paulo037/starcoder · Datasets at Hugging Face

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InvenTree/build/views.py	mpdgraev/InvenTree	0	152474	""" Django views for interacting with Build objects """ # -- coding: utf-8 -- from __future__ import unicode_literals from django.utils.translation import ugettext as _ from django.core.exceptions import ValidationError from django.views.generic import DetailView, ListView, UpdateView from django.forms import HiddenInput from django.urls import reverse from part.models import Part from .models import Build, BuildItem from . import forms from stock.models import StockLocation, StockItem from InvenTree.views import AjaxUpdateView, AjaxCreateView, AjaxDeleteView from InvenTree.views import InvenTreeRoleMixin from InvenTree.helpers import str2bool, ExtractSerialNumbers from InvenTree.status_codes import BuildStatus class BuildIndex(InvenTreeRoleMixin, ListView): """ View for displaying list of Builds """ model = Build template_name = 'build/index.html' context_object_name = 'builds' role_required = 'build.view' def get_queryset(self): """ Return all Build objects (order by date, newest first) """ return Build.objects.order_by('status', '-completion_date') def get_context_data(self, kwargs): context = super(BuildIndex, self).get_context_data(kwargs).copy() context['BuildStatus'] = BuildStatus context['active'] = self.get_queryset().filter(status__in=BuildStatus.ACTIVE_CODES) context['completed'] = self.get_queryset().filter(status=BuildStatus.COMPLETE) context['cancelled'] = self.get_queryset().filter(status=BuildStatus.CANCELLED) return context class BuildCancel(AjaxUpdateView): """ View to cancel a Build. Provides a cancellation information dialog """ model = Build ajax_template_name = 'build/cancel.html' ajax_form_title = _('Cancel Build') context_object_name = 'build' form_class = forms.CancelBuildForm role_required = 'build.change' def post(self, request, args, kwargs): """ Handle POST request. Mark the build status as CANCELLED """ build = self.get_object() form = self.get_form() valid = form.is_valid() confirm = str2bool(request.POST.get('confirm_cancel', False)) if confirm: build.cancelBuild(request.user) else: form.errors['confirm_cancel'] = [_('Confirm build cancellation')] valid = False data = { 'form_valid': valid, 'danger': _('Build was cancelled') } return self.renderJsonResponse(request, form, data=data) class BuildAutoAllocate(AjaxUpdateView): """ View to auto-allocate parts for a build. Follows a simple set of rules to automatically allocate StockItem objects. Ref: build.models.Build.getAutoAllocations() """ model = Build form_class = forms.ConfirmBuildForm context_object_name = 'build' ajax_form_title = _('Allocate Stock') ajax_template_name = 'build/auto_allocate.html' role_required = 'build.change' def get_context_data(self, args, *kwargs): """ Get the context data for form rendering. """ context = {} try: build = Build.objects.get(id=self.kwargs['pk']) context['build'] = build context['allocations'] = build.getAutoAllocations() except Build.DoesNotExist: context['error'] = _('No matching build found') return context def post(self, request, args, *kwargs): """ Handle POST request. Perform auto allocations. - If the form validation passes, perform allocations - Otherwise, the form is passed back to the client """ build = self.get_object() form = self.get_form() confirm = request.POST.get('confirm', False) valid = False if confirm is False: form.errors['confirm'] = [_('Confirm stock allocation')] form.non_field_errors = [_('Check the confirmation box at the bottom of the list')] else: build.autoAllocate() valid = True data = { 'form_valid': valid, } return self.renderJsonResponse(request, form, data, context=self.get_context_data()) class BuildUnallocate(AjaxUpdateView): """ View to un-allocate all parts from a build. Provides a simple confirmation dialog with a BooleanField checkbox. """ model = Build form_class = forms.ConfirmBuildForm ajax_form_title = _("Unallocate Stock") ajax_template_name = "build/unallocate.html" form_required = 'build.change' def post(self, request, args, kwargs): build = self.get_object() form = self.get_form() confirm = request.POST.get('confirm', False) valid = False if confirm is False: form.errors['confirm'] = [_('Confirm unallocation of build stock')] form.non_field_errors = [_('Check the confirmation box')] else: build.unallocateStock() valid = True data = { 'form_valid': valid, } return self.renderJsonResponse(request, form, data) class BuildComplete(AjaxUpdateView): """ View to mark a build as Complete. - Notifies the user of which parts will be removed from stock. - Removes allocated items from stock - Deletes pending BuildItem objects """ model = Build form_class = forms.CompleteBuildForm context_object_name = "build" ajax_form_title = _("Complete Build") ajax_template_name = "build/complete.html" role_required = 'build.change' def get_form(self): """ Get the form object. If the part is trackable, include a field for serial numbers. """ build = self.get_object() form = super().get_form() if not build.part.trackable: form.fields.pop('serial_numbers') else: form.field_placeholder['serial_numbers'] = build.part.getSerialNumberString(build.quantity) form.rebuild_layout() return form def get_initial(self): """ Get initial form data for the CompleteBuild form - If the part being built has a default location, pre-select that location """ initials = super(BuildComplete, self).get_initial().copy() build = self.get_object() if build.part.default_location is not None: try: location = StockLocation.objects.get(pk=build.part.default_location.id) initials['location'] = location except StockLocation.DoesNotExist: pass return initials def get_context_data(self, kwargs): """ Get context data for passing to the rendered form - Build information is required """ build = Build.objects.get(id=self.kwargs['pk']) context = {} # Build object context['build'] = build # Items to be removed from stock taking = BuildItem.objects.filter(build=build.id) context['taking'] = taking return context def post(self, request, args, kwargs): """ Handle POST request. Mark the build as COMPLETE - If the form validation passes, the Build objects completeBuild() method is called - Otherwise, the form is passed back to the client """ build = self.get_object() form = self.get_form() confirm = str2bool(request.POST.get('confirm', False)) loc_id = request.POST.get('location', None) valid = False if confirm is False: form.errors['confirm'] = [ _('Confirm completion of build'), ] else: try: location = StockLocation.objects.get(id=loc_id) valid = True except (ValueError, StockLocation.DoesNotExist): form.errors['location'] = [_('Invalid location selected')] serials = [] if build.part.trackable: # A build for a trackable part may optionally specify serial numbers. sn = request.POST.get('serial_numbers', '') sn = str(sn).strip() # If the user has specified serial numbers, check they are valid if len(sn) > 0: try: # Exctract a list of provided serial numbers serials = ExtractSerialNumbers(sn, build.quantity) existing = [] for serial in serials: if build.part.checkIfSerialNumberExists(serial): existing.append(serial) if len(existing) > 0: exists = ",".join([str(x) for x in existing]) form.errors['serial_numbers'] = [_('The following serial numbers already exist: ({sn})'.format(sn=exists))] valid = False except ValidationError as e: form.errors['serial_numbers'] = e.messages valid = False if valid: if not build.completeBuild(location, serials, request.user): form.non_field_errors = [('Build could not be completed')] valid = False data = { 'form_valid': valid, } return self.renderJsonResponse(request, form, data, context=self.get_context_data()) def get_data(self): """ Provide feedback data back to the form """ return { 'info': _('Build marked as COMPLETE') } class BuildNotes(UpdateView): """ View for editing the 'notes' field of a Build object. """ context_object_name = 'build' template_name = 'build/notes.html' model = Build role_required = 'build.view' fields = ['notes'] def get_success_url(self): return reverse('build-notes', kwargs={'pk': self.get_object().id}) def get_context_data(self, kwargs): ctx = super().get_context_data(kwargs) ctx['editing'] = str2bool(self.request.GET.get('edit', '')) return ctx class BuildDetail(DetailView): """ Detail view of a single Build object. """ model = Build template_name = 'build/detail.html' context_object_name = 'build' role_required = 'build.view' def get_context_data(self, kwargs): ctx = super(DetailView, self).get_context_data(kwargs) build = self.get_object() ctx['bom_price'] = build.part.get_price_info(build.quantity, buy=False) ctx['BuildStatus'] = BuildStatus return ctx class BuildAllocate(DetailView): """ View for allocating parts to a Build """ model = Build context_object_name = 'build' template_name = 'build/allocate.html' role_required = ['build.change'] def get_context_data(self, kwargs): """ Provide extra context information for the Build allocation page """ context = super(DetailView, self).get_context_data(*kwargs) build = self.get_object() part = build.part bom_items = part.bom_items context['part'] = part context['bom_items'] = bom_items context['BuildStatus'] = BuildStatus context['bom_price'] = build.part.get_price_info(build.quantity, buy=False) if str2bool(self.request.GET.get('edit', None)): context['editing'] = True return context class BuildCreate(AjaxCreateView): """ View to create a new Build object """ model = Build context_object_name = 'build' form_class = forms.EditBuildForm ajax_form_title = _('Start new Build') ajax_template_name = 'modal_form.html' role_required = 'build.add' def get_initial(self): """ Get initial parameters for Build creation. If 'part' is specified in the GET query, initialize the Build with the specified Part """ initials = super(BuildCreate, self).get_initial().copy() # User has provided a Part ID initials['part'] = self.request.GET.get('part', None) initials['parent'] = self.request.GET.get('parent', None) # User has provided a SalesOrder ID initials['sales_order'] = self.request.GET.get('sales_order', None) initials['quantity'] = self.request.GET.get('quantity', 1) return initials def get_data(self): return { 'success': _('Created new build'), } class BuildUpdate(AjaxUpdateView): """ View for editing a Build object """ model = Build form_class = forms.EditBuildForm context_object_name = 'build' ajax_form_title = _('Edit Build Details') ajax_template_name = 'modal_form.html' role_required = 'build.change' def get_data(self): return { 'info': _('Edited build'), } class BuildDelete(AjaxDeleteView): """ View to delete a build """ model = Build ajax_template_name = 'build/delete_build.html' ajax_form_title = _('Delete Build') role_required = 'build.delete' class BuildItemDelete(AjaxDeleteView): """ View to 'unallocate' a BuildItem. Really we are deleting the BuildItem object from the database. """ model = BuildItem ajax_template_name = 'build/delete_build_item.html' ajax_form_title = _('Unallocate Stock') context_object_name = 'item' role_required = 'build.delete' def get_data(self): return { 'danger': _('Removed parts from build allocation') } class BuildItemCreate(AjaxCreateView): """ View for allocating a new part to a build """ model = BuildItem form_class = forms.EditBuildItemForm ajax_template_name = 'build/create_build_item.html' ajax_form_title = _('Allocate new Part') role_required = 'build.add' part = None available_stock = None def get_context_data(self): ctx = super(AjaxCreateView, self).get_context_data() if self.part: ctx['part'] = self.part if self.available_stock: ctx['stock'] = self.available_stock else: ctx['no_stock'] = True return ctx def get_form(self): """ Create Form for making / editing new Part object """ form = super(AjaxCreateView, self).get_form() # If the Build object is specified, hide the input field. # We do not want the users to be able to move a BuildItem to a different build build_id = form['build'].value() if build_id is not None: form.fields['build'].widget = HiddenInput() # If the sub_part is supplied, limit to matching stock items part_id = self.get_param('part') if part_id: try: self.part = Part.objects.get(pk=part_id) query = form.fields['stock_item'].queryset # Only allow StockItem objects which match the current part query = query.filter(part=part_id) if build_id is not None: try: build = Build.objects.get(id=build_id) if build.take_from is not None: # Limit query to stock items that are downstream of the 'take_from' location query = query.filter(location__in=[loc for loc in build.take_from.getUniqueChildren()]) except Build.DoesNotExist: pass # Exclude StockItem objects which are already allocated to this build and part query = query.exclude(id__in=[item.stock_item.id for item in BuildItem.objects.filter(build=build_id, stock_item__part=part_id)]) form.fields['stock_item'].queryset = query stocks = query.all() self.available_stock = stocks # If there is only one item selected, select it if len(stocks) == 1: form.fields['stock_item'].initial = stocks[0].id # There is no stock available elif len(stocks) == 0: # TODO - Add a message to the form describing the problem pass except Part.DoesNotExist: self.part = None pass return form def get_initial(self): """ Provide initial data for BomItem. Look for the folllowing in the GET data: - build: pk of the Build object """ initials = super(AjaxCreateView, self).get_initial().copy() build_id = self.get_param('build') part_id = self.get_param('part') # Reference to a Part object part = None # Reference to a StockItem object item = None # Reference to a Build object build = None if part_id: try: part = Part.objects.get(pk=part_id) initials['part'] = part except Part.DoesNotExist: pass if build_id: try: build = Build.objects.get(pk=build_id) initials['build'] = build except Build.DoesNotExist: pass quantity = self.request.GET.get('quantity', None) if quantity is not None: quantity = float(quantity) if quantity is None: # Work out how many parts remain to be alloacted for the build if part: quantity = build.getUnallocatedQuantity(part) item_id = self.get_param('item') # If the request specifies a particular StockItem if item_id: try: item = StockItem.objects.get(pk=item_id) except: pass # If a StockItem is not selected, try to auto-select one if item is None and part is not None: items = StockItem.objects.filter(part=part) if items.count() == 1: item = items.first() # Finally, if a StockItem is selected, ensure the quantity is not too much if item is not None: if quantity is None: quantity = item.unallocated_quantity() else: quantity = min(quantity, item.unallocated_quantity()) if quantity is not None: initials['quantity'] = quantity return initials class BuildItemEdit(AjaxUpdateView): """ View to edit a BuildItem object """ model = BuildItem ajax_template_name = 'modal_form.html' form_class = forms.EditBuildItemForm ajax_form_title = _('Edit Stock Allocation') role_required = 'build.change' def get_data(self): return { 'info': _('Updated Build Item'), } def get_form(self): """ Create form for editing a BuildItem. - Limit the StockItem options to items that match the part """ build_item = self.get_object() form = super(BuildItemEdit, self).get_form() query = StockItem.objects.all() if build_item.stock_item: 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13, 4706, 883, 29889, 9621, 1839, 17712, 29918, 667, 13359, 1972, 842, 353, 2346, 13, 13, 4706, 883, 29889, 9621, 1839, 4282, 13359, 8030, 353, 379, 4215, 4290, 580, 13, 13, 4706, 736, 883, 13, 2 ]
tests/test_config.py	dstarner/pacyam	3	139474	<gh_stars>1-10 import os import unittest from pacyam.pacyam import Configuration, BuildException REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) class ConfigurationTestCase(unittest.TestCase): test_configs_root = os.path.join(REPO_ROOT, 'tests', 'test-configs') project_root = os.path.join(REPO_ROOT, 'tests', 'project') def test_invalid_root_dir(self): root = 'totes-invalid-dir' config_path = 'config.json' with self.assertRaises(BuildException): Configuration.load( root_directory=root, config_file_name=config_path, ) def test_missing_config_path(self): root = self.test_configs_root config_path = 'not-available-config.json' with self.assertRaises(BuildException): Configuration.load( root_directory=root, config_file_name=config_path ) def test_invalid_json_file(self): root = self.test_configs_root config_path = 'bad_json.json' with self.assertRaises(BuildException): Configuration.load( root_directory=root, config_file_name=config_path ) def test_unique_config_path(self): root = self.test_configs_root config_path = 'some-unique-name.json' config = Configuration.load( root_directory=root, config_file_name=config_path ) self.assertTrue( os.path.basename(config.config_file_path), config_path ) self.assertEqual(config.root_directory, root) def test_invalid_config(self): root = self.test_configs_root config_path = 'config.json' with self.assertRaises(BuildException): Configuration.load( root_directory=root, config_file_name=config_path ) def test_template_paths(self): root = self.project_root config_path = 'config.json' config = Configuration.load( root_directory=root, config_file_name=config_path ) expected = [ "builders/virtualbox.yaml", "builders/virtualbox.yaml", "post-processors/vagrant.yaml", "provisioners/core.yaml" ] self.assertEqual(config.template_paths, expected) def test_variable_paths(self): root = self.project_root config_path = 'config.json' config = Configuration.load( root_directory=root, config_file_name=config_path ) expected = [ "variables/default.yaml" ] self.assertEqual(config.variable_paths, expected)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 2897, 13, 5215, 443, 27958, 13, 13, 3166, 282, 4135, 314, 29889, 29886, 4135, 314, 1053, 20999, 29892, 8878, 2451, 13, 13, 1525, 13152, 29918, 21289, 353, 2897, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 370, 1028, 493, 22168, 1445, 1649, 4961, 13, 13, 1990, 20999, 3057, 8259, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 13, 1678, 1243, 29918, 2917, 29879, 29918, 4632, 353, 2897, 29889, 2084, 29889, 7122, 29898, 1525, 13152, 29918, 21289, 29892, 525, 21150, 742, 525, 1688, 29899, 2917, 29879, 1495, 13, 1678, 2060, 29918, 4632, 353, 2897, 29889, 2084, 29889, 7122, 29898, 1525, 13152, 29918, 21289, 29892, 525, 21150, 742, 525, 4836, 1495, 13, 13, 1678, 822, 1243, 29918, 20965, 29918, 4632, 29918, 3972, 29898, 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plugins/simsimi.py	messense/wechat-bot	115	1617087	#coding=utf-8 """ Copyright (c) 2012 wong2 <<EMAIL>> 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 sys sys.path.append('..') import requests import cookielib from tornado.options import options __name__ = 'simsimi' SIMSIMI_KEY = options.simsimi_key or '' class SimSimi(object): def __init__(self): self.headers = { 'Referer': 'http://www.simsimi.com/talk.htm' } self.chat_url = 'http://www.simsimi.com/func/req?lc=ch&msg=%s' self.api_url = 'http://api.simsimi.com/request.p?key=%s&lc=ch&ft=1.0&text=%s' if not SIMSIMI_KEY: self.initSimSimiCookie() def initSimSimiCookie(self): r = requests.get('http://www.simsimi.com/talk.htm') self.chat_cookies = r.cookies def getSimSimiResult(self, message, method='normal'): if method == 'normal': r = requests.get(self.chat_url % message, cookies=self.chat_cookies, headers=self.headers) self.chat_cookies = r.cookies else: url = self.api_url % (SIMSIMI_KEY, message) r = requests.get(url) return r def chat(self, message=''): if message: r = self.getSimSimiResult( message, 'normal' if not SIMSIMI_KEY else 'api') try: answer = r.json()['response'] return answer.encode('utf-8') except: return u'呵呵' else: return u'叫我干嘛' simsimi = SimSimi() def test(data, msg=None, bot=None): return True def respond(data, msg=None, bot=None): response = simsimi.chat(data) if "Unauthorized access" in response: # try again response = simsimi.chat(data) response = response.replace('xiaohuangji', options.username) response = response.replace('小黄鸡', '我') if "Unauthorized access" in response: # still can't , give up response = u'矮油，这个问题我暂时回答不了喵~' if u'微信' in response and u'搜' in response: # remove some ads response = u'呵呵' return response	[ 1, 396, 29883, 3689, 29922, 9420, 29899, 29947, 13, 13, 15945, 29908, 13, 11882, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29906, 281, 549, 29906, 3532, 26862, 6227, 6778, 13, 13, 27293, 338, 1244, 1609, 16896, 29892, 3889, 310, 8323, 29892, 304, 738, 2022, 4017, 292, 13, 29874, 3509, 310, 445, 7047, 322, 6942, 5106, 2066, 313, 1552, 13, 29915, 6295, 14093, 5477, 304, 5376, 297, 278, 18540, 1728, 24345, 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train.py	JanFschr/gangealing	2	132801	<filename>train.py """ GANgealing training script. """ import torch from torch import nn, optim import numpy as np from tqdm import tqdm import json import os from models import Generator, get_stn, DirectionInterpolator, PCA, get_perceptual_loss, kmeans_plusplus, BilinearDownsample, accumulate, requires_grad from models import gangealing_loss, gangealing_cluster_loss, total_variation_loss, flow_identity_loss from datasets import img_dataloader, sample_infinite_data from utils.vis_tools.training_vis import GANgealingWriter, create_training_visuals, create_training_cluster_visuals from utils.distributed import all_gather, get_rank, setup_distributed, reduce_loss_dict, get_world_size, primary from utils.base_argparse import base_training_argparse from utils.annealing import DecayingCosineAnnealingWarmRestarts, lr_cycle_iters, get_psi_annealing_fn from utils.download import find_model def save_state_dict(ckpt_name, generator, t_module, t_ema, t_optim, t_sched, ll_module, ll_optim, ll_sched, args): ckpt_dict = {"g_ema": generator.state_dict(), "t": t_module.state_dict(), "t_ema": t_ema.state_dict(), "t_optim": t_optim.state_dict(), "t_sched": t_sched.state_dict(), "ll": ll_module.state_dict(), "ll_optim": ll_optim.state_dict(), "ll_sched": ll_sched.state_dict(), "args": args} torch.save(ckpt_dict, f'{results_path}/checkpoints/{ckpt_name}.pt') def train(args, loader, generator, stn, t_ema, ll, t_optim, ll_optim, t_sched, ll_sched, loss_fn, anneal_fn, device, writer): # If using real data, select some fixed samples used to visualize training: vis_reals = loader is not None if vis_reals: if args.random_reals: real_indices = torch.randint(0, len(loader.dataset), (args.n_sample,)).numpy() else: real_indices = range(args.n_sample) sample_reals = torch.stack([loader.dataset[ix] for ix in real_indices]).to(device) loader = sample_infinite_data(loader, args.seed) else: sample_reals = None # Progress bar for monitoring training: pbar = range(args.iter) if primary(): pbar = tqdm(pbar, initial=args.start_iter, dynamic_ncols=True, smoothing=0.2) # Record modules to make saving checkpoints easier: if args.distributed: t_module = stn.module ll_module = ll.module else: t_module = stn ll_module = ll sample_z = torch.randn(args.n_sample // args.num_heads, args.dim_latent, device=device) # Used for generating a fixed set of GAN samples if args.clustering: # A much larger fixed set of GAN samples used for generating clustering-specific visuals: big_sample_z = torch.randn(args.n_mean // get_world_size(), args.dim_latent, device=device) resize_fake2stn = BilinearDownsample(args.gen_size // args.flow_size, 3).to(device) if args.gen_size > args.flow_size else nn.Sequential() generator.eval() requires_grad(generator, False) # G is frozen throughout this entire process requires_grad(stn, True) requires_grad(ll, True) # A model checkpoint will be saved whenever the learning rate is zero: zero_lr_iters = lr_cycle_iters(args.anneal_psi, args.period, args.iter, args.tm) early_ckpt_iters = set(zero_lr_iters) early_vis_iters = {100} early_vis_iters.update(early_ckpt_iters) # Initialize various training variables and constants: zero = torch.tensor(0.0, device='cuda') accum = 0.5 ** (32 / (10 * 1000)) psi = 1.0 # initially there is no truncation # Create initial training visualizations: if args.clustering: create_training_cluster_visuals(generator, t_ema, ll, loss_fn, loader, resize_fake2stn, sample_z, big_sample_z, psi, device, args.n_mean, args.n_sample, args.num_heads, args.flips, args.vis_batch_size, args.flow_size, 0, writer, padding_mode=args.padding_mode) else: create_training_visuals(generator, t_ema, ll, loader, sample_reals, resize_fake2stn, sample_z, psi, device, args.n_mean, args.n_sample, 0, writer, padding_mode=args.padding_mode) for idx in pbar: # main training loop i = idx + args.start_iter + 1 if i <= args.anneal_psi: psi = anneal_fn(i, 1.0, 0.0, args.anneal_psi).item() psi_is_fixed = False else: psi = 0.0 psi_is_fixed = True if i > args.iter: print("Done!") break #################################### ######### TRAIN STN and LL ######### #################################### if args.clustering or args.flips: # Clustering-specific perceptual loss: perceptual_loss, delta_flow = gangealing_cluster_loss(generator, stn, ll, loss_fn, resize_fake2stn, psi, args.batch, args.dim_latent, args.freeze_ll, args.num_heads, args.flips, device, sample_from_full_res=args.sample_from_full_res, padding_mode=args.padding_mode) else: # Standard GANgealing perceptual loss (unimodal): perceptual_loss, delta_flow = gangealing_loss(generator, stn, ll, loss_fn, resize_fake2stn, psi, args.batch, args.dim_latent, args.freeze_ll, device, sample_from_full_res=args.sample_from_full_res, padding_mode=args.padding_mode) tv_loss = total_variation_loss(delta_flow) if args.tv_weight > 0 else zero flow_idty_loss = flow_identity_loss(delta_flow) if args.flow_identity_weight > 0 else zero loss_dict = {} loss_dict["p"] = perceptual_loss loss_dict["tv"] = tv_loss loss_dict["f"] = flow_idty_loss stn.zero_grad() ll.zero_grad() full_stn_loss = perceptual_loss + args.tv_weight * tv_loss + args.flow_identity_weight * flow_idty_loss full_stn_loss.backward() t_optim.step() if not args.freeze_ll: ll_optim.step() if psi_is_fixed: # Step learning rate schedulers once psi has been fully-annealed to zero: epoch = max(0, (i - args.anneal_psi) / args.period) t_sched.step(epoch) ll_sched.step(epoch) accumulate(t_ema, t_module, accum) loss_reduced = reduce_loss_dict(loss_dict) # Aggregate loss information across GPUs if primary(): # Display losses on the progress bar: perceptual_loss_val = loss_reduced["p"].mean().item() tv_loss_val = loss_reduced["tv"].mean().item() flow_idty_loss_val = loss_reduced["f"].mean().item() f_str = f"identity loss: {flow_idty_loss_val:.4f}; " if args.flow_identity_weight > 0 else "" tv_str = f"tv loss: {tv_loss_val:.6f}; " if args.tv_weight > 0 else "" pbar.set_description(f"perceptual loss: {perceptual_loss_val:.4f}; {tv_str}{f_str}psi: {psi:.4f}") # Log losses and others metrics to TensorBoard: if i % args.log_every == 0 or i in early_ckpt_iters: writer.add_scalar('Loss/Reconstruction', perceptual_loss_val, i) writer.add_scalar('Loss/TotalVariation', tv_loss_val, i) writer.add_scalar('Loss/FlowIdentity', flow_idty_loss_val, i) writer.add_scalar('Progress/psi', psi, i) writer.add_scalar('Progress/LL_LearningRate', ll_sched.get_last_lr()[0], i) writer.add_scalar('Progress/STN_LearningRate', t_sched.get_last_lr()[0], i) if i % args.ckpt_every == 0 or i in early_ckpt_iters: # Save model checkpoint save_state_dict(str(i).zfill(7), generator, t_module, t_ema, t_optim, t_sched, ll_module, ll_optim, ll_sched, args) if i % args.vis_every == 0 or i in early_vis_iters: # Save visualizations to TensorBoard if primary() and i in early_ckpt_iters: pbar.write(f'{i:07}: Learning Rate = {t_sched.get_last_lr()[0]}') if args.clustering: create_training_cluster_visuals(generator, t_ema, ll, loss_fn, loader, resize_fake2stn, sample_z, big_sample_z, psi, device, args.n_mean, args.n_sample, args.num_heads, args.flips, args.vis_batch_size, args.flow_size, i, writer, padding_mode=args.padding_mode) else: create_training_visuals(generator, t_ema, ll, loader, sample_reals, resize_fake2stn, sample_z, psi, device, args.n_mean, args.n_sample, i, writer, padding_mode=args.padding_mode) if __name__ == "__main__": device = "cuda" parser = base_training_argparse() args = parser.parse_args() if args.transform == 'similarity': assert args.tv_weight == 0, 'Total Variation loss is not currently supported for similarity-only STNs' args.n_mean = 200 if args.debug else args.n_mean args.vis_batch_size //= args.num_heads # Keep visualization batch size reasonable for clustering models # Setup distributed PyTorch and create results directory: args.distributed = setup_distributed(args.local_rank) args.clustering = args.num_heads > 1 results_path = os.path.join(args.results, args.exp_name) if primary(): writer = GANgealingWriter(results_path) with open(f'{results_path}/opt.txt', 'w') as f: json.dump(args.__dict__, f, indent=2) else: writer = None # Seed RNG: torch.manual_seed(args.seed * get_world_size() + get_rank()) np.random.seed(args.seed * get_world_size() + get_rank()) # Initialize models: generator = Generator(args.gen_size, args.dim_latent, args.n_mlp, channel_multiplier=args.gen_channel_multiplier, num_fp16_res=args.num_fp16_res).to(device) stn = get_stn(args.transform, flow_size=args.flow_size, supersize=args.real_size, channel_multiplier=args.stn_channel_multiplier, num_heads=args.num_heads).to(device) t_ema = get_stn(args.transform, flow_size=args.flow_size, supersize=args.real_size, channel_multiplier=args.stn_channel_multiplier, num_heads=args.num_heads).to(device) ll = DirectionInterpolator(pca_path=None, n_comps=args.ndirs, inject_index=args.inject, n_latent=generator.n_latent, num_heads=args.num_heads).to(device) accumulate(t_ema, stn, 0) # Setup optimizers and learning rate schedulers: t_optim = optim.Adam(stn.parameters(), lr=args.stn_lr, betas=(0.9, 0.999), eps=1e-8) ll_optim = optim.Adam(ll.parameters(), lr=args.ll_lr, betas=(0.9, 0.999), eps=1e-8) t_sched = DecayingCosineAnnealingWarmRestarts(t_optim, T_0=1, T_mult=args.tm, decay=args.decay) ll_sched = DecayingCosineAnnealingWarmRestarts(ll_optim, T_0=1, T_mult=args.tm, decay=args.decay) # Setup the perceptual loss function: loss_fn = get_perceptual_loss(args.loss_fn, device) # Get the function used to anneal psi: anneal_fn = get_psi_annealing_fn(args.anneal_fn) # Load pre-trained generator (and optionally resume from a GANgealing checkpoint): print(f"Loading model from {args.ckpt}") ckpt, _ = find_model(args.ckpt) generator.load_state_dict(ckpt["g_ema"]) # NOTE: We load g_ema as generator since G is frozen! try: # Restore from full checkpoint, including the optimizer if args.load_G_only: # Don't attempt to load GANgealing checkpoint; jump straight to the except block raise KeyError # This doesn't actually raise an error stn.load_state_dict(ckpt["t"]) t_ema.load_state_dict(ckpt["t_ema"]) t_optim.load_state_dict(ckpt["t_optim"]) t_sched.load_state_dict(ckpt["t_sched"]) ll.load_state_dict(ckpt["ll"]) ll_optim.load_state_dict(ckpt["ll_optim"]) ll_sched.load_state_dict(ckpt["ll_sched"]) except KeyError: # Initialize the target mode c (ll) print('Only G_EMA has been loaded from checkpoint. Other nets are random!') n_pca = 1000 if args.debug else 1000000 with torch.no_grad(): batch_w = generator.batch_latent(n_pca // get_world_size()) batch_w = all_gather(batch_w) pca = PCA(args.ndirs, batch_w) ll.assign_buffers(pca) if args.clustering: # For clustering models, initialize using K-Means++ on W-Space print('Running K-Means++ Initialization') if args.debug: centroids = generator.batch_latent(args.num_heads).detach().requires_grad_(False) else: centroids = kmeans_plusplus(args.num_heads, 50000, generator, loss_fn, args.inject) decomposed = pca.encode(centroids) ll.assign_coefficients(decomposed) # See if the start iteration can be recovered when resuming training: args.start_iter = 0 try: ckpt_name = os.path.basename(args.ckpt) if ckpt_name.startswith('best_'): ckpt_name = ckpt_name[5:] # Remove prefix args.start_iter = int(os.path.splitext(ckpt_name)[0]) except ValueError: pass # Move models to DDP if distributed training is enabled: if args.distributed: stn = nn.parallel.DistributedDataParallel(stn, device_ids=[args.local_rank], output_device=args.local_rank, broadcast_buffers=False) ll = nn.parallel.DistributedDataParallel(ll, device_ids=[args.local_rank], output_device=args.local_rank, broadcast_buffers=False) # Setup real data for visualizations (optional): loader = img_dataloader(args.real_data_path, shuffle=False, batch_size=args.vis_batch_size, resolution=args.real_size, infinite=False) if args.real_data_path is not None else None # Begin training: train(args, loader, generator, stn, t_ema, ll, t_optim, ll_optim, t_sched, ll_sched, loss_fn, anneal_fn, device, writer)	[ 1, 529, 9507, 29958, 14968, 29889, 2272, 13, 15945, 29908, 13, 29954, 2190, 479, 12818, 6694, 2471, 29889, 13, 15945, 29908, 13, 13, 5215, 4842, 305, 13, 3166, 4842, 305, 1053, 302, 29876, 29892, 5994, 13, 5215, 12655, 408, 7442, 13, 3166, 260, 29939, 18933, 1053, 260, 29939, 18933, 13, 5215, 4390, 13, 5215, 2897, 13, 13, 3166, 4733, 1053, 3251, 1061, 29892, 679, 29918, 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pygame/PygameSuperMario/source/setup.py	Tom-Li1/py_modules	0	66309	<filename>pygame/PygameSuperMario/source/setup.py<gh_stars>0 import pygame from . import constants as C from . import tools pygame.init() SCREEN = pygame.display.set_mode(C.SCREEN_SIZE) GRAPHICS = tools.load_graphics(r'resources/graphics')	[ 1, 529, 9507, 29958, 2272, 11802, 29914, 29925, 4790, 420, 19111, 29924, 2628, 29914, 4993, 29914, 14669, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 22028, 13, 3166, 869, 1053, 17727, 408, 315, 13, 3166, 869, 1053, 8492, 13, 13, 2272, 11802, 29889, 2344, 580, 13, 7187, 1525, 1430, 353, 22028, 29889, 4990, 29889, 842, 29918, 8513, 29898, 29907, 29889, 7187, 1525, 1430, 29918, 14226, 29897, 13, 14345, 3301, 29950, 2965, 29903, 353, 8492, 29889, 1359, 29918, 6420, 29898, 29878, 29915, 13237, 29914, 6420, 1495, 13, 2 ]
unifier/apps/core/migrations/0010_auto_20210225_0227.py	sosolidkk/manga-unifier	6	151580	<reponame>sosolidkk/manga-unifier<gh_stars>1-10 # Generated by Django 3.1.7 on 2021-02-25 02:27 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('core', '0009_auto_20210223_0124'), ] operations = [ migrations.AddField( model_name='mangachapter', name='images', field=models.JSONField(default=list, verbose_name='Images'), ), migrations.DeleteModel( name='Image', ), ]	[ 1, 529, 276, 1112, 420, 29958, 29879, 359, 17211, 6859, 29914, 29885, 12686, 29899, 348, 3709, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 3251, 630, 491, 15337, 29871, 29941, 29889, 29896, 29889, 29955, 373, 29871, 29906, 29900, 29906, 29896, 29899, 29900, 29906, 29899, 29906, 29945, 29871, 29900, 29906, 29901, 29906, 29955, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 3221, 742, 525, 29900, 29900, 29900, 29929, 29918, 6921, 29918, 29906, 29900, 29906, 29896, 29900, 29906, 29906, 29941, 29918, 29900, 29896, 29906, 29946, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2528, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 29885, 574, 496, 3314, 742, 13, 9651, 1024, 2433, 8346, 742, 13, 9651, 1746, 29922, 9794, 29889, 7249, 3073, 29898, 4381, 29922, 1761, 29892, 26952, 29918, 978, 2433, 20163, 5477, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 12498, 3195, 29898, 13, 9651, 1024, 2433, 2940, 742, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
demisto_sdk/commands/lint/linter.py	nericksen/demisto-sdk	0	77399	# STD python packages import copy import hashlib import logging import os import platform import traceback from typing import Any, Dict, List, Optional, Tuple import docker import docker.errors import docker.models.containers import git import requests.exceptions import urllib3.exceptions from packaging.version import parse from wcmatch.pathlib import NEGATE, Path from demisto_sdk.commands.common.constants import (INTEGRATIONS_DIR, PACKS_PACK_META_FILE_NAME, TYPE_PWSH, TYPE_PYTHON) from demisto_sdk.commands.common.handlers import JSON_Handler, YAML_Handler from demisto_sdk.commands.common.timers import timer from demisto_sdk.commands.common.tools import (get_all_docker_images, run_command_os) from demisto_sdk.commands.lint.commands_builder import ( build_bandit_command, build_flake8_command, build_mypy_command, build_pwsh_analyze_command, build_pwsh_test_command, build_pylint_command, build_pytest_command, build_vulture_command, build_xsoar_linter_command) from demisto_sdk.commands.lint.docker_helper import Docker from demisto_sdk.commands.lint.helpers import (EXIT_CODES, FAIL, RERUN, RL, SUCCESS, WARNING, add_tmp_lint_files, add_typing_module, coverage_report_editor, get_file_from_container, get_python_version_from_image, pylint_plugin, split_warnings_errors, stream_docker_container_output) json = JSON_Handler() # 3-rd party packages # Local packages logger = logging.getLogger('demisto-sdk') class Linter: """ Linter used to activate lint command on single package Attributes: pack_dir(Path): Pack to run lint on. content_repo(Path): Git repo object of content repo. req_2(list): requirements for docker using python2. req_3(list): requirements for docker using python3. docker_engine(bool): Whether docker engine detected by docker-sdk. """ def __init__(self, pack_dir: Path, content_repo: Path, req_3: list, req_2: list, docker_engine: bool, docker_timeout: int): self._req_3 = req_3 self._req_2 = req_2 self._content_repo = content_repo self._pack_abs_dir = pack_dir self._pack_name = None self.docker_timeout = docker_timeout # Docker client init if docker_engine: self._docker_client: docker.DockerClient = docker.from_env(timeout=docker_timeout) self._docker_hub_login = self._docker_login() # Facts gathered regarding pack lint and test self._facts: Dict[str, Any] = { "images": [], "python_version": 0, "env_vars": {}, "test": False, "lint_files": [], "support_level": None, "is_long_running": False, "lint_unittest_files": [], "additional_requirements": [], "docker_engine": docker_engine, "is_script": False, "commands": None, } # Pack lint status object - visualize it self._pkg_lint_status: Dict = { "pkg": None, "pack_type": None, "path": str(self._content_repo), "errors": [], "images": [], "flake8_errors": None, "XSOAR_linter_errors": None, "bandit_errors": None, "mypy_errors": None, "vulture_errors": None, "flake8_warnings": None, "XSOAR_linter_warnings": None, "bandit_warnings": None, "mypy_warnings": None, "vulture_warnings": None, "exit_code": SUCCESS, "warning_code": SUCCESS, } @timer(group_name='lint') def run_dev_packages(self, no_flake8: bool, no_bandit: bool, no_mypy: bool, no_pylint: bool, no_vulture: bool, no_xsoar_linter: bool, no_pwsh_analyze: bool, no_pwsh_test: bool, no_test: bool, modules: dict, keep_container: bool, test_xml: str, no_coverage: bool) -> dict: """ Run lint and tests on single package Performing the follow: 1. Run the lint on OS - flake8, bandit, mypy. 2. Run in package docker - pylint, pytest. Args: no_flake8(bool): Whether to skip flake8 no_bandit(bool): Whether to skip bandit no_mypy(bool): Whether to skip mypy no_vulture(bool): Whether to skip vulture no_pylint(bool): Whether to skip pylint no_test(bool): Whether to skip pytest no_pwsh_analyze(bool): Whether to skip powershell code analyzing no_pwsh_test(bool): whether to skip powershell tests modules(dict): Mandatory modules to locate in pack path (CommonServerPython.py etc) keep_container(bool): Whether to keep the test container test_xml(str): Path for saving pytest xml results no_coverage(bool): Run pytest without coverage report Returns: dict: lint and test all status, pkg status) """ # Gather information for lint check information skip = self._gather_facts(modules) # If not python pack - skip pack if skip: return self._pkg_lint_status try: # Locate mandatory files in pack path - for more info checkout the context manager LintFiles with add_tmp_lint_files(content_repo=self._content_repo, # type: ignore pack_path=self._pack_abs_dir, lint_files=self._facts["lint_files"], modules=modules, pack_type=self._pkg_lint_status["pack_type"]): # Run lint check on host - flake8, bandit, mypy if self._pkg_lint_status["pack_type"] == TYPE_PYTHON: self._run_lint_in_host(no_flake8=no_flake8, no_bandit=no_bandit, no_mypy=no_mypy, no_vulture=no_vulture, no_xsoar_linter=no_xsoar_linter) # Run lint and test check on pack docker image if self._facts["docker_engine"]: self._run_lint_on_docker_image(no_pylint=no_pylint, no_test=no_test, no_pwsh_analyze=no_pwsh_analyze, no_pwsh_test=no_pwsh_test, keep_container=keep_container, test_xml=test_xml, no_coverage=no_coverage) except Exception as ex: err = f'{self._pack_abs_dir}: Unexpected fatal exception: {str(ex)}' logger.error(f"{err}. Traceback: {traceback.format_exc()}") self._pkg_lint_status["errors"].append(err) self._pkg_lint_status['exit_code'] += FAIL return self._pkg_lint_status @timer(group_name='lint') def _gather_facts(self, modules: dict) -> bool: """ Gathering facts about the package - python version, docker images, valid docker image, yml parsing Args: modules(dict): Test mandatory modules to be ignore in lint check Returns: bool: Indicating if to continue further or not, if False exit Thread, Else continue. """ # Looking for pkg yaml yml_file: Optional[Path] = self._pack_abs_dir.glob([r'.yaml', r'.yml', r'!unified.yml'], flags=NEGATE) if not yml_file: logger.info(f"{self._pack_abs_dir} - Skipping no yaml file found {yml_file}") self._pkg_lint_status["errors"].append('Unable to find yml file in package') return True else: try: yml_file = next(yml_file) except StopIteration: return True # Get pack name self._pack_name = yml_file.stem log_prompt = f"{self._pack_name} - Facts" self._pkg_lint_status["pkg"] = yml_file.stem logger.info(f"{log_prompt} - Using yaml file {yml_file}") # Parsing pack yaml - in order to verify if check needed try: script_obj: Dict = {} yml_obj: Dict = YAML_Handler().load(yml_file) if isinstance(yml_obj, dict): script_obj = yml_obj.get('script', {}) if isinstance(yml_obj.get('script'), dict) else yml_obj self._facts['is_script'] = True if 'Scripts' in yml_file.parts else False self._facts['is_long_running'] = script_obj.get('longRunning') self._facts['commands'] = self._get_commands_list(script_obj) self._pkg_lint_status["pack_type"] = script_obj.get('type') except (FileNotFoundError, IOError, KeyError): self._pkg_lint_status["errors"].append('Unable to parse package yml') return True # return no check needed if not python pack if self._pkg_lint_status["pack_type"] not in (TYPE_PYTHON, TYPE_PWSH): logger.info(f"{log_prompt} - Skipping due to not Python, Powershell package - Pack is" f" {self._pkg_lint_status['pack_type']}") return True # Docker images if self._facts["docker_engine"]: logger.info(f"{log_prompt} - Pulling docker images, can take up to 1-2 minutes if not exists locally ") self._facts["images"] = [[image, -1] for image in get_all_docker_images(script_obj=script_obj)] # Gather environment variables for docker execution self._facts["env_vars"] = { "CI": os.getenv("CI", False), "DEMISTO_LINT_UPDATE_CERTS": os.getenv('DEMISTO_LINT_UPDATE_CERTS', "yes") } lint_files = set() # Facts for python pack if self._pkg_lint_status["pack_type"] == TYPE_PYTHON: self._update_support_level() if self._facts["docker_engine"]: # Getting python version from docker image - verifying if not valid docker image configured for image in self._facts["images"]: py_num: str = get_python_version_from_image(image=image[0], timeout=self.docker_timeout) image[1] = py_num logger.info(f"{self._pack_name} - Facts - {image[0]} - Python {py_num}") if not self._facts["python_version"]: self._facts["python_version"] = py_num # Checking whatever test exists in package self._facts["test"] = True if next(self._pack_abs_dir.glob([r'test_.py', r'_test.py']), None) else False if self._facts["test"]: logger.info(f"{log_prompt} - Tests found") else: logger.info(f"{log_prompt} - Tests not found") # Gather package requirements embedded test-requirements.py file test_requirements = self._pack_abs_dir / 'test-requirements.txt' if test_requirements.exists(): try: additional_req = test_requirements.read_text(encoding='utf-8').strip().split('\n') self._facts["additional_requirements"].extend(additional_req) logger.info(f"{log_prompt} - Additional package Pypi packages found - {additional_req}") except (FileNotFoundError, IOError): self._pkg_lint_status["errors"].append('Unable to parse test-requirements.txt in package') elif not self._facts["python_version"]: # get python version from yml pynum = '3.7' if (script_obj.get('subtype', 'python3') == 'python3') else '2.7' self._facts["python_version"] = pynum logger.info(f"{log_prompt} - Using python version from yml: {pynum}") # Get lint files lint_files = set(self._pack_abs_dir.glob([".py", "!__init__.py", "!.tmp"], flags=NEGATE)) # Facts for Powershell pack elif self._pkg_lint_status["pack_type"] == TYPE_PWSH: # Get lint files lint_files = set( self._pack_abs_dir.glob([".ps1", "!Tests.ps1", "CommonServerPowerShell.ps1", "demistomock.ps1'"], flags=NEGATE)) # Add CommonServer to the lint checks if 'commonserver' in self._pack_abs_dir.name.lower(): # Powershell if self._pkg_lint_status["pack_type"] == TYPE_PWSH: self._facts["lint_files"] = [Path(self._pack_abs_dir / 'CommonServerPowerShell.ps1')] # Python elif self._pkg_lint_status["pack_type"] == TYPE_PYTHON: self._facts["lint_files"] = [Path(self._pack_abs_dir / 'CommonServerPython.py')] else: test_modules = {self._pack_abs_dir / module.name for module in modules.keys()} lint_files = lint_files.difference(test_modules) self._facts["lint_files"] = list(lint_files) if self._facts["lint_files"]: self._remove_gitignore_files(log_prompt) for lint_file in self._facts["lint_files"]: logger.info(f"{log_prompt} - Lint file {lint_file}") else: logger.info(f"{log_prompt} - Lint files not found") # Remove files that are in gitignore self._split_lint_files() return False def _remove_gitignore_files(self, log_prompt: str) -> None: """ Skipping files that matches gitignore patterns. Args: log_prompt(str): log prompt string Returns: """ try: repo = git.Repo(self._content_repo) files_to_ignore = repo.ignored(self._facts['lint_files']) for file in files_to_ignore: logger.info(f"{log_prompt} - Skipping gitignore file {file}") self._facts["lint_files"] = [path for path in self._facts['lint_files'] if path not in files_to_ignore] except (git.InvalidGitRepositoryError, git.NoSuchPathError): logger.debug("No gitignore files is available") def _split_lint_files(self): """ Remove unit test files from _facts['lint_files'] and put into their own list _facts['lint_unittest_files'] This is because not all lints should be done on unittest files. """ lint_files_list = copy.deepcopy(self._facts["lint_files"]) for lint_file in lint_files_list: if lint_file.name.startswith('test_') or lint_file.name.endswith('_test.py'): self._facts['lint_unittest_files'].append(lint_file) self._facts["lint_files"].remove(lint_file) @timer(group_name='lint') def _run_lint_in_host(self, no_flake8: bool, no_bandit: bool, no_mypy: bool, no_vulture: bool, no_xsoar_linter: bool): """ Run lint check on host Args: no_flake8(bool): Whether to skip flake8. no_bandit(bool): Whether to skip bandit. no_mypy(bool): Whether to skip mypy. no_vulture(bool): Whether to skip Vulture. """ warning = [] error = [] other = [] exit_code: int = 0 for lint_check in ["flake8", "XSOAR_linter", "bandit", "mypy", "vulture"]: exit_code = SUCCESS output = "" if self._facts["lint_files"] or self._facts["lint_unittest_files"]: if lint_check == "flake8" and not no_flake8: flake8_lint_files = copy.deepcopy(self._facts["lint_files"]) # if there are unittest.py then we would run flake8 on them too. if self._facts['lint_unittest_files']: flake8_lint_files.extend(self._facts['lint_unittest_files']) exit_code, output = self._run_flake8(py_num=self._facts["python_version"], lint_files=flake8_lint_files) if self._facts["lint_files"]: if lint_check == "XSOAR_linter" and not no_xsoar_linter: exit_code, output = self._run_xsoar_linter(py_num=self._facts["python_version"], lint_files=self._facts["lint_files"]) elif lint_check == "bandit" and not no_bandit: exit_code, output = self._run_bandit(lint_files=self._facts["lint_files"]) elif lint_check == "mypy" and not no_mypy: exit_code, output = self._run_mypy(py_num=self._facts["python_version"], lint_files=self._facts["lint_files"]) elif lint_check == "vulture" and not no_vulture: exit_code, output = self._run_vulture(py_num=self._facts["python_version"], lint_files=self._facts["lint_files"]) # check for any exit code other than 0 if exit_code: error, warning, other = split_warnings_errors(output) if exit_code and warning: self._pkg_lint_status["warning_code"] \|= EXIT_CODES[lint_check] self._pkg_lint_status[f"{lint_check}_warnings"] = "\n".join(warning) if exit_code & FAIL: self._pkg_lint_status["exit_code"] \|= EXIT_CODES[lint_check] # if the error were extracted correctly as they start with E if error: self._pkg_lint_status[f"{lint_check}_errors"] = "\n".join(error) # if there were errors but they do not start with E else: self._pkg_lint_status[f"{lint_check}_errors"] = "\n".join(other) @timer(group_name='lint') def _run_flake8(self, py_num: str, lint_files: List[Path]) -> Tuple[int, str]: """ Runs flake8 in pack dir Args: py_num(str): The python version in use lint_files(List[Path]): file to perform lint Returns: int: 0 on successful else 1, errors str: Bandit errors """ log_prompt = f"{self._pack_name} - Flake8" logger.info(f"{log_prompt} - Start") stdout, stderr, exit_code = run_command_os(command=build_flake8_command(lint_files, py_num), cwd=self._content_repo) logger.debug(f"{log_prompt} - Finished, exit-code: {exit_code}") logger.debug(f"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}") logger.debug(f"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}") if stderr or exit_code: logger.error(f"{log_prompt}- Finished, errors found") if stderr: return FAIL, stderr else: return FAIL, stdout logger.info(f"{log_prompt} - Successfully finished") return SUCCESS, "" @timer(group_name='lint') def _run_xsoar_linter(self, py_num: str, lint_files: List[Path]) -> Tuple[int, str]: """ Runs Xsaor linter in pack dir Args: lint_files(List[Path]): file to perform lint Returns: int: 0 on successful else 1, errors str: Xsoar linter errors """ status = SUCCESS FAIL_PYLINT = 0b10 with pylint_plugin(self._pack_abs_dir): log_prompt = f"{self._pack_name} - XSOAR Linter" logger.info(f"{log_prompt} - Start") myenv = os.environ.copy() if myenv.get('PYTHONPATH'): myenv['PYTHONPATH'] += ':' + str(self._pack_abs_dir) else: myenv['PYTHONPATH'] = str(self._pack_abs_dir) if self._facts['is_long_running']: myenv['LONGRUNNING'] = 'True' py_ver = parse(py_num).major if py_ver < 3: myenv['PY2'] = 'True' myenv['is_script'] = str(self._facts['is_script']) # as Xsoar checker is a pylint plugin and runs as part of pylint code, we can not pass args to it. # as a result we can use the env vars as a getway. myenv['commands'] = ','.join([str(elem) for elem in self._facts['commands']]) \ if self._facts['commands'] else '' stdout, stderr, exit_code = run_command_os( command=build_xsoar_linter_command(lint_files, py_num, self._facts.get('support_level', 'base')), cwd=self._pack_abs_dir, env=myenv) if exit_code & FAIL_PYLINT: logger.error(f"{log_prompt}- Finished, errors found") status = FAIL if exit_code & WARNING: logger.warning(f"{log_prompt} - Finished, warnings found") if not status: status = WARNING # if pylint did not run and failure exit code has been returned from run commnad elif exit_code & FAIL: status = FAIL logger.debug(f"{log_prompt} - Actual XSOAR linter error -") logger.debug(f"{log_prompt} - Full format stdout: {RL if stdout else ''}{stdout}") # for contrib prs which are not merged from master and do not have pylint in dev-requirements-py2. if os.environ.get('CI'): stdout = "Xsoar linter could not run, Please merge from master" else: stdout = "Xsoar linter could not run, please make sure you have" \ " the necessary Pylint version for both py2 and py3" logger.error(f"{log_prompt}- Finished, errors found") logger.debug(f"{log_prompt} - Finished, exit-code: {exit_code}") logger.debug(f"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}") logger.debug(f"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}") if not exit_code: logger.info(f"{log_prompt} - Successfully finished") return status, stdout @timer(group_name='lint') def _run_bandit(self, lint_files: List[Path]) -> Tuple[int, str]: """ Run bandit in pack dir Args: lint_files(List[Path]): file to perform lint Returns: int: 0 on successful else 1, errors str: Bandit errors """ log_prompt = f"{self._pack_name} - Bandit" logger.info(f"{log_prompt} - Start") stdout, stderr, exit_code = run_command_os(command=build_bandit_command(lint_files), cwd=self._pack_abs_dir) logger.debug(f"{log_prompt} - Finished, exit-code: {exit_code}") logger.debug(f"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}") logger.debug(f"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}") if stderr or exit_code: logger.error(f"{log_prompt}- Finished, errors found") if stderr: return FAIL, stderr else: return FAIL, stdout logger.info(f"{log_prompt} - Successfully finished") return SUCCESS, "" @timer(group_name='lint') def _run_mypy(self, py_num: str, lint_files: List[Path]) -> Tuple[int, str]: """ Run mypy in pack dir Args: py_num(str): The python version in use lint_files(List[Path]): file to perform lint Returns: int: 0 on successful else 1, errors str: Bandit errors """ log_prompt = f"{self._pack_name} - Mypy" logger.info(f"{log_prompt} - Start") with add_typing_module(lint_files=lint_files, python_version=py_num): mypy_command = build_mypy_command(files=lint_files, version=py_num, content_repo=self._content_repo) stdout, stderr, exit_code = run_command_os(command=mypy_command, cwd=self._pack_abs_dir) logger.debug(f"{log_prompt} - Finished, exit-code: {exit_code}") logger.debug(f"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}") logger.debug(f"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}") if stderr or exit_code: logger.error(f"{log_prompt}- Finished, errors found") if stderr: return FAIL, stderr else: return FAIL, stdout logger.info(f"{log_prompt} - Successfully finished") return SUCCESS, "" @timer(group_name='lint') def _run_vulture(self, py_num: str, lint_files: List[Path]) -> Tuple[int, str]: """ Run mypy in pack dir Args: py_num(str): The python version in use lint_files(List[Path]): file to perform lint Returns: int: 0 on successful else 1, errors str: Vulture errors """ log_prompt = f"{self._pack_name} - Vulture" logger.info(f"{log_prompt} - Start") stdout, stderr, exit_code = run_command_os(command=build_vulture_command(files=lint_files, pack_path=self._pack_abs_dir, py_num=py_num), cwd=self._pack_abs_dir) logger.debug(f"{log_prompt} - Finished, exit-code: {exit_code}") logger.debug(f"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}") logger.debug(f"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}") if stderr or exit_code: logger.error(f"{log_prompt}- Finished, errors found") if stderr: return FAIL, stderr else: return FAIL, stdout logger.info(f"{log_prompt} - Successfully finished") return SUCCESS, "" @timer(group_name='lint') def _run_lint_on_docker_image(self, no_pylint: bool, no_test: bool, no_pwsh_analyze: bool, no_pwsh_test: bool, keep_container: bool, test_xml: str, no_coverage: bool): """ Run lint check on docker image Args: no_pylint(bool): Whether to skip pylint no_test(bool): Whether to skip pytest no_pwsh_analyze(bool): Whether to skip powershell code analyzing no_pwsh_test(bool): whether to skip powershell tests keep_container(bool): Whether to keep the test container test_xml(str): Path for saving pytest xml results no_coverage(bool): Run pytest without coverage report """ for image in self._facts["images"]: # Docker image status - visualize status = { "image": image[0], "image_errors": "", "pylint_errors": "", "pytest_errors": "", "pytest_json": {}, "pwsh_analyze_errors": "", "pwsh_test_errors": "" } # Creating image if pylint specified or found tests and tests specified image_id = "" errors = "" for trial in range(2): image_id, errors = self._docker_image_create(docker_base_image=image) if not errors: break if image_id and not errors: # Set image creation status for check in ["pylint", "pytest", "pwsh_analyze", "pwsh_test"]: exit_code = SUCCESS output = "" for trial in range(2): if self._pkg_lint_status["pack_type"] == TYPE_PYTHON: # Perform pylint if not no_pylint and check == "pylint" and self._facts["lint_files"]: exit_code, output = self._docker_run_pylint(test_image=image_id, keep_container=keep_container) # Perform pytest elif not no_test and self._facts["test"] and check == "pytest": exit_code, output, test_json = self._docker_run_pytest(test_image=image_id, keep_container=keep_container, test_xml=test_xml, no_coverage=no_coverage) status["pytest_json"] = test_json elif self._pkg_lint_status["pack_type"] == TYPE_PWSH: # Perform powershell analyze if not no_pwsh_analyze and check == "pwsh_analyze" and self._facts["lint_files"]: exit_code, output = self._docker_run_pwsh_analyze(test_image=image_id, keep_container=keep_container) # Perform powershell test elif not no_pwsh_test and check == "pwsh_test": exit_code, output = self._docker_run_pwsh_test(test_image=image_id, keep_container=keep_container) # If lint check perfrom and failed on reason related to enviorment will run twice, # But it failing in second time it will count as test failure. if (exit_code == RERUN and trial == 1) or exit_code == FAIL or exit_code == SUCCESS: if exit_code in [RERUN, FAIL]: self._pkg_lint_status["exit_code"] \|= EXIT_CODES[check] status[f"{check}_errors"] = output break else: status["image_errors"] = str(errors) self._pkg_lint_status["exit_code"] += EXIT_CODES["image"] # Add image status to images self._pkg_lint_status["images"].append(status) def _docker_login(self) -> bool: """ Login to docker-hub using environment variables: 1. DOCKERHUB_USER - User for docker hub. 2. DOCKERHUB_PASSWORD - Password for docker-hub. Used in Circle-CI for pushing into repo devtestdemisto Returns: bool: True if logged in successfully. """ docker_user = os.getenv('DOCKERHUB_USER') docker_pass = os.getenv('DOCKERHUB_PASSWORD') try: self._docker_client.login(username=docker_user, password=<PASSWORD>, registry="https://index.docker.io/v1") return self._docker_client.ping() except docker.errors.APIError: return False @timer(group_name='lint') def _docker_image_create(self, docker_base_image: List[Any]) -> Tuple[str, str]: """ Create docker image: 1. Installing 'build base' if required in alpine images version - https://wiki.alpinelinux.org/wiki/GCC 2. Installing pypi packs - if only pylint required - only pylint installed otherwise all pytest and pylint installed, packages which being install can be found in path demisto_sdk/commands/lint/dev_envs 3. The docker image build done by Dockerfile template located in demisto_sdk/commands/lint/templates/dockerfile.jinja2 Args: docker_base_image(list): docker image to use as base for installing dev deps and python version. Returns: str, str. image name to use and errors string. """ log_prompt = f"{self._pack_name} - Image create" # Get requirements file for image requirements = [] if docker_base_image[1] != -1: py_ver = parse(docker_base_image[1]).major if py_ver == 2: requirements = self._req_2 elif py_ver == 3: requirements = self._req_3 # Using DockerFile template pip_requirements = requirements + self._facts["additional_requirements"] # Trying to pull image based on dockerfile hash, will check if something changed errors = "" identifier = hashlib.md5("\n".join(sorted(pip_requirements)).encode("utf-8")).hexdigest() test_image_name = f'devtest{docker_base_image[0]}-{identifier}' test_image = None try: logger.info(f"{log_prompt} - Trying to pull existing image {test_image_name}") test_image = Docker.pull_image(test_image_name) except (docker.errors.APIError, docker.errors.ImageNotFound): logger.info(f"{log_prompt} - Unable to find image {test_image_name}") # Creatng new image if existing image isn't found if not test_image: logger.info( f"{log_prompt} - Creating image based on {docker_base_image[0]} - Could take 2-3 minutes at first " f"time") try: Docker.create_image(docker_base_image[0], test_image_name, container_type=self._pkg_lint_status["pack_type"], install_packages=pip_requirements) if self._docker_hub_login: for _ in range(2): try: self._docker_client.images.push(test_image_name) logger.info(f"{log_prompt} - Image {test_image_name} pushed to repository") break except (requests.exceptions.ConnectionError, urllib3.exceptions.ReadTimeoutError, requests.exceptions.ReadTimeout): logger.info(f"{log_prompt} - Unable to push image {test_image_name} to repository") except (docker.errors.BuildError, docker.errors.APIError, Exception) as e: logger.critical(f"{log_prompt} - Build errors occurred {e}") errors = str(e) return test_image_name, errors def _docker_remove_container(self, container_name: str): try: container = self._docker_client.containers.get(container_name) container.remove(force=True) except docker.errors.NotFound: pass except requests.exceptions.ChunkedEncodingError as err: # see: https://github.com/docker/docker-py/issues/2696#issuecomment-721322548 if platform.system() != 'Darwin' or 'Connection broken' not in str(err): raise @timer(group_name='lint') def _docker_run_pylint(self, test_image: str, keep_container: bool) -> Tuple[int, str]: """ Run Pylint in created test image Args: test_image(str): test image id/name keep_container(bool): True if to keep container after execution finished Returns: int: 0 on successful, errors 1, need to retry 2 str: Container log """ log_prompt = f'{self._pack_name} - Pylint - Image {test_image}' logger.info(f"{log_prompt} - Start") container_name = f"{self._pack_name}-pylint" # Check if previous run left container a live if it do, we remove it self._docker_remove_container(container_name) # Run container exit_code = SUCCESS output = "" try: container: docker.models.containers.Container = Docker.create_container( name=container_name, image=test_image, command=[ build_pylint_command( self._facts["lint_files"], docker_version=self._facts.get('python_version')) ], user=f"{os.getuid()}:4000", files_to_push=[('/devwork', self._pack_abs_dir)], environment=self._facts["env_vars"], ) container.start() stream_docker_container_output(container.logs(stream=True)) # wait for container to finish container_status = container.wait(condition="exited") # Get container exit code container_exit_code = container_status.get("StatusCode") # Getting container logs container_log = container.logs().decode("utf-8") logger.info(f"{log_prompt} - exit-code: {container_exit_code}") if container_exit_code in [1, 2]: # 1-fatal message issued # 2-Error message issued exit_code = FAIL output = container_log logger.error(f"{log_prompt} - Finished, errors found") elif container_exit_code in [4, 8, 16]: # 4-Warning message issued # 8-refactor message issued # 16-convention message issued logger.info(f"{log_prompt} - Successfully finished - warnings found") exit_code = SUCCESS elif container_exit_code == 32: # 32-usage error logger.critical(f"{log_prompt} - Finished - Usage error") exit_code = RERUN else: logger.info(f"{log_prompt} - Successfully finished") # Keeping container if needed or remove it if keep_container: print(f"{log_prompt} - container name {container_name}") container.commit(repository=container_name.lower(), tag="pylint") else: try: container.remove(force=True) except docker.errors.NotFound as e: logger.critical(f"{log_prompt} - Unable to delete container - {e}") except Exception as e: logger.exception(f"{log_prompt} - Unable to run pylint") exit_code = RERUN output = str(e) return exit_code, output @timer(group_name='lint') def _docker_run_pytest(self, test_image: str, keep_container: bool, test_xml: str, no_coverage: bool = False) -> Tuple[int, str, dict]: """ Run Pytest in created test image Args: test_image(str): Test image id/name keep_container(bool): True if to keep container after execution finished test_xml(str): Xml saving path no_coverage(bool): Run pytest without coverage report Returns: int: 0 on successful, errors 1, need to retry 2 str: Unit test json report """ log_prompt = f'{self._pack_name} - Pytest - Image {test_image}' logger.info(f"{log_prompt} - Start") container_name = f"{self._pack_name}-pytest" # Check if previous run left container a live if it does, Remove it self._docker_remove_container(container_name) # Collect tests exit_code = SUCCESS output = '' test_json = {} try: # Running pytest container cov = '' if no_coverage else self._pack_abs_dir.stem uid = os.getuid() or 4000 logger.debug(f'{log_prompt} - user uid for running lint/test: {uid}') # lgtm[py/clear-text-logging-sensitive-data] container = Docker.create_container( name=container_name, image=test_image, user=f"{uid}:4000", command=[build_pytest_command(test_xml=test_xml, json=True, cov=cov)], environment=self._facts["env_vars"], files_to_push=[('/devwork', self._pack_abs_dir)] ) container.start() stream_docker_container_output(container.logs(stream=True)) # Waiting for container to be finished container_status: dict = container.wait(condition="exited") # Getting container exit code container_exit_code = container_status.get("StatusCode") # Getting container logs logger.info(f"{log_prompt} - exit-code: {container_exit_code}") if container_exit_code in [0, 1, 2, 5]: # 0-All tests passed # 1-Tests were collected and run but some of the tests failed # 2-Test execution was interrupted by the user # 5-No tests were collected if test_xml: test_data_xml = get_file_from_container(container_obj=container, container_path="/devwork/report_pytest.xml") xml_apth = Path(test_xml) / f'{self._pack_name}_pytest.xml' with open(file=xml_apth, mode='bw') as f: f.write(test_data_xml) # type: ignore if not no_coverage: cov_file_path = os.path.join(self._pack_abs_dir, '.coverage') cov_data = get_file_from_container(container_obj=container, container_path="/devwork/.coverage") cov_data = cov_data if isinstance(cov_data, bytes) else cov_data.encode() with open(cov_file_path, 'wb') as coverage_file: coverage_file.write(cov_data) coverage_report_editor(cov_file_path, os.path.join(self._pack_abs_dir, f'{self._pack_abs_dir.stem}.py')) test_json = json.loads(get_file_from_container(container_obj=container, container_path="/devwork/report_pytest.json", encoding="utf-8")) for test in test_json.get('report', {}).get("tests"): if test.get("call", {}).get("longrepr"): test["call"]["longrepr"] = test["call"]["longrepr"].split('\n') if container_exit_code in [0, 5]: logger.info(f"{log_prompt} - Successfully finished") exit_code = SUCCESS elif container_exit_code in [2]: output = container.logs().decode('utf-8') exit_code = FAIL else: logger.error(f"{log_prompt} - Finished, errors found") exit_code = FAIL elif container_exit_code in [3, 4]: # 3-Internal error happened while executing tests # 4-pytest command line usage error logger.critical(f"{log_prompt} - Usage error") exit_code = RERUN output = container.logs().decode('utf-8') else: # Any other container exit code logger.error(f"{log_prompt} - Finished, docker container error found ({container_exit_code})") exit_code = FAIL # Remove container if not needed if keep_container: print(f"{log_prompt} - Container name {container_name}") container.commit(repository=container_name.lower(), tag="pytest") else: try: container.remove(force=True) except docker.errors.NotFound as e: logger.critical(f"{log_prompt} - Unable to remove container {e}") except (docker.errors.ImageNotFound, docker.errors.APIError) as e: logger.critical(f"{log_prompt} - Unable to run pytest container {e}") exit_code = RERUN return exit_code, output, test_json def _docker_run_pwsh_analyze(self, test_image: str, keep_container: bool) -> Tuple[int, str]: """ Run Powershell code analyze in created test image Args: test_image(str): test image id/name keep_container(bool): True if to keep container after excution finished Returns: int: 0 on successful, errors 1, need to retry 2 str: Container log """ log_prompt = f'{self._pack_name} - Powershell analyze - Image {test_image}' logger.info(f"{log_prompt} - Start") container_name = f"{self._pack_name}-pwsh-analyze" # Check if previous run left container a live if it do, we remove it container: docker.models.containers.Container try: container = self._docker_client.containers.get(container_name) container.remove(force=True) except docker.errors.NotFound: pass # Run container exit_code = SUCCESS output = "" try: uid = os.getuid() or 4000 logger.debug(f'{log_prompt} - user uid for running lint/test: {uid}') # lgtm[py/clear-text-logging-sensitive-data] container = Docker.create_container(name=container_name, image=test_image, user=f"{uid}:4000", environment=self._facts["env_vars"], files_to_push=[('/devwork', self._pack_abs_dir)], command=build_pwsh_analyze_command( self._facts["lint_files"][0]) ) container.start() stream_docker_container_output(container.logs(stream=True)) # wait for container to finish container_status = container.wait(condition="exited") # Get container exit code container_exit_code = container_status.get("StatusCode") # Getting container logs container_log = container.logs().decode("utf-8") logger.info(f"{log_prompt} - exit-code: {container_exit_code}") if container_exit_code: # 1-fatal message issued # 2-Error message issued logger.error(f"{log_prompt} - Finished, errors found") output = container_log exit_code = FAIL else: logger.info(f"{log_prompt} - Successfully finished") # Keeping container if needed or remove it if keep_container: print(f"{log_prompt} - container name {container_name}") container.commit(repository=container_name.lower(), tag="pwsh_analyze") else: try: container.remove(force=True) except docker.errors.NotFound as e: logger.critical(f"{log_prompt} - Unable to delete container - {e}") except (docker.errors.ImageNotFound, docker.errors.APIError, requests.exceptions.ReadTimeout) as e: logger.critical(f"{log_prompt} - Unable to run powershell test - {e}") exit_code = RERUN return exit_code, output def _update_support_level(self): pack_dir = self._pack_abs_dir.parent if self._pack_abs_dir.parts[-1] == INTEGRATIONS_DIR else \ self._pack_abs_dir.parent.parent pack_meta_content: Dict = json.load((pack_dir / PACKS_PACK_META_FILE_NAME).open()) self._facts['support_level'] = pack_meta_content.get('support') if self._facts['support_level'] == 'partner' and pack_meta_content.get('Certification'): self._facts['support_level'] = 'certified partner' def _docker_run_pwsh_test(self, test_image: str, keep_container: bool) -> Tuple[int, str]: """ Run Powershell tests in created test image Args: test_image(str): test image id/name keep_container(bool): True if to keep container after excution finished Returns: int: 0 on successful, errors 1, neet to retry 2 str: Container log """ log_prompt = f'{self._pack_name} - Powershell test - Image {test_image}' logger.info(f"{log_prompt} - Start") container_name = f"{self._pack_name}-pwsh-test" # Check if previous run left container a live if it do, we remove it self._docker_remove_container(container_name) # Run container exit_code = SUCCESS output = "" try: uid = os.getuid() or 4000 logger.debug(f'{log_prompt} - user uid for running lint/test: {uid}') # lgtm[py/clear-text-logging-sensitive-data] container: docker.models.containers.Container = Docker.create_container( files_to_push=[('/devwork', self._pack_abs_dir)], name=container_name, image=test_image, command=build_pwsh_test_command(), user=f"{uid}:4000", environment=self._facts["env_vars"]) container.start() stream_docker_container_output(container.logs(stream=True)) # wait for container to finish container_status = container.wait(condition="exited") # Get container exit code container_exit_code = container_status.get("StatusCode") # Getting container logs container_log = container.logs().decode("utf-8") logger.info(f"{log_prompt} - exit-code: {container_exit_code}") if container_exit_code: # 1-fatal message issued # 2-Error message issued logger.error(f"{log_prompt} - Finished, errors found") output = container_log exit_code = FAIL else: logger.info(f"{log_prompt} - Successfully finished") # Keeping container if needed or remove it if keep_container: print(f"{log_prompt} - container name {container_name}") container.commit(repository=container_name.lower(), tag='pwsh_test') else: try: container.remove(force=True) except docker.errors.NotFound as e: logger.critical(f"{log_prompt} - Unable to delete container - {e}") except (docker.errors.ImageNotFound, docker.errors.APIError, requests.exceptions.ReadTimeout) as e: logger.critical(f"{log_prompt} - Unable to run powershell test - {e}") exit_code = RERUN return exit_code, output def _get_commands_list(self, script_obj: dict): """ Get all commands from yml file of the pack Args: script_obj(dict): the script section of the yml file. Returns: list: list of all commands """ commands_list = [] try: commands_obj = script_obj.get('commands', {}) for command in commands_obj: commands_list.append(command.get('name', '')) except Exception: logger.debug("Failed getting the commands from the yml file") return commands_list	[ 1, 396, 6850, 29928, 3017, 9741, 13, 5215, 3509, 13, 5215, 6608, 1982, 13, 5215, 12183, 13, 5215, 2897, 13, 5215, 7481, 13, 5215, 9637, 1627, 13, 3166, 19229, 1053, 3139, 29892, 360, 919, 29892, 2391, 29892, 28379, 29892, 12603, 552, 13, 13, 5215, 10346, 13, 5215, 10346, 29889, 12523, 13, 5215, 10346, 29889, 9794, 29889, 1285, 475, 414, 13, 5215, 6315, 13, 5215, 7274, 29889, 11739, 29879, 13, 5215, 3142, 1982, 29941, 29889, 11739, 29879, 13, 3166, 4870, 6751, 29889, 3259, 1053, 6088, 13, 3166, 28678, 4352, 29889, 2084, 1982, 1053, 405, 11787, 3040, 29892, 10802, 13, 13, 3166, 1261, 5137, 29918, 15348, 29889, 26381, 29889, 9435, 29889, 3075, 1934, 1053, 313, 1177, 4330, 14345, 8098, 29903, 29918, 9464, 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giggleliu/tba/lattice/latticelib.py	Lynn-015/Test_01	2	63116	#!/usr/bin/python #--coding:utf-8-- #By <NAME> from numpy import * from lattice import Lattice from bzone import BZone from group import C6vGroup,C4vGroup,C3vGroup __all__=['Honeycomb_Lattice','Square_Lattice','Triangular_Lattice','Chain','construct_lattice','resize_lattice'] class Honeycomb_Lattice(Lattice): ''' HoneyComb Lattice class. Construct ---------------- Honeycomb_Lattice(N,form=1.) form: The form of lattice. `1` -> traditional one with 0 point at a vertex, using C3v group. `2` -> the C6v form with 0 point at the center of hexagon, using C6v group. ''' def __init__(self,N,form=1): if form==1: catoms=[(0.,0.),(0.5,sqrt(3.)/6)] pg=C3vGroup() elif form==2: catoms=array[(0.,1./sqrt(3.)),(0.5,sqrt(3.)/6)] pg=C6vGroup() else: raise ValueError('Form %s not defined.'%form) super(Honeycomb_Lattice,self).__init__(name='honeycomb',a=array([(1.,0),(0.5,sqrt(3.)/2)]),N=N,catoms=catoms) self.usegroup(pg) @property def kspace(self): ''' Get the <KSpace> instance. ''' ks=super(Honeycomb_Lattice,self).kspace M0=ks.b[1]/2.0 K0=(ks.b[0]+2ks.b[1])/3.0 c6vg=C6vGroup() M=[] K=[] for i in xrange(6): M.append(c6vg.actonK(M0,i)) K.append(c6vg.actonK(K0,i)) ks.special_points['M']=M ks.special_points['K']=K ks.usegroup(c6vg) return ks class Square_Lattice(Lattice): ''' Square Lattice, using C4v Group. Construct ---------------- Square_Lattice(N,catoms=[(0.,0.)]) ''' def __init__(self,N,catoms=[(0.,0.)]): a=array([(1.,0),(0.,1.)]) super(Square_Lattice,self).__init__(N=N,a=a,catoms=catoms,name='square') c4vg=C4vGroup() self.usegroup(c4vg) @property def kspace(self): ''' Get the <KSpace> instance. ''' ks=super(Square_Lattice,self).kspace M0=ks.b[1]/2.0 K0=(ks.b[0]+ks.b[1])/2.0 c4vg=C4vGroup() M=[] K=[] for i in xrange(4): M.append(c4vg.actonK(M0,i)) K.append(c4vg.actonK(K0,i)) ks.special_points['M']=M ks.special_points['K']=K ks.usegroup(c4vg) return ks class Triangular_Lattice(Lattice): ''' Triangular Lattice, using C6v Group. Construct ---------------- Triangular_Lattice(N,catoms=[(0.,0.)]) ''' def __init__(self,N,catoms=[(0.,0.)]): '''Basic information of Triangular Lattice''' a=array([(1.,0),(0.5,sqrt(3.)/2)]) super(Triangular_Lattice,self).__init__(a=a,catoms=catoms,name='triangular',N=N) c6vg=C6vGroup() self.usegroup(c6vg) @property def kspace(self): ''' Get the <KSpace> instance. ''' ks=super(Triangular_Lattice,self).kspace M0=ks.b[1]/2.0 K0=(ks.b[0]+2ks.b[1])/3.0 c6vg=C6vGroup() M=[] K=[] for i in xrange(6): M.append(c6vg.actonK(M0,i)) K.append(c6vg.actonK(K0,i)) ks.special_points['M']=M ks.special_points['K']=K ks.usegroup(c6vg) return ks class Chain(Lattice): ''' Lattice of Chain. Construct ---------------- Chain(N,a=(1.),catoms=[(0.,0.)]) ''' def __init__(self,N,a=(1.),catoms=[(0.)]): ''' N: Number of cells, integer. a: Lattice vector, 1D array. catoms: Atom positions in a unit cell. ''' super(Chain,self).__init__(a=[a],N=[N],name='chain',catoms=catoms) @property def kspace(self): '''The <KSpace> instance correspond to a chain.''' a=self.a[0] b=2pia/a.dot(a) ks=KSpace(N=self.N,b=b) ks.special_points['K']=array([-b/2.,b/2.]) return ks def construct_lattice(N,lattice_shape='',a=None,catoms=None,args={}): ''' Uniform construct method for lattice. N: The size of lattice. lattice_shape: The shape of lattice. * '' -> the anonymous lattice. * 'square' -> square lattice. * 'honeycomb' -> honeycomb lattice. * 'triangular' -> triangular lattice. * 'chain' -> a chain. a: The unit vector. catoms: The atoms in a unit cell. args: Other arguments, * `form` -> the form used in constructing honeycomb lattice. ''' if lattice_shape=='': assert(a is not None) if catoms is None: catoms=zeros(shape(a)[-1]) return Lattice(name='anonymous',N=N,a=a,catoms=catoms) elif lattice_shape=='honeycomb': return Honeycomb_Lattice(N=N,form=args.get('form',1)) elif lattice_shape=='square': if catoms is None: catoms=zeros([1,2]) return Square_Lattice(N=N,catoms=catoms) elif lattice_shape=='triangular': if catoms is None: catoms=zeros([1,2]) return Triangular_Lattice(N=N,catoms=catoms) elif lattice_shape=='chain': if a is None: a=[1.] if catoms is None: catoms=zeros([1,1]) if ndim(N)==1: N=N[0] return Chain(N=N,catoms=catoms) def resize_lattice(lattice,N): ''' Resize the lattice to specific size. lattice: The target lattice. N: 1D - array, the size of new lattice. 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teste.py	EduardoFockinkSilva/python-exercises	0	164734	<gh_stars>0 #test print('==================================') print(' this is just a test ') print('==================================') num = int(input('insert a number: ')) print((num * 5) % 3)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 1688, 13, 13, 2158, 877, 9166, 9166, 1360, 1495, 13, 2158, 877, 4706, 445, 338, 925, 263, 1243, 539, 25710, 13, 2158, 877, 9166, 9166, 1360, 1495, 13, 13, 1949, 353, 938, 29898, 2080, 877, 7851, 263, 1353, 29901, 525, 876, 13, 13, 2158, 3552, 1949, 334, 29871, 29945, 29897, 1273, 29871, 29941, 29897, 13, 13, 2 ]
src/bbapp/migrations/0004_auto_20190303_1557.py	levinsamuel/baseballdb	0	84901	<reponame>levinsamuel/baseballdb # Generated by Django 2.1.7 on 2019-03-03 23:57 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('bbapp', '0003_fielding'), ] operations = [ migrations.AlterField( model_name='batting', name='teamID', field=models.CharField(max_length=5), ), ]	[ 1, 529, 276, 1112, 420, 29958, 2608, 1144, 314, 2491, 29914, 3188, 5521, 430, 29890, 13, 29937, 3251, 630, 491, 15337, 29871, 29906, 29889, 29896, 29889, 29955, 373, 29871, 29906, 29900, 29896, 29929, 29899, 29900, 29941, 29899, 29900, 29941, 29871, 29906, 29941, 29901, 29945, 29955, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 1327, 932, 742, 525, 29900, 29900, 29900, 29941, 29918, 2671, 292, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 29890, 23980, 742, 13, 9651, 1024, 2433, 14318, 1367, 742, 13, 9651, 1746, 29922, 9794, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29945, 511, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
tests/test_wps_dummy.py	f-PLT/emu	3	14210	from pywps import Service from pywps.tests import assert_response_success from .common import client_for, get_output from emu.processes.wps_dummy import Dummy def test_wps_dummy(): client = client_for(Service(processes=[Dummy()])) datainputs = "input1=10;input2=2" resp = client.get( service='WPS', request='Execute', version='1.0.0', identifier='dummyprocess', datainputs=datainputs) assert_response_success(resp) assert get_output(resp.xml) == {'output1': '11', 'output2': '1'}	[ 1, 515, 282, 5693, 567, 1053, 6692, 13, 3166, 282, 5693, 567, 29889, 21150, 1053, 4974, 29918, 5327, 29918, 8698, 13, 13, 3166, 869, 9435, 1053, 3132, 29918, 1454, 29892, 679, 29918, 4905, 13, 3166, 953, 29884, 29889, 5014, 267, 29889, 29893, 567, 29918, 29881, 11770, 1053, 360, 11770, 13, 13, 13, 1753, 1243, 29918, 29893, 567, 29918, 29881, 11770, 7295, 13, 1678, 3132, 353, 3132, 29918, 1454, 29898, 3170, 29898, 5014, 267, 11759, 29928, 11770, 580, 12622, 13, 1678, 1418, 475, 649, 29879, 353, 376, 2080, 29896, 29922, 29896, 29900, 29936, 2080, 29906, 29922, 29906, 29908, 13, 1678, 4613, 353, 3132, 29889, 657, 29898, 13, 4706, 2669, 2433, 29956, 7024, 742, 2009, 2433, 12296, 742, 1873, 2433, 29896, 29889, 29900, 29889, 29900, 742, 13, 4706, 15882, 2433, 29881, 11770, 5014, 742, 13, 4706, 1418, 475, 649, 29879, 29922, 4130, 475, 649, 29879, 29897, 13, 1678, 4974, 29918, 5327, 29918, 8698, 29898, 13713, 29897, 13, 1678, 4974, 679, 29918, 4905, 29898, 13713, 29889, 3134, 29897, 1275, 11117, 4905, 29896, 2396, 525, 29896, 29896, 742, 525, 4905, 29906, 2396, 525, 29896, 10827, 13, 2 ]
data/split_data_num.py	xiangyue9607/QVE	4	148648	import json import random from argparse import ArgumentParser from numpy.random import default_rng parser = ArgumentParser() parser.add_argument("--in_file", type=str, default="data/NewsQA.train.json",) parser.add_argument("--out_file_dev", type=str, default="dataNewsQA.sample.dev.json") parser.add_argument("--out_file_train", type=str, default="data/NewsQA.sample.train.json") parser.add_argument("--num", type=int, default=1000, required=False) parser.add_argument("--seed", type=int, default=42, required=False) args = parser.parse_args() def subsample_dataset_random(data_json, sample_num=1000, seed=55): total = 0 context_num=0 id_lists=[] for paras in data_json['data']: for para in paras['paragraphs']: context_num+=1 qa_num = len(para['qas']) id_lists+=[qa['id'] for qa in para['qas']] total += qa_num print('Total QA Num: %d, Total Context: %d' % (total,context_num)) random.seed(seed) rng = default_rng() sampled_list = list(rng.choice(id_lists, size=sample_num,replace=False)) new_passages_dev = [] new_passages_train=[] for passages in data_json['data']: new_paras_dev = [] new_paras_train = [] for para in passages['paragraphs']: context = para['context'] new_qas_dev = [] new_qas_train = [] for qa in para['qas']: if qa['id'] in sampled_list: new_qas_dev.append(qa) else: new_qas_train.append(qa) if len(new_qas_dev) > 0: new_paras_dev.append({'context': context, 'qas': new_qas_dev}) if len(new_qas_train) > 0: new_paras_train.append({'context': context, 'qas': new_qas_train}) if len(new_paras_dev) > 0: new_passages_dev.append({'title': passages['title'], 'paragraphs': new_paras_dev}) if len(new_paras_train) > 0: new_passages_train.append({'title': passages['title'], 'paragraphs': new_paras_train}) dev_data_json = {'data': new_passages_dev, 'version': data_json['version']} train_data_json = {'data': new_passages_train, 'version': data_json['version']} total = 0 context_num=0 for paras in dev_data_json['data']: for para in paras['paragraphs']: context_num+=1 qa_num = len(para['qas']) id_lists+=[qa['id'] for qa in para['qas']] total += qa_num print('Sample Dev QA Num: %d, Total Context: %d' % (total,context_num)) total = 0 context_num = 0 for paras in train_data_json['data']: for para in paras['paragraphs']: context_num += 1 qa_num = len(para['qas']) id_lists += [qa['id'] for qa in para['qas']] total += qa_num print('Sample Train QA Num: %d, Total Context: %d' % (total, context_num)) return train_data_json,dev_data_json def main(args): dataset = json.load(open(args.in_file, 'r')) train_data_json,dev_data_json=subsample_dataset_random(dataset, args.num, args.seed) json.dump(train_data_json, open(args.out_file_train, 'w')) json.dump(dev_data_json, open(args.out_file_dev, 'w')) if __name__ == '__main__': args = parser.parse_args() random.seed(args.seed) main(args)	[ 1, 1053, 4390, 30004, 13, 5215, 4036, 30004, 13, 3166, 1852, 5510, 1053, 23125, 11726, 30004, 13, 3166, 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code/ch14/chord.py	raionik/Py4Bio	66	65754	<filename>code/ch14/chord.py from bokeh.charts import output_file, Chord from bokeh.io import show import pandas as pd data = pd.read_csv('../../samples/test3.csv') chord_from_df = Chord(data, source='name_x', target='name_y', value='value') output_file('chord.html') show(chord_from_df)	[ 1, 529, 9507, 29958, 401, 29914, 305, 29896, 29946, 29914, 305, 536, 29889, 2272, 13, 3166, 1045, 446, 29882, 29889, 18366, 1053, 1962, 29918, 1445, 29892, 678, 536, 13, 3166, 1045, 446, 29882, 29889, 601, 1053, 1510, 13, 5215, 11701, 408, 10518, 13, 1272, 353, 10518, 29889, 949, 29918, 7638, 877, 21546, 27736, 29914, 1688, 29941, 29889, 7638, 1495, 13, 305, 536, 29918, 3166, 29918, 2176, 353, 678, 536, 29898, 1272, 29892, 2752, 2433, 978, 29918, 29916, 742, 3646, 2433, 978, 29918, 29891, 742, 13, 462, 418, 995, 2433, 1767, 1495, 13, 4905, 29918, 1445, 877, 305, 536, 29889, 1420, 1495, 13, 4294, 29898, 305, 536, 29918, 3166, 29918, 2176, 29897, 13, 2 ]
var/spack/repos/builtin/packages/perl-ipc-run/package.py	adrianjhpc/spack	2	8105	<reponame>adrianjhpc/spack # Copyright 2013-2019 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class PerlIpcRun(PerlPackage): """IPC::Run allows you to run and interact with child processes using files, pipes, and pseudo-ttys. Both system()-style and scripted usages are supported and may be mixed. Likewise, functional and OO API styles are both supported and may be mixed.""" homepage = "https://metacpan.org/pod/IPC::Run" url = "https://cpan.metacpan.org/authors/id/T/TO/TODDR/IPC-Run-20180523.0.tar.gz" version('20180523.0', sha256='3850d7edf8a4671391c6e99bb770698e1c45da55b323b31c76310913349b6c2f') depends_on('perl-io-tty', type=('build', 'run')) depends_on('perl-readonly', type='build')	[ 1, 529, 276, 1112, 420, 29958, 328, 6392, 29926, 29882, 6739, 29914, 1028, 547, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29896, 29941, 29899, 29906, 29900, 29896, 29929, 19520, 22469, 5514, 3086, 14223, 29892, 365, 12182, 322, 916, 13, 29937, 1706, 547, 8010, 10682, 414, 29889, 2823, 278, 2246, 29899, 5563, 315, 4590, 29979, 22789, 3912, 934, 363, 4902, 29889, 13, 29937, 13, 29937, 10937, 29928, 29990, 29899, 29931, 293, 1947, 29899, 12889, 29901, 313, 17396, 1829, 29899, 29906, 29889, 29900, 6323, 341, 1806, 29897, 13, 13, 3166, 805, 547, 1053, 334, 13, 13, 13, 1990, 23907, 29902, 6739, 6558, 29898, 5894, 29880, 14459, 1125, 13, 1678, 9995, 5690, 29907, 1057, 6558, 6511, 366, 304, 1065, 322, 16254, 411, 2278, 10174, 773, 13, 1678, 2066, 29892, 8450, 267, 29892, 322, 17381, 29899, 698, 952, 29889, 9134, 1788, 580, 29899, 3293, 322, 2471, 287, 502, 1179, 526, 13, 1678, 6969, 322, 1122, 367, 12849, 29889, 8502, 3538, 29892, 13303, 322, 438, 29949, 3450, 11949, 526, 1716, 13, 1678, 6969, 322, 1122, 367, 12849, 1213, 15945, 13, 13, 1678, 3271, 3488, 353, 376, 991, 597, 2527, 562, 8357, 29889, 990, 29914, 15334, 29914, 5690, 29907, 1057, 6558, 29908, 13, 1678, 3142, 418, 353, 376, 991, 597, 6814, 273, 29889, 2527, 562, 8357, 29889, 990, 29914, 5150, 943, 29914, 333, 29914, 29911, 29914, 4986, 29914, 4986, 7858, 29934, 29914, 5690, 29907, 29899, 6558, 29899, 29906, 29900, 29896, 29947, 29900, 29945, 29906, 29941, 29889, 29900, 29889, 12637, 29889, 18828, 29908, 13, 13, 1678, 1873, 877, 29906, 29900, 29896, 29947, 29900, 29945, 29906, 29941, 29889, 29900, 742, 528, 29874, 29906, 29945, 29953, 2433, 29941, 29947, 29945, 29900, 29881, 29955, 287, 29888, 29947, 29874, 29946, 29953, 29955, 29896, 29941, 29929, 29896, 29883, 29953, 29872, 29929, 29929, 1327, 29955, 29955, 29900, 29953, 29929, 29947, 29872, 29896, 29883, 29946, 29945, 1388, 29945, 29945, 29890, 29941, 29906, 29941, 29890, 29941, 29896, 29883, 29955, 29953, 29941, 29896, 29900, 29929, 29896, 29941, 29941, 29946, 29929, 29890, 29953, 29883, 29906, 29888, 1495, 13, 13, 1678, 7111, 29918, 265, 877, 22032, 29899, 601, 29899, 4349, 742, 1134, 29922, 877, 4282, 742, 525, 3389, 8785, 13, 1678, 7111, 29918, 265, 877, 22032, 29899, 949, 6194, 742, 1134, 2433, 4282, 1495, 13, 2 ]
testing/logging/test_formatter.py	christian-steinmeyer/pytest	4	24001	import logging from typing import Any from _pytest._io import TerminalWriter from _pytest.logging import ColoredLevelFormatter def test_coloredlogformatter() -> None: logfmt = "%(filename)-25s %(lineno)4d %(levelname)-8s %(message)s" record = logging.LogRecord( name="dummy", level=logging.INFO, pathname="dummypath", lineno=10, msg="Test Message", args=(), exc_info=None, ) class ColorConfig: class option: pass tw = TerminalWriter() tw.hasmarkup = True formatter = ColoredLevelFormatter(tw, logfmt) output = formatter.format(record) assert output == ( "dummypath 10 \x1b[32mINFO \x1b[0m Test Message" ) tw.hasmarkup = False formatter = ColoredLevelFormatter(tw, logfmt) output = formatter.format(record) assert output == ("dummypath 10 INFO Test Message") def test_multiline_message() -> None: from _pytest.logging import PercentStyleMultiline logfmt = "%(filename)-25s %(lineno)4d %(levelname)-8s %(message)s" record: Any = logging.LogRecord( name="dummy", level=logging.INFO, pathname="dummypath", lineno=10, msg="Test Message line1\nline2", args=(), exc_info=None, ) # this is called by logging.Formatter.format record.message = record.getMessage() ai_on_style = PercentStyleMultiline(logfmt, True) output = ai_on_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\n" " line2" ) ai_off_style = PercentStyleMultiline(logfmt, False) output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) ai_none_style = PercentStyleMultiline(logfmt, None) output = ai_none_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) record.auto_indent = False output = ai_on_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) record.auto_indent = True output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\n" " line2" ) record.auto_indent = "False" output = ai_on_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) record.auto_indent = "True" output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\n" " line2" ) # bad string values default to False record.auto_indent = "junk" output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) # anything other than string or int will default to False record.auto_indent = dict() output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) record.auto_indent = "5" output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\n line2" ) record.auto_indent = 5 output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\n line2" ) def test_colored_short_level() -> None: logfmt = "%(levelname).1s %(message)s" record = logging.LogRecord( name="dummy", level=logging.INFO, pathname="dummypath", lineno=10, msg="Test Message", args=(), exc_info=None, ) class ColorConfig: class option: pass tw = TerminalWriter() tw.hasmarkup = True formatter = ColoredLevelFormatter(tw, logfmt) output = formatter.format(record) # the I (of INFO) is colored assert output == ("\x1b[32mI\x1b[0m Test Message")	[ 1, 1053, 12183, 13, 3166, 19229, 1053, 3139, 13, 13, 3166, 903, 2272, 1688, 3032, 601, 1053, 29175, 10507, 13, 3166, 903, 2272, 1688, 29889, 21027, 1053, 1530, 4395, 10108, 18522, 13, 13, 13, 1753, 1243, 29918, 2780, 287, 1188, 689, 2620, 580, 1599, 6213, 29901, 13, 1678, 1480, 23479, 353, 11860, 29898, 9507, 6817, 29906, 29945, 29879, 1273, 29898, 1915, 8154, 29897, 29946, 29881, 1273, 29898, 5563, 978, 6817, 29947, 29879, 1273, 29898, 4906, 29897, 29879, 29908, 13, 13, 1678, 2407, 353, 12183, 29889, 3403, 9182, 29898, 13, 4706, 1024, 543, 29881, 11770, 613, 13, 4706, 3233, 29922, 21027, 29889, 11690, 29892, 13, 4706, 2224, 978, 543, 29881, 11770, 2084, 613, 13, 4706, 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waffle/defaults.py	PrimarySite/django-waffle	0	199550	from __future__ import unicode_literals COOKIE = 'dwf_%s' TEST_COOKIE = 'dwft_%s' SECURE = True MAX_AGE = 2592000 # 1 month in seconds CACHE_PREFIX = 'waffle:' CACHE_NAME = 'default' FLAG_CACHE_KEY = 'flag:%s' FLAG_USERS_CACHE_KEY = 'flag:%s:users' FLAG_GROUPS_CACHE_KEY = 'flag:%s:groups' ALL_FLAGS_CACHE_KEY = 'flags:all' SAMPLE_CACHE_KEY = 'sample:%s' ALL_SAMPLES_CACHE_KEY = 'samples:all' SWITCH_CACHE_KEY = 'switch:%s' ALL_SWITCHES_CACHE_KEY = 'switches:all' FLAG_DEFAULT = False SAMPLE_DEFAULT = False SWITCH_DEFAULT = False READ_FROM_WRITE_DB = False CREATE_MISSING_FLAGS = False CREATE_MISSING_SAMPLES = False CREATE_MISSING_SWITCHES = False LOG_MISSING_FLAGS = None LOG_MISSING_SAMPLES = None LOG_MISSING_SWITCHES = None OVERRIDE = False UNIQUE_FLAG_NAME = True	[ 1, 515, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 13, 3217, 8949, 8673, 353, 525, 28012, 29888, 29918, 29995, 29879, 29915, 13, 18267, 29918, 3217, 8949, 8673, 353, 525, 28012, 615, 29918, 29995, 29879, 29915, 13, 1660, 29907, 11499, 353, 5852, 13, 12648, 29918, 10461, 353, 29871, 29906, 29945, 29929, 29906, 29900, 29900, 29900, 29871, 396, 29871, 29896, 4098, 297, 6923, 13, 13, 29907, 2477, 9606, 29918, 15094, 25634, 353, 525, 2766, 600, 280, 11283, 13, 29907, 2477, 9606, 29918, 5813, 353, 525, 4381, 29915, 13, 26516, 29918, 29907, 2477, 9606, 29918, 10818, 353, 525, 15581, 16664, 29879, 29915, 13, 26516, 29918, 11889, 29903, 29918, 29907, 2477, 9606, 29918, 10818, 353, 525, 15581, 16664, 29879, 29901, 7193, 29915, 13, 26516, 29918, 26284, 29903, 29918, 29907, 2477, 9606, 29918, 10818, 353, 525, 15581, 16664, 29879, 29901, 13155, 29915, 13, 9818, 29918, 18823, 10749, 29918, 29907, 2477, 9606, 29918, 10818, 353, 525, 15764, 29901, 497, 29915, 13, 8132, 3580, 1307, 29918, 29907, 2477, 9606, 29918, 10818, 353, 525, 11249, 16664, 29879, 29915, 13, 9818, 29918, 8132, 3580, 17101, 29918, 29907, 2477, 9606, 29918, 10818, 353, 525, 27736, 29901, 497, 29915, 13, 23066, 1806, 3210, 29918, 29907, 2477, 9606, 29918, 10818, 353, 525, 15123, 16664, 29879, 29915, 13, 9818, 29918, 23066, 1806, 3210, 2890, 29918, 29907, 2477, 9606, 29918, 10818, 353, 525, 15123, 267, 29901, 497, 29915, 13, 13, 26516, 29918, 23397, 353, 7700, 13, 8132, 3580, 1307, 29918, 23397, 353, 7700, 13, 23066, 1806, 3210, 29918, 23397, 353, 7700, 13, 13, 16310, 29918, 21482, 29918, 16365, 29918, 4051, 353, 7700, 13, 13, 27045, 29918, 10403, 1799, 4214, 29918, 18823, 10749, 353, 7700, 13, 27045, 29918, 10403, 1799, 4214, 29918, 8132, 3580, 17101, 353, 7700, 13, 27045, 29918, 10403, 1799, 4214, 29918, 23066, 1806, 3210, 2890, 353, 7700, 13, 13, 14480, 29918, 10403, 1799, 4214, 29918, 18823, 10749, 353, 6213, 13, 14480, 29918, 10403, 1799, 4214, 29918, 8132, 3580, 17101, 353, 6213, 13, 14480, 29918, 10403, 1799, 4214, 29918, 23066, 1806, 3210, 2890, 353, 6213, 13, 13, 29949, 5348, 29934, 22027, 353, 7700, 13, 13, 3904, 29902, 11144, 29918, 26516, 29918, 5813, 353, 5852, 13, 2 ]
magda/pipeline/parallel/group/group.py	p-mielniczuk/magda	0	1606451	<filename>magda/pipeline/parallel/group/group.py from __future__ import annotations from typing import List from magda.module.module import Module from magda.pipeline.parallel.group.runtime import GroupRuntime class Group: Runtime = GroupRuntime def __init__( self, name: str, , replicas: int = 1, state_type=None, options, ): self.name = name self.replicas = replicas self.options = options self._state_type = state_type def set_replicas(self, replicas: int) -> Group: self.replicas = replicas return self def set_options(self, *options) -> Group: self.options = options return self @property def state_type(self): return self._state_type @state_type.setter def state_type(self, state_type): self._state_type = state_type def build( self, modules: List[Module], dependent_modules: List[Module], dependent_modules_nonregular: List[Module], ) -> Group.Runtime: return self.Runtime( name=self.name, modules=modules, dependent_modules=dependent_modules, dependent_modules_nonregular=dependent_modules_nonregular, replicas=self.replicas, state_type=self.state_type, options=self.options, )	[ 1, 529, 9507, 29958, 11082, 1388, 29914, 13096, 5570, 29914, 23482, 29914, 2972, 29914, 2972, 29889, 2272, 13, 3166, 4770, 29888, 9130, 1649, 1053, 25495, 13, 13, 3166, 19229, 1053, 2391, 13, 13, 3166, 2320, 1388, 29889, 5453, 29889, 5453, 1053, 15591, 13, 3166, 2320, 1388, 29889, 13096, 5570, 29889, 23482, 29889, 2972, 29889, 15634, 1053, 6431, 7944, 13, 13, 13, 1990, 6431, 29901, 13, 1678, 24875, 353, 6431, 7944, 13, 13, 1678, 822, 4770, 2344, 12035, 13, 4706, 1583, 29892, 1024, 29901, 851, 29892, 13, 4706, 334, 29892, 13, 4706, 1634, 506, 294, 29901, 938, 353, 29871, 29896, 29892, 13, 4706, 2106, 29918, 1853, 29922, 8516, 29892, 13, 4706, 3579, 6768, 29892, 13, 268, 1125, 13, 4706, 1583, 29889, 978, 353, 1024, 13, 4706, 1583, 29889, 3445, 506, 294, 353, 1634, 506, 294, 13, 4706, 1583, 29889, 6768, 353, 3987, 13, 4706, 1583, 3032, 3859, 29918, 1853, 353, 2106, 29918, 1853, 13, 13, 1678, 822, 731, 29918, 3445, 506, 294, 29898, 1311, 29892, 1634, 506, 294, 29901, 938, 29897, 1599, 6431, 29901, 13, 4706, 1583, 29889, 3445, 506, 294, 353, 1634, 506, 294, 13, 4706, 736, 1583, 13, 13, 1678, 822, 731, 29918, 6768, 29898, 1311, 29892, 3579, 6768, 29897, 1599, 6431, 29901, 13, 4706, 1583, 29889, 6768, 353, 3987, 13, 4706, 736, 1583, 13, 13, 1678, 732, 6799, 13, 1678, 822, 2106, 29918, 1853, 29898, 1311, 1125, 13, 4706, 736, 1583, 3032, 3859, 29918, 1853, 13, 13, 1678, 732, 3859, 29918, 1853, 29889, 842, 357, 13, 1678, 822, 2106, 29918, 1853, 29898, 1311, 29892, 2106, 29918, 1853, 1125, 13, 4706, 1583, 3032, 3859, 29918, 1853, 353, 2106, 29918, 1853, 13, 13, 1678, 822, 2048, 29898, 13, 4706, 1583, 29892, 13, 4706, 10585, 29901, 2391, 29961, 7355, 1402, 13, 4706, 14278, 29918, 7576, 29901, 2391, 29961, 7355, 1402, 13, 4706, 14278, 29918, 7576, 29918, 5464, 15227, 29901, 2391, 29961, 7355, 1402, 13, 1678, 1723, 1599, 6431, 29889, 7944, 29901, 13, 4706, 736, 1583, 29889, 7944, 29898, 13, 9651, 1024, 29922, 1311, 29889, 978, 29892, 13, 9651, 10585, 29922, 7576, 29892, 13, 9651, 14278, 29918, 7576, 29922, 18980, 29918, 7576, 29892, 13, 9651, 14278, 29918, 7576, 29918, 5464, 15227, 29922, 18980, 29918, 7576, 29918, 5464, 15227, 29892, 13, 9651, 1634, 506, 294, 29922, 1311, 29889, 3445, 506, 294, 29892, 13, 9651, 2106, 29918, 1853, 29922, 1311, 29889, 3859, 29918, 1853, 29892, 13, 9651, 3987, 29922, 1311, 29889, 6768, 29892, 13, 4706, 1723, 13, 2 ]
Code/GMM Test Scripts/yellowTest.py	Praveen1098/Gaussian_Mixture_Modelling	0	24954	<gh_stars>0 #!/usr/bin/env python3 # -- coding: utf-8 -- import cv2 import numpy as np from matplotlib import pyplot as plt import os import math def gaussian(x, mu, sig): return ((1/(sigmath.sqrt(2math.pi)))np.exp(-np.power(x - mu, 2.) / (2 np.power(sig, 2.)))) x=list(range(0, 256)) g1=gaussian(x,np.array([239.82]), np.array([2.53])) g2=gaussian(x,np.array([221.93]), np.array([23.83])) r2=gaussian(x, np.array([217.47]),np.array([29.43])) r1=gaussian(x, np.array([230.01]),np.array([3.675])) b1=gaussian(x, np.array([103.60]),np.array([18.64])) b2=gaussian(x, np.array([164.96]),np.array([27.56])) path=os.getcwd()+"/"+"detectbuoy.avi" c=cv2.VideoCapture(path) fourcc = cv2.VideoWriter_fourcc(*'MP4V') out = cv2.VideoWriter('yellowDetection.mp4', fourcc, 15, (640, 480)) while (True): ret,image=c.read() if ret==False: break b,g,r=cv2.split(image) if ret == True: img_out3=np.zeros(g.shape, dtype = np.uint8) for index, v in np.ndenumerate(g): av=int((int(v)+int(r[index]))/2) if ( g1[v]>0.08) and (r1[r[index]] >0.04) and (b1[b[index]]>0.01 ): img_out3[index]=255 else: img_out3[index]=0 ret, threshold3 = cv2.threshold(img_out3, 240, 255, cv2.THRESH_BINARY) kernel3 = np.ones((2,2),np.uint8) dilation3 = cv2.dilate(threshold3,kernel3,iterations =8) contours3, _= cv2.findContours(dilation3, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) for contour in contours3: if cv2.contourArea(contour) > 50: (x,y),radius = cv2.minEnclosingCircle(contour) center = (int(x),int(y)) radius = int(radius) # print(radius) if radius > 12: cv2.circle(image,center,radius,(0,255,255),2) cv2.imshow("Threshold",dilation3) cv2.imshow('Yellow Ball Segmentation', image) cv2.imwrite('yellow.jpg', image) out.write(image) k = cv2.waitKey(1) & 0xff if k == 27: break # wait for ESC key to exit else: break c.release() out.release() cv2.destroyAllWindows()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 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configs/benchmarks/classification/imagenet/imagenet_per_1/r50_sz224_4xb64_head1_lr0_01_step_ep20.py	Westlake-AI/openmixup	10	108070	<gh_stars>1-10 _base_ = [ 'r50_sz224_4xb64_head1_lr0_1_step_ep20.py', ] # optimizer optimizer = dict(lr=0.01)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29918, 3188, 29918, 353, 518, 13, 1678, 525, 29878, 29945, 29900, 29918, 3616, 29906, 29906, 29946, 29918, 29946, 29916, 29890, 29953, 29946, 29918, 2813, 29896, 29918, 29212, 29900, 29918, 29896, 29918, 10568, 29918, 1022, 29906, 29900, 29889, 2272, 742, 13, 29962, 13, 13, 29937, 5994, 3950, 13, 20640, 3950, 353, 9657, 29898, 29212, 29922, 29900, 29889, 29900, 29896, 29897, 13, 2 ]
test/utilities/test_contains_none_values.py	elifesciences/profiles	2	1607954	import pytest from profiles.utilities import contains_none_values EMPTY_DICT = {} DICT_WITH_NONE_VALUE = {'test': None} DICT_WITH_NESTED_DICTS_WITHOUT_NONE_VALUE = {'test': {'test': {'test': 1}}} DICT_WITH_NESTED_DICTS_WITH_NONE_VALUE = {'test': {'test': {'test': 1}, 'this': {'test': None}} } DICT_WITH_NESTED_DICTS_WITH_NONE_VALUE_AND_FOLLOWING_VALUES = { 'test': {'test': {'test': 1}, 'this': {'test': {'test': 'this'}}, 'and': {'test': {'test': None}}, 'that': {'test': 2}} } DICT_WITH_NESTED_DICT_AND_LIST_WITH_NONE_VALUE = { 'test': {'test': [1, None, 3]}} LIST_WITH_NONE_VALUE = [1, '2', None, 4] LIST_WITHOUT_NONE_VALUE = [1, '2', '3', 4] LIST_WITH_NESTED_LISTS_WITH_NONE_VALUE_AND_FOLLOWING_VALUES = [ 1, [1, 2, 3], [1, 2, [1, 2, None] ], [1, 2, 3], 4] @pytest.mark.parametrize('test_data, expected_result', [ (EMPTY_DICT, False), (DICT_WITH_NONE_VALUE, True), (DICT_WITH_NESTED_DICTS_WITHOUT_NONE_VALUE, False), (DICT_WITH_NESTED_DICTS_WITH_NONE_VALUE, True), (DICT_WITH_NESTED_DICTS_WITH_NONE_VALUE_AND_FOLLOWING_VALUES, True), (DICT_WITH_NESTED_DICT_AND_LIST_WITH_NONE_VALUE, True), (LIST_WITH_NONE_VALUE, True), (LIST_WITHOUT_NONE_VALUE, False), (LIST_WITH_NESTED_LISTS_WITH_NONE_VALUE_AND_FOLLOWING_VALUES, True) ]) def test_contains_none_values(test_data, expected_result) -> None: assert contains_none_values(test_data) is expected_result	[ 1, 1053, 11451, 1688, 13, 13, 3166, 28723, 29889, 4422, 1907, 1053, 3743, 29918, 9290, 29918, 5975, 13, 13, 29923, 3580, 15631, 29918, 4571, 1783, 353, 6571, 13, 4571, 1783, 29918, 29956, 13054, 29918, 29940, 12413, 29918, 19143, 353, 11117, 1688, 2396, 6213, 29913, 13, 4571, 1783, 29918, 29956, 13054, 29918, 8186, 1254, 3352, 29918, 4571, 1783, 29903, 29918, 29956, 1806, 8187, 2692, 29918, 29940, 12413, 29918, 19143, 353, 11117, 1688, 2396, 11117, 1688, 2396, 11117, 1688, 2396, 29871, 29896, 12499, 13, 4571, 1783, 29918, 29956, 13054, 29918, 8186, 1254, 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mailmerge/smtp_dummy.py	Denzeldeveloper/Python-Auto-MailMerge-	0	29996	"""Dummy SMTP API.""" class SMTP_dummy(object): # pylint: disable=useless-object-inheritance # pylint: disable=invalid-name, no-self-use """Dummy SMTP API.""" # Class variables track member function calls for later checking. msg_from = None msg_to = None msg = None def login(self, login, password): """Do nothing.""" def sendmail(self, msg_from, msg_to, msg): """Remember the recipients.""" SMTP_dummy.msg_from = msg_from SMTP_dummy.msg_to = msg_to SMTP_dummy.msg = msg def close(self): """Do nothing.""" def clear(self): """Reset class variables.""" SMTP_dummy.msg_from = None SMTP_dummy.msg_to = [] SMTP_dummy.msg = None	[ 1, 9995, 29928, 11770, 13766, 3557, 3450, 1213, 15945, 13, 13, 13, 1990, 13766, 3557, 29918, 29881, 11770, 29898, 3318, 1125, 29871, 396, 282, 2904, 524, 29901, 11262, 29922, 375, 6393, 29899, 3318, 29899, 262, 27069, 749, 13, 1678, 396, 282, 2904, 524, 29901, 11262, 29922, 20965, 29899, 978, 29892, 694, 29899, 1311, 29899, 1509, 13, 1678, 9995, 29928, 11770, 13766, 3557, 3450, 1213, 15945, 13, 13, 1678, 396, 4134, 3651, 5702, 4509, 740, 5717, 363, 2678, 8454, 29889, 13, 1678, 10191, 29918, 3166, 353, 6213, 13, 1678, 10191, 29918, 517, 353, 6213, 13, 1678, 10191, 353, 6213, 13, 13, 1678, 822, 6464, 29898, 1311, 29892, 6464, 29892, 4800, 1125, 13, 4706, 9995, 6132, 3078, 1213, 15945, 13, 13, 1678, 822, 3638, 2549, 29898, 1311, 29892, 10191, 29918, 3166, 29892, 10191, 29918, 517, 29892, 10191, 1125, 13, 4706, 9995, 7301, 1096, 278, 23957, 10070, 1213, 15945, 13, 4706, 13766, 3557, 29918, 29881, 11770, 29889, 7645, 29918, 3166, 353, 10191, 29918, 3166, 13, 4706, 13766, 3557, 29918, 29881, 11770, 29889, 7645, 29918, 517, 353, 10191, 29918, 517, 13, 4706, 13766, 3557, 29918, 29881, 11770, 29889, 7645, 353, 10191, 13, 13, 1678, 822, 3802, 29898, 1311, 1125, 13, 4706, 9995, 6132, 3078, 1213, 15945, 13, 13, 1678, 822, 2821, 29898, 1311, 1125, 13, 4706, 9995, 27175, 770, 3651, 1213, 15945, 13, 4706, 13766, 3557, 29918, 29881, 11770, 29889, 7645, 29918, 3166, 353, 6213, 13, 4706, 13766, 3557, 29918, 29881, 11770, 29889, 7645, 29918, 517, 353, 5159, 13, 4706, 13766, 3557, 29918, 29881, 11770, 29889, 7645, 353, 6213, 13, 2 ]
phpme/binx/console.py	eghojansu/phpme	0	39822	import os, json, subprocess class Console(): """Run PHP job""" def get_interface_methods(namespace): try: output = Console.run_command('interface-methods', [namespace]) return json.loads(output) except Exception as e: return {} def get_class_methods(namespace): try: output = Console.run_command('class-methods', [namespace]) return json.loads(output) except Exception as e: return {} def get_classes(symbol): try: output = Console.run_command('classes', [symbol]) return json.loads(output) except Exception as e: return [] def git_config(config): try: return subprocess.check_output(['git', 'config', '--get', config]).decode('utf-8') except Exception as e: print('[Phpme]', 'error: ' + str(e)) def run_command(command, args): try: console = os.path.dirname(os.path.abspath(__file__)) + os.sep + 'console.php' output = subprocess.check_output(['php', '-f', console, command] + args).decode('utf-8') if output.startswith('error'): print('[Phpme]', output) else: return output except Exception as e: print('[Phpme]', 'error: ' + str(e))	[ 1, 1053, 2897, 29892, 4390, 29892, 1014, 5014, 13, 13, 13, 1990, 9405, 7295, 13, 1678, 9995, 6558, 5048, 4982, 15945, 29908, 13, 13, 1678, 822, 679, 29918, 13248, 29918, 23515, 29898, 22377, 1125, 13, 4706, 1018, 29901, 13, 9651, 1962, 353, 9405, 29889, 3389, 29918, 6519, 877, 13248, 29899, 23515, 742, 518, 22377, 2314, 13, 13, 9651, 736, 4390, 29889, 18132, 29898, 4905, 29897, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 736, 6571, 13, 13, 1678, 822, 679, 29918, 1990, 29918, 23515, 29898, 22377, 1125, 13, 4706, 1018, 29901, 13, 9651, 1962, 353, 9405, 29889, 3389, 29918, 6519, 877, 1990, 29899, 23515, 742, 518, 22377, 2314, 13, 13, 9651, 736, 4390, 29889, 18132, 29898, 4905, 29897, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 736, 6571, 13, 13, 1678, 822, 679, 29918, 13203, 29898, 18098, 1125, 13, 4706, 1018, 29901, 13, 9651, 1962, 353, 9405, 29889, 3389, 29918, 6519, 877, 13203, 742, 518, 18098, 2314, 13, 13, 9651, 736, 4390, 29889, 18132, 29898, 4905, 29897, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 736, 5159, 13, 13, 1678, 822, 6315, 29918, 2917, 29898, 2917, 1125, 13, 4706, 1018, 29901, 13, 9651, 736, 1014, 5014, 29889, 3198, 29918, 4905, 18959, 5559, 742, 525, 2917, 742, 525, 489, 657, 742, 2295, 14664, 13808, 877, 9420, 29899, 29947, 1495, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 1596, 877, 29961, 4819, 29886, 1004, 29962, 742, 525, 2704, 29901, 525, 718, 851, 29898, 29872, 876, 13, 13, 1678, 822, 1065, 29918, 6519, 29898, 6519, 29892, 6389, 1125, 13, 4706, 1018, 29901, 13, 9651, 2991, 353, 2897, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 370, 1028, 493, 22168, 1445, 1649, 876, 718, 2897, 29889, 19570, 718, 525, 11058, 29889, 1961, 29915, 13, 9651, 1962, 353, 1014, 5014, 29889, 3198, 29918, 4905, 18959, 1961, 742, 17411, 29888, 742, 2991, 29892, 1899, 29962, 718, 6389, 467, 13808, 877, 9420, 29899, 29947, 1495, 13, 13, 9651, 565, 1962, 29889, 27382, 2541, 877, 2704, 29374, 13, 18884, 1596, 877, 29961, 4819, 29886, 1004, 29962, 742, 1962, 29897, 13, 9651, 1683, 29901, 13, 18884, 736, 1962, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 1596, 877, 29961, 4819, 29886, 1004, 29962, 742, 525, 2704, 29901, 525, 718, 851, 29898, 29872, 876, 13, 2 ]
rllib/agents/dqn/learner_thread.py	firebolt55439/ray	3	74756	<gh_stars>1-10 import queue import threading from ray.rllib.evaluation.metrics import get_learner_stats from ray.rllib.policy.policy import LEARNER_STATS_KEY from ray.rllib.utils.framework import try_import_tf from ray.rllib.utils.timer import TimerStat from ray.rllib.utils.window_stat import WindowStat LEARNER_QUEUE_MAX_SIZE = 16 tf1, tf, tfv = try_import_tf() class LearnerThread(threading.Thread): """Background thread that updates the local model from replay data. The learner thread communicates with the main thread through Queues. This is needed since Ray operations can only be run on the main thread. In addition, moving heavyweight gradient ops session runs off the main thread improves overall throughput. """ def __init__(self, local_worker): threading.Thread.__init__(self) self.learner_queue_size = WindowStat("size", 50) self.local_worker = local_worker self.inqueue = queue.Queue(maxsize=LEARNER_QUEUE_MAX_SIZE) self.outqueue = queue.Queue() self.queue_timer = TimerStat() self.grad_timer = TimerStat() self.overall_timer = TimerStat() self.daemon = True self.weights_updated = False self.stopped = False self.stats = {} def run(self): # Switch on eager mode if configured. if self.local_worker.policy_config.get("framework") in ["tf2", "tfe"]: tf1.enable_eager_execution() while not self.stopped: self.step() def step(self): with self.overall_timer: with self.queue_timer: ra, replay = self.inqueue.get() if replay is not None: prio_dict = {} with self.grad_timer: grad_out = self.local_worker.learn_on_batch(replay) for pid, info in grad_out.items(): td_error = info.get( "td_error", info[LEARNER_STATS_KEY].get("td_error")) prio_dict[pid] = (replay.policy_batches[pid].data.get( "batch_indexes"), td_error) self.stats[pid] = get_learner_stats(info) self.grad_timer.push_units_processed(replay.count) self.outqueue.put((ra, prio_dict, replay.count)) self.learner_queue_size.push(self.inqueue.qsize()) self.weights_updated = True self.overall_timer.push_units_processed(replay and replay.count or 0)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 9521, 13, 5215, 3244, 292, 13, 13, 3166, 15570, 29889, 2096, 1982, 29889, 24219, 362, 29889, 2527, 10817, 1053, 679, 29918, 1945, 1089, 29918, 16202, 13, 3166, 15570, 29889, 2096, 1982, 29889, 22197, 29889, 22197, 1053, 11060, 1718, 13865, 29918, 17816, 29903, 29918, 10818, 13, 3166, 15570, 29889, 2096, 1982, 29889, 13239, 29889, 4468, 1053, 1018, 29918, 5215, 29918, 13264, 13, 3166, 15570, 29889, 2096, 1982, 29889, 13239, 29889, 20404, 1053, 29168, 9513, 13, 3166, 15570, 29889, 2096, 1982, 29889, 13239, 29889, 7165, 29918, 6112, 1053, 18379, 9513, 13, 13, 1307, 1718, 13865, 29918, 11144, 4462, 29918, 12648, 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leetcode/python/359_logger_rate_limiter.py	VVKot/leetcode-solutions	4	194667	class Logger: PRINT_INTERVAL = 10 def __init__(self): self.buckets = [-1 for _ in range(self.PRINT_INTERVAL)] self.sets = [set() for _ in range(self.PRINT_INTERVAL)] def shouldPrintMessage(self, timestamp: int, message: str) -> bool: bucket_index = timestamp % self.PRINT_INTERVAL if self.buckets[bucket_index] != timestamp: self.sets[bucket_index].clear() self.buckets[bucket_index] = timestamp for i, bucket_timestamp in enumerate(self.buckets): if timestamp - bucket_timestamp < self.PRINT_INTERVAL: if message in self.sets[i]: return False self.sets[bucket_index].add(message) return True	[ 1, 770, 28468, 29901, 13, 13, 1678, 12089, 10192, 29918, 23845, 8932, 353, 29871, 29896, 29900, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1583, 29889, 2423, 9737, 353, 21069, 29896, 363, 903, 297, 3464, 29898, 1311, 29889, 10593, 10192, 29918, 23845, 8932, 4638, 13, 4706, 1583, 29889, 7224, 353, 518, 842, 580, 363, 903, 297, 3464, 29898, 1311, 29889, 10593, 10192, 29918, 23845, 8932, 4638, 13, 13, 1678, 822, 881, 11816, 3728, 29898, 1311, 29892, 14334, 29901, 938, 29892, 2643, 29901, 851, 29897, 1599, 6120, 29901, 13, 4706, 20968, 29918, 2248, 353, 14334, 1273, 1583, 29889, 10593, 10192, 29918, 23845, 8932, 13, 4706, 565, 1583, 29889, 2423, 9737, 29961, 21454, 29918, 2248, 29962, 2804, 14334, 29901, 13, 9651, 1583, 29889, 7224, 29961, 21454, 29918, 2248, 1822, 8551, 580, 13, 9651, 1583, 29889, 2423, 9737, 29961, 21454, 29918, 2248, 29962, 353, 14334, 13, 4706, 363, 474, 29892, 20968, 29918, 16394, 297, 26985, 29898, 1311, 29889, 2423, 9737, 1125, 13, 9651, 565, 14334, 448, 20968, 29918, 16394, 529, 1583, 29889, 10593, 10192, 29918, 23845, 8932, 29901, 13, 18884, 565, 2643, 297, 1583, 29889, 7224, 29961, 29875, 5387, 13, 462, 1678, 736, 7700, 13, 4706, 1583, 29889, 7224, 29961, 21454, 29918, 2248, 1822, 1202, 29898, 4906, 29897, 13, 4706, 736, 5852, 13, 2 ]
super32assembler/super32assembler/preprocessor/asmdirectives.py	Projektstudium-Mikroprozessor/Super32	1	19108	""" Enum Assembler-Directives """ from enum import Enum, auto class AssemblerDirectives(Enum): START = auto() END = auto() ORG = auto() DEFINE = auto() @classmethod def to_string(cls): return "{START},{END},{ORG},{DEFINE}".format( START=cls.START.name, END=cls.END.name, ORG=cls.ORG.name, DEFINE=cls.DEFINE.name )	[ 1, 9995, 13, 16854, 4007, 1590, 1358, 29899, 17392, 3145, 13, 15945, 29908, 13, 13, 3166, 14115, 1053, 1174, 398, 29892, 4469, 13, 13, 13, 1990, 4007, 1590, 1358, 17392, 3145, 29898, 16854, 1125, 13, 1678, 6850, 8322, 353, 4469, 580, 13, 1678, 11056, 353, 4469, 580, 13, 1678, 6323, 29954, 353, 4469, 580, 13, 1678, 5012, 29943, 8895, 353, 4469, 580, 13, 13, 1678, 732, 1990, 5696, 13, 1678, 822, 304, 29918, 1807, 29898, 25932, 1125, 13, 4706, 736, 29850, 25826, 29087, 11794, 29087, 1955, 29954, 29087, 24405, 8895, 29913, 1642, 4830, 29898, 13, 9651, 6850, 8322, 29922, 25932, 29889, 25826, 29889, 978, 29892, 13, 9651, 11056, 29922, 25932, 29889, 11794, 29889, 978, 29892, 13, 9651, 6323, 29954, 29922, 25932, 29889, 1955, 29954, 29889, 978, 29892, 13, 9651, 5012, 29943, 8895, 29922, 25932, 29889, 24405, 8895, 29889, 978, 13, 4706, 1723, 13, 2 ]
FSPSFiles.py	AlexaVillaume/FSPS	1	91898	import os ''' Read an FSPS spec file ''' class specmodel(object): def __init__(self, attr, wave, flux): self.agegyr = round(10*attr[0] / 1e9, 3) self.mass = attr[1] self.lbol = attr[2] self.sfr = attr[3] self.wave = wave self.flux = flux class readspec(object): def __init__(self, fname): self.data = open(fname, "r") # Burn header while True: line = self.data.readline() if line[0] != '#': break cols = line.split() self.tstep = float(cols[0]) self.sdim = float(cols[1]) # Get wavelength line = self.data.readline() cols = line.split() self.wave = map(lambda col: float(col)1e-4, cols) def next(self): line = self.data.readline() if line == "": self.data.close() raise StopIteration() cols = line.split() attr = map(lambda col: float(col), cols) line = self.data.readline() cols = line.split() flux = map(lambda col: float(col), cols) return specmodel(attr, self.wave, flux) def __iter__(self): return self ''' For FSPS .cmd files ''' class readcmd: def __init__(self, fname): self.data = open(fname, 'r') # Read header to find the columns in the file while True: line = self.data.readline() line = line.lstrip() if line[0] == '#': header = line.replace('#', '') header = header.replace('mags', '') header = header.split() break self.header = header def next(self): line = self.data.readline() if line == "": self.data.close() raise StopIteration() # Read in and assign the attributes of the models cols = line.split() for i, name in enumerate(self.header): self.__dict__[name] = float(cols[i]) # Update the attributes with filter names and values with open('/Users/alexawork/FSPS_python/filters.txt', 'r') as names: for i, _filter in enumerate(names): _filter = _filter.rstrip() #print _filter, cols[i + len(self.header)] self.__dict__.update({_filter:float(cols[i + len(self.header)])}) return self.__dict__.copy() def __iter__(self): return self ''' For FSPS .mags files ''' class readmags: def __init__(self, fname): self.data = open(fname, 'r') # Read header to find the columns in the file line_count = 0 while True: if line_count < 7: self.data.readline() else: line = self.data.readline() line = line.lstrip() if line[0] == '#': header = line.replace('#', '') header = header.replace('mags', '') header = header.replace('(see FILTER_LIST)', '') header = header.split() break line_count+=1 self.header = header def next(self): line = self.data.readline() if line == "": self.data.close() raise StopIteration() # Read in and assign the attributes of the models cols = line.split() for i, name in enumerate(self.header): self.__dict__[name] = float(cols[i]) # Update the attributes with filter names and values with open('/Users/alexawork/FSPS_python/filters.txt', 'r') as names: for i, _filter in enumerate(names): _filter = _filter.rstrip() #print _filter, cols[i + len(self.header)] self.__dict__.update({_filter:float(cols[i + len(self.header)])}) return self.__dict__.copy() def __iter__(self): return self	[ 1, 1053, 2897, 13, 13, 12008, 13, 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tests/test_properties.py	Kapiche/gcloud-python-orm	1	45505	import six import unittest2 from gcloud.datastore import helpers, key, set_default_dataset_id from gcloudorm import model, properties class TestProperties(unittest2.TestCase): _DATASET_ID = 'DATASET' def setUp(self): set_default_dataset_id(self._DATASET_ID) def testBooleanProperty(self): class TestModel(model.Model): test_bool = properties.BooleanProperty() m = TestModel() self.assertEqual(m.test_bool, None) self.assertEqual(m['test_bool'], None) m = TestModel(test_bool=False) self.assertEqual(m.test_bool, False) self.assertEqual(m['test_bool'], False) m.test_bool = True self.assertEqual(m.test_bool, True) self.assertEqual(m['test_bool'], True) class TestModel(model.Model): test_bool = properties.BooleanProperty(default=True) m = TestModel() self.assertEqual(m.test_bool, True) self.assertEqual(m['test_bool'], True) def testIdProperty(self): class TestModel(model.Model): test_id = properties.IdProperty() m = TestModel() self.assertIsInstance(m.test_id, six.string_types) self.assertIs(m.test_id, m.key.id_or_name) def testIntegerProperty(self): class TestModel(model.Model): test_int = properties.IntegerProperty() m = TestModel() self.assertEqual(m.test_int, None) self.assertEqual(m['test_int'], None) class TestModel(model.Model): test_int = properties.IntegerProperty(default=3) m = TestModel() self.assertEqual(m['test_int'], 3) m.test_int = 4 self.assertEqual(m.test_int, 4) self.assertEqual(m['test_int'], 4) def testFloatproperty(self): class TestModel(model.Model): test_float = properties.FloatProperty() m = TestModel() self.assertEqual(m.test_float, None) self.assertEqual(m['test_float'], None) class TestModel(model.Model): test_float = properties.FloatProperty(default=0.1) m = TestModel() self.assertEqual(m['test_float'], 0.1) m.test_float = 0.2 self.assertEqual(m['test_float'], 0.2) def testTextProperty(self): class TestModel(model.Model): test_text = properties.TextProperty() m = TestModel() self.assertEqual(m.test_text, None) class TestModel(model.Model): test_text = properties.TextProperty(default="") m = TestModel() self.assertEqual(m['test_text'], "") class TestModel(model.Model): test_text = properties.TextProperty(default=lambda: "") m = TestModel() self.assertEqual(m['test_text'], "") def testPickleProperty(self): class TestModel(model.Model): test_pickle = properties.PickleProperty() m = TestModel() self.assertEqual(m.test_pickle, None) m = TestModel(test_pickle={"123": "456"}) self.assertEqual(m.test_pickle, {"123": "456"}) m.test_pickle = {'456': '789'} self.assertEqual(m.test_pickle, {'456': '789'}) def testJsonProperty(self): class TestModel(model.Model): test_pickle = properties.JsonProperty() m = TestModel() self.assertEqual(m.test_pickle, None) m = TestModel(test_pickle={"123": "456"}) self.assertEqual(m.test_pickle, {"123": "456"}) m.test_pickle = {'456': '789'} self.assertEqual(m.test_pickle, {'456': '789'}) def testDataTimeProperty(self): import datetime 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mall/celery_tasks/sms/tasks.py	DanaLee1990/meiduo	0	54871	""" 任务： 1、就是普通函数 2、该函数必须通过celery的实例对象的tasks装饰其装饰 3、该任务需要让celery实例对象自动检测 4、任务（函数）需要使用任务名（函数名）.delay() 进行调用 """ from libs.yuntongxun.sms import CCP from celery_tasks.main import app @app.task def send_sms_code(mobile,sms_code): ccp = CCP() ccp.send_template_sms(mobile, [sms_code, 5], 1)	[ 1, 29871, 13, 13, 13, 15945, 29908, 13, 31450, 31358, 30383, 13, 268, 29896, 30330, 31238, 30392, 233, 156, 177, 30768, 31629, 30354, 13, 268, 29906, 30330, 31751, 31629, 30354, 31641, 236, 164, 190, 30768, 31138, 2242, 708, 30210, 31195, 31507, 30783, 31133, 30210, 20673, 31905, 236, 168, 179, 31149, 31905, 236, 168, 179, 13, 268, 29941, 30330, 31751, 31450, 31358, 31383, 30698, 235, 177, 172, 2242, 708, 31195, 31507, 30783, 31133, 30688, 30846, 233, 166, 131, 31851, 13, 268, 29946, 30330, 31450, 31358, 30419, 31629, 30354, 30409, 31383, 30698, 30785, 30406, 31450, 31358, 30548, 30419, 31629, 30354, 30548, 30409, 29889, 18829, 580, 29871, 31174, 30448, 31268, 30406, 13, 15945, 29908, 13, 3166, 4303, 29879, 29889, 29891, 1657, 549, 29916, 348, 29889, 29879, 1516, 1053, 315, 6271, 13, 3166, 6432, 708, 29918, 20673, 29889, 3396, 1053, 623, 13, 13, 13, 29992, 932, 29889, 7662, 13, 1753, 3638, 29918, 29879, 1516, 29918, 401, 29898, 16769, 29892, 29879, 1516, 29918, 401, 1125, 13, 1678, 274, 6814, 353, 315, 6271, 580, 13, 1678, 274, 6814, 29889, 6717, 29918, 6886, 29918, 29879, 1516, 29898, 16769, 29892, 518, 29879, 1516, 29918, 401, 29892, 29871, 29945, 1402, 29871, 29896, 29897, 13, 2 ]
api/resources/__init__.py	wopost/opencmdb	2	101142	from flask import Blueprint from flask_restful import Api from api.resources.demo import DemoResource from api.resources.login import LoginResource api_bp_v1 = Blueprint('bp_v1', __name__) api_v1 = Api(api_bp_v1, '/v1') api_v1.add_resource(DemoResource, '/demo') api_v1.add_resource(LoginResource, '/login') BLUEPRINTS = [api_bp_v1] __all__ = ['BLUEPRINTS']	[ 1, 515, 29784, 1053, 10924, 2158, 13, 3166, 29784, 29918, 5060, 1319, 1053, 29749, 13, 13, 3166, 7882, 29889, 13237, 29889, 17482, 1053, 27819, 6848, 13, 3166, 7882, 29889, 13237, 29889, 7507, 1053, 19130, 6848, 13, 13, 2754, 29918, 25288, 29918, 29894, 29896, 353, 10924, 2158, 877, 25288, 29918, 29894, 29896, 742, 4770, 978, 1649, 29897, 13, 2754, 29918, 29894, 29896, 353, 29749, 29898, 2754, 29918, 25288, 29918, 29894, 29896, 29892, 8207, 29894, 29896, 1495, 13, 13, 2754, 29918, 29894, 29896, 29889, 1202, 29918, 10314, 29898, 23444, 6848, 29892, 8207, 17482, 1495, 13, 2754, 29918, 29894, 29896, 29889, 1202, 29918, 10314, 29898, 11049, 6848, 29892, 8207, 7507, 1495, 13, 13, 13367, 4462, 10593, 1177, 9375, 353, 518, 2754, 29918, 25288, 29918, 29894, 29896, 29962, 13, 13, 1649, 497, 1649, 353, 6024, 13367, 4462, 10593, 1177, 9375, 2033, 13, 2 ]
src/simulation/entity.py	rah/optimal-search	0	32758	class Entity(object): ''' An entity has: - energy - position(x,y) - size(length, width) An entity may have a parent entity An entity may have child entities ''' def __init__( self, p, parent=None, children=None): self.p = p self.energy = p.getfloat("ENTITY", "energy") self.x_pos = p.getfloat("ENTITY", "x_pos") self.y_pos = p.getfloat("ENTITY", "y_pos") self.length = p.getint("ENTITY", "length") self.width = p.getint("ENTITY", "width") self.parent = parent if children is None: self.children = [] else: self.children = children def add_child(self, entity): self.children.append(entity) def remove_child(self, entity): self.children.remove(entity) def remove_self(self): if self.parent is not None: self.parent.children.remove(self) def number_children(self): if self.children: return len(self.children) else: return 0 def total_energy(self): ''' returns the sum of all energy ''' total_energy = self.energy for child in self.children: total_energy += child.energy return total_energy def set_bounds(self, x, y): ''' Ensure that x, y are within the bounds of this entity. Reset x,y so that a torus is formed ''' if x < 0.0: x = self.length if x > self.length: x = 0.0 if y < 0.0: y = self.width if y > self.width: y = 0.0 return x, y	[ 1, 770, 14945, 29898, 3318, 1125, 30004, 13, 1678, 14550, 30004, 13, 1678, 530, 7855, 756, 29901, 30004, 13, 418, 448, 5864, 30004, 13, 418, 448, 2602, 29898, 29916, 29892, 29891, 8443, 13, 418, 448, 2159, 29898, 2848, 29892, 2920, 8443, 13, 1678, 530, 7855, 1122, 505, 263, 3847, 7855, 30004, 13, 1678, 530, 7855, 1122, 505, 2278, 16212, 30004, 13, 1678, 14550, 30004, 13, 30004, 13, 1678, 822, 4770, 2344, 12035, 30004, 13, 9651, 1583, 11167, 13, 9651, 282, 11167, 13, 9651, 3847, 29922, 8516, 11167, 13, 9651, 4344, 29922, 8516, 1125, 30004, 13, 4706, 1583, 29889, 29886, 353, 282, 30004, 13, 4706, 1583, 29889, 27548, 353, 282, 29889, 657, 7411, 703, 3919, 11937, 613, 376, 27548, 1159, 30004, 13, 4706, 1583, 29889, 29916, 29918, 1066, 353, 282, 29889, 657, 7411, 703, 3919, 11937, 613, 376, 29916, 29918, 1066, 1159, 30004, 13, 4706, 1583, 29889, 29891, 29918, 1066, 353, 282, 29889, 657, 7411, 703, 3919, 11937, 613, 376, 29891, 29918, 1066, 1159, 30004, 13, 4706, 1583, 29889, 2848, 353, 282, 29889, 657, 524, 703, 3919, 11937, 613, 376, 2848, 1159, 30004, 13, 4706, 1583, 29889, 2103, 353, 282, 29889, 657, 524, 703, 3919, 11937, 613, 376, 2103, 1159, 30004, 13, 4706, 1583, 29889, 3560, 353, 3847, 30004, 13, 4706, 565, 4344, 338, 6213, 29901, 30004, 13, 9651, 1583, 29889, 11991, 353, 5159, 30004, 13, 4706, 1683, 29901, 30004, 13, 9651, 1583, 29889, 11991, 353, 4344, 30004, 13, 30004, 13, 1678, 822, 788, 29918, 5145, 29898, 1311, 29892, 7855, 1125, 30004, 13, 4706, 1583, 29889, 11991, 29889, 4397, 29898, 10041, 8443, 13, 30004, 13, 1678, 822, 3349, 29918, 5145, 29898, 1311, 29892, 7855, 1125, 30004, 13, 4706, 1583, 29889, 11991, 29889, 5992, 29898, 10041, 8443, 13, 30004, 13, 1678, 822, 3349, 29918, 1311, 29898, 1311, 1125, 30004, 13, 4706, 565, 1583, 29889, 3560, 338, 451, 6213, 29901, 30004, 13, 9651, 1583, 29889, 3560, 29889, 11991, 29889, 5992, 29898, 1311, 8443, 13, 30004, 13, 1678, 822, 1353, 29918, 11991, 29898, 1311, 1125, 30004, 13, 4706, 565, 1583, 29889, 11991, 29901, 30004, 13, 9651, 736, 7431, 29898, 1311, 29889, 11991, 8443, 13, 4706, 1683, 29901, 30004, 13, 9651, 736, 29871, 29900, 30004, 13, 30004, 13, 1678, 822, 3001, 29918, 27548, 29898, 1311, 1125, 30004, 13, 4706, 14550, 30004, 13, 4706, 3639, 278, 2533, 310, 599, 5864, 30004, 13, 4706, 14550, 30004, 13, 4706, 3001, 29918, 27548, 353, 1583, 29889, 27548, 30004, 13, 4706, 363, 2278, 297, 1583, 29889, 11991, 29901, 30004, 13, 9651, 3001, 29918, 27548, 4619, 2278, 29889, 27548, 30004, 13, 30004, 13, 4706, 736, 3001, 29918, 27548, 30004, 13, 30004, 13, 1678, 822, 731, 29918, 23687, 29898, 1311, 29892, 921, 29892, 343, 1125, 30004, 13, 4706, 14550, 30004, 13, 4706, 22521, 545, 393, 921, 29892, 343, 526, 2629, 278, 13451, 310, 445, 7855, 22993, 13, 4706, 2538, 300, 921, 29892, 29891, 577, 393, 263, 4842, 375, 338, 8429, 30004, 13, 4706, 14550, 30004, 13, 4706, 565, 921, 529, 29871, 29900, 29889, 29900, 29901, 30004, 13, 9651, 921, 353, 1583, 29889, 2848, 30004, 13, 4706, 565, 921, 1405, 1583, 29889, 2848, 29901, 30004, 13, 9651, 921, 353, 29871, 29900, 29889, 29900, 30004, 13, 30004, 13, 4706, 565, 343, 529, 29871, 29900, 29889, 29900, 29901, 30004, 13, 9651, 343, 353, 1583, 29889, 2103, 30004, 13, 4706, 565, 343, 1405, 1583, 29889, 2103, 29901, 30004, 13, 9651, 343, 353, 29871, 29900, 29889, 29900, 30004, 13, 30004, 13, 4706, 736, 921, 29892, 343, 30004, 13, 2 ]
mygallary/urls.py	mangowilliam/my_gallary	0	5799	<gh_stars>0 from django.conf import settings from django.conf.urls.static import static from django.conf.urls import url from . import views urlpatterns = [ url('^$', views.gallary,name = 'gallary'), url(r'^search/', views.search_image, name='search_image'), url(r'^details/(\d+)',views.search_location,name ='images') ] if settings.DEBUG: urlpatterns+= static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 29889, 5527, 29889, 26045, 29889, 7959, 1053, 2294, 13, 3166, 9557, 29889, 5527, 29889, 26045, 1053, 3142, 13, 3166, 869, 1053, 8386, 13, 13, 13, 13, 2271, 11037, 29879, 353, 518, 13, 1678, 3142, 877, 29985, 29938, 742, 8386, 29889, 29887, 497, 653, 29892, 978, 353, 525, 29887, 497, 653, 5477, 13, 1678, 3142, 29898, 29878, 29915, 29985, 4478, 29914, 742, 8386, 29889, 4478, 29918, 3027, 29892, 1024, 2433, 4478, 29918, 3027, 5477, 13, 1678, 3142, 29898, 29878, 29915, 29985, 14144, 29914, 1194, 29881, 28135, 742, 7406, 29889, 4478, 29918, 5479, 29892, 978, 353, 29915, 8346, 1495, 13, 29962, 13, 361, 6055, 29889, 18525, 29901, 13, 1678, 3142, 11037, 29879, 23661, 2294, 29898, 11027, 29889, 2303, 4571, 29909, 29918, 4219, 29892, 1842, 29918, 4632, 353, 6055, 29889, 2303, 4571, 29909, 29918, 21289, 29897, 13, 268, 13, 268, 13, 268, 13, 268, 13, 268, 13, 268, 13, 268, 13, 268, 13, 268, 2 ]
install/app_store/tk-framework-shotgunutils/v5.2.4/python/shotgun_globals/icon.py	JoanAzpeitia/lp_sg	0	59064	<reponame>JoanAzpeitia/lp_sg # Copyright (c) 2015 Shotgun Software Inc. # # CONFIDENTIAL AND PROPRIETARY # # This work is provided "AS IS" and subject to the Shotgun Pipeline Toolkit # Source Code License included in this distribution package. See LICENSE. # By accessing, using, copying or modifying this work you indicate your # agreement to the Shotgun Pipeline Toolkit Source Code License. All rights # not expressly granted therein are reserved by Shotgun Software Inc. from sgtk.platform.qt import QtCore, QtGui from .ui import resources_rc # list of all entity types for which an icon exists _entity_types_with_icons = ["Asset", "ClientUser", "EventLogEntry", "Group", "HumanUser", "PublishedFile", "TankPublishedFile", "Note", "Playlist", "Project", "Sequence", "Shot", "Tag", "Task", "Ticket", "Version", ] _cached_entity_icons = {} def get_entity_type_icon_url(entity_type): """ Retrieve the icon resource path for the specified entity type if available. This is useful if you want to include an icon in a ``QLabel`` using an ``<img>`` html tag. :param entity_type: The entity type to retrieve the icon for :returns: A string url with a qt resource path """ if entity_type in _entity_types_with_icons: return ":/tk-framework-shotgunutils/icon_%s_dark.png" % entity_type else: return None def get_entity_type_icon(entity_type): """ Retrieve the icon for the specified entity type if available. :param entity_type: The entity type to retrieve the icon for :returns: A QIcon if an icon was found for the specified entity type, otherwise None. """ global _cached_entity_icons if entity_type not in _cached_entity_icons: # not yet cached icon = None url = get_entity_type_icon_url(entity_type) if url: # create a QIcon for it icon = QtGui.QIcon(QtGui.QPixmap(url)) # cache it _cached_entity_icons[entity_type] = icon # we've previously asked for the icon return _cached_entity_icons[entity_type]	[ 1, 529, 276, 1112, 420, 29958, 10844, 273, 16748, 412, 277, 423, 29914, 22833, 29918, 5311, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29945, 1383, 327, 28798, 18540, 9266, 29889, 13, 29937, 29871, 13, 29937, 8707, 29943, 1367, 3919, 25758, 5300, 13756, 29829, 2544, 19926, 13, 29937, 29871, 13, 29937, 910, 664, 338, 4944, 376, 3289, 8519, 29908, 322, 4967, 304, 278, 1383, 327, 28798, 349, 23828, 21704, 7354, 29871, 13, 29937, 7562, 5920, 19245, 5134, 297, 445, 4978, 3577, 29889, 2823, 365, 2965, 1430, 1660, 29889, 13, 29937, 2648, 17378, 29892, 773, 29892, 17596, 470, 23815, 445, 664, 366, 12266, 596, 29871, 13, 29937, 17327, 304, 278, 1383, 327, 28798, 349, 23828, 21704, 7354, 7562, 5920, 19245, 29889, 2178, 10462, 29871, 13, 29937, 451, 4653, 368, 16896, 727, 262, 526, 21676, 491, 1383, 327, 28798, 18540, 9266, 29889, 13, 13, 3166, 269, 4141, 29895, 29889, 12120, 29889, 17915, 1053, 14705, 9203, 29892, 14705, 28707, 13, 3166, 869, 1481, 1053, 7788, 29918, 2214, 13, 13, 29937, 1051, 310, 599, 7855, 4072, 363, 607, 385, 9849, 4864, 13, 29918, 10041, 29918, 8768, 29918, 2541, 29918, 27078, 353, 6796, 26405, 613, 29871, 13, 462, 965, 376, 4032, 2659, 613, 13, 462, 965, 376, 2624, 3403, 9634, 613, 13, 462, 965, 376, 4782, 613, 13, 462, 965, 376, 29950, 7889, 2659, 613, 13, 462, 965, 376, 21076, 3726, 2283, 613, 13, 462, 965, 376, 29911, 804, 21076, 3726, 2283, 613, 13, 462, 965, 376, 9842, 613, 13, 462, 965, 376, 13454, 1761, 613, 13, 462, 965, 376, 7653, 613, 13, 462, 965, 376, 20529, 613, 13, 462, 965, 376, 2713, 327, 613, 13, 462, 965, 376, 8176, 613, 13, 462, 965, 376, 5398, 613, 13, 462, 965, 376, 29911, 8522, 613, 13, 462, 965, 376, 6594, 613, 13, 462, 965, 4514, 13, 13, 29918, 29883, 3791, 29918, 10041, 29918, 27078, 353, 6571, 13, 13, 13, 1753, 679, 29918, 10041, 29918, 1853, 29918, 4144, 29918, 2271, 29898, 10041, 29918, 1853, 1125, 13, 1678, 9995, 13, 1678, 4649, 29878, 2418, 278, 9849, 6503, 2224, 363, 278, 6790, 7855, 1134, 565, 3625, 29889, 13, 268, 13, 1678, 910, 338, 5407, 565, 366, 864, 304, 3160, 385, 9849, 297, 263, 4954, 2239, 1107, 16159, 773, 13, 1678, 385, 4954, 29966, 2492, 13885, 29952, 3472, 4055, 29889, 13, 13, 1678, 584, 3207, 7855, 29918, 1853, 29901, 450, 7855, 1134, 304, 10563, 278, 9849, 363, 13, 1678, 584, 18280, 29901, 965, 319, 1347, 3142, 411, 263, 3855, 29873, 6503, 2224, 13, 1678, 9995, 13, 1678, 565, 7855, 29918, 1853, 297, 903, 10041, 29918, 8768, 29918, 2541, 29918, 27078, 29901, 13, 4706, 736, 376, 8419, 11178, 29899, 4468, 29899, 8962, 28798, 13239, 29914, 4144, 29918, 29995, 29879, 29918, 26031, 29889, 2732, 29908, 1273, 7855, 29918, 1853, 13, 1678, 1683, 29901, 13, 4706, 736, 6213, 13, 13, 1753, 679, 29918, 10041, 29918, 1853, 29918, 4144, 29898, 10041, 29918, 1853, 1125, 13, 1678, 9995, 13, 1678, 4649, 29878, 2418, 278, 9849, 363, 278, 6790, 7855, 1134, 565, 3625, 29889, 13, 13, 1678, 584, 3207, 7855, 29918, 1853, 29901, 450, 7855, 1134, 304, 10563, 278, 9849, 363, 13, 1678, 584, 18280, 29901, 965, 319, 660, 12492, 565, 385, 9849, 471, 1476, 363, 278, 6790, 7855, 13, 462, 4706, 1134, 29892, 6467, 6213, 29889, 13, 1678, 9995, 13, 1678, 5534, 903, 29883, 3791, 29918, 10041, 29918, 27078, 13, 1678, 565, 7855, 29918, 1853, 451, 297, 903, 29883, 3791, 29918, 10041, 29918, 27078, 29901, 13, 4706, 396, 451, 3447, 22152, 13, 4706, 9849, 353, 6213, 13, 4706, 3142, 353, 679, 29918, 10041, 29918, 1853, 29918, 4144, 29918, 2271, 29898, 10041, 29918, 1853, 29897, 29871, 13, 4706, 565, 3142, 29901, 13, 9651, 396, 1653, 263, 660, 12492, 363, 372, 13, 9651, 9849, 353, 14705, 28707, 29889, 29984, 12492, 29898, 17303, 28707, 29889, 29984, 29925, 861, 1958, 29898, 2271, 876, 13, 4706, 396, 7090, 372, 13, 4706, 903, 29883, 3791, 29918, 10041, 29918, 27078, 29961, 10041, 29918, 1853, 29962, 353, 9849, 13, 308, 13, 1678, 396, 591, 29915, 345, 9251, 4433, 363, 278, 9849, 13, 1678, 736, 903, 29883, 3791, 29918, 10041, 29918, 27078, 29961, 10041, 29918, 1853, 29962, 13, 268, 13, 13, 13, 2 ]
Codewars/you're a square/you're a square.py	adoreblvnk/code_solutions	0	26653	from math import isqrt is_square = lambda n: isqrt(n) ** 2 == n if n >= 0 else False def is_square_soln(n): pass print(is_square(-1))	[ 1, 515, 5844, 1053, 338, 29939, 2273, 13, 13, 275, 29918, 17619, 353, 14013, 302, 29901, 338, 29939, 2273, 29898, 29876, 29897, 3579, 29871, 29906, 1275, 302, 565, 302, 6736, 29871, 29900, 1683, 7700, 13, 13, 1753, 338, 29918, 17619, 29918, 2929, 29876, 29898, 29876, 1125, 13, 1678, 1209, 13, 13, 13, 2158, 29898, 275, 29918, 17619, 6278, 29896, 876, 2 ]
pylibcamera_src/__init__.py	Jiangshan00001/pylibcamera	0	191614	import os from .cffi_helpers import get_lib_handle _this_path = os.path.dirname(os.path.realpath(__file__)) _library_dir = os.getenv('PI_LIBRARY_DIR') if _library_dir is None: _library_dir = os.path.join(_this_path, 'lib') _include_dir = os.getenv('PI_INCLUDE_DIR') if _include_dir is None: _include_dir = os.path.join(_this_path, 'include') lraw_api, ffi_raw = get_lib_handle( ['-DPI_API='], 'libcamera_raw_api.h', 'camera_raw_api', _library_dir, _include_dir ) lvid_api, ffi_vid = get_lib_handle( ['-DPI_API='], 'libcamera_vid_api.h', 'camera_vid_api', _library_dir, _include_dir )	[ 1, 1053, 2897, 13, 3166, 869, 29883, 600, 29875, 29918, 3952, 6774, 1053, 679, 29918, 1982, 29918, 8411, 13, 13, 13, 29918, 1366, 29918, 2084, 353, 2897, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 6370, 2084, 22168, 1445, 1649, 876, 13, 13, 29918, 5258, 29918, 3972, 353, 2897, 29889, 657, 6272, 877, 2227, 29918, 5265, 15176, 19926, 29918, 9464, 1495, 13, 361, 903, 5258, 29918, 3972, 338, 6213, 29901, 13, 1678, 903, 5258, 29918, 3972, 353, 2897, 29889, 2084, 29889, 7122, 7373, 1366, 29918, 2084, 29892, 525, 1982, 1495, 13, 13, 29918, 2856, 29918, 3972, 353, 2897, 29889, 657, 6272, 877, 2227, 29918, 1177, 6154, 29965, 2287, 29918, 9464, 1495, 13, 361, 903, 2856, 29918, 3972, 338, 6213, 29901, 13, 1678, 903, 2856, 29918, 3972, 353, 2897, 29889, 2084, 29889, 7122, 7373, 1366, 29918, 2084, 29892, 525, 2856, 1495, 13, 13, 29880, 1610, 29918, 2754, 29892, 285, 7241, 29918, 1610, 353, 679, 29918, 1982, 29918, 8411, 29898, 13, 1678, 6024, 29899, 29928, 2227, 29918, 8787, 2433, 1402, 13, 1678, 525, 1982, 26065, 29918, 1610, 29918, 2754, 29889, 29882, 742, 13, 1678, 525, 26065, 29918, 1610, 29918, 2754, 742, 13, 1678, 903, 5258, 29918, 3972, 29892, 13, 1678, 903, 2856, 29918, 3972, 13, 29897, 13, 13, 29880, 8590, 29918, 2754, 29892, 285, 7241, 29918, 8590, 353, 679, 29918, 1982, 29918, 8411, 29898, 13, 1678, 6024, 29899, 29928, 2227, 29918, 8787, 2433, 1402, 13, 1678, 525, 1982, 26065, 29918, 8590, 29918, 2754, 29889, 29882, 742, 13, 1678, 525, 26065, 29918, 8590, 29918, 2754, 742, 13, 1678, 903, 5258, 29918, 3972, 29892, 13, 1678, 903, 2856, 29918, 3972, 13, 29897, 13, 13, 2 ]
boards/models.py	6ba/bbgo	22	113622	<filename>boards/models.py # -- coding: utf-8 -- from __future__ import unicode_literals from django.conf import settings from django.contrib.auth.models import User from django.db import models from django.urls import reverse_lazy from django.utils.translation import ugettext as _ class Board(models.Model): """Board of boards""" BOARD_STATUS = { ('1normal', _('status_normal')), ('2temp', _('status_temp')), ('3notice', _('status_notice')), ('4warning', _('status_warning')), ('5hidden', _('status_hidden')), ('6deleted', _('status_deleted')), } table = models.IntegerField(default=0) status = models.CharField(max_length=10, choices=BOARD_STATUS, default='1normal') user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE) created_at = models.DateTimeField(auto_now_add=True) modified_at = models.DateTimeField(auto_now_add=True) ip = models.GenericIPAddressField() category = models.CharField(max_length=23, blank=True) subject = models.CharField(max_length=41) content = models.TextField() view_count = models.IntegerField(default=0) reply_count = models.IntegerField(default=0) like_count = models.IntegerField(default=0) dislike_count = models.IntegerField(default=0) like_users = models.ManyToManyField( User, related_name="board_like_users", default='', blank=True) dislike_users = models.ManyToManyField( User, related_name="board_dislike_users", default='', blank=True) reference = models.CharField(max_length=1855, default='', blank=True) has_image = models.BooleanField(default=False) has_video = models.BooleanField(default=False) def get_absolute_url(self): """Back to list""" return reverse_lazy('boards:show_list', args=[self.table, 1]) def get_article_url(self): """Back to article""" return reverse_lazy('boards:show_article', args=[self.id]) def get_edit_url(self): """Stay editing""" return reverse_lazy('boards:edit_article', args=[self.id]) def get_status_text(self): """Get status text""" if self.status == '1normal': return _('status_normal') elif self.status == '2temp': return _('status_temp') elif self.status == '3notice': return _('status_notice') elif self.status == '4warning': return _('status_warning') elif self.status == '5hidden': return _('status_hidden') elif self.status == '6deleted': return _('status_deleted') def get_image_text(self): """Get image text""" return '<img src="/upload/django-summernote/' def get_video_text(self): """Get video text""" return '<iframe frameborder="0" src="//www.youtube.com/' class Reply(models.Model): """Reply of boards""" REPLY_STATUS = { ('1normal', _('status_normal')), ('5hidden', _('status_hidden')), ('6deleted', _('status_deleted')), } article_id = models.IntegerField(default=0) reply_id = models.IntegerField(default=0) reply_to = models.CharField(max_length=150, default='', blank=True) status = models.CharField( max_length=10, choices=REPLY_STATUS, default='1normal') user = models.ForeignKey( settings.AUTH_USER_MODEL, 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src/pyphoplacecellanalysis/GUI/PyQtPlot/Flowchart/CustomNodes/Mixins/CtrlNodeMixins.py	CommanderPho/pyPhoPlaceCellAnalysis	0	112471	import numpy as np class KeysListAccessingMixin: """ Provides a helper function to get the keys from a variety of different data-types. Used for combo-boxes.""" @classmethod def get_keys_list(cls, data): if isinstance(data, dict): keys = list(data.keys()) elif isinstance(data, list) or isinstance(data, tuple): keys = data elif isinstance(data, np.ndarray) or isinstance(data, np.void): keys = data.dtype.names else: print("get_keys_list(data): Unknown data type:", type(data), data) raise return keys	[ 1, 1053, 12655, 408, 7442, 13, 13, 1990, 4813, 952, 1293, 6638, 292, 29924, 861, 262, 29901, 13, 1678, 9995, 9133, 2247, 263, 16876, 740, 304, 679, 278, 6611, 515, 263, 12875, 310, 1422, 848, 29899, 8768, 29889, 501, 8485, 363, 419, 833, 29899, 1884, 267, 1213, 15945, 13, 1678, 732, 1990, 5696, 13, 1678, 822, 679, 29918, 8149, 29918, 1761, 29898, 25932, 29892, 848, 1125, 13, 4706, 565, 338, 8758, 29898, 1272, 29892, 9657, 1125, 13, 9651, 6611, 353, 1051, 29898, 1272, 29889, 8149, 3101, 13, 4706, 25342, 338, 8758, 29898, 1272, 29892, 1051, 29897, 470, 338, 8758, 29898, 1272, 29892, 18761, 1125, 13, 9651, 6611, 353, 848, 13, 4706, 25342, 338, 8758, 29898, 1272, 29892, 7442, 29889, 299, 2378, 29897, 470, 338, 8758, 29898, 1272, 29892, 7442, 29889, 5405, 1125, 13, 9651, 6611, 353, 848, 29889, 29881, 1853, 29889, 7039, 13, 4706, 1683, 29901, 13, 9651, 1596, 703, 657, 29918, 8149, 29918, 1761, 29898, 1272, 1125, 853, 5203, 848, 1134, 29901, 613, 1134, 29898, 1272, 511, 848, 29897, 13, 9651, 12020, 13, 4706, 736, 6611, 2 ]
src/_zkapauthorizer/replicate.py	PrivateStorageio/SecureAccessTokenAuthorizer	1	59898	<reponame>PrivateStorageio/SecureAccessTokenAuthorizer # Copyright 2022 PrivateStorage.io, 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 # # 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. from __future__ import annotations """ A system for replicating local SQLite3 database state to remote storage. Theory of Operation =================== A function to wrap a ``sqlite3.Connection`` in a new type is provided. This new type provides facilities for accomplishing two goals: * It (can someday) presents an expanded connection interface which includes the ability to switch the database into "replicated" mode. This is an application-facing interface meant to be used when the application is ready to discharge its responsibilities in the replication process. * It (can someday) expose the usual cursor interface wrapped around the usual cursor behavior combined with extra logic to record statements which change the underlying database (DDL and DML statements). This recorded data then feeds into the above replication process once it is enabled. An application's responsibilities in the replication process are to arrange for remote storage of "snapshots" and "event streams". See the replication/recovery design document for details of these concepts. Once replication has been enabled, the application (can someday be) informed whenever the event stream changes (respecting database transactionality) and data can be shipped to remote storage as desired. It is essential to good replication performance that once replication is enabled all database-modifying actions are captured in the event stream. This is the reason for providing a ``sqlite3.Connection``-like object for use by application code rather than a separate side-car interface: it minimizes the opportunities for database changes which are overlooked by this replication system. """ __all__ = [ "ReplicationAlreadySetup", "fail_setup_replication", "setup_tahoe_lafs_replication", "with_replication", "statements_to_snapshot", "connection_to_statements", "snapshot", ] import os import re from enum import Enum from io import BytesIO from sqlite3 import Connection as _SQLite3Connection from sqlite3 import Cursor as _SQLite3Cursor from typing import ( IO, Any, Awaitable, Callable, ClassVar, Generator, Iterable, Iterator, Optional, Protocol, Sequence, ) import cbor2 from attrs import Factory, define, field, frozen from compose import compose from eliot import log_call from twisted.application.service import IService, Service from twisted.internet.defer import CancelledError, Deferred, DeferredQueue, succeed from twisted.logger import Logger from twisted.python.filepath import FilePath from twisted.python.lockfile import FilesystemLock from ._types import CapStr from .config import REPLICA_RWCAP_BASENAME, Config from .sql import Connection, Cursor, SQLRuntimeType, SQLType, statement_mutates from .tahoe import DataProvider, DirectoryEntry, ITahoeClient, attenuate_writecap # function which can set remote ZKAPAuthorizer state. Uploader = Callable[[str, DataProvider], Awaitable[None]] # function which can remove entries from ZKAPAuthorizer state. Pruner = Callable[[Callable[[str], bool]], Awaitable[None]] # functions which can list all entries in ZKAPAuthorizer state Lister = Callable[[], Awaitable[list[str]]] EntryLister = Callable[[], Awaitable[dict[str, DirectoryEntry]]] class SnapshotPolicy(Protocol): """ Encode policy rules about when to take and upload a new snapshot. """ def should_snapshot(self, snapshot_size: int, replica_sizes: list[int]) -> bool: """ Given the size of a new snapshot and the size of an existing replica (snapshot and event streams), is now a good time to take a new snapshot? """ SNAPSHOT_NAME = "snapshot" @frozen class Replica: """ Manage a specific replica. """ upload: Uploader prune: Pruner entry_lister: EntryLister async def list(self) -> list[str]: return list(await self.entry_lister()) class ReplicationJob(Enum): """ The kinds of jobs that the Replication queue knows about :ivar startup: The job that is run once when the replication service starts and which is responsible for inspecting local and remote state to determine if any actions are immediately necessary (even before any further local changes are made). :ivar event_stream: The job to upload a new event stream object. :ivar snapshot: The job to upload a new snapshot object and prune now-obsolete event stream objects. :ivar consider_snapshot: The job to inspect replica event stream and snapshot state and potentially schedule a new snapshot which will allow pruning of existing event streams. """ startup = 1 event_stream = 2 snapshot = 3 consider_snapshot = 4 @frozen class Change: """ Represent an item in a replication event stream :ivar sequence: The sequence number of this event. :ivar statement: The SQL statement associated with this event. :ivar important: Whether this change was "important" or not. :ivar arguments: Any arguments for the SQL statement. """ sequence: int statement: str arguments: Sequence[SQLType] = field(converter=tuple) important: bool @arguments.validator def _validate_arguments(self, attribute, value) -> None: """ Require that the value has as elements only values are legal SQL values. :note: attrs validators run after attrs converters. """ if all(isinstance(o, SQLRuntimeType) for o in value): return None raise ValueError("sequence contains values incompatible with SQL") @frozen class EventStream: """ A series of database operations represented as `Change` instances. :ivar version: An identifier for the schema of the serialized form of this event stream. This will appear inside the serialized form. A change to the schema will be accompanied with an increment to this value. """ changes: Sequence[Change] = field(converter=tuple) version: ClassVar[int] = 1 def highest_sequence(self) -> Optional[int]: """ :returns: the highest sequence number in this EventStream (or None if there are no events) """ if not self.changes: return None return max(change.sequence for change in self.changes) def to_bytes(self) -> IO[bytes]: """ :returns: a producer of bytes representing this EventStream. """ return BytesIO( cbor2.dumps( { "version": self.version, "events": tuple( ( event.sequence, event.statement, event.arguments, event.important, ) for event in self.changes ), } ) ) @classmethod def from_bytes(cls, stream: IO[bytes]) -> EventStream: """ :returns EventStream: an instance of EventStream from the given bytes (which should have been produced by a prior call to ``to_bytes``) """ data = cbor2.load(stream) serial_version = data.get("version", None) if serial_version != cls.version: raise ValueError( f"Unknown serialized event stream version {serial_version}" ) return cls( changes=[ # List comprehension has incompatible type List[Change]; expected List[_T_co] # https://github.com/python-attrs/attrs/issues/519 Change(args) # type: ignore for args in data["events"] ] ) class AlreadySettingUp(Exception): """ Another setup attempt is currently in progress. """ class ReplicationAlreadySetup(Exception): """ An attempt was made to setup of replication but it is already set up. """ async def fail_setup_replication(): """ A replication setup function that always fails. """ raise Exception("Test not set up for replication") async def setup_tahoe_lafs_replication(client: ITahoeClient) -> str: """ Configure the ZKAPAuthorizer plugin that lives in the Tahoe-LAFS node with the given configuration to replicate its state onto Tahoe-LAFS storage servers using that Tahoe-LAFS node. """ # Find the configuration path for this node's replica. config_path = client.get_private_path(REPLICA_RWCAP_BASENAME) # Take an advisory lock on the configuration path to avoid concurrency # shennanigans. config_lock = FilesystemLock(config_path.asTextMode().path + ".lock") if not config_lock.lock(): raise AlreadySettingUp() try: # Check to see if there is already configuration. if config_path.exists(): raise ReplicationAlreadySetup() # Create a directory with it rw_cap = await client.make_directory() # Store the resulting write-cap in the node's private directory config_path.setContent(rw_cap.encode("ascii")) finally: # On success and failure, release the lock since we're done with the # file for now. config_lock.unlock() # Attenuate it to a read-cap rocap = attenuate_writecap(rw_cap) # Return the read-cap return rocap def is_replication_setup(config: Config) -> bool: """ :return: ``True`` if and only if replication has previously been setup for the Tahoe-LAFS node associated with the given configuration. """ # Find the configuration path for this node's replica. return FilePath(config.get_private_path(REPLICA_RWCAP_BASENAME)).exists() def get_replica_rwcap(config: Config) -> CapStr: """ :return: a mutable directory capability for our replica. :raises: Exception if replication is not setup """ rwcap_file = FilePath(config.get_private_path(REPLICA_RWCAP_BASENAME)) return rwcap_file.getContent().decode("ascii") @define class _Important: """ A context-manager to set and unset the ._important flag on a _ReplicationCapableConnection """ _replication_cursor: _ReplicationCapableCursor def __enter__(self) -> None: self._replication_cursor._important = True def __exit__(self, args) -> None: self._replication_cursor._important = False return None def with_replication( connection: _SQLite3Connection, enable_replication: bool ) -> _ReplicationCapableConnection: """ Wrap the given connection in a layer which is capable of entering a "replication mode". In replication mode, the wrapper stores all changes made through the connection so that they are available to be replicated by another component. In normal mode, changes are not stored. :param connection: The SQLite3 connection to wrap. :param enable_replication: If ``True`` then the wrapper is placed in "replication mode" initially. Otherwise it is not but it can be switched into that mode later. :return: The wrapper object. """ return _ReplicationCapableConnection(connection, enable_replication) Mutation = tuple[bool, str, Iterable[tuple[SQLType, ...]]] MutationObserver = Callable[[_SQLite3Cursor, Iterable[Mutation]], Callable[[], None]] @define class _ReplicationCapableConnection: """ Wrap a ``sqlite3.Connection`` to provide additional snapshot- and streaming replication-related features. All of this type's methods are intended to behave the same way as ``sqlite3.Connection``\ 's methods except they may also add some additional functionality to support replication. :ivar _replicating: ``True`` if this connection is currently in replication mode and is recording all executed DDL and DML statements, ``False`` otherwise. """ # the "real" / normal sqlite connection _conn: _SQLite3Connection _replicating: bool _observers: tuple[MutationObserver, ...] = Factory(tuple) _mutations: list[Mutation] = Factory(list) def enable_replication(self) -> None: """ Turn on replication support. """ self._replicating = True def add_mutation_observer(self, fn: MutationObserver) -> None: """ Add another observer of changes made through this connection. :param fn: An object to call after any transaction with changes is committed on this connection. """ self._observers = self._observers + (fn,) def iterdump(self) -> Iterator[str]: """ :return: SQL statements which can be used to reconstruct the database state. """ return self._conn.iterdump() def close(self) -> None: return self._conn.close() def __enter__(self) -> _ReplicationCapableConnection: self._conn.__enter__() return self def __exit__( self, exc_type: Optional[type], exc_value: Optional[BaseException], exc_tb: Optional[Any], ) -> bool: propagate = self._conn.__exit__(exc_type, exc_value, exc_tb) if exc_type is None: # There was no exception, signal observers that a change has been # committed. post_txn_fns: list[Callable[[], None]] = [] with self._conn: curse = self._conn.cursor() curse.execute("BEGIN IMMEDIATE TRANSACTION") post_txn_fns.extend(self._maybe_signal_observers(curse)) for f in post_txn_fns: f() # Respect the underlying propagation decision. return propagate def _maybe_signal_observers( self, cursor ) -> Generator[Callable[[], None], None, None]: """ If there are recorded mutations, deliver them to each of the observers and then forget about them. :return: A generator of the return values of the observers. """ if self._mutations: to_signal = self._mutations self._mutations = list() for ob in self._observers: yield ob(cursor, to_signal) def cursor(self, factory: Optional[type] = None) -> _ReplicationCapableCursor: """ Get a replication-capable cursor for this connection. """ kwargs = {} if factory is not None: kwargs["factory"] = factory cursor = self._conn.cursor(*kwargs) # this cursor honors the ._replicating flag in this instance return _ReplicationCapableCursor(cursor, self) @define class _ReplicationCapableCursor: """ Wrap a ``sqlite3.Cursor`` to provide additional streaming replication-related features. All of this type's attributes and methods are intended to behave the same way as ``sqlite3.Cursor``\ 's methods except they may also add some additional functionality to support replication. """ _cursor: _SQLite3Cursor _connection: _ReplicationCapableConnection # true while statements are "important" (which is pased along to # the observers and interpreted as being "important data that the # user will be interested in preserving") _important: bool = field(init=False, default=False) @property def lastrowid(self): return self._cursor.lastrowid @property def rowcount(self): return self._cursor.rowcount def close(self): return self._cursor.close() def execute(self, statement: str, row: Iterable[SQLType] = ()) -> Cursor: """ sqlite's Cursor API :param row: the arguments """ assert isinstance(row, tuple) self._cursor.execute(statement, row) if self._connection._replicating and statement_mutates(statement): # note that this interface is for multiple statements, so # we turn our single row into a one-tuple self._connection._mutations.append((self._important, statement, (row,))) return self def fetchall(self): return self._cursor.fetchall() def fetchmany(self, n): return self._cursor.fetchmany(n) def fetchone(self): return self._cursor.fetchone() def executemany(self, statement: str, rows: Iterable[Any]) -> Cursor: self._cursor.executemany(statement, rows) if self._connection._replicating and statement_mutates(statement): self._connection._mutations.append((self._important, statement, rows)) return self def important(self) -> _Important: """ Create a new context-manager that -- while active -- sets the 'important' flag to true and resets it afterwards. """ return _Important(self) def statements_to_snapshot(statements: Iterator[str]) -> bytes: """ Take a snapshot of the database reachable via the given connection. The snapshot is consistent and write transactions on the given connection are blocked until it has been completed. """ return cbor2.dumps({"version": 1, "statements": [x for x in statements]}) def connection_to_statements(connection: Connection) -> Iterator[str]: """ Create an iterator of SQL statements as strings representing a consistent, self-contained snapshot of the database reachable via the given connection. """ return iter(connection.iterdump()) # Convenience API to dump statements and encode them for storage. snapshot: Callable[[Connection], bytes] = compose( statements_to_snapshot, connection_to_statements ) async def tahoe_lafs_uploader( client: ITahoeClient, recovery_cap: str, get_snapshot_data: DataProvider, entry_name: str, ) -> None: """ Upload a replica to Tahoe, linking the result into the given recovery mutable capbility under the name given by :py:data:`SNAPSHOT_NAME`. """ snapshot_immutable_cap = await client.upload(get_snapshot_data) await client.link(recovery_cap, entry_name, snapshot_immutable_cap) def get_tahoe_lafs_direntry_uploader( client: ITahoeClient, directory_mutable_cap: str, ) -> Callable[[str, DataProvider], Awaitable[None]]: """ Bind a Tahoe client to a mutable directory in a callable that will upload some data and link it into the mutable directory under the given name. :return: A callable that will upload some data as the latest replica snapshot. The data isn't given directly, but instead from a zero-argument callable itself to facilitate retrying. """ async def upload(entry_name: str, get_data_provider: DataProvider) -> None: await tahoe_lafs_uploader( client, directory_mutable_cap, get_data_provider, entry_name ) return upload def get_tahoe_lafs_direntry_pruner( client: ITahoeClient, directory_mutable_cap: str, ) -> Callable[[Callable[[str], bool]], Awaitable[None]]: """ Bind a Tahoe client to a mutable directory in a callable that will unlink some entries. Which entries to unlink are controlled by a predicate. :return: A callable that will unlink some entries given a predicate. The prediate is given a filename inside the mutable to consider. """ async def maybe_unlink(predicate: Callable[[str], bool]) -> None: """ For each child of `directory_mutable_cap` delete it iff the predicate returns True for that name """ entries = await client.list_directory(directory_mutable_cap) for name in entries.keys(): if predicate(name): await client.unlink(directory_mutable_cap, name) return maybe_unlink def get_tahoe_lafs_direntry_lister( client: ITahoeClient, directory_mutable_cap: str ) -> EntryLister: """ Bind a Tahoe client to a mutable directory in a callable that will list the entries of that directory. """ async def lister() -> dict[str, DirectoryEntry]: entries = await client.list_directory(directory_mutable_cap) return { name: DirectoryEntry(kind, entry.get("size", 0)) for name, (kind, entry) in entries.items() } return lister def get_tahoe_lafs_direntry_replica( client: ITahoeClient, directory_mutable_cap: str ) -> Replica: """ Get an object that can interact with a replica stored in a Tahoe-LAFS mutable directory. """ uploader = get_tahoe_lafs_direntry_uploader(client, directory_mutable_cap) pruner = get_tahoe_lafs_direntry_pruner(client, directory_mutable_cap) lister = get_tahoe_lafs_direntry_lister(client, directory_mutable_cap) return Replica(uploader, pruner, lister) def add_events( cursor: _SQLite3Cursor, events: Iterable[tuple[str, Sequence[SQLType]]], important: bool, ) -> None: """ Add some new changes to the event-log. """ sql_args = [] for sql, args in events: assert all( isinstance(a, SQLRuntimeType) for a in args ), f"{args} contains non-SQL value" sql_args.append((sql, cbor2.dumps(args), important)) cursor.executemany( """ INSERT INTO [event-stream]([statement], [serialized_arguments], [important]) VALUES (?, ?, ?) """, sql_args, ) def get_events(conn: _SQLite3Connection) -> EventStream: """ Return all events currently in our event-log. """ with conn: cursor = conn.cursor() cursor.execute( """ SELECT [sequence-number], [statement], [serialized_arguments], [important] FROM [event-stream] """ ) rows = cursor.fetchall() return EventStream( changes=[ # List comprehension has incompatible type List[Change]; expected List[_T_co] # https://github.com/python-attrs/attrs/issues/519 Change(seq, stmt, cbor2.loads(arguments), important) # type: ignore for seq, stmt, arguments, important in rows ] ) def prune_events_to(conn: _SQLite3Connection, sequence_number: int) -> None: """ Remove all events <= sequence_number """ with conn: cursor = conn.cursor() cursor.execute( """ DELETE FROM [event-stream] WHERE [sequence-number] <= (?) """, (sequence_number,), ) cursor.fetchall() @frozen class AccumulatedChanges: """ A summary of some changes that have been made. :ivar important: Are any of these "important" changes? :ivar size: The approximate size in bytes to represent all of the changes. """ important: bool size: int @classmethod def no_changes(cls) -> AccumulatedChanges: """ Create an ``AccumulatedChanges`` that represents no changes. """ return cls(False, 0) @classmethod def from_connection(cls, connection: _SQLite3Connection) -> AccumulatedChanges: """ Load information about unreplicated changes from the database. """ # this size is larger than what we would have computed via # `from_changes` which only counts the statement-sizes .. but # maybe fine? (also could fix by just accumulating the # statement-sizes instead below) events = get_events(connection) data = events.to_bytes() size = data.seek(0, os.SEEK_END) any_important = any(change.important for change in events.changes) return cls(any_important, size) @classmethod def from_statements( cls, important: bool, statements: Iterable[tuple[str, Sequence[SQLType]]], ) -> AccumulatedChanges: """ Load information about unreplicated changes from SQL statements giving those changes. """ # note that we're ignoring a certain amount of size overhead here: the # _actual_ size will be some CBOR information and the sequence number, # although the statement text should still dominate. # XXX Fix the size calculation return cls(important, sum(len(sql) for (sql, _) in statements)) def __add__(self, other: AccumulatedChanges) -> AccumulatedChanges: return AccumulatedChanges( self.important or other.important, self.size + other.size ) def event_stream_name(high_seq: int) -> str: """ Construct the basename of the event stream object containing the given highest sequence number. """ return f"event-stream-{high_seq}" @define class _ReplicationService(Service): """ Perform all activity related to maintaining a remote replica of the local ZKAPAuthorizer database. If this service is running for a database then the database is in replication mode and changes will be uploaded. :ivar _connection: A connection to the database being replicated. :ivar _replicating: The long-running replication operation. This is never expected to complete but it will be cancelled when the service stops. """ name = "replication-service" # type: ignore # Service assigns None, screws up type inference _logger = Logger() _connection: _ReplicationCapableConnection = field() _replica: Replica _snapshot_policy: SnapshotPolicy _replicating: Optional[Deferred] = field(init=False, default=None) _changes: AccumulatedChanges = AccumulatedChanges.no_changes() _jobs: DeferredQueue = field(factory=DeferredQueue) @property def _unreplicated_connection(self): """ A normal SQLite3 connection object, changes made via which will not be replicated. """ return self._connection._conn def startService(self) -> None: super().startService() # Register ourselves as a change observer (first! we don't want to # miss anything) and then put the database into replication mode so # that there are recorded events for us to work with. self._connection.add_mutation_observer(self.observed_event) self._connection.enable_replication() # Reflect whatever state is left over in the database from previous # efforts. self._changes = AccumulatedChanges.from_connection( self._unreplicated_connection ) self.queue_job(ReplicationJob.startup) # Start the actual work of reacting to changes by uploading them (as # appropriate). self._replicating = Deferred.fromCoroutine(self._replicate()) async def _replicate(self) -> None: """ React to changes by replicating them to remote storage. """ try: await self.wait_for_uploads() except CancelledError: # Ignore cancels; this will be the normal way we quit -- see # stopService. pass except Exception: # If something besides a cancel happens, at least make it visible. self._logger.failure("unexpected wait_for_uploads error") return None def queue_job(self, job: ReplicationJob) -> None: """ Queue a job, if it is not already queued, to be executed after any other queued jobs. """ if job not in self._jobs.pending: self._jobs.put(job) @log_call(action_type="zkapauthorizer:replicate:queue-event-upload") def queue_event_upload(self) -> None: """ Request an event-stream upload of outstanding events. """ self.queue_job(ReplicationJob.event_stream) @log_call(action_type="zkapauthorizer:replicate:queue-snapshot-upload") def queue_snapshot_upload(self) -> None: """ Request that an upload of a new snapshot occur. Stale event-streams will also be pruned after the snapshot is successfully uploaded. """ self.queue_job(ReplicationJob.snapshot) async def wait_for_uploads(self) -> None: """ An infinite async loop that processes uploads of event-streams or snapshots """ while True: job = await self._jobs.get() if job == ReplicationJob.event_stream: await self._do_one_event_upload() elif job == ReplicationJob.snapshot: await self._do_one_snapshot_upload() elif job == ReplicationJob.consider_snapshot: await self._do_consider_snapshot() elif job == ReplicationJob.startup: await self._do_startup() else: raise Exception("internal error") # pragma: nocover async def _do_startup(self) -> None: """ Check local and remote state to determine if there is any work that should be done immediately. Currently, this will upload a snapshot if none exists in the replica, or upload an event stream if there are events that warrant immediate upload. """ if await self.should_upload_snapshot(): self.queue_snapshot_upload() elif self.should_upload_eventstream(self._changes): self.queue_event_upload() @log_call(action_type="zkapauthorizer:replicate:snapshot-upload") async def _do_one_snapshot_upload(self) -> None: """ Perform a single snapshot upload, including pruning event-streams from the replica that are no longer relevant. """ # extract sequence-number and snapshot data seqnum = 1 rows = ( self._connection.cursor() .execute( "SELECT seq FROM sqlite_sequence WHERE name = 'event-stream'", tuple() ) .fetchall() ) if len(rows): seqnum = int(rows[0][0]) snap = snapshot(self._connection) # upload snapshot await self._replica.upload("snapshot", lambda: BytesIO(snap)) # remove local event history (that should now be encapsulated # by the snapshot we just uploaded) prune_events_to(self._connection._conn, seqnum) # if we crash here, there will be extra event-stream objects # in the replica. This will be fixed correctly upon our next # snapshot upload. The extra event-stream objects will be # ignored by the recovery code. # prune old events from the replica def is_old_eventstream(fname: str) -> bool: """ :returns: True if the `fname` is an event-stream object and the sequence number is strictly less than our snapshot's maximum sequence. """ m = re.match("event-stream-([0-9])", fname) if m: seq = int(m.group(1)) if seq <= seqnum: return True return False await self._replica.prune(is_old_eventstream) @log_call(action_type="zkapauthorizer:replicate:event-upload") async def _do_one_event_upload(self) -> None: """ Process a single upload of all current events and then delete them from our database. """ events = get_events(self._unreplicated_connection) high_seq = events.highest_sequence() # if this is None there are no events at all if high_seq is None: return # otherwise, upload the events we found. name = event_stream_name(high_seq) await self._replica.upload(name, events.to_bytes) # then discard the uploaded events from the local database. prune_events_to(self._unreplicated_connection, high_seq) # Arrange to examine replica state soon to determine whether taking a # new snapshot and pruning existing event streams is useful. self.queue_job(ReplicationJob.consider_snapshot) async def _do_consider_snapshot(self) -> None: """ Inspect local and remote state to decide if the cost of taking and uploading a new snapshot is worth the resulting savings in storage. """ local_size = await self._new_snapshot_size() replica_size = await self._replica_size() if self._snapshot_policy.should_snapshot(local_size, replica_size): self.queue_snapshot_upload() async def _new_snapshot_size(self) -> int: """ Measure the size of snapshot of the current database state, in bytes. """ return len(snapshot(self._connection)) async def _replica_size(self) -> list[int]: """ Retrieve the size of all the files that are part of the current on-grid replica. """ entries = await self._replica.entry_lister() return [ entry.size for (name, entry) in entries.items() if entry.kind == "filenode" and name == "snapshot" or name.startswith("event-stream-") ] def stopService(self) -> Deferred[None]: """ Cancel the replication operation and then wait for it to complete. """ super().stopService() replicating = self._replicating if replicating is None: return succeed(None) self._replicating = None replicating.cancel() return replicating def observed_event( self, unobserved_cursor: _SQLite3Cursor, all_changes: Iterable[Mutation], ) -> Callable[[], None]: """ A mutating SQL statement was observed by the cursor. This is like the executemany interface: there is always a list of args. For a single statement, we call this with the len(args) == 1 :param all_changes: 3-tuples of (important, statement, args) where important is whether this should trigger an immediate upload; statement is the SQL statement; and args are the arguments for the SQL. """ # A mutation contains one statement and one or more rows of arguments # that go with it. We're going to generate an event per # statement/argument pair - so "unroll" those rows and pair each # individual argument tuple with its statement. events = [] any_important = False for (important, sql, manyargs) in all_changes: any_important = any_important or important for args in manyargs: events.append((sql, args)) add_events(unobserved_cursor, events, any_important) changes = AccumulatedChanges.from_statements(any_important, events) self._changes = self._changes + changes if self.should_upload_eventstream(self._changes): return self._complete_upload else: return lambda: None def _complete_upload(self) -> None: """ This is called after the transaction closes (because we return it from our observer function). See _ReplicationCapableConnection.__exit__ """ self.queue_event_upload() self._changes = AccumulatedChanges.no_changes() async def should_upload_snapshot(self) -> bool: """ :returns: True if there is no remote snapshot """ entries = await self._replica.list() return SNAPSHOT_NAME not in entries def should_upload_eventstream(self, changes: AccumulatedChanges) -> bool: """ :returns: True if we have accumulated enough statements to upload an event-stream record. """ return changes.important or changes.size >= 570000 def replication_service( replicated_connection: _ReplicationCapableConnection, replica: Replica, snapshot_policy: SnapshotPolicy, ) -> IService: """ Return a service which implements the replication process documented in the ``backup-recovery`` design document. 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ultracart/models/item_digital_item.py	gstingy/uc_python_api	0	152087	<reponame>gstingy/uc_python_api # coding: utf-8 """ UltraCart Rest API V2 UltraCart REST API Version 2 OpenAPI spec version: 2.0.0 Contact: <EMAIL> Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class ItemDigitalItem(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'creation_dts': 'str', 'description': 'str', 'file_size': 'int', 'mime_type': 'str', 'original_filename': 'str' } attribute_map = { 'creation_dts': 'creation_dts', 'description': 'description', 'file_size': 'file_size', 'mime_type': 'mime_type', 'original_filename': 'original_filename' } def __init__(self, creation_dts=None, description=None, file_size=None, mime_type=None, original_filename=None): """ ItemDigitalItem - a model defined in Swagger """ self._creation_dts = None self._description = None self._file_size = None self._mime_type = None self._original_filename = None self.discriminator = None if creation_dts is not None: self.creation_dts = creation_dts if description is not None: self.description = description if file_size is not None: self.file_size = file_size if mime_type is not None: self.mime_type = mime_type if original_filename is not None: self.original_filename = original_filename @property def creation_dts(self): """ Gets the creation_dts of this ItemDigitalItem. File creation date :return: The creation_dts of this ItemDigitalItem. :rtype: str """ return self._creation_dts @creation_dts.setter def creation_dts(self, creation_dts): """ Sets the creation_dts of this ItemDigitalItem. File creation date :param creation_dts: The creation_dts of this ItemDigitalItem. :type: str """ self._creation_dts = creation_dts @property def description(self): """ Gets the description of this ItemDigitalItem. Description of the digital item :return: The description of this ItemDigitalItem. :rtype: str """ return self._description @description.setter def description(self, description): """ Sets the description of this ItemDigitalItem. Description of the digital item :param description: The description of this ItemDigitalItem. :type: str """ if description is not None and len(description) > 200: raise ValueError("Invalid value for `description`, length must be less than or equal to `200`") self._description = description @property def file_size(self): """ Gets the file_size of this ItemDigitalItem. File size :return: The file_size of this ItemDigitalItem. :rtype: int """ return self._file_size @file_size.setter def file_size(self, file_size): """ Sets the file_size of this ItemDigitalItem. File size :param file_size: The file_size of this ItemDigitalItem. :type: int """ self._file_size = file_size @property def mime_type(self): """ Gets the mime_type of this ItemDigitalItem. Mime type associated with the file :return: The mime_type of this ItemDigitalItem. :rtype: str """ return self._mime_type @mime_type.setter def mime_type(self, mime_type): """ Sets the mime_type of this ItemDigitalItem. Mime type associated with the file :param mime_type: The mime_type of this ItemDigitalItem. :type: str """ if mime_type is not None and len(mime_type) > 100: raise ValueError("Invalid value for `mime_type`, length must be less than or equal to `100`") self._mime_type = mime_type @property def original_filename(self): """ Gets the original_filename of this ItemDigitalItem. Original filename :return: The original_filename of this ItemDigitalItem. :rtype: str """ return self._original_filename @original_filename.setter def original_filename(self, original_filename): """ Sets the original_filename of this ItemDigitalItem. Original filename :param original_filename: The original_filename of this ItemDigitalItem. :type: str """ if original_filename is not None and len(original_filename) > 250: raise ValueError("Invalid value for `original_filename`, length must be less than or equal to `250`") self._original_filename = original_filename def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` 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resources/mstats/__init__.py	mvelezg99/distribuciones-de-probabilidad-python	8	178649	<reponame>mvelezg99/distribuciones-de-probabilidad-python """ mstats: A statistics and probability library for Python ======================================================= """ from mstats import anova from mstats import distributions from mstats import generals from mstats import hypothesis from mstats import intervals from mstats import categorical from mstats import linregr from mstats import graph from mstats import multiregr from mstats import timeseries	[ 1, 529, 276, 1112, 420, 29958, 29885, 345, 11867, 29887, 29929, 29929, 29914, 5721, 1091, 1682, 2884, 29899, 311, 29899, 22795, 4427, 2368, 29899, 4691, 13, 15945, 29908, 13, 29885, 16202, 29901, 319, 13964, 322, 6976, 3489, 363, 5132, 13, 9166, 9166, 9166, 2751, 25512, 13, 13, 15945, 29908, 13, 3166, 286, 16202, 1053, 385, 4273, 13, 3166, 286, 16202, 1053, 18822, 13, 3166, 286, 16202, 1053, 1176, 1338, 13, 3166, 286, 16202, 1053, 20051, 13, 3166, 286, 16202, 1053, 18747, 13, 3166, 286, 16202, 1053, 11608, 936, 13, 3166, 286, 16202, 1053, 6276, 276, 629, 13, 3166, 286, 16202, 1053, 3983, 13, 3166, 286, 16202, 1053, 1773, 533, 629, 13, 3166, 286, 16202, 1053, 3064, 6358, 13, 2 ]
src/creeps/__init__.py	ppolewicz/screeps-starter-python	0	98283	<reponame>ppolewicz/screeps-starter-python from creeps.builder import Builder from creeps.harvester import Harvester from creeps.miner import Miner from creeps.hauler import Hauler from creeps.scout import Scout from creeps.claimer import Claimer from creeps.upgrader import Upgrader from creeps.settler import Settler from creeps.cleanser import Cleanser CREEP_CLASSES = dict({ 'builder': Builder, 'harvester': Harvester, 'miner': Miner, 'hauler': Hauler, 'scout': Scout, 'claimer': Claimer, 'upgrader': Upgrader, 'settler': Settler, 'cleanser': Cleanser, })	[ 1, 529, 276, 1112, 420, 29958, 407, 1772, 29893, 5175, 29914, 1557, 929, 567, 29899, 303, 4254, 29899, 4691, 13, 3166, 907, 8961, 29889, 16409, 1053, 5373, 2700, 13, 3166, 907, 8961, 29889, 8222, 29894, 4156, 1053, 3536, 29894, 4156, 13, 3166, 907, 8961, 29889, 1195, 261, 1053, 3080, 261, 13, 3166, 907, 8961, 29889, 2350, 8584, 1053, 5952, 8584, 13, 3166, 907, 8961, 29889, 1557, 449, 1053, 2522, 449, 13, 3166, 907, 8961, 29889, 16398, 4193, 1053, 6015, 4193, 13, 3166, 907, 8961, 29889, 786, 629, 1664, 1053, 5020, 629, 1664, 13, 3166, 907, 8961, 29889, 9915, 1358, 1053, 317, 1803, 1358, 13, 3166, 907, 8961, 29889, 2841, 550, 261, 1053, 21386, 550, 261, 13, 13, 13, 22245, 15488, 29918, 6154, 3289, 1660, 29903, 353, 9657, 3319, 13, 1678, 525, 16409, 2396, 5373, 2700, 29892, 13, 1678, 525, 8222, 29894, 4156, 2396, 3536, 29894, 4156, 29892, 13, 1678, 525, 1195, 261, 2396, 3080, 261, 29892, 13, 1678, 525, 2350, 8584, 2396, 5952, 8584, 29892, 13, 1678, 525, 1557, 449, 2396, 2522, 449, 29892, 13, 1678, 525, 16398, 4193, 2396, 6015, 4193, 29892, 13, 1678, 525, 786, 629, 1664, 2396, 5020, 629, 1664, 29892, 13, 1678, 525, 9915, 1358, 2396, 317, 1803, 1358, 29892, 13, 1678, 525, 2841, 550, 261, 2396, 21386, 550, 261, 29892, 13, 1800, 13, 2 ]
AngularJSLibrary/test/testExecAsyncWithCallback.py	klaasderks/robotframework-angularjs	35	127472	<filename>AngularJSLibrary/test/testExecAsyncWithCallback.py import unittest from selenium import webdriver js_waiting_var=""" var waiting = true; var callback = arguments[arguments.length - 1]; var el = document.querySelector('#nested-ng-app'); angular.element(el).injector().get('$browser'). notifyWhenNoOutstandingRequests(callback); return waiting; """ js_return_callback=""" var callback = arguments[arguments.length - 1]; return callback; """ js_callback=""" var callback = arguments[arguments.length - 1]; """ js_timeout=""" var h = setTimeout(console.log('.'),2000); """ js_async_with_callback=""" var callback = arguments[arguments.length - 1]; var rootSelector = '[ng-app]' functions.waitForAngular = function(rootSelector, callback) { var el = document.querySelector(rootSelector); try { if (window.getAngularTestability) { window.getAngularTestability(el).whenStable(callback); return; } if (!window.angular) { throw new Error('window.angular is undefined. This could be either ' + 'because this is a non-angular page or because your test involves ' + 'client-side navigation, which can interfere with Protractor\'s ' + 'bootstrapping. See http://git.io/v4gXM for details'); } if (angular.getTestability) { angular.getTestability(el).whenStable(callback); } else { if (!angular.element(el).injector()) { throw new Error('root element (' + rootSelector + ') has no injector.' + ' this may mean it is not inside ng-app.'); } angular.element(el).injector().get('$browser'). notifyWhenNoOutstandingRequests(callback); } } catch (err) { callback(err.message); } }; """ js_tada=""" var done=arguments[0]; setTimeout(function() { done('tada'); }, 10000); """ class ExecuteAsyncJavascriptWithCallback(unittest.TestCase): def setUp(self): self.driver = webdriver.Firefox() self.driver.set_script_timeout(30) def test_exe_javascript(self): driver = self.driver driver.get("http://localhost:7000/testapp/ng1/alt_root_index.html#/async") # waiting = driver.execute_script(js_tada) # print('%s' % waiting) try: while (True): #waiting = driver.execute_async_script(js_async_with_callback) #waiting = driver.execute_async_script(js_waiting_var) #waiting = driver.execute_async_script(js_callback,"print 'Hello World\|'") #waiting = driver.execute_script(js_return_callback,"return false;") waiting = driver.execute_script(js_tada) if waiting != None: print('%s' % waiting) except KeyboardInterrupt: pass def tearDown(self): # self.driver.close() pass if __name__ == "__main__": unittest.main()	[ 1, 529, 9507, 29958, 9928, 1070, 8700, 12284, 29914, 1688, 29914, 1688, 5379, 8123, 3047, 10717, 29889, 2272, 13, 5215, 443, 27958, 13, 3166, 18866, 1053, 1856, 9465, 13, 13, 1315, 29918, 10685, 292, 29918, 1707, 13776, 29908, 13, 1678, 722, 10534, 353, 1565, 29936, 13, 1678, 722, 6939, 353, 6273, 29961, 25699, 29889, 2848, 448, 29871, 29896, 1385, 13, 1678, 722, 560, 353, 1842, 29889, 18825, 14237, 27420, 29899, 865, 29899, 932, 2157, 13, 1678, 6401, 29889, 5029, 29898, 295, 467, 21920, 272, 2141, 657, 877, 29938, 15965, 2824, 13, 18884, 26051, 10401, 3782, 3744, 11235, 3089, 29879, 29898, 14035, 416, 539, 13, 1678, 736, 10534, 29936, 13, 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Code/Testing/AlexNet Testing Code.py	apoorva2398/APS360-Project-Team-1	1	86236	#!/usr/bin/env python # coding: utf-8 # In[7]: import torch import torch.nn as nn from torchvision import datasets, models, transforms import os # In[8]: # Load the test data. data_transforms = { 'test': transforms.Compose([ transforms.Resize([224, 224]), transforms.ToTensor() ]) } data_dir = 'D:\data (augmented, 4 classes, tif)' image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['test']} batch_size = 128 dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True, num_workers=4) for x in ['test']} dataset_sizes = {x: len(image_datasets[x]) for x in ['test']} class_names = image_datasets['test'].classes device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # In[9]: # Load the saved model state_dict for inference (done later to keep len(class_names) after the dataloader dynamic). model_ft = models.alexnet(pretrained=True) model_ft.classifier[6] = nn.Linear(4096, len(class_names)) model_ft.classifier.add_module("7", nn.Dropout()) # Comment this out if the AlexNet model did not include the last dropout layer. PATH = "D:\Models\model_20190411-093358.pth" model_ft.load_state_dict(torch.load(PATH)) model_ft = model_ft.to(device) # In[10]: was_training = model_ft.training model_ft.eval() all_labels = [] all_preds = [] with torch.no_grad(): for inputs, labels in dataloaders['test']: # The labels will correspond to the alphabetical order of the class names (https://discuss.pytorch.org/t/how-to-get-the-class-names-to-class-label-mapping/470). inputs = inputs.to(device) labels = labels.to(device) labels_list = labels.tolist() all_labels.extend(labels_list) outputs = model_ft(inputs) _, preds = torch.max(outputs, 1) preds_list = preds.tolist() all_preds.extend(preds_list) model_ft.train(mode=was_training) # In[11]: from pandas_ml import ConfusionMatrix cm = ConfusionMatrix(all_labels, all_preds) cm.print_stats()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 14137, 29901, 23616, 29899, 29947, 13, 13, 29937, 512, 29961, 29955, 5387, 13, 13, 13, 5215, 4842, 305, 13, 5215, 4842, 305, 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src/plog/mixins.py	Strangemother/project-conceptnet-graphing	0	160338	<reponame>Strangemother/project-conceptnet-graphing from io import StringIO from plog import patterns class MixinBase(object): ''' Base mixin object of all Plog Mixins to inherit''' def __init__(self, args, kwargs): pass class PlogFileMixin(MixinBase): ''' A PlogFileMixin is designed to wrap a file object to represent correctly for enumeration. You can pass a string or a file object. Strings are converted to StringIO.StringIO before use. Pass this to the class for easy get_file, set_file methods ''' def __init__(self, args, *kwargs): ''' initial file can be given ''' self._file = None self._data = None if len(args) > 0: ''' could be file or string''' self.set_data(args[0]) super(PlogFileMixin, self).__init__(args, *kwargs) def set_file(self, log_file): ''' Add a file to the class to parse This will return a StringIO if a string was passed as data.''' self.set_data(log_file) def set_data(self, data): ''' wrapper for applying the file content to the class''' if type(data) == str: output = StringIO.StringIO(data) self._data = output else: self._data = data def get_data(self): return self._data def get_file(self): ''' return the internal file, This will return a StringIO if a string was passed as data ''' return self.get_data() class PlogBlockMixin(MixinBase): ''' Methods to assist block design on on a class ''' def __init__(self, args, *kwargs): self._blocks = [] super(PlogBlockMixin, self).__init__(args, kwargs) def blocks(): doc = "The blocks property." def fget(self): return self._blocks def fset(self, value): self._blocks = value def fdel(self): del self._blocks return locals() blocks = property(blocks()) def block_match(self, block, line, string, reg): ''' Method called upon a successfull match of a block header block {PlogBlock} - The block containing the matched PlogLine line {PlogLine} - PlogLine matched string {str} - String matched from file reg {regex} - Regex SRE match object (if it exists) ''' #print '> Block', block #print ' Match', line #print ' Found', string, '\n' def compile_blocks(self): '''Call each block to compile as needed''' for block in self.blocks: block.compile() def close_all_blocks(self): ''' Close any dangling blocks (blocks open at the end of parsing) and return a list of closed blocks. ''' closed =[] for block in self.blocks: if block.is_open: print('force close', block.ref) closed.append(block) block.close() return closed def add_blocks(self, args): ''' Add a list of blocks to append add_blocks(block1, block2, block3, ... ) add_blocks(blocks) ''' for block in args: self.add_block(block) def add_block(self, block): ''' Append a plog block to all valid blocks. ''' _block = block if type(block) == str: _block = patterns.PlogBlock(block) elif hasattr(_block, 'block'): _block = _block.block self._blocks.append(_block) def get_blocks_with_header(self, args): ''' Pass one or many PlogLines and return all matching blocks with the headers of that type. ''' header_line = args[0] # Loop blocks. _blocks = [] for block in self.blocks: mtch, reg = block.header.match(header_line) if mtch: _blocks.append(block) self.block_match(block, block.header, header_line, reg) return _blocks def get_blocks_with_header_footer(self, args): ''' Pass one or many PlogLines and return all matching blocks with the headers of that type. ''' mline = args[0] # Loop blocks. _blocks = [] mtch = None mtch_f = None reg = None reg_f = None for block in self.blocks: if block.header: mtch, reg = block.header.match(mline) if block.footer: mtch_f, reg_f = block.footer.match(mline) if mtch_f or mtch: _blocks.append(block) if mtch: self.block_match(block, block.header, mline, reg or reg_f) if mtch_f: self.block_match(block, block.footer, mline, reg or reg_f) return (_blocks, True if mtch else False, )	[ 1, 529, 276, 1112, 420, 29958, 5015, 574, 331, 1228, 29914, 4836, 29899, 535, 1547, 1212, 29899, 4262, 292, 13, 3166, 12013, 1053, 1714, 5971, 30004, 13, 3166, 282, 1188, 1053, 15038, 30004, 13, 30004, 13, 1990, 23478, 262, 5160, 29898, 3318, 1125, 30004, 13, 1678, 14550, 7399, 6837, 262, 1203, 310, 599, 349, 1188, 23478, 1144, 304, 13125, 12008, 30004, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 334, 5085, 29892, 3579, 19290, 1125, 30004, 13, 4706, 1209, 30004, 13, 30004, 13, 1990, 349, 1188, 2283, 29924, 861, 262, 29898, 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gdcv/db/base_match_state.py	the-blue-alliance/gdcv-backend	4	179250	<filename>gdcv/db/base_match_state.py from sqlalchemy import Column, String, Integer, BigInteger, PrimaryKeyConstraint class BaseMatchState(object): __table_args__ = ( PrimaryKeyConstraint('event_key', 'match_id', 'wall_time'), ) event_key = Column(String(16), nullable=False) # Like 2017nyny match_id = Column(String(16), nullable=False) # Like qm1 play = Column(Integer, nullable=False) # Accounts for replays wall_time = Column(BigInteger, nullable=False) mode = Column(String(16)) # pre_match, auto, teleop, post_match time_remaining = Column(Integer) # Number of seconds remaining in mode red_score = Column(Integer) blue_score = Column(Integer)	[ 1, 529, 9507, 29958, 29887, 29881, 11023, 29914, 2585, 29914, 3188, 29918, 4352, 29918, 3859, 29889, 2272, 13, 3166, 4576, 284, 305, 6764, 1053, 12481, 29892, 1714, 29892, 8102, 29892, 7997, 7798, 29892, 28267, 2558, 21529, 13, 13, 1990, 7399, 9652, 2792, 29898, 3318, 1125, 13, 13, 1678, 4770, 2371, 29918, 5085, 1649, 353, 313, 13, 4706, 28267, 2558, 21529, 877, 3696, 29918, 1989, 742, 525, 4352, 29918, 333, 742, 525, 11358, 29918, 2230, 5477, 13, 1678, 1723, 13, 1678, 1741, 29918, 1989, 353, 12481, 29898, 1231, 29898, 29896, 29953, 511, 1870, 519, 29922, 8824, 29897, 29871, 396, 8502, 29871, 29906, 29900, 29896, 29955, 29876, 948, 29891, 13, 1678, 1993, 29918, 333, 353, 12481, 29898, 1231, 29898, 29896, 29953, 511, 1870, 519, 29922, 8824, 29897, 29871, 396, 8502, 3855, 29885, 29896, 13, 1678, 1708, 353, 12481, 29898, 7798, 29892, 1870, 519, 29922, 8824, 29897, 29871, 396, 16535, 29879, 363, 337, 12922, 13, 1678, 10090, 29918, 2230, 353, 12481, 29898, 6970, 7798, 29892, 1870, 519, 29922, 8824, 29897, 13, 1678, 4464, 353, 12481, 29898, 1231, 29898, 29896, 29953, 876, 29871, 396, 758, 29918, 4352, 29892, 4469, 29892, 4382, 459, 29892, 1400, 29918, 4352, 13, 1678, 931, 29918, 1745, 17225, 353, 12481, 29898, 7798, 29897, 29871, 396, 9681, 310, 6923, 9886, 297, 4464, 13, 13, 1678, 2654, 29918, 13628, 353, 12481, 29898, 7798, 29897, 13, 1678, 7254, 29918, 13628, 353, 12481, 29898, 7798, 29897, 13, 2 ]
trainer.py	Xavierxhq/tableware	0	81347	import os, random, sys, time import torch from os import path as osp from pprint import pprint import numpy as np from torch import nn from torch.backends import cudnn import utils.loss as loss from models import get_baseline_model from utils.meters import AverageMeter from utils.loss import TripletLoss from utils.serialization import save_checkpoint from tester import get_feature, pickle_read, pickle_write, dist, evaluate_model def load_model(base_model, model_path): model_parameter = torch.load(model_path) base_model.load_state_dict(model_parameter['state_dict']) base_model = base_model.cuda() print('model', model_path.split('/')[-1], 'loaded.') return base_model def _compute_center_with_label(model, label, data_pth, sample_count): temp_name = './evaluate_result/feature_map/%f.pkl' % time.time() processed_imgs = [data_pth + x for x in os.listdir(data_pth) if x.split('.')[0].split('_')[-1] == str(label)] random.shuffle(processed_imgs) # use sample_count4 pictures to get the center for img_path in processed_imgs[:sample_count4]: f = get_feature(img_path, model) _store_features_temp(temp_name, str(label), f) obj = pickle_read(temp_name) _avg_feature = None for _f in obj[str(label)]: if _avg_feature is None: _avg_feature = _f else: _avg_feature = _f.add(_avg_feature) # get the center _avg_feature /= len(obj[str(label)]) dist_dict = {} for _i, _f in enumerate(obj[str(label)]): _d = dist(_avg_feature, _f) dist_dict[str(_i)] = _d dist_dict = sorted(dist_dict.items(), key=lambda d: d[1]) # for index, _d in dist_dict: # print(index, _d) # exit(1000) _avg_feature = obj[str(label)][int(dist_dict[0][0])] _max_feature_ls, _min_feature_ls = [], [] # the feature with smalleset distances for index, _d in dist_dict[1:sample_count+1]: _min_feature_ls.append(obj[str(label)][int(index)]) # those with largest distances for index, _d in dist_dict[-1sample_count:]: _max_feature_ls.append(obj[str(label)][int(index)]) os.remove(temp_name) return _avg_feature, _max_feature_ls, _min_feature_ls def _store_features_temp(feature_map_name, label, feature): if os.path.exists(feature_map_name): obj = pickle_read(feature_map_name) obj[label].append(feature) else: obj = { label: [feature] } pickle_write(feature_map_name, obj) def _get_negative_feature(model, label, data_pth): all_nagative_imgs = [data_pth + x for x in os.listdir(data_pth) if x.split('.')[0].split('_')[-1] != str(label)] random.shuffle(all_nagative_imgs) return get_feature(all_nagative_imgs[0], model) def _get_positive_feature(model, label, data_pth): all_positive_imgs = [data_pth + x for x in os.listdir(data_pth) if x.split('.')[0].split('_')[-1] == str(label)] random.shuffle(all_positive_imgs) return get_feature(all_positive_imgs[0], model) def _get_input_samples(model, labels, data_pth, sample_count=64): if sample_count % len(labels) != 0: print('sample_count should times the count of labels') return sample_count_for_one_label = int(sample_count / len(labels)) candidate_count_for_one_label = sample_count_for_one_label 4 anchors, positives, negatives = [], [], [] for label in labels: _avg_feature, _max_feature_ls, _min_feature_ls = _compute_center_with_label(model, label, data_pth, sample_count=sample_count_for_one_label) for i in range(len(_max_feature_ls)): anchors.append(_avg_feature) if random.randint(1, 10) > 50: positives.append(_max_feature_ls[i]) # positives.append(_min_feature_ls[i]) else : positives.append(_get_positive_feature(model, label, data_pth)) negatives.append(_get_negative_feature(model, label, data_pth)) return anchors, positives, negatives def train(model, optimizer, criterion, epoch, print_freq, data_loader, data_pth): # model.train() losses = AverageMeter() is_add_margin = False labels_count_in_one_batch = 8 labels_count_for_all = 40 iteration_count = 40 start = time.time() for _j in range(iteration_count): for i in range(int(labels_count_for_all / labels_count_in_one_batch)): start_label = i * labels_count_in_one_batch + 1 model.eval() feat1, feat2, feat3 = _get_input_samples(model, [x for x in range(start_label, start_label + labels_count_in_one_batch)], data_pth) model.train() loss = criterion(torch.stack(feat1), torch.stack(feat2), torch.stack(feat3)) optimizer.zero_grad() # backward loss.backward() optimizer.step() losses.update(loss.item()) if (_j * int(labels_count_for_all / labels_count_in_one_batch) + i + 1) % print_freq == 0: print('Epoch: [{}][{}/{}]\t' 'Loss {:.6f} ({:.6f})\t' .format(epoch, _j * int(labels_count_for_all / labels_count_in_one_batch) + i + 1, iteration_count * int(labels_count_for_all / labels_count_in_one_batch), losses.val, losses.mean)) if losses.val < 1e-5: is_add_margin = True param_group = optimizer.param_groups print('Epoch: [{}]\tEpoch Time {:.1f} s\tLoss {:.6f}\t' 'Lr {:.2e}' .format(epoch, (time.time() - start), losses.mean, param_group[0]['lr'])) return is_add_margin def trainer(data_pth, a, b, _time=0, layers=18): seed = 0 # dataset options height, width = 128, 128 # optimization options optim = 'Adam' max_epoch = 20 train_batch = 64 test_batch = 64 lr = 0.1 step_size = 40 gamma = 0.1 weight_decay = 5e-4 momentum = 0.9 test_margin = b margin = a num_instances = 4 num_gpu = 1 # model options last_stride = 1 pretrained_model_18 = 'model/resnet18-5c106cde.pth' pretrained_model_50 = 'model/resnet50-19c8e357.pth' pretrained_model_34 = 'model/resnet34-333f7ec4.pth' pretrained_model_101 = 'model/resnet101-5d3b4d8f.pth' pretrained_model_152 = 'model/resnet152-b121ed2d.pth' # miscs print_freq = 10 eval_step = 1 save_dir = 'model/pytorch-ckpt/time%d' % _time workers = 1 torch.manual_seed(seed) use_gpu = torch.cuda.is_available() if use_gpu: print('currently using GPU') cudnn.benchmark = True torch.cuda.manual_seed_all(seed) else: print('currently using cpu') pin_memory = True if use_gpu else False # model, optim_policy = get_baseline_model(model_path=pretrained_model) if layers == 18: model, optim_policy = get_baseline_model(model_path=pretrained_model_18, layers=18) else: model, optim_policy = get_baseline_model(model_path=pretrained_model_50, layers=50) # model, optim_policy = get_baseline_model(model_path=pretrained_model_18, layers=18) # model, optim_policy = get_baseline_model(model_path=pretrained_model_34, layers=34) # model, optim_policy = get_baseline_model(model_path=pretrained_model_101, layers=101) # model = load_model(model, model_path='./model/pytorch-ckpt/87_layers18_margin20_epoch87.tar') print('model\'s parameters size: {:.5f} M'.format(sum(p.numel() for p in model.parameters()) / 1e6)) tri_criterion = TripletLoss(margin) # get optimizer optimizer = torch.optim.Adam( optim_policy, lr=lr, weight_decay=weight_decay ) def adjust_lr(optimizer, ep): if ep < 20: lr = 1e-4 * (ep + 1) / 2 elif ep < 80: lr = 1e-3 * num_gpu elif ep < 180: lr = 1e-4 * num_gpu elif ep < 300: lr = 1e-5 * num_gpu elif ep < 320: lr = 1e-5 * 0.1 ** ((ep - 320) / 80) num_gpu elif ep < 400: lr = 1e-6 elif ep < 480: lr = 1e-4 num_gpu else: lr = 1e-5 * num_gpu for p in optimizer.param_groups: p['lr'] = lr if use_gpu: model = nn.DataParallel(model).cuda() max_acc = .0 for epoch in range(max_epoch): if step_size > 0: adjust_lr(optimizer, epoch + 1) next_margin = margin # skip if not save model if eval_step > 0 and (epoch + 1) % eval_step == 0 or (epoch + 1) == max_epoch: _t1 =time.time() train(model, optimizer, tri_criterion, epoch + 1, print_freq, None, data_pth=data_pth) _t2 = time.time() print('time for training:', '%.2f' % (_t2 - _t1), 's') acc = evaluate_model(model, margin=20, epoch=1) if acc > max_acc: max_acc = acc print('max acc:', max_acc, ', epoch:', epoch + 1) if use_gpu: state_dict = model.module.state_dict() else: state_dict = model.state_dict() save_model_name = 'layers{}_margin{}_epoch{}.tar'.format(layers, margin, epoch+1) save_checkpoint({ 'state_dict': state_dict, 'epoch': epoch + 1, }, is_best=False, save_dir=save_dir, filename=save_model_name) margin = next_margin return save_model_name if __name__ == "__main__": for _i in range(1): trainer('/home/ubuntu/Program/xhq/dataset/temp/train_data/', 20, 0, _time=_i, layers=50)	[ 1, 1053, 2897, 29892, 4036, 29892, 10876, 29892, 931, 13, 5215, 4842, 305, 13, 3166, 2897, 1053, 2224, 408, 288, 1028, 13, 3166, 282, 2158, 1053, 282, 2158, 13, 5215, 12655, 408, 7442, 13, 3166, 4842, 305, 1053, 302, 29876, 13, 3166, 4842, 305, 29889, 1627, 1975, 1053, 274, 566, 15755, 13, 5215, 3667, 29879, 29889, 6758, 408, 6410, 13, 3166, 4733, 1053, 679, 29918, 6500, 5570, 29918, 4299, 13, 3166, 3667, 29879, 29889, 2527, 414, 1053, 319, 19698, 29924, 1308, 13, 3166, 3667, 29879, 29889, 6758, 1053, 8602, 552, 29873, 29931, 2209, 13, 3166, 3667, 29879, 29889, 15550, 2133, 1053, 4078, 29918, 3198, 3149, 13, 3166, 1243, 261, 1053, 679, 29918, 14394, 29892, 5839, 280, 29918, 949, 29892, 5839, 280, 29918, 3539, 29892, 1320, 29892, 14707, 29918, 4299, 13, 13, 13, 1753, 2254, 29918, 4299, 29898, 3188, 29918, 4299, 29892, 1904, 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care/facility/migrations/0010_auto_20200321_0758.py	tucosaurus/care	189	1617112	<reponame>tucosaurus/care<filename>care/facility/migrations/0010_auto_20200321_0758.py # Generated by Django 2.2.11 on 2020-03-21 07:58 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('facility', '0009_auto_20200320_1850'), ] operations = [ migrations.AlterField( model_name='hospitaldoctors', name='area', field=models.IntegerField(choices=[(1, 'General Medicine'), (2, 'Pulmonology'), (3, 'Critical Care'), (4, 'Paediatrics')]), ), ]	[ 1, 529, 276, 1112, 420, 29958, 29873, 1682, 3628, 17142, 29914, 18020, 29966, 9507, 29958, 18020, 29914, 17470, 1793, 29914, 26983, 800, 29914, 29900, 29900, 29896, 29900, 29918, 6921, 29918, 29906, 29900, 29906, 29900, 29900, 29941, 29906, 29896, 29918, 29900, 29955, 29945, 29947, 29889, 2272, 13, 29937, 3251, 630, 491, 15337, 29871, 29906, 29889, 29906, 29889, 29896, 29896, 373, 29871, 29906, 29900, 29906, 29900, 29899, 29900, 29941, 29899, 29906, 29896, 29871, 29900, 29955, 29901, 29945, 29947, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 17470, 1793, 742, 525, 29900, 29900, 29900, 29929, 29918, 6921, 29918, 29906, 29900, 29906, 29900, 29900, 29941, 29906, 29900, 29918, 29896, 29947, 29945, 29900, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 29882, 8189, 1867, 14359, 742, 13, 9651, 1024, 2433, 6203, 742, 13, 9651, 1746, 29922, 9794, 29889, 7798, 3073, 29898, 1859, 1575, 11759, 29898, 29896, 29892, 525, 15263, 27529, 5477, 313, 29906, 29892, 525, 29925, 352, 3712, 3002, 5477, 313, 29941, 29892, 525, 29907, 768, 936, 10057, 5477, 313, 29946, 29892, 525, 11868, 287, 7163, 10817, 1495, 11724, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
cepan/_utils.py	kanga333/cepan	1	178845	<filename>cepan/_utils.py from typing import Any, Callable, Dict, Iterator, Optional import boto3 def client( service: str, session: Optional[boto3.Session] = None, ) -> boto3.client: if session is None: return boto3.client(service) return session.client(service) def call_with_pagination( client: boto3.client, func_name: str, args: Dict[str, Any], ) -> Iterator[Dict[str, Any]]: func: Callable[..., Dict[str, Any]] = getattr(client, func_name) response: Dict[str, Any] = func(args) yield response token_key: Optional[str] = None if "NextToken" in response: token_key = "NextToken" if "NextPageToken" in response: token_key = "NextPageToken" while token_key in response: args[token_key] = response[token_key] response = func(args) yield response	[ 1, 529, 9507, 29958, 346, 8357, 19891, 13239, 29889, 2272, 13, 3166, 19229, 1053, 3139, 29892, 8251, 519, 29892, 360, 919, 29892, 20504, 1061, 29892, 28379, 13, 13, 5215, 289, 3747, 29941, 13, 13, 13, 1753, 3132, 29898, 13, 1678, 2669, 29901, 851, 29892, 13, 1678, 4867, 29901, 28379, 29961, 29890, 3747, 29941, 29889, 7317, 29962, 353, 6213, 29892, 13, 29897, 1599, 289, 3747, 29941, 29889, 4645, 29901, 13, 1678, 565, 4867, 338, 6213, 29901, 13, 4706, 736, 289, 3747, 29941, 29889, 4645, 29898, 5509, 29897, 13, 13, 1678, 736, 4867, 29889, 4645, 29898, 5509, 29897, 13, 13, 13, 1753, 1246, 29918, 2541, 29918, 13573, 3381, 29898, 13, 1678, 3132, 29901, 289, 3747, 29941, 29889, 4645, 29892, 13, 1678, 3653, 29918, 978, 29901, 851, 29892, 13, 1678, 6389, 29901, 360, 919, 29961, 710, 29892, 3139, 1402, 13, 29897, 1599, 20504, 1061, 29961, 21533, 29961, 710, 29892, 3139, 5262, 29901, 13, 1678, 3653, 29901, 8251, 519, 29961, 16361, 360, 919, 29961, 710, 29892, 3139, 5262, 353, 679, 5552, 29898, 4645, 29892, 3653, 29918, 978, 29897, 13, 1678, 2933, 29901, 360, 919, 29961, 710, 29892, 3139, 29962, 353, 3653, 29898, 1068, 5085, 29897, 13, 1678, 7709, 2933, 13, 1678, 5993, 29918, 1989, 29901, 28379, 29961, 710, 29962, 353, 6213, 13, 1678, 565, 376, 9190, 6066, 29908, 297, 2933, 29901, 13, 4706, 5993, 29918, 1989, 353, 376, 9190, 6066, 29908, 13, 1678, 565, 376, 9190, 5074, 6066, 29908, 297, 2933, 29901, 13, 4706, 5993, 29918, 1989, 353, 376, 9190, 5074, 6066, 29908, 13, 1678, 1550, 5993, 29918, 1989, 297, 2933, 29901, 13, 4706, 6389, 29961, 6979, 29918, 1989, 29962, 353, 2933, 29961, 6979, 29918, 1989, 29962, 13, 4706, 2933, 353, 3653, 29898, 1068, 5085, 29897, 13, 4706, 7709, 2933, 13, 2 ]
src/ai/backend/kernel/git/__init__.py	hephaex/backend.ai-kernel-runner	1	157332	import asyncio import json import logging import os from pathlib import Path import re import subprocess # import pygit2 # from pygit2 import GIT_SORT_TOPOLOGICAL, GIT_SORT_REVERSE from .. import BaseRunner, Terminal log = logging.getLogger() CHILD_ENV = { 'TERM': 'xterm', 'LANG': 'C.UTF-8', 'SHELL': '/bin/bash', 'USER': 'work', 'HOME': '/home/work', 'PATH': '/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin', 'LD_PRELOAD': os.environ.get('LD_PRELOAD', '/home/sorna/libbaihook.so'), } class Runner(BaseRunner): log_prefix = 'shell-kernel' def __init__(self): super().__init__() self.child_env.update(CHILD_ENV) async def init_with_loop(self): self.user_input_queue = asyncio.Queue() self.term = Terminal( '/bin/bash', self.stopped, self.outsock, auto_restart=True, ) parser_show = self.term.subparsers.add_parser('show') parser_show.add_argument('target', choices=('graph',), default='graph') parser_show.add_argument('path', type=str) parser_show.set_defaults(func=self.do_show) await self.term.start() async def build_heuristic(self) -> int: raise NotImplementedError async def execute_heuristic(self) -> int: raise NotImplementedError async def query(self, code_text) -> int: return await self.term.handle_command(code_text) async def complete(self, data): return [] async def interrupt(self): # subproc interrupt is already handled by BaseRunner pass async def shutdown(self): await self.term.shutdown() def do_show(self, args): if args.target == 'graph': commit_branch_table = {} commit_info = [] if args.path in ['.', None]: current_dir = Path(f'/proc/{self.term.pid}/cwd').resolve() else: current_dir = Path(args.path).resolve() os.chdir(current_dir) # Create commit-branch matching table. tree_cmd = ['git', 'log', '--pretty=oneline', '--graph', '--source', '--branches'] run_res = subprocess.run(tree_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = run_res.stdout.decode('utf-8') stderr = run_res.stderr.decode('utf-8') prog = re.compile(r'([a-z0-9]+)\s+(\S+).*') if stderr: self.outsock.send_multipart([b'stderr', stderr.encode('utf-8')]) return for line in stdout.split('\n'): r = prog.search(line) if r and hasattr(r, 'group') and r.group(1) and r.group(2): oid = r.group(1)[:7] # short oid branch = r.group(2) commit_branch_table[oid] = branch # Gather commit info w/ branch name. log_cmd = ['git', 'log', '--pretty=format:%h\|\|%p\|\|%s\|\|%cn', '--all', '--topo-order', '--reverse'] run_res = subprocess.run(log_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = run_res.stdout.decode('utf-8') for gitlog in stdout.split('\n'): items = gitlog.split('\|\|') oid = items[0] parent_ids = items[1].split(' ') message = items[2] author = items[3] branch = commit_branch_table.get(oid, None) parent_branches = [commit_branch_table.get(pid, None) for pid in parent_ids] info = dict( oid=oid, parent_ids=parent_ids, author=author, message=message, branch=branch, parent_branches=parent_branches ) commit_info.append(info) self.outsock.send_multipart([ b'media', json.dumps({ 'type': 'application/vnd.sorna.gitgraph', 'data': commit_info }).encode('utf-8') ]) else: raise ValueError('Unsupported show target', args.target)	[ 1, 1053, 408, 948, 3934, 13, 5215, 4390, 13, 5215, 12183, 13, 5215, 2897, 13, 3166, 2224, 1982, 1053, 10802, 13, 5215, 337, 13, 5215, 1014, 5014, 13, 13, 29937, 1053, 19484, 277, 29906, 13, 29937, 515, 19484, 277, 29906, 1053, 402, 1806, 29918, 29903, 8476, 29918, 29911, 4590, 29949, 14480, 2965, 1964, 29892, 402, 1806, 29918, 29903, 8476, 29918, 1525, 5348, 1660, 13, 13, 3166, 6317, 1053, 7399, 16802, 29892, 29175, 13, 13, 1188, 353, 12183, 29889, 657, 16363, 580, 13, 13, 3210, 6227, 29928, 29918, 25838, 353, 426, 13, 1678, 525, 4945, 29924, 2396, 525, 29916, 8489, 742, 13, 1678, 525, 29931, 19453, 2396, 525, 29907, 29889, 10496, 29899, 29947, 742, 13, 1678, 525, 7068, 29923, 2208, 2396, 8207, 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code/bp_chrono/pull.py	niplav/site	2	170857	<gh_stars>1-10 import urllib2 from bs4 import BeautifulSoup import sys import datetime for year in range(2006, datetime.datetime.now().year+1): yearposts=[] for page in range(1, 1000): url='http://bit-player.org/{}/page/{}'.format(year, page) req=urllib2.Request(url, headers={'User-Agent' : "Firefox"}) try: con=urllib2.urlopen(req) except urllib2.HTTPError, e: break data=con.read() soup=BeautifulSoup(data, 'html.parser') posts=soup.find_all(class_="post") for p in posts: title=p.find_all(class_="entry-title")[0].a.text link=p.find_all(class_="entry-title")[0].a.get('href') meta=p.find_all(class_="entry-meta") author=p.find_all(class_="entry-meta")[0].find_all(class_='author')[0].a.text date=p.find_all(class_="entry-meta")[0].find_all(class_='entry-date')[0].text entry='* [{}]({}) ({}, {})'.format(title.encode('utf_8'), str(link), str(author), str(date)) yearposts.append(entry) print('\n### {}\n'.format(year)) for t in reversed(yearposts): print(t)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 3142, 1982, 29906, 13, 3166, 24512, 29946, 1053, 25685, 29903, 1132, 13, 5215, 10876, 13, 5215, 12865, 13, 13, 1454, 1629, 297, 3464, 29898, 29906, 29900, 29900, 29953, 29892, 12865, 29889, 12673, 29889, 3707, 2141, 6360, 29974, 29896, 1125, 13, 12, 6360, 14080, 29922, 2636, 13, 12, 1454, 1813, 297, 3464, 29898, 29896, 29892, 29871, 29896, 29900, 29900, 29900, 1125, 13, 12, 12, 2271, 2433, 1124, 597, 2966, 29899, 9106, 29889, 990, 19248, 6822, 3488, 29914, 8875, 4286, 4830, 29898, 6360, 29892, 1813, 29897, 13, 12, 12, 7971, 29922, 2271, 1982, 29906, 29889, 3089, 29898, 2271, 29892, 9066, 3790, 29915, 2659, 29899, 19661, 29915, 584, 376, 18654, 8944, 29908, 1800, 13, 12, 12, 2202, 29901, 13, 12, 12, 12, 535, 29922, 2271, 1982, 29906, 29889, 332, 417, 2238, 29898, 7971, 29897, 13, 12, 12, 19499, 3142, 1982, 29906, 29889, 10493, 2392, 29892, 321, 29901, 13, 12, 12, 12, 8690, 13, 12, 12, 1272, 29922, 535, 29889, 949, 580, 13, 12, 12, 29879, 1132, 29922, 3629, 1300, 6845, 29903, 1132, 29898, 1272, 29892, 525, 1420, 29889, 16680, 1495, 13, 12, 12, 14080, 29922, 29879, 1132, 29889, 2886, 29918, 497, 29898, 1990, 29918, 543, 2490, 1159, 13, 12, 12, 1454, 282, 297, 11803, 29901, 13, 12, 12, 12, 3257, 29922, 29886, 29889, 2886, 29918, 497, 29898, 1990, 29918, 543, 8269, 29899, 3257, 1159, 29961, 29900, 1822, 29874, 29889, 726, 13, 12, 12, 12, 2324, 29922, 29886, 29889, 2886, 29918, 497, 29898, 1990, 29918, 543, 8269, 29899, 3257, 1159, 29961, 29900, 1822, 29874, 29889, 657, 877, 12653, 1495, 13, 12, 12, 12, 7299, 29922, 29886, 29889, 2886, 29918, 497, 29898, 1990, 29918, 543, 8269, 29899, 7299, 1159, 13, 12, 12, 12, 8921, 29922, 29886, 29889, 2886, 29918, 497, 29898, 1990, 29918, 543, 8269, 29899, 7299, 1159, 29961, 29900, 1822, 2886, 29918, 497, 29898, 1990, 29918, 2433, 8921, 29861, 29900, 1822, 29874, 29889, 726, 13, 12, 12, 12, 1256, 29922, 29886, 29889, 2886, 29918, 497, 29898, 1990, 29918, 543, 8269, 29899, 7299, 1159, 29961, 29900, 1822, 2886, 29918, 497, 29898, 1990, 29918, 2433, 8269, 29899, 1256, 29861, 29900, 1822, 726, 13, 12, 12, 12, 8269, 2433, 29930, 518, 8875, 850, 29912, 1800, 21313, 1118, 426, 1800, 4286, 4830, 29898, 3257, 29889, 12508, 877, 9420, 29918, 29947, 5477, 851, 29898, 2324, 511, 851, 29898, 8921, 511, 851, 29898, 1256, 876, 13, 12, 12, 12, 6360, 14080, 29889, 4397, 29898, 8269, 29897, 13, 12, 2158, 28909, 29876, 2277, 29937, 426, 1012, 29876, 4286, 4830, 29898, 6360, 876, 13, 12, 1454, 260, 297, 18764, 287, 29898, 6360, 14080, 1125, 13, 12, 12, 2158, 29898, 29873, 29897, 13, 2 ]
sdk/python/pulumi_openstack/__init__.py	Frassle/pulumi-openstack	0	68419	# coding=utf-8 # * WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. * # * Do not edit by hand unless you're certain you know what you are doing! * # Make subpackages available: __all__ = ['blockstorage', 'compute', 'config', 'database', 'dns', 'firewall', 'identity', 'images', 'loadbalancer', 'networking', 'objectstorage', 'vpnaas']	[ 1, 396, 14137, 29922, 9420, 29899, 29947, 13, 29937, 18610, 399, 25614, 29901, 445, 934, 471, 5759, 491, 278, 27477, 15547, 20839, 689, 16230, 313, 13264, 1885, 29897, 21704, 29889, 18610, 13, 29937, 18610, 1938, 451, 3863, 491, 1361, 6521, 366, 29915, 276, 3058, 366, 1073, 825, 366, 526, 2599, 29991, 18610, 13, 13, 29937, 8561, 1014, 8318, 3625, 29901, 13, 1649, 497, 1649, 353, 6024, 1271, 12925, 742, 525, 26017, 742, 525, 2917, 742, 525, 9803, 742, 525, 29881, 1983, 742, 525, 8696, 11358, 742, 525, 22350, 742, 525, 8346, 742, 525, 1359, 5521, 25856, 742, 525, 11618, 292, 742, 525, 3318, 12925, 742, 525, 29894, 29886, 1056, 294, 2033, 13, 2 ]
src/mem/slicc/ast/PeekStatementAST.py	qianlong4526888/haha	31	143045	# Copyright (c) 1999-2008 <NAME> and <NAME> # Copyright (c) 2009 The Hewlett-Packard Development Company # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer; # redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution; # neither the name of the copyright holders nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. from slicc.ast.StatementAST import StatementAST from slicc.symbols import Var class PeekStatementAST(StatementAST): def __init__(self, slicc, queue_name, type_ast, pairs, statements, method): super(PeekStatementAST, self).__init__(slicc, pairs) self.queue_name = queue_name self.type_ast = type_ast self.statements = statements self.method = method def __repr__(self): return "[PeekStatementAST: %r queue_name: %r type: %r %r]" % \ (self.method, self.queue_name, self.type_ast, self.statements) def generate(self, code, return_type): self.symtab.pushFrame() msg_type = self.type_ast.type # Add new local var to symbol table var = Var(self.symtab, "in_msg", self.location, msg_type, "(in_msg_ptr)", self.pairs) self.symtab.newSymbol(var) # Check the queue type self.queue_name.assertType("InPort") # Declare the new "in_msg_ptr" variable mtid = msg_type.ident qcode = self.queue_name.var.code code(''' { // Declare message const $mtid in_msg_ptr M5_VAR_USED; in_msg_ptr = dynamic_cast<const $mtid *>(($qcode).${{self.method}}()); assert(in_msg_ptr != NULL); // Check the cast result ''') if self.pairs.has_key("block_on"): address_field = self.pairs['block_on'] code(''' if (m_is_blocking && (m_block_map.count(in_msg_ptr->m_$address_field) == 1) && (m_block_map[in_msg_ptr->m_$address_field] != &$qcode)) { $qcode.delayHead(); continue; } ''') if self.pairs.has_key("wake_up"): address_field = self.pairs['wake_up'] code(''' if (m_waiting_buffers.count(in_msg_ptr->m_$address_field) > 0) { wakeUpBuffers(in_msg_ptr->m_$address_field); } ''') # The other statements self.statements.generate(code, return_type) self.symtab.popFrame() code("}") def findResources(self, resources): self.statements.findResources(resources)	[ 1, 396, 14187, 1266, 313, 29883, 29897, 29871, 29896, 29929, 29929, 29929, 29899, 29906, 29900, 29900, 29947, 529, 5813, 29958, 322, 529, 5813, 29958, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29900, 29929, 450, 379, 809, 13650, 29899, 16638, 538, 14650, 6938, 13, 29937, 2178, 10462, 21676, 29889, 13, 29937, 13, 29937, 4367, 391, 3224, 322, 671, 297, 2752, 322, 7581, 7190, 29892, 411, 470, 1728, 13, 29937, 21733, 29892, 526, 21905, 4944, 393, 278, 1494, 5855, 526, 13, 29937, 1539, 29901, 2654, 391, 3224, 29879, 310, 2752, 775, 1818, 11551, 278, 2038, 3509, 1266, 13, 29937, 8369, 29892, 445, 1051, 310, 5855, 322, 278, 1494, 2313, 433, 4193, 29936, 13, 29937, 2654, 391, 3224, 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image_classification/T2T_ViT/load_pytorch_weights.py	RangeKing/PaddleViT	0	3288	<reponame>RangeKing/PaddleViT<filename>image_classification/T2T_ViT/load_pytorch_weights.py # Copyright (c) 2021 PPViT 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. """convert pytorch model weights to paddle pdparams""" import os import numpy as np import paddle import torch import timm from config import get_config from t2t_vit import build_t2t_vit as build_model from T2T_ViT_torch.models.t2t_vit import * from T2T_ViT_torch.utils import load_for_transfer_learning def print_model_named_params(model): print('----------------------------------') for name, param in model.named_parameters(): print(name, param.shape) print('----------------------------------') def print_model_named_buffers(model): print('----------------------------------') for name, param in model.named_buffers(): print(name, param.shape) print('----------------------------------') def torch_to_paddle_mapping(model_name, config): # (torch_param_name, paddle_param_name) mapping = [ ('cls_token', 'cls_token'), ('pos_embed', 'pos_embed'), ] for idx in range(1, 3): th_prefix = f'tokens_to_token.attention{idx}' pp_prefix = f'patch_embed.attn{idx}' if '_t_' in model_name: layer_mapping = [ (f'{th_prefix}.attn.qkv', f'{pp_prefix}.attn.qkv'), (f'{th_prefix}.attn.proj', f'{pp_prefix}.attn.proj'), (f'{th_prefix}.norm1', f'{pp_prefix}.norm1'), (f'{th_prefix}.norm2', f'{pp_prefix}.norm2'), (f'{th_prefix}.mlp.fc1', f'{pp_prefix}.mlp.fc1'), (f'{th_prefix}.mlp.fc2', f'{pp_prefix}.mlp.fc2'), ] else: layer_mapping = [ (f'{th_prefix}.w', f'{pp_prefix}.w'), (f'{th_prefix}.kqv', f'{pp_prefix}.kqv'), (f'{th_prefix}.proj', f'{pp_prefix}.proj'), (f'{th_prefix}.norm1', f'{pp_prefix}.norm1'), (f'{th_prefix}.norm2', f'{pp_prefix}.norm2'), (f'{th_prefix}.mlp.0', f'{pp_prefix}.mlp.0'), (f'{th_prefix}.mlp.2', f'{pp_prefix}.mlp.2'), ] mapping.extend(layer_mapping) mapping.append(('tokens_to_token.project','patch_embed.proj')) num_layers = config.MODEL.DEPTH for idx in range(num_layers): th_prefix = f'blocks.{idx}' pp_prefix = f'blocks.{idx}' layer_mapping = [ (f'{th_prefix}.norm1', f'{pp_prefix}.norm1'), (f'{th_prefix}.attn.qkv', f'{pp_prefix}.attn.qkv'), (f'{th_prefix}.attn.proj', f'{pp_prefix}.attn.proj'), (f'{th_prefix}.norm2', f'{pp_prefix}.norm2'), (f'{th_prefix}.mlp.fc1', f'{pp_prefix}.mlp.fc1'), (f'{th_prefix}.mlp.fc2', f'{pp_prefix}.mlp.fc2'), ] mapping.extend(layer_mapping) head_mapping = [ ('norm', 'norm'), ('head', 'head'), ] mapping.extend(head_mapping) return mapping def convert(torch_model, paddle_model, model_name, config): def _set_value(th_name, pd_name, transpose=True): th_shape = th_params[th_name].shape pd_shape = tuple(pd_params[pd_name].shape) # paddle shape default type is list #assert th_shape == pd_shape, f'{th_shape} != {pd_shape}' print(f'SET {th_name} {th_shape} TO {pd_name} {pd_shape}') if isinstance(th_params[th_name], torch.nn.parameter.Parameter): value = th_params[th_name].data.numpy() else: value = th_params[th_name].numpy() if len(value.shape) == 2 and transpose: value = value.transpose((1, 0)) pd_params[pd_name].set_value(value) # 1. get paddle and torch model parameters pd_params = {} th_params = {} for name, param in paddle_model.named_parameters(): pd_params[name] = param for name, param in torch_model.named_parameters(): th_params[name] = param for name, param in paddle_model.named_buffers(): pd_params[name] = param for name, param in torch_model.named_buffers(): th_params[name] = param # 2. get name mapping pairs mapping = torch_to_paddle_mapping(model_name, config) missing_keys_th = [] missing_keys_pd = [] zip_map = list(zip(*mapping)) th_keys = list(zip_map[0]) pd_keys = list(zip_map[1]) for key in th_params: missing = False if key not in th_keys: missing = True if key.endswith('.weight'): if key[:-7] in th_keys: missing = False if key.endswith('.bias'): if key[:-5] in th_keys: missing = False if missing: missing_keys_th.append(key) for key in pd_params: missing = False if key not in pd_keys: missing = True if key.endswith('.weight'): if key[:-7] in pd_keys: missing = False if key.endswith('.bias'): if key[:-5] in pd_keys: missing = False if missing: missing_keys_pd.append(key) print('====================================') print('missing_keys_pytorch:') print(missing_keys_th) print('missing_keys_paddle:') print(missing_keys_pd) print('====================================') # 3. set torch param values to paddle params: may needs transpose on weights for th_name, pd_name in mapping: if th_name in th_params and pd_name in pd_params: # nn.Parameters if th_name.endswith('w'): _set_value(th_name, pd_name, transpose=False) else: _set_value(th_name, pd_name) else: if f'{th_name}.weight' in th_params and f'{pd_name}.weight' in pd_params: th_name_w = f'{th_name}.weight' pd_name_w = f'{pd_name}.weight' _set_value(th_name_w, pd_name_w) if f'{th_name}.bias' in th_params and f'{pd_name}.bias' in pd_params: th_name_b = f'{th_name}.bias' pd_name_b = f'{pd_name}.bias' _set_value(th_name_b, pd_name_b) if f'{th_name}.running_mean' in th_params and f'{pd_name}._mean' in pd_params: th_name_b = f'{th_name}.running_mean' pd_name_b = f'{pd_name}._mean' _set_value(th_name_b, pd_name_b) if f'{th_name}.running_var' in th_params and f'{pd_name}._variance' in pd_params: th_name_b = f'{th_name}.running_var' pd_name_b = f'{pd_name}._variance' _set_value(th_name_b, pd_name_b) return paddle_model def main(): paddle.set_device('cpu') model_name_list = ['t2t_vit_7', 't2t_vit_10', 't2t_vit_12', 't2t_vit_14', 't2t_vit_14_384', 't2t_vit_19', 't2t_vit_24', 't2t_vit_24_token_labeling', 't2t_vit_t_14', 't2t_vit_t_19', 't2t_vit_t_24'] pth_model_path_list = ['./T2T_ViT_torch/t2t-vit-pth-models/71.7_T2T_ViT_7.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/75.2_T2T_ViT_10.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/76.5_T2T_ViT_12.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/81.5_T2T_ViT_14.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/83.3_T2T_ViT_14.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/81.9_T2T_ViT_19.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/82.3_T2T_ViT_24.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/84.2_T2T_ViT_24.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/81.7_T2T_ViTt_14.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/82.4_T2T_ViTt_19.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/82.6_T2T_ViTt_24.pth.tar'] for model_name, pth_model_path in zip(model_name_list, pth_model_path_list): print(f'============= NOW: {model_name} =============') sz = 384 if '384' in model_name else 224 if 'token_labeling' in model_name: config = get_config(f'./configs/{model_name[:-15]}.yaml') else: config = get_config(f'./configs/{model_name}.yaml') paddle_model = build_model(config) paddle_model.eval() print_model_named_params(paddle_model) print_model_named_buffers(paddle_model) print('+++++++++++++++++++++++++++++++++++') device = torch.device('cpu') if 'token_labeling' in model_name: torch_model = eval(f'{model_name[:-15]}(img_size={sz})') else: if '384' in model_name: torch_model = eval(f'{model_name[:-4]}(img_size={sz})') else: torch_model = eval(f'{model_name}(img_size={sz})') load_for_transfer_learning(torch_model, pth_model_path, use_ema=True, strict=False, num_classes=1000) torch_model = torch_model.to(device) torch_model.eval() print_model_named_params(torch_model) print_model_named_buffers(torch_model) # convert weights paddle_model = convert(torch_model, paddle_model, model_name, config) # check correctness x = np.random.randn(2, 3, sz, sz).astype('float32') x_paddle = paddle.to_tensor(x) x_torch = torch.Tensor(x).to(device) out_torch = torch_model(x_torch) out_paddle = paddle_model(x_paddle) out_torch = out_torch.data.cpu().numpy() out_paddle = out_paddle.cpu().numpy() print(out_torch.shape, out_paddle.shape) print(out_torch[0, 0:100]) print('========================================================') print(out_paddle[0, 0:100]) assert np.allclose(out_torch, out_paddle, atol = 1e-2) # save weights for paddle model model_path = os.path.join(f'./{model_name}.pdparams') paddle.save(paddle_model.state_dict(), model_path) print(f'{model_name} done') print('all done') if __name__ == "__main__": 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Acquire/Client/_user.py	michellab/BioSimSpaceCloud	2	15502	<filename>Acquire/Client/_user.py import os as _os from enum import Enum as _Enum from datetime import datetime as _datetime import time as _time from Acquire.Service import call_function as _call_function from Acquire.Service import Service as _Service from Acquire.ObjectStore import bytes_to_string as _bytes_to_string from Acquire.ObjectStore import string_to_bytes as _string_to_bytes from Acquire.Crypto import PrivateKey as _PrivateKey from Acquire.Crypto import PublicKey as _PublicKey from ._qrcode import create_qrcode as _create_qrcode from ._qrcode import has_qrcode as _has_qrcode from ._errors import UserError, LoginError # If we can, import socket to get the hostname and IP address try: import socket as _socket _has_socket = True except: _has_socket = False __all__ = ["User", "username_to_uid", "uid_to_username", "get_session_keys"] class _LoginStatus(_Enum): EMPTY = 0 LOGGING_IN = 1 LOGGED_IN = 2 LOGGED_OUT = 3 ERROR = 4 def _get_identity_url(): """Function to discover and return the default identity url""" return "http://172.16.17.32:8080/t/identity" def _get_identity_service(identity_url=None): """Function to return the identity service for the system""" if identity_url is None: identity_url = _get_identity_url() privkey = _PrivateKey() response = _call_function(identity_url, response_key=privkey) try: service = _Service.from_data(response["service_info"]) except: raise LoginError("Have not received the identity service info from " "the identity service at '%s' - got '%s'" % (identity_url, response)) if not service.is_identity_service(): raise LoginError( "You can only use a valid identity service to log in! " "The service at '%s' is a '%s'" % (identity_url, service.service_type())) if identity_url != service.service_url(): service.update_service_url(identity_url) return service def uid_to_username(user_uid, identity_url=None): """Function to return the username for the passed uid""" if identity_url is None: identity_url = _get_identity_url() response = _call_function(identity_url, "whois", user_uid=str(user_uid)) return response["username"] def username_to_uid(username, identity_url=None): """Function to return the uid for the passed username""" if identity_url is None: identity_url = _get_identity_url() response = _call_function(identity_url, "whois", username=str(username)) return response["user_uid"] def get_session_keys(username=None, user_uid=None, session_uid=None, identity_url=None): """Function to return the session keys for the specified user""" if username is None and user_uid is None: raise ValueError("You must supply either the username or user_uid!") if session_uid is None: raise ValueError("You must supply a valid UID for a login session") if identity_url is None: identity_url = _get_identity_url() response = _call_function(identity_url, "whois", username=username, user_uid=user_uid, session_uid=session_uid) try: response["public_key"] = _PublicKey.from_data(response["public_key"]) except: pass try: response["public_cert"] = _PublicKey.from_data(response["public_cert"]) except: pass return response class User: """This class holds all functionality that would be used by a user to authenticate with and access the service. This represents a single client login, and is the user-facing part of Acquire """ def __init__(self, username, identity_url=None): """Construct a null user""" self._username = username self._status = _LoginStatus.EMPTY self._identity_service = None if identity_url: self._identity_url = identity_url self._user_uid = None def __str__(self): return "User(name='%s', status=%s)" % (self.username(), self.status()) def __enter__(self): """Enter function used by 'with' statements'""" pass def __exit__(self, exception_type, exception_value, traceback): """Ensure that we logout""" self.logout() def __del__(self): """Make sure that we log out before deleting this object""" self.logout() def _set_status(self, status): """Internal function used to set the status from the string obtained from the LoginSession""" if status == "approved": self._status = _LoginStatus.LOGGED_IN elif status == "denied": self._set_error_state("Permission to log in was denied!") elif status == "logged_out": self._status = _LoginStatus.LOGGED_OUT def username(self): """Return the username of the user""" return self._username def uid(self): """Return the UID of this user. This uniquely identifies the user across all systems """ if self._user_uid is None: self._user_uid = username_to_uid(self.username(), self.identity_service_url()) return self._user_uid def status(self): """Return the current status of this user""" return self._status def _check_for_error(self): """Call to ensure that this object is not in an error state. If it is in an error state then raise an exception""" if self._status == _LoginStatus.ERROR: raise LoginError(self._error_string) def _set_error_state(self, message): """Put this object into an error state, displaying the passed message if anyone tries to use this object""" self._status = _LoginStatus.ERROR self._error_string = message def session_key(self): """Return the session key for the current login session""" self._check_for_error() try: return self._session_key except: return None def signing_key(self): """Return the signing key used for the current login session""" self._check_for_error() try: return self._signing_key except: return None def identity_service(self): """Return the identity service info object for the identity service used to validate the identity of this user """ if self._identity_service: return self._identity_service self._identity_service = _get_identity_service( self.identity_service_url()) return self._identity_service def identity_service_url(self): """Return the URL to the identity service. This is the full URL to the service, minus the actual function to be called, e.g. https://function_service.com/t/identity """ self._check_for_error() try: return self._identity_url except: # return the default URL - this should be discovered... return _get_identity_url() def login_url(self): """Return the URL that the user must connect to to authenticate this login session""" self._check_for_error() try: return self._login_url except: return None def login_qr_code(self): """Return a QR code of the login URL that the user must connect to to authenticate this login session""" self._check_for_error() try: return self._login_qrcode except: return None def session_uid(self): """Return the UID of the current login session. Returns None if there is no valid login session""" self._check_for_error() try: return self._session_uid except: return None def is_empty(self): """Return whether or not this is an empty login (so has not been used for anything yet...""" return self._status == _LoginStatus.EMPTY def is_logged_in(self): """Return whether or not the user has successfully logged in""" return self._status == _LoginStatus.LOGGED_IN def is_logging_in(self): """Return whether or not the user is in the process of loggin in""" return self._status == _LoginStatus.LOGGING_IN def logout(self): """Log out from the current session""" if self.is_logged_in() or self.is_logging_in(): identity_url = self.identity_service_url() if identity_url is None: return # create a permission message that can be signed # and then validated by the user permission = "Log out request for %s" % self._session_uid signature = self.signing_key().sign(permission) print("Logging out %s from session %s" % (self._username, self._session_uid)) result = _call_function( identity_url, "logout", args_key=self.identity_service().public_key(), username=self._username, session_uid=self._session_uid, permission=permission, signature=_bytes_to_string(signature)) print(result) self._status = _LoginStatus.LOGGED_OUT return result def register(self, password, identity_url=None): """Request to register this user with the identity service running at 'identity_url', using the supplied 'password'. This will return a QR code that you must use immediately to add this user on the identity service to a QR code generator""" if self._username is None: return None if identity_url is None: identity_url = _get_identity_url() privkey = _PrivateKey() result = _call_function( identity_url, "register", args_key=self.identity_service().public_key(), response_key=privkey, public_cert=self.identity_service().public_certificate(), username=self._username, password=password) try: provisioning_uri = result["provisioning_uri"] except: raise UserError( "Cannot register the user '%s' on " "the identity service at '%s'!" % (self._username, identity_url)) # return a QR code for the provisioning URI return (provisioning_uri, _create_qrcode(provisioning_uri)) def request_login(self, login_message=None): """Request to authenticate as this user. This returns a login URL that you must connect to to supply your login credentials If 'login_message' is supplied, then this is passed to the identity service so that it can be displayed when the user accesses the login page. This helps the user validate that they have accessed the correct login page. Note that if the message is None, then a random message will be generated. """ self._check_for_error() if not self.is_empty(): raise LoginError("You cannot try to log in twice using the same " "User object. Create another object if you want " "to try to log in again.") # first, create a private key that will be used # to sign all requests and identify this login session_key = _PrivateKey() signing_key = _PrivateKey() args = {"username": self._username, "public_key": session_key.public_key().to_data(), "public_certificate": signing_key.public_key().to_data(), "ipaddr": None} # get information from the local machine to help # the user validate that the login details are correct if _has_socket: hostname = _socket.gethostname() ipaddr = _socket.gethostbyname(hostname) args["ipaddr"] = ipaddr args["hostname"] = hostname if login_message is None: login_message = "User '%s' in process '%s' wants to log in..." % \ (_os.getlogin(), _os.getpid()) args["message"] = login_message result = _call_function( self.identity_service_url(), "request_login", args_key=self.identity_service().public_key(), response_key=session_key, public_cert=self.identity_service().public_certificate(), username=self._username, public_key=session_key.public_key().to_data(), public_certificate=signing_key.public_key().to_data(), ipaddr=None, message=login_message) # look for status = 0 try: status = int(result["status"]) except: status = -1 try: message = result["message"] except: message = str(result) try: prune_message = result["prune_message"] print("Pruning old sessions...\n%s" % "\n".join(prune_message)) except: pass if status != 0: error = "Failed to login. Error = %d. Message = %s" % \ (status, message) self._set_error_state(error) raise LoginError(error) try: login_url = result["login_url"] except: login_url = None if login_url is None: error = "Failed to login. Could not extract the login URL! " \ "Result is %s" % (str(result)) self._set_error_state(error) raise LoginError(error) try: session_uid = result["session_uid"] except: session_uid = None if session_uid is None: error = "Failed to login. Could not extract the login " \ "session UID! Result is %s" % (str(result)) self._set_error_state(error) raise LoginError(error) # now save all of the needed data self._login_url = result["login_url"] self._session_key = session_key self._signing_key = signing_key self._session_uid = session_uid self._status = _LoginStatus.LOGGING_IN self._user_uid = result["user_uid"] qrcode = None if _has_qrcode(): try: self._login_qrcode = _create_qrcode(self._login_url) qrcode = self._login_qrcode except: pass return (self._login_url, qrcode) def _poll_session_status(self): """Function used to query the identity service for this session to poll for the session status""" identity_url = self.identity_service_url() if identity_url is None: return result = _call_function(identity_url, "get_status", username=self._username, session_uid=self._session_uid) # look for status = 0 try: status = int(result["status"]) except: status = -1 try: message = result["message"] except: message = str(result) if status != 0: error = "Failed to query identity service. Error = %d. " \ "Message = %s" % (status, message) self._set_error_state(error) raise LoginError(error) # now update the status... status = result["session_status"] self._set_status(status) def wait_for_login(self, timeout=None, polling_delta=5): """Block until the user has logged in. If 'timeout' is set then we will wait for a maximum of that number of seconds This will check whether we have logged in by polling the identity service every 'polling_delta' seconds. 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tests/compilation/request/test_request_compiler.py	ymoch/preacher	3	10668	<reponame>ymoch/preacher<gh_stars>1-10 from unittest.mock import NonCallableMock, sentinel from pytest import mark, raises, fixture from preacher.compilation.argument import Argument from preacher.compilation.error import CompilationError, NamedNode, IndexedNode from preacher.compilation.request.request import RequestCompiler, RequestCompiled from preacher.compilation.request.request_body import RequestBodyCompiler from preacher.core.request import Method PKG = "preacher.compilation.request.request" @fixture def body(): body = NonCallableMock(RequestBodyCompiler) body.of_default.return_value = sentinel.new_body_compiler return body @fixture def default() -> RequestCompiled: return RequestCompiled( method=sentinel.default_method, path=sentinel.default_path, headers=sentinel.default_headers, params=sentinel.default_params, body=sentinel.default_body, ) @fixture def compiler(body, default: RequestCompiled) -> RequestCompiler: return RequestCompiler(body=body, default=default) @mark.parametrize( ("obj", "expected_path"), ( ([], []), ({"method": 1}, [NamedNode("method")]), ({"method": "invalid"}, [NamedNode("method")]), ({"path": {"key": "value"}}, [NamedNode("path")]), ({"headers": ""}, [NamedNode("headers")]), ({"headers": {"int": 1}}, [NamedNode("headers")]), ({"headers": {1: "not-a-string-key"}}, [NamedNode("headers")]), ), ) def test_given_an_invalid_obj(compiler: RequestCompiler, obj, expected_path): with raises(CompilationError) as error_info: compiler.compile(obj) assert error_info.value.path == expected_path def test_given_an_empty_mapping(compiler: RequestCompiler): compiled = compiler.compile({}) assert compiled.method is sentinel.default_method assert compiled.path is sentinel.default_path assert compiled.headers is sentinel.default_headers assert compiled.params is sentinel.default_params assert compiled.body is sentinel.default_body @mark.parametrize( ("method_obj", "expected"), ( ("get", Method.GET), ("POST", Method.POST), ("Put", Method.PUT), ("Delete", Method.DELETE), ), ) def test_given_a_valid_method(compiler: RequestCompiler, method_obj, expected): obj = {"method": Argument("method")} arguments = {"method": method_obj} compiled = compiler.compile(obj, arguments) assert compiled.method is expected @mark.parametrize( ("headers_obj", "expected"), ( ({}, {}), ({"null": None, "empty": ""}, {"empty": ""}), ({"n1": "v1", "n2": "v2"}, {"n1": "v1", "n2": "v2"}), ), ) def test_given_valid_headers(compiler: RequestCompiler, headers_obj, expected): obj = {"headers": Argument("headers")} arguments = {"headers": headers_obj} compiled = compiler.compile(obj, arguments) assert compiled.headers == expected def test_given_an_invalid_params(compiler: RequestCompiler, mocker): compile_params = mocker.patch(f"{PKG}.compile_url_params") compile_params.side_effect = CompilationError("msg", node=NamedNode("x")) with raises(CompilationError) as error_info: compiler.compile({"params": sentinel.params}) assert error_info.value.path == [NamedNode("params"), NamedNode("x")] compile_params.assert_called_once_with(sentinel.params, None) def test_given_valid_params(compiler: RequestCompiler, mocker): compile_params = mocker.patch(f"{PKG}.compile_url_params") compile_params.return_value = sentinel.compiled_params compiled = compiler.compile({"params": sentinel.params}, sentinel.args) assert compiled.params == sentinel.compiled_params compile_params.assert_called_once_with(sentinel.params, sentinel.args) def test_given_invalid_body(compiler: RequestCompiler, body): body.compile.side_effect = CompilationError("x", node=IndexedNode(1)) with raises(CompilationError) as error_info: compiler.compile({"body": sentinel.body_obj}) assert error_info.value.path == [NamedNode("body"), IndexedNode(1)] body.compile.assert_called_once_with(sentinel.body_obj, None) def test_given_valid_body(compiler: RequestCompiler, body): body.compile.return_value = sentinel.body compiled = compiler.compile({"body": sentinel.body_obj}, sentinel.args) assert compiled.body is sentinel.body body.compile.assert_called_once_with(sentinel.body_obj, sentinel.args) def test_given_a_string(compiler: RequestCompiler): compiled = compiler.compile(Argument("path"), {"path": "/path"}) assert compiled.method is sentinel.default_method assert compiled.path == "/path" assert compiled.headers is sentinel.default_headers assert compiled.params is sentinel.default_params assert compiled.body is sentinel.default_body def test_of_default_no_body(compiler: RequestCompiler, body, mocker): ctor = mocker.patch(f"{PKG}.RequestCompiler") ctor.return_value = sentinel.compiler_of_default new_default = RequestCompiled( method=sentinel.new_default_method, path=sentinel.new_default_path, headers=sentinel.new_default_headers, params=sentinel.new_default_params, ) new_compiler = compiler.of_default(new_default) assert new_compiler is sentinel.compiler_of_default ctor.assert_called_once_with( body=body, default=RequestCompiled( method=sentinel.new_default_method, path=sentinel.new_default_path, headers=sentinel.new_default_headers, params=sentinel.new_default_params, body=sentinel.default_body, ), ) body.of_default.assert_not_called() def test_of_default_body(compiler: RequestCompiler, body, mocker): ctor = mocker.patch(f"{PKG}.RequestCompiler") ctor.return_value = sentinel.compiler_of_default new_default = RequestCompiled(body=sentinel.new_default_body) new_compiler = compiler.of_default(new_default) assert new_compiler is sentinel.compiler_of_default ctor.assert_called_once_with( body=sentinel.new_body_compiler, default=RequestCompiled( method=sentinel.default_method, path=sentinel.default_path, headers=sentinel.default_headers, params=sentinel.default_params, body=sentinel.new_default_body, ), ) body.of_default.assert_called_once_with(sentinel.new_default_body)	[ 1, 529, 276, 1112, 420, 29958, 962, 2878, 29914, 1457, 11665, 29966, 12443, 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skpr/inout/frc.py	PhilippPelz/scikit-pr-open	0	1601830	from pyE17.utils import imsave from matplotlib import pyplot as plt import numpy as np from numpy.fft import fft2, ifftshift, fftshift, ifft2, fft, ifft from scipy.ndimage.filters import gaussian_filter1d from .plot import imsave def taperarray(shape, edge): xx, yy = np.mgrid[0:shape[0], 0:shape[1]] xx1 = np.flipud(xx) xx2 = np.minimum(xx, xx1) yy1 = np.fliplr(yy) yy2 = np.minimum(yy, yy1) rr = np.minimum(xx2, yy2).astype(np.float) rr[rr <= edge] /= edge rr[rr > edge] = 1 rr = np.pi / 2 rr = np.sin(rr) # print xx2 # fig, ax = plt.subplots() # imax = ax.imshow(rr) # plt.colorbar(imax) # plt.show() return rr def tapercircle(shape, edge, edgeloc=8 / 20.0, loc=(0, 0)): x0, y0 = loc xx, yy = np.meshgrid(np.arange(-shape[0] / 2, shape[0] / 2), np.arange(-shape[1] / 2, shape[1] / 2)) rr = np.sqrt((xx - x0) * 2 + (yy - y0) ** 2) rr -= (shape[0] * edgeloc - edge) one = rr < 0 taper = np.logical_and(rr >= 0, rr <= edge) zero = rr > edge # rr[taper] rr[zero] = 0 rr[taper] = np.abs(rr[taper] - np.max(rr[taper])) / np.max(rr[taper]) rr[one] = 1 return rr def frc(im1, im2, annulus_width=1, edgetaper=5, edgeloc=8 / 20.0, loc=(0, 0), smooth=None, working_mask=None, x=None, y=None, rmax=None, taper=None): """ r = radial_data(data,annulus_width,working_mask,x,y) A function to reduce an image to a radial cross-section. :INPUT: data - whatever data you are radially averaging. Data is binned into a series of annuli of width 'annulus_width' pixels. annulus_width - width of each annulus. Default is 1. working_mask - array of same size as 'data', with zeros at whichever 'data' points you don't want included in the radial data computations. x,y - coordinate system in which the data exists (used to set the center of the data). By default, these are set to integer meshgrids rmax -- maximum radial value over which to compute statistics :OUTPUT: r - a data structure containing the following statistics, computed across each annulus: .r - the radial coordinate used (outer edge of annulus) .mean - mean of the data in the annulus .sum - the sum of all enclosed values at the given radius .std - standard deviation of the data in the annulus .median - median value in the annulus .max - maximum value in the annulus .min - minimum value in the annulus .numel - number of elements in the annulus :EXAMPLE: :: import numpy as np import pylab as py import radial_data as rad # Create coordinate grid npix = 50. x = np.arange(npix) - npix/2. xx, yy = np.meshgrid(x, x) r = np.sqrt(xx2 + yy2) fake_psf = np.exp(-(r/5.)*2) noise = 0.1 np.random.normal(0, 1, r.size).reshape(r.shape) simulation = fake_psf + noise rad_stats = rad.radial_data(simulation, x=xx, y=yy) py.figure() py.plot(rad_stats.r, rad_stats.mean / rad_stats.std) py.xlabel('Radial coordinate') py.ylabel('Signal to Noise') """ # 2012-02-25 20:40 IJMC: Empty bins now have numel=0, not nan. # 2012-02-04 17:41 IJMC: Added "SUM" flag # 2010-11-19 16:36 IJC: Updated documentation for Sphinx # 2010-03-10 19:22 IJC: Ported to python from Matlab # 2005/12/19 Added 'working_region' option (IJC) # 2005/12/15 Switched order of outputs (IJC) # 2005/12/12 IJC: Removed decifact, changed name, wrote comments. # 2005/11/04 by <NAME> at the Jet Propulsion Laboratory import numpy as ny class radialDat: """Empty object container. """ def __init__(self): self.num = None self.denom = None self.T1bit = None self.Thalfbit = None # --------------------- # Set up input parameters # --------------------- if working_mask == None: working_mask = ny.ones(im1.shape, bool) npix, npiy = im1.shape if taper is not None: taper0 = taper else: taper0 = tapercircle(im1.shape, edgetaper, edgeloc, loc) f, a = plt.subplots() a.imshow(imsave(im1 * taper0)) plt.title('tapered') plt.show() # taper0 = 1 F1 = fftshift(fft2(ifftshift(im1 * taper0))) F2 = fftshift(fft2(ifftshift(im2 * taper0))) F1F2_star = F1 * F2.conj() if x == None or y == None: x1 = ny.arange(-npix / 2., npix / 2.) y1 = ny.arange(-npiy / 2., npiy / 2.) x, y = ny.meshgrid(y1, x1) r = abs(x + 1j * y) if rmax == None: rmax = r[working_mask].max() # --------------------- # Prepare the data container # --------------------- dr = ny.abs([x[0, 0] - x[0, 1]]) * annulus_width radial = ny.arange(rmax / dr) * dr + dr / 2. nrad = len(radial) radialdata = radialDat() radialdata.num = ny.zeros(nrad) radialdata.denom = ny.zeros(nrad) radialdata.T1bit = ny.zeros(nrad) radialdata.Thalfbit = ny.zeros(nrad) radialdata.r = radial / (npix / 2) # --------------------- # Loop through the bins # --------------------- for irad in range(nrad): # = 1:numel(radial) minrad = irad * dr maxrad = minrad + dr thisindex = (r >= minrad) * (r < maxrad) * working_mask # import pylab as py # pdb.set_trace() if not thisindex.ravel().any(): radialdata.num[irad] = ny.nan radialdata.denom[irad] = ny.nan radialdata.T1bit[irad] = ny.nan radialdata.Thalfbit[irad] = ny.nan else: sqrt_n = np.sqrt(thisindex.astype(np.int).sum()) radialdata.num[irad] = np.real(F1F2_star[thisindex].sum()) radialdata.denom[irad] = ny.sqrt((ny.abs(F1[thisindex]) ** 2).sum() * (ny.abs(F2[thisindex]) ** 2).sum()) radialdata.T1bit[irad] = (0.5 + 2.4142 / sqrt_n) / (1.5 + 1.4142 / sqrt_n) radialdata.Thalfbit[irad] = (0.2071 + 1.9102 / sqrt_n) / (1.2071 + 0.9102 / sqrt_n) # --------------------- # Return with data # --------------------- radialdata.frc = ny.nan_to_num(radialdata.num / radialdata.denom) radialdata.frc[radialdata.frc < 0] = 0 if smooth is not None: radialdata.frc = gaussian_filter1d(radialdata.frc, smooth) take = radialdata.r <= 1.1 radialdata.r = radialdata.r[take] radialdata.frc = radialdata.frc[take] radialdata.T1bit = radialdata.T1bit[take] radialdata.Thalfbit = radialdata.Thalfbit[take] return radialdata	[ 1, 515, 11451, 29923, 29896, 29955, 29889, 13239, 1053, 527, 7620, 13, 3166, 22889, 1053, 11451, 5317, 408, 14770, 13, 5215, 12655, 408, 7442, 13, 3166, 12655, 29889, 600, 29873, 1053, 285, 615, 29906, 29892, 565, 615, 10889, 29892, 285, 615, 10889, 29892, 565, 615, 29906, 29892, 285, 615, 29892, 565, 615, 13, 3166, 4560, 2272, 29889, 299, 3027, 29889, 26705, 1053, 330, 17019, 29918, 4572, 29896, 29881, 13, 3166, 869, 5317, 1053, 527, 7620, 13, 13, 1753, 260, 7202, 2378, 29898, 12181, 29892, 7636, 1125, 13, 1678, 15473, 29892, 343, 29891, 353, 7442, 29889, 29885, 7720, 29961, 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eval/beat_scores_calc.py	StoneYeY/Beat-Detection	0	61620	#!/usr/bin/env python # encoding: utf-8 """ calc beat score of files copyright: www.mgtv.com """ import os import sys import argparse import numpy as np import traceback import beat_evaluation_toolbox as be def calc_beat_score_of_file(annotation_file, beat_file): #check input params if os.path.exists(annotation_file) == False: print("failed! annotation_file:%s not exist\n" % (annotation_file)) return False, 0.0 if os.path.exists(beat_file) == False: print("failed! beat_file:%s not exist\n" % (beat_file)) return False, 0.0 data_annotation = np.loadtxt(annotation_file, usecols=(0)) data_annotation = np.expand_dims(data_annotation, axis=0) data_beat = np.loadtxt(beat_file, usecols=(0)) data_beat = np.expand_dims(data_beat, axis=0) R = be.evaluate_db(data_annotation, data_beat, 'all', doCI=False) #输出结果 print(R['scores']) pscore = R['scores']['pScore'][0] f_measure = R['scores']['fMeasure'][0] aml_c = R['scores']['amlC'][0] aml_t = R['scores']['amlT'][0] cml_c = R['scores']['cmlC'][0] cml_t = R['scores']['cmlT'][0] cem_acc = R['scores']['cemgilAcc'][0] total_score = (aml_c + cem_acc + cml_c + f_measure + pscore + cml_t + aml_t) / 7 print("[%s] score:%.4f"%(beat_file, total_score)) return True, total_score def calc_avg_score_of_files(annotation_files_dir, beat_files_dir, file_extension): #check input params if os.path.exists(annotation_files_dir) == False: print("failed! annotation_files_dir:%s not exist\n" % (annotation_files_dir)) return False, 0.0 if os.path.exists(beat_files_dir) == False: print("failed! beat_files_dir:%s not exist\n" % (beat_files_dir)) return False, 0.0 if not annotation_files_dir.endswith("/"): annotation_files_dir += "/" if not beat_files_dir.endswith("/"): beat_files_dir += "/" annotation_files_url = [f for f in os.listdir(annotation_files_dir) if f.endswith((file_extension))] nb_annotation_files = len(annotation_files_url) beat_files_url = [f for f in os.listdir(beat_files_dir) if f.endswith((file_extension))] nb_beat_files = len(beat_files_url) if nb_annotation_files != nb_beat_files or nb_annotation_files == 0: print("failed! annotation files num:%d beat files num:%d\n" % (nb_annotation_files, nb_beat_files)) return False, 0.0 sum_score = 0.0 for i in range(nb_annotation_files): annotation_file = annotation_files_dir + annotation_files_url[i] beat_file = beat_files_dir + annotation_files_url[i] if os.path.exists(beat_file) == False: print("failed! beat file:%s not exist\n" % (beat_file)) return False, 0.0 ret, score = calc_beat_score_of_file(annotation_file, beat_file) if ret == False: print("failed! calc_beat_score_of_file failed for file:%s\n" % (beat_file)) return False, 0.0 sum_score = sum_score + score avg_score = sum_score / nb_annotation_files return True, avg_score if __name__ == '__main__': parser = argparse.ArgumentParser(description="calc avg score of beat(downbeat) files") parser.add_argument("--annotation_files_dir", required=True, help="Path to input annotation files dir", default="") parser.add_argument("--beat_files_dir", required=True, help="Path to input beats files dir", default="") parser.add_argument("--file_extension", required=True, help="File ext, beat or downbeat", default="") # 获得工作目录，程序模块名称，并切换工作目录 s_work_path, s_module_name = os.path.split(os.path.abspath(sys.argv[0])) print(s_work_path, s_module_name) os.chdir(s_work_path) try: args = parser.parse_args() ret, score = calc_avg_score_of_files(args.annotation_files_dir, args.beat_files_dir, args.file_extension) print("Final score:%.4f" % score) except Exception as e: traceback.print_exc() print("Exception running beat_score_calc: [%s]" % (str(e))) ret = False if ret == True: sys.exit(0) else: sys.exit(1)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 8025, 29901, 23616, 29899, 29947, 13, 15945, 29908, 13, 28667, 16646, 8158, 310, 2066, 13, 8552, 1266, 29901, 7821, 29889, 29885, 4141, 29894, 29889, 510, 13, 15945, 29908, 13, 13, 5215, 2897, 13, 5215, 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Proxy_network/seq2seqSummariser.py	quilan78/MSC_project	0	1601164	<reponame>quilan78/MSC_project import numpy as np from encoder import * from embedding import * from decoder import * from lstm import * from utils import * import sys sys.path.append('/home/dwt17/MSc_project/neural_sum_1/code/Commons/') sys.path.append('../Commons/') from vocab import * from stateReducer import * from denseLayer import * class Seq2seqSummariser: """ Class that represent the entire architecture end to end Variable : cellSize is the dimension of the hidden layers in the LSTM cell embeddingSize is the dimension of the input attentionSize is the dimension of the attention vector vocabSize is the number of words max_encoding_length is the size of the input sequence max_decoding_length is the size of the output sequence """ def __init__(self, cellSize = 128, embeddingSize=64, attentionSize=128, vocabSize=150000, max_encoding_length=200, max_decoding_length=50, vocab_path="../data/useful files/"): self.cellSize = cellSize self.embeddingSize = embeddingSize self.attentionSize = attentionSize self.vocabSize = vocabSize self.max_encoding_length = max_encoding_length self.max_decoding_length = max_decoding_length self.embedding = None self.encoder = None self.decoder = None self.stateReducer = None self.denseLayer = None self.vocab = Vocab(path=vocab_path) self.vocabSize =self. vocab.LoadVocab(max_size=vocabSize) def _initialise_embedding(self, embedding_matrix): self.embedding = Embedding(embedding_matrix) assert self.vocabSize == self.embedding.vocabSize assert self.embeddingSize == self.embedding.embeddingSize def _initialise_encoder(self, kernel_forward, bias_forward, kernel_backward, bias_backward): """ Initialise the encoder The kernel are as given by Tensorflow, so of size (embedding + cellSize) x 4cellSize Bias are of shape (4cellSize, ) """ lstm_forward = Lstm(kernel_forward, bias_forward, self.cellSize) lstm_backward = Lstm(kernel_backward, bias_backward, self.cellSize) assert lstm_forward.embeddingSize == self.embeddingSize assert lstm_backward.embeddingSize == self.embeddingSize self.encoder = Encoder( lstm_forward=lstm_forward, lstm_backward=lstm_backward, cellSize=self.cellSize, embeddingSize = self.embeddingSize, max_encoding_length=self.max_encoding_length) def _initialise_stateReducer(self, W_h, W_c, b_h, b_c): """ Initialise the state Reducer from the encoder and the decoder The matrices as given by tensorflow are transposed """ self.stateReducer = StateReducer(W_h = W_h, W_c = W_c, b_h = b_h, b_c = b_c) def _initialise_decoder(self, kernel, bias, memoryLayer, queryLayer, attentionV, attentonLayer): cell = Lstm(kernel, bias, self.cellSize ) self.decoder = Decoder(lstm_cell=cell, cellSize=self.cellSize, attentionSize=self.attentionSize, max_decoding_length=self.max_decoding_length, memoryLayer=memoryLayer, queryLayer=queryLayer, attentionV = attentionV, attentonLayer=attentonLayer) def _initialise_DenseLayer(self, kernel, bias): self.denseLayer = DenseLayer(kernel = kernel, bias = bias) def compute_forward(self, input_seq_int, dec_seq_int): """ Compute all the states of the network for the specific input input : sequence of words """ #integer_seq_inp = self.vocab.TransalteSentence(input_seq) #print(dec_seq_int) embedded_inp_seq = self.embedding.transform_input(input_seq_int) forward_encoder_states, backward_encoder_states = self.encoder.compute_encoding(embedded_inp_seq) #print("Comparing the transmitted states C") #print(np.linalg.norm(forward_encoder_states[-1]["c"])) #print(np.linalg.norm(backward_encoder_states[0]["c"])) h, c = self.stateReducer.compute_reduction(forward_encoder_states[-1], backward_encoder_states[0]) #integer_seq_out = self.vocab.TransalteSentence(dec_seq) embedded_dec_seq = self.embedding.transform_input([self.vocab.start_decode_id] + dec_seq_int) decoder_states = self.decoder.compute_decoding(embedded_dec_seq, h, c, forward_encoder_states, backward_encoder_states, self.denseLayer, self.embedding) #print("compare context vectors") #print(decoder_states[0]["lstm"]["last_c"][:10]) #print(np.max(decoder_states[0]["lstm"]["f"])) #print(decoder_states[0]["lstm"]["f"][:10]) #print(c[:10]) return forward_encoder_states, backward_encoder_states, h, c, decoder_states def countParams(self): count = 0 count += self.embedding.countParams() count += self.encoder.countParams() count += self.decoder.countParams() count += self.stateReducer.countParams() count += self.denseLayer.countParams() return count def computeLRP(self, targets, start_decode=False): """ Compute the LRP for the entire network Input : targets : of shape (max_decoding_length, vocab_size). Table of True of False. If the position (i,j) is true, then we want to compute the relevance for the prediction we had of word number j at the decoding step i """ decoder_states = self.decoder.outputs forward_encoder_states = self.encoder.forward_encoder_states backward_encoder_states = self.encoder.backward_encoder_states output_relevance = [] for i in range(self.max_decoding_length): output_relevance.append(np.zeros((self.vocabSize, 1))) for j in range(self.vocabSize): if targets[i][j]: output_relevance[i][j] = softmax(decoder_states[i]["output"])[j] print(np.sum(output_relevance)) h_relevance, c_relevance, forward_encoder_relevance, backward_encoder_relevance, decoder_input_relevance, rest_relevance = self.decoder.computeLRP(output_relevance, self.denseLayer, forward_encoder_states, backward_encoder_states, transmit_input=False, start_decode=start_decode) #h_relevance = np.random.random_sample((self.cellSize, 1)) 10 -5 #c_relevance = np.random.random_sample((self.cellSize, 1)) 10 -5 print("Check conservation After decoding") print(np.sum(output_relevance)) print(np.sum(h_relevance)+ np.sum(c_relevance)+ np.sum(forward_encoder_relevance)+ np.sum(backward_encoder_relevance) + np.sum(np.sum(decoder_input_relevance)) + np.sum(rest_relevance)) #print(rest_relevance) #print("Length of the attention shit") #print(len(forward_encoder_relevance)) last_forward_h_relevance, last_backward_h_relevance, last_forward_c_relevance, last_backward_c_relevance = self.stateReducer.computeLRP(h_relevance, c_relevance) #print(np.linalg.norm(last_forward_h_relevance)) #print(np.linalg.norm(last_backward_h_relevance)) #print(np.linalg.norm(last_forward_c_relevance)) #print(np.linalg.norm(last_backward_c_relevance)) print("Check conservation State reducer") print(np.sum(h_relevance) + np.sum(c_relevance)) print(np.sum(last_forward_h_relevance)+ np.sum(last_backward_h_relevance)+ np.sum(last_forward_c_relevance)+ np.sum(last_backward_c_relevance)) input_relevance_forward, input_relevance_backward, h_relevance_rest_forward, c_relevance_rest_forward, h_relevance_rest_backward, c_relevance_rest_backward = self.encoder.computeLRP(last_forward_h_relevance, last_backward_h_relevance, last_forward_c_relevance, last_backward_c_relevance, forward_encoder_relevance, backward_encoder_relevance) print("Check conservation State reducer") print(np.sum(last_forward_h_relevance)+ np.sum(last_backward_h_relevance)+ np.sum(last_forward_c_relevance)+ np.sum(last_backward_c_relevance)+ np.sum(forward_encoder_relevance)+ np.sum(backward_encoder_relevance)) print(np.sum(input_relevance_forward)+ np.sum(input_relevance_backward) + np.sum(h_relevance_rest_forward)+ np.sum(c_relevance_rest_forward) + np.sum(h_relevance_rest_backward)+ np.sum(c_relevance_rest_backward)) print("Check conservation LRP") print(np.sum(output_relevance)) print(np.sum(input_relevance_forward)+ np.sum(input_relevance_backward)+ np.sum(np.sum(decoder_input_relevance)) + np.sum(rest_relevance)+ np.sum(h_relevance_rest_forward)+ np.sum(c_relevance_rest_forward) + np.sum(h_relevance_rest_backward)+ np.sum(c_relevance_rest_backward)) print("Difference between start and attributed LRP") print(np.sum(output_relevance)) print(np.sum(input_relevance_forward)+ np.sum(input_relevance_backward)+ np.sum(np.sum(decoder_input_relevance))) print("Rest relevance in attention") print(np.sum(rest_relevance)) print("Rest relevance in forward (h, c)") print(np.sum(h_relevance_rest_forward)) print(np.sum(c_relevance_rest_forward)) print("Rest relevance in backward (h, c)") print(np.sum(h_relevance_rest_backward)) print(np.sum(c_relevance_rest_backward)) # + np.sum(input_relevance_backward) + np.sum(decoder_input_relevance) return input_relevance_forward, input_relevance_backward, decoder_input_relevance	[ 1, 529, 276, 1112, 420, 29958, 339, 309, 273, 29955, 29947, 29914, 4345, 29907, 29918, 4836, 13, 5215, 12655, 408, 7442, 30004, 13, 3166, 2094, 6119, 1053, 334, 30004, 13, 3166, 23655, 1053, 334, 30004, 13, 3166, 1602, 6119, 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src/lesson_mathematics/math_frexp.py	jasonwee/asus-rt-n14uhp-mrtg	3	114105	import math print('{:^7} {:^7} {:^7}'.format('x', 'm', 'e')) print('{:-^7} {:-^7} {:-^7}'.format('', '', '')) for x in [0.1, 0.5, 4.0]: m, e = math.frexp(x) print('{:7.2f} {:7.2f} {:7d}'.format(x, m, e))	[ 1, 1053, 5844, 13, 13, 2158, 877, 25641, 29985, 29955, 29913, 12365, 29985, 29955, 29913, 12365, 29985, 29955, 29913, 4286, 4830, 877, 29916, 742, 525, 29885, 742, 525, 29872, 8785, 13, 2158, 877, 29912, 13018, 29985, 29955, 29913, 12365, 29899, 29985, 29955, 29913, 12365, 29899, 29985, 29955, 29913, 4286, 4830, 877, 742, 15516, 6629, 876, 13, 13, 1454, 921, 297, 518, 29900, 29889, 29896, 29892, 29871, 29900, 29889, 29945, 29892, 29871, 29946, 29889, 29900, 5387, 13, 1678, 286, 29892, 321, 353, 5844, 29889, 10745, 26330, 29898, 29916, 29897, 13, 1678, 1596, 877, 25641, 29955, 29889, 29906, 29888, 29913, 12365, 29955, 29889, 29906, 29888, 29913, 12365, 29955, 29881, 29913, 4286, 4830, 29898, 29916, 29892, 286, 29892, 321, 876, 13, 2 ]
apps/plea/exceptions.py	uk-gov-mirror/ministryofjustice.manchester_traffic_offences_pleas	3	173209	""" Exceptions ========== """ class AuditEventException(BaseException): pass	[ 1, 9995, 13, 2451, 29879, 13, 4936, 1360, 13, 15945, 29908, 13, 13, 1990, 8612, 277, 2624, 2451, 29898, 5160, 2451, 1125, 13, 1678, 1209, 13, 13, 13, 2 ]
zenbot/logging/__init__.py	Dmunch04/ZenBot	7	125957	<filename>zenbot/logging/__init__.py<gh_stars>1-10 from .console_logger import ConsoleLogger from .file_logger import FileLogger from .logger import Logger from .logmanager import LogManager	[ 1, 529, 9507, 29958, 2256, 7451, 29914, 21027, 29914, 1649, 2344, 26914, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 869, 11058, 29918, 21707, 1053, 9405, 16363, 13, 3166, 869, 1445, 29918, 21707, 1053, 3497, 16363, 13, 3166, 869, 21707, 1053, 28468, 13, 3166, 869, 1188, 12847, 1053, 4522, 3260, 13, 2 ]
10_ExtractUniqueSents.py	bjascob/SmartLMVocabs	10	125060	#!/usr/bin/python3 # Copyright 2018 <NAME> # # 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. from __future__ import print_function import os import io import math import random from subprocess import Popen, PIPE from tflmlib import ProgressBar from configs import config # Billion Word Corpus # To create the training-monolingual directory .. # wget http://statmt.org/wmt11/training-monolingual.tgz # tar xvf training-monolingual.tgz --wildcards training-monolingual/news.20??.en.shuffled # The 9.9 GB file extracts to 25GB without the wildcards. Other files are non-english. bw_raw_dir = config.bw_rawdir bw_unique_dir = os.path.join(config.bw_corpus, 'BWUniqueSents_FirstPass') # Get the number of lines in a file def getFileLines(fname): p = Popen(['wc', '-l', fname], stdout=PIPE, stderr=PIPE) result, err = p.communicate() if p.returncode != 0: raise IOError(err) return int(result.strip().split()[0]) if __name__ == '__main__': print('' 80) print() nshards = 100 test = False # Loop through the corpus # Note this takes about 3 minutes, uses 6.5GB of RAM and 3.9GB disk space print('#' * 40) print('Loading the raw corpus') fns = sorted([os.path.join(bw_raw_dir, fn) for fn in os.listdir(bw_raw_dir)]) bw_set = set() sent_ctr = 0 for i, fn in enumerate(fns): print(' %d/%d : %s' % (i + 1, len(fns), fn)) nlines = getFileLines(fn) pb = ProgressBar(nlines) with io.open(fn, 'r', encoding='utf8') as f: for j, line in enumerate(f): line = line.strip() bw_set.add(line) if 0 == j % 100: pb.update(j) if test and j > 100000: break pb.clear() sent_ctr += nlines if test and i >= 0: break nunique = len(bw_set) print('Corpus has {:,} unique sentences out of {:,} read.'.format(nunique, sent_ctr)) print() # Create the output directory if not os.path.exists(bw_unique_dir): os.mkdir(bw_unique_dir) # Split the sentences into shards and save them print('Converting to a list and shuffling') bw_set = list(bw_set) random.shuffle(bw_set) sents_per_shard = int(math.ceil(nunique / float(nshards))) for i in range(nshards): fn = os.path.join(bw_unique_dir, 'bw_%02d.txt' % i) print('Saving the data to ', fn) with io.open(fn, 'w', encoding='utf8') as f: for sent in bw_set[i * sents_per_shard:(i + 1) * sents_per_shard]: f.write('%s\n' % sent) print('done') print()	[ 1, 18787, 4855, 29914, 2109, 29914, 4691, 29941, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29896, 29947, 529, 5813, 29958, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 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29918, 2616, 13364, 29892, 525, 29933, 29956, 8110, 802, 29903, 1237, 29918, 6730, 7129, 1495, 13, 13, 13, 29937, 3617, 278, 1353, 310, 3454, 297, 263, 934, 13, 1753, 679, 2283, 20261, 29898, 29888, 978, 1125, 13, 1678, 282, 353, 349, 3150, 18959, 29893, 29883, 742, 17411, 29880, 742, 285, 978, 1402, 27591, 29922, 2227, 4162, 29892, 380, 20405, 29922, 2227, 4162, 29897, 13, 1678, 1121, 29892, 4589, 353, 282, 29889, 27820, 403, 580, 13, 1678, 565, 282, 29889, 2457, 401, 2804, 29871, 29900, 29901, 13, 4706, 12020, 10663, 2392, 29898, 3127, 29897, 13, 1678, 736, 938, 29898, 2914, 29889, 17010, 2141, 5451, 580, 29961, 29900, 2314, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1596, 877, 29930, 29915, 334, 29871, 29947, 29900, 29897, 13, 1678, 1596, 580, 13, 13, 1678, 302, 845, 3163, 353, 29871, 29896, 29900, 29900, 13, 1678, 1243, 353, 7700, 13, 13, 1678, 396, 21493, 1549, 278, 1034, 13364, 13, 1678, 396, 3940, 445, 4893, 1048, 29871, 29941, 6233, 29892, 3913, 29871, 29953, 29889, 29945, 7210, 310, 18113, 322, 29871, 29941, 29889, 29929, 7210, 8086, 2913, 13, 1678, 1596, 14237, 29915, 334, 29871, 29946, 29900, 29897, 13, 1678, 1596, 877, 23456, 278, 10650, 1034, 13364, 1495, 13, 1678, 285, 1983, 353, 12705, 4197, 359, 29889, 2084, 29889, 7122, 29898, 29890, 29893, 29918, 1610, 29918, 3972, 29892, 7876, 29897, 363, 7876, 297, 2897, 29889, 1761, 3972, 29898, 29890, 29893, 29918, 1610, 29918, 3972, 29897, 2314, 13, 1678, 289, 29893, 29918, 842, 353, 731, 580, 13, 1678, 2665, 29918, 9988, 353, 29871, 29900, 13, 1678, 363, 474, 29892, 7876, 297, 26985, 29898, 29888, 1983, 1125, 13, 4706, 1596, 877, 29871, 1273, 29881, 22584, 29881, 584, 1273, 29879, 29915, 1273, 313, 29875, 718, 29871, 29896, 29892, 7431, 29898, 29888, 1983, 511, 7876, 876, 13, 4706, 302, 9012, 353, 679, 2283, 20261, 29898, 9144, 29897, 13, 4706, 282, 29890, 353, 20018, 4297, 29898, 29876, 9012, 29897, 13, 4706, 411, 12013, 29889, 3150, 29898, 9144, 29892, 525, 29878, 742, 8025, 2433, 9420, 29947, 1495, 408, 285, 29901, 13, 9651, 363, 432, 29892, 1196, 297, 26985, 29898, 29888, 1125, 13, 18884, 1196, 353, 1196, 29889, 17010, 580, 13, 18884, 289, 29893, 29918, 842, 29889, 1202, 29898, 1220, 29897, 13, 18884, 565, 29871, 29900, 1275, 432, 1273, 29871, 29896, 29900, 29900, 29901, 282, 29890, 29889, 5504, 29898, 29926, 29897, 13, 18884, 565, 1243, 322, 432, 1405, 29871, 29896, 29900, 29900, 29900, 29900, 29900, 29901, 2867, 13, 4706, 282, 29890, 29889, 8551, 580, 13, 4706, 2665, 29918, 9988, 4619, 302, 9012, 13, 4706, 565, 1243, 322, 474, 6736, 29871, 29900, 29901, 2867, 13, 1678, 11923, 1387, 353, 7431, 29898, 29890, 29893, 29918, 842, 29897, 13, 1678, 1596, 877, 12521, 13364, 756, 12365, 29892, 29913, 5412, 25260, 714, 310, 12365, 29892, 29913, 1303, 29889, 4286, 4830, 29898, 29876, 13092, 29892, 2665, 29918, 9988, 876, 13, 1678, 1596, 580, 13, 13, 1678, 396, 6204, 278, 1962, 3884, 13, 1678, 565, 451, 2897, 29889, 2084, 29889, 9933, 29898, 29890, 29893, 29918, 13092, 29918, 3972, 1125, 13, 4706, 2897, 29889, 11256, 3972, 29898, 29890, 29893, 29918, 13092, 29918, 3972, 29897, 13, 13, 1678, 396, 26178, 278, 25260, 964, 528, 3163, 322, 4078, 963, 13, 1678, 1596, 877, 1168, 369, 1259, 304, 263, 1051, 322, 528, 3096, 1847, 1495, 13, 1678, 289, 29893, 29918, 842, 353, 1051, 29898, 29890, 29893, 29918, 842, 29897, 13, 1678, 4036, 29889, 845, 21897, 29898, 29890, 29893, 29918, 842, 29897, 13, 1678, 269, 1237, 29918, 546, 29918, 845, 538, 353, 938, 29898, 755, 29889, 27696, 29898, 29876, 13092, 847, 5785, 29898, 29876, 845, 3163, 4961, 13, 1678, 363, 474, 297, 3464, 29898, 29876, 845, 3163, 1125, 13, 4706, 7876, 353, 2897, 29889, 2084, 29889, 7122, 29898, 29890, 29893, 29918, 13092, 29918, 3972, 29892, 525, 29890, 29893, 29918, 29995, 29900, 29906, 29881, 29889, 3945, 29915, 1273, 474, 29897, 13, 4706, 1596, 877, 29903, 5555, 278, 848, 304, 13420, 7876, 29897, 13, 4706, 411, 12013, 29889, 3150, 29898, 9144, 29892, 525, 29893, 742, 8025, 2433, 9420, 29947, 1495, 408, 285, 29901, 13, 9651, 363, 2665, 297, 289, 29893, 29918, 842, 29961, 29875, 334, 269, 1237, 29918, 546, 29918, 845, 538, 5919, 29875, 718, 29871, 29896, 29897, 334, 269, 1237, 29918, 546, 29918, 845, 538, 5387, 13, 18884, 285, 29889, 3539, 877, 29995, 29879, 29905, 29876, 29915, 1273, 2665, 29897, 13, 1678, 1596, 877, 15091, 1495, 13, 1678, 1596, 580, 13, 2 ]
23_Basic_testbench_1.0/testbench.py	raysalemi/python4uvm_examples	1	107834	<gh_stars>1-10 # ## Importing modules # Figure 3: Importing needed resources import cocotb from cocotb.triggers import FallingEdge import random # ### The tinyalu_utils module # All testbenches use tinyalu_utils, so store it in a central # place and add its path to the sys path so we can import it from pathlib import Path parent_path = Path("..").resolve() import sys # noqa: E402 sys.path.insert(0, str(parent_path)) from tinyalu_utils import Ops, alu_prediction, logger, get_int # noqa: E402 # ## Setting up the cocotb TinyALU test # Figure 7: The start of the TinyALU. Reset the DUT @cocotb.test() async def alu_test(dut): passed = True cvg = set() # functional coverage await FallingEdge(dut.clk) dut.reset_n.value = 0 dut.start.value = 0 await FallingEdge(dut.clk) dut.reset_n.value = 1 # ### Sending commands # Figure 8: Creating one transaction for each operation cmd_count = 1 op_list = list(Ops) num_ops = len(op_list) while cmd_count <= num_ops: await FallingEdge(dut.clk) st = get_int(dut.start) dn = get_int(dut.done) # ### Sending a command and waiting for it to complete # Figure 9: Creating a TinyALU command if st == 0 and dn == 0: aa = random.randint(0, 255) bb = random.randint(0, 255) op = op_list.pop(0) cvg.add(op) dut.A.value = aa dut.B.value = bb dut.op.value = op dut.start.value = 1 # Figure 10: Asserting that a failure state never happens if st == 0 and dn == 1: raise AssertionError("DUT Error: done set to 1 without start") # Figure 11: If we are in an operation, continue if st == 1 and dn == 0: continue # ### Checking the result # Figure 12: The operation is complete if st == 1 and dn == 1: dut.start.value = 0 cmd_count += 1 result = get_int(dut.result) # Figure 13: Checking results against the prediction pr = alu_prediction(aa, bb, op) if result == pr: logger.info( f"PASSED: {aa:2x} {op.name} {bb:2x} = {result:04x}") else: logger.error( f"FAILED: {aa:2x} {op.name} {bb:2x} =" f" {result:04x} - predicted {pr:04x}") passed = False # ### Finishing the test # Figure 14: Checking functional coverage using a set if len(set(Ops) - cvg) > 0: logger.error(f"Functional coverage error. Missed: {set(Ops)-cvg}") passed = False else: logger.info("Covered all operations") # Figure 15: This assertion relays pass/fail to cocotb assert passed	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 444, 16032, 292, 10585, 13, 13, 29937, 11479, 29871, 29941, 29901, 16032, 292, 4312, 7788, 13, 5215, 274, 542, 327, 29890, 13, 3166, 274, 542, 327, 29890, 29889, 509, 335, 5743, 1053, 14053, 292, 23894, 13, 5215, 4036, 13, 13, 29937, 835, 450, 27773, 4605, 29884, 29918, 13239, 3883, 13, 13, 29937, 2178, 1243, 1785, 6609, 671, 27773, 4605, 29884, 29918, 13239, 29892, 577, 3787, 372, 297, 263, 6555, 13, 29937, 2058, 322, 788, 967, 2224, 304, 278, 10876, 2224, 577, 591, 508, 1053, 372, 13, 13, 3166, 2224, 1982, 1053, 10802, 13, 3560, 29918, 2084, 353, 10802, 703, 636, 2564, 17863, 580, 13, 13, 5215, 10876, 29871, 396, 694, 25621, 29901, 382, 29946, 29900, 29906, 13, 9675, 29889, 2084, 29889, 7851, 29898, 29900, 29892, 851, 29898, 3560, 29918, 2084, 876, 13, 13, 3166, 27773, 4605, 29884, 29918, 13239, 1053, 438, 567, 29892, 394, 29884, 29918, 11965, 2463, 29892, 17927, 29892, 679, 29918, 524, 29871, 396, 694, 25621, 29901, 382, 29946, 29900, 29906, 13, 13, 13, 29937, 444, 21605, 701, 278, 274, 542, 327, 29890, 323, 4901, 1964, 29965, 1243, 13, 13, 29937, 11479, 29871, 29955, 29901, 450, 1369, 310, 278, 323, 4901, 1964, 29965, 29889, 2538, 300, 278, 360, 2692, 13, 29992, 29883, 542, 327, 29890, 29889, 1688, 580, 13, 12674, 822, 394, 29884, 29918, 1688, 29898, 29881, 329, 1125, 13, 1678, 4502, 353, 5852, 13, 1678, 13850, 29887, 353, 731, 580, 29871, 396, 13303, 23746, 13, 1678, 7272, 14053, 292, 23894, 29898, 29881, 329, 29889, 20495, 29897, 13, 1678, 9379, 29889, 12071, 29918, 29876, 29889, 1767, 353, 29871, 29900, 13, 1678, 9379, 29889, 2962, 29889, 1767, 353, 29871, 29900, 13, 1678, 7272, 14053, 292, 23894, 29898, 29881, 329, 29889, 20495, 29897, 13, 1678, 9379, 29889, 12071, 29918, 29876, 29889, 1767, 353, 29871, 29896, 13, 29937, 835, 317, 2548, 8260, 13, 29937, 11479, 29871, 29947, 29901, 26221, 697, 10804, 363, 1269, 5858, 13, 1678, 9920, 29918, 2798, 353, 29871, 29896, 13, 1678, 1015, 29918, 1761, 353, 1051, 29898, 29949, 567, 29897, 13, 1678, 954, 29918, 3554, 353, 7431, 29898, 459, 29918, 1761, 29897, 13, 1678, 1550, 9920, 29918, 2798, 5277, 954, 29918, 3554, 29901, 13, 4706, 7272, 14053, 292, 23894, 29898, 29881, 329, 29889, 20495, 29897, 13, 4706, 380, 353, 679, 29918, 524, 29898, 29881, 329, 29889, 2962, 29897, 13, 4706, 270, 29876, 353, 679, 29918, 524, 29898, 29881, 329, 29889, 15091, 29897, 13, 29937, 835, 317, 2548, 263, 1899, 322, 10534, 363, 372, 304, 4866, 13, 29937, 11479, 29871, 29929, 29901, 26221, 263, 323, 4901, 1964, 29965, 1899, 13, 4706, 565, 380, 1275, 29871, 29900, 322, 270, 29876, 1275, 29871, 29900, 29901, 13, 9651, 29099, 353, 4036, 29889, 9502, 524, 29898, 29900, 29892, 29871, 29906, 29945, 29945, 29897, 13, 9651, 289, 29890, 353, 4036, 29889, 9502, 524, 29898, 29900, 29892, 29871, 29906, 29945, 29945, 29897, 13, 9651, 1015, 353, 1015, 29918, 1761, 29889, 7323, 29898, 29900, 29897, 13, 9651, 13850, 29887, 29889, 1202, 29898, 459, 29897, 13, 9651, 9379, 29889, 29909, 29889, 1767, 353, 29099, 13, 9651, 9379, 29889, 29933, 29889, 1767, 353, 289, 29890, 13, 9651, 9379, 29889, 459, 29889, 1767, 353, 1015, 13, 9651, 9379, 29889, 2962, 29889, 1767, 353, 29871, 29896, 13, 29937, 11479, 29871, 29896, 29900, 29901, 1094, 643, 1259, 393, 263, 10672, 2106, 2360, 5930, 13, 4706, 565, 380, 1275, 29871, 29900, 322, 270, 29876, 1275, 29871, 29896, 29901, 13, 9651, 12020, 16499, 291, 2392, 703, 29928, 2692, 4829, 29901, 2309, 731, 304, 29871, 29896, 1728, 1369, 1159, 13, 29937, 11479, 29871, 29896, 29896, 29901, 960, 591, 526, 297, 385, 5858, 29892, 6773, 13, 4706, 565, 380, 1275, 29871, 29896, 322, 270, 29876, 1275, 29871, 29900, 29901, 13, 9651, 6773, 13, 29937, 835, 5399, 292, 278, 1121, 13, 29937, 11479, 29871, 29896, 29906, 29901, 450, 5858, 338, 4866, 13, 4706, 565, 380, 1275, 29871, 29896, 322, 270, 29876, 1275, 29871, 29896, 29901, 13, 9651, 9379, 29889, 2962, 29889, 1767, 353, 29871, 29900, 13, 9651, 9920, 29918, 2798, 4619, 29871, 29896, 13, 9651, 1121, 353, 679, 29918, 524, 29898, 29881, 329, 29889, 2914, 29897, 13, 29937, 11479, 29871, 29896, 29941, 29901, 5399, 292, 2582, 2750, 278, 18988, 13, 9651, 544, 353, 394, 29884, 29918, 11965, 2463, 29898, 7340, 29892, 289, 29890, 29892, 1015, 29897, 13, 9651, 565, 1121, 1275, 544, 29901, 13, 18884, 17927, 29889, 3888, 29898, 13, 462, 1678, 285, 29908, 29925, 3289, 1660, 29928, 29901, 426, 7340, 29901, 29906, 29916, 29913, 426, 459, 29889, 978, 29913, 426, 1327, 29901, 29906, 29916, 29913, 353, 426, 2914, 29901, 29900, 29946, 29916, 27195, 13, 9651, 1683, 29901, 13, 18884, 17927, 29889, 2704, 29898, 13, 462, 1678, 285, 29908, 4519, 29902, 20566, 29901, 426, 7340, 29901, 29906, 29916, 29913, 426, 459, 29889, 978, 29913, 426, 1327, 29901, 29906, 29916, 29913, 29465, 13, 462, 1678, 285, 29908, 426, 2914, 29901, 29900, 29946, 29916, 29913, 448, 25383, 426, 558, 29901, 29900, 29946, 29916, 27195, 13, 18884, 4502, 353, 7700, 13, 29937, 835, 4231, 14424, 278, 1243, 13, 29937, 11479, 29871, 29896, 29946, 29901, 5399, 292, 13303, 23746, 773, 263, 731, 13, 1678, 565, 7431, 29898, 842, 29898, 29949, 567, 29897, 448, 13850, 29887, 29897, 1405, 29871, 29900, 29901, 13, 4706, 17927, 29889, 2704, 29898, 29888, 29908, 6678, 284, 23746, 1059, 29889, 4750, 287, 29901, 426, 842, 29898, 29949, 567, 6817, 11023, 29887, 27195, 13, 4706, 4502, 353, 7700, 13, 1678, 1683, 29901, 13, 4706, 17927, 29889, 3888, 703, 29907, 957, 287, 599, 6931, 1159, 13, 29937, 11479, 29871, 29896, 29945, 29901, 910, 28306, 1104, 1036, 1209, 29914, 14057, 304, 274, 542, 327, 29890, 13, 1678, 4974, 4502, 13, 2 ]
nutsml/examples/pytorch_/mnist/mlp_train.py	maet3608/nuts-ml	39	19605	""" .. module:: cnn_train :synopsis: Example nuts-ml pipeline for training a MLP on MNIST """ import torch import torch.nn.functional as F import torch.nn as nn import torch.optim as optim import nutsflow as nf import nutsml as nm import numpy as np from nutsml.network import PytorchNetwork from utils import download_mnist, load_mnist class Model(nn.Module): """Pytorch model""" def __init__(self, device): """Construct model on given device, e.g. 'cpu' or 'cuda'""" super(Model, self).__init__() self.fc1 = nn.Linear(28 * 28, 500) self.fc2 = nn.Linear(500, 256) self.fc3 = nn.Linear(256, 10) self.to(device) # set device before constructing optimizer # required properties of a model to be wrapped as PytorchNetwork! self.device = device # 'cuda', 'cuda:0' or 'gpu' self.losses = nn.CrossEntropyLoss() # can be list of loss functions self.optimizer = optim.Adam(self.parameters()) def forward(self, x): """Forward pass through network for input x""" x = x.view(-1, 28 * 28) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x def accuracy(y_true, y_pred): """Compute accuracy""" from sklearn.metrics import accuracy_score y_pred = [yp.argmax() for yp in y_pred] return 100 * accuracy_score(y_true, y_pred) def evaluate(network, x, y): """Evaluate network performance (here accuracy)""" metrics = [accuracy] build_batch = (nm.BuildBatch(64) .input(0, 'vector', 'float32') .output(1, 'number', 'int64')) acc = zip(x, y) >> build_batch >> network.evaluate(metrics) return acc def train(network, epochs=3): """Train network for given number of epochs""" print('loading data...') filepath = download_mnist() x_train, y_train, x_test, y_test = load_mnist(filepath) plot = nm.PlotLines(None, every_sec=0.2) build_batch = (nm.BuildBatch(128) .input(0, 'vector', 'float32') .output(1, 'number', 'int64')) for epoch in range(epochs): print('epoch', epoch + 1) losses = (zip(x_train, y_train) >> nf.PrintProgress(x_train) >> nf.Shuffle(1000) >> build_batch >> network.train() >> plot >> nf.Collect()) acc_test = evaluate(network, x_test, y_test) acc_train = evaluate(network, x_train, y_train) print('train loss : {:.6f}'.format(np.mean(losses))) print('train acc : {:.1f}'.format(acc_train)) print('test acc : {:.1f}'.format(acc_test)) if __name__ == '__main__': print('creating model...') device = 'cuda' if torch.cuda.is_available() else 'cpu' model = Model(device) network = PytorchNetwork(model) # network.load_weights() network.print_layers((28 * 28,)) print('training network...') train(network, epochs=3)	[ 1, 9995, 13, 636, 3883, 1057, 274, 15755, 29918, 14968, 13, 259, 584, 19274, 15368, 29901, 8741, 302, 8842, 29899, 828, 16439, 363, 6694, 263, 341, 13208, 373, 341, 29940, 9047, 13, 15945, 29908, 13, 13, 5215, 4842, 305, 13, 5215, 4842, 305, 29889, 15755, 29889, 2220, 284, 408, 383, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 29876, 13, 5215, 4842, 305, 29889, 20640, 408, 5994, 13, 5215, 302, 8842, 1731, 408, 302, 29888, 13, 5215, 302, 8842, 828, 408, 302, 29885, 13, 5215, 12655, 408, 7442, 13, 13, 3166, 302, 8842, 828, 29889, 11618, 1053, 349, 3637, 25350, 13724, 13, 3166, 3667, 29879, 1053, 5142, 29918, 23521, 391, 29892, 2254, 29918, 23521, 391, 13, 13, 13, 1990, 8125, 29898, 15755, 29889, 7355, 1125, 13, 1678, 9995, 29925, 3637, 25350, 1904, 15945, 29908, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 4742, 1125, 13, 4706, 9995, 1168, 4984, 1904, 373, 2183, 4742, 29892, 321, 29889, 29887, 29889, 525, 21970, 29915, 470, 525, 29883, 6191, 11838, 15945, 13, 4706, 2428, 29898, 3195, 29892, 1583, 467, 1649, 2344, 1649, 580, 13, 4706, 1583, 29889, 13801, 29896, 353, 302, 29876, 29889, 12697, 29898, 29906, 29947, 334, 29871, 29906, 29947, 29892, 29871, 29945, 29900, 29900, 29897, 13, 4706, 1583, 29889, 13801, 29906, 353, 302, 29876, 29889, 12697, 29898, 29945, 29900, 29900, 29892, 29871, 29906, 29945, 29953, 29897, 13, 4706, 1583, 29889, 13801, 29941, 353, 302, 29876, 29889, 12697, 29898, 29906, 29945, 29953, 29892, 29871, 29896, 29900, 29897, 13, 13, 4706, 1583, 29889, 517, 29898, 10141, 29897, 29871, 396, 731, 4742, 1434, 3386, 292, 5994, 3950, 13, 13, 4706, 396, 3734, 4426, 310, 263, 1904, 304, 367, 21021, 408, 349, 3637, 25350, 13724, 29991, 13, 4706, 1583, 29889, 10141, 353, 4742, 29871, 396, 525, 29883, 6191, 742, 525, 29883, 6191, 29901, 29900, 29915, 470, 525, 29887, 3746, 29915, 13, 4706, 1583, 29889, 6758, 267, 353, 302, 29876, 29889, 29907, 2124, 5292, 14441, 29931, 2209, 580, 29871, 396, 508, 367, 1051, 310, 6410, 3168, 13, 4706, 1583, 29889, 20640, 3950, 353, 5994, 29889, 3253, 314, 29898, 1311, 29889, 16744, 3101, 13, 13, 1678, 822, 6375, 29898, 1311, 29892, 921, 1125, 13, 4706, 9995, 2831, 1328, 1209, 1549, 3564, 363, 1881, 921, 15945, 29908, 13, 4706, 921, 353, 921, 29889, 1493, 6278, 29896, 29892, 29871, 29906, 29947, 334, 29871, 29906, 29947, 29897, 13, 4706, 921, 353, 383, 29889, 2674, 29884, 29898, 1311, 29889, 13801, 29896, 29898, 29916, 876, 13, 4706, 921, 353, 383, 29889, 2674, 29884, 29898, 1311, 29889, 13801, 29906, 29898, 29916, 876, 13, 4706, 921, 353, 1583, 29889, 13801, 29941, 29898, 29916, 29897, 13, 4706, 736, 921, 13, 13, 13, 1753, 13600, 29898, 29891, 29918, 3009, 29892, 343, 29918, 11965, 1125, 13, 1678, 9995, 20606, 29872, 13600, 15945, 29908, 13, 1678, 515, 2071, 19668, 29889, 2527, 10817, 1053, 13600, 29918, 13628, 13, 1678, 343, 29918, 11965, 353, 518, 1478, 29889, 1191, 3317, 580, 363, 343, 29886, 297, 343, 29918, 11965, 29962, 13, 1678, 736, 29871, 29896, 29900, 29900, 334, 13600, 29918, 13628, 29898, 29891, 29918, 3009, 29892, 343, 29918, 11965, 29897, 13, 13, 13, 1753, 14707, 29898, 11618, 29892, 921, 29892, 343, 1125, 13, 1678, 9995, 29923, 4387, 403, 3564, 4180, 313, 4150, 13600, 5513, 15945, 13, 1678, 21556, 353, 518, 562, 2764, 4135, 29962, 13, 1678, 2048, 29918, 16175, 353, 313, 22882, 29889, 8893, 23145, 29898, 29953, 29946, 29897, 13, 462, 259, 869, 2080, 29898, 29900, 29892, 525, 8111, 742, 525, 7411, 29941, 29906, 1495, 13, 462, 259, 869, 4905, 29898, 29896, 29892, 525, 4537, 742, 525, 524, 29953, 29946, 8785, 13, 1678, 1035, 353, 14319, 29898, 29916, 29892, 343, 29897, 5099, 2048, 29918, 16175, 5099, 3564, 29889, 24219, 403, 29898, 2527, 10817, 29897, 13, 1678, 736, 1035, 13, 13, 13, 1753, 7945, 29898, 11618, 29892, 21502, 12168, 29922, 29941, 1125, 13, 1678, 9995, 5323, 262, 3564, 363, 2183, 1353, 310, 21502, 12168, 15945, 29908, 13, 1678, 1596, 877, 13234, 848, 856, 1495, 13, 1678, 934, 2084, 353, 5142, 29918, 23521, 391, 580, 13, 1678, 921, 29918, 14968, 29892, 343, 29918, 14968, 29892, 921, 29918, 1688, 29892, 343, 29918, 1688, 353, 2254, 29918, 23521, 391, 29898, 1445, 2084, 29897, 13, 13, 1678, 6492, 353, 302, 29885, 29889, 20867, 20261, 29898, 8516, 29892, 1432, 29918, 3471, 29922, 29900, 29889, 29906, 29897, 13, 1678, 2048, 29918, 16175, 353, 313, 22882, 29889, 8893, 23145, 29898, 29896, 29906, 29947, 29897, 13, 462, 259, 869, 2080, 29898, 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gcp-cloud-function/send_email/main.py	duongphannamhung/atom-assignments	3	89885	<gh_stars>1-10 def send_email(request): """Responds to any HTTP request. Args: request (flask.Request): HTTP request object. Returns: The response text or any set of values that can be turned into a Response object using `make_response <http://flask.pocoo.org/docs/1.0/api/#flask.Flask.make_response>`. """ import os import json import email, smtplib, ssl from email import encoders from email.mime.base import MIMEBase from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText import datetime as dt # to work with date, time import sys request_json = request.get_json() #--> get the request json if request_json and 'subject' in request_json: print('subject ==> {}'.format(request_json['subject'])) if request_json and 'receiver_email' in request_json: print('receiver_email ==> {}'.format(request_json['receiver_email'])) ## 1A - Take input from request json receiver_email = request_json['receiver_email'] subject = request_json['subject'] ## 1B - Get ENV set in Cloud #sender_email = os.getenv('SENDER_EMAIL') ## PLEASE MEMBER TO SET THE ENV VARIABLES IN YOUR CLOUD FUNC sender_email = os.environ.get('SENDER_EMAIL') password = os.environ.get('PWD_EMAIL') print('=== Step 1: Get Input') ## 2 - Email Set email = MIMEMultipart() email["From"] = sender_email email["To"] = receiver_email email["Subject"] = subject ## 3 - Email Contents # We use html, you can convert word to html: https://wordtohtml.net/ html1 = """ <html> <h1><strong>Hello World</strong></h1> <body> <p>Hi!<br> How are you?<br> Here is the <a href="https://docs.python.org/3.4/library/email-examples.html">link</a> you wanted. </p> </body> </html> """ html2 = """ <html> Email sent at <b>{}</b><br> </html> """.format(dt.datetime.now().isoformat()) text3 = '--- End ----' # Combine parts part1 = MIMEText(html1, 'html') part2 = MIMEText(html2, 'html') part3 = MIMEText(text3, 'plain') email.attach(part1) email.attach(part2) email.attach(part3) print('=== Step 2: Prep Contents') ## 4 - Create SMTP session for sending the mail try: session = smtplib.SMTP('smtp.gmail.com', 587) #use gmail with port session.starttls() #enable security print('=== Step 3: Enable Security') session.login(sender_email, password) #login with mail_id and password print('=== Step 4: Login Success!') text = email.as_string() session.sendmail(sender_email, receiver_email, text) session.quit() print('DONE! Mail Sent from {} to {}'.format(sender_email, receiver_email)) message = 'DONE! Mail Sent from {} to {}'.format(sender_email, receiver_email) status = 'OK' except: print('=== ERROR: {}'.format(sys.exc_info()[0])) message = 'ERROR: {}'.format(sys.exc_info()[0]) status = 'FAIL' out = {'status': status, 'message': message} headers= { 'Access-Control-Allow-Origin': '*', 'Content-Type':'application/json' } return (json.dumps(out), 200, headers)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 1753, 3638, 29918, 5269, 29898, 3827, 1125, 13, 1678, 9995, 1666, 2818, 29879, 304, 738, 7331, 2009, 29889, 13, 1678, 826, 3174, 29901, 13, 4706, 2009, 313, 1579, 1278, 29889, 3089, 1125, 7331, 2009, 1203, 29889, 13, 1678, 16969, 29901, 13, 4706, 450, 2933, 1426, 470, 738, 731, 310, 1819, 393, 508, 367, 6077, 964, 263, 13, 4706, 13291, 1203, 773, 13, 4706, 421, 5675, 29918, 5327, 529, 1124, 597, 1579, 1278, 29889, 29886, 542, 3634, 29889, 990, 29914, 2640, 29914, 29896, 29889, 29900, 29914, 2754, 8484, 1579, 1278, 29889, 8754, 1278, 29889, 5675, 29918, 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model_based/svms/test_model_compression.py	vohoaiviet/tag-image-retrieval	50	28844	import sys import os import time from basic.common import ROOT_PATH,checkToSkip,makedirsforfile from basic.util import readImageSet from simpleknn.bigfile import BigFile, StreamFile from basic.annotationtable import readConcepts,readAnnotationsFrom from basic.metric import getScorer if __name__ == "__main__": try: rootpath = sys.argv[1] except: rootpath = ROOT_PATH metric = 'AP' feature = "dsift" trainCollection = 'voc2008train' trainAnnotationName = 'conceptsvoc2008train.txt' testCollection = 'voc2008val' testAnnotationName = 'conceptsvoc2008val.txt' testset = testCollection modelName = 'fik50' modelName = 'fastlinear' if 'fastlinear' == modelName: from fastlinear.fastlinear import fastlinear_load_model as load_model else: from fiksvm.fiksvm import fiksvm_load_model as load_model scorer = getScorer(metric) imset = readImageSet(testCollection,testset,rootpath=rootpath) concepts = readConcepts(testCollection,testAnnotationName,rootpath=rootpath) feat_dir = os.path.join(rootpath, testCollection, "FeatureData", feature) feat_file = BigFile(feat_dir) _renamed, _vectors = feat_file.read(imset) nr_of_images = len(_renamed) nr_of_concepts = len(concepts) mAP = 0.0 models = [None] * len(concepts) for i,concept in enumerate(concepts): model_file_name = os.path.join(rootpath,trainCollection,'Models',trainAnnotationName,feature, modelName, '%s.model'%concept) model1 = load_model(model_file_name) (pA,pB) = model1.get_probAB() model2 = load_model(model_file_name) model2.add_fastsvm(model1, 0.8, 1) names,labels = readAnnotationsFrom(testCollection, testAnnotationName, concept, rootpath=rootpath) name2label = dict(zip(names,labels)) ranklist1 = [(_id, model1.predict(_vec)) for _id,_vec in zip(_renamed, _vectors)] ranklist2 = [(_id, model2.predict(_vec)) for _id,_vec in zip(_renamed, _vectors)] model_file_name = os.path.join(rootpath,trainCollection,'Models', 'bag' + trainAnnotationName,feature, modelName, '%s.model'%concept) model3 = load_model(model_file_name) ranklist3 = [(_id, model3.predict(_vec)) for _id,_vec in zip(_renamed, _vectors)] print concept, for ranklist in [ranklist1, ranklist2, ranklist3]: ranklist.sort(key=lambda v:v[1], reverse=True) sorted_labels = [name2label[x[0]] for x in ranklist if x[0] in name2label] print '%.3f'%scorer.score(sorted_labels), print ''	[ 1, 29871, 13, 5215, 10876, 13, 5215, 2897, 13, 5215, 931, 13, 13, 3166, 6996, 29889, 9435, 1053, 16641, 2891, 29918, 10145, 29892, 3198, 1762, 15797, 666, 29892, 29885, 12535, 12935, 1454, 1445, 13, 3166, 6996, 29889, 4422, 1053, 1303, 2940, 2697, 13, 3166, 2560, 3959, 29876, 29889, 3752, 1445, 1053, 7997, 2283, 29892, 13763, 2283, 13, 3166, 6996, 29889, 18317, 2371, 1053, 1303, 1168, 1547, 29879, 29892, 949, 2744, 1333, 800, 4591, 13, 3166, 6996, 29889, 16414, 1053, 679, 29903, 2616, 261, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1018, 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airtableio/api.py	Parapheen/airtableio	0	175213	<reponame>Parapheen/airtableio<filename>airtableio/api.py import logging import os from http import HTTPStatus import aiohttp from .exceptions import AirtableAPIError LOG = logging.getLogger("airtable") API_URL = "https://api.airtable.com/v0/{app_id}/{table_name}" async def make_request( session, token, method, app_id, table_name, data=None, record_id=None, kwargs ): LOG.debug('Make request: "%s" with data: "%r', method, data) url = Methods.api_url(app_id=app_id, table_name=table_name, record_id=record_id) req = data headers = {"Authorization": "Bearer {}".format(token)} method = method.lower() if method == "post": try: async with session.post( url, json=req, headers=headers, kwargs ) as response: result = await response.json() return result except aiohttp.ClientError as e: raise AirtableAPIError( f"aiohttp client throws an error: {e.__class__.__name__}: {e}" ) elif method == "get": try: async with session.get(url, headers=headers, kwargs) as response: result = await response.json() LOG.debug("Got result from get query: {}".format(result)) return result except aiohttp.ClientError as e: raise AirtableAPIError( f"aiohttp client throws an error: {e.__class__.__name__}: {e}" ) elif method == "put": try: async with session.put( url, data=req, headers=headers, kwargs ) as response: result = await response.json() return result except aiohttp.ClientError as e: raise AirtableAPIError( f"aiohttp client throws an error: {e.__class__.__name__}: {e}" ) elif method == "patch": try: async with session.patch( url, data=req, headers=headers, **kwargs ) as response: result = await response.json() return result except aiohttp.ClientError as e: raise AirtableAPIError( f"aiohttp client throws an error: {e.__class__.__name__}: {e}" ) def compose_data(params=None): """ Prepare request data :param params: :return: """ data = aiohttp.formdata.FormData(quote_fields=False) if params: for key, value in params.items(): data.add_field(key, str(value)) return data class Methods: GET_RECORDS = "get" GET_RECORD = "get" CREATE_RECORDS = "post" UPDATE_RECORDS = "patch" @staticmethod def api_url(app_id, table_name, record_id=None): """ Generate API URL with included token and method name :param app_id: :param table_name: :return: """ if record_id: return ( API_URL.format(app_id=app_id, table_name=table_name) + "/" + record_id ) else: return API_URL.format(app_id=app_id, table_name=table_name)	[ 1, 529, 276, 1112, 420, 29958, 2177, 481, 354, 264, 29914, 1466, 2371, 601, 29966, 9507, 29958, 1466, 2371, 601, 29914, 2754, 29889, 2272, 13, 5215, 12183, 13, 5215, 2897, 13, 3166, 1732, 1053, 7331, 5709, 13, 13, 5215, 263, 601, 1124, 13, 13, 3166, 869, 11739, 29879, 1053, 5593, 2371, 8787, 2392, 13, 13, 14480, 353, 12183, 29889, 657, 16363, 703, 1466, 2371, 1159, 13, 13, 8787, 29918, 4219, 353, 376, 991, 597, 2754, 29889, 1466, 2371, 29889, 510, 29914, 29894, 29900, 19248, 932, 29918, 333, 6822, 29912, 2371, 29918, 978, 5038, 13, 13, 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office365/onedrive/permissions/sharing_invitation.py	theodoriss/Office365-REST-Python-Client	0	193186	from office365.directory.identities.identity_set import IdentitySet from office365.runtime.client_value import ClientValue class SharingInvitation(ClientValue): """The SharingInvitation resource groups invitation-related data items into a single structure.""" def __init__(self, email=None, invited_by=IdentitySet(), redeemed_by=None, signin_required=None): """ :param str email: The email address provided for the recipient of the sharing invitation. Read-only. :param IdentitySet invited_by: Provides information about who sent the invitation that created this permission, if that information is available. Read-only. :param str redeemed_by: :param bool signin_required: If true the recipient of the invitation needs to sign in in order to access the shared item. Read-only. """ super(SharingInvitation, self).__init__() self.email = email self.invitedBy = invited_by self.redeemedBy = redeemed_by self.signInRequired = signin_required	[ 1, 515, 8034, 29941, 29953, 29945, 29889, 12322, 29889, 1693, 1907, 29889, 22350, 29918, 842, 1053, 27486, 2697, 13, 3166, 8034, 29941, 29953, 29945, 29889, 15634, 29889, 4645, 29918, 1767, 1053, 12477, 1917, 13, 13, 13, 1990, 1383, 4362, 12165, 7018, 29898, 4032, 1917, 1125, 13, 1678, 9995, 1576, 1383, 4362, 12165, 7018, 6503, 6471, 2437, 7018, 29899, 12817, 848, 4452, 964, 263, 2323, 3829, 1213, 15945, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 4876, 29922, 8516, 29892, 23610, 29918, 1609, 29922, 18415, 2697, 3285, 337, 311, 22580, 29918, 1609, 29922, 8516, 29892, 1804, 262, 29918, 12403, 29922, 8516, 1125, 13, 4706, 9995, 13, 4706, 584, 3207, 851, 4876, 29901, 450, 4876, 3211, 4944, 363, 278, 23957, 993, 310, 278, 19383, 2437, 7018, 29889, 7523, 29899, 6194, 29889, 13, 4706, 584, 3207, 27486, 2697, 23610, 29918, 1609, 29901, 9133, 2247, 2472, 1048, 1058, 2665, 278, 2437, 7018, 393, 2825, 445, 10751, 29892, 13, 9651, 565, 393, 2472, 338, 3625, 29889, 7523, 29899, 6194, 29889, 13, 4706, 584, 3207, 851, 337, 311, 22580, 29918, 1609, 29901, 13, 4706, 584, 3207, 6120, 1804, 262, 29918, 12403, 29901, 960, 1565, 278, 23957, 993, 310, 278, 2437, 7018, 4225, 304, 1804, 297, 297, 1797, 13, 9651, 304, 2130, 278, 7258, 2944, 29889, 7523, 29899, 6194, 29889, 13, 4706, 9995, 13, 4706, 2428, 29898, 2713, 4362, 12165, 7018, 29892, 1583, 467, 1649, 2344, 1649, 580, 13, 4706, 1583, 29889, 5269, 353, 4876, 13, 4706, 1583, 29889, 11569, 1573, 2059, 353, 23610, 29918, 1609, 13, 4706, 1583, 29889, 276, 311, 22580, 2059, 353, 337, 311, 22580, 29918, 1609, 13, 4706, 1583, 29889, 4530, 797, 19347, 353, 1804, 262, 29918, 12403, 13, 2 ]
Python/Advanced OOP/Problems/10. Rhombus of stars.py	teodoramilcheva/softuni-software-engineering	0	155320	<gh_stars>0 INCREASE: int = 1 # global namespace DECREASE: int = -1 space = ' ' sign = '* ' def print_rhombus(n: int): # global namespace # fn-in-fn: closure def print_line(i: int, direction: int): # local namespace visible in print_rhombus if i == 0: # i is part of the local namespace of print_line return line = (n - i) * space + i * sign print(line.rstrip()) if i == n: direction = DECREASE # change is happening in the local namespace of print_line print_line(i + direction, direction) # recusrion print_line(1, INCREASE) n = int(input()) print_rhombus(n)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 1177, 22245, 8127, 29901, 938, 353, 29871, 29896, 396, 5534, 7397, 30004, 13, 2287, 22245, 8127, 29901, 938, 353, 448, 29896, 30004, 13, 3493, 353, 525, 525, 30004, 13, 4530, 353, 525, 29930, 525, 30004, 13, 30004, 13, 30004, 13, 1753, 1596, 29918, 19046, 3424, 375, 29898, 29876, 29901, 938, 1125, 396, 5534, 7397, 30004, 13, 1678, 396, 7876, 29899, 262, 29899, 9144, 29901, 18424, 30004, 13, 1678, 822, 1596, 29918, 1220, 29898, 29875, 29901, 938, 29892, 5305, 29901, 938, 1125, 396, 1887, 7397, 7962, 297, 1596, 29918, 19046, 3424, 375, 30004, 13, 4706, 565, 474, 1275, 29871, 29900, 29901, 396, 474, 338, 760, 310, 278, 1887, 7397, 310, 1596, 29918, 1220, 30004, 13, 9651, 736, 30004, 13, 4706, 1196, 353, 313, 29876, 448, 474, 29897, 334, 2913, 718, 474, 334, 1804, 30004, 13, 4706, 1596, 29898, 1220, 29889, 29878, 17010, 3101, 30004, 13, 4706, 565, 474, 1275, 302, 29901, 30004, 13, 9651, 5305, 353, 5012, 22245, 8127, 396, 1735, 338, 10464, 297, 278, 1887, 7397, 310, 1596, 29918, 1220, 30004, 13, 4706, 1596, 29918, 1220, 29898, 29875, 718, 5305, 29892, 5305, 29897, 396, 1162, 4855, 291, 30004, 13, 30004, 13, 1678, 1596, 29918, 1220, 29898, 29896, 29892, 2672, 22245, 8127, 8443, 13, 30004, 13, 30004, 13, 29876, 353, 938, 29898, 2080, 3101, 30004, 13, 2158, 29918, 19046, 3424, 375, 29898, 29876, 29897, 2 ]
main.py	angmont/PIA_PC	1	48521	import subprocess import cifrado import enviocorreos import puertos import metadata import webscraping import argparse import os, time import logging logging.basicConfig(filename='app.log', level=logging.INFO) if __name__ == "__main__": description= ("Este script realiza una gran diversa cantidad de tareas " + "las cuales son las siguientes: realizar cifrados, obtener metadata, " + "escaneo de puertos, envio de correos y webscraping") parser = argparse.ArgumentParser(description="PIA", epilog=description, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument("-t", metavar='TAREA', dest="tarea", choices=['cifrar','correos', 'dns', 'puertos', 'metadata', 'web', 'hash'] , help='Se elige la tarea a realizar', required=True) parser.add_argument("-m", metavar='MODO', dest="modo", choices=['cifmensaje', 'desmensaje', 'cifgithub', 'destxt', 'ciftxt', 'busqueda', 'emails', 'pdf', 'img'] , help='Si desea utilizar la tarea de cifrado o de web scraping, es necesario especificiar el modo') parser.add_argument("-msj", metavar='MENSAJE', dest="mensaje", type=str, help='Se debe poner un mensaje el cual se quiera cifrar o descifrar.') parser.add_argument("-key", metavar='LLAVE', dest="llave", type=int, help='Se utiliza para saber a base de cual llave se cifra o descifra el mensaje') parser.add_argument("-user", metavar='USUARIO', dest="usuario", type=str, help='Es un argumento necesario para la funcion de cifrar los resultados obtenidos de la API de Github') parser.add_argument("-ru", metavar='RUTA', dest="ruta", type=str, help='Ruta necesaria para el txt que se va a descifrar o donde se encuentran los arctivos pata la funcion de metadata') parser.add_argument("-rem", metavar='REMITENTE', dest="remitente", type=str, help='Correo del que se enviará el mensaje.') parser.add_argument("-des", metavar='DESTINATARIO', dest="destinatario", type=str, help='Correo que recibirá el mensaje.') parser.add_argument("-url", metavar= 'URL', dest="dominio", type=str, help='Url a investigar.') parser.add_argument("-cont", metavar='CONTENIDO', dest="contenido", type=str, help='Se debe poner un mensaje el cual se quiera enviar.', default="Hola mundo mundial") parser.add_argument("-asu", metavar='ASUNTO', dest="asunto", type=str, help='Se utiliza para poner el titulo que tendrá el correo.', default="Hola!") parser.add_argument("-ip", metavar='IP', dest="ip", type=str, help='Se debe introducir la ip a consultar, solo el ID de red.', default="172.217.15.") parser.add_argument("-ports", metavar='PUERTOS', dest="puertos", help='Introduce los puertos a revisar separados por una coma [80,800]', default= "80,800") parser.add_argument("-a", metavar='ARCHIVO', dest="archivo", choices=['imagen', 'imagenes', 'pdf', 'pdfs', 'word', 'words', 'mp3', 'mp3s'] , help='Si desea utilizar la tarea de sacar la metadata, es necesario especificiar el tipo de archivo') parser.add_argument("-mp", metavar= 'METAPATH', dest="metapath", type=str, help='Ruta donde se guardarán los metadatas encontrados.') parser.add_argument("-bus", metavar='BUSQUEDA', dest="busqueda", type=str, help='Busqueda para realizar en google') params = parser.parse_args() try: logging.info("Se escogió la tarea: ") logging.info(params.tarea) tarea = (params.tarea) except Exception as e: logging.error("Ocurrió un error: " + str(e)) print(e) exit try: if tarea == 'cifrar': modo = (params.modo) logging.info("El modo es: " + modo) llave = (params.llave) if (modo == 'cifmensaje') or (modo == 'desmensaje'): mensaje = (params.mensaje) logging.info("El mensaje es: " + str(mensaje)) if modo == 'cifmensaje': print(cifrado.cifrar_mensaje(mensaje, llave)) else: print(cifrado.descifrar_mensaje(mensaje, llave)) elif modo == 'cifgithub': usuario = (params.usuario) logging.info("El usuario es: " + usuario) cifrado.cifrar_github(usuario, llave) elif modo == 'destxt': ruta = (params.ruta) logging.info("Usaremos la ruta: " + ruta) cifrado.descifrar_txt(ruta, llave) elif modo == 'ciftxt': ruta = params.ruta logging.info("Usaremos la ruta: " + ruta) cifrado.cifrar_txt(ruta, llave) else: logging.error("Opcion no válida para cifrado") print('Opción no válida para cifrado') elif tarea == 'correos': remitente = (params.remitente) logging.info("El remitente es: " + remitente) destinatario = (params.destinatario) logging.info("El destinatario es: " + destinatario) mensaje = (params.contenido) logging.info("El mensaje es: " + mensaje) asunto = (params.asunto) logging.info("El asunto es: " + asunto) orga = (params.dominio) logging.info("La organizacion es: " + orga) datos_encontrados = enviocorreos.Busqueda(orga) if datos_encontrados is None: logging.info("No se encontró nada") print("No se encontró nada") exit() else: enviocorreos.GuardarInformacion(datos_encontrados, orga, remitente, destinatario, asunto, mensaje) elif tarea == 'dns': logging.info("Se inicia la tarea de dns") print() script_p = subprocess.Popen([r'C:\WINDOWS\system32\WindowsPowerShell\v1.0\powershell.exe','-ExecutionPolicy', 'Unrestricted', './dns.ps1'], cwd=os.getcwd()) script_p.wait() elif tarea == 'metadata': logging.info("Tipo de archivo:") archivo = (params.archivo) logging.info(archivo) if (archivo == 'imagen') or (archivo == 'imagenes'): ruta = (params.ruta) logging.info("En la ruta: " + ruta) metapath = (params.metapath) logging.info("El metadata se guardará en la ruta: " + metapath) if archivo == 'imagen': logging.info("Ingresamos a la función printOneMetaImg") metadata.printOneMetaImg(ruta, metapath) else: logging.info("Ingresamos a la función printAllMetaImg") metadata.printAllMetaImg(ruta, metapath) elif (archivo == 'pdf') or (archivo == 'pdfs'): ruta = (params.ruta) logging.info("Usaremos la ruta: " + ruta) metapath = (params.metapath) logging.info("Guardaremos la metadata en: " + metapath) if archivo == 'pdf': logging.info("Ingresamos a la función printOneMetaPDF") metadata.printOneMetaPDf(ruta, metapath) else: logging.info("Ingresamos a la función printAllMetaPDF") metadata.printAllMetaPDf(ruta, metapath) elif (archivo == 'word') or (archivo == 'words'): ruta = (params.ruta) logging.info("Usaremos la ruta: " + ruta) metapath = (params.metapath) logging.info("Guardaremos la metadata en: " + metapath) if archivo == 'word': logging.info("Ingresamos a la función printOneMetaDocx") metadata.printOneMetaDocx(ruta, metapath) else: logging.info("Ingresamos a la función printAllMetaDocx") metadata.printAllMetaDocx(ruta, metapath) else: ruta = (params.ruta) logging.info("Usaremos la ruta: " + ruta) metapath = (params.metapath) logging.info("Guardaremos la metadata en: " + metapath) if archivo == 'mp3': logging.info("Ingresamos a la función printOneMetaMp3") metadata.printOneMetaMp3(ruta, metapath) else: logging.info("Ingresamos a la función printAllMetaMp3") metadata.printAllMetaMp3(ruta, metapath) elif tarea == 'puertos': logging.info("Se introdujo la ip: ") ip = params.ip logging.info(ip) print("Se revisará la ip: " + ip) logging.info("Se escanearan los puertos: ") puertoss = params.puertos logging.info(puertoss) portlist = params.puertos.split(',') for i in range (len(portlist)): print("Con los puertos: " + portlist[i]) portlist[i] = int(portlist[i]) puertos.checoPuertos(ip, portlist, puertoss) elif tarea == 'web': logging.info("Con el modo: ") modo = params.modo logging.info(modo) if modo == 'emails' or modo == 'pdf' or modo == 'img': url = params.dominio logging.info("El dominio es: ") logging.info(url) if modo == 'emails': logging.info("Si el \"modo\" es: emails") webscraping.find_mails(url) elif modo == 'pdf': logging.info("Si el \"modo\" es: pdf") webscraping.descargar_pdfs(url) else: logging.info("Si el \"modo\" es: img") webscraping.download_images(url) elif modo == 'busqueda': logging.info("Se buscará:") busqueda = params.busqueda logging.info(busqueda) webscraping.busqueda_google(busqueda) else: logging.info("Ninguna opción es valida para hacer Web Scrapping" ) print('Opcion no válida para web scraping') else: print() script_p = subprocess.Popen([r'C:\WINDOWS\system32\WindowsPowerShell\v1.0\powershell.exe', '-ExecutionPolicy', 'Unrestricted', './rutas.ps1'], cwd=os.getcwd()) script_p.wait() except Exception as e: logging.error("Ha ocurrido un error: " + str(e)) print(e) exit	[ 1, 1053, 1014, 5014, 13, 5215, 274, 361, 26881, 13, 5215, 21061, 542, 272, 276, 359, 13, 5215, 2653, 814, 359, 13, 5215, 15562, 13, 5215, 1856, 1557, 2390, 292, 13, 5215, 1852, 5510, 13, 5215, 2897, 29892, 931, 13, 5215, 12183, 29871, 13, 13, 21027, 29889, 16121, 3991, 29898, 9507, 2433, 932, 29889, 1188, 742, 3233, 29922, 21027, 29889, 11690, 29897, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 29871, 6139, 29922, 4852, 29923, 1655, 2471, 1855, 6619, 1185, 3803, 6894, 29874, 5107, 2368, 316, 260, 598, 294, 376, 718, 13, 29871, 376, 3333, 28276, 1487, 1869, 29673, 29901, 8869, 279, 274, 361, 29878, 2255, 29892, 14403, 759, 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8636, 29889, 666, 13, 418, 12183, 29889, 3888, 29898, 666, 29897, 13, 418, 1596, 703, 2008, 23484, 20484, 425, 10377, 29901, 376, 718, 10377, 29897, 13, 13, 418, 12183, 29889, 3888, 703, 2008, 831, 3068, 799, 273, 1232, 2653, 814, 359, 29901, 16521, 13, 418, 2653, 814, 2209, 353, 8636, 29889, 3746, 814, 359, 13, 418, 12183, 29889, 3888, 29898, 3746, 814, 2209, 29897, 13, 418, 2011, 1761, 353, 8636, 29889, 3746, 814, 359, 29889, 5451, 29317, 1495, 13, 418, 363, 474, 297, 3464, 313, 2435, 29898, 637, 1761, 22164, 13, 4706, 1596, 703, 1168, 1232, 2653, 814, 359, 29901, 376, 718, 2011, 1761, 29961, 29875, 2314, 13, 4706, 2011, 1761, 29961, 29875, 29962, 353, 938, 29898, 637, 1761, 29961, 29875, 2314, 13, 12, 12, 13, 418, 2653, 814, 359, 29889, 305, 687, 29877, 29925, 29884, 814, 359, 29898, 666, 29892, 2011, 1761, 29892, 2653, 814, 2209, 29897, 13, 13, 13, 1678, 25342, 260, 6203, 1275, 525, 2676, 2396, 13, 13, 4706, 12183, 29889, 3888, 703, 1168, 560, 13963, 29901, 16521, 13, 4706, 13963, 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1557, 2390, 292, 29889, 10382, 29918, 8346, 29898, 2271, 29897, 13, 4706, 25342, 13963, 1275, 525, 8262, 339, 8710, 2396, 13, 9651, 12183, 29889, 3888, 703, 2008, 3593, 4287, 29976, 29901, 1159, 13, 9651, 3593, 339, 8710, 353, 8636, 29889, 8262, 339, 8710, 13, 9651, 12183, 29889, 3888, 29898, 8262, 339, 8710, 29897, 13, 9651, 1856, 1557, 2390, 292, 29889, 8262, 339, 8710, 29918, 3608, 29898, 8262, 339, 8710, 29897, 13, 4706, 1683, 29901, 13, 9651, 12183, 29889, 3888, 703, 29940, 292, 4347, 1015, 1290, 831, 659, 1458, 1702, 14557, 2563, 2522, 336, 3262, 29908, 1723, 13, 9651, 1596, 877, 29949, 6739, 291, 694, 12196, 1458, 1702, 1856, 885, 2390, 292, 1495, 13, 1678, 1683, 29901, 13, 13, 12, 1678, 1596, 580, 13, 12, 1678, 2471, 29918, 29886, 353, 1014, 5014, 29889, 29925, 3150, 4197, 29878, 29915, 29907, 3583, 25152, 3970, 7811, 29905, 5205, 29941, 29906, 29905, 7685, 21472, 16037, 29905, 29894, 29896, 29889, 29900, 29905, 12248, 27456, 29889, 8097, 742, 13, 462, 9651, 17411, 20418, 15644, 742, 525, 2525, 5060, 4146, 287, 742, 19283, 23798, 294, 29889, 567, 29896, 7464, 274, 9970, 29922, 359, 29889, 657, 29883, 9970, 3101, 13, 268, 13, 12, 1678, 2471, 29918, 29886, 29889, 10685, 580, 4706, 13, 19499, 8960, 408, 321, 29901, 13, 1678, 12183, 29889, 2704, 703, 24704, 12954, 1038, 1941, 443, 1059, 29901, 376, 718, 851, 29898, 29872, 876, 13, 1678, 1596, 29898, 29872, 29897, 13, 1678, 6876, 13, 2 ]
ch10/myproject_virtualenv/src/django-myproject/myproject/settings/production.py	PacktPublishing/Django-3-Web-Development-Cookbook	159	22663	from ._base import * DEBUG = False WEBSITE_URL = "https://example.com" # without trailing slash MEDIA_URL = f"{WEBSITE_URL}/media/"	[ 1, 515, 869, 29918, 3188, 1053, 334, 13, 13, 18525, 353, 7700, 13, 13, 8851, 9851, 9094, 29918, 4219, 353, 376, 991, 597, 4773, 29889, 510, 29908, 29871, 396, 1728, 25053, 24765, 13, 2303, 4571, 29909, 29918, 4219, 353, 285, 29908, 29912, 8851, 9851, 9094, 29918, 4219, 6822, 9799, 12975, 13, 2 ]
tests/unit/server/test_get_model_metadata_utils.py	rasapala/OpenVINO-model-server	0	78469	<reponame>rasapala/OpenVINO-model-server<gh_stars>0 # # Copyright (c) 2018 Intel Corporation # # 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. # from tensorflow.python.framework import dtypes as dtypes import numpy as np import pytest from ie_serving.server.get_model_metadata_utils import \ _prepare_signature, prepare_get_metadata_output from conftest import MockedIOInfo @pytest.mark.parametrize("layers, tensor_key, np_type", [ ({'tensor': MockedIOInfo('FP32', (1, 1, 1), 'NCHW'), 'test_tensor': MockedIOInfo('FP32', (1, 1, 1), 'NCHW')}, {'new_key': 'tensor', 'client_key': 'test_tensor'}, np.float32), ({'tensor': MockedIOInfo('I32', (1, 1, 1), 'NCHW')}, {'new_key': 'tensor'}, np.int32), ]) def test_prepare_signature(layers, tensor_key, np_type): dtype_model = dtypes.as_dtype(np_type) output = _prepare_signature( layers=layers, model_keys=tensor_key) for key, value in tensor_key.items(): assert key in output assert value in output[key].name shape = [d.size for d in output[key].tensor_shape.dim] assert list(layers[value].shape) == shape tensor_dtype = dtypes.as_dtype(output[key].dtype) assert dtype_model == tensor_dtype def test_prepare_get_metadata_output(): inputs = {'tensor_input': MockedIOInfo('FP32', (1, 1, 1), 'NCHW')} outputs = {'tensor_output': MockedIOInfo('FP32', (1, 1, 1), 'NCHW')} model_keys = {'inputs': {'name': 'tensor_input'}, 'outputs': {'output_name': 'tensor_output'}} output = prepare_get_metadata_output( inputs=inputs, outputs=outputs, model_keys=model_keys) assert "tensorflow/serving/predict" == output.method_name	[ 1, 529, 276, 1112, 420, 29958, 3417, 481, 2883, 29914, 6585, 29963, 1177, 29949, 29899, 4299, 29899, 2974, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 30004, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29947, 18555, 15025, 30004, 13, 29937, 30004, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 18584, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 22993, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 30004, 13, 29937, 30004, 13, 29937, 418, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 30004, 13, 29937, 30004, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 30004, 13, 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29889, 5696, 29918, 978, 30004, 13, 2 ]
faketranslate/metadata.py	HeywoodKing/faketranslate	0	23745	<filename>faketranslate/metadata.py # -- encoding: utf-8 -- """ @File : metadata.py @Time : 2020/1/1 @Author : flack @Email : <EMAIL> @ide : PyCharm @project : faketranslate @description : 描述 """	[ 1, 529, 9507, 29958, 29888, 557, 18184, 550, 9632, 29914, 19635, 29889, 2272, 13, 29937, 448, 29930, 29899, 8025, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 13, 29992, 2283, 965, 584, 15562, 29889, 2272, 13, 29992, 2481, 965, 584, 29871, 29906, 29900, 29906, 29900, 29914, 29896, 29914, 29896, 13, 29992, 13720, 308, 584, 285, 2364, 13, 29992, 9823, 3986, 584, 529, 26862, 6227, 29958, 13, 29992, 680, 9651, 584, 10772, 1451, 2817, 13, 29992, 4836, 4706, 584, 285, 557, 18184, 550, 9632, 13, 29992, 8216, 1678, 584, 29871, 233, 146, 146, 235, 194, 179, 13, 15945, 29908, 2 ]
problems/problem3.py	JakobHavtorn/euler	0	23420	"""Largest prime factor The prime factors of 13195 are 5, 7, 13 and 29. What is the largest prime factor of the number 600851475143? """ import math import numpy as np def largest_prime_factor_naive(number): """ Let the given number be n and let k = 2, 3, 4, 5, ... . For each k, if it is a factor of n then we divide n by k and completely divide out each k before moving to the next k. It can be seen that when k is a factor it will necessarily be prime, as all smaller factors have been removed, and the final result of this process will be n = 1. """ factor = 2 factors = [] while number > 1: if number % factor == 0: factors.append(factor) number = number // factor # Remainder guarenteed to be zero while number % factor == 0: number = number // factor # Remainder guarenteed to be zero factor += 1 return factors def largest_prime_factor_even_optimized(number): """ We know that, excluding 2, there are no even prime numbers. So we can increase factor by 2 per iteration after having found the """ factors = [] factor = 2 if number % factor == 0: number = number // factor factors.append(factor) while number % factor == 0: number = number // factor factor = 3 while number > 1: if number % factor == 0: factors.append(factor) number = number // factor # Remainder guarenteed to be zero while number % factor == 0: number = number // factor # Remainder guarenteed to be zero factor += 2 return factors def largest_prime_factor_square_optimized(number): """ Every number n can at most have one prime factor greater than n. If we, after dividing out some prime factor, calculate the square root of the remaining number we can use that square root as upper limit for factor. If factor exceeds this square root we know the remaining number is prime. """ factors = [] factor = 2 if number % factor == 0: number = number // factor factors.append(factor) while number % factor == 0: number = number // factor factor = 3 max_factor = math.sqrt(number) while number > 1 and factor <= max_factor: if number % factor == 0: factors.append(factor) number = number // factor while number % factor == 0: number = number // factor max_factor = math.sqrt(number) factor += 2 return factors def idx_sieve(length): """Static length sieve-based prime generator""" primes = [] is_prime = np.array([True]*length) i = 2 while i < length: if is_prime[i]: is_prime[np.arange(i, length, i)] = False primes.append(i) else: i += 1 return primes def prime_factor(n, primes): i = 0 factors = [] while n != 1: while (n % primes[i]) != 0: i += 1 factors.append(primes[i]) n = n / primes[i] return factors if __name__ == '__main__': number = 600851475143 print(largest_prime_factor_naive(number)) print(largest_prime_factor_even_optimized(number)) print(largest_prime_factor_square_optimized(number)) number = 600851475143 primes = idx_sieve(20000) print(max(prime_factor(number, primes)))	[ 1, 9995, 29931, 1191, 342, 6019, 7329, 13, 13, 1576, 6019, 13879, 310, 29871, 29896, 29941, 29896, 29929, 29945, 526, 29871, 29945, 29892, 29871, 29955, 29892, 29871, 29896, 29941, 322, 29871, 29906, 29929, 29889, 13, 5618, 338, 278, 10150, 6019, 7329, 310, 278, 1353, 29871, 29953, 29900, 29900, 29947, 29945, 29896, 29946, 29955, 29945, 29896, 29946, 29941, 29973, 13, 15945, 29908, 13, 13, 5215, 5844, 13, 5215, 12655, 408, 7442, 13, 13, 13, 1753, 10150, 29918, 10080, 29918, 19790, 29918, 1056, 573, 29898, 4537, 1125, 13, 1678, 9995, 13, 1678, 2803, 278, 2183, 1353, 367, 302, 322, 1235, 413, 353, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29892, 29871, 29945, 29892, 2023, 869, 13, 13, 1678, 1152, 1269, 413, 29892, 565, 372, 338, 263, 7329, 310, 302, 769, 591, 16429, 302, 491, 413, 322, 6446, 16429, 714, 1269, 413, 1434, 8401, 304, 278, 2446, 413, 29889, 13, 13, 1678, 739, 508, 367, 3595, 393, 746, 413, 338, 263, 7329, 372, 674, 12695, 367, 6019, 29892, 408, 599, 7968, 13879, 505, 1063, 6206, 29892, 13, 1678, 322, 278, 2186, 1121, 310, 445, 1889, 674, 367, 302, 353, 29871, 29896, 29889, 13, 1678, 9995, 13, 1678, 7329, 353, 29871, 29906, 13, 1678, 13879, 353, 5159, 13, 1678, 1550, 1353, 1405, 29871, 29896, 29901, 13, 4706, 565, 1353, 1273, 7329, 1275, 29871, 29900, 29901, 13, 9651, 13879, 29889, 4397, 29898, 19790, 29897, 13, 9651, 1353, 353, 1353, 849, 7329, 29871, 396, 5240, 475, 672, 1410, 279, 2016, 287, 304, 367, 5225, 13, 9651, 1550, 1353, 1273, 7329, 1275, 29871, 29900, 29901, 13, 18884, 1353, 353, 1353, 849, 7329, 29871, 396, 5240, 475, 672, 1410, 279, 2016, 287, 304, 367, 5225, 13, 4706, 7329, 4619, 29871, 29896, 13, 1678, 736, 13879, 13, 13, 13, 1753, 10150, 29918, 10080, 29918, 19790, 29918, 11884, 29918, 20640, 1891, 29898, 4537, 1125, 13, 1678, 9995, 13, 1678, 1334, 1073, 393, 29892, 429, 22368, 29871, 29906, 29892, 727, 526, 694, 1584, 6019, 3694, 29889, 13, 1678, 1105, 591, 508, 7910, 7329, 491, 29871, 29906, 639, 12541, 1156, 2534, 1476, 278, 29871, 13, 1678, 9995, 13, 1678, 13879, 353, 5159, 13, 1678, 7329, 353, 29871, 29906, 13, 1678, 565, 1353, 1273, 7329, 1275, 29871, 29900, 29901, 13, 4706, 1353, 353, 1353, 849, 7329, 13, 4706, 13879, 29889, 4397, 29898, 19790, 29897, 13, 4706, 1550, 1353, 1273, 7329, 1275, 29871, 29900, 29901, 13, 9651, 1353, 353, 1353, 849, 7329, 13, 13, 1678, 7329, 353, 29871, 29941, 13, 1678, 1550, 1353, 1405, 29871, 29896, 29901, 13, 4706, 565, 1353, 1273, 7329, 1275, 29871, 29900, 29901, 13, 9651, 13879, 29889, 4397, 29898, 19790, 29897, 13, 9651, 1353, 353, 1353, 849, 7329, 29871, 396, 5240, 475, 672, 1410, 279, 2016, 287, 304, 367, 5225, 13, 9651, 1550, 1353, 1273, 7329, 1275, 29871, 29900, 29901, 13, 18884, 1353, 353, 1353, 849, 7329, 29871, 396, 5240, 475, 672, 1410, 279, 2016, 287, 304, 367, 5225, 13, 4706, 7329, 4619, 29871, 29906, 13, 1678, 736, 13879, 13, 13, 13, 1753, 10150, 29918, 10080, 29918, 19790, 29918, 17619, 29918, 20640, 1891, 29898, 4537, 1125, 13, 1678, 9995, 13, 1678, 7569, 1353, 302, 508, 472, 1556, 505, 697, 6019, 7329, 7621, 1135, 302, 29889, 13, 13, 1678, 960, 591, 29892, 1156, 1933, 4821, 714, 777, 6019, 7329, 29892, 8147, 278, 6862, 3876, 310, 278, 9886, 1353, 13, 1678, 591, 508, 671, 393, 6862, 3876, 408, 7568, 4046, 363, 7329, 29889, 13, 268, 13, 1678, 960, 7329, 13461, 29879, 445, 6862, 3876, 591, 1073, 278, 9886, 1353, 338, 6019, 29889, 13, 1678, 9995, 13, 1678, 13879, 353, 5159, 13, 1678, 7329, 353, 29871, 29906, 13, 1678, 565, 1353, 1273, 7329, 1275, 29871, 29900, 29901, 13, 4706, 1353, 353, 1353, 849, 7329, 13, 4706, 13879, 29889, 4397, 29898, 19790, 29897, 13, 4706, 1550, 1353, 1273, 7329, 1275, 29871, 29900, 29901, 13, 9651, 1353, 353, 1353, 849, 7329, 13, 13, 1678, 7329, 353, 29871, 29941, 13, 1678, 4236, 29918, 19790, 353, 5844, 29889, 3676, 29898, 4537, 29897, 13, 1678, 1550, 1353, 1405, 29871, 29896, 322, 7329, 5277, 4236, 29918, 19790, 29901, 13, 4706, 565, 1353, 1273, 7329, 1275, 29871, 29900, 29901, 13, 9651, 13879, 29889, 4397, 29898, 19790, 29897, 13, 9651, 1353, 353, 1353, 849, 7329, 13, 9651, 1550, 1353, 1273, 7329, 1275, 29871, 29900, 29901, 13, 18884, 1353, 353, 1353, 849, 7329, 13, 18884, 4236, 29918, 19790, 353, 5844, 29889, 3676, 29898, 4537, 29897, 13, 4706, 7329, 4619, 29871, 29906, 13, 13, 1678, 736, 13879, 13, 13, 13, 1753, 22645, 29918, 29879, 2418, 29898, 2848, 1125, 13, 1678, 9995, 17046, 3309, 269, 2418, 29899, 6707, 6019, 15299, 15945, 29908, 13, 1678, 544, 1355, 353, 5159, 13, 1678, 338, 29918, 10080, 353, 7442, 29889, 2378, 4197, 5574, 14178, 2848, 29897, 13, 1678, 474, 353, 29871, 29906, 13, 1678, 1550, 474, 529, 3309, 29901, 13, 4706, 565, 338, 29918, 10080, 29961, 29875, 5387, 13, 9651, 338, 29918, 10080, 29961, 9302, 29889, 279, 927, 29898, 29875, 29892, 3309, 29892, 474, 4638, 353, 7700, 13, 9651, 544, 1355, 29889, 4397, 29898, 29875, 29897, 13, 4706, 1683, 29901, 13, 9651, 474, 4619, 29871, 29896, 13, 1678, 736, 544, 1355, 13, 13, 13, 1753, 6019, 29918, 19790, 29898, 29876, 29892, 544, 1355, 1125, 13, 1678, 474, 353, 29871, 29900, 13, 1678, 13879, 353, 5159, 13, 1678, 1550, 302, 2804, 29871, 29896, 29901, 13, 4706, 1550, 313, 29876, 1273, 544, 1355, 29961, 29875, 2314, 2804, 29871, 29900, 29901, 13, 9651, 474, 4619, 29871, 29896, 13, 4706, 13879, 29889, 4397, 29898, 558, 1355, 29961, 29875, 2314, 13, 4706, 302, 353, 302, 847, 544, 1355, 29961, 29875, 29962, 13, 1678, 736, 13879, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1353, 353, 29871, 29953, 29900, 29900, 29947, 29945, 29896, 29946, 29955, 29945, 29896, 29946, 29941, 13, 1678, 1596, 29898, 27489, 342, 29918, 10080, 29918, 19790, 29918, 1056, 573, 29898, 4537, 876, 13, 1678, 1596, 29898, 27489, 342, 29918, 10080, 29918, 19790, 29918, 11884, 29918, 20640, 1891, 29898, 4537, 876, 13, 1678, 1596, 29898, 27489, 342, 29918, 10080, 29918, 19790, 29918, 17619, 29918, 20640, 1891, 29898, 4537, 876, 13, 13, 1678, 1353, 353, 29871, 29953, 29900, 29900, 29947, 29945, 29896, 29946, 29955, 29945, 29896, 29946, 29941, 13, 1678, 544, 1355, 353, 22645, 29918, 29879, 2418, 29898, 29906, 29900, 29900, 29900, 29900, 29897, 13, 268, 13, 1678, 1596, 29898, 3317, 29898, 10080, 29918, 19790, 29898, 4537, 29892, 544, 1355, 4961, 13, 2 ]
apps/utils/errors.py	osw4l/villas-de-san-pablo	0	171375	<filename>apps/utils/errors.py __author__ = 'osw4l' from django.shortcuts import render def error400(request): return render(request, '400.html', status=400) def error403(request): return render(request, '403.html', status=403) def error404(request): return render(request, '404.html', status=404) def error500(request): return render(request, '500.html', status=500)	[ 1, 529, 9507, 29958, 13371, 29914, 13239, 29914, 12523, 29889, 2272, 13, 1649, 8921, 1649, 353, 525, 359, 29893, 29946, 29880, 29915, 13, 13, 13, 3166, 9557, 29889, 12759, 7582, 29879, 1053, 4050, 13, 13, 13, 1753, 1059, 29946, 29900, 29900, 29898, 3827, 1125, 13, 1678, 736, 4050, 29898, 3827, 29892, 525, 29946, 29900, 29900, 29889, 1420, 742, 4660, 29922, 29946, 29900, 29900, 29897, 13, 13, 1753, 1059, 29946, 29900, 29941, 29898, 3827, 1125, 13, 1678, 736, 4050, 29898, 3827, 29892, 525, 29946, 29900, 29941, 29889, 1420, 742, 4660, 29922, 29946, 29900, 29941, 29897, 13, 13, 1753, 1059, 29946, 29900, 29946, 29898, 3827, 1125, 13, 1678, 736, 4050, 29898, 3827, 29892, 525, 29946, 29900, 29946, 29889, 1420, 742, 4660, 29922, 29946, 29900, 29946, 29897, 13, 13, 1753, 1059, 29945, 29900, 29900, 29898, 3827, 1125, 13, 1678, 736, 4050, 29898, 3827, 29892, 525, 29945, 29900, 29900, 29889, 1420, 742, 4660, 29922, 29945, 29900, 29900, 29897, 2 ]
molior/api/mirror.py	randombenj/molior	0	182905	<gh_stars>0 """ Provides functions for managing aptly mirrors. """ from aiohttp import web import logging from molior.model.build import Build from molior.model.project import Project from molior.model.projectversion import ProjectVersion from molior.model.buildvariant import BuildVariant from molior.molior.utils import get_aptly_connection # FIXME: cannot import in here from molior.molior.notifier import build_added from .app import app logger = logging.getLogger("molior-web") def error(status, msg, args): """ Logs an error message and returns an error to the web client. Args: status (int): The http response status. msg (str): The message to display. args (tuple): Arguments for string format on msg. """ logger.error(msg.format(args)) return web.Response(status=status, text=msg.format(args)) @app.http_post("/api/mirror") @app.req_admin # FIXME: req_role async def create_mirror(request): """ Create a debian aptly mirror. --- description: Create a debian aptly mirror. tags: - Mirrors consumes: - application/x-www-form-urlencoded parameters: - name: name in: query required: true type: string description: name of the mirror - name: url in: query required: true type: string description: http://host of source - name: distribution in: query required: true type: string description: trusty, wheezy, jessie, etc. - name: components in: query required: false type: array description: components to be mirrored default: main - name: keys in: query required: false type: array uniqueItems: true collectionFormat: multi allowEmptyValue: true minItems: 0 items: type: string description: repository keys - name: keyserver in: query required: false type: string description: host name where the keys are - name: is_basemirror in: query required: false type: boolean description: use this mirror for chroot - name: architectures in: query required: false type: array description: i386,amd64,arm64,armhf,... - name: version in: query required: false type: string - name: armored_key_url in: query required: false type: string - name: basemirror_id in: query required: false type: string - name: download_sources in: query required: false type: boolean - name: download_installer in: query required: false type: boolean produces: - text/json responses: "200": description: successful "400": description: mirror creation failed. "412": description: key error. "409": description: mirror already exists. "500": description: internal server error. "503": description: aptly not available. "501": description: database error occurred. """ params = await request.json() mirror = params.get("name") url = params.get("url") mirror_distribution = params.get("distribution") components = params.get("components", []) keys = params.get("keys", []) keyserver = params.get("keyserver") is_basemirror = params.get("is_basemirror") architectures = params.get("architectures", []) version = params.get("version") key_url = params.get("armored_key_url") basemirror_id = params.get("basemirror_id") download_sources = params.get("download_sources") download_installer = params.get("download_installer") if not components: components = ["main"] if not isinstance(is_basemirror, bool): return web.Response(status=400, text="is_basemirror not a bool") args = { "create_mirror": [ mirror, url, mirror_distribution, components, keys, keyserver, is_basemirror, architectures, version, key_url, basemirror_id, download_sources, download_installer, ] } await request.cirrina.aptly_queue.put(args) return web.Response( status=200, text="Mirror {} successfully created.".format(mirror) ) @app.http_get("/api/mirror") @app.authenticated async def get_mirrors(request): """ Returns all mirrors from database. --- description: Returns mirrors from database. tags: - Mirrors consumes: - application/x-www-form-urlencoded parameters: - name: page in: query required: false type: integer default: 1 description: page number - name: page_size in: query required: false type: integer default: 10 description: max. mirrors per page - name: q in: query required: false type: string description: filter criteria produces: - text/json responses: "200": description: successful "400": description: bad request """ page = request.GET.getone("page", None) page_size = request.GET.getone("page_size", None) filter_name = request.GET.getone("q", "") if page: try: page = int(page) except (ValueError, TypeError): return web.Response(text="Incorrect value for page", status=400) if page_size: try: page_size = int(page_size) except (ValueError, TypeError): return web.Response(text="Incorrect value for page_size", status=400) page_size = 1 if page_size < 1 else page_size query = request.cirrina.db_session.query( ProjectVersion ) # pylint: disable=no-member query = query.join(Project, Project.id == ProjectVersion.project_id) query = query.filter(Project.is_mirror == "true").order_by(Project.name) if filter_name: query = query.filter(Project.name.like("%{}%".format(filter_name))) query = query.order_by(Project.name, ProjectVersion.name) nb_results = query.count() if page is not None and page_size: query = query.offset(page page_size) query = query.limit(page_size) results = query.all() data = {"total_result_count": nb_results, "results": []} for mirror in results: apt_url = mirror.get_apt_repo(url_only=True) base_mirror_url = str() if not mirror.project.is_basemirror and mirror.buildvariants: # FIXME: only one buildvariant supported base_mirror = mirror.buildvariants[0].base_mirror base_mirror_url = base_mirror.get_apt_repo(url_only=True) data["results"].append( { "id": mirror.id, "name": mirror.project.name, "version": mirror.name, "url": mirror.mirror_url, "base_mirror": base_mirror_url, "distribution": mirror.mirror_distribution, "components": mirror.mirror_components, "is_basemirror": mirror.project.is_basemirror, "architectures": mirror.mirror_architectures[1:-1], "is_locked": mirror.is_locked, "with_sources": mirror.mirror_with_sources, "with_installer": mirror.mirror_with_installer, "project_id": mirror.project.id, "state": mirror.mirror_state, "apt_url": apt_url, } ) return web.json_response(data) @app.http_delete("/api/mirror/{id}") @app.req_admin # FIXME: req_role async def delete_mirror(request): """ Delete a single mirror on aptly and from database. --- description: Delete a single mirror on aptly and from database. tags: - Mirrors consumes: - application/x-www-form-urlencoded parameters: - name: id in: path required: true type: integer description: id of the mirror produces: - text/json responses: "200": description: successful "400": description: removal failed from aptly. "404": description: mirror not found on aptly. "500": description: internal server error. "503": description: removal failed from database. """ apt = get_aptly_connection() mirror_id = request.match_info["id"] query = request.cirrina.db_session.query(ProjectVersion) # pylint: disable=no-member query = query.join(Project, Project.id == ProjectVersion.project_id) query = query.filter(Project.is_mirror.is_(True)) entry = query.filter(ProjectVersion.id == mirror_id).first() if not entry: logger.warning("error deleting mirror '%s': mirror not found", mirror_id) return error(404, "Error deleting mirror '%d': mirror not found", mirror_id) # FIXME: check state, do not delete ready/updating/... mirrorname = "{}-{}".format(entry.project.name, entry.name) # check relations if entry.sourcerepositories: logger.warning("error deleting mirror '%s': referenced by one or more source repositories", mirrorname) return error(412, "Error deleting mirror {}: still referenced by one or more source repositories", mirrorname) if entry.buildconfiguration: logger.warning("error deleting mirror '%s': referenced by one or more build configurations", mirrorname) return error(412, "Error deleting mirror {}: still referenced by one or more build configurations", mirrorname) if entry.dependents: logger.warning("error deleting mirror '%s': referenced by one or project versions", mirrorname) return error(412, "Error deleting mirror {}: still referenced by one or more project versions", mirrorname) base_mirror = "" if not entry.project.is_basemirror: basemirror = entry.buildvariants[0].base_mirror base_mirror = basemirror.project.name + "-" + basemirror.name # FIXME: cleanup chroot table, schroots, debootstrap, try: # FIXME: use altpy queue ! await apt.mirror_delete(base_mirror, entry.project.name, entry.name, entry.mirror_distribution) except Exception as exc: # mirror did not exist # FIXME: handle mirror has snapshots and cannot be deleted? pass project = entry.project bvs = request.cirrina.db_session.query(BuildVariant).filter( BuildVariant.base_mirror_id == entry.id).all() # pylint: disable=no-member for bvariant in bvs: # FIXME: delete buildconfigurations request.cirrina.db_session.delete(bvariant) # pylint: disable=no-member builds = request.cirrina.db_session.query(Build) .filter(Build.projectversion_id == entry.id).all() for build in builds: # FIXME: delete buildconfigurations # FIXME: remove buildout dir request.cirrina.db_session.delete(build) # pylint: disable=no-member request.cirrina.db_session.delete(entry) # pylint: disable=no-member request.cirrina.db_session.commit() # pylint: disable=no-member if not project.projectversions: request.cirrina.db_session.delete(project) # pylint: disable=no-member request.cirrina.db_session.commit() # pylint: disable=no-member return web.Response(status=200, text="Successfully deleted mirror: {}".format(mirrorname)) @app.http_put("/api/mirror/{id}") @app.req_admin # FIXME: req_role async def put_update_mirror(request): """ Updates a mirror. --- description: Updates a mirror. tags: - Mirrors consumes: - application/x-www-form-urlencoded parameters: - name: name in: path required: true type: integer description: name of the mirror produces: - text/json responses: "200": description: Mirror update successfully started. "400": description: Mirror not in error state. "500": description: Internal server error. """ mirror_id = request.match_info["id"] project_v = ( request.cirrina.db_session.query(ProjectVersion) .filter(ProjectVersion.id == mirror_id) # pylint: disable=no-member .first() ) if project_v.is_locked: return error(400, "Mirror locked. Update not allowed.") if project_v.mirror_state != "error": return error(400, "Mirror not in error state.") components = project_v.mirror_components.split(",") # FIXME: only one build variant supported base_mirror = None base_mirror_version = None if not project_v.project.is_basemirror: base_mirror = project_v.buildvariants[0].base_mirror.project.name base_mirror_version = project_v.buildvariants[0].base_mirror.name build = ( request.cirrina.db_session.query(Build) .filter(Build.buildtype == "mirror", Build.projectversion_id == project_v.id) .first() ) if not build: logger.error("update mirror: no build found for mirror %d", mirror_id) return error(400, "no build found for mirror") args = { "update_mirror": [ build.id, project_v.id, base_mirror, base_mirror_version, project_v.project.name, project_v.name, components, ] } await request.cirrina.aptly_queue.put(args) return web.Response(status=200, text="Successfully started update on mirror")	[ 1, 529, 12443, 29918, 303, 1503, 29958, 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QA system/main.py	yiwen26/NLP_banking_chatbot	2	199785	import os import argparse from gensim.models.keyedvectors import KeyedVectors from preprocess import * from model import * import torch if __name__ == '__main__': # parse command line argument parser = argparse.ArgumentParser() parser.add_argument('--mode', default='training', type=str, help='training, evaluation and simulation mode are supported') parser.add_argument('--testset', default='test1', type=str, help='Define testset type: [dev/test1/test2] ') parser.add_argument('--nn_type', default='attention', type=str, help='3 type to reduce rnn output: max_pooling, mean_pooling, attention') parser.add_argument('--data_path', default='./data', type=str, metavar='PATH', help='insurance input data directory') parser.add_argument('--checkpoint_path', default='./checkpoints', type=str, metavar='PATH', help='model saving directory') parser.add_argument('--embd_type', default='google', type=str, help='word embedding mode selection [none/google], default set to google embedding') parser.add_argument("--embed_path", default="./embedding/GoogleNews-vectors-negative300.bin", type=str, metavar='PATH', help="pre-trained word embedding path (Google news word embedding)") parser.add_argument("--model_path", default="./model/demo.model", type=str, metavar='PATH', help="pre-trained QA model for evaluation and simulation") parser.add_argument('--data_version', default='V1', type=str, help='insurance QA dataset version') parser.add_argument('--batch_size', type=int, default=128, help='input batch size') parser.add_argument('--n_epochs', type=int, default=5, help='epoch num') parser.add_argument('--embd_size', type=int, default=300, help='word embedding size') parser.add_argument('--hidden_size', type=int, default=128, help='hidden size of one-directional LSTM') parser.add_argument('--max_sent_len', type=int, default=200, help='max sentence length') parser.add_argument('--margin', type=float, default=0.2, help='margin for loss function') parser.add_argument('--neg_pos_rate', type=int, default=2, help='training negative samples and positive samples rate') args = parser.parse_args() mode = args.mode model_type = args.nn_type model_path = args.model_path testset_type = args.testset data_path = os.path.join(args.data_path, args.data_version) checkpoint_path = os.path.join(args.checkpoint_path, model_type) if not os.path.exists(checkpoint_path): os.mkdir(checkpoint_path) version = args.data_version embedding_type = args.embd_type embedding_size = args.embd_size hidden_size = args.hidden_size max_sentence_len = args.max_sent_len batch_size = args.batch_size margin = args.margin epoch_num = args.n_epochs neg_pos_rate = args.neg_pos_rate print("============================ Parameters ==================================") print("-- Model bi-lstm +", model_type) print("-- Dataset Version = ", version) print("-- Hidden Size = ", hidden_size) print("-- Embedding type = ", embedding_type) print("-- Embedding size = ", embedding_size) print("-- Epoch Num = ", epoch_num) print("-- Batch Size = ", batch_size) print("-- Margin = ", margin) print("-- Negative / Positive = ", neg_pos_rate) print("-- Checkpoint path: ", checkpoint_path) print("============================ Parameters ==================================") vocabulary_path = os.path.join(data_path, "vocabulary") if version == "V1": answer_path = os.path.join(data_path, "answers.label.token_idx") qa_training_data_path = os.path.join(data_path, "question.train.token_idx.label") qa_test_data_path = os.path.join(data_path, "question.{}.label.token_idx.pool".format(testset_type)) elif version == "V2": answer_path = os.path.join(data_path, "InsuranceQA.label2answer.token.encoded") qa_training_data_path = os.path.join(data_path, "InsuranceQA.question.anslabel.token.100.pool.solr.train.encoded") qa_test_data_path = os.path.join(data_path, "InsuranceQA.question.anslabel.token.100.pool.solr.test.encoded") else: print("[Error]: invalid dataset version setting, please use -h to check legal parameter setting") exit(1) id_2_word = load_vocabulary(vocabulary_path) word_to_id = {w: i for i, w in enumerate(id_2_word.values(), 1)} word_to_id['<PAD>'] = 0 vocabulary_size = len(word_to_id) id_2_answers, id_2_answer_text = load_id_2_answer_data(answer_path, id_2_word) print("answer size: ", len(id_2_answer_text)) print("raw vocabulary size: ", len(word_to_id)) if version == "V1": training_data = load_v1_training_answer_data(qa_training_data_path, id_2_word, id_2_answer_text) test_data = load_v1_test_answer_data(qa_test_data_path, id_2_word) validation_data = test_data[:100] if version == "V2": training_data = load_training_answer_data(qa_training_data_path, id_2_word, id_2_answer_text) test_data = load_test_answer_data(qa_test_data_path, id_2_word) validation_data = test_data[:100] print("============================ Checking data ==================================") print("word to id sample: ") head_data(word_to_id, 10) print("data sample: ") sample_id = random.randint(0, len(test_data)) print("-- question: ", " ".join(test_data[sample_id][0])) print("-- pos: ", " ".join(id_2_answer_text[test_data[sample_id][1][0]])) print("-- neg: ", " ".join(id_2_answer_text[test_data[sample_id][2][0]])) print("============================ Checking data done =============================") word_embedding = None if embedding_type == "google" and mode == "training": print('[Word Embedding]: load google news word embedding') word2vec = KeyedVectors.load_word2vec_format(args.embed_path, binary=True) word_embedding = load_word_embedding(word2vec, vocabulary_size, args.embd_size, word_to_id) model = QA_LSTM(vocabulary_size, args.embd_size, args.hidden_size, word_embedding, model_type) if torch.cuda.is_available(): model.cuda() optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters())) if mode == "training": train_batch(model, training_data, validation_data, optimizer, word_to_id, id_2_answer_text, max_sentence_len, epoch_num, batch_size, margin, neg_pos_rate, checkpoint_path) if mode == "evaluation": print("Load pre-trained model at ", model_path) checkpoint = torch.load(model_path) model.load_state_dict(checkpoint['state_dict']) test(model, test_data, id_2_answer_text, max_sentence_len, word_to_id) if mode == "simulation": print("============================ Simulation ========================") print("Load pre-trained model at ", model_path) checkpoint = torch.load(model_path) model.load_state_dict(checkpoint['state_dict']) # candidate question questions = generate_candidate_questions(training_data) print("candidate questions: ", questions) simulation(model, list(id_2_answer_text.values()), word_to_id, max_sentence_len)	[ 1, 1053, 2897, 30004, 13, 5215, 1852, 5510, 30004, 13, 3166, 26943, 326, 29889, 9794, 29889, 1989, 287, 345, 14359, 1053, 7670, 287, 29963, 11142, 30004, 13, 30004, 13, 3166, 758, 5014, 1053, 334, 30004, 13, 3166, 1904, 1053, 334, 30004, 13, 30004, 13, 5215, 4842, 305, 30004, 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old-code/enzfam-molfunc3.py	rwst/wikidata-molbio	2	84836	import pronto, six, csv from sys import * reader = csv.DictReader(open('goid.tab', 'r'), delimiter='\t') efs = {} for item in reader: go = item.get('goid') iturl = item.get('p') it = iturl[iturl.rfind('/')+1:] git = efs.get(go) if git is None: efs[go] = it else: print('============= {}'.format(go)) reader = csv.DictReader(open('t.tab', 'r'), delimiter='\t') mfs = {} for item in reader: ec = item.get('ecLabel') iturl = item.get('p') it = iturl[iturl.rfind('/')+1:] git = mfs.get(it) if git is None: mfs[it] = ec else: print(it) reader = csv.DictReader(open('efnames.tab', 'r'), delimiter='\t') ns = {} for item in reader: ec = item.get('pLabel') iturl = item.get('p') it = iturl[iturl.rfind('/')+1:] git = ns.get(it) if git is None: ns[it] = ec else: print(it) reader = csv.DictReader(open('mf.tab', 'r'), delimiter='\t') ms = {} for item in reader: lab = item.get('pLabel') iturl = item.get('p') it = iturl[iturl.rfind('/')+1:] git = ms.get(it) if git is None: ms[it] = lab else: print(it) reader = csv.DictReader(open('ec2go.tab', 'r'), delimiter='\t') ecgo = {} for item in reader: ec = item.get('ec') go = item.get('goid') git = ecgo.get(ec) if git is None: ecgo[ec] = [go] else: git.append(go) for tup in mfs.items(): red = False ec = tup[1].replace('.-', '') goecl = ecgo.get(ec) while goecl is None: red = True ec = ec[:ec.rfind('.')] goecl = ecgo.get(ec) if None is efs.get(goecl[0]): print(tup[0]) continue if len(goecl) == 1: pass #print('{}\|P680\|{}'.format(tup[0], efs.get(goecl[0]), goecl[0])) else: for e in goecl: print('{}\|P680\|{} x {} {}'.format(tup[0], efs.get(e), ns.get(tup[0]), ms.get(efs.get(e))))	[ 1, 1053, 544, 10268, 29892, 4832, 29892, 11799, 13, 3166, 10876, 1053, 334, 13, 13, 16950, 353, 11799, 29889, 21533, 6982, 29898, 3150, 877, 1484, 333, 29889, 3891, 742, 525, 29878, 5477, 28552, 2433, 29905, 29873, 1495, 13, 1389, 29879, 353, 6571, 13, 1454, 2944, 297, 9591, 29901, 13, 1678, 748, 353, 2944, 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nekoyume/app.py	lux600/nekoyume	0	80594	<reponame>lux600/nekoyume import os from flask import Flask from raven.contrib.flask import Sentry from nekoyume.models import cache, db from nekoyume.api import api from nekoyume.game import babel, game from nekoyume.tasks import celery def make_celery(app): celery.name = app.import_name celery.conf.update( result_backend=app.config['CELERY_RESULT_BACKEND'], broker_url=app.config['CELERY_BROKER_URL']) celery.conf.update(app.config) TaskBase = celery.Task class ContextTask(TaskBase): abstract = True def __call__(self, args, kwargs): with app.app_context(): return TaskBase.__call__(self, args, **kwargs) celery.Task = ContextTask return celery def create_app(): app = Flask(__name__) app.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get( 'DATABASE_URL', 'sqlite:///yume.db') app.config['TEMPLATES_AUTO_RELOAD'] = True app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False try: with open('.secret_key', 'rb') as f: app.secret_key = f.read() except FileNotFoundError: app.secret_key = os.urandom(64) f = open('.secret_key', 'wb') f.write(app.secret_key) f.close() app.register_blueprint(api) app.register_blueprint(game) db.app = app db.init_app(app) babel.app = app babel.init_app(app) app.config.update( CELERY_BROKER_URL=os.environ.get( 'CELERY_BROKER_URL', 'sqla+sqlite:///yume_broker.db'), CELERY_RESULT_BACKEND=os.environ.get( 'CELERY_RESULT_BACKEND', 'db+sqlite:///yume_reuslt.db')) return app app = create_app() cel = make_celery(app) cache_type = os.environ.get('CACHE_TYPE', 'filesystem') if cache_type == 'redis': cache_config = { 'CACHE_TYPE': cache_type, 'CACHE_REDIS_URL': os.environ.get( 'REDIS_URL', 'redis://localhost:6379' ), } else: cache_config = { 'CACHE_TYPE': cache_type, 'CACHE_DIR': os.environ.get('CACHE_DIR', '.yumecache'), } cache.init_app(app, cache_config) sentry = Sentry(app) def run(): from gunicorn.app import wsgiapp wsgiapp.run()	[ 1, 529, 276, 1112, 420, 29958, 29880, 1314, 29953, 29900, 29900, 29914, 484, 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dagology/__init__.py	JamesClough/dagology	5	89263	<gh_stars>1-10 from algorithms import * from generators import * from utils import * from metrics import * from matrix import *	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 14009, 1053, 334, 13, 3166, 1176, 4097, 1053, 334, 13, 3166, 3667, 29879, 1053, 334, 13, 3166, 21556, 1053, 334, 13, 3166, 4636, 1053, 334, 13, 2 ]
setup.py	aidan-fitz/opwen-cloudserver	0	124067	<filename>setup.py from setuptools import find_packages from setuptools import setup with open('requirements.txt') as fobj: install_requires = [line.strip() for line in fobj] with open('README.rst') as fobj: long_description = fobj.read() with open('version.txt') as fobj: version = fobj.read().strip() packages = find_packages(exclude=['tests*']) scripts = [ 'runserver.py', 'registerclient.py', ] setup( name='opwen_email_server', version=version, author='<NAME>', author_email='<EMAIL>', packages=packages, url='https://github.com/ascoderu/opwen-cloudserver', license='Apache Software License', description='Email server for the Lokole project: https://ascoderu.ca', long_description=long_description, scripts=scripts, include_package_data=True, install_requires=install_requires, classifiers=[ 'Development Status :: 3 - Alpha', 'Environment :: Web Environment', 'License :: OSI Approved :: Apache Software License', 'Programming Language :: Python :: 3', 'Topic :: Communications :: Email', ])	[ 1, 529, 9507, 29958, 14669, 29889, 2272, 13, 3166, 731, 21245, 8789, 1053, 1284, 29918, 8318, 13, 3166, 731, 21245, 8789, 1053, 6230, 13, 13, 13, 2541, 1722, 877, 12277, 1860, 29889, 3945, 1495, 408, 285, 5415, 29901, 13, 1678, 2601, 29918, 276, 339, 2658, 353, 518, 1220, 29889, 17010, 580, 363, 1196, 297, 285, 5415, 29962, 13, 13, 13, 2541, 1722, 877, 16310, 2303, 29889, 29878, 303, 1495, 408, 285, 5415, 29901, 13, 1678, 1472, 29918, 8216, 353, 285, 5415, 29889, 949, 580, 13, 13, 13, 2541, 1722, 877, 3259, 29889, 3945, 1495, 408, 285, 5415, 29901, 13, 1678, 1873, 353, 285, 5415, 29889, 949, 2141, 17010, 580, 13, 13, 13, 8318, 353, 1284, 29918, 8318, 29898, 735, 2325, 29922, 1839, 21150, 29930, 11287, 13, 16713, 353, 518, 13, 1678, 525, 3389, 2974, 29889, 2272, 742, 13, 1678, 525, 9573, 4645, 29889, 2272, 742, 13, 29962, 13, 13, 13, 14669, 29898, 13, 1678, 1024, 2433, 459, 15556, 29918, 5269, 29918, 2974, 742, 13, 1678, 1873, 29922, 3259, 29892, 13, 1678, 4148, 2433, 29966, 5813, 29958, 742, 13, 1678, 4148, 29918, 5269, 2433, 29966, 26862, 6227, 29958, 742, 13, 1678, 9741, 29922, 8318, 29892, 13, 1678, 3142, 2433, 991, 597, 3292, 29889, 510, 29914, 6151, 6119, 29884, 29914, 459, 15556, 29899, 9274, 2974, 742, 13, 1678, 19405, 2433, 17396, 1829, 18540, 19245, 742, 13, 1678, 6139, 2433, 9823, 1923, 363, 278, 26469, 1772, 2060, 29901, 2045, 597, 6151, 6119, 29884, 29889, 1113, 742, 13, 1678, 1472, 29918, 8216, 29922, 5426, 29918, 8216, 29892, 13, 1678, 12078, 29922, 16713, 29892, 13, 1678, 3160, 29918, 5113, 29918, 1272, 29922, 5574, 29892, 13, 1678, 2601, 29918, 276, 339, 2658, 29922, 6252, 29918, 276, 339, 2658, 29892, 13, 1678, 770, 14903, 11759, 13, 4706, 525, 21956, 358, 16034, 4761, 29871, 29941, 448, 838, 2026, 742, 13, 4706, 525, 18649, 4761, 2563, 16738, 742, 13, 4706, 525, 29931, 293, 1947, 4761, 438, 5425, 28268, 1490, 4761, 13380, 18540, 19245, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 742, 13, 4706, 525, 7031, 293, 4761, 22365, 800, 4761, 22608, 742, 13, 268, 2314, 13, 2 ]
setup.py	walwe/autolabel	1	25027	#!/usr/bin/env python from pkg_resources import get_distribution from setuptools import setup, find_packages with open("README.md", "r") as f: long_description = f.read() version = get_distribution("autolabel").version setup( packages=find_packages(), install_requires=[ 'click', 'more-itertools', 'torchvision', 'torch', 'pillow', 'numpy' ], entry_points=''' [console_scripts] autolabel=autolabel.cli:main ''', url='https://github.com/walwe/autolabel', version=version, author='walwe', python_requires='>=3.6', description='Autolabel is an image labeling tool using Neural Network', long_description_content_type="text/markdown", long_description=long_description, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ] )	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 3166, 282, 9415, 29918, 13237, 1053, 679, 29918, 27691, 13, 3166, 731, 21245, 8789, 1053, 6230, 29892, 1284, 29918, 8318, 13, 13, 2541, 1722, 703, 16310, 2303, 29889, 3487, 613, 376, 29878, 1159, 408, 285, 29901, 13, 1678, 1472, 29918, 8216, 353, 285, 29889, 949, 580, 13, 13, 3259, 353, 679, 29918, 27691, 703, 1300, 324, 1107, 2564, 3259, 13, 13, 14669, 29898, 13, 1678, 9741, 29922, 2886, 29918, 8318, 3285, 13, 1678, 2601, 29918, 276, 339, 2658, 11759, 13, 4706, 525, 3808, 742, 13, 4706, 525, 5514, 29899, 1524, 8504, 742, 13, 4706, 525, 7345, 305, 4924, 742, 13, 4706, 525, 7345, 305, 742, 13, 4706, 525, 29886, 453, 340, 742, 13, 4706, 525, 23749, 29915, 13, 1678, 21251, 13, 1678, 6251, 29918, 9748, 2433, 4907, 13, 4706, 518, 11058, 29918, 16713, 29962, 13, 4706, 1120, 324, 1107, 29922, 1300, 324, 1107, 29889, 11303, 29901, 3396, 13, 1678, 6629, 742, 13, 1678, 3142, 2433, 991, 597, 3292, 29889, 510, 29914, 14625, 705, 29914, 1300, 324, 1107, 742, 13, 1678, 1873, 29922, 3259, 29892, 13, 1678, 4148, 2433, 14625, 705, 742, 13, 1678, 3017, 29918, 276, 339, 2658, 2433, 18572, 29941, 29889, 29953, 742, 13, 1678, 6139, 2433, 6147, 324, 1107, 338, 385, 1967, 3858, 292, 5780, 773, 2448, 3631, 8527, 742, 13, 1678, 1472, 29918, 8216, 29918, 3051, 29918, 1853, 543, 726, 29914, 3502, 3204, 613, 13, 1678, 1472, 29918, 8216, 29922, 5426, 29918, 8216, 29892, 13, 1678, 770, 14903, 11759, 13, 4706, 376, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 613, 13, 4706, 376, 29931, 293, 1947, 4761, 438, 5425, 28268, 1490, 4761, 341, 1806, 19245, 613, 13, 4706, 376, 7094, 1218, 2184, 4761, 6570, 25266, 613, 13, 1678, 4514, 13, 29897, 13, 2 ]
workflow/scripts/sample_comp_plot.py	snakemake-workflows/dna-seq-neoantigen-prediction	6	120081	import os import glob import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt from pysam import VariantFile variant_df = pd.read_csv(snakemake.input[0], sep='\t').fillna(0.0) variant_df = variant_df[["CHROM", "POS"] + [c for c in variant_df.columns if c.endswith("Freq")]] ## tidy data - for facet plot tidy_df = variant_df.melt(id_vars=["CHROM", "POS"], var_name="Sample", value_name="VAF") g = sns.FacetGrid(tidy_df, col="Sample") g = g.map(sns.distplot, "VAF", hist=False) g.savefig(snakemake.output["facet"]) plt.close() ## pairplot sns.pairplot(variant_df.drop(["CHROM", "POS"],axis=1), diag_kind="kde") plt.savefig(snakemake.output["pairplot"]) plt.close() def overlap_pct(x, y, kws): n = 0 for i in range(0,len(x)): if (x[i] > 0) & (y[i] > 0): n += 1 overlap=n/len([e for e in x if e > 0]) ax = plt.gca() ax.annotate("Shared Fraction: {:.2f}".format(overlap), xy=(.2, .4)) ax.annotate("Shared Variants: {}".format(n), xy=(.2, .6)) def variants(x, kws): positive = len([e for e in x if e > 0]) ax = plt.gca() ax.annotate("#Variants: {}".format(positive), xy=(.2, .5), xycoords=ax.transAxes) g = sns.PairGrid(variant_df.drop(["CHROM", "POS"], axis=1)) g.map_offdiag(overlap_pct) g.map_diag(variants) g.savefig(snakemake.output["grid"]) plt.close() #def neg_overlap_pct(x, y, kws): # n = 0 # for i in range(0,len(x)): # if (x[i] == 0) & (y[i] == 0): # n += 1 # overlap=n/len([e for e in x if e == 0]) # ax = plt.gca() # ax.annotate("Shared Fraction: {:.2f}".format(overlap), xy=(.2, .4)) # ax.annotate("Shared missing variants: {}".format(n), xy=(.2, .6)) #def neg_variants(x, kws): # zero = len([e for e in x if e == 0]) # ax = plt.gca() # ax.annotate("#Missing Variants: {}".format(zero), xy=(.2, .5), #xycoords=ax.transAxes) #g = sns.PairGrid(variant_df.drop(["CHROM", "POS"], axis=1)) #g.map_offdiag(neg_overlap_pct) #g.map_diag(neg_variants) #g.savefig("plots/Mssing_Variant_table.pdf") #plt.close() for c in variant_df.drop(["CHROM", "POS"], axis=1).columns: sns.distplot(variant_df[[c]][variant_df[c] > 0]) plt.title(c) plt.savefig("plots/positive_" + c +".distplot.pdf") plt.close() sns.distplot(variant_df[[c]]) plt.savefig("plots/all_" + c +".distplot.pdf") plt.close()	[ 1, 1053, 2897, 13, 5215, 13149, 13, 5215, 11701, 408, 10518, 13, 5215, 12655, 408, 7442, 13, 5215, 409, 370, 1398, 408, 269, 1983, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 3166, 282, 952, 314, 1053, 9586, 424, 2283, 13, 13, 19365, 29918, 2176, 353, 10518, 29889, 949, 29918, 7638, 29898, 29879, 8546, 331, 1296, 29889, 2080, 29961, 29900, 1402, 16345, 2433, 29905, 29873, 2824, 5589, 1056, 29898, 29900, 29889, 29900, 29897, 13, 19365, 29918, 2176, 353, 17305, 29918, 2176, 29961, 3366, 3210, 3491, 613, 376, 24815, 3108, 718, 518, 29883, 363, 274, 297, 17305, 29918, 2176, 29889, 13099, 565, 274, 29889, 1975, 2541, 703, 29943, 7971, 1159, 5262, 13, 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LossAug/OpticalFlowLoss.py	gexahedron/pytti	0	30730	<reponame>gexahedron/pytti from pytti.LossAug import MSELoss, LatentLoss import sys, os, gc import argparse import os import cv2 import glob import math, copy import numpy as np import torch from torch import nn from torch.nn import functional as F from PIL import Image import imageio import matplotlib.pyplot as plt from pytti.Notebook import Rotoscoper from torchvision.transforms import functional as TF os.chdir('GMA') try: sys.path.append('core') from network import RAFTGMA from utils import flow_viz from utils.utils import InputPadder finally: os.chdir('..') from pytti.Transforms import apply_flow from pytti import fetch, to_pil, DEVICE, vram_usage_mode from pytti.Image.RGBImage import RGBImage GMA = None def init_GMA(checkpoint_path): global GMA if GMA is None: with vram_usage_mode('GMA'): parser = argparse.ArgumentParser() parser.add_argument('--model', help="restore checkpoint", default=checkpoint_path) parser.add_argument('--model_name', help="define model name", default="GMA") parser.add_argument('--path', help="dataset for evaluation") parser.add_argument('--num_heads', default=1, type=int, help='number of heads in attention and aggregation') parser.add_argument('--position_only', default=False, action='store_true', help='only use position-wise attention') parser.add_argument('--position_and_content', default=False, action='store_true', help='use position and content-wise attention') parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision') args = parser.parse_args([]) GMA = torch.nn.DataParallel(RAFTGMA(args)) GMA.load_state_dict(torch.load(checkpoint_path)) GMA.to(DEVICE) GMA.eval() def sample(tensor, uv, device=DEVICE): height, width = tensor.shape[-2:] max_pos = torch.tensor([width-1,height-1], device=device).view(2,1,1) grid = uv.div(max_pos/2).sub(1).movedim(0,-1).unsqueeze(0) return F.grid_sample(tensor.unsqueeze(0), grid, align_corners = True).squeeze(0) class TargetFlowLoss(MSELoss): def __init__(self, comp, weight = 0.5, stop = -math.inf, name = "direct target loss", image_shape = None): super().__init__(comp, weight, stop, name, image_shape) with torch.no_grad(): self.register_buffer('last_step', comp.clone()) self.mag = 1 @torch.no_grad() def set_target_flow(self, flow, device=DEVICE): self.comp.set_(flow.movedim(-1,1).to(device, memory_format = torch.channels_last)) self.mag = float(torch.linalg.norm(self.comp, dim = 1).square().mean()) @torch.no_grad() def set_last_step(self, last_step_pil, device = DEVICE): last_step = TF.to_tensor(last_step_pil).unsqueeze(0).to(device, memory_format = torch.channels_last) self.last_step.set_(last_step) def get_loss(self, input, img, device=DEVICE): os.chdir('GMA') try: init_GMA('checkpoints/gma-sintel.pth') image1 = self.last_step image2 = input padder = InputPadder(image1.shape) image1, image2 = padder.pad(image1, image2) _, flow = GMA(image1, image2, iters=3, test_mode=True) flow = flow.to(device, memory_format = torch.channels_last) finally: os.chdir('..') return super().get_loss(TF.resize(flow, self.comp.shape[-2:]), img)/self.mag class OpticalFlowLoss(MSELoss): @staticmethod @torch.no_grad() def motion_edge_map(flow_forward, flow_backward, img, border_mode = 'smear', sampling_mode = 'bilinear',device=DEVICE): # algorithm based on https://github.com/manuelruder/artistic-videos/blob/master/consistencyChecker/consistencyChecker.cpp # reimplemented in pytorch by <NAME> # // consistencyChecker # // Check consistency of forward flow via backward flow. # // # // (c) <NAME>, <NAME>, <NAME> 2016 dx_ker = torch.tensor([[[[0,0,0],[1,0,-1],[0, 0,0]]]], device = device).float().div(2).repeat(2,2,1,1) dy_ker = torch.tensor([[[[0,1,0],[0,0, 0],[0,-1,0]]]], device = device).float().div(2).repeat(2,2,1,1) f_x = nn.functional.conv2d(flow_backward, dx_ker, padding='same') f_y = nn.functional.conv2d(flow_backward, dy_ker, padding='same') motionedge = torch.cat([f_x,f_y]).square().sum(dim=(0,1)) height, width = flow_forward.shape[-2:] y,x = torch.meshgrid([torch.arange(0,height), torch.arange(0,width)]) x = x.to(device) y = y.to(device) p1 = torch.stack([x,y]) v1 = flow_forward.squeeze(0) p0 = p1 + flow_backward.squeeze() v0 = sample(v1, p0) p1_back = p0 + v0 v1_back = flow_backward.squeeze(0) r1 = torch.floor(p0) r2 = r1 + 1 max_pos = torch.tensor([width-1,height-1], device=device).view(2,1,1) min_pos = torch.tensor([0, 0], device=device).view(2,1,1) overshoot = torch.logical_or(r1.lt(min_pos),r2.gt(max_pos)) overshoot = torch.logical_or(overshoot[0],overshoot[1]) missed = (p1_back - p1).square().sum(dim=0).ge(torch.stack([v1_back,v0]).square().sum(dim=(0,1)).mul(0.01).add(0.5)) motion_boundary = motionedge.ge(v1_back.square().sum(dim=0).mul(0.01).add(0.002)) reliable = torch.ones((height, width), device=device) reliable[motion_boundary] = 0 reliable[missed] = -1 reliable[overshoot] = 0 mask = TF.gaussian_blur(reliable.unsqueeze(0), 3).clip(0,1) return mask @staticmethod @torch.no_grad() def get_flow(image1, image2, device=DEVICE): os.chdir('GMA') try: init_GMA('checkpoints/gma-sintel.pth') if isinstance(image1, Image.Image): image1 = TF.to_tensor(image1).unsqueeze(0).to(device) if isinstance(image2, Image.Image): image2 = TF.to_tensor(image2).unsqueeze(0).to(device) padder = InputPadder(image1.shape) image1, image2 = padder.pad(image1, image2) flow_low, flow_up = GMA(image1, image2, iters=12, test_mode=True) finally: os.chdir('..') return flow_up def __init__(self, comp, weight = 0.5, stop = -math.inf, name = "direct target loss", image_shape = None): super().__init__(comp, weight, stop, name, image_shape) with torch.no_grad(): self.latent_loss = MSELoss(comp.new_zeros((1,1,1,1)), weight, stop, name, image_shape) self.register_buffer('bg_mask',comp.new_zeros((1,1,1,1))) @torch.no_grad() def set_flow(self, frame_prev, frame_next, img, path, border_mode = 'smear', sampling_mode = 'bilinear', device = DEVICE): if path is not None: img = img.clone() state_dict = torch.load(path) img.load_state_dict(state_dict) gc.collect() torch.cuda.empty_cache() image1 = TF.to_tensor(frame_prev).unsqueeze(0).to(device) image2 = TF.to_tensor(frame_next).unsqueeze(0).to(device) if self.bg_mask.shape[-2:] != image1.shape[-2:]: bg_mask = TF.resize(self.bg_mask, image1.shape[-2:]) self.bg_mask.set_(bg_mask) noise = torch.empty_like(image2) noise.normal_(mean = 0, std = 0.05) noise.mul_(self.bg_mask) #adding the same noise vectors to both images forces #the flow model to match those parts of the frame, effectively #disabling the flow in those areas. image1.add_(noise) image2.add_(noise) # bdy = image2.clone().squeeze(0).mean(dim = 0) # h, w = bdy.shape # s = 4 # bdy[s:-s,s:-s] = 0 # mean = bdy.sum().div(wh - (w-2s)(h-s2)) # overlay = image2.gt(0.5) if mean > 0.5 else image2.lt(0.5) # noise = torch.empty_like(image2) # noise.normal_(mean = 0, std = 0.05) # noise[torch.logical_not(overlay)] = 0 # image1.add_(noise) # image2.add_(noise) flow_forward = OpticalFlowLoss.get_flow(image1, image2) flow_backward = OpticalFlowLoss.get_flow(image2, image1) unwarped_target_direct = img.decode_tensor() flow_target_direct = apply_flow(img, -flow_backward, border_mode = border_mode, sampling_mode = sampling_mode) fancy_mask = OpticalFlowLoss.motion_edge_map(flow_forward, flow_backward, img, border_mode, sampling_mode) target_direct = flow_target_direct target_latent = img.get_latent_tensor(detach = True) mask = fancy_mask.unsqueeze(0) self.comp.set_(target_direct) self.latent_loss.comp.set_(target_latent) self.set_flow_mask(mask) array = flow_target_direct.squeeze(0).movedim(0,-1).mul(255).clamp(0, 255).cpu().detach().numpy().astype(np.uint8)[:,:,:] return Image.fromarray(array), fancy_mask @torch.no_grad() def set_flow_mask(self,mask): super().set_mask(TF.resize(mask, self.comp.shape[-2:])) if mask is not None: self.latent_loss.set_mask(TF.resize(mask, self.latent_loss.comp.shape[-2:])) else: self.latent_loss.set_mask(None) @torch.no_grad() def set_mask(self, mask, inverted = False, device = DEVICE): if isinstance(mask, str) and mask != '': if mask[0] == '-': mask = mask[1:] inverted = True if mask.strip()[-4:] == '.mp4': r = Rotoscoper(mask,self) r.update(0) return mask = Image.open(fetch(mask)).convert('L') if isinstance(mask, Image.Image): with vram_usage_mode('Masks'): mask = TF.to_tensor(mask).unsqueeze(0).to(device, memory_format = torch.channels_last) if mask not in ['',None]: #this is where the inversion is. 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pythainlp/corpus/__init__.py	petetanru/pythainlp	1	21571	# -- coding: utf-8 -- from __future__ import absolute_import,unicode_literals from pythainlp.tools import get_path_db,get_path_data from tinydb import TinyDB,Query from future.moves.urllib.request import urlopen from tqdm import tqdm import requests import os import math import requests from nltk.corpus import names #__all__ = ["thaipos", "thaiword","alphabet","tone","country","wordnet"] path_db_=get_path_db() def get_file(name): db=TinyDB(path_db_) temp = Query() if len(db.search(temp.name==name))>0: path= get_path_data(db.search(temp.name==name)[0]['file']) db.close() if not os.path.exists(path): download(name) return path def download_(url, dst): """ @param: url to download file @param: dst place to put the file """ file_size = int(urlopen(url).info().get('Content-Length', -1)) if os.path.exists(dst): first_byte = os.path.getsize(dst) else: first_byte = 0 if first_byte >= file_size: return file_size header = {"Range": "bytes=%s-%s" % (first_byte, file_size)} pbar = tqdm( total=file_size, initial=first_byte, unit='B', unit_scale=True, desc=url.split('/')[-1]) req = requests.get(url, headers=header, stream=True) with(open(get_path_data(dst), 'wb')) as f: for chunk in req.iter_content(chunk_size=1024): if chunk: f.write(chunk) pbar.update(1024) pbar.close() #return file_size def download(name,force=False): db=TinyDB(path_db_) temp = Query() data=requests.get("https://raw.githubusercontent.com/PyThaiNLP/pythainlp-corpus/master/db.json") data_json=data.json() if name in list(data_json.keys()): temp_name=data_json[name] print("Download : "+name) if len(db.search(temp.name==name))==0: print(name+" "+temp_name['version']) download_(temp_name['download'],temp_name['file_name']) db.insert({'name': name, 'version': temp_name['version'],'file':temp_name['file_name']}) else: if len(db.search(temp.name==name and temp.version==temp_name['version']))==0: print("have update") print("from "+name+" "+db.search(temp.name==name)[0]['version']+" update to "+name+" "+temp_name['version']) yes_no="y" if force==False: yes_no=str(input("y or n : ")).lower() if "y"==yes_no: download_(temp_name['download'],temp_name['file_name']) db.update({'version':temp_name['version']},temp.name==name) else: print("re-download") print("from "+name+" "+db.search(temp.name==name)[0]['version']+" update to "+name+" "+temp_name['version']) yes_no="y" if force==False: yes_no=str(input("y or n : ")).lower() if "y"==yes_no: download_(temp_name['download'],temp_name['file_name']) db.update({'version':temp_name['version']},temp.name==name) db.close()	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 3166, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 29892, 2523, 356, 29918, 20889, 1338, 13, 3166, 282, 1541, 475, 22833, 29889, 8504, 1053, 679, 29918, 2084, 29918, 2585, 29892, 657, 29918, 2084, 29918, 1272, 13, 3166, 21577, 2585, 1053, 323, 4901, 4051, 29892, 3010, 13, 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paper/figures/make_photometry_table.py	abostroem/asassn15oz	0	39479	<reponame>abostroem/asassn15oz # coding: utf-8 # Creates a table of all imaging observations in the database for paper: # * lc_obs.tex # In[1]: import numpy as np from astropy import table from astropy.table import Table from astropy.time import Time from utilities_az import supernova, connect_to_sndavis # In[2]: db, cursor = connect_to_sndavis.get_cursor() # In[3]: sn15oz = supernova.LightCurve2('asassn-15oz') # In[4]: query_str = ''' SELECT DISTINCT mag, magerr, BINARY(filter), jd, source FROM photometry WHERE targetid = 322 ORDER BY jd''' # In[5]: cursor.execute(query_str) results = cursor.fetchall() # In[6]: loc_dict = { 1: {'short':'OGG 2m', 'long': 'Haleakala Observatory - 2m'}, #ogg2m001-fs02 2m0 2: {'short':'COJ 2m', 'long': 'Siding Springs Observatory - 2m'}, #coj2m002-fs03 3: {'short':'COJ 1m', 'long': 'Siding Springs Observatory - 1m'}, #coj1m003-kb71 4: {'short':'LSC 1m', 'long': 'CTIO - Region IV'}, #lsc1m004-kb77 1m0 5: {'short':'LSC 1m', 'long': 'CTIO - Region IV'}, #lsc1m005-kb78 1m0 8: {'short':'ELP 1m', 'long': 'McDonald Observatory - 1m'},#elp1m008-kb74 1m0 9: {'short':'LSC 1m', 'long': 'CTIO - Region IV'}, #lsc1m009-fl03 1m0 10: {'short': 'CPT 1m', 'long': 'SAAO - Sutherland Facilities - 1m'}, #cpt1m010 11: {'short': 'COJ 1m', 'long': 'Siding Springs Observatory - 1m'}, #coj1m011-kb05 1m0 12: {'short': 'CPT 1m', 'long': 'SAAO - Sutherland Facilities - 1m'}, #cpt1m012-kb75 1m0 13: {'short': 'CPT 1m', 'long': 'SAAO - Sutherland Facilities - 1m '},#cpt1m013-kb76 1m0 88: {'short': 'Swift', 'long': 'Swift'}, #Swift -88: {'short': 'Swift', 'long': 'Swift'}} #Swift; non-detections in the DB have negative sources # In[7]: band = [] jd = [] date = [] mag = [] mag_err = [] source = [] phase = [] for iresult in results: #rename the Swift filters to their proper names ifilter = iresult['BINARY(filter)'] if ifilter in [b'us', b'bs', b'vs']: band.append(ifilter.decode('utf-8')[0]) elif ifilter in [b'uw1', b'uw2', b'um2']: band.append('uv'+ifilter.decode('utf-8')[1:]) else: band.append(ifilter) jd.append(iresult['jd']) mag.append(iresult['mag']) mag_err.append(iresult['magerr']) if ifilter in [b'J', b'H', b'K']: source.append('NTT') else: source.append(loc_dict[iresult['source']]['short']) date.append(Time(iresult['jd'], format='jd', out_subfmt='date').iso) phase.append((Time(iresult['jd'], format='jd') - Time(sn15oz.jdexpl, format='jd')).value) tbdata = Table([date, jd, phase, mag, mag_err, band, source], names=['Date-Obs','JD', 'Phase (Day)', 'Apparent Magnitude', 'Apparent Magnitude Error', 'Filter', 'Source']) tbdata.sort(keys=['JD', 'Filter']) # In[8]: #tbdata.write('../lc_obs.tex', format='aastex', # formats={'JD':'%8.2f', # 'Phase (Day)':'%4.2f', # 'Apparent Magnitude':'%2.2f', # 'Apparent Magnitude Error': '%1.2f'}, overwrite=True, # latexdict={'preamble':r'\centering', # 'caption':r'Imaging Observations of ASASSN-15oz.\label{tab:LcObs}', # 'data_start':r'\hline'}) # In[9]: tbdata_short = tbdata[0:5].copy() tbdata_short.write('../lc_obs_short.tex', format='latex', formats={'JD':'%8.2f', 'Phase (Day)':'%4.2f', 'Apparent Magnitude':'%2.2f', 'Apparent Magnitude Error': '%1.2f'}, overwrite=True, latexdict={'preamble':r'\centering', 'caption':r'Sample of Imaging Observations of ASASSN-15oz. Full table available on-line.\label{tab:LcObs}', 'data_start':r'\hline', 'data_end':r'\hline', 'header_start':r'\hline', 'tabletype': 'table*'}) # In[10]: #tbdata.write('../lc_obs.dat', overwrite=True, format='ascii') #ofile = open('../lc_obs.dat', 'r') #all_lines = ofile.readlines() #ofile.close() #header = '''#Photometric observations of ASASSN-15oz. ##Columns: ##Date-Obs: (str) Human readable date of observation ##JD: (float) Julian Date of observation ##Phase: (float) Days since explosion, where explosion is defined as {} ##Apparent Magnitude: (float) ##Apparent Magntidue Error: (float) ##Filter: (str) Filter used for observation ##Source: (str) Observatory used to take the data. OGG, COJ, LSC, ELP, and CPT are Las Cumbres Observatory Telescopes.\n #'''.format(sn15oz.jdexpl) #ofile = open('../asassn15oz_lc_obs.dat', 'w') #ofile.write(header) #for iline in all_lines[1:]: # ofile.write(iline) #ofile.close() # In[11]: tbdata.write('../lc_obs.csv', overwrite=True) ofile = open('../lc_obs.csv', 'r') all_lines = ofile.readlines() ofile.close() header = '''#Photometric observations of ASASSN-15oz. #Columns: #Date-Obs: (str) Human readable date of observation #JD: (float) Julian Date of observation #Phase: (float) Days since explosion, where explosion is defined as {} #Apparent Magnitude: (float) #Apparent Magntidue Error: (float) #Filter: (str) Filter used for observation #Source: (str) Observatory used to take the data. OGG, COJ, LSC, ELP, and CPT are Las Cumbres Observatory Telescopes.\n '''.format(sn15oz.jdexpl) ofile = open('../asassn15oz_lc_obs.csv', 'w') ofile.write(header) for iline in all_lines[1:]: ofile.write(iline) ofile.close()	[ 1, 529, 276, 1112, 420, 29958, 370, 520, 307, 331, 29914, 294, 465, 29876, 29896, 29945, 2112, 13, 29871, 13, 29937, 14137, 29901, 23616, 29899, 29947, 13, 13, 29937, 6760, 1078, 263, 1591, 310, 599, 6382, 292, 13917, 297, 278, 2566, 363, 5650, 29901, 13, 29937, 334, 301, 29883, 29918, 26290, 29889, 4776, 13, 13, 29871, 13, 29937, 512, 29961, 29896, 5387, 13, 13, 13, 5215, 12655, 408, 7442, 13, 3166, 8717, 14441, 1053, 1591, 13, 3166, 8717, 14441, 29889, 2371, 1053, 6137, 13, 3166, 8717, 14441, 29889, 2230, 1053, 5974, 13, 13, 3166, 3667, 1907, 29918, 834, 1053, 2428, 29876, 4273, 29892, 4511, 29918, 517, 29918, 29879, 299, 485, 275, 13, 13, 13, 29871, 13, 29871, 13, 29937, 512, 29961, 29906, 5387, 13, 13, 13, 2585, 29892, 10677, 353, 4511, 29918, 517, 29918, 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datasets/few_shot_test_pickle.py	PengWan-Yang/few-shot-transformer	4	23240	<filename>datasets/few_shot_test_pickle.py import pickle # modify validation data _few_shot_pickle_file = 'few_shot_test_data.pkl' _few_shot_file = open(_few_shot_pickle_file, 'rb') data_few_shot = pickle.load(_few_shot_file) _few_shot_pickle_file = 'few_shot_val_data.pkl' _few_shot_file = open(_few_shot_pickle_file, 'rb') data_val = pickle.load(_few_shot_file) _few_shot_pickle_file = 'few_shot_train_data.pkl' _few_shot_file = open(_few_shot_pickle_file, 'rb') data_train = pickle.load(_few_shot_file) raise 1 for _list in data_few_shot: for _video in _list: _video['fg_name'] = _video['fg_name'].replace('/home/tao/dataset/v1-3/train_val_frames_3', 'datasets/activitynet13') _video['bg_name'] = _video['bg_name'].replace('/home/tao/dataset/v1-3/train_val_frames_3', 'datasets/activitynet13') pickle.dump(data_few_shot, open(_few_shot_pickle_file, "wb")) print("done") # modify testing data _few_shot_pickle_file = 'few_shot_test_data.pkl' _few_shot_file = open(_few_shot_pickle_file, 'rb') data_few_shot = pickle.load(_few_shot_file) for _list in data_few_shot: for _video in _list: _video['fg_name'] = _video['fg_name'].replace('/home/tao/dataset/v1-3/train_val_frames_3', 'datasets/activitynet13') _video['bg_name'] = _video['bg_name'].replace('/home/tao/dataset/v1-3/train_val_frames_3', 'datasets/activitynet13') pickle.dump(data_few_shot, open(_few_shot_pickle_file, "wb")) print("done") # modify training data _few_shot_pickle_file = 'few_shot_train_data.pkl' _few_shot_file = open(_few_shot_pickle_file, 'rb') data_few_shot = pickle.load(_few_shot_file) index = 0 for k, _list in data_few_shot.items(): for _video in _list: _video['video_id'] = "query_{:0>5d}".format(index) _video['fg_name'] = _video['fg_name'].replace('dataset/activitynet13/train_val_frames_3', 'datasets/activitynet13') _video['bg_name'] = _video['bg_name'].replace('dataset/activitynet13/train_val_frames_3', 'datasets/activitynet13') 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var/spack/repos/builtin/packages/r-xde/package.py	nkianggiss/spack	3	168095	<filename>var/spack/repos/builtin/packages/r-xde/package.py # Copyright 2013-2019 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class RXde(RPackage): """Multi-level model for cross-study detection of differential gene expression.""" homepage = "https://www.bioconductor.org/packages/XDE/" git = "https://git.bioconductor.org/packages/XDE.git" version('2.22.0', commit='<PASSWORD>') depends_on('r-biobase', type=('build', 'run')) depends_on('r-biocgenerics', type=('build', 'run')) depends_on('r-genefilter', type=('build', 'run')) depends_on('r-gtools', type=('build', 'run')) depends_on('r-mergemaid', type=('build', 'run')) depends_on('r-mvtnorm', type=('build', 'run')) depends_on('[email protected]:3.4.9', when='@2.22.0')	[ 1, 529, 9507, 29958, 1707, 29914, 1028, 547, 29914, 276, 1066, 29914, 16145, 262, 29914, 8318, 29914, 29878, 29899, 29916, 311, 29914, 5113, 29889, 2272, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29896, 29941, 29899, 29906, 29900, 29896, 29929, 19520, 22469, 5514, 3086, 14223, 29892, 365, 12182, 322, 916, 13, 29937, 1706, 547, 8010, 10682, 414, 29889, 2823, 278, 2246, 29899, 5563, 315, 4590, 29979, 22789, 3912, 934, 363, 4902, 29889, 13, 29937, 13, 29937, 10937, 29928, 29990, 29899, 29931, 293, 1947, 29899, 12889, 29901, 313, 17396, 1829, 29899, 29906, 29889, 29900, 6323, 341, 1806, 29897, 13, 13, 3166, 805, 547, 1053, 334, 13, 13, 13, 1990, 390, 29990, 311, 29898, 29934, 14459, 1125, 13, 1678, 9995, 15329, 29899, 5563, 1904, 363, 4891, 29899, 18082, 29891, 15326, 310, 16712, 18530, 13, 539, 4603, 1213, 15945, 13, 13, 1678, 3271, 3488, 353, 376, 991, 597, 1636, 29889, 24840, 535, 2199, 272, 29889, 990, 29914, 8318, 29914, 29990, 2287, 12975, 13, 1678, 6315, 418, 353, 376, 991, 597, 5559, 29889, 24840, 535, 2199, 272, 29889, 990, 29914, 8318, 29914, 29990, 2287, 29889, 5559, 29908, 13, 13, 1678, 1873, 877, 29906, 29889, 29906, 29906, 29889, 29900, 742, 9063, 2433, 29966, 25711, 17013, 29958, 1495, 13, 13, 1678, 7111, 29918, 265, 877, 29878, 29899, 24840, 3188, 742, 1134, 29922, 877, 4282, 742, 525, 3389, 8785, 13, 1678, 7111, 29918, 265, 877, 29878, 29899, 5365, 542, 4738, 1199, 742, 1134, 29922, 877, 4282, 742, 525, 3389, 8785, 13, 1678, 7111, 29918, 265, 877, 29878, 29899, 1885, 1389, 309, 357, 742, 1134, 29922, 877, 4282, 742, 525, 3389, 8785, 13, 1678, 7111, 29918, 265, 877, 29878, 29899, 29887, 8504, 742, 1134, 29922, 877, 4282, 742, 525, 3389, 8785, 13, 1678, 7111, 29918, 265, 877, 29878, 29899, 1050, 29887, 2603, 333, 742, 1134, 29922, 877, 4282, 742, 525, 3389, 8785, 13, 1678, 7111, 29918, 265, 877, 29878, 29899, 29324, 6277, 555, 742, 1134, 29922, 877, 4282, 742, 525, 3389, 8785, 13, 1678, 7111, 29918, 265, 877, 29878, 29992, 29941, 29889, 29946, 29889, 29900, 29901, 29941, 29889, 29946, 29889, 29929, 742, 746, 2433, 29992, 29906, 29889, 29906, 29906, 29889, 29900, 1495, 13, 2 ]
tests/test_engagement_query_set.py	Asday/keiyakusha	2	1605076	from datetime import date, timedelta import pytest from engagements.models import Engagement from factories.auth import UserFactory from factories.clients import ClientFactory from factories.engagements import EngagementFactory @pytest.mark.django_db def test_active_at(): user = UserFactory() client_a = ClientFactory() client_b = ClientFactory() engagement_a_active = EngagementFactory( start=date(2018, 8, 23), duration=timedelta(days=1), user=user, client=client_a, ) EngagementFactory( start=date(2018, 8, 21), duration=timedelta(days=1), user=user, client=client_a, ) EngagementFactory( start=date(2018, 8, 23), duration=timedelta(days=1), user=user, client=client_b, ) assert Engagement.objects.all().count() == 3 # Just checkin'. active_at = Engagement.objects \ .filter(client=client_a) \ .active_at(date(2018, 8, 23)) assert engagement_a_active in active_at assert active_at.count() == 1	[ 1, 515, 12865, 1053, 2635, 29892, 5335, 287, 2554, 13, 13, 5215, 11451, 1688, 13, 13, 3166, 27010, 4110, 29889, 9794, 1053, 2201, 5049, 13, 3166, 2114, 3842, 29889, 5150, 1053, 4911, 5126, 13, 3166, 2114, 3842, 29889, 11303, 1237, 1053, 12477, 5126, 13, 3166, 2114, 3842, 29889, 996, 351, 4110, 1053, 2201, 5049, 5126, 13, 13, 13, 29992, 2272, 1688, 29889, 3502, 29889, 14095, 29918, 2585, 13, 1753, 1243, 29918, 4925, 29918, 271, 7295, 13, 1678, 1404, 353, 4911, 5126, 580, 13, 13, 1678, 3132, 29918, 29874, 353, 12477, 5126, 580, 13, 1678, 3132, 29918, 29890, 353, 12477, 5126, 580, 13, 13, 1678, 3033, 5049, 29918, 29874, 29918, 4925, 353, 2201, 5049, 5126, 29898, 13, 4706, 1369, 29922, 1256, 29898, 29906, 29900, 29896, 29947, 29892, 29871, 29947, 29892, 29871, 29906, 29941, 511, 13, 4706, 14385, 29922, 9346, 287, 2554, 29898, 16700, 29922, 29896, 511, 13, 4706, 1404, 29922, 1792, 29892, 13, 4706, 3132, 29922, 4645, 29918, 29874, 29892, 13, 1678, 1723, 13, 1678, 2201, 5049, 5126, 29898, 13, 4706, 1369, 29922, 1256, 29898, 29906, 29900, 29896, 29947, 29892, 29871, 29947, 29892, 29871, 29906, 29896, 511, 13, 4706, 14385, 29922, 9346, 287, 2554, 29898, 16700, 29922, 29896, 511, 13, 4706, 1404, 29922, 1792, 29892, 13, 4706, 3132, 29922, 4645, 29918, 29874, 29892, 13, 1678, 1723, 13, 1678, 2201, 5049, 5126, 29898, 13, 4706, 1369, 29922, 1256, 29898, 29906, 29900, 29896, 29947, 29892, 29871, 29947, 29892, 29871, 29906, 29941, 511, 13, 4706, 14385, 29922, 9346, 287, 2554, 29898, 16700, 29922, 29896, 511, 13, 4706, 1404, 29922, 1792, 29892, 13, 4706, 3132, 29922, 4645, 29918, 29890, 29892, 13, 1678, 1723, 13, 13, 1678, 4974, 2201, 5049, 29889, 12650, 29889, 497, 2141, 2798, 580, 1275, 29871, 29941, 29871, 396, 3387, 1423, 262, 4286, 13, 13, 1678, 6136, 29918, 271, 353, 2201, 5049, 29889, 12650, 320, 13, 4706, 869, 4572, 29898, 4645, 29922, 4645, 29918, 29874, 29897, 320, 13, 4706, 869, 4925, 29918, 271, 29898, 1256, 29898, 29906, 29900, 29896, 29947, 29892, 29871, 29947, 29892, 29871, 29906, 29941, 876, 13, 13, 1678, 4974, 3033, 5049, 29918, 29874, 29918, 4925, 297, 6136, 29918, 271, 13, 1678, 4974, 6136, 29918, 271, 29889, 2798, 580, 1275, 29871, 29896, 13, 2 ]
lib/utils/__init__.py	jwyang/C3Net.pytorch	43	19886	from .verbo import *	[ 1, 515, 869, 369, 833, 1053, 334, 13, 2 ]
training/rootnav2/accuracy/__init__.py	robail-yasrab/6-RootNav-2.0	23	187358	<reponame>robail-yasrab/6-RootNav-2.0<gh_stars>10-100 from .nms import nonmaximalsuppression, rrtree, evaluate_points	[ 1, 529, 276, 1112, 420, 29958, 13716, 737, 29899, 29891, 294, 4201, 29914, 29953, 29899, 10303, 22107, 29899, 29906, 29889, 29900, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 3166, 869, 29876, 1516, 1053, 1661, 27525, 1338, 14889, 23881, 29892, 364, 2273, 929, 29892, 14707, 29918, 9748, 2 ]
utils/experiment_utils.py	samuelfneumann/RLControl	0	119224	#!/usr/bin/env python3 import pickle import os import numpy as np def get_sweep_parameters(parameters, env_config, index): """ Gets the parameters for the hyperparameter sweep defined by the index. Each hyperparameter setting has a specific index number, and this function will get the appropriate parameters for the argument index. In addition, this the indices will wrap around, so if there are a total of 10 different hyperparameter settings, then the indices 0 and 10 will return the same hyperparameter settings. This is useful for performing loops. For example, if you had 10 hyperparameter settings and you wanted to do 10 runs, the you could just call this for indices in range(0, 1010). If you only wanted to do runs for hyperparameter setting i, then you would use indices in range(i, 10, 1010) Parameters ---------- parameters : dict The dictionary of parameters, as found in the agent's json configuration file env_config : dict The environment configuration dictionary, as found in the environment's json configuration file index : int The index of the hyperparameters configuration to return Returns ------- dict, int The dictionary of hyperparameters to use for the agent and the total number of combinations of hyperparameters (highest possible unique index) """ out_params = {} out_params["gamma"] = env_config["gamma"] accum = 1 for key in parameters: num = len(parameters[key]) out_params[key] = parameters[key][(index // accum) % num] accum = num return (out_params, accum) def get_sweep_num(parameters): """ Similar to get_sweep_parameters but only returns the total number of hyperparameter combinations. This number is the total number of distinct hyperparameter settings. If this function returns k, then there are k distinct hyperparameter settings, and indices 0 and k refer to the same distinct hyperparameter setting. Parameters ---------- parameters : dict The dictionary of parameters, as found in the agent's json configuration file Returns ------- int The number of distinct hyperparameter settings """ accum = 1 for key in parameters: num = len(parameters[key]) accum = num return accum def get_hyperparam_indices(data, hp_name, hp_value): """ Gets all hyperparameter indices that have the hyperparameter hp_name having the value hp_value. Parameters ---------- data : dict The data dictionary generated from running main.py hp_name : str The name of the hyperparameter to check the value of hp_value : object The value that the hyperparameter should have in each hyperparameter settings index Returns ------- list of int The hyperparameter settings that have the argument hyperparameter hp_name having the argument value hp_value """ agent_param = data["experiment"]["agent"]["parameters"] env_config = data["experiment"]["environment"] hp_indices = [] for i in range(get_sweep_num(agent_param)): # Get the hyperparameters for each hyperparameter setting hp_setting = get_sweep_parameters(agent_param, env_config, i)[0] if hp_setting[hp_name] == hp_value: hp_indices.append(i) return hp_indices def get_varying_single_hyperparam(data, hp_name): """ Gets the hyperparameter indices where only a single hyperparameter is varying and all other hyperparameters remain constant. Parameters ---------- data : dict The data dictionary generated from running main.py hp_name : str The name of the hyperparameter to vary Returns ------- n-tuple of m-tuple of int Gets and returns the hyperparameter indices where only a single hyperparameter is varying and all others remain constant. The total number of values that the varying hyperparameter can take on is m; n is the total number of hyperparameter combinations // m. For example, if the hyperparameter is the decay rate and it can take on values in [0.0, 0.1, 0.5] and there are a total of 81 hyperparameter settings combinations, then m = 3 and n = 81 // 3 = 27 """ agent_param = data["experiment"]["agent"]["parameters"] hps = [] # set(range(exp.get_sweep_num(agent_param))) for hp_value in agent_param[hp_name]: hps.append(get_hyperparam_indices(data, hp_name, hp_value)) return tuple(zip(hps)) def get_best_hp(data, type_, after=0): """ Gets and returns a list of the hyperparameter settings, sorted by average return. Parameters ---------- data : dict They Python data dictionary generated from running main.py type_ : str The type of return by which to compare hyperparameter settings, one of "train" or "eval" after : int, optional Hyperparameters will only be compared by their performance after training for this many episodes (in continuing tasks, this is the number of times the task is restarted). For example, if after = -10, then only the last 10 returns from training/evaluation are taken into account when comparing the hyperparameters. As usual, positive values index from the front, and negative values index from the back. Returns ------- n-tuple of 2-tuple(int, float) A tuple with the number of elements equal to the total number of hyperparameter combinations. Each sub-tuple is a tuple of (hyperparameter setting number, mean return over all runs and episodes) """ if type_ not in ("train", "eval"): raise ValueError("type_ should be one of 'train', 'eval'") return_type = "train_episode_rewards" if type_ == "train" \ else "eval_episode_rewards" mean_returns = [] hp_settings = sorted(list(data["experiment_data"].keys())) for hp_setting in hp_settings: hp_returns = [] for run in data["experiment_data"][hp_setting]["runs"]: hp_returns.append(run[return_type]) hp_returns = np.stack(hp_returns) # If evaluating, use the mean return over all episodes for each # evaluation interval. That is, if 10 eval episodes for each evaluation # the take the average return over all these eval episodes if type_ == "eval": hp_returns = hp_returns.mean(axis=-1) # Calculate the average return over all runs hp_returns = hp_returns[after:, :].mean(axis=0) # Calculate the average return over all "episodes" hp_returns = hp_returns.mean(axis=0) # Save mean return mean_returns.append(hp_returns) # Return the best hyperparam settings in order with the # mean returns sorted by hyperparmater setting performance best_hp_settings = np.argsort(mean_returns) mean_returns = np.array(mean_returns)[best_hp_settings] return tuple(zip(best_hp_settings, mean_returns)) def combine_runs(data1, data2): """ Adds the runs for each hyperparameter setting in data2 to the runs for the corresponding hyperparameter setting in data1. Given two data dictionaries, this function will get each hyperparameter setting and extend the runs done on this hyperparameter setting and saved in data1 by the runs of this hyperparameter setting and saved in data2. In short, this function extends the lists data1["experiment_data"][i]["runs"] by the lists data2["experiment_data"][i]["runs"] for all i. This is useful if multiple runs are done at different times, and the two data files need to be combined. Parameters ---------- data1 : dict A data dictionary as generated by main.py data2 : dict A data dictionary as generated by main.py Raises ------ KeyError If a hyperparameter setting exists in data2 but not in data1. This signals that the hyperparameter settings indices are most likely different, so the hyperparameter index i in data1 does not correspond to the same hyperparameter index in data2. In addition, all other functions expect the number of runs to be consistent for each hyperparameter setting, which would be violated in this case. """ for hp_setting in data1["experiment_data"]: if hp_setting not in data2.keys(): # Ensure consistent hyperparam settings indices raise KeyError("hyperparameter settings are different " + "between the two experiments") extra_runs = data2["experiment_data"][hp_setting]["runs"] data1["experiment_data"][hp_setting]["runs"].extend(extra_runs) def get_returns(data, type_, ind): """ Gets the returns seen by an agent Gets the online or offline returns seen by an agent trained with hyperparameter settings index ind. Parameters ---------- data : dict The Python data dictionary generated from running main.py type_ : str Whether to get the training or evaluation returns, one of 'train', 'eval' ind : int Gets the returns of the agent trained with this hyperparameter settings index Returns ------- array_like The array of returns of the form (N, R, C) where N is the number of runs, R is the number of times a performance was measured, and C is the number of returns generated each time performance was measured (offline >= 1; online = 1). For the online setting, N is the number of runs, and R is the number of episodes and C = 1. For the offline setting, N is the number of runs, R is the number of times offline evaluation was performed, and C is the number of episodes run each time performance was evaluated offline. """ returns = [] if type_ == "eval": # Get the offline evaluation episode returns per run for run in data["experiment_data"][ind]["runs"]: returns.append(run["eval_episode_rewards"]) returns = np.stack(returns) elif type_ == "train": # Get the returns per episode per run for run in data["experiment_data"][ind]["runs"]: returns.append(run["train_episode_rewards"]) returns = np.expand_dims(np.stack(returns), axis=2) return returns def get_hyperparams(data, ind): """ Gets the hyperparameters for hyperparameter settings index ind data : dict The Python data dictionary generated from running main.py ind : int Gets the returns of the agent trained with this hyperparameter settings index Returns ------- dict The dictionary of hyperparameters """ return data["experiment_data"][ind]["agent_params"] def get_mean_returns_with_stderr_hp_varying(data, type_, hp_name, combo, after=0): """ Calculate mean and standard error of return for each hyperparameter value. Gets the mean returns for each variation of a single hyperparameter, with all other hyperparameters remaining constant. Since there are many different ways this can happen (the hyperparameter can vary with all other remaining constant, but there are many combinations of these constant hyperparameters), the combo argument cycles through the combinations of constant hyperparameters. Given hyperparameters a, b, and c, let's say we want to get all hyperparameter settings indices where a varies, and b and c are constant. if a, b, and c can each be 1 or 2, then there are four ways that a can vary with b and c remaining constant: [ ((a=1, b=1, c=1), (a=2, b=1, c=1)), combo = 0 ((a=1, b=2, c=1), (a=2, b=2, c=1)), combo = 1 ((a=1, b=1, c=2), (a=2, b=1, c=2)), combo = 2 ((a=1, b=2, c=2), (a=2, b=2, c=2)) combo = 3 ] The combo argument indexes into this list of hyperparameter settings Parameters ---------- data : dict The Python data dictionary generated from running main.py type_ : str Which type of data to plot, one of "eval" or "train" combo : int Determines the values of the constant hyperparameters. Given that only one hyperparameter may vary, there are many different sets having this hyperparameter varying with all others remaining constant since each constant hyperparameter may take on many values. This argument cycles through all sets of hyperparameter settings indices that have only one hyperparameter varying and all others constant. """ hp_combo = get_varying_single_hyperparam(data, hp_name)[combo] mean_returns = [] stderr_returns = [] for hp in hp_combo: mean_return = get_returns(data, type_, hp) # If evaluating, use the mean return over all episodes for each # evaluation interval. That is, if 10 eval episodes for each evaluation # the take the average return over all these eval episodes. # If online returns, this axis has a length of 1 so we reduce it mean_return = mean_return.mean(axis=-1) # Calculate the average return over all "episodes" # print(mean_return[:, after:].shape) mean_return = mean_return[:, after:].mean(axis=1) # Calculate the average return over all runs stderr_return = np.std(mean_return, axis=0) / \ np.sqrt(mean_return.shape[0]) mean_return = mean_return.mean(axis=0) mean_returns.append(mean_return) stderr_returns.append(stderr_return) # Get each hp value and sort all results by hp value hp_values = np.array(data["experiment"]["agent"]["parameters"][hp_name]) indices = np.argsort(hp_values) mean_returns = np.array(mean_returns)[indices] stderr_returns = np.array(stderr_returns)[indices] hp_values = hp_values[indices] return hp_values, mean_returns, stderr_returns def get_mean_stderr(data, type_, ind, smooth_over): """ Gets the timesteps, mean, and standard error to be plotted for a given hyperparameter settings index Parameters ---------- data : dict The Python data dictionary generated from running main.py type_ : str Which type of data to plot, one of "eval" or "train" ind : int The hyperparameter settings index to plot smooth_over : int The number of previous data points to smooth over. Note that this is not* the number of timesteps to smooth over, but rather the number of data points to smooth over. For example, if you save the return every 1,000 timesteps, then setting this value to 15 will smooth over the last 15 readings, or 15,000 timesteps. Returns ------- 3-tuple of list(int), list(float), list(float) The timesteps, mean episodic returns, and standard errors of the episodic returns """ timesteps = None # So the linter doesn't have a temper tantrum # Determine the timesteps to plot at if type_ == "eval": timesteps = \ data["experiment_data"][ind]["runs"][0]["timesteps_at_eval"] elif type_ == "train": timesteps_per_ep = \ data["experiment_data"][ind]["runs"][0]["train_episode_steps"] timesteps = get_cumulative_timesteps(timesteps_per_ep) # Get the mean over all episodes per evaluation step (for online # returns, this axis will have length 1 so we squeeze it) returns = get_returns(data, type_, ind) mean = returns.mean(axis=-1) # Get the standard deviation of mean episodes per evaluation # step over all runs runs = returns.shape[0] std = np.std(mean, axis=0) / np.sqrt(runs) # Get the mean over all runs mean = mean.mean(axis=0) # Smooth of last k returns if applicable, convolve will initially only # start sliding from the beginning, so we need to cut off the initial # averages which are incomplete if smooth_over > 1: mean = np.convolve(mean, np.ones(smooth_over) / smooth_over, mode="valid") #[smooth_over:mean.shape[0]] std = np.convolve(std, np.ones(smooth_over) / smooth_over, mode="valid") #[smooth_over:std.shape[0]] # np.convolve only goes until the last element of the convolution # lines up with the last element of the data, so smooth_over elements # will be lost timesteps = timesteps[:-smooth_over + 1] return timesteps, mean, std def get_cumulative_timesteps(timesteps_per_episode): """ Creates an array of cumulative timesteps. Creates an array of timesteps, where each timestep is the cumulative number of timesteps up until that point. This is needed for plotting the training data, where the training timesteps are stored for each episode, and we need to plot on the x-axis the cumulative timesteps, not the timesteps per episode. Parameters ---------- timesteps_per_episode : list A list where each element in the list denotes the amount of timesteps for the corresponding episode. Returns ------- array_like An array where each element is the cumulative number of timesteps up until that point. """ timesteps_per_episode = np.array(timesteps_per_episode) cumulative_timesteps = [timesteps_per_episode[:i].sum() for i in range(timesteps_per_episode.shape[0])] return np.array(cumulative_timesteps) def combine_data_dictionaries(files, save=True, save_dir=".", filename="data"): """ Combine data dictionaries given a list of filenames Given a list of paths to data dictionaries, combines each data dictionary into a single one. Parameters ---------- files : list of str A list of the paths to data dictionary files to combine save : bool Whether or not to save the data save_dir : str, optional The directory containing the saved dictionaries filename : str, optional The name of the file to save which stores the combined data, by default 'data' Returns ------- dict The combined dictionary """ # Use first dictionary as base dictionary with open(os.path.join(save_dir, files[0]), "rb") as in_file: data = pickle.load(in_file) # Add data from all other dictionaries for file in files[1:]: with open(os.path.join(save_dir, file), "rb") as in_file: # Read in the new dictionary in_data = pickle.load(in_file) # Add experiment data to running dictionary for key in in_data["experiment_data"]: # Check if key exists if key in data["experiment_data"]: # Append data if existing data["experiment_data"][key]["runs"] \ .extend(in_data["experiment_data"][key]["runs"]) else: # Key doesn't exist - add data to dictionary data["experiment_data"][key] = \ in_data["experiment_data"][key] if save: with open(os.path.join(save_dir, f"{filename}.pkl"), "wb") as out_file: pickle.dump(data, out_file) return data	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 5215, 5839, 280, 13, 5215, 2897, 13, 5215, 12655, 408, 7442, 13, 13, 13, 1753, 679, 29918, 29879, 705, 1022, 29918, 16744, 29898, 16744, 29892, 8829, 29918, 2917, 29892, 2380, 1125, 13, 1678, 9995, 13, 1678, 402, 1691, 278, 4128, 363, 278, 11266, 15501, 7901, 1022, 3342, 491, 278, 2380, 29889, 13, 13, 1678, 7806, 11266, 15501, 4444, 756, 263, 2702, 2380, 1353, 29892, 322, 445, 740, 13, 1678, 674, 679, 278, 8210, 4128, 363, 278, 2980, 2380, 29889, 512, 6124, 29892, 13, 1678, 445, 278, 16285, 674, 12244, 2820, 29892, 577, 565, 727, 526, 263, 3001, 310, 29871, 29896, 29900, 1422, 13, 1678, 11266, 15501, 6055, 29892, 769, 278, 16285, 29871, 29900, 322, 29871, 29896, 29900, 674, 736, 278, 1021, 13, 1678, 11266, 15501, 6055, 29889, 910, 338, 5407, 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29897, 13, 13, 1678, 736, 848, 13, 2 ]
lib/ExtraFM.py	KanHatakeyama/anneal_project2	0	98890	<reponame>KanHatakeyama/anneal_project2 from scipy.sparse import csr_matrix from pyfm import pylibfm import numpy as np from numba import jit from ScaleRegressor import ScaleRegressor default_model = pylibfm.FM(task="regression", num_iter=30, initial_learning_rate=10*-3, num_factors=10, verbose=False ) class ExtraFM: def __init__(self, model=None): if model is None: model = default_model self.model = model def fit(self, X, y): sparse_X = csr_matrix(X.astype("double")) self.model.fit(sparse_X, y) # self.qubo=calc_qubo(self.model.v,self.model.v[0].shape[0],self.model.v.shape[0])+np.diag(self.model.w) # calc offset self.b = self.model.predict(csr_matrix( np.zeros(X[0].shape[0]).astype("double"))) # self.y_max=max(y) self.y_max = 0 # self.y_max=max(y) self.y_min = 0 def predict(self, X): y = self.original_predict(X, reg_mode=False) # print(X.shape,y.shape) y = -np.log((1-y)/y) # fill nan nan_ids = np.where(y == np.inf) y[nan_ids] = self.y_max nan_ids = np.where(y == -np.inf) y[nan_ids] = self.y_min return y def original_predict(self, X, reg_mode=True): if reg_mode: self.model.fm_fast.task = 0 else: self.model.fm_fast.task = 1 sparse_X = csr_matrix(X.astype("double")) return self.model.predict(sparse_X) @jit def calc_qubo(v, dim1, dim2): qubo = np.zeros((dim1, dim1)) for k in range(dim2): for i in range(dim1): for j in range(i): val = v[k][j]v[k][i] qubo[j, i] += val return qubo class FMRegressor(ScaleRegressor): def fit(self, X, y): X = csr_matrix(X).astype(np.double) return super().fit(X, y) def predict(self, X): X = csr_matrix(X).astype(np.double) return super().predict(X)	[ 1, 529, 276, 1112, 420, 29958, 29968, 273, 29950, 532, 1989, 3304, 29914, 11276, 284, 29918, 4836, 29906, 13, 3166, 4560, 2272, 29889, 29879, 5510, 1053, 5939, 29878, 29918, 5344, 13, 3166, 11451, 24826, 1053, 11451, 492, 1635, 29885, 13, 5215, 12655, 408, 7442, 13, 3166, 954, 2291, 1053, 432, 277, 13, 3166, 2522, 744, 4597, 1253, 272, 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test/swe2d/test_steady_state_basin_mms.py	jrper/thetis	0	193326	<reponame>jrper/thetis """ MMS test for 2d shallow water equations. - setup functions define analytical expressions for fields and boundary conditions. Expressions were derived with sympy. - run function runs the MMS setup with a single mesh resolution, returning L2 errors. - run_convergence runs a scaling test, computes and asserts convergence rate. """ from thetis import * import numpy from scipy import stats import pytest def setup7(x, lx, ly, h0, f0, nu0, g): """ Non-trivial Coriolis, bath, elev, u and v, tangential velocity is zero at bnd to test flux BCs """ out = {} out['bath_expr'] = h0sqrt(0.3x[0]*2 + 0.2x[1]*2 + 0.1)/lx + 4.0 out['cori_expr'] = f0cos(pi(x[0] + x[1])/lx) out['elev_expr'] = cos(pi(3.0x[0] + 1.0x[1])/lx) out['uv_expr'] = as_vector( [ sin(pi(-2.0x[0] + 1.0x[1])/lx)sin(pix[1]/ly), 0.5sin(pix[0]/lx)sin(pi(-3.0x[0] + 1.0x[1])/lx), ]) out['res_elev_expr'] = (0.3h0x[0]/(lxsqrt(0.3x[0]2 + 0.2x[1]*2 + 0.1)) - 3.0pisin(pi(3.0x[0] + 1.0x[1])/lx)/lx)sin(pi(-2.0x[0] + 1.0x[1])/lx)sin(pix[1]/ly) + 0.5(0.2h0x[1]/(lxsqrt(0.3x[0]2 + 0.2x[1]*2 + 0.1)) - 1.0pisin(pi(3.0x[0] + 1.0x[1])/lx)/lx)sin(pix[0]/lx)sin(pi(-3.0x[0] + 1.0x[1])/lx) + 0.5pi(h0sqrt(0.3x[0]*2 + 0.2x[1]*2 + 0.1)/lx + cos(pi(3.0x[0] + 1.0x[1])/lx) + 4.0)sin(pix[0]/lx)cos(pi(-3.0x[0] + 1.0x[1])/lx)/lx - 2.0pi(h0sqrt(0.3x[0]*2 + 0.2x[1]*2 + 0.1)/lx + cos(pi(3.0x[0] + 1.0x[1])/lx) + 4.0)sin(pix[1]/ly)cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx out['res_uv_expr'] = as_vector( [ -0.5f0sin(pix[0]/lx)sin(pi(-3.0x[0] + 1.0x[1])/lx)cos(pi(x[0] + x[1])/lx) - 3.0pigsin(pi(3.0x[0] + 1.0x[1])/lx)/lx + 0.5(pisin(pi(-2.0x[0] + 1.0x[1])/lx)cos(pix[1]/ly)/ly + 1.0pisin(pix[1]/ly)cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx)sin(pix[0]/lx)sin(pi(-3.0x[0] + 1.0x[1])/lx) - 2.0pisin(pi(-2.0x[0] + 1.0x[1])/lx)sin(pix[1]/ly)*2cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx, f0sin(pi(-2.0x[0] + 1.0x[1])/lx)sin(pix[1]/ly)cos(pi(x[0] + x[1])/lx) - 1.0pigsin(pi(3.0x[0] + 1.0x[1])/lx)/lx + (-1.5pisin(pix[0]/lx)cos(pi(-3.0x[0] + 1.0x[1])/lx)/lx + 0.5pisin(pi(-3.0x[0] + 1.0x[1])/lx)cos(pix[0]/lx)/lx)sin(pi(-2.0x[0] + 1.0x[1])/lx)sin(pix[1]/ly) + 0.25pisin(pix[0]/lx)*2sin(pi(-3.0x[0] + 1.0x[1])/lx)cos(pi(-3.0x[0] + 1.0x[1])/lx)/lx, ]) out['bnd_funcs'] = {1: {'elev': None, 'flux_left': None}, 2: {'flux_right': None}, 3: {'elev': None, 'flux_lower': None}, 4: {'un_upper': None}, } return out def setup8(x, lx, ly, h0, f0, nu0, g): """ Non-trivial Coriolis, bath, elev, u and v, tangential velocity is non-zero at bnd, must prescribe uv at boundary. """ out = {} out['bath_expr'] = h0sqrt(0.3x[0]2 + 0.2x[1]*2 + 0.1)/lx + 4.0 out['cori_expr'] = f0cos(pi(x[0] + x[1])/lx) out['elev_expr'] = cos(pi(3.0x[0] + 1.0x[1])/lx) out['uv_expr'] = as_vector( [ sin(pi(-2.0x[0] + 1.0x[1])/lx), 0.5sin(pi(-3.0x[0] + 1.0x[1])/lx), ]) out['res_elev_expr'] = (0.3h0x[0]/(lxsqrt(0.3x[0]2 + 0.2x[1]*2 + 0.1)) - 3.0pisin(pi(3.0x[0] + 1.0x[1])/lx)/lx)sin(pi(-2.0x[0] + 1.0x[1])/lx) + 0.5(0.2h0x[1]/(lxsqrt(0.3x[0]2 + 0.2x[1]*2 + 0.1)) - 1.0pisin(pi(3.0x[0] + 1.0x[1])/lx)/lx)sin(pi(-3.0x[0] + 1.0x[1])/lx) + 0.5pi(h0sqrt(0.3x[0]*2 + 0.2x[1]*2 + 0.1)/lx + cos(pi(3.0x[0] + 1.0x[1])/lx) + 4.0)cos(pi(-3.0x[0] + 1.0x[1])/lx)/lx - 2.0pi(h0sqrt(0.3x[0]*2 + 0.2x[1]*2 + 0.1)/lx + cos(pi(3.0x[0] + 1.0x[1])/lx) + 4.0)cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx out['res_uv_expr'] = as_vector( [ -0.5f0sin(pi(-3.0x[0] + 1.0x[1])/lx)cos(pi(x[0] + x[1])/lx) - 3.0pigsin(pi(3.0x[0] + 1.0x[1])/lx)/lx + 0.5pisin(pi(-3.0x[0] + 1.0x[1])/lx)cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx - 2.0pisin(pi(-2.0x[0] + 1.0x[1])/lx)cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx, f0sin(pi(-2.0x[0] + 1.0x[1])/lx)cos(pi(x[0] + x[1])/lx) - 1.0pigsin(pi(3.0x[0] + 1.0x[1])/lx)/lx + 0.25pisin(pi(-3.0x[0] + 1.0x[1])/lx)cos(pi(-3.0x[0] + 1.0x[1])/lx)/lx - 1.5pisin(pi(-2.0x[0] + 1.0x[1])/lx)cos(pi(-3.0x[0] + 1.0x[1])/lx)/lx, ]) # NOTE uv condition alone does not work out['bnd_funcs'] = {1: {'elev': None, 'uv': None}, 2: {'elev': None, 'uv': None}, 3: {'elev': None, 'uv': None}, 4: {'elev': None, 'uv': None}, } return out def setup9(x, lx, ly, h0, f0, nu0, g): """ No Coriolis, non-trivial bath, viscosity, elev, u and v. """ out = {} out['bath_expr'] = h0sqrt(0.3x[0]2 + 0.2x[1]*2 + 0.1)/lx + 4.0 out['visc_expr'] = nu0(1.0 + x[0]/lx) out['elev_expr'] = cos(pi(3.0x[0] + 1.0x[1])/lx) out['uv_expr'] = as_vector( [ sin(pi(-2.0x[0] + 1.0x[1])/lx), 0.5sin(pi(-3.0x[0] + 1.0x[1])/lx), ]) out['res_elev_expr'] = (0.3h0x[0]/(lxsqrt(0.3x[0]*2 + 0.2x[1]*2 + 0.1)) - 3.0pisin(pi(3.0x[0] + 1.0x[1])/lx)/lx)sin(pi(-2.0x[0] + 1.0x[1])/lx) + 0.5(0.2h0x[1]/(lxsqrt(0.3x[0]2 + 0.2x[1]*2 + 0.1)) - 1.0pisin(pi(3.0x[0] + 1.0x[1])/lx)/lx)sin(pi(-3.0x[0] + 1.0x[1])/lx) + 0.5pi(h0sqrt(0.3x[0]*2 + 0.2x[1]*2 + 0.1)/lx + cos(pi(3.0x[0] + 1.0x[1])/lx) + 4.0)cos(pi(-3.0x[0] + 1.0x[1])/lx)/lx - 2.0pi(h0sqrt(0.3x[0]*2 + 0.2x[1]*2 + 0.1)/lx + cos(pi(3.0x[0] + 1.0x[1])/lx) + 4.0)cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx out['res_uv_expr'] = as_vector( [ -3.0pigsin(pi(3.0x[0] + 1.0x[1])/lx)/lx - 4.0pinu0(1.0 + x[0]/lx)(-0.3h0x[0]/(lxsqrt(0.3x[0]*2 + 0.2x[1]*2 + 0.1)) + 3.0pisin(pi(3.0x[0] + 1.0x[1])/lx)/lx)cos(pi(-2.0x[0] + 1.0x[1])/lx)/(lx(h0sqrt(0.3x[0]2 + 0.2x[1]*2 + 0.1)/lx + cos(pi(3.0x[0] + 1.0x[1])/lx) + 4.0)) + 0.5pisin(pi(-3.0x[0] + 1.0x[1])/lx)cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx - 2.0pisin(pi(-2.0x[0] + 1.0x[1])/lx)cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx - 1.5pi2nu0(1.0 + x[0]/lx)sin(pi(-3.0x[0] + 1.0x[1])/lx)/lx2 + 9.0pi*2nu0(1.0 + x[0]/lx)sin(pi(-2.0x[0] + 1.0x[1])/lx)/lx2 + 4.0pinu0cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx2, -1.0pigsin(pi(3.0x[0] + 1.0x[1])/lx)/lx + 1.0pinu0(1.0 + x[0]/lx)(-0.2h0x[1]/(lxsqrt(0.3x[0]2 + 0.2x[1]*2 + 0.1)) + 1.0pisin(pi(3.0x[0] + 1.0x[1])/lx)/lx)cos(pi(-3.0x[0] + 1.0x[1])/lx)/(lx(h0sqrt(0.3x[0]2 + 0.2x[1]*2 + 0.1)/lx + cos(pi(3.0x[0] + 1.0x[1])/lx) + 4.0)) + 0.25pisin(pi(-3.0x[0] + 1.0x[1])/lx)cos(pi(-3.0x[0] + 1.0x[1])/lx)/lx - 1.5pisin(pi(-2.0x[0] + 1.0x[1])/lx)cos(pi(-3.0x[0] + 1.0x[1])/lx)/lx + 5.5pi2nu0(1.0 + x[0]/lx)sin(pi(-3.0x[0] + 1.0x[1])/lx)/lx2 - 2.0pi*2nu0(1.0 + x[0]/lx)sin(pi(-2.0x[0] + 1.0x[1])/lx)/lx2 + 1.5pinu0cos(pi(-3.0x[0] + 1.0x[1])/lx)/lx2 - 1.0pinu0cos(pi(-2.0x[0] + 1.0x[1])/lx)/lx2, ]) out['bnd_funcs'] = {1: {'uv': None}, 2: {'uv': None}, 3: {'uv': None}, 4: {'uv': None}, } out['options'] = { 'use_grad_div_viscosity_term': True, 'use_grad_depth_viscosity_term': True, } return out def run(setup, refinement, order, do_export=True, options=None, solver_parameters=None): """Run single test and return L2 error""" print_output('--- running {:} refinement {:}'.format(setup.__name__, refinement)) # domain dimensions lx = 15e3 ly = 10e3 area = lxly f0 = 5e-3 # NOTE large value to make Coriolis terms larger nu0 = 100.0 g = physical_constants['g_grav'] depth = 10.0 t_period = 5000.0 # period of signals t_end = 1000.0 # 500.0 # 3T_period t_export = t_period/100.0 # export interval # mesh nx = 5refinement ny = 5refinement mesh2d = RectangleMesh(nx, ny, lx, ly) dt = 4.0/refinement if options is not None and options.get('timestepper_type') == 'CrankNicolson': dt = 100. x = SpatialCoordinate(mesh2d) sdict = setup(x, lx, ly, depth, f0, nu0, g) # outputs outputdir = 'outputs' # bathymetry p1_2d = FunctionSpace(mesh2d, 'CG', 1) bathymetry_2d = Function(p1_2d, name='Bathymetry') bathymetry_2d.project(sdict['bath_expr']) if bathymetry_2d.dat.data.min() < 0.0: print_output('bath {:} {:}'.format(bathymetry_2d.dat.data.min(), bathymetry_2d.dat.data.max())) raise Exception('Negative bathymetry') solver_obj = solver2d.FlowSolver2d(mesh2d, bathymetry_2d) solver_obj.options.polynomial_degree = order solver_obj.options.element_family = 'rt-dg' solver_obj.options.horizontal_velocity_scale = Constant(1.0) solver_obj.options.no_exports = not do_export solver_obj.options.output_directory = outputdir solver_obj.options.simulation_end_time = t_end solver_obj.options.timestep = dt solver_obj.options.simulation_export_time = t_export if 'options' in sdict: solver_obj.options.update(sdict['options']) if options is not None: solver_obj.options.update(options) if hasattr(solver_obj.options.timestepper_options, 'use_automatic_timestep'): solver_obj.options.timestepper_options.use_automatic_timestep = False solver_obj.create_function_spaces() # analytical solution in high-order space for computing L2 norms h_2d_ho = FunctionSpace(solver_obj.mesh2d, 'DG', order+3) u_2d_ho = VectorFunctionSpace(solver_obj.mesh2d, 'DG', order+4) elev_ana_ho = Function(h_2d_ho, name='Analytical elevation') elev_ana_ho.project(sdict['elev_expr']) uv_ana_ho = Function(u_2d_ho, name='Analytical velocity') uv_ana_ho.project(sdict['uv_expr']) # functions for source terms source_uv = Function(solver_obj.function_spaces.U_2d, name='momentum source') source_uv.project(sdict['res_uv_expr']) source_elev = Function(solver_obj.function_spaces.H_2d, name='continuity source') source_elev.project(sdict['res_elev_expr']) solver_obj.options.momentum_source_2d = source_uv solver_obj.options.volume_source_2d = source_elev if 'cori_expr' in sdict: coriolis_func = Function(solver_obj.function_spaces.H_2d, name='coriolis') coriolis_func.project(sdict['cori_expr']) solver_obj.options.coriolis_frequency = coriolis_func if 'visc_expr' in sdict: viscosity_space = FunctionSpace(solver_obj.mesh2d, "CG", order) viscosity_func = Function(viscosity_space, name='viscosity') viscosity_func.project(sdict['visc_expr']) solver_obj.options.horizontal_viscosity = viscosity_func # functions for boundary conditions # analytical elevation elev_ana = Function(solver_obj.function_spaces.H_2d, name='Analytical elevation') elev_ana.project(sdict['elev_expr']) # analytical uv uv_ana = Function(solver_obj.function_spaces.U_2d, name='Analytical velocity') uv_ana.project(sdict['uv_expr']) # normal velocity (scalar field, will be interpreted as unnormal vector) # left/right bnds un_ana_x = Function(solver_obj.function_spaces.H_2d, name='Analytical normal velocity x') un_ana_x.project(uv_ana[0]) # lower/uppser bnds un_ana_y = Function(solver_obj.function_spaces.H_2d, name='Analytical normal velocity y') un_ana_y.project(uv_ana[1]) # flux through left/right bnds flux_ana_x = Function(solver_obj.function_spaces.H_2d, name='Analytical x flux') flux_ana_x.project(uv_ana[0](bathymetry_2d + elev_ana)ly) # flux through lower/upper bnds flux_ana_y = Function(solver_obj.function_spaces.H_2d, name='Analytical x flux') flux_ana_y.project(uv_ana[1](bathymetry_2d + elev_ana)lx) # construct bnd conditions from setup bnd_funcs = sdict['bnd_funcs'] # correct values to replace None in bnd_funcs # NOTE: scalar velocity (flux\|un) sign def: positive out of domain bnd_field_mapping = {'symm': None, 'elev': elev_ana, 'uv': uv_ana, 'un_left': -un_ana_x, 'un_right': un_ana_x, 'un_lower': -un_ana_y, 'un_upper': un_ana_y, 'flux_left': -flux_ana_x, 'flux_right': flux_ana_x, 'flux_lower': -flux_ana_y, 'flux_upper': flux_ana_y, } for bnd_id in bnd_funcs: d = {} # values for this bnd e.g. {'elev': elev_ana, 'uv': uv_ana} for bnd_field in bnd_funcs[bnd_id]: field_name = bnd_field.split('_')[0] d[field_name] = bnd_field_mapping[bnd_field] # set to the correct bnd_id solver_obj.bnd_functions['shallow_water'][bnd_id] = d # # print a fancy description # bnd_str = '' # for k in d: # if isinstance(d[k], ufl.algebra.Product): # a, b = d[k].operands() # name = '{:} {:}'.format(a.value(), b.name()) # else: # if d[k] is not None: # name = d[k].name() # else: # name = str(d[k]) # bnd_str += '{:}: {:}, '.format(k, name) # print_output('bnd {:}: {:}'.format(bnd_id, bnd_str)) solver_obj.assign_initial_conditions(elev=elev_ana, uv=uv_ana) if solver_parameters is not None: # HACK: need to change prefix of solver options in order to overwrite them solver_obj.timestepper.name += '_' solver_obj.timestepper.solver_parameters.update(solver_parameters) solver_obj.timestepper.update_solver() solver_obj.iterate() elev_l2_err = errornorm(elev_ana_ho, solver_obj.fields.solution_2d.split()[1])/numpy.sqrt(area) uv_l2_err = errornorm(uv_ana_ho, solver_obj.fields.solution_2d.split()[0])/numpy.sqrt(area) print_output('elev L2 error {:.12f}'.format(elev_l2_err)) print_output('uv L2 error {:.12f}'.format(uv_l2_err)) return elev_l2_err, uv_l2_err def run_convergence(setup, ref_list, order, do_export=False, save_plot=False, options=None, solver_parameters=None): """Runs test for a list of refinements and computes error convergence rate""" l2_err = [] for r in ref_list: l2_err.append(run(setup, r, order, do_export=do_export, options=options, solver_parameters=solver_parameters)) x_log = numpy.log10(numpy.array(ref_list, dtype=float)*-1) y_log = numpy.log10(numpy.array(l2_err)) y_log_elev = y_log[:, 0] y_log_uv = y_log[:, 1] def check_convergence(x_log, y_log, expected_slope, field_str, save_plot): slope_rtol = 0.2 slope, intercept, r_value, p_value, std_err = stats.linregress(x_log, y_log) setup_name = setup.__name__ if save_plot: import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(5, 5)) # plot points ax.plot(x_log, y_log, 'k.') x_min = x_log.min() x_max = x_log.max() offset = 0.05(x_max - x_min) n = 50 xx = numpy.linspace(x_min - offset, x_max + offset, n) yy = intercept + slopexx # plot line ax.plot(xx, yy, linestyle='--', linewidth=0.5, color='k') ax.text(xx[2n/3], yy[2*n/3], '{:4.2f}'.format(slope), verticalalignment='top', horizontalalignment='left') ax.set_xlabel('log10(dx)') ax.set_ylabel('log10(L2 error)') ax.set_title(field_str) ref_str = 'ref-' + '-'.join([str(r) for r in ref_list]) order_str = 'o{:}'.format(order) imgfile = '_'.join(['convergence', setup_name, field_str, ref_str, order_str]) imgfile += '.png' img_dir = create_directory('plots') imgfile = os.path.join(img_dir, imgfile) print_output('saving figure {:}'.format(imgfile)) plt.savefig(imgfile, dpi=200, bbox_inches='tight') if expected_slope is not None: err_msg = '{:}: Wrong convergence rate {:.4f}, expected {:.4f}'.format(setup_name, slope, expected_slope) assert abs(slope - expected_slope)/expected_slope < slope_rtol, err_msg print_output('{:}: convergence rate {:.4f} PASSED'.format(setup_name, slope)) else: print_output('{:}: {:} convergence rate {:.4f}'.format(setup_name, field_str, slope)) return slope check_convergence(x_log, y_log_elev, order+1, 'elev', save_plot) check_convergence(x_log, y_log_uv, order+1, 'uv', save_plot) # NOTE nontrivial velocity implies slower convergence # NOTE try time dependent solution: need to update source terms # NOTE using Lax-Friedrichs stabilization in mom. advection term improves convergence of velocity # --------------------------- # standard tests for pytest # --------------------------- @pytest.fixture(params=[setup7, setup8, setup9], ids=["Setup7", "Setup8", "Setup9"]) def setup(request): return request.param @pytest.fixture(params=[ {'element_family': 'dg-dg', 'timestepper_type': 'CrankNicolson'}, {'element_family': 'rt-dg', 'timestepper_type': 'CrankNicolson'}, {'element_family': 'dg-cg', 'timestepper_type': 'CrankNicolson'}], ids=["dg-dg", "rt-dg", "dg-cg"] ) def options(request): return request.param def 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tests/py_tests/test_faint/test_color.py	lukas-ke/faint-graphics-editor	10	190551	<filename>tests/py_tests/test_faint/test_color.py #!/usr/bin/env python3 # -- coding: utf-8 -- import unittest import os from faint import Color class TestColor(unittest.TestCase): """Tests the faint.Color class""" def test_color(self): # Default construction self.assertEqual(Color(), (0,0,0,255)) self.assertNotEqual(Color(), (0,1,0,255)) # Comparison self.assertEqual(Color(1,2,3), Color(1,2,3)) self.assertEqual(Color(1,2,3), Color(1,2,3,255)) # Indexing self.assertEqual(Color(0,1,2,3)[0], 0) self.assertEqual(Color(0,1,2,3)[1], 1) self.assertEqual(Color(0,1,2,3)[2], 2) self.assertEqual(Color(0,1,2,3)[3], 3) # Slicing self.assertEqual(Color(0,1,2,3)[0:0], ()) self.assertEqual(Color(0,1,2,3)[0:1], (0,)) self.assertEqual(Color(0,1,2,3)[0:2], (0,1)) self.assertEqual(Color(0,1,2,3)[:], (0,1,2,3)) self.assertEqual(Color(0,1,2,3)[:-1], (0,1,2)) # Attributes self.assertEqual(Color(0,1,2,3).r, 0) self.assertEqual(Color(0,1,2,3).g, 1) self.assertEqual(Color(0,1,2,3).b, 2) self.assertEqual(Color(0,1,2,3).a, 3)	[ 1, 529, 9507, 29958, 21150, 29914, 2272, 29918, 21150, 29914, 1688, 29918, 29888, 2365, 29914, 1688, 29918, 2780, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 30004, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 30004, 13, 30004, 13, 5215, 443, 27958, 30004, 13, 5215, 2897, 30004, 13, 30004, 13, 3166, 24732, 1053, 9159, 30004, 13, 30004, 13, 1990, 4321, 3306, 29898, 348, 27958, 29889, 3057, 8259, 1125, 30004, 13, 1678, 9995, 24376, 278, 24732, 29889, 3306, 770, 15945, 19451, 13, 30004, 13, 1678, 822, 1243, 29918, 2780, 29898, 1311, 1125, 30004, 13, 4706, 396, 13109, 7632, 30004, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 3285, 313, 29900, 29892, 29900, 29892, 29900, 29892, 29906, 29945, 29945, 876, 30004, 13, 4706, 1583, 29889, 9294, 3664, 9843, 29898, 3306, 3285, 313, 29900, 29892, 29896, 29892, 29900, 29892, 29906, 29945, 29945, 876, 30004, 13, 30004, 13, 4706, 396, 422, 20941, 30004, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29896, 29892, 29906, 29892, 29941, 511, 9159, 29898, 29896, 29892, 29906, 29892, 29941, 876, 30004, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29896, 29892, 29906, 29892, 29941, 511, 9159, 29898, 29896, 29892, 29906, 29892, 29941, 29892, 29906, 29945, 29945, 876, 30004, 13, 30004, 13, 4706, 396, 11374, 292, 30004, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 9601, 29900, 1402, 29871, 29900, 8443, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 9601, 29896, 1402, 29871, 29896, 8443, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 9601, 29906, 1402, 29871, 29906, 8443, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 9601, 29941, 1402, 29871, 29941, 8443, 13, 30004, 13, 4706, 396, 317, 506, 292, 30004, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 9601, 29900, 29901, 29900, 1402, 313, 876, 30004, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 9601, 29900, 29901, 29896, 1402, 313, 29900, 29892, 876, 30004, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 9601, 29900, 29901, 29906, 1402, 313, 29900, 29892, 29896, 876, 30004, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 29897, 7503, 1402, 313, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 876, 30004, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 29897, 7503, 29899, 29896, 1402, 313, 29900, 29892, 29896, 29892, 29906, 876, 30004, 13, 30004, 13, 4706, 396, 6212, 5026, 30004, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 467, 29878, 29892, 29871, 29900, 8443, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 467, 29887, 29892, 29871, 29896, 8443, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 467, 29890, 29892, 29871, 29906, 8443, 13, 4706, 1583, 29889, 9294, 9843, 29898, 3306, 29898, 29900, 29892, 29896, 29892, 29906, 29892, 29941, 467, 29874, 29892, 29871, 29941, 8443, 13, 2 ]
transtool/checks/missing_translations.py	MarcinOrlowski/prop-tool	1	194480	<reponame>MarcinOrlowski/prop-tool<gh_stars>1-10 """ # trans-tool # The translation files checker and syncing tool. # # Copyright ©2021 <NAME> <mail [@] <EMAIL>> # https://github.com/MarcinOrlowski/trans-tool/ # """ from typing import Dict from transtool.decorators.overrides import overrides from transtool.report.group import ReportGroup from .base.check import Check # noinspection PyUnresolvedReferences class MissingTranslations(Check): """ This check checks if given base key is also present in translation file. """ @overrides(Check) # Do NOT "fix" the PropFile reference and do not import it, or you step on circular dependency! def check(self, translation_file: 'PropFile', reference_file: 'PropFile' = None) -> ReportGroup: self.need_valid_config() self.need_both_files(translation_file, reference_file) report = ReportGroup('Missing translations') missing_keys: List[str] = [ref_key for ref_key in reference_file.keys if ref_key not in translation_file.keys] # Commented out keys are also considered present in the translation # unless we run in strict check mode. if not self.config['strict']: for comm_key in translation_file.commented_out_keys: if comm_key in missing_keys: del missing_keys[missing_keys.index(comm_key)] for missing_key in missing_keys: report.warn(None, 'Missing translation.', missing_key) return report @overrides(Check) def get_default_config(self) -> Dict: return { 'strict': False, }	[ 1, 529, 276, 1112, 420, 29958, 29924, 5666, 262, 2816, 677, 2574, 29914, 7728, 29899, 10154, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 15945, 29908, 13, 29937, 1301, 29899, 10154, 13, 29937, 450, 13962, 2066, 1423, 261, 322, 16523, 292, 5780, 29889, 13, 29937, 13, 29937, 14187, 1266, 29871, 30211, 29906, 29900, 29906, 29896, 529, 5813, 29958, 529, 2549, 518, 29992, 29962, 529, 26862, 6227, 6778, 13, 29937, 2045, 597, 3292, 29889, 510, 29914, 29924, 5666, 262, 2816, 677, 2574, 29914, 3286, 29899, 10154, 29914, 13, 29937, 13, 15945, 29908, 13, 13, 3166, 19229, 1053, 360, 919, 13, 3166, 534, 16220, 1507, 29889, 19557, 4097, 29889, 957, 24040, 1053, 975, 24040, 13, 3166, 534, 16220, 1507, 29889, 12276, 29889, 2972, 1053, 13969, 4782, 13, 3166, 869, 3188, 29889, 3198, 1053, 5399, 13, 13, 13, 29937, 694, 1144, 27988, 10772, 2525, 9778, 1490, 1123, 10662, 13, 1990, 4750, 292, 4300, 29880, 800, 29898, 5596, 1125, 13, 1678, 9995, 13, 1678, 910, 1423, 12747, 565, 2183, 2967, 1820, 338, 884, 2198, 297, 13962, 934, 29889, 13, 1678, 9995, 13, 13, 1678, 732, 957, 24040, 29898, 5596, 29897, 13, 1678, 396, 1938, 6058, 376, 5878, 29908, 278, 18264, 2283, 3407, 322, 437, 451, 1053, 372, 29892, 470, 366, 4331, 373, 19308, 10609, 29991, 13, 1678, 822, 1423, 29898, 1311, 29892, 13962, 29918, 1445, 29901, 525, 20420, 2283, 742, 3407, 29918, 1445, 29901, 525, 20420, 2283, 29915, 353, 6213, 29897, 1599, 13969, 4782, 29901, 13, 4706, 1583, 29889, 26180, 29918, 3084, 29918, 2917, 580, 13, 4706, 1583, 29889, 26180, 29918, 20313, 29918, 5325, 29898, 3286, 18411, 29918, 1445, 29892, 3407, 29918, 1445, 29897, 13, 13, 4706, 3461, 353, 13969, 4782, 877, 18552, 292, 5578, 800, 1495, 13, 13, 4706, 4567, 29918, 8149, 29901, 2391, 29961, 710, 29962, 353, 518, 999, 29918, 1989, 363, 2143, 29918, 1989, 297, 3407, 29918, 1445, 29889, 8149, 565, 2143, 29918, 1989, 451, 297, 13962, 29918, 1445, 29889, 8149, 29962, 13, 13, 4706, 396, 461, 287, 714, 6611, 526, 884, 5545, 2198, 297, 278, 13962, 13, 4706, 396, 6521, 591, 1065, 297, 9406, 1423, 4464, 29889, 13, 4706, 565, 451, 1583, 29889, 2917, 1839, 710, 919, 2033, 29901, 13, 9651, 363, 844, 29918, 1989, 297, 13962, 29918, 1445, 29889, 9342, 287, 29918, 449, 29918, 8149, 29901, 13, 18884, 565, 844, 29918, 1989, 297, 4567, 29918, 8149, 29901, 13, 462, 1678, 628, 4567, 29918, 8149, 29961, 27259, 29918, 8149, 29889, 2248, 29898, 2055, 29918, 1989, 4638, 13, 13, 4706, 363, 4567, 29918, 1989, 297, 4567, 29918, 8149, 29901, 13, 9651, 3461, 29889, 25442, 29898, 8516, 29892, 525, 18552, 292, 13962, 29889, 742, 4567, 29918, 1989, 29897, 13, 13, 4706, 736, 3461, 13, 13, 1678, 732, 957, 24040, 29898, 5596, 29897, 13, 1678, 822, 679, 29918, 4381, 29918, 2917, 29898, 1311, 29897, 1599, 360, 919, 29901, 13, 4706, 736, 426, 13, 9651, 525, 710, 919, 2396, 7700, 29892, 13, 4706, 500, 13, 2 ]
coordinator/api/management/commands/fakedata.py	kids-first/kf-api-release-coordinator	2	80688	<gh_stars>1-10 from django.core.management.base import BaseCommand from coordinator.api.factories.release import ReleaseFactory class Command(BaseCommand): help = "Pre-populate the database with fake data" def handle(self, args, *options): ReleaseFactory.create_batch(100) self.stdout.write(self.style.SUCCESS("Created releases"))	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 9557, 29889, 3221, 29889, 21895, 29889, 3188, 1053, 7399, 6255, 13, 3166, 6615, 1061, 29889, 2754, 29889, 17028, 3842, 29889, 14096, 1053, 23708, 5126, 13, 13, 13, 1990, 10516, 29898, 5160, 6255, 1125, 13, 1678, 1371, 353, 376, 6572, 29899, 7323, 5987, 278, 2566, 411, 25713, 848, 29908, 13, 13, 1678, 822, 4386, 29898, 1311, 29892, 334, 5085, 29892, 3579, 6768, 1125, 13, 4706, 23708, 5126, 29889, 3258, 29918, 16175, 29898, 29896, 29900, 29900, 29897, 13, 4706, 1583, 29889, 25393, 29889, 3539, 29898, 1311, 29889, 3293, 29889, 14605, 26925, 703, 20399, 27474, 5783, 13, 2 ]
desafiosCursoEmVideo/ex018.py	gomesGabriel/Pythonicos	1	156246	<filename>desafiosCursoEmVideo/ex018.py import math n1 = float(input('Digite um valor: ')) print('O seno: {:.5f}' .format(math.sin(math.radians(n1)))) print('O cosseno: {:.5f}' .format(math.cos(math.radians(n1)))) print('O tg: {:.5f}' .format(math.tan(math.radians(n1))))	[ 1, 529, 9507, 29958, 2783, 2142, 2363, 23902, 578, 6026, 15167, 29914, 735, 29900, 29896, 29947, 29889, 2272, 13, 5215, 5844, 13, 29876, 29896, 353, 5785, 29898, 2080, 877, 14991, 568, 1922, 16497, 29901, 525, 876, 13, 2158, 877, 29949, 6940, 29877, 29901, 12365, 29889, 29945, 29888, 10162, 869, 4830, 29898, 755, 29889, 5223, 29898, 755, 29889, 3665, 5834, 29898, 29876, 29896, 13697, 13, 2158, 877, 29949, 274, 2209, 8154, 29901, 12365, 29889, 29945, 29888, 10162, 869, 4830, 29898, 755, 29889, 3944, 29898, 755, 29889, 3665, 5834, 29898, 29876, 29896, 13697, 13, 2158, 877, 29949, 260, 29887, 29901, 12365, 29889, 29945, 29888, 10162, 869, 4830, 29898, 755, 29889, 13161, 29898, 755, 29889, 3665, 5834, 29898, 29876, 29896, 13697, 13, 2 ]
updates.py	knowledgetechnologyuhh/hipss	0	16314	import torch import numpy as np import torch.nn.functional as F from torch.nn.utils.clip_grad import clip_grad_norm_ from mpi_utils.mpi_utils import sync_grads def update_entropy(alpha, log_alpha, target_entropy, log_pi, alpha_optim, cfg): if cfg.automatic_entropy_tuning: alpha_loss = -(log_alpha * (log_pi + target_entropy).detach()).mean() alpha_optim.zero_grad() alpha_loss.backward() alpha_optim.step() alpha = log_alpha.exp() alpha_tlogs = alpha.clone() else: alpha_loss = torch.tensor(0.) alpha_tlogs = torch.tensor(alpha) return alpha_loss, alpha_tlogs def update_flat(actor_network, critic_network, critic_target_network, policy_optim, critic_optim, alpha, log_alpha, target_entropy, alpha_optim, obs_norm, ag_norm, g_norm, obs_next_norm, actions, rewards, cfg): inputs_norm = np.concatenate([obs_norm, ag_norm, g_norm], axis=1) inputs_next_norm = np.concatenate([obs_next_norm, ag_norm, g_norm], axis=1) inputs_norm_tensor = torch.tensor(inputs_norm, dtype=torch.float32) inputs_next_norm_tensor = torch.tensor(inputs_next_norm, dtype=torch.float32) actions_tensor = torch.tensor(actions, dtype=torch.float32) r_tensor = torch.tensor(rewards, dtype=torch.float32).reshape(rewards.shape[0], 1) if cfg.cuda: inputs_norm_tensor = inputs_norm_tensor.cuda() inputs_next_norm_tensor = inputs_next_norm_tensor.cuda() actions_tensor = actions_tensor.cuda() r_tensor = r_tensor.cuda() with torch.no_grad(): actions_next, log_pi_next, _ = actor_network.sample(inputs_next_norm_tensor) qf_next_target = critic_target_network(inputs_next_norm_tensor, actions_next) min_qf_next_target = torch.min(qf_next_target, dim=0).values - alpha * log_pi_next next_q_value = r_tensor + cfg.gamma * min_qf_next_target # the q loss qf = critic_network(inputs_norm_tensor, actions_tensor) qf_loss = torch.stack([F.mse_loss(_qf, next_q_value) for _qf in qf]).mean() # the actor loss pi, log_pi, _ = actor_network.sample(inputs_norm_tensor) qf_pi = critic_network(inputs_norm_tensor, pi) min_qf_pi = torch.min(qf_pi, dim=0).values policy_loss = ((alpha * log_pi) - min_qf_pi).mean() # update actor network policy_optim.zero_grad() policy_loss.backward() sync_grads(actor_network) policy_optim.step() # update the critic_network critic_optim.zero_grad() qf_loss.backward() if cfg.clip_grad_norm: clip_grad_norm_(critic_network.parameters(), cfg.max_norm) sync_grads(critic_network) critic_optim.step() alpha_loss, alpha_tlogs = update_entropy(alpha, log_alpha, target_entropy, log_pi, alpha_optim, cfg) train_metrics = dict(q_loss=qf_loss.item(), next_q=next_q_value.mean().item(), policy_loss=policy_loss.item(), alpha_loss=alpha_loss.item(), alpha_tlogs=alpha_tlogs.item()) for idx, (_qf, _qtarget) in enumerate(zip(qf, qf_next_target)): train_metrics[f'q_{idx}'] = _qf.mean().item() train_metrics[f'q_target_{idx}'] = _qtarget.mean().item() return train_metrics def update_language(actor_network, critic_network, critic_target_network, policy_optim, critic_optim, alpha, log_alpha, target_entropy, alpha_optim, obs_norm, instruction, obs_next_norm, actions, rewards, cfg): inputs_norm = obs_norm inputs_next_norm = obs_next_norm inputs_norm_tensor = torch.tensor(inputs_norm, dtype=torch.float32) inputs_next_norm_tensor = torch.tensor(inputs_next_norm, dtype=torch.float32) actions_tensor = torch.tensor(actions, dtype=torch.float32) r_tensor = torch.tensor(rewards, dtype=torch.float32).reshape(rewards.shape[0], 1) instruction_tensor = torch.tensor(instruction, dtype=torch.long) if cfg.cuda: inputs_norm_tensor = inputs_norm_tensor.cuda() inputs_next_norm_tensor = inputs_next_norm_tensor.cuda() actions_tensor = actions_tensor.cuda() r_tensor = r_tensor.cuda() instruction_tensor = instruction_tensor.cuda() with torch.no_grad(): actions_next, log_pi_next, _ = actor_network.sample(inputs_next_norm_tensor, instruction_tensor) qf_next_target = critic_target_network(inputs_next_norm_tensor, actions_next, instruction_tensor) min_qf_next_target = torch.min(qf_next_target, dim=0).values - alpha * log_pi_next next_q_value = r_tensor + cfg.gamma * min_qf_next_target # the q loss qf = critic_network(inputs_norm_tensor, actions_tensor, instruction_tensor) qf_loss = torch.stack([F.mse_loss(_qf, next_q_value) for _qf in qf]).mean() # the actor loss pi, log_pi, _ = actor_network.sample(inputs_norm_tensor, instruction_tensor) qf_pi = critic_network(inputs_norm_tensor, pi, instruction_tensor) min_qf_pi = torch.min(qf_pi, dim=0).values policy_loss = ((alpha * log_pi) - min_qf_pi).mean() # update actor network policy_optim.zero_grad() policy_loss.backward() sync_grads(actor_network) policy_optim.step() # update the critic_network critic_optim.zero_grad() qf_loss.backward() if cfg.clip_grad_norm: clip_grad_norm_(critic_network.parameters(), cfg.max_norm) sync_grads(critic_network) critic_optim.step() alpha_loss, alpha_tlogs = update_entropy(alpha, log_alpha, target_entropy, log_pi, alpha_optim, cfg) train_metrics = dict(q_loss=qf_loss.item(), next_q=next_q_value.mean().item(), policy_loss=policy_loss.item(), alpha_loss=alpha_loss.item(), alpha_tlogs=alpha_tlogs.item()) for idx, (_qf, _qtarget) in enumerate(zip(qf, qf_next_target)): train_metrics[f'q_{idx}'] = _qf.mean().item() train_metrics[f'q_target_{idx}'] = _qtarget.mean().item() return train_metrics	[ 1, 1053, 4842, 305, 13, 5215, 12655, 408, 7442, 13, 5215, 4842, 305, 29889, 15755, 29889, 2220, 284, 408, 383, 13, 3166, 4842, 305, 29889, 15755, 29889, 13239, 29889, 24049, 29918, 5105, 1053, 20102, 29918, 5105, 29918, 12324, 29918, 13, 3166, 286, 1631, 29918, 13239, 29889, 1526, 29875, 29918, 13239, 1053, 16523, 29918, 5105, 29879, 13, 13, 13, 1753, 2767, 29918, 296, 14441, 29898, 2312, 29892, 1480, 29918, 2312, 29892, 3646, 29918, 296, 14441, 29892, 1480, 29918, 1631, 29892, 15595, 29918, 20640, 29892, 274, 16434, 1125, 13, 1678, 565, 274, 16434, 29889, 17405, 2454, 29918, 296, 14441, 29918, 29873, 27964, 29901, 13, 4706, 15595, 29918, 6758, 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search/urls.py	MubongwoNdasi/pms	0	1607683	from django.urls import path from search import views app_name = 'search' urlpatterns = [ path('', views.DrugSearchListView.as_view(), name="list"), path('pharmacist_list', views.PharmacistSearchListView.as_view(), name="pharmacist_list"), ]	[ 1, 515, 9557, 29889, 26045, 1053, 2224, 13, 3166, 2740, 1053, 8386, 13, 13, 13, 932, 29918, 978, 353, 525, 4478, 29915, 13, 13, 2271, 11037, 29879, 353, 518, 13, 1678, 2224, 877, 742, 8386, 29889, 29928, 11124, 7974, 15660, 29889, 294, 29918, 1493, 3285, 1024, 543, 1761, 4968, 13, 1678, 2224, 877, 561, 2817, 562, 391, 29918, 1761, 742, 8386, 29889, 4819, 2817, 562, 391, 7974, 15660, 29889, 294, 29918, 1493, 3285, 13, 308, 1024, 543, 561, 2817, 562, 391, 29918, 1761, 4968, 13, 29962, 13, 2 ]
test.py	zhanghanduo/calc_new	1	41595	#!/usr/bin/env python3 import layers import cv2 from os.path import join import numpy as np # import tensorflow as tf import Augmentor vw = 320 vh = 320 class Augment: def __init__(self): self.w = 2 * 640 self.h = 2 * 480 self.canvas = np.zeros((self.h, self.w, 3), dtype=np.uint8) def update(self, _im): # sc = .4 # h, w = (int(sc * _im.shape[0]), int(sc * _im.shape[1])) vh = _im.shape[0] vw = _im.shape[1] # im = cv2.resize(_im, (w, h)) self.canvas[100:(100 + vh), :vw, :] = _im cv2.putText(self.canvas, "Original", (0, 50), cv2.FONT_HERSHEY_COMPLEX, 1.0, (255, 255, 255)) cv2.putText(self.canvas, "Distorted", (0, 150 + vh), cv2.FONT_HERSHEY_COMPLEX, 1.0, (255, 255, 255)) self.canvas[(200 + vh):(200 + 2 * vh), :vw, :] = _im cv2.imshow("Image Augment", self.canvas) cv2.waitKey(0) if __name__ == "__main__": data_root = "/mnt/4102422c-af52-4b55-988f-df7544b35598/dataset/KITTI/KITTI_Odometry/" seq = "14" vo_fn = data_root + "dataset/poses/" + seq.zfill(2) + ".txt" im_dir = data_root + "dataset/sequences/" + seq.zfill(2) aux_dir = "/home/handuo/projects/paper/image_base/downloads" i = 0 gui = Augment() # with tf.Session() as sess: ims = [] p = Augmentor.Pipeline(join(im_dir, "image_0/"), output_directory="../../../output", save_format="JPEG") # print("Has %s samples." % (len(p.augmentor_images))) p.zoom(probability=0.3, min_factor=0.9, max_factor=1.2) p.skew(probability=0.75, magnitude=0.3) # p.random_erasing(probability=0.5, rectangle_area=0.3) p.multi_erasing(probability=0.5, max_x_axis=0.3, max_y_axis=0.15, max_num=4) # p.rotate(probability=0.5, max_left_rotation=6, max_right_rotation=6) p.sample(10)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 5215, 15359, 13, 5215, 13850, 29906, 13, 3166, 2897, 29889, 2084, 1053, 5988, 13, 5215, 12655, 408, 7442, 13, 29937, 1053, 26110, 408, 15886, 13, 5215, 22333, 358, 272, 13, 13, 29894, 29893, 353, 29871, 29941, 29906, 29900, 13, 29894, 29882, 353, 29871, 29941, 29906, 29900, 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29900, 29900, 5919, 29896, 29900, 29900, 718, 325, 29882, 511, 584, 29894, 29893, 29892, 584, 29962, 353, 903, 326, 13, 13, 4706, 13850, 29906, 29889, 649, 1626, 29898, 1311, 29889, 15257, 29892, 376, 26036, 613, 313, 29900, 29892, 29871, 29945, 29900, 511, 13850, 29906, 29889, 29943, 1164, 29911, 29918, 4448, 7068, 13282, 29918, 21514, 1307, 29990, 29892, 29871, 29896, 29889, 29900, 29892, 313, 29906, 29945, 29945, 29892, 29871, 29906, 29945, 29945, 29892, 29871, 29906, 29945, 29945, 876, 13, 4706, 13850, 29906, 29889, 649, 1626, 29898, 1311, 29889, 15257, 29892, 376, 13398, 18054, 613, 313, 29900, 29892, 29871, 29896, 29945, 29900, 718, 325, 29882, 511, 13850, 29906, 29889, 29943, 1164, 29911, 29918, 4448, 7068, 13282, 29918, 21514, 1307, 29990, 29892, 29871, 29896, 29889, 29900, 29892, 313, 29906, 29945, 29945, 29892, 29871, 29906, 29945, 29945, 29892, 29871, 29906, 29945, 29945, 876, 13, 13, 4706, 1583, 29889, 15257, 15625, 29906, 29900, 29900, 718, 325, 29882, 1125, 29898, 29906, 29900, 29900, 718, 29871, 29906, 334, 325, 29882, 511, 584, 29894, 29893, 29892, 584, 29962, 353, 903, 326, 13, 13, 4706, 13850, 29906, 29889, 326, 4294, 703, 2940, 22333, 358, 613, 1583, 29889, 15257, 29897, 13, 4706, 13850, 29906, 29889, 10685, 2558, 29898, 29900, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 848, 29918, 4632, 353, 5591, 29885, 593, 29914, 29946, 29896, 29900, 29906, 29946, 29906, 29906, 29883, 29899, 2142, 29945, 29906, 29899, 29946, 29890, 29945, 29945, 29899, 29929, 29947, 29947, 29888, 29899, 2176, 29955, 29945, 29946, 29946, 29890, 29941, 29945, 29945, 29929, 29947, 29914, 24713, 29914, 29968, 1806, 24301, 29914, 29968, 1806, 24301, 29918, 29949, 3129, 27184, 12975, 13, 1678, 19359, 353, 376, 29896, 29946, 29908, 13, 1678, 992, 29918, 9144, 353, 848, 29918, 4632, 718, 376, 24713, 29914, 10590, 12975, 718, 19359, 29889, 29920, 5589, 29898, 29906, 29897, 718, 11393, 3945, 29908, 13, 1678, 527, 29918, 3972, 353, 848, 29918, 4632, 718, 376, 24713, 29914, 6831, 2063, 12975, 718, 19359, 29889, 29920, 5589, 29898, 29906, 29897, 13, 13, 1678, 3479, 29918, 3972, 353, 5591, 5184, 29914, 3179, 25608, 29914, 16418, 29914, 19773, 29914, 3027, 29918, 3188, 29914, 10382, 29879, 29908, 13, 13, 1678, 474, 353, 29871, 29900, 13, 1678, 1410, 29875, 353, 22333, 358, 580, 13, 13, 1678, 396, 411, 15886, 29889, 7317, 580, 408, 27937, 29901, 13, 1678, 527, 29879, 353, 5159, 13, 1678, 282, 353, 22333, 358, 272, 29889, 29925, 23828, 29898, 7122, 29898, 326, 29918, 3972, 29892, 376, 3027, 29918, 29900, 29914, 4968, 1962, 29918, 12322, 543, 21546, 6995, 4905, 613, 4078, 29918, 4830, 543, 29967, 4162, 29954, 1159, 13, 1678, 396, 1596, 703, 14510, 1273, 29879, 11916, 1213, 1273, 313, 2435, 29898, 29886, 29889, 2987, 358, 272, 29918, 8346, 4961, 13, 1678, 282, 29889, 2502, 290, 29898, 22795, 3097, 29922, 29900, 29889, 29941, 29892, 1375, 29918, 19790, 29922, 29900, 29889, 29929, 29892, 4236, 29918, 19790, 29922, 29896, 29889, 29906, 29897, 13, 1678, 282, 29889, 26050, 29893, 29898, 22795, 3097, 29922, 29900, 29889, 29955, 29945, 29892, 18497, 29922, 29900, 29889, 29941, 29897, 13, 1678, 396, 282, 29889, 8172, 29918, 261, 5832, 29898, 22795, 3097, 29922, 29900, 29889, 29945, 29892, 16701, 29918, 6203, 29922, 29900, 29889, 29941, 29897, 13, 1678, 282, 29889, 9910, 29918, 261, 5832, 29898, 22795, 3097, 29922, 29900, 29889, 29945, 29892, 4236, 29918, 29916, 29918, 8990, 29922, 29900, 29889, 29941, 29892, 4236, 29918, 29891, 29918, 8990, 29922, 29900, 29889, 29896, 29945, 29892, 4236, 29918, 1949, 29922, 29946, 29897, 13, 1678, 396, 282, 29889, 23361, 29898, 22795, 3097, 29922, 29900, 29889, 29945, 29892, 4236, 29918, 1563, 29918, 5450, 362, 29922, 29953, 29892, 4236, 29918, 1266, 29918, 5450, 362, 29922, 29953, 29897, 13, 1678, 282, 29889, 11249, 29898, 29896, 29900, 29897, 13, 13, 13, 2 ]

InvenTree/build/views.py

mpdgraev/InvenTree

0

152474

""" Django views for interacting with Build objects """ # -*- coding: utf-8 -*- from __future__ import unicode_literals from django.utils.translation import ugettext as _ from django.core.exceptions import ValidationError from django.views.generic import DetailView, ListView, UpdateView from django.forms import HiddenInput from django.urls import reverse from part.models import Part from .models import Build, BuildItem from . import forms from stock.models import StockLocation, StockItem from InvenTree.views import AjaxUpdateView, AjaxCreateView, AjaxDeleteView from InvenTree.views import InvenTreeRoleMixin from InvenTree.helpers import str2bool, ExtractSerialNumbers from InvenTree.status_codes import BuildStatus class BuildIndex(InvenTreeRoleMixin, ListView): """ View for displaying list of Builds """ model = Build template_name = 'build/index.html' context_object_name = 'builds' role_required = 'build.view' def get_queryset(self): """ Return all Build objects (order by date, newest first) """ return Build.objects.order_by('status', '-completion_date') def get_context_data(self, **kwargs): context = super(BuildIndex, self).get_context_data(**kwargs).copy() context['BuildStatus'] = BuildStatus context['active'] = self.get_queryset().filter(status__in=BuildStatus.ACTIVE_CODES) context['completed'] = self.get_queryset().filter(status=BuildStatus.COMPLETE) context['cancelled'] = self.get_queryset().filter(status=BuildStatus.CANCELLED) return context class BuildCancel(AjaxUpdateView): """ View to cancel a Build. Provides a cancellation information dialog """ model = Build ajax_template_name = 'build/cancel.html' ajax_form_title = _('Cancel Build') context_object_name = 'build' form_class = forms.CancelBuildForm role_required = 'build.change' def post(self, request, *args, **kwargs): """ Handle POST request. Mark the build status as CANCELLED """ build = self.get_object() form = self.get_form() valid = form.is_valid() confirm = str2bool(request.POST.get('confirm_cancel', False)) if confirm: build.cancelBuild(request.user) else: form.errors['confirm_cancel'] = [_('Confirm build cancellation')] valid = False data = { 'form_valid': valid, 'danger': _('Build was cancelled') } return self.renderJsonResponse(request, form, data=data) class BuildAutoAllocate(AjaxUpdateView): """ View to auto-allocate parts for a build. Follows a simple set of rules to automatically allocate StockItem objects. Ref: build.models.Build.getAutoAllocations() """ model = Build form_class = forms.ConfirmBuildForm context_object_name = 'build' ajax_form_title = _('Allocate Stock') ajax_template_name = 'build/auto_allocate.html' role_required = 'build.change' def get_context_data(self, *args, **kwargs): """ Get the context data for form rendering. """ context = {} try: build = Build.objects.get(id=self.kwargs['pk']) context['build'] = build context['allocations'] = build.getAutoAllocations() except Build.DoesNotExist: context['error'] = _('No matching build found') return context def post(self, request, *args, **kwargs): """ Handle POST request. Perform auto allocations. - If the form validation passes, perform allocations - Otherwise, the form is passed back to the client """ build = self.get_object() form = self.get_form() confirm = request.POST.get('confirm', False) valid = False if confirm is False: form.errors['confirm'] = [_('Confirm stock allocation')] form.non_field_errors = [_('Check the confirmation box at the bottom of the list')] else: build.autoAllocate() valid = True data = { 'form_valid': valid, } return self.renderJsonResponse(request, form, data, context=self.get_context_data()) class BuildUnallocate(AjaxUpdateView): """ View to un-allocate all parts from a build. Provides a simple confirmation dialog with a BooleanField checkbox. """ model = Build form_class = forms.ConfirmBuildForm ajax_form_title = _("Unallocate Stock") ajax_template_name = "build/unallocate.html" form_required = 'build.change' def post(self, request, *args, **kwargs): build = self.get_object() form = self.get_form() confirm = request.POST.get('confirm', False) valid = False if confirm is False: form.errors['confirm'] = [_('Confirm unallocation of build stock')] form.non_field_errors = [_('Check the confirmation box')] else: build.unallocateStock() valid = True data = { 'form_valid': valid, } return self.renderJsonResponse(request, form, data) class BuildComplete(AjaxUpdateView): """ View to mark a build as Complete. - Notifies the user of which parts will be removed from stock. - Removes allocated items from stock - Deletes pending BuildItem objects """ model = Build form_class = forms.CompleteBuildForm context_object_name = "build" ajax_form_title = _("Complete Build") ajax_template_name = "build/complete.html" role_required = 'build.change' def get_form(self): """ Get the form object. If the part is trackable, include a field for serial numbers. """ build = self.get_object() form = super().get_form() if not build.part.trackable: form.fields.pop('serial_numbers') else: form.field_placeholder['serial_numbers'] = build.part.getSerialNumberString(build.quantity) form.rebuild_layout() return form def get_initial(self): """ Get initial form data for the CompleteBuild form - If the part being built has a default location, pre-select that location """ initials = super(BuildComplete, self).get_initial().copy() build = self.get_object() if build.part.default_location is not None: try: location = StockLocation.objects.get(pk=build.part.default_location.id) initials['location'] = location except StockLocation.DoesNotExist: pass return initials def get_context_data(self, **kwargs): """ Get context data for passing to the rendered form - Build information is required """ build = Build.objects.get(id=self.kwargs['pk']) context = {} # Build object context['build'] = build # Items to be removed from stock taking = BuildItem.objects.filter(build=build.id) context['taking'] = taking return context def post(self, request, *args, **kwargs): """ Handle POST request. Mark the build as COMPLETE - If the form validation passes, the Build objects completeBuild() method is called - Otherwise, the form is passed back to the client """ build = self.get_object() form = self.get_form() confirm = str2bool(request.POST.get('confirm', False)) loc_id = request.POST.get('location', None) valid = False if confirm is False: form.errors['confirm'] = [ _('Confirm completion of build'), ] else: try: location = StockLocation.objects.get(id=loc_id) valid = True except (ValueError, StockLocation.DoesNotExist): form.errors['location'] = [_('Invalid location selected')] serials = [] if build.part.trackable: # A build for a trackable part may optionally specify serial numbers. sn = request.POST.get('serial_numbers', '') sn = str(sn).strip() # If the user has specified serial numbers, check they are valid if len(sn) > 0: try: # Exctract a list of provided serial numbers serials = ExtractSerialNumbers(sn, build.quantity) existing = [] for serial in serials: if build.part.checkIfSerialNumberExists(serial): existing.append(serial) if len(existing) > 0: exists = ",".join([str(x) for x in existing]) form.errors['serial_numbers'] = [_('The following serial numbers already exist: ({sn})'.format(sn=exists))] valid = False except ValidationError as e: form.errors['serial_numbers'] = e.messages valid = False if valid: if not build.completeBuild(location, serials, request.user): form.non_field_errors = [('Build could not be completed')] valid = False data = { 'form_valid': valid, } return self.renderJsonResponse(request, form, data, context=self.get_context_data()) def get_data(self): """ Provide feedback data back to the form """ return { 'info': _('Build marked as COMPLETE') } class BuildNotes(UpdateView): """ View for editing the 'notes' field of a Build object. """ context_object_name = 'build' template_name = 'build/notes.html' model = Build role_required = 'build.view' fields = ['notes'] def get_success_url(self): return reverse('build-notes', kwargs={'pk': self.get_object().id}) def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['editing'] = str2bool(self.request.GET.get('edit', '')) return ctx class BuildDetail(DetailView): """ Detail view of a single Build object. """ model = Build template_name = 'build/detail.html' context_object_name = 'build' role_required = 'build.view' def get_context_data(self, **kwargs): ctx = super(DetailView, self).get_context_data(**kwargs) build = self.get_object() ctx['bom_price'] = build.part.get_price_info(build.quantity, buy=False) ctx['BuildStatus'] = BuildStatus return ctx class BuildAllocate(DetailView): """ View for allocating parts to a Build """ model = Build context_object_name = 'build' template_name = 'build/allocate.html' role_required = ['build.change'] def get_context_data(self, **kwargs): """ Provide extra context information for the Build allocation page """ context = super(DetailView, self).get_context_data(**kwargs) build = self.get_object() part = build.part bom_items = part.bom_items context['part'] = part context['bom_items'] = bom_items context['BuildStatus'] = BuildStatus context['bom_price'] = build.part.get_price_info(build.quantity, buy=False) if str2bool(self.request.GET.get('edit', None)): context['editing'] = True return context class BuildCreate(AjaxCreateView): """ View to create a new Build object """ model = Build context_object_name = 'build' form_class = forms.EditBuildForm ajax_form_title = _('Start new Build') ajax_template_name = 'modal_form.html' role_required = 'build.add' def get_initial(self): """ Get initial parameters for Build creation. If 'part' is specified in the GET query, initialize the Build with the specified Part """ initials = super(BuildCreate, self).get_initial().copy() # User has provided a Part ID initials['part'] = self.request.GET.get('part', None) initials['parent'] = self.request.GET.get('parent', None) # User has provided a SalesOrder ID initials['sales_order'] = self.request.GET.get('sales_order', None) initials['quantity'] = self.request.GET.get('quantity', 1) return initials def get_data(self): return { 'success': _('Created new build'), } class BuildUpdate(AjaxUpdateView): """ View for editing a Build object """ model = Build form_class = forms.EditBuildForm context_object_name = 'build' ajax_form_title = _('Edit Build Details') ajax_template_name = 'modal_form.html' role_required = 'build.change' def get_data(self): return { 'info': _('Edited build'), } class BuildDelete(AjaxDeleteView): """ View to delete a build """ model = Build ajax_template_name = 'build/delete_build.html' ajax_form_title = _('Delete Build') role_required = 'build.delete' class BuildItemDelete(AjaxDeleteView): """ View to 'unallocate' a BuildItem. Really we are deleting the BuildItem object from the database. """ model = BuildItem ajax_template_name = 'build/delete_build_item.html' ajax_form_title = _('Unallocate Stock') context_object_name = 'item' role_required = 'build.delete' def get_data(self): return { 'danger': _('Removed parts from build allocation') } class BuildItemCreate(AjaxCreateView): """ View for allocating a new part to a build """ model = BuildItem form_class = forms.EditBuildItemForm ajax_template_name = 'build/create_build_item.html' ajax_form_title = _('Allocate new Part') role_required = 'build.add' part = None available_stock = None def get_context_data(self): ctx = super(AjaxCreateView, self).get_context_data() if self.part: ctx['part'] = self.part if self.available_stock: ctx['stock'] = self.available_stock else: ctx['no_stock'] = True return ctx def get_form(self): """ Create Form for making / editing new Part object """ form = super(AjaxCreateView, self).get_form() # If the Build object is specified, hide the input field. # We do not want the users to be able to move a BuildItem to a different build build_id = form['build'].value() if build_id is not None: form.fields['build'].widget = HiddenInput() # If the sub_part is supplied, limit to matching stock items part_id = self.get_param('part') if part_id: try: self.part = Part.objects.get(pk=part_id) query = form.fields['stock_item'].queryset # Only allow StockItem objects which match the current part query = query.filter(part=part_id) if build_id is not None: try: build = Build.objects.get(id=build_id) if build.take_from is not None: # Limit query to stock items that are downstream of the 'take_from' location query = query.filter(location__in=[loc for loc in build.take_from.getUniqueChildren()]) except Build.DoesNotExist: pass # Exclude StockItem objects which are already allocated to this build and part query = query.exclude(id__in=[item.stock_item.id for item in BuildItem.objects.filter(build=build_id, stock_item__part=part_id)]) form.fields['stock_item'].queryset = query stocks = query.all() self.available_stock = stocks # If there is only one item selected, select it if len(stocks) == 1: form.fields['stock_item'].initial = stocks[0].id # There is no stock available elif len(stocks) == 0: # TODO - Add a message to the form describing the problem pass except Part.DoesNotExist: self.part = None pass return form def get_initial(self): """ Provide initial data for BomItem. Look for the folllowing in the GET data: - build: pk of the Build object """ initials = super(AjaxCreateView, self).get_initial().copy() build_id = self.get_param('build') part_id = self.get_param('part') # Reference to a Part object part = None # Reference to a StockItem object item = None # Reference to a Build object build = None if part_id: try: part = Part.objects.get(pk=part_id) initials['part'] = part except Part.DoesNotExist: pass if build_id: try: build = Build.objects.get(pk=build_id) initials['build'] = build except Build.DoesNotExist: pass quantity = self.request.GET.get('quantity', None) if quantity is not None: quantity = float(quantity) if quantity is None: # Work out how many parts remain to be alloacted for the build if part: quantity = build.getUnallocatedQuantity(part) item_id = self.get_param('item') # If the request specifies a particular StockItem if item_id: try: item = StockItem.objects.get(pk=item_id) except: pass # If a StockItem is not selected, try to auto-select one if item is None and part is not None: items = StockItem.objects.filter(part=part) if items.count() == 1: item = items.first() # Finally, if a StockItem is selected, ensure the quantity is not too much if item is not None: if quantity is None: quantity = item.unallocated_quantity() else: quantity = min(quantity, item.unallocated_quantity()) if quantity is not None: initials['quantity'] = quantity return initials class BuildItemEdit(AjaxUpdateView): """ View to edit a BuildItem object """ model = BuildItem ajax_template_name = 'modal_form.html' form_class = forms.EditBuildItemForm ajax_form_title = _('Edit Stock Allocation') role_required = 'build.change' def get_data(self): return { 'info': _('Updated Build Item'), } def get_form(self): """ Create form for editing a BuildItem. - Limit the StockItem options to items that match the part """ build_item = self.get_object() form = super(BuildItemEdit, self).get_form() query = StockItem.objects.all() if build_item.stock_item: part_id = build_item.stock_item.part.id query = query.filter(part=part_id) form.fields['stock_item'].queryset = query form.fields['build'].widget = HiddenInput() return form

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tests/test_config.py

dstarner/pacyam

3

139474

<gh_stars>1-10 import os import unittest from pacyam.pacyam import Configuration, BuildException REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) class ConfigurationTestCase(unittest.TestCase): test_configs_root = os.path.join(REPO_ROOT, 'tests', 'test-configs') project_root = os.path.join(REPO_ROOT, 'tests', 'project') def test_invalid_root_dir(self): root = 'totes-invalid-dir' config_path = 'config.json' with self.assertRaises(BuildException): Configuration.load( root_directory=root, config_file_name=config_path, ) def test_missing_config_path(self): root = self.test_configs_root config_path = 'not-available-config.json' with self.assertRaises(BuildException): Configuration.load( root_directory=root, config_file_name=config_path ) def test_invalid_json_file(self): root = self.test_configs_root config_path = 'bad_json.json' with self.assertRaises(BuildException): Configuration.load( root_directory=root, config_file_name=config_path ) def test_unique_config_path(self): root = self.test_configs_root config_path = 'some-unique-name.json' config = Configuration.load( root_directory=root, config_file_name=config_path ) self.assertTrue( os.path.basename(config.config_file_path), config_path ) self.assertEqual(config.root_directory, root) def test_invalid_config(self): root = self.test_configs_root config_path = 'config.json' with self.assertRaises(BuildException): Configuration.load( root_directory=root, config_file_name=config_path ) def test_template_paths(self): root = self.project_root config_path = 'config.json' config = Configuration.load( root_directory=root, config_file_name=config_path ) expected = [ "builders/virtualbox.yaml", "builders/virtualbox.yaml", "post-processors/vagrant.yaml", "provisioners/core.yaml" ] self.assertEqual(config.template_paths, expected) def test_variable_paths(self): root = self.project_root config_path = 'config.json' config = Configuration.load( root_directory=root, config_file_name=config_path ) expected = [ "variables/default.yaml" ] self.assertEqual(config.variable_paths, expected)

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plugins/simsimi.py

messense/wechat-bot

115

1617087

#coding=utf-8 """ Copyright (c) 2012 wong2 <<EMAIL>> 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 sys sys.path.append('..') import requests import cookielib from tornado.options import options __name__ = 'simsimi' SIMSIMI_KEY = options.simsimi_key or '' class SimSimi(object): def __init__(self): self.headers = { 'Referer': 'http://www.simsimi.com/talk.htm' } self.chat_url = 'http://www.simsimi.com/func/req?lc=ch&msg=%s' self.api_url = 'http://api.simsimi.com/request.p?key=%s&lc=ch&ft=1.0&text=%s' if not SIMSIMI_KEY: self.initSimSimiCookie() def initSimSimiCookie(self): r = requests.get('http://www.simsimi.com/talk.htm') self.chat_cookies = r.cookies def getSimSimiResult(self, message, method='normal'): if method == 'normal': r = requests.get(self.chat_url % message, cookies=self.chat_cookies, headers=self.headers) self.chat_cookies = r.cookies else: url = self.api_url % (SIMSIMI_KEY, message) r = requests.get(url) return r def chat(self, message=''): if message: r = self.getSimSimiResult( message, 'normal' if not SIMSIMI_KEY else 'api') try: answer = r.json()['response'] return answer.encode('utf-8') except: return u'呵呵' else: return u'叫我干嘛' simsimi = SimSimi() def test(data, msg=None, bot=None): return True def respond(data, msg=None, bot=None): response = simsimi.chat(data) if "Unauthorized access" in response: # try again response = simsimi.chat(data) response = response.replace('xiaohuangji', options.username) response = response.replace('小黄鸡', '我') if "Unauthorized access" in response: # still can't , give up response = u'矮油，这个问题我暂时回答不了喵~' if u'微信' in response and u'搜' in response: # remove some ads response = u'呵呵' return response

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train.py

JanFschr/gangealing

2

132801

<filename>train.py """ GANgealing training script. """ import torch from torch import nn, optim import numpy as np from tqdm import tqdm import json import os from models import Generator, get_stn, DirectionInterpolator, PCA, get_perceptual_loss, kmeans_plusplus, BilinearDownsample, accumulate, requires_grad from models import gangealing_loss, gangealing_cluster_loss, total_variation_loss, flow_identity_loss from datasets import img_dataloader, sample_infinite_data from utils.vis_tools.training_vis import GANgealingWriter, create_training_visuals, create_training_cluster_visuals from utils.distributed import all_gather, get_rank, setup_distributed, reduce_loss_dict, get_world_size, primary from utils.base_argparse import base_training_argparse from utils.annealing import DecayingCosineAnnealingWarmRestarts, lr_cycle_iters, get_psi_annealing_fn from utils.download import find_model def save_state_dict(ckpt_name, generator, t_module, t_ema, t_optim, t_sched, ll_module, ll_optim, ll_sched, args): ckpt_dict = {"g_ema": generator.state_dict(), "t": t_module.state_dict(), "t_ema": t_ema.state_dict(), "t_optim": t_optim.state_dict(), "t_sched": t_sched.state_dict(), "ll": ll_module.state_dict(), "ll_optim": ll_optim.state_dict(), "ll_sched": ll_sched.state_dict(), "args": args} torch.save(ckpt_dict, f'{results_path}/checkpoints/{ckpt_name}.pt') def train(args, loader, generator, stn, t_ema, ll, t_optim, ll_optim, t_sched, ll_sched, loss_fn, anneal_fn, device, writer): # If using real data, select some fixed samples used to visualize training: vis_reals = loader is not None if vis_reals: if args.random_reals: real_indices = torch.randint(0, len(loader.dataset), (args.n_sample,)).numpy() else: real_indices = range(args.n_sample) sample_reals = torch.stack([loader.dataset[ix] for ix in real_indices]).to(device) loader = sample_infinite_data(loader, args.seed) else: sample_reals = None # Progress bar for monitoring training: pbar = range(args.iter) if primary(): pbar = tqdm(pbar, initial=args.start_iter, dynamic_ncols=True, smoothing=0.2) # Record modules to make saving checkpoints easier: if args.distributed: t_module = stn.module ll_module = ll.module else: t_module = stn ll_module = ll sample_z = torch.randn(args.n_sample // args.num_heads, args.dim_latent, device=device) # Used for generating a fixed set of GAN samples if args.clustering: # A much larger fixed set of GAN samples used for generating clustering-specific visuals: big_sample_z = torch.randn(args.n_mean // get_world_size(), args.dim_latent, device=device) resize_fake2stn = BilinearDownsample(args.gen_size // args.flow_size, 3).to(device) if args.gen_size > args.flow_size else nn.Sequential() generator.eval() requires_grad(generator, False) # G is frozen throughout this entire process requires_grad(stn, True) requires_grad(ll, True) # A model checkpoint will be saved whenever the learning rate is zero: zero_lr_iters = lr_cycle_iters(args.anneal_psi, args.period, args.iter, args.tm) early_ckpt_iters = set(zero_lr_iters) early_vis_iters = {100} early_vis_iters.update(early_ckpt_iters) # Initialize various training variables and constants: zero = torch.tensor(0.0, device='cuda') accum = 0.5 ** (32 / (10 * 1000)) psi = 1.0 # initially there is no truncation # Create initial training visualizations: if args.clustering: create_training_cluster_visuals(generator, t_ema, ll, loss_fn, loader, resize_fake2stn, sample_z, big_sample_z, psi, device, args.n_mean, args.n_sample, args.num_heads, args.flips, args.vis_batch_size, args.flow_size, 0, writer, padding_mode=args.padding_mode) else: create_training_visuals(generator, t_ema, ll, loader, sample_reals, resize_fake2stn, sample_z, psi, device, args.n_mean, args.n_sample, 0, writer, padding_mode=args.padding_mode) for idx in pbar: # main training loop i = idx + args.start_iter + 1 if i <= args.anneal_psi: psi = anneal_fn(i, 1.0, 0.0, args.anneal_psi).item() psi_is_fixed = False else: psi = 0.0 psi_is_fixed = True if i > args.iter: print("Done!") break #################################### ######### TRAIN STN and LL ######### #################################### if args.clustering or args.flips: # Clustering-specific perceptual loss: perceptual_loss, delta_flow = gangealing_cluster_loss(generator, stn, ll, loss_fn, resize_fake2stn, psi, args.batch, args.dim_latent, args.freeze_ll, args.num_heads, args.flips, device, sample_from_full_res=args.sample_from_full_res, padding_mode=args.padding_mode) else: # Standard GANgealing perceptual loss (unimodal): perceptual_loss, delta_flow = gangealing_loss(generator, stn, ll, loss_fn, resize_fake2stn, psi, args.batch, args.dim_latent, args.freeze_ll, device, sample_from_full_res=args.sample_from_full_res, padding_mode=args.padding_mode) tv_loss = total_variation_loss(delta_flow) if args.tv_weight > 0 else zero flow_idty_loss = flow_identity_loss(delta_flow) if args.flow_identity_weight > 0 else zero loss_dict = {} loss_dict["p"] = perceptual_loss loss_dict["tv"] = tv_loss loss_dict["f"] = flow_idty_loss stn.zero_grad() ll.zero_grad() full_stn_loss = perceptual_loss + args.tv_weight * tv_loss + args.flow_identity_weight * flow_idty_loss full_stn_loss.backward() t_optim.step() if not args.freeze_ll: ll_optim.step() if psi_is_fixed: # Step learning rate schedulers once psi has been fully-annealed to zero: epoch = max(0, (i - args.anneal_psi) / args.period) t_sched.step(epoch) ll_sched.step(epoch) accumulate(t_ema, t_module, accum) loss_reduced = reduce_loss_dict(loss_dict) # Aggregate loss information across GPUs if primary(): # Display losses on the progress bar: perceptual_loss_val = loss_reduced["p"].mean().item() tv_loss_val = loss_reduced["tv"].mean().item() flow_idty_loss_val = loss_reduced["f"].mean().item() f_str = f"identity loss: {flow_idty_loss_val:.4f}; " if args.flow_identity_weight > 0 else "" tv_str = f"tv loss: {tv_loss_val:.6f}; " if args.tv_weight > 0 else "" pbar.set_description(f"perceptual loss: {perceptual_loss_val:.4f}; {tv_str}{f_str}psi: {psi:.4f}") # Log losses and others metrics to TensorBoard: if i % args.log_every == 0 or i in early_ckpt_iters: writer.add_scalar('Loss/Reconstruction', perceptual_loss_val, i) writer.add_scalar('Loss/TotalVariation', tv_loss_val, i) writer.add_scalar('Loss/FlowIdentity', flow_idty_loss_val, i) writer.add_scalar('Progress/psi', psi, i) writer.add_scalar('Progress/LL_LearningRate', ll_sched.get_last_lr()[0], i) writer.add_scalar('Progress/STN_LearningRate', t_sched.get_last_lr()[0], i) if i % args.ckpt_every == 0 or i in early_ckpt_iters: # Save model checkpoint save_state_dict(str(i).zfill(7), generator, t_module, t_ema, t_optim, t_sched, ll_module, ll_optim, ll_sched, args) if i % args.vis_every == 0 or i in early_vis_iters: # Save visualizations to TensorBoard if primary() and i in early_ckpt_iters: pbar.write(f'{i:07}: Learning Rate = {t_sched.get_last_lr()[0]}') if args.clustering: create_training_cluster_visuals(generator, t_ema, ll, loss_fn, loader, resize_fake2stn, sample_z, big_sample_z, psi, device, args.n_mean, args.n_sample, args.num_heads, args.flips, args.vis_batch_size, args.flow_size, i, writer, padding_mode=args.padding_mode) else: create_training_visuals(generator, t_ema, ll, loader, sample_reals, resize_fake2stn, sample_z, psi, device, args.n_mean, args.n_sample, i, writer, padding_mode=args.padding_mode) if __name__ == "__main__": device = "cuda" parser = base_training_argparse() args = parser.parse_args() if args.transform == 'similarity': assert args.tv_weight == 0, 'Total Variation loss is not currently supported for similarity-only STNs' args.n_mean = 200 if args.debug else args.n_mean args.vis_batch_size //= args.num_heads # Keep visualization batch size reasonable for clustering models # Setup distributed PyTorch and create results directory: args.distributed = setup_distributed(args.local_rank) args.clustering = args.num_heads > 1 results_path = os.path.join(args.results, args.exp_name) if primary(): writer = GANgealingWriter(results_path) with open(f'{results_path}/opt.txt', 'w') as f: json.dump(args.__dict__, f, indent=2) else: writer = None # Seed RNG: torch.manual_seed(args.seed * get_world_size() + get_rank()) np.random.seed(args.seed * get_world_size() + get_rank()) # Initialize models: generator = Generator(args.gen_size, args.dim_latent, args.n_mlp, channel_multiplier=args.gen_channel_multiplier, num_fp16_res=args.num_fp16_res).to(device) stn = get_stn(args.transform, flow_size=args.flow_size, supersize=args.real_size, channel_multiplier=args.stn_channel_multiplier, num_heads=args.num_heads).to(device) t_ema = get_stn(args.transform, flow_size=args.flow_size, supersize=args.real_size, channel_multiplier=args.stn_channel_multiplier, num_heads=args.num_heads).to(device) ll = DirectionInterpolator(pca_path=None, n_comps=args.ndirs, inject_index=args.inject, n_latent=generator.n_latent, num_heads=args.num_heads).to(device) accumulate(t_ema, stn, 0) # Setup optimizers and learning rate schedulers: t_optim = optim.Adam(stn.parameters(), lr=args.stn_lr, betas=(0.9, 0.999), eps=1e-8) ll_optim = optim.Adam(ll.parameters(), lr=args.ll_lr, betas=(0.9, 0.999), eps=1e-8) t_sched = DecayingCosineAnnealingWarmRestarts(t_optim, T_0=1, T_mult=args.tm, decay=args.decay) ll_sched = DecayingCosineAnnealingWarmRestarts(ll_optim, T_0=1, T_mult=args.tm, decay=args.decay) # Setup the perceptual loss function: loss_fn = get_perceptual_loss(args.loss_fn, device) # Get the function used to anneal psi: anneal_fn = get_psi_annealing_fn(args.anneal_fn) # Load pre-trained generator (and optionally resume from a GANgealing checkpoint): print(f"Loading model from {args.ckpt}") ckpt, _ = find_model(args.ckpt) generator.load_state_dict(ckpt["g_ema"]) # NOTE: We load g_ema as generator since G is frozen! try: # Restore from full checkpoint, including the optimizer if args.load_G_only: # Don't attempt to load GANgealing checkpoint; jump straight to the except block raise KeyError # This doesn't actually raise an error stn.load_state_dict(ckpt["t"]) t_ema.load_state_dict(ckpt["t_ema"]) t_optim.load_state_dict(ckpt["t_optim"]) t_sched.load_state_dict(ckpt["t_sched"]) ll.load_state_dict(ckpt["ll"]) ll_optim.load_state_dict(ckpt["ll_optim"]) ll_sched.load_state_dict(ckpt["ll_sched"]) except KeyError: # Initialize the target mode c (ll) print('Only G_EMA has been loaded from checkpoint. Other nets are random!') n_pca = 1000 if args.debug else 1000000 with torch.no_grad(): batch_w = generator.batch_latent(n_pca // get_world_size()) batch_w = all_gather(batch_w) pca = PCA(args.ndirs, batch_w) ll.assign_buffers(pca) if args.clustering: # For clustering models, initialize using K-Means++ on W-Space print('Running K-Means++ Initialization') if args.debug: centroids = generator.batch_latent(args.num_heads).detach().requires_grad_(False) else: centroids = kmeans_plusplus(args.num_heads, 50000, generator, loss_fn, args.inject) decomposed = pca.encode(centroids) ll.assign_coefficients(decomposed) # See if the start iteration can be recovered when resuming training: args.start_iter = 0 try: ckpt_name = os.path.basename(args.ckpt) if ckpt_name.startswith('best_'): ckpt_name = ckpt_name[5:] # Remove prefix args.start_iter = int(os.path.splitext(ckpt_name)[0]) except ValueError: pass # Move models to DDP if distributed training is enabled: if args.distributed: stn = nn.parallel.DistributedDataParallel(stn, device_ids=[args.local_rank], output_device=args.local_rank, broadcast_buffers=False) ll = nn.parallel.DistributedDataParallel(ll, device_ids=[args.local_rank], output_device=args.local_rank, broadcast_buffers=False) # Setup real data for visualizations (optional): loader = img_dataloader(args.real_data_path, shuffle=False, batch_size=args.vis_batch_size, resolution=args.real_size, infinite=False) if args.real_data_path is not None else None # Begin training: train(args, loader, generator, stn, t_ema, ll, t_optim, ll_optim, t_sched, ll_sched, loss_fn, anneal_fn, device, writer)

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pygame/PygameSuperMario/source/setup.py

Tom-Li1/py_modules

0

66309

<filename>pygame/PygameSuperMario/source/setup.py<gh_stars>0 import pygame from . import constants as C from . import tools pygame.init() SCREEN = pygame.display.set_mode(C.SCREEN_SIZE) GRAPHICS = tools.load_graphics(r'resources/graphics')

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unifier/apps/core/migrations/0010_auto_20210225_0227.py

sosolidkk/manga-unifier

6

151580

<reponame>sosolidkk/manga-unifier<gh_stars>1-10 # Generated by Django 3.1.7 on 2021-02-25 02:27 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('core', '0009_auto_20210223_0124'), ] operations = [ migrations.AddField( model_name='mangachapter', name='images', field=models.JSONField(default=list, verbose_name='Images'), ), migrations.DeleteModel( name='Image', ), ]

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demisto_sdk/commands/lint/linter.py

nericksen/demisto-sdk

0

77399

# STD python packages import copy import hashlib import logging import os import platform import traceback from typing import Any, Dict, List, Optional, Tuple import docker import docker.errors import docker.models.containers import git import requests.exceptions import urllib3.exceptions from packaging.version import parse from wcmatch.pathlib import NEGATE, Path from demisto_sdk.commands.common.constants import (INTEGRATIONS_DIR, PACKS_PACK_META_FILE_NAME, TYPE_PWSH, TYPE_PYTHON) from demisto_sdk.commands.common.handlers import JSON_Handler, YAML_Handler from demisto_sdk.commands.common.timers import timer from demisto_sdk.commands.common.tools import (get_all_docker_images, run_command_os) from demisto_sdk.commands.lint.commands_builder import ( build_bandit_command, build_flake8_command, build_mypy_command, build_pwsh_analyze_command, build_pwsh_test_command, build_pylint_command, build_pytest_command, build_vulture_command, build_xsoar_linter_command) from demisto_sdk.commands.lint.docker_helper import Docker from demisto_sdk.commands.lint.helpers import (EXIT_CODES, FAIL, RERUN, RL, SUCCESS, WARNING, add_tmp_lint_files, add_typing_module, coverage_report_editor, get_file_from_container, get_python_version_from_image, pylint_plugin, split_warnings_errors, stream_docker_container_output) json = JSON_Handler() # 3-rd party packages # Local packages logger = logging.getLogger('demisto-sdk') class Linter: """ Linter used to activate lint command on single package Attributes: pack_dir(Path): Pack to run lint on. content_repo(Path): Git repo object of content repo. req_2(list): requirements for docker using python2. req_3(list): requirements for docker using python3. docker_engine(bool): Whether docker engine detected by docker-sdk. """ def __init__(self, pack_dir: Path, content_repo: Path, req_3: list, req_2: list, docker_engine: bool, docker_timeout: int): self._req_3 = req_3 self._req_2 = req_2 self._content_repo = content_repo self._pack_abs_dir = pack_dir self._pack_name = None self.docker_timeout = docker_timeout # Docker client init if docker_engine: self._docker_client: docker.DockerClient = docker.from_env(timeout=docker_timeout) self._docker_hub_login = self._docker_login() # Facts gathered regarding pack lint and test self._facts: Dict[str, Any] = { "images": [], "python_version": 0, "env_vars": {}, "test": False, "lint_files": [], "support_level": None, "is_long_running": False, "lint_unittest_files": [], "additional_requirements": [], "docker_engine": docker_engine, "is_script": False, "commands": None, } # Pack lint status object - visualize it self._pkg_lint_status: Dict = { "pkg": None, "pack_type": None, "path": str(self._content_repo), "errors": [], "images": [], "flake8_errors": None, "XSOAR_linter_errors": None, "bandit_errors": None, "mypy_errors": None, "vulture_errors": None, "flake8_warnings": None, "XSOAR_linter_warnings": None, "bandit_warnings": None, "mypy_warnings": None, "vulture_warnings": None, "exit_code": SUCCESS, "warning_code": SUCCESS, } @timer(group_name='lint') def run_dev_packages(self, no_flake8: bool, no_bandit: bool, no_mypy: bool, no_pylint: bool, no_vulture: bool, no_xsoar_linter: bool, no_pwsh_analyze: bool, no_pwsh_test: bool, no_test: bool, modules: dict, keep_container: bool, test_xml: str, no_coverage: bool) -> dict: """ Run lint and tests on single package Performing the follow: 1. Run the lint on OS - flake8, bandit, mypy. 2. Run in package docker - pylint, pytest. Args: no_flake8(bool): Whether to skip flake8 no_bandit(bool): Whether to skip bandit no_mypy(bool): Whether to skip mypy no_vulture(bool): Whether to skip vulture no_pylint(bool): Whether to skip pylint no_test(bool): Whether to skip pytest no_pwsh_analyze(bool): Whether to skip powershell code analyzing no_pwsh_test(bool): whether to skip powershell tests modules(dict): Mandatory modules to locate in pack path (CommonServerPython.py etc) keep_container(bool): Whether to keep the test container test_xml(str): Path for saving pytest xml results no_coverage(bool): Run pytest without coverage report Returns: dict: lint and test all status, pkg status) """ # Gather information for lint check information skip = self._gather_facts(modules) # If not python pack - skip pack if skip: return self._pkg_lint_status try: # Locate mandatory files in pack path - for more info checkout the context manager LintFiles with add_tmp_lint_files(content_repo=self._content_repo, # type: ignore pack_path=self._pack_abs_dir, lint_files=self._facts["lint_files"], modules=modules, pack_type=self._pkg_lint_status["pack_type"]): # Run lint check on host - flake8, bandit, mypy if self._pkg_lint_status["pack_type"] == TYPE_PYTHON: self._run_lint_in_host(no_flake8=no_flake8, no_bandit=no_bandit, no_mypy=no_mypy, no_vulture=no_vulture, no_xsoar_linter=no_xsoar_linter) # Run lint and test check on pack docker image if self._facts["docker_engine"]: self._run_lint_on_docker_image(no_pylint=no_pylint, no_test=no_test, no_pwsh_analyze=no_pwsh_analyze, no_pwsh_test=no_pwsh_test, keep_container=keep_container, test_xml=test_xml, no_coverage=no_coverage) except Exception as ex: err = f'{self._pack_abs_dir}: Unexpected fatal exception: {str(ex)}' logger.error(f"{err}. Traceback: {traceback.format_exc()}") self._pkg_lint_status["errors"].append(err) self._pkg_lint_status['exit_code'] += FAIL return self._pkg_lint_status @timer(group_name='lint') def _gather_facts(self, modules: dict) -> bool: """ Gathering facts about the package - python version, docker images, valid docker image, yml parsing Args: modules(dict): Test mandatory modules to be ignore in lint check Returns: bool: Indicating if to continue further or not, if False exit Thread, Else continue. """ # Looking for pkg yaml yml_file: Optional[Path] = self._pack_abs_dir.glob([r'*.yaml', r'*.yml', r'!*unified*.yml'], flags=NEGATE) if not yml_file: logger.info(f"{self._pack_abs_dir} - Skipping no yaml file found {yml_file}") self._pkg_lint_status["errors"].append('Unable to find yml file in package') return True else: try: yml_file = next(yml_file) except StopIteration: return True # Get pack name self._pack_name = yml_file.stem log_prompt = f"{self._pack_name} - Facts" self._pkg_lint_status["pkg"] = yml_file.stem logger.info(f"{log_prompt} - Using yaml file {yml_file}") # Parsing pack yaml - in order to verify if check needed try: script_obj: Dict = {} yml_obj: Dict = YAML_Handler().load(yml_file) if isinstance(yml_obj, dict): script_obj = yml_obj.get('script', {}) if isinstance(yml_obj.get('script'), dict) else yml_obj self._facts['is_script'] = True if 'Scripts' in yml_file.parts else False self._facts['is_long_running'] = script_obj.get('longRunning') self._facts['commands'] = self._get_commands_list(script_obj) self._pkg_lint_status["pack_type"] = script_obj.get('type') except (FileNotFoundError, IOError, KeyError): self._pkg_lint_status["errors"].append('Unable to parse package yml') return True # return no check needed if not python pack if self._pkg_lint_status["pack_type"] not in (TYPE_PYTHON, TYPE_PWSH): logger.info(f"{log_prompt} - Skipping due to not Python, Powershell package - Pack is" f" {self._pkg_lint_status['pack_type']}") return True # Docker images if self._facts["docker_engine"]: logger.info(f"{log_prompt} - Pulling docker images, can take up to 1-2 minutes if not exists locally ") self._facts["images"] = [[image, -1] for image in get_all_docker_images(script_obj=script_obj)] # Gather environment variables for docker execution self._facts["env_vars"] = { "CI": os.getenv("CI", False), "DEMISTO_LINT_UPDATE_CERTS": os.getenv('DEMISTO_LINT_UPDATE_CERTS', "yes") } lint_files = set() # Facts for python pack if self._pkg_lint_status["pack_type"] == TYPE_PYTHON: self._update_support_level() if self._facts["docker_engine"]: # Getting python version from docker image - verifying if not valid docker image configured for image in self._facts["images"]: py_num: str = get_python_version_from_image(image=image[0], timeout=self.docker_timeout) image[1] = py_num logger.info(f"{self._pack_name} - Facts - {image[0]} - Python {py_num}") if not self._facts["python_version"]: self._facts["python_version"] = py_num # Checking whatever *test* exists in package self._facts["test"] = True if next(self._pack_abs_dir.glob([r'test_*.py', r'*_test.py']), None) else False if self._facts["test"]: logger.info(f"{log_prompt} - Tests found") else: logger.info(f"{log_prompt} - Tests not found") # Gather package requirements embedded test-requirements.py file test_requirements = self._pack_abs_dir / 'test-requirements.txt' if test_requirements.exists(): try: additional_req = test_requirements.read_text(encoding='utf-8').strip().split('\n') self._facts["additional_requirements"].extend(additional_req) logger.info(f"{log_prompt} - Additional package Pypi packages found - {additional_req}") except (FileNotFoundError, IOError): self._pkg_lint_status["errors"].append('Unable to parse test-requirements.txt in package') elif not self._facts["python_version"]: # get python version from yml pynum = '3.7' if (script_obj.get('subtype', 'python3') == 'python3') else '2.7' self._facts["python_version"] = pynum logger.info(f"{log_prompt} - Using python version from yml: {pynum}") # Get lint files lint_files = set(self._pack_abs_dir.glob(["*.py", "!__init__.py", "!*.tmp"], flags=NEGATE)) # Facts for Powershell pack elif self._pkg_lint_status["pack_type"] == TYPE_PWSH: # Get lint files lint_files = set( self._pack_abs_dir.glob(["*.ps1", "!*Tests.ps1", "CommonServerPowerShell.ps1", "demistomock.ps1'"], flags=NEGATE)) # Add CommonServer to the lint checks if 'commonserver' in self._pack_abs_dir.name.lower(): # Powershell if self._pkg_lint_status["pack_type"] == TYPE_PWSH: self._facts["lint_files"] = [Path(self._pack_abs_dir / 'CommonServerPowerShell.ps1')] # Python elif self._pkg_lint_status["pack_type"] == TYPE_PYTHON: self._facts["lint_files"] = [Path(self._pack_abs_dir / 'CommonServerPython.py')] else: test_modules = {self._pack_abs_dir / module.name for module in modules.keys()} lint_files = lint_files.difference(test_modules) self._facts["lint_files"] = list(lint_files) if self._facts["lint_files"]: self._remove_gitignore_files(log_prompt) for lint_file in self._facts["lint_files"]: logger.info(f"{log_prompt} - Lint file {lint_file}") else: logger.info(f"{log_prompt} - Lint files not found") # Remove files that are in gitignore self._split_lint_files() return False def _remove_gitignore_files(self, log_prompt: str) -> None: """ Skipping files that matches gitignore patterns. Args: log_prompt(str): log prompt string Returns: """ try: repo = git.Repo(self._content_repo) files_to_ignore = repo.ignored(self._facts['lint_files']) for file in files_to_ignore: logger.info(f"{log_prompt} - Skipping gitignore file {file}") self._facts["lint_files"] = [path for path in self._facts['lint_files'] if path not in files_to_ignore] except (git.InvalidGitRepositoryError, git.NoSuchPathError): logger.debug("No gitignore files is available") def _split_lint_files(self): """ Remove unit test files from _facts['lint_files'] and put into their own list _facts['lint_unittest_files'] This is because not all lints should be done on unittest files. """ lint_files_list = copy.deepcopy(self._facts["lint_files"]) for lint_file in lint_files_list: if lint_file.name.startswith('test_') or lint_file.name.endswith('_test.py'): self._facts['lint_unittest_files'].append(lint_file) self._facts["lint_files"].remove(lint_file) @timer(group_name='lint') def _run_lint_in_host(self, no_flake8: bool, no_bandit: bool, no_mypy: bool, no_vulture: bool, no_xsoar_linter: bool): """ Run lint check on host Args: no_flake8(bool): Whether to skip flake8. no_bandit(bool): Whether to skip bandit. no_mypy(bool): Whether to skip mypy. no_vulture(bool): Whether to skip Vulture. """ warning = [] error = [] other = [] exit_code: int = 0 for lint_check in ["flake8", "XSOAR_linter", "bandit", "mypy", "vulture"]: exit_code = SUCCESS output = "" if self._facts["lint_files"] or self._facts["lint_unittest_files"]: if lint_check == "flake8" and not no_flake8: flake8_lint_files = copy.deepcopy(self._facts["lint_files"]) # if there are unittest.py then we would run flake8 on them too. if self._facts['lint_unittest_files']: flake8_lint_files.extend(self._facts['lint_unittest_files']) exit_code, output = self._run_flake8(py_num=self._facts["python_version"], lint_files=flake8_lint_files) if self._facts["lint_files"]: if lint_check == "XSOAR_linter" and not no_xsoar_linter: exit_code, output = self._run_xsoar_linter(py_num=self._facts["python_version"], lint_files=self._facts["lint_files"]) elif lint_check == "bandit" and not no_bandit: exit_code, output = self._run_bandit(lint_files=self._facts["lint_files"]) elif lint_check == "mypy" and not no_mypy: exit_code, output = self._run_mypy(py_num=self._facts["python_version"], lint_files=self._facts["lint_files"]) elif lint_check == "vulture" and not no_vulture: exit_code, output = self._run_vulture(py_num=self._facts["python_version"], lint_files=self._facts["lint_files"]) # check for any exit code other than 0 if exit_code: error, warning, other = split_warnings_errors(output) if exit_code and warning: self._pkg_lint_status["warning_code"] |= EXIT_CODES[lint_check] self._pkg_lint_status[f"{lint_check}_warnings"] = "\n".join(warning) if exit_code & FAIL: self._pkg_lint_status["exit_code"] |= EXIT_CODES[lint_check] # if the error were extracted correctly as they start with E if error: self._pkg_lint_status[f"{lint_check}_errors"] = "\n".join(error) # if there were errors but they do not start with E else: self._pkg_lint_status[f"{lint_check}_errors"] = "\n".join(other) @timer(group_name='lint') def _run_flake8(self, py_num: str, lint_files: List[Path]) -> Tuple[int, str]: """ Runs flake8 in pack dir Args: py_num(str): The python version in use lint_files(List[Path]): file to perform lint Returns: int: 0 on successful else 1, errors str: Bandit errors """ log_prompt = f"{self._pack_name} - Flake8" logger.info(f"{log_prompt} - Start") stdout, stderr, exit_code = run_command_os(command=build_flake8_command(lint_files, py_num), cwd=self._content_repo) logger.debug(f"{log_prompt} - Finished, exit-code: {exit_code}") logger.debug(f"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}") logger.debug(f"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}") if stderr or exit_code: logger.error(f"{log_prompt}- Finished, errors found") if stderr: return FAIL, stderr else: return FAIL, stdout logger.info(f"{log_prompt} - Successfully finished") return SUCCESS, "" @timer(group_name='lint') def _run_xsoar_linter(self, py_num: str, lint_files: List[Path]) -> Tuple[int, str]: """ Runs Xsaor linter in pack dir Args: lint_files(List[Path]): file to perform lint Returns: int: 0 on successful else 1, errors str: Xsoar linter errors """ status = SUCCESS FAIL_PYLINT = 0b10 with pylint_plugin(self._pack_abs_dir): log_prompt = f"{self._pack_name} - XSOAR Linter" logger.info(f"{log_prompt} - Start") myenv = os.environ.copy() if myenv.get('PYTHONPATH'): myenv['PYTHONPATH'] += ':' + str(self._pack_abs_dir) else: myenv['PYTHONPATH'] = str(self._pack_abs_dir) if self._facts['is_long_running']: myenv['LONGRUNNING'] = 'True' py_ver = parse(py_num).major if py_ver < 3: myenv['PY2'] = 'True' myenv['is_script'] = str(self._facts['is_script']) # as Xsoar checker is a pylint plugin and runs as part of pylint code, we can not pass args to it. # as a result we can use the env vars as a getway. myenv['commands'] = ','.join([str(elem) for elem in self._facts['commands']]) \ if self._facts['commands'] else '' stdout, stderr, exit_code = run_command_os( command=build_xsoar_linter_command(lint_files, py_num, self._facts.get('support_level', 'base')), cwd=self._pack_abs_dir, env=myenv) if exit_code & FAIL_PYLINT: logger.error(f"{log_prompt}- Finished, errors found") status = FAIL if exit_code & WARNING: logger.warning(f"{log_prompt} - Finished, warnings found") if not status: status = WARNING # if pylint did not run and failure exit code has been returned from run commnad elif exit_code & FAIL: status = FAIL logger.debug(f"{log_prompt} - Actual XSOAR linter error -") logger.debug(f"{log_prompt} - Full format stdout: {RL if stdout else ''}{stdout}") # for contrib prs which are not merged from master and do not have pylint in dev-requirements-py2. if os.environ.get('CI'): stdout = "Xsoar linter could not run, Please merge from master" else: stdout = "Xsoar linter could not run, please make sure you have" \ " the necessary Pylint version for both py2 and py3" logger.error(f"{log_prompt}- Finished, errors found") logger.debug(f"{log_prompt} - Finished, exit-code: {exit_code}") logger.debug(f"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}") logger.debug(f"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}") if not exit_code: logger.info(f"{log_prompt} - Successfully finished") return status, stdout @timer(group_name='lint') def _run_bandit(self, lint_files: List[Path]) -> Tuple[int, str]: """ Run bandit in pack dir Args: lint_files(List[Path]): file to perform lint Returns: int: 0 on successful else 1, errors str: Bandit errors """ log_prompt = f"{self._pack_name} - Bandit" logger.info(f"{log_prompt} - Start") stdout, stderr, exit_code = run_command_os(command=build_bandit_command(lint_files), cwd=self._pack_abs_dir) logger.debug(f"{log_prompt} - Finished, exit-code: {exit_code}") logger.debug(f"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}") logger.debug(f"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}") if stderr or exit_code: logger.error(f"{log_prompt}- Finished, errors found") if stderr: return FAIL, stderr else: return FAIL, stdout logger.info(f"{log_prompt} - Successfully finished") return SUCCESS, "" @timer(group_name='lint') def _run_mypy(self, py_num: str, lint_files: List[Path]) -> Tuple[int, str]: """ Run mypy in pack dir Args: py_num(str): The python version in use lint_files(List[Path]): file to perform lint Returns: int: 0 on successful else 1, errors str: Bandit errors """ log_prompt = f"{self._pack_name} - Mypy" logger.info(f"{log_prompt} - Start") with add_typing_module(lint_files=lint_files, python_version=py_num): mypy_command = build_mypy_command(files=lint_files, version=py_num, content_repo=self._content_repo) stdout, stderr, exit_code = run_command_os(command=mypy_command, cwd=self._pack_abs_dir) logger.debug(f"{log_prompt} - Finished, exit-code: {exit_code}") logger.debug(f"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}") logger.debug(f"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}") if stderr or exit_code: logger.error(f"{log_prompt}- Finished, errors found") if stderr: return FAIL, stderr else: return FAIL, stdout logger.info(f"{log_prompt} - Successfully finished") return SUCCESS, "" @timer(group_name='lint') def _run_vulture(self, py_num: str, lint_files: List[Path]) -> Tuple[int, str]: """ Run mypy in pack dir Args: py_num(str): The python version in use lint_files(List[Path]): file to perform lint Returns: int: 0 on successful else 1, errors str: Vulture errors """ log_prompt = f"{self._pack_name} - Vulture" logger.info(f"{log_prompt} - Start") stdout, stderr, exit_code = run_command_os(command=build_vulture_command(files=lint_files, pack_path=self._pack_abs_dir, py_num=py_num), cwd=self._pack_abs_dir) logger.debug(f"{log_prompt} - Finished, exit-code: {exit_code}") logger.debug(f"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}") logger.debug(f"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}") if stderr or exit_code: logger.error(f"{log_prompt}- Finished, errors found") if stderr: return FAIL, stderr else: return FAIL, stdout logger.info(f"{log_prompt} - Successfully finished") return SUCCESS, "" @timer(group_name='lint') def _run_lint_on_docker_image(self, no_pylint: bool, no_test: bool, no_pwsh_analyze: bool, no_pwsh_test: bool, keep_container: bool, test_xml: str, no_coverage: bool): """ Run lint check on docker image Args: no_pylint(bool): Whether to skip pylint no_test(bool): Whether to skip pytest no_pwsh_analyze(bool): Whether to skip powershell code analyzing no_pwsh_test(bool): whether to skip powershell tests keep_container(bool): Whether to keep the test container test_xml(str): Path for saving pytest xml results no_coverage(bool): Run pytest without coverage report """ for image in self._facts["images"]: # Docker image status - visualize status = { "image": image[0], "image_errors": "", "pylint_errors": "", "pytest_errors": "", "pytest_json": {}, "pwsh_analyze_errors": "", "pwsh_test_errors": "" } # Creating image if pylint specified or found tests and tests specified image_id = "" errors = "" for trial in range(2): image_id, errors = self._docker_image_create(docker_base_image=image) if not errors: break if image_id and not errors: # Set image creation status for check in ["pylint", "pytest", "pwsh_analyze", "pwsh_test"]: exit_code = SUCCESS output = "" for trial in range(2): if self._pkg_lint_status["pack_type"] == TYPE_PYTHON: # Perform pylint if not no_pylint and check == "pylint" and self._facts["lint_files"]: exit_code, output = self._docker_run_pylint(test_image=image_id, keep_container=keep_container) # Perform pytest elif not no_test and self._facts["test"] and check == "pytest": exit_code, output, test_json = self._docker_run_pytest(test_image=image_id, keep_container=keep_container, test_xml=test_xml, no_coverage=no_coverage) status["pytest_json"] = test_json elif self._pkg_lint_status["pack_type"] == TYPE_PWSH: # Perform powershell analyze if not no_pwsh_analyze and check == "pwsh_analyze" and self._facts["lint_files"]: exit_code, output = self._docker_run_pwsh_analyze(test_image=image_id, keep_container=keep_container) # Perform powershell test elif not no_pwsh_test and check == "pwsh_test": exit_code, output = self._docker_run_pwsh_test(test_image=image_id, keep_container=keep_container) # If lint check perfrom and failed on reason related to enviorment will run twice, # But it failing in second time it will count as test failure. if (exit_code == RERUN and trial == 1) or exit_code == FAIL or exit_code == SUCCESS: if exit_code in [RERUN, FAIL]: self._pkg_lint_status["exit_code"] |= EXIT_CODES[check] status[f"{check}_errors"] = output break else: status["image_errors"] = str(errors) self._pkg_lint_status["exit_code"] += EXIT_CODES["image"] # Add image status to images self._pkg_lint_status["images"].append(status) def _docker_login(self) -> bool: """ Login to docker-hub using environment variables: 1. DOCKERHUB_USER - User for docker hub. 2. DOCKERHUB_PASSWORD - Password for docker-hub. Used in Circle-CI for pushing into repo devtestdemisto Returns: bool: True if logged in successfully. """ docker_user = os.getenv('DOCKERHUB_USER') docker_pass = os.getenv('DOCKERHUB_PASSWORD') try: self._docker_client.login(username=docker_user, password=<PASSWORD>, registry="https://index.docker.io/v1") return self._docker_client.ping() except docker.errors.APIError: return False @timer(group_name='lint') def _docker_image_create(self, docker_base_image: List[Any]) -> Tuple[str, str]: """ Create docker image: 1. Installing 'build base' if required in alpine images version - https://wiki.alpinelinux.org/wiki/GCC 2. Installing pypi packs - if only pylint required - only pylint installed otherwise all pytest and pylint installed, packages which being install can be found in path demisto_sdk/commands/lint/dev_envs 3. The docker image build done by Dockerfile template located in demisto_sdk/commands/lint/templates/dockerfile.jinja2 Args: docker_base_image(list): docker image to use as base for installing dev deps and python version. Returns: str, str. image name to use and errors string. """ log_prompt = f"{self._pack_name} - Image create" # Get requirements file for image requirements = [] if docker_base_image[1] != -1: py_ver = parse(docker_base_image[1]).major if py_ver == 2: requirements = self._req_2 elif py_ver == 3: requirements = self._req_3 # Using DockerFile template pip_requirements = requirements + self._facts["additional_requirements"] # Trying to pull image based on dockerfile hash, will check if something changed errors = "" identifier = hashlib.md5("\n".join(sorted(pip_requirements)).encode("utf-8")).hexdigest() test_image_name = f'devtest{docker_base_image[0]}-{identifier}' test_image = None try: logger.info(f"{log_prompt} - Trying to pull existing image {test_image_name}") test_image = Docker.pull_image(test_image_name) except (docker.errors.APIError, docker.errors.ImageNotFound): logger.info(f"{log_prompt} - Unable to find image {test_image_name}") # Creatng new image if existing image isn't found if not test_image: logger.info( f"{log_prompt} - Creating image based on {docker_base_image[0]} - Could take 2-3 minutes at first " f"time") try: Docker.create_image(docker_base_image[0], test_image_name, container_type=self._pkg_lint_status["pack_type"], install_packages=pip_requirements) if self._docker_hub_login: for _ in range(2): try: self._docker_client.images.push(test_image_name) logger.info(f"{log_prompt} - Image {test_image_name} pushed to repository") break except (requests.exceptions.ConnectionError, urllib3.exceptions.ReadTimeoutError, requests.exceptions.ReadTimeout): logger.info(f"{log_prompt} - Unable to push image {test_image_name} to repository") except (docker.errors.BuildError, docker.errors.APIError, Exception) as e: logger.critical(f"{log_prompt} - Build errors occurred {e}") errors = str(e) return test_image_name, errors def _docker_remove_container(self, container_name: str): try: container = self._docker_client.containers.get(container_name) container.remove(force=True) except docker.errors.NotFound: pass except requests.exceptions.ChunkedEncodingError as err: # see: https://github.com/docker/docker-py/issues/2696#issuecomment-721322548 if platform.system() != 'Darwin' or 'Connection broken' not in str(err): raise @timer(group_name='lint') def _docker_run_pylint(self, test_image: str, keep_container: bool) -> Tuple[int, str]: """ Run Pylint in created test image Args: test_image(str): test image id/name keep_container(bool): True if to keep container after execution finished Returns: int: 0 on successful, errors 1, need to retry 2 str: Container log """ log_prompt = f'{self._pack_name} - Pylint - Image {test_image}' logger.info(f"{log_prompt} - Start") container_name = f"{self._pack_name}-pylint" # Check if previous run left container a live if it do, we remove it self._docker_remove_container(container_name) # Run container exit_code = SUCCESS output = "" try: container: docker.models.containers.Container = Docker.create_container( name=container_name, image=test_image, command=[ build_pylint_command( self._facts["lint_files"], docker_version=self._facts.get('python_version')) ], user=f"{os.getuid()}:4000", files_to_push=[('/devwork', self._pack_abs_dir)], environment=self._facts["env_vars"], ) container.start() stream_docker_container_output(container.logs(stream=True)) # wait for container to finish container_status = container.wait(condition="exited") # Get container exit code container_exit_code = container_status.get("StatusCode") # Getting container logs container_log = container.logs().decode("utf-8") logger.info(f"{log_prompt} - exit-code: {container_exit_code}") if container_exit_code in [1, 2]: # 1-fatal message issued # 2-Error message issued exit_code = FAIL output = container_log logger.error(f"{log_prompt} - Finished, errors found") elif container_exit_code in [4, 8, 16]: # 4-Warning message issued # 8-refactor message issued # 16-convention message issued logger.info(f"{log_prompt} - Successfully finished - warnings found") exit_code = SUCCESS elif container_exit_code == 32: # 32-usage error logger.critical(f"{log_prompt} - Finished - Usage error") exit_code = RERUN else: logger.info(f"{log_prompt} - Successfully finished") # Keeping container if needed or remove it if keep_container: print(f"{log_prompt} - container name {container_name}") container.commit(repository=container_name.lower(), tag="pylint") else: try: container.remove(force=True) except docker.errors.NotFound as e: logger.critical(f"{log_prompt} - Unable to delete container - {e}") except Exception as e: logger.exception(f"{log_prompt} - Unable to run pylint") exit_code = RERUN output = str(e) return exit_code, output @timer(group_name='lint') def _docker_run_pytest(self, test_image: str, keep_container: bool, test_xml: str, no_coverage: bool = False) -> Tuple[int, str, dict]: """ Run Pytest in created test image Args: test_image(str): Test image id/name keep_container(bool): True if to keep container after execution finished test_xml(str): Xml saving path no_coverage(bool): Run pytest without coverage report Returns: int: 0 on successful, errors 1, need to retry 2 str: Unit test json report """ log_prompt = f'{self._pack_name} - Pytest - Image {test_image}' logger.info(f"{log_prompt} - Start") container_name = f"{self._pack_name}-pytest" # Check if previous run left container a live if it does, Remove it self._docker_remove_container(container_name) # Collect tests exit_code = SUCCESS output = '' test_json = {} try: # Running pytest container cov = '' if no_coverage else self._pack_abs_dir.stem uid = os.getuid() or 4000 logger.debug(f'{log_prompt} - user uid for running lint/test: {uid}') # lgtm[py/clear-text-logging-sensitive-data] container = Docker.create_container( name=container_name, image=test_image, user=f"{uid}:4000", command=[build_pytest_command(test_xml=test_xml, json=True, cov=cov)], environment=self._facts["env_vars"], files_to_push=[('/devwork', self._pack_abs_dir)] ) container.start() stream_docker_container_output(container.logs(stream=True)) # Waiting for container to be finished container_status: dict = container.wait(condition="exited") # Getting container exit code container_exit_code = container_status.get("StatusCode") # Getting container logs logger.info(f"{log_prompt} - exit-code: {container_exit_code}") if container_exit_code in [0, 1, 2, 5]: # 0-All tests passed # 1-Tests were collected and run but some of the tests failed # 2-Test execution was interrupted by the user # 5-No tests were collected if test_xml: test_data_xml = get_file_from_container(container_obj=container, container_path="/devwork/report_pytest.xml") xml_apth = Path(test_xml) / f'{self._pack_name}_pytest.xml' with open(file=xml_apth, mode='bw') as f: f.write(test_data_xml) # type: ignore if not no_coverage: cov_file_path = os.path.join(self._pack_abs_dir, '.coverage') cov_data = get_file_from_container(container_obj=container, container_path="/devwork/.coverage") cov_data = cov_data if isinstance(cov_data, bytes) else cov_data.encode() with open(cov_file_path, 'wb') as coverage_file: coverage_file.write(cov_data) coverage_report_editor(cov_file_path, os.path.join(self._pack_abs_dir, f'{self._pack_abs_dir.stem}.py')) test_json = json.loads(get_file_from_container(container_obj=container, container_path="/devwork/report_pytest.json", encoding="utf-8")) for test in test_json.get('report', {}).get("tests"): if test.get("call", {}).get("longrepr"): test["call"]["longrepr"] = test["call"]["longrepr"].split('\n') if container_exit_code in [0, 5]: logger.info(f"{log_prompt} - Successfully finished") exit_code = SUCCESS elif container_exit_code in [2]: output = container.logs().decode('utf-8') exit_code = FAIL else: logger.error(f"{log_prompt} - Finished, errors found") exit_code = FAIL elif container_exit_code in [3, 4]: # 3-Internal error happened while executing tests # 4-pytest command line usage error logger.critical(f"{log_prompt} - Usage error") exit_code = RERUN output = container.logs().decode('utf-8') else: # Any other container exit code logger.error(f"{log_prompt} - Finished, docker container error found ({container_exit_code})") exit_code = FAIL # Remove container if not needed if keep_container: print(f"{log_prompt} - Container name {container_name}") container.commit(repository=container_name.lower(), tag="pytest") else: try: container.remove(force=True) except docker.errors.NotFound as e: logger.critical(f"{log_prompt} - Unable to remove container {e}") except (docker.errors.ImageNotFound, docker.errors.APIError) as e: logger.critical(f"{log_prompt} - Unable to run pytest container {e}") exit_code = RERUN return exit_code, output, test_json def _docker_run_pwsh_analyze(self, test_image: str, keep_container: bool) -> Tuple[int, str]: """ Run Powershell code analyze in created test image Args: test_image(str): test image id/name keep_container(bool): True if to keep container after excution finished Returns: int: 0 on successful, errors 1, need to retry 2 str: Container log """ log_prompt = f'{self._pack_name} - Powershell analyze - Image {test_image}' logger.info(f"{log_prompt} - Start") container_name = f"{self._pack_name}-pwsh-analyze" # Check if previous run left container a live if it do, we remove it container: docker.models.containers.Container try: container = self._docker_client.containers.get(container_name) container.remove(force=True) except docker.errors.NotFound: pass # Run container exit_code = SUCCESS output = "" try: uid = os.getuid() or 4000 logger.debug(f'{log_prompt} - user uid for running lint/test: {uid}') # lgtm[py/clear-text-logging-sensitive-data] container = Docker.create_container(name=container_name, image=test_image, user=f"{uid}:4000", environment=self._facts["env_vars"], files_to_push=[('/devwork', self._pack_abs_dir)], command=build_pwsh_analyze_command( self._facts["lint_files"][0]) ) container.start() stream_docker_container_output(container.logs(stream=True)) # wait for container to finish container_status = container.wait(condition="exited") # Get container exit code container_exit_code = container_status.get("StatusCode") # Getting container logs container_log = container.logs().decode("utf-8") logger.info(f"{log_prompt} - exit-code: {container_exit_code}") if container_exit_code: # 1-fatal message issued # 2-Error message issued logger.error(f"{log_prompt} - Finished, errors found") output = container_log exit_code = FAIL else: logger.info(f"{log_prompt} - Successfully finished") # Keeping container if needed or remove it if keep_container: print(f"{log_prompt} - container name {container_name}") container.commit(repository=container_name.lower(), tag="pwsh_analyze") else: try: container.remove(force=True) except docker.errors.NotFound as e: logger.critical(f"{log_prompt} - Unable to delete container - {e}") except (docker.errors.ImageNotFound, docker.errors.APIError, requests.exceptions.ReadTimeout) as e: logger.critical(f"{log_prompt} - Unable to run powershell test - {e}") exit_code = RERUN return exit_code, output def _update_support_level(self): pack_dir = self._pack_abs_dir.parent if self._pack_abs_dir.parts[-1] == INTEGRATIONS_DIR else \ self._pack_abs_dir.parent.parent pack_meta_content: Dict = json.load((pack_dir / PACKS_PACK_META_FILE_NAME).open()) self._facts['support_level'] = pack_meta_content.get('support') if self._facts['support_level'] == 'partner' and pack_meta_content.get('Certification'): self._facts['support_level'] = 'certified partner' def _docker_run_pwsh_test(self, test_image: str, keep_container: bool) -> Tuple[int, str]: """ Run Powershell tests in created test image Args: test_image(str): test image id/name keep_container(bool): True if to keep container after excution finished Returns: int: 0 on successful, errors 1, neet to retry 2 str: Container log """ log_prompt = f'{self._pack_name} - Powershell test - Image {test_image}' logger.info(f"{log_prompt} - Start") container_name = f"{self._pack_name}-pwsh-test" # Check if previous run left container a live if it do, we remove it self._docker_remove_container(container_name) # Run container exit_code = SUCCESS output = "" try: uid = os.getuid() or 4000 logger.debug(f'{log_prompt} - user uid for running lint/test: {uid}') # lgtm[py/clear-text-logging-sensitive-data] container: docker.models.containers.Container = Docker.create_container( files_to_push=[('/devwork', self._pack_abs_dir)], name=container_name, image=test_image, command=build_pwsh_test_command(), user=f"{uid}:4000", environment=self._facts["env_vars"]) container.start() stream_docker_container_output(container.logs(stream=True)) # wait for container to finish container_status = container.wait(condition="exited") # Get container exit code container_exit_code = container_status.get("StatusCode") # Getting container logs container_log = container.logs().decode("utf-8") logger.info(f"{log_prompt} - exit-code: {container_exit_code}") if container_exit_code: # 1-fatal message issued # 2-Error message issued logger.error(f"{log_prompt} - Finished, errors found") output = container_log exit_code = FAIL else: logger.info(f"{log_prompt} - Successfully finished") # Keeping container if needed or remove it if keep_container: print(f"{log_prompt} - container name {container_name}") container.commit(repository=container_name.lower(), tag='pwsh_test') else: try: container.remove(force=True) except docker.errors.NotFound as e: logger.critical(f"{log_prompt} - Unable to delete container - {e}") except (docker.errors.ImageNotFound, docker.errors.APIError, requests.exceptions.ReadTimeout) as e: logger.critical(f"{log_prompt} - Unable to run powershell test - {e}") exit_code = RERUN return exit_code, output def _get_commands_list(self, script_obj: dict): """ Get all commands from yml file of the pack Args: script_obj(dict): the script section of the yml file. Returns: list: list of all commands """ commands_list = [] try: commands_obj = script_obj.get('commands', {}) for command in commands_obj: commands_list.append(command.get('name', '')) except Exception: logger.debug("Failed getting the commands from the yml file") return commands_list

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giggleliu/tba/lattice/latticelib.py

Lynn-015/Test_01

2

63116

#!/usr/bin/python #-*-coding:utf-8-*- #By <NAME> from numpy import * from lattice import Lattice from bzone import BZone from group import C6vGroup,C4vGroup,C3vGroup __all__=['Honeycomb_Lattice','Square_Lattice','Triangular_Lattice','Chain','construct_lattice','resize_lattice'] class Honeycomb_Lattice(Lattice): ''' HoneyComb Lattice class. Construct ---------------- Honeycomb_Lattice(N,form=1.) form: The form of lattice. `1` -> traditional one with 0 point at a vertex, using C3v group. `2` -> the C6v form with 0 point at the center of hexagon, using C6v group. ''' def __init__(self,N,form=1): if form==1: catoms=[(0.,0.),(0.5,sqrt(3.)/6)] pg=C3vGroup() elif form==2: catoms=array[(0.,1./sqrt(3.)),(0.5,sqrt(3.)/6)] pg=C6vGroup() else: raise ValueError('Form %s not defined.'%form) super(Honeycomb_Lattice,self).__init__(name='honeycomb',a=array([(1.,0),(0.5,sqrt(3.)/2)]),N=N,catoms=catoms) self.usegroup(pg) @property def kspace(self): ''' Get the <KSpace> instance. ''' ks=super(Honeycomb_Lattice,self).kspace M0=ks.b[1]/2.0 K0=(ks.b[0]+2*ks.b[1])/3.0 c6vg=C6vGroup() M=[] K=[] for i in xrange(6): M.append(c6vg.actonK(M0,i)) K.append(c6vg.actonK(K0,i)) ks.special_points['M']=M ks.special_points['K']=K ks.usegroup(c6vg) return ks class Square_Lattice(Lattice): ''' Square Lattice, using C4v Group. Construct ---------------- Square_Lattice(N,catoms=[(0.,0.)]) ''' def __init__(self,N,catoms=[(0.,0.)]): a=array([(1.,0),(0.,1.)]) super(Square_Lattice,self).__init__(N=N,a=a,catoms=catoms,name='square') c4vg=C4vGroup() self.usegroup(c4vg) @property def kspace(self): ''' Get the <KSpace> instance. ''' ks=super(Square_Lattice,self).kspace M0=ks.b[1]/2.0 K0=(ks.b[0]+ks.b[1])/2.0 c4vg=C4vGroup() M=[] K=[] for i in xrange(4): M.append(c4vg.actonK(M0,i)) K.append(c4vg.actonK(K0,i)) ks.special_points['M']=M ks.special_points['K']=K ks.usegroup(c4vg) return ks class Triangular_Lattice(Lattice): ''' Triangular Lattice, using C6v Group. Construct ---------------- Triangular_Lattice(N,catoms=[(0.,0.)]) ''' def __init__(self,N,catoms=[(0.,0.)]): '''Basic information of Triangular Lattice''' a=array([(1.,0),(0.5,sqrt(3.)/2)]) super(Triangular_Lattice,self).__init__(a=a,catoms=catoms,name='triangular',N=N) c6vg=C6vGroup() self.usegroup(c6vg) @property def kspace(self): ''' Get the <KSpace> instance. ''' ks=super(Triangular_Lattice,self).kspace M0=ks.b[1]/2.0 K0=(ks.b[0]+2*ks.b[1])/3.0 c6vg=C6vGroup() M=[] K=[] for i in xrange(6): M.append(c6vg.actonK(M0,i)) K.append(c6vg.actonK(K0,i)) ks.special_points['M']=M ks.special_points['K']=K ks.usegroup(c6vg) return ks class Chain(Lattice): ''' Lattice of Chain. Construct ---------------- Chain(N,a=(1.),catoms=[(0.,0.)]) ''' def __init__(self,N,a=(1.),catoms=[(0.)]): ''' N: Number of cells, integer. a: Lattice vector, 1D array. catoms: Atom positions in a unit cell. ''' super(Chain,self).__init__(a=[a],N=[N],name='chain',catoms=catoms) @property def kspace(self): '''The <KSpace> instance correspond to a chain.''' a=self.a[0] b=2*pi*a/a.dot(a) ks=KSpace(N=self.N,b=b) ks.special_points['K']=array([-b/2.,b/2.]) return ks def construct_lattice(N,lattice_shape='',a=None,catoms=None,args={}): ''' Uniform construct method for lattice. N: The size of lattice. lattice_shape: The shape of lattice. * '' -> the anonymous lattice. * 'square' -> square lattice. * 'honeycomb' -> honeycomb lattice. * 'triangular' -> triangular lattice. * 'chain' -> a chain. a: The unit vector. catoms: The atoms in a unit cell. args: Other arguments, * `form` -> the form used in constructing honeycomb lattice. ''' if lattice_shape=='': assert(a is not None) if catoms is None: catoms=zeros(shape(a)[-1]) return Lattice(name='anonymous',N=N,a=a,catoms=catoms) elif lattice_shape=='honeycomb': return Honeycomb_Lattice(N=N,form=args.get('form',1)) elif lattice_shape=='square': if catoms is None: catoms=zeros([1,2]) return Square_Lattice(N=N,catoms=catoms) elif lattice_shape=='triangular': if catoms is None: catoms=zeros([1,2]) return Triangular_Lattice(N=N,catoms=catoms) elif lattice_shape=='chain': if a is None: a=[1.] if catoms is None: catoms=zeros([1,1]) if ndim(N)==1: N=N[0] return Chain(N=N,catoms=catoms) def resize_lattice(lattice,N): ''' Resize the lattice to specific size. lattice: The target lattice. N: 1D - array, the size of new lattice. ''' return construct_lattice(a=lattice.a,N=N,catoms=lattice.catoms,args={'form':getattr(lattice,'form',None)})

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teste.py

EduardoFockinkSilva/python-exercises

0

164734

<gh_stars>0 #test print('==================================') print(' this is just a test ') print('==================================') num = int(input('insert a number: ')) print((num * 5) % 3)

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src/bbapp/migrations/0004_auto_20190303_1557.py

levinsamuel/baseballdb

0

84901

<reponame>levinsamuel/baseballdb # Generated by Django 2.1.7 on 2019-03-03 23:57 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('bbapp', '0003_fielding'), ] operations = [ migrations.AlterField( model_name='batting', name='teamID', field=models.CharField(max_length=5), ), ]

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tests/test_wps_dummy.py

f-PLT/emu

3

14210

from pywps import Service from pywps.tests import assert_response_success from .common import client_for, get_output from emu.processes.wps_dummy import Dummy def test_wps_dummy(): client = client_for(Service(processes=[Dummy()])) datainputs = "input1=10;input2=2" resp = client.get( service='WPS', request='Execute', version='1.0.0', identifier='dummyprocess', datainputs=datainputs) assert_response_success(resp) assert get_output(resp.xml) == {'output1': '11', 'output2': '1'}

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data/split_data_num.py

xiangyue9607/QVE

4

148648

import json import random from argparse import ArgumentParser from numpy.random import default_rng parser = ArgumentParser() parser.add_argument("--in_file", type=str, default="data/NewsQA.train.json",) parser.add_argument("--out_file_dev", type=str, default="dataNewsQA.sample.dev.json") parser.add_argument("--out_file_train", type=str, default="data/NewsQA.sample.train.json") parser.add_argument("--num", type=int, default=1000, required=False) parser.add_argument("--seed", type=int, default=42, required=False) args = parser.parse_args() def subsample_dataset_random(data_json, sample_num=1000, seed=55): total = 0 context_num=0 id_lists=[] for paras in data_json['data']: for para in paras['paragraphs']: context_num+=1 qa_num = len(para['qas']) id_lists+=[qa['id'] for qa in para['qas']] total += qa_num print('Total QA Num: %d, Total Context: %d' % (total,context_num)) random.seed(seed) rng = default_rng() sampled_list = list(rng.choice(id_lists, size=sample_num,replace=False)) new_passages_dev = [] new_passages_train=[] for passages in data_json['data']: new_paras_dev = [] new_paras_train = [] for para in passages['paragraphs']: context = para['context'] new_qas_dev = [] new_qas_train = [] for qa in para['qas']: if qa['id'] in sampled_list: new_qas_dev.append(qa) else: new_qas_train.append(qa) if len(new_qas_dev) > 0: new_paras_dev.append({'context': context, 'qas': new_qas_dev}) if len(new_qas_train) > 0: new_paras_train.append({'context': context, 'qas': new_qas_train}) if len(new_paras_dev) > 0: new_passages_dev.append({'title': passages['title'], 'paragraphs': new_paras_dev}) if len(new_paras_train) > 0: new_passages_train.append({'title': passages['title'], 'paragraphs': new_paras_train}) dev_data_json = {'data': new_passages_dev, 'version': data_json['version']} train_data_json = {'data': new_passages_train, 'version': data_json['version']} total = 0 context_num=0 for paras in dev_data_json['data']: for para in paras['paragraphs']: context_num+=1 qa_num = len(para['qas']) id_lists+=[qa['id'] for qa in para['qas']] total += qa_num print('Sample Dev QA Num: %d, Total Context: %d' % (total,context_num)) total = 0 context_num = 0 for paras in train_data_json['data']: for para in paras['paragraphs']: context_num += 1 qa_num = len(para['qas']) id_lists += [qa['id'] for qa in para['qas']] total += qa_num print('Sample Train QA Num: %d, Total Context: %d' % (total, context_num)) return train_data_json,dev_data_json def main(args): dataset = json.load(open(args.in_file, 'r')) train_data_json,dev_data_json=subsample_dataset_random(dataset, args.num, args.seed) json.dump(train_data_json, open(args.out_file_train, 'w')) json.dump(dev_data_json, open(args.out_file_dev, 'w')) if __name__ == '__main__': args = parser.parse_args() random.seed(args.seed) main(args)

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code/ch14/chord.py

raionik/Py4Bio

66

65754

<filename>code/ch14/chord.py from bokeh.charts import output_file, Chord from bokeh.io import show import pandas as pd data = pd.read_csv('../../samples/test3.csv') chord_from_df = Chord(data, source='name_x', target='name_y', value='value') output_file('chord.html') show(chord_from_df)

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var/spack/repos/builtin/packages/perl-ipc-run/package.py

adrianjhpc/spack

2

8105

<reponame>adrianjhpc/spack # Copyright 2013-2019 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class PerlIpcRun(PerlPackage): """IPC::Run allows you to run and interact with child processes using files, pipes, and pseudo-ttys. Both system()-style and scripted usages are supported and may be mixed. Likewise, functional and OO API styles are both supported and may be mixed.""" homepage = "https://metacpan.org/pod/IPC::Run" url = "https://cpan.metacpan.org/authors/id/T/TO/TODDR/IPC-Run-20180523.0.tar.gz" version('20180523.0', sha256='3850d7edf8a4671391c6e99bb770698e1c45da55b323b31c76310913349b6c2f') depends_on('perl-io-tty', type=('build', 'run')) depends_on('perl-readonly', type='build')

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testing/logging/test_formatter.py

christian-steinmeyer/pytest

4

24001

import logging from typing import Any from _pytest._io import TerminalWriter from _pytest.logging import ColoredLevelFormatter def test_coloredlogformatter() -> None: logfmt = "%(filename)-25s %(lineno)4d %(levelname)-8s %(message)s" record = logging.LogRecord( name="dummy", level=logging.INFO, pathname="dummypath", lineno=10, msg="Test Message", args=(), exc_info=None, ) class ColorConfig: class option: pass tw = TerminalWriter() tw.hasmarkup = True formatter = ColoredLevelFormatter(tw, logfmt) output = formatter.format(record) assert output == ( "dummypath 10 \x1b[32mINFO \x1b[0m Test Message" ) tw.hasmarkup = False formatter = ColoredLevelFormatter(tw, logfmt) output = formatter.format(record) assert output == ("dummypath 10 INFO Test Message") def test_multiline_message() -> None: from _pytest.logging import PercentStyleMultiline logfmt = "%(filename)-25s %(lineno)4d %(levelname)-8s %(message)s" record: Any = logging.LogRecord( name="dummy", level=logging.INFO, pathname="dummypath", lineno=10, msg="Test Message line1\nline2", args=(), exc_info=None, ) # this is called by logging.Formatter.format record.message = record.getMessage() ai_on_style = PercentStyleMultiline(logfmt, True) output = ai_on_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\n" " line2" ) ai_off_style = PercentStyleMultiline(logfmt, False) output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) ai_none_style = PercentStyleMultiline(logfmt, None) output = ai_none_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) record.auto_indent = False output = ai_on_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) record.auto_indent = True output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\n" " line2" ) record.auto_indent = "False" output = ai_on_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) record.auto_indent = "True" output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\n" " line2" ) # bad string values default to False record.auto_indent = "junk" output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) # anything other than string or int will default to False record.auto_indent = dict() output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\nline2" ) record.auto_indent = "5" output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\n line2" ) record.auto_indent = 5 output = ai_off_style.format(record) assert output == ( "dummypath 10 INFO Test Message line1\n line2" ) def test_colored_short_level() -> None: logfmt = "%(levelname).1s %(message)s" record = logging.LogRecord( name="dummy", level=logging.INFO, pathname="dummypath", lineno=10, msg="Test Message", args=(), exc_info=None, ) class ColorConfig: class option: pass tw = TerminalWriter() tw.hasmarkup = True formatter = ColoredLevelFormatter(tw, logfmt) output = formatter.format(record) # the I (of INFO) is colored assert output == ("\x1b[32mI\x1b[0m Test Message")

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waffle/defaults.py

PrimarySite/django-waffle

0

199550

from __future__ import unicode_literals COOKIE = 'dwf_%s' TEST_COOKIE = 'dwft_%s' SECURE = True MAX_AGE = 2592000 # 1 month in seconds CACHE_PREFIX = 'waffle:' CACHE_NAME = 'default' FLAG_CACHE_KEY = 'flag:%s' FLAG_USERS_CACHE_KEY = 'flag:%s:users' FLAG_GROUPS_CACHE_KEY = 'flag:%s:groups' ALL_FLAGS_CACHE_KEY = 'flags:all' SAMPLE_CACHE_KEY = 'sample:%s' ALL_SAMPLES_CACHE_KEY = 'samples:all' SWITCH_CACHE_KEY = 'switch:%s' ALL_SWITCHES_CACHE_KEY = 'switches:all' FLAG_DEFAULT = False SAMPLE_DEFAULT = False SWITCH_DEFAULT = False READ_FROM_WRITE_DB = False CREATE_MISSING_FLAGS = False CREATE_MISSING_SAMPLES = False CREATE_MISSING_SWITCHES = False LOG_MISSING_FLAGS = None LOG_MISSING_SAMPLES = None LOG_MISSING_SWITCHES = None OVERRIDE = False UNIQUE_FLAG_NAME = True

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magda/pipeline/parallel/group/group.py

p-mielniczuk/magda

0

1606451

<filename>magda/pipeline/parallel/group/group.py from __future__ import annotations from typing import List from magda.module.module import Module from magda.pipeline.parallel.group.runtime import GroupRuntime class Group: Runtime = GroupRuntime def __init__( self, name: str, *, replicas: int = 1, state_type=None, **options, ): self.name = name self.replicas = replicas self.options = options self._state_type = state_type def set_replicas(self, replicas: int) -> Group: self.replicas = replicas return self def set_options(self, **options) -> Group: self.options = options return self @property def state_type(self): return self._state_type @state_type.setter def state_type(self, state_type): self._state_type = state_type def build( self, modules: List[Module], dependent_modules: List[Module], dependent_modules_nonregular: List[Module], ) -> Group.Runtime: return self.Runtime( name=self.name, modules=modules, dependent_modules=dependent_modules, dependent_modules_nonregular=dependent_modules_nonregular, replicas=self.replicas, state_type=self.state_type, options=self.options, )

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Code/GMM Test Scripts/yellowTest.py

Praveen1098/Gaussian_Mixture_Modelling

0

24954

<gh_stars>0 #!/usr/bin/env python3 # -*- coding: utf-8 -*- import cv2 import numpy as np from matplotlib import pyplot as plt import os import math def gaussian(x, mu, sig): return ((1/(sig*math.sqrt(2*math.pi)))*np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))) x=list(range(0, 256)) g1=gaussian(x,np.array([239.82]), np.array([2.53])) g2=gaussian(x,np.array([221.93]), np.array([23.83])) r2=gaussian(x, np.array([217.47]),np.array([29.43])) r1=gaussian(x, np.array([230.01]),np.array([3.675])) b1=gaussian(x, np.array([103.60]),np.array([18.64])) b2=gaussian(x, np.array([164.96]),np.array([27.56])) path=os.getcwd()+"/"+"detectbuoy.avi" c=cv2.VideoCapture(path) fourcc = cv2.VideoWriter_fourcc(*'MP4V') out = cv2.VideoWriter('yellowDetection.mp4', fourcc, 15, (640, 480)) while (True): ret,image=c.read() if ret==False: break b,g,r=cv2.split(image) if ret == True: img_out3=np.zeros(g.shape, dtype = np.uint8) for index, v in np.ndenumerate(g): av=int((int(v)+int(r[index]))/2) if ( g1[v]>0.08) and (r1[r[index]] >0.04) and (b1[b[index]]>0.01 ): img_out3[index]=255 else: img_out3[index]=0 ret, threshold3 = cv2.threshold(img_out3, 240, 255, cv2.THRESH_BINARY) kernel3 = np.ones((2,2),np.uint8) dilation3 = cv2.dilate(threshold3,kernel3,iterations =8) contours3, _= cv2.findContours(dilation3, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) for contour in contours3: if cv2.contourArea(contour) > 50: (x,y),radius = cv2.minEnclosingCircle(contour) center = (int(x),int(y)) radius = int(radius) # print(radius) if radius > 12: cv2.circle(image,center,radius,(0,255,255),2) cv2.imshow("Threshold",dilation3) cv2.imshow('Yellow Ball Segmentation', image) cv2.imwrite('yellow.jpg', image) out.write(image) k = cv2.waitKey(1) & 0xff if k == 27: break # wait for ESC key to exit else: break c.release() out.release() cv2.destroyAllWindows()

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configs/benchmarks/classification/imagenet/imagenet_per_1/r50_sz224_4xb64_head1_lr0_01_step_ep20.py

Westlake-AI/openmixup

10

108070

<gh_stars>1-10 _base_ = [ 'r50_sz224_4xb64_head1_lr0_1_step_ep20.py', ] # optimizer optimizer = dict(lr=0.01)

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test/utilities/test_contains_none_values.py

elifesciences/profiles

2

1607954

import pytest from profiles.utilities import contains_none_values EMPTY_DICT = {} DICT_WITH_NONE_VALUE = {'test': None} DICT_WITH_NESTED_DICTS_WITHOUT_NONE_VALUE = {'test': {'test': {'test': 1}}} DICT_WITH_NESTED_DICTS_WITH_NONE_VALUE = {'test': {'test': {'test': 1}, 'this': {'test': None}} } DICT_WITH_NESTED_DICTS_WITH_NONE_VALUE_AND_FOLLOWING_VALUES = { 'test': {'test': {'test': 1}, 'this': {'test': {'test': 'this'}}, 'and': {'test': {'test': None}}, 'that': {'test': 2}} } DICT_WITH_NESTED_DICT_AND_LIST_WITH_NONE_VALUE = { 'test': {'test': [1, None, 3]}} LIST_WITH_NONE_VALUE = [1, '2', None, 4] LIST_WITHOUT_NONE_VALUE = [1, '2', '3', 4] LIST_WITH_NESTED_LISTS_WITH_NONE_VALUE_AND_FOLLOWING_VALUES = [ 1, [1, 2, 3], [1, 2, [1, 2, None] ], [1, 2, 3], 4] @pytest.mark.parametrize('test_data, expected_result', [ (EMPTY_DICT, False), (DICT_WITH_NONE_VALUE, True), (DICT_WITH_NESTED_DICTS_WITHOUT_NONE_VALUE, False), (DICT_WITH_NESTED_DICTS_WITH_NONE_VALUE, True), (DICT_WITH_NESTED_DICTS_WITH_NONE_VALUE_AND_FOLLOWING_VALUES, True), (DICT_WITH_NESTED_DICT_AND_LIST_WITH_NONE_VALUE, True), (LIST_WITH_NONE_VALUE, True), (LIST_WITHOUT_NONE_VALUE, False), (LIST_WITH_NESTED_LISTS_WITH_NONE_VALUE_AND_FOLLOWING_VALUES, True) ]) def test_contains_none_values(test_data, expected_result) -> None: assert contains_none_values(test_data) is expected_result

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mailmerge/smtp_dummy.py

Denzeldeveloper/Python-Auto-MailMerge-

0

29996

"""Dummy SMTP API.""" class SMTP_dummy(object): # pylint: disable=useless-object-inheritance # pylint: disable=invalid-name, no-self-use """Dummy SMTP API.""" # Class variables track member function calls for later checking. msg_from = None msg_to = None msg = None def login(self, login, password): """Do nothing.""" def sendmail(self, msg_from, msg_to, msg): """Remember the recipients.""" SMTP_dummy.msg_from = msg_from SMTP_dummy.msg_to = msg_to SMTP_dummy.msg = msg def close(self): """Do nothing.""" def clear(self): """Reset class variables.""" SMTP_dummy.msg_from = None SMTP_dummy.msg_to = [] SMTP_dummy.msg = None

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phpme/binx/console.py

eghojansu/phpme

0

39822

import os, json, subprocess class Console(): """Run PHP job""" def get_interface_methods(namespace): try: output = Console.run_command('interface-methods', [namespace]) return json.loads(output) except Exception as e: return {} def get_class_methods(namespace): try: output = Console.run_command('class-methods', [namespace]) return json.loads(output) except Exception as e: return {} def get_classes(symbol): try: output = Console.run_command('classes', [symbol]) return json.loads(output) except Exception as e: return [] def git_config(config): try: return subprocess.check_output(['git', 'config', '--get', config]).decode('utf-8') except Exception as e: print('[Phpme]', 'error: ' + str(e)) def run_command(command, args): try: console = os.path.dirname(os.path.abspath(__file__)) + os.sep + 'console.php' output = subprocess.check_output(['php', '-f', console, command] + args).decode('utf-8') if output.startswith('error'): print('[Phpme]', output) else: return output except Exception as e: print('[Phpme]', 'error: ' + str(e))

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rllib/agents/dqn/learner_thread.py

firebolt55439/ray

3

74756

<gh_stars>1-10 import queue import threading from ray.rllib.evaluation.metrics import get_learner_stats from ray.rllib.policy.policy import LEARNER_STATS_KEY from ray.rllib.utils.framework import try_import_tf from ray.rllib.utils.timer import TimerStat from ray.rllib.utils.window_stat import WindowStat LEARNER_QUEUE_MAX_SIZE = 16 tf1, tf, tfv = try_import_tf() class LearnerThread(threading.Thread): """Background thread that updates the local model from replay data. The learner thread communicates with the main thread through Queues. This is needed since Ray operations can only be run on the main thread. In addition, moving heavyweight gradient ops session runs off the main thread improves overall throughput. """ def __init__(self, local_worker): threading.Thread.__init__(self) self.learner_queue_size = WindowStat("size", 50) self.local_worker = local_worker self.inqueue = queue.Queue(maxsize=LEARNER_QUEUE_MAX_SIZE) self.outqueue = queue.Queue() self.queue_timer = TimerStat() self.grad_timer = TimerStat() self.overall_timer = TimerStat() self.daemon = True self.weights_updated = False self.stopped = False self.stats = {} def run(self): # Switch on eager mode if configured. if self.local_worker.policy_config.get("framework") in ["tf2", "tfe"]: tf1.enable_eager_execution() while not self.stopped: self.step() def step(self): with self.overall_timer: with self.queue_timer: ra, replay = self.inqueue.get() if replay is not None: prio_dict = {} with self.grad_timer: grad_out = self.local_worker.learn_on_batch(replay) for pid, info in grad_out.items(): td_error = info.get( "td_error", info[LEARNER_STATS_KEY].get("td_error")) prio_dict[pid] = (replay.policy_batches[pid].data.get( "batch_indexes"), td_error) self.stats[pid] = get_learner_stats(info) self.grad_timer.push_units_processed(replay.count) self.outqueue.put((ra, prio_dict, replay.count)) self.learner_queue_size.push(self.inqueue.qsize()) self.weights_updated = True self.overall_timer.push_units_processed(replay and replay.count or 0)

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leetcode/python/359_logger_rate_limiter.py

VVKot/leetcode-solutions

4

194667

class Logger: PRINT_INTERVAL = 10 def __init__(self): self.buckets = [-1 for _ in range(self.PRINT_INTERVAL)] self.sets = [set() for _ in range(self.PRINT_INTERVAL)] def shouldPrintMessage(self, timestamp: int, message: str) -> bool: bucket_index = timestamp % self.PRINT_INTERVAL if self.buckets[bucket_index] != timestamp: self.sets[bucket_index].clear() self.buckets[bucket_index] = timestamp for i, bucket_timestamp in enumerate(self.buckets): if timestamp - bucket_timestamp < self.PRINT_INTERVAL: if message in self.sets[i]: return False self.sets[bucket_index].add(message) return True

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super32assembler/super32assembler/preprocessor/asmdirectives.py

Projektstudium-Mikroprozessor/Super32

1

19108

""" Enum Assembler-Directives """ from enum import Enum, auto class AssemblerDirectives(Enum): START = auto() END = auto() ORG = auto() DEFINE = auto() @classmethod def to_string(cls): return "{START},{END},{ORG},{DEFINE}".format( START=cls.START.name, END=cls.END.name, ORG=cls.ORG.name, DEFINE=cls.DEFINE.name )

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FSPSFiles.py

AlexaVillaume/FSPS

1

91898

import os ''' Read an FSPS spec file ''' class specmodel(object): def __init__(self, attr, wave, flux): self.agegyr = round(10**attr[0] / 1e9, 3) self.mass = attr[1] self.lbol = attr[2] self.sfr = attr[3] self.wave = wave self.flux = flux class readspec(object): def __init__(self, fname): self.data = open(fname, "r") # Burn header while True: line = self.data.readline() if line[0] != '#': break cols = line.split() self.tstep = float(cols[0]) self.sdim = float(cols[1]) # Get wavelength line = self.data.readline() cols = line.split() self.wave = map(lambda col: float(col)*1e-4, cols) def next(self): line = self.data.readline() if line == "": self.data.close() raise StopIteration() cols = line.split() attr = map(lambda col: float(col), cols) line = self.data.readline() cols = line.split() flux = map(lambda col: float(col), cols) return specmodel(attr, self.wave, flux) def __iter__(self): return self ''' For FSPS .cmd files ''' class readcmd: def __init__(self, fname): self.data = open(fname, 'r') # Read header to find the columns in the file while True: line = self.data.readline() line = line.lstrip() if line[0] == '#': header = line.replace('#', '') header = header.replace('mags', '') header = header.split() break self.header = header def next(self): line = self.data.readline() if line == "": self.data.close() raise StopIteration() # Read in and assign the attributes of the models cols = line.split() for i, name in enumerate(self.header): self.__dict__[name] = float(cols[i]) # Update the attributes with filter names and values with open('/Users/alexawork/FSPS_python/filters.txt', 'r') as names: for i, _filter in enumerate(names): _filter = _filter.rstrip() #print _filter, cols[i + len(self.header)] self.__dict__.update({_filter:float(cols[i + len(self.header)])}) return self.__dict__.copy() def __iter__(self): return self ''' For FSPS .mags files ''' class readmags: def __init__(self, fname): self.data = open(fname, 'r') # Read header to find the columns in the file line_count = 0 while True: if line_count < 7: self.data.readline() else: line = self.data.readline() line = line.lstrip() if line[0] == '#': header = line.replace('#', '') header = header.replace('mags', '') header = header.replace('(see FILTER_LIST)', '') header = header.split() break line_count+=1 self.header = header def next(self): line = self.data.readline() if line == "": self.data.close() raise StopIteration() # Read in and assign the attributes of the models cols = line.split() for i, name in enumerate(self.header): self.__dict__[name] = float(cols[i]) # Update the attributes with filter names and values with open('/Users/alexawork/FSPS_python/filters.txt', 'r') as names: for i, _filter in enumerate(names): _filter = _filter.rstrip() #print _filter, cols[i + len(self.header)] self.__dict__.update({_filter:float(cols[i + len(self.header)])}) return self.__dict__.copy() def __iter__(self): return self

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tests/test_properties.py

Kapiche/gcloud-python-orm

1

45505

import six import unittest2 from gcloud.datastore import helpers, key, set_default_dataset_id from gcloudorm import model, properties class TestProperties(unittest2.TestCase): _DATASET_ID = 'DATASET' def setUp(self): set_default_dataset_id(self._DATASET_ID) def testBooleanProperty(self): class TestModel(model.Model): test_bool = properties.BooleanProperty() m = TestModel() self.assertEqual(m.test_bool, None) self.assertEqual(m['test_bool'], None) m = TestModel(test_bool=False) self.assertEqual(m.test_bool, False) self.assertEqual(m['test_bool'], False) m.test_bool = True self.assertEqual(m.test_bool, True) self.assertEqual(m['test_bool'], True) class TestModel(model.Model): test_bool = properties.BooleanProperty(default=True) m = TestModel() self.assertEqual(m.test_bool, True) self.assertEqual(m['test_bool'], True) def testIdProperty(self): class TestModel(model.Model): test_id = properties.IdProperty() m = TestModel() self.assertIsInstance(m.test_id, six.string_types) self.assertIs(m.test_id, m.key.id_or_name) def testIntegerProperty(self): class TestModel(model.Model): test_int = properties.IntegerProperty() m = TestModel() self.assertEqual(m.test_int, None) self.assertEqual(m['test_int'], None) class TestModel(model.Model): test_int = properties.IntegerProperty(default=3) m = TestModel() self.assertEqual(m['test_int'], 3) m.test_int = 4 self.assertEqual(m.test_int, 4) self.assertEqual(m['test_int'], 4) def testFloatproperty(self): class TestModel(model.Model): test_float = properties.FloatProperty() m = TestModel() self.assertEqual(m.test_float, None) self.assertEqual(m['test_float'], None) class TestModel(model.Model): test_float = properties.FloatProperty(default=0.1) m = TestModel() self.assertEqual(m['test_float'], 0.1) m.test_float = 0.2 self.assertEqual(m['test_float'], 0.2) def testTextProperty(self): class TestModel(model.Model): test_text = properties.TextProperty() m = TestModel() self.assertEqual(m.test_text, None) class TestModel(model.Model): test_text = properties.TextProperty(default="") m = TestModel() self.assertEqual(m['test_text'], "") class TestModel(model.Model): test_text = properties.TextProperty(default=lambda: "") m = TestModel() self.assertEqual(m['test_text'], "") def testPickleProperty(self): class TestModel(model.Model): test_pickle = properties.PickleProperty() m = TestModel() self.assertEqual(m.test_pickle, None) m = TestModel(test_pickle={"123": "456"}) self.assertEqual(m.test_pickle, {"123": "456"}) m.test_pickle = {'456': '789'} self.assertEqual(m.test_pickle, {'456': '789'}) def testJsonProperty(self): class TestModel(model.Model): test_pickle = properties.JsonProperty() m = TestModel() self.assertEqual(m.test_pickle, None) m = TestModel(test_pickle={"123": "456"}) self.assertEqual(m.test_pickle, {"123": "456"}) m.test_pickle = {'456': '789'} self.assertEqual(m.test_pickle, {'456': '789'}) def testDataTimeProperty(self): import datetime class TestModel(model.Model): test_datetime = properties.DateTimeProperty() m = TestModel() self.assertEqual(m.test_datetime, None) utcnow = datetime.datetime.utcnow() m.test_datetime = utcnow self.assertEqual(m.test_datetime, utcnow) def testDateProperty(self): import datetime class TestModel(model.Model): test_date = properties.DateProperty() m = TestModel() self.assertEqual(m.test_date, None) today = datetime.date.today() m.test_date = today self.assertEqual(m.test_date, today) def testTimeProperty(self): import datetime class TestModel(model.Model): test_time = properties.TimeProperty() m = TestModel() self.assertEqual(m.test_time, None) t = datetime.time() m.test_time = t self.assertEqual(m.test_time, t)

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mall/celery_tasks/sms/tasks.py

DanaLee1990/meiduo

0

54871

""" 任务： 1、就是普通函数 2、该函数必须通过celery的实例对象的tasks装饰其装饰 3、该任务需要让celery实例对象自动检测 4、任务（函数）需要使用任务名（函数名）.delay() 进行调用 """ from libs.yuntongxun.sms import CCP from celery_tasks.main import app @app.task def send_sms_code(mobile,sms_code): ccp = CCP() ccp.send_template_sms(mobile, [sms_code, 5], 1)

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api/resources/__init__.py

wopost/opencmdb

2

101142

from flask import Blueprint from flask_restful import Api from api.resources.demo import DemoResource from api.resources.login import LoginResource api_bp_v1 = Blueprint('bp_v1', __name__) api_v1 = Api(api_bp_v1, '/v1') api_v1.add_resource(DemoResource, '/demo') api_v1.add_resource(LoginResource, '/login') BLUEPRINTS = [api_bp_v1] __all__ = ['BLUEPRINTS']

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src/simulation/entity.py

rah/optimal-search

0

32758

class Entity(object): ''' An entity has: - energy - position(x,y) - size(length, width) An entity may have a parent entity An entity may have child entities ''' def __init__( self, p, parent=None, children=None): self.p = p self.energy = p.getfloat("ENTITY", "energy") self.x_pos = p.getfloat("ENTITY", "x_pos") self.y_pos = p.getfloat("ENTITY", "y_pos") self.length = p.getint("ENTITY", "length") self.width = p.getint("ENTITY", "width") self.parent = parent if children is None: self.children = [] else: self.children = children def add_child(self, entity): self.children.append(entity) def remove_child(self, entity): self.children.remove(entity) def remove_self(self): if self.parent is not None: self.parent.children.remove(self) def number_children(self): if self.children: return len(self.children) else: return 0 def total_energy(self): ''' returns the sum of all energy ''' total_energy = self.energy for child in self.children: total_energy += child.energy return total_energy def set_bounds(self, x, y): ''' Ensure that x, y are within the bounds of this entity. Reset x,y so that a torus is formed ''' if x < 0.0: x = self.length if x > self.length: x = 0.0 if y < 0.0: y = self.width if y > self.width: y = 0.0 return x, y

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mygallary/urls.py

mangowilliam/my_gallary

0

5799

<gh_stars>0 from django.conf import settings from django.conf.urls.static import static from django.conf.urls import url from . import views urlpatterns = [ url('^$', views.gallary,name = 'gallary'), url(r'^search/', views.search_image, name='search_image'), url(r'^details/(\d+)',views.search_location,name ='images') ] if settings.DEBUG: urlpatterns+= static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT)

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install/app_store/tk-framework-shotgunutils/v5.2.4/python/shotgun_globals/icon.py

JoanAzpeitia/lp_sg

0

59064

<reponame>JoanAzpeitia/lp_sg # Copyright (c) 2015 Shotgun Software Inc. # # CONFIDENTIAL AND PROPRIETARY # # This work is provided "AS IS" and subject to the Shotgun Pipeline Toolkit # Source Code License included in this distribution package. See LICENSE. # By accessing, using, copying or modifying this work you indicate your # agreement to the Shotgun Pipeline Toolkit Source Code License. All rights # not expressly granted therein are reserved by Shotgun Software Inc. from sgtk.platform.qt import QtCore, QtGui from .ui import resources_rc # list of all entity types for which an icon exists _entity_types_with_icons = ["Asset", "ClientUser", "EventLogEntry", "Group", "HumanUser", "PublishedFile", "TankPublishedFile", "Note", "Playlist", "Project", "Sequence", "Shot", "Tag", "Task", "Ticket", "Version", ] _cached_entity_icons = {} def get_entity_type_icon_url(entity_type): """ Retrieve the icon resource path for the specified entity type if available. This is useful if you want to include an icon in a ``QLabel`` using an ``<img>`` html tag. :param entity_type: The entity type to retrieve the icon for :returns: A string url with a qt resource path """ if entity_type in _entity_types_with_icons: return ":/tk-framework-shotgunutils/icon_%s_dark.png" % entity_type else: return None def get_entity_type_icon(entity_type): """ Retrieve the icon for the specified entity type if available. :param entity_type: The entity type to retrieve the icon for :returns: A QIcon if an icon was found for the specified entity type, otherwise None. """ global _cached_entity_icons if entity_type not in _cached_entity_icons: # not yet cached icon = None url = get_entity_type_icon_url(entity_type) if url: # create a QIcon for it icon = QtGui.QIcon(QtGui.QPixmap(url)) # cache it _cached_entity_icons[entity_type] = icon # we've previously asked for the icon return _cached_entity_icons[entity_type]

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Codewars/you're a square/you're a square.py

adoreblvnk/code_solutions

0

26653

from math import isqrt is_square = lambda n: isqrt(n) ** 2 == n if n >= 0 else False def is_square_soln(n): pass print(is_square(-1))

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pylibcamera_src/__init__.py

Jiangshan00001/pylibcamera

0

191614

import os from .cffi_helpers import get_lib_handle _this_path = os.path.dirname(os.path.realpath(__file__)) _library_dir = os.getenv('PI_LIBRARY_DIR') if _library_dir is None: _library_dir = os.path.join(_this_path, 'lib') _include_dir = os.getenv('PI_INCLUDE_DIR') if _include_dir is None: _include_dir = os.path.join(_this_path, 'include') lraw_api, ffi_raw = get_lib_handle( ['-DPI_API='], 'libcamera_raw_api.h', 'camera_raw_api', _library_dir, _include_dir ) lvid_api, ffi_vid = get_lib_handle( ['-DPI_API='], 'libcamera_vid_api.h', 'camera_vid_api', _library_dir, _include_dir )

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boards/models.py

6ba/bbgo

22

113622

<filename>boards/models.py # -*- coding: utf-8 -*- from __future__ import unicode_literals from django.conf import settings from django.contrib.auth.models import User from django.db import models from django.urls import reverse_lazy from django.utils.translation import ugettext as _ class Board(models.Model): """Board of boards""" BOARD_STATUS = { ('1normal', _('status_normal')), ('2temp', _('status_temp')), ('3notice', _('status_notice')), ('4warning', _('status_warning')), ('5hidden', _('status_hidden')), ('6deleted', _('status_deleted')), } table = models.IntegerField(default=0) status = models.CharField(max_length=10, choices=BOARD_STATUS, default='1normal') user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE) created_at = models.DateTimeField(auto_now_add=True) modified_at = models.DateTimeField(auto_now_add=True) ip = models.GenericIPAddressField() category = models.CharField(max_length=23, blank=True) subject = models.CharField(max_length=41) content = models.TextField() view_count = models.IntegerField(default=0) reply_count = models.IntegerField(default=0) like_count = models.IntegerField(default=0) dislike_count = models.IntegerField(default=0) like_users = models.ManyToManyField( User, related_name="board_like_users", default='', blank=True) dislike_users = models.ManyToManyField( User, related_name="board_dislike_users", default='', blank=True) reference = models.CharField(max_length=1855, default='', blank=True) has_image = models.BooleanField(default=False) has_video = models.BooleanField(default=False) def get_absolute_url(self): """Back to list""" return reverse_lazy('boards:show_list', args=[self.table, 1]) def get_article_url(self): """Back to article""" return reverse_lazy('boards:show_article', args=[self.id]) def get_edit_url(self): """Stay editing""" return reverse_lazy('boards:edit_article', args=[self.id]) def get_status_text(self): """Get status text""" if self.status == '1normal': return _('status_normal') elif self.status == '2temp': return _('status_temp') elif self.status == '3notice': return _('status_notice') elif self.status == '4warning': return _('status_warning') elif self.status == '5hidden': return _('status_hidden') elif self.status == '6deleted': return _('status_deleted') def get_image_text(self): """Get image text""" return '<img src="/upload/django-summernote/' def get_video_text(self): """Get video text""" return '<iframe frameborder="0" src="//www.youtube.com/' class Reply(models.Model): """Reply of boards""" REPLY_STATUS = { ('1normal', _('status_normal')), ('5hidden', _('status_hidden')), ('6deleted', _('status_deleted')), } article_id = models.IntegerField(default=0) reply_id = models.IntegerField(default=0) reply_to = models.CharField(max_length=150, default='', blank=True) status = models.CharField( max_length=10, choices=REPLY_STATUS, default='1normal') user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE) created_at = models.DateTimeField(auto_now_add=True) modified_at = models.DateTimeField(auto_now_add=True) ip = models.GenericIPAddressField() content = models.TextField(max_length=settings.REPLY_TEXT_MAX) image = models.ImageField(upload_to="reply-images/%Y-%m-%d/", blank=True) like_count = models.IntegerField(default=0) dislike_count = models.IntegerField(default=0) like_users = models.ManyToManyField( User, related_name="reply_like_users", default='', blank=True) dislike_users = models.ManyToManyField( User, related_name="reply_dislike_users", default='', blank=True)

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src/pyphoplacecellanalysis/GUI/PyQtPlot/Flowchart/CustomNodes/Mixins/CtrlNodeMixins.py

CommanderPho/pyPhoPlaceCellAnalysis

0

112471

import numpy as np class KeysListAccessingMixin: """ Provides a helper function to get the keys from a variety of different data-types. Used for combo-boxes.""" @classmethod def get_keys_list(cls, data): if isinstance(data, dict): keys = list(data.keys()) elif isinstance(data, list) or isinstance(data, tuple): keys = data elif isinstance(data, np.ndarray) or isinstance(data, np.void): keys = data.dtype.names else: print("get_keys_list(data): Unknown data type:", type(data), data) raise return keys

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src/_zkapauthorizer/replicate.py

PrivateStorageio/SecureAccessTokenAuthorizer

1

59898

<reponame>PrivateStorageio/SecureAccessTokenAuthorizer # Copyright 2022 PrivateStorage.io, 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 # # 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. from __future__ import annotations """ A system for replicating local SQLite3 database state to remote storage. Theory of Operation =================== A function to wrap a ``sqlite3.Connection`` in a new type is provided. This new type provides facilities for accomplishing two goals: * It (can someday) presents an expanded connection interface which includes the ability to switch the database into "replicated" mode. This is an application-facing interface meant to be used when the application is ready to discharge its responsibilities in the replication process. * It (can someday) expose the usual cursor interface wrapped around the usual cursor behavior combined with extra logic to record statements which change the underlying database (DDL and DML statements). This recorded data then feeds into the above replication process once it is enabled. An application's responsibilities in the replication process are to arrange for remote storage of "snapshots" and "event streams". See the replication/recovery design document for details of these concepts. Once replication has been enabled, the application (can someday be) informed whenever the event stream changes (respecting database transactionality) and data can be shipped to remote storage as desired. It is essential to good replication performance that once replication is enabled all database-modifying actions are captured in the event stream. This is the reason for providing a ``sqlite3.Connection``-like object for use by application code rather than a separate side-car interface: it minimizes the opportunities for database changes which are overlooked by this replication system. """ __all__ = [ "ReplicationAlreadySetup", "fail_setup_replication", "setup_tahoe_lafs_replication", "with_replication", "statements_to_snapshot", "connection_to_statements", "snapshot", ] import os import re from enum import Enum from io import BytesIO from sqlite3 import Connection as _SQLite3Connection from sqlite3 import Cursor as _SQLite3Cursor from typing import ( IO, Any, Awaitable, Callable, ClassVar, Generator, Iterable, Iterator, Optional, Protocol, Sequence, ) import cbor2 from attrs import Factory, define, field, frozen from compose import compose from eliot import log_call from twisted.application.service import IService, Service from twisted.internet.defer import CancelledError, Deferred, DeferredQueue, succeed from twisted.logger import Logger from twisted.python.filepath import FilePath from twisted.python.lockfile import FilesystemLock from ._types import CapStr from .config import REPLICA_RWCAP_BASENAME, Config from .sql import Connection, Cursor, SQLRuntimeType, SQLType, statement_mutates from .tahoe import DataProvider, DirectoryEntry, ITahoeClient, attenuate_writecap # function which can set remote ZKAPAuthorizer state. Uploader = Callable[[str, DataProvider], Awaitable[None]] # function which can remove entries from ZKAPAuthorizer state. Pruner = Callable[[Callable[[str], bool]], Awaitable[None]] # functions which can list all entries in ZKAPAuthorizer state Lister = Callable[[], Awaitable[list[str]]] EntryLister = Callable[[], Awaitable[dict[str, DirectoryEntry]]] class SnapshotPolicy(Protocol): """ Encode policy rules about when to take and upload a new snapshot. """ def should_snapshot(self, snapshot_size: int, replica_sizes: list[int]) -> bool: """ Given the size of a new snapshot and the size of an existing replica (snapshot and event streams), is now a good time to take a new snapshot? """ SNAPSHOT_NAME = "snapshot" @frozen class Replica: """ Manage a specific replica. """ upload: Uploader prune: Pruner entry_lister: EntryLister async def list(self) -> list[str]: return list(await self.entry_lister()) class ReplicationJob(Enum): """ The kinds of jobs that the Replication queue knows about :ivar startup: The job that is run once when the replication service starts and which is responsible for inspecting local and remote state to determine if any actions are immediately necessary (even before any further local changes are made). :ivar event_stream: The job to upload a new event stream object. :ivar snapshot: The job to upload a new snapshot object and prune now-obsolete event stream objects. :ivar consider_snapshot: The job to inspect replica event stream and snapshot state and potentially schedule a new snapshot which will allow pruning of existing event streams. """ startup = 1 event_stream = 2 snapshot = 3 consider_snapshot = 4 @frozen class Change: """ Represent an item in a replication event stream :ivar sequence: The sequence number of this event. :ivar statement: The SQL statement associated with this event. :ivar important: Whether this change was "important" or not. :ivar arguments: Any arguments for the SQL statement. """ sequence: int statement: str arguments: Sequence[SQLType] = field(converter=tuple) important: bool @arguments.validator def _validate_arguments(self, attribute, value) -> None: """ Require that the value has as elements only values are legal SQL values. :note: attrs validators run after attrs converters. """ if all(isinstance(o, SQLRuntimeType) for o in value): return None raise ValueError("sequence contains values incompatible with SQL") @frozen class EventStream: """ A series of database operations represented as `Change` instances. :ivar version: An identifier for the schema of the serialized form of this event stream. This will appear inside the serialized form. A change to the schema will be accompanied with an increment to this value. """ changes: Sequence[Change] = field(converter=tuple) version: ClassVar[int] = 1 def highest_sequence(self) -> Optional[int]: """ :returns: the highest sequence number in this EventStream (or None if there are no events) """ if not self.changes: return None return max(change.sequence for change in self.changes) def to_bytes(self) -> IO[bytes]: """ :returns: a producer of bytes representing this EventStream. """ return BytesIO( cbor2.dumps( { "version": self.version, "events": tuple( ( event.sequence, event.statement, event.arguments, event.important, ) for event in self.changes ), } ) ) @classmethod def from_bytes(cls, stream: IO[bytes]) -> EventStream: """ :returns EventStream: an instance of EventStream from the given bytes (which should have been produced by a prior call to ``to_bytes``) """ data = cbor2.load(stream) serial_version = data.get("version", None) if serial_version != cls.version: raise ValueError( f"Unknown serialized event stream version {serial_version}" ) return cls( changes=[ # List comprehension has incompatible type List[Change]; expected List[_T_co] # https://github.com/python-attrs/attrs/issues/519 Change(*args) # type: ignore for args in data["events"] ] ) class AlreadySettingUp(Exception): """ Another setup attempt is currently in progress. """ class ReplicationAlreadySetup(Exception): """ An attempt was made to setup of replication but it is already set up. """ async def fail_setup_replication(): """ A replication setup function that always fails. """ raise Exception("Test not set up for replication") async def setup_tahoe_lafs_replication(client: ITahoeClient) -> str: """ Configure the ZKAPAuthorizer plugin that lives in the Tahoe-LAFS node with the given configuration to replicate its state onto Tahoe-LAFS storage servers using that Tahoe-LAFS node. """ # Find the configuration path for this node's replica. config_path = client.get_private_path(REPLICA_RWCAP_BASENAME) # Take an advisory lock on the configuration path to avoid concurrency # shennanigans. config_lock = FilesystemLock(config_path.asTextMode().path + ".lock") if not config_lock.lock(): raise AlreadySettingUp() try: # Check to see if there is already configuration. if config_path.exists(): raise ReplicationAlreadySetup() # Create a directory with it rw_cap = await client.make_directory() # Store the resulting write-cap in the node's private directory config_path.setContent(rw_cap.encode("ascii")) finally: # On success and failure, release the lock since we're done with the # file for now. config_lock.unlock() # Attenuate it to a read-cap rocap = attenuate_writecap(rw_cap) # Return the read-cap return rocap def is_replication_setup(config: Config) -> bool: """ :return: ``True`` if and only if replication has previously been setup for the Tahoe-LAFS node associated with the given configuration. """ # Find the configuration path for this node's replica. return FilePath(config.get_private_path(REPLICA_RWCAP_BASENAME)).exists() def get_replica_rwcap(config: Config) -> CapStr: """ :return: a mutable directory capability for our replica. :raises: Exception if replication is not setup """ rwcap_file = FilePath(config.get_private_path(REPLICA_RWCAP_BASENAME)) return rwcap_file.getContent().decode("ascii") @define class _Important: """ A context-manager to set and unset the ._important flag on a _ReplicationCapableConnection """ _replication_cursor: _ReplicationCapableCursor def __enter__(self) -> None: self._replication_cursor._important = True def __exit__(self, *args) -> None: self._replication_cursor._important = False return None def with_replication( connection: _SQLite3Connection, enable_replication: bool ) -> _ReplicationCapableConnection: """ Wrap the given connection in a layer which is capable of entering a "replication mode". In replication mode, the wrapper stores all changes made through the connection so that they are available to be replicated by another component. In normal mode, changes are not stored. :param connection: The SQLite3 connection to wrap. :param enable_replication: If ``True`` then the wrapper is placed in "replication mode" initially. Otherwise it is not but it can be switched into that mode later. :return: The wrapper object. """ return _ReplicationCapableConnection(connection, enable_replication) Mutation = tuple[bool, str, Iterable[tuple[SQLType, ...]]] MutationObserver = Callable[[_SQLite3Cursor, Iterable[Mutation]], Callable[[], None]] @define class _ReplicationCapableConnection: """ Wrap a ``sqlite3.Connection`` to provide additional snapshot- and streaming replication-related features. All of this type's methods are intended to behave the same way as ``sqlite3.Connection``\ 's methods except they may also add some additional functionality to support replication. :ivar _replicating: ``True`` if this connection is currently in replication mode and is recording all executed DDL and DML statements, ``False`` otherwise. """ # the "real" / normal sqlite connection _conn: _SQLite3Connection _replicating: bool _observers: tuple[MutationObserver, ...] = Factory(tuple) _mutations: list[Mutation] = Factory(list) def enable_replication(self) -> None: """ Turn on replication support. """ self._replicating = True def add_mutation_observer(self, fn: MutationObserver) -> None: """ Add another observer of changes made through this connection. :param fn: An object to call after any transaction with changes is committed on this connection. """ self._observers = self._observers + (fn,) def iterdump(self) -> Iterator[str]: """ :return: SQL statements which can be used to reconstruct the database state. """ return self._conn.iterdump() def close(self) -> None: return self._conn.close() def __enter__(self) -> _ReplicationCapableConnection: self._conn.__enter__() return self def __exit__( self, exc_type: Optional[type], exc_value: Optional[BaseException], exc_tb: Optional[Any], ) -> bool: propagate = self._conn.__exit__(exc_type, exc_value, exc_tb) if exc_type is None: # There was no exception, signal observers that a change has been # committed. post_txn_fns: list[Callable[[], None]] = [] with self._conn: curse = self._conn.cursor() curse.execute("BEGIN IMMEDIATE TRANSACTION") post_txn_fns.extend(self._maybe_signal_observers(curse)) for f in post_txn_fns: f() # Respect the underlying propagation decision. return propagate def _maybe_signal_observers( self, cursor ) -> Generator[Callable[[], None], None, None]: """ If there are recorded mutations, deliver them to each of the observers and then forget about them. :return: A generator of the return values of the observers. """ if self._mutations: to_signal = self._mutations self._mutations = list() for ob in self._observers: yield ob(cursor, to_signal) def cursor(self, factory: Optional[type] = None) -> _ReplicationCapableCursor: """ Get a replication-capable cursor for this connection. """ kwargs = {} if factory is not None: kwargs["factory"] = factory cursor = self._conn.cursor(**kwargs) # this cursor honors the ._replicating flag in this instance return _ReplicationCapableCursor(cursor, self) @define class _ReplicationCapableCursor: """ Wrap a ``sqlite3.Cursor`` to provide additional streaming replication-related features. All of this type's attributes and methods are intended to behave the same way as ``sqlite3.Cursor``\ 's methods except they may also add some additional functionality to support replication. """ _cursor: _SQLite3Cursor _connection: _ReplicationCapableConnection # true while statements are "important" (which is pased along to # the observers and interpreted as being "important data that the # user will be interested in preserving") _important: bool = field(init=False, default=False) @property def lastrowid(self): return self._cursor.lastrowid @property def rowcount(self): return self._cursor.rowcount def close(self): return self._cursor.close() def execute(self, statement: str, row: Iterable[SQLType] = ()) -> Cursor: """ sqlite's Cursor API :param row: the arguments """ assert isinstance(row, tuple) self._cursor.execute(statement, row) if self._connection._replicating and statement_mutates(statement): # note that this interface is for multiple statements, so # we turn our single row into a one-tuple self._connection._mutations.append((self._important, statement, (row,))) return self def fetchall(self): return self._cursor.fetchall() def fetchmany(self, n): return self._cursor.fetchmany(n) def fetchone(self): return self._cursor.fetchone() def executemany(self, statement: str, rows: Iterable[Any]) -> Cursor: self._cursor.executemany(statement, rows) if self._connection._replicating and statement_mutates(statement): self._connection._mutations.append((self._important, statement, rows)) return self def important(self) -> _Important: """ Create a new context-manager that -- while active -- sets the 'important' flag to true and resets it afterwards. """ return _Important(self) def statements_to_snapshot(statements: Iterator[str]) -> bytes: """ Take a snapshot of the database reachable via the given connection. The snapshot is consistent and write transactions on the given connection are blocked until it has been completed. """ return cbor2.dumps({"version": 1, "statements": [x for x in statements]}) def connection_to_statements(connection: Connection) -> Iterator[str]: """ Create an iterator of SQL statements as strings representing a consistent, self-contained snapshot of the database reachable via the given connection. """ return iter(connection.iterdump()) # Convenience API to dump statements and encode them for storage. snapshot: Callable[[Connection], bytes] = compose( statements_to_snapshot, connection_to_statements ) async def tahoe_lafs_uploader( client: ITahoeClient, recovery_cap: str, get_snapshot_data: DataProvider, entry_name: str, ) -> None: """ Upload a replica to Tahoe, linking the result into the given recovery mutable capbility under the name given by :py:data:`SNAPSHOT_NAME`. """ snapshot_immutable_cap = await client.upload(get_snapshot_data) await client.link(recovery_cap, entry_name, snapshot_immutable_cap) def get_tahoe_lafs_direntry_uploader( client: ITahoeClient, directory_mutable_cap: str, ) -> Callable[[str, DataProvider], Awaitable[None]]: """ Bind a Tahoe client to a mutable directory in a callable that will upload some data and link it into the mutable directory under the given name. :return: A callable that will upload some data as the latest replica snapshot. The data isn't given directly, but instead from a zero-argument callable itself to facilitate retrying. """ async def upload(entry_name: str, get_data_provider: DataProvider) -> None: await tahoe_lafs_uploader( client, directory_mutable_cap, get_data_provider, entry_name ) return upload def get_tahoe_lafs_direntry_pruner( client: ITahoeClient, directory_mutable_cap: str, ) -> Callable[[Callable[[str], bool]], Awaitable[None]]: """ Bind a Tahoe client to a mutable directory in a callable that will unlink some entries. Which entries to unlink are controlled by a predicate. :return: A callable that will unlink some entries given a predicate. The prediate is given a filename inside the mutable to consider. """ async def maybe_unlink(predicate: Callable[[str], bool]) -> None: """ For each child of `directory_mutable_cap` delete it iff the predicate returns True for that name """ entries = await client.list_directory(directory_mutable_cap) for name in entries.keys(): if predicate(name): await client.unlink(directory_mutable_cap, name) return maybe_unlink def get_tahoe_lafs_direntry_lister( client: ITahoeClient, directory_mutable_cap: str ) -> EntryLister: """ Bind a Tahoe client to a mutable directory in a callable that will list the entries of that directory. """ async def lister() -> dict[str, DirectoryEntry]: entries = await client.list_directory(directory_mutable_cap) return { name: DirectoryEntry(kind, entry.get("size", 0)) for name, (kind, entry) in entries.items() } return lister def get_tahoe_lafs_direntry_replica( client: ITahoeClient, directory_mutable_cap: str ) -> Replica: """ Get an object that can interact with a replica stored in a Tahoe-LAFS mutable directory. """ uploader = get_tahoe_lafs_direntry_uploader(client, directory_mutable_cap) pruner = get_tahoe_lafs_direntry_pruner(client, directory_mutable_cap) lister = get_tahoe_lafs_direntry_lister(client, directory_mutable_cap) return Replica(uploader, pruner, lister) def add_events( cursor: _SQLite3Cursor, events: Iterable[tuple[str, Sequence[SQLType]]], important: bool, ) -> None: """ Add some new changes to the event-log. """ sql_args = [] for sql, args in events: assert all( isinstance(a, SQLRuntimeType) for a in args ), f"{args} contains non-SQL value" sql_args.append((sql, cbor2.dumps(args), important)) cursor.executemany( """ INSERT INTO [event-stream]([statement], [serialized_arguments], [important]) VALUES (?, ?, ?) """, sql_args, ) def get_events(conn: _SQLite3Connection) -> EventStream: """ Return all events currently in our event-log. """ with conn: cursor = conn.cursor() cursor.execute( """ SELECT [sequence-number], [statement], [serialized_arguments], [important] FROM [event-stream] """ ) rows = cursor.fetchall() return EventStream( changes=[ # List comprehension has incompatible type List[Change]; expected List[_T_co] # https://github.com/python-attrs/attrs/issues/519 Change(seq, stmt, cbor2.loads(arguments), important) # type: ignore for seq, stmt, arguments, important in rows ] ) def prune_events_to(conn: _SQLite3Connection, sequence_number: int) -> None: """ Remove all events <= sequence_number """ with conn: cursor = conn.cursor() cursor.execute( """ DELETE FROM [event-stream] WHERE [sequence-number] <= (?) """, (sequence_number,), ) cursor.fetchall() @frozen class AccumulatedChanges: """ A summary of some changes that have been made. :ivar important: Are any of these "important" changes? :ivar size: The approximate size in bytes to represent all of the changes. """ important: bool size: int @classmethod def no_changes(cls) -> AccumulatedChanges: """ Create an ``AccumulatedChanges`` that represents no changes. """ return cls(False, 0) @classmethod def from_connection(cls, connection: _SQLite3Connection) -> AccumulatedChanges: """ Load information about unreplicated changes from the database. """ # this size is larger than what we would have computed via # `from_changes` which only counts the statement-sizes .. but # maybe fine? (also could fix by just accumulating the # statement-sizes instead below) events = get_events(connection) data = events.to_bytes() size = data.seek(0, os.SEEK_END) any_important = any(change.important for change in events.changes) return cls(any_important, size) @classmethod def from_statements( cls, important: bool, statements: Iterable[tuple[str, Sequence[SQLType]]], ) -> AccumulatedChanges: """ Load information about unreplicated changes from SQL statements giving those changes. """ # note that we're ignoring a certain amount of size overhead here: the # _actual_ size will be some CBOR information and the sequence number, # although the statement text should still dominate. # XXX Fix the size calculation return cls(important, sum(len(sql) for (sql, _) in statements)) def __add__(self, other: AccumulatedChanges) -> AccumulatedChanges: return AccumulatedChanges( self.important or other.important, self.size + other.size ) def event_stream_name(high_seq: int) -> str: """ Construct the basename of the event stream object containing the given highest sequence number. """ return f"event-stream-{high_seq}" @define class _ReplicationService(Service): """ Perform all activity related to maintaining a remote replica of the local ZKAPAuthorizer database. If this service is running for a database then the database is in replication mode and changes will be uploaded. :ivar _connection: A connection to the database being replicated. :ivar _replicating: The long-running replication operation. This is never expected to complete but it will be cancelled when the service stops. """ name = "replication-service" # type: ignore # Service assigns None, screws up type inference _logger = Logger() _connection: _ReplicationCapableConnection = field() _replica: Replica _snapshot_policy: SnapshotPolicy _replicating: Optional[Deferred] = field(init=False, default=None) _changes: AccumulatedChanges = AccumulatedChanges.no_changes() _jobs: DeferredQueue = field(factory=DeferredQueue) @property def _unreplicated_connection(self): """ A normal SQLite3 connection object, changes made via which will not be replicated. """ return self._connection._conn def startService(self) -> None: super().startService() # Register ourselves as a change observer (first! we don't want to # miss anything) and then put the database into replication mode so # that there are recorded events for us to work with. self._connection.add_mutation_observer(self.observed_event) self._connection.enable_replication() # Reflect whatever state is left over in the database from previous # efforts. self._changes = AccumulatedChanges.from_connection( self._unreplicated_connection ) self.queue_job(ReplicationJob.startup) # Start the actual work of reacting to changes by uploading them (as # appropriate). self._replicating = Deferred.fromCoroutine(self._replicate()) async def _replicate(self) -> None: """ React to changes by replicating them to remote storage. """ try: await self.wait_for_uploads() except CancelledError: # Ignore cancels; this will be the normal way we quit -- see # stopService. pass except Exception: # If something besides a cancel happens, at least make it visible. self._logger.failure("unexpected wait_for_uploads error") return None def queue_job(self, job: ReplicationJob) -> None: """ Queue a job, if it is not already queued, to be executed after any other queued jobs. """ if job not in self._jobs.pending: self._jobs.put(job) @log_call(action_type="zkapauthorizer:replicate:queue-event-upload") def queue_event_upload(self) -> None: """ Request an event-stream upload of outstanding events. """ self.queue_job(ReplicationJob.event_stream) @log_call(action_type="zkapauthorizer:replicate:queue-snapshot-upload") def queue_snapshot_upload(self) -> None: """ Request that an upload of a new snapshot occur. Stale event-streams will also be pruned after the snapshot is successfully uploaded. """ self.queue_job(ReplicationJob.snapshot) async def wait_for_uploads(self) -> None: """ An infinite async loop that processes uploads of event-streams or snapshots """ while True: job = await self._jobs.get() if job == ReplicationJob.event_stream: await self._do_one_event_upload() elif job == ReplicationJob.snapshot: await self._do_one_snapshot_upload() elif job == ReplicationJob.consider_snapshot: await self._do_consider_snapshot() elif job == ReplicationJob.startup: await self._do_startup() else: raise Exception("internal error") # pragma: nocover async def _do_startup(self) -> None: """ Check local and remote state to determine if there is any work that should be done immediately. Currently, this will upload a snapshot if none exists in the replica, or upload an event stream if there are events that warrant immediate upload. """ if await self.should_upload_snapshot(): self.queue_snapshot_upload() elif self.should_upload_eventstream(self._changes): self.queue_event_upload() @log_call(action_type="zkapauthorizer:replicate:snapshot-upload") async def _do_one_snapshot_upload(self) -> None: """ Perform a single snapshot upload, including pruning event-streams from the replica that are no longer relevant. """ # extract sequence-number and snapshot data seqnum = 1 rows = ( self._connection.cursor() .execute( "SELECT seq FROM sqlite_sequence WHERE name = 'event-stream'", tuple() ) .fetchall() ) if len(rows): seqnum = int(rows[0][0]) snap = snapshot(self._connection) # upload snapshot await self._replica.upload("snapshot", lambda: BytesIO(snap)) # remove local event history (that should now be encapsulated # by the snapshot we just uploaded) prune_events_to(self._connection._conn, seqnum) # if we crash here, there will be extra event-stream objects # in the replica. This will be fixed correctly upon our next # snapshot upload. The extra event-stream objects will be # ignored by the recovery code. # prune old events from the replica def is_old_eventstream(fname: str) -> bool: """ :returns: True if the `fname` is an event-stream object and the sequence number is strictly less than our snapshot's maximum sequence. """ m = re.match("event-stream-([0-9]*)", fname) if m: seq = int(m.group(1)) if seq <= seqnum: return True return False await self._replica.prune(is_old_eventstream) @log_call(action_type="zkapauthorizer:replicate:event-upload") async def _do_one_event_upload(self) -> None: """ Process a single upload of all current events and then delete them from our database. """ events = get_events(self._unreplicated_connection) high_seq = events.highest_sequence() # if this is None there are no events at all if high_seq is None: return # otherwise, upload the events we found. name = event_stream_name(high_seq) await self._replica.upload(name, events.to_bytes) # then discard the uploaded events from the local database. prune_events_to(self._unreplicated_connection, high_seq) # Arrange to examine replica state soon to determine whether taking a # new snapshot and pruning existing event streams is useful. self.queue_job(ReplicationJob.consider_snapshot) async def _do_consider_snapshot(self) -> None: """ Inspect local and remote state to decide if the cost of taking and uploading a new snapshot is worth the resulting savings in storage. """ local_size = await self._new_snapshot_size() replica_size = await self._replica_size() if self._snapshot_policy.should_snapshot(local_size, replica_size): self.queue_snapshot_upload() async def _new_snapshot_size(self) -> int: """ Measure the size of snapshot of the current database state, in bytes. """ return len(snapshot(self._connection)) async def _replica_size(self) -> list[int]: """ Retrieve the size of all the files that are part of the current on-grid replica. """ entries = await self._replica.entry_lister() return [ entry.size for (name, entry) in entries.items() if entry.kind == "filenode" and name == "snapshot" or name.startswith("event-stream-") ] def stopService(self) -> Deferred[None]: """ Cancel the replication operation and then wait for it to complete. """ super().stopService() replicating = self._replicating if replicating is None: return succeed(None) self._replicating = None replicating.cancel() return replicating def observed_event( self, unobserved_cursor: _SQLite3Cursor, all_changes: Iterable[Mutation], ) -> Callable[[], None]: """ A mutating SQL statement was observed by the cursor. This is like the executemany interface: there is always a list of args. For a single statement, we call this with the len(args) == 1 :param all_changes: 3-tuples of (important, statement, args) where important is whether this should trigger an immediate upload; statement is the SQL statement; and args are the arguments for the SQL. """ # A mutation contains one statement and one or more rows of arguments # that go with it. We're going to generate an event per # statement/argument pair - so "unroll" those rows and pair each # individual argument tuple with its statement. events = [] any_important = False for (important, sql, manyargs) in all_changes: any_important = any_important or important for args in manyargs: events.append((sql, args)) add_events(unobserved_cursor, events, any_important) changes = AccumulatedChanges.from_statements(any_important, events) self._changes = self._changes + changes if self.should_upload_eventstream(self._changes): return self._complete_upload else: return lambda: None def _complete_upload(self) -> None: """ This is called after the transaction closes (because we return it from our observer function). See _ReplicationCapableConnection.__exit__ """ self.queue_event_upload() self._changes = AccumulatedChanges.no_changes() async def should_upload_snapshot(self) -> bool: """ :returns: True if there is no remote snapshot """ entries = await self._replica.list() return SNAPSHOT_NAME not in entries def should_upload_eventstream(self, changes: AccumulatedChanges) -> bool: """ :returns: True if we have accumulated enough statements to upload an event-stream record. """ return changes.important or changes.size >= 570000 def replication_service( replicated_connection: _ReplicationCapableConnection, replica: Replica, snapshot_policy: SnapshotPolicy, ) -> IService: """ Return a service which implements the replication process documented in the ``backup-recovery`` design document. """ return _ReplicationService( connection=replicated_connection, replica=replica, snapshot_policy=snapshot_policy, )

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ultracart/models/item_digital_item.py

gstingy/uc_python_api

0

152087

<reponame>gstingy/uc_python_api # coding: utf-8 """ UltraCart Rest API V2 UltraCart REST API Version 2 OpenAPI spec version: 2.0.0 Contact: <EMAIL> Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class ItemDigitalItem(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'creation_dts': 'str', 'description': 'str', 'file_size': 'int', 'mime_type': 'str', 'original_filename': 'str' } attribute_map = { 'creation_dts': 'creation_dts', 'description': 'description', 'file_size': 'file_size', 'mime_type': 'mime_type', 'original_filename': 'original_filename' } def __init__(self, creation_dts=None, description=None, file_size=None, mime_type=None, original_filename=None): """ ItemDigitalItem - a model defined in Swagger """ self._creation_dts = None self._description = None self._file_size = None self._mime_type = None self._original_filename = None self.discriminator = None if creation_dts is not None: self.creation_dts = creation_dts if description is not None: self.description = description if file_size is not None: self.file_size = file_size if mime_type is not None: self.mime_type = mime_type if original_filename is not None: self.original_filename = original_filename @property def creation_dts(self): """ Gets the creation_dts of this ItemDigitalItem. File creation date :return: The creation_dts of this ItemDigitalItem. :rtype: str """ return self._creation_dts @creation_dts.setter def creation_dts(self, creation_dts): """ Sets the creation_dts of this ItemDigitalItem. File creation date :param creation_dts: The creation_dts of this ItemDigitalItem. :type: str """ self._creation_dts = creation_dts @property def description(self): """ Gets the description of this ItemDigitalItem. Description of the digital item :return: The description of this ItemDigitalItem. :rtype: str """ return self._description @description.setter def description(self, description): """ Sets the description of this ItemDigitalItem. Description of the digital item :param description: The description of this ItemDigitalItem. :type: str """ if description is not None and len(description) > 200: raise ValueError("Invalid value for `description`, length must be less than or equal to `200`") self._description = description @property def file_size(self): """ Gets the file_size of this ItemDigitalItem. File size :return: The file_size of this ItemDigitalItem. :rtype: int """ return self._file_size @file_size.setter def file_size(self, file_size): """ Sets the file_size of this ItemDigitalItem. File size :param file_size: The file_size of this ItemDigitalItem. :type: int """ self._file_size = file_size @property def mime_type(self): """ Gets the mime_type of this ItemDigitalItem. Mime type associated with the file :return: The mime_type of this ItemDigitalItem. :rtype: str """ return self._mime_type @mime_type.setter def mime_type(self, mime_type): """ Sets the mime_type of this ItemDigitalItem. Mime type associated with the file :param mime_type: The mime_type of this ItemDigitalItem. :type: str """ if mime_type is not None and len(mime_type) > 100: raise ValueError("Invalid value for `mime_type`, length must be less than or equal to `100`") self._mime_type = mime_type @property def original_filename(self): """ Gets the original_filename of this ItemDigitalItem. Original filename :return: The original_filename of this ItemDigitalItem. :rtype: str """ return self._original_filename @original_filename.setter def original_filename(self, original_filename): """ Sets the original_filename of this ItemDigitalItem. Original filename :param original_filename: The original_filename of this ItemDigitalItem. :type: str """ if original_filename is not None and len(original_filename) > 250: raise ValueError("Invalid value for `original_filename`, length must be less than or equal to `250`") self._original_filename = original_filename def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ if not isinstance(other, ItemDigitalItem): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

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resources/mstats/__init__.py

mvelezg99/distribuciones-de-probabilidad-python

8

178649

<reponame>mvelezg99/distribuciones-de-probabilidad-python """ mstats: A statistics and probability library for Python ======================================================= """ from mstats import anova from mstats import distributions from mstats import generals from mstats import hypothesis from mstats import intervals from mstats import categorical from mstats import linregr from mstats import graph from mstats import multiregr from mstats import timeseries

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src/creeps/__init__.py

ppolewicz/screeps-starter-python

0

98283

<reponame>ppolewicz/screeps-starter-python from creeps.builder import Builder from creeps.harvester import Harvester from creeps.miner import Miner from creeps.hauler import Hauler from creeps.scout import Scout from creeps.claimer import Claimer from creeps.upgrader import Upgrader from creeps.settler import Settler from creeps.cleanser import Cleanser CREEP_CLASSES = dict({ 'builder': Builder, 'harvester': Harvester, 'miner': Miner, 'hauler': Hauler, 'scout': Scout, 'claimer': Claimer, 'upgrader': Upgrader, 'settler': Settler, 'cleanser': Cleanser, })

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AngularJSLibrary/test/testExecAsyncWithCallback.py

klaasderks/robotframework-angularjs

35

127472

<filename>AngularJSLibrary/test/testExecAsyncWithCallback.py import unittest from selenium import webdriver js_waiting_var=""" var waiting = true; var callback = arguments[arguments.length - 1]; var el = document.querySelector('#nested-ng-app'); angular.element(el).injector().get('$browser'). notifyWhenNoOutstandingRequests(callback); return waiting; """ js_return_callback=""" var callback = arguments[arguments.length - 1]; return callback; """ js_callback=""" var callback = arguments[arguments.length - 1]; """ js_timeout=""" var h = setTimeout(console.log('.'),2000); """ js_async_with_callback=""" var callback = arguments[arguments.length - 1]; var rootSelector = '[ng-app]' functions.waitForAngular = function(rootSelector, callback) { var el = document.querySelector(rootSelector); try { if (window.getAngularTestability) { window.getAngularTestability(el).whenStable(callback); return; } if (!window.angular) { throw new Error('window.angular is undefined. This could be either ' + 'because this is a non-angular page or because your test involves ' + 'client-side navigation, which can interfere with Protractor\'s ' + 'bootstrapping. See http://git.io/v4gXM for details'); } if (angular.getTestability) { angular.getTestability(el).whenStable(callback); } else { if (!angular.element(el).injector()) { throw new Error('root element (' + rootSelector + ') has no injector.' + ' this may mean it is not inside ng-app.'); } angular.element(el).injector().get('$browser'). notifyWhenNoOutstandingRequests(callback); } } catch (err) { callback(err.message); } }; """ js_tada=""" var done=arguments[0]; setTimeout(function() { done('tada'); }, 10000); """ class ExecuteAsyncJavascriptWithCallback(unittest.TestCase): def setUp(self): self.driver = webdriver.Firefox() self.driver.set_script_timeout(30) def test_exe_javascript(self): driver = self.driver driver.get("http://localhost:7000/testapp/ng1/alt_root_index.html#/async") # waiting = driver.execute_script(js_tada) # print('%s' % waiting) try: while (True): #waiting = driver.execute_async_script(js_async_with_callback) #waiting = driver.execute_async_script(js_waiting_var) #waiting = driver.execute_async_script(js_callback,"print 'Hello World|'") #waiting = driver.execute_script(js_return_callback,"return false;") waiting = driver.execute_script(js_tada) if waiting != None: print('%s' % waiting) except KeyboardInterrupt: pass def tearDown(self): # self.driver.close() pass if __name__ == "__main__": unittest.main()

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Code/Testing/AlexNet Testing Code.py

apoorva2398/APS360-Project-Team-1

1

86236

#!/usr/bin/env python # coding: utf-8 # In[7]: import torch import torch.nn as nn from torchvision import datasets, models, transforms import os # In[8]: # Load the test data. data_transforms = { 'test': transforms.Compose([ transforms.Resize([224, 224]), transforms.ToTensor() ]) } data_dir = 'D:\data (augmented, 4 classes, tif)' image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['test']} batch_size = 128 dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True, num_workers=4) for x in ['test']} dataset_sizes = {x: len(image_datasets[x]) for x in ['test']} class_names = image_datasets['test'].classes device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # In[9]: # Load the saved model state_dict for inference (done later to keep len(class_names) after the dataloader dynamic). model_ft = models.alexnet(pretrained=True) model_ft.classifier[6] = nn.Linear(4096, len(class_names)) model_ft.classifier.add_module("7", nn.Dropout()) # Comment this out if the AlexNet model did not include the last dropout layer. PATH = "D:\Models\model_20190411-093358.pth" model_ft.load_state_dict(torch.load(PATH)) model_ft = model_ft.to(device) # In[10]: was_training = model_ft.training model_ft.eval() all_labels = [] all_preds = [] with torch.no_grad(): for inputs, labels in dataloaders['test']: # The labels will correspond to the alphabetical order of the class names (https://discuss.pytorch.org/t/how-to-get-the-class-names-to-class-label-mapping/470). inputs = inputs.to(device) labels = labels.to(device) labels_list = labels.tolist() all_labels.extend(labels_list) outputs = model_ft(inputs) _, preds = torch.max(outputs, 1) preds_list = preds.tolist() all_preds.extend(preds_list) model_ft.train(mode=was_training) # In[11]: from pandas_ml import ConfusionMatrix cm = ConfusionMatrix(all_labels, all_preds) cm.print_stats()

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src/plog/mixins.py

Strangemother/project-conceptnet-graphing

0

160338

<reponame>Strangemother/project-conceptnet-graphing from io import StringIO from plog import patterns class MixinBase(object): ''' Base mixin object of all Plog Mixins to inherit''' def __init__(self, *args, **kwargs): pass class PlogFileMixin(MixinBase): ''' A PlogFileMixin is designed to wrap a file object to represent correctly for enumeration. You can pass a string or a file object. Strings are converted to StringIO.StringIO before use. Pass this to the class for easy get_file, set_file methods ''' def __init__(self, *args, **kwargs): ''' initial file can be given ''' self._file = None self._data = None if len(args) > 0: ''' could be file or string''' self.set_data(args[0]) super(PlogFileMixin, self).__init__(*args, **kwargs) def set_file(self, log_file): ''' Add a file to the class to parse This will return a StringIO if a string was passed as data.''' self.set_data(log_file) def set_data(self, data): ''' wrapper for applying the file content to the class''' if type(data) == str: output = StringIO.StringIO(data) self._data = output else: self._data = data def get_data(self): return self._data def get_file(self): ''' return the internal file, This will return a StringIO if a string was passed as data ''' return self.get_data() class PlogBlockMixin(MixinBase): ''' Methods to assist block design on on a class ''' def __init__(self, *args, **kwargs): self._blocks = [] super(PlogBlockMixin, self).__init__(*args, **kwargs) def blocks(): doc = "The blocks property." def fget(self): return self._blocks def fset(self, value): self._blocks = value def fdel(self): del self._blocks return locals() blocks = property(**blocks()) def block_match(self, block, line, string, reg): ''' Method called upon a successfull match of a block header block {PlogBlock} - The block containing the matched PlogLine line {PlogLine} - PlogLine matched string {str} - String matched from file reg {regex} - Regex SRE match object (if it exists) ''' #print '> Block', block #print ' Match', line #print ' Found', string, '\n' def compile_blocks(self): '''Call each block to compile as needed''' for block in self.blocks: block.compile() def close_all_blocks(self): ''' Close any dangling blocks (blocks open at the end of parsing) and return a list of closed blocks. ''' closed =[] for block in self.blocks: if block.is_open: print('force close', block.ref) closed.append(block) block.close() return closed def add_blocks(self, *args): ''' Add a list of blocks to append add_blocks(block1, block2, block3, ... ) add_blocks(*blocks) ''' for block in args: self.add_block(block) def add_block(self, block): ''' Append a plog block to all valid blocks. ''' _block = block if type(block) == str: _block = patterns.PlogBlock(block) elif hasattr(_block, 'block'): _block = _block.block self._blocks.append(_block) def get_blocks_with_header(self, *args): ''' Pass one or many PlogLines and return all matching blocks with the headers of that type. ''' header_line = args[0] # Loop blocks. _blocks = [] for block in self.blocks: mtch, reg = block.header.match(header_line) if mtch: _blocks.append(block) self.block_match(block, block.header, header_line, reg) return _blocks def get_blocks_with_header_footer(self, *args): ''' Pass one or many PlogLines and return all matching blocks with the headers of that type. ''' mline = args[0] # Loop blocks. _blocks = [] mtch = None mtch_f = None reg = None reg_f = None for block in self.blocks: if block.header: mtch, reg = block.header.match(mline) if block.footer: mtch_f, reg_f = block.footer.match(mline) if mtch_f or mtch: _blocks.append(block) if mtch: self.block_match(block, block.header, mline, reg or reg_f) if mtch_f: self.block_match(block, block.footer, mline, reg or reg_f) return (_blocks, True if mtch else False, )

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gdcv/db/base_match_state.py

the-blue-alliance/gdcv-backend

4

179250

<filename>gdcv/db/base_match_state.py from sqlalchemy import Column, String, Integer, BigInteger, PrimaryKeyConstraint class BaseMatchState(object): __table_args__ = ( PrimaryKeyConstraint('event_key', 'match_id', 'wall_time'), ) event_key = Column(String(16), nullable=False) # Like 2017nyny match_id = Column(String(16), nullable=False) # Like qm1 play = Column(Integer, nullable=False) # Accounts for replays wall_time = Column(BigInteger, nullable=False) mode = Column(String(16)) # pre_match, auto, teleop, post_match time_remaining = Column(Integer) # Number of seconds remaining in mode red_score = Column(Integer) blue_score = Column(Integer)

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trainer.py

Xavierxhq/tableware

0

81347

import os, random, sys, time import torch from os import path as osp from pprint import pprint import numpy as np from torch import nn from torch.backends import cudnn import utils.loss as loss from models import get_baseline_model from utils.meters import AverageMeter from utils.loss import TripletLoss from utils.serialization import save_checkpoint from tester import get_feature, pickle_read, pickle_write, dist, evaluate_model def load_model(base_model, model_path): model_parameter = torch.load(model_path) base_model.load_state_dict(model_parameter['state_dict']) base_model = base_model.cuda() print('model', model_path.split('/')[-1], 'loaded.') return base_model def _compute_center_with_label(model, label, data_pth, sample_count): temp_name = './evaluate_result/feature_map/%f.pkl' % time.time() processed_imgs = [data_pth + x for x in os.listdir(data_pth) if x.split('.')[0].split('_')[-1] == str(label)] random.shuffle(processed_imgs) # use sample_count*4 pictures to get the center for img_path in processed_imgs[:sample_count*4]: f = get_feature(img_path, model) _store_features_temp(temp_name, str(label), f) obj = pickle_read(temp_name) _avg_feature = None for _f in obj[str(label)]: if _avg_feature is None: _avg_feature = _f else: _avg_feature = _f.add(_avg_feature) # get the center _avg_feature /= len(obj[str(label)]) dist_dict = {} for _i, _f in enumerate(obj[str(label)]): _d = dist(_avg_feature, _f) dist_dict[str(_i)] = _d dist_dict = sorted(dist_dict.items(), key=lambda d: d[1]) # for index, _d in dist_dict: # print(index, _d) # exit(1000) _avg_feature = obj[str(label)][int(dist_dict[0][0])] _max_feature_ls, _min_feature_ls = [], [] # the feature with smalleset distances for index, _d in dist_dict[1:sample_count+1]: _min_feature_ls.append(obj[str(label)][int(index)]) # those with largest distances for index, _d in dist_dict[-1*sample_count:]: _max_feature_ls.append(obj[str(label)][int(index)]) os.remove(temp_name) return _avg_feature, _max_feature_ls, _min_feature_ls def _store_features_temp(feature_map_name, label, feature): if os.path.exists(feature_map_name): obj = pickle_read(feature_map_name) obj[label].append(feature) else: obj = { label: [feature] } pickle_write(feature_map_name, obj) def _get_negative_feature(model, label, data_pth): all_nagative_imgs = [data_pth + x for x in os.listdir(data_pth) if x.split('.')[0].split('_')[-1] != str(label)] random.shuffle(all_nagative_imgs) return get_feature(all_nagative_imgs[0], model) def _get_positive_feature(model, label, data_pth): all_positive_imgs = [data_pth + x for x in os.listdir(data_pth) if x.split('.')[0].split('_')[-1] == str(label)] random.shuffle(all_positive_imgs) return get_feature(all_positive_imgs[0], model) def _get_input_samples(model, labels, data_pth, sample_count=64): if sample_count % len(labels) != 0: print('sample_count should times the count of labels') return sample_count_for_one_label = int(sample_count / len(labels)) candidate_count_for_one_label = sample_count_for_one_label * 4 anchors, positives, negatives = [], [], [] for label in labels: _avg_feature, _max_feature_ls, _min_feature_ls = _compute_center_with_label(model, label, data_pth, sample_count=sample_count_for_one_label) for i in range(len(_max_feature_ls)): anchors.append(_avg_feature) if random.randint(1, 10) > 50: positives.append(_max_feature_ls[i]) # positives.append(_min_feature_ls[i]) else : positives.append(_get_positive_feature(model, label, data_pth)) negatives.append(_get_negative_feature(model, label, data_pth)) return anchors, positives, negatives def train(model, optimizer, criterion, epoch, print_freq, data_loader, data_pth): # model.train() losses = AverageMeter() is_add_margin = False labels_count_in_one_batch = 8 labels_count_for_all = 40 iteration_count = 40 start = time.time() for _j in range(iteration_count): for i in range(int(labels_count_for_all / labels_count_in_one_batch)): start_label = i * labels_count_in_one_batch + 1 model.eval() feat1, feat2, feat3 = _get_input_samples(model, [x for x in range(start_label, start_label + labels_count_in_one_batch)], data_pth) model.train() loss = criterion(torch.stack(feat1), torch.stack(feat2), torch.stack(feat3)) optimizer.zero_grad() # backward loss.backward() optimizer.step() losses.update(loss.item()) if (_j * int(labels_count_for_all / labels_count_in_one_batch) + i + 1) % print_freq == 0: print('Epoch: [{}][{}/{}]\t' 'Loss {:.6f} ({:.6f})\t' .format(epoch, _j * int(labels_count_for_all / labels_count_in_one_batch) + i + 1, iteration_count * int(labels_count_for_all / labels_count_in_one_batch), losses.val, losses.mean)) if losses.val < 1e-5: is_add_margin = True param_group = optimizer.param_groups print('Epoch: [{}]\tEpoch Time {:.1f} s\tLoss {:.6f}\t' 'Lr {:.2e}' .format(epoch, (time.time() - start), losses.mean, param_group[0]['lr'])) return is_add_margin def trainer(data_pth, a, b, _time=0, layers=18): seed = 0 # dataset options height, width = 128, 128 # optimization options optim = 'Adam' max_epoch = 20 train_batch = 64 test_batch = 64 lr = 0.1 step_size = 40 gamma = 0.1 weight_decay = 5e-4 momentum = 0.9 test_margin = b margin = a num_instances = 4 num_gpu = 1 # model options last_stride = 1 pretrained_model_18 = 'model/resnet18-5c106cde.pth' pretrained_model_50 = 'model/resnet50-19c8e357.pth' pretrained_model_34 = 'model/resnet34-333f7ec4.pth' pretrained_model_101 = 'model/resnet101-5d3b4d8f.pth' pretrained_model_152 = 'model/resnet152-b121ed2d.pth' # miscs print_freq = 10 eval_step = 1 save_dir = 'model/pytorch-ckpt/time%d' % _time workers = 1 torch.manual_seed(seed) use_gpu = torch.cuda.is_available() if use_gpu: print('currently using GPU') cudnn.benchmark = True torch.cuda.manual_seed_all(seed) else: print('currently using cpu') pin_memory = True if use_gpu else False # model, optim_policy = get_baseline_model(model_path=pretrained_model) if layers == 18: model, optim_policy = get_baseline_model(model_path=pretrained_model_18, layers=18) else: model, optim_policy = get_baseline_model(model_path=pretrained_model_50, layers=50) # model, optim_policy = get_baseline_model(model_path=pretrained_model_18, layers=18) # model, optim_policy = get_baseline_model(model_path=pretrained_model_34, layers=34) # model, optim_policy = get_baseline_model(model_path=pretrained_model_101, layers=101) # model = load_model(model, model_path='./model/pytorch-ckpt/87_layers18_margin20_epoch87.tar') print('model\'s parameters size: {:.5f} M'.format(sum(p.numel() for p in model.parameters()) / 1e6)) tri_criterion = TripletLoss(margin) # get optimizer optimizer = torch.optim.Adam( optim_policy, lr=lr, weight_decay=weight_decay ) def adjust_lr(optimizer, ep): if ep < 20: lr = 1e-4 * (ep + 1) / 2 elif ep < 80: lr = 1e-3 * num_gpu elif ep < 180: lr = 1e-4 * num_gpu elif ep < 300: lr = 1e-5 * num_gpu elif ep < 320: lr = 1e-5 * 0.1 ** ((ep - 320) / 80) *num_gpu elif ep < 400: lr = 1e-6 elif ep < 480: lr = 1e-4 * num_gpu else: lr = 1e-5 * num_gpu for p in optimizer.param_groups: p['lr'] = lr if use_gpu: model = nn.DataParallel(model).cuda() max_acc = .0 for epoch in range(max_epoch): if step_size > 0: adjust_lr(optimizer, epoch + 1) next_margin = margin # skip if not save model if eval_step > 0 and (epoch + 1) % eval_step == 0 or (epoch + 1) == max_epoch: _t1 =time.time() train(model, optimizer, tri_criterion, epoch + 1, print_freq, None, data_pth=data_pth) _t2 = time.time() print('time for training:', '%.2f' % (_t2 - _t1), 's') acc = evaluate_model(model, margin=20, epoch=1) if acc > max_acc: max_acc = acc print('max acc:', max_acc, ', epoch:', epoch + 1) if use_gpu: state_dict = model.module.state_dict() else: state_dict = model.state_dict() save_model_name = 'layers{}_margin{}_epoch{}.tar'.format(layers, margin, epoch+1) save_checkpoint({ 'state_dict': state_dict, 'epoch': epoch + 1, }, is_best=False, save_dir=save_dir, filename=save_model_name) margin = next_margin return save_model_name if __name__ == "__main__": for _i in range(1): trainer('/home/ubuntu/Program/xhq/dataset/temp/train_data/', 20, 0, _time=_i, layers=50)

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care/facility/migrations/0010_auto_20200321_0758.py

tucosaurus/care

189

1617112

<reponame>tucosaurus/care<filename>care/facility/migrations/0010_auto_20200321_0758.py # Generated by Django 2.2.11 on 2020-03-21 07:58 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('facility', '0009_auto_20200320_1850'), ] operations = [ migrations.AlterField( model_name='hospitaldoctors', name='area', field=models.IntegerField(choices=[(1, 'General Medicine'), (2, 'Pulmonology'), (3, 'Critical Care'), (4, 'Paediatrics')]), ), ]

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cepan/_utils.py

kanga333/cepan

1

178845

<filename>cepan/_utils.py from typing import Any, Callable, Dict, Iterator, Optional import boto3 def client( service: str, session: Optional[boto3.Session] = None, ) -> boto3.client: if session is None: return boto3.client(service) return session.client(service) def call_with_pagination( client: boto3.client, func_name: str, args: Dict[str, Any], ) -> Iterator[Dict[str, Any]]: func: Callable[..., Dict[str, Any]] = getattr(client, func_name) response: Dict[str, Any] = func(**args) yield response token_key: Optional[str] = None if "NextToken" in response: token_key = "NextToken" if "NextPageToken" in response: token_key = "NextPageToken" while token_key in response: args[token_key] = response[token_key] response = func(**args) yield response

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src/ai/backend/kernel/git/__init__.py

hephaex/backend.ai-kernel-runner

1

157332

import asyncio import json import logging import os from pathlib import Path import re import subprocess # import pygit2 # from pygit2 import GIT_SORT_TOPOLOGICAL, GIT_SORT_REVERSE from .. import BaseRunner, Terminal log = logging.getLogger() CHILD_ENV = { 'TERM': 'xterm', 'LANG': 'C.UTF-8', 'SHELL': '/bin/bash', 'USER': 'work', 'HOME': '/home/work', 'PATH': '/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin', 'LD_PRELOAD': os.environ.get('LD_PRELOAD', '/home/sorna/libbaihook.so'), } class Runner(BaseRunner): log_prefix = 'shell-kernel' def __init__(self): super().__init__() self.child_env.update(CHILD_ENV) async def init_with_loop(self): self.user_input_queue = asyncio.Queue() self.term = Terminal( '/bin/bash', self.stopped, self.outsock, auto_restart=True, ) parser_show = self.term.subparsers.add_parser('show') parser_show.add_argument('target', choices=('graph',), default='graph') parser_show.add_argument('path', type=str) parser_show.set_defaults(func=self.do_show) await self.term.start() async def build_heuristic(self) -> int: raise NotImplementedError async def execute_heuristic(self) -> int: raise NotImplementedError async def query(self, code_text) -> int: return await self.term.handle_command(code_text) async def complete(self, data): return [] async def interrupt(self): # subproc interrupt is already handled by BaseRunner pass async def shutdown(self): await self.term.shutdown() def do_show(self, args): if args.target == 'graph': commit_branch_table = {} commit_info = [] if args.path in ['.', None]: current_dir = Path(f'/proc/{self.term.pid}/cwd').resolve() else: current_dir = Path(args.path).resolve() os.chdir(current_dir) # Create commit-branch matching table. tree_cmd = ['git', 'log', '--pretty=oneline', '--graph', '--source', '--branches'] run_res = subprocess.run(tree_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = run_res.stdout.decode('utf-8') stderr = run_res.stderr.decode('utf-8') prog = re.compile(r'([a-z0-9]+)\s+(\S+).*') if stderr: self.outsock.send_multipart([b'stderr', stderr.encode('utf-8')]) return for line in stdout.split('\n'): r = prog.search(line) if r and hasattr(r, 'group') and r.group(1) and r.group(2): oid = r.group(1)[:7] # short oid branch = r.group(2) commit_branch_table[oid] = branch # Gather commit info w/ branch name. log_cmd = ['git', 'log', '--pretty=format:%h||%p||%s||%cn', '--all', '--topo-order', '--reverse'] run_res = subprocess.run(log_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = run_res.stdout.decode('utf-8') for gitlog in stdout.split('\n'): items = gitlog.split('||') oid = items[0] parent_ids = items[1].split(' ') message = items[2] author = items[3] branch = commit_branch_table.get(oid, None) parent_branches = [commit_branch_table.get(pid, None) for pid in parent_ids] info = dict( oid=oid, parent_ids=parent_ids, author=author, message=message, branch=branch, parent_branches=parent_branches ) commit_info.append(info) self.outsock.send_multipart([ b'media', json.dumps({ 'type': 'application/vnd.sorna.gitgraph', 'data': commit_info }).encode('utf-8') ]) else: raise ValueError('Unsupported show target', args.target)

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code/bp_chrono/pull.py

niplav/site

2

170857

<gh_stars>1-10 import urllib2 from bs4 import BeautifulSoup import sys import datetime for year in range(2006, datetime.datetime.now().year+1): yearposts=[] for page in range(1, 1000): url='http://bit-player.org/{}/page/{}'.format(year, page) req=urllib2.Request(url, headers={'User-Agent' : "Firefox"}) try: con=urllib2.urlopen(req) except urllib2.HTTPError, e: break data=con.read() soup=BeautifulSoup(data, 'html.parser') posts=soup.find_all(class_="post") for p in posts: title=p.find_all(class_="entry-title")[0].a.text link=p.find_all(class_="entry-title")[0].a.get('href') meta=p.find_all(class_="entry-meta") author=p.find_all(class_="entry-meta")[0].find_all(class_='author')[0].a.text date=p.find_all(class_="entry-meta")[0].find_all(class_='entry-date')[0].text entry='* [{}]({}) ({}, {})'.format(title.encode('utf_8'), str(link), str(author), str(date)) yearposts.append(entry) print('\n### {}\n'.format(year)) for t in reversed(yearposts): print(t)

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sdk/python/pulumi_openstack/__init__.py

Frassle/pulumi-openstack

0

68419

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** # Make subpackages available: __all__ = ['blockstorage', 'compute', 'config', 'database', 'dns', 'firewall', 'identity', 'images', 'loadbalancer', 'networking', 'objectstorage', 'vpnaas']

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src/mem/slicc/ast/PeekStatementAST.py

qianlong4526888/haha

31

143045

# Copyright (c) 1999-2008 <NAME> and <NAME> # Copyright (c) 2009 The Hewlett-Packard Development Company # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer; # redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution; # neither the name of the copyright holders nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. from slicc.ast.StatementAST import StatementAST from slicc.symbols import Var class PeekStatementAST(StatementAST): def __init__(self, slicc, queue_name, type_ast, pairs, statements, method): super(PeekStatementAST, self).__init__(slicc, pairs) self.queue_name = queue_name self.type_ast = type_ast self.statements = statements self.method = method def __repr__(self): return "[PeekStatementAST: %r queue_name: %r type: %r %r]" % \ (self.method, self.queue_name, self.type_ast, self.statements) def generate(self, code, return_type): self.symtab.pushFrame() msg_type = self.type_ast.type # Add new local var to symbol table var = Var(self.symtab, "in_msg", self.location, msg_type, "(*in_msg_ptr)", self.pairs) self.symtab.newSymbol(var) # Check the queue type self.queue_name.assertType("InPort") # Declare the new "in_msg_ptr" variable mtid = msg_type.ident qcode = self.queue_name.var.code code(''' { // Declare message const $mtid* in_msg_ptr M5_VAR_USED; in_msg_ptr = dynamic_cast<const $mtid *>(($qcode).${{self.method}}()); assert(in_msg_ptr != NULL); // Check the cast result ''') if self.pairs.has_key("block_on"): address_field = self.pairs['block_on'] code(''' if (m_is_blocking && (m_block_map.count(in_msg_ptr->m_$address_field) == 1) && (m_block_map[in_msg_ptr->m_$address_field] != &$qcode)) { $qcode.delayHead(); continue; } ''') if self.pairs.has_key("wake_up"): address_field = self.pairs['wake_up'] code(''' if (m_waiting_buffers.count(in_msg_ptr->m_$address_field) > 0) { wakeUpBuffers(in_msg_ptr->m_$address_field); } ''') # The other statements self.statements.generate(code, return_type) self.symtab.popFrame() code("}") def findResources(self, resources): self.statements.findResources(resources)

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image_classification/T2T_ViT/load_pytorch_weights.py

RangeKing/PaddleViT

0

3288

<reponame>RangeKing/PaddleViT<filename>image_classification/T2T_ViT/load_pytorch_weights.py # Copyright (c) 2021 PPViT 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. """convert pytorch model weights to paddle pdparams""" import os import numpy as np import paddle import torch import timm from config import get_config from t2t_vit import build_t2t_vit as build_model from T2T_ViT_torch.models.t2t_vit import * from T2T_ViT_torch.utils import load_for_transfer_learning def print_model_named_params(model): print('----------------------------------') for name, param in model.named_parameters(): print(name, param.shape) print('----------------------------------') def print_model_named_buffers(model): print('----------------------------------') for name, param in model.named_buffers(): print(name, param.shape) print('----------------------------------') def torch_to_paddle_mapping(model_name, config): # (torch_param_name, paddle_param_name) mapping = [ ('cls_token', 'cls_token'), ('pos_embed', 'pos_embed'), ] for idx in range(1, 3): th_prefix = f'tokens_to_token.attention{idx}' pp_prefix = f'patch_embed.attn{idx}' if '_t_' in model_name: layer_mapping = [ (f'{th_prefix}.attn.qkv', f'{pp_prefix}.attn.qkv'), (f'{th_prefix}.attn.proj', f'{pp_prefix}.attn.proj'), (f'{th_prefix}.norm1', f'{pp_prefix}.norm1'), (f'{th_prefix}.norm2', f'{pp_prefix}.norm2'), (f'{th_prefix}.mlp.fc1', f'{pp_prefix}.mlp.fc1'), (f'{th_prefix}.mlp.fc2', f'{pp_prefix}.mlp.fc2'), ] else: layer_mapping = [ (f'{th_prefix}.w', f'{pp_prefix}.w'), (f'{th_prefix}.kqv', f'{pp_prefix}.kqv'), (f'{th_prefix}.proj', f'{pp_prefix}.proj'), (f'{th_prefix}.norm1', f'{pp_prefix}.norm1'), (f'{th_prefix}.norm2', f'{pp_prefix}.norm2'), (f'{th_prefix}.mlp.0', f'{pp_prefix}.mlp.0'), (f'{th_prefix}.mlp.2', f'{pp_prefix}.mlp.2'), ] mapping.extend(layer_mapping) mapping.append(('tokens_to_token.project','patch_embed.proj')) num_layers = config.MODEL.DEPTH for idx in range(num_layers): th_prefix = f'blocks.{idx}' pp_prefix = f'blocks.{idx}' layer_mapping = [ (f'{th_prefix}.norm1', f'{pp_prefix}.norm1'), (f'{th_prefix}.attn.qkv', f'{pp_prefix}.attn.qkv'), (f'{th_prefix}.attn.proj', f'{pp_prefix}.attn.proj'), (f'{th_prefix}.norm2', f'{pp_prefix}.norm2'), (f'{th_prefix}.mlp.fc1', f'{pp_prefix}.mlp.fc1'), (f'{th_prefix}.mlp.fc2', f'{pp_prefix}.mlp.fc2'), ] mapping.extend(layer_mapping) head_mapping = [ ('norm', 'norm'), ('head', 'head'), ] mapping.extend(head_mapping) return mapping def convert(torch_model, paddle_model, model_name, config): def _set_value(th_name, pd_name, transpose=True): th_shape = th_params[th_name].shape pd_shape = tuple(pd_params[pd_name].shape) # paddle shape default type is list #assert th_shape == pd_shape, f'{th_shape} != {pd_shape}' print(f'**SET** {th_name} {th_shape} **TO** {pd_name} {pd_shape}') if isinstance(th_params[th_name], torch.nn.parameter.Parameter): value = th_params[th_name].data.numpy() else: value = th_params[th_name].numpy() if len(value.shape) == 2 and transpose: value = value.transpose((1, 0)) pd_params[pd_name].set_value(value) # 1. get paddle and torch model parameters pd_params = {} th_params = {} for name, param in paddle_model.named_parameters(): pd_params[name] = param for name, param in torch_model.named_parameters(): th_params[name] = param for name, param in paddle_model.named_buffers(): pd_params[name] = param for name, param in torch_model.named_buffers(): th_params[name] = param # 2. get name mapping pairs mapping = torch_to_paddle_mapping(model_name, config) missing_keys_th = [] missing_keys_pd = [] zip_map = list(zip(*mapping)) th_keys = list(zip_map[0]) pd_keys = list(zip_map[1]) for key in th_params: missing = False if key not in th_keys: missing = True if key.endswith('.weight'): if key[:-7] in th_keys: missing = False if key.endswith('.bias'): if key[:-5] in th_keys: missing = False if missing: missing_keys_th.append(key) for key in pd_params: missing = False if key not in pd_keys: missing = True if key.endswith('.weight'): if key[:-7] in pd_keys: missing = False if key.endswith('.bias'): if key[:-5] in pd_keys: missing = False if missing: missing_keys_pd.append(key) print('====================================') print('missing_keys_pytorch:') print(missing_keys_th) print('missing_keys_paddle:') print(missing_keys_pd) print('====================================') # 3. set torch param values to paddle params: may needs transpose on weights for th_name, pd_name in mapping: if th_name in th_params and pd_name in pd_params: # nn.Parameters if th_name.endswith('w'): _set_value(th_name, pd_name, transpose=False) else: _set_value(th_name, pd_name) else: if f'{th_name}.weight' in th_params and f'{pd_name}.weight' in pd_params: th_name_w = f'{th_name}.weight' pd_name_w = f'{pd_name}.weight' _set_value(th_name_w, pd_name_w) if f'{th_name}.bias' in th_params and f'{pd_name}.bias' in pd_params: th_name_b = f'{th_name}.bias' pd_name_b = f'{pd_name}.bias' _set_value(th_name_b, pd_name_b) if f'{th_name}.running_mean' in th_params and f'{pd_name}._mean' in pd_params: th_name_b = f'{th_name}.running_mean' pd_name_b = f'{pd_name}._mean' _set_value(th_name_b, pd_name_b) if f'{th_name}.running_var' in th_params and f'{pd_name}._variance' in pd_params: th_name_b = f'{th_name}.running_var' pd_name_b = f'{pd_name}._variance' _set_value(th_name_b, pd_name_b) return paddle_model def main(): paddle.set_device('cpu') model_name_list = ['t2t_vit_7', 't2t_vit_10', 't2t_vit_12', 't2t_vit_14', 't2t_vit_14_384', 't2t_vit_19', 't2t_vit_24', 't2t_vit_24_token_labeling', 't2t_vit_t_14', 't2t_vit_t_19', 't2t_vit_t_24'] pth_model_path_list = ['./T2T_ViT_torch/t2t-vit-pth-models/71.7_T2T_ViT_7.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/75.2_T2T_ViT_10.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/76.5_T2T_ViT_12.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/81.5_T2T_ViT_14.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/83.3_T2T_ViT_14.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/81.9_T2T_ViT_19.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/82.3_T2T_ViT_24.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/84.2_T2T_ViT_24.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/81.7_T2T_ViTt_14.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/82.4_T2T_ViTt_19.pth.tar', './T2T_ViT_torch/t2t-vit-pth-models/82.6_T2T_ViTt_24.pth.tar'] for model_name, pth_model_path in zip(model_name_list, pth_model_path_list): print(f'============= NOW: {model_name} =============') sz = 384 if '384' in model_name else 224 if 'token_labeling' in model_name: config = get_config(f'./configs/{model_name[:-15]}.yaml') else: config = get_config(f'./configs/{model_name}.yaml') paddle_model = build_model(config) paddle_model.eval() print_model_named_params(paddle_model) print_model_named_buffers(paddle_model) print('+++++++++++++++++++++++++++++++++++') device = torch.device('cpu') if 'token_labeling' in model_name: torch_model = eval(f'{model_name[:-15]}(img_size={sz})') else: if '384' in model_name: torch_model = eval(f'{model_name[:-4]}(img_size={sz})') else: torch_model = eval(f'{model_name}(img_size={sz})') load_for_transfer_learning(torch_model, pth_model_path, use_ema=True, strict=False, num_classes=1000) torch_model = torch_model.to(device) torch_model.eval() print_model_named_params(torch_model) print_model_named_buffers(torch_model) # convert weights paddle_model = convert(torch_model, paddle_model, model_name, config) # check correctness x = np.random.randn(2, 3, sz, sz).astype('float32') x_paddle = paddle.to_tensor(x) x_torch = torch.Tensor(x).to(device) out_torch = torch_model(x_torch) out_paddle = paddle_model(x_paddle) out_torch = out_torch.data.cpu().numpy() out_paddle = out_paddle.cpu().numpy() print(out_torch.shape, out_paddle.shape) print(out_torch[0, 0:100]) print('========================================================') print(out_paddle[0, 0:100]) assert np.allclose(out_torch, out_paddle, atol = 1e-2) # save weights for paddle model model_path = os.path.join(f'./{model_name}.pdparams') paddle.save(paddle_model.state_dict(), model_path) print(f'{model_name} done') print('all done') if __name__ == "__main__": main()

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Acquire/Client/_user.py

michellab/BioSimSpaceCloud

2

15502

<filename>Acquire/Client/_user.py import os as _os from enum import Enum as _Enum from datetime import datetime as _datetime import time as _time from Acquire.Service import call_function as _call_function from Acquire.Service import Service as _Service from Acquire.ObjectStore import bytes_to_string as _bytes_to_string from Acquire.ObjectStore import string_to_bytes as _string_to_bytes from Acquire.Crypto import PrivateKey as _PrivateKey from Acquire.Crypto import PublicKey as _PublicKey from ._qrcode import create_qrcode as _create_qrcode from ._qrcode import has_qrcode as _has_qrcode from ._errors import UserError, LoginError # If we can, import socket to get the hostname and IP address try: import socket as _socket _has_socket = True except: _has_socket = False __all__ = ["User", "username_to_uid", "uid_to_username", "get_session_keys"] class _LoginStatus(_Enum): EMPTY = 0 LOGGING_IN = 1 LOGGED_IN = 2 LOGGED_OUT = 3 ERROR = 4 def _get_identity_url(): """Function to discover and return the default identity url""" return "http://172.16.17.32:8080/t/identity" def _get_identity_service(identity_url=None): """Function to return the identity service for the system""" if identity_url is None: identity_url = _get_identity_url() privkey = _PrivateKey() response = _call_function(identity_url, response_key=privkey) try: service = _Service.from_data(response["service_info"]) except: raise LoginError("Have not received the identity service info from " "the identity service at '%s' - got '%s'" % (identity_url, response)) if not service.is_identity_service(): raise LoginError( "You can only use a valid identity service to log in! " "The service at '%s' is a '%s'" % (identity_url, service.service_type())) if identity_url != service.service_url(): service.update_service_url(identity_url) return service def uid_to_username(user_uid, identity_url=None): """Function to return the username for the passed uid""" if identity_url is None: identity_url = _get_identity_url() response = _call_function(identity_url, "whois", user_uid=str(user_uid)) return response["username"] def username_to_uid(username, identity_url=None): """Function to return the uid for the passed username""" if identity_url is None: identity_url = _get_identity_url() response = _call_function(identity_url, "whois", username=str(username)) return response["user_uid"] def get_session_keys(username=None, user_uid=None, session_uid=None, identity_url=None): """Function to return the session keys for the specified user""" if username is None and user_uid is None: raise ValueError("You must supply either the username or user_uid!") if session_uid is None: raise ValueError("You must supply a valid UID for a login session") if identity_url is None: identity_url = _get_identity_url() response = _call_function(identity_url, "whois", username=username, user_uid=user_uid, session_uid=session_uid) try: response["public_key"] = _PublicKey.from_data(response["public_key"]) except: pass try: response["public_cert"] = _PublicKey.from_data(response["public_cert"]) except: pass return response class User: """This class holds all functionality that would be used by a user to authenticate with and access the service. This represents a single client login, and is the user-facing part of Acquire """ def __init__(self, username, identity_url=None): """Construct a null user""" self._username = username self._status = _LoginStatus.EMPTY self._identity_service = None if identity_url: self._identity_url = identity_url self._user_uid = None def __str__(self): return "User(name='%s', status=%s)" % (self.username(), self.status()) def __enter__(self): """Enter function used by 'with' statements'""" pass def __exit__(self, exception_type, exception_value, traceback): """Ensure that we logout""" self.logout() def __del__(self): """Make sure that we log out before deleting this object""" self.logout() def _set_status(self, status): """Internal function used to set the status from the string obtained from the LoginSession""" if status == "approved": self._status = _LoginStatus.LOGGED_IN elif status == "denied": self._set_error_state("Permission to log in was denied!") elif status == "logged_out": self._status = _LoginStatus.LOGGED_OUT def username(self): """Return the username of the user""" return self._username def uid(self): """Return the UID of this user. This uniquely identifies the user across all systems """ if self._user_uid is None: self._user_uid = username_to_uid(self.username(), self.identity_service_url()) return self._user_uid def status(self): """Return the current status of this user""" return self._status def _check_for_error(self): """Call to ensure that this object is not in an error state. If it is in an error state then raise an exception""" if self._status == _LoginStatus.ERROR: raise LoginError(self._error_string) def _set_error_state(self, message): """Put this object into an error state, displaying the passed message if anyone tries to use this object""" self._status = _LoginStatus.ERROR self._error_string = message def session_key(self): """Return the session key for the current login session""" self._check_for_error() try: return self._session_key except: return None def signing_key(self): """Return the signing key used for the current login session""" self._check_for_error() try: return self._signing_key except: return None def identity_service(self): """Return the identity service info object for the identity service used to validate the identity of this user """ if self._identity_service: return self._identity_service self._identity_service = _get_identity_service( self.identity_service_url()) return self._identity_service def identity_service_url(self): """Return the URL to the identity service. This is the full URL to the service, minus the actual function to be called, e.g. https://function_service.com/t/identity """ self._check_for_error() try: return self._identity_url except: # return the default URL - this should be discovered... return _get_identity_url() def login_url(self): """Return the URL that the user must connect to to authenticate this login session""" self._check_for_error() try: return self._login_url except: return None def login_qr_code(self): """Return a QR code of the login URL that the user must connect to to authenticate this login session""" self._check_for_error() try: return self._login_qrcode except: return None def session_uid(self): """Return the UID of the current login session. Returns None if there is no valid login session""" self._check_for_error() try: return self._session_uid except: return None def is_empty(self): """Return whether or not this is an empty login (so has not been used for anything yet...""" return self._status == _LoginStatus.EMPTY def is_logged_in(self): """Return whether or not the user has successfully logged in""" return self._status == _LoginStatus.LOGGED_IN def is_logging_in(self): """Return whether or not the user is in the process of loggin in""" return self._status == _LoginStatus.LOGGING_IN def logout(self): """Log out from the current session""" if self.is_logged_in() or self.is_logging_in(): identity_url = self.identity_service_url() if identity_url is None: return # create a permission message that can be signed # and then validated by the user permission = "Log out request for %s" % self._session_uid signature = self.signing_key().sign(permission) print("Logging out %s from session %s" % (self._username, self._session_uid)) result = _call_function( identity_url, "logout", args_key=self.identity_service().public_key(), username=self._username, session_uid=self._session_uid, permission=permission, signature=_bytes_to_string(signature)) print(result) self._status = _LoginStatus.LOGGED_OUT return result def register(self, password, identity_url=None): """Request to register this user with the identity service running at 'identity_url', using the supplied 'password'. This will return a QR code that you must use immediately to add this user on the identity service to a QR code generator""" if self._username is None: return None if identity_url is None: identity_url = _get_identity_url() privkey = _PrivateKey() result = _call_function( identity_url, "register", args_key=self.identity_service().public_key(), response_key=privkey, public_cert=self.identity_service().public_certificate(), username=self._username, password=password) try: provisioning_uri = result["provisioning_uri"] except: raise UserError( "Cannot register the user '%s' on " "the identity service at '%s'!" % (self._username, identity_url)) # return a QR code for the provisioning URI return (provisioning_uri, _create_qrcode(provisioning_uri)) def request_login(self, login_message=None): """Request to authenticate as this user. This returns a login URL that you must connect to to supply your login credentials If 'login_message' is supplied, then this is passed to the identity service so that it can be displayed when the user accesses the login page. This helps the user validate that they have accessed the correct login page. Note that if the message is None, then a random message will be generated. """ self._check_for_error() if not self.is_empty(): raise LoginError("You cannot try to log in twice using the same " "User object. Create another object if you want " "to try to log in again.") # first, create a private key that will be used # to sign all requests and identify this login session_key = _PrivateKey() signing_key = _PrivateKey() args = {"username": self._username, "public_key": session_key.public_key().to_data(), "public_certificate": signing_key.public_key().to_data(), "ipaddr": None} # get information from the local machine to help # the user validate that the login details are correct if _has_socket: hostname = _socket.gethostname() ipaddr = _socket.gethostbyname(hostname) args["ipaddr"] = ipaddr args["hostname"] = hostname if login_message is None: login_message = "User '%s' in process '%s' wants to log in..." % \ (_os.getlogin(), _os.getpid()) args["message"] = login_message result = _call_function( self.identity_service_url(), "request_login", args_key=self.identity_service().public_key(), response_key=session_key, public_cert=self.identity_service().public_certificate(), username=self._username, public_key=session_key.public_key().to_data(), public_certificate=signing_key.public_key().to_data(), ipaddr=None, message=login_message) # look for status = 0 try: status = int(result["status"]) except: status = -1 try: message = result["message"] except: message = str(result) try: prune_message = result["prune_message"] print("Pruning old sessions...\n%s" % "\n".join(prune_message)) except: pass if status != 0: error = "Failed to login. Error = %d. Message = %s" % \ (status, message) self._set_error_state(error) raise LoginError(error) try: login_url = result["login_url"] except: login_url = None if login_url is None: error = "Failed to login. Could not extract the login URL! " \ "Result is %s" % (str(result)) self._set_error_state(error) raise LoginError(error) try: session_uid = result["session_uid"] except: session_uid = None if session_uid is None: error = "Failed to login. Could not extract the login " \ "session UID! Result is %s" % (str(result)) self._set_error_state(error) raise LoginError(error) # now save all of the needed data self._login_url = result["login_url"] self._session_key = session_key self._signing_key = signing_key self._session_uid = session_uid self._status = _LoginStatus.LOGGING_IN self._user_uid = result["user_uid"] qrcode = None if _has_qrcode(): try: self._login_qrcode = _create_qrcode(self._login_url) qrcode = self._login_qrcode except: pass return (self._login_url, qrcode) def _poll_session_status(self): """Function used to query the identity service for this session to poll for the session status""" identity_url = self.identity_service_url() if identity_url is None: return result = _call_function(identity_url, "get_status", username=self._username, session_uid=self._session_uid) # look for status = 0 try: status = int(result["status"]) except: status = -1 try: message = result["message"] except: message = str(result) if status != 0: error = "Failed to query identity service. Error = %d. " \ "Message = %s" % (status, message) self._set_error_state(error) raise LoginError(error) # now update the status... status = result["session_status"] self._set_status(status) def wait_for_login(self, timeout=None, polling_delta=5): """Block until the user has logged in. If 'timeout' is set then we will wait for a maximum of that number of seconds This will check whether we have logged in by polling the identity service every 'polling_delta' seconds. """ self._check_for_error() if not self.is_logging_in(): return self.is_logged_in() polling_delta = int(polling_delta) if polling_delta > 60: polling_delta = 60 elif polling_delta < 1: polling_delta = 1 if timeout is None: # block forever.... while True: self._poll_session_status() if self.is_logged_in(): return True elif not self.is_logging_in(): return False _time.sleep(polling_delta) else: # only block until the timeout has been reached timeout = int(timeout) if timeout < 1: timeout = 1 start_time = _datetime.now() while (_datetime.now() - start_time).seconds < timeout: self._poll_session_status() if self.is_logged_in(): return True elif not self.is_logging_in(): return False _time.sleep(polling_delta) return False

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tests/compilation/request/test_request_compiler.py

ymoch/preacher

3

10668

<reponame>ymoch/preacher<gh_stars>1-10 from unittest.mock import NonCallableMock, sentinel from pytest import mark, raises, fixture from preacher.compilation.argument import Argument from preacher.compilation.error import CompilationError, NamedNode, IndexedNode from preacher.compilation.request.request import RequestCompiler, RequestCompiled from preacher.compilation.request.request_body import RequestBodyCompiler from preacher.core.request import Method PKG = "preacher.compilation.request.request" @fixture def body(): body = NonCallableMock(RequestBodyCompiler) body.of_default.return_value = sentinel.new_body_compiler return body @fixture def default() -> RequestCompiled: return RequestCompiled( method=sentinel.default_method, path=sentinel.default_path, headers=sentinel.default_headers, params=sentinel.default_params, body=sentinel.default_body, ) @fixture def compiler(body, default: RequestCompiled) -> RequestCompiler: return RequestCompiler(body=body, default=default) @mark.parametrize( ("obj", "expected_path"), ( ([], []), ({"method": 1}, [NamedNode("method")]), ({"method": "invalid"}, [NamedNode("method")]), ({"path": {"key": "value"}}, [NamedNode("path")]), ({"headers": ""}, [NamedNode("headers")]), ({"headers": {"int": 1}}, [NamedNode("headers")]), ({"headers": {1: "not-a-string-key"}}, [NamedNode("headers")]), ), ) def test_given_an_invalid_obj(compiler: RequestCompiler, obj, expected_path): with raises(CompilationError) as error_info: compiler.compile(obj) assert error_info.value.path == expected_path def test_given_an_empty_mapping(compiler: RequestCompiler): compiled = compiler.compile({}) assert compiled.method is sentinel.default_method assert compiled.path is sentinel.default_path assert compiled.headers is sentinel.default_headers assert compiled.params is sentinel.default_params assert compiled.body is sentinel.default_body @mark.parametrize( ("method_obj", "expected"), ( ("get", Method.GET), ("POST", Method.POST), ("Put", Method.PUT), ("Delete", Method.DELETE), ), ) def test_given_a_valid_method(compiler: RequestCompiler, method_obj, expected): obj = {"method": Argument("method")} arguments = {"method": method_obj} compiled = compiler.compile(obj, arguments) assert compiled.method is expected @mark.parametrize( ("headers_obj", "expected"), ( ({}, {}), ({"null": None, "empty": ""}, {"empty": ""}), ({"n1": "v1", "n2": "v2"}, {"n1": "v1", "n2": "v2"}), ), ) def test_given_valid_headers(compiler: RequestCompiler, headers_obj, expected): obj = {"headers": Argument("headers")} arguments = {"headers": headers_obj} compiled = compiler.compile(obj, arguments) assert compiled.headers == expected def test_given_an_invalid_params(compiler: RequestCompiler, mocker): compile_params = mocker.patch(f"{PKG}.compile_url_params") compile_params.side_effect = CompilationError("msg", node=NamedNode("x")) with raises(CompilationError) as error_info: compiler.compile({"params": sentinel.params}) assert error_info.value.path == [NamedNode("params"), NamedNode("x")] compile_params.assert_called_once_with(sentinel.params, None) def test_given_valid_params(compiler: RequestCompiler, mocker): compile_params = mocker.patch(f"{PKG}.compile_url_params") compile_params.return_value = sentinel.compiled_params compiled = compiler.compile({"params": sentinel.params}, sentinel.args) assert compiled.params == sentinel.compiled_params compile_params.assert_called_once_with(sentinel.params, sentinel.args) def test_given_invalid_body(compiler: RequestCompiler, body): body.compile.side_effect = CompilationError("x", node=IndexedNode(1)) with raises(CompilationError) as error_info: compiler.compile({"body": sentinel.body_obj}) assert error_info.value.path == [NamedNode("body"), IndexedNode(1)] body.compile.assert_called_once_with(sentinel.body_obj, None) def test_given_valid_body(compiler: RequestCompiler, body): body.compile.return_value = sentinel.body compiled = compiler.compile({"body": sentinel.body_obj}, sentinel.args) assert compiled.body is sentinel.body body.compile.assert_called_once_with(sentinel.body_obj, sentinel.args) def test_given_a_string(compiler: RequestCompiler): compiled = compiler.compile(Argument("path"), {"path": "/path"}) assert compiled.method is sentinel.default_method assert compiled.path == "/path" assert compiled.headers is sentinel.default_headers assert compiled.params is sentinel.default_params assert compiled.body is sentinel.default_body def test_of_default_no_body(compiler: RequestCompiler, body, mocker): ctor = mocker.patch(f"{PKG}.RequestCompiler") ctor.return_value = sentinel.compiler_of_default new_default = RequestCompiled( method=sentinel.new_default_method, path=sentinel.new_default_path, headers=sentinel.new_default_headers, params=sentinel.new_default_params, ) new_compiler = compiler.of_default(new_default) assert new_compiler is sentinel.compiler_of_default ctor.assert_called_once_with( body=body, default=RequestCompiled( method=sentinel.new_default_method, path=sentinel.new_default_path, headers=sentinel.new_default_headers, params=sentinel.new_default_params, body=sentinel.default_body, ), ) body.of_default.assert_not_called() def test_of_default_body(compiler: RequestCompiler, body, mocker): ctor = mocker.patch(f"{PKG}.RequestCompiler") ctor.return_value = sentinel.compiler_of_default new_default = RequestCompiled(body=sentinel.new_default_body) new_compiler = compiler.of_default(new_default) assert new_compiler is sentinel.compiler_of_default ctor.assert_called_once_with( body=sentinel.new_body_compiler, default=RequestCompiled( method=sentinel.default_method, path=sentinel.default_path, headers=sentinel.default_headers, params=sentinel.default_params, body=sentinel.new_default_body, ), ) body.of_default.assert_called_once_with(sentinel.new_default_body)

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skpr/inout/frc.py

PhilippPelz/scikit-pr-open

0

1601830

from pyE17.utils import imsave from matplotlib import pyplot as plt import numpy as np from numpy.fft import fft2, ifftshift, fftshift, ifft2, fft, ifft from scipy.ndimage.filters import gaussian_filter1d from .plot import imsave def taperarray(shape, edge): xx, yy = np.mgrid[0:shape[0], 0:shape[1]] xx1 = np.flipud(xx) xx2 = np.minimum(xx, xx1) yy1 = np.fliplr(yy) yy2 = np.minimum(yy, yy1) rr = np.minimum(xx2, yy2).astype(np.float) rr[rr <= edge] /= edge rr[rr > edge] = 1 rr *= np.pi / 2 rr = np.sin(rr) # print xx2 # fig, ax = plt.subplots() # imax = ax.imshow(rr) # plt.colorbar(imax) # plt.show() return rr def tapercircle(shape, edge, edgeloc=8 / 20.0, loc=(0, 0)): x0, y0 = loc xx, yy = np.meshgrid(np.arange(-shape[0] / 2, shape[0] / 2), np.arange(-shape[1] / 2, shape[1] / 2)) rr = np.sqrt((xx - x0) ** 2 + (yy - y0) ** 2) rr -= (shape[0] * edgeloc - edge) one = rr < 0 taper = np.logical_and(rr >= 0, rr <= edge) zero = rr > edge # rr[taper] rr[zero] = 0 rr[taper] = np.abs(rr[taper] - np.max(rr[taper])) / np.max(rr[taper]) rr[one] = 1 return rr def frc(im1, im2, annulus_width=1, edgetaper=5, edgeloc=8 / 20.0, loc=(0, 0), smooth=None, working_mask=None, x=None, y=None, rmax=None, taper=None): """ r = radial_data(data,annulus_width,working_mask,x,y) A function to reduce an image to a radial cross-section. :INPUT: data - whatever data you are radially averaging. Data is binned into a series of annuli of width 'annulus_width' pixels. annulus_width - width of each annulus. Default is 1. working_mask - array of same size as 'data', with zeros at whichever 'data' points you don't want included in the radial data computations. x,y - coordinate system in which the data exists (used to set the center of the data). By default, these are set to integer meshgrids rmax -- maximum radial value over which to compute statistics :OUTPUT: r - a data structure containing the following statistics, computed across each annulus: .r - the radial coordinate used (outer edge of annulus) .mean - mean of the data in the annulus .sum - the sum of all enclosed values at the given radius .std - standard deviation of the data in the annulus .median - median value in the annulus .max - maximum value in the annulus .min - minimum value in the annulus .numel - number of elements in the annulus :EXAMPLE: :: import numpy as np import pylab as py import radial_data as rad # Create coordinate grid npix = 50. x = np.arange(npix) - npix/2. xx, yy = np.meshgrid(x, x) r = np.sqrt(xx**2 + yy**2) fake_psf = np.exp(-(r/5.)**2) noise = 0.1 * np.random.normal(0, 1, r.size).reshape(r.shape) simulation = fake_psf + noise rad_stats = rad.radial_data(simulation, x=xx, y=yy) py.figure() py.plot(rad_stats.r, rad_stats.mean / rad_stats.std) py.xlabel('Radial coordinate') py.ylabel('Signal to Noise') """ # 2012-02-25 20:40 IJMC: Empty bins now have numel=0, not nan. # 2012-02-04 17:41 IJMC: Added "SUM" flag # 2010-11-19 16:36 IJC: Updated documentation for Sphinx # 2010-03-10 19:22 IJC: Ported to python from Matlab # 2005/12/19 Added 'working_region' option (IJC) # 2005/12/15 Switched order of outputs (IJC) # 2005/12/12 IJC: Removed decifact, changed name, wrote comments. # 2005/11/04 by <NAME> at the Jet Propulsion Laboratory import numpy as ny class radialDat: """Empty object container. """ def __init__(self): self.num = None self.denom = None self.T1bit = None self.Thalfbit = None # --------------------- # Set up input parameters # --------------------- if working_mask == None: working_mask = ny.ones(im1.shape, bool) npix, npiy = im1.shape if taper is not None: taper0 = taper else: taper0 = tapercircle(im1.shape, edgetaper, edgeloc, loc) f, a = plt.subplots() a.imshow(imsave(im1 * taper0)) plt.title('tapered') plt.show() # taper0 = 1 F1 = fftshift(fft2(ifftshift(im1 * taper0))) F2 = fftshift(fft2(ifftshift(im2 * taper0))) F1F2_star = F1 * F2.conj() if x == None or y == None: x1 = ny.arange(-npix / 2., npix / 2.) y1 = ny.arange(-npiy / 2., npiy / 2.) x, y = ny.meshgrid(y1, x1) r = abs(x + 1j * y) if rmax == None: rmax = r[working_mask].max() # --------------------- # Prepare the data container # --------------------- dr = ny.abs([x[0, 0] - x[0, 1]]) * annulus_width radial = ny.arange(rmax / dr) * dr + dr / 2. nrad = len(radial) radialdata = radialDat() radialdata.num = ny.zeros(nrad) radialdata.denom = ny.zeros(nrad) radialdata.T1bit = ny.zeros(nrad) radialdata.Thalfbit = ny.zeros(nrad) radialdata.r = radial / (npix / 2) # --------------------- # Loop through the bins # --------------------- for irad in range(nrad): # = 1:numel(radial) minrad = irad * dr maxrad = minrad + dr thisindex = (r >= minrad) * (r < maxrad) * working_mask # import pylab as py # pdb.set_trace() if not thisindex.ravel().any(): radialdata.num[irad] = ny.nan radialdata.denom[irad] = ny.nan radialdata.T1bit[irad] = ny.nan radialdata.Thalfbit[irad] = ny.nan else: sqrt_n = np.sqrt(thisindex.astype(np.int).sum()) radialdata.num[irad] = np.real(F1F2_star[thisindex].sum()) radialdata.denom[irad] = ny.sqrt((ny.abs(F1[thisindex]) ** 2).sum() * (ny.abs(F2[thisindex]) ** 2).sum()) radialdata.T1bit[irad] = (0.5 + 2.4142 / sqrt_n) / (1.5 + 1.4142 / sqrt_n) radialdata.Thalfbit[irad] = (0.2071 + 1.9102 / sqrt_n) / (1.2071 + 0.9102 / sqrt_n) # --------------------- # Return with data # --------------------- radialdata.frc = ny.nan_to_num(radialdata.num / radialdata.denom) radialdata.frc[radialdata.frc < 0] = 0 if smooth is not None: radialdata.frc = gaussian_filter1d(radialdata.frc, smooth) take = radialdata.r <= 1.1 radialdata.r = radialdata.r[take] radialdata.frc = radialdata.frc[take] radialdata.T1bit = radialdata.T1bit[take] radialdata.Thalfbit = radialdata.Thalfbit[take] return radialdata

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eval/beat_scores_calc.py

StoneYeY/Beat-Detection

0

61620

#!/usr/bin/env python # encoding: utf-8 """ calc beat score of files copyright: www.mgtv.com """ import os import sys import argparse import numpy as np import traceback import beat_evaluation_toolbox as be def calc_beat_score_of_file(annotation_file, beat_file): #check input params if os.path.exists(annotation_file) == False: print("failed! annotation_file:%s not exist\n" % (annotation_file)) return False, 0.0 if os.path.exists(beat_file) == False: print("failed! beat_file:%s not exist\n" % (beat_file)) return False, 0.0 data_annotation = np.loadtxt(annotation_file, usecols=(0)) data_annotation = np.expand_dims(data_annotation, axis=0) data_beat = np.loadtxt(beat_file, usecols=(0)) data_beat = np.expand_dims(data_beat, axis=0) R = be.evaluate_db(data_annotation, data_beat, 'all', doCI=False) #输出结果 print(R['scores']) pscore = R['scores']['pScore'][0] f_measure = R['scores']['fMeasure'][0] aml_c = R['scores']['amlC'][0] aml_t = R['scores']['amlT'][0] cml_c = R['scores']['cmlC'][0] cml_t = R['scores']['cmlT'][0] cem_acc = R['scores']['cemgilAcc'][0] total_score = (aml_c + cem_acc + cml_c + f_measure + pscore + cml_t + aml_t) / 7 print("[%s] score:%.4f"%(beat_file, total_score)) return True, total_score def calc_avg_score_of_files(annotation_files_dir, beat_files_dir, file_extension): #check input params if os.path.exists(annotation_files_dir) == False: print("failed! annotation_files_dir:%s not exist\n" % (annotation_files_dir)) return False, 0.0 if os.path.exists(beat_files_dir) == False: print("failed! beat_files_dir:%s not exist\n" % (beat_files_dir)) return False, 0.0 if not annotation_files_dir.endswith("/"): annotation_files_dir += "/" if not beat_files_dir.endswith("/"): beat_files_dir += "/" annotation_files_url = [f for f in os.listdir(annotation_files_dir) if f.endswith((file_extension))] nb_annotation_files = len(annotation_files_url) beat_files_url = [f for f in os.listdir(beat_files_dir) if f.endswith((file_extension))] nb_beat_files = len(beat_files_url) if nb_annotation_files != nb_beat_files or nb_annotation_files == 0: print("failed! annotation files num:%d beat files num:%d\n" % (nb_annotation_files, nb_beat_files)) return False, 0.0 sum_score = 0.0 for i in range(nb_annotation_files): annotation_file = annotation_files_dir + annotation_files_url[i] beat_file = beat_files_dir + annotation_files_url[i] if os.path.exists(beat_file) == False: print("failed! beat file:%s not exist\n" % (beat_file)) return False, 0.0 ret, score = calc_beat_score_of_file(annotation_file, beat_file) if ret == False: print("failed! calc_beat_score_of_file failed for file:%s\n" % (beat_file)) return False, 0.0 sum_score = sum_score + score avg_score = sum_score / nb_annotation_files return True, avg_score if __name__ == '__main__': parser = argparse.ArgumentParser(description="calc avg score of beat(downbeat) files") parser.add_argument("--annotation_files_dir", required=True, help="Path to input annotation files dir", default="") parser.add_argument("--beat_files_dir", required=True, help="Path to input beats files dir", default="") parser.add_argument("--file_extension", required=True, help="File ext, beat or downbeat", default="") # 获得工作目录，程序模块名称，并切换工作目录 s_work_path, s_module_name = os.path.split(os.path.abspath(sys.argv[0])) print(s_work_path, s_module_name) os.chdir(s_work_path) try: args = parser.parse_args() ret, score = calc_avg_score_of_files(args.annotation_files_dir, args.beat_files_dir, args.file_extension) print("Final score:%.4f" % score) except Exception as e: traceback.print_exc() print("Exception running beat_score_calc: [%s]" % (str(e))) ret = False if ret == True: sys.exit(0) else: sys.exit(1)

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Proxy_network/seq2seqSummariser.py

quilan78/MSC_project

0

1601164

<reponame>quilan78/MSC_project import numpy as np from encoder import * from embedding import * from decoder import * from lstm import * from utils import * import sys sys.path.append('/home/dwt17/MSc_project/neural_sum_1/code/Commons/') sys.path.append('../Commons/') from vocab import * from stateReducer import * from denseLayer import * class Seq2seqSummariser: """ Class that represent the entire architecture end to end Variable : cellSize is the dimension of the hidden layers in the LSTM cell embeddingSize is the dimension of the input attentionSize is the dimension of the attention vector vocabSize is the number of words max_encoding_length is the size of the input sequence max_decoding_length is the size of the output sequence """ def __init__(self, cellSize = 128, embeddingSize=64, attentionSize=128, vocabSize=150000, max_encoding_length=200, max_decoding_length=50, vocab_path="../data/useful files/"): self.cellSize = cellSize self.embeddingSize = embeddingSize self.attentionSize = attentionSize self.vocabSize = vocabSize self.max_encoding_length = max_encoding_length self.max_decoding_length = max_decoding_length self.embedding = None self.encoder = None self.decoder = None self.stateReducer = None self.denseLayer = None self.vocab = Vocab(path=vocab_path) self.vocabSize =self. vocab.LoadVocab(max_size=vocabSize) def _initialise_embedding(self, embedding_matrix): self.embedding = Embedding(embedding_matrix) assert self.vocabSize == self.embedding.vocabSize assert self.embeddingSize == self.embedding.embeddingSize def _initialise_encoder(self, kernel_forward, bias_forward, kernel_backward, bias_backward): """ Initialise the encoder The kernel are as given by Tensorflow, so of size (embedding + cellSize) x 4*cellSize Bias are of shape (4*cellSize, ) """ lstm_forward = Lstm(kernel_forward, bias_forward, self.cellSize) lstm_backward = Lstm(kernel_backward, bias_backward, self.cellSize) assert lstm_forward.embeddingSize == self.embeddingSize assert lstm_backward.embeddingSize == self.embeddingSize self.encoder = Encoder( lstm_forward=lstm_forward, lstm_backward=lstm_backward, cellSize=self.cellSize, embeddingSize = self.embeddingSize, max_encoding_length=self.max_encoding_length) def _initialise_stateReducer(self, W_h, W_c, b_h, b_c): """ Initialise the state Reducer from the encoder and the decoder The matrices as given by tensorflow are transposed """ self.stateReducer = StateReducer(W_h = W_h, W_c = W_c, b_h = b_h, b_c = b_c) def _initialise_decoder(self, kernel, bias, memoryLayer, queryLayer, attentionV, attentonLayer): cell = Lstm(kernel, bias, self.cellSize ) self.decoder = Decoder(lstm_cell=cell, cellSize=self.cellSize, attentionSize=self.attentionSize, max_decoding_length=self.max_decoding_length, memoryLayer=memoryLayer, queryLayer=queryLayer, attentionV = attentionV, attentonLayer=attentonLayer) def _initialise_DenseLayer(self, kernel, bias): self.denseLayer = DenseLayer(kernel = kernel, bias = bias) def compute_forward(self, input_seq_int, dec_seq_int): """ Compute all the states of the network for the specific input input : sequence of words """ #integer_seq_inp = self.vocab.TransalteSentence(input_seq) #print(dec_seq_int) embedded_inp_seq = self.embedding.transform_input(input_seq_int) forward_encoder_states, backward_encoder_states = self.encoder.compute_encoding(embedded_inp_seq) #print("Comparing the transmitted states C") #print(np.linalg.norm(forward_encoder_states[-1]["c"])) #print(np.linalg.norm(backward_encoder_states[0]["c"])) h, c = self.stateReducer.compute_reduction(forward_encoder_states[-1], backward_encoder_states[0]) #integer_seq_out = self.vocab.TransalteSentence(dec_seq) embedded_dec_seq = self.embedding.transform_input([self.vocab.start_decode_id] + dec_seq_int) decoder_states = self.decoder.compute_decoding(embedded_dec_seq, h, c, forward_encoder_states, backward_encoder_states, self.denseLayer, self.embedding) #print("compare context vectors") #print(decoder_states[0]["lstm"]["last_c"][:10]) #print(np.max(decoder_states[0]["lstm"]["f"])) #print(decoder_states[0]["lstm"]["f"][:10]) #print(c[:10]) return forward_encoder_states, backward_encoder_states, h, c, decoder_states def countParams(self): count = 0 count += self.embedding.countParams() count += self.encoder.countParams() count += self.decoder.countParams() count += self.stateReducer.countParams() count += self.denseLayer.countParams() return count def computeLRP(self, targets, start_decode=False): """ Compute the LRP for the entire network Input : targets : of shape (max_decoding_length, vocab_size). Table of True of False. If the position (i,j) is true, then we want to compute the relevance for the prediction we had of word number j at the decoding step i """ decoder_states = self.decoder.outputs forward_encoder_states = self.encoder.forward_encoder_states backward_encoder_states = self.encoder.backward_encoder_states output_relevance = [] for i in range(self.max_decoding_length): output_relevance.append(np.zeros((self.vocabSize, 1))) for j in range(self.vocabSize): if targets[i][j]: output_relevance[i][j] = softmax(decoder_states[i]["output"])[j] print(np.sum(output_relevance)) h_relevance, c_relevance, forward_encoder_relevance, backward_encoder_relevance, decoder_input_relevance, rest_relevance = self.decoder.computeLRP(output_relevance, self.denseLayer, forward_encoder_states, backward_encoder_states, transmit_input=False, start_decode=start_decode) #h_relevance = np.random.random_sample((self.cellSize, 1)) *10 -5 #c_relevance = np.random.random_sample((self.cellSize, 1)) *10 -5 print("Check conservation After decoding") print(np.sum(output_relevance)) print(np.sum(h_relevance)+ np.sum(c_relevance)+ np.sum(forward_encoder_relevance)+ np.sum(backward_encoder_relevance) + np.sum(np.sum(decoder_input_relevance)) + np.sum(rest_relevance)) #print(rest_relevance) #print("Length of the attention shit") #print(len(forward_encoder_relevance)) last_forward_h_relevance, last_backward_h_relevance, last_forward_c_relevance, last_backward_c_relevance = self.stateReducer.computeLRP(h_relevance, c_relevance) #print(np.linalg.norm(last_forward_h_relevance)) #print(np.linalg.norm(last_backward_h_relevance)) #print(np.linalg.norm(last_forward_c_relevance)) #print(np.linalg.norm(last_backward_c_relevance)) print("Check conservation State reducer") print(np.sum(h_relevance) + np.sum(c_relevance)) print(np.sum(last_forward_h_relevance)+ np.sum(last_backward_h_relevance)+ np.sum(last_forward_c_relevance)+ np.sum(last_backward_c_relevance)) input_relevance_forward, input_relevance_backward, h_relevance_rest_forward, c_relevance_rest_forward, h_relevance_rest_backward, c_relevance_rest_backward = self.encoder.computeLRP(last_forward_h_relevance, last_backward_h_relevance, last_forward_c_relevance, last_backward_c_relevance, forward_encoder_relevance, backward_encoder_relevance) print("Check conservation State reducer") print(np.sum(last_forward_h_relevance)+ np.sum(last_backward_h_relevance)+ np.sum(last_forward_c_relevance)+ np.sum(last_backward_c_relevance)+ np.sum(forward_encoder_relevance)+ np.sum(backward_encoder_relevance)) print(np.sum(input_relevance_forward)+ np.sum(input_relevance_backward) + np.sum(h_relevance_rest_forward)+ np.sum(c_relevance_rest_forward) + np.sum(h_relevance_rest_backward)+ np.sum(c_relevance_rest_backward)) print("Check conservation LRP") print(np.sum(output_relevance)) print(np.sum(input_relevance_forward)+ np.sum(input_relevance_backward)+ np.sum(np.sum(decoder_input_relevance)) + np.sum(rest_relevance)+ np.sum(h_relevance_rest_forward)+ np.sum(c_relevance_rest_forward) + np.sum(h_relevance_rest_backward)+ np.sum(c_relevance_rest_backward)) print("Difference between start and attributed LRP") print(np.sum(output_relevance)) print(np.sum(input_relevance_forward)+ np.sum(input_relevance_backward)+ np.sum(np.sum(decoder_input_relevance))) print("Rest relevance in attention") print(np.sum(rest_relevance)) print("Rest relevance in forward (h, c)") print(np.sum(h_relevance_rest_forward)) print(np.sum(c_relevance_rest_forward)) print("Rest relevance in backward (h, c)") print(np.sum(h_relevance_rest_backward)) print(np.sum(c_relevance_rest_backward)) # + np.sum(input_relevance_backward) + np.sum(decoder_input_relevance) return input_relevance_forward, input_relevance_backward, decoder_input_relevance

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src/lesson_mathematics/math_frexp.py

jasonwee/asus-rt-n14uhp-mrtg

3

114105

import math print('{:^7} {:^7} {:^7}'.format('x', 'm', 'e')) print('{:-^7} {:-^7} {:-^7}'.format('', '', '')) for x in [0.1, 0.5, 4.0]: m, e = math.frexp(x) print('{:7.2f} {:7.2f} {:7d}'.format(x, m, e))

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apps/plea/exceptions.py

uk-gov-mirror/ministryofjustice.manchester_traffic_offences_pleas

3

173209

""" Exceptions ========== """ class AuditEventException(BaseException): pass

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zenbot/logging/__init__.py

Dmunch04/ZenBot

7

125957

<filename>zenbot/logging/__init__.py<gh_stars>1-10 from .console_logger import ConsoleLogger from .file_logger import FileLogger from .logger import Logger from .logmanager import LogManager

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10_ExtractUniqueSents.py

bjascob/SmartLMVocabs

10

125060

#!/usr/bin/python3 # Copyright 2018 <NAME> # # 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. from __future__ import print_function import os import io import math import random from subprocess import Popen, PIPE from tflmlib import ProgressBar from configs import config # Billion Word Corpus # To create the training-monolingual directory .. # wget http://statmt.org/wmt11/training-monolingual.tgz # tar xvf training-monolingual.tgz --wildcards training-monolingual/news.20??.en.shuffled # The 9.9 GB file extracts to 25GB without the wildcards. Other files are non-english. bw_raw_dir = config.bw_rawdir bw_unique_dir = os.path.join(config.bw_corpus, 'BWUniqueSents_FirstPass') # Get the number of lines in a file def getFileLines(fname): p = Popen(['wc', '-l', fname], stdout=PIPE, stderr=PIPE) result, err = p.communicate() if p.returncode != 0: raise IOError(err) return int(result.strip().split()[0]) if __name__ == '__main__': print('*' * 80) print() nshards = 100 test = False # Loop through the corpus # Note this takes about 3 minutes, uses 6.5GB of RAM and 3.9GB disk space print('#' * 40) print('Loading the raw corpus') fns = sorted([os.path.join(bw_raw_dir, fn) for fn in os.listdir(bw_raw_dir)]) bw_set = set() sent_ctr = 0 for i, fn in enumerate(fns): print(' %d/%d : %s' % (i + 1, len(fns), fn)) nlines = getFileLines(fn) pb = ProgressBar(nlines) with io.open(fn, 'r', encoding='utf8') as f: for j, line in enumerate(f): line = line.strip() bw_set.add(line) if 0 == j % 100: pb.update(j) if test and j > 100000: break pb.clear() sent_ctr += nlines if test and i >= 0: break nunique = len(bw_set) print('Corpus has {:,} unique sentences out of {:,} read.'.format(nunique, sent_ctr)) print() # Create the output directory if not os.path.exists(bw_unique_dir): os.mkdir(bw_unique_dir) # Split the sentences into shards and save them print('Converting to a list and shuffling') bw_set = list(bw_set) random.shuffle(bw_set) sents_per_shard = int(math.ceil(nunique / float(nshards))) for i in range(nshards): fn = os.path.join(bw_unique_dir, 'bw_%02d.txt' % i) print('Saving the data to ', fn) with io.open(fn, 'w', encoding='utf8') as f: for sent in bw_set[i * sents_per_shard:(i + 1) * sents_per_shard]: f.write('%s\n' % sent) print('done') print()

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23_Basic_testbench_1.0/testbench.py

raysalemi/python4uvm_examples

1

107834

<gh_stars>1-10 # ## Importing modules # Figure 3: Importing needed resources import cocotb from cocotb.triggers import FallingEdge import random # ### The tinyalu_utils module # All testbenches use tinyalu_utils, so store it in a central # place and add its path to the sys path so we can import it from pathlib import Path parent_path = Path("..").resolve() import sys # noqa: E402 sys.path.insert(0, str(parent_path)) from tinyalu_utils import Ops, alu_prediction, logger, get_int # noqa: E402 # ## Setting up the cocotb TinyALU test # Figure 7: The start of the TinyALU. Reset the DUT @cocotb.test() async def alu_test(dut): passed = True cvg = set() # functional coverage await FallingEdge(dut.clk) dut.reset_n.value = 0 dut.start.value = 0 await FallingEdge(dut.clk) dut.reset_n.value = 1 # ### Sending commands # Figure 8: Creating one transaction for each operation cmd_count = 1 op_list = list(Ops) num_ops = len(op_list) while cmd_count <= num_ops: await FallingEdge(dut.clk) st = get_int(dut.start) dn = get_int(dut.done) # ### Sending a command and waiting for it to complete # Figure 9: Creating a TinyALU command if st == 0 and dn == 0: aa = random.randint(0, 255) bb = random.randint(0, 255) op = op_list.pop(0) cvg.add(op) dut.A.value = aa dut.B.value = bb dut.op.value = op dut.start.value = 1 # Figure 10: Asserting that a failure state never happens if st == 0 and dn == 1: raise AssertionError("DUT Error: done set to 1 without start") # Figure 11: If we are in an operation, continue if st == 1 and dn == 0: continue # ### Checking the result # Figure 12: The operation is complete if st == 1 and dn == 1: dut.start.value = 0 cmd_count += 1 result = get_int(dut.result) # Figure 13: Checking results against the prediction pr = alu_prediction(aa, bb, op) if result == pr: logger.info( f"PASSED: {aa:2x} {op.name} {bb:2x} = {result:04x}") else: logger.error( f"FAILED: {aa:2x} {op.name} {bb:2x} =" f" {result:04x} - predicted {pr:04x}") passed = False # ### Finishing the test # Figure 14: Checking functional coverage using a set if len(set(Ops) - cvg) > 0: logger.error(f"Functional coverage error. Missed: {set(Ops)-cvg}") passed = False else: logger.info("Covered all operations") # Figure 15: This assertion relays pass/fail to cocotb assert passed

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nutsml/examples/pytorch_/mnist/mlp_train.py

maet3608/nuts-ml

39

19605

""" .. module:: cnn_train :synopsis: Example nuts-ml pipeline for training a MLP on MNIST """ import torch import torch.nn.functional as F import torch.nn as nn import torch.optim as optim import nutsflow as nf import nutsml as nm import numpy as np from nutsml.network import PytorchNetwork from utils import download_mnist, load_mnist class Model(nn.Module): """Pytorch model""" def __init__(self, device): """Construct model on given device, e.g. 'cpu' or 'cuda'""" super(Model, self).__init__() self.fc1 = nn.Linear(28 * 28, 500) self.fc2 = nn.Linear(500, 256) self.fc3 = nn.Linear(256, 10) self.to(device) # set device before constructing optimizer # required properties of a model to be wrapped as PytorchNetwork! self.device = device # 'cuda', 'cuda:0' or 'gpu' self.losses = nn.CrossEntropyLoss() # can be list of loss functions self.optimizer = optim.Adam(self.parameters()) def forward(self, x): """Forward pass through network for input x""" x = x.view(-1, 28 * 28) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x def accuracy(y_true, y_pred): """Compute accuracy""" from sklearn.metrics import accuracy_score y_pred = [yp.argmax() for yp in y_pred] return 100 * accuracy_score(y_true, y_pred) def evaluate(network, x, y): """Evaluate network performance (here accuracy)""" metrics = [accuracy] build_batch = (nm.BuildBatch(64) .input(0, 'vector', 'float32') .output(1, 'number', 'int64')) acc = zip(x, y) >> build_batch >> network.evaluate(metrics) return acc def train(network, epochs=3): """Train network for given number of epochs""" print('loading data...') filepath = download_mnist() x_train, y_train, x_test, y_test = load_mnist(filepath) plot = nm.PlotLines(None, every_sec=0.2) build_batch = (nm.BuildBatch(128) .input(0, 'vector', 'float32') .output(1, 'number', 'int64')) for epoch in range(epochs): print('epoch', epoch + 1) losses = (zip(x_train, y_train) >> nf.PrintProgress(x_train) >> nf.Shuffle(1000) >> build_batch >> network.train() >> plot >> nf.Collect()) acc_test = evaluate(network, x_test, y_test) acc_train = evaluate(network, x_train, y_train) print('train loss : {:.6f}'.format(np.mean(losses))) print('train acc : {:.1f}'.format(acc_train)) print('test acc : {:.1f}'.format(acc_test)) if __name__ == '__main__': print('creating model...') device = 'cuda' if torch.cuda.is_available() else 'cpu' model = Model(device) network = PytorchNetwork(model) # network.load_weights() network.print_layers((28 * 28,)) print('training network...') train(network, epochs=3)

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gcp-cloud-function/send_email/main.py

duongphannamhung/atom-assignments

3

89885

<gh_stars>1-10 def send_email(request): """Responds to any HTTP request. Args: request (flask.Request): HTTP request object. Returns: The response text or any set of values that can be turned into a Response object using `make_response <http://flask.pocoo.org/docs/1.0/api/#flask.Flask.make_response>`. """ import os import json import email, smtplib, ssl from email import encoders from email.mime.base import MIMEBase from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText import datetime as dt # to work with date, time import sys request_json = request.get_json() #--> get the request json if request_json and 'subject' in request_json: print('subject ==> {}'.format(request_json['subject'])) if request_json and 'receiver_email' in request_json: print('receiver_email ==> {}'.format(request_json['receiver_email'])) ## 1A - Take input from request json receiver_email = request_json['receiver_email'] subject = request_json['subject'] ## 1B - Get ENV set in Cloud #sender_email = os.getenv('SENDER_EMAIL') ## PLEASE MEMBER TO SET THE ENV VARIABLES IN YOUR CLOUD FUNC sender_email = os.environ.get('SENDER_EMAIL') password = os.environ.get('PWD_EMAIL') print('=== Step 1: Get Input') ## 2 - Email Set email = MIMEMultipart() email["From"] = sender_email email["To"] = receiver_email email["Subject"] = subject ## 3 - Email Contents # We use html, you can convert word to html: https://wordtohtml.net/ html1 = """ <html> <h1><strong>Hello World</strong></h1> <body> <p>Hi!<br> How are you?<br> Here is the <a href="https://docs.python.org/3.4/library/email-examples.html">link</a> you wanted. </p> </body> </html> """ html2 = """ <html> Email sent at <b>{}</b><br> </html> """.format(dt.datetime.now().isoformat()) text3 = '--- End ----' # Combine parts part1 = MIMEText(html1, 'html') part2 = MIMEText(html2, 'html') part3 = MIMEText(text3, 'plain') email.attach(part1) email.attach(part2) email.attach(part3) print('=== Step 2: Prep Contents') ## 4 - Create SMTP session for sending the mail try: session = smtplib.SMTP('smtp.gmail.com', 587) #use gmail with port session.starttls() #enable security print('=== Step 3: Enable Security') session.login(sender_email, password) #login with mail_id and password print('=== Step 4: Login Success!') text = email.as_string() session.sendmail(sender_email, receiver_email, text) session.quit() print('DONE! Mail Sent from {} to {}'.format(sender_email, receiver_email)) message = 'DONE! Mail Sent from {} to {}'.format(sender_email, receiver_email) status = 'OK' except: print('=== ERROR: {}'.format(sys.exc_info()[0])) message = 'ERROR: {}'.format(sys.exc_info()[0]) status = 'FAIL' out = {'status': status, 'message': message} headers= { 'Access-Control-Allow-Origin': '*', 'Content-Type':'application/json' } return (json.dumps(out), 200, headers)

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model_based/svms/test_model_compression.py

vohoaiviet/tag-image-retrieval

50

28844

import sys import os import time from basic.common import ROOT_PATH,checkToSkip,makedirsforfile from basic.util import readImageSet from simpleknn.bigfile import BigFile, StreamFile from basic.annotationtable import readConcepts,readAnnotationsFrom from basic.metric import getScorer if __name__ == "__main__": try: rootpath = sys.argv[1] except: rootpath = ROOT_PATH metric = 'AP' feature = "dsift" trainCollection = 'voc2008train' trainAnnotationName = 'conceptsvoc2008train.txt' testCollection = 'voc2008val' testAnnotationName = 'conceptsvoc2008val.txt' testset = testCollection modelName = 'fik50' modelName = 'fastlinear' if 'fastlinear' == modelName: from fastlinear.fastlinear import fastlinear_load_model as load_model else: from fiksvm.fiksvm import fiksvm_load_model as load_model scorer = getScorer(metric) imset = readImageSet(testCollection,testset,rootpath=rootpath) concepts = readConcepts(testCollection,testAnnotationName,rootpath=rootpath) feat_dir = os.path.join(rootpath, testCollection, "FeatureData", feature) feat_file = BigFile(feat_dir) _renamed, _vectors = feat_file.read(imset) nr_of_images = len(_renamed) nr_of_concepts = len(concepts) mAP = 0.0 models = [None] * len(concepts) for i,concept in enumerate(concepts): model_file_name = os.path.join(rootpath,trainCollection,'Models',trainAnnotationName,feature, modelName, '%s.model'%concept) model1 = load_model(model_file_name) (pA,pB) = model1.get_probAB() model2 = load_model(model_file_name) model2.add_fastsvm(model1, 0.8, 1) names,labels = readAnnotationsFrom(testCollection, testAnnotationName, concept, rootpath=rootpath) name2label = dict(zip(names,labels)) ranklist1 = [(_id, model1.predict(_vec)) for _id,_vec in zip(_renamed, _vectors)] ranklist2 = [(_id, model2.predict(_vec)) for _id,_vec in zip(_renamed, _vectors)] model_file_name = os.path.join(rootpath,trainCollection,'Models', 'bag' + trainAnnotationName,feature, modelName, '%s.model'%concept) model3 = load_model(model_file_name) ranklist3 = [(_id, model3.predict(_vec)) for _id,_vec in zip(_renamed, _vectors)] print concept, for ranklist in [ranklist1, ranklist2, ranklist3]: ranklist.sort(key=lambda v:v[1], reverse=True) sorted_labels = [name2label[x[0]] for x in ranklist if x[0] in name2label] print '%.3f'%scorer.score(sorted_labels), print ''

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airtableio/api.py

Parapheen/airtableio

0

175213

<reponame>Parapheen/airtableio<filename>airtableio/api.py import logging import os from http import HTTPStatus import aiohttp from .exceptions import AirtableAPIError LOG = logging.getLogger("airtable") API_URL = "https://api.airtable.com/v0/{app_id}/{table_name}" async def make_request( session, token, method, app_id, table_name, data=None, record_id=None, **kwargs ): LOG.debug('Make request: "%s" with data: "%r', method, data) url = Methods.api_url(app_id=app_id, table_name=table_name, record_id=record_id) req = data headers = {"Authorization": "Bearer {}".format(token)} method = method.lower() if method == "post": try: async with session.post( url, json=req, headers=headers, **kwargs ) as response: result = await response.json() return result except aiohttp.ClientError as e: raise AirtableAPIError( f"aiohttp client throws an error: {e.__class__.__name__}: {e}" ) elif method == "get": try: async with session.get(url, headers=headers, **kwargs) as response: result = await response.json() LOG.debug("Got result from get query: {}".format(result)) return result except aiohttp.ClientError as e: raise AirtableAPIError( f"aiohttp client throws an error: {e.__class__.__name__}: {e}" ) elif method == "put": try: async with session.put( url, data=req, headers=headers, **kwargs ) as response: result = await response.json() return result except aiohttp.ClientError as e: raise AirtableAPIError( f"aiohttp client throws an error: {e.__class__.__name__}: {e}" ) elif method == "patch": try: async with session.patch( url, data=req, headers=headers, **kwargs ) as response: result = await response.json() return result except aiohttp.ClientError as e: raise AirtableAPIError( f"aiohttp client throws an error: {e.__class__.__name__}: {e}" ) def compose_data(params=None): """ Prepare request data :param params: :return: """ data = aiohttp.formdata.FormData(quote_fields=False) if params: for key, value in params.items(): data.add_field(key, str(value)) return data class Methods: GET_RECORDS = "get" GET_RECORD = "get" CREATE_RECORDS = "post" UPDATE_RECORDS = "patch" @staticmethod def api_url(app_id, table_name, record_id=None): """ Generate API URL with included token and method name :param app_id: :param table_name: :return: """ if record_id: return ( API_URL.format(app_id=app_id, table_name=table_name) + "/" + record_id ) else: return API_URL.format(app_id=app_id, table_name=table_name)

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office365/onedrive/permissions/sharing_invitation.py

theodoriss/Office365-REST-Python-Client

0

193186

from office365.directory.identities.identity_set import IdentitySet from office365.runtime.client_value import ClientValue class SharingInvitation(ClientValue): """The SharingInvitation resource groups invitation-related data items into a single structure.""" def __init__(self, email=None, invited_by=IdentitySet(), redeemed_by=None, signin_required=None): """ :param str email: The email address provided for the recipient of the sharing invitation. Read-only. :param IdentitySet invited_by: Provides information about who sent the invitation that created this permission, if that information is available. Read-only. :param str redeemed_by: :param bool signin_required: If true the recipient of the invitation needs to sign in in order to access the shared item. Read-only. """ super(SharingInvitation, self).__init__() self.email = email self.invitedBy = invited_by self.redeemedBy = redeemed_by self.signInRequired = signin_required

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Python/Advanced OOP/Problems/10. Rhombus of stars.py

teodoramilcheva/softuni-software-engineering

0

155320

<gh_stars>0 INCREASE: int = 1 # global namespace DECREASE: int = -1 space = ' ' sign = '* ' def print_rhombus(n: int): # global namespace # fn-in-fn: closure def print_line(i: int, direction: int): # local namespace visible in print_rhombus if i == 0: # i is part of the local namespace of print_line return line = (n - i) * space + i * sign print(line.rstrip()) if i == n: direction = DECREASE # change is happening in the local namespace of print_line print_line(i + direction, direction) # recusrion print_line(1, INCREASE) n = int(input()) print_rhombus(n)

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main.py

angmont/PIA_PC

1

48521

import subprocess import cifrado import enviocorreos import puertos import metadata import webscraping import argparse import os, time import logging logging.basicConfig(filename='app.log', level=logging.INFO) if __name__ == "__main__": description= ("Este script realiza una gran diversa cantidad de tareas " + "las cuales son las siguientes: realizar cifrados, obtener metadata, " + "escaneo de puertos, envio de correos y webscraping") parser = argparse.ArgumentParser(description="PIA", epilog=description, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument("-t", metavar='TAREA', dest="tarea", choices=['cifrar','correos', 'dns', 'puertos', 'metadata', 'web', 'hash'] , help='Se elige la tarea a realizar', required=True) parser.add_argument("-m", metavar='MODO', dest="modo", choices=['cifmensaje', 'desmensaje', 'cifgithub', 'destxt', 'ciftxt', 'busqueda', 'emails', 'pdf', 'img'] , help='Si desea utilizar la tarea de cifrado o de web scraping, es necesario especificiar el modo') parser.add_argument("-msj", metavar='MENSAJE', dest="mensaje", type=str, help='Se debe poner un mensaje el cual se quiera cifrar o descifrar.') parser.add_argument("-key", metavar='LLAVE', dest="llave", type=int, help='Se utiliza para saber a base de cual llave se cifra o descifra el mensaje') parser.add_argument("-user", metavar='USUARIO', dest="usuario", type=str, help='Es un argumento necesario para la funcion de cifrar los resultados obtenidos de la API de Github') parser.add_argument("-ru", metavar='RUTA', dest="ruta", type=str, help='Ruta necesaria para el txt que se va a descifrar o donde se encuentran los arctivos pata la funcion de metadata') parser.add_argument("-rem", metavar='REMITENTE', dest="remitente", type=str, help='Correo del que se enviará el mensaje.') parser.add_argument("-des", metavar='DESTINATARIO', dest="destinatario", type=str, help='Correo que recibirá el mensaje.') parser.add_argument("-url", metavar= 'URL', dest="dominio", type=str, help='Url a investigar.') parser.add_argument("-cont", metavar='CONTENIDO', dest="contenido", type=str, help='Se debe poner un mensaje el cual se quiera enviar.', default="Hola mundo mundial") parser.add_argument("-asu", metavar='ASUNTO', dest="asunto", type=str, help='Se utiliza para poner el titulo que tendrá el correo.', default="Hola!") parser.add_argument("-ip", metavar='IP', dest="ip", type=str, help='Se debe introducir la ip a consultar, solo el ID de red.', default="172.217.15.") parser.add_argument("-ports", metavar='PUERTOS', dest="puertos", help='Introduce los puertos a revisar separados por una coma [80,800]', default= "80,800") parser.add_argument("-a", metavar='ARCHIVO', dest="archivo", choices=['imagen', 'imagenes', 'pdf', 'pdfs', 'word', 'words', 'mp3', 'mp3s'] , help='Si desea utilizar la tarea de sacar la metadata, es necesario especificiar el tipo de archivo') parser.add_argument("-mp", metavar= 'METAPATH', dest="metapath", type=str, help='Ruta donde se guardarán los metadatas encontrados.') parser.add_argument("-bus", metavar='BUSQUEDA', dest="busqueda", type=str, help='Busqueda para realizar en google') params = parser.parse_args() try: logging.info("Se escogió la tarea: ") logging.info(params.tarea) tarea = (params.tarea) except Exception as e: logging.error("Ocurrió un error: " + str(e)) print(e) exit try: if tarea == 'cifrar': modo = (params.modo) logging.info("El modo es: " + modo) llave = (params.llave) if (modo == 'cifmensaje') or (modo == 'desmensaje'): mensaje = (params.mensaje) logging.info("El mensaje es: " + str(mensaje)) if modo == 'cifmensaje': print(cifrado.cifrar_mensaje(mensaje, llave)) else: print(cifrado.descifrar_mensaje(mensaje, llave)) elif modo == 'cifgithub': usuario = (params.usuario) logging.info("El usuario es: " + usuario) cifrado.cifrar_github(usuario, llave) elif modo == 'destxt': ruta = (params.ruta) logging.info("Usaremos la ruta: " + ruta) cifrado.descifrar_txt(ruta, llave) elif modo == 'ciftxt': ruta = params.ruta logging.info("Usaremos la ruta: " + ruta) cifrado.cifrar_txt(ruta, llave) else: logging.error("Opcion no válida para cifrado") print('Opción no válida para cifrado') elif tarea == 'correos': remitente = (params.remitente) logging.info("El remitente es: " + remitente) destinatario = (params.destinatario) logging.info("El destinatario es: " + destinatario) mensaje = (params.contenido) logging.info("El mensaje es: " + mensaje) asunto = (params.asunto) logging.info("El asunto es: " + asunto) orga = (params.dominio) logging.info("La organizacion es: " + orga) datos_encontrados = enviocorreos.Busqueda(orga) if datos_encontrados is None: logging.info("No se encontró nada") print("No se encontró nada") exit() else: enviocorreos.GuardarInformacion(datos_encontrados, orga, remitente, destinatario, asunto, mensaje) elif tarea == 'dns': logging.info("Se inicia la tarea de dns") print() script_p = subprocess.Popen([r'C:\WINDOWS\system32\WindowsPowerShell\v1.0\powershell.exe','-ExecutionPolicy', 'Unrestricted', './dns.ps1'], cwd=os.getcwd()) script_p.wait() elif tarea == 'metadata': logging.info("Tipo de archivo:") archivo = (params.archivo) logging.info(archivo) if (archivo == 'imagen') or (archivo == 'imagenes'): ruta = (params.ruta) logging.info("En la ruta: " + ruta) metapath = (params.metapath) logging.info("El metadata se guardará en la ruta: " + metapath) if archivo == 'imagen': logging.info("Ingresamos a la función printOneMetaImg") metadata.printOneMetaImg(ruta, metapath) else: logging.info("Ingresamos a la función printAllMetaImg") metadata.printAllMetaImg(ruta, metapath) elif (archivo == 'pdf') or (archivo == 'pdfs'): ruta = (params.ruta) logging.info("Usaremos la ruta: " + ruta) metapath = (params.metapath) logging.info("Guardaremos la metadata en: " + metapath) if archivo == 'pdf': logging.info("Ingresamos a la función printOneMetaPDF") metadata.printOneMetaPDf(ruta, metapath) else: logging.info("Ingresamos a la función printAllMetaPDF") metadata.printAllMetaPDf(ruta, metapath) elif (archivo == 'word') or (archivo == 'words'): ruta = (params.ruta) logging.info("Usaremos la ruta: " + ruta) metapath = (params.metapath) logging.info("Guardaremos la metadata en: " + metapath) if archivo == 'word': logging.info("Ingresamos a la función printOneMetaDocx") metadata.printOneMetaDocx(ruta, metapath) else: logging.info("Ingresamos a la función printAllMetaDocx") metadata.printAllMetaDocx(ruta, metapath) else: ruta = (params.ruta) logging.info("Usaremos la ruta: " + ruta) metapath = (params.metapath) logging.info("Guardaremos la metadata en: " + metapath) if archivo == 'mp3': logging.info("Ingresamos a la función printOneMetaMp3") metadata.printOneMetaMp3(ruta, metapath) else: logging.info("Ingresamos a la función printAllMetaMp3") metadata.printAllMetaMp3(ruta, metapath) elif tarea == 'puertos': logging.info("Se introdujo la ip: ") ip = params.ip logging.info(ip) print("Se revisará la ip: " + ip) logging.info("Se escanearan los puertos: ") puertoss = params.puertos logging.info(puertoss) portlist = params.puertos.split(',') for i in range (len(portlist)): print("Con los puertos: " + portlist[i]) portlist[i] = int(portlist[i]) puertos.checoPuertos(ip, portlist, puertoss) elif tarea == 'web': logging.info("Con el modo: ") modo = params.modo logging.info(modo) if modo == 'emails' or modo == 'pdf' or modo == 'img': url = params.dominio logging.info("El dominio es: ") logging.info(url) if modo == 'emails': logging.info("Si el \"modo\" es: emails") webscraping.find_mails(url) elif modo == 'pdf': logging.info("Si el \"modo\" es: pdf") webscraping.descargar_pdfs(url) else: logging.info("Si el \"modo\" es: img") webscraping.download_images(url) elif modo == 'busqueda': logging.info("Se buscará:") busqueda = params.busqueda logging.info(busqueda) webscraping.busqueda_google(busqueda) else: logging.info("Ninguna opción es valida para hacer Web Scrapping" ) print('Opcion no válida para web scraping') else: print() script_p = subprocess.Popen([r'C:\WINDOWS\system32\WindowsPowerShell\v1.0\powershell.exe', '-ExecutionPolicy', 'Unrestricted', './rutas.ps1'], cwd=os.getcwd()) script_p.wait() except Exception as e: logging.error("Ha ocurrido un error: " + str(e)) print(e) exit

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ch10/myproject_virtualenv/src/django-myproject/myproject/settings/production.py

PacktPublishing/Django-3-Web-Development-Cookbook

159

22663

from ._base import * DEBUG = False WEBSITE_URL = "https://example.com" # without trailing slash MEDIA_URL = f"{WEBSITE_URL}/media/"

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tests/unit/server/test_get_model_metadata_utils.py

rasapala/OpenVINO-model-server

0

78469

<reponame>rasapala/OpenVINO-model-server<gh_stars>0 # # Copyright (c) 2018 Intel Corporation # # 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. # from tensorflow.python.framework import dtypes as dtypes import numpy as np import pytest from ie_serving.server.get_model_metadata_utils import \ _prepare_signature, prepare_get_metadata_output from conftest import MockedIOInfo @pytest.mark.parametrize("layers, tensor_key, np_type", [ ({'tensor': MockedIOInfo('FP32', (1, 1, 1), 'NCHW'), 'test_tensor': MockedIOInfo('FP32', (1, 1, 1), 'NCHW')}, {'new_key': 'tensor', 'client_key': 'test_tensor'}, np.float32), ({'tensor': MockedIOInfo('I32', (1, 1, 1), 'NCHW')}, {'new_key': 'tensor'}, np.int32), ]) def test_prepare_signature(layers, tensor_key, np_type): dtype_model = dtypes.as_dtype(np_type) output = _prepare_signature( layers=layers, model_keys=tensor_key) for key, value in tensor_key.items(): assert key in output assert value in output[key].name shape = [d.size for d in output[key].tensor_shape.dim] assert list(layers[value].shape) == shape tensor_dtype = dtypes.as_dtype(output[key].dtype) assert dtype_model == tensor_dtype def test_prepare_get_metadata_output(): inputs = {'tensor_input': MockedIOInfo('FP32', (1, 1, 1), 'NCHW')} outputs = {'tensor_output': MockedIOInfo('FP32', (1, 1, 1), 'NCHW')} model_keys = {'inputs': {'name': 'tensor_input'}, 'outputs': {'output_name': 'tensor_output'}} output = prepare_get_metadata_output( inputs=inputs, outputs=outputs, model_keys=model_keys) assert "tensorflow/serving/predict" == output.method_name

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faketranslate/metadata.py

HeywoodKing/faketranslate

0

23745

<filename>faketranslate/metadata.py # -*- encoding: utf-8 -*- """ @File : metadata.py @Time : 2020/1/1 @Author : flack @Email : <EMAIL> @ide : PyCharm @project : faketranslate @description : 描述 """

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problems/problem3.py

JakobHavtorn/euler

0

23420

"""Largest prime factor The prime factors of 13195 are 5, 7, 13 and 29. What is the largest prime factor of the number 600851475143? """ import math import numpy as np def largest_prime_factor_naive(number): """ Let the given number be n and let k = 2, 3, 4, 5, ... . For each k, if it is a factor of n then we divide n by k and completely divide out each k before moving to the next k. It can be seen that when k is a factor it will necessarily be prime, as all smaller factors have been removed, and the final result of this process will be n = 1. """ factor = 2 factors = [] while number > 1: if number % factor == 0: factors.append(factor) number = number // factor # Remainder guarenteed to be zero while number % factor == 0: number = number // factor # Remainder guarenteed to be zero factor += 1 return factors def largest_prime_factor_even_optimized(number): """ We know that, excluding 2, there are no even prime numbers. So we can increase factor by 2 per iteration after having found the """ factors = [] factor = 2 if number % factor == 0: number = number // factor factors.append(factor) while number % factor == 0: number = number // factor factor = 3 while number > 1: if number % factor == 0: factors.append(factor) number = number // factor # Remainder guarenteed to be zero while number % factor == 0: number = number // factor # Remainder guarenteed to be zero factor += 2 return factors def largest_prime_factor_square_optimized(number): """ Every number n can at most have one prime factor greater than n. If we, after dividing out some prime factor, calculate the square root of the remaining number we can use that square root as upper limit for factor. If factor exceeds this square root we know the remaining number is prime. """ factors = [] factor = 2 if number % factor == 0: number = number // factor factors.append(factor) while number % factor == 0: number = number // factor factor = 3 max_factor = math.sqrt(number) while number > 1 and factor <= max_factor: if number % factor == 0: factors.append(factor) number = number // factor while number % factor == 0: number = number // factor max_factor = math.sqrt(number) factor += 2 return factors def idx_sieve(length): """Static length sieve-based prime generator""" primes = [] is_prime = np.array([True]*length) i = 2 while i < length: if is_prime[i]: is_prime[np.arange(i, length, i)] = False primes.append(i) else: i += 1 return primes def prime_factor(n, primes): i = 0 factors = [] while n != 1: while (n % primes[i]) != 0: i += 1 factors.append(primes[i]) n = n / primes[i] return factors if __name__ == '__main__': number = 600851475143 print(largest_prime_factor_naive(number)) print(largest_prime_factor_even_optimized(number)) print(largest_prime_factor_square_optimized(number)) number = 600851475143 primes = idx_sieve(20000) print(max(prime_factor(number, primes)))

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apps/utils/errors.py

osw4l/villas-de-san-pablo

0

171375

<filename>apps/utils/errors.py __author__ = 'osw4l' from django.shortcuts import render def error400(request): return render(request, '400.html', status=400) def error403(request): return render(request, '403.html', status=403) def error404(request): return render(request, '404.html', status=404) def error500(request): return render(request, '500.html', status=500)

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molior/api/mirror.py

randombenj/molior

0

182905

<gh_stars>0 """ Provides functions for managing aptly mirrors. """ from aiohttp import web import logging from molior.model.build import Build from molior.model.project import Project from molior.model.projectversion import ProjectVersion from molior.model.buildvariant import BuildVariant from molior.molior.utils import get_aptly_connection # FIXME: cannot import in here from molior.molior.notifier import build_added from .app import app logger = logging.getLogger("molior-web") def error(status, msg, *args): """ Logs an error message and returns an error to the web client. Args: status (int): The http response status. msg (str): The message to display. args (tuple): Arguments for string format on msg. """ logger.error(msg.format(*args)) return web.Response(status=status, text=msg.format(*args)) @app.http_post("/api/mirror") @app.req_admin # FIXME: req_role async def create_mirror(request): """ Create a debian aptly mirror. --- description: Create a debian aptly mirror. tags: - Mirrors consumes: - application/x-www-form-urlencoded parameters: - name: name in: query required: true type: string description: name of the mirror - name: url in: query required: true type: string description: http://host of source - name: distribution in: query required: true type: string description: trusty, wheezy, jessie, etc. - name: components in: query required: false type: array description: components to be mirrored default: main - name: keys in: query required: false type: array uniqueItems: true collectionFormat: multi allowEmptyValue: true minItems: 0 items: type: string description: repository keys - name: keyserver in: query required: false type: string description: host name where the keys are - name: is_basemirror in: query required: false type: boolean description: use this mirror for chroot - name: architectures in: query required: false type: array description: i386,amd64,arm64,armhf,... - name: version in: query required: false type: string - name: armored_key_url in: query required: false type: string - name: basemirror_id in: query required: false type: string - name: download_sources in: query required: false type: boolean - name: download_installer in: query required: false type: boolean produces: - text/json responses: "200": description: successful "400": description: mirror creation failed. "412": description: key error. "409": description: mirror already exists. "500": description: internal server error. "503": description: aptly not available. "501": description: database error occurred. """ params = await request.json() mirror = params.get("name") url = params.get("url") mirror_distribution = params.get("distribution") components = params.get("components", []) keys = params.get("keys", []) keyserver = params.get("keyserver") is_basemirror = params.get("is_basemirror") architectures = params.get("architectures", []) version = params.get("version") key_url = params.get("armored_key_url") basemirror_id = params.get("basemirror_id") download_sources = params.get("download_sources") download_installer = params.get("download_installer") if not components: components = ["main"] if not isinstance(is_basemirror, bool): return web.Response(status=400, text="is_basemirror not a bool") args = { "create_mirror": [ mirror, url, mirror_distribution, components, keys, keyserver, is_basemirror, architectures, version, key_url, basemirror_id, download_sources, download_installer, ] } await request.cirrina.aptly_queue.put(args) return web.Response( status=200, text="Mirror {} successfully created.".format(mirror) ) @app.http_get("/api/mirror") @app.authenticated async def get_mirrors(request): """ Returns all mirrors from database. --- description: Returns mirrors from database. tags: - Mirrors consumes: - application/x-www-form-urlencoded parameters: - name: page in: query required: false type: integer default: 1 description: page number - name: page_size in: query required: false type: integer default: 10 description: max. mirrors per page - name: q in: query required: false type: string description: filter criteria produces: - text/json responses: "200": description: successful "400": description: bad request """ page = request.GET.getone("page", None) page_size = request.GET.getone("page_size", None) filter_name = request.GET.getone("q", "") if page: try: page = int(page) except (ValueError, TypeError): return web.Response(text="Incorrect value for page", status=400) if page_size: try: page_size = int(page_size) except (ValueError, TypeError): return web.Response(text="Incorrect value for page_size", status=400) page_size = 1 if page_size < 1 else page_size query = request.cirrina.db_session.query( ProjectVersion ) # pylint: disable=no-member query = query.join(Project, Project.id == ProjectVersion.project_id) query = query.filter(Project.is_mirror == "true").order_by(Project.name) if filter_name: query = query.filter(Project.name.like("%{}%".format(filter_name))) query = query.order_by(Project.name, ProjectVersion.name) nb_results = query.count() if page is not None and page_size: query = query.offset(page * page_size) query = query.limit(page_size) results = query.all() data = {"total_result_count": nb_results, "results": []} for mirror in results: apt_url = mirror.get_apt_repo(url_only=True) base_mirror_url = str() if not mirror.project.is_basemirror and mirror.buildvariants: # FIXME: only one buildvariant supported base_mirror = mirror.buildvariants[0].base_mirror base_mirror_url = base_mirror.get_apt_repo(url_only=True) data["results"].append( { "id": mirror.id, "name": mirror.project.name, "version": mirror.name, "url": mirror.mirror_url, "base_mirror": base_mirror_url, "distribution": mirror.mirror_distribution, "components": mirror.mirror_components, "is_basemirror": mirror.project.is_basemirror, "architectures": mirror.mirror_architectures[1:-1], "is_locked": mirror.is_locked, "with_sources": mirror.mirror_with_sources, "with_installer": mirror.mirror_with_installer, "project_id": mirror.project.id, "state": mirror.mirror_state, "apt_url": apt_url, } ) return web.json_response(data) @app.http_delete("/api/mirror/{id}") @app.req_admin # FIXME: req_role async def delete_mirror(request): """ Delete a single mirror on aptly and from database. --- description: Delete a single mirror on aptly and from database. tags: - Mirrors consumes: - application/x-www-form-urlencoded parameters: - name: id in: path required: true type: integer description: id of the mirror produces: - text/json responses: "200": description: successful "400": description: removal failed from aptly. "404": description: mirror not found on aptly. "500": description: internal server error. "503": description: removal failed from database. """ apt = get_aptly_connection() mirror_id = request.match_info["id"] query = request.cirrina.db_session.query(ProjectVersion) # pylint: disable=no-member query = query.join(Project, Project.id == ProjectVersion.project_id) query = query.filter(Project.is_mirror.is_(True)) entry = query.filter(ProjectVersion.id == mirror_id).first() if not entry: logger.warning("error deleting mirror '%s': mirror not found", mirror_id) return error(404, "Error deleting mirror '%d': mirror not found", mirror_id) # FIXME: check state, do not delete ready/updating/... mirrorname = "{}-{}".format(entry.project.name, entry.name) # check relations if entry.sourcerepositories: logger.warning("error deleting mirror '%s': referenced by one or more source repositories", mirrorname) return error(412, "Error deleting mirror {}: still referenced by one or more source repositories", mirrorname) if entry.buildconfiguration: logger.warning("error deleting mirror '%s': referenced by one or more build configurations", mirrorname) return error(412, "Error deleting mirror {}: still referenced by one or more build configurations", mirrorname) if entry.dependents: logger.warning("error deleting mirror '%s': referenced by one or project versions", mirrorname) return error(412, "Error deleting mirror {}: still referenced by one or more project versions", mirrorname) base_mirror = "" if not entry.project.is_basemirror: basemirror = entry.buildvariants[0].base_mirror base_mirror = basemirror.project.name + "-" + basemirror.name # FIXME: cleanup chroot table, schroots, debootstrap, try: # FIXME: use altpy queue ! await apt.mirror_delete(base_mirror, entry.project.name, entry.name, entry.mirror_distribution) except Exception as exc: # mirror did not exist # FIXME: handle mirror has snapshots and cannot be deleted? pass project = entry.project bvs = request.cirrina.db_session.query(BuildVariant).filter( BuildVariant.base_mirror_id == entry.id).all() # pylint: disable=no-member for bvariant in bvs: # FIXME: delete buildconfigurations request.cirrina.db_session.delete(bvariant) # pylint: disable=no-member builds = request.cirrina.db_session.query(Build) .filter(Build.projectversion_id == entry.id).all() for build in builds: # FIXME: delete buildconfigurations # FIXME: remove buildout dir request.cirrina.db_session.delete(build) # pylint: disable=no-member request.cirrina.db_session.delete(entry) # pylint: disable=no-member request.cirrina.db_session.commit() # pylint: disable=no-member if not project.projectversions: request.cirrina.db_session.delete(project) # pylint: disable=no-member request.cirrina.db_session.commit() # pylint: disable=no-member return web.Response(status=200, text="Successfully deleted mirror: {}".format(mirrorname)) @app.http_put("/api/mirror/{id}") @app.req_admin # FIXME: req_role async def put_update_mirror(request): """ Updates a mirror. --- description: Updates a mirror. tags: - Mirrors consumes: - application/x-www-form-urlencoded parameters: - name: name in: path required: true type: integer description: name of the mirror produces: - text/json responses: "200": description: Mirror update successfully started. "400": description: Mirror not in error state. "500": description: Internal server error. """ mirror_id = request.match_info["id"] project_v = ( request.cirrina.db_session.query(ProjectVersion) .filter(ProjectVersion.id == mirror_id) # pylint: disable=no-member .first() ) if project_v.is_locked: return error(400, "Mirror locked. Update not allowed.") if project_v.mirror_state != "error": return error(400, "Mirror not in error state.") components = project_v.mirror_components.split(",") # FIXME: only one build variant supported base_mirror = None base_mirror_version = None if not project_v.project.is_basemirror: base_mirror = project_v.buildvariants[0].base_mirror.project.name base_mirror_version = project_v.buildvariants[0].base_mirror.name build = ( request.cirrina.db_session.query(Build) .filter(Build.buildtype == "mirror", Build.projectversion_id == project_v.id) .first() ) if not build: logger.error("update mirror: no build found for mirror %d", mirror_id) return error(400, "no build found for mirror") args = { "update_mirror": [ build.id, project_v.id, base_mirror, base_mirror_version, project_v.project.name, project_v.name, components, ] } await request.cirrina.aptly_queue.put(args) return web.Response(status=200, text="Successfully started update on mirror")

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QA system/main.py

yiwen26/NLP_banking_chatbot

2

199785

import os import argparse from gensim.models.keyedvectors import KeyedVectors from preprocess import * from model import * import torch if __name__ == '__main__': # parse command line argument parser = argparse.ArgumentParser() parser.add_argument('--mode', default='training', type=str, help='training, evaluation and simulation mode are supported') parser.add_argument('--testset', default='test1', type=str, help='Define testset type: [dev/test1/test2] ') parser.add_argument('--nn_type', default='attention', type=str, help='3 type to reduce rnn output: max_pooling, mean_pooling, attention') parser.add_argument('--data_path', default='./data', type=str, metavar='PATH', help='insurance input data directory') parser.add_argument('--checkpoint_path', default='./checkpoints', type=str, metavar='PATH', help='model saving directory') parser.add_argument('--embd_type', default='google', type=str, help='word embedding mode selection [none/google], default set to google embedding') parser.add_argument("--embed_path", default="./embedding/GoogleNews-vectors-negative300.bin", type=str, metavar='PATH', help="pre-trained word embedding path (Google news word embedding)") parser.add_argument("--model_path", default="./model/demo.model", type=str, metavar='PATH', help="pre-trained QA model for evaluation and simulation") parser.add_argument('--data_version', default='V1', type=str, help='insurance QA dataset version') parser.add_argument('--batch_size', type=int, default=128, help='input batch size') parser.add_argument('--n_epochs', type=int, default=5, help='epoch num') parser.add_argument('--embd_size', type=int, default=300, help='word embedding size') parser.add_argument('--hidden_size', type=int, default=128, help='hidden size of one-directional LSTM') parser.add_argument('--max_sent_len', type=int, default=200, help='max sentence length') parser.add_argument('--margin', type=float, default=0.2, help='margin for loss function') parser.add_argument('--neg_pos_rate', type=int, default=2, help='training negative samples and positive samples rate') args = parser.parse_args() mode = args.mode model_type = args.nn_type model_path = args.model_path testset_type = args.testset data_path = os.path.join(args.data_path, args.data_version) checkpoint_path = os.path.join(args.checkpoint_path, model_type) if not os.path.exists(checkpoint_path): os.mkdir(checkpoint_path) version = args.data_version embedding_type = args.embd_type embedding_size = args.embd_size hidden_size = args.hidden_size max_sentence_len = args.max_sent_len batch_size = args.batch_size margin = args.margin epoch_num = args.n_epochs neg_pos_rate = args.neg_pos_rate print("============================ Parameters ==================================") print("-- Model bi-lstm +", model_type) print("-- Dataset Version = ", version) print("-- Hidden Size = ", hidden_size) print("-- Embedding type = ", embedding_type) print("-- Embedding size = ", embedding_size) print("-- Epoch Num = ", epoch_num) print("-- Batch Size = ", batch_size) print("-- Margin = ", margin) print("-- Negative / Positive = ", neg_pos_rate) print("-- Checkpoint path: ", checkpoint_path) print("============================ Parameters ==================================") vocabulary_path = os.path.join(data_path, "vocabulary") if version == "V1": answer_path = os.path.join(data_path, "answers.label.token_idx") qa_training_data_path = os.path.join(data_path, "question.train.token_idx.label") qa_test_data_path = os.path.join(data_path, "question.{}.label.token_idx.pool".format(testset_type)) elif version == "V2": answer_path = os.path.join(data_path, "InsuranceQA.label2answer.token.encoded") qa_training_data_path = os.path.join(data_path, "InsuranceQA.question.anslabel.token.100.pool.solr.train.encoded") qa_test_data_path = os.path.join(data_path, "InsuranceQA.question.anslabel.token.100.pool.solr.test.encoded") else: print("[Error]: invalid dataset version setting, please use -h to check legal parameter setting") exit(1) id_2_word = load_vocabulary(vocabulary_path) word_to_id = {w: i for i, w in enumerate(id_2_word.values(), 1)} word_to_id['<PAD>'] = 0 vocabulary_size = len(word_to_id) id_2_answers, id_2_answer_text = load_id_2_answer_data(answer_path, id_2_word) print("answer size: ", len(id_2_answer_text)) print("raw vocabulary size: ", len(word_to_id)) if version == "V1": training_data = load_v1_training_answer_data(qa_training_data_path, id_2_word, id_2_answer_text) test_data = load_v1_test_answer_data(qa_test_data_path, id_2_word) validation_data = test_data[:100] if version == "V2": training_data = load_training_answer_data(qa_training_data_path, id_2_word, id_2_answer_text) test_data = load_test_answer_data(qa_test_data_path, id_2_word) validation_data = test_data[:100] print("============================ Checking data ==================================") print("word to id sample: ") head_data(word_to_id, 10) print("data sample: ") sample_id = random.randint(0, len(test_data)) print("-- question: ", " ".join(test_data[sample_id][0])) print("-- pos: ", " ".join(id_2_answer_text[test_data[sample_id][1][0]])) print("-- neg: ", " ".join(id_2_answer_text[test_data[sample_id][2][0]])) print("============================ Checking data done =============================") word_embedding = None if embedding_type == "google" and mode == "training": print('[Word Embedding]: load google news word embedding') word2vec = KeyedVectors.load_word2vec_format(args.embed_path, binary=True) word_embedding = load_word_embedding(word2vec, vocabulary_size, args.embd_size, word_to_id) model = QA_LSTM(vocabulary_size, args.embd_size, args.hidden_size, word_embedding, model_type) if torch.cuda.is_available(): model.cuda() optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters())) if mode == "training": train_batch(model, training_data, validation_data, optimizer, word_to_id, id_2_answer_text, max_sentence_len, epoch_num, batch_size, margin, neg_pos_rate, checkpoint_path) if mode == "evaluation": print("Load pre-trained model at ", model_path) checkpoint = torch.load(model_path) model.load_state_dict(checkpoint['state_dict']) test(model, test_data, id_2_answer_text, max_sentence_len, word_to_id) if mode == "simulation": print("============================ Simulation ========================") print("Load pre-trained model at ", model_path) checkpoint = torch.load(model_path) model.load_state_dict(checkpoint['state_dict']) # candidate question questions = generate_candidate_questions(training_data) print("candidate questions: ", questions) simulation(model, list(id_2_answer_text.values()), word_to_id, max_sentence_len)

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old-code/enzfam-molfunc3.py

rwst/wikidata-molbio

2

84836

import pronto, six, csv from sys import * reader = csv.DictReader(open('goid.tab', 'r'), delimiter='\t') efs = {} for item in reader: go = item.get('goid') iturl = item.get('p') it = iturl[iturl.rfind('/')+1:] git = efs.get(go) if git is None: efs[go] = it else: print('============= {}'.format(go)) reader = csv.DictReader(open('t.tab', 'r'), delimiter='\t') mfs = {} for item in reader: ec = item.get('ecLabel') iturl = item.get('p') it = iturl[iturl.rfind('/')+1:] git = mfs.get(it) if git is None: mfs[it] = ec else: print(it) reader = csv.DictReader(open('efnames.tab', 'r'), delimiter='\t') ns = {} for item in reader: ec = item.get('pLabel') iturl = item.get('p') it = iturl[iturl.rfind('/')+1:] git = ns.get(it) if git is None: ns[it] = ec else: print(it) reader = csv.DictReader(open('mf.tab', 'r'), delimiter='\t') ms = {} for item in reader: lab = item.get('pLabel') iturl = item.get('p') it = iturl[iturl.rfind('/')+1:] git = ms.get(it) if git is None: ms[it] = lab else: print(it) reader = csv.DictReader(open('ec2go.tab', 'r'), delimiter='\t') ecgo = {} for item in reader: ec = item.get('ec') go = item.get('goid') git = ecgo.get(ec) if git is None: ecgo[ec] = [go] else: git.append(go) for tup in mfs.items(): red = False ec = tup[1].replace('.-', '') goecl = ecgo.get(ec) while goecl is None: red = True ec = ec[:ec.rfind('.')] goecl = ecgo.get(ec) if None is efs.get(goecl[0]): print(tup[0]) continue if len(goecl) == 1: pass #print('{}|P680|{}'.format(tup[0], efs.get(goecl[0]), goecl[0])) else: for e in goecl: print('{}|P680|{} x {} {}'.format(tup[0], efs.get(e), ns.get(tup[0]), ms.get(efs.get(e))))

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nekoyume/app.py

lux600/nekoyume

0

80594

<reponame>lux600/nekoyume import os from flask import Flask from raven.contrib.flask import Sentry from nekoyume.models import cache, db from nekoyume.api import api from nekoyume.game import babel, game from nekoyume.tasks import celery def make_celery(app): celery.name = app.import_name celery.conf.update( result_backend=app.config['CELERY_RESULT_BACKEND'], broker_url=app.config['CELERY_BROKER_URL']) celery.conf.update(app.config) TaskBase = celery.Task class ContextTask(TaskBase): abstract = True def __call__(self, *args, **kwargs): with app.app_context(): return TaskBase.__call__(self, *args, **kwargs) celery.Task = ContextTask return celery def create_app(): app = Flask(__name__) app.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get( 'DATABASE_URL', 'sqlite:///yume.db') app.config['TEMPLATES_AUTO_RELOAD'] = True app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False try: with open('.secret_key', 'rb') as f: app.secret_key = f.read() except FileNotFoundError: app.secret_key = os.urandom(64) f = open('.secret_key', 'wb') f.write(app.secret_key) f.close() app.register_blueprint(api) app.register_blueprint(game) db.app = app db.init_app(app) babel.app = app babel.init_app(app) app.config.update( CELERY_BROKER_URL=os.environ.get( 'CELERY_BROKER_URL', 'sqla+sqlite:///yume_broker.db'), CELERY_RESULT_BACKEND=os.environ.get( 'CELERY_RESULT_BACKEND', 'db+sqlite:///yume_reuslt.db')) return app app = create_app() cel = make_celery(app) cache_type = os.environ.get('CACHE_TYPE', 'filesystem') if cache_type == 'redis': cache_config = { 'CACHE_TYPE': cache_type, 'CACHE_REDIS_URL': os.environ.get( 'REDIS_URL', 'redis://localhost:6379' ), } else: cache_config = { 'CACHE_TYPE': cache_type, 'CACHE_DIR': os.environ.get('CACHE_DIR', '.yumecache'), } cache.init_app(app, cache_config) sentry = Sentry(app) def run(): from gunicorn.app import wsgiapp wsgiapp.run()

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dagology/__init__.py

JamesClough/dagology

5

89263

<gh_stars>1-10 from algorithms import * from generators import * from utils import * from metrics import * from matrix import *

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setup.py

aidan-fitz/opwen-cloudserver

0

124067

<filename>setup.py from setuptools import find_packages from setuptools import setup with open('requirements.txt') as fobj: install_requires = [line.strip() for line in fobj] with open('README.rst') as fobj: long_description = fobj.read() with open('version.txt') as fobj: version = fobj.read().strip() packages = find_packages(exclude=['tests*']) scripts = [ 'runserver.py', 'registerclient.py', ] setup( name='opwen_email_server', version=version, author='<NAME>', author_email='<EMAIL>', packages=packages, url='https://github.com/ascoderu/opwen-cloudserver', license='Apache Software License', description='Email server for the Lokole project: https://ascoderu.ca', long_description=long_description, scripts=scripts, include_package_data=True, install_requires=install_requires, classifiers=[ 'Development Status :: 3 - Alpha', 'Environment :: Web Environment', 'License :: OSI Approved :: Apache Software License', 'Programming Language :: Python :: 3', 'Topic :: Communications :: Email', ])

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setup.py

walwe/autolabel

1

25027

#!/usr/bin/env python from pkg_resources import get_distribution from setuptools import setup, find_packages with open("README.md", "r") as f: long_description = f.read() version = get_distribution("autolabel").version setup( packages=find_packages(), install_requires=[ 'click', 'more-itertools', 'torchvision', 'torch', 'pillow', 'numpy' ], entry_points=''' [console_scripts] autolabel=autolabel.cli:main ''', url='https://github.com/walwe/autolabel', version=version, author='walwe', python_requires='>=3.6', description='Autolabel is an image labeling tool using Neural Network', long_description_content_type="text/markdown", long_description=long_description, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ] )

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workflow/scripts/sample_comp_plot.py

snakemake-workflows/dna-seq-neoantigen-prediction

6

120081

import os import glob import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt from pysam import VariantFile variant_df = pd.read_csv(snakemake.input[0], sep='\t').fillna(0.0) variant_df = variant_df[["CHROM", "POS"] + [c for c in variant_df.columns if c.endswith("Freq")]] ## tidy data - for facet plot tidy_df = variant_df.melt(id_vars=["CHROM", "POS"], var_name="Sample", value_name="VAF") g = sns.FacetGrid(tidy_df, col="Sample") g = g.map(sns.distplot, "VAF", hist=False) g.savefig(snakemake.output["facet"]) plt.close() ## pairplot sns.pairplot(variant_df.drop(["CHROM", "POS"],axis=1), diag_kind="kde") plt.savefig(snakemake.output["pairplot"]) plt.close() def overlap_pct(x, y, **kws): n = 0 for i in range(0,len(x)): if (x[i] > 0) & (y[i] > 0): n += 1 overlap=n/len([e for e in x if e > 0]) ax = plt.gca() ax.annotate("Shared Fraction: {:.2f}".format(overlap), xy=(.2, .4)) ax.annotate("Shared Variants: {}".format(n), xy=(.2, .6)) def variants(x, **kws): positive = len([e for e in x if e > 0]) ax = plt.gca() ax.annotate("#Variants: {}".format(positive), xy=(.2, .5), xycoords=ax.transAxes) g = sns.PairGrid(variant_df.drop(["CHROM", "POS"], axis=1)) g.map_offdiag(overlap_pct) g.map_diag(variants) g.savefig(snakemake.output["grid"]) plt.close() #def neg_overlap_pct(x, y, **kws): # n = 0 # for i in range(0,len(x)): # if (x[i] == 0) & (y[i] == 0): # n += 1 # overlap=n/len([e for e in x if e == 0]) # ax = plt.gca() # ax.annotate("Shared Fraction: {:.2f}".format(overlap), xy=(.2, .4)) # ax.annotate("Shared missing variants: {}".format(n), xy=(.2, .6)) #def neg_variants(x, **kws): # zero = len([e for e in x if e == 0]) # ax = plt.gca() # ax.annotate("#Missing Variants: {}".format(zero), xy=(.2, .5), #xycoords=ax.transAxes) #g = sns.PairGrid(variant_df.drop(["CHROM", "POS"], axis=1)) #g.map_offdiag(neg_overlap_pct) #g.map_diag(neg_variants) #g.savefig("plots/Mssing_Variant_table.pdf") #plt.close() for c in variant_df.drop(["CHROM", "POS"], axis=1).columns: sns.distplot(variant_df[[c]][variant_df[c] > 0]) plt.title(c) plt.savefig("plots/positive_" + c +".distplot.pdf") plt.close() sns.distplot(variant_df[[c]]) plt.savefig("plots/all_" + c +".distplot.pdf") plt.close()

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LossAug/OpticalFlowLoss.py

gexahedron/pytti

0

30730

<reponame>gexahedron/pytti from pytti.LossAug import MSELoss, LatentLoss import sys, os, gc import argparse import os import cv2 import glob import math, copy import numpy as np import torch from torch import nn from torch.nn import functional as F from PIL import Image import imageio import matplotlib.pyplot as plt from pytti.Notebook import Rotoscoper from torchvision.transforms import functional as TF os.chdir('GMA') try: sys.path.append('core') from network import RAFTGMA from utils import flow_viz from utils.utils import InputPadder finally: os.chdir('..') from pytti.Transforms import apply_flow from pytti import fetch, to_pil, DEVICE, vram_usage_mode from pytti.Image.RGBImage import RGBImage GMA = None def init_GMA(checkpoint_path): global GMA if GMA is None: with vram_usage_mode('GMA'): parser = argparse.ArgumentParser() parser.add_argument('--model', help="restore checkpoint", default=checkpoint_path) parser.add_argument('--model_name', help="define model name", default="GMA") parser.add_argument('--path', help="dataset for evaluation") parser.add_argument('--num_heads', default=1, type=int, help='number of heads in attention and aggregation') parser.add_argument('--position_only', default=False, action='store_true', help='only use position-wise attention') parser.add_argument('--position_and_content', default=False, action='store_true', help='use position and content-wise attention') parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision') args = parser.parse_args([]) GMA = torch.nn.DataParallel(RAFTGMA(args)) GMA.load_state_dict(torch.load(checkpoint_path)) GMA.to(DEVICE) GMA.eval() def sample(tensor, uv, device=DEVICE): height, width = tensor.shape[-2:] max_pos = torch.tensor([width-1,height-1], device=device).view(2,1,1) grid = uv.div(max_pos/2).sub(1).movedim(0,-1).unsqueeze(0) return F.grid_sample(tensor.unsqueeze(0), grid, align_corners = True).squeeze(0) class TargetFlowLoss(MSELoss): def __init__(self, comp, weight = 0.5, stop = -math.inf, name = "direct target loss", image_shape = None): super().__init__(comp, weight, stop, name, image_shape) with torch.no_grad(): self.register_buffer('last_step', comp.clone()) self.mag = 1 @torch.no_grad() def set_target_flow(self, flow, device=DEVICE): self.comp.set_(flow.movedim(-1,1).to(device, memory_format = torch.channels_last)) self.mag = float(torch.linalg.norm(self.comp, dim = 1).square().mean()) @torch.no_grad() def set_last_step(self, last_step_pil, device = DEVICE): last_step = TF.to_tensor(last_step_pil).unsqueeze(0).to(device, memory_format = torch.channels_last) self.last_step.set_(last_step) def get_loss(self, input, img, device=DEVICE): os.chdir('GMA') try: init_GMA('checkpoints/gma-sintel.pth') image1 = self.last_step image2 = input padder = InputPadder(image1.shape) image1, image2 = padder.pad(image1, image2) _, flow = GMA(image1, image2, iters=3, test_mode=True) flow = flow.to(device, memory_format = torch.channels_last) finally: os.chdir('..') return super().get_loss(TF.resize(flow, self.comp.shape[-2:]), img)/self.mag class OpticalFlowLoss(MSELoss): @staticmethod @torch.no_grad() def motion_edge_map(flow_forward, flow_backward, img, border_mode = 'smear', sampling_mode = 'bilinear',device=DEVICE): # algorithm based on https://github.com/manuelruder/artistic-videos/blob/master/consistencyChecker/consistencyChecker.cpp # reimplemented in pytorch by <NAME> # // consistencyChecker # // Check consistency of forward flow via backward flow. # // # // (c) <NAME>, <NAME>, <NAME> 2016 dx_ker = torch.tensor([[[[0,0,0],[1,0,-1],[0, 0,0]]]], device = device).float().div(2).repeat(2,2,1,1) dy_ker = torch.tensor([[[[0,1,0],[0,0, 0],[0,-1,0]]]], device = device).float().div(2).repeat(2,2,1,1) f_x = nn.functional.conv2d(flow_backward, dx_ker, padding='same') f_y = nn.functional.conv2d(flow_backward, dy_ker, padding='same') motionedge = torch.cat([f_x,f_y]).square().sum(dim=(0,1)) height, width = flow_forward.shape[-2:] y,x = torch.meshgrid([torch.arange(0,height), torch.arange(0,width)]) x = x.to(device) y = y.to(device) p1 = torch.stack([x,y]) v1 = flow_forward.squeeze(0) p0 = p1 + flow_backward.squeeze() v0 = sample(v1, p0) p1_back = p0 + v0 v1_back = flow_backward.squeeze(0) r1 = torch.floor(p0) r2 = r1 + 1 max_pos = torch.tensor([width-1,height-1], device=device).view(2,1,1) min_pos = torch.tensor([0, 0], device=device).view(2,1,1) overshoot = torch.logical_or(r1.lt(min_pos),r2.gt(max_pos)) overshoot = torch.logical_or(overshoot[0],overshoot[1]) missed = (p1_back - p1).square().sum(dim=0).ge(torch.stack([v1_back,v0]).square().sum(dim=(0,1)).mul(0.01).add(0.5)) motion_boundary = motionedge.ge(v1_back.square().sum(dim=0).mul(0.01).add(0.002)) reliable = torch.ones((height, width), device=device) reliable[motion_boundary] = 0 reliable[missed] = -1 reliable[overshoot] = 0 mask = TF.gaussian_blur(reliable.unsqueeze(0), 3).clip(0,1) return mask @staticmethod @torch.no_grad() def get_flow(image1, image2, device=DEVICE): os.chdir('GMA') try: init_GMA('checkpoints/gma-sintel.pth') if isinstance(image1, Image.Image): image1 = TF.to_tensor(image1).unsqueeze(0).to(device) if isinstance(image2, Image.Image): image2 = TF.to_tensor(image2).unsqueeze(0).to(device) padder = InputPadder(image1.shape) image1, image2 = padder.pad(image1, image2) flow_low, flow_up = GMA(image1, image2, iters=12, test_mode=True) finally: os.chdir('..') return flow_up def __init__(self, comp, weight = 0.5, stop = -math.inf, name = "direct target loss", image_shape = None): super().__init__(comp, weight, stop, name, image_shape) with torch.no_grad(): self.latent_loss = MSELoss(comp.new_zeros((1,1,1,1)), weight, stop, name, image_shape) self.register_buffer('bg_mask',comp.new_zeros((1,1,1,1))) @torch.no_grad() def set_flow(self, frame_prev, frame_next, img, path, border_mode = 'smear', sampling_mode = 'bilinear', device = DEVICE): if path is not None: img = img.clone() state_dict = torch.load(path) img.load_state_dict(state_dict) gc.collect() torch.cuda.empty_cache() image1 = TF.to_tensor(frame_prev).unsqueeze(0).to(device) image2 = TF.to_tensor(frame_next).unsqueeze(0).to(device) if self.bg_mask.shape[-2:] != image1.shape[-2:]: bg_mask = TF.resize(self.bg_mask, image1.shape[-2:]) self.bg_mask.set_(bg_mask) noise = torch.empty_like(image2) noise.normal_(mean = 0, std = 0.05) noise.mul_(self.bg_mask) #adding the same noise vectors to both images forces #the flow model to match those parts of the frame, effectively #disabling the flow in those areas. image1.add_(noise) image2.add_(noise) # bdy = image2.clone().squeeze(0).mean(dim = 0) # h, w = bdy.shape # s = 4 # bdy[s:-s,s:-s] = 0 # mean = bdy.sum().div(w*h - (w-2*s)*(h-s*2)) # overlay = image2.gt(0.5) if mean > 0.5 else image2.lt(0.5) # noise = torch.empty_like(image2) # noise.normal_(mean = 0, std = 0.05) # noise[torch.logical_not(overlay)] = 0 # image1.add_(noise) # image2.add_(noise) flow_forward = OpticalFlowLoss.get_flow(image1, image2) flow_backward = OpticalFlowLoss.get_flow(image2, image1) unwarped_target_direct = img.decode_tensor() flow_target_direct = apply_flow(img, -flow_backward, border_mode = border_mode, sampling_mode = sampling_mode) fancy_mask = OpticalFlowLoss.motion_edge_map(flow_forward, flow_backward, img, border_mode, sampling_mode) target_direct = flow_target_direct target_latent = img.get_latent_tensor(detach = True) mask = fancy_mask.unsqueeze(0) self.comp.set_(target_direct) self.latent_loss.comp.set_(target_latent) self.set_flow_mask(mask) array = flow_target_direct.squeeze(0).movedim(0,-1).mul(255).clamp(0, 255).cpu().detach().numpy().astype(np.uint8)[:,:,:] return Image.fromarray(array), fancy_mask @torch.no_grad() def set_flow_mask(self,mask): super().set_mask(TF.resize(mask, self.comp.shape[-2:])) if mask is not None: self.latent_loss.set_mask(TF.resize(mask, self.latent_loss.comp.shape[-2:])) else: self.latent_loss.set_mask(None) @torch.no_grad() def set_mask(self, mask, inverted = False, device = DEVICE): if isinstance(mask, str) and mask != '': if mask[0] == '-': mask = mask[1:] inverted = True if mask.strip()[-4:] == '.mp4': r = Rotoscoper(mask,self) r.update(0) return mask = Image.open(fetch(mask)).convert('L') if isinstance(mask, Image.Image): with vram_usage_mode('Masks'): mask = TF.to_tensor(mask).unsqueeze(0).to(device, memory_format = torch.channels_last) if mask not in ['',None]: #this is where the inversion is. This mask is naturally inverted :) #since it selects the background self.bg_mask.set_(mask if inverted else (1-mask)) def get_loss(self, input, img): l1 = super().get_loss(input, img) l2 = self.latent_loss.get_loss(img.get_latent_tensor(), img) #print(float(l1),float(l2)) return l1+l2*img.latent_strength

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pythainlp/corpus/__init__.py

petetanru/pythainlp

1

21571

# -*- coding: utf-8 -*- from __future__ import absolute_import,unicode_literals from pythainlp.tools import get_path_db,get_path_data from tinydb import TinyDB,Query from future.moves.urllib.request import urlopen from tqdm import tqdm import requests import os import math import requests from nltk.corpus import names #__all__ = ["thaipos", "thaiword","alphabet","tone","country","wordnet"] path_db_=get_path_db() def get_file(name): db=TinyDB(path_db_) temp = Query() if len(db.search(temp.name==name))>0: path= get_path_data(db.search(temp.name==name)[0]['file']) db.close() if not os.path.exists(path): download(name) return path def download_(url, dst): """ @param: url to download file @param: dst place to put the file """ file_size = int(urlopen(url).info().get('Content-Length', -1)) if os.path.exists(dst): first_byte = os.path.getsize(dst) else: first_byte = 0 if first_byte >= file_size: return file_size header = {"Range": "bytes=%s-%s" % (first_byte, file_size)} pbar = tqdm( total=file_size, initial=first_byte, unit='B', unit_scale=True, desc=url.split('/')[-1]) req = requests.get(url, headers=header, stream=True) with(open(get_path_data(dst), 'wb')) as f: for chunk in req.iter_content(chunk_size=1024): if chunk: f.write(chunk) pbar.update(1024) pbar.close() #return file_size def download(name,force=False): db=TinyDB(path_db_) temp = Query() data=requests.get("https://raw.githubusercontent.com/PyThaiNLP/pythainlp-corpus/master/db.json") data_json=data.json() if name in list(data_json.keys()): temp_name=data_json[name] print("Download : "+name) if len(db.search(temp.name==name))==0: print(name+" "+temp_name['version']) download_(temp_name['download'],temp_name['file_name']) db.insert({'name': name, 'version': temp_name['version'],'file':temp_name['file_name']}) else: if len(db.search(temp.name==name and temp.version==temp_name['version']))==0: print("have update") print("from "+name+" "+db.search(temp.name==name)[0]['version']+" update to "+name+" "+temp_name['version']) yes_no="y" if force==False: yes_no=str(input("y or n : ")).lower() if "y"==yes_no: download_(temp_name['download'],temp_name['file_name']) db.update({'version':temp_name['version']},temp.name==name) else: print("re-download") print("from "+name+" "+db.search(temp.name==name)[0]['version']+" update to "+name+" "+temp_name['version']) yes_no="y" if force==False: yes_no=str(input("y or n : ")).lower() if "y"==yes_no: download_(temp_name['download'],temp_name['file_name']) db.update({'version':temp_name['version']},temp.name==name) db.close()

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paper/figures/make_photometry_table.py

abostroem/asassn15oz

0

39479

<reponame>abostroem/asassn15oz # coding: utf-8 # Creates a table of all imaging observations in the database for paper: # * lc_obs.tex # In[1]: import numpy as np from astropy import table from astropy.table import Table from astropy.time import Time from utilities_az import supernova, connect_to_sndavis # In[2]: db, cursor = connect_to_sndavis.get_cursor() # In[3]: sn15oz = supernova.LightCurve2('asassn-15oz') # In[4]: query_str = ''' SELECT DISTINCT mag, magerr, BINARY(filter), jd, source FROM photometry WHERE targetid = 322 ORDER BY jd''' # In[5]: cursor.execute(query_str) results = cursor.fetchall() # In[6]: loc_dict = { 1: {'short':'OGG 2m', 'long': 'Haleakala Observatory - 2m'}, #ogg2m001-fs02 2m0 2: {'short':'COJ 2m', 'long': 'Siding Springs Observatory - 2m'}, #coj2m002-fs03 3: {'short':'COJ 1m', 'long': 'Siding Springs Observatory - 1m'}, #coj1m003-kb71 4: {'short':'LSC 1m', 'long': 'CTIO - Region IV'}, #lsc1m004-kb77 1m0 5: {'short':'LSC 1m', 'long': 'CTIO - Region IV'}, #lsc1m005-kb78 1m0 8: {'short':'ELP 1m', 'long': 'McDonald Observatory - 1m'},#elp1m008-kb74 1m0 9: {'short':'LSC 1m', 'long': 'CTIO - Region IV'}, #lsc1m009-fl03 1m0 10: {'short': 'CPT 1m', 'long': 'SAAO - Sutherland Facilities - 1m'}, #cpt1m010 11: {'short': 'COJ 1m', 'long': 'Siding Springs Observatory - 1m'}, #coj1m011-kb05 1m0 12: {'short': 'CPT 1m', 'long': 'SAAO - Sutherland Facilities - 1m'}, #cpt1m012-kb75 1m0 13: {'short': 'CPT 1m', 'long': 'SAAO - Sutherland Facilities - 1m '},#cpt1m013-kb76 1m0 88: {'short': 'Swift', 'long': 'Swift'}, #Swift -88: {'short': 'Swift', 'long': 'Swift'}} #Swift; non-detections in the DB have negative sources # In[7]: band = [] jd = [] date = [] mag = [] mag_err = [] source = [] phase = [] for iresult in results: #rename the Swift filters to their proper names ifilter = iresult['BINARY(filter)'] if ifilter in [b'us', b'bs', b'vs']: band.append(ifilter.decode('utf-8')[0]) elif ifilter in [b'uw1', b'uw2', b'um2']: band.append('uv'+ifilter.decode('utf-8')[1:]) else: band.append(ifilter) jd.append(iresult['jd']) mag.append(iresult['mag']) mag_err.append(iresult['magerr']) if ifilter in [b'J', b'H', b'K']: source.append('NTT') else: source.append(loc_dict[iresult['source']]['short']) date.append(Time(iresult['jd'], format='jd', out_subfmt='date').iso) phase.append((Time(iresult['jd'], format='jd') - Time(sn15oz.jdexpl, format='jd')).value) tbdata = Table([date, jd, phase, mag, mag_err, band, source], names=['Date-Obs','JD', 'Phase (Day)', 'Apparent Magnitude', 'Apparent Magnitude Error', 'Filter', 'Source']) tbdata.sort(keys=['JD', 'Filter']) # In[8]: #tbdata.write('../lc_obs.tex', format='aastex', # formats={'JD':'%8.2f', # 'Phase (Day)':'%4.2f', # 'Apparent Magnitude':'%2.2f', # 'Apparent Magnitude Error': '%1.2f'}, overwrite=True, # latexdict={'preamble':r'\centering', # 'caption':r'Imaging Observations of ASASSN-15oz.\label{tab:LcObs}', # 'data_start':r'\hline'}) # In[9]: tbdata_short = tbdata[0:5].copy() tbdata_short.write('../lc_obs_short.tex', format='latex', formats={'JD':'%8.2f', 'Phase (Day)':'%4.2f', 'Apparent Magnitude':'%2.2f', 'Apparent Magnitude Error': '%1.2f'}, overwrite=True, latexdict={'preamble':r'\centering', 'caption':r'Sample of Imaging Observations of ASASSN-15oz. Full table available on-line.\label{tab:LcObs}', 'data_start':r'\hline', 'data_end':r'\hline', 'header_start':r'\hline', 'tabletype': 'table*'}) # In[10]: #tbdata.write('../lc_obs.dat', overwrite=True, format='ascii') #ofile = open('../lc_obs.dat', 'r') #all_lines = ofile.readlines() #ofile.close() #header = '''#Photometric observations of ASASSN-15oz. ##Columns: ##Date-Obs: (str) Human readable date of observation ##JD: (float) Julian Date of observation ##Phase: (float) Days since explosion, where explosion is defined as {} ##Apparent Magnitude: (float) ##Apparent Magntidue Error: (float) ##Filter: (str) Filter used for observation ##Source: (str) Observatory used to take the data. OGG, COJ, LSC, ELP, and CPT are Las Cumbres Observatory Telescopes.\n #'''.format(sn15oz.jdexpl) #ofile = open('../asassn15oz_lc_obs.dat', 'w') #ofile.write(header) #for iline in all_lines[1:]: # ofile.write(iline) #ofile.close() # In[11]: tbdata.write('../lc_obs.csv', overwrite=True) ofile = open('../lc_obs.csv', 'r') all_lines = ofile.readlines() ofile.close() header = '''#Photometric observations of ASASSN-15oz. #Columns: #Date-Obs: (str) Human readable date of observation #JD: (float) Julian Date of observation #Phase: (float) Days since explosion, where explosion is defined as {} #Apparent Magnitude: (float) #Apparent Magntidue Error: (float) #Filter: (str) Filter used for observation #Source: (str) Observatory used to take the data. OGG, COJ, LSC, ELP, and CPT are Las Cumbres Observatory Telescopes.\n '''.format(sn15oz.jdexpl) ofile = open('../asassn15oz_lc_obs.csv', 'w') ofile.write(header) for iline in all_lines[1:]: ofile.write(iline) ofile.close()

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datasets/few_shot_test_pickle.py

PengWan-Yang/few-shot-transformer

4

23240

<filename>datasets/few_shot_test_pickle.py import pickle # modify validation data _few_shot_pickle_file = 'few_shot_test_data.pkl' _few_shot_file = open(_few_shot_pickle_file, 'rb') data_few_shot = pickle.load(_few_shot_file) _few_shot_pickle_file = 'few_shot_val_data.pkl' _few_shot_file = open(_few_shot_pickle_file, 'rb') data_val = pickle.load(_few_shot_file) _few_shot_pickle_file = 'few_shot_train_data.pkl' _few_shot_file = open(_few_shot_pickle_file, 'rb') data_train = pickle.load(_few_shot_file) raise 1 for _list in data_few_shot: for _video in _list: _video['fg_name'] = _video['fg_name'].replace('/home/tao/dataset/v1-3/train_val_frames_3', 'datasets/activitynet13') _video['bg_name'] = _video['bg_name'].replace('/home/tao/dataset/v1-3/train_val_frames_3', 'datasets/activitynet13') pickle.dump(data_few_shot, open(_few_shot_pickle_file, "wb")) print("done") # modify testing data _few_shot_pickle_file = 'few_shot_test_data.pkl' _few_shot_file = open(_few_shot_pickle_file, 'rb') data_few_shot = pickle.load(_few_shot_file) for _list in data_few_shot: for _video in _list: _video['fg_name'] = _video['fg_name'].replace('/home/tao/dataset/v1-3/train_val_frames_3', 'datasets/activitynet13') _video['bg_name'] = _video['bg_name'].replace('/home/tao/dataset/v1-3/train_val_frames_3', 'datasets/activitynet13') pickle.dump(data_few_shot, open(_few_shot_pickle_file, "wb")) print("done") # modify training data _few_shot_pickle_file = 'few_shot_train_data.pkl' _few_shot_file = open(_few_shot_pickle_file, 'rb') data_few_shot = pickle.load(_few_shot_file) index = 0 for k, _list in data_few_shot.items(): for _video in _list: _video['video_id'] = "query_{:0>5d}".format(index) _video['fg_name'] = _video['fg_name'].replace('dataset/activitynet13/train_val_frames_3', 'datasets/activitynet13') _video['bg_name'] = _video['bg_name'].replace('dataset/activitynet13/train_val_frames_3', 'datasets/activitynet13') index = index + 1 pickle.dump(data_few_shot, open(_few_shot_pickle_file, "wb")) print("done")

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var/spack/repos/builtin/packages/r-xde/package.py

nkianggiss/spack

3

168095

<filename>var/spack/repos/builtin/packages/r-xde/package.py # Copyright 2013-2019 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class RXde(RPackage): """Multi-level model for cross-study detection of differential gene expression.""" homepage = "https://www.bioconductor.org/packages/XDE/" git = "https://git.bioconductor.org/packages/XDE.git" version('2.22.0', commit='<PASSWORD>') depends_on('r-biobase', type=('build', 'run')) depends_on('r-biocgenerics', type=('build', 'run')) depends_on('r-genefilter', type=('build', 'run')) depends_on('r-gtools', type=('build', 'run')) depends_on('r-mergemaid', type=('build', 'run')) depends_on('r-mvtnorm', type=('build', 'run')) depends_on('[email protected]:3.4.9', when='@2.22.0')

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tests/test_engagement_query_set.py

Asday/keiyakusha

2

1605076

from datetime import date, timedelta import pytest from engagements.models import Engagement from factories.auth import UserFactory from factories.clients import ClientFactory from factories.engagements import EngagementFactory @pytest.mark.django_db def test_active_at(): user = UserFactory() client_a = ClientFactory() client_b = ClientFactory() engagement_a_active = EngagementFactory( start=date(2018, 8, 23), duration=timedelta(days=1), user=user, client=client_a, ) EngagementFactory( start=date(2018, 8, 21), duration=timedelta(days=1), user=user, client=client_a, ) EngagementFactory( start=date(2018, 8, 23), duration=timedelta(days=1), user=user, client=client_b, ) assert Engagement.objects.all().count() == 3 # Just checkin'. active_at = Engagement.objects \ .filter(client=client_a) \ .active_at(date(2018, 8, 23)) assert engagement_a_active in active_at assert active_at.count() == 1

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lib/utils/__init__.py

jwyang/C3Net.pytorch

43

19886

from .verbo import *

[ 1, 515, 869, 369, 833, 1053, 334, 13, 2 ]

training/rootnav2/accuracy/__init__.py

robail-yasrab/6-RootNav-2.0

23

187358

<reponame>robail-yasrab/6-RootNav-2.0<gh_stars>10-100 from .nms import nonmaximalsuppression, rrtree, evaluate_points

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utils/experiment_utils.py

samuelfneumann/RLControl

0

119224

#!/usr/bin/env python3 import pickle import os import numpy as np def get_sweep_parameters(parameters, env_config, index): """ Gets the parameters for the hyperparameter sweep defined by the index. Each hyperparameter setting has a specific index number, and this function will get the appropriate parameters for the argument index. In addition, this the indices will wrap around, so if there are a total of 10 different hyperparameter settings, then the indices 0 and 10 will return the same hyperparameter settings. This is useful for performing loops. For example, if you had 10 hyperparameter settings and you wanted to do 10 runs, the you could just call this for indices in range(0, 10*10). If you only wanted to do runs for hyperparameter setting i, then you would use indices in range(i, 10, 10*10) Parameters ---------- parameters : dict The dictionary of parameters, as found in the agent's json configuration file env_config : dict The environment configuration dictionary, as found in the environment's json configuration file index : int The index of the hyperparameters configuration to return Returns ------- dict, int The dictionary of hyperparameters to use for the agent and the total number of combinations of hyperparameters (highest possible unique index) """ out_params = {} out_params["gamma"] = env_config["gamma"] accum = 1 for key in parameters: num = len(parameters[key]) out_params[key] = parameters[key][(index // accum) % num] accum *= num return (out_params, accum) def get_sweep_num(parameters): """ Similar to get_sweep_parameters but only returns the total number of hyperparameter combinations. This number is the total number of distinct hyperparameter settings. If this function returns k, then there are k distinct hyperparameter settings, and indices 0 and k refer to the same distinct hyperparameter setting. Parameters ---------- parameters : dict The dictionary of parameters, as found in the agent's json configuration file Returns ------- int The number of distinct hyperparameter settings """ accum = 1 for key in parameters: num = len(parameters[key]) accum *= num return accum def get_hyperparam_indices(data, hp_name, hp_value): """ Gets all hyperparameter indices that have the hyperparameter hp_name having the value hp_value. Parameters ---------- data : dict The data dictionary generated from running main.py hp_name : str The name of the hyperparameter to check the value of hp_value : object The value that the hyperparameter should have in each hyperparameter settings index Returns ------- list of int The hyperparameter settings that have the argument hyperparameter hp_name having the argument value hp_value """ agent_param = data["experiment"]["agent"]["parameters"] env_config = data["experiment"]["environment"] hp_indices = [] for i in range(get_sweep_num(agent_param)): # Get the hyperparameters for each hyperparameter setting hp_setting = get_sweep_parameters(agent_param, env_config, i)[0] if hp_setting[hp_name] == hp_value: hp_indices.append(i) return hp_indices def get_varying_single_hyperparam(data, hp_name): """ Gets the hyperparameter indices where only a single hyperparameter is varying and all other hyperparameters remain constant. Parameters ---------- data : dict The data dictionary generated from running main.py hp_name : str The name of the hyperparameter to vary Returns ------- n-tuple of m-tuple of int Gets and returns the hyperparameter indices where only a single hyperparameter is varying and all others remain constant. The total number of values that the varying hyperparameter can take on is m; n is the total number of hyperparameter combinations // m. For example, if the hyperparameter is the decay rate and it can take on values in [0.0, 0.1, 0.5] and there are a total of 81 hyperparameter settings combinations, then m = 3 and n = 81 // 3 = 27 """ agent_param = data["experiment"]["agent"]["parameters"] hps = [] # set(range(exp.get_sweep_num(agent_param))) for hp_value in agent_param[hp_name]: hps.append(get_hyperparam_indices(data, hp_name, hp_value)) return tuple(zip(*hps)) def get_best_hp(data, type_, after=0): """ Gets and returns a list of the hyperparameter settings, sorted by average return. Parameters ---------- data : dict They Python data dictionary generated from running main.py type_ : str The type of return by which to compare hyperparameter settings, one of "train" or "eval" after : int, optional Hyperparameters will only be compared by their performance after training for this many episodes (in continuing tasks, this is the number of times the task is restarted). For example, if after = -10, then only the last 10 returns from training/evaluation are taken into account when comparing the hyperparameters. As usual, positive values index from the front, and negative values index from the back. Returns ------- n-tuple of 2-tuple(int, float) A tuple with the number of elements equal to the total number of hyperparameter combinations. Each sub-tuple is a tuple of (hyperparameter setting number, mean return over all runs and episodes) """ if type_ not in ("train", "eval"): raise ValueError("type_ should be one of 'train', 'eval'") return_type = "train_episode_rewards" if type_ == "train" \ else "eval_episode_rewards" mean_returns = [] hp_settings = sorted(list(data["experiment_data"].keys())) for hp_setting in hp_settings: hp_returns = [] for run in data["experiment_data"][hp_setting]["runs"]: hp_returns.append(run[return_type]) hp_returns = np.stack(hp_returns) # If evaluating, use the mean return over all episodes for each # evaluation interval. That is, if 10 eval episodes for each evaluation # the take the average return over all these eval episodes if type_ == "eval": hp_returns = hp_returns.mean(axis=-1) # Calculate the average return over all runs hp_returns = hp_returns[after:, :].mean(axis=0) # Calculate the average return over all "episodes" hp_returns = hp_returns.mean(axis=0) # Save mean return mean_returns.append(hp_returns) # Return the best hyperparam settings in order with the # mean returns sorted by hyperparmater setting performance best_hp_settings = np.argsort(mean_returns) mean_returns = np.array(mean_returns)[best_hp_settings] return tuple(zip(best_hp_settings, mean_returns)) def combine_runs(data1, data2): """ Adds the runs for each hyperparameter setting in data2 to the runs for the corresponding hyperparameter setting in data1. Given two data dictionaries, this function will get each hyperparameter setting and extend the runs done on this hyperparameter setting and saved in data1 by the runs of this hyperparameter setting and saved in data2. In short, this function extends the lists data1["experiment_data"][i]["runs"] by the lists data2["experiment_data"][i]["runs"] for all i. This is useful if multiple runs are done at different times, and the two data files need to be combined. Parameters ---------- data1 : dict A data dictionary as generated by main.py data2 : dict A data dictionary as generated by main.py Raises ------ KeyError If a hyperparameter setting exists in data2 but not in data1. This signals that the hyperparameter settings indices are most likely different, so the hyperparameter index i in data1 does not correspond to the same hyperparameter index in data2. In addition, all other functions expect the number of runs to be consistent for each hyperparameter setting, which would be violated in this case. """ for hp_setting in data1["experiment_data"]: if hp_setting not in data2.keys(): # Ensure consistent hyperparam settings indices raise KeyError("hyperparameter settings are different " + "between the two experiments") extra_runs = data2["experiment_data"][hp_setting]["runs"] data1["experiment_data"][hp_setting]["runs"].extend(extra_runs) def get_returns(data, type_, ind): """ Gets the returns seen by an agent Gets the online or offline returns seen by an agent trained with hyperparameter settings index ind. Parameters ---------- data : dict The Python data dictionary generated from running main.py type_ : str Whether to get the training or evaluation returns, one of 'train', 'eval' ind : int Gets the returns of the agent trained with this hyperparameter settings index Returns ------- array_like The array of returns of the form (N, R, C) where N is the number of runs, R is the number of times a performance was measured, and C is the number of returns generated each time performance was measured (offline >= 1; online = 1). For the online setting, N is the number of runs, and R is the number of episodes and C = 1. For the offline setting, N is the number of runs, R is the number of times offline evaluation was performed, and C is the number of episodes run each time performance was evaluated offline. """ returns = [] if type_ == "eval": # Get the offline evaluation episode returns per run for run in data["experiment_data"][ind]["runs"]: returns.append(run["eval_episode_rewards"]) returns = np.stack(returns) elif type_ == "train": # Get the returns per episode per run for run in data["experiment_data"][ind]["runs"]: returns.append(run["train_episode_rewards"]) returns = np.expand_dims(np.stack(returns), axis=2) return returns def get_hyperparams(data, ind): """ Gets the hyperparameters for hyperparameter settings index ind data : dict The Python data dictionary generated from running main.py ind : int Gets the returns of the agent trained with this hyperparameter settings index Returns ------- dict The dictionary of hyperparameters """ return data["experiment_data"][ind]["agent_params"] def get_mean_returns_with_stderr_hp_varying(data, type_, hp_name, combo, after=0): """ Calculate mean and standard error of return for each hyperparameter value. Gets the mean returns for each variation of a single hyperparameter, with all other hyperparameters remaining constant. Since there are many different ways this can happen (the hyperparameter can vary with all other remaining constant, but there are many combinations of these constant hyperparameters), the combo argument cycles through the combinations of constant hyperparameters. Given hyperparameters a, b, and c, let's say we want to get all hyperparameter settings indices where a varies, and b and c are constant. if a, b, and c can each be 1 or 2, then there are four ways that a can vary with b and c remaining constant: [ ((a=1, b=1, c=1), (a=2, b=1, c=1)), combo = 0 ((a=1, b=2, c=1), (a=2, b=2, c=1)), combo = 1 ((a=1, b=1, c=2), (a=2, b=1, c=2)), combo = 2 ((a=1, b=2, c=2), (a=2, b=2, c=2)) combo = 3 ] The combo argument indexes into this list of hyperparameter settings Parameters ---------- data : dict The Python data dictionary generated from running main.py type_ : str Which type of data to plot, one of "eval" or "train" combo : int Determines the values of the constant hyperparameters. Given that only one hyperparameter may vary, there are many different sets having this hyperparameter varying with all others remaining constant since each constant hyperparameter may take on many values. This argument cycles through all sets of hyperparameter settings indices that have only one hyperparameter varying and all others constant. """ hp_combo = get_varying_single_hyperparam(data, hp_name)[combo] mean_returns = [] stderr_returns = [] for hp in hp_combo: mean_return = get_returns(data, type_, hp) # If evaluating, use the mean return over all episodes for each # evaluation interval. That is, if 10 eval episodes for each evaluation # the take the average return over all these eval episodes. # If online returns, this axis has a length of 1 so we reduce it mean_return = mean_return.mean(axis=-1) # Calculate the average return over all "episodes" # print(mean_return[:, after:].shape) mean_return = mean_return[:, after:].mean(axis=1) # Calculate the average return over all runs stderr_return = np.std(mean_return, axis=0) / \ np.sqrt(mean_return.shape[0]) mean_return = mean_return.mean(axis=0) mean_returns.append(mean_return) stderr_returns.append(stderr_return) # Get each hp value and sort all results by hp value hp_values = np.array(data["experiment"]["agent"]["parameters"][hp_name]) indices = np.argsort(hp_values) mean_returns = np.array(mean_returns)[indices] stderr_returns = np.array(stderr_returns)[indices] hp_values = hp_values[indices] return hp_values, mean_returns, stderr_returns def get_mean_stderr(data, type_, ind, smooth_over): """ Gets the timesteps, mean, and standard error to be plotted for a given hyperparameter settings index Parameters ---------- data : dict The Python data dictionary generated from running main.py type_ : str Which type of data to plot, one of "eval" or "train" ind : int The hyperparameter settings index to plot smooth_over : int The number of previous data points to smooth over. Note that this is *not* the number of timesteps to smooth over, but rather the number of data points to smooth over. For example, if you save the return every 1,000 timesteps, then setting this value to 15 will smooth over the last 15 readings, or 15,000 timesteps. Returns ------- 3-tuple of list(int), list(float), list(float) The timesteps, mean episodic returns, and standard errors of the episodic returns """ timesteps = None # So the linter doesn't have a temper tantrum # Determine the timesteps to plot at if type_ == "eval": timesteps = \ data["experiment_data"][ind]["runs"][0]["timesteps_at_eval"] elif type_ == "train": timesteps_per_ep = \ data["experiment_data"][ind]["runs"][0]["train_episode_steps"] timesteps = get_cumulative_timesteps(timesteps_per_ep) # Get the mean over all episodes per evaluation step (for online # returns, this axis will have length 1 so we squeeze it) returns = get_returns(data, type_, ind) mean = returns.mean(axis=-1) # Get the standard deviation of mean episodes per evaluation # step over all runs runs = returns.shape[0] std = np.std(mean, axis=0) / np.sqrt(runs) # Get the mean over all runs mean = mean.mean(axis=0) # Smooth of last k returns if applicable, convolve will initially only # start sliding from the beginning, so we need to cut off the initial # averages which are incomplete if smooth_over > 1: mean = np.convolve(mean, np.ones(smooth_over) / smooth_over, mode="valid") #[smooth_over:mean.shape[0]] std = np.convolve(std, np.ones(smooth_over) / smooth_over, mode="valid") #[smooth_over:std.shape[0]] # np.convolve only goes until the last element of the convolution # lines up with the last element of the data, so smooth_over elements # will be lost timesteps = timesteps[:-smooth_over + 1] return timesteps, mean, std def get_cumulative_timesteps(timesteps_per_episode): """ Creates an array of cumulative timesteps. Creates an array of timesteps, where each timestep is the cumulative number of timesteps up until that point. This is needed for plotting the training data, where the training timesteps are stored for each episode, and we need to plot on the x-axis the cumulative timesteps, not the timesteps per episode. Parameters ---------- timesteps_per_episode : list A list where each element in the list denotes the amount of timesteps for the corresponding episode. Returns ------- array_like An array where each element is the cumulative number of timesteps up until that point. """ timesteps_per_episode = np.array(timesteps_per_episode) cumulative_timesteps = [timesteps_per_episode[:i].sum() for i in range(timesteps_per_episode.shape[0])] return np.array(cumulative_timesteps) def combine_data_dictionaries(files, save=True, save_dir=".", filename="data"): """ Combine data dictionaries given a list of filenames Given a list of paths to data dictionaries, combines each data dictionary into a single one. Parameters ---------- files : list of str A list of the paths to data dictionary files to combine save : bool Whether or not to save the data save_dir : str, optional The directory containing the saved dictionaries filename : str, optional The name of the file to save which stores the combined data, by default 'data' Returns ------- dict The combined dictionary """ # Use first dictionary as base dictionary with open(os.path.join(save_dir, files[0]), "rb") as in_file: data = pickle.load(in_file) # Add data from all other dictionaries for file in files[1:]: with open(os.path.join(save_dir, file), "rb") as in_file: # Read in the new dictionary in_data = pickle.load(in_file) # Add experiment data to running dictionary for key in in_data["experiment_data"]: # Check if key exists if key in data["experiment_data"]: # Append data if existing data["experiment_data"][key]["runs"] \ .extend(in_data["experiment_data"][key]["runs"]) else: # Key doesn't exist - add data to dictionary data["experiment_data"][key] = \ in_data["experiment_data"][key] if save: with open(os.path.join(save_dir, f"{filename}.pkl"), "wb") as out_file: pickle.dump(data, out_file) return data

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lib/ExtraFM.py

KanHatakeyama/anneal_project2

0

98890

<reponame>KanHatakeyama/anneal_project2 from scipy.sparse import csr_matrix from pyfm import pylibfm import numpy as np from numba import jit from ScaleRegressor import ScaleRegressor default_model = pylibfm.FM(task="regression", num_iter=30, initial_learning_rate=10**-3, num_factors=10, verbose=False ) class ExtraFM: def __init__(self, model=None): if model is None: model = default_model self.model = model def fit(self, X, y): sparse_X = csr_matrix(X.astype("double")) self.model.fit(sparse_X, y) # self.qubo=calc_qubo(self.model.v,self.model.v[0].shape[0],self.model.v.shape[0])+np.diag(self.model.w) # calc offset self.b = self.model.predict(csr_matrix( np.zeros(X[0].shape[0]).astype("double"))) # self.y_max=max(y) self.y_max = 0 # self.y_max=max(y) self.y_min = 0 def predict(self, X): y = self.original_predict(X, reg_mode=False) # print(X.shape,y.shape) y = -np.log((1-y)/y) # fill nan nan_ids = np.where(y == np.inf) y[nan_ids] = self.y_max nan_ids = np.where(y == -np.inf) y[nan_ids] = self.y_min return y def original_predict(self, X, reg_mode=True): if reg_mode: self.model.fm_fast.task = 0 else: self.model.fm_fast.task = 1 sparse_X = csr_matrix(X.astype("double")) return self.model.predict(sparse_X) @jit def calc_qubo(v, dim1, dim2): qubo = np.zeros((dim1, dim1)) for k in range(dim2): for i in range(dim1): for j in range(i): val = v[k][j]*v[k][i] qubo[j, i] += val return qubo class FMRegressor(ScaleRegressor): def fit(self, X, y): X = csr_matrix(X).astype(np.double) return super().fit(X, y) def predict(self, X): X = csr_matrix(X).astype(np.double) return super().predict(X)

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test/swe2d/test_steady_state_basin_mms.py

jrper/thetis

0

193326

<reponame>jrper/thetis """ MMS test for 2d shallow water equations. - setup functions define analytical expressions for fields and boundary conditions. Expressions were derived with sympy. - run function runs the MMS setup with a single mesh resolution, returning L2 errors. - run_convergence runs a scaling test, computes and asserts convergence rate. """ from thetis import * import numpy from scipy import stats import pytest def setup7(x, lx, ly, h0, f0, nu0, g): """ Non-trivial Coriolis, bath, elev, u and v, tangential velocity is zero at bnd to test flux BCs """ out = {} out['bath_expr'] = h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + 4.0 out['cori_expr'] = f0*cos(pi*(x[0] + x[1])/lx) out['elev_expr'] = cos(pi*(3.0*x[0] + 1.0*x[1])/lx) out['uv_expr'] = as_vector( [ sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*sin(pi*x[1]/ly), 0.5*sin(pi*x[0]/lx)*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx), ]) out['res_elev_expr'] = (0.3*h0*x[0]/(lx*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)) - 3.0*pi*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx)*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*sin(pi*x[1]/ly) + 0.5*(0.2*h0*x[1]/(lx*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)) - 1.0*pi*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx)*sin(pi*x[0]/lx)*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx) + 0.5*pi*(h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + cos(pi*(3.0*x[0] + 1.0*x[1])/lx) + 4.0)*sin(pi*x[0]/lx)*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx - 2.0*pi*(h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + cos(pi*(3.0*x[0] + 1.0*x[1])/lx) + 4.0)*sin(pi*x[1]/ly)*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx out['res_uv_expr'] = as_vector( [ -0.5*f0*sin(pi*x[0]/lx)*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx)*cos(pi*(x[0] + x[1])/lx) - 3.0*pi*g*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx + 0.5*(pi*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*cos(pi*x[1]/ly)/ly + 1.0*pi*sin(pi*x[1]/ly)*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx)*sin(pi*x[0]/lx)*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx) - 2.0*pi*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*sin(pi*x[1]/ly)**2*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx, f0*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*sin(pi*x[1]/ly)*cos(pi*(x[0] + x[1])/lx) - 1.0*pi*g*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx + (-1.5*pi*sin(pi*x[0]/lx)*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx + 0.5*pi*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx)*cos(pi*x[0]/lx)/lx)*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*sin(pi*x[1]/ly) + 0.25*pi*sin(pi*x[0]/lx)**2*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx)*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx, ]) out['bnd_funcs'] = {1: {'elev': None, 'flux_left': None}, 2: {'flux_right': None}, 3: {'elev': None, 'flux_lower': None}, 4: {'un_upper': None}, } return out def setup8(x, lx, ly, h0, f0, nu0, g): """ Non-trivial Coriolis, bath, elev, u and v, tangential velocity is non-zero at bnd, must prescribe uv at boundary. """ out = {} out['bath_expr'] = h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + 4.0 out['cori_expr'] = f0*cos(pi*(x[0] + x[1])/lx) out['elev_expr'] = cos(pi*(3.0*x[0] + 1.0*x[1])/lx) out['uv_expr'] = as_vector( [ sin(pi*(-2.0*x[0] + 1.0*x[1])/lx), 0.5*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx), ]) out['res_elev_expr'] = (0.3*h0*x[0]/(lx*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)) - 3.0*pi*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx)*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx) + 0.5*(0.2*h0*x[1]/(lx*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)) - 1.0*pi*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx)*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx) + 0.5*pi*(h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + cos(pi*(3.0*x[0] + 1.0*x[1])/lx) + 4.0)*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx - 2.0*pi*(h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + cos(pi*(3.0*x[0] + 1.0*x[1])/lx) + 4.0)*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx out['res_uv_expr'] = as_vector( [ -0.5*f0*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx)*cos(pi*(x[0] + x[1])/lx) - 3.0*pi*g*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx + 0.5*pi*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx)*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx - 2.0*pi*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx, f0*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*cos(pi*(x[0] + x[1])/lx) - 1.0*pi*g*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx + 0.25*pi*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx)*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx - 1.5*pi*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx, ]) # NOTE uv condition alone does not work out['bnd_funcs'] = {1: {'elev': None, 'uv': None}, 2: {'elev': None, 'uv': None}, 3: {'elev': None, 'uv': None}, 4: {'elev': None, 'uv': None}, } return out def setup9(x, lx, ly, h0, f0, nu0, g): """ No Coriolis, non-trivial bath, viscosity, elev, u and v. """ out = {} out['bath_expr'] = h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + 4.0 out['visc_expr'] = nu0*(1.0 + x[0]/lx) out['elev_expr'] = cos(pi*(3.0*x[0] + 1.0*x[1])/lx) out['uv_expr'] = as_vector( [ sin(pi*(-2.0*x[0] + 1.0*x[1])/lx), 0.5*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx), ]) out['res_elev_expr'] = (0.3*h0*x[0]/(lx*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)) - 3.0*pi*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx)*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx) + 0.5*(0.2*h0*x[1]/(lx*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)) - 1.0*pi*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx)*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx) + 0.5*pi*(h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + cos(pi*(3.0*x[0] + 1.0*x[1])/lx) + 4.0)*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx - 2.0*pi*(h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + cos(pi*(3.0*x[0] + 1.0*x[1])/lx) + 4.0)*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx out['res_uv_expr'] = as_vector( [ -3.0*pi*g*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx - 4.0*pi*nu0*(1.0 + x[0]/lx)*(-0.3*h0*x[0]/(lx*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)) + 3.0*pi*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx)*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/(lx*(h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + cos(pi*(3.0*x[0] + 1.0*x[1])/lx) + 4.0)) + 0.5*pi*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx)*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx - 2.0*pi*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx - 1.5*pi**2*nu0*(1.0 + x[0]/lx)*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx**2 + 9.0*pi**2*nu0*(1.0 + x[0]/lx)*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx**2 + 4.0*pi*nu0*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx**2, -1.0*pi*g*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx + 1.0*pi*nu0*(1.0 + x[0]/lx)*(-0.2*h0*x[1]/(lx*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)) + 1.0*pi*sin(pi*(3.0*x[0] + 1.0*x[1])/lx)/lx)*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/(lx*(h0*sqrt(0.3*x[0]**2 + 0.2*x[1]**2 + 0.1)/lx + cos(pi*(3.0*x[0] + 1.0*x[1])/lx) + 4.0)) + 0.25*pi*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx)*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx - 1.5*pi*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx + 5.5*pi**2*nu0*(1.0 + x[0]/lx)*sin(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx**2 - 2.0*pi**2*nu0*(1.0 + x[0]/lx)*sin(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx**2 + 1.5*pi*nu0*cos(pi*(-3.0*x[0] + 1.0*x[1])/lx)/lx**2 - 1.0*pi*nu0*cos(pi*(-2.0*x[0] + 1.0*x[1])/lx)/lx**2, ]) out['bnd_funcs'] = {1: {'uv': None}, 2: {'uv': None}, 3: {'uv': None}, 4: {'uv': None}, } out['options'] = { 'use_grad_div_viscosity_term': True, 'use_grad_depth_viscosity_term': True, } return out def run(setup, refinement, order, do_export=True, options=None, solver_parameters=None): """Run single test and return L2 error""" print_output('--- running {:} refinement {:}'.format(setup.__name__, refinement)) # domain dimensions lx = 15e3 ly = 10e3 area = lx*ly f0 = 5e-3 # NOTE large value to make Coriolis terms larger nu0 = 100.0 g = physical_constants['g_grav'] depth = 10.0 t_period = 5000.0 # period of signals t_end = 1000.0 # 500.0 # 3*T_period t_export = t_period/100.0 # export interval # mesh nx = 5*refinement ny = 5*refinement mesh2d = RectangleMesh(nx, ny, lx, ly) dt = 4.0/refinement if options is not None and options.get('timestepper_type') == 'CrankNicolson': dt *= 100. x = SpatialCoordinate(mesh2d) sdict = setup(x, lx, ly, depth, f0, nu0, g) # outputs outputdir = 'outputs' # bathymetry p1_2d = FunctionSpace(mesh2d, 'CG', 1) bathymetry_2d = Function(p1_2d, name='Bathymetry') bathymetry_2d.project(sdict['bath_expr']) if bathymetry_2d.dat.data.min() < 0.0: print_output('bath {:} {:}'.format(bathymetry_2d.dat.data.min(), bathymetry_2d.dat.data.max())) raise Exception('Negative bathymetry') solver_obj = solver2d.FlowSolver2d(mesh2d, bathymetry_2d) solver_obj.options.polynomial_degree = order solver_obj.options.element_family = 'rt-dg' solver_obj.options.horizontal_velocity_scale = Constant(1.0) solver_obj.options.no_exports = not do_export solver_obj.options.output_directory = outputdir solver_obj.options.simulation_end_time = t_end solver_obj.options.timestep = dt solver_obj.options.simulation_export_time = t_export if 'options' in sdict: solver_obj.options.update(sdict['options']) if options is not None: solver_obj.options.update(options) if hasattr(solver_obj.options.timestepper_options, 'use_automatic_timestep'): solver_obj.options.timestepper_options.use_automatic_timestep = False solver_obj.create_function_spaces() # analytical solution in high-order space for computing L2 norms h_2d_ho = FunctionSpace(solver_obj.mesh2d, 'DG', order+3) u_2d_ho = VectorFunctionSpace(solver_obj.mesh2d, 'DG', order+4) elev_ana_ho = Function(h_2d_ho, name='Analytical elevation') elev_ana_ho.project(sdict['elev_expr']) uv_ana_ho = Function(u_2d_ho, name='Analytical velocity') uv_ana_ho.project(sdict['uv_expr']) # functions for source terms source_uv = Function(solver_obj.function_spaces.U_2d, name='momentum source') source_uv.project(sdict['res_uv_expr']) source_elev = Function(solver_obj.function_spaces.H_2d, name='continuity source') source_elev.project(sdict['res_elev_expr']) solver_obj.options.momentum_source_2d = source_uv solver_obj.options.volume_source_2d = source_elev if 'cori_expr' in sdict: coriolis_func = Function(solver_obj.function_spaces.H_2d, name='coriolis') coriolis_func.project(sdict['cori_expr']) solver_obj.options.coriolis_frequency = coriolis_func if 'visc_expr' in sdict: viscosity_space = FunctionSpace(solver_obj.mesh2d, "CG", order) viscosity_func = Function(viscosity_space, name='viscosity') viscosity_func.project(sdict['visc_expr']) solver_obj.options.horizontal_viscosity = viscosity_func # functions for boundary conditions # analytical elevation elev_ana = Function(solver_obj.function_spaces.H_2d, name='Analytical elevation') elev_ana.project(sdict['elev_expr']) # analytical uv uv_ana = Function(solver_obj.function_spaces.U_2d, name='Analytical velocity') uv_ana.project(sdict['uv_expr']) # normal velocity (scalar field, will be interpreted as un*normal vector) # left/right bnds un_ana_x = Function(solver_obj.function_spaces.H_2d, name='Analytical normal velocity x') un_ana_x.project(uv_ana[0]) # lower/uppser bnds un_ana_y = Function(solver_obj.function_spaces.H_2d, name='Analytical normal velocity y') un_ana_y.project(uv_ana[1]) # flux through left/right bnds flux_ana_x = Function(solver_obj.function_spaces.H_2d, name='Analytical x flux') flux_ana_x.project(uv_ana[0]*(bathymetry_2d + elev_ana)*ly) # flux through lower/upper bnds flux_ana_y = Function(solver_obj.function_spaces.H_2d, name='Analytical x flux') flux_ana_y.project(uv_ana[1]*(bathymetry_2d + elev_ana)*lx) # construct bnd conditions from setup bnd_funcs = sdict['bnd_funcs'] # correct values to replace None in bnd_funcs # NOTE: scalar velocity (flux|un) sign def: positive out of domain bnd_field_mapping = {'symm': None, 'elev': elev_ana, 'uv': uv_ana, 'un_left': -un_ana_x, 'un_right': un_ana_x, 'un_lower': -un_ana_y, 'un_upper': un_ana_y, 'flux_left': -flux_ana_x, 'flux_right': flux_ana_x, 'flux_lower': -flux_ana_y, 'flux_upper': flux_ana_y, } for bnd_id in bnd_funcs: d = {} # values for this bnd e.g. {'elev': elev_ana, 'uv': uv_ana} for bnd_field in bnd_funcs[bnd_id]: field_name = bnd_field.split('_')[0] d[field_name] = bnd_field_mapping[bnd_field] # set to the correct bnd_id solver_obj.bnd_functions['shallow_water'][bnd_id] = d # # print a fancy description # bnd_str = '' # for k in d: # if isinstance(d[k], ufl.algebra.Product): # a, b = d[k].operands() # name = '{:} * {:}'.format(a.value(), b.name()) # else: # if d[k] is not None: # name = d[k].name() # else: # name = str(d[k]) # bnd_str += '{:}: {:}, '.format(k, name) # print_output('bnd {:}: {:}'.format(bnd_id, bnd_str)) solver_obj.assign_initial_conditions(elev=elev_ana, uv=uv_ana) if solver_parameters is not None: # HACK: need to change prefix of solver options in order to overwrite them solver_obj.timestepper.name += '_' solver_obj.timestepper.solver_parameters.update(solver_parameters) solver_obj.timestepper.update_solver() solver_obj.iterate() elev_l2_err = errornorm(elev_ana_ho, solver_obj.fields.solution_2d.split()[1])/numpy.sqrt(area) uv_l2_err = errornorm(uv_ana_ho, solver_obj.fields.solution_2d.split()[0])/numpy.sqrt(area) print_output('elev L2 error {:.12f}'.format(elev_l2_err)) print_output('uv L2 error {:.12f}'.format(uv_l2_err)) return elev_l2_err, uv_l2_err def run_convergence(setup, ref_list, order, do_export=False, save_plot=False, options=None, solver_parameters=None): """Runs test for a list of refinements and computes error convergence rate""" l2_err = [] for r in ref_list: l2_err.append(run(setup, r, order, do_export=do_export, options=options, solver_parameters=solver_parameters)) x_log = numpy.log10(numpy.array(ref_list, dtype=float)**-1) y_log = numpy.log10(numpy.array(l2_err)) y_log_elev = y_log[:, 0] y_log_uv = y_log[:, 1] def check_convergence(x_log, y_log, expected_slope, field_str, save_plot): slope_rtol = 0.2 slope, intercept, r_value, p_value, std_err = stats.linregress(x_log, y_log) setup_name = setup.__name__ if save_plot: import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(5, 5)) # plot points ax.plot(x_log, y_log, 'k.') x_min = x_log.min() x_max = x_log.max() offset = 0.05*(x_max - x_min) n = 50 xx = numpy.linspace(x_min - offset, x_max + offset, n) yy = intercept + slope*xx # plot line ax.plot(xx, yy, linestyle='--', linewidth=0.5, color='k') ax.text(xx[2*n/3], yy[2*n/3], '{:4.2f}'.format(slope), verticalalignment='top', horizontalalignment='left') ax.set_xlabel('log10(dx)') ax.set_ylabel('log10(L2 error)') ax.set_title(field_str) ref_str = 'ref-' + '-'.join([str(r) for r in ref_list]) order_str = 'o{:}'.format(order) imgfile = '_'.join(['convergence', setup_name, field_str, ref_str, order_str]) imgfile += '.png' img_dir = create_directory('plots') imgfile = os.path.join(img_dir, imgfile) print_output('saving figure {:}'.format(imgfile)) plt.savefig(imgfile, dpi=200, bbox_inches='tight') if expected_slope is not None: err_msg = '{:}: Wrong convergence rate {:.4f}, expected {:.4f}'.format(setup_name, slope, expected_slope) assert abs(slope - expected_slope)/expected_slope < slope_rtol, err_msg print_output('{:}: convergence rate {:.4f} PASSED'.format(setup_name, slope)) else: print_output('{:}: {:} convergence rate {:.4f}'.format(setup_name, field_str, slope)) return slope check_convergence(x_log, y_log_elev, order+1, 'elev', save_plot) check_convergence(x_log, y_log_uv, order+1, 'uv', save_plot) # NOTE nontrivial velocity implies slower convergence # NOTE try time dependent solution: need to update source terms # NOTE using Lax-Friedrichs stabilization in mom. advection term improves convergence of velocity # --------------------------- # standard tests for pytest # --------------------------- @pytest.fixture(params=[setup7, setup8, setup9], ids=["Setup7", "Setup8", "Setup9"]) def setup(request): return request.param @pytest.fixture(params=[ {'element_family': 'dg-dg', 'timestepper_type': 'CrankNicolson'}, {'element_family': 'rt-dg', 'timestepper_type': 'CrankNicolson'}, {'element_family': 'dg-cg', 'timestepper_type': 'CrankNicolson'}], ids=["dg-dg", "rt-dg", "dg-cg"] ) def options(request): return request.param def test_steady_state_basin_convergence(setup, options): sp = {'ksp_type': 'preonly', 'pc_type': 'lu', 'snes_monitor': None, 'mat_type': 'aij'} run_convergence(setup, [1, 2, 4, 6], 1, options=options, solver_parameters=sp, save_plot=False)

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tests/py_tests/test_faint/test_color.py

lukas-ke/faint-graphics-editor

10

190551

<filename>tests/py_tests/test_faint/test_color.py #!/usr/bin/env python3 # -*- coding: utf-8 -*- import unittest import os from faint import Color class TestColor(unittest.TestCase): """Tests the faint.Color class""" def test_color(self): # Default construction self.assertEqual(Color(), (0,0,0,255)) self.assertNotEqual(Color(), (0,1,0,255)) # Comparison self.assertEqual(Color(1,2,3), Color(1,2,3)) self.assertEqual(Color(1,2,3), Color(1,2,3,255)) # Indexing self.assertEqual(Color(0,1,2,3)[0], 0) self.assertEqual(Color(0,1,2,3)[1], 1) self.assertEqual(Color(0,1,2,3)[2], 2) self.assertEqual(Color(0,1,2,3)[3], 3) # Slicing self.assertEqual(Color(0,1,2,3)[0:0], ()) self.assertEqual(Color(0,1,2,3)[0:1], (0,)) self.assertEqual(Color(0,1,2,3)[0:2], (0,1)) self.assertEqual(Color(0,1,2,3)[:], (0,1,2,3)) self.assertEqual(Color(0,1,2,3)[:-1], (0,1,2)) # Attributes self.assertEqual(Color(0,1,2,3).r, 0) self.assertEqual(Color(0,1,2,3).g, 1) self.assertEqual(Color(0,1,2,3).b, 2) self.assertEqual(Color(0,1,2,3).a, 3)

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transtool/checks/missing_translations.py

MarcinOrlowski/prop-tool

1

194480

<reponame>MarcinOrlowski/prop-tool<gh_stars>1-10 """ # trans-tool # The translation files checker and syncing tool. # # Copyright ©2021 <NAME> <mail [@] <EMAIL>> # https://github.com/MarcinOrlowski/trans-tool/ # """ from typing import Dict from transtool.decorators.overrides import overrides from transtool.report.group import ReportGroup from .base.check import Check # noinspection PyUnresolvedReferences class MissingTranslations(Check): """ This check checks if given base key is also present in translation file. """ @overrides(Check) # Do NOT "fix" the PropFile reference and do not import it, or you step on circular dependency! def check(self, translation_file: 'PropFile', reference_file: 'PropFile' = None) -> ReportGroup: self.need_valid_config() self.need_both_files(translation_file, reference_file) report = ReportGroup('Missing translations') missing_keys: List[str] = [ref_key for ref_key in reference_file.keys if ref_key not in translation_file.keys] # Commented out keys are also considered present in the translation # unless we run in strict check mode. if not self.config['strict']: for comm_key in translation_file.commented_out_keys: if comm_key in missing_keys: del missing_keys[missing_keys.index(comm_key)] for missing_key in missing_keys: report.warn(None, 'Missing translation.', missing_key) return report @overrides(Check) def get_default_config(self) -> Dict: return { 'strict': False, }

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coordinator/api/management/commands/fakedata.py

kids-first/kf-api-release-coordinator

2

80688

<gh_stars>1-10 from django.core.management.base import BaseCommand from coordinator.api.factories.release import ReleaseFactory class Command(BaseCommand): help = "Pre-populate the database with fake data" def handle(self, *args, **options): ReleaseFactory.create_batch(100) self.stdout.write(self.style.SUCCESS("Created releases"))

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desafiosCursoEmVideo/ex018.py

gomesGabriel/Pythonicos

1

156246

<filename>desafiosCursoEmVideo/ex018.py import math n1 = float(input('Digite um valor: ')) print('O seno: {:.5f}' .format(math.sin(math.radians(n1)))) print('O cosseno: {:.5f}' .format(math.cos(math.radians(n1)))) print('O tg: {:.5f}' .format(math.tan(math.radians(n1))))

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updates.py

knowledgetechnologyuhh/hipss

0

16314

import torch import numpy as np import torch.nn.functional as F from torch.nn.utils.clip_grad import clip_grad_norm_ from mpi_utils.mpi_utils import sync_grads def update_entropy(alpha, log_alpha, target_entropy, log_pi, alpha_optim, cfg): if cfg.automatic_entropy_tuning: alpha_loss = -(log_alpha * (log_pi + target_entropy).detach()).mean() alpha_optim.zero_grad() alpha_loss.backward() alpha_optim.step() alpha = log_alpha.exp() alpha_tlogs = alpha.clone() else: alpha_loss = torch.tensor(0.) alpha_tlogs = torch.tensor(alpha) return alpha_loss, alpha_tlogs def update_flat(actor_network, critic_network, critic_target_network, policy_optim, critic_optim, alpha, log_alpha, target_entropy, alpha_optim, obs_norm, ag_norm, g_norm, obs_next_norm, actions, rewards, cfg): inputs_norm = np.concatenate([obs_norm, ag_norm, g_norm], axis=1) inputs_next_norm = np.concatenate([obs_next_norm, ag_norm, g_norm], axis=1) inputs_norm_tensor = torch.tensor(inputs_norm, dtype=torch.float32) inputs_next_norm_tensor = torch.tensor(inputs_next_norm, dtype=torch.float32) actions_tensor = torch.tensor(actions, dtype=torch.float32) r_tensor = torch.tensor(rewards, dtype=torch.float32).reshape(rewards.shape[0], 1) if cfg.cuda: inputs_norm_tensor = inputs_norm_tensor.cuda() inputs_next_norm_tensor = inputs_next_norm_tensor.cuda() actions_tensor = actions_tensor.cuda() r_tensor = r_tensor.cuda() with torch.no_grad(): actions_next, log_pi_next, _ = actor_network.sample(inputs_next_norm_tensor) qf_next_target = critic_target_network(inputs_next_norm_tensor, actions_next) min_qf_next_target = torch.min(qf_next_target, dim=0).values - alpha * log_pi_next next_q_value = r_tensor + cfg.gamma * min_qf_next_target # the q loss qf = critic_network(inputs_norm_tensor, actions_tensor) qf_loss = torch.stack([F.mse_loss(_qf, next_q_value) for _qf in qf]).mean() # the actor loss pi, log_pi, _ = actor_network.sample(inputs_norm_tensor) qf_pi = critic_network(inputs_norm_tensor, pi) min_qf_pi = torch.min(qf_pi, dim=0).values policy_loss = ((alpha * log_pi) - min_qf_pi).mean() # update actor network policy_optim.zero_grad() policy_loss.backward() sync_grads(actor_network) policy_optim.step() # update the critic_network critic_optim.zero_grad() qf_loss.backward() if cfg.clip_grad_norm: clip_grad_norm_(critic_network.parameters(), cfg.max_norm) sync_grads(critic_network) critic_optim.step() alpha_loss, alpha_tlogs = update_entropy(alpha, log_alpha, target_entropy, log_pi, alpha_optim, cfg) train_metrics = dict(q_loss=qf_loss.item(), next_q=next_q_value.mean().item(), policy_loss=policy_loss.item(), alpha_loss=alpha_loss.item(), alpha_tlogs=alpha_tlogs.item()) for idx, (_qf, _qtarget) in enumerate(zip(qf, qf_next_target)): train_metrics[f'q_{idx}'] = _qf.mean().item() train_metrics[f'q_target_{idx}'] = _qtarget.mean().item() return train_metrics def update_language(actor_network, critic_network, critic_target_network, policy_optim, critic_optim, alpha, log_alpha, target_entropy, alpha_optim, obs_norm, instruction, obs_next_norm, actions, rewards, cfg): inputs_norm = obs_norm inputs_next_norm = obs_next_norm inputs_norm_tensor = torch.tensor(inputs_norm, dtype=torch.float32) inputs_next_norm_tensor = torch.tensor(inputs_next_norm, dtype=torch.float32) actions_tensor = torch.tensor(actions, dtype=torch.float32) r_tensor = torch.tensor(rewards, dtype=torch.float32).reshape(rewards.shape[0], 1) instruction_tensor = torch.tensor(instruction, dtype=torch.long) if cfg.cuda: inputs_norm_tensor = inputs_norm_tensor.cuda() inputs_next_norm_tensor = inputs_next_norm_tensor.cuda() actions_tensor = actions_tensor.cuda() r_tensor = r_tensor.cuda() instruction_tensor = instruction_tensor.cuda() with torch.no_grad(): actions_next, log_pi_next, _ = actor_network.sample(inputs_next_norm_tensor, instruction_tensor) qf_next_target = critic_target_network(inputs_next_norm_tensor, actions_next, instruction_tensor) min_qf_next_target = torch.min(qf_next_target, dim=0).values - alpha * log_pi_next next_q_value = r_tensor + cfg.gamma * min_qf_next_target # the q loss qf = critic_network(inputs_norm_tensor, actions_tensor, instruction_tensor) qf_loss = torch.stack([F.mse_loss(_qf, next_q_value) for _qf in qf]).mean() # the actor loss pi, log_pi, _ = actor_network.sample(inputs_norm_tensor, instruction_tensor) qf_pi = critic_network(inputs_norm_tensor, pi, instruction_tensor) min_qf_pi = torch.min(qf_pi, dim=0).values policy_loss = ((alpha * log_pi) - min_qf_pi).mean() # update actor network policy_optim.zero_grad() policy_loss.backward() sync_grads(actor_network) policy_optim.step() # update the critic_network critic_optim.zero_grad() qf_loss.backward() if cfg.clip_grad_norm: clip_grad_norm_(critic_network.parameters(), cfg.max_norm) sync_grads(critic_network) critic_optim.step() alpha_loss, alpha_tlogs = update_entropy(alpha, log_alpha, target_entropy, log_pi, alpha_optim, cfg) train_metrics = dict(q_loss=qf_loss.item(), next_q=next_q_value.mean().item(), policy_loss=policy_loss.item(), alpha_loss=alpha_loss.item(), alpha_tlogs=alpha_tlogs.item()) for idx, (_qf, _qtarget) in enumerate(zip(qf, qf_next_target)): train_metrics[f'q_{idx}'] = _qf.mean().item() train_metrics[f'q_target_{idx}'] = _qtarget.mean().item() return train_metrics

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search/urls.py

MubongwoNdasi/pms

0

1607683

from django.urls import path from search import views app_name = 'search' urlpatterns = [ path('', views.DrugSearchListView.as_view(), name="list"), path('pharmacist_list', views.PharmacistSearchListView.as_view(), name="pharmacist_list"), ]

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test.py

zhanghanduo/calc_new

1

41595

#!/usr/bin/env python3 import layers import cv2 from os.path import join import numpy as np # import tensorflow as tf import Augmentor vw = 320 vh = 320 class Augment: def __init__(self): self.w = 2 * 640 self.h = 2 * 480 self.canvas = np.zeros((self.h, self.w, 3), dtype=np.uint8) def update(self, _im): # sc = .4 # h, w = (int(sc * _im.shape[0]), int(sc * _im.shape[1])) vh = _im.shape[0] vw = _im.shape[1] # im = cv2.resize(_im, (w, h)) self.canvas[100:(100 + vh), :vw, :] = _im cv2.putText(self.canvas, "Original", (0, 50), cv2.FONT_HERSHEY_COMPLEX, 1.0, (255, 255, 255)) cv2.putText(self.canvas, "Distorted", (0, 150 + vh), cv2.FONT_HERSHEY_COMPLEX, 1.0, (255, 255, 255)) self.canvas[(200 + vh):(200 + 2 * vh), :vw, :] = _im cv2.imshow("Image Augment", self.canvas) cv2.waitKey(0) if __name__ == "__main__": data_root = "/mnt/4102422c-af52-4b55-988f-df7544b35598/dataset/KITTI/KITTI_Odometry/" seq = "14" vo_fn = data_root + "dataset/poses/" + seq.zfill(2) + ".txt" im_dir = data_root + "dataset/sequences/" + seq.zfill(2) aux_dir = "/home/handuo/projects/paper/image_base/downloads" i = 0 gui = Augment() # with tf.Session() as sess: ims = [] p = Augmentor.Pipeline(join(im_dir, "image_0/"), output_directory="../../../output", save_format="JPEG") # print("Has %s samples." % (len(p.augmentor_images))) p.zoom(probability=0.3, min_factor=0.9, max_factor=1.2) p.skew(probability=0.75, magnitude=0.3) # p.random_erasing(probability=0.5, rectangle_area=0.3) p.multi_erasing(probability=0.5, max_x_axis=0.3, max_y_axis=0.15, max_num=4) # p.rotate(probability=0.5, max_left_rotation=6, max_right_rotation=6) p.sample(10)

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