xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

61994048e974aac7ccedc29d996420c49db567d8

879

py

Python

examples/renderimage.py

acgessler/pysfml2-cython

dfb81e399af2eb389a1953bbe78c2e73778e3440

[ "Zlib", "BSD-2-Clause" ]

1

2019-08-16T16:33:12.000Z

examples/renderimage.py

acgessler/pysfml2-cython

dfb81e399af2eb389a1953bbe78c2e73778e3440

[ "Zlib", "BSD-2-Clause" ]

null

examples/renderimage.py

acgessler/pysfml2-cython

dfb81e399af2eb389a1953bbe78c2e73778e3440

[ "Zlib", "BSD-2-Clause" ]

null

#! /usr/bin/env python2 # -*- coding: utf-8 -*- import sf import sys def main(): window = sf.RenderWindow(sf.VideoMode(640, 480), 'RenderImage example') window.framerate_limit = 60 running = True rect0 = sf.Shape.rectangle(5, 5, 90, 50, sf.Color.GREEN, 2, sf.Color.BLUE) rect1 = sf.Shape.rectangle(20.0, 30.0, 50.0, 50.0, sf.Color.CYAN) ri = sf.RenderTexture(110, 110) ri.clear(sf.Color(0, 0, 0, 0)) ri.draw(rect0) ri.draw(rect1) ri.display() s = sf.Sprite(ri.texture) s.origin = (55, 55) s.position = (320, 240) while running: for event in window.iter_events(): if event.type == sf.Event.CLOSED: running = False window.clear(sf.Color.WHITE) s.rotate(5) window.draw(s) window.display() window.close() if __name__ == '__main__': main()

23.131579

78

0.583618

127

879

3.96063

0.527559

0.069583

0.063618

0

0.083333

0.262799

879

37

79

23.756757

0.692901

0.050057

0

0.032413

0

1

0.037037

false

0

0.074074

0

0.111111

0

null

0

null

0

1

0

619fe24fa47471571e21443afa6062ff41ae9a71

453

py

Python

src/settings.py

tokku5552/google-photo-backup

6508f98d979faea9de617af91e7f660b2b5de4d1

[ "MIT" ]

1

2022-03-06T22:55:02.000Z

src/settings.py

tokku5552/google-photo-backup

6508f98d979faea9de617af91e7f660b2b5de4d1

[ "MIT" ]

3

2021-09-03T15:28:31.000Z

2021-09-09T14:22:52.000Z

src/settings.py

tokku5552/google-photo-backup

6508f98d979faea9de617af91e7f660b2b5de4d1

[ "MIT" ]

null

# -*- coding: utf_8 -*- SCOPES = ['https://www.googleapis.com/auth/photoslibrary'] API_SERVICE_NAME = 'photoslibrary' API_VERSION = 'v1' CLIENT_SECRET_FILE = 'client_secret.json' CREDENTIAL_FILE = 'credential.json' AQUIRED_MEDIA_LIST = 'aquired_list.json' TMP_DIR = 'tmp' DESTINATION_DIR = '/gpbk' QUERY_FILTER = True PAST_YEARS = 0 PAST_MONTHS = 1 PAST_DAYS = 0 LOGGING_LEVEL = 20 # DEBUG=10,INFO=20 LOG_FILENAME = '/var/log/google_photos_backup.log'

28.3125

58

0.754967

66

453

4.863636

0.727273

0.099688

0

0.027295

0.110375

453

15

59

30.2

0.769231

0.083885

0

0.366505

0.080097

0

1

0

false

0

null

0

null

0

1

0

61a1e5af27a2cafee845ea0db041e5f1132696f9

10,968

py

Python

marketplace/views/views_internalpackage.py

MOOCworkbench/MOOCworkbench

c478dd4f185c50e0a48319e2b30d418533c32a34

[ "MIT" ]

null

marketplace/views/views_internalpackage.py

MOOCworkbench/MOOCworkbench

c478dd4f185c50e0a48319e2b30d418533c32a34

[ "MIT" ]

1

2017-07-09T17:38:21.000Z

2017-07-09T17:38:22.000Z

marketplace/views/views_internalpackage.py

MOOCworkbench/MOOCworkbench

c478dd4f185c50e0a48319e2b30d418533c32a34

[ "MIT" ]

null

import logging from django.contrib import messages from django.contrib.auth.decorators import login_required from django.core.urlresolvers import reverse from django.http import JsonResponse from django.shortcuts import get_object_or_404, redirect, render from django.views.generic import CreateView, DetailView, UpdateView, View from django.views.generic.list import ListView from markdown2 import Markdown from experiments_manager.helper import verify_and_get_experiment from experiments_manager.mixins import ActiveExperimentsList from experiments_manager.models import ChosenExperimentSteps from git_manager.helpers.github_helper import GitHubHelper, get_github_helper from git_manager.mixins.repo_file_list import get_files_for_repository from helpers.helper_mixins import ExperimentPackageTypeMixin from marketplace.forms import InternalPackageForm from marketplace.helpers.helper import (create_tag_for_package_version, update_setup_py_with_new_version) from marketplace.mixins import IsInternalPackageMixin, ObjectTypeIdMixin from marketplace.models import InternalPackage, PackageResource, PackageVersion from marketplace.tasks import (task_create_package_from_experiment, task_publish_update_package, task_remove_package, task_create_package) from requirements_manager.helper import add_internalpackage_to_experiment from user_manager.models import get_workbench_user logger = logging.getLogger(__name__) class InternalPackageBaseView(ObjectTypeIdMixin, IsInternalPackageMixin): class Meta: abstract = True class InternalPackageCreateView(ExperimentPackageTypeMixin, CreateView): """View for InternalPackage Create, this view is used for creating an empty package from the Packages index""" model = InternalPackage form_class = InternalPackageForm template_name = 'marketplace/package_create/package_form.html' success_url = '/packages/new/status' def get_context_data(self, **kwargs): context = super(InternalPackageCreateView, self).get_context_data(**kwargs) logger.info('%s started on package creation for own code', self.request.user) return context def form_valid(self, form): form.instance.owner = get_workbench_user(self.request.user) form.instance.template_id = 1 response = super(InternalPackageCreateView, self).form_valid(form) task_create_package.delay(form.instance.pk) return response class InternalPackageCreateFromExperimentView(ExperimentPackageTypeMixin, CreateView): """View of InternalPackage Create from Experiment. After completing an experiment step, if users wish, they can create a package and are redirected to this view.""" model = InternalPackage form_class = InternalPackageForm template_name = 'marketplace/package_create/package_form.html' success_url = '/packages/new/status' def get_context_data(self, **kwargs): context = super(InternalPackageCreateFromExperimentView, self).get_context_data(**kwargs) context['experiment_id'] = self.kwargs['experiment_id'] context['step_id'] = self.kwargs['step_id'] logger.info('%s started on package creation for %s', self.request.user, self.kwargs['experiment_id']) return context def form_valid(self, form): step_folder = self.get_step().location experiment = self.get_experiment() form.instance.owner = experiment.owner form.instance.template_id = 1 response = super(InternalPackageCreateFromExperimentView, self).form_valid(form) task_create_package_from_experiment.delay(form.instance.pk, experiment.pk, step_folder) return response def get_experiment(self): experiment_id = self.kwargs['experiment_id'] experiment = verify_and_get_experiment(self.request, experiment_id) return experiment def get_step(self): step_id = self.kwargs['step_id'] step = ChosenExperimentSteps.objects.get(pk=step_id) return step class InternalPackageListView(ListView): model = InternalPackage def get_queryset(self): qs = super(InternalPackageListView, self).get_queryset() return qs.filter(owner__user_id=self.request.user.id) class InternalPackageDashboard(ExperimentPackageTypeMixin, View): def get(self, request, pk): package = get_object_or_404(InternalPackage, pk=pk) assert package.owner.user == self.request.user context = {'docs': package.docs, 'package': package, 'object_id': package.pk, 'object_type': package.get_object_type(), 'edit_form': InternalPackageForm(instance=package), 'dashboard_active': True, 'is_internal': True} return render(request, 'marketplace/package_detail/internalpackage_dashboard.html', context) class InternalPackageUpdateView(UpdateView): """Updates the information associated with an InternalPackage.""" model = InternalPackage form_class = InternalPackageForm def get_success_url(self): return reverse('internalpackage_dashboard', kwargs={'pk': self.kwargs['pk']}) def form_valid(self, form): assert form.instance.owner.user == self.request.user messages.add_message(self.request, messages.SUCCESS, 'Package successfully updated') return super(InternalPackageUpdateView, self).form_valid(form) class InternalPackageVersionCreateView(CreateView): """View for creating a new version of an InternalPackage. Starts task to publish this new package and publish it. This is only needed for Python experiments.""" model = PackageVersion fields = ['version_nr', 'changelog', 'pre_release'] template_name = 'marketplace/package_detail/packageversion_form.html' def get_context_data(self, **kwargs): context = super(InternalPackageVersionCreateView, self).get_context_data(**kwargs) context['package'] = InternalPackage.objects.get(id=self.kwargs['package_id']) return context def form_valid(self, form): package = InternalPackage.objects.get(id=self.kwargs['package_id']) form.instance.package = package assert form.instance.package.owner.user == self.request.user form.instance.added_by = get_workbench_user(self.request.user) response = super(InternalPackageVersionCreateView, self).form_valid(form) create_tag_for_package_version(form.instance.id) if 'Python' in package.language.language: update_setup_py_with_new_version(form.instance.id) task_publish_update_package.delay(package.pk) return response def get_success_url(self): return reverse('internalpackage_dashboard', kwargs={'pk': self.kwargs['package_id']}) @login_required def internalpackage_publish(request, pk): """Publish a package, starts task doing the actual publish work""" package = InternalPackage.objects.get(id=pk) assert package.owner.user == request.user task_publish_update_package.delay(package.pk) logger.info('%s published the package %s', request.user, package) return redirect(to=package.get_absolute_url()) @login_required def internalpackage_publish_checklist(request, pk): """View for displaying the InternalPackage checklist before publishing a package.""" package = InternalPackage.objects.get(id=pk) assert package.owner.user == request.user dependencies_defined = package.requirements.count() != 0 getting_started_guide = PackageResource.objects.filter(package=package.id, title='Getting started') getting_started = False if getting_started_guide: getting_started_guide = getting_started_guide[0] getting_started = len(getting_started_guide.resource) != 0 return render(request, 'marketplace/package_publish.html', {'object': package, 'dependencies_defined': dependencies_defined, 'getting_started': getting_started}) @login_required def internalpackage_remove(request, pk): """View for removing an internal package. This is an action that can only be performed by the owner of the package.""" package = InternalPackage.objects.get(id=pk) assert package.owner.user == request.user task_remove_package.delay(package.pk) logger.info("%s removed the package %s", request.user, package) messages.add_message(request, messages.INFO, "Removing package...") return redirect(to=package.success_url_dict()['dashboard']) class InternalPackageDetailView(InternalPackageBaseView, ActiveExperimentsList, DetailView): model = InternalPackage template_name = 'marketplace/package_detail/package_detail.html' def get_context_data(self, **kwargs): self.object_type = ExperimentPackageTypeMixin.PACKAGE_TYPE context = super(InternalPackageDetailView, self).get_context_data(**kwargs) github_helper = get_github_helper(context['package'].owner, context['package']) package_id = self.kwargs['pk'] context['version_history'] = PackageVersion.objects.filter(package=package_id).order_by('-created')[:5] context['index_active'] = True context['git_list'] = get_files_for_repository(github_helper, self.object) if InternalPackage.objects.filter(pk=self.object.pk): context['readme'] = self.readme_file_of_package() return context def readme_file_of_package(self): internalpackage = InternalPackage.objects.get(id=self.kwargs['pk']) github_helper = GitHubHelper(internalpackage.owner, internalpackage.git_repo.name) readme = github_helper.view_file('README.md') md = Markdown() content_file = md.convert(readme) return content_file @login_required def internalpackage_install(request, pk): """View for installing a package in own project. Adds this package to the requirements file of the chosen experiment and starts task to update this file in GitHub""" internal_package = InternalPackage.objects.get(pk=pk) assert 'experiment_id' in request.POST experiment_id = request.POST['experiment_id'] experiment = verify_and_get_experiment(request, experiment_id) result = add_internalpackage_to_experiment(internal_package, experiment) if result: logger.info('%s installed the package %s in experiment %s', request.user, internal_package, experiment) messages.add_message(request, messages.SUCCESS, 'Added package to your experiment') return JsonResponse({'added': True}) else: messages.add_message(request, messages.ERROR, 'Could not add package to your experiment') return JsonResponse({'added': False})

45.510373

122

0.728209

1,245

10,968

6.217671

0.180723

0.019377

0.014468

0.024803

0.346467

0.25901

0.193903

0.128924

0.105284

0.09159

0

0.001456

0.185904

10,968

240

123

45.7

0.865494

0.081236

0

0.236264

0

0.105342

0.032351

0

0.038462

1

0.104396

false

0

0.120879

0.010989

0.472527

0

null

0

null

0

1

0

61a8845e1ccc4a6f9d7930913c7112d6c98c2b1b

1,049

py

Python

solutions/day13/p2/main.py

tosmun/AdventOfCode

62f4f3a8cc3761ee5d5eaf682ae9c2c985cd80b5

[ "Apache-2.0" ]

1

2017-07-15T19:01:03.000Z

solutions/day13/p2/main.py

tosmun/Python-AdventOfCode

62f4f3a8cc3761ee5d5eaf682ae9c2c985cd80b5

[ "Apache-2.0" ]

null

solutions/day13/p2/main.py

tosmun/Python-AdventOfCode

62f4f3a8cc3761ee5d5eaf682ae9c2c985cd80b5

[ "Apache-2.0" ]

null

from itertools import permutations if __name__ == "__main__": map = { } with open('../input.txt', 'r') as fp: while True: line=fp.readline() if line is None or line == '': break parts = line.split(" ") person = parts[0] value = int(parts[3]) if parts[2] == 'lose': value = value * -1 neighbour = parts[10].split('.')[0] if person not in map: map[person] = { } person_map = map[person] person_map[neighbour] = value #Include myself my_map = { } for person in map: map[person]['me'] = 0 my_map[person] = 0 map['me'] = my_map best = (-1, None) #For each possible combination for arrangement in permutations(map.keys(), len(map.keys())): #Calculate total total = 0 for i, person in enumerate(arrangement): #Have to account for boundaries of the list left = (i-1 if i > 0 else -1) right = (i+1 if i < len(arrangement)-1 else 0) total += map[person][arrangement[left]] total += map[person][arrangement[right]] if total > best[0]: best = (total, arrangement) print(best)

26.225

62

0.622498

155

1,049

4.129032

0.412903

0.084375

0.05625

0.04375

0.060938

0

0.022086

0.22307

1,049

39

63

26.897436

0.76319

0.095329

0

0.032804

0

1

0

false

0

0.029412

0

0.029412

0

null

0

null

0

1

0

61aa4cd0aa9e08c093a70a1f326924e6e96fd86c

13,031

py

Python

review/models.py

osaimola/django-review

e4f10838a88b84749bda5bd8febaf7d85447fcdf

[ "MIT" ]

null

review/models.py

osaimola/django-review

e4f10838a88b84749bda5bd8febaf7d85447fcdf

[ "MIT" ]

null

review/models.py

osaimola/django-review

e4f10838a88b84749bda5bd8febaf7d85447fcdf

[ "MIT" ]

null

"""Just an empty models file to let the testrunner recognize this as app.""" from django.conf import settings from django.contrib.contenttypes import fields from django.contrib.contenttypes.models import ContentType from django.db import models from django.utils import timezone from django.utils.encoding import python_2_unicode_compatible from django.utils.translation import ugettext, ugettext_lazy as _ from hvad.models import TranslatableModel, TranslatedFields DEFAULT_CHOICES = ( ('5', '5'), ('4', '4'), ('3', '3'), ('2', '2'), ('1', '1'), ) @python_2_unicode_compatible class Review(models.Model): """ Represents a user review, which includes free text and images. :reviewed_item: Object, which is reviewed. :user (optional): User, which posted the rating. :content (optional): Running text. :images (optional): Review-related images. :language (optional): Language shortcut to filter reviews. :creation_date: The date and time, this review was created. :average_rating: Should always be calculated and updated when the object is saved. This is for improving performance and reducing db queries when calculating ratings for reviewed items. Currently it gets updated at the end of the save method of the ``ReviewForm``. This means that when you manually save a Review via the Django admin, this field will not be updated. :extra_item: Optional object, which should be attached to the review. """ # GFK 'reviewed_item' content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE) object_id = models.PositiveIntegerField() reviewed_item = fields.GenericForeignKey('content_type', 'object_id') user = models.ForeignKey( getattr(settings, 'AUTH_USER_MODEL', 'auth.User'), verbose_name=_('User'), blank=True, null=True, on_delete=models.CASCADE ) content = models.TextField( max_length=1024, verbose_name=_('Content'), blank=True, ) images = fields.GenericRelation( 'user_media.UserMediaImage', ) language = models.CharField( max_length=5, verbose_name=_('Language'), blank=True, ) creation_date = models.DateTimeField( auto_now_add=True, verbose_name=_('Creation date'), ) average_rating = models.FloatField( verbose_name=_('Average rating'), default=0, ) # GFK 'extra_item' extra_content_type = models.ForeignKey( ContentType, related_name='reviews_attached', null=True, blank=True, on_delete=models.CASCADE ) extra_object_id = models.PositiveIntegerField(null=True, blank=True) extra_item = fields.GenericForeignKey( 'extra_content_type', 'extra_object_id') class Meta: ordering = ['-creation_date'] def __str__(self): return '{0} - {1}'.format(self.reviewed_item, self.get_user()) # TODO: Add magic to get ReviewExtraInfo content objects here def get_user(self): """Returns the user who wrote this review or ``Anonymous``.""" if self.user: return self.user.email return ugettext('Anonymous') def get_averages(self, max_value=None): """ Centralized average calculation. Returns category averages and total average. :param max_value: By default the app is set to a rating from 1 to 5. So if nothing is changed, we can just calculate the average of all rating values and be good. We then have an average that is between 1 and 5 as well. BUT if we have custom choices, we could end up having one category with a range of 1 to 10 and one category with 1 to 5. The result then must be abstracted to fit into the given range set by max_value. This can also be used to calculate percentages by setting max_value to 100. """ max_rating_value = 0 category_maximums = {} category_averages = {} categories = RatingCategory.objects.filter(counts_for_average=True, rating__review=self) # find the highest rating possible across all categories for category in categories: category_max = category.get_rating_max_from_choices() category_maximums.update({category: category_max}) if max_value is not None: max_rating_value = max_value else: if category_max > max_rating_value: max_rating_value = category_max # calculate the average of every distinct category, normalized to the # recently found max for category in categories: category_average = None ratings = Rating.objects.filter( review=self, category=category, value__isnull=False).exclude(value='') category_max = category_maximums[category] for rating in ratings: if category_average is None: category_average = float(rating.value) else: category_average += float(rating.value) if category_average is not None: category_average *= float(max_rating_value) / float( category_max) category_averages[category] = ( category_average / ratings.count()) # calculate the total average of all categories total_average = 0 for category, category_average in category_averages.items(): total_average += category_average if not len(category_averages): return (False, False) total_average /= len(category_averages) return total_average, category_averages def get_average_rating(self, max_value=None): """ Returns the average rating for all categories of this review. A shortcut for get_averages. Look there for more details. """ total_average, category_averages = self.get_averages( max_value=max_value) return total_average def get_category_averages(self, max_value=None): """ Returns the average ratings for every category of this review. A shortcut for get_averages. Look there for more details. """ total_average, category_averages = self.get_averages( max_value=max_value) return category_averages def is_editable(self): """ Returns True, if the time period to update this review hasn't ended yet. If the period setting has not been set, it always return True. This is the general case. If the user has used this setting to define an update period it returns False, if this period has expired. """ if getattr(settings, 'REVIEW_UPDATE_PERIOD', False): period_end = self.creation_date + timezone.timedelta( seconds=getattr(settings, 'REVIEW_UPDATE_PERIOD') * 60) if timezone.now() > period_end: return False return True @python_2_unicode_compatible class ReviewExtraInfo(models.Model): """ Model to add any extra information to a review. This can be useful if you need to save more information about a reviewer than just the User instance. Let's say you are building a site for theme park reviews and you want to allow the user to select the weather conditions for the day of his visit (which will surely influence his review). This model would allow you to tie any model of your app to a review. :type: Callable type of the extra info. This should be unique per review. We will soon add a hack to the Review model which allows you to get the content_object of this instance from a review instance (i.e. by calling ``my_review.weather_conditions.name``). So for this example you would set the type to ``weather_conditions``. :review: Related review. :content_object: The related object that stores this extra information. """ type = models.CharField( max_length=256, verbose_name=_('Type'), ) review = models.ForeignKey( 'review.Review', verbose_name=_('Review'), on_delete=models.CASCADE ) # GFK 'content_object' content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE) object_id = models.PositiveIntegerField() content_object = fields.GenericForeignKey('content_type', 'object_id') class Meta: ordering = ['type'] def __str__(self): return '{0} - {1}'.format(self.review, self.type) @python_2_unicode_compatible class RatingCategory(TranslatableModel): """ Represents a rating category. If your reviews are just text based, you don't have to use this. This can be useful if you want to allow users to rate one or more categories, like ``Food``, ``Room service``, ``Cleansines`` and so on. :identifier: Optional identifier. :name: Name of the category. Also used as label for the category form. :question: If you want to render a more explicit question in addition to the name, use this field. It is added to the form fields as help text. :counts_for_average: If True, the ratings of this category will be used to calculate the average rating. Default is True. """ identifier = models.SlugField( max_length=32, verbose_name=_('Identifier'), blank=True, ) counts_for_average = models.BooleanField( verbose_name=_('Counts for average rating'), default=True, ) translations = TranslatedFields( name=models.CharField(max_length=256), question=models.CharField(max_length=512, blank=True, null=True), ) def __str__(self): return self.lazy_translation_getter('name', 'Untranslated') @property def required(self): """Returns False, if the choices include a None value.""" if not hasattr(self, '_required'): # get_choices sets _required self.get_choices() return self._required def get_choices(self): """Returns the tuple of choices for this category.""" choices = () self._required = True for choice in self.choices.all(): if choice.value is None or choice.value == '': self._required = False choices += (choice.value, choice.label), if not choices: return DEFAULT_CHOICES return choices def get_rating_max_from_choices(self): """Returns the maximun value a rating can have in this catgory.""" return int(list(self.get_choices())[0][0]) @python_2_unicode_compatible class RatingCategoryChoice(TranslatableModel): """ Defines an optional choice for a `RatingCategory`. If `RatingChoice` exists, the choices will not be loaded from the settings. :label: The label that is displayed for this choice. :ratingcategory: The `RatingCategory` this choice belongs to. :value: The value that this choice has. If a `RatingChoice` with value=None is created and chosen by the user, this category is not taken into account when the average is calculated. """ ratingcategory = models.ForeignKey( RatingCategory, verbose_name=_('Rating category'), related_name='choices', on_delete=models.CASCADE ) value = models.CharField( verbose_name=_('Value'), max_length=20, blank=True, null=True, ) translations = TranslatedFields( label=models.CharField( verbose_name=_('Label'), max_length=128, ), ) def __str__(self): return self.lazy_translation_getter('label', self.ratingcategory.identifier) class Meta: ordering = ('-value', ) @python_2_unicode_compatible class Rating(models.Model): """ Represents a rating for one rating category. :rating: Rating value. :review: The review the rating belongs to. :category: The rating category the rating belongs to. """ rating_choices = DEFAULT_CHOICES value = models.CharField( max_length=20, verbose_name=_('Value'), choices=getattr(settings, 'REVIEW_RATING_CHOICES', rating_choices), blank=True, null=True, ) review = models.ForeignKey( 'review.Review', verbose_name=_('Review'), related_name='ratings', on_delete=models.CASCADE ) category = models.ForeignKey( 'review.RatingCategory', verbose_name=_('Category'), on_delete=models.CASCADE ) class Meta: ordering = ['category', 'review'] def __str__(self): return '{0}/{1} - {2}'.format(self.category, self.review, self.value)

33.412821

79

0.647149

1,586

13,031

5.163304

0.210593

0.020149

0.013677

0.020515

0.180364

0.107461

0.090121

0.082061

0.052754

0

0.006861

0.272964

13,031

389

80

33.498715

0.857505

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0

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0

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0.00844

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0

null

0

1

0

61aabee9e72f77565b8fbe0b0256f2a2d385c406

2,170

py

Python

python/oneflow/test/modules/test_consistent_stateful_kernel_with_cache.py

L-Net-1992/oneflow

4dc08d65caea36fdd137841ac95551218897e730

[ "Apache-2.0" ]

1

2022-03-14T11:17:56.000Z

python/oneflow/test/modules/test_consistent_stateful_kernel_with_cache.py

L-Net-1992/oneflow

4dc08d65caea36fdd137841ac95551218897e730

[ "Apache-2.0" ]

null

python/oneflow/test/modules/test_consistent_stateful_kernel_with_cache.py

L-Net-1992/oneflow

4dc08d65caea36fdd137841ac95551218897e730

[ "Apache-2.0" ]

1

2021-12-15T02:14:49.000Z

""" Copyright 2020 The OneFlow Authors. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import unittest import numpy as np import oneflow as flow import oneflow.unittest from oneflow.test_utils.automated_test_util import * def _test_global_stateful_kernel_with_inpersistent_state(test_case, placement, sbp): x = ( flow.arange(64) .reshape(8, 8) .to_global(flow.env.all_device_placement("cpu"), flow.sbp.broadcast) ) x = x.to_global(placement, sbp) y = flow._C.logical_slice(x, [0, 0], [3, 1], [1, 1]) y_np = np.array([[0], [8], [16]]) test_case.assertTrue( np.array_equal( y.to_global(flow.env.all_device_placement("cpu"), flow.sbp.broadcast) .to_local() .numpy(), y_np, ) ) x = x.to_global(sbp=flow.sbp.split(1)) y = flow._C.logical_slice(x, [0, 0], [3, 1], [1, 1]) test_case.assertTrue( np.array_equal( y.to_global(flow.env.all_device_placement("cpu"), flow.sbp.broadcast) .to_local() .numpy(), y_np, ) ) class TestStatefulKernelWithInpersistentState(flow.unittest.TestCase): @globaltest def test_global_stateful_kernel_with_inpersistent_state(test_case): for placement in all_placement(): # logical_slice only support 1d sbp if len(placement.ranks.shape) != 1: continue for sbp in all_sbp(placement, max_dim=2): _test_global_stateful_kernel_with_inpersistent_state( test_case, placement, sbp ) if __name__ == "__main__": unittest.main()

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null

0

null

0

1

0

61ab1ecec0d561e9b48dbeb6b94f8d2303697a63

1,437

py

Python

schools3/ml/experiments/multi_dataset_experiment.py

dssg/mlpolicylab_fall20_schools3_public

f8eff4c56e9bada1eb81ddaca03686d7ef53c2c4

[ "MIT" ]

null

schools3/ml/experiments/multi_dataset_experiment.py

dssg/mlpolicylab_fall20_schools3_public

f8eff4c56e9bada1eb81ddaca03686d7ef53c2c4

[ "MIT" ]

null

schools3/ml/experiments/multi_dataset_experiment.py

dssg/mlpolicylab_fall20_schools3_public

f8eff4c56e9bada1eb81ddaca03686d7ef53c2c4

[ "MIT" ]

null

import pandas as pd from tqdm import tqdm from schools3.data.datasets.datasets_generator import DatasetsGenerator from schools3.ml.experiments.models_experiment import ModelsExperiment from schools3.config import main_config # an experiment that trains models and reports metrics for multiple grades class MultiDatasetExperiment(ModelsExperiment): def __init__( self, name='ignore', features_list=main_config.features, labels=main_config.labels, models=main_config.models, metrics=main_config.metrics, use_cache=main_config.use_cache ): super(MultiDatasetExperiment, self).__init__( name, features_list, labels, models, metrics, use_cache=use_cache ) def perform( self, grades=main_config.multi_grades, include_all_train_hist=True, *args, **kwargs ): df = pd.DataFrame() t_grades = tqdm(grades) for grade in t_grades: t_grades.set_description(f'grade {grade}:') generator = DatasetsGenerator(grade) cohorts = \ tqdm(generator.get_all_train_test_pairs(include_all_train_hist)) for train_cohort, test_cohort in cohorts: cohorts.set_description(train_cohort.get_identifier()) metrics_df = self.get_train_test_metrics(train_cohort, test_cohort) df = pd.concat([df, metrics_df], ignore_index=True) return df

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1,437

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0.3

0

null

0

null

0

1

0

61ab642b229d604ba34d9d88a40117fa3578d4e2

276

py

Python

wrappers/bcftools/view/wrapper.py

delvinso/crg2

366f9dd6f89db2243765688bd0c2d9a2b3d170f4

[ "Apache-2.0" ]

null

wrappers/bcftools/view/wrapper.py

delvinso/crg2

366f9dd6f89db2243765688bd0c2d9a2b3d170f4

[ "Apache-2.0" ]

null

wrappers/bcftools/view/wrapper.py

delvinso/crg2

366f9dd6f89db2243765688bd0c2d9a2b3d170f4

[ "Apache-2.0" ]

null

__author__ = "Johannes Köster" __copyright__ = "Copyright 2016, Johannes Köster" __email__ = "koester@jimmy.harvard.edu" __license__ = "MIT" from snakemake.shell import shell shell( "bcftools view {snakemake.params} {snakemake.input[0]} " "-o {snakemake.output[0]}" )

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0.125

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null

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null

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1

0

61ac0e6db30abca8f91a026b4103c099bb566e16

4,728

py

Python

tensorflow/python/ops/ragged/ragged_range_op_test.py

uve/tensorflow

e08079463bf43e5963acc41da1f57e95603f8080

[ "Apache-2.0" ]

null

tensorflow/python/ops/ragged/ragged_range_op_test.py

uve/tensorflow

e08079463bf43e5963acc41da1f57e95603f8080

[ "Apache-2.0" ]

null

tensorflow/python/ops/ragged/ragged_range_op_test.py

uve/tensorflow

e08079463bf43e5963acc41da1f57e95603f8080

[ "Apache-2.0" ]

null

# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for ragged_range op.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import errors from tensorflow.python.framework import test_util from tensorflow.python.ops.ragged import ragged_math_ops from tensorflow.python.platform import googletest @test_util.run_all_in_graph_and_eager_modes class RaggedRangeOpTest(test_util.TensorFlowTestCase): def testDocStringExamples(self): """Examples from ragged_range.__doc__.""" rt1 = ragged_math_ops.range([3, 5, 2]) self.assertAllEqual(rt1, [[0, 1, 2], [0, 1, 2, 3, 4], [0, 1]]) rt2 = ragged_math_ops.range([0, 5, 8], [3, 3, 12]) self.assertAllEqual(rt2, [[0, 1, 2], [], [8, 9, 10, 11]]) rt3 = ragged_math_ops.range([0, 5, 8], [3, 3, 12], 2) self.assertAllEqual(rt3, [[0, 2], [], [8, 10]]) def testBasicRanges(self): # Specify limits only. self.assertAllEqual( ragged_math_ops.range([0, 3, 5]), [list(range(0)), list(range(3)), list(range(5))]) # Specify starts and limits. self.assertAllEqual( ragged_math_ops.range([0, 3, 5], [2, 3, 10]), [list(range(0, 2)), list(range(3, 3)), list(range(5, 10))]) # Specify starts, limits, and deltas. self.assertAllEqual( ragged_math_ops.range([0, 3, 5], [4, 4, 15], [2, 3, 4]), [list(range(0, 4, 2)), list(range(3, 4, 3)), list(range(5, 15, 4))]) def testFloatRanges(self): expected = [[0.0, 0.4, 0.8, 1.2, 1.6, 2.0, 2.4, 2.8, 3.2, 3.6], [3.0], [5.0, 7.2, 9.4, 11.6, 13.8]] actual = ragged_math_ops.range([0.0, 3.0, 5.0], [3.9, 4.0, 15.0], [0.4, 1.5, 2.2]) self.assertAllClose(actual, expected) def testNegativeDeltas(self): self.assertAllEqual( ragged_math_ops.range([0, 3, 5], limits=0, deltas=-1), [list(range(0, 0, -1)), list(range(3, 0, -1)), list(range(5, 0, -1))]) self.assertAllEqual( ragged_math_ops.range([0, -3, 5], limits=0, deltas=[-1, 1, -2]), [list(range(0, 0, -1)), list(range(-3, 0, 1)), list(range(5, 0, -2))]) def testBroadcast(self): # Specify starts and limits, broadcast deltas. self.assertAllEqual( ragged_math_ops.range([0, 3, 5], [4, 4, 15], 3), [list(range(0, 4, 3)), list(range(3, 4, 3)), list(range(5, 15, 3))]) # Broadcast all arguments. self.assertAllEqual( ragged_math_ops.range(0, 5, 1), [list(range(0, 5, 1))]) def testEmptyRanges(self): rt1 = ragged_math_ops.range([0, 5, 3], [0, 3, 5]) rt2 = ragged_math_ops.range([0, 5, 5], [0, 3, 5], -1) self.assertAllEqual(rt1, [[], [], [3, 4]]) self.assertAllEqual(rt2, [[], [5, 4], []]) def testShapeFnErrors(self): self.assertRaises((ValueError, errors.InvalidArgumentError), ragged_math_ops.range, [[0]], 5) self.assertRaises((ValueError, errors.InvalidArgumentError), ragged_math_ops.range, 0, [[5]]) self.assertRaises((ValueError, errors.InvalidArgumentError), ragged_math_ops.range, 0, 5, [[0]]) self.assertRaises((ValueError, errors.InvalidArgumentError), ragged_math_ops.range, [0], [1, 2]) def testKernelErrors(self): with self.assertRaisesRegexp(errors.InvalidArgumentError, r'Requires delta != 0'): self.evaluate(ragged_math_ops.range(0, 0, 0)) def testShape(self): self.assertAllEqual( ragged_math_ops.range(0, 0, 1).shape.as_list(), [1, None]) self.assertAllEqual( ragged_math_ops.range([1, 2, 3]).shape.as_list(), [3, None]) self.assertAllEqual( ragged_math_ops.range([1, 2, 3], [4, 5, 6]).shape.as_list(), [3, None]) if __name__ == '__main__': googletest.main()

37.52381

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0.10632

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4,728

125

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1

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false

0

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0

0.204819

0.012048

0

null

0

null

0

1

0

61ac853aed3414f5df7058083f22b577f7f6c8d2

256

py

Python

run/dev.py

LuxQuad/ozet-core-api

bf0cd9e4b58bf9b7e805843df4dfe7320afa7e4b

[ "MIT" ]

null

run/dev.py

LuxQuad/ozet-core-api

bf0cd9e4b58bf9b7e805843df4dfe7320afa7e4b

[ "MIT" ]

5

2021-08-10T03:38:31.000Z

2021-08-11T12:39:34.000Z

run/dev.py

LuxQuad/ozet-core-api

bf0cd9e4b58bf9b7e805843df4dfe7320afa7e4b

[ "MIT" ]

null

import uvicorn HOST = "127.0.0.1" PORT = 8000 ENV = ".misc/env/dev.env" SERVICE = "app.main:service" LOG_LEVEL = "trace" if __name__ == "__main__": uvicorn.run(SERVICE, host=HOST, port=PORT, log_level=LOG_LEVEL, workers=4, env_file=ENV, reload=True)

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42

256

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0.125

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null

0

null

0

1

0

61aea3b9d53c7af9cfa86fadc4caee11c448c970

4,307

py

Python

vgg16_convadd.py

kkkumar2/Vegetable-recognition-with-VGG-16-19-SCRATCH

a1f16bcca0608ae87cbf9b0973ac16d9d2274ae3

[ "Apache-2.0" ]

null

vgg16_convadd.py

kkkumar2/Vegetable-recognition-with-VGG-16-19-SCRATCH

a1f16bcca0608ae87cbf9b0973ac16d9d2274ae3

[ "Apache-2.0" ]

null

vgg16_convadd.py

kkkumar2/Vegetable-recognition-with-VGG-16-19-SCRATCH

a1f16bcca0608ae87cbf9b0973ac16d9d2274ae3

[ "Apache-2.0" ]

null

from tensorflow.keras.layers import Input, Lambda, Dense, Flatten from tensorflow.keras.models import Model from tensorflow.keras.applications.vgg16 import VGG16 from tensorflow.keras.applications.vgg16 import preprocess_input from tensorflow.keras.preprocessing import image from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.models import Sequential from tensorflow.keras import optimizers import numpy as np from glob import glob import matplotlib.pyplot as plt from datetime import datetime from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler from tensorflow.keras.callbacks import ReduceLROnPlateau import warnings warnings.filterwarnings("ignore", category=FutureWarning) #Give dataset path train_path = r'D:\Data science\ineuron\Assignments\Vegetable edited\train' test_path = r'D:\Data science\ineuron\Assignments\Vegetable edited\test' validation_path = r'D:\Data science\ineuron\Assignments\Vegetable edited\validation' vgg = VGG16(input_shape=(224,224,3), weights='imagenet', include_top=False) for layer in vgg.layers[:16]: layer.trainable = False for layer in vgg.layers: print(layer,layer.trainable) print("Total layers in VGG 16 are : ",len(vgg.layers)) # useful for getting number of classes folders = glob(train_path + '\*') print(len(folders)) x = Flatten()(vgg.output) prediction = Dense(len(folders), activation='softmax')(x) model = Model(inputs=vgg.input, outputs=prediction) model.summary() sgd = optimizers.SGD(learning_rate=0.01, decay=1e-6, momentum=0.9) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # Data Augmentation train_datagen = ImageDataGenerator( preprocessing_function=preprocess_input, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') # Data Augmentation test_datagen = ImageDataGenerator( preprocessing_function=preprocess_input, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') # Data Augmentation valid_datagen = ImageDataGenerator( preprocessing_function=preprocess_input, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') # Make sure you provide the same target size as initialied for the image size train_set = train_datagen.flow_from_directory(train_path, target_size = (224, 224), batch_size = 32, class_mode = 'categorical') test_set = train_datagen.flow_from_directory(test_path, target_size = (224, 224), batch_size = 32, class_mode = 'categorical') valid_set = train_datagen.flow_from_directory(validation_path, target_size = (224, 224), batch_size = 32, class_mode = 'categorical') #lr_scheduler = LearningRateScheduler(lr_schedule) lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1), cooldown=0, patience=5, min_lr=0.5e-6) #num_epochs = 1000 #num_batch_size = 32 checkpoint = ModelCheckpoint(filepath='vgg16_tl_convadd.h5', verbose=1, save_best_only=True) callbacks = [checkpoint, lr_reducer] #callbacks = [checkpoint] start = datetime.now() model.fit_generator( train_set, validation_data=test_set, epochs=5, steps_per_epoch=5, validation_steps=32, callbacks=callbacks ,verbose=1) duration = datetime.now() - start print("Training completed in time: ", duration)

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null

0

null

0

1

0

61af0d4043273f66c7507d94d876e3a5dbecbd8a

4,888

py

Python

scripts/make_phenicx_anechoic_index.py

magdalenafuentes/soundata

4d2dde8e9ef61483bc202bf94d6a0ccc1601c52b

[ "BSD-3-Clause" ]

null

scripts/make_phenicx_anechoic_index.py

magdalenafuentes/soundata

4d2dde8e9ef61483bc202bf94d6a0ccc1601c52b

[ "BSD-3-Clause" ]

null

scripts/make_phenicx_anechoic_index.py

magdalenafuentes/soundata

4d2dde8e9ef61483bc202bf94d6a0ccc1601c52b

[ "BSD-3-Clause" ]

1

2021-05-03T19:34:46.000Z

import argparse import glob import hashlib import json import os import string DATASET_INDEX_PATH = '../mirdata/datasets/indexes/phenicx_anechoic_index.json' def md5(file_path): """Get md5 hash of a file. Parameters ---------- file_path: str File path. Returns ------- md5_hash: str md5 hash of data in file_path """ hash_md5 = hashlib.md5() with open(file_path, 'rb') as fhandle: for chunk in iter(lambda: fhandle.read(4096), b''): hash_md5.update(chunk) return hash_md5.hexdigest() def make_dataset_index(data_path): pieces = ['beethoven', 'bruckner', 'mahler', 'mozart'] families = { 'doublebass': 'strings', 'cello': 'strings', 'clarinet': 'woodwinds', 'viola': 'strings', 'violin': 'strings', 'oboe': 'woodwinds', 'flute': 'woodwinds', 'trumpet': 'brass', 'bassoon': 'woodwinds', 'horn': 'brass', } totalinstruments = [20, 39, 30, 10] ninstruments = [10, 10, 10, 8] index = {'version':1} index['tracks'] = {} index['multitracks'] = {} for ip, piece in enumerate(pieces): index['multitracks'][piece] = {} audio_files = sorted( glob.glob(os.path.join(data_path, 'audio', piece, '*.wav')) ) instruments = [ os.path.basename(audio_path).split('.')[0].rstrip(string.digits) for audio_path in audio_files ] set_instruments = list(set(instruments)) assert ( len(instruments) == totalinstruments[ip] ), 'audio files for some instruments are missing' assert ( len(set_instruments) == ninstruments[ip] ), 'some instruments are missing from the dataset' index['multitracks'][piece]['tracks'] = [] for instrument in set_instruments: assert ( instrument in families.keys() ), "instrument {} is not in the list of dataset instruments".format( instrument ) index['tracks'][piece+'-'+instrument] = {} index['multitracks'][piece]['tracks'].append(piece+'-'+instrument) #### add audios instrument_audio_files = sorted( glob.glob(os.path.join(data_path, 'audio', piece, instrument + '*.wav')) ) assert ( len(instrument_audio_files) > 0 ), 'no audio has been found for {}'.format(instrument) for i, audio_file in enumerate(instrument_audio_files): audio_checksum = md5( os.path.join( data_path, 'audio', piece, os.path.basename(audio_file) ) ) source = os.path.basename(audio_file).replace('.wav', '') index['tracks'][piece+'-'+instrument]['audio_'+source] = ( 'audio/{}/{}'.format(piece, os.path.basename(audio_file)), audio_checksum, ) #### add scores assert os.path.exists( os.path.join( data_path, 'annotations', piece, '{}.txt'.format(instrument) ) ), 'cannot find score file {}'.formatos.path.join( data_path, 'annotations', piece, '{}.txt'.format(instrument) ) assert os.path.exists( os.path.join( data_path, 'annotations', piece, '{}_o.txt'.format(instrument) ) ), 'cannot find score file {}'.formatos.path.join( data_path, 'annotations', piece, '{}_o.txt'.format(instrument) ) score_checksum = md5( os.path.join( data_path, 'annotations', piece, '{}.txt'.format(instrument) ) ) score_original_checksum = md5( os.path.join( data_path, 'annotations', piece, '{}_o.txt'.format(instrument) ) ) index['tracks'][piece+'-'+instrument]['notes'] = ( 'annotations/{}/{}.txt'.format(piece, instrument), score_checksum, ) index['tracks'][piece+'-'+instrument]['notes_original'] = ( 'annotations/{}/{}_o.txt'.format(piece, instrument), score_original_checksum, ) with open(DATASET_INDEX_PATH, 'w') as fhandle: json.dump(index, fhandle, indent=2) def main(args): make_dataset_index(args.data_path) if __name__ == '__main__': PARSER = argparse.ArgumentParser(description='Make Phenicx-anechoic index file.') PARSER.add_argument( 'data_path', type=str, help='Path to Phenicx-anechoic data folder.' ) main(PARSER.parse_args())

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null

0

null

0

1

0

61af771f46003607f0d27ebb336cdcdad337f2c1

4,341

py

Python

Pzzzzz/plugins/code_runner.py

Pzzzzz5142/animal-forest-QQ-group-bot

a9141a212a7746ac95d28459ec9cec5b6c188b35

[ "MIT" ]

5

2020-05-28T06:29:33.000Z

2020-09-30T12:14:46.000Z

Pzzzzz/plugins/code_runner.py

Pzzzzz5142/xjbx-QQ-group-bot

a9141a212a7746ac95d28459ec9cec5b6c188b35

[ "MIT" ]

null

Pzzzzz/plugins/code_runner.py

Pzzzzz5142/xjbx-QQ-group-bot

a9141a212a7746ac95d28459ec9cec5b6c188b35

[ "MIT" ]

null

from nonebot import on_command, CommandSession, get_bot from nonebot.command import call_command from nonebot.message import escape as message_escape import aiohttp from nonebot.argparse import ArgumentParser __plugin_name__ = "运行代码" RUN_API_URL_FORMAT = "https://glot.io/run/{}?version=latest" SUPPORTED_LANGUAGES = { "assembly": {"ext": "asm"}, "bash": {"ext": "sh"}, "c": {"ext": "c"}, "clojure": {"ext": "clj"}, "coffeescript": {"ext": "coffe"}, "cpp": {"ext": "cpp"}, "csharp": {"ext": "cs"}, "erlang": {"ext": "erl"}, "fsharp": {"ext": "fs"}, "go": {"ext": "go"}, "groovy": {"ext": "groovy"}, "haskell": {"ext": "hs"}, "java": {"ext": "java", "name": "Main"}, "javascript": {"ext": "js"}, "julia": {"ext": "jl"}, "kotlin": {"ext": "kt"}, "lua": {"ext": "lua"}, "perl": {"ext": "pl"}, "php": {"ext": "php"}, "python": {"ext": "py"}, "ruby": {"ext": "rb"}, "rust": {"ext": "rs"}, "scala": {"ext": "scala"}, "swift": {"ext": "swift"}, "typescript": {"ext": "ts"}, } headers = { "Authorization": "Token {}".format(get_bot().config.RUNCODEAPI), "Content-type": "application/json", } @on_command("run", aliases=["运行代码", "运行"], only_to_me=False) async def run(session: CommandSession): supported_languages = ", ".join(sorted(SUPPORTED_LANGUAGES.keys())) language = session.get( "language", prompt="你想运行的代码是什么语言？\n" f"目前支持 {supported_languages}" ) code = session.get("code", prompt="你想运行的代码是？") await session.send("正在运行，请稍等……") async with aiohttp.ClientSession(headers=headers) as sess: async with sess.post( RUN_API_URL_FORMAT.format(language), json={ "files": [ { "name": ( SUPPORTED_LANGUAGES[language].get("name", "main") + f'.{SUPPORTED_LANGUAGES[language]["ext"]}' ), "content": code, } ], "stdin": "", "command": "", }, ) as resp: if resp.status != 200: session.finish("运行失败，服务可能暂时不可用，请稍后再试") payload = await resp.json() if not isinstance(payload, dict): session.finish("运行失败，服务可能暂时不可用，请稍后再试") sent = False for k in ["stdout", "stderr", "error"]: v = payload.get(k) lines = v.splitlines() lines, remained_lines = lines[:10], lines[10:] out = "\n".join(lines) out, remained_out = out[: 60 * 10], out[60 * 10 :] if remained_lines or remained_out: out += f"\n（输出过多，已忽略剩余内容）" out = message_escape(out) if out: await session.send(f"{k}:\n\n{out}") sent = True if not sent: session.finish("运行成功，没有任何输出") @on_command("cal", only_to_me=False) async def cal(session: CommandSession): args = session.current_arg_text.strip() if args == "": session.finish("没有输入内容哦！") await call_command( session.bot, session.event, "run", current_arg="""from math import * print({})""".format( args ), ) @run.args_parser async def _(session: CommandSession): stripped_arg = session.current_arg_text.strip() if session.is_first_run: if stripped_arg == "": return parser = ArgumentParser(session=session) parser.add_argument("-l", "--language", default="python", help="指定编程语言") parser.add_argument("source", help="运行源代码", nargs="*") argv = parser.parse_args(stripped_arg.split(" ")) language = argv.language if language not in SUPPORTED_LANGUAGES: session.finish("暂时不支持运行你输入的编程语言") session.state["language"] = language source = " ".join(argv.source) if source == "": return session.state["code"] = " ".join(argv.source) return if not stripped_arg: return if not stripped_arg: session.pause("请输入有效内容") if session.current_key == "language": if stripped_arg not in SUPPORTED_LANGUAGES: session.finish("暂时不支持运行你输入的编程语言") session.state[session.current_key] = stripped_arg

30.356643

80

0.539968

461

4,341

4.97397

0.381779

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0.013083

0.143916

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0.004847

0.287031

4,341

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0.008065

0

null

0

null

0

1

0

61b14520dd49127de7f32813b04d71f1ebbf9aa7

2,905

py

Python

jobs.py

gomes-lab/HCLMP

6770579404f6fa76948f688ae3c626ad621284ec

[ "MIT" ]

4

2021-06-04T12:34:19.000Z

2022-01-08T07:12:41.000Z

jobs.py

sk2299/HCLMP

d8b3e4dbf39af8d324c4f57e5d56f5846f2b136e

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

1

2022-01-07T05:16:26.000Z

2022-01-07T05:55:17.000Z

jobs.py

sk2299/HCLMP

d8b3e4dbf39af8d324c4f57e5d56f5846f2b136e

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

1

2022-02-21T19:12:14.000Z

import os from os import listdir from os.path import isfile, join ''' Author: Shufeng KONG, Cornell University, USA Contact: sk2299@cornell.edu This is an example script to run jobs. Set single_job to be True if you only have one setting or dataset to run. In our experiments, we have 69 systems to run, so we set single_job to be False by default. The trained models will be saved in the "models" folder by default. For testing, please set train to be 0. The testing results will be saved in the "results" folder by defaults. We have provided trained models for our 69 systems. One can run the script to output results. The transfer_type indicates whether to use the GAN transfer learning. 'None' represents no transfer learning is used. ''' model = 'run_HCLMP.py' data_path = 'data/uvis_dataset_no_redundancy/uvis_dict.chkpt' single_job = True train = 0 # 0 for testing, 1 for training transfer_type = 'gen_feat' # choices ['gen_feat', 'None'] #transfer_type = 'None' epochs = 40 # Run on the ramdom split setting if single_job: train_path = 'data/uvis_dataset_no_redundancy/idx/rd_idx_jh/train/rd_idx_jh.npy' test_path = 'data/uvis_dataset_no_redundancy/idx/rd_idx_jh/test/rd_idx_jh.npy' val_path = 'data/uvis_dataset_no_redundancy/idx/rd_idx_jh/val/rd_idx_jh.npy' if train==1: command = "CUDA_VISIBLE_DEVICES=0 python %s --train --epochs %d --transfer-type %s --data-path %s --train-path %s --val-path %s"\ %(model, epochs, transfer_type, data_path, train_path, val_path) else: command = "CUDA_VISIBLE_DEVICES=0 python %s --evaluate --epochs %d --transfer-type %s --data-path %s --test-path %s"\ %(model, epochs, transfer_type, data_path, test_path) print() print(command) print() os.system(command) # Run on 69 ternary systems else: train_dir = 'data/uvis_dataset_no_redundancy/idx/train/' val_dir = 'data/uvis_dataset_no_redundancy/idx/val_from_train/' test_dir = 'data/uvis_dataset_no_redundancy/idx/test/' system_files = sorted([f.split('.')[0] for f in listdir(train_dir) if isfile(join(train_dir, f))]) for sys in system_files: train_path = train_dir + sys + '.npy' val_path = val_dir + sys + '.npy' test_path = test_dir + sys + '.npy' if train==1: command = "CUDA_VISIBLE_DEVICES=0 python %s --train --epochs %d --transfer-type %s --data-path %s --train-path %s --val-path %s"\ %(model, epochs, transfer_type, data_path, train_path, val_path) else: command = "CUDA_VISIBLE_DEVICES=0 python %s --transfer-type %s --evaluate --epochs %d --data-path %s --test-path %s" \ % (model, transfer_type, epochs, data_path, test_path) print() print(command) print() os.system(command) print('Finish running all systems!!!')

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0.679862

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2,905

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0.263393

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0.055556

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0.341799

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0.162791

0

null

0

null

0

1

0

61b34cc74049ca79c83591c3b423846191259210

3,460

py

Python

tests/test_readout.py

msohaibalam/forest-benchmarking

40f5fd5235803204b34fa8ba1ced4ef2e0f3098d

[ "Apache-2.0" ]

null

tests/test_readout.py

msohaibalam/forest-benchmarking

40f5fd5235803204b34fa8ba1ced4ef2e0f3098d

[ "Apache-2.0" ]

null

tests/test_readout.py

msohaibalam/forest-benchmarking

40f5fd5235803204b34fa8ba1ced4ef2e0f3098d

[ "Apache-2.0" ]

null

import re import numpy as np from pyquil import Program from pyquil.device import gates_in_isa from pyquil.gates import I, RX, CNOT, MEASURE from pyquil.noise import decoherence_noise_with_asymmetric_ro from forest_benchmarking.readout import get_flipped_program, estimate_confusion_matrix, \ estimate_joint_confusion_in_set, marginalize_confusion_matrix, estimate_joint_reset_confusion def test_get_flipped_program(): program = Program() ro = program.declare('ro', memory_type='BIT', memory_size=2) program += Program([ I(0), RX(2.3, 1), CNOT(0, 1), MEASURE(0, ro[0]), MEASURE(1, ro[1]), ]) flipped_program = get_flipped_program(program) lines = flipped_program.out().splitlines() matched = 0 for l1, l2 in zip(lines, lines[1:]): ma = re.match(r'MEASURE (\d) ro\[(\d)\]', l2) if ma is not None: matched += 1 assert int(ma.group(1)) == int(ma.group(2)) assert l1 == 'RX(pi) {}'.format(int(ma.group(1))) assert matched == 2 def test_readout_confusion_matrix_consistency(qvm): noise_model = decoherence_noise_with_asymmetric_ro(gates=gates_in_isa(qvm.device.get_isa())) qvm.qam.noise_model = noise_model qvm.qam.random_seed = 1 num_shots = 500 qubits = (0, 1, 2) qubit = (0,) # parameterized confusion matrices cm_3q_param = estimate_joint_confusion_in_set(qvm, qubits, num_shots=num_shots, joint_group_size=len(qubits))[qubits] cm_1q_param = estimate_joint_confusion_in_set(qvm, qubit, num_shots=num_shots, joint_group_size=1)[qubit] # non-parameterized confusion matrices cm_3q = estimate_joint_confusion_in_set(qvm, qubits, num_shots=num_shots, joint_group_size=len(qubits), use_param_program=False)[qubits] cm_1q = estimate_joint_confusion_in_set(qvm, qubit, num_shots=num_shots, joint_group_size=1, use_param_program=False, use_active_reset=True)[qubit] # single qubit cm single_q = estimate_confusion_matrix(qvm, qubit[0], num_shots) # marginals from 3q above marginal_1q_param = marginalize_confusion_matrix(cm_3q_param, qubits, qubit) marginal_1q = marginalize_confusion_matrix(cm_3q, qubits, qubit) atol = .03 np.testing.assert_allclose(cm_3q_param, cm_3q, atol=atol) np.testing.assert_allclose(cm_1q_param, single_q, atol=atol) np.testing.assert_allclose(cm_1q, single_q, atol=atol) np.testing.assert_allclose(cm_1q_param, marginal_1q_param, atol=atol) np.testing.assert_allclose(cm_1q, marginal_1q, atol=atol) np.testing.assert_allclose(marginal_1q_param, single_q, atol=atol) def test_reset_confusion_consistency(qvm): noise_model = decoherence_noise_with_asymmetric_ro(gates=gates_in_isa(qvm.device.get_isa())) qvm.qam.noise_model = noise_model qvm.qam.random_seed = 1 num_trials = 10 qubits = (0, 1) passive_reset = estimate_joint_reset_confusion(qvm, qubits, num_trials, len(qubits), use_active_reset=False)[qubits] atol = .1 np.testing.assert_allclose(passive_reset[:, 0], np.ones(4).T, atol=atol)

39.318182

97

0.650867

462

3,460

4.549784

0.222944

0.038059

0.049952

0.076594

0.468601

0.367745

0.342531

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0.321123

0.299715

0

0.023653

0.254624

3,460

87

98

39.770115

0.791392

0.031503

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1

0.045455

false

0.030303

0.106061

0

0.151515

0

null

0

null

0

1

0

61b823d8585b20f092c787b7b2c50b78ac5048c4

1,166

py

Python

freshmaker/parsers/errata/signing_change.py

hluk/freshmaker

224875b104b5be9fa6688af31363a387eeb1b05f

[ "MIT" ]

5

2020-06-17T11:29:16.000Z

2022-03-24T07:20:16.000Z

freshmaker/parsers/errata/signing_change.py

ronnyhlim/freshmaker

b7635dcfe631759e917c85e6ef6654024a3fb91c

[ "MIT" ]

96

2020-06-29T15:01:23.000Z

2022-03-30T08:07:06.000Z

freshmaker/parsers/errata/signing_change.py

ronnyhlim/freshmaker

b7635dcfe631759e917c85e6ef6654024a3fb91c

[ "MIT" ]

20

2020-06-16T01:30:08.000Z

2022-02-19T15:34:55.000Z

# SPDX-License-Identifier: MIT from freshmaker.parsers import BaseParser from freshmaker.events import FlatpakModuleAdvisoryReadyEvent from freshmaker.errata import Errata, ErrataAdvisory class ErrataAdvisorySigningChangedParser(BaseParser): """ Parses errata.activity.signing messages (a build attached to advisory is signed). Creates FlatpakModuleAdvisoryReadyEvent if a new flatpak advisory can be created for module security advisory. """ name = "ErrataAdvisorySigningChangedParser" topic_suffixes = ["errata.activity.signing"] def can_parse(self, topic, msg): return any(topic.endswith(s) for s in self.topic_suffixes) def parse(self, topic, msg): msg_id = msg.get("msg_id") inner_msg = msg.get("msg") if "module" not in inner_msg["content_types"] or inner_msg["errata_status"] != "QE": return errata_id = int(inner_msg.get("errata_id")) errata = Errata() advisory = ErrataAdvisory.from_advisory_id(errata, errata_id) if advisory.is_flatpak_module_advisory_ready(): return FlatpakModuleAdvisoryReadyEvent(msg_id, advisory)

32.388889

92

0.715266

134

1,166

6.067164

0.440299

0.03936

0.051661

0.04182

0

0.199828

1,166

35

93

33.314286

0.871383

0.191252

0

0.119126

0.062295

0

1

0.111111

false

0

0.166667

0.055556

0.611111

0

null

0

null

0

1

0

61b8dfc3b67dbc326456271271e5948b8211186a

1,940

py

Python

music163/api_test.py

WMLHUST/scrapy_wangyiyun_music

bd6b76d852dc6ead7f88a14843886c233c5a52c7

[ "Apache-2.0" ]

3

2019-07-01T14:20:26.000Z

2019-12-16T01:50:23.000Z

music163/api_test.py

WMLHUST/scrapy_wangyiyun_music

bd6b76d852dc6ead7f88a14843886c233c5a52c7

[ "Apache-2.0" ]

null

music163/api_test.py

WMLHUST/scrapy_wangyiyun_music

bd6b76d852dc6ead7f88a14843886c233c5a52c7

[ "Apache-2.0" ]

5

2018-10-15T12:47:55.000Z

2019-09-13T13:33:53.000Z

# coding: utf-8 import requests def get_song_comments(music_id, offset=0, total='false', limit=100): action = 'http://music.163.com/api/v1/resource/comments/R_SO_4_{}/?rid=R_SO_4_{}&\ offset={}&total={}&limit={}'.format(music_id, music_id, offset, total, limit) # proxy = {"http": "http://dev-proxy.oa.com:8080"} # proxy = {"http": "http://194.182.74.160:3128"} # proxy = {"http": "http://127.0.0.1:1080"} headers = {"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8", "Accept-Encoding": "gzip, deflate", 'User-Agent': "Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36", "Referer": "Referer:http://music.163.com/", "Accept-Encoding": "zh-CN,zh;q=0.8,en;q=0.6", "Content-Type": "application/x-www-form-urlencoded", } # rep = requests.get(action, proxies=proxy, headers=headers) rep = requests.get(action, headers=headers) print("status code:", rep.status_code) return rep def get_hot_comments(rep): comments_list = [] comments = rep.json()['hotComments'] for comment in comments: tmp_dict = {} tmp_dict['nickname'] = comment['user']['nickname'] tmp_dict['star_cnt'] = comment['likedCount'] tmp_dict['content'] = comment['content'] if len(comment['beReplied']) > 0: tmp_dict['quote'] = comment['beReplied'][0]['content'] # log.msg(tmp_dict, _level=log.INFO) comments_list.append(tmp_dict) return comments_list if __name__ == "__main__": rep = get_song_comments(520521342) print(rep.text) # print(get_hot_comments(rep)) # tmp_proxy = ProxyHandler.random_get() # proxy = {"http": tmp_proxy} # rep = requests.get("http://httpbin.org/ip", timeout=(2, 8)) # print(rep.text)

38.039216

146

0.610309

262

1,940

4.362595

0.469466

0.04287

0.034121

0.026247

0

0.059701

0.20567

1,940

50

147

38.8

0.682025

0.214433

0

0.103448

0.31746

0.093254

0

1

0.068966

false

0

0.034483

0

0.172414

0.068966

0

null

0

null

0

1

0

61b9588fe2949fa1516521e6875b226d81b8b6f7

2,356

py

Python

src/data_modules/image_dataset_data_module.py

gmum/lcw-generator

fde1128505194bd04f04bbddcbe7fcec453b0052

[ "MIT" ]

4

2020-09-17T22:16:48.000Z

2022-02-21T19:07:48.000Z

src/data_modules/image_dataset_data_module.py

gmum/lcw-generator

fde1128505194bd04f04bbddcbe7fcec453b0052

[ "MIT" ]

null

src/data_modules/image_dataset_data_module.py

gmum/lcw-generator

fde1128505194bd04f04bbddcbe7fcec453b0052

[ "MIT" ]

null

from typing import Union from torch.utils.data.dataset import Dataset from data_modules.dataset_factory import DatasetFactory from torch.utils.data import DataLoader import pytorch_lightning as pl class ImageDatasetDataModule(pl.LightningDataModule): def __init__(self, dataset_factory: Union[DatasetFactory], train_batch_size: int, validation_batch_size: int, workers: int): super().__init__() self.__dataset_factory = dataset_factory self.train_batch_size = train_batch_size self.validation_batch_size = validation_batch_size self.workers = workers self.__validation_dataset = None self.__train_dataset = None self.__geneval_dataset = None def dataset_name(self) -> str: return self.__dataset_factory.get_dataset_name() def setup(self, stage=None): if self.__validation_dataset is None: self.__validation_dataset = self.__dataset_factory.get_dataset(False) print(f'Size of validation dataset: {len(self.__validation_dataset)}') if self.__train_dataset is None: self.__train_dataset = self.__dataset_factory.get_dataset(True) print(f'Size of train dataset: {len(self.__train_dataset)}') if self.__geneval_dataset is None: self.__geneval_dataset = self.__dataset_factory.get_eval_dataset() print(f'Size of geneval dataset: {len(self.__geneval_dataset)}') def train_dataset_elements_count(self) -> int: assert self.__train_dataset is not None return len(self.__train_dataset) def train_dataloader(self, drop_last=True, shuffle=True) -> DataLoader: assert self.__train_dataset is not None return DataLoader(self.__train_dataset, batch_size=self.train_batch_size, shuffle=shuffle, num_workers=self.workers, drop_last=drop_last, pin_memory=False) def val_dataloader(self) -> DataLoader: assert self.__validation_dataset is not None return DataLoader(self.__validation_dataset, batch_size=self.validation_batch_size, num_workers=4, drop_last=True, pin_memory=False) def generative_eval_dataset(self) -> Dataset: assert self.__geneval_dataset is not None return self.__geneval_dataset

43.62963

129

0.702886

287

2,356

5.324042

0.205575

0.058901

0.08377

0.054974

0.219895

0.168848

0.081152

0.048429

0

0.000548

0.225382

2,356

53

130

44.45283

0.836712

0

0.04878

0

0.071211

0.038211

0

0.097561

1

0.170732

false

0

0.121951

0.02439

0.439024

0.073171

0

null

0

null

0

1

0

61b978854383493ee32031f63ea4d378d345d545

3,429

py

Python

src/methods/model_investigate.py

clownjiahui/kdd2018_air_pollution_prediction

c76c3ee87132a923cf499d9be17d49b2c9b6eac1

[ "MIT" ]

19

2019-03-31T09:06:49.000Z

2022-03-29T12:25:29.000Z

src/methods/model_investigate.py

clownjiahui/kdd2018_air_pollution_prediction

c76c3ee87132a923cf499d9be17d49b2c9b6eac1

[ "MIT" ]

null

src/methods/model_investigate.py

clownjiahui/kdd2018_air_pollution_prediction

c76c3ee87132a923cf499d9be17d49b2c9b6eac1

[ "MIT" ]

11

2019-04-02T07:59:45.000Z

2022-03-18T08:32:28.000Z

import settings import const import pandas as pd import numpy as np import matplotlib from matplotlib import rcParams rcParams.update({'figure.autolayout': True}) # to prevent labels going out of plot! matplotlib.use('TkAgg') import seaborn as sns import matplotlib.pyplot as plt from src.preprocess import reform from src import util config = settings.config[const.DEFAULT] feature_dir = config[const.FEATURE_DIR] suffix = '_12_3_7_24_8_6_12_1_7_24_hybrid_tests.csv' paths = { 'BJ': { # 'PM2.5': feature_dir + const.BJ_PM25 + suffix, # 'PM10': feature_dir + const.BJ_PM10 + suffix, # 'O3': feature_dir + const.BJ_O3 + suffix, }, 'LD': { 'PM2.5': feature_dir + const.LD_PM25 + suffix, # 'PM10': feature_dir + const.LD_PM10 + suffix, } } smape_columns = ['city', const.ID, const.LONG, const.LAT, 'pollutant', 'SMAPE', 'count'] smapes = pd.DataFrame(columns=smape_columns) for city in paths: station_path = config[const.BJ_STATIONS] if city == 'BJ' else config[const.LD_STATIONS] stations = pd.read_csv(station_path, sep=";", low_memory=False) stations_dict = stations.to_dict(orient='index') for pollutant, path in paths[city].items(): ts = pd.read_csv(path, sep=";", low_memory=False) station_data = reform.group_by_station(ts=ts, stations=stations) local_smapes = pd.DataFrame(data=[], columns=smape_columns) for _, station in stations_dict.items(): data = station_data[station[const.ID]] if station[const.PREDICT] == 1 else pd.DataFrame() if len(data.index) == 0: continue # no prediction for this station actual = data[[pollutant + '__' + str(i) for i in range(1, 49)]].as_matrix() forecast = data[['f' + str(i) for i in range(0, 48)]].as_matrix() station['SMAPE'] = util.SMAPE(actual=actual, forecast=forecast) smape = pd.DataFrame( data=[[city, station[const.ID], station[const.LONG], station[const.LAT], pollutant, station['SMAPE'], actual.size]], columns=smape_columns) local_smapes = local_smapes.append(other=smape, ignore_index=True) smapes = smapes.append(other=smape, ignore_index=True) fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 3)) # Plot SMAPE values sorted local_smapes.sort_values(by='SMAPE', inplace=True) g = sns.stripplot(x=const.ID, y='SMAPE', data=local_smapes, ax=axes[0]) g.set_xticklabels(labels=g.get_xticklabels(), rotation=90) # rotate station names for readability # Plot SMAPE values on map local_smapes.plot.scatter(x=const.LONG, y=const.LAT, s=util.normalize(local_smapes['SMAPE'], multiplier=150), title=city + '_' + pollutant, fontsize=13, ax=axes[1]) # Plot station names on positions for _, station in stations_dict.items(): if 'SMAPE' in station: label = ('%d ' % (100 * station['SMAPE'])) + station[const.ID][0:2] # 64 be axes[1].annotate(label, xy=(station[const.LONG], station[const.LAT]), xytext=(5, 0), textcoords='offset points', ) plt.draw() # Calculate total error total_smape = np.sum(smapes['SMAPE'] * smapes['count']) / np.sum(smapes['count']) print('Total SMAPE:', total_smape) plt.show()

45.72

117

0.633712

458

3,429

4.60262

0.349345

0.045541

0.035579

0.024194

0.171727

0.133776

0.035104

0

0.024288

0.231554

3,429

75

118

45.72

0.775712

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0

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false

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0

null

0

null

0

1

0

61b9871edd93122c9a1e0cadd169ef1f8e7cc7a9

6,002

py

Python

src/obj.py

LemnX4/PointDipole

79223498f6adce9d4f33939bd0173e9f7cd9132a

[ "MIT" ]

null

src/obj.py

LemnX4/PointDipole

79223498f6adce9d4f33939bd0173e9f7cd9132a

[ "MIT" ]

null

src/obj.py

LemnX4/PointDipole

79223498f6adce9d4f33939bd0173e9f7cd9132a

[ "MIT" ]

null

# vim: set et sw=4 ts=4 nu fdm=indent: # coding: utf8 import numpy as np import random from demag import demagnetization from llg import Bth class Object: def __init__(self, position, magnetization, a=1.0, b=1.0, h=1.0, angle=0, M0=1720e3): if len(magnetization) == 2 and len(position) == 2: self.dim = "2D" elif len(magnetization) == 3 and len(position) == 3: self.dim = "3D" elif len(magnetization) != len(position): print("\nError : island dimension undefined.\n") self.dim == "undefined" self._frozen = False self._atomic = False self._island = False self.pos = position self._a = a*1e-9 self._b = b*1e-9 self._h = h*1e-9 self.mag = magnetization self.thermic_field = [] self.time_evolved = False self.time = [] self.mag_history = [] self.angle = angle self.M0 = M0 self.Ku = 0.0 self.har = a/b self.var = a/h self.coupled_with = [] @property def frozen(self): return self._frozen @frozen.setter def frozen(self, value): self._frozen = value @property def atomic(self): return self._atomic @atomic.setter def atomic(self, value): self._atomic = value @property def island(self): return self._island @island.setter def island(self, value): self._island = value @property def a(self): return self._a @a.setter def a(self, value): self._a = value*1e-9 self.update_values() @property def b(self): return self._b @b.setter def b(self, value): self._b = value*1e-9 self.update_values() @property def h(self): return self._h @h.setter def h(self, value): self._h = value*1e-9 self.update_values() @property def M0(self): return self._M0 @M0.setter def M0(self, value): self._M0 = value self.update_values() @h.setter def h(self, value): self._h = value*1e-9 self.update_values() @property def mag(self): return self._mag @mag.setter def mag(self, value): if np.linalg.norm(value) == 0: if self.dim == "2D": print("Error : null vector magnetization. Set to [1, 0] by default.") self._mag = [1, 0] elif self.dim == "3D": print("Error : null vector magnetization. Set to [1, 0, 0] by default.") self._mag = [1, 0, 0] else: self._mag = value / np.linalg.norm(value) def randomize_magnetization(self, angle=180): theta = 2*(0.5-random.random())*angle*np.pi/180 if self.dim == "2D": new_angle = np.arctan2(self.mag[1], self.mag[0]) + theta self.mag = [np.cos(new_angle), np.sin(new_angle)] elif self.dim == "3D": self.mag = [random.random()-0.5, random.random()-0.5, random.random()-0.5] def update_values(self): if self.atomic: self.factors = [1/3.0, 1/3.0, 1/3.0] self.ku = 0 return self.V = np.pi * (self.a/2.0)*(self.b/2.0)*self.h self.factors = demagnetization(self.a, self.b, self.h) self.M = self.V*self.M0 if self.dim == "2D": self.E0 = (2*np.pi*1e-7) * self.M0**2 * self.V * self.factors[0] self.ku = self.M0**2 *(2*np.pi*1e-7)*(1 - 2*self.factors[0] - self.factors[2]) def update_caracteristics(self): if self.atomic: c = "\n##########\tCaracteristics of the atom:\t##########\n\n" elif self.island: c = "\n##########\tCaracteristics of the nano-island:\t##########\n\n" c += "Position (nm) : {}\n".format(self.pos) if self.atomic: c += "Magnetic moment : {} µB\n".format(self.m) else: c += "Magnetic moment : {} µB\n".format(self.M/9.74e-24) f = "" if self.frozen: f = " (frozen)" c += "Magnetization direction{} : {}\n".format(f, self.mag) if not self.atomic: c += "Angle : {}°\n".format(self.angle) c += "Large diamater (a) : {} nm\n".format(self.a/1e-9) c += "Small diamater (b) : {} nm\n".format(self.b/1e-9) c += "Height (h) : {} nm\n".format(self.h/1e-9) c += "Horizontal aspect ratio : {} \n".format(self.har) c += "Vertical aspect ratio : {} \n".format(self.var) else: c += "Radius : {} nm\n".format(self.radius) c += "Volume : {} m³\n".format(self.V) if not self.atomic: if self.dim == "2D": c += "Uniaxial horizontal constant Ku : {} kJ/m³\n".format(self.Ku/1e3) c += "Self dipolar energy : {} J\n".format(self.E0) elif self.dim == "3D": c += "Demagnetization factors : {}\n".format(self.factors) elif self.Ku != 0: c += "Uniaxial constant Ku : {} kJ/m³\n".format(self.Ku/1e3) c += "Uniaxial axis : {}\n".format(self.u_axis) if len(self.coupled_with) !=0 : c += "\nCoupled with islands : {}\n".format(self.coupled_with) else: c += "\nNot coupled with other islands.\n" c += "\n###################################################################\n" self.caracteristics = c def initialize_thermic_field(self, gamma, alpha, T, N): self.thermic_field = [] for i in range(N+1): self.thermic_field.append(Bth(gamma, alpha, self.M, T))

26.794643

90

0.488004

770

6,002

3.738961

0.192208

0.043765

0.064953

0.022577

0.202154

0.16603

0.162904

0.150052

0.091004

0.063216

0

0.034581

0.354382

6,002

223

91

26.914798

0.708129

0.008164

0

0.237179

0

0.150422

0.02968

0

1

0.141026

false

0

0.025641

0.051282

0.230769

0.019231

0

null

0

null

0

1

0

61ba96c4c02b0b326e2005e4e9d043a4842e135b

828

py

Python

rubbish.bin/legacy/examples/normal_textures.py

Jack12xl/taichi_three

4785aeefd9e0bccd33cc9b564046dc566b03c714

[ "MIT" ]

null

rubbish.bin/legacy/examples/normal_textures.py

Jack12xl/taichi_three

4785aeefd9e0bccd33cc9b564046dc566b03c714

[ "MIT" ]

null

rubbish.bin/legacy/examples/normal_textures.py

Jack12xl/taichi_three

4785aeefd9e0bccd33cc9b564046dc566b03c714

[ "MIT" ]

null

import taichi as ti import taichi_three as t3 import numpy as np ti.init(ti.cpu) scene = t3.Scene() obj = t3.readobj('assets/cube.obj', scale=0.6) model = t3.Model(t3.Mesh.from_obj(obj)) model.material = t3.Material(t3.CookTorrance( color=t3.Texture(ti.imread('assets/cloth.jpg')), normal=t3.NormalMap(texture=t3.Texture(ti.imread('assets/normal.png'))), )) scene.add_model(model) camera = t3.Camera() camera.ctl = t3.CameraCtl(pos=[0, 1, 1.8]) scene.add_camera(camera) light = t3.Light([0.4, -0.8, -1.7]) scene.add_light(light) gui = ti.GUI('Normal map', camera.res) while gui.running: gui.get_event(None) gui.running = not gui.is_pressed(ti.GUI.ESCAPE) camera.from_mouse(gui) scene.render() gui.set_image(camera.img) #gui.set_image(camera.fb['normal'].to_numpy() * 0.5 + 0.5) gui.show()

27.6

76

0.693237

143

828

3.937063

0.433566

0.042629

0.039076

0.060391

0.081705

0

0.040111

0.126812

828

29

77

28.551724

0.738589

0.068841

0

0.075325

0

1

0

false

0

0.12

0

0.12

0

null

0

null

0

1

0

61c0a34abeefd2557216aff867ba7f8de9c65cae

799

py

Python

forms-flow-api/src/api/utils/util.py

McCoySmith/forms-flow-ai

5555c1b2a9a5496f1ab98e5339d66537e25974c2

[ "Apache-2.0" ]

null

forms-flow-api/src/api/utils/util.py

McCoySmith/forms-flow-ai

5555c1b2a9a5496f1ab98e5339d66537e25974c2

[ "Apache-2.0" ]

11

2021-06-02T04:42:50.000Z

2022-02-14T07:24:15.000Z

forms-flow-api/src/api/utils/util.py

McCoySmith/forms-flow-ai

5555c1b2a9a5496f1ab98e5339d66537e25974c2

[ "Apache-2.0" ]

null

"""Common utils. * CORS pre-flight decorator. A simple decorator to add the options method to a Request Class. """ from .constants import ALLOW_ALL_ORIGINS def cors_preflight(methods: str = "GET"): """Render an option method on the class.""" def wrapper(f): # pylint: disable=invalid-name def options(self, *args, **kwargs): # pylint: disable=unused-argument return ( {"Allow": "GET"}, 200, { "Access-Control-Allow-Origin": ALLOW_ALL_ORIGINS, "Access-Control-Allow-Methods": methods, "Access-Control-Allow-Headers": "Authorization, Content-Type", }, ) setattr(f, "options", options) return f return wrapper

29.592593

93

0.560701

84

799

5.27381

0.607143

0.088036

0.121896

0

0.005576

0.326658

799

26

94

30.730769

0.817844

0.260325

0

0.221453

0.143599

0

1

0.1875

false

0

0.0625

0.4375

0

null

0

null

0

1

0

61c6722666daed5da1e5465a987ae0f5c8c2f1c8

11,323

py

Python

wk3_time_space_est.py

pc0179/RomeTaxiData

dff19a538482810e1b84486cdc4299fb820f5051

[ "MIT" ]

null

wk3_time_space_est.py

pc0179/RomeTaxiData

dff19a538482810e1b84486cdc4299fb820f5051

[ "MIT" ]

null

wk3_time_space_est.py

pc0179/RomeTaxiData

dff19a538482810e1b84486cdc4299fb820f5051

[ "MIT" ]

null

""" # week3 madness... # filter, process (map-match) and output data to new postgres database # aim by thurs, to be able to answer question, where are (best-guess/estimate) all the taxis at time T. # then, work out, how far each one is from one another, likely a) search with in BBox, then b) do some fast osrm routing (get that line of sight distance?) # to aid map-matching, best to use traces of one taxi at at time (allows for timestamp to be used... better approx. should result.) # get a list of all taxi_IDs... within dataset #execution_str = "SELECT DISTINCT taxi_id FROM rometaxidata" #currently all designed to run on C207... """ import psycopg2 import pandas.io.sql as pdsql import pandas as pd from sqlalchemy import create_engine #import matplotlib.pyplot as plt #from mpl_toolkits.basemap import Basemap import numpy as np import osrm #1. querying database # connection string for working on c207: connect_str = "dbname='rometaxitraces' user='postgres' host='localhost' password='postgres'" # connection string for Klara: # taxi_ids = pd.read_csv('/home/user/RomeTaxiData/all_rome_taxi_ids.csv', header=None, sep="\n") # list_taxi_ids = list(taxi_ids[0]) #I was bored and this seemed easier than figuring out exactly how pandas.iterrows() bullshit works #osrm.RequestConfig.host = "http://localhost:5000" # quick reminder of available columns in database: # cols = ['taxi_id','ts_dt','sim_t','sim_day_num','weekday_num','Lat1','Long1','x','y','unix_ts'] #interesting queries... #"SELECT * FROM rometaxidata WHERE taxi_id =225" #execution_str = "SELECT sim_t, Lat1, Long1 FROM rometaxidata WHERE taxi_id = 225" #execution_str = "SELECT DISTINCT taxi_id FROM rometaxidata" #execution_str = "SELECT sim_t, x, y FROM rometaxidata WHERE taxi_id = %s" #"SELECT * FROM rometaxidata WHERE sim_day_num=1" #execution_str = "SELECT * FROM rometaxidata WHERE (x BETWEEN -1000 AND 1000) AND (y BETWEEN -1000 AND 1000)" #execution_str = "SELECT * FROM rometaxidata WHERE sim_day_num = 10 AND (x BETWEEN -1000 AND 1000) AND (y BETWEEN -1000 AND 1000)" #execution_str = "SELECT * FROM rometaxidata WHERE weekday_num = 0 AND taxi_id = 225" #execution_str = ("SELECT DISTINCT taxi_id FROM rometaxidata WHERE sim_day_num = %s" % (str(sim_day_num))) #taxi_ids = pdsql.read_sql_query(execution_str,connection) """ bunch of taxi ids on day 3 129 195 106 120 285 8 264 305 318 179 209 276 """ #taxi_id = 129 #129 #taxi_ids['taxi_id'][0] #execution_str = ("SELECT unix_ts,lat1,long1 FROM rometaxidata WHERE (taxi_id = %s AND sim_day_num = %s)" % (str(taxi_id),str(sim_day_num))) connect_str = "dbname='c207rometaxitraces' user='postgres' host='localhost' password='postgres'" #sim_day_num = 4 connection = psycopg2.connect(connect_str) #connection = psycopg2.connect(connect_str) #1. get all taxi trace data for one day. # List unique values in a DataFrame column # h/t @makmanalp for the updated syntax! # Grab DataFrame rows where column has certain values #valuelist = ['value1', 'value2', 'value3'] #df = df[df.column.isin(valuelist)] #0-13 completed, start again at 14 for k in range(15,27): sim_day_num = k execution_str = ("SELECT taxi_id,unix_ts,lat1,long1 FROM rometaxidata WHERE sim_day_num =%s" % (str(sim_day_num))) taxidf = pdsql.read_sql_query(execution_str,connection) taxi_IDs = list(taxidf['taxi_id'].unique()) # for each taxi_id that was working on that day number for j in range(0,len(taxi_IDs)): trace_data2match = taxidf[taxidf['taxi_id']==taxi_IDs[j]] trace_data2match = trace_data2match.drop_duplicates() if len(trace_data2match)>1: trace_data2match = trace_data2match.sort_values('unix_ts') #VERY IMPORTANT for osrm. big deal! #search_radius = np.zeros_like(np.array(taxidf['unix_ts']))+10 #time_stamps = taxidf['unix_ts'] #going back to shitty python wrapper: #m = 0#50 #between 50-60 there is an error... the timestamps are not monotonically increasing.. need to sort this, jokes. #n = 1260 #60 #900 #len(gps_subset) #1260 #mpmatched_points = osrm.match(gps_positions[m:n], overview="simplified", timestamps=taxidf['unix_ts'][m:n], radius=None) #bear in mind... i might need to flip lats/longs order... hmmm.... gps_subset = trace_data2match[['long1','lat1']] gps_positions = [tuple(x) for x in gps_subset.values] mpmatched_points = osrm.match(gps_positions, overview="simplified", timestamps=trace_data2match['unix_ts'], radius=None) nobody_index = [] matched_longitude = [] matched_latitude = [] matched_unix_ts = [] matched_cols = ['taxi_id','day_num','unix_ts','mlatitude','mlongitude'] # loop each outputed point from the mapmatched trace (osrm), add correct timestampts,etc... build pandas dataframe for i in range(0,len(mpmatched_points['tracepoints'])): if mpmatched_points['tracepoints'][i] is None: nobody_index.append(i) else: matched_unix_ts.append(taxidf['unix_ts'][i]) matched_longitude.append(mpmatched_points['tracepoints'][i]['location'][1]) matched_latitude.append(mpmatched_points['tracepoints'][i]['location'][0]) matched_taxi_id = np.ones_like(matched_unix_ts)*taxi_IDs[j] matched_day_num = np.ones_like(matched_taxi_id)*sim_day_num matched_df = pd.DataFrame(np.column_stack([matched_taxi_id,matched_day_num,matched_unix_ts, matched_longitude, matched_latitude]), columns = matched_cols) matched_df.taxi_id = matched_df.taxi_id.astype(int) matched_df.day_num = matched_df.day_num.astype(int) matched_df.unix_ts = matched_df.unix_ts.astype(int) if j>0: entire_day_matched_traces = pd.concat([entire_day_matched_traces, matched_df], axis=0, join='outer', join_axes=None, ignore_index=True, keys=None, levels=None, names=None, verify_integrity=False, copy=True) else: entire_day_matched_traces = matched_df else: fail = j #at the entire sim day level. K loop file_name = ("/home/pdawg/Downloads/matched_traces/day_%s.csv" % (str(sim_day_num))) entire_day_matched_traces.to_csv(file_name,sep=',',index=False) #con.execute('TRUNCATE matchedta ;') #df.to_sql('my_table', con, if_exists='append') #connect_str2 = "dbname='matchedtaxitraces' user='postgres' host='localhost' password='postgres'" #connection2 = psycopg2.connect(connect_str2) #execution_str2 = ("TRUNCATE matchedtaxidata;") #really stupid code. #matched_df.to_sql(execution_str2,connection2) # code insert to postgres table, but first, set up table.... #could numpy row stack, and write to database at the end of the 'sim_day_num'... would reduce some shiz... # similarily, I should import a days worth of traces, divide into unique taxi_ids, then iterate! ''' my attempt... 0. will need to sort out matched database table etc.... this might mean care sigfigs etc... 1. load maybe 1GB a time from psql... 2. chunk it up, per chunk - psql query (yeah it will be slower, deal with it.... lets get this pig up and running,) - map match: 1000 points? <-- look at above not regards 'gaps=false?'? maybe need to edit fucking pyosrm shit. - convert results to - pandas dataframe, with ['unix_ts','latitude','longitude'] <-- ORDER IS IMPORTANT BE CAREFUL. - save 'overview=full' json file (although writing this out for everything coul be slooow) or could I save this to yet another database... nah, easy, just save to disk in another directory should have IterationNum,sim_day_num_taxi_id - save pandas dataframe to new matched_db search_radius = [20,20,20,20,20] url0 = ['http://localhost:5000/match/v1/driving/'] overview = 'full' steps='false' geometry='polyline' gps_points2match = gps_positions[500:1000] timestamps = taxidf['unix_ts'][500:1000] #url1 = [url0,';'.join([','.join([str(coord[0]),str(coord[1])]) for coord in gps_points2match])] #url2 = [join([url0,';'.join([','.join([str(coord[0]),str(coord[1])]) for coord in gps_points2match])])] #GPS coords... url0.append(';'.join([','.join([str(coord[0]),str(coord[1])]) for coord in gps_points2match])) #radiuses url0.append(';'.join([','.join([str(radii)]) for radii in search_radius])) url0.append(';'.join([','.join([str(ts)]) for ts in timestamps])) #timestamps// url1 = ''.join([url0[0],url0[1]]) url1 = ''.join([url1,'?overview={}&steps={}&geometries={}'.format(overview,str(steps).lower(), geometry)]) #url1 = '&radiuses='.join([url1,url0[2]]) # bold. url1 = '&timestamps='.join([url1,url0[3]]) url2txt_file = open("url2test.txt","w") url2txt_file.write(url1) url2txt_file.close() #url2txt = np.array(url1 #url_filename = '/home/user/RomeTaxiData/url2test.txt' #np.savetxt(url_filename,url2txt,fmt=str) #url = [host, '/match/', url_config.version, '/', url_config.profile, '/',';'.join([','.join([str(coord[0]), str(coord[1])]) for coord in points]),"?overview={}&steps={}&geometries={}".format(overview,str(steps).lower(), geometry)] ''' #mpmatched_points = osrm.match(gps_positions[0:5], overview="full", timestamps=taxidf['unix_ts'][0:5], radius =[10]) #---- from osrm-python wrapper ---- """ next steps: 1. edit/start from scratch making new python-osrm map-match function, need to get that url query just right http://localhost:5000/match/v1/driving/{gps points long1,lat1;... longN,latN}&radiuses={r1;r2;r3...rN}&timestamps{ts1;ts2;ts3...tsN} url = 'http://localhost:5000/match/v1/driving/' gps_points2match = gps_positions[500:505] for i in gps_points2match: url = [url.join(str(coord[0]),str(coord[1]) for coord in gps_points2match] #-------original.... shit. points = gps_positions # host = check_host(url_config.host) url = [host, '/match/', url_config.version, '/', url_config.profile, '/',';'.join( [','.join([str(coord[0]), str(coord[1])]) for coord in points]), "?overview={}&steps={}&geometries={}" .format(overview, str(steps).lower(), geometry)] if radius: url.append(";".join([str(rad) for rad in radius])) if timestamps: url.append(";".join([str(timestamp) for timestamp in timestamps])) r = urlopen("".join(url)) r_json = json.loads(r.read().decode('utf-8')) for taxi_id in list_taxi_ids: execution_str = ("SELECT lat1,long1,unix_ts FROM rometaxidata WHERE taxi_id = %s" % (str(taxi_id)) MOAR NOTES these two seem to work reasonably well, however give slightly different results which may/may not be worrying... curl "http://router.project-osrm.org/match/v1/driving/12.457089,41.895786;12.457089,41.895786;12.487011,41.893273;12.498969,41.902191;12.501389,41.901612?radiuses=20;20;20;20;20&timestamps=1391371881;1391371882;1391372422;1391372747;1391372791" curl "http://localhost:5000/match/v1/driving/12.457089,41.895786;12.457089,41.895786;12.487011,41.893273;12.498969,41.902191;12.501389,41.901612?radiuses=20;20;20;20;20&timestamps=1391371881;1391371882;1391372422;1391372747;1391372791" -- """

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0.122449

0

null

0

null

0

1

0

61c780171c158e14c78061e8324344923f5412cc

668

py

Python

lgy/algorithm/9251 LCS.py

Einere/boostcamp_study

63a52253c0ee01354a81dcac6349cc84d738b9ca

[ "MIT" ]

2

2019-06-25T14:18:34.000Z

2019-11-21T01:19:35.000Z

lgy/algorithm/9251 LCS.py

Einere/boostcamp_study

63a52253c0ee01354a81dcac6349cc84d738b9ca

[ "MIT" ]

null

lgy/algorithm/9251 LCS.py

Einere/boostcamp_study

63a52253c0ee01354a81dcac6349cc84d738b9ca

[ "MIT" ]

1

2019-06-26T05:09:39.000Z

import sys sys.path.append('.') from lgy.algorithm.StdIOTestContainer import StdIOTestContainer as T def main(): v1 = input() v2 = input() length = max(len(v1), len(v2)) dp = [[0 for i in range(len(v2) + 1)] for j in range(len(v1) + 1)] ans = 0 for i in range(1, len(v1) + 1): for j in range(1, len(v2) + 1): if v1[i-1:i] == v2[j-1:j]: dp[i][j] = dp[i-1][j-1] + 1 else: dp[i][j] = max(dp[i][j-1], dp[i-1][j]) print(dp[len(v1)][len(v2)]) print("ab"[0]) user_input = ''' ACAYKP CAPCAK ''' expected = ''' 4 ''' T.runningTest(user_input.strip(), expected.lstrip(), main)

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0.034985

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668

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0.08

0

null

0

null

0

1

0

61c87be5497f3881250ad3985df824448fa55d00

427

py

Python

md2html/weixin/pre_processor.py

g0man/md2html

c7f021298556e60984497464c1f523ac2443e868

[ "MIT" ]

1

2018-08-03T01:25:38.000Z

md2html/weixin/pre_processor.py

g0man/md2html

c7f021298556e60984497464c1f523ac2443e868

[ "MIT" ]

null

md2html/weixin/pre_processor.py

g0man/md2html

c7f021298556e60984497464c1f523ac2443e868

[ "MIT" ]

null

from markdown.preprocessors import Preprocessor class CalcReadingTimePreprocessor(Preprocessor): def __init__(self, cfg, *args, **kwargs): self.cfg = cfg super(CalcReadingTimePreprocessor, self).__init__(*args, **kwargs) def run(self, root): word_count = len(root) print("word count: %d" % word_count) self.cfg['READING_MINUTES'] = int(word_count/700) return root

30.5

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427

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427

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1

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false

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0.1

0

null

0

null

0

1

0

61c9624c9f9d3fcfa45f472861b7d2d95733b378

19,842

py

Python

data/scripts/model.py

Honorates/covid19_scenarios

c0d6d1a34d3650e9812744932b9b661ddac57643

[ "MIT" ]

3

2020-05-23T03:22:08.000Z

2020-05-23T03:22:16.000Z

data/scripts/model.py

Honorates/covid19_scenarios

c0d6d1a34d3650e9812744932b9b661ddac57643

[ "MIT" ]

null

data/scripts/model.py

Honorates/covid19_scenarios

c0d6d1a34d3650e9812744932b9b661ddac57643

[ "MIT" ]

1

2020-05-25T13:50:23.000Z

import csv import importlib import sys sys.path.append('..') import os import json import argparse import copy from enum import IntEnum from datetime import datetime import numpy as np import scipy.integrate as solve import scipy.optimize as opt import matplotlib.pylab as plt from scripts.tsv import parse as parse_tsv from scripts.R0_estimator import get_Re_guess from paths import BASE_PATH # ------------------------------------------------------------------------ # Globals PATH_UN_AGES = os.path.join(BASE_PATH, "../src/assets/data/ageDistribution.json") PATH_UN_CODES = os.path.join(BASE_PATH,"country_codes.csv") PATH_POP_DATA = os.path.join(BASE_PATH,"populationData.tsv") JAN1_2019 = datetime.strptime("2019-01-01", "%Y-%m-%d").toordinal() JUN1_2019 = datetime.strptime("2019-06-01", "%Y-%m-%d").toordinal() JAN1_2020 = datetime.strptime("2020-01-01", "%Y-%m-%d").toordinal() CASES = importlib.import_module(f"scripts.tsv") CASE_DATA = CASES.parse() def load_distribution(path): dist = {} with open(path, 'r') as fd: db = json.load(fd) for data in db["all"]: key = data["name"] ageDis = sorted(data["data"], key=lambda x: x["ageGroup"]) dist[key] = np.array([float(elt["population"]) for elt in ageDis]) dist[key] = dist[key]/np.sum(dist[key]) return dist def load_country_codes(path): db = {} with open(path, 'r') as fd: rdr = csv.reader(fd) next(rdr) for entry in rdr: db[entry[0]] = entry[2] return db def load_population_data(path): db = {} with open(path, 'r') as fd: rdr = csv.reader(fd, delimiter='\t') next(rdr) for entry in rdr: db[entry[0]] = {'size':int(entry[1]), 'ageDistribution':entry[2]} return db AGES = load_distribution(PATH_UN_AGES) POPDATA = load_population_data(PATH_POP_DATA) CODES = load_country_codes(PATH_UN_CODES) # ------------------------------------------------------------------------ # Indexing enums compartments = ['S', 'E1', 'E2', 'E3', 'I', 'H', 'C', 'D', 'R', 'T', 'NUM'] Sub = IntEnum('Sub', compartments, start=0) groups = ['_0', '_1', '_2', '_3', '_4', '_5', '_6', '_7', '_8', 'NUM'] Age = IntEnum('Age', groups , start=0) # ------------------------------------------------------------------------ # Organizational classes class Data(object): def __str__(self): return str({k : str(v) for k, v in self.__dict__.items()}) class Rates(Data): def __init__(self, latency, logR0, infection, hospital, critical, imports, efficacy): self.latency = latency self.logR0 = logR0 self.infectivity = np.exp(self.logR0) * infection self.infection = infection self.hospital = hospital self.critical = critical self.imports = imports self.efficacy = efficacy # NOTE: Pulled from default severe table on neherlab.org/covid19 # Keep in sync! # TODO: Allow custom values? class Fracs(Data): confirmed = np.array([5, 5, 10, 15, 20, 25, 30, 40, 50]) / 100 severe = np.array([1, 3, 3, 3, 6, 10, 25, 35, 50]) / 100 severe *= confirmed critical = np.array([5, 10, 10, 15, 20, 25, 35, 45, 55]) / 100 fatality = np.array([30, 30, 30, 30, 30, 40, 40, 50, 50]) / 100 recovery = 1 - severe discharge = 1 - critical stabilize = 1 - fatality def __init__(self, reported=1/30): self.reported = reported class TimeRange(Data): def __init__(self, day0, start, end, delta=1): self.day0 = day0 self.start = start self.end = end self.delta = delta class Params(Data): def __init__(self, ages=None, size=None, date=None, times=None, rates=None, fracs=None): self.ages = ages self.rates = rates self.fracs = fracs self.size = size self.time = times self.date = date # Make infection function beta = self.rates.infectivity self.rates.infectivity = lambda t,date,eff : beta if t<date else beta*(1-eff) # ------------------------------------------------------------------------ # Default parameters DefaultRates = Rates(latency=1/3.0, logR0=1.0, infection=1/3.0, hospital=1/3.0, critical=1/14, imports=.1, efficacy=0.5) RateFields = [ f for f in dir(DefaultRates) \ if not callable(getattr(DefaultRates, f)) \ and not f.startswith("__") ] RateFields.remove('infectivity') # ------------------------------------------------------------------------ # Functions # ------------------------------------------ # Modeling def make_evolve(params): # Equations for coupled ODEs def evolve(t, pop): pop2d = np.reshape(pop, (Sub.NUM, Age.NUM)) fracI = pop2d[Sub.I, :].sum() / params.size dpop = np.zeros_like(pop2d) flux_S = params.rates.infectivity(t, params.date, params.rates.efficacy)*fracI*pop2d[Sub.S] + (params.rates.imports / Sub.NUM) flux_E1 = params.rates.latency*pop2d[Sub.E1]*3 flux_E2 = params.rates.latency*pop2d[Sub.E2]*3 flux_E3 = params.rates.latency*pop2d[Sub.E3]*3 flux_I_R = params.rates.infection*params.fracs.recovery*pop2d[Sub.I] flux_I_H = params.rates.infection*params.fracs.severe*pop2d[Sub.I] flux_H_R = params.rates.hospital*params.fracs.discharge*pop2d[Sub.H] flux_H_C = params.rates.hospital*params.fracs.critical*pop2d[Sub.H] flux_C_H = params.rates.critical*params.fracs.stabilize*pop2d[Sub.C] flux_C_D = params.rates.critical*params.fracs.fatality*pop2d[Sub.C] # Add fluxes to states dpop[Sub.S] = -flux_S dpop[Sub.E1] = +flux_S - flux_E1 dpop[Sub.E2] = +flux_E1 - flux_E2 dpop[Sub.E3] = +flux_E2 - flux_E3 dpop[Sub.I] = +flux_E3 - flux_I_R - flux_I_H dpop[Sub.H] = +flux_I_H + flux_C_H - flux_H_R - flux_H_C dpop[Sub.C] = +flux_H_C - flux_C_D - flux_C_H dpop[Sub.R] = +flux_H_R + flux_I_R dpop[Sub.D] = +flux_C_D dpop[Sub.T] = +flux_E3*params.fracs.reported return np.reshape(dpop, Sub.NUM*Age.NUM) return evolve def init_pop(ages, size, cases): pop = np.zeros((Sub.NUM, Age.NUM)) ages = np.array(ages) / np.sum(ages) pop[Sub.S, :] = size * ages pop[Sub.S, :] -= cases*ages pop[Sub.I, :] += cases*ages*0.3 pop[Sub.E1, :] += cases*ages*0.7/3 pop[Sub.E2, :] += cases*ages*0.7/3 pop[Sub.E3, :] += cases*ages*0.7/3 return pop def solve_ode(params, init_pop): t_beg = params.time[0] num_tp = len(params.time) evolve = make_evolve(params) solver = solve.ode(evolve) # TODO: Add Jacobian solver.set_initial_value(init_pop.flatten(), t_beg) solution = np.zeros((num_tp, init_pop.shape[0], init_pop.shape[1])) solution[0, :, :] = init_pop i = 1 while solver.successful() and i<num_tp: solution[i, :, :] = np.reshape(solver.integrate(params.time[i]), (Sub.NUM, Age.NUM)) i += 1 return solution def trace_ages(solution): return solution.sum(axis=-1) # ------------------------------------------ # Parameter estimation def is_cumulative(vec): return not False in (vec[~vec.mask][:-1]<=vec[~vec.mask][1:]) def poissonNegLogLH(n,lam, eps=0.1): L = np.abs(lam) N = np.abs(n) return (L-N) - N*np.log((L+eps)/(N+eps)) def assess_model(params, data, cases): sol = solve_ode(params, init_pop(params.ages, params.size, cases)) model = trace_ages(sol) eps = 1e-2 diff_cases = data[Sub.T][3:] - data[Sub.T][:-3] diff_cases_model = model[3:, Sub.T] - model[:-3, Sub.T] case_cost = np.ma.sum(poissonNegLogLH(diff_cases, diff_cases_model, eps)) diff_deaths = data[Sub.D][3:] - data[Sub.D][:-3] diff_deaths_model = model[3:, Sub.D] - model[:-3, Sub.D] death_cost = np.ma.sum(poissonNegLogLH(diff_deaths, diff_deaths_model, eps)) hospital_cost = 0 ICU_cost = 0 if data[Sub.H] is not None: hospital_cost = np.ma.sum(poissonNegLogLH(data[Sub.H], model[:,Sub.H], eps)) if data[Sub.C] is not None: ICU_cost = np.ma.sum(poissonNegLogLH(data[Sub.C], model[:,Sub.C], eps)) return case_cost + 10*death_cost # + hospital_cost + ICU_cost # Any parameters given in guess are fit. The remaining are fixed and set by DefaultRates def fit_params(key, time_points, data, guess, fixed_params=None, bounds=None): if fixed_params is None: fixed_params = {} if key not in POPDATA: return (Params(ages=None, size=None, date=None, times=None, rates=DefaultRates, fracs=Fracs()), 10, (False, "Not within population database")) params_to_fit = {key : i for i, key in enumerate(guess.keys())} def pack(x, as_list=False): data = [x[key] for key in params_to_fit.keys()] if not as_list: return np.array(data) return data def unpack(x): vals = {} for f in RateFields: if f in guess: vals[f] = x[params_to_fit[f]] elif f in fixed_params: vals[f] = fixed_params[f] else: vals[f] = getattr(DefaultRates, f) return Rates(**vals), Fracs(x[params_to_fit['reported']]) if 'reported' in params_to_fit else Fracs() def fit(x): # TODO(nnoll): Need a better default here! if POPDATA[key]["ageDistribution"] in AGES: ages = AGES[POPDATA[key]["ageDistribution"]] else: ages = AGES["Switzerland"] rates, fracs = unpack(x) param = Params(ages=AGES[POPDATA[key]["ageDistribution"]], size=POPDATA[key]["size"], date=fixed_params.get('containment_start', None), times=time_points, rates=rates, fracs=fracs) return assess_model(param, data, np.exp(x[params_to_fit['logInitial']])) if bounds is None: fit_param = opt.minimize(fit, pack(guess), method='Nelder-Mead') else: fit_param = opt.minimize(fit, pack(guess), method='L-BFGS-B', bounds=bounds) err = (fit_param.success, fit_param.message) print(key, fit_param.x) if POPDATA[key]["ageDistribution"] in AGES: ages = AGES[POPDATA[key]["ageDistribution"]] else: ages = AGES["Switzerland"] rates, fracs = unpack(fit_param.x) return (Params(ages=AGES[POPDATA[key]["ageDistribution"]], size=POPDATA[key]["size"], date=fixed_params.get('containment_start', None), times=time_points, rates=rates, fracs=fracs), np.exp(fit_param.x[params_to_fit['logInitial']]), err) # ------------------------------------------ # Data loading def load_data(key, ts): if key in POPDATA: popsize = POPDATA[key]["size"] else: popsize = 1e6 case_min = 20 data = [[] if (i == Sub.D or i == Sub.T or i == Sub.H or i == Sub.C) else None for i in range(Sub.NUM)] days = [] for tp in ts: #replace all zeros by np.nan data[Sub.T].append(tp['cases'] or np.nan) data[Sub.H].append(tp['hospitalized'] or np.nan) data[Sub.D].append(tp['deaths'] or np.nan) data[Sub.C].append(tp['icu'] or np.nan) data = [ np.ma.array(d) if d is not None else d for d in data] good_idx = np.array(np.logical_or(case_min <= data[Sub.T], case_min <= data[Sub.D])) for ii in [Sub.D, Sub.T, Sub.H, Sub.C]: data[ii] = data[ii][good_idx] data[ii].mask = np.isnan(data[ii]) if False not in data[ii].mask: data[ii] = None days = np.array([datetime.strptime(d['time'].split('T')[0], "%Y-%m-%d").toordinal() for d in ts]) return days[good_idx], data def get_fit_data(days, data_original, end_discard=3): """ Select the relevant part of the data for the fitting procedure. The early datapoints where there is less than 20 cases are removed. The last 3 days are also removed (due to latency of reporting) """ data = copy.deepcopy(data_original) case_min = 20 day0 = days[case_min <= data[Sub.T]][0] # Filter points good_idx = np.bitwise_and(days >= day0, days < days[-1] - end_discard) for idx in [Sub.D, Sub.T, Sub.H, Sub.C]: if data[idx] is None: data[idx] = np.ma.array([np.nan]) data[idx].mask = np.isnan(data[idx]) else: data[idx] = np.ma.array(np.concatenate([[np.nan], data[idx][good_idx]])) data[idx].mask = np.isnan(data[idx]) for ii in [Sub.T, Sub.D, Sub.H, Sub.C]: # remove data if whole array is masked if False not in data[ii].mask: data[ii] = None # start the model 3 weeks prior. time = np.concatenate(([day0-14], days[good_idx])) return time, data def fit_population_iterative(key, time_points, data, guess=None, second_fit=False, FRA=False): """ Iterative fitting procedure. First, R_effective is estimated from the data and fitted using a stair function to deduce R0, the containment start date and the efficacy of the containement. Secondly, these parameters are used to optimize the reported fraction and the initial number of infected people using the fit_params function. """ if data is None or data[Sub.D] is None or len(data[Sub.D]) <= 14: return None res = get_Re_guess(time_points, data, only_deaths=FRA) fit = res['fit'] if fit is None or fit[0]<1 or fit[0]>6 or fit[1]>fit[0] or fit[1]<0: return None fixed_params = {} fixed_params['logR0'] = np.log(fit[0]) fixed_params['efficacy'] = 1-fit[1]/fit[0] fixed_params['containment_start'] = fit[2] if guess is None: guess = { "reported" : 0.1, "logInitial" : 1, } bounds=None for ii in [Sub.T, Sub.D]: if not is_cumulative(data[ii]): print("Cases / deaths count is not cumulative.", data[ii]) t1 = datetime.now().timestamp() param, init_cases, err = fit_params(key, time_points, data, guess, fixed_params, bounds=bounds) t2 = datetime.now().timestamp() print(round(t2 - t1,2), fixed_params) if second_fit: guess = { "reported" : param.fracs.reported, "logInitial" : np.log(init_cases), "logR0": param.rates.logR0, "efficacy": param.rates.efficacy } param, init_cases, err = fit_params(key, time_points, data, guess, {'containment_start':fixed_params['containment_start']}, bounds=None) t3 = datetime.now().timestamp() print(round(t3 - t2, 2), fixed_params) tMin = datetime.strftime(datetime.fromordinal(time_points[0]), '%Y-%m-%d') res = {'params': param, 'initialCases': init_cases, 'tMin': tMin, 'data': data, 'error':err} if param.date is not None: res['containment_start'] = datetime.fromordinal(int(param.date)).strftime('%Y-%m-%d') return res def fit_population(key, time_points, data, containment_start=None, guess=None): if data is None or data[Sub.D] is None or len(data[Sub.D]) <= 5: return None if guess is None: guess = { "logR0": 1.0, "reported" : 0.2, "logInitial" : 1, "efficacy" : 0.8 } # bounds = ((0.4,2),(0.01,0.8),(1,None),(0,1)) bounds=None for ii in [Sub.T, Sub.D]: if not is_cumulative(data[ii]): print("Cases / deaths count is not cumulative.", data[ii]) param, init_cases, err = fit_params(key, time_points, data, guess, {'containment_start':containment_start}, bounds=bounds) tMin = datetime.strftime(datetime.fromordinal(time_points[0]), '%Y-%m-%d') res = {'params': param, 'initialCases': init_cases, 'tMin': tMin, 'data': data, 'error':err} if param.date is not None: res['containment_start'] = datetime.fromordinal(param.date).strftime('%Y-%m-%d') return res # ------------------------------------------------------------------------ # Testing entry def fit_error(data, model): err = [[] if (i == Sub.D or i == Sub.T or i == Sub.H or i == Sub.C) else None for i in range(Sub.NUM)] eps = 1e-2 for idx in [Sub.T, Sub.D, Sub.H, Sub.C]: if data[idx] is not None: err[idx] = poissonNegLogLH(data[idx], model[:,idx], eps) return err if __name__ == "__main__": parser = argparse.ArgumentParser(description = "", usage="fit data") parser.add_argument('--key', type=str, help="key for region, e.g 'USA-California'") args = parser.parse_args() # NOTE: For debugging purposes only # rates = DefaultRates # fracs = Fracs() # times = TimeRange(0, 100) # param = Params(AGES[COUNTRY], POPDATA[make_key(COUNTRY, REGION)], times, rates, fracs) # model = trace_ages(solve_ode(param, init_pop(param.ages, param.size, 1))) key = args.key or "USA-New York" # key = "CHE-Basel-Stadt" # key = "DEU-Berlin" # Raw data and time points time, data = load_data(key, CASE_DATA[key]) model_tps, fit_data = get_fit_data(time, data) # Fitting over the pre-confinement days res = fit_population_iterative(key, model_tps, fit_data, FRA=False) model = trace_ages(solve_ode(res['params'], init_pop(res['params'].ages, res['params'].size, res['initialCases']))) err = fit_error(fit_data, model) time -= res['params'].time[0] tp = res['params'].time - res['params'].time[0] # plt.figure() # plt.title(f"{key}") # plt.plot(time, data[Sub.T], 'o', color='#a9a9a9', label="cases") # plt.plot(tp, model[:,Sub.T], color="#a9a9a9", label="predicted cases") # # plt.plot(time, data[Sub.D], 'o', color="#cab2d6", label="deaths") # plt.plot(tp, model[:,Sub.D], color="#cab2d6", label="predicated deaths") # # plt.plot(time, data[Sub.H], 'o', color="#fb9a98", label="Hospitalized") # plt.plot(tp, model[:,Sub.H], color="#fb9a98", label="Predicted hospitalized") # # plt.plot(time, data[Sub.C], 'o', color="#e31a1c", label="ICU") # plt.plot(tp, model[:,Sub.C], color="#e31a1c", label="Predicted ICU") # # plt.plot(tp, model[:,Sub.I], color="#fdbe6e", label="infected") # plt.plot(tp, model[:,Sub.R], color="#36a130", label="recovered") # # # plt.xlabel("Time [days]") # plt.ylabel("Number of people") # plt.legend(loc="best") # plt.tight_layout() # # plt.yscale('log') # # plt.ylim([-100,1000]) # plt.savefig("Basel-Stadt", format="png") # plt.show() plt.figure() plt.title(f"{key}") plt.plot(time, data[Sub.T], 'o', color='#a9a9a9', label="cases") plt.plot(tp, model[:,Sub.T], color="#a9a9a9", label="predicted cases") plt.plot(tp, err[Sub.T], '--', color="#a9a9a9", label="cases error") plt.plot(time, data[Sub.D], 'o', color="#cab2d6", label="deaths") plt.plot(tp, model[:,Sub.D], color="#cab2d6") plt.plot(tp, err[Sub.D], '--', color="#cab2d6") if data[Sub.H] is not None: plt.plot(time, data[Sub.H], 'o', color="#fb9a98", label="Hospitalized") plt.plot(tp, model[:,Sub.H], color="#fb9a98") plt.plot(tp, err[Sub.H], '--', color="#fb9a98") if data[Sub.C] is not None: plt.plot(time, data[Sub.C], 'o', color="#e31a1c", label="ICU") plt.plot(tp, model[:,Sub.C], color="#e31a1c") plt.plot(tp, err[Sub.C], '--', color="#e31a1c") plt.plot(tp, model[:,Sub.I], color="#fdbe6e", label="infected") plt.plot(tp, model[:,Sub.R], color="#36a130", label="recovered") plt.xlabel("Time [days]") plt.ylabel("Number of people") plt.legend(loc="best") plt.tight_layout() plt.yscale("log") # plt.savefig(f"{key}-Poisson_max_likelihood", format="png") plt.show()

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null

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61c99cc2811581dd1cdf3ced3f55a324a3a575c9

3,396

py

Python

mxfold2/fold/fold.py

n-mikamo/mxfold2

8c195c77f824bdd5899d3d01d6a096de95cd0e9b

[ "MIT" ]

46

2020-09-17T04:50:22.000Z

2022-03-22T08:14:15.000Z

mxfold2/fold/fold.py

n-mikamo/mxfold2

8c195c77f824bdd5899d3d01d6a096de95cd0e9b

[ "MIT" ]

7

2021-02-09T10:09:03.000Z

2022-01-14T21:19:02.000Z

mxfold2/fold/fold.py

n-mikamo/mxfold2

8c195c77f824bdd5899d3d01d6a096de95cd0e9b

[ "MIT" ]

20

2020-10-15T09:03:59.000Z

2022-03-09T07:16:20.000Z

import torch import torch.nn as nn import torch.nn.functional as F class AbstractFold(nn.Module): def __init__(self, predict, partfunc): super(AbstractFold, self).__init__() self.predict = predict self.partfunc = partfunc def clear_count(self, param): param_count = {} for n, p in param.items(): if n.startswith("score_"): param_count["count_"+n[6:]] = torch.zeros_like(p) param.update(param_count) return param def calculate_differentiable_score(self, v, param, count): s = 0 for n, p in param.items(): if n.startswith("score_"): s += torch.sum(p * count["count_"+n[6:]].to(p.device)) s += v - s.item() return s def forward(self, seq, return_param=False, param=None, return_partfunc=False, max_internal_length=30, max_helix_length=30, constraint=None, reference=None, loss_pos_paired=0.0, loss_neg_paired=0.0, loss_pos_unpaired=0.0, loss_neg_unpaired=0.0): param = self.make_param(seq) if param is None else param # reuse param or not ss = [] preds = [] pairs = [] pfs = [] bpps = [] for i in range(len(seq)): param_on_cpu = { k: v.to("cpu") for k, v in param[i].items() } param_on_cpu = self.clear_count(param_on_cpu) with torch.no_grad(): v, pred, pair = self.predict(seq[i], param_on_cpu, max_internal_length=max_internal_length if max_internal_length is not None else len(seq[i]), max_helix_length=max_helix_length, constraint=constraint[i].tolist() if constraint is not None else None, reference=reference[i].tolist() if reference is not None else None, loss_pos_paired=loss_pos_paired, loss_neg_paired=loss_neg_paired, loss_pos_unpaired=loss_pos_unpaired, loss_neg_unpaired=loss_neg_unpaired) if return_partfunc: pf, bpp = self.partfunc(seq[i], param_on_cpu, max_internal_length=max_internal_length if max_internal_length is not None else len(seq[i]), max_helix_length=max_helix_length, constraint=constraint[i].tolist() if constraint is not None else None, reference=reference[i].tolist() if reference is not None else None, loss_pos_paired=loss_pos_paired, loss_neg_paired=loss_neg_paired, loss_pos_unpaired=loss_pos_unpaired, loss_neg_unpaired=loss_neg_unpaired) pfs.append(pf) bpps.append(bpp) if torch.is_grad_enabled(): v = self.calculate_differentiable_score(v, param[i], param_on_cpu) ss.append(v) preds.append(pred) pairs.append(pair) device = next(iter(param[0].values())).device ss = torch.stack(ss) if torch.is_grad_enabled() else torch.tensor(ss, device=device) if return_param: return ss, preds, pairs, param elif return_partfunc: return ss, preds, pairs, pfs, bpps else: return ss, preds, pairs

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4edee1ef5f03774068d2d400a1fd45f888b489f3

3,651

py

Python

test/connectivity/acts/tests/google/ble/api/GattApiTest.py

Keneral/atools

055e76621340c7dced125e9de56e2645b5e1cdfb

[ "Unlicense" ]

null

test/connectivity/acts/tests/google/ble/api/GattApiTest.py

Keneral/atools

055e76621340c7dced125e9de56e2645b5e1cdfb

[ "Unlicense" ]

null

test/connectivity/acts/tests/google/ble/api/GattApiTest.py

Keneral/atools

055e76621340c7dced125e9de56e2645b5e1cdfb

[ "Unlicense" ]

1

2018-02-24T19:13:01.000Z

#/usr/bin/env python3.4 # # Copyright (C) 2016 The Android Open Source Project # # 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. """ Test script to exercise Gatt Apis. """ from acts.controllers.android import SL4AAPIError from acts.test_utils.bt.BluetoothBaseTest import BluetoothBaseTest from acts.test_utils.bt.bt_test_utils import log_energy_info from acts.test_utils.bt.bt_test_utils import setup_multiple_devices_for_bt_test class GattApiTest(BluetoothBaseTest): def __init__(self, controllers): BluetoothBaseTest.__init__(self, controllers) self.ad = self.android_devices[0] def setup_class(self): return setup_multiple_devices_for_bt_test(self.android_devices) def setup_test(self): self.log.debug(log_energy_info(self.android_devices, "Start")) for a in self.android_devices: a.ed.clear_all_events() return True def teardown_test(self): self.log.debug(log_energy_info(self.android_devices, "End")) return True @BluetoothBaseTest.bt_test_wrap def test_open_gatt_server(self): """Test a gatt server. Test opening a gatt server. Steps: 1. Create a gatt server callback. 2. Open the gatt server. Expected Result: Api to open gatt server should not fail. Returns: Pass if True Fail if False TAGS: LE, GATT Priority: 1 """ gatt_server_cb = self.ad.droid.gattServerCreateGattServerCallback() self.ad.droid.gattServerOpenGattServer(gatt_server_cb) return True @BluetoothBaseTest.bt_test_wrap def test_open_gatt_server_on_same_callback(self): """Test repetitive opening of a gatt server. Test opening a gatt server on the same callback twice in a row. Steps: 1. Create a gatt server callback. 2. Open the gatt server. 3. Open the gatt server on the same callback as step 2. Expected Result: Api to open gatt server should not fail. Returns: Pass if True Fail if False TAGS: LE, GATT Priority: 2 """ gatt_server_cb = self.ad.droid.gattServerCreateGattServerCallback() self.ad.droid.gattServerOpenGattServer(gatt_server_cb) self.ad.droid.gattServerOpenGattServer(gatt_server_cb) return True @BluetoothBaseTest.bt_test_wrap def test_open_gatt_server_on_invalid_callback(self): """Test gatt server an an invalid callback. Test opening a gatt server with an invalid callback. Steps: 1. Open a gatt server with the gall callback set to -1. Expected Result: Api should fail to open a gatt server. Returns: Pass if True Fail if False TAGS: LE, GATT Priority: 2 """ invalid_callback_index = -1 try: self.ad.droid.gattServerOpenGattServer(invalid_callback_index) except SL4AAPIError as e: self.log.info("Failed successfully with exception: {}.".format(e)) return True return False

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0.5

0

null

0

null

0

1

0

4ee166755be43836f7c47c4a1c08fb0f9fe27932

663

py

Python

url/forms.py

mad-skull/URL-Shortener

09fdf179a2ae0f0f5bc9309e53c0aebf352a9b02

[ "MIT" ]

null

url/forms.py

mad-skull/URL-Shortener

09fdf179a2ae0f0f5bc9309e53c0aebf352a9b02

[ "MIT" ]

null

url/forms.py

mad-skull/URL-Shortener

09fdf179a2ae0f0f5bc9309e53c0aebf352a9b02

[ "MIT" ]

null

from flask_wtf import FlaskForm # , RecaptchaField from wtforms import validators, StringField from wtforms.validators import Length # from wtforms.fields.html5 import URLField class UrlForm(FlaskForm): old = StringField('Title', [ validators.InputRequired(), validators.Length( min=4, max=2027, message="If URL\'s were that short, would you even be here?") ]) # recaptcha = RecaptchaField() def save_url(self, url): self.populate_obj(url) if not "http" in url.old: url.old = "https://" + url.old if not "." in url.old: url.old = url.old + ".com/" return url

30.136364

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0.624434

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663

5.012195

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0.065693

0.087591

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0.26546

663

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91

31.571429

0.831622

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false

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0

0.4375

0

null

0

null

0

1

0

4ee1ab036fce9ec6e8e4a17a9395246651c2444f

645

py

Python

Leetcode/0051-0100/0053-maximum-subarray.py

harshbhandari7/Data-Structures-and-Algorithms

0ce0a5bc64d112ff38ae0de51d19ce3751b70eca

[ "MIT" ]

null

Leetcode/0051-0100/0053-maximum-subarray.py

harshbhandari7/Data-Structures-and-Algorithms

0ce0a5bc64d112ff38ae0de51d19ce3751b70eca

[ "MIT" ]

null

Leetcode/0051-0100/0053-maximum-subarray.py

harshbhandari7/Data-Structures-and-Algorithms

0ce0a5bc64d112ff38ae0de51d19ce3751b70eca

[ "MIT" ]

1

2019-10-06T15:46:14.000Z

''' Author : MiKueen Level : Easy Problem Statement : Maximum Subarray Given an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum. Example: Input: [-2,1,-3,4,-1,2,1,-5,4], Output: 6 Explanation: [4,-1,2,1] has the largest sum = 6. ''' class Solution: def maxSubArray(self, nums): """ :type nums: List[int] :rtype: int """ max_sum = curr = nums[0] for i in range(1, len(nums)): curr = max(nums[i], curr + nums[i]) max_sum = max(max_sum, curr) return max_sum

25.8

136

0.575194

94

645

3.904255

0.585106

0.065395

0.070845

0.087193

0

0.037694

0.300775

645

25

137

25.8

0.776053

0.525581

0

1

0.142857

false

0

0.428571

0

null

0

null

0

1

0

4ee4c9bda6034b23325fab78be4485408477098a

1,727

py

Python

faker_extras/biology.py

nyimbi/faker_extras

37967d9101af217f7671fc2ad071b49258711c29

[ "MIT" ]

null

faker_extras/biology.py

nyimbi/faker_extras

37967d9101af217f7671fc2ad071b49258711c29

[ "MIT" ]

null

faker_extras/biology.py

nyimbi/faker_extras

37967d9101af217f7671fc2ad071b49258711c29

[ "MIT" ]

1

2019-05-23T16:02:45.000Z

"""Faker data providers for biological data.""" from random import choice from faker.providers import BaseProvider from . import utils class GeneticProvider(BaseProvider): """Genomic data provider. Acid data source: http://www.cryst.bbk.ac.uk/education/AminoAcid/the_twenty.html """ acids = { 'alanine': 'ala', 'arginine': 'arg', 'asparagine': 'asn', 'aspartic acid': 'asp', 'cysteine': 'cys', 'glutamine': 'gln', 'glutamic acid': 'glu', 'glycine': 'gly', 'histidine': 'his', 'isoleucine': 'ile', 'leucine': 'leu', 'lysine': 'lys', 'methionine': 'met', 'phenylalanine': 'phe', 'proline': 'pro', 'serine': 'ser', 'threonine': 'thr', 'tryptophan': 'trp', 'tyrosine': 'tyr', 'valine': 'val', } def amino_acid_group(self): """Return an amino acid group.""" return choice([ 'Aliphatic', 'Aromatic', 'Acidic', 'Basic', 'Hydroxylic', 'Sulphur-containing', 'Amidic', ]) def amino_acid(self, symbol=True): """Return a random amino symbol or acid.""" if symbol: vals = self.acids.keys() return choice(vals) return choice(self.acids.keys()) def rna(self): """Return some RNA sequence. >>> rna() >>> AAACUAGCUG """ return utils._choice_str(['U', 'C', 'G', 'A'], 10) def dna(self): """Return some DNA sequence. >>> dna() >>> CTATAGAGCT """ return utils._choice_str(['T', 'C', 'G', 'A'], 10)

23.337838

66

0.491025

164

1,727

5.121951

0.621951

0.032143

0.028571

0.047619

0

0.003527

0.34337

1,727

73

67

23.657534

0.737213

0.182976

0

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1

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false

0

0.066667

0

0.311111

0

null

0

null

0

1

0

4eea6491efcd84bda94636814b33bd0e8caf7e12

351

py

Python

check_file.py

aideyisu/english_study

87f188655f9858dff32ea3f0dc5e86bc79c267ad

[ "MIT" ]

1

2021-08-14T13:42:15.000Z

check_file.py

aideyisu/english_study

87f188655f9858dff32ea3f0dc5e86bc79c267ad

[ "MIT" ]

null

check_file.py

aideyisu/english_study

87f188655f9858dff32ea3f0dc5e86bc79c267ad

[ "MIT" ]

null

''' 检查系统内文件是否完备 ''' from pathlib import Path import os basepath = os.path.dirname(__file__) # 当前文件所在路径 file_list = [] for file in file_list: my_file = Path(f'{basepath}/analysis_result/asd') if not my_file.exists(): # 检测是否存在id路径不存在 os.makedirs(my_file) # 只能创建单级目录 =.=对这个用法表示怀疑 print(f'路径不存在 {my_file} 创建路径')

18.473684

53

0.652422

46

351

4.73913

0.608696

0.110092

0

0.225071

351

19

54

18.473684

0.801471

0.162393

0

0.179211

0.107527

0

1

0

false

0

0.222222

0

0.222222

0.111111

0

null

0

null

0

1

0

4eeb090fcce31a4d12b4e9146d28f91e0bc82c1a

7,983

py

Python

neutron/tests/unit/plugins/wrs/test_extension_host.py

ericho/stx-neutron

d4a8ad548c4afed73269575c48526a704dd09a9c

[ "Apache-2.0" ]

4

2018-08-05T00:43:03.000Z

2021-10-13T00:45:45.000Z

neutron/tests/unit/plugins/wrs/test_extension_host.py

ericho/stx-neutron

d4a8ad548c4afed73269575c48526a704dd09a9c

[ "Apache-2.0" ]

8

2018-06-14T14:50:16.000Z

2018-11-13T16:30:42.000Z

neutron/tests/unit/plugins/wrs/test_extension_host.py

ericho/stx-neutron

d4a8ad548c4afed73269575c48526a704dd09a9c

[ "Apache-2.0" ]

7

2018-06-12T18:57:04.000Z

2019-05-09T15:42:30.000Z

# Copyright 2013 OpenStack Foundation # # 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. # # Copyright (c) 2013-2014 Wind River Systems, Inc. # import contextlib import copy import six import webob.exc from neutron_lib.utils import helpers as lib_helpers from oslo_log import log as logging from neutron.common import constants from neutron.tests.common import helpers from neutron.tests.unit.plugins.wrs import test_wrs_plugin LOG = logging.getLogger(__name__) HOST1 = {'name': 'compute-0', 'id': '065aa1d1-84ed-4d59-a777-16b0ea8a5640', 'availability': constants.HOST_UP} HOST2 = {'name': 'compute-1', 'id': '31df579d-d9ea-4623-a5a6-1bb0ccad22ef', 'availability': constants.HOST_DOWN} class HostTestCaseMixin(object): def _update_host(self, id, body): data = {'host': body} request = self.new_update_request('hosts', data, id) response = request.get_response(self.ext_api) return self.deserialize(self.fmt, response) def _bind_interface(self, id, body): data = {'interface': body} request = self.new_action_request('hosts', data, id, 'bind_interface') return request.get_response(self.ext_api) def _unbind_interface(self, id, body): data = {'interface': body} request = self.new_action_request('hosts', data, id, 'unbind_interface') return request.get_response(self.ext_api) def _create_host(self, host): data = {'host': {'name': host['name'], 'tenant_id': self._tenant_id}} for arg in ('id', 'availability'): data['host'][arg] = host[arg] request = self.new_create_request('hosts', data) return request.get_response(self.ext_api) def _make_host(self, host): response = self._create_host(host) if response.status_int >= 400: raise webob.exc.HTTPClientError(code=response.status_int) return self.deserialize(self.fmt, response) def _make_interface(self, id, interface): response = self._bind_interface(id, interface) if response.status_int >= 400: raise webob.exc.HTTPClientError(code=response.status_int) return self.deserialize(self.fmt, response) def _delete_interface(self, id, interface): response = self._unbind_interface(id, interface) if response.status_int >= 400: raise webob.exc.HTTPClientError(code=response.status_int) return self.deserialize(self.fmt, response) @contextlib.contextmanager def host(self, host, no_delete=False): host = self._make_host(host) try: yield host finally: if not no_delete: self._delete('hosts', host['host']['id']) def _create_test_interfaces(self, interfaces): self._interfaces = copy.deepcopy(interfaces) for name, host in six.iteritems(self._hosts): interface = self._interfaces.get(host['name']) if not interface: continue # Add to "sysinv" first self._host_driver.add_interface(host['name'], interface) # Then, add to the plugin self._make_interface(host['id'], interface) def _delete_test_interfaces(self): for name, host in six.iteritems(self._hosts): interface = self._interfaces.get(host['name']) if not interface: continue self._delete_interface(host['id'], interface) def _create_test_hosts(self, hosts): for host in hosts: data = self._make_host(host) self._hosts[host['name']] = data['host'] self._host_driver.add_host(data['host']) def _delete_test_hosts(self): for name, host in six.iteritems(self._hosts): self._delete('hosts', host['id']) self._hosts = [] def _get_pnet(self, name): return self._pnets.get(name, None) def _create_test_providernets(self, pnets, pnet_ranges): for pnet in pnets: data = self._make_pnet(pnet) self._pnets[pnet['name']] = data['providernet'] # create segmentation ranges for each provider network pnet_ranges.setdefault(pnet['name'], []) for pnet_range in pnet_ranges[pnet['name']]: data = self._make_pnet_range(data['providernet'], pnet_range) self._pnet_ranges[pnet_range['name']] = data def _delete_test_providernets(self): for name, pnet in six.iteritems(self._pnets): self._delete('wrs-provider/providernets', pnet['id']) self._pnets = [] def _register_avs_agent(self, host=None, mappings=None): agent = helpers._get_l2_agent_dict( host, constants.AGENT_TYPE_WRS_VSWITCH, 'neutron-avs-agent') agent['configurations']['mappings'] = mappings return helpers._register_agent(agent, self._plugin) def _create_l2_agents(self): for name, host in six.iteritems(self._hosts): iface = self._interfaces[name] mappings = ['%s:%s' % (p, iface['uuid']) for p in iface['providernets'].split(',')] mappings_dict = lib_helpers.parse_mappings(mappings, unique_values=False) self._register_avs_agent( host=name, mappings=mappings_dict) def _update_host_states(self): for name, host in six.iteritems(self._hosts): updates = {'availability': constants.HOST_UP} data = self._update_host(host['id'], updates) self._hosts[name] = data['host'] def _prepare_test_dependencies(self, hosts, providernets, providernet_ranges, interfaces): self._create_test_hosts(hosts) self._create_test_providernets(providernets, providernet_ranges) self._create_test_interfaces(interfaces) self._create_l2_agents() self._update_host_states() def _cleanup_test_dependencies(self): self._delete_test_interfaces() self._delete_test_hosts() self._delete_test_providernets() class HostTestCase(HostTestCaseMixin, test_wrs_plugin.WrsMl2PluginV2TestCase): def setUp(self, plugin=None, ext_mgr=None): self.host1 = HOST1 self.host2 = HOST2 super(HostTestCase, self).setUp() def tearDown(self): super(HostTestCase, self).tearDown() def test_create_host(self): with self.host(self.host1) as host: self.assertEqual(host['host']['name'], self.host1['name']) self.assertIsNotNone(host['host']['id']) def test_update_host(self): with self.host(self.host1) as host: self.assertEqual(host['host']['availability'], constants.HOST_UP) data = {'host': {'availability': constants.HOST_DOWN}} request = self.new_update_request('hosts', data, host['host']['id']) response = request.get_response(self.ext_api) self.assertEqual(response.status_int, 200) body = self.deserialize(self.fmt, response) self.assertEqual(body['host']['availability'], constants.HOST_DOWN)

38.014286

78

0.621571

925

7,983

5.144865

0.220541

0.021853

0.025005

0.022694

0.321286

0.276739

0.26161

0.238285

0.214541

0.170414

0

0.01392

0.271076

7,983

209

79

38.196172

0.803918

0.09182

0

0.207792

0

0.06971

0.013416

0

0.032468

1

0.155844

false

0

0.058442

0.006494

0.285714

0

null

0

null

0

1

0

4eeb19e644b70e38ac9382dd07fad932a634f7b9

1,792

py

Python

project4/task1_ground_truth.py

yushanweng/github_projects

e2263a04a37291b767014239c2c23fa25a0811bd

[ "MIT" ]

null

project4/task1_ground_truth.py

yushanweng/github_projects

e2263a04a37291b767014239c2c23fa25a0811bd

[ "MIT" ]

6

2020-05-18T05:02:09.000Z

2022-02-27T05:41:49.000Z

project4/task1_ground_truth.py

yushanweng/projects

e2263a04a37291b767014239c2c23fa25a0811bd

[ "MIT" ]

null

from pyspark import SparkContext import os import re import json import sys import time import logging import collections from itertools import combinations from itertools import product s_logger = logging.getLogger('py4j.java_gateway') s_logger.setLevel(logging.ERROR) sc = SparkContext('local[*]', 'task1') sc.setLogLevel('ERROR') input_file_path = '../../PycharmProjects/553hw3/train_review.json' textRDD = sc.textFile(input_file_path,20) # 20: number of partition output_file_path = '../../PycharmProjects/553hw3/task1truth.txt' #output_file_path=sys.argv[4] start = time.time() # def Jac_func(x): # x = (bus1,bus2) user_id_v1 =dict_pre_min[x[0]] user_id_v2 = dict_pre_min[x[1]] inter_set = set(user_id_v1).intersection(set(user_id_v2)) union_set= set(user_id_v1).union(set(user_id_v2)) sim=len(inter_set)/len(union_set) return (x,sim) # ground truth no minhash (no bands) pre_min=textRDD.map(lambda x: json.loads(x)).map(lambda x: (x['business_id'],[x['user_id']])).reduceByKey(lambda a,b: a+b) unique_bs_id=pre_min.map(lambda x:x[0]).collect() pair_list = [unique_bs_id] # print(pre_min) dict_pre_min={} pre=pre_min.collect() for i in pre: dict_pre_min.update({i[0]:i[1]}) unique_bs_id_comb=sc.parallelize(pair_list)\ .map(lambda x: list(combinations(sorted(x),2)))\ .flatMap(lambda x: x)\ .map(Jac_func)\ .filter(lambda x: x[1]>=0.05)\ .sortBy(lambda x:x[1])\ .collect() f = open(output_file_path, 'w') for i in unique_bs_id_comb: dict_result={} dict_result.update({"b1":i[0][0]}) dict_result.update({"b2": i[0][1]}) dict_result.update({"sim":i[1]}) json_string = json.dumps(dict_result) f.write(str(json_string)) f.write('\n') end= time.time() case_time = end - start print('Duration:',case_time)

28.444444

122

0.707589

299

1,792

4.016722

0.371237

0.039967

0.033306

0.048293

0.023314

0

0.026854

0.127232

1,792

63

123

28.444444

0.741049

0.06529

0

0.096465

0.053325

0

1

0.019608

false

0

0.196078

0

0.235294

0.019608

0

null

0

null

0

1

0

4eec905aa21dad34da72aba28caa72b9f1341da1

3,917

py

Python

preprocess_kb.py

ruinunca/NeuralDialog-ZSDG

c20359541036ea876a126d1c7c172b820476dcb2

[ "Apache-2.0" ]

null

preprocess_kb.py

ruinunca/NeuralDialog-ZSDG

c20359541036ea876a126d1c7c172b820476dcb2

[ "Apache-2.0" ]

null

preprocess_kb.py

ruinunca/NeuralDialog-ZSDG

c20359541036ea876a126d1c7c172b820476dcb2

[ "Apache-2.0" ]

1

2020-09-24T15:09:34.000Z

import os from argparse import ArgumentParser import json import copy import shutil def process_kb(in_kb): all_values = set({}) items = in_kb.get('items', {}) if items is None: items = {} for item in items: for key, value in item.items(): all_values.add(value) return sorted(all_values, key=len) def flatten_entities(in_entities_map): result = [] for key, values_list in in_entities_map.items(): for value in values_list: if isinstance(value, dict): result += map(lambda x: str(x).lower(), value.values()) else: result.append(str(value).lower()) return sorted(result, key=len, reverse=True) def extract_entities(in_utterance, in_kb_entries): result = set([]) for kb_entry in in_kb_entries: if kb_entry in in_utterance: in_utterance = in_utterance.replace(kb_entry, '__entity__') result.add(kb_entry) return result def extract_entities_from_dialog(in_dialog, in_entities): result = set([]) for turn in in_dialog['dialogue']: result.update(extract_entities(turn['data']['utterance'], in_entities)) return result def kb_entry_contains_all_entities(in_kb_entry, in_entities): found = set([]) kb_entry_str = json.dumps(in_kb_entry) for entity in in_entities: if entity in kb_entry_str: found.add(entity) return len(found) == len(in_entities) def delexicalize_dialog(in_dialog, in_entities_list): result = copy.deepcopy(in_dialog) result['scenario']['kb'] = json.loads(json.dumps(result['scenario']['kb']).lower()) for turn in result['dialogue']: turn['data']['utterance'] = turn['data']['utterance'].lower() dialog_entities = extract_entities_from_dialog(in_dialog, in_entities_list) if result['scenario']['kb']['items']: for entry in result['scenario']['kb']['items']: if kb_entry_contains_all_entities(entry, dialog_entities): result['scenario']['kb']['items'] = [entry] print('New kb: {}'.format(json.dumps(entry))) break return result def process_dataset(in_dataset_folder): datasets = {} for dataset_name in ['train', 'dev', 'test']: filename = 'kvret_{}_public.json'.format(dataset_name) with open(os.path.join(in_dataset_folder, filename)) as dataset_in: datasets[filename] = json.load(dataset_in) with open(os.path.join(in_dataset_folder, 'kvret_entities.json')) as entities_in: entities = json.load(entities_in) entities_flat = flatten_entities(entities) for dataset_name, dataset in datasets.items(): for idx, dialog in enumerate(dataset): dataset[idx] = delexicalize_dialog(dialog, entities_flat) return datasets def save_dataset(in_src_folder, in_tgt_folder, in_datasets): if not os.path.exists(in_tgt_folder): os.makedirs(in_tgt_folder) for filename in os.listdir(in_src_folder): if filename not in in_datasets: if os.path.isdir(os.path.join(in_src_folder, filename)): shutil.copytree(os.path.join(in_src_folder, filename), os.path.join(in_tgt_folder, filename)) else: shutil.copy(os.path.join(in_src_folder, filename), in_tgt_folder) else: with open(os.path.join(in_tgt_folder, filename), 'w') as json_out: json.dump(in_datasets[filename], json_out) def configure_argument_parser(): parser = ArgumentParser() parser.add_argument('dataset_folder') parser.add_argument('output_folder') return parser if __name__ == '__main__': parser = configure_argument_parser() args = parser.parse_args() datasets = process_dataset(args.dataset_folder) save_dataset(args.dataset_folder, args.output_folder, datasets)

34.359649

87

0.658667

514

3,917

4.745136

0.200389

0.0451

0.0287

0.03444

0.159492

0.138171

0.121771

0.062321

0

0.225428

3,917

113

88

34.663717

0.803889

0

0.087912

0

0.059244

0

1

0.098901

false

0

0.054945

0

0.241758

0.010989

0

null

0

null

0

1

0

4eeebae26c14fcd330b4a36375b13a6878c29835

1,038

py

Python

Prettified Stopwatch.py

adrian88szymanski/Automate_the_Boring_Stuff_with_Python_by_Sweigart

a5fa40a3e27c4f0c79d6406926456c3d1a54c0c1

[ "MIT" ]

1

2020-08-15T11:36:24.000Z

Prettified Stopwatch.py

adrian88szymanski/Automate_the_Boring_Stuff_with_Python_by_Sweigart

a5fa40a3e27c4f0c79d6406926456c3d1a54c0c1

[ "MIT" ]

2

2022-01-13T03:18:08.000Z

2022-03-12T00:48:23.000Z

Prettified Stopwatch.py

adrian88szymanski/Sweigart_tasks

a5fa40a3e27c4f0c79d6406926456c3d1a54c0c1

[ "MIT" ]

null

#! python3 """A stopwatch program with a prettier output and pyperclip functionality.""" import time import pyperclip # Display the programs instructions. print('Press ENTER to begin. Afterwards, press ENTER to "click" the stopwatch.' 'Press Ctrl-c to quit.') input() print('Started.') start_time = time.time() last_time = start_time lap_num = 1 # Start tracking the lap times. try: while True: input() lap_time = round(time.time() - last_time, 2) total_time = round(time.time() - start_time, 2) lap = 'lap # {} {} ({})'.format((str(lap_num)+ ':').ljust(3), str(total_time).rjust(5), str(lap_time).rjust(6)) print(lap, end='') lap_num += 1 last_time = time.time() # Reset the last lap time. pyperclip.copy(lap) # Copy latest lap to clipboard. except KeyboardInterrupt: # Handle the Ctrl-C exception to keep its error message from displaying. print('\nDone.')

28.833333

79

0.595376

132

1,038

4.583333

0.484848

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null

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1

0

4eefc422a0f1ecd6f321d7742b73d4e9a3dbaace

2,768

py

Python

src/tspf/main.py

Javernaver/ProyectoTitulo

de8406b13bf62c3f96409ce95675c95a9e00c7f1

[ "Apache-2.0" ]

2

2022-01-28T02:15:55.000Z

2022-01-28T02:16:00.000Z

src/tspf/main.py

Javernaver/TSP-Framework

de8406b13bf62c3f96409ce95675c95a9e00c7f1

[ "Apache-2.0" ]

null

src/tspf/main.py

Javernaver/TSP-Framework

de8406b13bf62c3f96409ce95675c95a9e00c7f1

[ "Apache-2.0" ]

null

"""Modulo principal que utiliza todas las demas clases para ejecutar el framework""" from .Algorithms import GeneticAlgorithm, SimulatedAnnealing, LocalSearch, IteratedLocalSearch, timer from . import sys, os, AlgorithmsOptions, MHType, Tsp, Tour, bcolors def main(argv=sys.argv) -> None: """ Funcion principal que ejecuta el framework algoritmos metaheristicos para resolver el problema del vendedor viajero (TSP) """ # Activa la secuencia VT100 en Windows 10 para que funcione ANSI y se puedan cambiar se color los textos en cmd y powershell os.system('') #bcolors.disable(bcolors) start = timer() # tiempo inicial de ejecucion # leer e inicializar las opciones options = AlgorithmsOptions(argv=argv) # leer e interpretar el problema TSP leido desde la instancia definida problem = Tsp(filename=options.instance) # Ejecutar Metaheuristica Simulated Annealing if (options.metaheuristic == MHType.SA): # Solucion inicial first_solution = Tour(type_initial_sol=options.initial_solution, problem=problem) # Crear solver solver = SimulatedAnnealing(options=options, problem=problem) # Ejecutar la busqueda solver.search(first_solution) # Ejecutar Metaheuristica Algoritmo Genetico elif (options.metaheuristic == MHType.GA): # Crear solver solver = GeneticAlgorithm(options=options, problem=problem) # Ejecutar la busqueda solver.search() elif (options.metaheuristic == MHType.LS): # Solucion inicial first_solution = Tour(type_initial_sol=options.initial_solution, problem=problem) # Crear solver solver = LocalSearch(options=options, problem=problem) # Ejecutar la busqueda solver.search(first_solution) elif (options.metaheuristic == MHType.ILS): # Solucion inicial first_solution = Tour(type_initial_sol=options.initial_solution, problem=problem) # Crear solver solver = IteratedLocalSearch(options=options, problem=problem) # Ejecutar la busqueda solver.search(first_solution) else: # Crear solver solver = GeneticAlgorithm(options=options, problem=problem) # Ejecutar la busqueda solver.search() # Guardar la solucion y trayectoria en archivo solver.printSolFile(options.solution) solver.printTraFile(options.trajectory) # Escribir la solucion por consola solver.print_best_solution() end = timer() # tiempo final de ejecucion print(f"{bcolors.BOLD}Tiempo total de ejecucion: {bcolors.ENDC}{bcolors.OKBLUE} {end-start:.3f} segundos{bcolors.ENDC}") if options.visualize: solver.visualize(options.replit)

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2,768

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0.125

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null

0

null

0

1

0

4ef1fb454c62d9675e2048e32596b30d112e5f10

4,838

py

Python

python_tools/test/test_memory_engine.py

ultimatezen/felix

5a7ad298ca4dcd5f1def05c60ae3c84519ec54c4

[ "MIT" ]

null

python_tools/test/test_memory_engine.py

ultimatezen/felix

5a7ad298ca4dcd5f1def05c60ae3c84519ec54c4

[ "MIT" ]

null

python_tools/test/test_memory_engine.py

ultimatezen/felix

5a7ad298ca4dcd5f1def05c60ae3c84519ec54c4

[ "MIT" ]

null

""" Tests for MemoryEngine::RemoteMemory """ import unittest import cPickle from cStringIO import StringIO import mock from FelixMemoryServes import MemoryEngine class WebMocker(unittest.TestCase): def set_request_val(self, val): self.req_val = cPickle.dumps(val) def get_request(self, x): return StringIO(self.req_val) def setUp(self): # mock COM stuff self.wrap = MemoryEngine.util.wrap self.unwrap = MemoryEngine.util.unwrap self.coll = MemoryEngine.util.NewCollection MemoryEngine.util.wrap = lambda x : x MemoryEngine.util.unwrap = lambda x : x MemoryEngine.util.NewCollection = lambda x : x # mock urllib stuff self.urlencode = MemoryEngine.urllib.urlencode self.Request = MemoryEngine.urllib2.Request self.urlopen = MemoryEngine.urllib2.urlopen MemoryEngine.urllib2.Request = lambda x, y : (x, y) self.req_val = cPickle.dumps(None) MemoryEngine.urllib2.urlopen = self.get_request MemoryEngine.urllib.urlencode = lambda x : x self.engine = MemoryEngine.FelixRemoteMemory() def tearDown(self): MemoryEngine.util.unwrap = self.unwrap MemoryEngine.util.wrap = self.wrap MemoryEngine.util.NewCollection = self.coll MemoryEngine.urllib2.Request = self.Request MemoryEngine.urllib2.urlopen = self.urlopen MemoryEngine.urllib.urlencode = self.urlencode class TestRecordById(WebMocker): def test_no_url(self): self.engine.commands = {} result = self.engine.RecordById(3) assert result is None, result def test_None_result(self): self.engine.commands = dict(rec_by_id="foo") result = self.engine.RecordById(3) assert result is None, result def test_a_b(self): self.engine.commands = dict(rec_by_id="foo") self.set_request_val(dict(source="a", trans="b")) result = self.engine.RecordById(3) assert result.Source() == "a", result.Source() assert result.Trans() == "b", result.Trans() class TestRecToRaw(WebMocker): def test_a_b(self): record = mock.Mock() record.data = dict(source="raw a", trans="raw b", context="context", created_by="Ryan", modified_by="Sam") record.commands = {} data = self.engine.rec_to_raw(record) assert data == record.data, data def test_a_b_record2dict(self): class Foo: pass record = Foo() record.data = dict(source="raw a", trans="raw b", context="context") record.Source = u"record2d source" record.Trans = u"record2d trans" record.Context = u"record2d context" record.Reliability = 5 record.Validated = 4 record.RefCount= 3 record.Created = 2 record.Modified = 1 record.CreatedBy = u"Created by" record.ModifiedBy = u"Modified by" record.Id = 3 record.commands = {} data = self.engine.rec_to_raw(record) assert data["source"] == "record2d source", data assert data["trans"] == "record2d trans", data assert data["context"] == "record2d context", data class TestPrepareHits(WebMocker): def test_empty(self): hits = self.engine.prepare_hits([]) assert hits == [], hits def test_two(self): rec1 = dict(source="sa", trans="ta") rec2 = dict(source="sb", trans="tb") h1, h2 = self.engine.prepare_hits([rec1, rec2]) assert h1.Source() == "sa", h1.Source() assert h2.Trans() == "tb", h2.Trans() class TestSetBase(unittest.TestCase): def setUp(self): self.engine = MemoryEngine.FelixRemoteMemory() def test_connection(self): connection = "http://ginstrom.com:8000/api/mems/1" expected = "http://ginstrom.com:8000" base = self.engine.set_base(connection) assert base == expected, base class TestGetLoginUrl(unittest.TestCase): def setUp(self): self.engine = MemoryEngine.FelixRemoteMemory() def test_commands(self): expected = "http://ginstrom.com:8000/api/login" self.engine.commands = dict(login=expected) login = self.engine.get_login_url() assert login == expected, login def test_no_command(self): self.engine.base = "http://felix-cat.com:8000" expected = "http://felix-cat.com:8000/api/login/" login = self.engine.get_login_url() assert login == expected, login

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0.596114

539

4,838

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0.207792

0.063358

0.029567

0.041183

0.330517

0.236888

0.223161

0.197818

0

0.015529

0.294543

4,838

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0.816877

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0

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0

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0.144144

false

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0.045045

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0.261261

0

null

0

null

0

1

0

4ef25d0f65d81e8e0d5a9796fa97079edac4afe1

6,033

py

Python

commands/print9.py

egigoka/commands

3431ccdb9b9e8b13957b6cfc10feb51c46188b48

[ "MIT" ]

1

2018-05-23T03:34:05.000Z

commands/print9.py

egigoka/commands

3431ccdb9b9e8b13957b6cfc10feb51c46188b48

[ "MIT" ]

null

commands/print9.py

egigoka/commands

3431ccdb9b9e8b13957b6cfc10feb51c46188b48

[ "MIT" ]

null

#! python3 # -*- coding: utf-8 -*- from typing import Union """Internal module with functions for print to console. """ __version__ = "0.13.0" class __Print: """Class with functions for print to console. """ def __init__(self, *args, **kwargs): from threading import Lock self.s_print_lock = Lock() self.colorama_inited = False self._color_output_enabled = None def __call__(self, *args, **kwargs) -> None: self.multithread_safe(*args, **kwargs) def multithread_safe(self, *args, **kwargs) -> None: """Thread safe print function""" with self.s_print_lock: print(*args, **kwargs) def debug(self, *strings: Union[str, bytes], raw: bool = False) -> None: """More notable print, used only for debugging <br>`param strings` prints separately <br>`param raw` print representation of strings <br>`return` """ from .console9 import Console line = "-" * Console.width() self.multithread_safe("<<<Debug sheet:>>>") for str_ in strings: self.multithread_safe(line, end="") if raw: self.multithread_safe(repr(str_)) else: self.multithread_safe(str_) self.multithread_safe(line) self.multithread_safe("<<<End of debug sheet>>>") def rewrite(self, *strings: str, sep: str = " ", fit: bool = True) -> None: """Print rewritable string. note, that you need to rewrite string to remove previous characters <br>`param strings` work as builtin print() <br>`param sep` sep as builtin print(sep) <br>`param fit` try to fit output in one line """ from .os9 import OS from .console9 import Console line = " " * Console.width() if OS.windows: # windows add symbol to end of string :( line = line[:-1] self.multithread_safe(line, end="\r") if fit: strings = Console.fit(*strings, sep=sep) self.multithread_safe(*strings, sep=sep, end="\r") def prettify(self, object_: Union[list, dict, tuple], indent: int = 4, quiet: bool = False) -> str: """Pretty print of list, dicts, tuples <br>`param object_` object to print <br>`param indent` indent to new nested level <br>`param quiet` suppress print to console <br>`return` from pprint.pformat """ import pprint pretty_printer = pprint.PrettyPrinter(indent=indent) pretty_string = pretty_printer.pformat(object=object_) if not quiet: self.multithread_safe(pretty_string) return pretty_string @property def color_output_enabled(self): if self._color_output_enabled is None: from .os9 import OS self._color_output_enabled = "NO_COLOR" not in OS.env.keys() return self._color_output_enabled def colored(self, *strings: Union[str, int, list, dict], attributes: list = None, end: str = "\n", sep: str = " ", flush: bool = False, verbose: bool = True) -> None: """Wrapper for termcolor.cprint, added some smartness <br>Usage` Print.colored("text1", "text2", "red") or Print.colored("text", "text2", "red", "on_white") <br>even Print.colored("text", "text2", "on_white", "red") now. You can pick colors from termcolor.COLORS, highlights from termcolor.HIGHLIGHTS. When environment variable NO_COLOR present (regardless of its value), prevents the addition of ANSI color. <br>`param strings` work as builtin print(*strings) <br>`param attributes` going to termcolor.cprint(attrs) argument <br>`param end` same as builtin print(end) <br>`param sep` same as builtin print(sep) <br>`param flush` same as builtin print(flush) """ import termcolor from contextlib import suppress termcolor.COLORS["gray"] = termcolor.COLORS["black"] = 30 termcolor.HIGHLIGHTS["on_gray"] = termcolor.HIGHLIGHTS["on_black"] = 40 from .os9 import OS if OS.windows and not self.colorama_inited and self.color_output_enabled: import colorama colorama.init() self.colorama_inited = True # check for colors in input highlight = None color = None color_args = 0 try: if str(strings[-1]) in termcolor.HIGHLIGHTS: highlight = strings[-1] color_args += 1 if str(strings[-2]) in termcolor.COLORS: color = strings[-2] color_args += 1 elif str(strings[-1]) in termcolor.COLORS: color = strings[-1] color_args += 1 if str(strings[-2]) in termcolor.HIGHLIGHTS: highlight = strings[-2] color_args += 1 except KeyError: pass # create single string to pass it into termcolor string = "" if color_args: strings = strings[:-color_args] if len(strings) > 1: for substring in strings[:-1]: # все строки добавляются в основную строку с сепаратором string += str(substring) + sep string += str(strings[-1]) # последняя без сепаратора else: # if there only one object string = strings[0] if self.color_output_enabled: colored_string = termcolor.colored(string, color=color, on_color=highlight, attrs=attributes) else: colored_string = string if verbose: self.multithread_safe(colored_string, end=end, flush=flush) with suppress(KeyError): # for work with multithreading termcolor.COLORS.pop("gray") termcolor.COLORS.pop("black") termcolor.HIGHLIGHTS.pop("on_gray") termcolor.HIGHLIGHTS.pop("on_black") return colored_string Print = __Print()

39.690789

114

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714

6,033

4.896359

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0.02603

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0

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6,033

151

115

39.953642

0.819237

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0

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0

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0

1

0.080808

false

0.010101

0.111111

0

0.232323

0.060606

0

null

0

null

0

1

0

4ef3dc5e360dade818b286abce16461dc436ebac

3,514

py

Python

src/function_file.py

cognitedata/function-action-oidc

6ba70da59aa1e94fff003def1082f48fc55bd6a2

[ "Apache-2.0" ]

1

2021-09-06T20:57:27.000Z

src/function_file.py

cognitedata/function-action-oidc

6ba70da59aa1e94fff003def1082f48fc55bd6a2

[ "Apache-2.0" ]

8

2021-09-06T12:16:39.000Z

2022-02-16T11:48:54.000Z

src/function_file.py

cognitedata/function-action-oidc

6ba70da59aa1e94fff003def1082f48fc55bd6a2

[ "Apache-2.0" ]

null

import io import logging import os from pathlib import Path from zipfile import ZipFile from cognite.client.data_classes import DataSet, FileMetadata from cognite.client.exceptions import CogniteAPIError from cognite.experimental import CogniteClient from retry import retry # type: ignore from configs import FunctionConfig from exceptions import FunctionDeployError from utils import retrieve_dataset, temporary_chdir logger = logging.getLogger(__name__) def _write_files_to_zip_buffer(zf: ZipFile, directory: Path): for dirpath, _, files in os.walk(directory): zf.write(dirpath) for f in files: zf.write(Path(dirpath) / f) @retry(exceptions=FunctionDeployError, tries=12, delay=2, jitter=2, max_delay=15, logger=None) def await_file_upload_status(client: CogniteClient, file_id: int): if not client.files.retrieve(file_id).uploaded: logger.info(f"- File (ID: {file_id}) not yet uploaded...") raise FunctionDeployError def upload_zipped_code_to_files( client: CogniteClient, file_bytes: bytes, xid: str, ds: DataSet, ) -> FileMetadata: file_meta = client.files.upload_bytes( file_bytes, name=xid, external_id=xid, data_set_id=ds.id, overwrite=True, ) await_file_upload_status(client, file_meta.id) return file_meta def zip_and_upload_folder(client: CogniteClient, fn_config: FunctionConfig, xid: str) -> int: logger.info(f"Uploading code from '{fn_config.function_folder}' to Files using external ID: '{xid}'") buf = io.BytesIO() # TempDir, who needs that?! :rocket: with ZipFile(buf, mode="a") as zf: with temporary_chdir(fn_config.function_folder): _write_files_to_zip_buffer(zf, directory=Path()) if (common_folder := fn_config.common_folder) is not None: with temporary_chdir(common_folder.parent): # Note .parent logger.info(f"- Added common directory: '{common_folder}' to the file/function") _write_files_to_zip_buffer(zf, directory=common_folder) if (ds_id := fn_config.data_set_id) is not None: ds = retrieve_dataset(client, ds_id) logger.info( f"- Using dataset '{ds.external_id}' (ID: {ds_id}) to govern the file " f"(has write protection: {ds.write_protected})." ) else: ds = DataSet(id=None) logger.info("- No dataset will be used to govern the function zip-file!") file_meta = upload_zipped_code_to_files(client, buf.getvalue(), xid, ds) if (file_id := file_meta.id) is not None: logger.info(f"- File uploaded successfully ({xid})!") return file_id raise FunctionDeployError(f"Failed to upload file ({xid}) to CDF Files") def delete_function_file(client: CogniteClient, xid: str): if (file_meta := client.files.retrieve(external_id=xid)) is None: logger.info(f"Unable to delete file! External ID: '{xid}' NOT found!") return logger.info(f"Deleting existing file '{xid}' (ID: {file_meta.id})") try: client.files.delete(external_id=xid) logger.info(f"- Delete of file '{xid}' successful!") except CogniteAPIError as err: reason = f"{type(err).__name__}: {err}" # 'CogniteAPIError' does not implement dunder repr... logger.error( "Unable to delete file! Trying to ignore and continue as this action will overwrite " f"the file later. Error message from the API: \n{reason}" )

37.382979

105

0.684121

476

3,514

4.863445

0.287815

0.038877

0.038013

0.019438

0.085961

0.062635

0.027646

0

0.002171

0.213432

3,514

93

106

37.784946

0.835384

0.031873

0

0.2199

0.021195

0

1

0.065789

false

0

0.157895

0

0.263158

0

null

0

null

0

1

0

4ef491194dad1406c21f05ef14e5bf3fd5cd294c

474

py

Python

learning_python/2.built-in_data_types/1.built-in_data_types.py

thekilian/Python-pratica

875661addd5b8eb4364bc638832c7ab55dcefce4

[ "MIT" ]

null

learning_python/2.built-in_data_types/1.built-in_data_types.py

thekilian/Python-pratica

875661addd5b8eb4364bc638832c7ab55dcefce4

[ "MIT" ]

null

learning_python/2.built-in_data_types/1.built-in_data_types.py

thekilian/Python-pratica

875661addd5b8eb4364bc638832c7ab55dcefce4

[ "MIT" ]

null

''' BUILT-IN DATA TYPES Mutable - the value can change Immutable - the value cannot change ''' # Immutable object age = 99 id(age) age = 100 id(age) ''' We didn't change 99 to 100. We actually just pointed age to a different location: the new int object whose value is 100. We print the IDs by calling the built-in id function. ''' # Mutable object ''' fab = Person(age=99) fab.age # 99 id(fab) # some numbers here fab.age = 100 id(fab) # same 'some numbers here' '''

14.8125

120

0.694093

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474

4.012195

0.5

0.045593

0.042553

0

0.052632

0.198312

474

32

121

14.8125

0.813158

0.253165

0

0.5

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1

0

false

0

null

0

null

0

1

0

4ef5599e07f8786bc1af735d52d729d2cbe1c992

3,147

py

Python

deep_dream.py

ewalldo/Deep-Dream---Keras

bd4b116c547b008a38cb9e15d7845b1d4f1d120b

[ "MIT" ]

null

deep_dream.py

ewalldo/Deep-Dream---Keras

bd4b116c547b008a38cb9e15d7845b1d4f1d120b

[ "MIT" ]

null

deep_dream.py

ewalldo/Deep-Dream---Keras

bd4b116c547b008a38cb9e15d7845b1d4f1d120b

[ "MIT" ]

null

from keras.applications import inception_v3 from keras import backend as K import scipy import imageio from keras.preprocessing import image import numpy as np K.set_learning_phase(0) model = inception_v3.InceptionV3(weights='imagenet', include_top=False) layer_contributions = {'mixed2': 0.2, 'mixed3': 3., 'mixed4': 2., 'mixed5': 1.5,} layer_dict = dict([(layer.name, layer) for layer in model.layers]) loss = K.variable(0.) for layer_name in layer_contributions: coeff = layer_contributions[layer_name] activation = layer_dict[layer_name].output scaling = K.prod(K.cast(K.shape(activation), 'float32')) loss = loss + coeff * K.sum(K.square(activation[:, 2: -2, 2: -2, :])) / scaling dream = model.input grads = K.gradients(loss, dream)[0] grads /= K.maximum(K.mean(K.abs(grads)), 1e-7) outputs = [loss, grads] fetch_loss_and_grads = K.function([dream], outputs) def eval_loss_and_grads(x): outs = fetch_loss_and_grads([x]) loss_value = outs[0] grad_values = outs[1] return loss_value, grad_values def gradient_ascent(x, iterations, step, max_loss=None): for i in range(iterations): loss_value, grad_values = eval_loss_and_grads(x) if max_loss is not None and loss_value > max_loss: break print('Loss at', i, ":", loss_value) x += step * grad_values return x def resize_img(img, size): img = np.copy(img) factors = (1, float(size[0]) / img.shape[1], float(size[1]) / img.shape[2], 1) return scipy.ndimage.zoom(img, factors, order=1) def save_img(img, fname): pil_image = deprocess_image(np.copy(img)) # scipy.misc.imsave(fname, pil_image) imageio.imwrite(fname, pil_image) def preprocess_image(image_path): img = image.load_img(image_path) img = image.img_to_array(img) img = np.expand_dims(img, axis=0) img = inception_v3.preprocess_input(img) return img def deprocess_image(x): if K.image_data_format() == 'channels_first': x = x.reshape((3, x.shape[2], x.shape[3])) x = x.transpose((1, 2, 0)) else: x = x.reshape((x.shape[1], x.shape[2], 3)) x /= 2. x += 0.5 x *= 255 x = np.clip(x, 0, 255).astype('uint8') return x step = 0.01 num_octave = 3 octave_scale = 1.4 iterations = 20 max_loss = 10. base_image_path = 'cats_and_dogs_small/train/cats/cat.0.jpg' img = preprocess_image(base_image_path) original_shape = img.shape[1:3] succesive_shapes = [original_shape] for i in range(1, num_octave): shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape]) succesive_shapes.append(shape) succesive_shapes = succesive_shapes[::-1] original_img = np.copy(img) shrunk_original_img = resize_img(img, succesive_shapes[0]) for shape in succesive_shapes: print("Processing image shape", shape) img = resize_img(img, shape) img = gradient_ascent(img, iterations=iterations, step=step, max_loss=max_loss) upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape) same_size_original = resize_img(original_img, shape) lost_detail = same_size_original - upscaled_shrunk_original_img img += lost_detail shrunk_original_img = resize_img(original_img, shape) save_img(img, fname='dream_at_scale'+str(shape)+'.png') save_img(img, fname='final_dream.png')

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82

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false

0

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null

0

null

0

1

0

4ef59607a8d06514cadf280683c16c602c23e215

978

py

Python

game.py

greymistcube/racing_game_ai

7e5e6ec781eb3c98729d370cbcc0ab6ed053962f

[ "MIT" ]

null

game.py

greymistcube/racing_game_ai

7e5e6ec781eb3c98729d370cbcc0ab6ed053962f

[ "MIT" ]

null

game.py

greymistcube/racing_game_ai

7e5e6ec781eb3c98729d370cbcc0ab6ed053962f

[ "MIT" ]

null

import pygame import argparser import lib import ai.neatinterface.neatcore pygame.init() if __name__ == "__main__": args = argparser.get_args() # pygame initialization pygame.init() # initialize properly and make links make them as common resources # for other modules # I admit this looks pretty hideous but python has no good way of # handling singletons lib.common.settings = settings = lib.Settings(args) lib.common.display = display = lib.Display() lib.common.clock = clock = lib.Clock() lib.common.events = events = lib.Events() # setting game mode if args.ai == "neat": lib.common.core = core = ai.neatinterface.neatcore.NeatCore() else: lib.common.core = core = lib.Core() core.new_game() # main loop while True: clock.tick() core.update() if core.game_over(): core.new_game() continue display.draw(core.get_surface())

22.744186

70

0.639059

122

978

5.016393

0.5

0.088235

0.075163

0.055556

0

0.260736

978

42

71

23.285714

0.846473

0.220859

0

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0

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false

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0.166667

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0.166667

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null

0

null

0

1

0

4ef6267fcd36a4e2159433da6c76f793b111f5da

39,946

py

Python

build/lib/biotas/radio_project.py

knebiolo/biotas

2ea06297fc2851bc54ce89f20f8f7aaa98dd8fc1

[ "MIT" ]

1

2021-12-30T14:25:39.000Z

build/lib/biotas/radio_project.py

knebiolo/biotas

2ea06297fc2851bc54ce89f20f8f7aaa98dd8fc1

[ "MIT" ]

17

2020-11-27T18:05:45.000Z

2022-01-27T02:46:46.000Z

build/lib/biotas/radio_project.py

knebiolo/biotas

2ea06297fc2851bc54ce89f20f8f7aaa98dd8fc1

[ "MIT" ]

1

2020-11-17T21:07:38.000Z

# -*- coding: utf-8 -*- ''' Module contains all of the functions to create a radio telemetry project.''' # import modules required for function dependencies import numpy as np import pandas as pd import os import sqlite3 import datetime import matplotlib.pyplot as plt import matplotlib import matplotlib.dates as mdates from mpl_toolkits.mplot3d import Axes3D import statsmodels.api as sm import statsmodels.formula.api as smf import networkx as nx from matplotlib import rcParams from scipy import interpolate font = {'family': 'serif','size': 6} rcParams['font.size'] = 6 rcParams['font.family'] = 'serif' def noiseRatio (duration,data,study_tags): ''' function calculates the ratio of miscoded, pure noise detections, to matching frequency/code detections within the duration specified. In other words, what is the ratio of miscoded to correctly coded detections within the duration specified duration = moving window length in minutes data = current data file study_tags = list or list like object of study tags ''' # identify miscodes data['miscode'] = np.isin(data.FreqCode.values, study_tags, invert = True) # bin everything into nearest 5 min time bin and count miscodes and total number of detections duration_s = str(int(duration * 60)) + 's' miscode = data.groupby(pd.Grouper(key = 'timeStamp', freq = duration_s)).miscode.sum().to_frame() total = data.groupby(pd.Grouper(key = 'timeStamp', freq = duration_s)).FreqCode.count().to_frame() # rename total.rename(columns = {'FreqCode':'total'}, inplace = True) # merge dataframes, calculate noise ratio noise = total.merge(miscode, left_on = 'timeStamp', right_on ='timeStamp') noise.reset_index(inplace = True) noise.fillna(value = 0, inplace = True) noise['noiseRatio'] = noise.miscode / noise.total noise.dropna(inplace = True) noise['Epoch'] = (noise['timeStamp'] - datetime.datetime(1970,1,1)).dt.total_seconds() # create function for noise ratio at time if len(noise) >= 2: noise_ratio_fun = interpolate.interp1d(noise.Epoch.values,noise.noiseRatio.values,kind = 'linear',bounds_error = False, fill_value ='extrapolate') # interpolate noise ratio as a function of time for every row in data data['noiseRatio'] = noise_ratio_fun(data.Epoch.values) data.drop(columns = ['miscode'], inplace = True) return data def createTrainDB(project_dir, dbName): ''' function creates empty project database, user can edit project parameters using DB Broswer for sqlite found at: http://sqlitebrowser.org/''' # first step creates a project directory if it doesn't already exist if not os.path.exists(project_dir): os.makedirs(project_dir) data_dir = os.path.join(project_dir,'Data') # raw data goes here if not os.path.exists(data_dir): os.makedirs(data_dir) training_dir = os.path.join(data_dir,'Training_Files') if not os.path.exists(training_dir): os.makedirs(training_dir) output_dir = os.path.join(project_dir, 'Output') # intermediate data products, final data products and images if not os.path.exists(output_dir): os.makedirs(output_dir) scratch_dir = os.path.join(output_dir,'Scratch') if not os.path.exists(scratch_dir): os.makedirs(scratch_dir) figures_dir = os.path.join(output_dir, 'Figures') if not os.path.exists(figures_dir): os.makedirs(figures_dir) # program_dir = os.path.join(project_dir, 'Program') # this is where we will create a local clone of the Git repository # if not os.path.exists(program_dir): # os.makedirs(program_dir) dbDir = os.path.join(data_dir,dbName) # connect to and create the project geodatabase conn = sqlite3.connect(dbDir, timeout=30.0) c = conn.cursor() # mandatory project tables c.execute('''DROP TABLE IF EXISTS tblMasterReceiver''') # receiver ID, receiver type c.execute('''DROP TABLE IF EXISTS tblMasterTag''') # tag ID, frequency, freqcode c.execute('''DROP TABLE IF EXISTS tblReceiverParameters''') # field crews fuck up, we need these parameters to correctly quantify detection history c.execute('''DROP TABLE IF EXISTS tblAlgParams''') c.execute('''DROP TABLE IF EXISTS tblNodes''') c.execute('''CREATE TABLE tblMasterReceiver(recID TEXT, Name TEXT, RecType TEXT, Node TEXT)''') c.execute('''CREATE TABLE tblReceiverParameters(recID TEXT, RecType TEXT, ScanTime REAL, Channels INTEGER, fileName TEXT)''') c.execute('''CREATE TABLE tblMasterTag(FreqCode TEXT, PIT_ID TEXT, PulseRate REAL, MortRate REAL, CapLoc TEXT, RelLoc TEXT, TagType TEXT, Length INTEGER, Sex TEXT, RelDate TIMESTAMP, Study TEXT, Species TEXT)''') c.execute('''CREATE TABLE tblAlgParams(det INTEGER, duration INTEGER)''') c.execute('''CREATE TABLE tblNodes(Node TEXT, Reach TEXT, RecType TEXT, X INTEGER, Y INTEGER)''') ''' note these top three tables are mandatory, depending upon how many receivers we train and/or use for a study we may not need all of these tables, or we may need more. This must be addressed in future iterations, can we keep adding empty tables at the onset of the project???''' c.execute('''DROP TABLE IF EXISTS tblRaw''') c.execute('''DROP TABLE IF EXISTS tblTrain''') c.execute('''CREATE TABLE tblTrain(Channels INTEGER, Detection INTEGER, FreqCode TEXT, Power REAL, lag INTEGER, lagDiff REAL, FishCount INTEGER, conRecLength INTEGER, miss_to_hit REAL, consDet INTEGER, detHist TEXT, hitRatio REAL, noiseRatio REAL, seriesHit INTEGER, timeStamp TIMESTAMP, Epoch INTEGER, Seconds INTEGER, fileName TEXT, recID TEXT, recType TEXT, ScanTime REAL)''') # create full radio table - table includes all records, final version will be designed for specific receiver types c.execute('''CREATE TABLE tblRaw(timeStamp TIMESTAMP, Epoch INTEGER, FreqCode TEXT, Power REAL,noiseRatio, fileName TEXT, recID TEXT, ScanTime REAL, Channels REAL, RecType TEXT)''') #c.execute('''CREATE INDEX idx_fileNameRaw ON tblRaw (fileName)''') c.execute('''CREATE INDEX idx_RecID_Raw ON tblRaw (recID)''') c.execute('''CREATE INDEX idx_FreqCode On tblRaw (FreqCode)''') #c.execute('''CREATE INDEX idx_fileNameTrain ON tblTrain (fileName)''') c.execute('''CREATE INDEX idx_RecType ON tblTrain (recType)''') conn.commit() c.close() def setAlgorithmParameters(det,duration,dbName): '''Function sets parameters for predictor variables used in the naive bayes classifier det = number of detections to look forward and backward in times for detection history strings duration = moving window around each detection, used to calculate the noise ratio and number of fish present (fish count) ''' conn = sqlite3.connect(dbName, timeout=30.0) c = conn.cursor() params = [(det,duration)] conn.executemany('INSERT INTO tblAlgParams VALUES (?,?)',params) conn.commit() conn.commit() c.close() def studyDataImport(dataFrame,dbName,tblName): '''function imports formatted data into project database. The code in its current function does not check for inconsistencies with data structures. If you're shit isn't right, this isn't going to work for you. Make sure your table data structures match exactly, that column names and datatypes match. I'm not your mother, clean up your shit. dataFrame = pandas dataframe imported from your structured file. dbName = full directory path to project database tblName = the name of the data you can import to. If you are brave, import to tblRaw, but really this is meant for tblMasterTag and tblMasterReceiver''' conn = sqlite3.connect(dbName) c = conn.cursor() dataFrame.to_sql(tblName,con = conn,index = False, if_exists = 'append') conn.commit() c.close() def orionImport(fileName,rxfile,dbName,recName,switch = False, scanTime = None, channels = None, ant_to_rec_dict = None): '''Function imports raw Sigma Eight orion data. Text parser uses simple column fixed column widths. ''' conn = sqlite3.connect(dbName, timeout=30.0) c = conn.cursor() study_tags = pd.read_sql('SELECT FreqCode, TagType FROM tblMasterTag',con = conn) study_tags = study_tags[study_tags.TagType == 'Study'].FreqCode.values recType = 'orion' if ant_to_rec_dict != None: scanTime = 1 channels = 1 # what orion firmware is it? the header row is the key o_file =open(fileName, encoding='utf-8') header = o_file.readline()[:-1] # read first line in file columns = str.split(header) o_file.close() if 'Type' in columns: # with our data row, extract information using pandas fwf import procedure telemDat = pd.read_fwf(fileName,colspecs = [(0,12),(13,23),(24,30),(31,35),(36,45),(46,54),(55,60),(61,65)], names = ['Date','Time','Site','Ant','Freq','Type','Code','Power'], skiprows = 1, dtype = {'Date':str,'Time':str,'Site':np.int32,'Ant':str,'Freq':str,'Type':str,'Code':str,'Power':np.float64}) telemDat = telemDat[telemDat.Type != 'STATUS'] telemDat.drop(['Type'], axis = 1, inplace = True) else: # with our data row, extract information using pandas fwf import procedure telemDat = pd.read_fwf(fileName,colspecs = [(0,11),(11,20),(20,26),(26,30),(30,37),(37,42),(42,48)], names = ['Date','Time','Site','Ant','Freq','Code','Power'], skiprows = 1, dtype = {'Date':str,'Time':str,'Site':str,'Ant':str,'Freq':str,'Code':str,'Power':str}) if len(telemDat) > 0: telemDat['fileName'] = np.repeat(rxfile,len(telemDat)) #Note I'm going back here to the actual file name without the path. Is that OK? I prefer it, but it's a potential source of confusion telemDat['FreqCode'] = telemDat['Freq'].astype(str) + ' ' + telemDat['Code'].astype(str) telemDat['timeStamp'] = pd.to_datetime(telemDat['Date'] + ' ' + telemDat['Time'],errors = 'coerce')# create timestamp field from date and time and apply to index telemDat['ScanTime'] = np.repeat(scanTime,len(telemDat)) telemDat['Channels'] = np.repeat(channels,len(telemDat)) telemDat['RecType'] = np.repeat('orion',len(telemDat)) telemDat = telemDat[telemDat.timeStamp.notnull()] if len(telemDat) == 0: print ("Invalid timestamps in raw data, cannot import") else: telemDat['Epoch'] = (telemDat['timeStamp'] - datetime.datetime(1970,1,1)).dt.total_seconds() telemDat.drop (['Date','Time','Freq','Code','Site'],axis = 1, inplace = True) telemDat = noiseRatio(5.0,telemDat,study_tags) if ant_to_rec_dict == None: telemDat.drop(['Ant'], axis = 1, inplace = True) telemDat['recID'] = np.repeat(recName,len(telemDat)) tuples = zip(telemDat.FreqCode.values,telemDat.recID.values,telemDat.Epoch.values) index = pd.MultiIndex.from_tuples(tuples, names=['FreqCode', 'recID','Epoch']) telemDat.set_index(index,inplace = True,drop = False) telemDat.to_sql('tblRaw',con = conn,index = False, if_exists = 'append') # recParamLine = [(recName,recType,scanTime,channels,fileName)] # conn.executemany('INSERT INTO tblReceiverParameters VALUES (?,?,?,?,?)',recParamLine) conn.commit() c.close() else: for i in ant_to_rec_dict: site = ant_to_rec_dict[i] telemDat_sub = telemDat[telemDat.Ant == str(i)] telemDat_sub['recID'] = np.repeat(site,len(telemDat_sub)) tuples = zip(telemDat_sub.FreqCode.values,telemDat_sub.recID.values,telemDat_sub.Epoch.values) index = pd.MultiIndex.from_tuples(tuples, names=['FreqCode', 'recID','Epoch']) telemDat_sub.set_index(index,inplace = True,drop = False) telemDat_sub.drop(['Ant'], axis = 1, inplace = True) telemDat_sub.to_sql('tblRaw',con = conn,index = False, if_exists = 'append') # recParamLine = [(site,recType,scanTime,channels,fileName)] # conn.executemany('INSERT INTO tblReceiverParameters VALUES (?,?,?,?,?)',recParamLine) conn.commit() c.close() def lotek_import(fileName,rxfile,dbName,recName,ant_to_rec_dict = None): ''' function imports raw lotek data, reads header data to find receiver parameters and automatically locates raw telemetry data. Import procedure works with standardized project database. Database must be created before function can be run''' '''to do: in future iterations create a check for project database, if project data base does not exist, throw an exception inputs: fileName = name of raw telemetry data file with full directory and extenstion dbName = name of project database with full directory and extension recName = official receiver name''' # declare the workspace - in practice we will identify all files in diretory and iterate over them as part of function, all this crap passed as parameters recType = 'lotek' headerDat = {} # create empty dictionary to hold Lotek header data indexed by line number - to be imported to Pandas dataframe lineCounter = [] # create empty array to hold line indices lineList = [] # generate a list of header lines - contains all data we need to write to project set up database o_file = open(fileName, encoding='utf-8') counter = 0 # start line counter line = o_file.readline()[:-1] # read first line in file lineCounter.append(counter) # append the current line counter to the counter array lineList.append(line) # append the current line of header data to the line list if line == "SRX800 / 800D Information:": # find where data begins and header data ends with o_file as f: for line in f: if "** Data Segment **" in line: counter = counter + 1 dataRow = counter + 5 # if this current line signifies the start of the data stream, the data starts three rows down from this break # break the loop, we have reached our stop point else: counter = counter + 1 # if we are still reading header data increase the line counter by 1 lineCounter.append(counter) # append the line counter to the count array lineList.append(line) # append line of data to the data array headerDat['idx'] = lineCounter # add count array to dictionary with field name 'idx' as key headerDat['line'] = lineList # add data line array to dictionary with field name 'line' as key headerDF = pd.DataFrame.from_dict(headerDat) # create pandas dataframe of header data indexed by row number headerDF.set_index('idx',inplace = True) # find scan time for row in headerDF.iterrows(): # for every header data row if 'Scan Time' in row[1][0]: # if the first 9 characters of the line say 'Scan Time' = we have found the scan time in the document scanTimeStr = row[1][0][-7:-1] # get the number value from the row scanTimeSplit = scanTimeStr.split(':') # split the string scanTime = float(scanTimeSplit[1]) # convert the scan time string to float break # stop that loop, we done del row # find number of channels and create channel dictionary scanChan = [] # create empty array of channel ID's channelDict = {} # create empty channel ID: frequency dictionary counter = 0 # create counter rows = headerDF.iterrows() # create row iterator for row in rows: # for every row if 'Active scan_table:' in row[1][0]: # if the first 18 characters say what that says idx0 = counter + 2 # channel dictionary data starts two rows from here while next(rows)[1][0] != '\n': # while the next row isn't empty counter = counter + 1 # increase the counter, when the row is empty we have reached the end of channels, break loop idx1 = counter + 1 # get index of last data row break # break that loop, we done else: counter = counter + 1 # if it isn't a data row, increase the counter by 1 del row, rows channelDat = headerDF.iloc[idx0:idx1] # extract channel dictionary data using rows identified earlier for row in channelDat.iterrows(): dat = row[1][0] channel = int(dat[0:4]) frequency = dat[10:17] channelDict[channel] = frequency scanChan.append(channel) # extract that channel ID from the data row and append to array channels = len(scanChan) conn = sqlite3.connect(dbName, timeout=30.0) c = conn.cursor() study_tags = pd.read_sql('SELECT FreqCode, TagType FROM tblMasterTag',con = conn) study_tags = study_tags[study_tags.TagType == 'Study'].FreqCode.values # with our data row, extract information using pandas fwf import procedure #Depending on firmware the data structure will change. This is for xxx firmware. See below for additional firmware configs # telemDat = pd.read_fwf(fileName,colspecs = [(0,8),(8,18),(18,28),(28,36),(36,51),(51,59)],names = ['Date','Time','ChannelID','TagID','Antenna','Power'],skiprows = dataRow) # telemDat = telemDat.iloc[:-2] # remove last two rows, Lotek adds garbage at the end #Master Firmware: Version 9.12.5 telemDat = pd.read_fwf(fileName,colspecs = [(0,8),(8,23),(23,33),(33,41),(41,56),(56,64)],names = ['Date','Time','ChannelID','TagID','Antenna','Power'],skiprows = dataRow) telemDat = telemDat.iloc[:-2] # remove last two telemDat['Antenna'] = telemDat['Antenna'].astype(str) #TCS Added this to get dict to line up with data telemDat['fileName'] = np.repeat(rxfile,len(telemDat)) # Adding the filename into the dataset...drop the path (note this may cause confusion because above we use filename with path. Decide what to do and fix) def id_to_freq(row,channelDict): if row[2] in channelDict: return channelDict[row[2]] else: return '888' if len(telemDat) > 0: if ant_to_rec_dict == None: telemDat['Frequency'] = telemDat.apply(id_to_freq, axis = 1, args = (channelDict,)) telemDat = telemDat[telemDat.Frequency != '888'] telemDat = telemDat[telemDat.TagID != 999] telemDat['FreqCode'] = telemDat['Frequency'].astype(str) + ' ' + telemDat['TagID'].astype(int).astype(str) telemDat['timeStamp'] = pd.to_datetime(telemDat['Date'] + ' ' + telemDat['Time'])# create timestamp field from date and time and apply to index telemDat['Epoch'] = (telemDat['timeStamp'] - datetime.datetime(1970,1,1)).dt.total_seconds() telemDat = noiseRatio(5.0,telemDat,study_tags) telemDat.drop (['Date','Time','Frequency','TagID','ChannelID','Antenna'],axis = 1, inplace = True) telemDat['ScanTime'] = np.repeat(scanTime,len(telemDat)) telemDat['Channels'] = np.repeat(channels,len(telemDat)) telemDat['RecType'] = np.repeat(recType,len(telemDat)) telemDat['recID'] = np.repeat(recName,len(telemDat)) telemDat.to_sql('tblRaw',con = conn,index = False, if_exists = 'append') else: for ant in ant_to_rec_dict: site = ant_to_rec_dict[ant] telemDat_sub = telemDat[telemDat.Antenna == str(ant)] telemDat_sub['Frequency'] = telemDat_sub.apply(id_to_freq, axis = 1, args = (channelDict,)) telemDat_sub = telemDat_sub[telemDat_sub.Frequency != '888'] telemDat_sub = telemDat_sub[telemDat_sub.TagID != 999] telemDat_sub['FreqCode'] = telemDat_sub['Frequency'].astype(str) + ' ' + telemDat_sub['TagID'].astype(int).astype(str) telemDat_sub['timeStamp'] = pd.to_datetime(telemDat_sub['Date'] + ' ' + telemDat_sub['Time'])# create timestamp field from date and time and apply to index telemDat_sub['Epoch'] = (telemDat_sub['timeStamp'] - datetime.datetime(1970,1,1)).dt.total_seconds() telemDat_sub = noiseRatio(5.0,telemDat_sub,study_tags) telemDat_sub.drop (['Date','Time','Frequency','TagID','ChannelID','Antenna'],axis = 1, inplace = True) telemDat_sub['ScanTime'] = np.repeat(scanTime,len(telemDat_sub)) telemDat_sub['Channels'] = np.repeat(channels,len(telemDat_sub)) telemDat_sub['RecType'] = np.repeat(recType,len(telemDat_sub)) telemDat_sub['recID'] = np.repeat(site,len(telemDat_sub)) telemDat_sub.to_sql('tblRaw',con = conn,index = False, if_exists = 'append') else: lotek400 = False # find where data begins and header data ends with o_file as f: for line in f: if "********************************* Data Segment *********************************" in line: counter = counter + 1 dataRow = counter + 5 # if this current line signifies the start of the data stream, the data starts three rows down from this break # break the loop, we have reached our stop point elif line[0:14] == "Code_log data:": counter = counter + 1 dataRow = counter + 3 lotek400 = True break else: counter = counter + 1 # if we are still reading header data increase the line counter by 1 lineCounter.append(counter) # append the line counter to the count array lineList.append(line) # append line of data to the data array headerDat['idx'] = lineCounter # add count array to dictionary with field name 'idx' as key headerDat['line'] = lineList # add data line array to dictionary with field name 'line' as key headerDF = pd.DataFrame.from_dict(headerDat) # create pandas dataframe of header data indexed by row number headerDF.set_index('idx',inplace = True) # find scan time for row in headerDF.iterrows(): # for every header data row if 'scan time' in row[1][0] or 'Scan time' in row[1][0]: # if the first 9 characters of the line say 'Scan Time' = we have found the scan time in the document scanTimeStr = row[1][0][-7:-1] # get the number value from the row scanTimeSplit = scanTimeStr.split(':') # split the string scanTime = float(scanTimeSplit[1]) # convert the scan time string to float break # stop that loop, we done del row # find number of channels and create channel dictionary scanChan = [] # create empty array of channel ID's channelDict = {} # create empty channel ID: frequency dictionary counter = 0 # create counter rows = headerDF.iterrows() # create row iterator for row in rows: # for every row if 'Active scan_table:' in row[1][0]: # if the first 18 characters say what that says idx0 = counter + 2 # channel dictionary data starts two rows from here while next(rows)[1][0] != '\n': # while the next row isn't empty counter = counter + 1 # increase the counter, when the row is empty we have reached the end of channels, break loop idx1 = counter + 1 # get index of last data row break # break that loop, we done else: counter = counter + 1 # if it isn't a data row, increase the counter by 1 del row, rows channelDat = headerDF.iloc[idx0:idx1] # extract channel dictionary data using rows identified earlier for row in channelDat.iterrows(): dat = row[1][0] channel = int(dat[0:4]) frequency = dat[10:17] channelDict[channel] = frequency scanChan.append(channel) # extract that channel ID from the data row and append to array channels = len(scanChan) conn = sqlite3.connect(dbName, timeout=30.0) c = conn.cursor() study_tags = pd.read_sql('SELECT FreqCode FROM tblMasterTag WHERE TagType == "Study" OR TagType == "Beacon"',con = conn).FreqCode.values def id_to_freq(row,channelDict): channel = row['ChannelID'] if np.int(channel) in channelDict: return channelDict[np.int(channel)] else: return '888' # with our data row, extract information using pandas fwf import procedure if lotek400 == False: telemDat = pd.read_fwf(os.path.join(fileName),colspecs = [(0,5),(5,14),(14,23),(23,31),(31,46),(46,54)],names = ['DayNumber','Time','ChannelID','TagID','Antenna','Power'],skiprows = dataRow) telemDat = telemDat.iloc[:-2] # remove last two rows, Lotek adds garbage at the end telemDat.dropna(inplace = True) if len(telemDat) > 0: if ant_to_rec_dict == None: telemDat['Frequency'] = telemDat.apply(id_to_freq, axis = 1, args = (channelDict,)) telemDat = telemDat[telemDat.Frequency != '888'] telemDat = telemDat[telemDat.TagID != 999] telemDat['FreqCode'] = telemDat['Frequency'].astype(str) + ' ' + telemDat['TagID'].astype(int).astype(str) telemDat['day0'] = np.repeat(pd.to_datetime("1900-01-01"),len(telemDat)) telemDat['Date'] = telemDat['day0'] + pd.to_timedelta(telemDat['DayNumber'].astype(int), unit='d') telemDat['Date'] = telemDat.Date.astype('str') telemDat['timeStamp'] = pd.to_datetime(telemDat['Date'] + ' ' + telemDat['Time'])# create timestamp field from date and time and apply to index telemDat.drop(['day0','DayNumber'],axis = 1, inplace = True) telemDat['Epoch'] = (telemDat['timeStamp'] - datetime.datetime(1970,1,1)).dt.total_seconds() telemDat.drop (['Date','Time','Frequency','TagID','ChannelID','Antenna'],axis = 1, inplace = True) telemDat['fileName'] = np.repeat(rxfile,len(telemDat)) #Made change here as above--taking jsut the file name and writing it to the dataset. Note naming issue. telemDat['recID'] = np.repeat(recName,len(telemDat)) telemDat['noiseRatio'] = noiseRatio(5.0,telemDat,study_tags) telemDat['ScanTime'] = np.repeat(scanTime,len(telemDat)) telemDat['Channels'] = np.repeat(channels,len(telemDat)) telemDat['RecType'] = np.repeat(recType,len(telemDat)) tuples = zip(telemDat.FreqCode.values,telemDat.recID.values,telemDat.Epoch.values) index = pd.MultiIndex.from_tuples(tuples, names=['FreqCode', 'recID','Epoch']) telemDat.set_index(index,inplace = True,drop = False) telemDat.to_sql('tblRaw',con = conn,index = False, if_exists = 'append') else: site = ant_to_rec_dict[ant] telemDat_sub = telemDat[telemDat.Antenna == str(ant)] telemDat_sub['Frequency'] = telemDat_sub.apply(id_to_freq, axis = 1, args = (channelDict,)) telemDat_sub = telemDat_sub[telemDat_sub.Frequency != '888'] telemDat_sub = telemDat_sub[telemDat_sub.TagID != 999] telemDat_sub['FreqCode'] = telemDat_sub['Frequency'].astype(str) + ' ' + telemDat_sub['TagID'].astype(int).astype(str) telemDat_sub['day0'] = np.repeat(pd.to_datetime("1900-01-01"),len(telemDat_sub)) telemDat_sub['Date'] = telemDat_sub['day0'] + pd.to_timedelta(telemDat_sub['DayNumber'].astype(int), unit='d') telemDat_sub['Date'] = telemDat_sub.Date.astype('str') telemDat_sub['timeStamp'] = pd.to_datetime(telemDat_sub['Date'] + ' ' + telemDat_sub['Time'])# create timestamp field from date and time and apply to index telemDat.drop(['day0','DayNumber'],axis = 1, inplace = True) telemDat_sub['Epoch'] = (telemDat_sub['timeStamp'] - datetime.datetime(1970,1,1)).dt.total_seconds() telemDat_sub.drop (['Date','Time','Frequency','TagID','ChannelID','Antenna'],axis = 1, inplace = True) telemDat_sub['fileName'] = np.repeat(rxfile,len(telemDat_sub)) #Made change here as above--taking jsut the file name and writing it to the dataset. Note naming issue. telemDat_sub['recID'] = np.repeat(recName,len(telemDat_sub)) telemDat_sub['noiseRatio'] = noiseRatio(5.0,telemDat_sub,study_tags) telemDat_sub['ScanTime'] = np.repeat(scanTime,len(telemDat_sub)) telemDat_sub['Channels'] = np.repeat(channels,len(telemDat_sub)) telemDat_sub['RecType'] = np.repeat(recType,len(telemDat_sub)) tuples = zip(telemDat_sub.FreqCode.values,telemDat_sub.recID.values,telemDat_sub.Epoch.values) index = pd.MultiIndex.from_tuples(tuples, names=['FreqCode', 'recID','Epoch']) telemDat_sub.set_index(index,inplace = True,drop = False) telemDat_sub.to_sql('tblRaw',con = conn,index = False, if_exists = 'append') else: telemDat = pd.read_fwf(os.path.join(fileName),colspecs = [(0,6),(6,14),(14,22),(22,27),(27,35),(35,41),(41,48),(48,56),(56,67),(67,80)],names = ['DayNumber_Start','StartTime','ChannelID','TagID','Antenna','Power','Data','Events','DayNumber_End','EndTime'],skiprows = dataRow) telemDat.dropna(inplace = True) # if len(telemDat) > 0: # telemDat['Frequency'] = telemDat.apply(id_to_freq, axis = 1, args = (channelDict,)) # telemDat = telemDat[telemDat.Frequency != '888'] # telemDat = telemDat[telemDat.TagID != 999] # telemDat['FreqCode'] = telemDat['Frequency'].astype(str) + ' ' + telemDat['TagID'].astype(int).astype(str) # telemDat['day0'] = np.repeat(pd.to_datetime("1900-01-01"),len(telemDat)) # telemDat['Date_Start'] = telemDat['day0'] + pd.to_timedelta(telemDat['DayNumber_Start'].astype(int), unit='d') # telemDat['Date_Start'] = telemDat.Date_Start.astype('str') # telemDat['Date_End'] = telemDat['day0'] + pd.to_timedelta(telemDat['DayNumber_End'].astype(int), unit='d') # telemDat['Date_End'] = telemDat.Date_End.astype('str') # telemDat['timeStamp'] = pd.to_datetime(telemDat['Date_Start'] + ' ' + telemDat['StartTime'])# create timestamp field from date and time and apply to index # telemDat['time_end'] = pd.to_datetime(telemDat['Date_End'] + ' ' + telemDat['EndTime'])# create timestamp field from date and time and apply to index # telemDat.drop(['day0','DayNumber_Start','DayNumber_End'],axis = 1, inplace = True) # telemDat['duration'] = (telemDat.time_end - telemDat.timeStamp).astype('timedelta64[s]') # telemDat['events_per_duration'] = telemDat.Events / telemDat.duration # telemDat['Epoch'] = (telemDat['timeStamp'] - datetime.datetime(1970,1,1)).dt.total_seconds() # telemDat.drop (['Date_Start','Date_End','time_end','Frequency','TagID','ChannelID','Antenna'],axis = 1, inplace = True) # telemDat['fileName'] = np.repeat(fileName,len(telemDat)) # telemDat['recID'] = np.repeat(recName,len(telemDat)) # tuples = zip(telemDat.FreqCode.values,telemDat.recID.values,telemDat.Epoch.values) # index = pd.MultiIndex.from_tuples(tuples, names=['FreqCode', 'recID','Epoch']) # telemDat.set_index(index,inplace = True,drop = False) # telemDat.to_sql('tblRaw_Lotek400',con = conn,index = False, if_exists = 'append') if len(telemDat) > 0: telemDat['Frequency'] = telemDat.apply(id_to_freq, axis = 1, args = (channelDict,)) telemDat = telemDat[telemDat.Frequency != '888'] telemDat = telemDat[telemDat.TagID != 999] telemDat['FreqCode'] = telemDat['Frequency'].astype(str) + ' ' + telemDat['TagID'].astype(int).astype(str) telemDat['day0'] = np.repeat(pd.to_datetime("1900-01-01"),len(telemDat)) telemDat['Date_Start'] = telemDat['day0'] + pd.to_timedelta(telemDat['DayNumber_Start'].astype(int), unit='d') telemDat['Date_Start'] = telemDat.Date_Start.astype('str') telemDat['Date_End'] = telemDat['day0'] + pd.to_timedelta(telemDat['DayNumber_End'].astype(int), unit='d') telemDat['Date_End'] = telemDat.Date_End.astype('str') telemDat['timeStamp'] = pd.to_datetime(telemDat['Date_Start'] + ' ' + telemDat['StartTime'])# create timestamp field from date and time and apply to index telemDat['time_end'] = pd.to_datetime(telemDat['Date_End'] + ' ' + telemDat['EndTime'])# create timestamp field from date and time and apply to index telemDat.drop(['day0','DayNumber_Start','DayNumber_End'],axis = 1, inplace = True) telemDat['duration'] = (telemDat.time_end - telemDat.timeStamp).astype('timedelta64[s]') telemDat['events_per_duration'] = telemDat.Events / telemDat.duration telemDat['Epoch'] = (telemDat['timeStamp'] - datetime.datetime(1970,1,1)).dt.total_seconds() telemDat.drop (['Date_Start','Date_End','time_end','Frequency','TagID','ChannelID','Antenna'],axis = 1, inplace = True) telemDat['fileName'] = np.repeat(rxfile,len(telemDat)) #This is the 4th time I'm assigning file to fileName in the saved data table. telemDat['recID'] = np.repeat(recName,len(telemDat)) telemDat['ScanTime'] = np.repeat(scanTime,len(telemDat)) telemDat['Channels'] = np.repeat(channels,len(telemDat)) telemDat['RecType'] = np.repeat(recType,len(telemDat)) telemDat.drop(['StartTime','Data','Events','EndTime','duration','events_per_duration'], axis = 1, inplace = True) tuples = zip(telemDat.FreqCode.values,telemDat.recID.values,telemDat.Epoch.values) index = pd.MultiIndex.from_tuples(tuples, names=['FreqCode', 'recID','Epoch']) telemDat.set_index(index,inplace = True,drop = False) telemDat.to_sql('tblRaw',con = conn,index = False, if_exists = 'append') # add receiver parameters to database # recParamLine = [(recName,recType,scanTime,channels,fileName)] # conn.executemany('INSERT INTO tblReceiverParameters VALUES (?,?,?,?,?)',recParamLine) conn.commit() c.close() def telemDataImport(site,recType,file_directory,projectDB,switch = False, scanTime = None, channels = None, ant_to_rec_dict = None): tFiles = os.listdir(file_directory) for f in tFiles: f_dir = os.path.join(file_directory,f) rxfile=f if recType == 'lotek': lotek_import(f_dir,rxfile,projectDB,site,ant_to_rec_dict) elif recType == 'orion': orionImport(f_dir,rxfile,projectDB,site,switch, scanTime, channels, ant_to_rec_dict) else: print ("There currently is not an import routine created for this receiver type. Please try again") print ("File %s imported"%(f)) print ("Raw Telemetry Data Import Completed")

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0.00995

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null

0

null

0

1

0

4ef9b9608e8bbaa101ff10ed781037b8288c8ad9

3,284

py

Python

cpp/python/libgdf_cffi/tests/test_prefixsum.py

tgravescs/cudf

b8e72d713c801afd6b7d3f4b11711ef90b9f1f51

[ "Apache-2.0" ]

1

2021-02-23T21:19:08.000Z

cpp/python/libgdf_cffi/tests/test_prefixsum.py

tgravescs/cudf

b8e72d713c801afd6b7d3f4b11711ef90b9f1f51

[ "Apache-2.0" ]

null

cpp/python/libgdf_cffi/tests/test_prefixsum.py

tgravescs/cudf

b8e72d713c801afd6b7d3f4b11711ef90b9f1f51

[ "Apache-2.0" ]

null

from __future__ import division, print_function import pytest from itertools import product import numpy as np from libgdf_cffi import ffi, libgdf from librmm_cffi import librmm as rmm from libgdf_cffi.tests.utils import (new_column, unwrap_devary, get_dtype, gen_rand, buffer_as_bits, count_nulls) params_dtype = [ np.int8, np.int16, np.int32, np.int64, np.float32, np.float64, ] params_sizes = [1, 2, 13, 64, 100, 1000] def _gen_params(): for t, n in product(params_dtype, params_sizes): if (t == np.int8, np.int16 ) and n > 20: # to keep data in range continue yield t, n @pytest.mark.parametrize('dtype,nelem', list(_gen_params())) def test_prefixsum(dtype, nelem): if dtype == np.int8: # to keep data in range data = gen_rand(dtype, nelem, low=-2, high=2) else: data = gen_rand(dtype, nelem) d_data = rmm.to_device(data) d_result = rmm.device_array(d_data.size, dtype=d_data.dtype) col_data = new_column() gdf_dtype = get_dtype(dtype) libgdf.gdf_column_view(col_data, unwrap_devary(d_data), ffi.NULL, nelem, gdf_dtype) col_result = new_column() libgdf.gdf_column_view(col_result, unwrap_devary(d_result), ffi.NULL, nelem, gdf_dtype) inclusive = True libgdf.gdf_prefixsum(col_data, col_result, inclusive) expect = np.cumsum(d_data.copy_to_host()) got = d_result.copy_to_host() if not inclusive: expect = expect[:-1] assert got[0] == 0 got = got[1:] decimal = 4 if dtype == np.float32 else 6 np.testing.assert_array_almost_equal(expect, got, decimal=decimal) @pytest.mark.parametrize('dtype,nelem', list(_gen_params())) def test_prefixsum_masked(dtype, nelem): if dtype == np.int8: data = gen_rand(dtype, nelem, low=-2, high=2) else: data = gen_rand(dtype, nelem) mask = gen_rand(np.int8, (nelem + 8 - 1) // 8) dummy_mask = gen_rand(np.int8, (nelem + 8 - 1) // 8) d_data = rmm.to_device(data) d_mask = rmm.to_device(mask) d_result = rmm.device_array(d_data.size, dtype=d_data.dtype) d_result_mask = rmm.to_device(dummy_mask) gdf_dtype = get_dtype(dtype) extra_dtype_info = ffi.new('gdf_dtype_extra_info*') extra_dtype_info.time_unit = libgdf.TIME_UNIT_NONE col_data = new_column() libgdf.gdf_column_view_augmented(col_data, unwrap_devary(d_data), unwrap_devary(d_mask), nelem, gdf_dtype, count_nulls(d_mask, nelem), extra_dtype_info[0]) col_result = new_column() libgdf.gdf_column_view(col_result, unwrap_devary(d_result), unwrap_devary(d_result_mask), nelem, gdf_dtype) inclusive = True libgdf.gdf_prefixsum(col_data, col_result, inclusive) boolmask = buffer_as_bits(mask)[:nelem] expect = np.cumsum(data[boolmask]) got = d_result.copy_to_host()[boolmask] if not inclusive: expect = expect[:-1] assert got[0] == 0 got = got[1:] decimal = 4 if dtype == np.float32 else 6 np.testing.assert_array_almost_equal(expect, got, decimal=decimal)

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0.012346

0

null

0

null

0

1

0

4efabc6bb17436720e7713819341d82858e58c26

1,889

py

Python

tests/contrib_django/test_types.py

jhgg/graphene

67904e8329de3d69fec8c82ba8c3b4fe598afa8e

[ "MIT" ]

1

2021-04-28T21:35:01.000Z

tests/contrib_django/test_types.py

jhgg/graphene

67904e8329de3d69fec8c82ba8c3b4fe598afa8e

[ "MIT" ]

null

tests/contrib_django/test_types.py

jhgg/graphene

67904e8329de3d69fec8c82ba8c3b4fe598afa8e

[ "MIT" ]

null

from py.test import raises from collections import namedtuple from pytest import raises from graphene.core.fields import ( Field, StringField, ) from graphql.core.type import ( GraphQLObjectType, GraphQLInterfaceType ) from graphene import Schema from graphene.contrib.django.types import ( DjangoNode, DjangoInterface ) from .models import Reporter, Article from tests.utils import assert_equal_lists class Character(DjangoInterface): '''Character description''' class Meta: model = Reporter class Human(DjangoNode): '''Human description''' def get_node(self, id): pass class Meta: model = Article schema = Schema() def test_django_interface(): assert DjangoNode._meta.interface is True def test_pseudo_interface(): object_type = Character.internal_type(schema) assert Character._meta.interface is True assert isinstance(object_type, GraphQLInterfaceType) assert Character._meta.model == Reporter assert_equal_lists( object_type.get_fields().keys(), ['articles', 'firstName', 'lastName', 'email', 'pets', 'id'] ) def test_interface_resolve_type(): resolve_type = Character.resolve_type(schema, Human(object())) assert isinstance(resolve_type, GraphQLObjectType) def test_object_type(): object_type = Human.internal_type(schema) fields_map = Human._meta.fields_map assert Human._meta.interface is False assert isinstance(object_type, GraphQLObjectType) assert object_type.get_fields() == { 'headline': fields_map['headline'].internal_field(schema), 'id': fields_map['id'].internal_field(schema), 'reporter': fields_map['reporter'].internal_field(schema), 'pubDate': fields_map['pub_date'].internal_field(schema), } assert object_type.get_interfaces() == [DjangoNode.internal_type(schema)]

24.855263

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null

0

null

0

1

0

4efd82d7035e7bb99b1ee82af5fcb6a562111dd9

10,225

py

Python

landia/runner.py

pistarlab/simpleland

e1d5f65ef6ffaf9e32536d46aa3a2526d3b57801

[ "MIT" ]

4

2021-08-19T21:41:34.000Z

2022-02-03T00:44:43.000Z

landia/runner.py

pistarlab/simpleland

e1d5f65ef6ffaf9e32536d46aa3a2526d3b57801

[ "MIT" ]

null

landia/runner.py

pistarlab/simpleland

e1d5f65ef6ffaf9e32536d46aa3a2526d3b57801

[ "MIT" ]

null

import argparse import json import logging import threading from pyinstrument import Profiler from landia.client import GameClient from landia.config import GameDef, PlayerDefinition, ServerConfig from landia.content import Content from landia.common import StateDecoder, StateEncoder from landia.registry import load_game_content, load_game_def from landia.renderer import Renderer from landia.utils import gen_id import traceback from landia import gamectx from landia.server import GameUDPServer, UDPHandler import signal import sys LOG_LEVELS = { 'critical': logging.CRITICAL, 'error': logging.ERROR, 'warn': logging.WARNING, 'warning': logging.WARNING, 'info': logging.INFO, 'debug': logging.DEBUG } def get_game_def( game_id, enable_server, remote_client, port, tick_rate=None, step_mode =False, config_filename="base_config.json", content_overrides={} ) -> GameDef: game_def = load_game_def(game_id, config_filename, content_overrides) game_def.server_config.enabled = enable_server game_def.server_config.hostname = '0.0.0.0' game_def.server_config.port = port game_def.game_config.step_mode = step_mode game_def.game_config.config_filename =config_filename # Game game_def.game_config.tick_rate = tick_rate game_def.game_config.client_only_mode = not enable_server and remote_client return game_def def get_player_def( enable_client, client_id, remote_client, hostname, port, player_type, player_name=None, resolution=None, fps=None, render_shapes=None, is_human=True, draw_grid = False, tile_size=16, debug_render_bodies=False, view_type=0, sound_enabled = True, show_console = True, enable_resize=False, include_state_observation = False, render_to_screen=True, disable_hud = False) -> PlayerDefinition: player_def = PlayerDefinition() player_def.client_config.player_type = player_type player_def.client_config.client_id = client_id player_def.client_config.player_name=player_name player_def.client_config.enabled = enable_client player_def.client_config.server_hostname = hostname player_def.client_config.server_port = port player_def.client_config.frames_per_second = fps player_def.client_config.is_remote = remote_client player_def.client_config.is_human = is_human player_def.client_config.include_state_observation = include_state_observation player_def.renderer_config.resolution = resolution player_def.renderer_config.render_shapes = render_shapes player_def.renderer_config.draw_grid = draw_grid player_def.renderer_config.tile_size = tile_size player_def.renderer_config.debug_render_bodies = debug_render_bodies player_def.renderer_config.view_type = view_type player_def.renderer_config.sound_enabled =sound_enabled player_def.renderer_config.show_console =show_console player_def.renderer_config.enable_resize = enable_resize player_def.renderer_config.render_to_screen = render_to_screen player_def.renderer_config.disable_hud = disable_hud if player_type == "admin": player_def.renderer_config.view_port_scale = 0.7,0.7 player_def.renderer_config.border_h_offset = 0.02 player_def.renderer_config.info_filter = set(['label']) # else: # player_def.renderer_config.view_port_scale = (0.7,0.7) # player_def.renderer_config.border_h_offset = 0 # player_def.renderer_config.info_filter = set(['label']) return player_def def get_arguments(override_args=None): parser = argparse.ArgumentParser() # Server parser.add_argument("--enable_server", action="store_true", help="Accepts remote clients") # Client parser.add_argument("--enable_client", action="store_true", help="Run Client") parser.add_argument("--remote_client", action="store_true", help="client uses server") parser.add_argument("--resolution", default="800x600", help="resolution eg, [f,640x480]") parser.add_argument("--hostname", default="localhost", help="hostname or ip, default is localhost") parser.add_argument("--client_id", default=gen_id(), help="user id, default is random") parser.add_argument("--render_shapes", action='store_true', help="render actual shapes") parser.add_argument("--fps", default=60, type=int, help="fps") parser.add_argument("--player_type", default="default", type=str, help="Player type ") parser.add_argument("--view_type", default=0, type=int, help="NOT USED at moment: View type (0=perspective, 1=world)") parser.add_argument("--tile_size", default=16, type=int, help="not = no grid") parser.add_argument("--debug_render_bodies", action="store_true", help=" render") parser.add_argument("--disable_sound", action="store_true", help="disable_sound") parser.add_argument("--draw_grid", action="store_true", help="draw_grid") parser.add_argument("--show_console", action="store_true", help="Show on screen info") parser.add_argument("--disable_hud", action="store_true", help="Disable all screen printing") parser.add_argument("--enable_resize", action="store_true", help="Enable Screen Resize") parser.add_argument("--player_name",help="player name") parser.add_argument("--config_filename",default="base_config.json") # used for both client and server parser.add_argument("--port", default=10001, help="the port the server is running on") # Game Options parser.add_argument("--enable_profiler", action="store_true", help="Enable Performance profiler") parser.add_argument("--tick_rate", default=60, type=int, help="tick_rate") parser.add_argument("--game_id", default="survival", help="id of game") parser.add_argument("--content_overrides", default="{}", type=str,help="Content overrides in JSON format Eg: --content_overrides='{\"maps\":{\"main\":{\"static_layers\":[\"map_layer_test.txt\"]}}}'") parser.add_argument("--log_level",default="info",help=", ".join(list(LOG_LEVELS.keys())),type=str) parser.add_argument("--step_mode", action="store_true", help="Step mode (requires input for game time to proceed)") return parser.parse_args(override_args) def main(override_args=None): args = get_arguments(override_args) run(args) def run(args): logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) logging.getLogger().setLevel(LOG_LEVELS.get(args.log_level)) if not args.enable_server and not args.enable_client and not args.remote_client: args.enable_client = True if args.enable_server and args.enable_client and args.remote_client: print("Error: Server and Remote Client cannot be started from the same process. Please run seperately.") exit(1) profiler = None if args.enable_profiler: print("Profiling Enabled..") profiler = Profiler() profiler.start() game_def = get_game_def( game_id=args.game_id, enable_server=args.enable_server, remote_client=args.remote_client, port=args.port, tick_rate=args.tick_rate, step_mode = args.step_mode, config_filename=args.config_filename, content_overrides = json.loads(args.content_overrides), ) # Get resolution if args.enable_client and args.resolution == 'f': import pygame pygame.init() infoObject = pygame.display.Info() resolution = (infoObject.current_w, infoObject.current_h) else: res_string = args.resolution.split("x") resolution = (int(res_string[0]), int(res_string[1])) # player_meta_st = f"{{{args.player_meta}}}" # print(player_meta_st) # player_meta = json.loads( player_meta_st) player_def = get_player_def( enable_client=args.enable_client, client_id=str(args.client_id), remote_client=args.remote_client, hostname=args.hostname, port=args.port, render_shapes=args.render_shapes, resolution=resolution, fps=args.fps, draw_grid = args.draw_grid, player_type=args.player_type, tile_size=args.tile_size, debug_render_bodies = args.debug_render_bodies, view_type = args.view_type, sound_enabled= not args.disable_sound, show_console= args.show_console, enable_resize = args.enable_resize, disable_hud = args.disable_hud, player_name=args.player_name ) content: Content = load_game_content(game_def) gamectx.initialize( game_def, content=content) if player_def.client_config.enabled: renderer = Renderer( config = player_def.renderer_config, asset_bundle=content.get_asset_bundle()) client = GameClient( renderer=renderer, config=player_def.client_config) gamectx.add_local_client(client) server = None def graceful_exit(signum=None, frame=None): print("Shutting down") if game_def.server_config.enabled: # server.shutdown() server.server_close() if args.enable_profiler: profiler.stop() print(profiler.output_text(unicode=True, color=True)) exit() signal.signal(signal.SIGINT, graceful_exit) try: if game_def.server_config.enabled: server = GameUDPServer( conn=(game_def.server_config.hostname, game_def.server_config.port), config=game_def.server_config) server_thread = threading.Thread(target=server.serve_forever) server_thread.daemon = True server_thread.start() print("Server started at {} port {}".format(game_def.server_config.hostname, game_def.server_config.port)) gamectx.run() except (Exception,KeyboardInterrupt) as e: print(traceback.format_exc()) print(e) finally: graceful_exit() if __name__ == "__main__": main()

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null

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null

0

1

0

4eff13c2701b38fb56a685cebe6a17525d5923cc

3,734

py

Python

ships.py

prime-ffxiv/primebot

7f30815d06f69bc0f61aeff6dd3a0a19f4002657

[ "MIT" ]

null

ships.py

prime-ffxiv/primebot

7f30815d06f69bc0f61aeff6dd3a0a19f4002657

[ "MIT" ]

10

2021-07-13T03:39:52.000Z

2021-07-14T05:47:20.000Z

ships.py

prime-ffxiv/primebot

7f30815d06f69bc0f61aeff6dd3a0a19f4002657

[ "MIT" ]

1

2021-07-13T03:24:15.000Z

import io import datetime class Vehicle: def __init__(self, name, rank, max_rank=50): self.name = name self.rank = rank self.max_rank = max_rank self.voyage = None def add_voyage(self, voyage): self.voyage = voyage def delete_voyage(self): self.voyage = None def rename(self, name): self.name = name def update_rank(self, rank): self.rank = rank def __str__(self): if self.voyage is None: return "Ship: {} -- Rank: {}/{} -- Docked".format(self.name, self.rank, self.max_rank) else: # datetime formatting courtesy of # https://stackoverflow.com/a/538687 time_left = self.voyage.end_time - datetime.datetime.now() time_left = ''.join(str(time_left).split('.')[0]) return "Ship: {} -- Rank: {}/{} -- {} -- Voyage complete in {}".format(\ self.name, self.rank, self.max_rank, self.voyage.purpose, time_left) class Voyage: def __init__(self, start_time=None, end_time=None, time_delta=None, purpose=None): if (start_time is not None) and (end_time is not None) and \ (time_delta is not None): # check that the times given agree with elapsed time if end_time - start_time != time_delta: raise ValueError("Start/end times do not agree with voyage length") self.start_time = start_time self.end_time = end_time self.time_delta = time_delta elif (start_time is not None) and (end_time is not None): self.start_time = start_time self.end_time = end_time self.time_delta = self.end_time - self.start_time elif (start_time is not None) and (time_delta is not None): self.start_time = start_time self.time_delta = time_delta self.end_time = self.start_time + self.time_delta elif (time_delta is not None) and (end_time is not None): self.time_delta = time_delta self.end_time = end_time self.start_time = self.end_time - self.time_delta elif end_time is not None: self.time_delta = None self.start_time = None self.end_time = end_time else: raise ValueError("Not enough information provided to determine voyage start/end time") if purpose is not None: self.purpose = purpose else: self.purpose = "n/a" def __str__(self): return "Start time: {}, End time: {}, Voyage_length: {}, Purpose: {}".format(self.start_time, \ self.end_time, self.time_delta, self.purpose) class VehicleList: def __init__(self, airships=list(), submersibles=list()): self.airships = airships self.submersibles = submersibles def update_airships(self, airships): self.airships = airships def update_submersibles(self, submersibles): self.submersibles = submersibles def clear(self): self.airships = [] self.submersibles = [] def __str__(self): out_buf = io.StringIO() out_buf.write(u"```") out_buf.write(u"Airships:\n") for airship in self.airships: out_buf.write(str(airship)) out_buf.write(u"\n") out_buf.write(u"\n") out_buf.write(u"Submersibles:\n") for submersible in self.submersibles: out_buf.write(str(submersible)) out_buf.write(u"\n") out_buf.write(u"\n") out_buf.write(u"```") out_buf.seek(0) out_str = str(out_buf.read()).rstrip() return out_str def print_list(self): print(self)

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103

0.594537

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0.069536

0.046831

0.045412

0.349101

0.328288

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0

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104

40.150538

0.804281

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0.363636

0

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false

0

0.022727

0.011364

0.261364

0.022727

0

null

0

null

0

1

0

4eff140b6ab6cbf7d2f94869396489708d4d2a0a

10,735

py

Python

preql/core/pql_types.py

otherJL0/Preql

958a8dfd3a040f9c40fa394a8bfc3295f32a3019

[ "MIT" ]

null

preql/core/pql_types.py

otherJL0/Preql

958a8dfd3a040f9c40fa394a8bfc3295f32a3019

[ "MIT" ]

null

preql/core/pql_types.py

otherJL0/Preql

958a8dfd3a040f9c40fa394a8bfc3295f32a3019

[ "MIT" ]

null

from collections import defaultdict, deque from contextlib import suppress from dataclasses import field from datetime import datetime from decimal import Decimal from typing import Union import arrow import runtype from runtype.typesystem import TypeSystem from preql.utils import dataclass from .base import Object global_methods = {} class Id: def __init__(self, *parts): assert all(isinstance(p, str) for p in parts), parts self.parts = parts def __repr__(self): return 'Id(%s)' % '.'.join(self.parts) def __str__(self): # Prevents accidents! raise Exception("Operation not allowed!") def __hash__(self): return hash(tuple(self.parts)) def __eq__(self, other): if not isinstance(other, Id): return NotImplemented return self.parts == other.parts @property def repr_name(self): return self.parts[-1] @property def name(self): return self.parts[-1] def lower(self): return Id(*[p.lower() for p in self.parts]) def _repr_type_elem(t, depth): return _repr_type(t, depth - 1) if isinstance(t, Type) else repr(t) def _repr_type(t, depth=2): if t.elems: if depth > 0: if isinstance(t.elems, dict): elems = '[%s]' % ', '.join( f'{k}: {_repr_type_elem(v, depth)}' for k, v in t.elems.items() ) else: elems = '[%s]' % ', '.join(_repr_type_elem(e, depth) for e in t.elems) else: elems = '[...]' else: elems = '' return f'{t._typename_with_q}{elems}' ITEM_NAME = 'item' @dataclass class Type(Object): typename: str supertypes: frozenset elems: Union[tuple, dict] = field(hash=False, default_factory=dict) options: dict = field(hash=False, compare=False, default_factory=dict) proto_attrs: dict = field( hash=False, compare=False, default_factory=lambda: dict(global_methods) ) _nullable: bool = field(default_factory=bool) @property def _typename_with_q(self): n = '?' if self._nullable else '' return f'{self.typename}{n}' @property def elem(self): if isinstance(self.elems, dict): (elem,) = self.elems.values() else: (elem,) = self.elems return elem def as_nullable(self): # assert not self.maybe_null() return self.replace(_nullable=True) def maybe_null(self): return self._nullable or self is T.nulltype def supertype_chain(self): res = {t2 for t1 in self.supertypes for t2 in t1.supertype_chain()} assert self not in res return res | {self} def __eq__(self, other, memo=None): "Repetitive nested equalities are assumed to be true" if not isinstance(other, Type): return False if memo is None: memo = set() a, b = id(self), id(other) if (a, b) in memo or (b, a) in memo: return True memo.add((a, b)) l1 = self.elems if isinstance(self.elems, dict) else dict(enumerate(self.elems)) l2 = ( other.elems if isinstance(other.elems, dict) else dict(enumerate(other.elems)) ) if len(l1) != len(l2): return False res = self.typename == other.typename and all( k1 == k2 and v1.__eq__(v2, memo) for (k1, v1), (k2, v2) in zip(l1.items(), l2.items()) ) return res @property def elem_types(self): if isinstance(self.elems, dict): return self.elems.values() return self.elems def issubtype(self, t): assert isinstance(t, Type), t if t.typename == 'union': # XXX a little hacky. Change to issupertype? return any(self.issubtype(t2) for t2 in t.elem_types) if self is T.nulltype: if t.maybe_null(): return True # TODO zip should be aware of lengths if t.typename in (s.typename for s in self.supertype_chain()): return all( e1.issubtype(e2) for e1, e2 in zip(self.elem_types, t.elem_types) ) return False def __le__(self, other): return self.issubtype(other) def __getitem__(self, elems): # TODO assert elems = (any,) assert not isinstance(elems, tuple), (self, elems) elems = {ITEM_NAME: elems} return self.replace(elems=elems) def __call__(self, elems=None, **options): return self.replace( elems=elems or self.elems, proto_attrs=dict(self.proto_attrs), options={**self.options, **options}, ) def __repr__(self): # TODO Move to dp_inst? return _repr_type(self) def get_attr(self, attr): if self is T.unknown: return self if isinstance(self.elems, dict): with suppress(KeyError): return self.elems[attr] with suppress(KeyError): return self.proto_attrs[attr] return super().get_attr(attr) def all_attrs(self): # return {'elems': self.elems} if isinstance(self.elems, dict): return self.elems return {} def repr(self): return repr(self) def __or__(self, other): return T.union[self, other] class TupleType(Type): def __getitem__(self, elems): assert not self.elems return self.replace(elems=tuple(elems)) def __or__(self, other): return self.replace(elems=self.elems + (other,)) class SumType(TupleType): def issubtype(self, other): return all(t.issubtype(other) for t in self.elem_types) class ProductType(TupleType): def issubtype(self, other): return all(a.issubtype(b) for a, b in zip(self.elem_types, other.elem_types)) class PhantomType(Type): def issubtype(self, other): return super().issubtype(other) or self.elem.issubtype(other) class TypeDict(dict): def _register(self, name, supertypes=(), elems=(), type_class=Type): t = type_class(name, frozenset(supertypes), elems) assert name not in self T[name] = t dict.__setattr__(self, name, t) def __setattr__(self, name, args): if isinstance(args, tuple): self._register(name, *args) else: self._register(name, args) T = TypeDict() T.any = () T.unknown = [T.any] # T.union = [T.any] T._register('union', type_class=SumType) T.type = [T.any] Type.type = T.type T.object = [T.any] T.nulltype = [T.object] T.primitive = [T.object] T.text = [T.primitive] T._rich = [T.text] T.string = [T.text] T.number = [T.primitive] T.int = [T.number] T.float = [T.number] T.bool = [T.primitive] # number? T.decimal = [T.number] # TODO datetime vs timestamp ! T.timestamp = [T.primitive] # struct? T.datetime = [T.primitive] # struct? T.date = [T.primitive] # struct? T.time = [T.primitive] # struct? T.container = [T.object] T.struct = [T.container] T.row = [T.struct] # T.collection = [T.container], {} # T.table = [T.container], {} T._register('table', [T.container], {}) T.list = [T.table], {ITEM_NAME: T.any} T.set = [T.table], {ITEM_NAME: T.any} T.t_id = [T.primitive], (T.table,) T.t_relation = [T.primitive], (T.any,) # t_id? # XXX sequence instead of container? T._register('aggregated', [T.container], (T.any,), type_class=PhantomType) T._register('projected', [T.container], (T.any,), type_class=PhantomType) T._register('aggregate_result', [T.object], (T.any,), type_class=PhantomType) T.json = [T.container], (T.any,) T.json_array = [T.json] T._register('function', [T.object], type_class=TupleType) T.property = [T.object] T.module = [T.object] T.signal = [T.object] # ----------- T.Exception = [T.signal] T.IOError = [T.Exception] T.CodeError = [T.Exception] T.EvalError = [T.Exception] # CodeError - Failures due to inherently unexecutable code T.SyntaxError = [T.CodeError] T.NotImplementedError = [T.CodeError] # IOError - All errors resulting directly from attempts at I/O communication T.FileError = [T.IOError] T.DbError = [T.IOError] T.DbQueryError = [T.DbError] T.DbConnectionError = [T.DbError] # EvalError - Errors that arise only when evaluating the code (either at run-time or compile-time) T.TypeError = [T.EvalError] T.ValueError = [T.EvalError] T.NameError = [T.EvalError] T.JoinError = [T.EvalError] T.CompileError = [T.EvalError] T.AttributeError = [T.NameError] T.AssertError = [T.ValueError] T.IndexError = [T.ValueError] T.CastError = [T.TypeError] T.ImportError = [T.Exception] def _get_subtypes(): d = defaultdict(list) for t in T.values(): for st in t.supertypes: d[st].append(t) return dict(d) subtypes = _get_subtypes() # ------------- _python_type_to_sql_type = { bool: T.bool, int: T.int, float: T.float, str: T.string, datetime: T.timestamp, Decimal: T.decimal, arrow.Arrow: T.timestamp, # datetime? } def from_python(t): # TODO throw proper exception if this fails return _python_type_to_sql_type[t] def common_type(t1, t2): "Returns a type which is the closest ancestor of both t1 and t2" v1 = {t1} v2 = {t2} o1 = deque([t1]) o2 = deque([t2]) while o1 or o2: x1 = o1.popleft() v1.add(x1) if x1 in v2: return x1 o1 += [t for t in x1.supertypes if t not in v1] x2 = o2.popleft() v2.add(x2) if x2 in v1: return x2 o2 += [t for t in x2.supertypes if t not in v2] assert False def union_types(types): # TODO flatten unions, remove duplications and subtypes ts = set(types) if len(ts) > 1: elem_type = T.union(elems=tuple(ts)) else: (elem_type,) = ts return elem_type class ProtoTS(TypeSystem): def issubclass(self, t1, t2): if t2 is object: return True is_t2 = isinstance(t2, Type) if isinstance(t1, Type): return is_t2 and t1 <= t2 elif is_t2: return False # Regular Python types return runtype.issubclass(t1, t2) default_type = object class TS_Preql(ProtoTS): def get_type(self, obj): try: return obj.type except AttributeError: return type(obj) class TS_Preql_subclass(ProtoTS): def get_type(self, obj): # Preql objects if isinstance(obj, Type): return obj # Regular Python return type(obj) dp_type = runtype.Dispatch(TS_Preql_subclass()) dp_inst = runtype.Dispatch(TS_Preql())

24.453303

98

0.604844

1,446

10,735

4.36722

0.183956

0.029929

0.006334

0.016627

0.152336

0.094378

0.0654

0.040222

0.013618

0

0.009701

0.270238

10,735

438

99

24.509132

0.7964

0.085794

0

0.135762

0

0.030202

0.002727

0

0.002283

0.02649

1

0.13245

false

0

0.039735

0.059603

0.407285

0

null

0

null

0

1

0

4eff47851c2ce7c0c90b9adfdf5d2fd11cd451cc

947

py

Python

setup.py

MichaelPHartmann/iexfinance-py

9d91002a45747a78d47d3ff364d9ebf0f11a6fba

[ "Apache-2.0" ]

null

setup.py

MichaelPHartmann/iexfinance-py

9d91002a45747a78d47d3ff364d9ebf0f11a6fba

[ "Apache-2.0" ]

null

setup.py

MichaelPHartmann/iexfinance-py

9d91002a45747a78d47d3ff364d9ebf0f11a6fba

[ "Apache-2.0" ]

null

""" Version naming has been simplified in 2.0 going forward. Production releases will be MAJOR.MINOR format. Increments to major are reserved for significant updates. Increments to minor are available for all new versions Test releases are MAJOR.MINOR.PATCH format. """ import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name = "FinMesh", version = "2.2", author = "Michael Hartmann", author_email = "michaelpeterhartmann94@gmail.com.com", description = "A Python wrapper to bring together various financial APIs.", long_description = long_description, long_description_content_type = "text/markdown", keywords = "Finance, API, DCF, IEX, EDGAR, FRED, interest rates", url = "https://finmesh.readthedocs.io/", packages=setuptools.find_packages(), classifiers = [ "Programming Language :: Python :: 3" ], python_requires = ">3.6", )

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947

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32.655172

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0

0.389381

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0

1

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false

0

0.052632

0

0.052632

0

null

0

null

0

1

0

f601696eb01af41cfb6bfff7cab1149a7ddd141f

1,433

py

Python

universum/analyzers/pylint.py

o-andrieiev/Universum

71c9494f59dbac58a378d29eb31f8724964c8067

[ "BSD-2-Clause" ]

21

2019-01-07T03:59:54.000Z

2021-12-13T10:51:54.000Z

universum/analyzers/pylint.py

o-andrieiev/Universum

71c9494f59dbac58a378d29eb31f8724964c8067

[ "BSD-2-Clause" ]

407

2019-01-29T11:50:29.000Z

2022-03-24T15:09:20.000Z

universum/analyzers/pylint.py

o-andrieiev/Universum

71c9494f59dbac58a378d29eb31f8724964c8067

[ "BSD-2-Clause" ]

14

2019-01-08T07:37:13.000Z

2022-02-03T17:00:19.000Z

import argparse import json from typing import List from . import utils def pylint_argument_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser(description="Pylint analyzer") parser.add_argument("--rcfile", dest="rcfile", type=str, help="Specify a configuration file.") utils.add_python_version_argument(parser) return parser @utils.sys_exit @utils.analyzer(pylint_argument_parser()) def main(settings: argparse.Namespace) -> List[utils.ReportData]: cmd = [f"python{settings.version}", '-m', 'pylint', '-f', 'json'] if settings.rcfile: cmd.append(f'--rcfile={settings.rcfile}') cmd.extend(settings.file_list) output, _ = utils.run_for_output(cmd) return pylint_output_parser(output) def pylint_output_parser(output: str) -> List[utils.ReportData]: result: List[utils.ReportData] = [] for data in json.loads(output): # pylint has its own escape rules for json output of "message" values. # it uses cgi.escape lib and escapes symbols <>& result.append(utils.ReportData( symbol=data["symbol"], message=data["message"].replace("<", "<").replace(">", ">").replace("&", "&"), path=data["path"], line=int(data["line"]) )) return result if __name__ == "__main__": main() # pylint: disable=no-value-for-parameter # see https://github.com/PyCQA/pylint/issues/259

33.325581

102

0.667132

176

1,433

5.289773

0.460227

0.064447

0.061224

0.051557

0

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0.183531

1,433

42

103

34.119048

0.793162

0.141661

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0.136327

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0

1

0.1

false

0

0.133333

0

0.333333

0

null

0

null

0

1

0

f60211b49b4a0be6744888f8409578cc9d61668a

5,485

py

Python

fn_jira/fn_jira/components/jira_common.py

rudimeyer/resilient-community-apps

7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00

[ "MIT" ]

1

2020-08-25T03:43:07.000Z

fn_jira/fn_jira/components/jira_common.py

rudimeyer/resilient-community-apps

7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00

[ "MIT" ]

1

2019-07-08T16:57:48.000Z

fn_jira/fn_jira/components/jira_common.py

rudimeyer/resilient-community-apps

7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00

[ "MIT" ]

null

# -*- coding: utf-8 -*- # pragma pylint: disable=unused-argument, no-self-use # (c) Copyright IBM Corp. 2010, 2019. All Rights Reserved. """ These are methods for accessing Jira. The Jira REST API is used for general access. Requirements: JIRA URL and basic authentication user/password """ import json import fn_jira.lib.constants as constants from resilient_lib import RequestsCommon """ This module implements the calls needed for jira api access. API operations supported: ) create an issue ) create a comment ) transition an issue """ # URL fragments needed along with the base jira URL ISSUE_URL = 'rest/api/2/issue' TRANSITION_PARAM = 'transitions' COMMENT_PARAM = 'comment' class JiraCommon: def __init__(self, opts, function_opts): self.req_common = RequestsCommon(opts=opts, function_opts=function_opts) def create_issue(self, log, appDict): """Function: create a jira issue. :return the raw JSON returned from the api call """ issue_url = '/'.join((appDict['url'], ISSUE_URL)) payload = self._mkCreatePayload(appDict) resp = self.req_common.execute_call_v2('post', issue_url, auth=(appDict['user'], appDict['password']), data=payload, verify=appDict['verifyFlag'], headers=constants.HTTP_HEADERS) log and log.debug(resp) return self.get_json_result(resp) def transition_issue(self, log, appDict): """Function: transition a jira issue. :return: the raw JSON returned from the api call """ url = '/'.join((appDict['url'], TRANSITION_PARAM)) payload = self._mkTransitionPayload(appDict) #find_transitions(log, appDict) # uncomment to see transitions for this enterprise log and log.debug(payload) resp = self.req_common.execute_call_v2('post', url, auth=(appDict['user'], appDict['password']), data=payload, verify=appDict['verifyFlag'], headers=constants.HTTP_HEADERS) log and log.debug(resp) return self.get_json_result(resp) def find_transitions(self, log, appDict): """ determine the ticket transitions for a given issue :param log: :param appDict: :return: None """ url = '/'.join((appDict['url'], TRANSITION_PARAM)) resp = self.req_common.execute_call_v2('get', url, auth=(appDict['user'], appDict['password']), verify=appDict['verifyFlag'], headers=constants.HTTP_HEADERS) log and log.debug(resp) return self.get_json_result(resp) def create_comment(self, log, appDict): """Function: create a jira comment in a Jira issue. No JSON is returned on success :return: dictionary for a comment """ url = '/'.join((appDict['url'], COMMENT_PARAM)) payload = self._mkCommentPayload(appDict) resp = self.req_common.execute_call_v2('post', url, auth=(appDict['user'], appDict['password']), data=payload, verify=appDict['verifyFlag'], headers=constants.HTTP_HEADERS) log and log.debug(resp) # successfully added comments return an empty dictionary: { } return self.get_json_result(resp) def get_json_result(self, resp): """ get the response in json format, if possible :param resp: :return: None if errors or not json """ try: result = resp.json() if resp and resp.content else None except: result = None return result def _mkCreatePayload(self, appDict): ''' Build the payload for creating a Jira issue :param **dict could be **kwargs: :return: json payload for jira update ''' payload = { "fields": { "project": { "key": appDict.get('project') }, "issuetype": { "name": appDict.get('issuetype') } } } for key in appDict['fields']: payload['fields'][key] = appDict['fields'][key] return json.dumps(payload) def _mkCommentPayload(self, appDict): ''' Build the payload for adding a Jira comment :param **dict could be **kwargs: :return: json payload for jira update ''' payload = {"body": appDict['comment']} return json.dumps(payload) def _mkTransitionPayload(self, appDict): ''' Build the payload needed to transition a Jira issue :param **dict could be **kwargs: :return: json payload for jira call ''' payload = { "transition": { "id": appDict['transitionId'] } } if appDict.get('comment'): comment = \ {"comment": [ { "add": { "body": appDict['comment'] } } ] } payload['update'] = comment if appDict.get('resolution'): resolution = { "resolution": { "name": appDict['resolution'] } } payload['fields'] = resolution return json.dumps(payload)

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0.256499

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0.021495

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5,485

178

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0

0.244186

0

null

0

null

0

1

0

f6073840233bba54bc871867d9a6348f670aaee9

22,428

py

Python

staticwebsync/__init__.py

staticwebsync/staticwebsync

84c5f39cdd82192e3ed24541f9208be915ffc6df

[ "MIT" ]

null

staticwebsync/__init__.py

staticwebsync/staticwebsync

84c5f39cdd82192e3ed24541f9208be915ffc6df

[ "MIT" ]

14

2015-12-20T16:10:44.000Z

2016-03-08T18:21:10.000Z

staticwebsync/__init__.py

staticwebsync/staticwebsync

84c5f39cdd82192e3ed24541f9208be915ffc6df

[ "MIT" ]

1

2017-02-19T01:05:40.000Z

__all__ = ('log', 'progress_callback_factory', 'progress_callback_divisions', 'BadUserError', 'setup') import binascii import hashlib import mimetypes import mmap import os import posixpath import re import time import boto3 import botocore import termcolor log = lambda msg: None progress_callback_factory = lambda: None progress_callback_divisions = 10 # this is no longer used, but is retained so as not to break the module API class BadUserError(Exception): def __init__(self, message): self.message = message def setup(args): def split_all(s, splitter): out = [] while len(s) != 0: s, tail = splitter(s) out.insert(0, tail) return out def md5_hex_digest_string(filename): digestor = hashlib.md5() with open(filename, 'rb') as opened_file: fd = opened_file.fileno() if os.fstat(fd).st_size > 0: # can't mmap empty files with mmap.mmap(fd, 0, access=mmap.ACCESS_READ) as mm: digestor.update(mm) return digestor.hexdigest() def log_check(msg): """Use this when reporting that we are about to check something.""" log(msg) def log_noop(msg): """Use this when reporting that we checked something and it was fine as-is so it didn't need to be changed.""" log(termcolor.colored(msg, 'cyan', attrs=['bold'])) def log_op(msg): """Use this when reporting that we changed something (uploaded a file, changed a setting etc.)""" log(termcolor.colored(msg, 'green', attrs=['bold'])) def log_warn(msg): """Use this when warning the user about something.""" log(termcolor.colored(msg, 'red', attrs=['bold'])) prefix = 'http://' if args.host_name.startswith(prefix): args.host_name = args.host_name[len(prefix):] suffix = '/' if args.host_name.endswith(suffix): args.host_name = args.host_name[:-len(suffix)] standard_bucket_name = args.host_name is_index_key = re.compile('(?P<path>^|.*?/)%s$' % re.escape(args.index)) session = boto3.session.Session( aws_access_key_id=args.access_key_id, aws_secret_access_key=args.secret_access_key) s3 = session.resource('s3') bucket = None region = None all_buckets = None try: log_check('looking for existing S3 bucket') all_buckets = list(s3.buckets.all()) except botocore.exceptions.ClientError as e: if e.response['ResponseMetadata']['HTTPStatusCode'] == 403: raise BadUserError('Access denied: %s' % e.response['Error']['Message']) else: raise e except botocore.exceptions.NoCredentialsError: raise BadUserError('No AWS credentials found. Please set up your ~/.aws/credentials file or specify them on the command line.') use_cloudfront = not args.no_cloudfront MARKER_KEY_NAME = '.staticwebsync' def install_marker_key(bucket): s3.Object(bucket.name, MARKER_KEY_NAME).put(Body=b'', ACL='private') def object_or_none(bucket, key): try: o = s3.Object(bucket.name, key) o.load() return o except botocore.exceptions.ClientError as e: if e.response['ResponseMetadata']['HTTPStatusCode'] == 404: return None else: raise e for b in all_buckets: if b.name == standard_bucket_name or b.name.startswith(standard_bucket_name + '-'): log_noop('found existing bucket %s' % b.name) # The bucket location must be set in boto so that it can use the # path addressing style: # http://boto3.readthedocs.org/en/latest/guide/s3.html?highlight=botocore.client.Config#changing-the-addressing-style # That's required because otherwise requests on buckets with dots # in their names fail HTTPS validation: # https://github.com/boto/boto/issues/2836 region = s3.meta.client.get_bucket_location(Bucket=b.name)['LocationConstraint'] # That API returns None when the region is us-east-1: # http://docs.aws.amazon.com/AmazonS3/latest/API/RESTBucketGETlocation.html if region is None: region = 'us-east-1' s3 = session.resource('s3', region_name=region) bucket = s3.Bucket(b.name) if not object_or_none(b, MARKER_KEY_NAME): if not args.take_over_existing_bucket: raise BadUserError("The S3 bucket %s already exists, but was not created by staticwebsync. If you wish to use it anyway and are happy for any existing files in it to be deleted if they don't have a corresponding local file then use the --take-over-existing-bucket option." % bucket.name) install_marker_key(bucket) break else: bucket_name = standard_bucket_name first_fail = True while True: try: log_op('creating bucket %s' % bucket_name) configuration = None region = args.bucket_location if not region or region == 'US': region = 'us-east-1' if region != 'us-east-1': configuration = { 'LocationConstraint': region } s3 = session.resource('s3', region_name=region) if configuration: bucket = s3.create_bucket(Bucket=bucket_name, CreateBucketConfiguration=configuration) else: bucket = s3.create_bucket(Bucket=bucket_name) install_marker_key(bucket) break except botocore.exceptions.ClientError as e: if e.response['Error']['Code'] == 'BucketAlreadyExists': log_warn('bucket %s was already used by another user' % bucket_name) if first_fail: log_warn('We can use an alternative bucket name, but this will only work with CloudFront and not with standard S3 web site hosting (because it requires the bucket name to match the host name).') first_fail = False if not use_cloudfront: raise BadUserError("Using CloudFront is disabled, so we can't continue.") bucket_name = standard_bucket_name + '-' + binascii.b2a_hex(os.urandom(8)).decode('ascii') continue else: raise e log_op('configuring bucket ACL policy') bucket.Acl().put(ACL='private') log_op('configuring bucket for website access') website_configuration = { 'IndexDocument': { 'Suffix': args.index } } if args.error_page is not None: website_configuration['ErrorDocument'] = { 'Key': args.error_page } bucket.Website().put(WebsiteConfiguration=website_configuration) # http://docs.aws.amazon.com/AmazonS3/latest/dev/WebsiteEndpoints.html website_endpoint = '%s.s3-website-%s.amazonaws.com' % (bucket.name, region) def set_caller_reference(options): options['CallerReference'] = binascii.b2a_hex(os.urandom(8)).decode('ascii') if use_cloudfront: cf = session.client('cloudfront') all_distribution_summaries = [] try: log_check('looking for existing CloudFront distribution') distribution_lists = list(cf.get_paginator('list_distributions').paginate()) for distribution_list in distribution_lists: all_distribution_summaries.extend(distribution_list['DistributionList'].get('Items', [])) except botocore.exceptions.ClientError as e: if e.response['Error']['Code'] == 'OptInRequired': raise BadUserError('Your AWS account is not signed up for CloudFront, please sign up at http://aws.amazon.com/cloudfront/') else: raise e def set_required_config(config): any_changed = False def get_or_set_default(d, k, default): nonlocal any_changed value = d.get(k) if value is None: any_changed = True d[k] = default return default return value def set_if_not_equal(d, k, value): nonlocal any_changed old_value = d.get(k) if old_value != value: any_changed = True d[k] = value aliases = get_or_set_default(config, 'Aliases', {}) aliases_items = get_or_set_default(aliases, 'Items', []) if args.host_name not in aliases_items: any_changed = True aliases_items.append(args.host_name) aliases['Quantity'] = len(aliases_items) origins = get_or_set_default(config, 'Origins', {}) origins_items = get_or_set_default(origins, 'Items', []) if len(origins_items) == 0: any_changed = True origin = {} origins_items[:] = [origin] elif len(origins_items) == 1: origin = origins_items[0] else: raise BadUserError("The existing distribution has multiple origins, and we can't configure distributions with more than one. Please delete all but the default origin or delete the distribution.") set_if_not_equal(origins, 'Quantity', len(origins_items)) set_if_not_equal(origin, 'DomainName', website_endpoint) set_if_not_equal(origin, 'Id', 'S3 Website') custom_origin_config = get_or_set_default(origin, 'CustomOriginConfig', {}) set_if_not_equal(custom_origin_config, 'OriginProtocolPolicy', 'http-only') set_if_not_equal(custom_origin_config, 'HTTPPort', 80) set_if_not_equal(custom_origin_config, 'HTTPSPort', 443) default_cache_behavior = get_or_set_default(config, 'DefaultCacheBehavior', {}) set_if_not_equal(default_cache_behavior, 'Compress', True) set_if_not_equal(default_cache_behavior, 'TargetOriginId', origin['Id']) forwarded_values = get_or_set_default(default_cache_behavior, 'ForwardedValues', {}) set_if_not_equal(forwarded_values, 'QueryString', False) cookies = get_or_set_default(forwarded_values, 'Cookies', {}) if cookies.get('Forward') != 'none': any_changed = True cookies.clear() cookies['Forward'] = 'none' set_if_not_equal(config, 'Enabled', True) return any_changed created_new_distribution = False for distribution_summary in all_distribution_summaries: origins = distribution_summary['Origins'].get('Items', []) if len(origins) == 1: origin = origins[0] if origin['DomainName'] == website_endpoint: distribution_id = distribution_summary['Id'] distribution_domain_name = distribution_summary['DomainName'] log_noop('found distribution: %s' % distribution_id) break if args.host_name in distribution_summary['Aliases'].get('Items', []): # TODO Remove the alias if a force option is given. raise BadUserError("Existing distribution %s has this hostname set as an alternate domain name (CNAME), but it isn't associated with the correct origin bucket. Please remove the alternate domain name from the distribution or delete the distribution." % distribution_summary['Id']) else: log_op('creating CloudFront distribution') creation_config = {} set_required_config(creation_config) # Set defaults for options that are required to create a distribution: creation_config.setdefault('Comment', '') default_cache_behavior = creation_config.setdefault('DefaultCacheBehavior', {}) trusted_signers = default_cache_behavior.setdefault('TrustedSigners', {}) trusted_signers.setdefault('Enabled', False) trusted_signers.setdefault('Quantity', 0) default_cache_behavior.setdefault('ViewerProtocolPolicy', 'allow-all') default_cache_behavior.setdefault('MinTTL', 0) set_caller_reference(creation_config) distribution_creation_response = cf.create_distribution(DistributionConfig=creation_config) distribution_id = distribution_creation_response['Distribution']['Id'] distribution_domain_name = distribution_creation_response['Distribution']['DomainName'] log_op('created distribution %s' % distribution_id) created_new_distribution = True if not created_new_distribution: log_check('checking distribution configuration') get_distribution_config_response = cf.get_distribution_config(Id=distribution_id) update_config = get_distribution_config_response['DistributionConfig'] if set_required_config(update_config): log_op('configuring distribution') cf.update_distribution( Id=distribution_id, IfMatch=get_distribution_config_response['ETag'], DistributionConfig=update_config) else: log_noop('distribution configuration already fine') # TODO Set up custom MIME types. mimetypes.init() # On my Windows system these get set to silly other values by some registry # key, which is, for the avoidance of doubt, super lame. mimetypes.types_map['.png'] = 'image/png' mimetypes.types_map['.jpg'] = 'image/jpeg' mimetypes.types_map['.js'] = 'application/javascript' # TODO Serialize these in case of failure, and resume when restarting: invalidations = [] dir = os.path.normpath(args.folder) if not os.path.exists(dir): raise BadUserError('Folder %s does not exist.' % args.folder) if not os.path.isdir(dir): raise BadUserError('%s is a file not a folder.' % args.folder) os.chdir(dir) for (dirpath, dirnames, filenames) in os.walk('.'): if not args.allow_dot_files: blacklisted = False for p in split_all(dirpath, os.path.split): if p.startswith('.') and p != '.': log_noop('skipping folder %s' % os.path.normpath(dirpath)) blacklisted = True break if blacklisted: continue for filename in filenames: if not args.allow_dot_files and filename.startswith('.'): log_noop('skipping file %s' % filename) continue inf = os.path.normpath(os.path.join(dirpath, filename)) d = os.path.normpath(dirpath) if d == '.': d = '' type = mimetypes.guess_type(filename, strict=False) upload_extra_args = {} if type[0] is not None: # the lack of hyphens in the keys is correct, because these are method arguments rather than HTTP headers: upload_extra_args['ContentType'] = type[0] if type[1] is not None: upload_extra_args['ContentEncoding'] = type[1] def upload(f): # We could re-use this when uploading the same file twice, but # the code would be a bit messy. md5 = None parts = list(split_all(d, os.path.split)) parts.append(f) outf = posixpath.join(*parts) if outf == '': outf = args.index log_check('processing "%s" -> "%s"' % (inf, outf)) obj = s3.Object(bucket.name, outf) try: obj.load() existed = True log_noop('%s exists in bucket' % outf) md5 = md5_hex_digest_string(inf) if obj.e_tag == '"%s"' % md5 and \ obj.content_type == upload_extra_args.get('ContentType', obj.content_type) and \ obj.content_encoding == upload_extra_args.get('ContentEncoding'): # TODO Check for other headers? log_noop('%s matches local file' % outf) if not args.repair: return acl = obj.Acl() user_grant_okay = False public_grant_okay = False for grant in acl.grants: grantee = grant['Grantee'] if grantee.get('ID') == acl.owner['ID']: user_grant_okay = grant['Permission'] == 'FULL_CONTROL' if not user_grant_okay: break elif grantee['Type'] == 'Group': public_grant_okay = \ grantee['URI'] == 'http://acs.amazonaws.com/groups/global/AllUsers' and \ grant['Permission'] == 'READ' if not public_grant_okay: break else: break else: if user_grant_okay and public_grant_okay: log_noop('%s ACL is fine' % outf) return log_op('%s ACL is wrong' % outf) except botocore.exceptions.ClientError as ce: if ce.response['Error']['Code'] != '404': raise ce existed = False log_op('uploading %s' % outf) upload_extra_args['ACL'] = 'public-read' # Convert our callbacks to be compatible with the boto3 upload callback API: class CallbackWrapper: def __init__(self, old_callback_factory, file_size): self.old_callback = old_callback_factory() self.file_size = file_size self.total_transferred = 0 def __call__(self, newly_transferred_bytes_count): self.total_transferred += newly_transferred_bytes_count self.old_callback(self.total_transferred, self.file_size) obj.upload_file(inf, ExtraArgs=upload_extra_args, Callback=CallbackWrapper(progress_callback_factory, os.path.getsize(inf))) if existed: key_name = obj.key invalidations.append(key_name) # Index pages are likely to be cached in CloudFront without the trailing filename instead (or as well). m = is_index_key.match(key_name) if m: invalidations.append(m.group('path')) upload(filename) log_check('checking for deleted files') for obj in list(bucket.objects.all()): name = obj.key if name == MARKER_KEY_NAME: continue if name.endswith('/'): name = posixpath.join(name, args.index) parts = split_all(name, posixpath.split) blacklisted = False if not args.allow_dot_files: for p in parts: if p.startswith('.'): blacklisted = True break if not blacklisted and os.path.isfile(os.path.join(*parts)): log_noop('%s has corresponding local file' % obj.key) continue log_op('deleting %s' % obj.key) obj.delete() invalidations.append(obj.key) def log_sync_complete(dns_entry_name, dns_entry_target): log_op('sync complete') log_check('a DNS entry needs to be set for\n%s\npointing to\n%s' % (dns_entry_name, dns_entry_target)) if not use_cloudfront: log_sync_complete(args.host_name, website_endpoint) return def cf_complete(): log_sync_complete(args.host_name, distribution_domain_name) if (args.dont_wait_for_cloudfront_propagation): log_noop('CloudFront may take up to 15 minutes to reflect any changes') return while True: log_check('checking if CloudFront propagation is complete') get_distribution_response = cf.get_distribution(Id=distribution_id)['Distribution'] if get_distribution_response['Status'] != 'InProgress' and \ get_distribution_response['InProgressInvalidationBatches'] == 0: log_op('CloudFront propagation is complete') return interval = 15 log_check('propagation still in progress; checking again in %d seconds' % interval) time.sleep(interval) if len(invalidations) == 0: cf_complete() return log_op('invalidating cached copies of changed or deleted files') def invalidate_all(paths): batch = { 'Paths': { 'Quantity': len(paths), 'Items': paths, }, } while True: try: set_caller_reference(batch) cf.create_invalidation(DistributionId=distribution_id, InvalidationBatch=batch) break except botocore.exceptions.ClientError as ce: if ce.response['Error']['Code'] != 'TooManyInvalidationsInProgress': raise ce interval = 60 log_check('too many invalidations in progress; trying again in %d seconds' % interval) time.sleep(interval) paths.clear() paths = [] def invalidate(path): paths.append(path) if len(paths) == 3000: invalidate_all(paths) for i in invalidations: invalidate('/' + i) if (i == args.index): invalidate('/') if len(paths) > 0: invalidate_all(paths) cf_complete()

40.852459

307

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2,487

22,428

5.063932

0.211902

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0.043195

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f608cd237ad400b7d70ddc6b3272e50852e00f2c

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py

Python

src/dsa/chapter4_exercises.py

AlexMGitHub/DS-A_Python

a4770c95ef2f76917fb1d8bc8c11433828a735a3

[ "MIT" ]

null

src/dsa/chapter4_exercises.py

AlexMGitHub/DS-A_Python

a4770c95ef2f76917fb1d8bc8c11433828a735a3

[ "MIT" ]

null

src/dsa/chapter4_exercises.py

AlexMGitHub/DS-A_Python

a4770c95ef2f76917fb1d8bc8c11433828a735a3

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """Solutions to chapter 4 exercises. ############################################################################### # chapter4_exercises.py # # Revision: 1.00 # Date: 6/27/2021 # Author: Alex # # Purpose: Solutions to chapter 4 exercises from "Data Structures and # Algorithms in Python" by Goodrich et. al. # ############################################################################### """ # %% Imports # Standard system imports from pathlib import Path # Related third party imports # Local application/library specific imports # %% Reinforcement Exercises def harmonic_number(n): """Solution to exercise R-4.6. Describe a recursive function for computing the nth Harmonic number. """ if n == 1: return 1/n # Base case return 1/n + harmonic_number(n-1) def str_to_int(string): """Solution to exercise R-4.7. Describe a recursive function for converting a string of digits into the integer it represents. For example, "13531" represents the integer 13,531. """ n = len(string) zero_unicode = ord('0') def recurse(idx): if idx == n: return 0 # Base case int_val = ord(string[idx]) - zero_unicode return int_val * 10 ** (n - 1 - idx) + recurse(idx + 1) return recurse(0) # %% Creativity Exercises def find_min_max(data): """Solution to exercise C-4.9. Write a short recursive Python function that finds the minimum and maximum values in a sequence without using any loops. """ n = len(data) min_val = data[0] max_val = data[0] def recurse_minmax(idx): nonlocal min_val, max_val if idx == n: return min_val, max_val # Base case if data[idx] > max_val: max_val = data[idx] elif data[idx] < min_val: min_val = data[idx] return recurse_minmax(idx + 1) return recurse_minmax(1) def recursive_log(num): """Solution to exercise C-4.10. Describe a recursive algorithm to compute the integer part of the base-two logarithm of n using only addition and integer division. """ def recurse(num, count): if num == 1: return count # Base case return recurse(num // 2, count + 1) return recurse(num, 0) def recursive_unique(sequence): """Solution to exercise C-4.11. Describe an efficient recursive function for solving the element uniqueness problem, which runs in time that is at most O(n^2) in the worst case without using sorting. -------------------------------------------------------------------------- Solution: -------------------------------------------------------------------------- The nonrecursive part of each call uses O(1) time, so the overall running time will be proportional to the total number of recursive invocations. However, unlike the "bad" recursion example in the text, my function does not make more than one recursive call per invocation. It is a linear recursion algorithm. The algorithm works by decrementing the stop index until it reaches the start index. Once that happens, the stop index is reset to the end of the sequence and the start index is incremented by 1. The first recursion call in the conditional statements only executes if the stop index hasn't reached the start index yet. The second recursion call only occurs if the start index hasn't reached the end of the sequence yet. Both calls are placed in an "elif" statement that makes them mutually exclusive. In other words, a maximum of one recursive call per invocation. Based on the above description, it's clear that the algorithm is worst case O(n^2). It's equivalent to a nested loop with n outer iterations and (n-1), (n-2) ... 1 inner iterations. This is well-known to be O(n^2). I used timeit to verify that the execution time of my algorithm grows approximately as n^2. """ n = len(sequence) def unique(start, stop): if sequence[start] == sequence[stop]: return False # Base case if not unique if stop > (start+1): return unique(start, stop-1) if start < (n-2): return unique(start+1, n-1) return True # Base case if unique return unique(0, n-1) def integer_product(num1, num2): """Solution to exercise C-4.12. Give a recursive algorithm to compute the product of two positive integers, m and n, using only addition and subtraction. """ def recurse(num1, idx): if idx == 0: return 0 # Base case return num1 + recurse(num1, idx-1) return recurse(num1, num2) def towers_of_hanoi(n): """Solution to exercise C-4.14. In the Towers of Hanoi puzzle, we are given a platform with three pegs, a, b, and c, sticking out of it. On peg a is a stack of n disks, each larger than the next, so that the smallest is on the top and the largest is on the bottom. The puzzle is to move all the disks from peg a to peg c, moving one disk at a time, so that we never place a larger disk on top of a smaller one. See Figure 4.15 for an example of the case n = 4. Describe a recursive algorithm for solving the Towers of Hanoi puzzle for arbitrary n. """ a = list(range(n, 0, -1)) b = [] c = [] def recurse(n, source, destination, temp): if n > 0: # Base case, bottom of stack of disks # Move n-1 disks from source to temporary storage recurse(n-1, source, temp, destination) # Move the nth (bottom) disk from source to destination destination.append(source.pop()) # Move the n-1 disks from temporary storage to destination recurse(n-1, temp, destination, source) recurse(n, a, c, b) return c def all_subsets(aset): """Solution to exercise C-4.15. Write a recursive function that will output all the subsets of a set of n elements (without repeating any subsets). -------------------------------------------------------------------------- Solution: -------------------------------------------------------------------------- I've made the following assumptions: 1. The input is a list of unique numbers 2. The set itself is considered a subset (not a proper subset) 3. The empty set is considered a subset """ def recurse(alist): if not alist: return [[]] # Base case, return empty set prev_lists = recurse(alist[1:]) return prev_lists + [[alist[0]] + y for y in prev_lists] return recurse(aset) def reverse_string(string): """Solution to exercise C-4.16. Write a short recursive Python function that takes a character string s and outputs its reverse. For example, the reverse of "pots&pans" would be "snap&stop". """ n = len(string) def recurse(idx): if idx == 0: return string[0] # Base case, decremented to beginning of string return string[idx] + recurse(idx-1) return recurse(n-1) def is_palindrome(string): """Solution to exercise C-4.17. Write a short recursive Python function that determines if a string s is a palindrome, that is, it is equal to its reverse. For example, "racecar" and "gohangasalamiimalasagnahog" are palindromes. """ n = len(string) def recurse(idx): if idx == n: return True # Base case, end of string and all letters matched if string[idx] == string[n-1-idx]: return recurse(idx+1) return False return recurse(0) def more_vowels(astring): """Solution to exercise C-4.18. Use recursion to write a Python function for determining if a string s has more vowels than consonants. """ string = astring.lower() vowels = 'aeiou' n = len(string) vowel_count = 0 def recurse(idx): nonlocal vowel_count if idx == n: return vowel_count > (n-vowel_count) # Base case, end of string if string[idx] in vowels: vowel_count += 1 return recurse(idx+1) return recurse(0) def evens_first(nums): """Solution to exercise C-4.19. Write a short recursive Python function that rearranges a sequence of integer values so that all the even values appear before all the odd values. """ n = len(nums) def recurse(start, stop): if start == stop: return nums # Base case, finished sorting list if nums[start] % 2 == 0: return recurse(start+1, stop) nums[stop], nums[start] = nums[start], nums[stop] return recurse(start, stop-1) return recurse(0, n-1) def rearrange_unsorted(nums, k): """Solution to exercise C-4.20. Given an unsorted sequence, S, of integers and an integer k, describe a recursive algorithm for rearranging the elements in S so that all elements less than or equal to k come before any elements larger than k. What is the running time of your algorithm on a sequence of n values? -------------------------------------------------------------------------- Solution: -------------------------------------------------------------------------- The algorithm terminates when the start index equals the stop index. That requires n recursive calls. Each recursive call will worst case swap two values in the list. Replacing a value in a list is O(1) according to the text (table 5.4), and so this algorithm is O(n). """ n = len(nums) def recurse(start, stop): if start == stop: return nums # Base case, finished sorting list if nums[start] <= k: return recurse(start+1, stop) nums[stop], nums[start] = nums[start], nums[stop] return recurse(start, stop-1) return recurse(0, n-1) def sum_to_k(nums, k): """Solution to exercise C-4.21. Suppose you are given an n-element sequence, S, containing distinct integers that are listed in increasing order. Given a number k, describe a recursive algorithm to find two integers in S that sum to k, if such a pair exists. What is the running time of your algorithm? -------------------------------------------------------------------------- Solution: -------------------------------------------------------------------------- All of the non-recursive operations are O(1). The running time is thus proportional to the number of recursive calls. Worst case, the algorithm will try every pairwise combination in the sequence. This is O(n^2), as there are n elements, each of which will be compared with n - k other elements: the familiar n*(n+1)/2 formula. """ n = len(nums) def recurse(start, stop): if nums[start] + nums[stop] == k: return nums[start], nums[stop] # Base case: pair found if stop > (start+1): return recurse(start, stop-1) if start < (n-2): return recurse(start+1, n-1) return None # Base case: no pair found return recurse(0, n-1) # %% Project Exercises def summation_puzzle(words): """Solution to exercise P-4.24. Write a program for solving summation puzzles by enumerating and testing all possible configurations. Using your program, solve the three puzzles given in Section 4.4.3. """ assert len(words) == 3, 'Summation puzzle must be three word phrase' digit_list = list(range(10)) chars = list(''.join(words)) unique_chars = list(set(chars)) n = len(unique_chars) char_dict = {} def solution_found(S): nonlocal char_dict char_dict = {unique_chars[idx]: S[idx] for idx in range(len(S))} word1 = [str(char_dict[x]) for x in words[0]] val1 = int(''.join(word1)) word2 = [str(char_dict[x]) for x in words[1]] val2 = int(''.join(word2)) word3 = [str(char_dict[x]) for x in words[2]] val3 = int(''.join(word3)) return (val1 + val2) == val3 def recurse(k, S, U): for idx, e in enumerate(U): S.append(U.pop(idx)) if k == 1: if solution_found(S): return (S, char_dict) # Base case: Solution found else: result = recurse(k-1, S.copy(), U.copy()) if result is not None: return result S.pop() U.insert(0, e) return None # Base case: No solution found return recurse(n, [], digit_list) def os_walk(path_str): """Solution to exercise P-4.27. Python’s os module provides a function with signature walk(path) that is a generator yielding the tuple (dirpath, dirnames, filenames) for each subdirectory of the directory identified by string path, such that string dirpath is the full path to the subdirectory, dirnames is a list of the names of the subdirectories within dirpath, and filenames is a list of the names of non-directory entries of dirpath. -------------------------------------------------------------------------- Solution: -------------------------------------------------------------------------- I used pytest's tmp_path fixture to create a temporary directory with a function-level scope. I then wrote a recursive function to create a simple directory with multiple levels of subdirectories and files. I compare the results of the os module's walk function to my own. Note that the order of the tuples reported by the two methods may be arbitrary, and so I sorted both results before comparing them. I also compared the lists of files and directories as sets so that a differing order does not cause the test to fail. """ results = [] path = Path(path_str) def walk(path): files = [] dirs = [] contents = Path.iterdir(path) for obj in contents: if Path.is_dir(obj): dirs.append(obj.name) walk(obj) else: files.append(obj.name) results.append((str(path), dirs, files)) walk(path) return results

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f60bbe8d92cacc0baec1190db4c4305ce847b01a

4,220

py

Python

spanish_inflections.py

mathigatti/spanish_inflections

bb5d9677edec8862aaaf4a4f7c6ad5bc4c151c50

[ "MIT" ]

null

spanish_inflections.py

mathigatti/spanish_inflections

bb5d9677edec8862aaaf4a4f7c6ad5bc4c151c50

[ "MIT" ]

null

spanish_inflections.py

mathigatti/spanish_inflections

bb5d9677edec8862aaaf4a4f7c6ad5bc4c151c50

[ "MIT" ]

null

rules_adjetive = [] with open("MM.adj.txt",'r') as f: for line in f.readlines(): rule = line.split() rules_adjetive.append({"word":rule[0], "lemma":rule[1], "code":rule[2]}) rules_noun = [] with open("MM.nom.txt",'r') as f: for line in f.readlines(): rule = line.split() rules_noun.append({"word":rule[0], "lemma":rule[1], "code":rule[2]}) rules_tanc = [] with open("MM.tanc.txt",'r') as f: for line in f.readlines(): rule = line.split() rules_tanc.append({"word":rule[0], "lemma":rule[1], "code":rule[2]}) rules_verb = [] with open("MM.verb.txt",'r') as f: for line in f.readlines(): rule = line.split() rules_verb.append({"word":rule[0], "lemma":rule[1], "code":rule[2]}) rules = {"DET": rules_tanc, "ADJ": rules_adjetive, "VERB": rules_verb, "NOUN": rules_noun} def search_rule(rules, word): for rule in rules: if word == rule["word"]: return rule return None def search_word(rules, lemma, code): for rule_i in rules: if lemma == rule_i["lemma"] and code == rule_i["code"]: return rule_i["word"] return "" def search_verb(verb): rule_i = search_rule(rules_verb, verb) if rule_i is None: return {"original": verb} else: lemma = rule["lemma"] code = rule["code"] result_i = {"original": noun} for rule in rules_verb: if rule["lemma"] == lemma: result_i[rule["code"]] = rule["word"] return result_i def search_noun(noun): rule = search_rule(rules_noun, noun) if rule is None: return {"original": noun} else: lemma = rule["lemma"] code = rule["code"] result_i = {"original": noun} for sub_code in ["S","P"]: code = code[:3] + sub_code + code[4:] result_i[code[2:4]] = search_word(rules_noun, lemma, code) return result_i def search_adjetive(adjetive): rule = search_rule(rules_adjetive, adjetive) if rule is None: return {"original": adjetive, "FS": "", "FP": "", "MS": "", "MP": ""} else: lemma = rule["lemma"] code = rule["code"] result_i = {"original": adjetive} for sub_code in ["FS","FP","MS", "MP", "CS", "CP"]: code = code[:3] + sub_code + code[5:] result_i[sub_code] = search_word(rules_adjetive, lemma, code) if result_i["CS"] != "": for sub_code in ["FS","MS"]: result_i[sub_code] = result_i["CS"] for sub_code in ["FP","MP"]: result_i[sub_code] = result_i["CP"] if result_i["FS"] == "": for sub_code in ["FS","MS"]: result_i[sub_code] = result_i["MS"] for sub_code in ["FP","MP"]: result_i[sub_code] = result_i["MP"] if result_i["MS"] == "": for sub_code in ["FS","MS"]: result_i[sub_code] = result_i["FS"] for sub_code in ["FP","MP"]: result_i[sub_code] = result_i["FP"] del result_i["CS"] del result_i["CP"] return result_i def basic_noun_data(word): code = search_rule(rules_noun, word)["code"] gender = code[2] number = code[3] return {"gender": gender, "number": number} def fix_verb(rules, word, gender, number): try: rule = search_rule(rules, word) lemma = rule["lemma"] code = rule["code"] for a, b in [("F", gender), ("M", gender), ("S", number), ("P", number)]: code = code[:-2] + code[-2:].replace(a,b) result = search_word(rules, lemma, code) if result != "": return result else: return word except: return word def fix_word(rules, word, gender, number): try: rule = search_rule(rules, word) lemma = rule["lemma"] code = rule["code"] for a, b in [("F", gender), ("M", gender), ("S", number), ("P", number)]: code = code.replace(a,b) result = search_word(rules, lemma, code) if result != "": return result else: return word except: return word

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f60d57417f085366ece3370fedcd7871a28b809a

1,746

py

Python

setup.py

T-FitAndFat/toucan-connectors

7d03454e4d06f5cb2e9c2c778d99dd655efd14a7

[ "BSD-3-Clause" ]

null

setup.py

T-FitAndFat/toucan-connectors

7d03454e4d06f5cb2e9c2c778d99dd655efd14a7

[ "BSD-3-Clause" ]

null

setup.py

T-FitAndFat/toucan-connectors

7d03454e4d06f5cb2e9c2c778d99dd655efd14a7

[ "BSD-3-Clause" ]

null

from setuptools import setup, find_packages auth_deps = ['oauthlib', 'requests_oauthlib'] extras_require = { 'adobe': ['adobe_analytics'], 'azure_mssql': ['pyodbc'], 'dataiku': ['dataiku-api-client'], 'elasticsearch': ['elasticsearch'], 'facebook': ['facebook-sdk'], 'google_analytics': ['google-api-python-client', 'oauth2client'], 'google_big_query': ['pandas_gbq'], 'google_cloud_mysql': ['PyMySQL>=0.8.0'], 'google_my_business': ['google-api-python-client>=1.7.5'], 'google_spreadsheet': ['gspread>=3', 'oauth2client'], 'hive': ['pyhive[hive]'], 'http_api': auth_deps, 'mongo': ['pymongo>=3.6.1'], 'mssql': ['pymssql>=2.1.3'], 'mysql': ['PyMySQL>=0.8.0'], 'odata': auth_deps + ['tctc_odata'], 'oracle_sql': ['cx_Oracle>=6.2.1'], 'postgres': ['psycopg2>=2.7.4'], 'sap_hana': ['pyhdb>=0.3.4'], 'snowflake': ['snowflake-connector-python'], 'toucan_toco': ['toucan_client'] } extras_require['all'] = sorted(set(sum(extras_require.values(), []))) install_requires = [ 'aiohttp', 'jq', 'jinja2', 'pydantic==0.31.1', 'requests', 'tenacity', 'toucan_data_sdk' ] classifiers = [ 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Programming Language :: Python :: 3.6' ] setup(name='toucan_connectors', version='0.23.4', description='Toucan Toco Connectors', author='Toucan Toco', author_email='dev@toucantoco.com', url='https://github.com/ToucanToco/toucan-connectors', license='BSD', classifiers=classifiers, packages=find_packages(), install_requires=install_requires, extras_require=extras_require, include_package_data=True)

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null

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0

f60dc5242dc79fcf961a2da0cfd7f24d3f799fd1

1,125

py

Python

pytest_unittest_filter.py

un-def/pytest-unittest-filter

7919aff50c6b86c60d2c808c18f5e973db33f338

[ "MIT" ]

4

2018-10-26T13:17:05.000Z

2019-03-22T06:51:50.000Z

pytest_unittest_filter.py

un-def/pytest-unittest-filter

7919aff50c6b86c60d2c808c18f5e973db33f338

[ "MIT" ]

null

pytest_unittest_filter.py

un-def/pytest-unittest-filter

7919aff50c6b86c60d2c808c18f5e973db33f338

[ "MIT" ]

null

import pytest from _pytest.unittest import UnitTestCase __version__ = '0.2.1' INI_OPTION_CLASSES = 'python_unittest_classes' INI_OPTION_UNDERSCORE = 'python_unittest_exclude_underscore' def pytest_addoption(parser): parser.addini( INI_OPTION_CLASSES, type='args', default=None, help='prefixes or glob names for unittest.TestCase subclass discovery', ) parser.addini( INI_OPTION_UNDERSCORE, type='bool', default=False, help='prefixes or glob names for unittest.TestCase subclass discovery', ) @pytest.hookimpl(hookwrapper=True, tryfirst=True) def pytest_pycollect_makeitem(collector, name): outcome = yield result = outcome.get_result() if result is None or not isinstance(result, UnitTestCase): return if collector.config.getini(INI_OPTION_UNDERSCORE) and name.startswith('_'): outcome.force_result(None) return if not collector.config.getini(INI_OPTION_CLASSES): return if not collector._matches_prefix_or_glob_option(INI_OPTION_CLASSES, name): outcome.force_result(None)

28.125

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null

0

null

0

1

0

f60f64227a4b588c259406487b84cb9a51b06bfb

5,058

py

Python

tests/unit/schema/wrappers/test_field.py

nsky80/gapic-generator-python

6dd7498438e87329c69a27ac57bb1693b02471d3

[ "Apache-2.0" ]

1

2019-08-15T05:41:02.000Z

tests/unit/schema/wrappers/test_field.py

nsky80/gapic-generator-python

6dd7498438e87329c69a27ac57bb1693b02471d3

[ "Apache-2.0" ]

null

tests/unit/schema/wrappers/test_field.py

nsky80/gapic-generator-python

6dd7498438e87329c69a27ac57bb1693b02471d3

[ "Apache-2.0" ]

1

2022-01-23T12:29:11.000Z

# Copyright 2018 Google LLC # # 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 # # https://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 pytest from google.api import field_behavior_pb2 from google.protobuf import descriptor_pb2 from gapic.schema import wrappers def test_field_properties(): Type = descriptor_pb2.FieldDescriptorProto.Type field = make_field(name='my_field', number=1, type=Type.Value('TYPE_BOOL')) assert field.name == 'my_field' assert field.number == 1 assert field.type.python_type == bool def test_field_is_primitive(): Type = descriptor_pb2.FieldDescriptorProto.Type primitive_field = make_field(type=Type.Value('TYPE_INT32')) assert primitive_field.is_primitive def test_field_proto_type(): Type = descriptor_pb2.FieldDescriptorProto.Type primitive_field = make_field(type=Type.Value('TYPE_INT32')) assert primitive_field.proto_type == 'INT32' def test_field_not_primitive(): Type = descriptor_pb2.FieldDescriptorProto.Type message = wrappers.MessageType( fields={}, nested_messages={}, nested_enums={}, message_pb=descriptor_pb2.DescriptorProto(), ) non_primitive_field = make_field( type=Type.Value('TYPE_MESSAGE'), type_name='bogus.Message', message=message, ) assert not non_primitive_field.is_primitive def test_ident(): Type = descriptor_pb2.FieldDescriptorProto.Type field = make_field(type=Type.Value('TYPE_BOOL')) assert str(field.ident) == 'bool' def test_ident_repeated(): Type = descriptor_pb2.FieldDescriptorProto.Type REP = descriptor_pb2.FieldDescriptorProto.Label.Value('LABEL_REPEATED') field = make_field(type=Type.Value('TYPE_BOOL'), label=REP) assert str(field.ident) == 'Sequence[bool]' def test_repeated(): REP = descriptor_pb2.FieldDescriptorProto.Label.Value('LABEL_REPEATED') field = make_field(label=REP) assert field.repeated def test_not_repeated(): OPT = descriptor_pb2.FieldDescriptorProto.Label.Value('LABEL_OPTIONAL') field = make_field(label=OPT) assert not field.repeated def test_required(): field = make_field() field.options.Extensions[field_behavior_pb2.field_behavior].append( field_behavior_pb2.FieldBehavior.Value('REQUIRED') ) assert field.required def test_not_required(): field = make_field() assert not field.required def test_ident_sphinx(): Type = descriptor_pb2.FieldDescriptorProto.Type field = make_field(type=Type.Value('TYPE_BOOL')) assert field.ident.sphinx == 'bool' def test_ident_sphinx_repeated(): Type = descriptor_pb2.FieldDescriptorProto.Type REP = descriptor_pb2.FieldDescriptorProto.Label.Value('LABEL_REPEATED') field = make_field(type=Type.Value('TYPE_BOOL'), label=REP) assert field.ident.sphinx == 'Sequence[bool]' def test_type_primitives(): T = descriptor_pb2.FieldDescriptorProto.Type assert make_field(type=T.Value('TYPE_FLOAT')).type.python_type == float assert make_field(type=T.Value('TYPE_INT64')).type.python_type == int assert make_field(type=T.Value('TYPE_BOOL')).type.python_type == bool assert make_field(type=T.Value('TYPE_STRING')).type.python_type == str assert make_field(type=T.Value('TYPE_BYTES')).type.python_type == bytes def test_type_message(): T = descriptor_pb2.FieldDescriptorProto.Type message = wrappers.MessageType( fields={}, nested_messages={}, nested_enums={}, message_pb=descriptor_pb2.DescriptorProto(), ) field = make_field( type=T.Value('TYPE_MESSAGE'), type_name='bogus.Message', message=message, ) assert field.type == message def test_type_enum(): T = descriptor_pb2.FieldDescriptorProto.Type enum = wrappers.EnumType( values={}, enum_pb=descriptor_pb2.EnumDescriptorProto(), ) field = make_field( type=T.Value('TYPE_ENUM'), type_name='bogus.Enumerable', enum=enum, ) assert field.type == enum def test_type_invalid(): T = descriptor_pb2.FieldDescriptorProto.Type with pytest.raises(TypeError): make_field(type=T.Value('TYPE_GROUP')).type def make_field(*, message=None, enum=None, **kwargs) -> wrappers.Field: kwargs.setdefault('name', 'my_field') kwargs.setdefault('number', 1) kwargs.setdefault('type', descriptor_pb2.FieldDescriptorProto.Type.Value('TYPE_BOOL'), ) field_pb = descriptor_pb2.FieldDescriptorProto(**kwargs) return wrappers.Field(field_pb=field_pb, message=message, enum=enum)

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null

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0

f612776e5655cc35f2ff3a2714bb44593975be1d

4,241

py

Python

src/expressivity.py

elijahc/vae

5cd80518f876d4ca9e97de2ece7c266e3df09cb7

[ "MIT" ]

null

src/expressivity.py

elijahc/vae

5cd80518f876d4ca9e97de2ece7c266e3df09cb7

[ "MIT" ]

null

src/expressivity.py

elijahc/vae

5cd80518f876d4ca9e97de2ece7c266e3df09cb7

[ "MIT" ]

null

import numpy as np # from ray.dataframe import pd # def pairwise_correlations( g_t) def euclidean_metric( g_t,delta): n_d = g_t.shape[0] v = np.gradient( g_t, delta, axis=0 ) vv = np.empty( n_d, dtype=np.float32 ) for t in np.arange( n_d ): vv[t] = np.dot( v[t], v[t].T ) return vv def curvature( g_t, delta ): n_d = g_t.shape[0] v = np.gradient( g_t, delta, axis=0 ) vv = np.empty( n_d, dtype=np.float32 ) vhat = np.empty_like( v ) for t in np.arange( n_d ): vv[t] = np.dot( v[t], v[t].T ) vhat[t] = v[t] / np.sqrt( vv[t] ) k = np.empty(n_d, dtype=np.float32) a = np.gradient(v, delta, axis=0) for i in np.arange( n_d ): aa = np.dot( a[i], a[i].T ) va = np.dot( v[i], a[i].T ) k[i] = ( vv[i]**-(3/2))*np.sqrt(( vv[i]*aa)-va**2) return k def grassmanian_metric(g_t,delta): k = curvature(g_t,delta) g_E = euclidean_metric(g_t,delta) return (k**2)*g_E def curvature_length(g_t,delta,N=None): # Number of examples, e.g. number of theta's n_d = g_t.shape[0] g_dt = np.ediff1d(delta) g_E = euclidean_metric(g_t,delta)[:-1] dL_E= np.sqrt(g_E)*g_dt L_E = dL_E.sum() if N is not None: L_E = L_E/np.sqrt(N) return L_E def grassmanian_length( g_t, delta ): n_d = g_t.shape[0] v = np.gradient( g_t, delta, axis=0 ) vv = np.empty( n_d, dtype=np.float32 ) vhat = np.empty_like( v ) for t in np.arange( n_d ): vv[t] = np.dot( v[t], v[t].T ) vhat[t] = v[t] / np.sqrt( vv[t] ) a_hat = np.gradient( vhat, delta, axis=0) gauss_metric = np.array([np.dot(a_hat[i],a_hat[i].T) for i in np.arange( n_d )]) if isinstance(delta,float): dG = np.sqrt(gauss_metric)[:-1]*delta elif isinstance(delta,np.ndarray): dG = np.sqrt(gauss_metric)[:-1]*np.ediff1d(delta) return dG.sum() class Expressivity(): def __init__(self,model,trajectory,delta,index=None): # evaluate expressivity on a specific layer if index is provided self.trajectory = trajectory self.delta = delta self.n_d = trajectory.shape[0] self.model = model if index is not None: activation_functors = gen_activation_functors(model) func = activation_functors[index] self.g_t = np.squeeze(func([self.trajectory])[0]) else: self.g_t = self.model.predict(self.trajectory,batch_size=32) self.v = np.gradient(self.g_t,self.delta,axis=0) self.vv = np.empty(self.n_d,dtype=np.float32) self.vhat = np.empty_like(self.v) for t in np.arange(self.n_d): self.vv[t] = np.dot(self.v[t],self.v[t].T) self.vhat[t] = self.v[t]/np.sqrt(self.vv[t]) def curvature(self): return curvature(self.g_t,self.n_d,self.delta) def curve_length(self): self.dL = np.sqrt(self.vv)*self.delta return self.dL.sum() def grassmanian_length(self): a_hat = np.gradient(self.vhat,self.delta,axis=0) gauss_metric = np.array([np.dot(a_hat[i],a_hat[i].T) for i in np.arange(self.n_d)]) self.dG = np.sqrt(gauss_metric)*self.delta return self.dG.sum() def salience(model,x_test,masks,x_iso=None): funcs = gen_activation_functors(model) outs = [] input_G = [] for i,mask in enumerate(masks): x_traj = x_test[mask] if x_iso is None: # Calc embedding print('Calculating Isomap embeddings...') x_traj,x_iso = gen_sorted_isomap(x_traj,n_neighbors=20,n_components=1,n_jobs=1) x_G = grassmanian_length(x_traj,delta=x_iso) input_G.append(x_G) g_t = [np.squeeze(f([x_traj])[0]) for f in funcs] y_G = [grassmanian_length(g,1.0/len(g)) for g in g_t] for l_idx,Y in enumerate(y_G): rec = { 'Grassmanian Length':Y, 'x_G':x_G, 'Layer': l_idx+1, 'Digit':i+1, 'G_delta':Y-x_G } outs.append(rec) return pd.DataFrame.from_records(outs) def manifold_overlap(): pass

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null

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f61517f627009ff7f376b1194a4e917a81ddfd11

5,243

py

Python

userbot/modules/misc.py

FS-Project/FeRuBoT

54cc12243ccbeb289ed37d691698fbd42fd8f740

[ "Naumen", "Condor-1.1", "MS-PL" ]

3

2021-01-24T20:35:11.000Z

2021-03-10T18:16:26.000Z

userbot/modules/misc.py

FS-Project/FeRuBoT

54cc12243ccbeb289ed37d691698fbd42fd8f740

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

userbot/modules/misc.py

FS-Project/FeRuBoT

54cc12243ccbeb289ed37d691698fbd42fd8f740

[ "Naumen", "Condor-1.1", "MS-PL" ]

1

2021-03-10T18:16:25.000Z

# INFO : ini merupakan copy source code dari repo one4ubot, dan sudah mendapatkan izin dari pemilik. # INFO : This is a copy of the source code from the One4ubot repo, and has the permission of the owner. # Copyright (C) 2019 The Raphielscape Company LLC. # # Licensed under the Raphielscape Public License, Version 1.d (the "License"); # you may not use this file except in compliance with the License. # # You can find misc modules, which dont fit in anything xD """ Userbot module for other small commands. """ import io import sys from os import execl from random import randint from time import sleep from userbot import BOTLOG, BOTLOG_CHATID, CMD_HELP, bot from userbot.events import register from userbot.utils import time_formatter @register(outgoing=True, pattern="^.random") async def randomise(items): """ For .random command, get a random item from the list of items. """ itemo = (items.text[8:]).split() if len(itemo) < 2: await items.edit( "`2 item atau lebih diperlukan! Periksa .help random untuk info lebih lanjut.`" ) return index = randint(1, len(itemo) - 1) await items.edit( "**Query: **\n`" + items.text[8:] + "`\n**Output: **\n`" + itemo[index] + "`" ) @register(outgoing=True, pattern="^.sleep ([0-9]+)$") async def sleepybot(time): """ For .sleep command, let the userbot snooze for a few second. """ counter = int(time.pattern_match.group(1)) await time.edit("`Saya merajuk dan tertidur...`") if BOTLOG: str_counter = time_formatter(counter) await time.client.send_message( BOTLOG_CHATID, f"Anda sudah membuat bot untuk tidur💤 {str_counter}.", ) sleep(counter) await time.edit("`Oke, saya sudah bangun sekarang.`") @register(outgoing=True, pattern="^.shutdown$") async def killbot(shut): """For .shutdown command, shut the bot down.""" await shut.edit("`Selamat tinggal *Suara shutdown Windows XP*....`") if BOTLOG: await shut.client.send_message(BOTLOG_CHATID, "#SHUTDOWN \n" "Bot sudah meninggal, hidupkan lagi ") await bot.disconnect() @register(outgoing=True, pattern="^.restart$") async def killdabot(reboot): await reboot.edit("`*saya akan kembali sebentar lagi*`") if BOTLOG: await reboot.client.send_message(BOTLOG_CHATID, "#RESTART \n" "Bot di nyalakan ulang") await bot.disconnect() # Spin a new instance of bot execl(sys.executable, sys.executable, *sys.argv) # Shut the existing one down exit() # Copyright (c) Gegham Zakaryan | 2019 @register(outgoing=True, pattern="^.repeat (.*)") async def repeat(rep): cnt, txt = rep.pattern_match.group(1).split(" ", 1) replyCount = int(cnt) toBeRepeated = txt replyText = toBeRepeated + "\n" for i in range(0, replyCount - 1): replyText += toBeRepeated + "\n" await rep.edit(replyText) @register(outgoing=True, pattern="^.repo$") async def repo_is_here(wannasee): """ For .repo command, just returns the repo URL. """ await wannasee.edit( "[🔗Sentuh ini](https://github.com/FS-Project/FeRuBoT) untuk membuka repo FeRuBoT." ) @register(outgoing=True, pattern="^.raw$") async def raw(rawtext): the_real_message = None reply_to_id = None if rawtext.reply_to_msg_id: previous_message = await rawtext.get_reply_message() the_real_message = previous_message.stringify() reply_to_id = rawtext.reply_to_msg_id else: the_real_message = rawtext.stringify() reply_to_id = rawtext.message.id with io.BytesIO(str.encode(the_real_message)) as out_file: out_file.name = "raw_message_data.txt" await rawtext.edit("`Periksa log userbot untuk data pesan yang didekodekan!!`") await rawtext.client.send_file( BOTLOG_CHATID, out_file, force_document=True, allow_cache=False, reply_to=reply_to_id, caption="`Berikut data pesan yang diterjemahkan!!`", ) CMD_HELP.update( { "random": ".random <item1> <item2> ... <itemN>\ \nPenggunaan: Dapatkan item acak dari daftar item." } ) CMD_HELP.update( { "sleep": ".sleep <seconds>\ \nPenggunaan: Userbot juga lelah. Biarkan punyamu tertidur selama beberapa detik💤." } ) CMD_HELP.update( { "shutdown": ".shutdown\ \nPenggunaan: Terkadang Anda perlu mematikan bot Anda. Terkadang Anda hanya berharap\ mendengar suara shutdown Windows XP ... tetapi Anda tidak melakukannya." } ) CMD_HELP.update( { "repo": ".repo\ \nPenggunaan: Jika Anda penasaran dengan apa yang membuat userbot bekerja, inilah yang Anda butuhkan." } ) CMD_HELP.update( { "readme": ".readme\ \nPenggunaan: Berikan tautan untuk menyiapkan bot pengguna dan modulnya." } ) CMD_HELP.update( { "repeat": ".repeat <no.> <text>\ \nPenggunaan: Mengulangi teks beberapa kali. Jangan bingung ini dengan spam." } ) CMD_HELP.update( { "restart": ".restart\ \nPenggunaan: Mulai ulang bot!!" } ) CMD_HELP.update( { "raw": ".raw\ \nPenggunaan: Dapatkan data rinci yang diformat seperti JSON tentang pesan yang dibalas." } )

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null

0

null

0

1

0

f617abbd37a250768e243f67f08bb7f3f81db933

1,675

py

Python

util_scripts/test_zoom.py

ShuaiW/kaggle-heart

022997f27add953c74af2b371c67d9d86cbdccc3

[ "MIT" ]

182

2016-03-15T01:51:29.000Z

2021-04-21T09:49:05.000Z

util_scripts/test_zoom.py

weidezhang/kaggle-heart

022997f27add953c74af2b371c67d9d86cbdccc3

[ "MIT" ]

1

2018-06-22T16:46:12.000Z

2018-06-22T21:08:09.000Z

util_scripts/test_zoom.py

weidezhang/kaggle-heart

022997f27add953c74af2b371c67d9d86cbdccc3

[ "MIT" ]

61

2016-03-15T00:58:28.000Z

2020-03-06T22:00:41.000Z

import numpy as np import matplotlib.pyplot as plt from matplotlib import animation from scipy.special import erf, erfinv import cPickle as pickle import glob import os import scipy import scipy.ndimage.interpolation #print glob.glob(os.path.expanduser("~/storage/metadata/kaggle-heart/predictions/j7_jeroen_ch.pkl")) #predictions = pickle.load(open(glob.glob(os.path.expanduser("~/storage/metadata/kaggle-heart/predictions/j7_jeroen_ch.pkl"))[0]))["predictions"] #scipy.ndimage.interpolation.zoom(input, zoom, output=None, order=3, mode='constant', cval=0.0, prefilter=True) p = np.array(range(0,600), dtype='float32') predictions = (erf( (p - 300)/50 )+1)/2 def zoom(array, zoom_factor): result = np.ones(array.shape) zoom = [1.0]*array.ndim zoom[-1] = zoom_factor zr = scipy.ndimage.interpolation.zoom(array, zoom, order=3, mode='nearest', prefilter=True) result[...,:min(zr.shape[-1],array.shape[-1])] = zr[...,:min(zr.shape[-1],array.shape[-1])] return result fig = plt.figure() mngr = plt.get_current_fig_manager() # to put it into the upper left corner for example: mngr.window.setGeometry(50, 100, 600, 300) im1 = fig.gca().plot(p, predictions) def init(): pp = predictions im1[0].set_ydata(pp) def animate(i): z = float(i)/50 pp = zoom(predictions,z) fig.suptitle("zoom %f"%z) im1[0].set_ydata(pp) return im1 anim = animation.FuncAnimation(fig, animate, init_func=init, frames=100, interval=50) #anim.save('my_animation.mp4') plt.show()

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0

null

0

null

0

1

0

f618ea5cffb6353292606a5b9b153b42064483ff

3,523

py

Python

train_liner.py

ALEXKIRNAS/Toxic-Comment-Classification-Challenge

1f86d113f38c6c9eaca03ec33c6467709bf2d652

[ "MIT" ]

2

2018-04-07T19:52:09.000Z

2018-04-24T11:37:58.000Z

train_liner.py

ALEXKIRNAS/Toxic-Comment-Classification-Challenge

1f86d113f38c6c9eaca03ec33c6467709bf2d652

[ "MIT" ]

null

train_liner.py

ALEXKIRNAS/Toxic-Comment-Classification-Challenge

1f86d113f38c6c9eaca03ec33c6467709bf2d652

[ "MIT" ]

null

import concurrent.futures import click import numpy as np import pandas as pd from sklearn.metrics import roc_auc_score from sklearn.model_selection import KFold from models import NbSvmClassifier from utils.constants import RANDOM_SEED, CLASS_NAMES from utils.data_loader import tf_idf_vectors cv_params = [ {'C': 0.7}, {'C': 0.25}, {'C': 0.27}, {'C': 0.25}, {'C': 0.25}, {'C': 0.25}, ] train_word_features, test_word_features, train, test = None, None, None, None def training(train_indices, val_indices, class_name, params): classifier = NbSvmClassifier(**params) csr = train_word_features.tocsr() X_train = csr[train_indices] y_train = np.array(train[class_name])[train_indices] X_test = csr[val_indices] y_test = np.array(train[class_name])[val_indices] classifier.fit(X_train, y_train) train_proba = classifier.predict_proba(X_train)[:, 1] val_proba = classifier.predict_proba(X_test)[:, 1] sub_proba = classifier.predict_proba(test_word_features)[:, 1] train_score = roc_auc_score(y_train, train_proba) val_score = roc_auc_score(y_test, val_proba) return train_score, val_score, val_proba, sub_proba, val_indices @click.command() @click.option('--train_df_path', default='./input/train.csv') @click.option('--test_df_path', default='./input/test.csv') @click.option('--stamp', default='lr') @click.option('--preprocess', default=False) def main(train_df_path, test_df_path, stamp, preprocess): global train_word_features, test_word_features, train, test train = pd.read_csv(train_df_path).fillna(' ') test = pd.read_csv(test_df_path).fillna(' ') print('Create features') train_word_features, test_word_features = tf_idf_vectors(train, test, preprocess) print('Start training') submission = pd.DataFrame.from_dict({'id': test['id']}) train_submission = pd.DataFrame.from_dict({'id': train['id']}) scores = [] for i, class_name in enumerate(CLASS_NAMES): print('Class: %s' % class_name) sub_probas = np.zeros(shape=(len(test),)) train_probas = np.zeros(shape=(len(train),)) kf = KFold(n_splits=5, shuffle=True, random_state=RANDOM_SEED) train_scores, val_scores = [], [] with concurrent.futures.ProcessPoolExecutor(max_workers=5) as executor: futures = (executor.submit(training, train_indices, val_indices, class_name, cv_params[i]) for train_indices, val_indices in kf.split(train)) for future in concurrent.futures.as_completed(futures): train_score, val_score, val_proba, sub_proba, val_indices = future.result() train_scores.append(train_score) val_scores.append(val_score) train_probas[val_indices] += val_proba sub_probas += sub_proba / 5. scores.append(np.mean(val_scores)) print('\tTrain ROC-AUC: %s' % np.mean(train_scores)) print('\tVal ROC-AUC: %s' % np.mean(val_scores)) submission[class_name] = sub_probas train_submission[class_name] = train_probas submission.to_csv('%s.csv' % stamp, index=False) train_submission.to_csv('%s.csv' % stamp, index=False) print('Total: %s' % np.mean(scores)) if __name__ == '__main__': main()

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null

0

null

0

1

0

f61be4718911e196ba9094bf4af6f045bc2cf0e9

4,527

py

Python

tools/python/bluebottle_flow_reader.py

GediZhou/bluebottle-3.0

645e6cbe257ad4f65456652a50d49e5f3059564f

[ "Apache-2.0" ]

11

2018-02-20T15:58:07.000Z

2021-12-27T09:02:30.000Z

tools/python/bluebottle_flow_reader.py

groundcherry/bluebottle-2.0

7adc8782ad269f06ab0edb0111500907757bea50

[ "Apache-2.0" ]

3

2018-12-11T13:44:45.000Z

2021-03-10T15:13:38.000Z

tools/python/bluebottle_flow_reader.py

groundcherry/bluebottle-2.0

7adc8782ad269f06ab0edb0111500907757bea50

[ "Apache-2.0" ]

6

2018-09-21T11:36:22.000Z

2021-03-13T09:15:35.000Z

################################################################################ ################################## BLUEBOTTLE ################################## ################################################################################ # # Copyright 2012 - 2018 Adam Sierakowski and Daniel Willen, # The Johns Hopkins University # # 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. # # Please contact the Johns Hopkins University to use Bluebottle for # commercial and/or for-profit applications. ################################################################################ import sys, os, glob import h5py as h5 import numpy # Initialize the reader by passing the directory containing the CGNS files. This # returns a list containing the rounded time values available for reading. def init(sys): # Parse command line args if len(sys.argv) == 2: basedir = sys.argv[1] else: print("Usage: requires ./path/to/some/output as command-line argument") sys.exit() if not basedir.endswith("/"): basedir = basedir + "/" global base base = basedir t_read = list() files = glob.glob(base + "/flow-*.cgns") if(len(files) == 0): print("cannot find any flow-*.cgns files in", base) sys.exit() else: for i in files: start = i.find("flow-") # XXX breaks on dirs with "flow-" in name t_read.append(i[start+5:-5]) return (sorted(t_read, key=float), basedir) # Open a particular CGNS file using a time value in the list returned by init(). def open(time): global f infile = base + "/flow-" + time + ".cgns" try: f = h5.File(infile, 'r') return f except OSError: f = None print("file", infile, "does not exist") return f def close(): f.close() # Read the time. def read_time(): t1 = f["/Base/Zone0/Etc/Time/ data"][0] try: t2 = g["/Base/Zone0/Etc/Time/ data"][0] except NameError: return t1 else: return (t1,t2) # Read flow parameters def read_flow_params(): rho_f = numpy.array(f["/Base/Zone0/Etc/Density/ data"]) nu = numpy.array(f["/Base/Zone0/Etc/KinematicViscosity/ data"]) return (rho_f, nu) # Read grid extents def read_flow_extents(basedir): infile = basedir + "/grid.cgns" try: gr = h5.File(infile, 'r') except OSError: gr = None print("file", infile, "does not exist") sys.exit() Nxyz = numpy.array(gr["/Base/Zone0/ data"]) Nx = Nxyz[1, 2] Ny = Nxyz[1, 1] Nz = Nxyz[1, 0] # These are output as x[k,j,i] x = numpy.array(gr["/Base/Zone0/GridCoordinates/CoordinateX/ data"]) y = numpy.array(gr["/Base/Zone0/GridCoordinates/CoordinateY/ data"]) z = numpy.array(gr["/Base/Zone0/GridCoordinates/CoordinateZ/ data"]) xs = numpy.min(x) xe = numpy.max(x) xl = xe - xs ys = numpy.min(y) ye = numpy.max(y) yl = ye - ys zs = numpy.min(z) ze = numpy.max(z) zl = ze - zs return (Nx, Ny, Nz, xs, xe, xl, ys, ye, yl, zs, ze, zl) # Read the flow positions. def read_flow_position(): # Open grid file #global gr infile = base + "/grid.cgns" try: gr = h5.File(infile, 'r') except OSError: gr = None print("file", infile, "does not exist") sys.exit() # These are output as x[k,j,i] x = numpy.array(gr["/Base/Zone0/GridCoordinates/CoordinateX/ data"]) y = numpy.array(gr["/Base/Zone0/GridCoordinates/CoordinateY/ data"]) z = numpy.array(gr["/Base/Zone0/GridCoordinates/CoordinateZ/ data"]) x = x[0,0,:] y = y[0,:,0] z = z[:,0,0] return (x,y,z) # Read the particle velocities. def read_flow_velocity(): u1 = numpy.array(f["/Base/Zone0/Solution/VelocityX/ data"]) v1 = numpy.array(f["/Base/Zone0/Solution/VelocityY/ data"]) w1 = numpy.array(f["/Base/Zone0/Solution/VelocityZ/ data"]) try: u2 = numpy.array(g["/Base/Zone0/Solution/VelocityX/ data"]) v2 = numpy.array(g["/Base/Zone0/Solution/VelocityY/ data"]) w2 = numpy.array(g["/Base/Zone0/Solution/VelocityZ/ data"]) except NameError: return (u1,v1,w1) else: return ((u1,v1,w1),(u2,v2,w2))

28.118012

80

0.608571

646

4,527

4.243034

0.321981

0.055819

0.030646

0.040861

0.326888

0.29077

0.197373

0.186063

0

0.019891

0.189309

4,527

160

81

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0.726975

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0.282453

0.186712

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1

0.080808

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0.212121

0.050505

0

null

0

null

0

1

0

f61d162134f520eaed2779c8b47fde1648352f14

954

py

Python

exerc_25.py

marcelocmedeiros/EstruturaDeRepeticao

23e917377e41083901a4ffbf3e0fda31e3a78982

[ "MIT" ]

null

exerc_25.py

marcelocmedeiros/EstruturaDeRepeticao

23e917377e41083901a4ffbf3e0fda31e3a78982

[ "MIT" ]

null

exerc_25.py

marcelocmedeiros/EstruturaDeRepeticao

23e917377e41083901a4ffbf3e0fda31e3a78982

[ "MIT" ]

null

# Marcelo Campos de Medeiros # ADS UNIFIP # Estrutura de Repetição # 25/03/2020 ''' 25 -Faça um programa que peça para n pessoas a sua idade, ao final o programa devera verificar se a média de idade da turma varia entre 0 e 25,26 e 60 e maior que 60; e então, dizer se a turma é jovem, adulta ou idosa, conforme a média calculada. ''' print('=' * 40) print('{:=^40}'.format(" 'MÉDIA DE IDADE DA TURMA' ")) print('=' * 40, '\n') # entrada de variável n = int(input('Quantas pessoas tem na turma: ')) soma = 0 # laço for c in range(1, n + 1): idade = int(input(f'Informe a idade da {c}° pessoa: ')) soma += idade media = soma / n # if media >= 0 and media <= 25: print(f'A média das idades é {media:.2f}, por tanto esssa turma é jovem!') elif media >= 26 and media <= 60: print(f'A média das idades é {media},por tanto esssa turma é adulta!') elif media > 60: print(f'A média das idades é {media},por tanto esssa turma é idosa!')

27.257143

78

0.659329

172

954

3.662791

0.453488

0.047619

0.033333

0.057143

0.301587

0.211111

0.168254

0

0.050599

0.212788

954

34

79

28.058824

0.786951

0.360587

0

0.477234

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1

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false

0

0.4

0

null

0

null

0

1

0

f61dc2bab26e07654cfc84f3ad968a59e5a75412

4,089

py

Python

appengine/main.py

luiscielak/bl.ocks.org

c971a001fa9c1c65a64b641937b88d078376e77d

[ "BSD-3-Clause" ]

6

2016-09-05T17:22:01.000Z

2021-11-16T13:44:07.000Z

appengine/main.py

luiscielak/bl.ocks.org

c971a001fa9c1c65a64b641937b88d078376e77d

[ "BSD-3-Clause" ]

null

appengine/main.py

luiscielak/bl.ocks.org

c971a001fa9c1c65a64b641937b88d078376e77d

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # # Copyright 2007 Google 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 os os.environ['DJANGO_SETTINGS_MODULE'] = 'settings' import wsgiref.handlers import yaml import re from cgi import escape from urllib import quote from markdown import markdown from google.appengine.ext import webapp from google.appengine.api.urlfetch import fetch from google.appengine.dist import use_library use_library('django', '1.1') from django.utils.encoding import smart_unicode class GistRedirectHandler(webapp.RequestHandler): def get(self, id): self.redirect('/%s' % id) class GistViewHandler(webapp.RequestHandler): def get(self, id): raw = fetch('http://gist.github.com/api/v1/yaml/%s' % id) meta = yaml.load(raw.content)['gists'][0] owner = meta[':owner'] or "" description = meta[':description'] or "" files = meta[':files'] or [] time = meta[':created_at'] title = "%s - %s" % (id, escape(description)) if description else id self.response.out.write(""" <!DOCTYPE html> <html> <head> <title>bl.ocks.org - %s</title> <style type="text/css"> @import url("/style.css"); </style> </head> <body> <div class="body"> <a href="/" class="about right">What’s all this then?</a> <h1>block <a href="http://gist.github.com/%s">#%s</a></h1> <h2> <span class="description">%s</span> by <a href="http://github.com/%s" class="owner">%s</a> </h2> <iframe marginwidth="0" marginheight="0" scrolling="no" src=\"/d/%s/\"></iframe> <div class="readme"> """ % (title, id, id, escape(description), quote(owner), escape(owner), id)) # display the README for f in files: if re.match("^readme\.(md|mkd|markdown)$", f, re.I): html = markdown(smart_unicode(fetch('http://gist.github.com/raw/%s/%s' % (id, quote(f))).content)) elif re.match("^readme(\.txt)?$", f, re.I): html = "<pre>%s</pre>" % escape(fetch('http://gist.github.com/raw/%s/%s' % (id, quote(f))).content) else: html = None if html: self.response.out.write(html) # display the creation time if time: self.response.out.write("<p class=\"time\">Created at %s.</p>" % time) self.response.out.write("</div>") # display the other files as source for f in files: if not re.match("^readme(\.[a-z]+)?$", f, re.I): self.response.out.write('<script src="http://gist.github.com/%s.js?file=%s"></script>' % (id, f)) self.response.out.write(""" <a href="/" class="about">about bl.ocks.org</a> </div> </body> </html> """) class GistDataHandler(webapp.RequestHandler): def get(self, id, file): if not file: file = 'index.html' raw = fetch('http://gist.github.com/raw/%s/%s' % (id, quote(file))) if re.search("\.css$", file): self.response.headers["Content-Type"] = "text/css" elif re.search("\.js$", file): self.response.headers["Content-Type"] = "text/javascript" elif re.search("\.json$", file): self.response.headers["Access-Control-Allow-Origin"] = "*" self.response.headers["Content-Type"] = "application/json" elif re.search("\.txt$", file): self.response.headers["Content-Type"] = "text/plain" self.response.out.write(raw.content) def main(): application = webapp.WSGIApplication([ ('/([0-9]+)', GistViewHandler), ('/([0-9]+)/', GistRedirectHandler), ('/d/([0-9]+)/(.*)', GistDataHandler) ], debug=True) wsgiref.handlers.CGIHandler().run(application) if __name__ == '__main__': main()

32.452381

107

0.63414

569

4,089

4.530756

0.340949

0.055857

0.040729

0.054306

0.192397

0.138092

0.089992

0.045772

0

0.007162

0.180484

4,089

125

108

32.712

0.762161

0.158474

0

0.066667

0

0.044444

0.361777

0.037697

0

1

0.044444

false

0

0.133333

0

0.211111

0

null

0

null

0

1

0

f61f02c078332b8fc380bf074e7157b43f51dde5

831

py

Python

junk.py

blue-army/azuretools-docker

255a17b0adf25ccaf9797079fd59c98b3fc48095

[ "MIT" ]

null

junk.py

blue-army/azuretools-docker

255a17b0adf25ccaf9797079fd59c98b3fc48095

[ "MIT" ]

1

2021-06-01T22:07:59.000Z

junk.py

blue-army/azuretools-docker

255a17b0adf25ccaf9797079fd59c98b3fc48095

[ "MIT" ]

null

import os, uuid, sys from azure.storage.blob import BlockBlobService, PublicAccess def run_sample(): try: # Create the BlockBlockService that is used to call the Blob service for the storage account block_blob_service = BlockBlobService(account_name='planck', account_key='dXskxcS8enXEWXbk2K4dAfh5ktJkF/LHx9er5I2UdW44jKqT/AYWBqI7M2IzkoDUCvmbzHRWdV3nCXUmUn1WPQ==') # Create a container called 'quickstartblobs'. container_name ='nutanix-pipeline' # List the blobs in the container print("\nList blobs in the container") generator = block_blob_service.list_blobs(container_name) for blob in generator: print("\t Blob name: " + blob.name) except Exception as e: print(e) # Main method. if __name__ == '__main__': run_sample()

36.130435

172

0.700361

94

831

5.989362

0.553191

0.058615

0.056838

0.067496

0

0.020155

0.223827

831

23

173

36.130435

0.852713

0.216607

0

0.248841

0.136012

0

1

0.071429

false

0

0.142857

0

0.214286

0

null

0

null

0

1

0

f621c4152cb5134441f2e29cffa3dab4475d0a8b

2,078

py

Python

examples/compare_methods_kld_air_quality.py

hsivan/automon

222b17651533bdb2abce7de36a80156ab7b9cc21

[ "BSD-3-Clause" ]

1

2022-02-25T17:50:32.000Z

examples/compare_methods_kld_air_quality.py

hsivan/automon

222b17651533bdb2abce7de36a80156ab7b9cc21

[ "BSD-3-Clause" ]

null

examples/compare_methods_kld_air_quality.py

hsivan/automon

222b17651533bdb2abce7de36a80156ab7b9cc21

[ "BSD-3-Clause" ]

1

2022-03-12T08:12:37.000Z

from automon import AutomonNode, AutomonCoordinator, RlvNode, RlvCoordinator from test_utils.functions_to_monitor import func_kld from test_utils.tune_neighborhood_size import tune_neighborhood_size from test_utils.data_generator import DataGeneratorKldAirQuality from test_utils.test_utils import start_test, end_test, run_test, write_config_to_file, read_config_file from test_utils.stats_analysis_utils import plot_monitoring_stats import logging from test_utils.object_factory import get_objects if __name__ == "__main__": try: test_folder = start_test("compare_methods_kld_air_quality") '''conf = get_config(num_nodes=12, num_iterations=30000, sliding_window_size=200, d=20, error_bound=0.1, slack_type=SlackType.Drift.value, sync_type=SyncType.LazyLRU.value, domain=(0, 1), neighborhood_size=1.0, num_iterations_for_tuning=300)''' data_folder = '../datasets/air_quality/' conf = read_config_file(data_folder) write_config_to_file(test_folder, conf) data_generator = DataGeneratorKldAirQuality(num_iterations=conf["num_iterations"], num_nodes=conf["num_nodes"], d=conf["d"], test_folder=test_folder, num_iterations_for_tuning=conf["num_iterations_for_tuning"], sliding_window_size=conf["sliding_window_size"]) logging.info("\n###################### Start KLD RLV test ######################") data_generator.reset() coordinator, nodes = get_objects(RlvNode, RlvCoordinator, conf, func_kld) run_test(data_generator, coordinator, nodes, test_folder) logging.info("\n###################### Start KLD AutoMon test ######################") data_generator.reset() coordinator, nodes = get_objects(AutomonNode, AutomonCoordinator, conf, func_kld) tune_neighborhood_size(coordinator, nodes, conf, data_generator) run_test(data_generator, coordinator, nodes, test_folder) plot_monitoring_stats(test_folder) finally: end_test()

53.282051

199

0.701155

250

2,078

5.428

0.316

0.046426

0.05748

0.048637

0.168018

0.138541

0

0.01231

0.179018

2,078

38

200

54.684211

0.783118

0

0.142857

0

0.149832

0.096521

0

1

0

false

0

0.285714

0

0.285714

0

null

0

null

0

1

0

f622c63183c5ab98481217bec0358ab5279f4cbd

643

py

Python

beecrowd-1007.py

jessicabessaoliveira/Python

c4732f5e9528a40721b7c16364e6310e7ed8d490

[ "MIT" ]

null

beecrowd-1007.py

jessicabessaoliveira/Python

c4732f5e9528a40721b7c16364e6310e7ed8d490

[ "MIT" ]

null

beecrowd-1007.py

jessicabessaoliveira/Python

c4732f5e9528a40721b7c16364e6310e7ed8d490

[ "MIT" ]

null

# https://www.beecrowd.com.br/judge/pt/problems/view/1007 ''' Leia quatro valores inteiros A, B, C e D. A seguir, calcule e mostre a diferença do produto de A e B pelo produto de C e D segundo a fórmula: DIFERENCA = (A * B - C * D). Entrada O arquivo de entrada contém 4 valores inteiros. Saída Imprima a mensagem DIFERENCA com todas as letras maiúsculas, conforme exemplo abaixo, com um espaço em branco antes e depois da igualdade. ''' a = int(input()) b = int(input()) c = int(input()) d = int(input()) diferenca = a*b - c*d print('DIFERENCA = {}'.format(diferenca)) ''' Usando f-string PYTHON 3.6 print(f'DIFERENCA = {diferenca}') '''

27.956522

170

0.7014

109

643

4.137615

0.577982

0.070953

0.019956

0.053215

0.05765

0

0.013109

0.169518

643

22

171

29.227273

0.831461

0.66874

0

0.10219

0

1

0

false

0

0.166667

0

null

0

null

0

1

0

f62329758cd5399f4ed0ac810b952ed937df5568

15,450

py

Python

earthquake/generator.py

viktorsapozhok/earthquake-prediction

aca49d2c7e25deb385f98ef030c904ebae96135e

[ "MIT" ]

8

2019-06-20T13:31:42.000Z

2020-05-01T21:53:20.000Z

earthquake/generator.py

viktorsapozhok/earthquake-prediction

aca49d2c7e25deb385f98ef030c904ebae96135e

[ "MIT" ]

null

earthquake/generator.py

viktorsapozhok/earthquake-prediction

aca49d2c7e25deb385f98ef030c904ebae96135e

[ "MIT" ]

1

2019-06-05T19:15:14.000Z

import argparse from itertools import product import warnings from joblib import Parallel, delayed import librosa import numpy as np import pandas as pd from scipy import signal, stats from sklearn.linear_model import LinearRegression from tqdm import tqdm from tsfresh.feature_extraction import feature_calculators from earthquake import config warnings.filterwarnings("ignore") class FeatureGenerator(object): """Feature engineering. """ def __init__( self, path_to_store, is_train=True, n_rows=1e6, n_jobs=1, segment_size=150000 ): """Decomposition of initial signal into the set of features. Args: path_to_store: Path to .hdf store with original signal data. is_train: True, if creating the training set. n_rows: Amount of rows in training store. n_jobs: Amount of parallel jobs. segment_size: Amount of observations in each segment """ self.path_to_store = path_to_store self.n_rows = n_rows self.n_jobs = n_jobs self.segment_size = segment_size self.is_train = is_train if self.is_train: self.total = int(self.n_rows / self.segment_size) self.store = None self.keys = None else: self.store = pd.HDFStore(self.path_to_store, mode='r') self.keys = self.store.keys() self.total = len(self.keys) def __del__(self): if self.store is not None: self.store.close() def segments(self): """Returns generator object to iterate over segments. """ if self.is_train: for i in range(self.total): start = i * self.segment_size stop = (i + 1) * self.segment_size # read one segment of data from .hdf store data = pd.read_hdf(self.path_to_store, start=start, stop=stop) x = data['acoustic_data'].values y = data['time_to_failure'].values[-1] seg_id = 'train_' + str(i) del data yield seg_id, x, y else: for key in self.keys: seg_id = key[1:] x = self.store[key]['acoustic_data'].values yield seg_id, x, -999 def get_features(self, x, y, seg_id): x = pd.Series(x) # fast fourier transform zc = np.fft.fft(x) # real part realFFT = pd.Series(np.real(zc)) # imaginary part imagFFT = pd.Series(np.imag(zc)) main_dict = self.features(x, y, seg_id) r_dict = self.features(realFFT, y, seg_id) i_dict = self.features(imagFFT, y, seg_id) for k, v in r_dict.items(): if k not in ['target', 'seg_id']: main_dict[f'fftr_{k}'] = v for k, v in i_dict.items(): if k not in ['target', 'seg_id']: main_dict[f'ffti_{k}'] = v return main_dict def features(self, x, y, seg_id): feature_dict = dict() feature_dict['target'] = y feature_dict['seg_id'] = seg_id # lists with parameters to iterate over them percentiles = [ 1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99] hann_windows = [ 50, 150, 1500, 15000] spans = [ 300, 3000, 30000, 50000] windows = [ 10, 50, 100, 500, 1000, 10000] borders = list(range(-4000, 4001, 1000)) peaks = [ 10, 20, 50, 100] coefs = [ 1, 5, 10, 50, 100] autocorr_lags = [ 5, 10, 50, 100, 500, 1000, 5000, 10000] # basic stats feature_dict['mean'] = x.mean() feature_dict['std'] = x.std() feature_dict['max'] = x.max() feature_dict['min'] = x.min() # basic stats on absolute values feature_dict['mean_change_abs'] = np.mean(np.diff(x)) feature_dict['abs_max'] = np.abs(x).max() feature_dict['abs_mean'] = np.abs(x).mean() feature_dict['abs_std'] = np.abs(x).std() # geometric and harmonic means feature_dict['hmean'] = stats.hmean(np.abs(x[np.nonzero(x)[0]])) feature_dict['gmean'] = stats.gmean(np.abs(x[np.nonzero(x)[0]])) # k-statistic and moments for i in range(1, 5): feature_dict[f'kstat_{i}'] = stats.kstat(x, i) feature_dict[f'moment_{i}'] = stats.moment(x, i) for i in [1, 2]: feature_dict[f'kstatvar_{i}'] = stats.kstatvar(x, i) # aggregations on various slices of data for agg_type, slice_length, direction in product( ['std', 'min', 'max', 'mean'], [1000, 10000, 50000], ['first', 'last']): if direction == 'first': feature_dict[f'{agg_type}_{direction}_{slice_length}'] = \ x[:slice_length].agg(agg_type) elif direction == 'last': feature_dict[f'{agg_type}_{direction}_{slice_length}'] = \ x[-slice_length:].agg(agg_type) feature_dict['max_to_min'] = x.max() / np.abs(x.min()) feature_dict['max_to_min_diff'] = x.max() - np.abs(x.min()) feature_dict['count_big'] = len(x[np.abs(x) > 500]) feature_dict['sum'] = x.sum() feature_dict['mean_change_rate'] = self.calc_change_rate(x) # calc_change_rate on slices of data for slice_length, direction in product( [1000, 10000, 50000], ['first', 'last']): if direction == 'first': feature_dict[f'mean_change_rate_{direction}_{slice_length}'] = \ self.calc_change_rate(x[:slice_length]) elif direction == 'last': feature_dict[f'mean_change_rate_{direction}_{slice_length}'] = \ self.calc_change_rate(x[-slice_length:]) # percentiles on original and absolute values for p in percentiles: feature_dict[f'percentile_{p}'] = np.percentile(x, p) feature_dict[f'abs_percentile_{p}'] = np.percentile(np.abs(x), p) feature_dict['trend'] = self.add_trend_feature(x) feature_dict['abs_trend'] = self.add_trend_feature(x, abs_values=True) feature_dict['mad'] = x.mad() feature_dict['kurt'] = x.kurtosis() feature_dict['skew'] = x.skew() feature_dict['med'] = x.median() feature_dict['Hilbert_mean'] = np.abs(signal.hilbert(x)).mean() for hw in hann_windows: feature_dict[f'Hann_window_mean_{hw}'] = \ (signal.convolve(x, signal.hann(hw), mode='same') / sum(signal.hann(hw))).mean() feature_dict['classic_sta_lta1_mean'] = \ self.classic_sta_lta(x, 500, 10000).mean() feature_dict['classic_sta_lta2_mean'] = \ self.classic_sta_lta(x, 5000, 100000).mean() feature_dict['classic_sta_lta3_mean'] = \ self.classic_sta_lta(x, 3333, 6666).mean() feature_dict['classic_sta_lta4_mean'] = \ self.classic_sta_lta(x, 10000, 25000).mean() feature_dict['classic_sta_lta5_mean'] = \ self.classic_sta_lta(x, 50, 1000).mean() feature_dict['classic_sta_lta6_mean'] = \ self.classic_sta_lta(x, 100, 5000).mean() feature_dict['classic_sta_lta7_mean'] = \ self.classic_sta_lta(x, 333, 666).mean() feature_dict['classic_sta_lta8_mean'] = \ self.classic_sta_lta(x, 4000, 10000).mean() # exponential rolling statistics ewma = pd.Series.ewm for s in spans: feature_dict[f'exp_Moving_average_{s}_mean'] = \ (ewma(x, span=s).mean(skipna=True)).mean(skipna=True) feature_dict[f'exp_Moving_average_{s}_std'] = \ (ewma(x, span=s).mean(skipna=True)).std(skipna=True) feature_dict[f'exp_Moving_std_{s}_mean'] = \ (ewma(x, span=s).std(skipna=True)).mean(skipna=True) feature_dict[f'exp_Moving_std_{s}_std'] = \ (ewma(x, span=s).std(skipna=True)).std(skipna=True) feature_dict['iqr'] = np.subtract(*np.percentile(x, [75, 25])) feature_dict['iqr1'] = np.subtract(*np.percentile(x, [95, 5])) feature_dict['ave10'] = stats.trim_mean(x, 0.1) for slice_length, threshold in product( [50000, 100000, 150000], [5, 10, 20, 50, 100]): feature_dict[f'count_big_{slice_length}_threshold_{threshold}'] = \ (np.abs(x[-slice_length:]) > threshold).sum() feature_dict[f'count_big_{slice_length}_less_threshold_{threshold}'] = \ (np.abs(x[-slice_length:]) < threshold).sum() feature_dict['range_minf_m4000'] = \ feature_calculators.range_count(x, -np.inf, -4000) feature_dict['range_p4000_pinf'] = \ feature_calculators.range_count(x, 4000, np.inf) for i, j in zip(borders, borders[1:]): feature_dict[f'range_{i}_{j}'] = feature_calculators.range_count(x, i, j) for autocorr_lag in autocorr_lags: feature_dict[f'autocorrelation_{autocorr_lag}'] = \ feature_calculators.autocorrelation(x, autocorr_lag) feature_dict[f'c3_{autocorr_lag}'] = \ feature_calculators.c3(x, autocorr_lag) for p in percentiles: feature_dict[f'binned_entropy_{p}'] = \ feature_calculators.binned_entropy(x, p) feature_dict['num_crossing_0'] = \ feature_calculators.number_crossing_m(x, 0) for peak in peaks: feature_dict[f'num_peaks_{peak}'] = feature_calculators.number_peaks(x, peak) for c in coefs: feature_dict[f'spkt_welch_density_{c}'] = \ list(feature_calculators.spkt_welch_density(x, [{'coeff': c}]))[0][1] feature_dict[f'time_rev_asym_stat_{c}'] = \ feature_calculators.time_reversal_asymmetry_statistic(x, c) for w in windows: x_roll_std = x.rolling(w).std().dropna().values x_roll_mean = x.rolling(w).mean().dropna().values feature_dict[f'ave_roll_std_{w}'] = x_roll_std.mean() feature_dict[f'std_roll_std_{w}'] = x_roll_std.std() feature_dict[f'max_roll_std_{w}'] = x_roll_std.max() feature_dict[f'min_roll_std_{w}'] = x_roll_std.min() for p in percentiles: feature_dict[f'percentile_roll_std_{p}_window_{w}'] = \ np.percentile(x_roll_std, p) feature_dict[f'av_change_abs_roll_std_{w}'] = \ np.mean(np.diff(x_roll_std)) feature_dict[f'av_change_rate_roll_std_{w}'] = \ np.mean(np.nonzero((np.diff(x_roll_std) / x_roll_std[:-1]))[0]) feature_dict[f'abs_max_roll_std_{w}'] = \ np.abs(x_roll_std).max() feature_dict[f'ave_roll_mean_{w}'] = x_roll_mean.mean() feature_dict[f'std_roll_mean_{w}'] = x_roll_mean.std() feature_dict[f'max_roll_mean_{w}'] = x_roll_mean.max() feature_dict[f'min_roll_mean_{w}'] = x_roll_mean.min() for p in percentiles: feature_dict[f'percentile_roll_mean_{p}_window_{w}'] = \ np.percentile(x_roll_mean, p) feature_dict[f'av_change_abs_roll_mean_{w}'] = \ np.mean(np.diff(x_roll_mean)) feature_dict[f'av_change_rate_roll_mean_{w}'] = \ np.mean(np.nonzero((np.diff(x_roll_mean) / x_roll_mean[:-1]))[0]) feature_dict[f'abs_max_roll_mean_{w}'] = \ np.abs(x_roll_mean).max() # Mel-frequency cepstral coefficients (MFCCs) x = x.values.astype('float32') mfcc = librosa.feature.mfcc(y=x) for i in range(len(mfcc)): feature_dict[f'mfcc_{i}_avg'] = np.mean(np.abs(mfcc[i])) # spectral features feature_dict['spectral_centroid'] = \ np.mean(np.abs(librosa.feature.spectral_centroid(y=x)[0])) feature_dict['zero_crossing_rate'] = \ np.mean(np.abs(librosa.feature.zero_crossing_rate(y=x)[0])) feature_dict['spectral_flatness'] = \ np.mean(np.abs(librosa.feature.spectral_flatness(y=x)[0])) feature_dict['spectral_contrast'] = \ np.mean(np.abs(librosa.feature.spectral_contrast(S=np.abs(librosa.stft(x)))[0])) feature_dict['spectral_bandwidth'] = \ np.mean(np.abs(librosa.feature.spectral_bandwidth(y=x)[0])) return feature_dict def generate(self): feature_list = [] res = Parallel(n_jobs=self.n_jobs, backend='threading')( delayed(self.get_features)(x, y, s) for s, x, y in tqdm(self.segments(), total=self.total, ncols=100, desc='generating features', ascii=True)) for r in res: feature_list.append(r) return pd.DataFrame(feature_list) @staticmethod def add_trend_feature(arr, abs_values=False): idx = np.array(range(len(arr))) if abs_values: arr = np.abs(arr) lr = LinearRegression() lr.fit(idx.reshape(-1, 1), arr) return lr.coef_[0] @staticmethod def classic_sta_lta(x, length_sta, length_lta): sta = np.cumsum(x ** 2) # Convert to float sta = np.require(sta, dtype=np.float) # Copy for LTA lta = sta.copy() # Compute the STA and the LTA sta[length_sta:] = sta[length_sta:] - sta[:-length_sta] sta /= length_sta lta[length_lta:] = lta[length_lta:] - lta[:-length_lta] lta /= length_lta # Pad zeros sta[:length_lta - 1] = 0 # Avoid division by zero by setting zero values to tiny float dtiny = np.finfo(0.0).tiny idx = lta < dtiny lta[idx] = dtiny return sta / lta @staticmethod def calc_change_rate(x): change = (np.diff(x) / x[:-1]).values change = change[np.nonzero(change)[0]] change = change[~np.isnan(change)] change = change[change != -np.inf] change = change[change != np.inf] return np.mean(change) def main(args): if args['train']: fg = FeatureGenerator( config.path_to_train_store, is_train=True, n_rows=config.n_rows_all, n_jobs=config.n_jobs, segment_size=config.segment_size) else: fg = FeatureGenerator( config.path_to_test_store, is_train=False, n_jobs=config.n_jobs) data = fg.generate() data.to_csv(config.path_to_test, index=False, float_format='%.5f') if __name__ == '__main__': arg_parser = argparse.ArgumentParser( description='features generator', formatter_class=argparse.RawTextHelpFormatter) arg_parser.add_argument( '--train', action='store_true', help='make train set') arg_parser.add_argument( '--test', action='store_true', help='make test set') main(vars(arg_parser.parse_args()))

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0

0.094771

0

null

0

null

0

1

0

f6262ab64348d90863c6a1288397d28b818c68b1

6,611

py

Python

main.py

haoyfan/SemiTime

9604900bd5d67513d128d33d395bf78e5186b467

[ "MIT" ]

14

2020-10-28T11:29:43.000Z

2022-03-31T02:20:27.000Z

main.py

haoyfan/SemiTime

9604900bd5d67513d128d33d395bf78e5186b467

[ "MIT" ]

1

2022-03-31T08:16:35.000Z

main.py

haoyfan/SemiTime

9604900bd5d67513d128d33d395bf78e5186b467

[ "MIT" ]

1

2021-06-21T16:30:43.000Z

# -*- coding: utf-8 -*- import datetime from optim.pretrain import * import argparse import torch from optim.train import supervised_train def parse_option(): parser = argparse.ArgumentParser('argument for training') parser.add_argument('--save_freq', type=int, default=200, help='save frequency') parser.add_argument('--batch_size', type=int, default=128, help='batch_size') parser.add_argument('--K', type=int, default=4, help='Number of augmentation for each sample') # Bigger is better. parser.add_argument('--alpha', type=float, default=0.5, help='Past-future split point') parser.add_argument('--feature_size', type=int, default=64, help='feature_size') parser.add_argument('--num_workers', type=int, default=16, help='num of workers to use') parser.add_argument('--epochs', type=int, default=1000, help='number of training epochs') parser.add_argument('--patience', type=int, default=200, help='training patience') parser.add_argument('--aug_type', type=str, default='none', help='Augmentation type') parser.add_argument('--class_type', type=str, default='3C', help='Classification type') parser.add_argument('--gpu', type=str, default='0', help='gpu id') # optimization parser.add_argument('--learning_rate', type=float, default=0.01, help='learning rate') # model dataset parser.add_argument('--dataset_name', type=str, default='MFPT', choices=['CricketX', 'UWaveGestureLibraryAll', 'InsectWingbeatSound', 'MFPT', 'XJTU', 'EpilepticSeizure', ], help='dataset') parser.add_argument('--nb_class', type=int, default=3, help='class number') # ucr_path = '../datasets/UCRArchive_2018' parser.add_argument('--ucr_path', type=str, default='./datasets', help='Data root for dataset.') parser.add_argument('--ckpt_dir', type=str, default='./ckpt/', help='Data path for checkpoint.') # method parser.add_argument('--backbone', type=str, default='SimConv4') parser.add_argument('--model_name', type=str, default='SemiTime', choices=['SupCE', 'SemiTime'], help='choose method') parser.add_argument('--label_ratio', type=float, default=0.1, help='label ratio') opt = parser.parse_args() return opt if __name__ == "__main__": import os import numpy as np opt = parse_option() os.environ['CUDA_VISIBLE_DEVICES']=opt.gpu exp = 'exp-cls' Seeds = [0, 1, 2, 3, 4] Runs = range(0, 10, 1) aug1 = ['magnitude_warp'] aug2 = ['time_warp'] if opt.model_name == 'SemiTime': model_paras='label{}_{}'.format(opt.label_ratio, opt.alpha) else: model_paras='label{}'.format(opt.label_ratio) if aug1 == aug2: opt.aug_type = [aug1] elif type(aug1) is list: opt.aug_type = aug1 + aug2 else: opt.aug_type = [aug1, aug2] log_dir = './results/{}/{}/{}/{}'.format( exp, opt.dataset_name, opt.model_name, model_paras) if not os.path.exists(log_dir): os.makedirs(log_dir) file2print_detail_train = open("{}/train_detail.log".format(log_dir), 'a+') print(datetime.datetime.now(), file=file2print_detail_train) print("Dataset\tTrain\tTest\tDimension\tClass\tSeed\tAcc_label\tAcc_unlabel\tEpoch_max", file=file2print_detail_train) file2print_detail_train.flush() file2print = open("{}/test.log".format(log_dir), 'a+') print(datetime.datetime.now(), file=file2print) print("Dataset\tAcc_mean\tAcc_std\tEpoch_max", file=file2print) file2print.flush() file2print_detail = open("{}/test_detail.log".format(log_dir), 'a+') print(datetime.datetime.now(), file=file2print_detail) print("Dataset\tTrain\tTest\tDimension\tClass\tSeed\tAcc_max\tEpoch_max", file=file2print_detail) file2print_detail.flush() ACCs = {} MAX_EPOCHs_seed = {} ACCs_seed = {} for seed in Seeds: np.random.seed(seed) torch.manual_seed(seed) opt.ckpt_dir = './ckpt/{}/{}/{}/{}/{}/{}'.format( exp, opt.model_name, opt.dataset_name, '_'.join(opt.aug_type), model_paras, str(seed)) if not os.path.exists(opt.ckpt_dir): os.makedirs(opt.ckpt_dir) print('[INFO] Running at:', opt.dataset_name) x_train, y_train, x_val, y_val, x_test, y_test, opt.nb_class, _ \ = load_ucr2018(opt.ucr_path, opt.dataset_name) ACCs_run={} MAX_EPOCHs_run = {} for run in Runs: ################ ## Train ####### ################ if opt.model_name == 'SupCE': acc_test, epoch_max = supervised_train( x_train, y_train, x_val, y_val, x_test, y_test, opt) acc_unlabel=0 elif 'SemiTime' in opt.model_name: acc_test, acc_unlabel, epoch_max = train_SemiTime( x_train, y_train, x_val, y_val, x_test, y_test,opt) print("{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}".format( opt.dataset_name, x_train.shape[0], x_test.shape[0], x_train.shape[1], opt.nb_class, seed, round(acc_test, 2), round(acc_unlabel, 2), epoch_max), file=file2print_detail_train) file2print_detail_train.flush() ACCs_run[run] = acc_test MAX_EPOCHs_run[run] = epoch_max ACCs_seed[seed] = round(np.mean(list(ACCs_run.values())), 2) MAX_EPOCHs_seed[seed] = np.max(list(MAX_EPOCHs_run.values())) print("{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}".format( opt.dataset_name, x_train.shape[0], x_test.shape[0], x_train.shape[1], opt.nb_class, seed, ACCs_seed[seed], MAX_EPOCHs_seed[seed]), file=file2print_detail) file2print_detail.flush() ACCs_seed_mean = round(np.mean(list(ACCs_seed.values())), 2) ACCs_seed_std = round(np.std(list(ACCs_seed.values())), 2) MAX_EPOCHs_seed_max = np.max(list(MAX_EPOCHs_seed.values())) print("{}\t{}\t{}\t{}".format( opt.dataset_name, ACCs_seed_mean, ACCs_seed_std, MAX_EPOCHs_seed_max), file=file2print) file2print.flush()

36.932961

122

0.588262

821

6,611

4.501827

0.218027

0.046266

0.087392

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0.118236

0

0.017795

0.260475

6,611

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false

0

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0

0.069231

0.184615

0

null

0

null

0

1

0

f6306e5dbd5f299747a7bcacf37b900f61ec3359

2,731

py

Python

boundingbox/distances.py

nickhalmagyi/boundingbox

76aae366171b64ed938aa22912e0c9684fecd351

[ "MIT" ]

1

2019-05-10T13:07:40.000Z

boundingbox/distances.py

nickhalmagyi/BoundingBox

76aae366171b64ed938aa22912e0c9684fecd351

[ "MIT" ]

null

boundingbox/distances.py

nickhalmagyi/BoundingBox

76aae366171b64ed938aa22912e0c9684fecd351

[ "MIT" ]

null

from boundingbox.boundingbox import BoundingBox from haversine import haversine import numpy as np import time from importlib import reload import boundingbox.validations; reload(boundingbox.validations) from boundingbox.validations.numbers import validate_strictly_positive_integer, validate_positive_number def get_points_within_distance(source, targets, length): """ It is possible for a point to be within the bbox but further than length from source. Here we remove such points. :param source: lat-lon tuple :param targets: iterable of the form [(lat, lon), dist] :param length: positive number :return: list of targets whose distance to source is less than length. """ validate_positive_number(length) boundingbox = BoundingBox(source, length) targets_in_bbox = boundingbox.get_points_within_bboxs(targets, boundingbox.bbox) targets_within_distance = targets_in_bbox[np.transpose(targets_in_bbox)[1] <= length] return targets_within_distance def closest_points_are_within_length(targets_distance, N, length): """ :param targets_dist: iterable of the form [(lat, lon), dist] :param N: strictly positive integer :param length: positive number :return: boolean, whether the distance from source to the N-th point in targets_dist is leq to length """ return targets_distance[:N][-1][1] <= length def get_closest_points(source_degrees, targets, N, length=None): validate_strictly_positive_integer(N) if N > len(targets): N = len(targets) boundingbox = BoundingBox(source_degrees, length) targets_filtered = boundingbox.filter_targets_in_bboxs(targets, boundingbox.bbox) targets_distance = boundingbox.compute_distances_from_source(source_degrees, targets_filtered) while (len(targets_distance) < N) or not closest_points_are_within_length(targets_distance, N, boundingbox.length): print('Rescaling box, consider using a larger initial length') # rescale if len(targets_distance) < N: boundingbox.length *= 1.25 else: # set length to be the distance from source to the N-th point. Nth_point_distance = targets_distance[:N][-1][1] if Nth_point_distance <= boundingbox.length: boundingbox.length *= 1.25 else: boundingbox.length = Nth_point_distance boundingbox.bbox = boundingbox.make_bounding_box(boundingbox.source_radians, boundingbox.length) targets_filtered = boundingbox.filter_targets_in_bboxs(targets, boundingbox.bbox) targets_distance = boundingbox.compute_distances_from_source(source_degrees, targets_filtered) return targets_distance[:N]

42.671875

119

0.737459

346

2,731

5.601156

0.263006

0.069659

0.057792

0.041796

0.379773

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0.158927

0

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2,731

63

120

43.349206

0.870764

0.220066

0

0.222222

0

0.025666

0

1

0.083333

false

0

0.194444

0

0.361111

0.027778

0

null

0

null

0

1

0

f630cd0ac8ff9fcf66251d9af12794677c609746

709

py

Python

apis/betterself/v1/constants.py

jeffshek/betterself

51468253fc31373eb96e0e82189b9413f3d76ff5

[ "MIT" ]

98

2017-07-29T14:26:36.000Z

2022-02-28T04:10:15.000Z

apis/betterself/v1/constants.py

jeffshek/betterself

51468253fc31373eb96e0e82189b9413f3d76ff5

[ "MIT" ]

1,483

2017-05-30T00:05:56.000Z

2022-03-31T12:37:06.000Z

apis/betterself/v1/constants.py

lawrendran/betterself

51468253fc31373eb96e0e82189b9413f3d76ff5

[ "MIT" ]

13

2017-11-08T00:02:35.000Z

2022-02-28T04:10:32.000Z

from events.models import SupplementLog from supplements.models import IngredientComposition, Supplement, Ingredient, Measurement from vendors.models import Vendor VALID_REST_RESOURCES = [ SupplementLog, Supplement, IngredientComposition, Ingredient, Measurement, Vendor ] # a lot of frontend (react) depends on a uniqueKey to render rows, in this case, do something here that makes rendering # all the rows a little bit easier. in most circumstances, for any resources that are directly related to a model # uuid is fine, but not all resources are django models, so uniqueKey comes in handy UNIQUE_KEY_CONSTANT = 'uniqueKey' DAILY_FREQUENCY = 'daily' MONTHLY_FREQUENCY = 'monthly'

33.761905

119

0.782793

93

709

5.903226

0.677419

0.065574

0

0.169252

709

20

120

35.45

0.932088

0.440056

0

0.053435

0

1

0

false

0

0.214286

0

0.214286

0

null

0

null

0

1

0

f630fb65819047b6f622503fb05a79444a7a296e

4,538

py

Python

Data/DataExploitation.py

Someone-42/BonapioSQL

1d059ed1796cfc33f1a580c6320b5e569db8cb5d

[ "MIT" ]

null

Data/DataExploitation.py

Someone-42/BonapioSQL

1d059ed1796cfc33f1a580c6320b5e569db8cb5d

[ "MIT" ]

1

2021-12-19T16:49:46.000Z

2021-12-19T16:49:58.000Z

Data/DataExploitation.py

Someone-42/BonapioSQL

1d059ed1796cfc33f1a580c6320b5e569db8cb5d

[ "MIT" ]

null

import os _NamePath = "Data/noms2008nat_text.txt" _SurnameGirlsPath = "Data/prenoms_donnees_filles.csv" _SurnameBoysPath = "Data/prenoms_donnees_garcons.csv" _FreqPathPrenom = "Data/freq_prenoms.csv" _FreqPathNom = "Data/freq_noms.csv" def _get_first_name_freq(file_name: str, d: dict) -> int: """ Modifies the dictionary with new names frequency - Returns the total added frequency""" total = 0 # Opening the bois file with open(file_name) as fp: print("Reading first names :", file_name) fp.readlines(5<<5) #Removes the first useless lines fp.readline() lines = fp.readlines() for line in lines: print(line) _region_, first_name, nombre = line.rstrip().split(";") #prenom_garcons = prenom_garcons.casefold() check le comment en bas if first_name not in d: d[first_name] = int(nombre.replace(' ', '')) else: d[first_name] += int(nombre.replace(' ', '')) total += int(nombre.replace(' ', '')) return total def _write_name_freqs_to_file(file_name: str, total: int, name_freqs: dict) -> None: with open(file_name, 'w') as f: print("Writing to names frequency file") f.write(f"{total}\n") for name, freq in name_freqs.values(): f.write(f"{name};{freq}\n") def _read_name_freqs_file(file_name: str) -> tuple: """ Returns a tuple, with the total frequencies added, and the list of names and their respective frequencies """ name_freqs = [] total = -1 with open(file_name) as f: total = int(f.readline) for line in f.readlines(): nom, freq = line.split(';') freq = int(freq) name_freqs.append((nom, freq)) return (total, name_freqs) def _create_prenoms_freq_file() -> None: """Creates a file containing the frequency of each prenom in the database for 2020""" total = 0 dict_prenoms_et_nombre = {} print("Creating prenoms frequency file...") total += _get_first_name_freq(_SurnameGirlsPath, dict_prenoms_et_nombre) total += _get_first_name_freq(_SurnameBoysPath, dict_prenoms_et_nombre) _write_name_freqs_to_file(_FreqPathPrenom, total, dict_prenoms_et_nombre) def _create_noms_freq_file(period: list = [(1991, 2000)]) -> None: """Creates a file containing the frequency of each name in the database for a given period""" assert period[0] < period[1], "The first year of the period must be lower than the second" total = 0 dict_noms_et_nombre = {} print("Creating noms frequency file...") with open(_NamePath) as f: print("Reading") liste_entete_avec_les_dates = f.readline().split("\t") period_indices = [] for p in period: s = '_' + str(p[0]) + '_' + str(p[1]) ind = liste_entete_avec_les_dates.index(s) assert ind > 0, "The period is not in the database" # bigger than 0 bc first index is the names column period_indices.append(ind) lines = f.readlines() for line in lines: name_and_dates_freq = line.split('\t') freq = sum([name_and_dates_freq[i] for i in period_indices]) # Gathers the sum of frequencies over every period selected name = name_and_dates_freq[0] dict_noms_et_nombre[name] = freq total += freq _write_name_freqs_to_file(_FreqPathNom, total, dict_noms_et_nombre) def get_prenoms(limit: int = None) -> tuple: """Returns a tuple, with the total frequency, and a list of surnames with their frequency""" freq_compiled_firstnames_exists = os.path.exists(_FreqPathPrenom) freq_boys_exists = os.path.exists(_SurnameBoysPath) freq_girls_exists = os.path.exists(_SurnameGirlsPath) if not freq_compiled_firstnames_exists: if freq_boys_exists and freq_girls_exists: _create_prenoms_freq_file() else: raise FileNotFoundError("A file for first names (boys or girls) is missing, cannot compile name frequencies") return _read_name_freqs_file(_FreqPathPrenom) def get_noms(limit: int = None, period: list = [(1991,2000)]) -> list: """Returns a list of names with their respective frequencies""" if not os.path.exists(_FreqPathNom): if os.path.exists(_NamePath): _create_noms_freq_file(period) else: raise FileNotFoundError("The file containing names does not exist") return _read_name_freqs_file(_FreqPathNom)

41.254545

132

0.659982

613

4,538

4.628059

0.239804

0.034896

0.021149

0.026789

0.196687

0.070497

0.052168

0.031019

0

0.010718

0.239312

4,538

109

133

41.633028

0.811124

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0.071429

0

null

0

null

0

1

0

f6323866975c2773aa564a86869863a47d476760

1,126

py

Python

backend/moves/signals.py

mnieber/lindyscience

468160aa6da42f45d8c37a2141a077a48410f81d

[ "MIT" ]

null

backend/moves/signals.py

mnieber/lindyscience

468160aa6da42f45d8c37a2141a077a48410f81d

[ "MIT" ]

21

2020-02-11T23:50:05.000Z

2022-02-27T17:44:29.000Z

backend/moves/signals.py

mnieber/lindyscience

468160aa6da42f45d8c37a2141a077a48410f81d

[ "MIT" ]

null

from django.dispatch import receiver from django_rtk.signals import account_activated from moves import models from profiles.models import Profile, ProfileToMoveList @receiver(account_activated) def create_profile_on_activation(sender, user, request, **kwargs): trash = models.MoveList( role="trash", name="Trash", slug="trash", is_private=True, description="", owner=user, ) trash.save() drafts = models.MoveList( role="drafts", name="Drafts", slug="drafts", is_private=True, description="", owner=user, ) drafts.save() moves = models.MoveList( role="", name="Moves", slug="moves", is_private=False, description="", owner=user, ) moves.save() profile = Profile( owner=user, recent_move_url="lists/%s/%s" % (user.username, moves.name) ) profile.save() for (idx, move_list) in enumerate([trash, drafts, moves]): p2m = ProfileToMoveList(profile=profile, move_list=move_list, order=idx + 1) p2m.save()

23.458333

84

0.60302

122

1,126

5.45082

0.401639

0.054135

0.081203

0.07218

0.099248

0

0.003667

0.273535

1,126

47

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23.957447

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false

0

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0

0.125

0

null

0

null

0

1

0

f6347a3ec7983daa3f16a31ca1dbd8d5e0049877

5,131

py

Python

Assets/Python/Victories.py

dguenms/Dawn-of-Civilization

1c4f510af97a869637cddb4c0859759158cea5ce

[ "MIT" ]

93

2015-11-20T04:13:36.000Z

2022-03-24T00:03:08.000Z

Assets/Python/Victories.py

dguenms/Dawn-of-Civilization

1c4f510af97a869637cddb4c0859759158cea5ce

[ "MIT" ]

206

2015-11-09T00:27:15.000Z

2021-12-04T19:05:18.000Z

Assets/Python/Victories.py

dguenms/Dawn-of-Civilization

1c4f510af97a869637cddb4c0859759158cea5ce

[ "MIT" ]

117

2015-11-08T02:43:46.000Z

2022-02-12T06:29:00.000Z

from Core import * from StoredData import data from Events import handler ### SCENARIO SETUP ### lLostIn1700AD = [iChina, iIndia, iTamils, iKorea, iVikings, iTurks, iSpain, iHolyRome, iPoland, iPortugal, iMughals, iOttomans, iThailand] lWonIn1700AD = [(iIran, 0), (iJapan, 0), (iFrance, 0), (iCongo, 0), (iNetherlands, 1)] ### DELAYED IMPORT ### dHistoricalGoals = None dReligiousGoals = None dPaganGoals = None @handler("fontsLoaded") def onFontsLoaded(): from HistoricalVictory import dGoals as dDefinedHistoricalGoals global dHistoricalGoals dHistoricalGoals = dDefinedHistoricalGoals from ReligiousVictory import dGoals as dDefinedReligiousGoals global dReligiousGoals dReligiousGoals = dDefinedReligiousGoals from ReligiousVictory import dAdditionalPaganGoal global dPaganGoals dPaganGoals = dAdditionalPaganGoal def getHistoricalGoals(iPlayer): return list(dHistoricalGoals[civ(iPlayer)]) def getReligiousGoals(iPlayer): iStateReligion = player(iPlayer).getStateReligion() if iStateReligion >= 0: return list(dReligiousGoals[iStateReligion]) elif player(iPlayer).isStateReligion(): return concat(dReligiousGoals[iPaganVictory], dPaganGoals[infos.civ(civ(iPlayer)).getPaganReligion()]) else: return dReligiousGoals[iSecularVictory] ### GOAL CHECKS ### class HistoricalVictoryCallback(object): def stateChange(self, goal): if goal.succeeded(): goal.announceSuccess() iCount = count(goal.succeeded() for goal in data.players[goal.iPlayer].historicalGoals) if iCount == 2: self.goldenAge(goal.iPlayer) elif iCount == 3: self.victory(goal.iPlayer) elif goal.failed(): goal.announceFailure() def goldenAge(self, iPlayer): data.players[iPlayer].bLaunchHistoricalGoldenAge = True def victory(self, iPlayer): if game.getWinner() == -1: game.setWinner(iPlayer, VictoryTypes.VICTORY_HISTORICAL) class ReligiousVictoryCallback(object): def check(self, goal): if goal: iCount = count(goal for goal in data.players[goal.iPlayer].religiousGoals) if iCount == 3: self.victory(goal.iPlayer) def victory(self, iPlayer): if game.getWinner() == -1: game.setWinner(iPlayer, VictoryTypes.VICTORY_RELIGIOUS) historicalVictoryCallback = HistoricalVictoryCallback() religiousVictoryCallback = ReligiousVictoryCallback() ### SETUP ### def createHistoricalGoals(iPlayer): goals = [goal.activate(iPlayer, historicalVictoryCallback) for goal in getHistoricalGoals(iPlayer)] goals = setupScenario(iPlayer, goals) return goals def createReligiousGoals(iPlayer): return [goal.passivate(iPlayer, religiousVictoryCallback) for goal in getReligiousGoals(iPlayer)] def disable(iPlayer=None): if iPlayer is None: iPlayer = active() for goal in data.players[iPlayer].historicalGoals + data.players[iPlayer].religiousGoals: goal.deactivate() data.players[iPlayer].historicalGoals = [] data.players[iPlayer].religiousGoals = [] def switchReligiousGoals(iPlayer): for goal in data.players[iPlayer].religiousGoals: goal.deactivate() data.players[iPlayer].religiousGoals = createReligiousGoals(iPlayer) def setupScenario(iPlayer, goals): iCiv = civ(iPlayer) if scenario() == i1700AD: if iCiv in lLostIn1700AD: for goal in goals: goal.fail() for iGoal in [iGoal for iGoalCiv, iGoal in lWonIn1700AD if iGoalCiv == iCiv]: goals[iGoal].succeed() # setup English tech goal if iCiv == iEngland: goals[2].accumulate(4, iRenaissance) # setup Congo slave trade goal if iCiv == iCongo: goals[1].accumulate(500) return goals ### GOLDEN AGE ### def goldenAge(iPlayer): iGoldenAgeTurns = player(iPlayer).getGoldenAgeLength() player(iPlayer).changeGoldenAgeTurns(iGoldenAgeTurns) message(iPlayer, "TXT_KEY_UHV_INTERMEDIATE", color=iPurple) if player(iPlayer).isHuman(): for iOtherPlayer in players.major().alive().without(iPlayer): player(iOtherPlayer).AI_changeAttitudeExtra(iPlayer, -2) @handler("GameStart") def setup(): iPlayer = active() data.players[iPlayer].historicalGoals = createHistoricalGoals(iPlayer) data.players[iPlayer].religiousGoals = createReligiousGoals(iPlayer) @handler("switch") def onSwitch(iPrevious, iCurrent): for goal in data.players[iPrevious].historicalGoals + data.players[iPrevious].religiousGoals: goal.deactivate() data.players[iPrevious].historicalGoals = [] data.players[iPrevious].religiousGoals = [] data.players[iCurrent].historicalGoals = createHistoricalGoals(iCurrent) data.players[iCurrent].religiousGoals = createReligiousGoals(iCurrent) @handler("civicChanged") def onCivicChanged(iPlayer, iOldCivic, iNewCivic): if iPlayer == active() and infos.civic(iOldCivic).isStateReligion() != infos.civic(iNewCivic).isStateReligion(): switchReligiousGoals(iPlayer) @handler("playerChangeStateReligion") def onStateReligionChanged(iPlayer): if iPlayer == active(): switchReligiousGoals(iPlayer) @handler("EndPlayerTurn") def checkHistoricalGoldenAge(iGameTurn, iPlayer): if data.players[iPlayer].bLaunchHistoricalGoldenAge: data.players[iPlayer].bLaunchHistoricalGoldenAge = False goldenAge(iPlayer)

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138

0.764958

519

5,131

7.55106

0.308285

0.05333

0.050523

0.016586

0.193672

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0.142894

0.127073

0.071447

0.038785

0

0.008473

0.125901

5,131

186

139

27.586022

0.865329

0.022413

0

0.142857

0

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0.009833

0

1

0.159664

false

0.008403

0.05042

0.016807

0.285714

0

null

0

null

0

1

0

f634d3bb4f093f386bc4f87d452b8f879ce45cb6

8,015

py

Python

src/cpePaser/day_extract.py

MAE-M/PotentiallyInactiveCpeAnalysisTool

58f897fb45437ff72a6db4d490f364061d779c50

[ "Apache-2.0" ]

null

src/cpePaser/day_extract.py

MAE-M/PotentiallyInactiveCpeAnalysisTool

58f897fb45437ff72a6db4d490f364061d779c50

[ "Apache-2.0" ]

null

src/cpePaser/day_extract.py

MAE-M/PotentiallyInactiveCpeAnalysisTool

58f897fb45437ff72a6db4d490f364061d779c50

[ "Apache-2.0" ]

null

# Copyright (c) 2020 Huawei Technologies Co., Ltd. # foss@huawei.com # # 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 os import time from typing import Dict, List import numpy as np import pandas as pd from collections import Counter from src.compress import compress # 日志器 from src.logger_setting.my_logger import get_logger from src.setting import setting LOGGER = get_logger() def groupby_calc(df): df['esn'] = df['esn'].astype('str') df = df.groupby(['esn']) return df def calc_total(series): series = series.values count = 0 for d in range(len(series)): if d < len(series) - 1: if pd.isna(series[d]) or pd.isna(series[d + 1]): continue if float(series[d]) <= float(series[d + 1]): count += float(series[d + 1]) - float(series[d]) else: count += float(series[d + 1]) return count def is_active(series): series = calc_total(series) if float(series) / setting.mb > 10: return 1 else: return 0 def get_max(series): if series: return np.max(series) else: return setting.INVALID_VALUE def get_min(series): if series: return np.min(series) else: return setting.INVALID_VALUE def get_avg(values, counts): count = sum(counts) if type(counts) == list else counts if count == 0: return setting.INVALID_VALUE else: return sum(values) / count if type(values) == list else values / count def get_avg_max_min(df, avg_name, max_name, min_name, counts): avg_list = list(filter(lambda x: int(x) != setting.INVALID_VALUE, df[avg_name].values)) sum_value = get_sum(avg_list) cnt = get_sum(list(df[counts].values)) avg = sum_value / cnt if cnt != 0 else setting.INVALID_VALUE max_list = list(filter(lambda x: int(x) != setting.INVALID_VALUE, df[max_name].values)) max_value = get_max(max_list) min_list = list(filter(lambda x: int(x) != setting.INVALID_VALUE, df[min_name].values)) min_value = get_min(min_list) return {avg_name: avg, max_name: max_value, min_name: min_value} def get_sum(series): if series: return np.sum(series) else: return setting.INVALID_VALUE def get_std(series): if series: return np.std(series) else: return setting.INVALID_VALUE def get_all_day(): all_day_file = compress.get_all_csv_file(os.path.join(setting.data_path, 'extractData')) day_list = [] for file in all_day_file: day_list.append(os.path.split(file)[1].split("\\")[-1].split('_')[0]) return list(set(day_list)) def merge_day_data(day_dict: Dict[str, List[str]]): for day in day_dict.keys(): file_list: List[str] = day_dict.get(day) df = pd.concat(pd.read_csv(file, error_bad_lines=False, index_col=False) for file in file_list) df.columns = setting.parameter_json["extract_data_columns"] df = df.sort_values('collectTime', ascending=True) # 把-9999变成了NaN,但是原来是空的值，在读进来的时候已经变成NaN了，所有空值和-9999都变成了NaN df = df.replace(setting.INVALID_VALUE, np.nan) grouped = groupby_calc(df).agg( MaxRSRP=pd.NamedAgg(column='RSRP', aggfunc=max), MinRSRP=pd.NamedAgg(column='RSRP', aggfunc=min), AvgRSRP=pd.NamedAgg(column='RSRP', aggfunc=sum), CntRSRP=pd.NamedAgg(column='RSRP', aggfunc="count"), MaxCQI=pd.NamedAgg(column='CQI', aggfunc=max), MinCQI=pd.NamedAgg(column='CQI', aggfunc=min), AvgCQI=pd.NamedAgg(column='CQI', aggfunc=sum), CntCQI=pd.NamedAgg(column='CQI', aggfunc="count"), MaxRSRQ=pd.NamedAgg(column='RSRQ', aggfunc=max), MinRSRQ=pd.NamedAgg(column='RSRQ', aggfunc=min), AvgRSRQ=pd.NamedAgg(column='RSRQ', aggfunc=sum), CntRSRQ=pd.NamedAgg(column='RSRQ', aggfunc="count"), MaxRSSI=pd.NamedAgg(column='RSSI', aggfunc=max), MinRSSI=pd.NamedAgg(column='RSSI', aggfunc=min), AvgRSSI=pd.NamedAgg(column='RSSI', aggfunc=sum), CntRSSI=pd.NamedAgg(column='RSSI', aggfunc="count"), MaxSINR=pd.NamedAgg(column='SINR', aggfunc=max), MinSINR=pd.NamedAgg(column='SINR', aggfunc=min), AvgSINR=pd.NamedAgg(column='SINR', aggfunc=sum), CntSINR=pd.NamedAgg(column='SINR', aggfunc="count"), TotalDownload=pd.NamedAgg(column='TotalDownload', aggfunc=calc_total), TotalUpload=pd.NamedAgg(column='TotalUpload', aggfunc=calc_total), TotalConnectTime=pd.NamedAgg(column='TotalConnectTime', aggfunc=calc_total), ModelName=pd.NamedAgg(column='ModelName', aggfunc=lambda x: x.iloc[-1]), IMSI=pd.NamedAgg(column='IMSI', aggfunc=lambda x: x.iloc[-1]), IMEI=pd.NamedAgg(column='IMEI', aggfunc=lambda x: x.iloc[-1]), MSISDN=pd.NamedAgg(column='MSISDN', aggfunc=lambda x: x.iloc[-1]), isActive=pd.NamedAgg(column='TotalDownload', aggfunc=is_active), AvgDlThroughput=pd.NamedAgg(column='MaxDLThroughput', aggfunc=sum), CntDlThroughput=pd.NamedAgg(column='MaxDLThroughput', aggfunc="count"), AvgUlThroughput=pd.NamedAgg(column='MaxULThroughput', aggfunc=sum), CntUlThroughput=pd.NamedAgg(column='MaxULThroughput', aggfunc="count"), WiFiUserQty=pd.NamedAgg(column='WiFiUserQty', aggfunc=sum), CntWiFiUserQty=pd.NamedAgg(column='WiFiUserQty', aggfunc="count"), HostNumberOfEntries=pd.NamedAgg(column='HostNumberOfEntries', aggfunc=sum), CntHostNumberOfEntries=pd.NamedAgg(column='HostNumberOfEntries', aggfunc="count"), ECGI=pd.NamedAgg(column='ECGI', aggfunc=get_main_cell),) grouped[['TotalDownload', 'TotalUpload', 'TotalConnectTime', 'ModelName', 'IMSI', 'IMEI', 'MSISDN']] = grouped.sort_values('esn')[ ['TotalDownload', 'TotalUpload', 'TotalConnectTime', 'ModelName', 'IMSI', 'IMEI', 'MSISDN']].fillna(0) grouped = grouped.reset_index() grouped['date'] = day # 除了 'TotalDownload', 'TotalUpload', 'TotalConnectTime', 'ModelName', 'IMSI', 'IMEI', 'MSISDN' 这几列 # 其他列的nan将转换还原为setting.INVALID_VALUE, 也就是-9999 grouped = grouped.replace(np.nan, setting.INVALID_VALUE) grouped.to_csv(os.path.join(setting.data_path, 'day', day + r".csv"), index=False) # return a dictionary with: # key: date # value: list of filenames of this date def get_day_df_dict() -> Dict[str, List[str]]: all_day_file = compress.get_all_csv_file(os.path.join(setting.data_path, 'extractData')) day_dict = dict() for file in all_day_file: date = os.path.split(file)[1].split("\\")[-1].split('_')[0] if date not in day_dict: day_dict[date] = list() day_dict[date].append(file) return day_dict def get_main_cell(series): count_map = Counter(list(filter(lambda x: x != setting.INVALID_STRING, series))) count = 0 main_cell = "-" for cell, nums in count_map.items(): if nums > count: count = nums main_cell = cell return main_cell def day_extract(): compress.empty_folder(os.path.join(setting.data_path, 'day')) day_dict = get_day_df_dict() merge_day_data(day_dict) if __name__ == '__main__': print(time.localtime(time.time())) day_extract() print(time.localtime(time.time()))

37.629108

106

0.648534

1,049

8,015

4.824595

0.224023

0.073108

0.116973

0.024699

0.405453

0.17131

0.147994

0.11065

0.065995

0.05493

0

0.007021

0.218091

8,015

212

107

37.806604

0.800543

0.108796

0

0.154839

0

0.076404

0

1

0.090323

false

0

0.058065

0

0.264516

0.012903

0

null

0

null

0

1

0

f635b3eca92319c1097c2f0cdb0375a22892f53e

1,138

py

Python

module/seeding/seeder/threshold_seed.py

ObliviousJamie/opic-prototype

a925ce9faa38b9a6c8976d4c63b47349a53fd07e

[ "BSD-3-Clause" ]

null

module/seeding/seeder/threshold_seed.py

ObliviousJamie/opic-prototype

a925ce9faa38b9a6c8976d4c63b47349a53fd07e

[ "BSD-3-Clause" ]

null

module/seeding/seeder/threshold_seed.py

ObliviousJamie/opic-prototype

a925ce9faa38b9a6c8976d4c63b47349a53fd07e

[ "BSD-3-Clause" ]

null

import peakutils from module.seeding.seeder.seeder import Seeder class ThresholdSeeder(Seeder): def __init__(self, threshold, return_type='string', s_filter=None, peak_filter=None): super(ThresholdSeeder, self).__init__(return_type) self.threshold = threshold self.s_filter = s_filter self.peak_filter = peak_filter def pick_peaks(self, x_axis, y_axis, G): seeds = [] indexes = peakutils.indexes(y_axis, thres=self.threshold / max(y_axis)) for seed in x_axis[indexes]: seed = self.seed_switch[self.return_type](seed) if seed not in seeds: for v in G[seed]: if v in seeds: break seeds.append(seed) return seeds def _gen_name(self, name): self.name = f'{name}' if self.peak_filter is not None: self.name = f'{self.name}_{self.peak_filter.name}' else: self.name = f'{self.name}_gaussian_peak{self.threshold}' if self.s_filter is not None: self.name = f'{self.name}_{self.s_filter.name}'

29.947368

89

0.598418

150

1,138

4.313333

0.3

0.098918

0.055641

0.060278

0.137558

0.111283

0

0.301406

1,138

37

90

30.756757

0.813836

0

0.105448

0.094903

0

1

0.111111

false

0

0.074074

0

0.259259

0

null

0

null

0

1

0

f635f47508977479e51aeb2a2d9a8692db618aa8

1,620

py

Python

ball.py

iliescua/Pong

88c62cc9db44b76b98130546582aa601859e0041

[ "MIT" ]

null

ball.py

iliescua/Pong

88c62cc9db44b76b98130546582aa601859e0041

[ "MIT" ]

null

ball.py

iliescua/Pong

88c62cc9db44b76b98130546582aa601859e0041

[ "MIT" ]

null

from turtle import Turtle TOP_BORDER = 280 SIDE_BORDER = 380 P_SIZE = 50 P_POSITION = 320 class Ball: def __init__(self): self.ball = Turtle("circle") self.ball.pu() self.ball.color("white") self.x_move = 10 self.y_move = 10 self.r_score = 0 self.l_score = 0 self.ball_speed = 0.1 def move(self): x_dir = self.ball.xcor() + self.x_move y_dir = self.ball.ycor() + self.y_move self.ball.goto(x_dir, y_dir) def bounce(self, r_pad, l_pad): if self.ball.ycor() > TOP_BORDER or self.ball.ycor() < -TOP_BORDER: self.y_move *= -1 check_right = self.ball.distance(r_pad) < P_SIZE and self.ball.xcor() > P_POSITION check_left = self.ball.distance(l_pad) < P_SIZE and self.ball.xcor() < -P_POSITION if check_right or check_left: self.ball_speed *= 0.9 self.x_move *= -1 def reset_ball_right(self): self.ball.goto(0, 0) self.ball_speed = 0.1 if self.x_move < 0: self.x_move *= -1 def reset_ball_left(self): self.ball.goto(0, 0) self.ball_speed = 0.1 if self.x_move > 0: self.x_move *= -1 def update_score(self): if self.ball.xcor() > SIDE_BORDER: self.l_score += 1 self.reset_ball_right() if self.ball.xcor() < -SIDE_BORDER: self.r_score += 1 self.reset_ball_left() def get_r_score(self): return self.r_score def get_l_score(self): return self.l_score

23.142857

90

0.559877

245

1,620

3.453061

0.204082

0.189125

0.074468

0.066194

0.429078

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0.315603

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0.137116

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0.327778

1,620

69

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0.25

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null

0

null

0

1

0

f63677a352c96443fb84314f00dd42cf61e97b33

2,404

py

Python

nicos_sinq/devices/epics/motor.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

null

nicos_sinq/devices/epics/motor.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

null

nicos_sinq/devices/epics/motor.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

null

# -*- coding: utf-8 -*- # ***************************************************************************** # NICOS, the Networked Instrument Control System of the MLZ # Copyright (c) 2009-2022 by the NICOS contributors (see AUTHORS) # # This program is free software; you can redistribute it and/or modify it under # the terms of the GNU General Public License as published by the Free Software # Foundation; either version 2 of the License, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU General Public License for more # details. # # You should have received a copy of the GNU General Public License along with # this program; if not, write to the Free Software Foundation, Inc., # 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA # # Module authors: # Michele Brambilla <mnichele.brambilla@psi.ch> # # ***************************************************************************** from nicos import session from nicos.core import MAIN, Param, status from nicos_ess.devices.epics import EpicsMotor from nicos_ess.devices.epics.extensions import HasDisablePv class MotorCanDisable(HasDisablePv, EpicsMotor): parameters = { 'auto_enable': Param('Automatically enable the motor when the setup is' ' loaded', type=bool, default=False, settable=False), } def doInit(self, mode): EpicsMotor.doInit(self, mode) if session.sessiontype == MAIN and self.auto_enable: self.enable() def doShutdown(self): if session.sessiontype == MAIN: self.disable() EpicsMotor.doShutdown(self) def doStatus(self, maxage=0): stat, message = self._get_status_message() self._motor_status = stat, message if stat == status.ERROR: return stat, message or 'Unknown problem in record' elif stat == status.WARN: return stat, message if not self.isEnabled: return status.WARN, 'Motor is disabled' return EpicsMotor.doStatus(self, maxage) def doIsAllowed(self, target): if not self.isEnabled: return False, 'Motor disabled' return EpicsMotor.doIsAllowed(self, target)

36.424242

79

0.635191

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0.037549

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0.036891

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0.013499

0.229617

2,404

65

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36.984615

0.806156

0.431364

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0.0625

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0.090909

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0.125

0

0.5

0

null

0

null

0

1

0

f637709339851920c290c85068ff77de0d14dc64

2,941

py

Python

tests/test_matrix.py

cmc333333/mapbox-sdk-py

c38d177fc4f235b08a9e8dcda711e09a6edf0e20

[ "MIT" ]

null

tests/test_matrix.py

cmc333333/mapbox-sdk-py

c38d177fc4f235b08a9e8dcda711e09a6edf0e20

[ "MIT" ]

null

tests/test_matrix.py

cmc333333/mapbox-sdk-py

c38d177fc4f235b08a9e8dcda711e09a6edf0e20

[ "MIT" ]

null

import pytest import responses from mapbox import DirectionsMatrix from mapbox.errors import MapboxDeprecationWarning points = [{ "type": "Feature", "properties": {}, "geometry": { "type": "Point", "coordinates": [ -87, 36]}}, { "type": "Feature", "properties": {}, "geometry": { "type": "Point", "coordinates": [ -86, 36]}}, { "type": "Feature", "properties": {}, "geometry": { "type": "Point", "coordinates": [ -88, 37]}}] def test_class_attrs(): """Get expected class attr values""" serv = DirectionsMatrix() assert serv.api_name == 'directions-matrix' assert serv.api_version == 'v1' def test_profile_invalid(): """'jetpack' is not a valid profile.""" with pytest.raises(ValueError): DirectionsMatrix(access_token='pk.test')._validate_profile('jetpack') @pytest.mark.parametrize('profile', ['mapbox/driving', 'mapbox/cycling', 'mapbox/walking']) def test_profile_valid(profile): """Profiles are valid""" assert profile == DirectionsMatrix( access_token='pk.test')._validate_profile(profile) @pytest.mark.parametrize('profile', ['mapbox.driving', 'mapbox.cycling', 'mapbox.walking']) def test_deprecated_profile(profile): """Profiles are deprecated""" service = DirectionsMatrix() with pytest.warns(MapboxDeprecationWarning): assert profile.replace('.', '/') == service._validate_profile(profile) def test_null_query(): service = DirectionsMatrix() assert service._make_query(None, None) == {} def test_query(): service = DirectionsMatrix() params = service._make_query([0, 3], [1, 2]) assert params['sources'] == '0;3' assert params['destinations'] == '1;2' @responses.activate @pytest.mark.parametrize('waypoints', [points, [p['geometry'] for p in points], [p['geometry']['coordinates'] for p in points]]) def test_matrix(waypoints): responses.add( responses.GET, 'https://api.mapbox.com/directions-matrix/v1/mapbox/driving/-87,36;-86,36;-88,37?access_token=pk.test', match_querystring=True, body='{"durations":[[0,4977,5951],[4963,0,9349],[5881,9317,0]]}', status=200, content_type='application/json') # We need a second response because of the difference in rounding between # Python 2 (leaves a '.0') and 3 (no unnecessary '.0'). responses.add( responses.GET, 'https://api.mapbox.com/directions-matrix/v1/mapbox/driving/-87.0,36.0;-86.0,36.0;-88.0,37.0?access_token=pk.test', match_querystring=True, body='{"durations":[[0,4977,5951],[4963,0,9349],[5881,9317,0]]}', status=200, content_type='application/json') res = DirectionsMatrix(access_token='pk.test').matrix(waypoints) matrix = res.json()['durations'] # 3x3 list assert len(matrix) == 3 assert len(matrix[0]) == 3

31.287234

128

0.633798

340

2,941

5.385294

0.338235

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0.0355

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0.43692

0.418897

0.339705

0.283998

0

0.051586

0.195852

2,941

94

129

31.287234

0.722622

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0

0.058824

0.269806

0.042601

0

0.132353

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0.102941

false

0

0.058824

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0.161765

0

null

0

null

0

1

0

f63ae4ae1659c3703f55d9ef00455b73d7a5f9b7

2,801

py

Python

scripts/no_bool_in_generic.py

umangino/pandas

c492672699110fe711b7f76ded5828ff24bce5ab

[ "BSD-3-Clause" ]

28,899

2016-10-13T03:32:12.000Z

2022-03-31T21:39:05.000Z

scripts/no_bool_in_generic.py

umangino/pandas

c492672699110fe711b7f76ded5828ff24bce5ab

[ "BSD-3-Clause" ]

31,004

2016-10-12T23:22:27.000Z

2022-03-31T23:17:38.000Z

scripts/no_bool_in_generic.py

umangino/pandas

c492672699110fe711b7f76ded5828ff24bce5ab

[ "BSD-3-Clause" ]

15,149

2016-10-13T03:21:31.000Z

2022-03-31T18:46:47.000Z

""" Check that pandas/core/generic.py doesn't use bool as a type annotation. There is already the method `bool`, so the alias `bool_t` should be used instead. This is meant to be run as a pre-commit hook - to run it manually, you can do: pre-commit run no-bool-in-core-generic --all-files The function `visit` is adapted from a function by the same name in pyupgrade: https://github.com/asottile/pyupgrade/blob/5495a248f2165941c5d3b82ac3226ba7ad1fa59d/pyupgrade/_data.py#L70-L113 """ from __future__ import annotations import argparse import ast import collections from typing import Sequence def visit(tree: ast.Module) -> dict[int, list[int]]: "Step through tree, recording when nodes are in annotations." in_annotation = False nodes: list[tuple[bool, ast.AST]] = [(in_annotation, tree)] to_replace = collections.defaultdict(list) while nodes: in_annotation, node = nodes.pop() if isinstance(node, ast.Name) and in_annotation and node.id == "bool": to_replace[node.lineno].append(node.col_offset) for name in reversed(node._fields): value = getattr(node, name) if name in {"annotation", "returns"}: next_in_annotation = True else: next_in_annotation = in_annotation if isinstance(value, ast.AST): nodes.append((next_in_annotation, value)) elif isinstance(value, list): for value in reversed(value): if isinstance(value, ast.AST): nodes.append((next_in_annotation, value)) return to_replace def replace_bool_with_bool_t(to_replace, content: str) -> str: new_lines = [] for n, line in enumerate(content.splitlines(), start=1): if n in to_replace: for col_offset in reversed(to_replace[n]): line = line[:col_offset] + "bool_t" + line[col_offset + 4 :] new_lines.append(line) return "\n".join(new_lines) def check_for_bool_in_generic(content: str) -> tuple[bool, str]: tree = ast.parse(content) to_replace = visit(tree) if not to_replace: mutated = False return mutated, content mutated = True return mutated, replace_bool_with_bool_t(to_replace, content) def main(argv: Sequence[str] | None = None) -> None: parser = argparse.ArgumentParser() parser.add_argument("paths", nargs="*") args = parser.parse_args(argv) for path in args.paths: with open(path, encoding="utf-8") as fd: content = fd.read() mutated, new_content = check_for_bool_in_generic(content) if mutated: with open(path, "w", encoding="utf-8") as fd: fd.write(new_content) if __name__ == "__main__": main()

31.829545

111

0.646912

377

2,801

4.633952

0.3687

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2,801

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0.193859

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null

0

null

0

1

0

f63b5c57bfdbb91b31f33dd30b40f9e108f599e0

4,786

py

Python

tests/unit/configuration_subsystem/test_sample_configurations.py

richm/ansible-navigator

c9cd9a4b2eeb424145d4953aca79c4cc8ee8afd4

[ "Apache-2.0", "MIT" ]

null

tests/unit/configuration_subsystem/test_sample_configurations.py

richm/ansible-navigator

c9cd9a4b2eeb424145d4953aca79c4cc8ee8afd4

[ "Apache-2.0", "MIT" ]

1

2022-02-04T02:38:15.000Z

tests/unit/configuration_subsystem/test_sample_configurations.py

richm/ansible-navigator

c9cd9a4b2eeb424145d4953aca79c4cc8ee8afd4

[ "Apache-2.0", "MIT" ]

1

2021-11-17T09:45:18.000Z

""" Some tests using a alternate test configurations to prove code paths not covered by the ansible-navigator configuration """ import pytest from ansible_navigator.configuration_subsystem.configurator import Configurator from ansible_navigator.configuration_subsystem.navigator_post_processor import ( NavigatorPostProcessor, ) from ansible_navigator.configuration_subsystem.definitions import ApplicationConfiguration from ansible_navigator.configuration_subsystem.definitions import CliParameters from ansible_navigator.configuration_subsystem.definitions import Entry from ansible_navigator.configuration_subsystem.definitions import EntryValue from ansible_navigator.configuration_subsystem.definitions import SubCommand from ansible_navigator.configuration_subsystem.parser import Parser # pylint: disable=protected-access def test_cmdline_source_not_set(): """Ensure a Config without a subparse entry fails""" test_config = ApplicationConfiguration( application_name="test_config1", post_processor=NavigatorPostProcessor(), subcommands=[ SubCommand(name="subcommand1", description="subcommand1"), ], entries=[ Entry( name="cmdline", short_description="cmdline", value=EntryValue(), ), ], ) configurator = Configurator(params=[], application_configuration=test_config) configurator._post_process() assert "Completed post processing for cmdline" in configurator._messages[0][1] assert configurator._exit_messages == [] def test_no_subcommand(): """Ensure a Config without a subparse entry fails""" test_config = ApplicationConfiguration( application_name="test_config1", post_processor=None, subcommands=[ SubCommand(name="subcommand1", description="subcommand1"), ], entries=[], ) with pytest.raises(ValueError, match="No entry with subparser value defined"): Configurator(params=[], application_configuration=test_config).configure() def test_many_subcommand(): """Ensure a Config without a subparse entry fails""" test_config = ApplicationConfiguration( application_name="test_config1", post_processor=None, subcommands=[ SubCommand(name="subcommand1", description="subcommand1"), ], entries=[ Entry( name="sb1", short_description="Subcommands", subcommand_value=True, value=EntryValue(default="welcome"), ), Entry( name="sb2", short_description="Subcommands", subcommand_value=True, value=EntryValue(default="welcome"), ), ], ) with pytest.raises(ValueError, match="Multiple entries with subparser value defined"): Configurator(params=[], application_configuration=test_config).configure() def test_invalid_choice_not_set(): """Ensure an error is raised for no choice""" test_config = ApplicationConfiguration( application_name="test_config1", post_processor=None, subcommands=[ SubCommand(name="subcommand1", description="subcommand1"), ], entries=[ Entry( name="sb1", short_description="Subcommands", subcommand_value=True, value=EntryValue(default="welcome"), ), Entry( name="e1", short_description="ex1", value=EntryValue(), ), ], ) with pytest.raises(ValueError, match="Current source not set for e1"): test_config.entry("e1").invalid_choice # pylint: disable=expression-not-assigned def test_cutom_nargs_for_postional(): """Ensure a nargs for a positional are carried forward""" test_config = ApplicationConfiguration( application_name="test_config1", post_processor=None, subcommands=[ SubCommand(name="subcommand1", description="subcommand1"), ], entries=[ Entry( name="sb1", short_description="Subcommands", subcommand_value=True, value=EntryValue(default="welcome"), ), Entry( name="e1", cli_parameters=CliParameters(positional=True, nargs=3), short_description="ex1", value=EntryValue(), subcommands=["subcommand1"], ), ], ) parser = Parser(test_config) assert parser.parser._actions[2].choices["subcommand1"]._actions[2].nargs == 3

34.431655

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4,786

138

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0

null

0

null

0

1

0

f63e8c61c7288fdc96e3a4c021f87342a2409094

563

py

Python

tests/shared.py

award7/dicomsort

b83a8d9468a6599cfc36f497dcdb38ea62c2c783

[ "MIT" ]

15

2015-02-26T17:27:48.000Z

2019-10-22T12:28:24.000Z

tests/shared.py

BigHeartDB/dicomsort

e4e73241cc08e2fe34ea5a3fae606c9ce5afd3ff

[ "MIT" ]

61

2020-02-07T21:56:23.000Z

2022-03-31T22:12:08.000Z

tests/shared.py

suever/dicomsort

d2a09887ebe7e3f2bcdc07eb1375d995ba365205

[ "MIT" ]

7

2015-09-07T04:47:29.000Z

2019-03-18T09:29:48.000Z

import wx class WxTestCase: def setup(self): self.app = wx.App() self.frame = wx.Frame(None) self.frame.Show() def teardown(self): def _cleanup(): for win in wx.GetTopLevelWindows(): if win: if isinstance(win, wx.Dialog) and win.IsModal(): win.EndModal(0) else: win.Close(force=True) wx.WakeUpIdle() timer = wx.PyTimer(_cleanup) timer.Start(100) self.app.MainLoop()

23.458333

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563

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0

1

0.166667

false

0

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0

0.277778

0

null

0

null

0

1

0

f6403a7748cde59ed1c5a1bdb63ee72fbf187a9c

637

py

Python

tests/utilities/read_properties.py

BSE21-13/FrontEndSite

3592b7c0f62bdc3868d93221b27e9d365e9120b8

[ "MIT" ]

null

tests/utilities/read_properties.py

BSE21-13/FrontEndSite

3592b7c0f62bdc3868d93221b27e9d365e9120b8

[ "MIT" ]

5

2021-12-28T12:48:56.000Z

2022-01-24T12:07:17.000Z

tests/utilities/read_properties.py

BSE21-13/FrontEndSite

3592b7c0f62bdc3868d93221b27e9d365e9120b8

[ "MIT" ]

null

import configparser config = configparser.RawConfigParser() config.read("../tests/configurations/config.ini") class ReadConfig: """This class consists of methods that retrieve information from the configuration file""" @staticmethod def get_application_url(): """This method retrieves the URL from config file""" url = config.get('Common required information', 'base_url') return url @staticmethod def get_cadise_page_title(): cadise_page_title = config.get( 'Common required information', 'cadise_page_title' ) return cadise_page_title

27.695652

67

0.676609

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637

6.072464

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0.109785

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637

22

68

28.954545

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0

0.4

0

null

0

null

0

1

0

f6406e119d2329e146c0e9ca048882d418cdc911

20,678

py

Python

s2protocol/decoders.py

karlgluck/heroes-of-the-storm-replay-parser

5dd407e3ce2bd06d1acd279dd85c2a2a924c3c62

[ "MIT" ]

31

2015-01-19T09:42:02.000Z

2021-01-02T12:42:07.000Z

s2protocol/decoders.py

karlgluck/heroes-of-the-storm-replay-parser

5dd407e3ce2bd06d1acd279dd85c2a2a924c3c62

[ "MIT" ]

null

s2protocol/decoders.py

karlgluck/heroes-of-the-storm-replay-parser

5dd407e3ce2bd06d1acd279dd85c2a2a924c3c62

[ "MIT" ]

9

2015-04-02T04:24:54.000Z

2017-09-08T11:17:19.000Z

# Copyright (c) 2013 Blizzard Entertainment # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import struct import base64 # A note on decoding blobs: # # The library Blizzard wrote has no distinction between strings and binary blobs # at the protocol level. Because the results are going to be formatted as JSON # and printed in utf-8 text, they must be properly encoded. # # I extended their decoders to support distinguishing these binary blobs. Every # blob will now return a dictionary instead of a string. This dictionary will # contain one or two members: # * 'utf8' - A utf-8 decoded Unicode string representing the blob, # created using Python's 'replace' option. This is what you expect # a string to be. # * 'base64' - A base-64 encoded version of the bytes from the blob. This member # is present if and only if the blob cannot be decoded correctly # into the utf8 member. # # This distinction is necessary because one cannot simply interpret all blobs # as base64, since strings would be unrecognizable, or as utf-8 Unicode, since # not all byte sequences form valid Unicode strings. This makes it easy to use # the most common case of just getting strings from the replay, but keeps it # possible to obtain the original binary blob by first checking for the base64 # member, then decoding either it or utf8. class TruncatedError(Exception): pass class CorruptedError(Exception): pass class BitPackedBuffer: def __init__(self, contents, endian='big'): self._data = contents or [] self._used = 0 self._next = None self._nextbits = 0 self._bigendian = (endian == 'big') def __str__(self): return 'buffer(%02x/%d,[%d]=%s)' % ( self._nextbits and self._next or 0, self._nextbits, self._used, '%02x' % (ord(self._data[self._used]),) if (self._used < len(self._data)) else '--') def copy(self, other): self._data = other._data self._used = other._used self._next = other._next self._nextbits = other._nextbits self._bigendian = other._bigendian def peek_bytes_as_hex_string(self, bytes=0): if bytes == 0: bytes = len(self._data) bpb = BitPackedBuffer([], self._bigendian) bpb.copy(self) return ''.join('{:02x}'.format(ord(x)) for x in bpb.read_unaligned_bytes(bytes)) def peek_bytes_as_bin_string(self, bytes=0): if bytes == 0: bytes = len(self._data) bpb = BitPackedBuffer([], self._bigendian) bpb.copy(self) return ''.join('{:08b}'.format(ord(x)) for x in bpb.read_unaligned_bytes(bytes)) def peek_bits_as_bin_string(self, bits=0): if bits == 0: bits = len(self._data) * 8 bpb = BitPackedBuffer([], self._bigendian) bpb.copy(self) return ('{:0%ib}'%bits).format(bpb.read_bits(bits)) def done(self): return self._nextbits == 0 and self._used >= len(self._data) def used_bits(self): return self._used * 8 - self._nextbits def byte_align(self): self._nextbits = 0 def read_aligned_bytes(self, bytes): self.byte_align() data = self._data[self._used:self._used + bytes] self._used += bytes if len(data) != bytes: raise TruncatedError(self) return data def state(self): return '{next=%i,nextbits=%i,used=%i}' % (self._next, self._nextbits, self._used) def read_bits(self, bits): result = 0 resultbits = 0 while resultbits != bits: if self._nextbits == 0: if self.done(): raise TruncatedError(self) self._next = ord(self._data[self._used]) self._used += 1 self._nextbits = 8 copybits = min(bits - resultbits, self._nextbits) copy = (self._next & ((1 << copybits) - 1)) if self._bigendian: result |= copy << (bits - resultbits - copybits) else: result |= copy << resultbits self._next >>= copybits self._nextbits -= copybits resultbits += copybits return result def read_unaligned_bytes(self, bytes): return ''.join([chr(self.read_bits(8)) for i in xrange(bytes)]) class BitPackedDecoder: def __init__(self, contents, typeinfos): self._buffer = BitPackedBuffer(contents) self._typeinfos = typeinfos def __str__(self): return self._buffer.__str__() def instance(self, typeid): if typeid >= len(self._typeinfos): raise CorruptedError(self) typeinfo = self._typeinfos[typeid] return getattr(self, typeinfo[0])(*typeinfo[1]) def byte_align(self): self._buffer.byte_align() def done(self): return self._buffer.done() def used_bits(self): return self._buffer.used_bits() def _array(self, bounds, typeid): length = self._int(bounds) return [self.instance(typeid) for i in xrange(length)] def _bitarray(self, bounds): length = self._int(bounds) return (length, self._buffer.read_bits(length)) def _blob(self, bounds): length = self._int(bounds) result = self._buffer.read_aligned_bytes(length) try: result = {'utf8': result.decode('utf-8', 'strict')} except UnicodeDecodeError: result = { 'utf8': result.decode('utf-8', 'replace'), 'base64': base64.b64encode(result) } return result def _bool(self): return self._int((0, 1)) != 0 def _choice(self, bounds, fields): tag = self._int(bounds) if tag not in fields: raise CorruptedError(self) field = fields[tag] return {field[0]: self.instance(field[1])} def _fourcc(self): return self._buffer.read_unaligned_bytes(4) def _int(self, bounds): return bounds[0] + self._buffer.read_bits(bounds[1]) def _null(self): return None def _optional(self, typeid): exists = self._bool() return self.instance(typeid) if exists else None def _real32(self): return struct.unpack('>f', self._buffer.read_unaligned_bytes(4)) def _real64(self): return struct.unpack('>d', self._buffer.read_unaligned_bytes(8)) def _struct(self, fields): result = {} for field in fields: if field[0] == '__parent': parent = self.instance(field[1]) if isinstance(parent, dict): result.update(parent) elif len(fields) == 1: result = parent else: result[field[0]] = parent else: result[field[0]] = self.instance(field[1]) return result class BitPackedDecoderDebug: def __init__(self, contents, typeinfos): self._buffer = BitPackedBuffer(contents) self._typeinfos = typeinfos self._markers = [] self._json = {} def __str__(self): return self._buffer.__str__() def peek_bytes_as_hex_string(self, bytes): return self._buffer.peek_bytes_as_hex_string(bytes) def peek_bytes_as_bin_string(self, bytes=0): return self._buffer.peek_bytes_as_bin_string(bytes) def space_binary_string_by_markers(self, bin_string, first_bit_index): retval = '' x = 0 while x < len(bin_string): for m in self._markers: if m['at'] == (first_bit_index + x): retval = retval + '{' + m['type'] + '}' retval = retval + bin_string[x] x += 1 for m in self._markers: if m['at'] == (first_bit_index + x): retval = retval + '{' + m['type'] + '}' return retval def get_json_and_reset(self): retval = self._json self._json = {} return retval def instance(self, typeid): used_bits = self._buffer.used_bits() self._markers.append({'at':self.used_bits(),'type':'instance(%i)'%typeid}) old_json = self._json self._json = {'bit_start': self.used_bits(), 'typeid': typeid} if typeid >= len(self._typeinfos): return {"ERROR":"Asked to instance typeid '%i' but there are only '%i' type IDs" % (typeid, len(self._typeinfos)), "hex": hex_string } typeinfo = self._typeinfos[typeid] retval = getattr(self, typeinfo[0])(*typeinfo[1]) self._json['bit_end'] = self.used_bits() old_json['instance%i'%self.used_bits()] = self._json self._json = old_json self._markers.append({'at':self.used_bits(),'type':'end-instance(%i)'%typeid}) return retval def byte_align(self): self._buffer.byte_align() def done(self): return self._buffer.done() def used_bits(self): return self._buffer.used_bits() def _array(self, bounds, typeid): self._markers.append({'at':self.used_bits(),'type':'array(%s,%s)'%(str(bounds),str(typeid))}) old_json = self._json self._json = {'bit_start': self.used_bits(), 'bounds': bounds, 'typeid': typeid} length = self._int(bounds) self._json['length'] = length retval = [self.instance(typeid) for i in xrange(length)] old_json['array%i' % self.used_bits()] = self._json self._json = old_json return retval def _bitarray(self, bounds): self._markers.append({'at':self.used_bits(),'type':'bitarray(%s)'%str(bounds)}) old_json = self._json self._json = {'bit_start': self.used_bits(), 'bounds': bounds} length = self._int(bounds) self._json['bits'] = self._buffer.peek_bits_as_bin_string(length) retval = (length, self._buffer.read_bits(length)) old_json['bitarray%i'%self.used_bits()] = self._json self._json = old_json return retval def _blob(self, bounds): self._markers.append({'at':self.used_bits(),'type':'blob(%s)'%str(bounds)}) old_json = self._json self._json = {'bit_start': self.used_bits(), 'bounds': bounds} length = self._int(bounds) self._json['length'] = length retval = self._buffer.read_aligned_bytes(length) self._json['bytes'] = ''.join('{:02x}'.format(ord(x)) for x in retval) old_json['blob%i'%self.used_bits()] = self._json self._json = old_json try: retval = {'utf8': retval.decode('utf-8', 'strict')} except UnicodeDecodeError: retval = { 'utf8': retval.decode('utf-8', 'replace'), 'base64': base64.b64encode(retval) } return retval def _bool(self): old_json = self._json self._json = {'bit_start': self.used_bits()} self._markers.append({'at':self.used_bits(),'type':'bool'}) retval = self._int((0, 1)) != 0 self._json['value'] = retval old_json['bool%i'%self.used_bits()] = self._json self._json = old_json return retval def _choice(self, bounds, fields): self._markers.append({'at':self.used_bits(),'type':'choice(%s,%s)'%(str(bounds),str(fields))}) old_json = self._json self._json = {'bit_start': self.used_bits(), 'bounds': bounds, 'fields':fields} tag = self._int(bounds) if tag not in fields: return {"ERROR":"Choice '%s' does not exist in available fields '%s'" % (str(tag), str(fields))} field = fields[tag] retval = {field[0]: self.instance(field[1])} self._json['value'] = retval old_json['choice%i'%self.used_bits()] = self._json self._json = old_json return retval def _fourcc(self): old_json = self._json self._json = {'bit_start': self.used_bits()} self._markers.append({'at':self.used_bits(),'type':'blob'}) retval = self._buffer.read_unaligned_bytes(4) old_json['fourcc%i'%self.used_bits()] = self._json self._json = old_json return retval def _int(self, bounds): old_json = self._json self._json = {'bit_start': self.used_bits(), 'bounds': bounds, 'bits':self._buffer.peek_bits_as_bin_string(bounds[1])} bitpos = self.used_bits() retval = bounds[0] + self._buffer.read_bits(bounds[1]) self._markers.append({'at':bitpos,'type':'int(%s)=%i @ %s'%(str(bounds), retval, self._buffer.state())}) self._json['value'] = retval old_json['int%i'%self.used_bits()] = self._json self._json = old_json return retval def _null(self): self._markers.append({'at':self.used_bits(),'type':'null'}) return None def _optional(self, typeid): old_json = self._json self._json = {'bit_start': self.used_bits(), 'typeid':typeid} self._markers.append({'at':self.used_bits(),'type':'optional(%s)'%str(typeid)}) exists = self._bool() retval = self.instance(typeid) if exists else None old_json['optional%i'%self.used_bits()] = self._json self._json = old_json return retval def _real32(self): old_json = self._json self._json = {'bit_start': self.used_bits()} self._markers.append({'at':self.used_bits(),'type':'real32'}) retval = struct.unpack('>f', self._buffer.read_unaligned_bytes(4)) self._json['value'] = retval old_json['real32%i'%self.used_bits()] = self._json self._json = old_json return retval def _real64(self): old_json = self._json self._json = {'bit_start': self.used_bits()} self._markers.append({'at':self.used_bits(),'type':'real64'}) retval = struct.unpack('>d', self._buffer.read_unaligned_bytes(8)) self._json['value'] = retval old_json['real64%i'%self.used_bits()] = self._json self._json = old_json return retval def _struct(self, fields): old_json = self._json self._json = {'bit_start': self.used_bits(), 'fields': fields} self._markers.append({'at':self.used_bits(),'type':'struct(%s)'%str(fields)}) result = {} for field in fields: if field[0] == '__parent': parent = self.instance(field[1]) if isinstance(parent, dict): result.update(parent) elif len(fields) == 1: result = parent else: result[field[0]] = parent else: result[field[0]] = self.instance(field[1]) old_json['struct%i'%self.used_bits()] = self._json self._json = old_json return result class VersionedDecoder: def __init__(self, contents, typeinfos): self._buffer = BitPackedBuffer(contents) self._typeinfos = typeinfos def __str__(self): return self._buffer.__str__() def instance(self, typeid): if typeid >= len(self._typeinfos): raise CorruptedError(self) typeinfo = self._typeinfos[typeid] return getattr(self, typeinfo[0])(*typeinfo[1]) def byte_align(self): self._buffer.byte_align() def done(self): return self._buffer.done() def used_bits(self): return self._buffer.used_bits() def _expect_skip(self, expected): if self._buffer.read_bits(8) != expected: raise CorruptedError(self) def _vint(self): b = self._buffer.read_bits(8) negative = b & 1 result = (b >> 1) & 0x3f bits = 6 while (b & 0x80) != 0: b = self._buffer.read_bits(8) result |= (b & 0x7f) << bits bits += 7 return -result if negative else result def _array(self, bounds, typeid): self._expect_skip(0) length = self._vint() return [self.instance(typeid) for i in xrange(length)] def _bitarray(self, bounds): self._expect_skip(1) length = self._vint() return (length, self._buffer.read_aligned_bytes((length + 7) / 8)) def _blob(self, bounds): self._expect_skip(2) length = self._vint() result = self._buffer.read_aligned_bytes(length) try: result = {'utf8': result.decode('utf-8', 'strict')} except UnicodeDecodeError: result = { 'utf8': result.decode('utf-8', 'replace'), 'base64': base64.b64encode(result) } return result def _bool(self): self._expect_skip(6) return self._buffer.read_bits(8) != 0 def _choice(self, bounds, fields): self._expect_skip(3) tag = self._vint() if tag not in fields: self._skip_instance() return {} field = fields[tag] return {field[0]: self.instance(field[1])} def _fourcc(self): self._expect_skip(7) return self._buffer.read_aligned_bytes(4) def _int(self, bounds): self._expect_skip(9) return self._vint() def _null(self): return None def _optional(self, typeid): self._expect_skip(4) exists = self._buffer.read_bits(8) != 0 return self.instance(typeid) if exists else None def _real32(self): self._expect_skip(7) return struct.unpack('>f', self._buffer.read_aligned_bytes(4)) def _real64(self): self._expect_skip(8) return struct.unpack('>d', self._buffer.read_aligned_bytes(8)) def _struct(self, fields): self._expect_skip(5) result = {} length = self._vint() for i in xrange(length): tag = self._vint() field = next((f for f in fields if f[2] == tag), None) if field: if field[0] == '__parent': parent = self.instance(field[1]) if isinstance(parent, dict): result.update(parent) elif len(fields) == 1: result = parent else: result[field[0]] = parent else: result[field[0]] = self.instance(field[1]) else: self._skip_instance() return result def _skip_instance(self): skip = self._buffer.read_bits(8) if skip == 0: # array length = self._vint() for i in xrange(length): self._skip_instance() elif skip == 1: # bitblob length = self._vint() self._buffer.read_aligned_bytes((length + 7) / 8) elif skip == 2: # blob length = self._vint() self._buffer.read_aligned_bytes(length) elif skip == 3: # choice tag = self._vint() self._skip_instance() elif skip == 4: # optional exists = self._buffer.read_bits(8) != 0 if exists: self._skip_instance() elif skip == 5: # struct length = self._vint() for i in xrange(length): tag = self._vint() self._skip_instance() elif skip == 6: # u8 self._buffer.read_aligned_bytes(1) elif skip == 7: # u32 self._buffer.read_aligned_bytes(4) elif skip == 8: # u64 self._buffer.read_aligned_bytes(8) elif skip == 9: # vint self._vint()

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0.034501

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0.325275

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null

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null

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1

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f646c5cb4299bf70a2742fabb4cedc310b92d3e0

6,019

py

Python

rl_rvo_nav/policy_test/post_train.py

hanruihua/rl_rvo_nav

9ce17853336ee3231860e0a0f8df9269515adfb4

[ "MIT" ]

8

2022-03-18T06:41:07.000Z

2022-03-31T03:49:55.000Z

rl_rvo_nav/policy_test/post_train.py

hanruihua/rl_rvo_nav

9ce17853336ee3231860e0a0f8df9269515adfb4

[ "MIT" ]

null

rl_rvo_nav/policy_test/post_train.py

hanruihua/rl_rvo_nav

9ce17853336ee3231860e0a0f8df9269515adfb4

[ "MIT" ]

2

2022-03-29T01:43:54.000Z

2022-03-29T07:47:02.000Z

import torch import numpy as np from pathlib import Path import platform from rl_rvo_nav.policy.policy_rnn_ac import rnn_ac from math import pi, sin, cos, sqrt import time class post_train: def __init__(self, env, num_episodes=100, max_ep_len=150, acceler_vel = 1.0, reset_mode=3, render=True, save=False, neighbor_region=4, neighbor_num=5, args=None, **kwargs): self.env = env self.num_episodes=num_episodes self.max_ep_len = max_ep_len self.acceler_vel = acceler_vel self.reset_mode = reset_mode self.render=render self.save=save self.robot_number = self.env.ir_gym.robot_number self.step_time = self.env.ir_gym.step_time self.inf_print = kwargs.get('inf_print', True) self.std_factor = kwargs.get('std_factor', 0.001) # self.show_traj = kwargs.get('show_traj', False) self.show_traj = False self.traj_type = '' self.figure_format = kwargs.get('figure_format', 'png') self.nr = neighbor_region self.nm = neighbor_num self.args = args def policy_test(self, policy_type='drl', policy_path=None, policy_name='policy', result_path=None, result_name='/result.txt', figure_save_path=None, ani_save_path=None, policy_dict=False, once=False): if policy_type == 'drl': model_action = self.load_policy(policy_path, self.std_factor, policy_dict=policy_dict) o, r, d, ep_ret, ep_len, n = self.env.reset(mode=self.reset_mode), 0, False, 0, 0, 0 ep_ret_list, speed_list, mean_speed_list, ep_len_list, sn = [], [], [], [], 0 print('Policy Test Start !') figure_id = 0 while n < self.num_episodes: # if n == 1: # self.show_traj = True action_time_list = [] if self.render or self.save: self.env.render(save=self.save, path=figure_save_path, i = figure_id, show_traj=self.show_traj, traj_type=self.traj_type) if policy_type == 'drl': abs_action_list =[] for i in range(self.robot_number): start_time = time.time() a_inc = np.round(model_action(o[i]), 2) end_time = time.time() temp = end_time - start_time action_time_list.append(temp) cur_vel = self.env.ir_gym.robot_list[i].vel_omni abs_action = self.acceler_vel * a_inc + np.squeeze(cur_vel) abs_action_list.append(abs_action) o, r, d, info = self.env.step_ir(abs_action_list, vel_type = 'omni') robot_speed_list = [np.linalg.norm(robot.vel_omni) for robot in self.env.ir_gym.robot_list] avg_speed = np.average(robot_speed_list) speed_list.append(avg_speed) ep_ret += r[0] ep_len += 1 figure_id += 1 if np.max(d) or (ep_len == self.max_ep_len) or np.min(info): speed = np.mean(speed_list) figure_id = 0 if np.min(info): ep_len_list.append(ep_len) if self.inf_print: print('Successful, Episode %d \t EpRet %.3f \t EpLen %d \t EpSpeed %.3f'%(n, ep_ret, ep_len, speed)) else: if self.inf_print: print('Fail, Episode %d \t EpRet %.3f \t EpLen %d \t EpSpeed %.3f'%(n, ep_ret, ep_len, speed)) ep_ret_list.append(ep_ret) mean_speed_list.append(speed) speed_list = [] o, r, d, ep_ret, ep_len = self.env.reset(mode=self.reset_mode), 0, False, 0, 0 n += 1 if np.min(info): sn+=1 # if n == 2: if once: self.env.ir_gym.world_plot.save_gif_figure(figure_save_path, 0, format='eps') break if self.save: self.env.ir_gym.save_ani(figure_save_path, ani_save_path, ani_name=policy_name) break mean_len = 0 if len(ep_len_list) == 0 else np.round(np.mean(ep_len_list), 2) std_len = 0 if len(ep_len_list) == 0 else np.round(np.std(ep_len_list), 2) average_speed = np.round(np.mean(mean_speed_list),2) std_speed = np.round(np.std(mean_speed_list), 2) f = open( result_path + result_name, 'a') print( 'policy_name: '+ policy_name, ' successful rate: {:.2%}'.format(sn/self.num_episodes), "average EpLen:", mean_len, "std length", std_len, 'average speed:', average_speed, 'std speed', std_speed, file = f) f.close() print( 'policy_name: '+ policy_name, ' successful rate: {:.2%}'.format(sn/self.num_episodes), "average EpLen:", mean_len, 'std length', std_len, 'average speed:', average_speed, 'std speed', std_speed) def load_policy(self, filename, std_factor=1, policy_dict=False): if policy_dict == True: model = rnn_ac(self.env.observation_space, self.env.action_space, self.args.state_dim, self.args.rnn_input_dim, self.args.rnn_hidden_dim, self.args.hidden_sizes_ac, self.args.hidden_sizes_v, self.args.activation, self.args.output_activation, self.args.output_activation_v, self.args.use_gpu, self.args.rnn_mode) check_point = torch.load(filename) model.load_state_dict(check_point['model_state'], strict=True) model.eval() else: model = torch.load(filename) model.eval() # model.train() def get_action(x): with torch.no_grad(): x = torch.as_tensor(x, dtype=torch.float32) action = model.act(x, std_factor) return action return get_action def dis(self, p1, p2): return sqrt( (p2.py - p1.py)**2 + (p2.px - p1.px)**2 )

41.510345

323

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849

6,019

3.839812

0.199058

0.026074

0.016564

0.022086

0.211963

0.177301

0.164417

0.156442

0

0.014166

0.308025

6,019

145

324

41.510345

0.768547

0.018275

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0.118812

0

0.066226

0

1

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false

0

0.069307

0.009901

0.158416

0.059406

0

null

0

null

0

1

0

f6483d57aeed490b4f308d885d8bb0758430e6ca

4,866

py

Python

scripts/NIRISS_AMI_tutorial.py

vandalt/ImPlaneIA

72b22e487ef45a8a665e4a6a88a91e99e382fdd0

[ "BSD-3-Clause" ]

6

2020-03-03T16:15:40.000Z

2022-03-23T16:15:09.000Z

scripts/NIRISS_AMI_tutorial.py

vandalt/ImPlaneIA

72b22e487ef45a8a665e4a6a88a91e99e382fdd0

[ "BSD-3-Clause" ]

5

2020-02-03T17:46:59.000Z

2022-03-07T20:00:59.000Z

scripts/NIRISS_AMI_tutorial.py

vandalt/ImPlaneIA

72b22e487ef45a8a665e4a6a88a91e99e382fdd0

[ "BSD-3-Clause" ]

1

2021-07-06T23:02:01.000Z

#!/usr/bin/env python # coding: utf-8 # # A short Tutorial to process sample NIRISS AMI simulations # # * fit fringes for a simulated target and calibrator sequence (no WFE evolution between them) # * calibrate target closure phases with the calibrator # * fit for a binary import glob import os, sys, time from astropy.io import fits import numpy as np from nrm_analysis import nrm_core, InstrumentData from nrm_analysis.misctools import utils print(InstrumentData.__file__) import matplotlib.pyplot as plt #get_ipython().run_line_magic('matplotlib', 'inline') debug = True home = os.path.expanduser('~') np.set_printoptions(precision=4) if debug: print("Current working directory is ", os.getcwd()) print("InstrumentData is file: ", InstrumentData.__file__) filt="F430M" oversample = 3 # ### Where the data lives: # small disk, noise, call name different cos of central pix kluge, but it's correct. # copied these from ami_sim output ~/scene_noise/..." datadir = home+"/Downloads/asoulain_arch2019.12.07/Simulated_data/" cr = "c_dsk_100mas__F430M_81_flat_x11__00_mir" tr = "t_dsk_100mas__F430M_81_flat_x11__00_mir" # Directories where ascii output files of fringe fitting will go: tsavedir = datadir+"tgt_ov%d"%oversample csavedir = datadir+"cal_ov%d"%oversample test_tar = datadir + tr + ".fits" test_cal = datadir + cr + ".fits" if debug: print("tsavedir:", tsavedir, "\ntest_tar:", test_tar) print("csavedir:", csavedir, "\ntest_cal:", test_cal) # ### First we specify the instrument & filter # (defaults: Spectral type set to A0V) # SET BANDPASS - or use NIRISS' default bandpass for the filter default = None # 'bandpass' defaults to None - it's here for clarity bp3= np.array([(0.1, 4.2e-6),(0.8, 4.3e-6),(0.1,4.4e-6)]) # for speedy development bpmono = np.array([(1.0, 4.3e-6),]) # for speedy development niriss = InstrumentData.NIRISS(filt, bandpass=bpmono) # ### Next: Extract fringe observables using image plane fringe-fitting # * Need to pass the InstrumentData object, some keywords. # * Observables are (over)written to a new savedir/input_datafile_root (eg cr or tr here) # * Initialize FringeFitter with save_txt_only=True to switch off diagnostic fits file writing # *files written out to these directories. ff_t = nrm_core.FringeFitter(niriss, datadir=datadir, savedir=tsavedir, oversample=oversample, interactive=False) ff_c = nrm_core.FringeFitter(niriss, datadir=datadir, savedir=csavedir, oversample=oversample, interactive=False) # set interactive to False unless you don't know what you are doing # This can take a little while -- there is a parallelization option, set threads=n_threads # output of this is long -- may also want to do this scripted instead of in notebook, # leaving off the output in this example. ff_t.fit_fringes(test_tar) ff_c.fit_fringes(test_cal) utils.compare_pistons(ff_t.nrm.phi*2*np.pi, ff_t.nrm.fringepistons, str="ff_t") utils.compare_pistons(ff_c.nrm.phi*2*np.pi, ff_t.nrm.fringepistons, str="ff_c") # Text files contain the observables you are trying to # measure, but some diagnostic fits files written: centered_nn # are the cropped/centered data, modelsolution_nn are the best fit model to the # data, and residual_nn is the data - model_solution print("oversample {:d} used in modelling the data".format(oversample)) print("observables text files in rootdir", home+"/Downloads/asoulain_arch2019.12.07/Simulated_data/") print("tgt observables in subdir", tsavedir) print("cal observables in subdir", csavedir) showfig = False if showfig: target_outputdir = tsavedir + "/" + tr data = fits.getdata(target_outputdir + "/centered_0.fits") fmodel = fits.getdata(target_outputdir + "/modelsolution_01.fits") res = fits.getdata(target_outputdir + "/residual_01.fits") plt.figure(figsize=(12,4)) plt.subplot(131) plt.title("Input data") im = plt.imshow(pow(data/data.max(), 0.5)) plt.axis("off") plt.colorbar(fraction=0.046, pad=0.04) plt.subplot(132) plt.title("best model") im = plt.imshow(pow(fmodel/data.max(), 0.5)) plt.axis("off") plt.colorbar(fraction=0.046, pad=0.04) plt.subplot(133) plt.title("residual") im = plt.imshow(res/data.max()) plt.axis("off") plt.colorbar(fraction=0.046, pad=0.04) plt.show() # If you don't want to clog up your hardrive with fits files you can initialize # FringeFitter with keyword "save_txt_only=True" -- but you may want to save # out everything the first time you reduce the data to check it. Above we can # see a pretty good fit the magnification of the model is a bit off. This shows # up as a radial pattern in the residual. Finely fitting the exact magnification # and rotation should be done before fringe fitting.

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null

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f64bb2f111d4579927862540613c0debd02f8fa0

3,817

py

Python

vilya/views/api/git.py

mubashshirjamal/code

d9c7adf7efed8e9c1ab3ff8cdeb94e7eb1a45382

[ "BSD-3-Clause" ]

1,582

2015-01-05T02:41:44.000Z

2022-03-30T20:03:22.000Z

vilya/views/api/git.py

mubashshirjamal/code

d9c7adf7efed8e9c1ab3ff8cdeb94e7eb1a45382

[ "BSD-3-Clause" ]

66

2015-01-23T07:58:04.000Z

2021-11-12T02:23:27.000Z

vilya/views/api/git.py

mubashshirjamal/code

d9c7adf7efed8e9c1ab3ff8cdeb94e7eb1a45382

[ "BSD-3-Clause" ]

347

2015-01-05T07:47:07.000Z

2021-09-20T21:22:32.000Z

# coding: UTF-8 from __future__ import absolute_import import json import PyRSS2Gen as RSS2 from vilya.config import DOMAIN from vilya.libs.reltime import compute_relative_time from vilya.views.api.utils import jsonize class GitUI(object): _q_exports = ['branches', 'allfiles', 'lastlog', 'lineblame'] def __init__(self, project): self.project = project @jsonize def branches(self, request): return self.project.repo.branches @jsonize def allfiles(self, request): branch = request.get_form_var('branch', 'HEAD') repo = self.project.repo # FIXME: path sort order in ellen tree = repo.get_tree(branch, recursive=True) return [f['path'] for f in tree] @jsonize def lastlog(self, request): path = request.get_form_var('path') repo = self.project.repo commit = repo.get_last_commit('HEAD', path=path) data = {"author": '', "age": '', "parents": [], "date": '', "commit": '', "message": '', "email": ''} if commit: data = commit.as_dict() data['commit'] = data['id'] data['age'] = compute_relative_time(commit.author_timestamp) return data @jsonize def lineblame(self, request): rev = request.get_form_var('rev', 'HEAD') path = request.get_form_var('path') lineno = request.get_form_var('lineno', 1) dumb = { 'author': '', 'time': '', 'summary': '', 'sha': '', } if not path: return dumb blame = self.project.repo.blame_file(rev, path, lineno=int(lineno)) for hunk in blame.hunks: for line in hunk.lines: if line.no == int(lineno): dumb['author'] = line.commit.author.name dumb['time'] = line.commit.author_time dumb['summary'] = line.commit.message_header dumb['sha'] = line.commit.sha return dumb class CommitsUI(object): _q_exports = [] def __init__(self, request, project): self.project = project def __call__(self, request): return self._index(request) def _q_index(self, request): return self._index(request) def _gen_rss(self, data): proj_name = self.project.name items = [] for d in data: items.append(RSS2.RSSItem( title=d.get('message', ''), link="%s/%s/commit/%s" % ( DOMAIN, proj_name, d.get('id', '')), author=d.get('email', ''), pubDate=d.get('date', ''), )) rss = RSS2.RSS2( title="%s RSS Feed" % proj_name, link="%s/api/%s/commits" % (DOMAIN, proj_name), description="%s RSS Feed" % proj_name, items=items, ) return rss.to_xml('utf-8') def _index(self, request): begin = request.get_form_var('begin') or 'HEAD~5' end = request.get_form_var('end') or 'HEAD' format = request.get_form_var('format') or 'json' repo = self.project.repo data = [] if repo: commits = repo.get_commits(end, from_ref=begin) data = [commit.as_dict(with_files=True) for commit in commits] if format == 'rss': return self._gen_rss(data) else: return json.dumps(data) def _q_lookup(self, request, sha): data = {} repo = self.project.repo commit = repo.get_commit(sha) if commit: data = commit.as_dict(with_files=True) return json.dumps(data)

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0.304762

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null

0

null

0

1

0

f64c8b2cad206f15a32b0791a1effdab5558a028

1,912

py

Python

src/graph.py

kb2ma/nethead-ui

950aa85806aa31e1d7857af8f933e75588c1fdc5

[ "Apache-2.0" ]

null

src/graph.py

kb2ma/nethead-ui

950aa85806aa31e1d7857af8f933e75588c1fdc5

[ "Apache-2.0" ]

null

src/graph.py

kb2ma/nethead-ui

950aa85806aa31e1d7857af8f933e75588c1fdc5

[ "Apache-2.0" ]

null

import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output, State from app import app from app import applog from datetime import datetime import json import numpy as np import pandas as pd import plotly.express as px import requests """Displays graph of time series data from Graphite. Refreshes graph once a minute. """ def _collect_data(device_sn): """Collects data from Graphite into a dataframe. """ url = 'http://localhost:8089/render' params = { 'target': 'device.{}.object.3303.0'.format(device_sn), 'from': 'now-1d', 'format': 'json' } data = json.loads(requests.get(url, params=params).text) series = np.array(data[0]['datapoints']) df = pd.DataFrame(series, index=series[:, 1], columns=['temp', 'time']) # time as hours:minutes df['time'] = df['time'].apply(lambda x: datetime.fromtimestamp(x).strftime('%H:%M')) return df def tab_layout(device_sn, device_desc): if device_sn and device_desc == 'Status: found': df = _collect_data(device_sn) fig = px.line(df, x="time", y="temp") return html.Div(id='graph-div', children=[ dcc.Graph(id='graph-ref', figure=fig, style={'height': '500px'}), dcc.Interval( id='graph-refresh', interval=60*1000, # in milliseconds n_intervals=0) ]) else: return html.Div(id='graph-div', children=[ 'No valid device' ]) @app.callback([Output('graph-ref', 'figure'), Output('device-desc2', 'children')], [Input('graph-refresh', 'n_intervals')], [State('device-sn', 'value')]) def updateGraph(n, device_sn): df = _collect_data(device_sn) return px.line(df, x="time", y="temp"), 'data as of {}'.format(df.iloc[-1,1])

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null

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null

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1

0

f64c8c0ad69d6d3faab6d5fe3c733e51363ca006

1,985

py

Python

xprize_robojudge.py

anibalsolon/covid-xprize

cafc2c65c7e4f4184c16a1793da85371b6bc3218

[ "Apache-2.0" ]

null

xprize_robojudge.py

anibalsolon/covid-xprize

cafc2c65c7e4f4184c16a1793da85371b6bc3218

[ "Apache-2.0" ]

null

xprize_robojudge.py

anibalsolon/covid-xprize

cafc2c65c7e4f4184c16a1793da85371b6bc3218

[ "Apache-2.0" ]

null

# Copyright 2020 (c) Cognizant Digital Business, Evolutionary AI. All rights reserved. Issued under the Apache 2.0 License. import numpy as np import pandas as pd LATEST_DATA_URL = 'https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/OxCGRT_latest.csv' LOCAL_DATA_URL = "tests/fixtures/OxCGRT_latest.csv" NPI_COLUMNS = ['C1_School closing', 'C2_Workplace closing', 'C3_Cancel public events', 'C4_Restrictions on gatherings', 'C5_Close public transport', 'C6_Stay at home requirements', 'C7_Restrictions on internal movement', 'C8_International travel controls', 'H1_Public information campaigns', 'H2_Testing policy', 'H3_Contact tracing'] class XPrizeRobojudge(object): def load_dataset(self, url: str = LATEST_DATA_URL) -> pd.DataFrame: latest_df = pd.read_csv(url, parse_dates=['Date'], encoding="ISO-8859-1", error_bad_lines=False) # Handle regions latest_df["RegionName"].fillna('', inplace=True) # Replace CountryName by CountryName / RegionName # np.where usage: if A then B else C latest_df["CountryName"] = np.where(latest_df["RegionName"] == '', latest_df["CountryName"], latest_df["CountryName"] + ' / ' + latest_df["RegionName"]) return latest_df def get_npis(self, start_date: np.datetime64, end_date: np.datetime64, url: str = LATEST_DATA_URL) -> pd.DataFrame: latest_df = self.load_dataset(url) npis_df = latest_df[["CountryName", "Date"] + NPI_COLUMNS] actual_npis_df = npis_df[(npis_df.Date >= start_date) & (npis_df.Date <= end_date)] return actual_npis_df

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1,985

5.045662

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1,985

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false

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0.205882

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null

0

null

0

1

0

f64c94177f64a2f9e4145e71f70d7ea87d65280f

3,282

py

Python

ooobuild/lo/frame/frame_action_event.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/lo/frame/frame_action_event.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/lo/frame/frame_action_event.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2022 :Barry-Thomas-Paul: Moss # # 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. # # Struct Class # this is a auto generated file generated by Cheetah # Namespace: com.sun.star.frame # Libre Office Version: 7.3 from ooo.oenv.env_const import UNO_NONE from ..lang.event_object import EventObject as EventObject_a3d70b03 from ..uno.x_interface import XInterface as XInterface_8f010a43 import typing from .frame_action import FrameAction as FrameAction_aef40b5c from .x_frame import XFrame as XFrame_7a570956 class FrameActionEvent(EventObject_a3d70b03): """ Struct Class this event struct is broadcast for actions which can happen to components within frames See Also: `API FrameActionEvent <https://api.libreoffice.org/docs/idl/ref/structcom_1_1sun_1_1star_1_1frame_1_1FrameActionEvent.html>`_ """ __ooo_ns__: str = 'com.sun.star.frame' __ooo_full_ns__: str = 'com.sun.star.frame.FrameActionEvent' __ooo_type_name__: str = 'struct' typeName: str = 'com.sun.star.frame.FrameActionEvent' """Literal Constant ``com.sun.star.frame.FrameActionEvent``""" def __init__(self, Source: typing.Optional[XInterface_8f010a43] = None, Frame: typing.Optional[XFrame_7a570956] = None, Action: typing.Optional[FrameAction_aef40b5c] = FrameAction_aef40b5c.COMPONENT_ATTACHED) -> None: """ Constructor Arguments: Source (XInterface, optional): Source value. Frame (XFrame, optional): Frame value. Action (FrameAction, optional): Action value. """ if isinstance(Source, FrameActionEvent): oth: FrameActionEvent = Source self.Source = oth.Source self.Frame = oth.Frame self.Action = oth.Action return kargs = { "Source": Source, "Frame": Frame, "Action": Action, } self._init(**kargs) def _init(self, **kwargs) -> None: self._frame = kwargs["Frame"] self._action = kwargs["Action"] inst_keys = ('Frame', 'Action') kargs = kwargs.copy() for key in inst_keys: del kargs[key] super()._init(**kargs) @property def Frame(self) -> XFrame_7a570956: """ contains the frame in which the event occurred """ return self._frame @Frame.setter def Frame(self, value: XFrame_7a570956) -> None: self._frame = value @property def Action(self) -> FrameAction_aef40b5c: """ specifies the concrete event """ return self._action @Action.setter def Action(self, value: FrameAction_aef40b5c) -> None: self._action = value __all__ = ['FrameActionEvent']

32.176471

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3,282

5.36962

0.41519

0.028289

0.023574

0.035361

0.057049

0.042433

0

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0.240707

3,282

101

222

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0.817416

0.360146

0

0.044444

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0.078836

0.037037

0

1

0.133333

false

0

0.133333

0

0.444444

0

null

0

null

0

1

0

f64d006c79ff144996e84e5aa827cbb38d274052

4,446

py

Python

actions/lib/bestfit.py

StackStorm-Exchange/manageiq

e94b3bb0f3d68fee4d749cee1b5d1d24012ffe81

[ "Apache-2.0" ]

1

2019-08-26T02:31:19.000Z

actions/lib/bestfit.py

StackStorm-Exchange/manageiq

e94b3bb0f3d68fee4d749cee1b5d1d24012ffe81

[ "Apache-2.0" ]

5

2018-09-27T16:51:18.000Z

2020-09-25T18:06:05.000Z

actions/lib/bestfit.py

StackStorm-Exchange/manageiq

e94b3bb0f3d68fee4d749cee1b5d1d24012ffe81

[ "Apache-2.0" ]

2

2018-09-25T23:21:03.000Z

2021-01-28T17:45:20.000Z

import base_action import re import json class BestFit(base_action.BaseAction): def __init__(self, config): """Creates a new BaseAction given a StackStorm config object (kwargs works too) :param config: StackStorm configuration object for the pack :returns: a new BaseAction """ super(BestFit, self).__init__(config) def _load_disks(self, client, disks): """If disks json is present this gets the first disk information and returns the proper information. """ datastoreName = None datastoreID = None first_disk = disks['all_disks'][0] datastore_name = first_disk['datastore'] if datastore_name != "automatic": datastoreName, datastoreID = self._find_storage(client, datastore_name) return (datastoreName, datastoreID) def _check_hosts(self, client, hosts, kwargs_dict): cluster = self._get_arg("clusterName", kwargs_dict) if not cluster: raise ValueError("Cluster Name can not be empty.") leastVMs = None hostID = None hostName = None datastoreName = None datastoreID = None disks = self._get_arg("disk_json", kwargs_dict) if disks is not None: datastoreName, datastoreID = self._load_disks(client, disks) for host in hosts: # Need to verify that the host is on and connected (not in maintenance mode) # power_state can be 'on' 'maintenance' 'off' if (host.v_owning_cluster == cluster and host.power_state == "on"): if (leastVMs is None or host.v_total_vms < leastVMs): hostID = str(host.id) hostName = str(host.name) leastVMs = host.v_total_vms if (datastoreName is None and datastoreID is None): datastoreName, datastoreID = self._check_storages(client, host.host_storages, kwargs_dict) # only success if all of these are not None # fail otherwise success = cluster is not None and hostID is not None and datastoreID is not None result = {'clusterName': cluster, 'hostName': hostName, 'hostID': hostID, 'datastoreName': datastoreName, 'datastoreID': datastoreID} return (success, result) def _check_storages(self, client, storages, kwargs_dict): mostSpace = 0 dName = None dId = None for datastore in storages: ds = client.collections.data_stores(datastore["storage_id"]) if self._filter_datastores(ds.name, kwargs_dict): if ds.free_space > mostSpace: dName = ds.name dId = str(ds.id) mostSpace = ds.free_space return (dName, dId) def _find_storage(self, client, datastore_name): all_datastores = client.collections.data_stores.all dName = None dId = None for datastore in all_datastores: if datastore.name == datastore_name: dName = datastore.name dId = str(datastore.id) break return (dName, dId) def _filter_datastores(self, datastore, kwargs_dict): datastoreFilters = self._get_arg("datastoreFilterRegEx", kwargs_dict) if not type(datastoreFilters) is dict: datastoreFilters = json.loads(datastoreFilters) datastoreFilterRegEx = datastoreFilters["filters"] """Filter out the datastores by name Include if the datastore name does NOT match any of the regex expressions """ for regex in datastoreFilterRegEx: if re.search(regex.strip(), datastore): return False return True def bestfit(self, client, kwargs_dict): attributes = self._attributes_str(["v_owning_cluster", "v_total_vms", "host_storages"]) allHosts = client.collections.hosts.query_string( # pylint: disable=no-member expand="resources", attributes=attributes) if not allHosts: raise ValueError("No Hosts were returned from ManageIQ") return self._check_hosts(client, allHosts, kwargs_dict)

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4,446

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false

0

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0

0.222222

0

null

0

null

0

1

0

f64d4cb44725ca01b18c26fa31d360e83ae96dc0

622

py

Python

tests/IOExpander.py

Footleg/rocky-rover-board

83d2f7a75fe362304fbc54bf71b6cd89718098f6

[ "MIT" ]

1

2022-01-15T17:51:08.000Z

tests/IOExpander.py

Footleg/rocky-rover-board

83d2f7a75fe362304fbc54bf71b6cd89718098f6

[ "MIT" ]

null

tests/IOExpander.py

Footleg/rocky-rover-board

83d2f7a75fe362304fbc54bf71b6cd89718098f6

[ "MIT" ]

null

#!/usr/bin/env python3 """ Test case for the Footleg Robotics Sentinel robot controller board Pulses all IO expander pins configured as outputs """ from time import sleep import digitalio from sentinelboard import SentinelHardware sh = SentinelHardware() # Set all pins as outputs, setting them LOW for pin in range(16): p = sh.mcp23017.get_pin(pin) p.direction = digitalio.Direction.OUTPUT p.value = False # Pulse each pin twice in turn for pin in range(16): p = sh.mcp23017.get_pin(pin) for i in range(2): p.value = True sleep(0.15) p.value = False sleep(0.3)

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622

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0.2

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0.2

0

null

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null

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f64ef28d2932094a9e57197502304041dd4681ae

882

py

Python

airflow/upload.py

jordanparker6/coronva-virus

bd9ea4e8f3b9ebd193b5425bf4268bf6c0baf275

[ "Apache-2.0" ]

2

2020-05-09T06:45:38.000Z

2020-06-01T21:50:27.000Z

airflow/upload.py

jordanparker6/coronva-virus

bd9ea4e8f3b9ebd193b5425bf4268bf6c0baf275

[ "Apache-2.0" ]

4

2021-03-10T11:46:52.000Z

2022-02-27T01:33:20.000Z

airflow/upload.py

jordanparker6/COVID-19

bd9ea4e8f3b9ebd193b5425bf4268bf6c0baf275

[ "Apache-2.0" ]

null

import boto3, os from datetime import datetime as dt from botocore.client import Config def main(): ACCESS_KEY = os.environ['DIGITAL_OCEAN_ACCESS_KEY'] SECRET = os.environ['DIGITAL_OCEAN_SECRET_KEY'] date = dt.today().strftime('%Y.%m.%d') files = ['data.csv', 'agg_data.csv', 'confirmed_cases.csv'] # Initialize a session using DigitalOcean Spaces. session = boto3.session.Session() client = session.client('s3', region_name='nyc3', endpoint_url='https://nyc3.digitaloceanspaces.com', aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET) # Upload Files for file in files: print('Uploading: ', file) fn = f"{date}/{file}" client.upload_file(fn, 'covid-19', file) if __name__ == "__main__": main()

33.923077

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882

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0.063618

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0.011006

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882

26

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33.923077

0.779874

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0.05

0

null

0

null

0

1

0

f64f2f22b9f3bcf769fdc52943788c73b7058eaa

3,100

py

Python

src/eduid_common/session/testing.py

SUNET/eduid-common

d666aec7e47e6b0ccb575d621bb6e9f40bcea4e4

[ "BSD-3-Clause" ]

1

2016-04-14T13:45:10.000Z

src/eduid_common/session/testing.py

SUNET/eduid-common

d666aec7e47e6b0ccb575d621bb6e9f40bcea4e4

[ "BSD-3-Clause" ]

16

2017-03-10T11:47:59.000Z

2020-03-19T13:51:01.000Z

src/eduid_common/session/testing.py

SUNET/eduid-common

d666aec7e47e6b0ccb575d621bb6e9f40bcea4e4

[ "BSD-3-Clause" ]

3

2016-11-21T11:39:49.000Z

2019-09-18T12:32:02.000Z

# # Copyright (c) 2016 NORDUnet A/S # Copyright (c) 2018 SUNET # All rights reserved. # # Redistribution and use in source and binary forms, with or # without modification, are permitted provided that the following # conditions are met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. 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. # 3. Neither the name of the NORDUnet 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 HOLDER 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. # import logging from typing import Sequence import redis from eduid_userdb.testing import EduidTemporaryInstance logger = logging.getLogger(__name__) class RedisTemporaryInstance(EduidTemporaryInstance): """Singleton to manage a temporary Redis instance Use this for testing purpose only. The instance is automatically destroyed at the end of the program. """ @property def command(self) -> Sequence[str]: return [ 'docker', 'run', '--rm', '-p', '{!s}:6379'.format(self.port), '-v', '{!s}:/data'.format(self.tmpdir), '-e', 'extra_args=--daemonize no --bind 0.0.0.0', 'docker.sunet.se/eduid/redis:latest', ] def setup_conn(self) -> bool: try: host, port, db = self.get_params() _conn = redis.Redis(host, port, db) _conn.set('dummy', 'dummy') self._conn = _conn except redis.exceptions.ConnectionError: return False return True @property def conn(self) -> redis.Redis: if self._conn is None: raise RuntimeError('Missing temporary Redis instance') return self._conn def get_params(self): """ Convenience function to get Redis connection parameters for the temporary database. :return: Host, port and database """ return 'localhost', self.port, 0

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0.027778

0.388889

0

null

0

null

0

1

0

f65146c393969c774797935b3458611949f43a82

285

py

Python

lectures/19-functional-ideas/examples/gematria2.py

mattmiller899/biosys-analytics

ab24a4c7206ed9a865e896daa57cee3c4e62df1f

[ "MIT" ]

14

2019-07-14T08:29:04.000Z

2022-03-07T06:33:26.000Z

lectures/19-functional-ideas/examples/gematria2.py

mattmiller899/biosys-analytics

ab24a4c7206ed9a865e896daa57cee3c4e62df1f

[ "MIT" ]

null

lectures/19-functional-ideas/examples/gematria2.py

mattmiller899/biosys-analytics

ab24a4c7206ed9a865e896daa57cee3c4e62df1f

[ "MIT" ]

33

2019-01-05T17:03:47.000Z

2019-11-11T20:48:24.000Z

#!/usr/bin/env python3 import os import sys args = sys.argv[1:] if len(args) != 1: print('Usage: {} WORD'.format(os.path.basename(sys.argv[0]))) sys.exit(1) word = args[0] number = 0 for letter in word: number += ord(letter) print('"{}" = "{}"'.format(word, number))

15

65

0.603509

45

285

3.822222

0.555556

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0.030043

0.182456

285

18

66

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0

0.181818

0

null

0

null

0

1

0

f653d42bc6c243aed7bff8e1f420a4f32e076ef4

1,113

py

Python

setup.py

andreped/livermask

4bfc3dd858fc8ed6ee87468458cae7a4736e1551

[ "MIT" ]

38

2020-02-11T08:34:33.000Z

2022-03-24T10:54:48.000Z

setup.py

andreped/livermask

4bfc3dd858fc8ed6ee87468458cae7a4736e1551

[ "MIT" ]

9

2020-02-06T22:49:21.000Z

2022-01-31T12:27:52.000Z

setup.py

andreped/livermask

4bfc3dd858fc8ed6ee87468458cae7a4736e1551

[ "MIT" ]

6

2020-02-29T16:01:11.000Z

2021-11-04T10:27:36.000Z

import setuptools with open("README.md", "r") as f: long_description = f.read() with open('requirements.txt', 'r', encoding='utf-16') as ff: required = ff.read().splitlines() setuptools.setup( name='livermask', version='1.2.0', author="André Pedersen", author_email="andrped94@gmail.com", license='MIT', description="A package for automatic segmentation of liver from CT data", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/andreped/livermask", packages=setuptools.find_packages(), entry_points={ 'console_scripts': [ 'livermask = livermask.livermask:main' ] }, install_requires=required, classifiers=[ "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.6', )

30.916667

78

0.619946

121

1,113

5.603306

0.644628

0.088496

0.147493

0.153392

0

0.020047

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1,113

35

79

31.8

0.779481

0

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0.021563

0

1

0

false

0

0.03125

0

0.03125

0

null

0

null

0

1

0

f654fd291c8b54b41d59a2fb5e8750235276c4ea

1,039

py

Python

pycontrol/libcpu/discovery.py

Velko/8-bit-CPU

30cab1bd157da01149898607a5c1a15961b56294

[ "MIT" ]

7

2021-02-22T19:29:35.000Z

2022-03-27T23:17:04.000Z

pycontrol/libcpu/discovery.py

Velko/8-bit-CPU

30cab1bd157da01149898607a5c1a15961b56294

[ "MIT" ]

8

2021-01-05T19:08:24.000Z

2021-08-16T20:50:13.000Z

pycontrol/libcpu/discovery.py

Velko/8-bit-CPU

30cab1bd157da01149898607a5c1a15961b56294

[ "MIT" ]

null

import sys from typing import Iterator, Tuple from .pin import PinBase, Pin, MuxPin, Mux from . import DeviceSetup def all_pins() -> Iterator[Tuple[str, PinBase]]: dupe_filter = set() for v_name, var in vars(DeviceSetup).items(): if not hasattr(var, "__dict__"): continue for a_name, attr in vars(var).items(): if (not a_name.startswith("_")) and (isinstance(attr, Pin) or isinstance(attr, MuxPin)) and attr not in dupe_filter: dupe_filter.add(attr) yield "{}.{}".format(var.name, a_name), attr def simple_pins() -> Iterator[Tuple[str, Pin]]: for name, pin in all_pins(): if isinstance(pin, Pin): yield name, pin def all_muxes() -> Iterator[Tuple[str, Mux]]: for v_name, var in vars(DeviceSetup).items(): if isinstance(var, Mux): yield v_name, var def mux_pins(mux: Mux) -> Iterator[Tuple[str, MuxPin]]: for name, pin in all_pins(): if isinstance(pin, MuxPin) and pin.mux == mux: yield name, pin

37.107143

128

0.623677

148

1,039

4.243243

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0.103503

0.101911

0.063694

0.219745

0

0.246391

1,039

28

129

37.107143

0.802043

0

0.25

0

0.013462

0

1

0.166667

false

0

0.166667

0

0.333333

0

null

0

null

0

1

0

f65533e82f38d22c049fe300eaa8d9c21a02aaae

749

py

Python

tests/apis/test_yara.py

ninoseki/uzen

93726f22f43902e17b22dd36142dac05171d0d84

[ "MIT" ]

76

2020-02-27T06:36:27.000Z

2022-03-10T20:18:03.000Z

tests/apis/test_yara.py

ninoseki/uzen

93726f22f43902e17b22dd36142dac05171d0d84

[ "MIT" ]

33

2020-03-13T02:04:14.000Z

2022-03-04T02:06:11.000Z

tests/apis/test_yara.py

ninoseki/uzen

93726f22f43902e17b22dd36142dac05171d0d84

[ "MIT" ]

6

2020-03-17T16:42:25.000Z

2021-04-27T06:35:46.000Z

import asyncio import pytest from fastapi.testclient import TestClient @pytest.mark.usefixtures("snapshots_setup") def test_yara_scan(client: TestClient, event_loop: asyncio.AbstractEventLoop): # it matches with all snapshots payload = {"source": 'rule foo: bar {strings: $a = "foo" condition: $a}'} response = client.post("/api/yara/scan", json=payload) assert response.status_code == 200 snapshot = response.json() assert snapshot.get("id") assert snapshot.get("type") == "yara" def test_yara_scan_with_invalid_input( client: TestClient, event_loop: asyncio.AbstractEventLoop ): payload = {"source": "boo"} response = client.post("/api/yara/scan", json=payload) assert response.status_code == 422

29.96

78

0.715621

93

749

5.634409

0.494624

0.061069

0.041985

0.057252

0.431298

0.244275

0

0.009509

0.157543

749

24

79

31.208333

0.820919

0.038718

0

0.117647

0

0.162953

0

0.235294

1

0.117647

false

0

0.176471

0

0.294118

0

null

0

null

0

1

0

f656ca916c5e4b551ec247056d8e8cd69797a168

576

py

Python

src/text/text_clean/rm_biaodian.py

TongtongSong/tools

598417a018ab01b8dcda7fdbce118ce261246ea3

[ "Apache-2.0" ]

null

src/text/text_clean/rm_biaodian.py

TongtongSong/tools

598417a018ab01b8dcda7fdbce118ce261246ea3

[ "Apache-2.0" ]

null

src/text/text_clean/rm_biaodian.py

TongtongSong/tools

598417a018ab01b8dcda7fdbce118ce261246ea3

[ "Apache-2.0" ]

null

import re import sys #!/bin/env python import sys import re if len(sys.argv) != 1: print >>sys.stderr,"%s < in > out"%(__file__) sys.exit(1) for line in sys.stdin: # 中文的正则匹配式 zh_patt = u'[\u4e00-\u9fa5]' # 英文的正则匹配式 en_patt = u'[A-Za-z]' src_word_list = [] for word in line.strip().split(' '): if re.findall(zh_patt, word): src_word_list.append(word) elif re.findall(en_patt, word): src_word_list.append(word) if src_word_list: string = ' '.join(src_word_list) print (string)

20.571429

49

0.581597

86

576

3.686047

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0.11041

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0.094637

0.182965

0

0.016827

0.277778

576

27

50

21.333333

0.745192

0.059028

0

0.315789

0

0.070501

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1

0

false

0

0.210526

0

0.210526

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0

null

0

null

0

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