain_f,amp_norm_dict['normalized_gain'],tau) fine_corr = calibrate.remove_cable_delay(fine_f,amp_norm_dict['normalized_fine'],tau) stream_corr = calibrate.remove_cable_delay(stream_f,amp_norm_dict['normalized_stream'],tau) plt.subplot(246) plt.title("Cable delay removed") plt.plot(np.real(gain_corr),np.imag(gain_corr),'o') plt.plot(np.real(fine_corr),np.imag(fine_corr),'o') plt.plot(np.real(stream_corr)[10:-10][::plot_period],np.imag(stream_corr)[10:-10][::plot_period],'.') # fit a cicle to the data xc, yc, R, residu = calibrate.leastsq_circle(np.real(fine_corr),np.imag(fine_corr)) #move the data to the origin gain_corr = gain_corr - xc -1j*yc fine_corr = fine_corr - xc -1j*yc stream_corr = stream_corr - xc -1j*yc # rotate so streaming data is at 0 pi phase_stream = np.arctan2(np.imag(stream_corr),np.real(stream_corr)) med_phase = np.median(phase_stream) gain_corr = gain_corr*np.exp(-1j*med_phase) fine_corr = fine_corr*np.exp(-1j*med_phase) stream_corr = stream_corr*np.exp(-1j*med_phase) plt.subplot(247) plt.title("Moved to 0,0 and rotated") plt.plot(np.real(stream_corr)[2:-1][::plot_period],np.imag(stream_corr)[2:-1][::plot_period],'.') plt.plot(np.real(gain_corr),np.imag(gain_corr),'o') plt.plot(np.real(fine_corr),np.imag(fine_corr),'o') calibrate.plot_data_circle(np.real(fine_corr)-xc,np.imag(fine_corr)-yc, 0, 0, R) phase_fine = np.arctan2(np.imag(fine_corr),np.real(fine_corr)) use_index = np.where((-np.pi/2.<phase_fine) & (phase_fine<np.pi/2)) phase_stream = np.arctan2(np.imag(stream_corr),np.real(stream_corr)) #interp phase to frequency f_interp = interpolate.interp1d(phase_fine, fine_f,kind = interp,bounds_error = False,fill_value = 0) phase_small = np.linspace(np.min(phase_fine),np.max(phase_fine),1000) freqs_stream = f_interp(phase_stream) stream_df_over_f_all = stream_df_over_f = freqs_stream/np.mean(freqs_stream)-1. plt.subplot(248) plt.plot(phase_fine,fine_f,'o') plt.plot(phase_small,f_interp(phase_small),'--') plt.plot(phase_stream[::plot_period],freqs_stream[::plot_period],'.') plt.ylim(np.min(freqs_stream)-(np.max(freqs_stream)-np.min(freqs_stream))*3,np.max(freqs_stream)+(np.max(freqs_stream)-np.min(freqs_stream))*3) plt.xlim(np.min(phase_stream)-np.pi/4,np.max(phase_stream)+np.pi/4) plt.xlabel("phase") plt.ylabel("Frequency") plt.savefig("calibration.pdf") cal_dict = {'fine_z': fine_z, 'gain_z': gain_z, 'stream_z': stream_z, 'fine_freqs':fine_f, 'gain_freqs':gain_f, 'stream_corr':stream_corr, 'gain_corr':gain_corr, 'fine_corr':fine_corr, 'stream_df_over_f':stream_df_over_f_all} pickle.dump( cal_dict, open( "cal.p", "wb" ),2 ) return cal_dict def noise(cal_dict, sample_rate): fft_freqs,Sxx,S_per,S_par = calibrate.fft_noise(cal_dict['stream_corr'],cal_dict['stream_df_over_f'],sample_rate) plot_bins = np.logspace(-3,np.log10(300),100) binnedfreq = binned_statistic(fft_freqs, fft_freqs, bins=plot_bins)[0] #bin the frequecy against itself binnedpsd = binned_statistic(fft_freqs, np.abs(Sxx), bins=plot_bins)[0] binnedper = binned_statistic(fft_freqs, np.abs(S_per), bins=plot_bins)[0] binnedpar = binned_statistic(fft_freqs, np.abs(S_par), bins=plot_bins)[0] amp_subtracted = np.abs(binnedpsd)*(binnedpar-binnedper)/binnedpar fig = plt.figure(4,figsize = (16,6)) plt.subplot(122) plt.title("Sxx") #plt.loglog(fft_freqs,np.abs(Sxx)) plt.loglog(binnedfreq,np.abs(binnedpsd),linewidth = 2,label = "Sxx raw") plt.loglog(binnedfreq,amp_subtracted,linewidth = 2,label = "raw amp subtracted") #plt.ylim(10**-18,10**-15) plt.ylabel("Sxx (1/Hz)") plt.xlabel("Frequency (Hz)") plt.legend() plt.subplot(121) #plt.loglog(fft_freqs,S_per) #plt.loglog(fft_freqs,S_par) plt.loglog(binnedfreq,binnedper,label = "amp noise") plt.loglog(binnedfreq,binnedpar,label = "detect noise") plt.legend() #plt.ylim(10**2,10**6) plt.xlabel("Frequency (Hz)") plt.savefig("psd.pdf") psd_dict = {'fft_freqs':fft_freqs, 'Sxx':Sxx, 'S_per':S_per, 'S_par':S_par, 'binned_freqs':binnedfreq, 'Sxx_binned':binnedpsd, 'S_per_binned':binnedper, 'S_par_binned':binnedpar, 'amp_subtracted':amp_subtracted} #save the psd dictionary pickle.dump( psd_dict, open("psd.p", "wb" ),2 ) return psd_dict

[ "Wheeler1711@gmail.com" ]

Wheeler1711@gmail.com

12e36e9537e9bd48715cc87299c6710dcc8d2484

4341c1c4fbf30032c50b66ca6ac2d4a2bfc0e83a

/translate/models.py

6d1a98c568bcfc0c779f1c1ebfd40552bd2fb9a1

[]

no_license

a574751346/transfer2nl

fec566835a62ebdc5388fcfef7526dbe72bf78d7

0251655603e2da0c3ca7cf597b2d7c10060804ba

refs/heads/master

2021-05-23T17:44:56.836659

2020-04-09T05:33:57

253,404,966

0

null

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py

import tensorflow as tf import math from tensorflow.contrib.rnn import BasicLSTMCell, RNNCell, DropoutWrapper, MultiRNNCell from rnn import stack_bidirectional_dynamic_rnn, CellInitializer, GRUCell, DropoutGRUCell import utils, beam_search def auto_reuse(fun): """ Wrapper that automatically handles the `reuse' parameter. This is rather risky, as it can lead to reusing variables by mistake. """ def fun_(*args, **kwargs): try: return fun(*args, **kwargs) except ValueError as e: if 'reuse' in str(e): with tf.variable_scope(tf.get_variable_scope(), reuse=True): return fun(*args, **kwargs) else: raise e return fun_ get_variable = auto_reuse(tf.get_variable) dense = auto_reuse(tf.layers.dense) class CellWrapper(RNNCell): """ Wrapper around LayerNormBasicLSTMCell, BasicLSTMCell and MultiRNNCell, to keep the state_is_tuple=False behavior (soon to be deprecated). """ def __init__(self, cell): super(CellWrapper, self).__init__() self.cell = cell self.num_splits = len(cell.state_size) if isinstance(cell.state_size, tuple) else 1 @property def state_size(self): return sum(self.cell.state_size) @property def output_size(self): return self.cell.output_size def __call__(self, inputs, state, scope=None): state = tf.split(value=state, num_or_size_splits=self.num_splits, axis=1) new_h, new_state = self.cell(inputs, state, scope=scope) return new_h, tf.concat(new_state, 1) def multi_encoder(encoder_inputs, encoders, encoder_input_length, other_inputs=None, **kwargs): """ Build multiple encoders according to the configuration in `encoders`, reading from `encoder_inputs`. The result is a list of the outputs produced by those encoders (for each time-step), and their final state. :param encoder_inputs: list of tensors of shape (batch_size, input_length), one tensor for each encoder. :param encoders: list of encoder configurations :param encoder_input_length: list of tensors of shape (batch_size,) (one tensor for each encoder) :return: encoder outputs: a list of tensors of shape (batch_size, input_length, encoder_cell_size), hidden states of the encoders. encoder state: concatenation of the final states of all encoders, tensor of shape (batch_size, sum_of_state_sizes) new_encoder_input_length: list of tensors of shape (batch_size,) with the true length of the encoder outputs. May be different than `encoder_input_length` because of maxout strides, and time pooling. """ encoder_states = [] encoder_outputs = [] # create embeddings in the global scope (allows sharing between encoder and decoder) embedding_variables = [] for encoder in encoders: if encoder.binary: embedding_variables.append(None) continue # inputs are token ids, which need to be mapped to vectors (embeddings) embedding_shape = [encoder.vocab_size, encoder.embedding_size] if encoder.embedding_initializer == 'sqrt3': initializer = tf.random_uniform_initializer(-math.sqrt(3), math.sqrt(3)) else: initializer = None device = '/cpu:0' if encoder.embeddings_on_cpu else None with tf.device(device): # embeddings can take a very large amount of memory, so # storing them in GPU memory can be impractical embedding = get_variable('embedding_{}'.format(encoder.name), shape=embedding_shape, initializer=initializer) embedding_variables.append(embedding) new_encoder_input_length = [] for i, encoder in enumerate(encoders): if encoder.use_lstm is False: encoder.cell_type = 'GRU' with tf.variable_scope('encoder_{}'.format(encoder.name)): encoder_inputs_ = encoder_inputs[i] encoder_input_length_ = encoder_input_length[i] def get_cell(input_size=None, reuse=False): if encoder.cell_type.lower() == 'lstm': cell = CellWrapper(BasicLSTMCell(encoder.cell_size, reuse=reuse)) elif encoder.cell_type.lower() == 'dropoutgru': cell = DropoutGRUCell(encoder.cell_size, reuse=reuse, layer_norm=encoder.layer_norm, input_size=input_size, input_keep_prob=encoder.rnn_input_keep_prob, state_keep_prob=encoder.rnn_state_keep_prob) elif encoder.cell_type.lower() == 'treelstm': # TODO cell = None return else: cell = GRUCell(encoder.cell_size, reuse=reuse, layer_norm=encoder.layer_norm) if encoder.use_dropout and encoder.cell_type.lower() != 'dropoutgru': cell = DropoutWrapper(cell, input_keep_prob=encoder.rnn_input_keep_prob, output_keep_prob=encoder.rnn_output_keep_prob, state_keep_prob=encoder.rnn_state_keep_prob, variational_recurrent=encoder.pervasive_dropout, dtype=tf.float32, input_size=input_size) return cell embedding = embedding_variables[i] batch_size = tf.shape(encoder_inputs_)[0] time_steps = tf.shape(encoder_inputs_)[1] if embedding is not None: flat_inputs = tf.reshape(encoder_inputs_, [tf.multiply(batch_size, time_steps)]) flat_inputs = tf.nn.embedding_lookup(embedding, flat_inputs) encoder_inputs_ = tf.reshape(flat_inputs, tf.stack([batch_size, time_steps, flat_inputs.get_shape()[1].value])) if other_inputs is not None: encoder_inputs_ = tf.concat([encoder_inputs_, other_inputs], axis=2) if encoder.use_dropout: noise_shape = [1, time_steps, 1] if encoder.pervasive_dropout else [batch_size, time_steps, 1] encoder_inputs_ = tf.nn.dropout(encoder_inputs_, keep_prob=encoder.word_keep_prob, noise_shape=noise_shape) size = tf.shape(encoder_inputs_)[2] noise_shape = [1, 1, size] if encoder.pervasive_dropout else [batch_size, time_steps, size] encoder_inputs_ = tf.nn.dropout(encoder_inputs_, keep_prob=encoder.embedding_keep_prob, noise_shape=noise_shape) if encoder.input_layers: for j, layer_size in enumerate(encoder.input_layers): if encoder.input_layer_activation is not None and encoder.input_layer_activation.lower() == 'relu': activation = tf.nn.relu else: activation = tf.tanh encoder_inputs_ = dense(encoder_inputs_, layer_size, activation=activation, use_bias=True, name='layer_{}'.format(j)) if encoder.use_dropout: encoder_inputs_ = tf.nn.dropout(encoder_inputs_, keep_prob=encoder.input_layer_keep_prob) # Contrary to Theano's RNN implementation, states after the sequence length are zero # (while Theano repeats last state) inter_layer_keep_prob = None if not encoder.use_dropout else encoder.inter_layer_keep_prob parameters = dict( inputs=encoder_inputs_, sequence_length=encoder_input_length_, dtype=tf.float32, parallel_iterations=encoder.parallel_iterations ) input_size = encoder_inputs_.get_shape()[2].value state_size = (encoder.cell_size * 2 if encoder.cell_type.lower() == 'lstm' else encoder.cell_size) def get_initial_state(name='initial_state'): if encoder.train_initial_states: initial_state = get_variable(name, initializer=tf.zeros(state_size)) return tf.tile(tf.expand_dims(initial_state, axis=0), [batch_size, 1]) else: return None if encoder.bidir: rnn = lambda reuse: stack_bidirectional_dynamic_rnn( cells_fw=[get_cell(input_size if j == 0 else 2 * encoder.cell_size, reuse=reuse) for j in range(encoder.layers)], cells_bw=[get_cell(input_size if j == 0 else 2 * encoder.cell_size, reuse=reuse) for j in range(encoder.layers)], initial_states_fw=[get_initial_state('initial_state_fw')] * encoder.layers, initial_states_bw=[get_initial_state('initial_state_bw')] * encoder.layers, time_pooling=encoder.time_pooling, pooling_avg=encoder.pooling_avg, **parameters) initializer = CellInitializer(encoder.cell_size) if encoder.orthogonal_init else None with tf.variable_scope(tf.get_variable_scope(), initializer=initializer): try: encoder_outputs_, _, encoder_states_ = rnn(reuse=False) except ValueError: # Multi-task scenario where we're reusing the same RNN parameters encoder_outputs_, _, encoder_states_ = rnn(reuse=True) else: if encoder.time_pooling or encoder.final_state == 'concat_last': raise NotImplementedError if encoder.layers > 1: cell = MultiRNNCell([get_cell(input_size if j == 0 else encoder.cell_size) for j in range(encoder.layers)]) initial_state = (get_initial_state(),) * encoder.layers else: cell = get_cell(input_size) initial_state = get_initial_state() encoder_outputs_, encoder_states_ = auto_reuse(tf.nn.dynamic_rnn)(cell=cell, initial_state=initial_state, **parameters) last_backward = encoder_outputs_[:, 0, encoder.cell_size:] indices = tf.stack([tf.range(batch_size), encoder_input_length_ - 1], axis=1) last_forward = tf.gather_nd(encoder_outputs_[:, :, :encoder.cell_size], indices) last_forward.set_shape([None, encoder.cell_size]) if encoder.final_state == 'concat_last': # concats last states of all backward layers (full LSTM states) encoder_state_ = tf.concat(encoder_states_, axis=1) elif encoder.final_state == 'average': mask = tf.sequence_mask(encoder_input_length_, maxlen=tf.shape(encoder_outputs_)[1], dtype=tf.float32) mask = tf.expand_dims(mask, axis=2) encoder_state_ = tf.reduce_sum(mask * encoder_outputs_, axis=1) / tf.reduce_sum(mask, axis=1) elif encoder.final_state == 'average_inputs': mask = tf.sequence_mask(encoder_input_length_, maxlen=tf.shape(encoder_inputs_)[1], dtype=tf.float32) mask = tf.expand_dims(mask, axis=2) encoder_state_ = tf.reduce_sum(mask * encoder_inputs_, axis=1) / tf.reduce_sum(mask, axis=1) elif encoder.bidir and encoder.final_state == 'last_both': encoder_state_ = tf.concat([last_forward, last_backward], axis=1) elif encoder.bidir and not encoder.final_state == 'last_forward': # last backward hidden state encoder_state_ = last_backward else: # last forward hidden state encoder_state_ = last_forward if encoder.bidir and encoder.bidir_projection: encoder_outputs_ = dense(encoder_outputs_, encoder.cell_size, use_bias=False, name='bidir_projection') encoder_outputs.append(encoder_outputs_) encoder_states.append(encoder_state_) new_encoder_input_length.append(encoder_input_length_) encoder_state = tf.concat(encoder_states, 1) return encoder_outputs, encoder_state, new_encoder_input_length def compute_energy(hidden, state, attn_size, attn_keep_prob=None, pervasive_dropout=False, layer_norm=False, mult_attn=False, **kwargs): if attn_keep_prob is not None: state_noise_shape = [1, tf.shape(state)[1]] if pervasive_dropout else None state = tf.nn.dropout(state, keep_prob=attn_keep_prob, noise_shape=state_noise_shape) hidden_noise_shape = [1, 1, tf.shape(hidden)[2]] if pervasive_dropout else None hidden = tf.nn.dropout(hidden, keep_prob=attn_keep_prob, noise_shape=hidden_noise_shape) if mult_attn: state = dense(state, attn_size, use_bias=False, name='state') hidden = dense(hidden, attn_size, use_bias=False, name='hidden') return tf.einsum('ijk,ik->ij', hidden, state) else: y = dense(state, attn_size, use_bias=not layer_norm, name='W_a') y = tf.expand_dims(y, axis=1) if layer_norm: y = tf.contrib.layers.layer_norm(y, scope='layer_norm_state') hidden = tf.contrib.layers.layer_norm(hidden, center=False, scope='layer_norm_hidden') f = dense(hidden, attn_size, use_bias=False, name='U_a') v = get_variable('v_a', [attn_size]) s = f + y return tf.reduce_sum(v * tf.tanh(s), axis=2) def compute_energy_with_filter(hidden, state, prev_weights, attn_filters, attn_filter_length, **kwargs): hidden = tf.expand_dims(hidden, 2) batch_size = tf.shape(hidden)[0] time_steps = tf.shape(hidden)[1] attn_size = hidden.get_shape()[3].value filter_shape = [attn_filter_length * 2 + 1, 1, 1, attn_filters] filter_ = get_variable('filter', filter_shape) u = get_variable('U', [attn_filters, attn_size]) prev_weights = tf.reshape(prev_weights, tf.stack([batch_size, time_steps, 1, 1])) conv = tf.nn.conv2d(prev_weights, filter_, [1, 1, 1, 1], 'SAME') shape = tf.stack([tf.multiply(batch_size, time_steps), attn_filters]) conv = tf.reshape(conv, shape) z = tf.matmul(conv, u) z = tf.reshape(z, tf.stack([batch_size, time_steps, 1, attn_size])) y = dense(state, attn_size, use_bias=True, name='y') y = tf.reshape(y, [-1, 1, 1, attn_size]) k = get_variable('W', [attn_size, attn_size]) # dot product between tensors requires reshaping hidden = tf.reshape(hidden, tf.stack([tf.multiply(batch_size, time_steps), attn_size])) f = tf.matmul(hidden, k) f = tf.reshape(f, tf.stack([batch_size, time_steps, 1, attn_size])) v = get_variable('V', [attn_size]) s = f + y + z return tf.reduce_sum(v * tf.tanh(s), [2, 3]) def global_attention(state, hidden_states, encoder, encoder_input_length, scope=None, context=None, **kwargs): with tf.variable_scope(scope or 'attention_{}'.format(encoder.name)): if context is not None and encoder.use_context: state = tf.concat([state, context], axis=1) if encoder.attn_filters: e = compute_energy_with_filter(hidden_states, state, attn_size=encoder.attn_size, attn_filters=encoder.attn_filters, attn_filter_length=encoder.attn_filter_length, **kwargs) else: e = compute_energy(hidden_states, state, attn_size=encoder.attn_size, attn_keep_prob=encoder.attn_keep_prob, pervasive_dropout=encoder.pervasive_dropout, layer_norm=encoder.layer_norm, mult_attn=encoder.mult_attn, **kwargs) e -= tf.reduce_max(e, axis=1, keep_dims=True) mask = tf.sequence_mask(encoder_input_length, maxlen=tf.shape(hidden_states)[1], dtype=tf.float32) T = encoder.attn_temperature or 1.0 exp = tf.exp(e / T) * mask weights = exp / tf.reduce_sum(exp, axis=-1, keep_dims=True) weighted_average = tf.reduce_sum(tf.expand_dims(weights, 2) * hidden_states, axis=1) return weighted_average, weights def no_attention(state, hidden_states, *args, **kwargs): batch_size = tf.shape(state)[0] weighted_average = tf.zeros(shape=tf.stack([batch_size, 0])) weights = tf.zeros(shape=[batch_size, tf.shape(hidden_states)[1]]) return weighted_average, weights def average_attention(hidden_states, encoder_input_length, *args, **kwargs): # attention with fixed weights (average of all hidden states) lengths = tf.to_float(tf.expand_dims(encoder_input_length, axis=1)) mask = tf.sequence_mask(encoder_input_length, maxlen=tf.shape(hidden_states)[1]) weights = tf.to_float(mask) / lengths weighted_average = tf.reduce_sum(hidden_states * tf.expand_dims(weights, axis=2), axis=1) return weighted_average, weights def last_state_attention(hidden_states, encoder_input_length, *args, **kwargs): weights = tf.one_hot(encoder_input_length - 1, tf.shape(hidden_states)[1]) weights = tf.to_float(weights) weighted_average = tf.reduce_sum(hidden_states * tf.expand_dims(weights, axis=2), axis=1) return weighted_average, weights def local_attention(state, hidden_states, encoder, encoder_input_length, pos=None, scope=None, context=None, **kwargs): batch_size = tf.shape(state)[0] attn_length = tf.shape(hidden_states)[1] if context is not None and encoder.use_context: state = tf.concat([state, context], axis=1) state_size = state.get_shape()[1].value with tf.variable_scope(scope or 'attention_{}'.format(encoder.name)): encoder_input_length = tf.to_float(tf.expand_dims(encoder_input_length, axis=1)) if pos is not None: pos = tf.reshape(pos, [-1, 1]) pos = tf.minimum(pos, encoder_input_length - 1) if pos is not None and encoder.attn_window_size > 0: # `pred_edits` scenario, where we know the aligned pos # when the windows size is non-zero, we concatenate consecutive encoder states # and map it to the right attention vector size. weights = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos, axis=1)), depth=attn_length)) weighted_average = [] for offset in range(-encoder.attn_window_size, encoder.attn_window_size + 1): pos_ = pos + offset pos_ = tf.minimum(pos_, encoder_input_length - 1) pos_ = tf.maximum(pos_, 0) # TODO: when pos is < 0, use <S> or </S> weights_ = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos_, axis=1)), depth=attn_length)) weighted_average_ = tf.reduce_sum(tf.expand_dims(weights_, axis=2) * hidden_states, axis=1) weighted_average.append(weighted_average_) weighted_average = tf.concat(weighted_average, axis=1) weighted_average = dense(weighted_average, encoder.attn_size) elif pos is not None: weights = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos, axis=1)), depth=attn_length)) weighted_average = tf.reduce_sum(tf.expand_dims(weights, axis=2) * hidden_states, axis=1) else: # Local attention of Luong et al. (http://arxiv.org/abs/1508.04025) wp = get_variable('Wp', [state_size, state_size]) vp = get_variable('vp', [state_size, 1]) pos = tf.nn.sigmoid(tf.matmul(tf.nn.tanh(tf.matmul(state, wp)), vp)) pos = tf.floor(encoder_input_length * pos) pos = tf.reshape(pos, [-1, 1]) pos = tf.minimum(pos, encoder_input_length - 1) idx = tf.tile(tf.to_float(tf.range(attn_length)), tf.stack([batch_size])) idx = tf.reshape(idx, [-1, attn_length]) low = pos - encoder.attn_window_size high = pos + encoder.attn_window_size mlow = tf.to_float(idx < low) mhigh = tf.to_float(idx > high) m = mlow + mhigh m += tf.to_float(idx >= encoder_input_length) mask = tf.to_float(tf.equal(m, 0.0)) e = compute_energy(hidden_states, state, attn_size=encoder.attn_size, **kwargs) weights = softmax(e, mask=mask) sigma = encoder.attn_window_size / 2 numerator = -tf.pow((idx - pos), tf.convert_to_tensor(2, dtype=tf.float32)) div = tf.truediv(numerator, 2 * sigma ** 2) weights *= tf.exp(div) # result of the truncated normal distribution # normalize to keep a probability distribution # weights /= (tf.reduce_sum(weights, axis=1, keep_dims=True) + 10e-12) weighted_average = tf.reduce_sum(tf.expand_dims(weights, axis=2) * hidden_states, axis=1) return weighted_average, weights def attention(encoder, **kwargs): attention_functions = { 'global': global_attention, 'local': local_attention, 'none': no_attention, 'average': average_attention, 'last_state': last_state_attention } attention_function = attention_functions.get(encoder.attention_type, global_attention) return attention_function(encoder=encoder, **kwargs) def multi_attention(state, hidden_states, encoders, encoder_input_length, pos=None, aggregation_method='sum', prev_weights=None, **kwargs): attns = [] weights = [] context_vector = None for i, (hidden, encoder, input_length) in enumerate(zip(hidden_states, encoders, encoder_input_length)): pos_ = pos[i] if pos is not None else None prev_weights_ = prev_weights[i] if prev_weights is not None else None hidden = beam_search.resize_like(hidden, state) input_length = beam_search.resize_like(input_length, state) context_vector, weights_ = attention(state=state, hidden_states=hidden, encoder=encoder, encoder_input_length=input_length, pos=pos_, context=context_vector, prev_weights=prev_weights_, **kwargs) attns.append(context_vector) weights.append(weights_) if aggregation_method == 'sum': context_vector = tf.reduce_sum(tf.stack(attns, axis=2), axis=2) else: context_vector = tf.concat(attns, axis=1) return context_vector, weights def attention_decoder(decoder_inputs, initial_state, attention_states, encoders, decoder, encoder_input_length, feed_previous=0.0, align_encoder_id=0, feed_argmax=True, **kwargs): """ :param decoder_inputs: int32 tensor of shape (batch_size, output_length) :param initial_state: initial state of the decoder (usually the final state of the encoder), as a float32 tensor of shape (batch_size, initial_state_size). This state is mapped to the correct state size for the decoder. :param attention_states: list of tensors of shape (batch_size, input_length, encoder_cell_size), the hidden states of the encoder(s) (one tensor for each encoder). :param encoders: configuration of the encoders :param decoder: configuration of the decoder :param encoder_input_length: list of int32 tensors of shape (batch_size,), tells for each encoder, the true length of each sequence in the batch (sequences in the same batch are padded to all have the same length). :param feed_previous: scalar tensor corresponding to the probability to use previous decoder output instead of the ground truth as input for the decoder (1 when decoding, between 0 and 1 when training) :param feed_argmax: boolean tensor, when True the greedy decoder outputs the word with the highest probability (argmax). When False, it samples a word from the probability distribution (softmax). :param align_encoder_id: outputs attention weights for this encoder. Also used when predicting edit operations (pred_edits), to specifify which encoder reads the sequence to post-edit (MT). :return: outputs of the decoder as a tensor of shape (batch_size, output_length, decoder_cell_size) attention weights as a tensor of shape (output_length, encoders, batch_size, input_length) """ assert not decoder.pred_maxout_layer or decoder.cell_size % 2 == 0, 'cell size must be a multiple of 2' if decoder.use_lstm is False: decoder.cell_type = 'GRU' embedding_shape = [decoder.vocab_size, decoder.embedding_size] if decoder.embedding_initializer == 'sqrt3': initializer = tf.random_uniform_initializer(-math.sqrt(3), math.sqrt(3)) else: initializer = None device = '/cpu:0' if decoder.embeddings_on_cpu else None with tf.device(device): embedding = get_variable('embedding_{}'.format(decoder.name), shape=embedding_shape, initializer=initializer) input_shape = tf.shape(decoder_inputs) batch_size = input_shape[0] time_steps = input_shape[1] scope_name = 'decoder_{}'.format(decoder.name) scope_name += '/' + '_'.join(encoder.name for encoder in encoders) def embed(input_): embedded_input = tf.nn.embedding_lookup(embedding, input_) if decoder.use_dropout and decoder.word_keep_prob is not None: noise_shape = [1, 1] if decoder.pervasive_dropout else [batch_size, 1] embedded_input = tf.nn.dropout(embedded_input, keep_prob=decoder.word_keep_prob, noise_shape=noise_shape) if decoder.use_dropout and decoder.embedding_keep_prob is not None: size = tf.shape(embedded_input)[1] noise_shape = [1, size] if decoder.pervasive_dropout else [batch_size, size] embedded_input = tf.nn.dropout(embedded_input, keep_prob=decoder.embedding_keep_prob, noise_shape=noise_shape) return embedded_input def get_cell(input_size=None, reuse=False): cells = [] for j in range(decoder.layers): input_size_ = input_size if j == 0 else decoder.cell_size if decoder.cell_type.lower() == 'lstm': cell = CellWrapper(BasicLSTMCell(decoder.cell_size, reuse=reuse)) elif decoder.cell_type.lower() == 'dropoutgru': cell = DropoutGRUCell(decoder.cell_size, reuse=reuse, layer_norm=decoder.layer_norm, input_size=input_size_, input_keep_prob=decoder.rnn_input_keep_prob, state_keep_prob=decoder.rnn_state_keep_prob) else: cell = GRUCell(decoder.cell_size, reuse=reuse, layer_norm=decoder.layer_norm) if decoder.use_dropout and decoder.cell_type.lower() != 'dropoutgru': cell = DropoutWrapper(cell, input_keep_prob=decoder.rnn_input_keep_prob, output_keep_prob=decoder.rnn_output_keep_prob, state_keep_prob=decoder.rnn_state_keep_prob, variational_recurrent=decoder.pervasive_dropout, dtype=tf.float32, input_size=input_size_) cells.append(cell) if len(cells) == 1: return cells[0] else: return CellWrapper(MultiRNNCell(cells)) def look(state, input_, prev_weights=None, pos=None): prev_weights_ = [prev_weights if i == align_encoder_id else None for i in range(len(encoders))] pos_ = None if decoder.pred_edits: pos_ = [pos if i == align_encoder_id else None for i in range(len(encoders))] if decoder.attn_prev_word: state = tf.concat([state, input_], axis=1) parameters = dict(hidden_states=attention_states, encoder_input_length=encoder_input_length, encoders=encoders, aggregation_method=decoder.aggregation_method) context, new_weights = multi_attention(state, pos=pos_, prev_weights=prev_weights_, **parameters) if decoder.context_mapping: with tf.variable_scope(scope_name): activation = tf.nn.tanh if decoder.context_mapping_activation == 'tanh' else None use_bias = not decoder.context_mapping_no_bias context = dense(context, decoder.context_mapping, use_bias=use_bias, activation=activation, name='context_mapping') return context, new_weights[align_encoder_id] def update(state, input_, context=None, symbol=None): if context is not None and decoder.rnn_feed_attn: input_ = tf.concat([input_, context], axis=1) input_size = input_.get_shape()[1].value initializer = CellInitializer(decoder.cell_size) if decoder.orthogonal_init else None with tf.variable_scope(tf.get_variable_scope(), initializer=initializer): try: output, new_state = get_cell(input_size)(input_, state) except ValueError: # auto_reuse doesn't work with LSTM cells output, new_state = get_cell(input_size, reuse=True)(input_, state) if decoder.skip_update and decoder.pred_edits and symbol is not None: is_del = tf.equal(symbol, utils.DEL_ID) new_state = tf.where(is_del, state, new_state) if decoder.cell_type.lower() == 'lstm' and decoder.use_lstm_full_state: output = new_state return output, new_state def update_pos(pos, symbol, max_pos=None): if not decoder.pred_edits: return pos is_keep = tf.equal(symbol, utils.KEEP_ID) is_del = tf.equal(symbol, utils.DEL_ID) is_not_ins = tf.logical_or(is_keep, is_del) pos = beam_search.resize_like(pos, symbol) max_pos = beam_search.resize_like(max_pos, symbol) pos += tf.to_float(is_not_ins) if max_pos is not None: pos = tf.minimum(pos, tf.to_float(max_pos)) return pos def generate(state, input_, context): if decoder.pred_use_lstm_state is False: # for back-compatibility state = state[:,-decoder.cell_size:] projection_input = [state, context] if decoder.use_previous_word: projection_input.insert(1, input_) # for back-compatibility output_ = tf.concat(projection_input, axis=1) if decoder.pred_deep_layer: deep_layer_size = decoder.pred_deep_layer_size or decoder.embedding_size if decoder.layer_norm: output_ = dense(output_, deep_layer_size, use_bias=False, name='deep_output') output_ = tf.contrib.layers.layer_norm(output_, activation_fn=tf.nn.tanh, scope='output_layer_norm') else: output_ = dense(output_, deep_layer_size, activation=tf.tanh, use_bias=True, name='deep_output') if decoder.use_dropout: size = tf.shape(output_)[1] noise_shape = [1, size] if decoder.pervasive_dropout else None output_ = tf.nn.dropout(output_, keep_prob=decoder.deep_layer_keep_prob, noise_shape=noise_shape) else: if decoder.pred_maxout_layer: maxout_size = decoder.maxout_size or decoder.cell_size output_ = dense(output_, maxout_size, use_bias=True, name='maxout') if decoder.old_maxout: # for back-compatibility with old models output_ = tf.nn.pool(tf.expand_dims(output_, axis=2), window_shape=[2], pooling_type='MAX', padding='SAME', strides=[2]) output_ = tf.squeeze(output_, axis=2) else: output_ = tf.maximum(*tf.split(output_, num_or_size_splits=2, axis=1)) if decoder.pred_embed_proj: # intermediate projection to embedding size (before projecting to vocabulary size) # this is useful to reduce the number of parameters, and # to use the output embeddings for output projection (tie_embeddings parameter) output_ = dense(output_, decoder.embedding_size, use_bias=False, name='softmax0') if decoder.tie_embeddings and (decoder.pred_embed_proj or decoder.pred_deep_layer): bias = get_variable('softmax1/bias', shape=[decoder.vocab_size]) output_ = tf.matmul(output_, tf.transpose(embedding)) + bias else: output_ = dense(output_, decoder.vocab_size, use_bias=True, name='softmax1') return output_ state_size = (decoder.cell_size * 2 if decoder.cell_type.lower() == 'lstm' else decoder.cell_size) * decoder.layers if decoder.use_dropout: initial_state = tf.nn.dropout(initial_state, keep_prob=decoder.initial_state_keep_prob) with tf.variable_scope(scope_name): if decoder.layer_norm: initial_state = dense(initial_state, state_size, use_bias=False, name='initial_state_projection') initial_state = tf.contrib.layers.layer_norm(initial_state, activation_fn=tf.nn.tanh, scope='initial_state_layer_norm') else: initial_state = dense(initial_state, state_size, use_bias=True, name='initial_state_projection', activation=tf.nn.tanh) if decoder.cell_type.lower() == 'lstm' and decoder.use_lstm_full_state: initial_output = initial_state else: initial_output = initial_state[:, -decoder.cell_size:] time = tf.constant(0, dtype=tf.int32, name='time') outputs = tf.TensorArray(dtype=tf.float32, size=time_steps) samples = tf.TensorArray(dtype=tf.int64, size=time_steps) inputs = tf.TensorArray(dtype=tf.int64, size=time_steps).unstack(tf.to_int64(tf.transpose(decoder_inputs))) states = tf.TensorArray(dtype=tf.float32, size=time_steps) weights = tf.TensorArray(dtype=tf.float32, size=time_steps) attns = tf.TensorArray(dtype=tf.float32, size=time_steps) initial_symbol = inputs.read(0) # first symbol is BOS initial_input = embed(initial_symbol) initial_pos = tf.zeros([batch_size], tf.float32) initial_weights = tf.zeros(tf.shape(attention_states[align_encoder_id])[:2]) initial_context, _ = look(initial_output, initial_input, pos=initial_pos, prev_weights=initial_weights) initial_data = tf.concat([initial_state, initial_context, tf.expand_dims(initial_pos, axis=1), initial_weights], axis=1) context_size = initial_context.shape[1].value def get_logits(state, ids, time): # for beam-search decoding with tf.variable_scope('decoder_{}'.format(decoder.name)): state, context, pos, prev_weights = tf.split(state, [state_size, context_size, 1, -1], axis=1) input_ = embed(ids) pos = tf.squeeze(pos, axis=1) pos = tf.cond(tf.equal(time, 0), lambda: pos, lambda: update_pos(pos, ids, encoder_input_length[align_encoder_id])) if decoder.cell_type.lower() == 'lstm' and decoder.use_lstm_full_state: output = state else: # output is always the right-most part of state. However, this only works at test time, # because different dropout operations can be used on state and output. output = state[:, -decoder.cell_size:] if decoder.conditional_rnn: with tf.variable_scope('conditional_1'): output, state = update(state, input_) elif decoder.update_first: output, state = update(state, input_, None, ids) elif decoder.generate_first: output, state = tf.cond(tf.equal(time, 0), lambda: (output, state), lambda: update(state, input_, context, ids)) context, new_weights = look(output, input_, pos=pos, prev_weights=prev_weights) if decoder.conditional_rnn: with tf.variable_scope('conditional_2'): output, state = update(state, context) elif not decoder.generate_first: output, state = update(state, input_, context, ids) logits = generate(output, input_, context) pos = tf.expand_dims(pos, axis=1) state = tf.concat([state, context, pos, new_weights], axis=1) return state, logits def _time_step(time, input_, input_symbol, pos, state, output, outputs, states, weights, attns, prev_weights, samples): if decoder.conditional_rnn: with tf.variable_scope('conditional_1'): output, state = update(state, input_) elif decoder.update_first: output, state = update(state, input_, None, input_symbol) context, new_weights = look(output, input_, pos=pos, prev_weights=prev_weights) if decoder.conditional_rnn: with tf.variable_scope('conditional_2'): output, state = update(state, context) elif not decoder.generate_first: output, state = update(state, input_, context, input_symbol) output_ = generate(output, input_, context) argmax = lambda: tf.argmax(output_, 1) target = lambda: inputs.read(time + 1) softmax = lambda: tf.squeeze(tf.multinomial(tf.log(tf.nn.softmax(output_)), num_samples=1), axis=1) use_target = tf.logical_and(time < time_steps - 1, tf.random_uniform([]) >= feed_previous) predicted_symbol = tf.case([ (use_target, target), (tf.logical_not(feed_argmax), softmax)], default=argmax) # default case is useful for beam-search predicted_symbol.set_shape([None]) predicted_symbol = tf.stop_gradient(predicted_symbol) samples = samples.write(time, predicted_symbol) input_ = embed(predicted_symbol) pos = update_pos(pos, predicted_symbol, encoder_input_length[align_encoder_id]) attns = attns.write(time, context) weights = weights.write(time, new_weights) states = states.write(time, state) outputs = outputs.write(time, output_) if not decoder.conditional_rnn and not decoder.update_first and decoder.generate_first: output, state = update(state, input_, context, predicted_symbol) return (time + 1, input_, predicted_symbol, pos, state, output, outputs, states, weights, attns, new_weights, samples) with tf.variable_scope('decoder_{}'.format(decoder.name)): _, _, _, new_pos, new_state, _, outputs, states, weights, attns, new_weights, samples = tf.while_loop( cond=lambda time, *_: time < time_steps, body=_time_step, loop_vars=(time, initial_input, initial_symbol, initial_pos, initial_state, initial_output, outputs, weights, states, attns, initial_weights, samples), parallel_iterations=decoder.parallel_iterations, swap_memory=decoder.swap_memory) outputs = outputs.stack() weights = weights.stack() # batch_size, encoders, output time, input time states = states.stack() attns = attns.stack() samples = samples.stack() # put batch_size as first dimension outputs = tf.transpose(outputs, perm=(1, 0, 2)) weights = tf.transpose(weights, perm=(1, 0, 2)) states = tf.transpose(states, perm=(1, 0, 2)) attns = tf.transpose(attns, perm=(1, 0, 2)) samples = tf.transpose(samples) return outputs, weights, states, attns, samples, get_logits, initial_data def encoder_decoder(encoders, decoders, encoder_inputs, targets, feed_previous, align_encoder_id=0, encoder_input_length=None, feed_argmax=True, **kwargs): decoder = decoders[0] targets = targets[0] # single decoder if encoder_input_length is None: encoder_input_length = [] for encoder_inputs_ in encoder_inputs: weights = get_weights(encoder_inputs_, utils.EOS_ID, include_first_eos=True) encoder_input_length.append(tf.to_int32(tf.reduce_sum(weights, axis=1))) parameters = dict(encoders=encoders, decoder=decoder, encoder_inputs=encoder_inputs, feed_argmax=feed_argmax) target_weights = get_weights(targets[:, 1:], utils.EOS_ID, include_first_eos=True) attention_states, encoder_state, encoder_input_length = multi_encoder( encoder_input_length=encoder_input_length, **parameters) outputs, attention_weights, _, _, samples, beam_fun, initial_data = attention_decoder( attention_states=attention_states, initial_state=encoder_state, feed_previous=feed_previous, decoder_inputs=targets[:, :-1], align_encoder_id=align_encoder_id, encoder_input_length=encoder_input_length, **parameters ) xent_loss = sequence_loss(logits=outputs, targets=targets[:, 1:], weights=target_weights) losses = xent_loss return losses, [outputs], encoder_state, attention_states, attention_weights, samples, beam_fun, initial_data def chained_encoder_decoder(encoders, decoders, encoder_inputs, targets, feed_previous, chaining_strategy=None, align_encoder_id=0, chaining_non_linearity=False, chaining_loss_ratio=1.0, chaining_stop_gradient=False, **kwargs): decoder = decoders[0] targets = targets[0] # single decoder assert len(encoders) == 2 encoder_input_length = [] input_weights = [] for encoder_inputs_ in encoder_inputs: weights = get_weights(encoder_inputs_, utils.EOS_ID, include_first_eos=True) input_weights.append(weights) encoder_input_length.append(tf.to_int32(tf.reduce_sum(weights, axis=1))) target_weights = get_weights(targets[:, 1:], utils.EOS_ID, include_first_eos=True) parameters = dict(encoders=encoders[1:], decoder=encoders[0]) attention_states, encoder_state, encoder_input_length[1:] = multi_encoder( encoder_inputs[1:], encoder_input_length=encoder_input_length[1:], **parameters) decoder_inputs = encoder_inputs[0][:, :-1] batch_size = tf.shape(decoder_inputs)[0] pad = tf.ones(shape=tf.stack([batch_size, 1]), dtype=tf.int32) * utils.BOS_ID decoder_inputs = tf.concat([pad, decoder_inputs], axis=1) outputs, _, states, attns, _, _, _ = attention_decoder( attention_states=attention_states, initial_state=encoder_state, decoder_inputs=decoder_inputs, encoder_input_length=encoder_input_length[1:], **parameters ) chaining_loss = sequence_loss(logits=outputs, targets=encoder_inputs[0], weights=input_weights[0]) if decoder.cell_type.lower() == 'lstm': size = states.get_shape()[2].value decoder_outputs = states[:, :, size // 2:] else: decoder_outputs = states if chaining_strategy == 'share_states': other_inputs = states elif chaining_strategy == 'share_outputs': other_inputs = decoder_outputs else: other_inputs = None if other_inputs is not None and chaining_stop_gradient: other_inputs = tf.stop_gradient(other_inputs) parameters = dict(encoders=encoders[:1], decoder=decoder, encoder_inputs=encoder_inputs[:1], other_inputs=other_inputs) attention_states, encoder_state, encoder_input_length[:1] = multi_encoder( encoder_input_length=encoder_input_length[:1], **parameters) if chaining_stop_gradient: attns = tf.stop_gradient(attns) states = tf.stop_gradient(states) decoder_outputs = tf.stop_gradient(decoder_outputs) if chaining_strategy == 'concat_attns': attention_states[0] = tf.concat([attention_states[0], attns], axis=2) elif chaining_strategy == 'concat_states': attention_states[0] = tf.concat([attention_states[0], states], axis=2) elif chaining_strategy == 'sum_attns': attention_states[0] += attns elif chaining_strategy in ('map_attns', 'map_states', 'map_outputs'): if chaining_strategy == 'map_attns': x = attns elif chaining_strategy == 'map_outputs': x = decoder_outputs else: x = states shape = [x.get_shape()[-1], attention_states[0].get_shape()[-1]] w = tf.get_variable("map_attns/matrix", shape=shape) b = tf.get_variable("map_attns/bias", shape=shape[-1:]) x = tf.einsum('ijk,kl->ijl', x, w) + b if chaining_non_linearity: x = tf.nn.tanh(x) attention_states[0] += x outputs, attention_weights, _, _, samples, beam_fun, initial_data = attention_decoder( attention_states=attention_states, initial_state=encoder_state, feed_previous=feed_previous, decoder_inputs=targets[:,:-1], align_encoder_id=align_encoder_id, encoder_input_length=encoder_input_length[:1], **parameters ) xent_loss = sequence_loss(logits=outputs, targets=targets[:, 1:], weights=target_weights) if chaining_loss is not None and chaining_loss_ratio: xent_loss += chaining_loss_ratio * chaining_loss losses = [xent_loss, None, None] return losses, [outputs], encoder_state, attention_states, attention_weights, samples, beam_fun, initial_data def softmax(logits, dim=-1, mask=None): e = tf.exp(logits) if mask is not None: e *= mask return e / tf.clip_by_value(tf.reduce_sum(e, axis=dim, keep_dims=True), 10e-37, 10e+37) def sequence_loss(logits, targets, weights, average_across_timesteps=False, average_across_batch=True): batch_size = tf.shape(targets)[0] time_steps = tf.shape(targets)[1] logits_ = tf.reshape(logits, tf.stack([time_steps * batch_size, logits.get_shape()[2].value])) targets_ = tf.reshape(targets, tf.stack([time_steps * batch_size])) crossent = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_, labels=targets_) crossent = tf.reshape(crossent, tf.stack([batch_size, time_steps])) log_perp = tf.reduce_sum(crossent * weights, axis=1) if average_across_timesteps: total_size = tf.reduce_sum(weights, axis=1) total_size += 1e-12 # just to avoid division by 0 for all-0 weights log_perp /= total_size cost = tf.reduce_sum(log_perp) if average_across_batch: return cost / tf.to_float(batch_size) else: return cost def get_weights(sequence, eos_id, include_first_eos=True): cumsum = tf.cumsum(tf.to_float(tf.not_equal(sequence, eos_id)), axis=1) range_ = tf.range(start=1, limit=tf.shape(sequence)[1] + 1) range_ = tf.tile(tf.expand_dims(range_, axis=0), [tf.shape(sequence)[0], 1]) weights = tf.to_float(tf.equal(cumsum, tf.to_float(range_))) if include_first_eos: weights = weights[:,:-1] shape = [tf.shape(weights)[0], 1] weights = tf.concat([tf.ones(tf.stack(shape)), weights], axis=1) return tf.stop_gradient(weights)

[ "574751346@qq.com" ]

574751346@qq.com

1e24144a7cf422eb8aed4964ee92309dbd9dafec

6b1dd40d16ae6169e7ed780c5062e88d10502c85

/Demo/Caffe-demo/demo_train.py

1e72565d91468c3f044f049942af2e183e29f6f1

[ "MIT" ]

permissive

hehuanlin123/DeepLearning

8a59680a341cfc525d50aa5afc3e44202ca4acc4

6b7feabbbde9ac9489f76da4c06eeb6703fb165a

refs/heads/master

2022-07-12T09:26:08.617883

2019-06-10T11:31:37

183,748,407

1

0

null

UTF-8

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194

py

import sys sys.path.append('/home/kuan/AM-softmax_caffe/python') import caffe solver = caffe.SGDSolver("/home/kuan/PycharmProjects/demo_cnn_net/cnn_net/alexnet/solver.prototxt") solver.solve()

[ "hehuanlin@13126771609@163.com" ]

hehuanlin@13126771609@163.com

6c8c5c41b634bf773212a73f580e465cefb4528b

0f38803a7536cbff35202d68b0eef948bd628a96

/common/datasets/asr/librispeech/oggzip.py

a17ef77e60c4187d1caa0b6028beaacda4822d57

[]

no_license

jotix16/returnn_common

b41523ffebac07f54061a2c16336c56d5f826dce

686bcb8b1a42002b8ab1c776b5055569c0f20682

refs/heads/main

2023-05-12T01:26:31.311641

2021-06-03T10:08:21

373,462,667

0

null

2021-06-03T10:08:21

2021-06-03T10:06:02

null

UTF-8

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false

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py

from __future__ import annotations from pathlib import Path from typing import Dict, Any from ..features import make_gt_features_opts from .vocabs import bpe1k, bpe10k from ...interface import DatasetConfig, VocabConfig from ....data import get_common_data_path _Parts = [ "train-clean-100", "train-clean-360", "train-other-500", "dev-clean", "dev-other", "test-clean", "test-other"] _norm_stats_dir = Path(__file__).absolute().parent / "norm_stats" class Librispeech(DatasetConfig): def __init__(self, *, audio_dim=50, audio_norm: str = "per_seq", vocab: VocabConfig = bpe1k, train_epoch_split=20, train_random_permute=None): """ :param audio_norm: "global" or "per_seq". "global" tries to read from standard location in repo """ super(Librispeech, self).__init__() self.audio_dim = audio_dim self.audio_norm = audio_norm self.vocab = vocab self.train_epoch_split = train_epoch_split self.train_random_permute = train_random_permute @classmethod def old_defaults(cls, audio_dim=40, audio_norm="global", vocab: VocabConfig = bpe10k, **kwargs) -> Librispeech: return Librispeech(audio_dim=audio_dim, audio_norm=audio_norm, vocab=vocab, **kwargs) def get_extern_data(self) -> Dict[str, Dict[str, Any]]: return { "data": {"dim": self.audio_dim}, "classes": { "sparse": True, "dim": self.vocab.get_num_classes(), "vocab": self.vocab.get_opts()}, } def get_train_dataset(self) -> Dict[str, Any]: return self.get_dataset("train", train=True, train_partition_epoch=self.train_epoch_split) def get_eval_datasets(self) -> Dict[str, Dict[str, Any]]: return { "dev": self.get_dataset("dev", train=False, subset=3000), "devtrain": self.get_dataset("train", train=False, subset=2000)} def get_dataset(self, key: str, *, train: bool, subset=None, train_partition_epoch=None): files = [] parts = [part for part in _Parts if part.startswith(key)] assert parts for part in parts: files += [ # (History: Changed data/dataset-ogg -> data-common/librispeech/dataset/dataset-ogg) get_common_data_path("librispeech/dataset/dataset-ogg/%s.zip" % part), get_common_data_path("librispeech/dataset/dataset-ogg/%s.txt.gz" % part)] def _make_norm_arg(k: str): if self.audio_norm == "per_seq": return "per_seq" if self.audio_norm == "global": return str(_norm_stats_dir / f"stats.{self.audio_dim}.{k}.txt") if not self.audio_norm: return None raise TypeError(f"Invalid audio norm {self.audio_norm}.") d = { "class": 'OggZipDataset', "path": files, "use_cache_manager": True, "zip_audio_files_have_name_as_prefix": False, "targets": self.vocab.get_opts(), "audio": { "norm_mean": _make_norm_arg("mean"), "norm_std_dev": _make_norm_arg("std_dev"), "num_feature_filters": self.audio_dim}, # make_gt_features_opts(dim=self.audio_dim), } # type: Dict[str, Any] if train: d["partition_epoch"] = train_partition_epoch if key == "train": d["epoch_wise_filter"] = { (1, 5): {'max_mean_len': 200}, (6, 10): {'max_mean_len': 500}, } if self.train_random_permute: d["audio"]["random_permute"] = self.train_random_permute d["seq_ordering"] = "laplace:.1000" else: d["targets"]['unknown_label'] = '<unk>' # only for non-train. for train, there never should be an unknown d["fixed_random_seed"] = 1 d["seq_ordering"] = "sorted_reverse" if subset: d["fixed_random_subset"] = subset # faster return d

[ "zeyer@i6.informatik.rwth-aachen.de" ]

zeyer@i6.informatik.rwth-aachen.de

52d1966e0b46fbd58e2c48d3edf2a752d4edf8bf

e4382c802e3c6d340d9ed9f2ba4e6c4068b5545b

/users/urls.py

ba84bbc2250b0a98b02c32e1b00cfea2f4c4d249

[]

no_license

Trishala13/COC2

23965cc30a9f8aeda468773889611595bda6c6b0

e3d3efe5f4fbd282e822b26a0d66bf01bc7f6d02

refs/heads/master

2021-05-07T02:08:05.692281

2017-11-13T06:16:59

110,505,973

0

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Python

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py

from django.conf.urls import url from . import views as user_views urlpatterns = [ url(r'user_site', user_views.user_site), url(r'sign-in', user_views.sign_in), url(r'sign-up', user_views.sign_up), url(r'sign_up_form',user_views.sign_up_form), url(r'sign_in_form',user_views.sign_in_form), url(r'complaint_form',user_views.complaint_form), url(r'complaint',user_views.complaint), url(r'zone_fill', user_views.zone_fill), url(r'zone', user_views.zone), url(r'resubmit',user_views.resubmit), url(r'feedback_form',user_views.feedback_form), url(r'update_fill',user_views.update_fill), url(r'update',user_views.update), url(r'division_fill',user_views.division_fill), url(r'division', user_views.division), url(r'official_login_fill', user_views.official_login_form), url(r'official-login', user_views.official_login), url(r'employee_site', user_views.emp_site), url(r'garbage_fill',user_views.garbage_fill), url(r'garbage_form_fill',user_views.garbage_entries), url(r'garbage_form',user_views.garbage_form), url(r'garbage', user_views.garbage), url(r'reset_passwrd',user_views.reset_passwrd), url(r'feedback',user_views.feedback), url(r'signout',user_views.signout), ]

[ "noreply@github.com" ]

Trishala13.noreply@github.com