xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

b9ad055e162f0001e288ab22dec6a5a4746fd51d

2,786

py

Python

Neuro-Cognitive Models/Runs/Nonhier_run/res_nonhier.py

AGhaderi/spatial_attenNCM

1f7edf17f55d804d2ae3360d23623c9ab5035518

[ "MIT" ]

null

Neuro-Cognitive Models/Runs/Nonhier_run/res_nonhier.py

AGhaderi/spatial_attenNCM

1f7edf17f55d804d2ae3360d23623c9ab5035518

[ "MIT" ]

null

Neuro-Cognitive Models/Runs/Nonhier_run/res_nonhier.py

AGhaderi/spatial_attenNCM

1f7edf17f55d804d2ae3360d23623c9ab5035518

[ "MIT" ]

null

#!/home/a.ghaderi/.conda/envs/envjm/bin/python # Model 2 import pystan import pandas as pd import numpy as np import sys sys.path.append('../../') import utils parts = 1 data = utils.get_data() #loading dateset data = data[data['participant']==parts] mis = np.where((data['n200lat']<.101)|(data['n200lat']>.248))[0] # missing data for n200lat obs = np.where((data['n200lat']>.101)&(data['n200lat']<.248))[0] # observation and missing data for n200lat N_mis = mis.shape[0] # number of missing data N_obs = obs.shape[0] # number of observed data modelfile = '../../stans/res_nonhier.stan' #reading the model span f = open(modelfile, 'r') model_wiener = f.read() sm = pystan.StanModel(model_code=model_wiener)# Compile the model stan ncohers = 2 #Number of coherence conditions nspats = 2 #Number of spatial conditions nconds = 4 #Number of conditions y = data['y'].to_numpy() cond_coher = data['cond_coher'].to_numpy() cond_spat = data['cond_spat'].to_numpy() conds = data['conds'].to_numpy() n200lat = data['n200lat'].to_numpy() #set inistial data for molde span data_winner = {'N_obs':N_obs, #Number of trial-level observations 'N_mis':N_mis, #Number of trial-level mising data 'ncohers':ncohers, #Number of coherence conditions 'nspats':nspats, #Number of spatial conditions 'nconds':nconds, #Number of conditions 'y':np.concatenate([y[obs],y[mis]]), #acc*rt in seconds for obervation and missing data 'cond_coher':np.concatenate([cond_coher[obs],cond_coher[mis]]), #Coherence index for each trial 'cond_spat':np.concatenate([cond_spat[obs],cond_spat[mis]]), #sptial index for each trial 'conds':np.concatenate([conds[obs],conds[mis]]), #sptial index for each trial 'n200lat_obs':n200lat[obs]}; #n200 latency for each trial observation # setting MCMC arguments niter = 10000 nwarmup = 4000 nchains = 1 thin = 1 initials = [] # initial sampling for c in range(0, nchains): chaininit = { 'delta': np.random.uniform(1, 3, size=ncohers), 'alpha': np.random.uniform(.5, 1.), 'eta': np.random.uniform(.01, .2), 'res': np.random.uniform(.01, .02, size=nspats), 'n200sub': np.random.uniform(.11, .2, size=nconds), 'lambda': np.random.uniform(.01, .02), 'n200lat_mis': np.random.uniform(.11, .2, size = N_mis) } initials.append(chaininit) # Train the model and generate samples fit = sm.sampling(data=data_winner, iter=niter, chains=nchains, warmup=nwarmup, thin=thin, init=initials) utils.to_pickle(stan_model=sm, stan_fit=fit, save_path='../../save/nonhier/'+str(parts)+'_res_nonhier.pkl')

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null

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b9adc3a3c0f82e03cf53dd13486c80b1bb9dbf85

6,691

py

Python

rq_dashboard/dashboard.py

refgenomics/rq-dashboard

cdfadd2b9aa9a66b0594fd5573e3c45fa8643f05

[ "BSD-2-Clause-FreeBSD" ]

null

rq_dashboard/dashboard.py

refgenomics/rq-dashboard

cdfadd2b9aa9a66b0594fd5573e3c45fa8643f05

[ "BSD-2-Clause-FreeBSD" ]

null

rq_dashboard/dashboard.py

refgenomics/rq-dashboard

cdfadd2b9aa9a66b0594fd5573e3c45fa8643f05

[ "BSD-2-Clause-FreeBSD" ]

null

from redis import Redis from redis import from_url from rq import push_connection, pop_connection from rq.job import Job from functools import wraps import times from flask import Blueprint from flask import current_app, url_for, abort from flask import render_template from rq import Queue, Worker from rq import cancel_job, requeue_job from rq import get_failed_queue from math import ceil dashboard = Blueprint('rq_dashboard', __name__, template_folder='templates', static_folder='static', ) @dashboard.before_request def authentication_hook(): """ Allow the parent app to authenticate user's access to the dashboard with it's own auth_handler method that must return True or False """ auth_handler = current_app.extensions['rq-dashboard'].auth_handler if auth_handler and not auth_handler(): abort(401) @dashboard.before_app_first_request def setup_rq_connection(): if current_app.config.get('REDIS_URL'): current_app.redis_conn = from_url(current_app.config.get('REDIS_URL')) else: current_app.redis_conn = Redis(host=current_app.config.get('REDIS_HOST', 'localhost'), port=current_app.config.get('REDIS_PORT', 6379), password=current_app.config.get('REDIS_PASSWORD', None), db=current_app.config.get('REDIS_DB', 0)) @dashboard.before_request def push_rq_connection(): push_connection(current_app.redis_conn) @dashboard.teardown_request def pop_rq_connection(exception=None): pop_connection() def jsonify(f): @wraps(f) def _wrapped(*args, **kwargs): from flask import jsonify as flask_jsonify try: result_dict = f(*args, **kwargs) except Exception as e: result_dict = dict(status='error') if current_app.config['DEBUG']: result_dict['reason'] = str(e) from traceback import format_exc result_dict['exc_info'] = format_exc() return flask_jsonify(**result_dict) return _wrapped def serialize_queues(queues): return [dict(name=q.name, count=q.count, url=url_for('.overview', queue_name=q.name)) for q in queues] def serialize_date(dt): if dt is None: return None return times.format(dt, 'UTC') def serialize_job(job): return dict( id=job.id, created_at=serialize_date(job.created_at), enqueued_at=serialize_date(job.enqueued_at), ended_at=serialize_date(job.ended_at), origin=job.origin, result=job._result, exc_info=job.exc_info, description=job.description) def remove_none_values(input_dict): return dict([ (k,v) for k,v in input_dict.items() if v is not None ]) def pagination_window(total_items, cur_page, per_page=5, window_size=10): all_pages = range(1, int(ceil(total_items / float(per_page))) + 1) results = all_pages if (window_size >= 1): pages_window_start = int(max(0, min(len(all_pages) - window_size, (cur_page-1) - ceil(window_size / 2.0)))) pages_window_end = int(pages_window_start + window_size) result = all_pages[pages_window_start:pages_window_end] return result @dashboard.route('/', defaults={'queue_name': None, 'page': '1'}) @dashboard.route('/<queue_name>', defaults={'page': '1'}) @dashboard.route('/<queue_name>/<page>') def overview(queue_name, page): if queue_name is None: # Show the failed queue by default if it contains any jobs failed = Queue('failed') if not failed.is_empty(): queue = failed else: queue = Queue() else: queue = Queue(queue_name) return render_template('rq_dashboard/dashboard.html', workers=Worker.all(), queue=queue, page=page, queues=Queue.all(), rq_url_prefix=url_for('.overview')) @dashboard.route('/job/<job_id>/cancel', methods=['POST']) @jsonify def cancel_job_view(job_id): rq_job = Job.fetch(job_id) if rq_job.status == "failed": rq_job.delete() else: rq_job.cancel() return dict(status='OK') @dashboard.route('/job/<job_id>/requeue', methods=['POST']) @jsonify def requeue_job_view(job_id): requeue_job(job_id) return dict(status='OK') @dashboard.route('/requeue-all', methods=['GET', 'POST']) @jsonify def requeue_all(): fq = get_failed_queue() job_ids = fq.job_ids count = len(job_ids) for job_id in job_ids: requeue_job(job_id) return dict(status='OK', count=count) @dashboard.route('/queue/<queue_name>/empty', methods=['POST']) @jsonify def empty_queue(queue_name): q = Queue(queue_name) q.empty() return dict(status='OK') @dashboard.route('/queue/<queue_name>/compact', methods=['POST']) @jsonify def compact_queue(queue_name): q = Queue(queue_name) q.compact() return dict(status='OK') @dashboard.route('/queues.json') @jsonify def list_queues(): queues = serialize_queues(sorted(Queue.all())) return dict(queues=queues) @dashboard.route('/jobs/<queue_name>/<page>.json') @jsonify def list_jobs(queue_name, page): current_page = int(page) queue = Queue(queue_name) per_page = 5 total_items = queue.count pages_numbers_in_window = pagination_window(total_items, current_page, per_page) pages_in_window = [ dict(number=p, url=url_for('.overview', queue_name=queue_name, page=p)) for p in pages_numbers_in_window ] last_page = int(ceil(total_items / float(per_page))) prev_page = None if current_page > 1: prev_page = dict(url=url_for('.overview', queue_name=queue_name, page=(current_page-1))) next_page = None if current_page < last_page: next_page = dict(url=url_for('.overview', queue_name=queue_name, page=(current_page+1))) pagination = remove_none_values( dict(pages_in_window=pages_in_window, next_page=next_page, prev_page=prev_page)) offset = (current_page - 1) * per_page jobs = [serialize_job(job) for job in queue.get_jobs(offset, per_page)] return dict(name=queue.name, jobs=jobs, pagination=pagination) @dashboard.route('/workers.json') @jsonify def list_workers(): def serialize_queue_names(worker): return [q.name for q in worker.queues] workers = [dict(name=worker.name, queues=serialize_queue_names(worker), state=worker.get_state()) for worker in Worker.all()] return dict(workers=workers) @dashboard.context_processor def inject_interval(): interval = current_app.config.get('RQ_POLL_INTERVAL', 2500) return dict(poll_interval=interval)

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0

null

0

null

0

1

0

b9b691941c62b002880bb1f21ca60b0e932e41c1

3,574

py

Python

peaksampl.py

Gattocrucco/sipmfilter

74215d6c53b998808fc6c677b46030234d996bdf

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

null

peaksampl.py

Gattocrucco/sipmfilter

74215d6c53b998808fc6c677b46030234d996bdf

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

null

peaksampl.py

Gattocrucco/sipmfilter

74215d6c53b998808fc6c677b46030234d996bdf

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

null

import numpy as np def _adddims(a, b): n = max(a.ndim, b.ndim) a = np.expand_dims(a, tuple(range(n - a.ndim))) b = np.expand_dims(b, tuple(range(n - b.ndim))) return a, b def _yz(y, z, t, yout): """ Shared implementation of peaksampl and sumpeaks. """ y = np.asarray(y) z = np.asarray(z) t = np.asarray(t) y = np.pad(y, [(0, 0)] * (y.ndim - 1) + [(1, 1)], constant_values=yout) offset = np.argmax(np.abs(y), axis=-1) ampl = np.take_along_axis(y, np.expand_dims(offset, -1), -1) ampl = np.squeeze(ampl, -1) indices = t[..., :, None] - t[..., None, :] + offset[..., None, None] indices = np.minimum(indices, y.shape[-1] - 1) indices = np.maximum(indices, 0) N = t.shape[-1] indices = indices.reshape(indices.shape[:-2] + (N * N,)) n = max(y.ndim, indices.ndim) y, indices = _adddims(y, indices) y = np.take_along_axis(y, indices, -1) eps = np.finfo(float).eps * N * N * ampl y[..., ::N + 1] += np.expand_dims(eps, -1) y = y.reshape(y.shape[:-1] + (N, N)) z = z[..., None] y, z = _adddims(y, z) return y, z def peaksampl(y, z, t, yout=0): """ Get peak amplitudes given their sum. This assumes that the position of the signals is given by peaks positions even when they are summed. Parameters ---------- y : array (..., M,) The single signal shape. z : array (..., N,) The peak height in the sum of the signals for each peak. t : int array (..., N,) The indices of the peaks in the sum. yout : scalar The value of the signal outside the provided values, default 0. Return ------ a : array (..., N), The amplitudes such that z_i = sum_j a_j * y[t_i - t_j]. Broadcasted along non-last axis. """ y, z = _yz(y, z, t, yout) a = np.linalg.solve(y, z) return np.squeeze(a, -1) def sumpeaks(y, a, t, yout=0): """ Compute the peak heights of a sum of signals. This assumes that the position of the peaks is given by the signal positions even when they are summed. Parameters ---------- y : array (..., M,) The single signal shape. a : array (..., N,) The amplitudes of the signals (`y` is multiplied by `a`). t : int array (..., N,) The indices of the position of the signals. yout : scalar The value of the signal outside the provided values, default 0. Return ------ z : array (..., N,) The peak height in the sum of the signals for each signal. Broadcasted along non-last axis. """ y, a = _yz(y, a, t, yout) z = np.matmul(y, a) return np.squeeze(z, axis=-1) if __name__ == '__main__': from matplotlib import pyplot as plt from scipy import signal y = np.exp(-np.linspace(0, 10, 1000) / 10) i = np.arange(1, 1000) t0 = np.array([10, 340, 523]) a0 = np.array([3, 2, 1]) indices = i - t0[:, None] z = np.take(y, indices, mode='clip') * a0[:, None] z = np.where((indices < 0) | (indices >= len(y)), 0, z) z = np.sum(z, axis=0) t, = signal.argrelmax(z) assert len(t) == len(t0) a = peaksampl(y, z[t], t) h = sumpeaks(y, a, t) fig, ax = plt.subplots(num='peaksampl', clear=True) ax.plot(z, color='#f55') ax.vlines(t0, 0, a0, color='gray', zorder=3) ax.vlines(t, 0, a, linestyle='--', zorder=3) ax.plot(t, h, 'ok') ax.grid('major', linestyle='--') fig.tight_layout() fig.show()

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0

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0.729412

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null

0

null

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1

0

b9b9340675c6ceead7ff166bf8fe4d65fa580b58

4,597

py

Python

backend/Washlist/tests.py

henrikhorluck/tdt4140-washlists

a75c3bc38a3f915eb48cf3e9ecba848f46a2bcaa

[ "MIT" ]

null

backend/Washlist/tests.py

henrikhorluck/tdt4140-washlists

a75c3bc38a3f915eb48cf3e9ecba848f46a2bcaa

[ "MIT" ]

2

2020-05-02T18:17:44.000Z

2020-05-02T18:18:02.000Z

backend/Washlist/tests.py

henrikhorluck/tdt4140-washlists

a75c3bc38a3f915eb48cf3e9ecba848f46a2bcaa

[ "MIT" ]

null

from django.test import TestCase from django.urls import reverse from rest_framework import status from Dormroom.models import Dormroom from SIFUser.mixins import AuthTestMixin from StudentVillage.models import StudentVillage from Washlist.jobs import reset_washlists from Washlist.models.Templates import TemplateListItem, TemplateWashList from Washlist.models.WashLists import ListItem from Washlist.serializer import TemplateWashListSerializer class WashListTemplateTest(TestCase): room = None def setUp(self): village = StudentVillage.objects.create(name="Moholt") self.room = Dormroom.objects.create(number=1, village=village) temp_list = TemplateWashList.objects.create(title="Moholt") village.templateWashList = temp_list village.save() def test_add_to_template_adds_to_each_list(self): desc = "Vask badet" temp_list = TemplateWashList.objects.get(title="Moholt") TemplateListItem.objects.create(description=desc, washlist=temp_list).save() self.assertEqual(desc, ListItem.objects.get(dormroom=self.room).description) class WeeklyResetOfWashlistsTest(TestCase): def setUp(self): """ Create a Washlist item that is completed the method also sets up a village and a room to relate the Washlist item to satisfy the db constraints """ village = StudentVillage.objects.create(name="Moholt") self.room = Dormroom.objects.create(number=1, village=village) temp_list = TemplateWashList.objects.create(title="Moholt") village.templateWashList = temp_list village.save() self.item = ListItem.objects.create( pk=1, dormroom=self.room, desc="Vask badet", completed=True ) self.item.save() def test_job_resets_items(self): """ Test that job to reset Washlist items when run manually actually rests the databases Washlist items """ reset_washlists() self.assertEqual(False, ListItem.objects.get(pk=1).completed) class WashlistTemplateAPITest(AuthTestMixin): def setUp(self): super().setUp() self.temp_list = TemplateWashList.objects.create(title="Moholt") village = StudentVillage.objects.create( name="Moholt", templateWashList=self.temp_list ) self.room = Dormroom.objects.create(number=1, village=village) self.item = ListItem.objects.create( pk=1, dormroom=self.room, desc="Vask badet", completed=True ) def test_get_template_list(self): url = reverse("templatewashlist-list") response = self.client.get(url, HTTP_AUTHORIZATION=self.auth) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual( response.data[0], TemplateWashListSerializer( TemplateWashList.objects.get(title="Moholt") ).data, ) def test_get_detail_template_list(self): url = reverse("templatewashlist-detail", args=[1]) response = self.client.get(url, HTTP_AUTHORIZATION=self.auth) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual( response.data, TemplateWashListSerializer( TemplateWashList.objects.get(title="Moholt") ).data, ) def test_add_template_washlist(self): url = reverse("templatewashlist-list") response = self.client.post( url, {"title": "Tyholt", "village": 1}, HTTP_AUTHORIZATION=self.auth ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( response.data, TemplateWashListSerializer( TemplateWashList.objects.get(title="Tyholt") ).data, ) def test_partial_update(self): url = reverse("templatewashlist-detail", args=[1]) response = self.client.patch( url, {"title": "Berg"}, HTTP_AUTHORIZATION=self.auth ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual( response.data, TemplateWashListSerializer(TemplateWashList.objects.get(title="Berg")).data, ) def test_destroy(self): url = reverse("templatewashlist-detail", args=[1]) response = self.client.delete(url, HTTP_AUTHORIZATION=self.auth) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) self.assertEqual(TemplateWashList.objects.count(), 0)

35.091603

92

0.67022

493

4,597

6.137931

0.227181

0.051553

0.068407

0.051223

0.575017

0.562789

0.542631

0.496034

0.421679

0

0.00766

0.233196

4,597

130

93

35.361538

0.85078

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0

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0

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0.025916

0

0.126316

1

0.105263

false

0

0.105263

0

0.252632

0

null

0

null

0

1

0

b9ba39e57d52ad0baaeb81fbe95a03b7bb17d4ad

3,792

py

Python

torchvision/prototype/models/mobilenetv3.py

piyush01123/vision

c6722307e6860057b4855483d237fe00a213dcf6

[ "BSD-3-Clause" ]

null

torchvision/prototype/models/mobilenetv3.py

piyush01123/vision

c6722307e6860057b4855483d237fe00a213dcf6

[ "BSD-3-Clause" ]

null

torchvision/prototype/models/mobilenetv3.py

piyush01123/vision

c6722307e6860057b4855483d237fe00a213dcf6

[ "BSD-3-Clause" ]

null

from functools import partial from typing import Any, Optional, List from torchvision.prototype.transforms import ImageNetEval from torchvision.transforms.functional import InterpolationMode from ...models.mobilenetv3 import MobileNetV3, _mobilenet_v3_conf, InvertedResidualConfig from ._api import WeightsEnum, Weights from ._meta import _IMAGENET_CATEGORIES from ._utils import handle_legacy_interface, _ovewrite_named_param __all__ = [ "MobileNetV3", "MobileNet_V3_Large_Weights", "MobileNet_V3_Small_Weights", "mobilenet_v3_large", "mobilenet_v3_small", ] def _mobilenet_v3( inverted_residual_setting: List[InvertedResidualConfig], last_channel: int, weights: Optional[WeightsEnum], progress: bool, **kwargs: Any, ) -> MobileNetV3: if weights is not None: _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs) if weights is not None: model.load_state_dict(weights.get_state_dict(progress=progress)) return model _COMMON_META = { "task": "image_classification", "architecture": "MobileNetV3", "publication_year": 2019, "size": (224, 224), "min_size": (1, 1), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR, } class MobileNet_V3_Large_Weights(WeightsEnum): ImageNet1K_V1 = Weights( url="https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth", transforms=partial(ImageNetEval, crop_size=224), meta={ **_COMMON_META, "num_params": 5483032, "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small", "acc@1": 74.042, "acc@5": 91.340, }, ) ImageNet1K_V2 = Weights( url="https://download.pytorch.org/models/mobilenet_v3_large-5c1a4163.pth", transforms=partial(ImageNetEval, crop_size=224, resize_size=232), meta={ **_COMMON_META, "num_params": 5483032, "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning", "acc@1": 75.274, "acc@5": 92.566, }, ) default = ImageNet1K_V2 class MobileNet_V3_Small_Weights(WeightsEnum): ImageNet1K_V1 = Weights( url="https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth", transforms=partial(ImageNetEval, crop_size=224), meta={ **_COMMON_META, "num_params": 2542856, "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small", "acc@1": 67.668, "acc@5": 87.402, }, ) default = ImageNet1K_V1 @handle_legacy_interface(weights=("pretrained", MobileNet_V3_Large_Weights.ImageNet1K_V1)) def mobilenet_v3_large( *, weights: Optional[MobileNet_V3_Large_Weights] = None, progress: bool = True, **kwargs: Any ) -> MobileNetV3: weights = MobileNet_V3_Large_Weights.verify(weights) inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs) return _mobilenet_v3(inverted_residual_setting, last_channel, weights, progress, **kwargs) @handle_legacy_interface(weights=("pretrained", MobileNet_V3_Small_Weights.ImageNet1K_V1)) def mobilenet_v3_small( *, weights: Optional[MobileNet_V3_Small_Weights] = None, progress: bool = True, **kwargs: Any ) -> MobileNetV3: weights = MobileNet_V3_Small_Weights.verify(weights) inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_small", **kwargs) return _mobilenet_v3(inverted_residual_setting, last_channel, weights, progress, **kwargs)

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0.238636

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null

0

null

0

1

0

b9bb907819b5835937644fde4b8d08e5dd987580

1,036

py

Python

crawler/tests.py

mental689/paddict

493268b62531c698687d42416edf61c602250133

[ "MIT" ]

1

2019-06-22T10:28:21.000Z

crawler/tests.py

mental689/paddict

493268b62531c698687d42416edf61c602250133

[ "MIT" ]

4

2020-09-05T01:48:18.000Z

2022-03-02T04:29:25.000Z

crawler/tests.py

mental689/paddict

493268b62531c698687d42416edf61c602250133

[ "MIT" ]

null

from django.test import TestCase # Create your tests here. from crawler.download import * from crawler.models import * class AnimalDownloadTestCase(TestCase): def setUp(self): self.stopWords = ["CVPR 2019", "Computer Vision Foundation."] self.url = "/Users/tuannguyenanh/Desktop/cvpr2019.html"#"http://openaccess.thecvf.com/CVPR2019.py" self.root = "http://openaccess.thecvf.com/" self.event = Event.objects.filter(shortname='CVPR2019').first() if self.event is None: self.event = Event(shortname='CVPR2019') self.event.save() def test_animal_can_download(self): #print(get_html(self.url)) f = open(self.url) soup = parse_html(f.read()) f.close() f = open('cvpr2019.bib', 'w') print(soup.title) bibtexs = soup.find_all("div", attrs={"class": "bibref"}) #print(bibtexs) for bib in bibtexs: print(bib.text) f.write(bib.text.replace('<br>', '\n')) f.close()

32.375

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0.086957

0

null

0

null

0

1

0

b9bfcc9ca3f71d3591d1b453eea9313adf491d9f

452

py

Python

test_scripts/xml_example.py

petervdb/testrep1

76b6eb3de2deb9596c055f252191e28587d5520c

[ "MIT" ]

1

2015-11-17T21:35:44.000Z

test_scripts/xml_example.py

petervdb/testrep1

76b6eb3de2deb9596c055f252191e28587d5520c

[ "MIT" ]

null

test_scripts/xml_example.py

petervdb/testrep1

76b6eb3de2deb9596c055f252191e28587d5520c

[ "MIT" ]

null

#!/usr/bin/python3 from urllib.request import urlopen from xml.etree.ElementTree import parse # Download the RSS feed and parse it u = urlopen('http://planet.python.org/rss20.xml') doc = parse(u) # Extract and output tags of interest for item in doc.iterfind('channel/item'): title = item.findtext('title') date = item.findtext('pubDate') link = item.findtext('link') print(title) print(date) print(link) print() print("Program executed.")

20.545455

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0.007673

0.134956

452

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21.52381

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0.384615

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null

0

null

0

1

0

b9c06414f6de5d6df932f87abe0ac2addfe2d410

1,489

py

Python

contacts/urls.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

[ "MIT" ]

1

2019-04-21T18:57:57.000Z

contacts/urls.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

[ "MIT" ]

null

contacts/urls.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

[ "MIT" ]

null

"""dedupper_app URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path from contacts import views admin.autodiscover() urlpatterns = [ path('', views.index, name='contact_index'), path('', views.index, name='lead_index'), path('contacts/', views.contacts, name='contacts'), path('leads/', views.leads, name='leads'), path('table/', views.table, name='table'), path('plotly/', views.plotly, name='plotly'), # url(r'^keys', views.upload, name='keys'), # path('key-gen/', views.key_gen, name='key-gen'), # path('heroku/', generic.ListView.as_view(model=models.Contact), name='heroku'), # path('run/', views.run, name='run'), # path('sorted/<id>', views.merge, name='merge'), # path('sorted/export/<type>', views.download, name='export'), # path('sorted/report/<type>', views.download_times, name='report'), ]

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null

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null

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0

b9c1d738b7414d020a32d72c8b5b4b39a4b6d1d4

2,667

py

Python

CPB100/lab2b/scheduled/ingestapp.py

pranaynanda/training-data-analyst

f10ab778589129239fd5b277cfdefb41638eded5

[ "Apache-2.0" ]

null

CPB100/lab2b/scheduled/ingestapp.py

pranaynanda/training-data-analyst

f10ab778589129239fd5b277cfdefb41638eded5

[ "Apache-2.0" ]

null

CPB100/lab2b/scheduled/ingestapp.py

pranaynanda/training-data-analyst

f10ab778589129239fd5b277cfdefb41638eded5

[ "Apache-2.0" ]

null

#!/usr/bin/env python # Copyright 2016 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # [START app] import os import logging import transform import flask import google.cloud.storage as gcs # [start config] app = flask.Flask(__name__) # Configure this environment variable via app.yaml CLOUD_STORAGE_BUCKET = os.environ['CLOUD_STORAGE_BUCKET'] # logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.INFO) # [end config] @app.route('/') def welcome(): return '<html><a href="ingest">ingest last week</a> earthquake data</html>' @app.route('/ingest') def ingest_last_week(): try: # verify that this is a cron job request is_cron = flask.request.headers['X-Appengine-Cron'] logging.info('Received cron request {}'.format(is_cron)) # create png url = 'http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_week.csv' outfile = 'earthquakes.png' status = 'scheduled ingest of {} to {}'.format(url, outfile) logging.info(status) transform.create_png(url, outfile) # upload to cloud storage client = gcs.Client() bucket = client.get_bucket(CLOUD_STORAGE_BUCKET) blob = gcs.Blob('earthquakes/earthquakes.png', bucket) blob.upload_from_filename(outfile) # change permissions blob.make_public() status = 'uploaded {} to {}'.format(outfile, blob.name) logging.info(status) except KeyError as e: status = '<html>Sorry, this capability is accessible only by the Cron service, but I got a KeyError for {} -- try invoking it from <a href="{}"> the GCP console / AppEngine / taskqueues </a></html>'.format( e, 'http://console.cloud.google.com/appengine/taskqueues?tab=CRON') logging.info('Rejected non-Cron request') return status @app.errorhandler(500) def server_error(e): logging.exception('An error occurred during a request.') return """ An internal error occurred: <pre>{}</pre> See logs for full stacktrace. """.format(e), 500 if __name__ == '__main__': app.run(host='0.0.0.0', port=8080, debug=True) # [END app]

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0.033426

0.030084

0.017827

0

0.011199

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2,667

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32.52439

0.826412

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0

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0.384697

0.014346

0

1

0.071429

false

0

0.119048

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0.261905

0

null

0

null

0

1

0

b9c731695680778a55c685fcfc15ab5e3eccf437

5,438

py

Python

dramkit/_tmp/VMD.py

Genlovy-Hoo/dramkit

fa3d2f35ebe9effea88a19e49d876b43d3c5c4c7

[ "MIT" ]

null

dramkit/_tmp/VMD.py

Genlovy-Hoo/dramkit

fa3d2f35ebe9effea88a19e49d876b43d3c5c4c7

[ "MIT" ]

null

dramkit/_tmp/VMD.py

Genlovy-Hoo/dramkit

fa3d2f35ebe9effea88a19e49d876b43d3c5c4c7

[ "MIT" ]

null

# -*- coding: utf-8 -*- import numpy as np def vmd( signal, alpha, tau, K, DC, init, tol): ''' 用VMD分解算法时只要把信号输入进行分解就行了，只是对信号进行分解，和采样频率没有关系， VMD的输入参数也没有采样频率。 VMD分解出的各分量在输出量 u 中，这个和信号的长度、信号的采样频率没有关系。迭代时各分量的中心频率在输出量omega，可以用2*pi/fs*omega求出中心频率，但迭代时的频率是变化的。 Input and Parameters: signal - the time domain signal (1D) to be decomposed alpha - the balancing parameter of the data-fidelity constraint tau - time-step of the dual ascent ( pick 0 for noise-slack ) K - the number of modes to be recovered DC - true if the first mode is put and kept at DC (0-freq) init - 0 = all omegas start at 0 1 = all omegas start uniformly distributed 2 = all omegas initialized randomly tol - tolerance of convergence criterion; typically around 1e-6 Output: u - the collection of decomposed modes u_hat - spectra of the modes omega - estimated mode center-frequencies ''' # Period and sampling frequency of input signal #分解算法中的采样频率和时间是标准化的，分解信号的采样时间为1s,然后就得到相应的采样频率。采样时间间隔：1/ length(signal)，频率： length(signal)。 save_T = len(signal) fs = 1 / save_T # extend the signal by mirroring镜像延拓 T = save_T f_mirror = [] temp = signal[0:T//2] f_mirror.extend(temp[::-1]) #temp[::-1] 倒序排列 f_mirror.extend(signal) temp = signal[T//2:T] f_mirror.extend(temp[::-1]) f = f_mirror # Time Domain 0 to T (of mirrored signal) T = len(f) t = [(i + 1) / T for i in range(T)] # 列表从1开始 # Spectral Domain discretization #freqs 进行移位是由于进行傅里叶变换时，会有正负对称的频率，分析时一般只有正频率，所以看到的频谱图是没有负频率的 freqs = np.array( [i - 0.5 - 1 / T for i in t] ) # Maximum number of iterations (if not converged yet, then it won't anyway) N = 500 # For future generalizations: individual alpha for each mode Alpha = alpha * np.ones(K) # Construct and center f_hat transformed = np.fft.fft(f) # 使用fft函数对信号进行快速傅里叶变换。 f_hat = np.fft.fftshift(transformed) # 使用fftshift函数进行移频操作。 f_hat_plus = f_hat f_hat_plus[0:T // 2] = 0 # f_hat_plus[0:T] = 1 #????????????????????????????//////////// # matrix keeping track of every iterant // could be discarded for mem u_hat_plus = [np.zeros((N, len(freqs)), dtype=complex) for i in range(K)] # Initialization of omega_k omega_plus = np.zeros((N, K)) if init == 1: for i in range(K): omega_plus[0, i] = (0.5 / K) * i elif init == 2: omega_plus[0, :] = np.sort(np.exp(np.log(fs) + (np.log(0.5) - np.log(fs)) * np.random.rand(K))) else: omega_plus[0, :] = 0 # if DC mode imposed, set its omega to 0 if DC: omega_plus[0, 0] = 0 # start with empty dual variables lambda_hat = np.zeros( (N, len(freqs)), dtype=complex) # other inits eps = 2.2204e-16 # python里没有eps功能 uDiff = tol + eps # update step n = 1 # loop counter sum_uk = 0 # accumulator #----------- Main loop for iterative updates---------- while (uDiff > tol and n < N ): #not converged and below iterations limit #update first mode accumulator k = 0 sum_uk = u_hat_plus[K-1][n-1,:]+ sum_uk - u_hat_plus[0][n-1,:] #sum_uk 一直都等于0（1,2000）???????????????? #update spectrum of first mode through Wiener filter of residuals u_hat_plus[k][n,:] = (f_hat_plus - sum_uk - lambda_hat[n-1,:]/2)/(1+Alpha[k]*(freqs - omega_plus[n-1,k])**2) #update first omega if not held at 0 if not DC: omega_plus[n,k] = (freqs[T//2:T]*np.mat(np.abs(u_hat_plus[k][n, T//2:T])**2).H)/np.sum(np.abs(u_hat_plus[k][n,T//2:T])**2) #update of any other mode for k in range(K-1): #accumulator sum_uk = u_hat_plus[k][n,:] + sum_uk - u_hat_plus[k+1][n-1,:] #mode spectrum u_hat_plus[k+1][n,:] = (f_hat_plus - sum_uk - lambda_hat[n-1,:]/2)/(1+Alpha[k+1]*(freqs - omega_plus[n-1,k+1])**2) #center frequencies omega_plus[n,k+1] = (freqs[T//2:T]*np.mat(np.abs(u_hat_plus[k+1][n, T//2:T])**2).H)/np.sum(np.abs(u_hat_plus[k+1][n,T//2:T])**2) #Dual ascent lambda_hat[n,:] = lambda_hat[n-1,:] + tau*(np.sum([ u_hat_plus[i][n,:] for i in range(K)],0) - f_hat_plus) #loop counter n = n+1 #converged yet? uDiff = eps for i in range(K): uDiff = uDiff + 1/T*(u_hat_plus[i][n-1,:]-u_hat_plus[i][n-2,:])*np.mat((u_hat_plus[i][n-1,:]-u_hat_plus[i][n-2,:]).conjugate()).H uDiff = np.abs(uDiff) # ------ Postprocessing and cleanup------- #discard empty space if converged early N = min(N,n) omega = omega_plus[0:N,:] #Signal reconstruction u_hat = np.zeros((T, K), dtype=complex) temp = [u_hat_plus[i][N-1,T//2:T] for i in range(K) ] u_hat[T//2:T,:] = np.squeeze(temp).T temp = np.squeeze(np.mat(temp).conjugate()) u_hat[1:(T//2+1),:] = temp.T[::-1] u_hat[0,:] = (u_hat[-1,:]).conjugate() u = np.zeros((K,len(t))) for k in range(K): u[k,:]=np.real(np.fft.ifft(np.fft.ifftshift(u_hat[:,k]))) #remove mirror part u = u[:,T//4:3*T//4] #recompute spectrum u_hat = np.zeros((T//2, K), dtype=complex) for k in range(K): u_hat[:,k]= np.squeeze( np.mat( np.fft.fftshift(np.fft.fft(u[k,:])) ).H) return u, u_hat, omega

37.503448

141

0.580912

878

5,438

3.494305

0.252847

0.035202

0.044329

0.026402

0.203716

0.133963

0.102999

0.084746

0.073664

0

0.029659

0.255976

5,438

144

142

37.763889

0.728621

0.400331

0

0.086957

0

1

0.014493

false

0

0.014493

0

0.043478

0

null

0

null

0

1

0

b9c81413c2bd63d72d0731352d31911ef52240f6

480

py

Python

forum/main.py

asmaasalih/my_project

89183d7a2578fa302e94ea29570ab527e9ca47b5

[ "MIT" ]

1

2018-03-21T07:51:36.000Z

forum/main.py

asmaasalih/my_project

89183d7a2578fa302e94ea29570ab527e9ca47b5

[ "MIT" ]

null

forum/main.py

asmaasalih/my_project

89183d7a2578fa302e94ea29570ab527e9ca47b5

[ "MIT" ]

null

import models import stores member1 =models.Member("ahmed",33) member2 =models.Member("mohamed",30) post1=models.Post("Post1", "Content1") post2= models.Post("Post2", "Content2") post3= models.Post("Post3", "Content3") #member store member_store=stores.MemberStore() member_store.add(member1) member_store.add(member2) print (member_store.get_all()) post_store=stores.PostStore() post_store.add(post1) post_store.add(post2) post_store.add(post3) print (post_store.get_all())

20.869565

39

0.772917

69

480

5.217391

0.347826

0.152778

0.1

0

0.044843

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480

22

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0

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0

1

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false

0

0.125

0

0.125

0

null

0

null

0

1

0

b9c964b752a9622a17123202e7aae50d1718a48a

1,345

py

Python

question3.py

nosisky/algo-solution

a9276f73ba63b1a0965c194885aea6cadfab0e0b

[ "MIT" ]

1

2019-08-14T12:32:49.000Z

question3.py

nosisky/algo-solution

a9276f73ba63b1a0965c194885aea6cadfab0e0b

[ "MIT" ]

null

question3.py

nosisky/algo-solution

a9276f73ba63b1a0965c194885aea6cadfab0e0b

[ "MIT" ]

null

# A string S consisting of N characters is considered to be properly nested if any of the following conditions is true: # S is empty; # S has the form "(U)" or "[U]" or "{U}" where U is a properly nested string; S has the form "VW" where V and W are properly nested strings. # For example, the string "{[()()]}" is properly nested but "([)()]" is not. # Write a function: # int solution(char *S); # that, given a string S consisting of N characters, returns 1 if S is properly nested and 0 otherwise. # For example, given S = "{[()()]}", the function should return 1 and given S = "([)()]", the function should return 0, as explained above. # Assume that: # N is an integer within the range [0..200,000]; # string S consists only of the following characters: "(", "{", "[", "]", "}" and/or ")". Complexity: # expected worst-case time complexity is O(N); # expected worst-case space complexity is O(N) (not counting the storage required for input arguments). def solution(s): sets = dict(zip('({[', ')}]')) if(not isinstance(s, str)): return "Invalid input" collector = [] for bracket in s: if(bracket in sets): collector.append(sets[bracket]) elif bracket not in(sets.values()): return "Invalid input" elif (bracket != collector.pop()): return False return not collector print(solution("()[]{}"))

42.03125

140

0.66171

203

1,345

4.384236

0.433498

0.078652

0.017978

0.040449

0.134831

0.069663

0

0.010148

0.194052

1,345

31

141

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0.071429

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null

0

null

0

1

0

b9ca4ff833bf2ee267f7f1b8ecf69069cd8c4b31

1,996

py

Python

Teil_27_Game_of_Life_3d.py

chrMenzel/A-beautiful-code-in-Python

92ee43c1fb03c299384d4de8bebb590c5ba1b623

[ "MIT" ]

50

2018-12-23T15:46:16.000Z

2022-03-28T15:49:59.000Z

Teil_27_Game_of_Life_3d.py

chrMenzel/A-beautiful-code-in-Python

92ee43c1fb03c299384d4de8bebb590c5ba1b623

[ "MIT" ]

9

2018-12-03T10:31:29.000Z

2022-01-20T14:41:33.000Z

Teil_27_Game_of_Life_3d.py

chrMenzel/A-beautiful-code-in-Python

92ee43c1fb03c299384d4de8bebb590c5ba1b623

[ "MIT" ]

69

2019-02-02T11:59:09.000Z

2022-03-28T15:54:28.000Z

import bpy import random as rnd from collections import Counter import itertools as iter feld_von, feld_bis = -4, 4 spielfeld_von, spielfeld_bis = feld_von-6, feld_bis+6 anz = int((feld_bis-feld_von)**3*.3) spielfeld = {(rnd.randint(feld_von, feld_bis), rnd.randint( feld_von, feld_bis), rnd.randint(feld_von, feld_bis)) for _ in range(anz)} animate_frame = 8 def nachbarn(pos): for x,y,z in iter.product(range(-1,2), repeat = 3): if z == y == x == 0: continue yield pos[0]+x, pos[1]+y, pos[2]+z def nächsteGeneration(spielfeld): nachb = Counter([p for pos in spielfeld for p in nachbarn(pos)]) return {pos for pos, anz in nachb.items() if anz == 6 or (anz in (5, 6, 7, 8) and pos in spielfeld)} def scale_rotate(ob, scale, rot, fr): ob.scale = (scale, scale, scale) ob.rotation_euler.rotate_axis("Z", rot) ob.keyframe_insert(data_path='rotation_euler', frame=fr) ob.keyframe_insert(data_path='scale', frame=fr) bpy.ops.mesh.primitive_cube_add(size=0.001, location=(0, 0, 0)) orig_cube = bpy.context.active_object n = "cube" m = orig_cube.data.copy() cubes = {} for x,y,z in iter.product(range(spielfeld_von,spielfeld_bis), repeat = 3): o = bpy.data.objects.new(n, m) o.location = (x, y, z) cubes[x, y, z] = o bpy.context.collection.objects.link(o) o.select_set(False) for i in range(200): print(f'Durchlauf No. {i}, Anz. Zellen = {len(spielfeld)}') spielfeld2 = nächsteGeneration(spielfeld) dead = spielfeld - spielfeld2 new = spielfeld2 - spielfeld spielfeld = spielfeld2 if not new and not dead: break for zelle in new | dead: if zelle not in cubes: continue ob = cubes[zelle] if zelle in new: scale_rotate(ob, 0.001, -3.141/2, (i-1)*animate_frame) scale_rotate(ob, 750, 3.141/2, i * animate_frame) else: scale_rotate(ob, 750, 3.141/2, (i-1) * animate_frame) scale_rotate(ob, 0.001, -3.141/2, i * animate_frame) if not spielfeld: break bpy.context.scene.frame_current = 1

28.927536

102

0.67986

341

1,996

3.859238

0.302053

0.031915

0.049392

0.042553

0.234043

0.197568

0.179331

0.12386

0.103343

0

0.043372

0.17986

1,996

68

103

29.352941

0.760538

0

0.037037

0

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0

1

0.055556

false

0

0.074074

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0.148148

0.018519

0

null

0

null

0

1

0

b9ca98991068e30844d7bcc8e336f70de5eef5a9

1,824

py

Python

power_perceiver/xr_batch_processor/reduce_num_pv_systems.py

openclimatefix/power_perceiver

bafcdfaf6abf42fbab09da641479f74709ddd395

[ "MIT" ]

null

power_perceiver/xr_batch_processor/reduce_num_pv_systems.py

openclimatefix/power_perceiver

bafcdfaf6abf42fbab09da641479f74709ddd395

[ "MIT" ]

33

2022-02-16T07:51:41.000Z

2022-03-31T11:24:11.000Z

power_perceiver/xr_batch_processor/reduce_num_pv_systems.py

openclimatefix/power_perceiver

bafcdfaf6abf42fbab09da641479f74709ddd395

[ "MIT" ]

null

from dataclasses import dataclass import numpy as np import xarray as xr from power_perceiver.load_prepared_batches.data_sources import PV from power_perceiver.load_prepared_batches.data_sources.prepared_data_source import XarrayBatch @dataclass class ReduceNumPVSystems: """Reduce the number of PV systems per example to `requested_num_pv_systems`. Randomly select PV systems for each example. If there are less PV systems available than requested, then randomly sample with duplicates allowed. This is implemented as an xr_batch_processor so it can run after SelectPVSystemsNearCenterOfImage. """ requested_num_pv_systems: int def __post_init__(self): self.rng = np.random.default_rng() # Seeded by seed_rngs worker_init_function def __call__(self, xr_batch: XarrayBatch) -> XarrayBatch: pv_batch = xr_batch[PV] num_examples = len(pv_batch.example) selection = np.zeros(shape=(num_examples, self.requested_num_pv_systems), dtype=np.int32) for example_i in range(num_examples): pv_mask_for_example = pv_batch.pv_mask.isel(example=example_i).values all_indicies = np.nonzero(pv_mask_for_example)[0] # Only allow a PV system to be chosen multiple times for this example if there are # less available PV systems than requested PV systems. replace = len(all_indicies) < self.requested_num_pv_systems chosen_indicies = self.rng.choice( all_indicies, size=self.requested_num_pv_systems, replace=replace ) selection[example_i] = chosen_indicies selection = xr.DataArray(selection, dims=("example", "pv_system")) pv_batch = pv_batch.isel(pv_system=selection) xr_batch[PV] = pv_batch return xr_batch

39.652174

97

0.721491

247

1,824

5.044534

0.421053

0.072231

0.05618

0.08427

0.170947

0.077047

0

0.002096

0.215461

1,824

45

98

40.533333

0.868623

0.272478

0

0.012327

0

1

0.076923

false

0

0.192308

0

0.384615

0

null

0

null

0

1

0

b9cc65aafe29eb9820f902e036880e65947e1e2d

857

py

Python

HelloWorld_python/log/demo_log_3.py

wang153723482/HelloWorld_my

b8642ad9742f95cfebafc61f25b00e917485e50c

[ "Apache-2.0" ]

null

HelloWorld_python/log/demo_log_3.py

wang153723482/HelloWorld_my

b8642ad9742f95cfebafc61f25b00e917485e50c

[ "Apache-2.0" ]

null

HelloWorld_python/log/demo_log_3.py

wang153723482/HelloWorld_my

b8642ad9742f95cfebafc61f25b00e917485e50c

[ "Apache-2.0" ]

null

#encoding=utf8 # 按天生成文件 import logging import time from logging.handlers import TimedRotatingFileHandler #---------------------------------------------------------------------- if __name__ == "__main__": logFilePath = "timed_test.log" logger = logging.getLogger("YouLoggerName") logger.setLevel(logging.INFO) handler = TimedRotatingFileHandler(logFilePath, when="d", interval=1, backupCount=7) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) handler.setLevel(logging.INFO) logger.addHandler(handler) for i in range(6): logger.info("This is a info!") logger.debug("This is a debug!") # time.sleep(61)

29.551724

89

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77

857

5.857143

0.597403

0.066519

0.084257

0

0.009756

0.28238

857

29

90

29.551724

0.723577

0.124854

0

0.159732

0

1

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false

0

0.166667

0

0.166667

0

null

0

null

0

1

0

b9cda5cbb2749647d6a78abf80d9eb5c24205425

341

py

Python

tests/test_gen_epub.py

ffreemt/tmx2epub

55a59cb2a9b7f42031a65f64c29e5c43fdb487ea

[ "MIT" ]

null

tests/test_gen_epub.py

ffreemt/tmx2epub

55a59cb2a9b7f42031a65f64c29e5c43fdb487ea

[ "MIT" ]

null

tests/test_gen_epub.py

ffreemt/tmx2epub

55a59cb2a9b7f42031a65f64c29e5c43fdb487ea

[ "MIT" ]

null

""" test gen_epub. """ from tmx2epub.gen_epub import gen_epub def test_gen_epub2(): """ test_gen_epub2. """ from pathlib import Path infile = r"tests\2.tmx" stem = Path(infile).absolute().stem outfile = f"{Path(infile).absolute().parent / stem}.epub" assert gen_epub(infile, debug=True) == outfile # assert 0

22.733333

61

0.653959

48

341

4.479167

0.5

0.130233

0.111628

0

0.018315

0.199413

341

14

62

24.357143

0.769231

0.123167

0

0.192308

0.108392

0

0.142857

1

0.142857

false

0

0.285714

0

0.428571

0

null

0

null

0

1

0

b9cde2fbd07898c518510cadb194827f6566c927

716

py

Python

pub_sub/python/http/checkout/app.py

amulyavarote/quickstarts

c21a8f58d515b28eaa8a3680388fa06995c2331b

[ "Apache-2.0" ]

null

pub_sub/python/http/checkout/app.py

amulyavarote/quickstarts

c21a8f58d515b28eaa8a3680388fa06995c2331b

[ "Apache-2.0" ]

null

pub_sub/python/http/checkout/app.py

amulyavarote/quickstarts

c21a8f58d515b28eaa8a3680388fa06995c2331b

[ "Apache-2.0" ]

null

import json import time import random import logging import requests import os logging.basicConfig(level=logging.INFO) base_url = os.getenv('BASE_URL', 'http://localhost') + ':' + os.getenv( 'DAPR_HTTP_PORT', '3500') PUBSUB_NAME = 'order_pub_sub' TOPIC = 'orders' logging.info('Publishing to baseURL: %s, Pubsub Name: %s, Topic: %s' % ( base_url, PUBSUB_NAME, TOPIC)) for i in range(1, 10): order = {'orderId': i} # Publish an event/message using Dapr PubSub via HTTP Post result = requests.post( url='%s/v1.0/publish/%s/%s' % (base_url, PUBSUB_NAME, TOPIC), json=order ) logging.info('Published data: ' + json.dumps(order)) time.sleep(1)

25.571429

72

0.642458

100

716

4.49

0.5

0.062361

0.035635

0.062361

0.10245

0

0.017825

0.21648

716

27

73

26.518519

0.782531

0.078212

0

0.241641

0.031915

0

1

0

false

0

0.285714

0

0.285714

0

null

0

null

0

1

0

b9ce404499c062b33e8623b446d27dfebe6f033f

52,312

py

Python

jj.py

smailedge/pro

f86347d4368bc97aa860b37caa9ba10e84a93738

[ "Unlicense" ]

1

2019-08-14T04:17:06.000Z

jj.py

smailedge/pro

f86347d4368bc97aa860b37caa9ba10e84a93738

[ "Unlicense" ]

null

jj.py

smailedge/pro

f86347d4368bc97aa860b37caa9ba10e84a93738

[ "Unlicense" ]

7

2018-10-27T11:58:45.000Z

2021-02-11T19:45:30.000Z

# -*- coding: utf-8 -*- from linepy import * from datetime import datetime from time import sleep from humanfriendly import format_timespan, format_size, format_number, format_length import time, random, sys, json, codecs, threading, glob, re, string, os, requests, subprocess, six, ast, pytz, urllib, urllib.parse #==============================================================================# botStart = time.time() cl = LINE() #cl = LINE("TOKEN KAMU") #cl = LINE("Email","Password") cl.log("Auth Token : " + str(cl.authToken)) channelToken = cl.getChannelResult() cl.log("Channel Token : " + str(channelToken)) clMID = cl.profile.mid clProfile = cl.getProfile() lineSettings = cl.getSettings() oepoll = OEPoll(cl) #==============================================================================# readOpen = codecs.open("read.json","r","utf-8") settingsOpen = codecs.open("temp.json","r","utf-8") read = json.load(readOpen) settings = json.load(settingsOpen) myProfile = { "displayName": "", "statusMessage": "", "pictureStatus": "" } msg_dict = {} bl = [""] myProfile["displayName"] = clProfile.displayName myProfile["statusMessage"] = clProfile.statusMessage myProfile["pictureStatus"] = clProfile.pictureStatus #==============================================================================# def restartBot(): print ("[ INFO ] BOT RESETTED") backupData() python = sys.executable os.execl(python, python, *sys.argv) def backupData(): try: backup = settings f = codecs.open('temp.json','w','utf-8') json.dump(backup, f, sort_keys=True, indent=4, ensure_ascii=False) backup = read f = codecs.open('read.json','w','utf-8') json.dump(backup, f, sort_keys=True, indent=4, ensure_ascii=False) return True except Exception as error: logError(error) return False def logError(text): cl.log("[ ERROR ] " + str(text)) time_ = datetime.now() with open("errorLog.txt","a") as error: error.write("\n[%s] %s" % (str(time), text)) def sendMessageWithMention(to, mid): try: aa = '{"S":"0","E":"3","M":'+json.dumps(mid)+'}' text_ = '@x ' cl.sendMessage(to, text_, contentMetadata={'MENTION':'{"MENTIONEES":['+aa+']}'}, contentType=0) except Exception as error: logError(error) def helpmessage(): helpMessage = """╔═════════════ ╠♥ ✿✿✿ 十香の特製Bot ✿✿✿ ♥ ╠SR 設定已讀點 ╠LR 查看誰已讀 ╠Nk @ 標註踢人 ╠Nk 全部再見 ╠══✪〘其他功能略〙✪═══ """ return helpMessage wait = { "share":False, "sender" :{}, } admin =['ud5ff1dff426cf9e3030c7ac2a61512f0','ua10c2ad470b4b6e972954e1140ad1891',clMID] owners = ["ua10c2ad470b4b6e972954e1140ad1891","ud5ff1dff426cf9e3030c7ac2a61512f0"] #if clMID not in owners: # python = sys.executable # os.execl(python, python, *sys.argv) #==============================================================================# def lineBot(op): try: if op.type == 0: print ("[ 0 ] END OF OPERATION") return if op.type == 5: print ("[ 5 ] NOTIFIED ADD CONTACT") if settings["autoAdd"] == True: cl.sendMessage(op.param1, "感謝您加入本帳為好友w".format(str(cl.getContact(op.param1).displayName))) if op.type == 13: print ("[ 13 ] NOTIFIED INVITE GROUP") group = cl.getGroup(op.param1) if settings["autoJoin"] == True: cl.acceptGroupInvitation(op.param1) if op.type == 19: if op.param2 not in owners: if op.param2 in owners: pass elif wait["protect"] == True: settings["blacklist"][op.param2] = True cl.kickoutFromGroup(op.param1,[op.param2]) else: cl.sendMessage(op.param1,"") else: cl.sendMessage(op.param1,"") if op.type == 24: print ("[ 24 ] NOTIFIED LEAVE ROOM") if settings["autoLeave"] == True: cl.leaveRoom(op.param1) if op.type == 25 or op.type == 26: K0 = admin msg = op.message if wait["share"] == True: K0 = msg._from else: K0 = admin # if op.type == 25: # to = msg.to # receiver = str(to.displayName) # print ("send" + receiver + str(text.lower())) # if op.type == 26: # to = msg._from # sender = str(to.displayName) # print ("receiver" + sender + str(text.lower())) if op.type == 26 or op.type == 25: print ("[ 25 ] SEND MESSAGE") msg = op.message text = msg.text msg_id = msg.id receiver = msg.to sender = msg._from if msg.toType == 0: if sender != cl.profile.mid: to = sender else: to = receiver else: to = receiver if msg.contentType == 0: if text is None: return #==============================================================================# if sender in K0: if text.lower() == 'help': helpMessage = helpmessage() cl.sendMessage(to, str(helpMessage)) cl.sendContact(to,"u0a59c278b1529476ddb210cb5e827ffc") cl.sendContact(to,"ufb30e2203f44bc7b72e28b09a88c9bbd") #==============================================================================# elif text.lower() == 'speed': start = time.time() cl.sendMessage(to, "計算中...") elapsed_time = time.time() - start cl.sendMessage(to,format(str(elapsed_time))) elif text.lower() == 'restart': cl.sendMessage(to, "重新啟動中...") time.sleep(5) cl.sendMessage(to, "重啟成功，請重新登入") restartBot() elif text.lower() == 'runtime': timeNow = time.time() runtime = timeNow - botStart runtime = format_timespan(runtime) cl.sendMessage(to, "系統已運作 {}".format(str(runtime))) elif text.lower() == 'about': try: arr = [] owner = "ua10c2ad470b4b6e972954e1140ad1891" creator = cl.getContact(owner) contact = cl.getContact(clMID) grouplist = cl.getGroupIdsJoined() contactlist = cl.getAllContactIds() blockedlist = cl.getBlockedContactIds() ret_ = "╔══[ 關於使用者 ]" ret_ += "\n╠ 使用者名稱 : {}".format(contact.displayName) ret_ += "\n╠ 群組數 : {}".format(str(len(grouplist))) ret_ += "\n╠ 好友數 : {}".format(str(len(contactlist))) ret_ += "\n╠ 已封鎖 : {}".format(str(len(blockedlist))) ret_ += "\n╠══[ 關於本bot ]" ret_ += "\n╠ 版本 : 最新" ret_ += "\n╠ 製作者 : {}".format(creator.displayName) ret_ += "\n╚══[ 感謝您的使用 ]" cl.sendMessage(to, str(ret_)) except Exception as e: cl.sendMessage(msg.to, str(e)) #==============================================================================# elif text.lower() == 'set': try: ret_ = "╔══[ 狀態 ]" if settings["autoAdd"] == True: ret_ += "\n╠ Auto Add ✅" else: ret_ += "\n╠ Auto Add ❌" if settings["autoJoin"] == True: ret_ += "\n╠ Auto Join ✅" else: ret_ += "\n╠ Auto Join ❌" if settings["autoLeave"] == True: ret_ += "\n╠ Auto Leave ✅" else: ret_ += "\n╠ Auto Leave ❌" if settings["autoRead"] == True: ret_ += "\n╠ Auto Read ✅" else: ret_ += "\n╠ Auto Read ❌" if settings["reread"] ==True: ret_+="\n╠ Reread ✅" else: ret_ += "\n╠ Reread ❌" ret_ += "\n╚══[ Finish ]" cl.sendMessage(to, str(ret_)) except Exception as e: cl.sendMessage(msg.to, str(e)) elif text.lower() == 'autoadd on': settings["autoAdd"] = True cl.sendMessage(to, "Auto Add on success") elif text.lower() == 'autoadd off': settings["autoAdd"] = False cl.sendMessage(to, "Auto Add off success") elif text.lower() == 'autojoin on': settings["autoJoin"] = True cl.sendMessage(to, "Auto Join on success") elif text.lower() == 'autojoin off': settings["autoJoin"] = False cl.sendMessage(to, "Auto Join off success") elif text.lower() == 'autoleave on': settings["autoLeave"] = True cl.sendMessage(to, "Auto Leave on success") elif text.lower() == 'autojoin off': settings["autoLeave"] = False cl.sendMessage(to, "Auto Leave off success") elif text.lower() == 'autoread on': settings["autoRead"] = True cl.sendMessage(to, "Auto Read on success") elif text.lower() == 'autoread off': settings["autoRead"] = False cl.sendMessage(to, "Auto Read off success") elif text.lower() == 'checksticker on': settings["checkSticker"] = True cl.sendMessage(to, "Berhasil mengaktifkan Check Details Sticker") elif text.lower() == 'checksticker off': settings["checkSticker"] = False cl.sendMessage(to, "Berhasil menonaktifkan Check Details Sticker") elif text.lower() == 'detectmention on': settings["datectMention"] = True cl.sendMessage(to, "Berhasil mengaktifkan Detect Mention") elif text.lower() == 'detectmention off': settings["datectMention"] = False cl.sendMessage(to, "Berhasil menonaktifkan Detect Mention") elif text.lower() == 'reread on': settings["reread"] = True cl.sendMessage(to,"reread on success") elif text.lower() == 'reread off': settings["reread"] = False cl.sendMessage(to,"reread off success") elif text.lower() == 'protect on': settings["protect"] = True cl.sendMessage(to, "Protect on success") elif text.lower() == 'protect off': settings["protect"] = False cl.sendMessage(to, "Protect off success") elif text.lower() == 'share on': wait["share"] = True cl.sendMessage(to, "已開啟分享") elif text.lower() == 'share off': wait["share"] = False cl.sendMessage(to, "已關閉分享") #==============================================================================# elif text.lower() == 'admin ': MENTION =eval(msg.contentMetadata['MENTION']) inkey =MENTION['MENTIONEES'][0]['M'] admin.append(str(inkey)) cl.sendMessage(to,"已新增權限") elif text.lower() == 'demin ': MENTION =eval(msg.contentMetadata['MENTION']) inkey =MENTION['MENTIONEES'][0]['M'] admin.remove(str(inkey)) cl.sendMessage(to,"已停止權限") elif text.lower() == 'adminlist': if admin == []: cl.sendMessage(to,"無擁有權限者!") else: mc = "╔══[ Admin List ]" for mi_d in admin: mc += "\n╠ "+cl.getContact(mi_d).displayName cl.sendMessage(to,mc + "\n╚══[ Finish ]") #==============================================================================# elif text.lower() == 'me': sendMessageWithMention(to, clMID) cl.sendContact(to, clMID) elif text.lower() == 'mymid': cl.sendMessage(msg.to,"[MID]\n" + clMID) elif text.lower() == 'myname': me = cl.getContact(clMID) cl.sendMessage(msg.to,"[Name]\n" + me.displayName) elif text.lower() == 'mytoken': me = cl.getContact(clMID) cl.sendMessage(msg.to,"[StatusMessage]\n" + me.statusMessage) elif text.lower() == 'mypicture': me = cl.getContact(clMID) cl.sendImageWithURL(msg.to,"http://dl.profile.line-cdn.net/" + me.pictureStatus) elif text.lower() == 'myvideoprofile': me = cl.getContact(clMID) cl.sendVideoWithURL(msg.to,"http://dl.profile.line-cdn.net/" + me.pictureStatus + "/vp") elif text.lower() == 'mycover': me = cl.getContact(clMID) cover = cl.getProfileCoverURL(clMID) cl.sendImageWithURL(msg.to, cover) elif msg.text.lower().startswith("contact "): if 'MENTION' in msg.contentMetadata.keys()!= None: names = re.findall(r'@(\w+)', text) mention = ast.literal_eval(msg.contentMetadata['MENTION']) mentionees = mention['MENTIONEES'] lists = [] for mention in mentionees: if mention["M"] not in lists: lists.append(mention["M"]) for ls in lists: contact = cl.getContact(ls) mi_d = contact.mid cl.sendContact(msg.to, mi_d) elif msg.text.lower().startswith("mid "): if 'MENTION' in msg.contentMetadata.keys()!= None: names = re.findall(r'@(\w+)', text) mention = ast.literal_eval(msg.contentMetadata['MENTION']) mentionees = mention['MENTIONEES'] lists = [] for mention in mentionees: if mention["M"] not in lists: lists.append(mention["M"]) ret_ = "[ Mid User ]" for ls in lists: ret_ += "\n" + ls cl.sendMessage(msg.to, str(ret_)) elif msg.text.lower().startswith("name "): if 'MENTION' in msg.contentMetadata.keys()!= None: names = re.findall(r'@(\w+)', text) mention = ast.literal_eval(msg.contentMetadata['MENTION']) mentionees = mention['MENTIONEES'] lists = [] for mention in mentionees: if mention["M"] not in lists: lists.append(mention["M"]) for ls in lists: contact = cl.getContact(ls) cl.sendMessage(msg.to, "[ 名字 ]\n" + contact.displayName) for ls in lists: contact = cl.getContact(ls) cl.sendMessage(msg.to, "[ 個簽 ]\n" + contact.statusMessage) for ls in lists: path = "http://dl.profile.cl.naver.jp/" + cl.getContact(ls).pictureStatus cl.sendImageWithURL(msg.to, str(path)) for ls in lists: path = cl.getProfileCoverURL(ls) pmath = "http://dl.profile.cl.naver.jp/" + cl.getContact(ls).pictureStatus cl.sendImageWithURL(msg.to, path) try: key = eval(msg.contentMetadata["MENTION"]) u = key["MENTIONEES"][0]["M"] cname = cl.getContact(u).displayName cmid = cl.getContact(u).mid cstatus = cl.getContact(u).statusMessage cpic = cl.getContact(u).picturePath cl.sendMessage(receiver, 'Nama : '+cname+'\nMID : '+cmid+'\nStatus Msg : '+cstatus+'\nPicture : http://dl.profile.line.naver.jp'+cpic) cl.sendMessage(receiver, None, contentMetadata={'mid': cmid}, contentType=13) if cl.getContact(u).videoProfile != None: cl.sendVideoWithURL(receiver, 'http://dl.profile.line.naver.jp'+cpic+'/vp.small') else: cl.sendImageWithURL(receiver, 'http://dl.profile.line.naver.jp'+cpic) except Exception as e: cl.sendMessage(receiver, str(e)) if line != None: if 'MENTION' in msg.contentMetadata.keys()!= None: names = re.findall(r'@(\w+)', text) mention = ast.literal_eval(msg.contentMetadata['MENTION']) mentionees = mention['MENTIONEES'] lists = [] for mention in mentionees: if mention["M"] not in lists: lists.append(mention["M"]) for ls in lists: path = cl.getProfileCoverURL(ls) cl.sendImageWithURL(msg.to, str(path)) elif msg.text.lower().startswith("cloneprofile "): if 'MENTION' in msg.contentMetadata.keys()!= None: names = re.findall(r'@(\w+)', text) mention = ast.literal_eval(msg.contentMetadata['MENTION']) mentionees = mention['MENTIONEES'] for mention in mentionees: contact = mention["M"] break try: cl.cloneContactProfile(contact) cl.sendMessage(msg.to, "Berhasil clone member tunggu beberapa saat sampai profile berubah") except: cl.sendMessage(msg.to, "Gagal clone member") elif text.lower() == 'restoreprofile': try: clProfile.displayName = str(myProfile["displayName"]) clProfile.statusMessage = str(myProfile["statusMessage"]) clProfile.pictureStatus = str(myProfile["pictureStatus"]) cl.updateProfileAttribute(8, clProfile.pictureStatus) cl.updateProfile(clProfile) cl.sendMessage(msg.to, "Berhasil restore profile tunggu beberapa saat sampai profile berubah") except: cl.sendMessage(msg.to, "Gagal restore profile") #==============================================================================# elif msg.text.lower().startswith("mimicadd "): targets = [] key = eval(msg.contentMetadata["MENTION"]) key["MENTIONEES"][0]["M"] for x in key["MENTIONEES"]: targets.append(x["M"]) for target in targets: try: settings["mimic"]["target"][target] = True cl.sendMessage(msg.to,"已加入模仿名單!") break except: cl.sendMessage(msg.to,"添加失敗 !") break elif msg.text.lower().startswith("mimicdel "): targets = [] key = eval(msg.contentMetadata["MENTION"]) key["MENTIONEES"][0]["M"] for x in key["MENTIONEES"]: targets.append(x["M"]) for target in targets: try: del settings["模仿名單"]["target"][target] cl.sendMessage(msg.to,"刪除成功 !") break except: cl.sendMessage(msg.to,"刪除失敗 !") break elif text.lower() == 'mimiclist': if settings["mimic"]["target"] == {}: cl.sendMessage(msg.to,"未設定模仿目標") else: mc = "╔══[ Mimic List ]" for mi_d in settings["mimic"]["target"]: mc += "\n╠ "+cl.getContact(mi_d).displayName cl.sendMessage(msg.to,mc + "\n╚══[ Finish ]") elif "mimic" in msg.text.lower(): sep = text.split(" ") mic = text.replace(sep[0] + " ","") if mic == "on": if settings["mimic"]["status"] == False: settings["mimic"]["status"] = True cl.sendMessage(msg.to,"Reply Message on") elif mic == "off": if settings["mimic"]["status"] == True: settings["mimic"]["status"] = False cl.sendMessage(msg.to,"Reply Message off") #==============================================================================# elif text.lower() == 'groupcreator': group = cl.getGroup(to) GS = group.creator.mid cl.sendContact(to, GS) elif text.lower() == 'groupid': gid = cl.getGroup(to) cl.sendMessage(to, "[ID Group : ]\n" + gid.id) elif text.lower() == 'grouppicture': group = cl.getGroup(to) path = "http://dl.profile.line-cdn.net/" + group.pictureStatus cl.sendImageWithURL(to, path) elif text.lower() == 'groupname': gid = cl.getGroup(to) cl.sendMessage(to, "[群組名稱 : ]\n" + gid.name) elif text.lower() == 'grouplink': if msg.toType == 2: group = cl.getGroup(to) if group.preventedJoinByTicket == False: ticket = cl.reissueGroupTicket(to) cl.sendMessage(to, "[ Group Ticket ]\nhttps://cl.me/R/ti/g/{}".format(str(ticket))) else: cl.sendMessage(to, "Grouplink未開啟 {}openlink".format(str(settings["keyCommand"]))) elif text.lower() == 'link off': if msg.toType == 2: group = cl.getGroup(to) if group.preventedJoinByTicket == False: cl.sendMessage(to, "群組網址已關") else: group.preventedJoinByTicket = False cl.updateGroup(group) cl.sendMessage(to, "關閉成功") elif text.lower() == 'link on': if msg.toType == 2: group = cl.getGroup(to) if group.preventedJoinByTicket == True: cl.sendMessage(to, "群組網址已開") else: group.preventedJoinByTicket = True cl.updateGroup(group) cl.sendMessage(to, "開啟成功") elif text.lower() == 'groupinfo': group = cl.getGroup(to) try: gCreator = group.creator.displayName except: gCreator = "不明" if group.invitee is None: gPending = "0" else: gPending = str(len(group.invitee)) if group.preventedJoinByTicket == True: gQr = "關閉" gTicket = "無" else: gQr = "開啟" gTicket = "https://cl.me/R/ti/g/{}".format(str(cl.reissueGroupTicket(group.id))) path = "http://dl.profile.line-cdn.net/" + group.pictureStatus ret_ = "╔══[ Group Info ]" ret_ += "\n╠ 群組名稱 : {}".format(str(group.name)) ret_ += "\n╠ 群組 Id : {}".format(group.id) ret_ += "\n╠ 創建者 : {}".format(str(gCreator)) ret_ += "\n╠ 群組人數 : {}".format(str(len(group.members))) ret_ += "\n╠ 邀請中 : {}".format(gPending) ret_ += "\n╠ 網址狀態 : {}".format(gQr) ret_ += "\n╠ 群組網址 : {}".format(gTicket) ret_ += "\n╚══[ Finish ]" cl.sendMessage(to, str(ret_)) cl.sendImageWithURL(to, path) elif text.lower() == 'groupmemberlist': if msg.toType == 2: group = cl.getGroup(to) ret_ = "╔══[ 成員名單 ]" no = 0 + 1 for mem in group.members: ret_ += "\n╠ {}. {}".format(str(no), str(mem.displayName)) no += 1 ret_ += "\n╚══[ 全部成員共 {} 人]".format(str(len(group.members))) cl.sendMessage(to, str(ret_)) elif text.lower() == 'grouplist': groups = cl.groups ret_ = "╔══[ Group List ]" no = 0 + 1 for gid in groups: group = cl.getGroup(gid) ret_ += "\n╠ {}. {} | {}".format(str(no), str(group.name), str(len(group.members))) no += 1 ret_ += "\n╚══[ Total {} Groups ]".format(str(len(groups))) cl.sendMessage(to, str(ret_)) elif msg.text.lower().startswith("nk "): targets = [] key = eval(msg.contentMetadata["MENTION"]) key["MENTIONEES"][0]["M"] for x in key["MENTIONEES"]: targets.append(x["M"]) for target in targets: try: cl.sendMessage(to,"Fuck you") cl.kickoutFromGroup(msg.to,[target]) except: cl.sendMessage(to,"Error") elif msg.text.lower().startswith("ri "): targets = [] key = eval(msg.contentMetadata["MENTION"]) key["MENTIONEES"][0]["M"] for x in key["MENTIONEES"]: targets.append(x["M"]) for target in targets: try: cl.sendMessage(to,"來回機票一張ww") cl.kickoutFromGroup(msg.to,[target]) cl.inviteIntoGroup(to,[target]) except: cl.sendMessage(to,"Error") elif text.lower() == 'nk': if msg.toType == 2: print ("[ 19 ] KICK ALL MEMBER") _name = msg.text.replace("Byeall","") gs = cl.getGroup(msg.to) cl.sendMessage(msg.to,"Sorry guys") targets = [] for g in gs.members: if _name in g.displayName: targets.append(g.mid) if targets == []: cl.sendMessage(msg.to,"Not Found") else: for target in targets: try: cl.kickoutFromGroup(msg.to,[target]) print (msg.to,[g.mid]) except: cl.sendMessage(msg.to,"") elif ("Gn " in msg.text): if msg.toType == 2: X = cl.getGroup(msg.to) X.name = msg.text.replace("Gn ","") cl.updateGroup(X) else: cl.sendMessage(msg.to,"It can't be used besides the group.") elif text.lower() == 'cancel': if msg.toType == 2: group = cl.getGroup(to) gMembMids = [contact.mid for contact in group.invitee] for _mid in gMembMids: cl.cancelGroupInvitation(msg.to,[_mid]) cl.sendMessage(msg.to,"已取消所有邀請!") elif ("Inv " in msg.text): if msg.toType == 2: midd = msg.text.replace("Inv ","") cl.findAndAddContactsByMid(midd) cl.inviteIntoGroup(to,[midd]) #==============================================================================# elif text.lower() == 'tagall': group = cl.getGroup(msg.to) nama = [contact.mid for contact in group.members] k = len(nama)//100 for a in range(k+1): txt = u'' s=0 b=[] for i in group.members[a*100 : (a+1)*100]: b.append({"S":str(s), "E" :str(s+6), "M":i.mid}) s += 7 txt += u'@Alin \n' cl.sendMessage(to, text=txt, contentMetadata={u'MENTION': json.dumps({'MENTIONEES':b})}, contentType=0) cl.sendMessage(to, "Total {} Mention".format(str(len(nama)))) elif text.lower() == 'sr': tz = pytz.timezone("Asia/Jakarta") timeNow = datetime.now(tz=tz) day = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday","Friday", "Saturday"] hari = ["Minggu", "Senin", "Selasa", "Rabu", "Kamis", "Jumat", "Sabtu"] bulan = ["Januari", "Februari", "Maret", "April", "Mei", "Juni", "Juli", "Agustus", "September", "Oktober", "November", "Desember"] hr = timeNow.strftime("%A") bln = timeNow.strftime("%m") for i in range(len(day)): if hr == day[i]: hasil = hari[i] for k in range(0, len(bulan)): if bln == str(k): bln = bulan[k-1] readTime = hasil + ", " + timeNow.strftime('%d') + " - " + bln + " - " + timeNow.strftime('%Y') + "\nJam : [ " + timeNow.strftime('%H:%M:%S') + " ]" if msg.to in read['readPoint']: try: del read['readPoint'][msg.to] del read['readMember'][msg.to] del read['readTime'][msg.to] except: pass read['readPoint'][msg.to] = msg.id read['readMember'][msg.to] = "" read['readTime'][msg.to] = datetime.now().strftime('%H:%M:%S') read['ROM'][msg.to] = {} with open('read.json', 'w') as fp: json.dump(read, fp, sort_keys=True, indent=4) cl.sendMessage(msg.to,"偵測點已設置") else: try: del read['readPoint'][msg.to] del read['readMember'][msg.to] del read['readTime'][msg.to] except: pass read['readPoint'][msg.to] = msg.id read['readMember'][msg.to] = "" read['readTime'][msg.to] = datetime.now().strftime('%H:%M:%S') read['ROM'][msg.to] = {} with open('read.json', 'w') as fp: json.dump(read, fp, sort_keys=True, indent=4) cl.sendMessage(msg.to, "Set reading point:\n" + readTime) elif text.lower() == 'readcancel': tz = pytz.timezone("Asia/Jakarta") timeNow = datetime.now(tz=tz) day = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday","Friday", "Saturday"] hari = ["Minggu", "Senin", "Selasa", "Rabu", "Kamis", "Jumat", "Sabtu"] bulan = ["Januari", "Februari", "Maret", "April", "Mei", "Juni", "Juli", "Agustus", "September", "Oktober", "November", "Desember"] hr = timeNow.strftime("%A") bln = timeNow.strftime("%m") for i in range(len(day)): if hr == day[i]: hasil = hari[i] for k in range(0, len(bulan)): if bln == str(k): bln = bulan[k-1] readTime = hasil + ", " + timeNow.strftime('%d') + " - " + bln + " - " + timeNow.strftime('%Y') + "\nJam : [ " + timeNow.strftime('%H:%M:%S') + " ]" if msg.to not in read['readPoint']: cl.sendMessage(msg.to,"偵測點已取消") else: try: del read['readPoint'][msg.to] del read['readMember'][msg.to] del read['readTime'][msg.to] except: pass cl.sendMessage(msg.to, "Delete reading point:\n" + readTime) elif text.lower() == 'resetread': tz = pytz.timezone("Asia/Jakarta") timeNow = datetime.now(tz=tz) day = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday","Friday", "Saturday"] hari = ["Minggu", "Senin", "Selasa", "Rabu", "Kamis", "Jumat", "Sabtu"] bulan = ["Januari", "Februari", "Maret", "April", "Mei", "Juni", "Juli", "Agustus", "September", "Oktober", "November", "Desember"] hr = timeNow.strftime("%A") bln = timeNow.strftime("%m") for i in range(len(day)): if hr == day[i]: hasil = hari[i] for k in range(0, len(bulan)): if bln == str(k): bln = bulan[k-1] readTime = hasil + ", " + timeNow.strftime('%d') + " - " + bln + " - " + timeNow.strftime('%Y') + "\nJam : [ " + timeNow.strftime('%H:%M:%S') + " ]" if msg.to in read["readPoint"]: try: del read["readPoint"][msg.to] del read["readMember"][msg.to] del read["readTime"][msg.to] except: pass cl.sendMessage(msg.to, "Reset reading point:\n" + readTime) else: cl.sendMessage(msg.to, "偵測點未設置?") elif text.lower() == 'lr': tz = pytz.timezone("Asia/Jakarta") timeNow = datetime.now(tz=tz) day = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday","Friday", "Saturday"] hari = ["Minggu", "Senin", "Selasa", "Rabu", "Kamis", "Jumat", "Sabtu"] bulan = ["Januari", "Februari", "Maret", "April", "Mei", "Juni", "Juli", "Agustus", "September", "Oktober", "November", "Desember"] hr = timeNow.strftime("%A") bln = timeNow.strftime("%m") for i in range(len(day)): if hr == day[i]: hasil = hari[i] for k in range(0, len(bulan)): if bln == str(k): bln = bulan[k-1] readTime = hasil + ", " + timeNow.strftime('%d') + " - " + bln + " - " + timeNow.strftime('%Y') + "\nJam : [ " + timeNow.strftime('%H:%M:%S') + " ]" if receiver in read['readPoint']: if read["ROM"][receiver].items() == []: cl.sendMessage(receiver,"[ 已讀的人 ]:\nNone") else: chiya = [] for rom in read["ROM"][receiver].items(): chiya.append(rom[1]) cmem = cl.getContacts(chiya) zx = "" zxc = "" zx2 = [] xpesan = '[ 已讀的人 ]:\n' for x in range(len(cmem)): xname = str(cmem[x].displayName) pesan = '' pesan2 = pesan+"@c\n" xlen = str(len(zxc)+len(xpesan)) xlen2 = str(len(zxc)+len(pesan2)+len(xpesan)-1) zx = {'S':xlen, 'E':xlen2, 'M':cmem[x].mid} zx2.append(zx) zxc += pesan2 text = xpesan+ zxc + "\n[ 已讀時間 ]: \n" + readTime try: cl.sendMessage(receiver, text, contentMetadata={'MENTION':str('{"MENTIONEES":'+json.dumps(zx2).replace(' ','')+'}')}, contentType=0) except Exception as error: print (error) pass else: cl.sendMessage(receiver,"尚未設置偵測點") #==============================================================================# elif msg.text.lower().startswith("ban "): targets = [] key = eval(msg.contentMetadata["MENTION"]) key["MENTIONEES"][0]["M"] for x in key["MENTIONEES"]: targets.append(x["M"]) for target in targets: try: settings["blacklist"][target] = True cl.sendMessage(msg.to,"已加入黑單!") break except: cl.sendMessage(msg.to,"添加失敗 !") break elif msg.text.lower().startswith("unban "): targets = [] key = eval(msg.contentMetadata["MENTION"]) key["MENTIONEES"][0]["M"] for x in key["MENTIONEES"]: targets.append(x["M"]) for target in targets: try: del settings["blacklist"][target] cl.sendMessage(msg.to,"刪除成功 !") break except: cl.sendMessage(msg.to,"刪除失敗 !") break elif text.lower() == 'banlist': if settings["blacklist"] == {}: cl.sendMessage(msg.to,"無黑單成員!") else: mc = "╔══[ Black List ]" for mi_d in settings["blacklist"]: mc += "\n╠ "+cl.getContact(mi_d).displayName cl.sendMessage(msg.to,mc + "\n╚══[ Finish ]") elif text.lower() == 'nkban': if msg.toType == 2: group = cl.getGroup(to) gMembMids = [contact.mid for contact in group.members] matched_list = [] for tag in settings["blacklist"]: matched_list+=filter(lambda str: str == tag, gMembMids) if matched_list == []: cl.sendMessage(msg.to,"There was no blacklist user") return for jj in matched_list: cl.kickoutFromGroup(msg.to,[jj]) cl.sendMessage(msg.to,"Blacklist kicked out") elif text.lower() == 'cleanban': settings["blacklist"] == {ok} for mi_d in settings["blacklist"]: try: del settings["blacklist"][mi_d] cl.sendMessage(msg.to,"已清空黑單!") break except: cl.sendMessage(msg.to,"刪除失敗 !") break elif text.lower() == 'banmidlist': if settings["blacklist"] == {}: cl.sendMessage(msg.to,"無黑單成員!") else: mc = "╔══[ Black List ]" for mi_d in settings["blacklist"]: mc += "\n╠ "+mi_d cl.sendMessage(to,mc + "\n╚══[ Finish ]") #==============================================================================# elif "Copy " in msg.text: targets = [] key = eval(msg.contentMetadata["MENTION"]) key["MENTIONEES"][0]["M"] for x in key["MENTIONEES"]: targets.append(x["M"]) for target in targets: try: contact = cl.getContact(target) X = contact.displayName profile = cl.getProfile() profile.displayName = X cl.updateProfile(profile) cl.sendMessage(to, "Success...") Y = contact.statusMessage lol = cl.getProfile() lol.statusMessage = Y cl.updateProfile(lol) P = contact.pictureStatus pic = cl.getProfile() pic.pictureStatus = P cl.updateProfilePicture(P) cl.cloneContactProfile(target) except Exception as e: cl.sendMessage(to, "Failed!") elif text.lower() == 'cc9487': if sender in ['ua10c2ad470b4b6e972954e1140ad1891']: python = sys.executable os.execl(python, python, *sys.argv) else: pass #==============================================================================# elif text.lower() == 'calender': tz = pytz.timezone("Asia/Makassar") timeNow = datetime.now(tz=tz) day = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday","Friday", "Saturday"] hari = ["Minggu", "Senin", "Selasa", "Rabu", "Kamis", "Jumat", "Sabtu"] bulan = ["Januari", "Februari", "Maret", "April", "Mei", "Juni", "Juli", "Agustus", "September", "Oktober", "November", "Desember"] hr = timeNow.strftime("%A") bln = timeNow.strftime("%m") for i in range(len(day)): if hr == day[i]: hasil = hari[i] for k in range(0, len(bulan)): if bln == str(k): bln = bulan[k-1] readTime = hasil + ", " + timeNow.strftime('%d') + " - " + bln + " - " + timeNow.strftime('%Y') + "\nJam : [ " + timeNow.strftime('%H:%M:%S') + " ]" cl.sendMessage(msg.to, readTime) elif "screenshotwebsite" in msg.text.lower(): sep = text.split(" ") query = text.replace(sep[0] + " ","") with requests.session() as web: r = web.get("http://rahandiapi.herokuapp.com/sswebAPI?key=betakey&link={}".format(urllib.parse.quote(query))) data = r.text data = json.loads(data) cl.sendImageWithURL(to, data["result"]) elif "checkdate" in msg.text.lower(): sep = msg.text.split(" ") tanggal = msg.text.replace(sep[0] + " ","") r=requests.get('https://script.google.com/macros/exec?service=AKfycbw7gKzP-WYV2F5mc9RaR7yE3Ve1yN91Tjs91hp_jHSE02dSv9w&nama=ervan&tanggal='+tanggal) data=r.text data=json.loads(data) ret_ = "╔══[ D A T E ]" ret_ += "\n╠ Date Of Birth : {}".format(str(data["data"]["lahir"])) ret_ += "\n╠ Age : {}".format(str(data["data"]["usia"])) ret_ += "\n╠ Birthday : {}".format(str(data["data"]["ultah"])) ret_ += "\n╠ Zodiak : {}".format(str(data["data"]["zodiak"])) ret_ += "\n╚══[ Success ]" cl.sendMessage(to, str(ret_)) elif msg.contentType == 7: if settings["checkSticker"] == True: stk_id = msg.contentMetadata['STKID'] stk_ver = msg.contentMetadata['STKVER'] pkg_id = msg.contentMetadata['STKPKGID'] ret_ = "╔══[ Sticker Info ]" ret_ += "\n╠ STICKER ID : {}".format(stk_id) ret_ += "\n╠ STICKER PACKAGES ID : {}".format(pkg_id) ret_ += "\n╠ STICKER VERSION : {}".format(stk_ver) ret_ += "\n╠ STICKER URL : line://shop/detail/{}".format(pkg_id) ret_ += "\n╚══[ Finish ]" cl.sendMessage(to, str(ret_)) elif msg.contentType == 13: if settings["copy"] == True: _name = msg.contentMetadata["displayName"] copy = msg.contentMetadata["mid"] groups = cl.getGroup(msg.to) targets = [] for s in groups.members: if _name in s.displayName: print ("[Target] Copy") break else: targets.append(copy) if targets == []: cl.sendMessage(msg.to, "Not Found...") pass else: for target in targets: try: cl.cloneContactProfile(target) cl.sendMessage(msg.to, "Berhasil clone member tunggu beberapa saat sampai profile berubah") settings['copy'] = False break except: msg.contentMetadata = {'mid': target} settings["copy"] = False break #==============================================================================# if op.type == 26: print ("[ 26 ] RECEIVE MESSAGE") msg = op.message text = msg.text msg_id = msg.id receiver = msg.to sender = msg._from if msg.toType == 0: if sender != cl.profile.mid: to = sender else: to = receiver else: to = receiver if settings["autoRead"] == True: cl.sendChatChecked(to, msg_id) if to in read["readPoint"]: if sender not in read["ROM"][to]: read["ROM"][to][sender] = True if sender in settings["mimic"]["target"] and settings["mimic"]["status"] == True and settings["mimic"]["target"][sender] == True: text = msg.text if text is not None: cl.sendMessage(msg.to,text) if msg.contentType == 0 and sender not in clMID and msg.toType == 2: if 'MENTION' in msg.contentMetadata.keys()!= None: names = re.findall(r'@(\w+)', text) mention = ast.literal_eval(msg.contentMetadata['MENTION']) mentionees = mention['MENTIONEES'] lists = [] for mention in mentionees: if clMID in mention["M"]: if settings["detectMention"] == True: contact = cl.getContact(sender) cl.sendMessage(to, "sundala nu") sendMessageWithMention(to, contact.mid) break #==============================================================================# if op.type == 65: print ("[ 65 ] REREAD") try: at = op.param1 msg_id = op.param2 if setting["reread"] == True: if msg_id in msg_dict: if msg_dict[msg_id]["from"] not in bl: cl.sendMessage(at,"[收回訊息者]\n%s\n[訊息內容]\n%s"%(cl.getContact(msg_dict[msg_id]["from"]).displayName,msg_dict[msg_id]["text"])) del msg_dict[msg_id] else: pass except Exception as e: print (e) #==============================================================================# if op.type == 55: print ("[ 55 ] NOTIFIED READ MESSAGE") try: if op.param1 in read['readPoint']: if op.param2 in read['readMember'][op.param1]: pass else: read['readMember'][op.param1] += op.param2 read['ROM'][op.param1][op.param2] = op.param2 backupData() else: pass except: pass except Exception as error: logError(error) #==============================================================================# while True: try: ops = oepoll.singleTrace(count=50) if ops is not None: for op in ops: lineBot(op) oepoll.setRevision(op.revision) except Exception as e: logError(e)

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0.014538

0

null

0

null

0

1

0

b9cf5fa54caecef97e6454178f438ce16bc99d7b

241

py

Python

fetch_data.py

bitfag/bt-macd-binance

eeffe52f8e561ff521629839078ff886e7bf700e

[ "MIT" ]

null

fetch_data.py

bitfag/bt-macd-binance

eeffe52f8e561ff521629839078ff886e7bf700e

[ "MIT" ]

null

fetch_data.py

bitfag/bt-macd-binance

eeffe52f8e561ff521629839078ff886e7bf700e

[ "MIT" ]

null

#!/usr/bin/env python from btmacd.binance_fetcher import BinanceFetcher def main(): fetcher = BinanceFetcher("BTCUSDT", filename="binance_ohlc.csv", start_date="01.01.2018") fetcher.fetch() if __name__ == "__main__": main()

18.538462

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0.186364

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null

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null

0

1

0

b9d0d7e9fc82e29bf1385d169d21f03d43d467e2

25,508

py

Python

tensorflow_probability/python/mcmc/diagnostic.py

Frightera/probability

deac4562cbc1056e6abebc7450218d38444fe65d

[ "Apache-2.0" ]

1

2022-03-06T15:37:18.000Z

tensorflow_probability/python/mcmc/diagnostic.py

Frightera/probability

deac4562cbc1056e6abebc7450218d38444fe65d

[ "Apache-2.0" ]

null

tensorflow_probability/python/mcmc/diagnostic.py

Frightera/probability

deac4562cbc1056e6abebc7450218d38444fe65d

[ "Apache-2.0" ]

null

# Copyright 2018 The TensorFlow Probability Authors. # # 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. # ============================================================================ """Utilities for Markov Chain Monte Carlo (MCMC) sampling. @@effective_sample_size @@potential_scale_reduction """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow.compat.v2 as tf from tensorflow_probability.python import stats from tensorflow_probability.python.internal import assert_util from tensorflow_probability.python.internal import dtype_util from tensorflow_probability.python.internal import nest_util from tensorflow_probability.python.internal import prefer_static as ps from tensorflow_probability.python.internal import tensorshape_util from tensorflow.python.util import nest # pylint: disable=g-direct-tensorflow-import __all__ = [ 'effective_sample_size', 'potential_scale_reduction', ] def effective_sample_size(states, filter_threshold=0., filter_beyond_lag=None, filter_beyond_positive_pairs=False, cross_chain_dims=None, validate_args=False, name=None): """Estimate a lower bound on effective sample size for each independent chain. Roughly speaking, "effective sample size" (ESS) is the size of an iid sample with the same variance as `state`. More precisely, given a stationary sequence of possibly correlated random variables `X_1, X_2, ..., X_N`, identically distributed, ESS is the number such that ``` Variance{ N**-1 * Sum{X_i} } = ESS**-1 * Variance{ X_1 }. ``` If the sequence is uncorrelated, `ESS = N`. If the sequence is positively auto-correlated, `ESS` will be less than `N`. If there are negative correlations, then `ESS` can exceed `N`. Some math shows that, with `R_k` the auto-correlation sequence, `R_k := Covariance{X_1, X_{1+k}} / Variance{X_1}`, we have ``` ESS(N) = N / [ 1 + 2 * ( (N - 1) / N * R_1 + ... + 1 / N * R_{N-1} ) ] ``` This function estimates the above by first estimating the auto-correlation. Since `R_k` must be estimated using only `N - k` samples, it becomes progressively noisier for larger `k`. For this reason, the summation over `R_k` should be truncated at some number `filter_beyond_lag < N`. This function provides two methods to perform this truncation. * `filter_threshold` -- since many MCMC methods generate chains where `R_k > 0`, a reasonable criterion is to truncate at the first index where the estimated auto-correlation becomes negative. This method does not estimate the `ESS` of super-efficient chains (where `ESS > N`) correctly. * `filter_beyond_positive_pairs` -- reversible MCMC chains produce an auto-correlation sequence with the property that pairwise sums of the elements of that sequence are positive [Geyer][1], i.e. `R_{2k} + R_{2k + 1} > 0` for `k in {0, ..., N/2}`. Deviations are only possible due to noise. This method truncates the auto-correlation sequence where the pairwise sums become non-positive. The arguments `filter_beyond_lag`, `filter_threshold` and `filter_beyond_positive_pairs` are filters intended to remove noisy tail terms from `R_k`. You can combine `filter_beyond_lag` with `filter_threshold` or `filter_beyond_positive_pairs. E.g., combining `filter_beyond_lag` and `filter_beyond_positive_pairs` means that terms are removed if they were to be filtered under the `filter_beyond_lag` OR `filter_beyond_positive_pairs` criteria. This function can also compute cross-chain ESS following [Vehtari et al. (2019)][2] by specifying the `cross_chain_dims` argument. Cross-chain ESS takes into account the cross-chain variance to reduce the ESS in cases where the chains are not mixing well. In general, this will be a smaller number than computing the ESS for individual chains and then summing them. In an extreme case where the chains have fallen into K non-mixing modes, this function will return ESS ~ K. Even when chains are mixing well it is still preferrable to compute cross-chain ESS via this method because it will reduce the noise in the estimate of `R_k`, reducing the need for truncation. Args: states: `Tensor` or Python structure of `Tensor` objects. Dimension zero should index identically distributed states. filter_threshold: `Tensor` or Python structure of `Tensor` objects. Must broadcast with `state`. The sequence of auto-correlations is truncated after the first appearance of a term less than `filter_threshold`. Setting to `None` means we use no threshold filter. Since `|R_k| <= 1`, setting to any number less than `-1` has the same effect. Ignored if `filter_beyond_positive_pairs` is `True`. filter_beyond_lag: `Tensor` or Python structure of `Tensor` objects. Must be `int`-like and scalar valued. The sequence of auto-correlations is truncated to this length. Setting to `None` means we do not filter based on the size of lags. filter_beyond_positive_pairs: Python boolean. If `True`, only consider the initial auto-correlation sequence where the pairwise sums are positive. cross_chain_dims: An integer `Tensor` or a structure of integer `Tensors` corresponding to each state component. If a list of `states` is provided, then this argument should also be a list of the same length. Which dimensions of `states` to treat as independent chains that ESS will be summed over. If `None`, no summation is performed. Note this requires at least 2 chains. validate_args: Whether to add runtime checks of argument validity. If False, and arguments are incorrect, correct behavior is not guaranteed. name: `String` name to prepend to created ops. Returns: ess: `Tensor` structure parallel to `states`. The effective sample size of each component of `states`. If `cross_chain_dims` is None, the shape will be `states.shape[1:]`. Otherwise, the shape is `tf.reduce_mean(states, cross_chain_dims).shape[1:]`. Raises: ValueError: If `states` and `filter_threshold` or `states` and `filter_beyond_lag` are both structures of different shapes. ValueError: If `cross_chain_dims` is not `None` and there are less than 2 chains. #### Examples We use ESS to estimate standard error. ``` import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions target = tfd.MultivariateNormalDiag(scale_diag=[1., 2.]) # Get 1000 states from one chain. states = tfp.mcmc.sample_chain( num_burnin_steps=200, num_results=1000, current_state=tf.constant([0., 0.]), trace_fn=None, kernel=tfp.mcmc.HamiltonianMonteCarlo( target_log_prob_fn=target.log_prob, step_size=0.05, num_leapfrog_steps=20)) states.shape ==> (1000, 2) ess = effective_sample_size(states, filter_beyond_positive_pairs=True) ==> Shape (2,) Tensor mean, variance = tf.nn.moments(states, axis=0) standard_error = tf.sqrt(variance / ess) ``` #### References [1]: Charles J. Geyer, Practical Markov chain Monte Carlo (with discussion). Statistical Science, 7:473-511, 1992. [2]: Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, Paul-Christian Burkner. Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC, 2019. Retrieved from http://arxiv.org/abs/1903.08008 """ if cross_chain_dims is None: cross_chain_dims = nest_util.broadcast_structure(states, None) filter_beyond_lag = nest_util.broadcast_structure(states, filter_beyond_lag) filter_threshold = nest_util.broadcast_structure(states, filter_threshold) filter_beyond_positive_pairs = nest_util.broadcast_structure( states, filter_beyond_positive_pairs) # Process items, one at a time. def single_state(*args): return _effective_sample_size_single_state( *args, validate_args=validate_args) with tf.name_scope('effective_sample_size' if name is None else name): return nest.map_structure_up_to( states, single_state, states, filter_beyond_lag, filter_threshold, filter_beyond_positive_pairs, cross_chain_dims) def _effective_sample_size_single_state(states, filter_beyond_lag, filter_threshold, filter_beyond_positive_pairs, cross_chain_dims, validate_args): """ESS computation for one single Tensor argument.""" with tf.name_scope('effective_sample_size_single_state'): states = tf.convert_to_tensor(states, name='states') dt = states.dtype # filter_beyond_lag == None ==> auto_corr is the full sequence. auto_cov = stats.auto_correlation( states, axis=0, max_lags=filter_beyond_lag, normalize=False) n = _axis_size(states, axis=0) if cross_chain_dims is not None: num_chains = _axis_size(states, cross_chain_dims) num_chains_ = tf.get_static_value(num_chains) assertions = [] msg = ('When `cross_chain_dims` is not `None`, there must be > 1 chain ' 'in `states`.') if num_chains_ is not None: if num_chains_ < 2: raise ValueError(msg) elif validate_args: assertions.append( assert_util.assert_greater(num_chains, 1., message=msg)) with tf.control_dependencies(assertions): # We're computing the R[k] from equation 10 of Vehtari et al. # (2019): # # R[k] := 1 - (W - 1/C * Sum_{c=1}^C s_c**2 R[k, c]) / (var^+), # # where: # C := number of chains # N := length of chains # x_hat[c] := 1 / N Sum_{n=1}^N x[n, c], chain mean. # x_hat := 1 / C Sum_{c=1}^C x_hat[c], overall mean. # W := 1/C Sum_{c=1}^C s_c**2, within-chain variance. # B := N / (C - 1) Sum_{c=1}^C (x_hat[c] - x_hat)**2, between chain # variance. # s_c**2 := 1 / (N - 1) Sum_{n=1}^N (x[n, c] - x_hat[c])**2, chain # variance # R[k, m] := auto_corr[k, m, ...], auto-correlation indexed by chain. # var^+ := (N - 1) / N * W + B / N cross_chain_dims = ps.non_negative_axis( cross_chain_dims, ps.rank(states)) # B / N between_chain_variance_div_n = _reduce_variance( tf.reduce_mean(states, axis=0), biased=False, # This makes the denominator be C - 1. axis=cross_chain_dims - 1) # W * (N - 1) / N biased_within_chain_variance = tf.reduce_mean(auto_cov[0], cross_chain_dims - 1) # var^+ approx_variance = ( biased_within_chain_variance + between_chain_variance_div_n) # 1/C * Sum_{c=1}^C s_c**2 R[k, c] mean_auto_cov = tf.reduce_mean(auto_cov, cross_chain_dims) auto_corr = 1. - (biased_within_chain_variance - mean_auto_cov) / approx_variance else: auto_corr = auto_cov / auto_cov[:1] num_chains = 1 # With R[k] := auto_corr[k, ...], # ESS = N / {1 + 2 * Sum_{k=1}^N R[k] * (N - k) / N} # = N / {-1 + 2 * Sum_{k=0}^N R[k] * (N - k) / N} (since R[0] = 1) # approx N / {-1 + 2 * Sum_{k=0}^M R[k] * (N - k) / N} # where M is the filter_beyond_lag truncation point chosen above. # Get the factor (N - k) / N, and give it shape [M, 1,...,1], having total # ndims the same as auto_corr k = tf.range(0., _axis_size(auto_corr, axis=0)) nk_factor = (n - k) / n if tensorshape_util.rank(auto_corr.shape) is not None: new_shape = [-1] + [1] * (tensorshape_util.rank(auto_corr.shape) - 1) else: new_shape = tf.concat( ([-1], tf.ones([tf.rank(auto_corr) - 1], dtype=tf.int32)), axis=0) nk_factor = tf.reshape(nk_factor, new_shape) weighted_auto_corr = nk_factor * auto_corr if filter_beyond_positive_pairs: def _sum_pairs(x): x_len = ps.shape(x)[0] # For odd sequences, we drop the final value. x = x[:x_len - x_len % 2] new_shape = ps.concat([[x_len // 2, 2], ps.shape(x)[1:]], axis=0) return tf.reduce_sum(tf.reshape(x, new_shape), 1) # Pairwise sums are all positive for auto-correlation spectra derived from # reversible MCMC chains. # E.g. imagine the pairwise sums are [0.2, 0.1, -0.1, -0.2] # Step 1: mask = [False, False, True, True] mask = _sum_pairs(auto_corr) < 0. # Step 2: mask = [0, 0, 1, 1] mask = tf.cast(mask, dt) # Step 3: mask = [0, 0, 1, 2] mask = tf.cumsum(mask, axis=0) # Step 4: mask = [1, 1, 0, 0] mask = tf.maximum(1. - mask, 0.) # N.B. this reduces the length of weighted_auto_corr by a factor of 2. # It still works fine in the formula below. weighted_auto_corr = _sum_pairs(weighted_auto_corr) * mask elif filter_threshold is not None: filter_threshold = tf.convert_to_tensor( filter_threshold, dtype=dt, name='filter_threshold') # Get a binary mask to zero out values of auto_corr below the threshold. # mask[i, ...] = 1 if auto_corr[j, ...] > threshold for all j <= i, # mask[i, ...] = 0, otherwise. # So, along dimension zero, the mask will look like [1, 1, ..., 0, 0,...] # Building step by step, # Assume auto_corr = [1, 0.5, 0.0, 0.3], and filter_threshold = 0.2. # Step 1: mask = [False, False, True, False] mask = auto_corr < filter_threshold # Step 2: mask = [0, 0, 1, 0] mask = tf.cast(mask, dtype=dt) # Step 3: mask = [0, 0, 1, 1] mask = tf.cumsum(mask, axis=0) # Step 4: mask = [1, 1, 0, 0] mask = tf.maximum(1. - mask, 0.) weighted_auto_corr *= mask return num_chains * n / (-1 + 2 * tf.reduce_sum(weighted_auto_corr, axis=0)) def potential_scale_reduction(chains_states, independent_chain_ndims=1, split_chains=False, validate_args=False, name=None): """Gelman and Rubin (1992)'s potential scale reduction for chain convergence. Given `N > 1` states from each of `C > 1` independent chains, the potential scale reduction factor, commonly referred to as R-hat, measures convergence of the chains (to the same target) by testing for equality of means. Specifically, R-hat measures the degree to which variance (of the means) between chains exceeds what one would expect if the chains were identically distributed. See [Gelman and Rubin (1992)][1]; [Brooks and Gelman (1998)][2]. Some guidelines: * The initial state of the chains should be drawn from a distribution overdispersed with respect to the target. * If all chains converge to the target, then as `N --> infinity`, R-hat --> 1. Before that, R-hat > 1 (except in pathological cases, e.g. if the chain paths were identical). * The above holds for any number of chains `C > 1`. Increasing `C` does improve effectiveness of the diagnostic. * Sometimes, R-hat < 1.2 is used to indicate approximate convergence, but of course this is problem-dependent. See [Brooks and Gelman (1998)][2]. * R-hat only measures non-convergence of the mean. If higher moments, or other statistics are desired, a different diagnostic should be used. See [Brooks and Gelman (1998)][2]. Args: chains_states: `Tensor` or Python structure of `Tensor`s representing the states of a Markov Chain at each result step. The `ith` state is assumed to have shape `[Ni, Ci1, Ci2,...,CiD] + A`. Dimension `0` indexes the `Ni > 1` result steps of the Markov Chain. Dimensions `1` through `D` index the `Ci1 x ... x CiD` independent chains to be tested for convergence to the same target. The remaining dimensions, `A`, can have any shape (even empty). independent_chain_ndims: Integer type `Tensor` with value `>= 1` giving the number of dimensions, from `dim = 1` to `dim = D`, holding independent chain results to be tested for convergence. split_chains: Python `bool`. If `True`, divide samples from each chain into first and second halves, treating these as separate chains. This makes R-hat more robust to non-stationary chains, and is recommended in [3]. validate_args: Whether to add runtime checks of argument validity. If False, and arguments are incorrect, correct behavior is not guaranteed. name: `String` name to prepend to created tf. Default: `potential_scale_reduction`. Returns: `Tensor` structure parallel to `chains_states` representing the R-hat statistic for the state(s). Same `dtype` as `state`, and shape equal to `state.shape[1 + independent_chain_ndims:]`. Raises: ValueError: If `independent_chain_ndims < 1`. #### Examples Diagnosing convergence by monitoring 10 chains that each attempt to sample from a 2-variate normal. ```python import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions target = tfd.MultivariateNormalDiag(scale_diag=[1., 2.]) # Get 10 (2x) overdispersed initial states. initial_state = target.sample(10) * 2. ==> (10, 2) # Get 1000 samples from the 10 independent chains. chains_states = tfp.mcmc.sample_chain( num_burnin_steps=200, num_results=1000, current_state=initial_state, trace_fn=None, kernel=tfp.mcmc.HamiltonianMonteCarlo( target_log_prob_fn=target.log_prob, step_size=0.05, num_leapfrog_steps=20)) chains_states.shape ==> (1000, 10, 2) rhat = tfp.mcmc.diagnostic.potential_scale_reduction( chains_states, independent_chain_ndims=1) # The second dimension needed a longer burn-in. rhat.eval() ==> [1.05, 1.3] ``` To see why R-hat is reasonable, let `X` be a random variable drawn uniformly from the combined states (combined over all chains). Then, in the limit `N, C --> infinity`, with `E`, `Var` denoting expectation and variance, ```R-hat = ( E[Var[X | chain]] + Var[E[X | chain]] ) / E[Var[X | chain]].``` Using the law of total variance, the numerator is the variance of the combined states, and the denominator is the total variance minus the variance of the the individual chain means. If the chains are all drawing from the same distribution, they will have the same mean, and thus the ratio should be one. #### References [1]: Stephen P. Brooks and Andrew Gelman. General Methods for Monitoring Convergence of Iterative Simulations. _Journal of Computational and Graphical Statistics_, 7(4), 1998. [2]: Andrew Gelman and Donald B. Rubin. Inference from Iterative Simulation Using Multiple Sequences. _Statistical Science_, 7(4):457-472, 1992. [3]: Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, Paul-Christian Burkner. Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC, 2019. Retrieved from http://arxiv.org/abs/1903.08008 """ # tf.get_static_value returns None iff a constant value (as a numpy # array) is not efficiently computable. Therefore, we try constant_value then # check for None. icn_const_ = tf.get_static_value( ps.convert_to_shape_tensor(independent_chain_ndims)) if icn_const_ is not None: independent_chain_ndims = icn_const_ if icn_const_ < 1: raise ValueError( 'Argument `independent_chain_ndims` must be `>= 1`, found: {}'.format( independent_chain_ndims)) def single_state(s): return _potential_scale_reduction_single_state( s, independent_chain_ndims, split_chains, validate_args) with tf.name_scope('potential_scale_reduction' if name is None else name): return tf.nest.map_structure(single_state, chains_states) def _potential_scale_reduction_single_state(state, independent_chain_ndims, split_chains, validate_args): """potential_scale_reduction for one single state `Tensor`.""" # casting integers to floats for floating-point division # check to see if the `state` is a numpy object for the numpy test suite if dtype_util.as_numpy_dtype(state.dtype) is np.int64: state = tf.cast(state, tf.float64) elif dtype_util.is_integer(state.dtype): state = tf.cast(state, tf.float32) with tf.name_scope('potential_scale_reduction_single_state'): # We assume exactly one leading dimension indexes e.g. correlated samples # from each Markov chain. state = tf.convert_to_tensor(state, name='state') n_samples_ = tf.compat.dimension_value(state.shape[0]) if n_samples_ is not None: # If available statically. if split_chains and n_samples_ < 4: raise ValueError( 'Must provide at least 4 samples when splitting chains. ' 'Found {}'.format(n_samples_)) if not split_chains and n_samples_ < 2: raise ValueError( 'Must provide at least 2 samples. Found {}'.format(n_samples_)) elif validate_args: if split_chains: assertions = [assert_util.assert_greater( ps.shape(state)[0], 4, message='Must provide at least 4 samples when splitting chains.')] with tf.control_dependencies(assertions): state = tf.identity(state) else: assertions = [assert_util.assert_greater( ps.shape(state)[0], 2, message='Must provide at least 2 samples.')] with tf.control_dependencies(assertions): state = tf.identity(state) # Define so it's not a magic number. # Warning! `if split_chains` logic assumes this is 1! sample_ndims = 1 if split_chains: # Split the sample dimension in half, doubling the number of # independent chains. # For odd number of samples, keep all but the last sample. state_shape = ps.shape(state) n_samples = state_shape[0] state = state[:n_samples - n_samples % 2] # Suppose state = [0, 1, 2, 3, 4, 5] # Step 1: reshape into [[0, 1, 2], [3, 4, 5]] # E.g. reshape states of shape [a, b] into [2, a//2, b]. state = tf.reshape( state, ps.concat([[2, n_samples // 2], state_shape[1:]], axis=0) ) # Step 2: Put the size `2` dimension in the right place to be treated as a # chain, changing [[0, 1, 2], [3, 4, 5]] into [[0, 3], [1, 4], [2, 5]], # reshaping [2, a//2, b] into [a//2, 2, b]. state = tf.transpose( a=state, perm=ps.concat( [[1, 0], ps.range(2, ps.rank(state))], axis=0)) # We're treating the new dim as indexing 2 chains, so increment. independent_chain_ndims += 1 sample_axis = ps.range(0, sample_ndims) chain_axis = ps.range(sample_ndims, sample_ndims + independent_chain_ndims) sample_and_chain_axis = ps.range( 0, sample_ndims + independent_chain_ndims) n = _axis_size(state, sample_axis) m = _axis_size(state, chain_axis) # In the language of Brooks and Gelman (1998), # B / n is the between chain variance, the variance of the chain means. # W is the within sequence variance, the mean of the chain variances. b_div_n = _reduce_variance( tf.reduce_mean(state, axis=sample_axis, keepdims=True), sample_and_chain_axis, biased=False) w = tf.reduce_mean( _reduce_variance(state, sample_axis, keepdims=True, biased=False), axis=sample_and_chain_axis) # sigma^2_+ is an estimate of the true variance, which would be unbiased if # each chain was drawn from the target. c.f. "law of total variance." sigma_2_plus = ((n - 1) / n) * w + b_div_n return ((m + 1.) / m) * sigma_2_plus / w - (n - 1.) / (m * n) # TODO(b/72873233) Move some variant of this to tfd.sample_stats. def _reduce_variance(x, axis=None, biased=True, keepdims=False): with tf.name_scope('reduce_variance'): x = tf.convert_to_tensor(x, name='x') mean = tf.reduce_mean(x, axis=axis, keepdims=True) biased_var = tf.reduce_mean( tf.math.squared_difference(x, mean), axis=axis, keepdims=keepdims) if biased: return biased_var n = _axis_size(x, axis) return (n / (n - 1.)) * biased_var def _axis_size(x, axis=None): """Get number of elements of `x` in `axis`, as type `x.dtype`.""" if axis is None: return ps.cast(ps.size(x), x.dtype) return ps.cast( ps.reduce_prod( ps.gather(ps.shape(x), axis)), x.dtype)

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b9d2c04ffcb32d5c9ad6c0f626a368e22db97763

4,504

py

Python

tests/data/s3_scrape_config.py

kids-first/kf-api-study-creator

93a79b108b6474f9b4135ace06c89ddcf63dd257

[ "Apache-2.0" ]

3

2019-05-04T02:07:28.000Z

2020-10-16T17:47:44.000Z

tests/data/s3_scrape_config.py

kids-first/kf-api-study-creator

93a79b108b6474f9b4135ace06c89ddcf63dd257

[ "Apache-2.0" ]

604

2019-02-21T18:14:51.000Z

2022-02-10T08:13:54.000Z

tests/data/s3_scrape_config.py

kids-first/kf-api-study-creator

93a79b108b6474f9b4135ace06c89ddcf63dd257

[ "Apache-2.0" ]

null

""" This is an extract config intended for S3 object manifests produced by TBD. To use it, you must import it in another extract config and override at least the `source_data_url`. You may also append additional operations to the `operations` list as well. For example you could have the following in your extract config module: from kf_ingest_packages.common.extract_configs.s3_object_info import * source_data_url = 'file://../data/kf-seq-data-bcm-chung-s3-objects.tsv' operations.append( value_map( in_col='Key', out_col=CONCEPT.BIOSPECIMEN.ID, m=lambda x: x ) ) """ import os from kf_lib_data_ingest.common import constants from kf_lib_data_ingest.common.constants import GENOMIC_FILE from kf_lib_data_ingest.common.concept_schema import CONCEPT from kf_lib_data_ingest.etl.extract.operations import ( keep_map, row_map, value_map, constant_map, ) def file_ext(x): """ Get genomic file extension """ matches = [ file_ext for file_ext in FILE_EXT_FORMAT_MAP if x.endswith(file_ext) ] if matches: file_ext = max(matches, key=len) else: file_ext = None return file_ext FILE_EXT_FORMAT_MAP = { "fq": GENOMIC_FILE.FORMAT.FASTQ, "fastq": GENOMIC_FILE.FORMAT.FASTQ, "fq.gz": GENOMIC_FILE.FORMAT.FASTQ, "fastq.gz": GENOMIC_FILE.FORMAT.FASTQ, "bam": GENOMIC_FILE.FORMAT.BAM, "hgv.bam": GENOMIC_FILE.FORMAT.BAM, "cram": GENOMIC_FILE.FORMAT.CRAM, "bam.bai": GENOMIC_FILE.FORMAT.BAI, "bai": GENOMIC_FILE.FORMAT.BAI, "cram.crai": GENOMIC_FILE.FORMAT.CRAI, "crai": GENOMIC_FILE.FORMAT.CRAI, "g.vcf.gz": GENOMIC_FILE.FORMAT.GVCF, "g.vcf.gz.tbi": GENOMIC_FILE.FORMAT.TBI, "vcf.gz": GENOMIC_FILE.FORMAT.VCF, "vcf": GENOMIC_FILE.FORMAT.VCF, "vcf.gz.tbi": GENOMIC_FILE.FORMAT.TBI, "peddy.html": "html", } DATA_TYPES = { GENOMIC_FILE.FORMAT.FASTQ: GENOMIC_FILE.DATA_TYPE.UNALIGNED_READS, GENOMIC_FILE.FORMAT.BAM: GENOMIC_FILE.DATA_TYPE.ALIGNED_READS, GENOMIC_FILE.FORMAT.CRAM: GENOMIC_FILE.DATA_TYPE.ALIGNED_READS, GENOMIC_FILE.FORMAT.BAI: "Aligned Reads Index", GENOMIC_FILE.FORMAT.CRAI: "Aligned Reads Index", GENOMIC_FILE.FORMAT.VCF: "Variant Calls", GENOMIC_FILE.FORMAT.GVCF: "gVCF", "g.vcf.gz.tbi": "gVCF Index", "vcf.gz.tbi": "Variant Calls Index", "html": "Other", } def filter_df_by_file_ext(df): """ Only keep rows where file extension is one of those in FILE_EXT_FORMAT_MAP.keys """ df[CONCEPT.GENOMIC_FILE.FILE_FORMAT] = df["Key"].apply( lambda x: file_format(x) ) return df[df[CONCEPT.GENOMIC_FILE.FILE_FORMAT].notnull()] source_data_url = ( 'https://localhost:5002/download/study/SD_ME0WME0W/' 'file/SF_Y1JMXTTS/version/FV_4RYEMD71' ) do_after_read = filter_df_by_file_ext def s3_url(row): """ Create S3 URL for object from S3 bucket and key """ return f's3://{row["Bucket"]}/{row["Key"]}' def file_format(x): """ Get genomic file format by looking genomic file ext up in FILE_EXT_FORMAT_MAP dict """ # File format return FILE_EXT_FORMAT_MAP.get(file_ext(x)) def data_type(x): """ Get genomic file data type by looking up file format in DATA_TYPES. However, if the file's extension has `tbi` in it, then use the file extension itself to do the data type lookup. """ ext = file_ext(x) if "tbi" in ext: data_type = DATA_TYPES.get(ext) else: data_type = DATA_TYPES.get(file_format(x)) return data_type operations = [ row_map(out_col=CONCEPT.GENOMIC_FILE.ID, m=lambda row: s3_url(row)), row_map( out_col=CONCEPT.GENOMIC_FILE.URL_LIST, m=lambda row: [s3_url(row)] ), value_map( in_col="Key", out_col=CONCEPT.GENOMIC_FILE.FILE_NAME, m=lambda x: os.path.split(x)[-1], ), keep_map(in_col="Size", out_col=CONCEPT.GENOMIC_FILE.SIZE), value_map( in_col="ETag", out_col=CONCEPT.GENOMIC_FILE.HASH_DICT, m=lambda x: {constants.FILE.HASH.S3_ETAG.lower(): x.replace('"', "")}, ), constant_map( out_col=CONCEPT.GENOMIC_FILE.AVAILABILITY, m=constants.GENOMIC_FILE.AVAILABILITY.IMMEDIATE, ), keep_map( in_col=CONCEPT.GENOMIC_FILE.FILE_FORMAT, out_col=CONCEPT.GENOMIC_FILE.FILE_FORMAT, ), value_map( in_col="Key", out_col=CONCEPT.GENOMIC_FILE.DATA_TYPE, m=lambda x: data_type(x), ), ]

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b9d3222fd93bbc8ba199ba7a401394dc7531a2ff

665

py

Python

hard-gists/5c973ec1b5ab2e387646/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

21

2019-07-08T08:26:45.000Z

2022-01-24T23:53:25.000Z

hard-gists/5c973ec1b5ab2e387646/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

5

2019-06-15T14:47:47.000Z

2022-02-26T05:02:56.000Z

hard-gists/5c973ec1b5ab2e387646/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

17

2019-05-16T03:50:34.000Z

2021-01-14T14:35:12.000Z

import bpy from bpy.app.handlers import persistent bl_info = { "name": "Playback Once", "author": "Adhi Hargo", "version": (1, 0, 0), "blender": (2, 67, 3), "location": "", "description": "Playback once.", "warning": "", "wiki_url": "", "tracker_url": "", "category": "Animation"} @persistent def stopPlaybackAtEnd(scene): if scene.frame_current >= scene.frame_end: bpy.ops.screen.animation_cancel() def register(): bpy.app.handlers.frame_change_pre.append(stopPlaybackAtEnd) def unregister(): bpy.app.handlers.frame_change_pre.remove(stopPlaybackAtEnd) if __name__ == "__main__": register()

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b9d47acd47b8bd0babe955a7bbbde7c4d9080b36

688

py

Python

Py3Challenges/saves/challenges/c6_min.py

AlbertUnruh/Py3Challenges

52f03f157860f6464f0c1710bf051a8099c29ea2

[ "MIT" ]

2

2022-02-13T04:57:10.000Z

2022-02-13T10:40:14.000Z

Py3Challenges/saves/challenges/c6_min.py

AlbertUnruh/Py3Challenges

52f03f157860f6464f0c1710bf051a8099c29ea2

[ "MIT" ]

null

Py3Challenges/saves/challenges/c6_min.py

AlbertUnruh/Py3Challenges

52f03f157860f6464f0c1710bf051a8099c29ea2

[ "MIT" ]

null

""" To master this you should consider using the builtin-``min``-function. """ from ...challenge import Challenge from random import randint x = [] for _ in range(randint(2, 10)): x.append(randint(1, 100)) intro = f"You have to print the lowest value of {', '.join(str(_) for _ in x[:-1])} and {x[-1]}. (values: x)" def validate_function(stdin: str, stdout: str, stderr: str, exc: tuple) -> bool: try: z = int(stdout.removesuffix("\n")) except ValueError: return False else: return min(x) == z challenge = Challenge( intro=intro, validate_function=validate_function, help=__doc__, values={"x": x}, capture_stdout=True, )

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null

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null

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1

0

b9d600352f466e38045c7614f4b0151d5eb8f878

4,625

py

Python

services/web/server/tests/unit/with_dbs/01/test_director_v2.py

mrnicegyu11/osparc-simcore

b6fa6c245dbfbc18cc74a387111a52de9b05d1f4

[ "MIT" ]

null

services/web/server/tests/unit/with_dbs/01/test_director_v2.py

mrnicegyu11/osparc-simcore

b6fa6c245dbfbc18cc74a387111a52de9b05d1f4

[ "MIT" ]

1

2021-11-29T13:38:09.000Z

services/web/server/tests/unit/with_dbs/01/test_director_v2.py

mrnicegyu11/osparc-simcore

b6fa6c245dbfbc18cc74a387111a52de9b05d1f4

[ "MIT" ]

null

# pylint:disable=unused-variable # pylint:disable=unused-argument # pylint:disable=redefined-outer-name from typing import AsyncIterator import pytest from aioresponses import aioresponses from faker import Faker from hypothesis import HealthCheck, given, settings from hypothesis import strategies as st from models_library.clusters import ClusterID from models_library.projects import ProjectID from models_library.projects_pipeline import ComputationTask from models_library.projects_state import RunningState from models_library.users import UserID from simcore_service_webserver import director_v2_api from simcore_service_webserver.director_v2_models import ( ClusterCreate, ClusterPatch, ClusterPing, ) @pytest.fixture() async def mocked_director_v2( director_v2_service_mock: aioresponses, ) -> AsyncIterator[aioresponses]: yield director_v2_service_mock @pytest.fixture def user_id(faker: Faker) -> UserID: return UserID(faker.pyint(min_value=1)) @pytest.fixture def project_id(faker: Faker) -> ProjectID: return ProjectID(faker.uuid4()) @pytest.fixture def cluster_id(faker: Faker) -> ClusterID: return ClusterID(faker.pyint(min_value=0)) async def test_create_pipeline( mocked_director_v2, client, user_id: UserID, project_id: ProjectID ): task_out = await director_v2_api.create_or_update_pipeline( client.app, user_id, project_id ) assert task_out assert isinstance(task_out, dict) assert task_out["state"] == RunningState.NOT_STARTED async def test_get_computation_task( mocked_director_v2, client, user_id: UserID, project_id: ProjectID, ): task_out = await director_v2_api.get_computation_task( client.app, user_id, project_id ) assert task_out assert isinstance(task_out, ComputationTask) assert task_out.state == RunningState.NOT_STARTED async def test_delete_pipeline( mocked_director_v2, client, user_id: UserID, project_id: ProjectID ): await director_v2_api.delete_pipeline(client.app, user_id, project_id) @settings(suppress_health_check=[HealthCheck.function_scoped_fixture]) @given(cluster_create=st.builds(ClusterCreate)) async def test_create_cluster( mocked_director_v2, client, user_id: UserID, cluster_create ): created_cluster = await director_v2_api.create_cluster( client.app, user_id=user_id, new_cluster=cluster_create ) assert created_cluster is not None assert isinstance(created_cluster, dict) assert "id" in created_cluster async def test_list_clusters(mocked_director_v2, client, user_id: UserID): list_of_clusters = await director_v2_api.list_clusters(client.app, user_id=user_id) assert isinstance(list_of_clusters, list) assert len(list_of_clusters) > 0 async def test_get_cluster( mocked_director_v2, client, user_id: UserID, cluster_id: ClusterID ): cluster = await director_v2_api.get_cluster( client.app, user_id=user_id, cluster_id=cluster_id ) assert isinstance(cluster, dict) assert cluster["id"] == cluster_id async def test_get_cluster_details( mocked_director_v2, client, user_id: UserID, cluster_id: ClusterID ): cluster_details = await director_v2_api.get_cluster_details( client.app, user_id=user_id, cluster_id=cluster_id ) assert isinstance(cluster_details, dict) @settings(suppress_health_check=[HealthCheck.function_scoped_fixture]) @given(cluster_patch=st.from_type(ClusterPatch)) async def test_update_cluster( mocked_director_v2, client, user_id: UserID, cluster_id: ClusterID, cluster_patch ): print(f"--> updating cluster with {cluster_patch=}") updated_cluster = await director_v2_api.update_cluster( client.app, user_id=user_id, cluster_id=cluster_id, cluster_patch=cluster_patch ) assert isinstance(updated_cluster, dict) assert updated_cluster["id"] == cluster_id async def test_delete_cluster( mocked_director_v2, client, user_id: UserID, cluster_id: ClusterID ): await director_v2_api.delete_cluster( client.app, user_id=user_id, cluster_id=cluster_id ) @settings(suppress_health_check=[HealthCheck.function_scoped_fixture]) @given(cluster_ping=st.builds(ClusterPing)) async def test_ping_cluster(mocked_director_v2, client, cluster_ping: ClusterPing): await director_v2_api.ping_cluster(client.app, cluster_ping=cluster_ping) async def test_ping_specific_cluster( mocked_director_v2, client, user_id: UserID, cluster_id: ClusterID ): await director_v2_api.ping_specific_cluster( client.app, user_id=user_id, cluster_id=cluster_id )

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null

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null

0

1

0

b9d6dd8bd3445675e1356c10ac0bb61cd00aba81

3,027

py

Python

generator.py

Geoalert/emergency-mapping

96668e4e5aa2b520e5727536f7a8f4c262ee3da6

[ "MIT" ]

3

2018-04-04T17:58:53.000Z

2021-10-14T08:50:13.000Z

generator.py

aeronetlab/map_augury

96668e4e5aa2b520e5727536f7a8f4c262ee3da6

[ "MIT" ]

null

generator.py

aeronetlab/map_augury

96668e4e5aa2b520e5727536f7a8f4c262ee3da6

[ "MIT" ]

1

2020-03-24T12:07:07.000Z

import numpy as np def random_augmentation(img, mask): #you can add any augmentations you need return img, mask def batch_generator(image, mask, batch_size=1, crop_size=0, patch_size=256, bbox= None, augmentation=False): ''' image: nparray, must have 3 dimension mask: nparray, 2 dimensions, same size as image batch_size: int, number of images in a batch patch_size: int, size of the image returned, patch is square crop_size: int, how much pixels should be cropped off the mask bbox: None or tuple of 4 ints, (min_y, max_y, min_x, max_x), the data is selected from within the bbox augmentation: turn on/off data augmentation. The augmentation function is random_augmentation() above returns batch of image and mask patches, image is turned to 'channels last' as required by unet ''' if np.ndim(mask) != 2 or np.ndim(image) != 3: raise ValueError('image must have 3 dims and mask 2 dims') if mask.shape != image.shape[1:]: raise ValueError('image and mask shape is different') im_max = float(np.max(image)) mask_max = 1.0 #select subimage if bbox is not None: # check bbox if bbox[0] < 0 or bbox [2] < 0 \ or bbox[1] > mask.shape[0] or bbox[3] > mask.shape[0] \ or bbox[0] + patch_size > bbox[1] or bbox[2] + patch_size > bbox[3] \ or patch_size <= 0: raise ValueError("Incorrect bbox or patch size") img_ = image[:, bbox[0] : bbox[1], bbox[2]:bbox[3]] mask_ = mask[bbox[0] : bbox[1], bbox[2]:bbox[3]] else: img_ = image mask_ = mask while 1: x = [] y = [] for i in range (batch_size): random_x = np.random.randint(0, mask_.shape[1] - patch_size) random_y = np.random.randint(0, mask_.shape[0] - patch_size) img_patch = img_[:, random_y : random_y + patch_size, random_x : random_x + patch_size] / im_max # transform the image from channels-first (rasterio format) to channels-last (default tensorflow format) img_patch = np.moveaxis(img_patch, 0, 2) mask_patch = mask_[random_y : random_y + patch_size, random_x : random_x + patch_size] / mask_max if augmentation: img_patch, mask_patch = random_augmentation(img_patch, mask_patch) # mask is cropped as it may be useful for some convnets that have output size less than input if crop_size > 0: mask_patch = mask_patch[crop_size : -crop_size, crop_size : -crop_size] mask_patch = np.expand_dims(mask_patch, 2) x.append(img_patch) y.append(mask_patch) yield (np.array(x), np.array(y))

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b9d71e12c5fdd4a3220a64251c8e0e2c9a302fe4

13,351

py

Python

awx/api/metadata.py

Avinesh/awx

6310a2edd890d6062a9f6bcdeb2b46c4b876c2bf

[ "Apache-2.0" ]

1

2021-09-07T14:53:57.000Z

awx/api/metadata.py

Avinesh/awx

6310a2edd890d6062a9f6bcdeb2b46c4b876c2bf

[ "Apache-2.0" ]

2

2020-02-04T05:01:38.000Z

2020-02-18T06:44:52.000Z

awx/api/metadata.py

Avinesh/awx

6310a2edd890d6062a9f6bcdeb2b46c4b876c2bf

[ "Apache-2.0" ]

1

2020-01-28T05:34:09.000Z

# Copyright (c) 2016 Ansible, Inc. # All Rights Reserved. from collections import OrderedDict # Django from django.core.exceptions import PermissionDenied from django.db.models.fields import PositiveIntegerField, BooleanField from django.db.models.fields.related import ForeignKey from django.http import Http404 from django.utils.encoding import force_text, smart_text from django.utils.translation import ugettext_lazy as _ # Django REST Framework from rest_framework import exceptions from rest_framework import metadata from rest_framework import serializers from rest_framework.relations import RelatedField, ManyRelatedField from rest_framework.fields import JSONField as DRFJSONField from rest_framework.request import clone_request # AWX from awx.main.fields import JSONField, ImplicitRoleField from awx.main.models import InventorySource, NotificationTemplate from awx.main.scheduler.kubernetes import PodManager class Metadata(metadata.SimpleMetadata): def get_field_info(self, field): field_info = OrderedDict() field_info['type'] = self.label_lookup[field] field_info['required'] = getattr(field, 'required', False) text_attrs = [ 'read_only', 'label', 'help_text', 'min_length', 'max_length', 'min_value', 'max_value', 'category', 'category_slug', 'defined_in_file' ] for attr in text_attrs: value = getattr(field, attr, None) if value is not None and value != '': field_info[attr] = force_text(value, strings_only=True) placeholder = getattr(field, 'placeholder', serializers.empty) if placeholder is not serializers.empty: field_info['placeholder'] = placeholder serializer = getattr(field, 'parent', None) if serializer and hasattr(serializer, 'Meta') and hasattr(serializer.Meta, 'model'): # Update help text for common fields. field_help_text = { 'id': _('Database ID for this {}.'), 'name': _('Name of this {}.'), 'description': _('Optional description of this {}.'), 'type': _('Data type for this {}.'), 'url': _('URL for this {}.'), 'related': _('Data structure with URLs of related resources.'), 'summary_fields': _('Data structure with name/description for related resources.'), 'created': _('Timestamp when this {} was created.'), 'modified': _('Timestamp when this {} was last modified.'), } if field.field_name in field_help_text: opts = serializer.Meta.model._meta.concrete_model._meta verbose_name = smart_text(opts.verbose_name) field_info['help_text'] = field_help_text[field.field_name].format(verbose_name) if field.field_name == 'type': field_info['filterable'] = True else: for model_field in serializer.Meta.model._meta.fields: if field.field_name == model_field.name: if getattr(model_field, '__accepts_json__', None): field_info['type'] = 'json' field_info['filterable'] = True break else: field_info['filterable'] = False # Indicate if a field has a default value. # FIXME: Still isn't showing all default values? try: default = field.get_default() if field.field_name == 'TOWER_URL_BASE' and default == 'https://towerhost': default = '{}://{}'.format(self.request.scheme, self.request.get_host()) field_info['default'] = default except serializers.SkipField: pass if getattr(field, 'child', None): field_info['child'] = self.get_field_info(field.child) elif getattr(field, 'fields', None): field_info['children'] = self.get_serializer_info(field) if not isinstance(field, (RelatedField, ManyRelatedField)) and hasattr(field, 'choices'): field_info['choices'] = [(choice_value, choice_name) for choice_value, choice_name in field.choices.items()] # Indicate if a field is write-only. if getattr(field, 'write_only', False): field_info['write_only'] = True # Special handling of inventory source_region choices that vary based on # selected inventory source. if field.field_name == 'source_regions': for cp in ('azure_rm', 'ec2', 'gce'): get_regions = getattr(InventorySource, 'get_%s_region_choices' % cp) field_info['%s_region_choices' % cp] = get_regions() # Special handling of group_by choices for EC2. if field.field_name == 'group_by': for cp in ('ec2',): get_group_by_choices = getattr(InventorySource, 'get_%s_group_by_choices' % cp) field_info['%s_group_by_choices' % cp] = get_group_by_choices() # Special handling of notification configuration where the required properties # are conditional on the type selected. if field.field_name == 'notification_configuration': for (notification_type_name, notification_tr_name, notification_type_class) in NotificationTemplate.NOTIFICATION_TYPES: field_info[notification_type_name] = notification_type_class.init_parameters # Special handling of notification messages where the required properties # are conditional on the type selected. try: view_model = field.context['view'].model except (AttributeError, KeyError): view_model = None if view_model == NotificationTemplate and field.field_name == 'messages': for (notification_type_name, notification_tr_name, notification_type_class) in NotificationTemplate.NOTIFICATION_TYPES: field_info[notification_type_name] = notification_type_class.default_messages # Update type of fields returned... model_field = None if serializer and hasattr(serializer, 'Meta') and hasattr(serializer.Meta, 'model'): try: model_field = serializer.Meta.model._meta.get_field(field.field_name) except Exception: pass if field.field_name == 'type': field_info['type'] = 'choice' elif field.field_name in ('url', 'custom_virtualenv', 'token'): field_info['type'] = 'string' elif field.field_name in ('related', 'summary_fields'): field_info['type'] = 'object' elif isinstance(field, PositiveIntegerField): field_info['type'] = 'integer' elif field.field_name in ('created', 'modified'): field_info['type'] = 'datetime' elif ( RelatedField in field.__class__.__bases__ or isinstance(model_field, ForeignKey) ): field_info['type'] = 'id' elif ( isinstance(field, JSONField) or isinstance(model_field, JSONField) or isinstance(field, DRFJSONField) or isinstance(getattr(field, 'model_field', None), JSONField) or field.field_name == 'credential_passwords' ): field_info['type'] = 'json' elif ( isinstance(field, ManyRelatedField) and field.field_name == 'credentials' # launch-time credentials ): field_info['type'] = 'list_of_ids' elif isinstance(model_field, BooleanField): field_info['type'] = 'boolean' return field_info def get_serializer_info(self, serializer, method=None): filterer = getattr(serializer, 'filter_field_metadata', lambda fields, method: fields) return filterer( super(Metadata, self).get_serializer_info(serializer), method ) def determine_actions(self, request, view): # Add field information for GET requests (so field names/labels are # available even when we can't POST/PUT). actions = {} for method in {'GET', 'PUT', 'POST'} & set(view.allowed_methods): view.request = clone_request(request, method) obj = None try: # Test global permissions if hasattr(view, 'check_permissions'): view.check_permissions(view.request) # Test object permissions if method == 'PUT' and hasattr(view, 'get_object'): obj = view.get_object() except (exceptions.APIException, PermissionDenied, Http404): continue else: # If user has appropriate permissions for the view, include # appropriate metadata about the fields that should be supplied. serializer = view.get_serializer(instance=obj) actions[method] = self.get_serializer_info(serializer, method=method) finally: view.request = request for field, meta in list(actions[method].items()): if not isinstance(meta, dict): continue if field == "pod_spec_override": meta['default'] = PodManager().pod_definition # Add type choices if available from the serializer. if field == 'type' and hasattr(serializer, 'get_type_choices'): meta['choices'] = serializer.get_type_choices() # For GET method, remove meta attributes that aren't relevant # when reading a field and remove write-only fields. if method == 'GET': attrs_to_remove = ('required', 'read_only', 'default', 'min_length', 'max_length', 'placeholder') for attr in attrs_to_remove: meta.pop(attr, None) meta.get('child', {}).pop(attr, None) if meta.pop('write_only', False): actions['GET'].pop(field) # For PUT/POST methods, remove read-only fields. if method in ('PUT', 'POST'): # This value should always be False for PUT/POST, so don't # show it (file-based read-only settings can't be updated) meta.pop('defined_in_file', False) if meta.pop('read_only', False): if field == 'id' and hasattr(view, 'attach'): continue actions[method].pop(field) return actions def determine_metadata(self, request, view): # store request on self so we can use it to generate field defaults # (such as TOWER_URL_BASE) self.request = request try: setattr(view, '_request', request) metadata = super(Metadata, self).determine_metadata(request, view) finally: delattr(view, '_request') # Add type(s) handled by this view/serializer. if hasattr(view, 'get_serializer'): serializer = view.get_serializer() if hasattr(serializer, 'get_types'): metadata['types'] = serializer.get_types() # Add search fields if available from the view. if getattr(view, 'search_fields', None): metadata['search_fields'] = view.search_fields # Add related search fields if available from the view. if getattr(view, 'related_search_fields', None): metadata['related_search_fields'] = view.related_search_fields # include role names in metadata roles = [] model = getattr(view, 'model', None) if model: for field in model._meta.get_fields(): if type(field) is ImplicitRoleField: roles.append(field.name) if len(roles) > 0: metadata['object_roles'] = roles from rest_framework import generics if isinstance(view, generics.ListAPIView) and hasattr(view, 'paginator'): metadata['max_page_size'] = view.paginator.max_page_size return metadata class RoleMetadata(Metadata): def determine_metadata(self, request, view): metadata = super(RoleMetadata, self).determine_metadata(request, view) if 'actions' in metadata: metadata['actions'].pop('POST') metadata['actions']['POST'] = { "id": {"type": "integer", "label": "ID", "help_text": "Database ID for this role."}, "disassociate": {"type": "integer", "label": "Disassociate", "help_text": "Provide to remove this role."}, } return metadata class SublistAttachDetatchMetadata(Metadata): def determine_actions(self, request, view): actions = super(SublistAttachDetatchMetadata, self).determine_actions(request, view) method = 'POST' if method in actions: for field in list(actions[method].keys()): if field == 'id': continue actions[method].pop(field) return actions

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null

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null

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0

b9d7834f2dd39b0c5b6da30b8ebfe19e7026adeb

1,985

py

Python

plugins/python/tasks.py

BBVA/deeptracy

40f4b6bba2bdd345e95e42d474c05fa90f15c3e9

[ "Apache-1.1" ]

85

2017-09-22T10:48:51.000Z

2021-06-11T18:33:28.000Z

plugins/python/tasks.py

BBVA/deeptracy

40f4b6bba2bdd345e95e42d474c05fa90f15c3e9

[ "Apache-1.1" ]

51

2017-10-17T10:16:16.000Z

2020-08-29T23:10:21.000Z

plugins/python/tasks.py

BBVA/deeptracy

40f4b6bba2bdd345e95e42d474c05fa90f15c3e9

[ "Apache-1.1" ]

14

2017-11-20T10:20:16.000Z

2021-02-02T21:35:07.000Z

import json from washer.worker.actions import AppendStdout, AppendStderr from washer.worker.actions import CreateNamedLog, AppendToLog from washer.worker.actions import SetProperty from washer.worker.commands import washertask def pipenv_graph2deps(rawgraph): graph = json.loads(rawgraph) def build_entry(data): if 'required_version' in data: spec = data['key'] + data['required_version'] else: spec = data['key'] return {'installer': 'pipenv', 'spec': spec, 'source': 'pypi', 'name': data['package_name'], 'version': data['installed_version']} def extract_dependencies(entries): for entry in entries: if 'package' in entry: package = entry['package'] dependencies = entry.get('dependencies', []) yield build_entry(package) yield from extract_dependencies(dependencies) else: yield build_entry(entry) yield from extract_dependencies(graph) @washertask def pip_install(repopath, path=".", **kwargs): import invoke c = invoke.Context() with c.cd(repopath): with c.cd(path): res = c.run("pipenv install .") deps = c.run("pipenv graph --json") yield AppendStdout(res.stdout) yield AppendStderr(res.stderr) yield SetProperty("dependencies", list(pipenv_graph2deps(deps.stdout))) return True @washertask def requirement_file(repopath, requirement="requirements.txt", path=".", **kwargs): import invoke c = invoke.Context() with c.cd(repopath): with c.cd(path): res = c.run("pipenv install -r %s" % requirement) deps = c.run("pipenv graph --json") yield AppendStdout(res.stdout) yield AppendStderr(res.stderr) yield SetProperty("dependencies", list(pipenv_graph2deps(deps.stdout))) return True

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null

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1

0

b9d84b2b4c7d4cbbbf84bcb2ee37459c480a1a5e

715

py

Python

senity/utils/getSiteProfile.py

pkokkinos/senity

c6e41678620bef558cc3600929a8320ff2a285cf

[ "MIT" ]

1

2017-10-26T12:30:04.000Z

senity/utils/getSiteProfile.py

pkokkinos/senity

c6e41678620bef558cc3600929a8320ff2a285cf

[ "MIT" ]

null

senity/utils/getSiteProfile.py

pkokkinos/senity

c6e41678620bef558cc3600929a8320ff2a285cf

[ "MIT" ]

null

import json import os # get site profile def getSiteProfile(site_file): with open(site_file) as json_file: json_data = json.load(json_file) return json_data # get all site profile def getAllSiteProfiles(site_folder): allSiteProfiles = {} allSiteFiles = os.listdir(site_folder) for sf in allSiteFiles: sp = getSiteProfile(site_folder + "/" + sf) allSiteProfiles[sp["siteName"]] = [] for device in sp["devicesAvailable"]: for i in range(device["deviceCounter"]): allSiteProfiles[sp["siteName"]].append(device["deviceName"]) return allSiteProfiles #sites_folder = "sites" #print getAllSiteProfiles(sites_folder)

23.833333

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null

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null

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1

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b9d87f8b647f237794f75914da625ea130e200c3

5,959

py

Python

ppo_new/baseline.py

QingXinHu123/Lane_change_RL

06c70e6f58d3478669b56800028e320ca03f5222

[ "MIT" ]

1

2022-03-17T03:40:57.000Z

ppo_new/baseline.py

QingXinHu123/Lane_change_RL

06c70e6f58d3478669b56800028e320ca03f5222

[ "MIT" ]

null

ppo_new/baseline.py

QingXinHu123/Lane_change_RL

06c70e6f58d3478669b56800028e320ca03f5222

[ "MIT" ]

null

import os, sys from env.LaneChangeEnv import LaneChangeEnv import random import numpy as np if 'SUMO_HOME' in os.environ: tools = os.path.join(os.environ['SUMO_HOME'], 'tools') sys.path.append(tools) print('success') else: sys.exit("please declare environment variable 'SUMO_HOME'") import traci def episode_generator(pi, env, is_gui, ttc, gap, sumoseed, randomseed): egoid = 'lane1.' + str(random.randint(1, 6)) ob = env.reset(egoid=egoid, tlane=0, tfc=2, is_gui=is_gui, sumoseed=sumoseed, randomseed=randomseed) traci.vehicle.setColor(egoid, (255, 69, 0)) cur_ep_ret = 0 # return in current episode cur_ep_ret_detail = 0 cur_ep_len = 0 # len of current episode cur_ep_obs = [] cur_ep_acs = [] while True: ac = pi(ob=ob, env=env, ttc=ttc, gap=gap) ob, rew, new, info = env.step(ac) cur_ep_ret += rew cur_ep_ret_detail += np.array(list(info['reward_dict'].values())) cur_ep_len += 1 cur_ep_obs.append(ob) cur_ep_acs.append(ac) if new: return {"ep_obs": cur_ep_obs, "ep_acs": cur_ep_acs, "ep_ret": cur_ep_ret, 'ep_rets_detail': cur_ep_ret_detail, "ep_len": cur_ep_len, 'ep_num_danger': info['num_danger'], 'ep_is_success': info['is_success'], 'ep_num_crash': info['num_crash'], 'ep_is_collision': info["is_collision"]} def pi_baseline(ob, env, ttc, gap): # safety gap set to seconds to collision if env.ego.trgt_leader: leader_speed = env.ego.trgt_leader.speed else: leader_speed = env.ego.speed if env.ego.trgt_follower: follower_speed = env.ego.trgt_follower.speed else: follower_speed = env.ego.speed leader_dis = abs(ob[3 * 4 + 0 + 1])*239.8 follower_dis = abs(ob[4 * 4 + 0 + 1])*239.8 TTC = (leader_dis - 5) / max(env.ego.speed, 0.001) TTC2 = (follower_dis - 5) / max(follower_speed, 0.001) # print(TTC, TTC) if TTC > ttc and TTC2 > ttc and leader_dis > gap and follower_dis > gap: ac_lat = 1 # change lane else: ac_lat = 0 # abort ac = ac_lat * 3 + 1 return ac def evaluate_baseline(num_eps, ttc, gap, is_gui): sumoseed = 0 randomseed = 0 pi = pi_baseline env = LaneChangeEnv(is_train=False) ret_eval = 0 ret_det_eval = 0 # not a integer, will be broadcasted danger_num = 0 crash_num = 0 level_1_danger = [] level_2_danger = [] collision_num = 0 ep_len_list = [] success_num = 0 for i in range(num_eps): ep_eval = episode_generator(pi, env, is_gui=is_gui, ttc=ttc, gap=gap, sumoseed=sumoseed, randomseed=randomseed) ret_eval += ep_eval['ep_ret'] ret_det_eval += ep_eval['ep_rets_detail'] danger_num += ep_eval['ep_num_danger'] crash_num += ep_eval['ep_num_crash'] level_1_danger.append(1 if ep_eval['ep_num_danger'] > 0 else 0) level_2_danger.append((1 if ep_eval['ep_num_crash'] > 0 else 0)) collision_num += ep_eval['ep_is_collision'] success_num += int(ep_eval['ep_is_success']) if ep_eval['ep_is_success']: ep_len_list.append(ep_eval['ep_len']) sumoseed += 1 randomseed += 1 ret_eval /= float(num_eps) ret_det_eval /= float(num_eps) danger_rate = danger_num / num_eps crash_rate = crash_num / num_eps level_1_danger_rate = np.mean(level_1_danger) level_2_danger_rate = np.mean(level_2_danger) coll_rate = collision_num / num_eps success_rate = success_num / float(num_eps) success_len = np.mean(ep_len_list) print('reward_detail: ', ret_det_eval) print('reward: ', ret_eval, '\ndanger_rate: ', danger_rate, '\ncrash_rate: ', crash_rate, '\nlevel-1-danger_rate: ', level_1_danger_rate, '\nlevel-2-danger_rate: ', level_2_danger_rate, '\ncollision_rate: ', coll_rate, '\nsuccess_rate: ', success_rate, '\nsucess_len: ', success_len) env.close() return ret_eval, danger_rate, crash_rate, level_1_danger_rate, level_2_danger_rate, coll_rate, success_rate, success_len NUM_EPS = 100 IS_GUI = False # f = open('../data/baseline_evaluation/testseed2.csv', 'w+') # safety_gap = 2 constraints_list = [3.0] # [1.0, 2.0, 3.0, 4.0, 5.0, 10.0, 20.0] ttcs = [0.1, 0.3, 0.5, 1, 2, 3] # ttcs = [2] gap = 0 reward_list = [] danger_rate_list = [] crash_rate_list = [] level_1_danger_list = [] level_2_danger_list = [] coll_rate_list = [] succ_rate_list = [] succ_len_list = [] for ttc in ttcs: ret_eval, danger_rate, crash_rate, level_1_danger_rate, level_2_danger_rate, coll_rate, success_rate, success_len = evaluate_baseline(NUM_EPS, ttc, gap, IS_GUI) reward_list.append(ret_eval) danger_rate_list.append(danger_rate) crash_rate_list.append(crash_rate) level_1_danger_list.append(level_1_danger_rate) level_2_danger_list.append(level_2_danger_rate) coll_rate_list.append(coll_rate) succ_rate_list.append(success_rate) succ_len_list.append(success_len) print('reward: ', reward_list) print('danger rate: ', danger_rate_list) print('crash rate: ', crash_rate_list) print('level-1-danger_rate: ', level_1_danger_list) print('level-2-danger_rate: ', level_2_danger_list) print('collison rate: ', coll_rate_list) print('success rate: ', succ_rate_list) print('sucess len: ', succ_len_list) # reward: [-89.12552753359037, -69.84537459892903, -73.81562785829651, -148.23580687485645, -227.71842861064192, -229.9101089174337] # danger rate: [2.13, 0.88, 0.77, 1.88, 3.82, 3.82] # crash rate: [0.58, 0.33, 0.5, 1.24, 2.09, 2.09] # level-1-danger_rate: [0.23, 0.09, 0.05, 0.14, 0.25, 0.25] # level-2-danger_rate: [0.05, 0.03, 0.05, 0.12, 0.2, 0.2] # collison rate: [0.0, 0.0, 0.02, 0.09, 0.14, 0.14] # success rate: [0.99, 0.99, 0.9, 0.6, 0.08, 0.05] # sucess len: [55.656565656565654, 62.43434343434343, 67.5, 90.1, 66.625, 73.4]

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5,959

3.770747

0.192946

0.071527

0.042916

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0.208424

5,959

162

165

36.783951

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null

0

null

0

1

0

b9d8a3bc2867b57ba7db6ffd06a68bdf7372909c

1,261

py

Python

clean_data.py

toogy/pendigits-hmm

03382e1457941714439d40b67e53eaf117fe4d08

[ "MIT" ]

null

clean_data.py

toogy/pendigits-hmm

03382e1457941714439d40b67e53eaf117fe4d08

[ "MIT" ]

null

clean_data.py

toogy/pendigits-hmm

03382e1457941714439d40b67e53eaf117fe4d08

[ "MIT" ]

null

import numpy as np import pickle from collections import defaultdict from parsing import parser from analysis import training def main(): parse = parser.Parser(); train_digits = parse.parse_file('data/pendigits-train'); test_digits = parse.parse_file('data/pendigits-test') centroids = training.get_digit_kmeans_centroids( train_digits, 256 - 3) training.set_digit_observations( train_digits, centroids, 256) training.set_digit_observations( test_digits, centroids, 256) train_sequences = defaultdict(list) test_sequences = [] n_test_sequences = len(test_digits) test_expected_labels = np.ndarray(shape=(n_test_sequences,)) for digit in train_digits: train_sequences[digit.label].append(digit.np_array_observations) for i, digit in enumerate(test_digits): test_sequences.append(digit.np_array_observations) test_expected_labels[i] = digit.label with open('train_sequences', 'wb') as f: pickle.dump(train_sequences, f) with open('test_sequences', 'wb') as f: pickle.dump(test_sequences, f) with open('test_expected_labels', 'wb') as f: pickle.dump(test_expected_labels, f) if __name__ == '__main__': main()

24.25

72

0.704996

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1,261

5.185185

0.314815

0.092857

0.085714

0.039286

0.286905

0.163095

0

0.009901

0.199048

1,261

51

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0

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0

0.1875

0

null

0

null

0

1

0

b9db09c1d1c26d802117168878ef76954cf77560

3,360

py

Python

matrixprofile/algorithms/snippets.py

KSaiRahul21/matrixprofile

d8250e30d90ed0453bb7c35bb34ab0c04ae7b334

[ "Apache-2.0" ]

null

matrixprofile/algorithms/snippets.py

KSaiRahul21/matrixprofile

d8250e30d90ed0453bb7c35bb34ab0c04ae7b334

[ "Apache-2.0" ]

null

matrixprofile/algorithms/snippets.py

KSaiRahul21/matrixprofile

d8250e30d90ed0453bb7c35bb34ab0c04ae7b334

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals range = getattr(__builtins__, 'xrange', range) # end of py2 compatability boilerplate import numpy as np from matrixprofile import core from matrixprofile.algorithms.mpdist import mpdist_vector def snippets(ts, snippet_size, num_snippets=2, window_size=None): """ The snippets algorithm is used to summarize your time series by identifying N number of representative subsequences. If you want to identify typical patterns in your time series, then this is the algorithm to use. Parameters ---------- ts : array_like The time series. snippet_size : int The size of snippet desired. num_snippets : int, Default 2 The number of snippets you would like to find. window_size : int, Default (snippet_size / 2) The window size. Returns ------- list : snippets A list of snippets as dictionary objects with the following structure. >>> { >>> fraction: fraction of the snippet, >>> index: the index of the snippet, >>> snippet: the snippet values >>> } """ ts = core.to_np_array(ts).astype('d') n = len(ts) if not isinstance(snippet_size, int) or snippet_size < 4: raise ValueError('snippet_size must be an integer >= 4') if n < (2 * snippet_size): raise ValueError('Time series is too short relative to snippet length') if not window_size: window_size = int(np.floor(snippet_size / 2)) if window_size >= snippet_size: raise ValueError('window_size must be smaller than snippet_size') # pad end of time series with zeros num_zeros = int(snippet_size * np.ceil(n / snippet_size) - n) ts = np.append(ts, np.zeros(num_zeros)) # compute all profiles indices = np.arange(0, len(ts) - snippet_size, snippet_size) distances = [] for j, i in enumerate(indices): distance = mpdist_vector(ts, ts[i:(i + snippet_size - 1)], int(window_size)) distances.append(distance) distances = np.array(distances) # find N snippets snippets = [] minis = np.inf total_min = None for n in range(num_snippets): minims = np.inf for i in range(len(indices)): s = np.sum(np.minimum(distances[i, :], minis)) if minims > s: minims = s index = i minis = np.minimum(distances[index, :], minis) actual_index = indices[index] snippet = ts[actual_index:actual_index + snippet_size] snippet_distance = distances[index] snippets.append({ 'index': actual_index, 'snippet': snippet, 'distance': snippet_distance }) if isinstance(total_min, type(None)): total_min = snippet_distance else: total_min = np.minimum(total_min, snippet_distance) # compute the fraction of each snippet for snippet in snippets: mask = (snippet['distance'] <= total_min) snippet['fraction'] = mask.sum() / (len(ts) - snippet_size) total_min = total_min - mask del snippet['distance'] return snippets

29.734513

84

0.633036

428

3,360

4.799065

0.32243

0.091042

0.031159

0.025316

0

0.004512

0.274405

3,360

112

85

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0.837982

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0

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0.160714

0.017857

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null

0

null

0

1

0

b9db24edad8766b6e734d6a8a9c26aff6bb04235

2,360

py

Python

jina/logging/formatter.py

yk/jina

ab66e233e74b956390f266881ff5dc4e0110d3ff

[ "Apache-2.0" ]

1

2020-12-23T12:34:00.000Z

jina/logging/formatter.py

yk/jina

ab66e233e74b956390f266881ff5dc4e0110d3ff

[ "Apache-2.0" ]

null

jina/logging/formatter.py

yk/jina

ab66e233e74b956390f266881ff5dc4e0110d3ff

[ "Apache-2.0" ]

null

import json import re from copy import copy from logging import Formatter from .profile import used_memory from ..helper import colored class ColorFormatter(Formatter): """Format the log into colored logs based on the log-level. """ MAPPING = { 'DEBUG': dict(color='white', on_color=None), # white 'INFO': dict(color='white', on_color=None), # cyan 'WARNING': dict(color='yellow', on_color='on_grey'), # yellow 'ERROR': dict(color='red', on_color=None), # 31 for red 'CRITICAL': dict(color='white', on_color='on_red'), # white on red bg 'SUCCESS': dict(color='green', on_color=None), # white on red bg } #: log-level to color mapping def format(self, record): cr = copy(record) seq = self.MAPPING.get(cr.levelname, self.MAPPING['INFO']) # default white cr.msg = colored(cr.msg, **seq) return super().format(cr) class PlainFormatter(Formatter): """Remove all control chars from the log and format it as plain text Also restrict the max-length of msg to 512 """ def format(self, record): cr = copy(record) if isinstance(cr.msg, str): cr.msg = re.sub(r'\u001b\[.*?[@-~]', '', str(cr.msg))[:512] return super().format(cr) class JsonFormatter(Formatter): """Format the log message as a JSON object so that it can be later used/parsed in browser with javascript. """ KEYS = {'created', 'filename', 'funcName', 'levelname', 'lineno', 'msg', 'module', 'name', 'pathname', 'process', 'thread', 'processName', 'threadName', 'log_id'} #: keys to extract from the log def format(self, record): cr = copy(record) cr.msg = re.sub(r'\u001b\[.*?[@-~]', '', str(cr.msg)) return json.dumps( {k: getattr(cr, k) for k in self.KEYS if hasattr(cr, k)}, sort_keys=True) class ProfileFormatter(Formatter): """Format the log message as JSON object and add the current used memory into it""" def format(self, record): cr = copy(record) if isinstance(cr.msg, dict): cr.msg.update({k: getattr(cr, k) for k in ['created', 'module', 'process', 'thread']}) cr.msg['memory'] = used_memory(unit=1) return json.dumps(cr.msg, sort_keys=True) else: return ''

34.705882

114

0.601695

319

2,360

4.410658

0.363636

0.03909

0.031272

0.054016

0.297797

0.248756

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0.102345

0.06823

0

0.008475

0.25

2,360

67

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0.786441

0.205085

0

0.222222

0

0.133297

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1

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false

0

0.133333

0

0.466667

0

null

0

null

0

1

0

b9dcf24da986778ebcd29602d923908626cfea3c

4,263

py

Python

mtl/util/pipeline.py

vandurme/TFMTL

5958187900bdf67089a237c523b6caa899f63ac1

[ "Apache-2.0" ]

10

2019-05-18T22:23:44.000Z

2022-01-25T15:24:45.000Z

mtl/util/pipeline.py

vandurme/TFMTL

5958187900bdf67089a237c523b6caa899f63ac1

[ "Apache-2.0" ]

1

2020-01-07T15:24:16.000Z

2020-01-15T00:39:01.000Z

mtl/util/pipeline.py

vandurme/TFMTL

5958187900bdf67089a237c523b6caa899f63ac1

[ "Apache-2.0" ]

1

2021-12-02T02:24:06.000Z

# Copyright 2018 Johns Hopkins University. 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. # ============================================================================== from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import namedtuple import tensorflow as tf from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib from tensorflow.python.ops import parsing_ops class Pipeline(object): def __init__(self, tfrecord_file, feature_map, batch_size=32, num_threads=4, prefetch_buffer_size=1, static_max_length=None, shuffle_buffer_size=10000, shuffle=True, num_epochs=None, one_shot=False): self._feature_map = feature_map self._batch_size = batch_size self._static_max_length = static_max_length # Initialize the dataset dataset = tf.data.TFRecordDataset(tfrecord_file) # Maybe randomize if shuffle: dataset = dataset.shuffle(shuffle_buffer_size) # Maybe repeat if num_epochs is None: dataset = dataset.repeat() # repeat indefinitely elif num_epochs > 1: dataset = dataset.repeat(count=num_epochs) dataset = dataset.batch(batch_size) dataset = dataset.map(self.parse_example, num_parallel_calls=num_threads) # Pre-fetch a batch for faster processing dataset = dataset.prefetch(prefetch_buffer_size) # Get the iterator if one_shot: self._iterator = dataset.make_one_shot_iterator() else: self._iterator = dataset.make_initializable_iterator() self._init_op = self._iterator.initializer # Get outputs self._outputs = self._iterator.get_next() # Map to features index = 0 result = {} for key in sorted(self._feature_map.keys()): result[key] = self._outputs[index] index += 1 self._result = result def pad(self, t): s = tf.shape(t) paddings = [[0, 0], [0, self._static_max_length - s[1]]] x = tf.pad(t, paddings, 'CONSTANT', constant_values=0) x = tf.reshape(x, [s[0], self._static_max_length]) assert x.get_shape().as_list()[1] is self._static_max_length return x def parse_example(self, serialized): parsed = parsing_ops.parse_example(serialized, self._feature_map) result = [] for key in sorted(self._feature_map.keys()): val = parsed[key] if isinstance(val, sparse_tensor_lib.SparseTensor): dense_tensor = tf.sparse_tensor_to_dense(val) if self._static_max_length is not None: dense_tensor = self.pad(dense_tensor) result.append(dense_tensor) else: result.append(val) return tuple(result) @property def iterator(self): return self._iterator @property def init_op(self): return self._init_op @property def batch(self): return self._result # namedtuple for bucket_info object (used in Pipeline) # func: a mapping from examples to tf.int64 keys # pads: a set of tf shapes that correspond to padded examples bucket_info = namedtuple("bucket_info", "func pads") def int64_feature(value): """ Takes a single int (e.g. 3) and converts it to a tf Feature """ return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) def int64_list_feature(sequence): """ Sequence of ints (e.g [1,2,3]) to TF feature """ return tf.train.Feature(int64_list=tf.train.Int64List(value=sequence))

34.658537

80

0.649073

546

4,263

4.847985

0.357143

0.023801

0.039668

0.03589

0.088402

0.073291

0.044579

0

0.014111

0.251935

4,263

122

81

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0.81593

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0

0.097222

0

0.00892

0

0.013889

1

0.111111

false

0

0.097222

0.041667

0.319444

0.013889

0

null

0

null

0

1

0

b9de795b7b1298f8cad5f30e914735224920a0f9

1,158

py

Python

core/views.py

moiyad/image

d4515ef3057794f38268a6887bfff157115f26f7

[ "MIT" ]

null

core/views.py

moiyad/image

d4515ef3057794f38268a6887bfff157115f26f7

[ "MIT" ]

null

core/views.py

moiyad/image

d4515ef3057794f38268a6887bfff157115f26f7

[ "MIT" ]

null

from django.core.files.storage import FileSystemStorage from django.shortcuts import render, redirect from core.forms import DocumentForm from core.models import Document from media import image_cv2 def home(request): documents = Document.objects.all() number = len(image_cv2.myList) return render(request, 'core/home.html', {'documents': documents, 'number': number}) def simple_upload(request): if request.method == 'POST' and request.FILES['myfile']: myfile = request.FILES['myfile'] fs = FileSystemStorage() filename = fs.save(myfile.name, myfile) uploaded_file_url = fs.url(filename) return render(request, 'core/simple_upload.html', { 'uploaded_file_url': uploaded_file_url }) return render(request, 'core/simple_upload.html') def model_form_upload(request): if request.method == 'POST': form = DocumentForm(request.POST, request.FILES) if form.is_valid(): form.save() return redirect('home') else: form = DocumentForm() return render(request, 'core/model_form_upload.html', { 'form': form })

30.473684

88

0.668394

137

1,158

5.532847

0.343066

0.063325

0.100264

0.121372

0.187335

0.102902

0

0.002208

0.217617

1,158

37

89

31.297297

0.834437

0

0.066667

0

0.126943

0.06304

0

1

0.1

false

0

0.166667

0

0.433333

0

null

0

null

0

1

0

b9dfea4e7beba7ec415b85a76c49ed3af214dec4

25,442

py

Python

ml4chem/atomistic/models/neuralnetwork.py

muammar/mlchem

365487c23ea3386657e178e56ab31adfe8d5d073

[ "BSD-3-Clause-LBNL" ]

77

2019-08-05T17:30:22.000Z

2022-03-28T14:31:35.000Z

ml4chem/atomistic/models/neuralnetwork.py

muammar/ml4chem

365487c23ea3386657e178e56ab31adfe8d5d073

[ "BSD-3-Clause-LBNL" ]

6

2019-07-31T18:59:38.000Z

2020-10-18T18:15:07.000Z

ml4chem/atomistic/models/neuralnetwork.py

muammar/mlchem

365487c23ea3386657e178e56ab31adfe8d5d073

[ "BSD-3-Clause-LBNL" ]

15

2020-02-28T10:11:21.000Z

2021-12-01T13:45:33.000Z

import dask import datetime import logging import time import torch import numpy as np import pandas as pd from collections import OrderedDict from ml4chem.metrics import compute_rmse from ml4chem.atomistic.models.base import DeepLearningModel, DeepLearningTrainer from ml4chem.atomistic.models.loss import AtomicMSELoss from ml4chem.optim.handler import get_optimizer, get_lr_scheduler, get_lr from ml4chem.utils import convert_elapsed_time, get_chunks, get_number_of_parameters from pprint import pformat # Setting precision and starting logger object torch.set_printoptions(precision=10) logger = logging.getLogger() class NeuralNetwork(DeepLearningModel, torch.nn.Module): """Atom-centered Neural Network Regression with Pytorch This model is based on Ref. 1 by Behler and Parrinello. Parameters ---------- hiddenlayers : tuple Structure of hidden layers in the neural network. activation : str Activation functions. Supported "tanh", "relu", or "celu". References ---------- 1. Behler, J. & Parrinello, M. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Phys. Rev. Lett. 98, 146401 (2007). 2. Khorshidi, A. & Peterson, A. A. Amp : A modular approach to machine learning in atomistic simulations. Comput. Phys. Commun. 207, 310–324 (2016). """ NAME = "PytorchPotentials" @classmethod def name(cls): """Returns name of class""" return cls.NAME def __init__(self, hiddenlayers=(3, 3), activation="relu", **kwargs): super(DeepLearningModel, self).__init__() self.hiddenlayers = hiddenlayers self.activation = activation def prepare_model(self, input_dimension, data=None, purpose="training"): """Prepare the model Parameters ---------- input_dimension : int Input's dimension. data : object Data object created from the handler. purpose : str Purpose of this model: 'training', 'inference'. """ self.input_dimension = input_dimension activation = { "tanh": torch.nn.Tanh, "relu": torch.nn.ReLU, "celu": torch.nn.CELU, } hl = len(self.hiddenlayers) if purpose == "training": logger.info(" ") logger.info("Model") logger.info("=====") now = datetime.datetime.now() logger.info( "Module accessed on {}.".format(now.strftime("%Y-%m-%d %H:%M:%S")) ) logger.info("Model name: {}.".format(self.name())) logger.info("Number of hidden-layers: {}".format(hl)) logger.info( "Structure of Neural Net: {}".format( "(input, " + str(self.hiddenlayers)[1:-1] + ", output)" ) ) layers = range(len(self.hiddenlayers) + 1) try: unique_element_symbols = data.unique_element_symbols[purpose] except TypeError: unique_element_symbols = data.get_unique_element_symbols(purpose=purpose) unique_element_symbols = unique_element_symbols[purpose] symbol_model_pair = [] for symbol in unique_element_symbols: linears = [] intercept_name = "intercept_" + symbol slope_name = "slope_" + symbol if purpose == "training": intercept = (data.max_energy + data.min_energy) / 2.0 intercept = torch.nn.Parameter( torch.tensor(intercept, requires_grad=True) ) slope = (data.max_energy - data.min_energy) / 2.0 slope = torch.nn.Parameter(torch.tensor(slope, requires_grad=True)) self.register_parameter(intercept_name, intercept) self.register_parameter(slope_name, slope) elif purpose == "inference": intercept = torch.nn.Parameter(torch.tensor(0.0)) slope = torch.nn.Parameter(torch.tensor(0.0)) self.register_parameter(intercept_name, intercept) self.register_parameter(slope_name, slope) for index in layers: # This is the input layer if index == 0: out_dimension = self.hiddenlayers[0] _linear = torch.nn.Linear(input_dimension, out_dimension) linears.append(_linear) linears.append(activation[self.activation]()) # This is the output layer elif index == len(self.hiddenlayers): inp_dimension = self.hiddenlayers[index - 1] out_dimension = 1 _linear = torch.nn.Linear(inp_dimension, out_dimension) linears.append(_linear) # These are hidden-layers else: inp_dimension = self.hiddenlayers[index - 1] out_dimension = self.hiddenlayers[index] _linear = torch.nn.Linear(inp_dimension, out_dimension) linears.append(_linear) linears.append(activation[self.activation]()) # Stacking up the layers. linears = torch.nn.Sequential(*linears) symbol_model_pair.append([symbol, linears]) self.linears = torch.nn.ModuleDict(symbol_model_pair) if purpose == "training": total_params, train_params = get_number_of_parameters(self) logger.info("Total number of parameters: {}.".format(total_params)) logger.info("Number of training parameters: {}.".format(train_params)) logger.info(" ") logger.info(self.linears) # Iterate over all modules and just intialize those that are # a linear layer. logger.warning( "Initialization of weights with Xavier Uniform by " "default." ) for m in self.modules(): if isinstance(m, torch.nn.Linear): # nn.init.normal_(m.weight) # , mean=0, std=0.01) torch.nn.init.xavier_uniform_(m.weight) def forward(self, X): """Forward propagation This is forward propagation and it returns the atomic energy. Parameters ---------- X : list List of inputs in the feature space. Returns ------- outputs : tensor A list of tensors with energies per image. """ outputs = [] for hash in X: image = X[hash] atomic_energies = [] for symbol, x in image: # FIXME this conditional can be removed after de/serialization # is fixed. if isinstance(symbol, bytes): symbol = symbol.decode("utf-8") x = self.linears[symbol](x) intercept_name = "intercept_" + symbol slope_name = "slope_" + symbol slope = getattr(self, slope_name) intercept = getattr(self, intercept_name) x = (slope * x) + intercept atomic_energies.append(x) atomic_energies = torch.cat(atomic_energies) image_energy = torch.sum(atomic_energies) outputs.append(image_energy) outputs = torch.stack(outputs) return outputs def get_activations(self, images, model=None, numpy=True): """Get activations of each hidden-layer This function allows to extract activations of each hidden-layer of the neural network. Parameters ---------- image : dict Image with structure hash, features. model : object A ML4Chem model object. numpy : bool Whether we want numpy arrays or tensors. Returns ------- activations : DataFrame A DataFrame with activations for each layer. """ activations = [] columns = ["hash", "atom.index", "atom.symbol"] if model is None: model = self model.eval() for hash, data in images.items(): for index, (symbol, features) in enumerate(data): counter = 0 layer_counter = 0 for l, layer in enumerate(model.linears[symbol].modules()): if isinstance(layer, torch.nn.Linear) and counter == 0: x = layer(features) if numpy: data_ = [hash, index, symbol, x.detach_().numpy()] else: data_ = [hash, index, symbol, x.detach_()] layer_column_name = f"layer{layer_counter}" if layer_column_name not in columns: columns.append(layer_column_name) counter += 1 layer_counter += 1 elif isinstance(layer, torch.nn.Linear) and counter > 0: x = layer(x) if numpy: data_.append(x.detach_().numpy()) else: data_.append(x.detach_()) layer_column_name = f"layer{layer_counter}" if layer_column_name not in columns: columns.append(layer_column_name) counter += 1 layer_counter += 1 activations.append(data_) del data_ # Create DataFrame from lists df = pd.DataFrame(activations, columns=columns) return df class train(DeepLearningTrainer): """Train the model Parameters ---------- inputs : dict Dictionary with hashed feature space. targets : list The expected values that the model has to learn aka y. model : object The NeuralNetwork class. data : object Data object created from the handler. optimizer : tuple The optimizer is a tuple with the structure: >>> ('adam', {'lr': float, 'weight_decay'=float}) epochs : int Number of full training cycles. regularization : float This is the L2 regularization. It is not the same as weight decay. convergence : dict Instead of using epochs, users can set a convergence criterion. Supported keys are "training" and "test". lossfxn : obj A loss function object. device : str Calculation can be run in the cpu or cuda (gpu). batch_size : int Number of data points per batch to use for training. Default is None. lr_scheduler : tuple Tuple with structure: scheduler's name and a dictionary with keyword arguments. >>> lr_scheduler = ('ReduceLROnPlateau', {'mode': 'min', 'patience': 10}) uncertainty : list A list of uncertainties that are used to penalize during the loss function evaluation. checkpoint : dict Set checkpoints. Dictionary with following structure: >>> checkpoint = {"label": label, "checkpoint": 100, "path": ""} `label` refers to the name used to save the checkpoint, `checkpoint` is a integer or -1 for saving all epochs, and the path is where the checkpoint is stored. Default is None and no checkpoint is saved. test : dict A dictionary used to compute the error over a validation/test set during training procedures. >>> test = {"features": test_space, "targets": test_targets, "data": data_test} The keys,values of the dictionary are: - "data": a `Data` object. - "targets": test set targets. - "features": a feature space obtained using `features.calculate()`. """ def __init__( self, inputs, targets, model=None, data=None, optimizer=(None, None), regularization=None, epochs=100, convergence=None, lossfxn=None, device="cpu", batch_size=None, lr_scheduler=None, uncertainty=None, checkpoint=None, test=None, ): self.initial_time = time.time() if lossfxn is None: lossfxn = AtomicMSELoss logger.info("") logger.info("Training") logger.info("========") logger.info(f"Convergence criteria: {convergence}") logger.info(f"Loss function: {lossfxn.__name__}") if uncertainty is not None: logger.info("Options:") logger.info(f" - Uncertainty penalization: {pformat(uncertainty)}") logger.info("") atoms_per_image = data.atoms_per_image if batch_size is None: batch_size = len(inputs.values()) if isinstance(batch_size, int): # Data batches chunks = list(get_chunks(inputs, batch_size, svm=False)) targets = list(get_chunks(targets, batch_size, svm=False)) atoms_per_image = list(get_chunks(atoms_per_image, batch_size, svm=False)) if uncertainty != None: uncertainty = list(get_chunks(uncertainty, batch_size, svm=False)) uncertainty = [ torch.tensor(u, requires_grad=False, dtype=torch.float) for u in uncertainty ] logger.info("") logging.info("Batch Information") logging.info("-----------------") logging.info("Number of batches: {}.".format(len(chunks))) logging.info("Batch size: {} elements per batch.".format(batch_size)) logger.info(" ") atoms_per_image = [ torch.tensor(n_atoms, requires_grad=False, dtype=torch.float) for n_atoms in atoms_per_image ] targets = [torch.tensor(t, requires_grad=False) for t in targets] if device == "cuda": logger.info("Moving data to CUDA...") atoms_per_image = atoms_per_image.cuda() targets = targets.cuda() _inputs = OrderedDict() for hash, f in inputs.items(): _inputs[hash] = [] for features in f: symbol, vector = features _inputs[hash].append((symbol, vector.cuda())) inputs = _inputs move_time = time.time() - self.initial_time h, m, s = convert_elapsed_time(move_time) logger.info( "Data moved to GPU in {} hours {} minutes {:.2f} \ seconds.".format( h, m, s ) ) logger.info(" ") # Define optimizer self.optimizer_name, self.optimizer = get_optimizer( optimizer, model.parameters() ) if lr_scheduler is not None: self.scheduler = get_lr_scheduler(self.optimizer, lr_scheduler) self.atoms_per_image = atoms_per_image self.convergence = convergence self.device = device self.epochs = epochs self.model = model self.lr_scheduler = lr_scheduler self.lossfxn = lossfxn self.checkpoint = checkpoint self.test = test # Data scattering client = dask.distributed.get_client() self.chunks = [client.scatter(chunk) for chunk in chunks] self.targets = [client.scatter(target) for target in targets] if uncertainty != None: self.uncertainty = [client.scatter(u) for u in uncertainty] else: self.uncertainty = uncertainty # Let the hunger games begin... self.trainer() def trainer(self): """Run the training class""" logger.info(" ") logger.info("Starting training...\n") if self.test is None: logger.info( "{:6s} {:19s} {:12s} {:12s} {:8s}".format( "Epoch", "Time Stamp", "Loss", "Error/img", "Error/atom" ) ) logger.info( "{:6s} {:19s} {:12s} {:8s} {:8s}".format( "------", "-------------------", "------------", "------------", "------------", ) ) else: test_features = self.test.get("features", None) test_targets = self.test.get("targets", None) test_data = self.test.get("data", None) logger.info( "{:6s} {:19s} {:12s} {:12s} {:12s} {:12s} {:16s}".format( "Epoch", "Time Stamp", "Loss", "Error/img", "Error/atom", "Error/img (t)", "Error/atom (t)", ) ) logger.info( "{:6s} {:19s} {:12s} {:8s} {:8s} {:8s} {:8s}".format( "------", "-------------------", "------------", "------------", "------------", "------------", "------------", ) ) converged = False _loss = [] _rmse = [] epoch = 0 client = dask.distributed.get_client() while not converged: epoch += 1 self.optimizer.zero_grad() # clear previous gradients loss, outputs_ = train.closure( self.chunks, self.targets, self.uncertainty, self.model, self.lossfxn, self.atoms_per_image, self.device, ) # We step the optimizer if self.optimizer_name != "LBFGS": self.optimizer.step() else: options = {"closure": self.closure, "current_loss": loss, "max_ls": 10} self.optimizer.step(options) # RMSE per image and per/atom rmse = client.submit(compute_rmse, *(outputs_, self.targets)) atoms_per_image = torch.cat(self.atoms_per_image) rmse_atom = client.submit( compute_rmse, *(outputs_, self.targets, atoms_per_image) ) rmse = rmse.result() rmse_atom = rmse_atom.result() _loss.append(loss.item()) _rmse.append(rmse) # In the case that lr_scheduler is not None if self.lr_scheduler is not None: self.scheduler.step(loss) print("Epoch {} lr {}".format(epoch, get_lr(self.optimizer))) ts = time.time() ts = datetime.datetime.fromtimestamp(ts).strftime("%Y-%m-%d " "%H:%M:%S") if self.test is None: logger.info( "{:6d} {} {:8e} {:4e} {:4e}".format( epoch, ts, loss.detach(), rmse, rmse_atom ) ) else: test_model = self.model.eval() test_predictions = test_model(test_features).detach() rmse_test = client.submit( compute_rmse, *(test_predictions, test_targets) ) atoms_per_image_test = torch.tensor( test_data.atoms_per_image, requires_grad=False ) rmse_atom_test = client.submit( compute_rmse, *(test_predictions, test_targets, atoms_per_image_test), ) rmse_test = rmse_test.result() rmse_atom_test = rmse_atom_test.result() logger.info( "{:6d} {} {:8e} {:4e} {:4e} {:4e} {:4e}".format( epoch, ts, loss.detach(), rmse, rmse_atom, rmse_test, rmse_atom_test, ) ) if self.checkpoint is not None: self.checkpoint_save(epoch, self.model, **self.checkpoint) if self.convergence is None and epoch == self.epochs: converged = True elif self.convergence is not None and rmse < self.convergence["energy"]: converged = True training_time = time.time() - self.initial_time h, m, s = convert_elapsed_time(training_time) logger.info( "Training finished in {} hours {} minutes {:.2f} seconds.".format(h, m, s) ) @classmethod def closure( Cls, chunks, targets, uncertainty, model, lossfxn, atoms_per_image, device ): """Closure This class method clears previous gradients, iterates over batches, accumulates the gradients, reduces the gradients, update model params, and finally returns loss and outputs_. Parameters ---------- Cls : object Class object. chunks : tensor or list Tensor with input data points in batch with index. targets : tensor or list The targets. uncertainty : list A list of uncertainties that are used to penalize during the loss function evaluation. model : obj Pytorch model to perform forward() and get gradients. lossfxn : obj A loss function object. atoms_per_image : list Atoms per image because we are doing atom-centered methods. device : str Are we running cuda or cpu? """ outputs_ = [] # Get client to send futures to the scheduler client = dask.distributed.get_client() running_loss = torch.tensor(0, dtype=torch.float) accumulation = [] grads = [] # Accumulation of gradients for index, chunk in enumerate(chunks): accumulation.append( client.submit( train.train_batches, *( index, chunk, targets, uncertainty, model, lossfxn, atoms_per_image, device, ), ) ) dask.distributed.wait(accumulation) accumulation = client.gather(accumulation) for outputs, loss, grad in accumulation: grad = np.array(grad, dtype=object) running_loss += loss outputs_.append(outputs) grads.append(grad) grads = sum(grads) for index, param in enumerate(model.parameters()): param.grad = torch.tensor(grads[index], dtype=torch.float) del accumulation del grads return running_loss, outputs_ @classmethod def train_batches( Cls, index, chunk, targets, uncertainty, model, lossfxn, atoms_per_image, device ): """A function that allows training per batches Parameters ---------- index : int Index of batch. chunk : tensor or list Tensor with input data points in batch with index. targets : tensor or list The targets. model : obj Pytorch model to perform forward() and get gradients. uncertainty : list A list of uncertainties that are used to penalize during the loss function evaluation. lossfxn : obj A loss function object. atoms_per_image : list Atoms per image because we are doing atom-centered methods. device : str Are we running cuda or cpu? Returns ------- loss : tensor The loss function of the batch. """ inputs = OrderedDict(chunk) outputs = model(inputs) if uncertainty == None: loss = lossfxn(outputs, targets[index], atoms_per_image[index]) else: loss = lossfxn( outputs, targets[index], atoms_per_image[index], uncertainty[index] ) loss.backward() gradients = [] for param in model.parameters(): try: gradient = param.grad.detach().numpy() except AttributeError: # This exception catches the case where an image does not # contain variable that is following the gradient of certain # atom. For example, suppose two batches with 2 molecules each. # In the first batch we have only C, H, O but it turns out that # N is also available only in the second batch. The # contribution of the total gradient from the first batch for N is 0. gradient = 0.0 gradients.append(gradient) return outputs, loss, gradients

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null

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null

0

1

0

b9e018d6290ebe7b0654b7e76a8df225914e3778

7,104

py

Python

hatsploit/core/db/db.py

EntySec/HatSploit

8e445804c252cc24e87888be2c2efc02750ce5ee

[ "MIT" ]

139

2021-02-17T15:52:30.000Z

2022-03-30T14:50:42.000Z

hatsploit/core/db/db.py

YurinDoctrine/HatSploit

b1550323e08336ec057cbafb77003c22a3bbee91

[ "MIT" ]

27

2021-03-24T17:14:30.000Z

2022-03-02T18:50:43.000Z

hatsploit/core/db/db.py

YurinDoctrine/HatSploit

b1550323e08336ec057cbafb77003c22a3bbee91

[ "MIT" ]

85

2021-02-17T15:39:03.000Z

2022-03-07T09:08:58.000Z

#!/usr/bin/env python3 # # MIT License # # Copyright (c) 2020-2022 EntySec # # 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 json import os from hatsploit.core.cli.badges import Badges from hatsploit.lib.config import Config from hatsploit.lib.storage import LocalStorage class DB: badges = Badges() config = Config() local_storage = LocalStorage() def disconnect_payload_database(self, name): if self.local_storage.get("connected_payload_databases"): if name in self.local_storage.get("connected_payload_databases"): self.local_storage.delete_element("connected_payload_databases", name) self.local_storage.delete_element("payloads", name) return self.badges.print_error("No such payload database connected!") def disconnect_module_database(self, name): if self.local_storage.get("connected_module_databases"): if name in self.local_storage.get("connected_module_databases"): self.local_storage.delete_element("connected_module_databases", name) self.local_storage.delete_element("modules", name) return self.badges.print_error("No such module database connected!") def disconnect_plugin_database(self, name): if self.local_storage.get("connected_plugin_databases"): if name in self.local_storage.get("connected_plugin_databases"): self.local_storage.delete_element("connected_plugin_databases", name) self.local_storage.delete_element("plugins", name) return self.badges.print_error("No such plugin database connected!") def connect_payload_database(self, name, path): if self.local_storage.get("connected_payload_databases"): if name in self.local_storage.get("connected_payload_databases"): self.badges.print_error("Payload database already connected!") return if not os.path.exists(path) or not str.endswith(path, "json"): self.badges.print_error("Not a payload database!") return try: database = json.load(open(path)) except Exception: self.badges.print_error("Failed to connect payload database!") return if '__database__' not in database: self.badges.print_error("No __database__ section found!") return if database['__database__']['type'] != "payloads": self.badges.print_error("Not a payload database!") return del database['__database__'] payloads = { name: database } data = { name: { 'path': path } } if not self.local_storage.get("connected_payload_databases"): self.local_storage.set("connected_payload_databases", {}) self.local_storage.update("connected_payload_databases", data) if self.local_storage.get("payloads"): self.local_storage.update("payloads", payloads) else: self.local_storage.set("payloads", payloads) def connect_module_database(self, name, path): if self.local_storage.get("connected_module_databases"): if name in self.local_storage.get("connected_module_databases"): self.badges.print_error("Module database already connected!") return if not os.path.exists(path) or not str.endswith(path, "json"): self.badges.print_error("Not a module database!") return try: database = json.load(open(path)) except Exception: self.badges.print_error("Failed to connect module database!") return if '__database__' not in database: self.badges.print_error("No __database__ section found!") return if database['__database__']['type'] != "modules": self.badges.print_error("Not a module database!") return del database['__database__'] modules = { name: database } data = { name: { 'path': path } } if not self.local_storage.get("connected_module_databases"): self.local_storage.set("connected_module_databases", {}) self.local_storage.update("connected_module_databases", data) if self.local_storage.get("modules"): self.local_storage.update("modules", modules) else: self.local_storage.set("modules", modules) def connect_plugin_database(self, name, path): if self.local_storage.get("connected_plugin_databases"): if name in self.local_storage.get("connected_plugin_databases"): self.badges.print_error("Plugin database already connected!") return if not os.path.exists(path) or not str.endswith(path, "json"): self.badges.print_error("Not a database!") return try: database = json.load(open(path)) except Exception: self.badges.print_error("Failed to connect plugin database!") return if '__database__' not in database: self.badges.print_error("No __database__ section found!") return if database['__database__']['type'] != "plugins": self.badges.print_error("Not a plugin database!") return del database['__database__'] plugins = { name: database } data = { name: { 'path': path } } if not self.local_storage.get("connected_plugin_databases"): self.local_storage.set("connected_plugin_databases", {}) self.local_storage.update("connected_plugin_databases", data) if self.local_storage.get("plugins"): self.local_storage.update("plugins", plugins) else: self.local_storage.set("plugins", plugins)

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0.190942

0.101579

0.131778

0.078243

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0.269285

7,104

185

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0.840301

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0.133333

0

null

0

null

0

1

0

b9e09def642ce98a753ac3053c44b1ba7d862f16

4,850

py

Python

shutTheBox/main.py

robi1467/shut-the-box

ed1a8f13bc74caa63361453e723768a9cbe1dac4

[ "MIT" ]

null

shutTheBox/main.py

robi1467/shut-the-box

ed1a8f13bc74caa63361453e723768a9cbe1dac4

[ "MIT" ]

null

shutTheBox/main.py

robi1467/shut-the-box

ed1a8f13bc74caa63361453e723768a9cbe1dac4

[ "MIT" ]

null

import random numbers_list = [1,2,3,4,5,6,7,8,9,10] game_won = False game_completed = False #Stats games_played = 0 games_won = 0 games_lost = 0 average_score = 0 total_score = 0 def welcome(): welcome_message = "Welcome to shut the box" print(welcome_message) i = 0 result = "" while i < len(numbers_list): if i < len(numbers_list)-1: result += str(numbers_list[i]) + " " else: result += str(numbers_list[i]) i+=1 print(result) def dice_roll(amount): total = 0 i = 0 while i < amount: total += random.randint(1, 6) i+=1 return total def choose_dice_amount(): amount = 0 while True: try: amount = int(input("You choose to roll one or two dice. Please enter either '1' or '2': ")) except ValueError: print("INVALID ENTRY PLEASE TRY AGAIN") continue if amount == 1 or amount == 2: return amount else: print("INVALID ENTRY PLEASE TRY AGAIN!") continue return amount def choose_number_to_drop(target_amount): entered = 0 goal = target_amount entered_numbers = list() while goal != 0: try: print("Available numbers: " + str(numbers_list) + " to get to " + str(target_amount)) entered = int(input("Please enter a number that is available: ")) except ValueError: print("Invalid Entry, please try again") continue if entered not in numbers_list or entered in entered_numbers: print("Invalid Entry, please try again") continue else: goal -= entered entered_numbers.append(entered) if goal < 0: goal = target_amount entered_numbers = list() i = 0 while i < len(entered_numbers): numbers_list.remove(entered_numbers[i]) i += 1 def check_lost_game(rolled): value = True if rolled not in numbers_list: i = 0 while i < len(numbers_list): j = i+1 while j< len(numbers_list): if numbers_list[i] + numbers_list[j] == rolled: return False k = j+1 while k < len(numbers_list): if numbers_list[i] + numbers_list[j] + numbers_list[k] == rolled: return False l = k+1 while l < len(numbers_list): if numbers_list[i] + numbers_list[j] + numbers_list[k] + numbers_list[l] == rolled: return False l+=1 k+=1 j+=1 i +=1 else: value = False return value def end_game(): game_completed = True return game_completed def win_game(): game_won = True return game_won def score_game(): score = 0 i = 0 while i < len(numbers_list): score += numbers_list[i] i+=1 return score def all_less_than_7(): less_than_7 = True i = 0 while i < len(numbers_list): if numbers_list[i] > 6: less_than_7 = False i += 1 return less_than_7 def keep_playing_input(): while True: try: continue_playing = (input("Do you wish to keep playing? y or n: ")) except ValueError: print("Invalid choice; please try again") continue if continue_playing.lower == "y": return True else: return False keep_playing = True while keep_playing: numbers_list = [1,2,3,4,5,6,7,8,9,10] welcome() roll_total = 0 while roll_total < 55: dice_amount = 2 if all_less_than_7(): dice_amount = choose_dice_amount() dice_total = dice_roll(dice_amount) print("Your roll is: " + str(dice_total)) if check_lost_game(dice_total): print("It is impossible to continue the game with this roll") break choose_number_to_drop(dice_total) roll_total += dice_total if roll_total == 55: game_won = win_game() if game_won: print("Congrats you won!!!!") games_played +=1 games_won +=1 else: print("You lose, your score is " + str(score_game())) print("Numbers remaining: " + str(numbers_list)) games_played += 1 games_lost += 1 total_score += score_game() average_score = total_score/games_played game_won = False print("STATS:\n Games Played: " + str(games_played) + "\nGames Won: " + str(games_won) + "\nGames Lost: " + str(games_lost) + "\nAverage Score: " + str(average_score) + "\nTotal Score: " + str(total_score)) keep_playing_input()

28.034682

127

0.549897

619

4,850

4.113086

0.177706

0.129615

0.042419

0.029458

0.261587

0.225452

0.216811

0.118617

0

0.023672

0.355464

4,850

172

128

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0.790787

0.001031

0

0.341935

0

0.117052

0

1

0.064516

false

0

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0

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0.090323

0

null

0

null

0

1

0

b9e379a95e3f4e855adb56ee1112dc1aa95e6a78

9,351

py

Python

main.py

mithi/semantic-segmentation

85e9df04397745e0c6ab252e30991fa9b514ec1a

[ "MIT" ]

33

2017-08-24T16:38:15.000Z

2022-03-17T15:55:52.000Z

main.py

mithi/semantic-segmentation

85e9df04397745e0c6ab252e30991fa9b514ec1a

[ "MIT" ]

3

2018-10-12T11:17:22.000Z

2019-05-30T09:49:11.000Z

main.py

mithi/semantic-segmentation

85e9df04397745e0c6ab252e30991fa9b514ec1a

[ "MIT" ]

26

2017-09-17T09:09:52.000Z

2020-01-14T02:48:56.000Z

import tensorflow as tf import os.path import warnings from distutils.version import LooseVersion import glob import helper import project_tests as tests #-------------------------- # USER-SPECIFIED DATA #-------------------------- # Tune these parameters NUMBER_OF_CLASSES = 2 IMAGE_SHAPE = (160, 576) EPOCHS = 20 BATCH_SIZE = 1 LEARNING_RATE = 0.0001 DROPOUT = 0.75 # Specify these directory paths DATA_DIRECTORY = './data' RUNS_DIRECTORY = './runs' TRAINING_DATA_DIRECTORY ='./data/data_road/training' NUMBER_OF_IMAGES = len(glob.glob('./data/data_road/training/calib/*.*')) VGG_PATH = './data/vgg' all_training_losses = [] # Used for plotting to visualize if our training is going well given parameters #-------------------------- # DEPENDENCY CHECK #-------------------------- # Check TensorFlow Version assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__) print('TensorFlow Version: {}'.format(tf.__version__)) # Check for a GPU if not tf.test.gpu_device_name(): warnings.warn('No GPU found. Please use a GPU to train your neural network.') else: print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) #-------------------------- # PLACEHOLDER TENSORS #-------------------------- correct_label = tf.placeholder(tf.float32, [None, IMAGE_SHAPE[0], IMAGE_SHAPE[1], NUMBER_OF_CLASSES]) learning_rate = tf.placeholder(tf.float32) keep_prob = tf.placeholder(tf.float32) #-------------------------- # FUNCTIONS #-------------------------- def load_vgg(sess, vgg_path): """ Load Pretrained VGG Model into TensorFlow. sess: TensorFlow Session vgg_path: Path to vgg folder, containing "variables/" and "saved_model.pb" return: Tuple of Tensors from VGG model (image_input, keep_prob, layer3, layer4, layer7) """ # load the model and weights model = tf.saved_model.loader.load(sess, ['vgg16'], vgg_path) # Get Tensors to be returned from graph graph = tf.get_default_graph() image_input = graph.get_tensor_by_name('image_input:0') keep_prob = graph.get_tensor_by_name('keep_prob:0') layer3 = graph.get_tensor_by_name('layer3_out:0') layer4 = graph.get_tensor_by_name('layer4_out:0') layer7 = graph.get_tensor_by_name('layer7_out:0') return image_input, keep_prob, layer3, layer4, layer7 def conv_1x1(layer, layer_name): """ Return the output of a 1x1 convolution of a layer """ return tf.layers.conv2d(inputs = layer, filters = NUMBER_OF_CLASSES, kernel_size = (1, 1), strides = (1, 1), name = layer_name) def upsample(layer, k, s, layer_name): """ Return the output of transpose convolution given kernel_size k and strides s """ return tf.layers.conv2d_transpose(inputs = layer, filters = NUMBER_OF_CLASSES, kernel_size = (k, k), strides = (s, s), padding = 'same', name = layer_name) def layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes = NUMBER_OF_CLASSES): """ Create the layers for a fully convolutional network. Build skip-layers using the vgg layers. vgg_layerX_out: TF Tensor for VGG Layer X output num_classes: Number of classes to classify return: The Tensor for the last layer of output """ # Use a shorter variable name for simplicity layer3, layer4, layer7 = vgg_layer3_out, vgg_layer4_out, vgg_layer7_out # Apply a 1x1 convolution to encoder layers layer3x = conv_1x1(layer = layer3, layer_name = "layer3conv1x1") layer4x = conv_1x1(layer = layer4, layer_name = "layer4conv1x1") layer7x = conv_1x1(layer = layer7, layer_name = "layer7conv1x1") # Add decoder layers to the network with skip connections and upsampling # Note: the kernel size and strides are the same as the example in Udacity Lectures # Semantic Segmentation Scene Understanding Lesson 10-9: FCN-8 - Decoder decoderlayer1 = upsample(layer = layer7x, k = 4, s = 2, layer_name = "decoderlayer1") decoderlayer2 = tf.add(decoderlayer1, layer4x, name = "decoderlayer2") decoderlayer3 = upsample(layer = decoderlayer2, k = 4, s = 2, layer_name = "decoderlayer3") decoderlayer4 = tf.add(decoderlayer3, layer3x, name = "decoderlayer4") decoderlayer_output = upsample(layer = decoderlayer4, k = 16, s = 8, layer_name = "decoderlayer_output") return decoderlayer_output def optimize(nn_last_layer, correct_label, learning_rate, num_classes = NUMBER_OF_CLASSES): """ Build the TensorFLow loss and optimizer operations. nn_last_layer: TF Tensor of the last layer in the neural network correct_label: TF Placeholder for the correct label image learning_rate: TF Placeholder for the learning rate num_classes: Number of classes to classify return: Tuple of (logits, train_op, cross_entropy_loss) """ # Reshape 4D tensors to 2D, each row represents a pixel, each column a class logits = tf.reshape(nn_last_layer, (-1, num_classes)) class_labels = tf.reshape(correct_label, (-1, num_classes)) # The cross_entropy_loss is the cost which we are trying to minimize to yield higher accuracy cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = class_labels) cross_entropy_loss = tf.reduce_mean(cross_entropy) # The model implements this operation to find the weights/parameters that would yield correct pixel labels train_op = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy_loss) return logits, train_op, cross_entropy_loss def train_nn(sess, epochs, batch_size, get_batches_fn, train_op, cross_entropy_loss, input_image, correct_label, keep_prob, learning_rate): """ Train neural network and print out the loss during training. sess: TF Session epochs: Number of epochs batch_size: Batch size get_batches_fn: Function to get batches of training data. Call using get_batches_fn(batch_size) train_op: TF Operation to train the neural network cross_entropy_loss: TF Tensor for the amount of loss input_image: TF Placeholder for input images correct_label: TF Placeholder for label images keep_prob: TF Placeholder for dropout keep probability learning_rate: TF Placeholder for learning rate """ for epoch in range(EPOCHS): losses, i = [], 0 for images, labels in get_batches_fn(BATCH_SIZE): i += 1 feed = { input_image: images, correct_label: labels, keep_prob: DROPOUT, learning_rate: LEARNING_RATE } _, partial_loss = sess.run([train_op, cross_entropy_loss], feed_dict = feed) print("---> iteration: ", i, " partial loss:", partial_loss) losses.append(partial_loss) training_loss = sum(losses) / len(losses) all_training_losses.append(training_loss) print("------------------") print("epoch: ", epoch + 1, " of ", EPOCHS, "training loss: ", training_loss) print("------------------") def run_tests(): tests.test_layers(layers) tests.test_optimize(optimize) tests.test_for_kitti_dataset(DATA_DIRECTORY) tests.test_train_nn(train_nn) def run(): """ Run a train a model and save output images resulting from the test image fed on the trained model """ # Get vgg model if we can't find it where it should be helper.maybe_download_pretrained_vgg(DATA_DIRECTORY) # A function to get batches get_batches_fn = helper.gen_batch_function(TRAINING_DATA_DIRECTORY, IMAGE_SHAPE) with tf.Session() as session: # Returns the three layers, keep probability and input layer from the vgg architecture image_input, keep_prob, layer3, layer4, layer7 = load_vgg(session, VGG_PATH) # The resulting network architecture from adding a decoder on top of the given vgg model model_output = layers(layer3, layer4, layer7, NUMBER_OF_CLASSES) # Returns the output logits, training operation and cost operation to be used # - logits: each row represents a pixel, each column a class # - train_op: function used to get the right parameters to the model to correctly label the pixels # - cross_entropy_loss: function outputting the cost which we are minimizing, lower cost should yield higher accuracy logits, train_op, cross_entropy_loss = optimize(model_output, correct_label, learning_rate, NUMBER_OF_CLASSES) # Initialize all variables session.run(tf.global_variables_initializer()) session.run(tf.local_variables_initializer()) # Train the neural network train_nn(session, EPOCHS, BATCH_SIZE, get_batches_fn, train_op, cross_entropy_loss, image_input, correct_label, keep_prob, learning_rate) # Run the model with the test images and save each painted output image (roads painted green) helper.save_inference_samples(RUNS_DIRECTORY, DATA_DIRECTORY, session, IMAGE_SHAPE, logits, keep_prob, image_input) #-------------------------- # MAIN #-------------------------- if __name__ == "__main__": run_tests() run() # Run a train a model and save output images resulting from the test image fed on the trained model print(all_training_losses)

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b9e38ca4d963e2aa4de106573e34682092b6337e

22,356

py

Python

tests/scanner/audit/log_sink_rules_engine_test.py

BrunoReboul/forseti-security

9d4a61b3e5a5d22a4330d15ddf61063fc9079071

[ "Apache-2.0" ]

null

tests/scanner/audit/log_sink_rules_engine_test.py

BrunoReboul/forseti-security

9d4a61b3e5a5d22a4330d15ddf61063fc9079071

[ "Apache-2.0" ]

null

tests/scanner/audit/log_sink_rules_engine_test.py

BrunoReboul/forseti-security

9d4a61b3e5a5d22a4330d15ddf61063fc9079071

[ "Apache-2.0" ]

null

# Copyright 2018 The Forseti Security Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests the LogSinkRulesEngine.""" import unittest import mock from tests.unittest_utils import ForsetiTestCase from tests.unittest_utils import get_datafile_path from google.cloud.forseti.common.gcp_type.billing_account import BillingAccount from google.cloud.forseti.common.gcp_type.folder import Folder from google.cloud.forseti.common.gcp_type.log_sink import LogSink from google.cloud.forseti.common.gcp_type.organization import Organization from google.cloud.forseti.common.gcp_type.project import Project from google.cloud.forseti.scanner.audit import log_sink_rules_engine as lsre from google.cloud.forseti.scanner.audit.errors import InvalidRulesSchemaError class LogSinkRulesEngineTest(ForsetiTestCase): """Tests for the LogSinkRulesEngine.""" def setUp(self): """Set up GCP resources for tests.""" self.lsre = lsre self.lsre.LOGGER = mock.MagicMock() # Set up resources in the following hierarchy: # +-----> billing_acct_abcd # | # | # +-----------------------> proj-1 # | # | # org_234 +-----> folder_56 +-----> proj-2 # | # | # +-----------------------> proj-3 self.org_234 = Organization( '234', display_name='Organization 234', full_name='organization/234/', data='fake_org_data_234') self.billing_acct_abcd = BillingAccount( 'ABCD-1234', display_name='Billing Account ABCD', full_name='organization/234/billingAccount/ABCD-1234/', data='fake_billing_account_data_abcd') self.folder_56 = Folder( '56', display_name='Folder 56', full_name='organization/234/folder/56/', data='fake_folder_data456456') self.proj_1 = Project( 'proj-1', project_number=11223344, display_name='My project 1', parent=self.org_234, full_name='organization/234/project/proj-1/', data='fake_project_data_2341') self.proj_2 = Project( 'proj-2', project_number=223344, display_name='My project 2', parent=self.folder_56, full_name='organization/234/folder/56/project/proj-2/', data='fake_project_data_4562') self.proj_3 = Project( 'proj-3', project_number=33445566, display_name='My project 3', parent=self.org_234, full_name='organization/234/project/proj-3/', data='fake_project_data_1233') def get_engine_with_valid_rules(self): """Create a rule engine build with a valid rules file.""" rules_local_path = get_datafile_path( __file__, 'log_sink_test_valid_rules.yaml') rules_engine = self.lsre.LogSinkRulesEngine( rules_file_path=rules_local_path) rules_engine.build_rule_book() return rules_engine def test_build_rule_book_from_local_yaml_file_works(self): """Tests that a RuleBook is built correctly with a yaml file.""" rules_engine = self.get_engine_with_valid_rules() # Creates 'self' rules for 5 difference resources and 'children' rules # for 2. self.assertEqual( 6, len(rules_engine.rule_book.resource_rules_map['self'])) self.assertEqual( 2, len(rules_engine.rule_book.resource_rules_map['children'])) self_rule_resources = [] for resource in rules_engine.rule_book.resource_rules_map['self']: self_rule_resources.append(resource.name) expected_rule_resources = [ 'billingAccounts/ABCD-1234', 'folders/56', 'organizations/234', 'projects/proj-1', 'projects/proj-2', 'projects/proj-3'] self.assertEqual(expected_rule_resources, sorted(self_rule_resources)) child_rule_resources = [] for resource in rules_engine.rule_book.resource_rules_map['children']: child_rule_resources.append(resource.name) expected_rule_resources = ['folders/56', 'organizations/234'] self.assertEqual(expected_rule_resources, sorted(child_rule_resources)) def test_build_rule_book_invalid_applies_to_fails(self): """Tests that a rule with invalid applies_to type cannot be created.""" rules_local_path = get_datafile_path( __file__, 'log_sink_test_invalid_rules.yaml') rules_engine = self.lsre.LogSinkRulesEngine( rules_file_path=rules_local_path) with self.assertRaises(InvalidRulesSchemaError): rules_engine.build_rule_book() def test_project_with_no_violations(self): """Tests that no violations are produced for a correct project.""" rules_engine = self.get_engine_with_valid_rules() # proj-1 needs an Audit Log sink. log_sinks = [ LogSink( sink_id='audit_logs_to_bq', destination=('bigquery.googleapis.com/projects/my-audit-logs/' 'datasets/proj_1_logs'), sink_filter='logName:"logs/cloudaudit.googleapis.com"', include_children=False, writer_identity='serviceAccount:logs@test.gserviceaccount.com', parent=self.proj_1, raw_json='_SINK_1_' ), LogSink( sink_id='compute_logs_saver', destination=('bigquery.googleapis.com/projects/proj_1/' 'datasets/compute_logs'), sink_filter='resource.type="gce_instance"', include_children=False, writer_identity=('serviceAccount:p12345-67890@' 'gcp-sa-logging.iam.gserviceaccount.com'), parent=self.proj_1, raw_json='_SINK_2_' ) ] actual_violations = rules_engine.find_violations( self.proj_1, log_sinks) self.assertEqual(set(), actual_violations) def test_folder_with_no_violations(self): """Tests that no violations are produced for a correct folder.""" rules_engine = self.get_engine_with_valid_rules() # Rules disallow any folder-level LogSinks. actual_violations = rules_engine.find_violations(self.folder_56, []) self.assertEqual(set(), actual_violations) def test_billing_account_with_no_violations(self): """Tests that no violations are produced for a correct billing acct.""" rules_engine = self.get_engine_with_valid_rules() log_sinks = [ LogSink( sink_id='billing_logs', destination=('bigquery.googleapis.com/projects/my-audit-logs/' 'datasets/billing_logs'), sink_filter='', include_children=False, writer_identity='serviceAccount:logs@test.gserviceaccount.com', parent=self.billing_acct_abcd, raw_json='__SINK_1__' ), ] actual_violations = rules_engine.find_violations( self.billing_acct_abcd, log_sinks) self.assertEqual(set(), actual_violations) def test_org_with_no_violations(self): """Tests that no violations are produced for a correct organization.""" rules_engine = self.get_engine_with_valid_rules() # Org needs an Audit Log sink, but to any destination. log_sinks = [ LogSink( sink_id='audit_logs_to_pubsub', destination=('pubsub.googleapis.com/projects/proj-3/topics/' 'org-audit-logs'), sink_filter='logName:"logs/cloudaudit.googleapis.com"', include_children=True, writer_identity='serviceAccount:logs@test.gserviceaccount.com', parent=self.org_234, raw_json='__SINK_1__' ) ] actual_violations = rules_engine.find_violations( self.org_234, log_sinks) self.assertEqual(set(), actual_violations) def test_project_missing_required_sinks(self): """Tests violations are produced for project missing required sinks.""" rules_engine = self.get_engine_with_valid_rules() # proj-2 needs an Audit Log sink, by org-level rules, and a pubsub # sink, by folder-level rules. log_sinks = [ LogSink( sink_id='non_audit_logs_to_bq', destination=('bigquery.googleapis.com/projects/my-audit-logs/' 'datasets/proj_2_logs'), sink_filter='logName:"logs/non-cloudaudit.googleapis.com"', include_children=False, writer_identity='serviceAccount:logs@test.gserviceaccount.com', parent=self.proj_2, raw_json='__SINK_1__' ), LogSink( sink_id='compute_logs_saver', destination=('bigquery.googleapis.com/projects/proj_2/' 'datasets/compute_logs'), sink_filter='resource.type="gce_instance"', include_children=False, writer_identity=('serviceAccount:p12345-67890@' 'gcp-sa-logging.iam.gserviceaccount.com'), parent=self.proj_2, raw_json='__SINK_2__' ) ] actual_violations = rules_engine.find_violations( self.proj_2, log_sinks) expected_violations = set([ lsre.Rule.RuleViolation( resource_name='proj-2', resource_type='project', resource_id='proj-2', full_name='organization/234/folder/56/project/proj-2/', rule_name='Require Audit Log sinks in all projects.', rule_index=0, violation_type='LOG_SINK_VIOLATION', sink_destination=('^bigquery\\.googleapis\\.com\\/projects\\/' 'my\\-audit\\-logs\\/datasets\\/.+$'), sink_filter=('^logName\\:\\"logs\\/' 'cloudaudit\\.googleapis\\.com\\"$'), sink_include_children='*', resource_data='' ), lsre.Rule.RuleViolation( resource_name='proj-2', resource_type='project', resource_id='proj-2', full_name='organization/234/folder/56/project/proj-2/', rule_name='Require a PubSub sink in folder-56 projects.', rule_index=3, violation_type='LOG_SINK_VIOLATION', sink_destination='^pubsub\\.googleapis\\.com\\/.+$', sink_filter='^$', sink_include_children='*', resource_data='' ) ]) self.assertEqual(expected_violations, actual_violations) def test_project_whitelist_violation(self): """Tests violations are produced for non-whitelisted sinks.""" rules_engine = self.get_engine_with_valid_rules() # proj-3 can only have BigQuery sinks. log_sinks = [ LogSink( sink_id='audit_logs_to_bq', destination=('bigquery.googleapis.com/projects/my-audit-logs/' 'datasets/proj_1_logs'), sink_filter='logName:"logs/cloudaudit.googleapis.com"', include_children=False, writer_identity='serviceAccount:logs@test.gserviceaccount.com', parent=self.proj_3, raw_json='__SINK_1__' ), LogSink( sink_id='audit_logs_to_pubsub', destination=('pubsub.googleapis.com/projects/proj-3/topics/' 'proj-audit-logs'), sink_filter='logName:"logs/cloudaudit.googleapis.com"', include_children=True, writer_identity='serviceAccount:logs@test.gserviceaccount.com', parent=self.proj_3, raw_json='__SINK_2__' ) ] actual_violations = rules_engine.find_violations( self.proj_3, log_sinks) expected_violations = set([ lsre.Rule.RuleViolation( resource_name='projects/proj-3/sinks/audit_logs_to_pubsub', resource_type='sink', resource_id='audit_logs_to_pubsub', full_name='organization/234/project/proj-3/audit_logs_to_pubsub/', rule_name='Only allow BigQuery sinks in Proj-1 and Proj-3.', rule_index=4, violation_type='LOG_SINK_VIOLATION', sink_destination=('pubsub.googleapis.com/projects/proj-3/' 'topics/proj-audit-logs'), sink_filter='logName:"logs/cloudaudit.googleapis.com"', sink_include_children=True, resource_data='__SINK_2__' ) ]) self.assertEqual(expected_violations, actual_violations) def test_folder_blacklist_violation(self): """Tests violations are produced for blacklisted sinks.""" rules_engine = self.get_engine_with_valid_rules() # Rules disallow any folder-level LogSinks. log_sinks = [ LogSink( sink_id='audit_logs_to_bq', destination=('bigquery.googleapis.com/projects/my-audit-logs/' 'datasets/folder_logs'), sink_filter='logName:"logs/cloudaudit.googleapis.com"', include_children=False, writer_identity='serviceAccount:logs@test.gserviceaccount.com', parent=self.folder_56, raw_json='__SINK_1__' ) ] actual_violations = rules_engine.find_violations( self.folder_56, log_sinks) expected_violations = set([ lsre.Rule.RuleViolation( resource_name='folders/56/sinks/audit_logs_to_bq', resource_type='sink', resource_id='audit_logs_to_bq', full_name='organization/234/folder/56/audit_logs_to_bq/', rule_name='Disallow folder sinks.', rule_index=2, violation_type='LOG_SINK_VIOLATION', sink_destination=('bigquery.googleapis.com/projects/' 'my-audit-logs/datasets/folder_logs'), sink_filter='logName:"logs/cloudaudit.googleapis.com"', sink_include_children=False, resource_data='__SINK_1__') ]) self.assertEqual(expected_violations, actual_violations) def test_billing_account_with_whitelist_violations(self): """Tests violations are produced for billing account sinks.""" rules_engine = self.get_engine_with_valid_rules() log_sinks = [ LogSink( sink_id='billing_logs', destination=('bigquery.googleapis.com/projects/my-audit-logs/' 'datasets/wrong_dataset'), sink_filter='', include_children=False, writer_identity='serviceAccount:logs@test.gserviceaccount.com', parent=self.billing_acct_abcd, raw_json='__SINK_1__' ), ] actual_violations = rules_engine.find_violations( self.billing_acct_abcd, log_sinks) expected_violations = set([ lsre.Rule.RuleViolation( resource_type='sink', resource_id='billing_logs', resource_name='billingAccounts/ABCD-1234/sinks/billing_logs', full_name='organization/234/billingAccount/ABCD-1234/billing_logs/', rule_name=('Only allow Billing Account sinks to audit logs ' 'project.'), rule_index=6, violation_type='LOG_SINK_VIOLATION', sink_destination=('bigquery.googleapis.com/projects/' 'my-audit-logs/datasets/wrong_dataset'), sink_filter='', sink_include_children=False, resource_data='__SINK_1__') ]) self.assertEqual(expected_violations, actual_violations) def test_org_missing_required_sinks(self): """Tests violations are produced for an org missing required sinks.""" rules_engine = self.get_engine_with_valid_rules() # Org needs an Audit Log sink, including children. log_sinks = [ LogSink( sink_id='sink_not_including_children', destination=('pubsub.googleapis.com/projects/proj-3/topics/' 'org-audit-logs'), sink_filter='logName:"logs/cloudaudit.googleapis.com"', include_children=False, writer_identity='serviceAccount:logs@test.gserviceaccount.com', parent=self.org_234, raw_json='__SINK_1__' ), LogSink( sink_id='sink_with_wrong_filter', destination=('pubsub.googleapis.com/projects/proj-3/topics/' 'org-more-logs'), sink_filter='logName:"logs/otherapi.googleapis.com"', include_children=True, writer_identity='serviceAccount:logs@test.gserviceaccount.com', parent=self.org_234, raw_json='__SINK_2__' ) ] actual_violations = rules_engine.find_violations( self.org_234, log_sinks) expected_violations = set([ lsre.Rule.RuleViolation( resource_name='234', resource_type='organization', resource_id='234', full_name='organization/234/', rule_name='Require an Org Level audit log sink.', rule_index=1, violation_type='LOG_SINK_VIOLATION', sink_destination='^.*$', sink_filter=('^logName\\:\\"logs\\/' 'cloudaudit\\.googleapis\\.com\\"$'), sink_include_children=True, resource_data='' ) ]) self.assertEqual(expected_violations, actual_violations) def test_add_invalid_rules(self): """Tests that adding invalid rules raises exceptions.""" rule_book = self.lsre.LogSinkRuleBook(global_configs=None) valid_resource = { 'type': 'organization', 'applies_to': 'children', 'resource_ids': ['1234'] } valid_sink_spec = { 'destination': 'bigquery.*', 'filter': '', 'include_children': '*' } rule_book.add_rule( { 'name': 'Valid rule', 'resource': [valid_resource], 'sink': valid_sink_spec, 'mode': 'whitelist' }, 0) bad_rules = [ {}, { 'name': 'Mising Resource', 'mode': 'whitelist', 'sink': valid_sink_spec, }, { 'name': 'Mising sink', 'resource': [valid_resource], 'mode': 'whitelist', }, { 'name': 'Bad mode', 'resource': [valid_resource], 'sink': valid_sink_spec, 'mode': 'other', }, { 'name': 'Bad resource type', 'resource': [{ 'type': 'bucket', 'applies_to': 'self', 'resource_ids': ['bucket-1'] }], 'sink': valid_sink_spec, 'mode': 'whitelist' }, { 'name': 'Bad applies to type', 'resource': [{ 'type': 'folder', 'applies_to': 'self_and_children', 'resource_ids': ['56'] }], 'sink': valid_sink_spec, 'mode': 'whitelist' }, { 'name': 'Bad applies to type', 'resource': [{ 'type': 'billing_account', 'applies_to': 'children', 'resource_ids': ['ABCD-1234'] }], 'sink': valid_sink_spec, 'mode': 'whitelist' }, { 'name': 'Empty resource_ids', 'resource': [{ 'type': 'project', 'applies_to': 'self', 'resource_ids': [] }], 'sink': valid_sink_spec, 'mode': 'whitelist' }, { 'name': 'Missing filter', 'resource': [valid_resource], 'sink': { 'destination': 'bigquery.*', 'include_children': '*' }, 'mode': 'whitelist' }, { 'name': 'Bad include_children', 'resource': [valid_resource], 'sink': { 'destination': 'bigquery.*', 'filter': '*', 'include_children': 'Yes' }, 'mode': 'whitelist' } ] for rule in bad_rules: with self.assertRaises(InvalidRulesSchemaError): rule_book.add_rule(rule, 1) if __name__ == '__main__': unittest.main()

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b9e3fca3aec04c54b087304757154615d5a67e58

2,852

py

Python

backend/api/ulca-ums-service/user-management/utilities/orgUtils.py

agupta54/ulca

c1f570ac254ce2ac73f40c49716458f4f7cbaee2

[ "MIT" ]

3

2022-01-12T06:51:51.000Z

2022-02-23T18:54:33.000Z

backend/api/ulca-ums-service/user-management/utilities/orgUtils.py

agupta54/ulca

c1f570ac254ce2ac73f40c49716458f4f7cbaee2

[ "MIT" ]

6

2021-08-31T19:21:26.000Z

2022-01-03T05:53:42.000Z

backend/api/ulca-ums-service/user-management/utilities/orgUtils.py

agupta54/ulca

c1f570ac254ce2ac73f40c49716458f4f7cbaee2

[ "MIT" ]

8

2021-08-12T08:07:49.000Z

2022-01-25T04:40:51.000Z

import uuid from config import USR_ORG_MONGO_COLLECTION, USR_MONGO_COLLECTION import db from models.response import post_error import logging log = logging.getLogger('file') class OrgUtils: def __init__(self): pass #orgId generation @staticmethod def generate_org_id(): """UUID generation for org registeration""" return(uuid.uuid4().hex) @staticmethod def validate_org(org_code): """Validating Org Org should be registered and active on Anuvaad system. """ try: #connecting to mongo instance/collection collections = db.get_db()[USR_ORG_MONGO_COLLECTION] #searching for active org record result = collections.find({"code": org_code}, {"_id": 0, "active": 1}) if result.count() == 0: return post_error("Invalid Organization", "No such registered organization with the given Org Id", None) for value in result: if value["active"] == False: return post_error("Invalid Organization", "Organization is currently inactive", None) except Exception as e: log.exception(f"Db connection exception : {e}") return post_error("Database connection exception", "An error occurred while connecting to the database:{}".format(str(e)), None) @staticmethod def validate_org_upsert(i,org): """Org validation on upsert deactivation of org allowed only once all the users in the corresponding org is inactive. """ if "code" not in org or not org["code"]: return post_error("Data Missing", "code not found", None) if "active" not in org: return post_error("Data Missing", "active not found", None) code = str(org["code"]).upper() active = org["active"] if not isinstance(active,bool): return post_error("Invalid format", "active should be bool", None), 400 if active == False: try: #connecting to mongo instance/collection collections = db.get_db()[USR_MONGO_COLLECTION] #searching for active users in the org result = collections.find({"orgID": code,"is_active":True}) if result.count()!=0: log.info("Deactivation request for org failed, {} active users with the orgID".format(str(result.count()))) return post_error("Deactivation Failed","There exist active users in {} hence this action cannot be performed".format(code),None) except Exception as e: log.exception(f"Db connection exception : {e}") return post_error("Database connection exception", "An error occurred while connecting to the database:{}".format(str(e)), None)

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2,852

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0.4

0

null

0

null

0

1

0

b9e478ed385905aa26b48748e1fbf896e8ced766

4,299

py

Python

setup.py

AntonBiryukovUofC/diffvg

e081098f52b82bfd0b7e91114d289d65ef969a60

[ "Apache-2.0" ]

null

setup.py

AntonBiryukovUofC/diffvg

e081098f52b82bfd0b7e91114d289d65ef969a60

[ "Apache-2.0" ]

null

setup.py

AntonBiryukovUofC/diffvg

e081098f52b82bfd0b7e91114d289d65ef969a60

[ "Apache-2.0" ]

null

# Adapted from https://github.com/pybind/cmake_example/blob/master/setup.py import os import re import sys import platform import subprocess import importlib from sysconfig import get_paths import importlib from setuptools import setup, Extension from setuptools.command.build_ext import build_ext from setuptools.command.install import install from distutils.sysconfig import get_config_var from distutils.version import LooseVersion class CMakeExtension(Extension): def __init__(self, name, sourcedir, build_with_cuda): Extension.__init__(self, name, sources=[]) self.sourcedir = os.path.abspath(sourcedir) self.build_with_cuda = build_with_cuda class Build(build_ext): def run(self): try: out = subprocess.check_output(['cmake', '--version']) except OSError: raise RuntimeError("CMake must be installed to build the following extensions: " + ", ".join(e.name for e in self.extensions)) super().run() def build_extension(self, ext): if isinstance(ext, CMakeExtension): extdir = os.path.abspath(os.path.dirname(self.get_ext_fullpath(ext.name))) info = get_paths() include_path = info['include'] cmake_args = ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=' + extdir, '-DPYTHON_INCLUDE_PATH=' + include_path, ] cfg = 'Debug' if self.debug else 'Release' build_args = ['--config', cfg] if platform.system() == "Windows": cmake_args += ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY_{}={}'.format(cfg.upper(), extdir), '-DCMAKE_RUNTIME_OUTPUT_DIRECTORY_{}={}'.format(cfg.upper(), extdir)] if sys.maxsize > 2 ** 32: cmake_args += ['-A', 'x64'] build_args += ['--', '/m'] else: cmake_args += ['-DCMAKE_BUILD_TYPE=' + cfg] build_args += ['--', '-j8'] if ext.build_with_cuda: cmake_args += ['-DDIFFVG_CUDA=1'] else: cmake_args += ['-DDIFFVG_CUDA=0'] env = os.environ.copy() env['CXXFLAGS'] = '{} -DVERSION_INFO=\\"{}\\"'.format(env.get('CXXFLAGS', ''), self.distribution.get_version()) env_build = env env["CXX"] = "/usr/bin/g++-5" env["CC"] = "/usr/bin/gcc-5" env_build["CXX"] = "/usr/bin/g++-5" env_build["CC"] = "/usr/bin/gcc-5" env["PATH"] = "/usr/local/cuda-10.1/bin" + ":" + os.environ['PATH'] env_build["PATH"] = "/usr/local/cuda-10.1/bin" + ":" + os.environ['PATH'] if not os.path.exists(self.build_temp): os.makedirs(self.build_temp) subprocess.check_call(['cmake', ext.sourcedir] + cmake_args, cwd=self.build_temp, env=env) subprocess.check_call(['cmake', '--build', '.'] + build_args, cwd=self.build_temp, env=env_build) else: super().build_extension(ext) torch_spec = importlib.util.find_spec("torch") tf_spec = importlib.util.find_spec("tensorflow") packages = [] build_with_cuda = False if torch_spec is not None: packages.append('pydiffvg') import torch if torch.cuda.is_available(): build_with_cuda = True if tf_spec is not None and sys.platform != 'win32': packages.append('pydiffvg_tensorflow') if not build_with_cuda: import tensorflow as tf if tf.test.is_gpu_available(cuda_only=True, min_cuda_compute_capability=None): build_with_cuda = True if len(packages) == 0: print('Error: PyTorch or Tensorflow must be installed. For Windows platform only PyTorch is supported.') exit() # Override build_with_cuda with environment variable if 'DIFFVG_CUDA' in os.environ: build_with_cuda = os.environ['DIFFVG_CUDA'] == '1' setup(name='diffvg', version='0.0.1', install_requires=["svgpathtools"], description='Differentiable Vector Graphics', ext_modules=[CMakeExtension('diffvg', '', build_with_cuda)], cmdclass=dict(build_ext=Build, install=install), packages=packages, zip_safe=False)

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4,299

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0.057685

0.017749

0.160145

0.124647

0.049213

0.028237

0

0.007939

0.267504

4,299

112

110

38.383929

0.779295

0.028844

0

0.075269

0

0.172339

0.048418

0.021505

0

1

0.032258

false

0

0.182796

0

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0.010753

0

null

0

null

0

1

0

b9e64ab7c515862e0dec6a8272d8a276b9bd86b9

14,587

py

Python

robotpy_ext/common_drivers/navx/registerio.py

twinters007/robotpy-wpilib-utilities

d2e18c16fc97a469e0621521e0fbed0093610d6e

[ "MIT", "BSD-3-Clause" ]

2

2017-01-16T03:10:57.000Z

2017-01-16T03:11:00.000Z

robotpy_ext/common_drivers/navx/registerio.py

twinters007/robotpy-wpilib-utilities

d2e18c16fc97a469e0621521e0fbed0093610d6e

[ "MIT", "BSD-3-Clause" ]

null

robotpy_ext/common_drivers/navx/registerio.py

twinters007/robotpy-wpilib-utilities

d2e18c16fc97a469e0621521e0fbed0093610d6e

[ "MIT", "BSD-3-Clause" ]

null

# validated: 2017-02-19 DS c5e3a8a9b642 roborio/java/navx_frc/src/com/kauailabs/navx/frc/RegisterIO.java #---------------------------------------------------------------------------- # Copyright (c) Kauai Labs 2015. All Rights Reserved. # # Created in support of Team 2465 (Kauaibots). Go Purple Wave! # # Open Source Software - may be modified and shared by FRC teams. Any # modifications to this code must be accompanied by the \License.txt file # in the root directory of the project #---------------------------------------------------------------------------- from ._impl import AHRSProtocol, IMUProtocol, IMURegisters from wpilib.timer import Timer import logging logger = logging.getLogger('navx') __all__ = ['RegisterIO'] IO_TIMEOUT_SECONDS = 1.0 DELAY_OVERHEAD_SECONDS = 0.004 class _BoardId: type = 0 hw_rev = 0 fw_ver_major = 0 fw_ver_minor = 0 fw_revision = 0 unique_id = [0]*12 class _BoardState: op_status = 0 sensor_status = 0 cal_status = 0 selftest_status = 0 capability_flags = 0 update_rate_hz = 0 accel_fsr_g = 0 gyro_fsr_dps = 0 class RegisterIO: def __init__(self, io_provider, update_rate_hz, notify_sink, board_capabilities): """ :param board_capabilities: must have the following callable attributes: _isOmniMountSupported, _isBoardYawResetSupported, _isDisplacementSupported :param notify_sink: must have the following callable attributes: _setYawPitchRoll, _setAHRSData, _setAHRSPosData, _setRawData, _setBoardID, _setBoardState, _yawResetComplete """ self.io_provider = io_provider self.update_rate_hz = update_rate_hz self.board_capabilities = board_capabilities self.notify_sink = notify_sink self.raw_data_update = IMUProtocol.GyroUpdate() self.ahrspos_update = AHRSProtocol.AHRSPosUpdate() self.board_state = _BoardState() self.board_id = _BoardId() self.last_update_time = 0 self.byte_count = 0 self.update_count = 0 self.last_sensor_timestamp = 0 self._stop = False def stop(self): self._stop = True def shutdown(self): self.io_provider.shutdown() def run(self): logger.info("NavX io thread starting") try: self.io_provider.init() # initial device configuration self.setUpdateRateHz(self.update_rate_hz) if not self.getConfiguration(): logger.warning("-- Did not get configuration data") else: logger.info("-- Board is %s (rev %s)", IMURegisters.model_type(self.board_id.type), self.board_id.hw_rev) logger.info("-- Firmware %s.%s", self.board_id.fw_ver_major, self.board_id.fw_ver_minor) log_error = True # Calculate delay to match configured update rate # Note: some additional time is removed from the # 1/update_rate value to ensure samples are not # dropped, esp. at higher update rates. update_rate = 1.0/(self.update_rate_hz & 0xFF) if update_rate > DELAY_OVERHEAD_SECONDS: update_rate -= DELAY_OVERHEAD_SECONDS logger.info("-- Update rate: %shz (%.4fs)", self.update_rate_hz, update_rate) # IO Loop while not self._stop: if self.board_state.update_rate_hz != self.update_rate_hz: self.setUpdateRateHz(self.update_rate_hz) try: self.getCurrentData() except IOError: if log_error: logger.exception("Error getting data") log_error = False else: log_error = True Timer.delay(update_rate) except Exception: logger.exception("Unhandled exception in NavX thread") finally: logger.info("NavX i/o thread exiting") def getConfiguration(self): success = False retry_count = 0 while retry_count < 5 and not success: try: config = self.io_provider.read(IMURegisters.NAVX_REG_WHOAMI, IMURegisters.NAVX_REG_SENSOR_STATUS_H+1) except IOError as e: logger.warning("Error reading configuration data, retrying (%s)", e) success = False Timer.delay(0.5) else: board_id = self.board_id board_id.hw_rev = config[IMURegisters.NAVX_REG_HW_REV] board_id.fw_ver_major = config[IMURegisters.NAVX_REG_FW_VER_MAJOR] board_id.fw_ver_minor = config[IMURegisters.NAVX_REG_FW_VER_MINOR] board_id.type = config[IMURegisters.NAVX_REG_WHOAMI] self.notify_sink._setBoardID(board_id) board_state = self.board_state board_state.cal_status = config[IMURegisters.NAVX_REG_CAL_STATUS] board_state.op_status = config[IMURegisters.NAVX_REG_OP_STATUS] board_state.selftest_status = config[IMURegisters.NAVX_REG_SELFTEST_STATUS] board_state.sensor_status = AHRSProtocol.decodeBinaryUint16(config,IMURegisters.NAVX_REG_SENSOR_STATUS_L) board_state.gyro_fsr_dps = AHRSProtocol.decodeBinaryUint16(config,IMURegisters.NAVX_REG_GYRO_FSR_DPS_L) board_state.accel_fsr_g = config[IMURegisters.NAVX_REG_ACCEL_FSR_G] board_state.update_rate_hz = config[IMURegisters.NAVX_REG_UPDATE_RATE_HZ] board_state.capability_flags = AHRSProtocol.decodeBinaryUint16(config,IMURegisters.NAVX_REG_CAPABILITY_FLAGS_L) self.notify_sink._setBoardState(board_state) success = True retry_count += 1 return success def getCurrentData(self): first_address = IMURegisters.NAVX_REG_UPDATE_RATE_HZ displacement_registers = self.board_capabilities._isDisplacementSupported() # If firmware supports displacement data, acquire it - otherwise implement # similar (but potentially less accurate) calculations on this processor. if displacement_registers: read_count = IMURegisters.NAVX_REG_LAST + 1 - first_address else: read_count = IMURegisters.NAVX_REG_QUAT_OFFSET_Z_H + 1 - first_address curr_data = self.io_provider.read(first_address, read_count) sensor_timestamp = AHRSProtocol.decodeBinaryUint32(curr_data, IMURegisters.NAVX_REG_TIMESTAMP_L_L-first_address) if sensor_timestamp == self.last_sensor_timestamp: return self.last_sensor_timestamp = sensor_timestamp ahrspos_update = self.ahrspos_update ahrspos_update.op_status = curr_data[IMURegisters.NAVX_REG_OP_STATUS - first_address] ahrspos_update.selftest_status = curr_data[IMURegisters.NAVX_REG_SELFTEST_STATUS - first_address] ahrspos_update.cal_status = curr_data[IMURegisters.NAVX_REG_CAL_STATUS] ahrspos_update.sensor_status = curr_data[IMURegisters.NAVX_REG_SENSOR_STATUS_L - first_address] ahrspos_update.yaw = AHRSProtocol.decodeProtocolSignedHundredthsFloat(curr_data, IMURegisters.NAVX_REG_YAW_L-first_address) ahrspos_update.pitch = AHRSProtocol.decodeProtocolSignedHundredthsFloat(curr_data, IMURegisters.NAVX_REG_PITCH_L-first_address) ahrspos_update.roll = AHRSProtocol.decodeProtocolSignedHundredthsFloat(curr_data, IMURegisters.NAVX_REG_ROLL_L-first_address) ahrspos_update.compass_heading = AHRSProtocol.decodeProtocolUnsignedHundredthsFloat(curr_data, IMURegisters.NAVX_REG_HEADING_L-first_address) ahrspos_update.mpu_temp_c = AHRSProtocol.decodeProtocolSignedHundredthsFloat(curr_data, IMURegisters.NAVX_REG_MPU_TEMP_C_L - first_address) ahrspos_update.world_linear_accel_x = AHRSProtocol.decodeProtocolSignedThousandthsFloat(curr_data, IMURegisters.NAVX_REG_LINEAR_ACC_X_L-first_address) ahrspos_update.world_linear_accel_y = AHRSProtocol.decodeProtocolSignedThousandthsFloat(curr_data, IMURegisters.NAVX_REG_LINEAR_ACC_Y_L-first_address) ahrspos_update.world_linear_accel_z = AHRSProtocol.decodeProtocolSignedThousandthsFloat(curr_data, IMURegisters.NAVX_REG_LINEAR_ACC_Z_L-first_address) ahrspos_update.altitude = AHRSProtocol.decodeProtocol1616Float(curr_data, IMURegisters.NAVX_REG_ALTITUDE_D_L - first_address) ahrspos_update.baro_pressure = AHRSProtocol.decodeProtocol1616Float(curr_data, IMURegisters.NAVX_REG_PRESSURE_DL - first_address) ahrspos_update.fused_heading = AHRSProtocol.decodeProtocolUnsignedHundredthsFloat(curr_data, IMURegisters.NAVX_REG_FUSED_HEADING_L-first_address) ahrspos_update.quaternionW = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_QUAT_W_L-first_address)/ 32768.0 ahrspos_update.quaternionX = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_QUAT_X_L-first_address)/ 32768.0 ahrspos_update.quaternionY = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_QUAT_Y_L-first_address)/ 32768.0 ahrspos_update.quaternionZ = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_QUAT_Z_L-first_address)/ 32768.0 if displacement_registers: ahrspos_update.vel_x = AHRSProtocol.decodeProtocol1616Float(curr_data, IMURegisters.NAVX_REG_VEL_X_I_L-first_address) ahrspos_update.vel_y = AHRSProtocol.decodeProtocol1616Float(curr_data, IMURegisters.NAVX_REG_VEL_Y_I_L-first_address) ahrspos_update.vel_z = AHRSProtocol.decodeProtocol1616Float(curr_data, IMURegisters.NAVX_REG_VEL_Z_I_L-first_address) ahrspos_update.disp_x = AHRSProtocol.decodeProtocol1616Float(curr_data, IMURegisters.NAVX_REG_DISP_X_I_L-first_address) ahrspos_update.disp_y = AHRSProtocol.decodeProtocol1616Float(curr_data, IMURegisters.NAVX_REG_DISP_Y_I_L-first_address) ahrspos_update.disp_z = AHRSProtocol.decodeProtocol1616Float(curr_data, IMURegisters.NAVX_REG_DISP_Z_I_L-first_address) self.notify_sink._setAHRSPosData(ahrspos_update, sensor_timestamp) else: self.notify_sink._setAHRSData(ahrspos_update, sensor_timestamp) board_state = self.board_state board_state.cal_status = curr_data[IMURegisters.NAVX_REG_CAL_STATUS-first_address] board_state.op_status = curr_data[IMURegisters.NAVX_REG_OP_STATUS-first_address] board_state.selftest_status = curr_data[IMURegisters.NAVX_REG_SELFTEST_STATUS-first_address] board_state.sensor_status = AHRSProtocol.decodeBinaryUint16(curr_data,IMURegisters.NAVX_REG_SENSOR_STATUS_L-first_address) board_state.update_rate_hz = curr_data[IMURegisters.NAVX_REG_UPDATE_RATE_HZ-first_address] board_state.gyro_fsr_dps = AHRSProtocol.decodeBinaryUint16(curr_data,IMURegisters.NAVX_REG_GYRO_FSR_DPS_L) board_state.accel_fsr_g = curr_data[IMURegisters.NAVX_REG_ACCEL_FSR_G] board_state.capability_flags= AHRSProtocol.decodeBinaryUint16(curr_data,IMURegisters.NAVX_REG_CAPABILITY_FLAGS_L-first_address) self.notify_sink._setBoardState(board_state) raw_data_update = self.raw_data_update raw_data_update.raw_gyro_x = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_GYRO_X_L-first_address) raw_data_update.raw_gyro_y = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_GYRO_Y_L-first_address) raw_data_update.raw_gyro_z = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_GYRO_Z_L-first_address) raw_data_update.raw_accel_x = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_ACC_X_L-first_address) raw_data_update.raw_accel_y = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_ACC_Y_L-first_address) raw_data_update.raw_accel_z = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_ACC_Z_L-first_address) raw_data_update.cal_mag_x = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_MAG_X_L-first_address) raw_data_update.cal_mag_y = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_MAG_Y_L-first_address) raw_data_update.cal_mag_z = AHRSProtocol.decodeBinaryInt16(curr_data, IMURegisters.NAVX_REG_MAG_Z_L-first_address) raw_data_update.mpu_temp_c = ahrspos_update.mpu_temp self.notify_sink._setRawData(raw_data_update, sensor_timestamp) self.last_update_time = Timer.getFPGATimestamp() self.byte_count += len(curr_data) self.update_count += 1 def isConnected(self): time_since_last_update = Timer.getFPGATimestamp() - self.last_update_time return time_since_last_update <= IO_TIMEOUT_SECONDS def getByteCount(self): return self.byte_count def getUpdateCount(self): return self.update_count def setUpdateRateHz(self, update_rate_hz): self.io_provider.write(IMURegisters.NAVX_REG_UPDATE_RATE_HZ, update_rate_hz) def zeroYaw(self): self.io_provider.write( IMURegisters.NAVX_REG_INTEGRATION_CTL, AHRSProtocol.NAVX_INTEGRATION_CTL_RESET_YAW ) self.notify_sink._yawResetComplete() def zeroDisplacement(self): self.io_provider.write( IMURegisters.NAVX_REG_INTEGRATION_CTL, (AHRSProtocol.NAVX_INTEGRATION_CTL_RESET_DISP_X | AHRSProtocol.NAVX_INTEGRATION_CTL_RESET_DISP_Y | AHRSProtocol.NAVX_INTEGRATION_CTL_RESET_DISP_Z ) )

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0.168088

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0.130222

0.112272

0.590078

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0.413621

0.311684

0.113903

0.06832

0

0.014578

0.252279

14,587

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0

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0

1

0.062176

false

0.005181

0.015544

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0.19171

0

null

0

null

0

1

0

b9e6a0bf2a4d3e860c6eb607624b101a086157b4

12,517

py

Python

RigolWFM/channel.py

wvdv2002/RigolWFM

849a1130c9194f052eaf5582dfa67e7a5708a3a3

[ "BSD-3-Clause" ]

null

RigolWFM/channel.py

wvdv2002/RigolWFM

849a1130c9194f052eaf5582dfa67e7a5708a3a3

[ "BSD-3-Clause" ]

null

RigolWFM/channel.py

wvdv2002/RigolWFM

849a1130c9194f052eaf5582dfa67e7a5708a3a3

[ "BSD-3-Clause" ]

null

#pylint: disable=invalid-name #pylint: disable=too-many-instance-attributes #pylint: disable=too-many-return-statements #pylint: disable=too-many-statements """ Class structure and methods for an oscilloscope channel. The idea is to collect all the relevant information from all the Rigol scope waveforms into a single structure that can be handled in a uniform and consistent manner. Specifically this lets one just use channel.times : numpy array of signal times channel.volts : numpy array of signal voltages or the stringification method to describe a channel print(channel) """ from enum import Enum import numpy as np class UnitEnum(Enum): """Enumerated units for scopes without them.""" w = 0 a = 1 v = 2 u = 3 def best_scale(number): """Scale and units for a number with proper prefix.""" absnr = abs(number) if absnr == 0: return 1, ' ' if absnr < 0.99999999e-9: return 1e12, 'p' if absnr < 0.99999999e-6: return 1e9, 'n' if absnr < 0.99999999e-3: return 1e6, 'µ' if absnr < 0.99999999: return 1e3, 'm' if absnr < 0.99999999e3: return 1, ' ' if absnr < 0.99999999e6: return 1e-3, 'k' if absnr < 0.999999991e9: return 1e-6, 'M' return 1e-9, 'G' def engineering_string(number, n_digits): """Format number with proper prefix.""" scale, prefix = best_scale(number) fformat = "%%.%df %%s" % n_digits s = fformat % (number * scale, prefix) return s def _channel_bytes(channel_number, w): """ Return right series of bytes for a channel for 1000Z scopes. Waveform points are interleaved stored in memory when two or more channels are saved. This unweaves them. Args: channel_number: the number of enabled channels before this one w: original waveform object Returns byte array for specified channel """ offset = 0 if w.header.stride == 2: # byte pattern CHx CHy # use odd bytes when this is the second enabled channel if any([w.header.ch[i].enabled for i in range(channel_number-1)]): offset = 1 elif w.header.stride == 4: # byte pattern CH4 CH3 CH2 CH1 offset = 4 - channel_number data = np.frombuffer(w.data.raw, dtype=np.uint8) raw_bytes = data[offset::w.header.stride] return raw_bytes class Channel(): """Base class for a single channel.""" def __init__(self, w, channel_number, scope, selected='1234'): """ Initialize a Channel Object. Args: w: Wfm object channel_number: 1, 2, 3, or 4 scope: string describing scope selected: string with channels chosen by user Returns: Channel object """ self.channel_number = channel_number self.name = "CH %d" % channel_number self.waveform = w self.seconds_per_point = w.header.seconds_per_point self.firmware = 'unknown' self.unit = UnitEnum.v self.points = 0 self.raw = None self.volts = None self.times = None self.coupling = 'unknown' self.roll_stop = 0 self.time_offset = 0 self.time_scale = 1 self.enabled = False self.enabled_and_selected = False self.volt_scale = 1 self.volt_offset = 0 self.y_scale = 1 self.y_offset = 0 self.volt_per_division = 1 self.probe_value = 1 self.inverted = False # determine if this channel is one of those chosen by user chosen = selected.find(str(channel_number)) != -1 if channel_number <= len(w.header.ch): channel = w.header.ch[channel_number-1] self.enabled = channel.enabled self.enabled_and_selected = channel.enabled and chosen self.volt_scale = channel.volt_scale self.volt_offset = channel.volt_offset self.y_scale = channel.volt_scale self.y_offset = channel.volt_offset self.volt_per_division = channel.volt_per_division self.probe_value = channel.probe_value self.unit = channel.unit self.inverted = channel.inverted if scope == 'wfm1000c': self.ds1000c(w, channel_number) elif scope == 'wfm1000d': self.ds1000d(w, channel_number) elif scope == 'wfm1000e': self.ds1000e(w, channel_number) elif scope == 'wfm1000z': self.ds1000z(w, channel_number) elif scope == 'wfm2000': self.ds2000(w, channel_number) elif scope == 'wfm4000': self.ds4000(w, channel_number) elif scope == 'wfm6000': self.ds6000(w, channel_number) def __str__(self): """Describe this channel.""" s = " Channel %d:\n" % self.channel_number s += " Coupling = %8s\n" % self.coupling.rjust(7, ' ') s += " Scale = %10sV/div\n" % engineering_string(self.volt_per_division, 2) s += " Offset = %10sV\n" % engineering_string(self.volt_offset, 2) s += " Probe = %7gX\n" % self.probe_value s += " Inverted = %8s\n\n" % self.inverted s += " Time Base = %10ss/div\n" % engineering_string(self.time_scale, 3) s += " Offset = %10ss\n" % engineering_string(self.time_offset, 3) s += " Delta = %10ss/point\n" % engineering_string(self.seconds_per_point, 3) s += " Points = %8d\n\n" % self.points if self.enabled_and_selected: s += " Count = [%9d,%9d,%9d ... %9d,%9d]\n" % ( 1, 2, 3, self.points-1, self.points) s += " Raw = [%9d,%9d,%9d ... %9d,%9d]\n" % ( self.raw[0], self.raw[1], self.raw[2], self.raw[-2], self.raw[-1]) t = [engineering_string(self.times[i], 3) + "s" for i in [0, 1, 2, -2, -1]] s += " Times = [%9s,%9s,%9s ... %9s,%9s]\n" % ( t[0], t[1], t[2], t[-2], t[-1]) v = [engineering_string(self.volts[i], 2) + "V" for i in [0, 1, 2, -2, -1]] s += " Volts = [%9s,%9s,%9s ... %9s,%9s]\n" % ( v[0], v[1], v[2], v[-2], v[-1]) return s def calc_times_and_volts(self): """Calculate the times and voltages for this channel.""" if self.enabled_and_selected: self.volts = self.y_scale * (127.0 - self.raw) - self.y_offset h = self.points * self.seconds_per_point / 2 self.times = np.linspace(-h, h, self.points) + self.time_offset def ds1000c(self, w, channel_number): """Interpret waveform data for 1000CD series scopes.""" self.time_scale = 1.0e-12 * w.header.time_scale self.time_offset = 1.0e-12 * w.header.time_offset if channel_number == 1: if self.enabled_and_selected: self.points = len(w.data.ch1) self.raw = np.frombuffer(w.data.ch1, dtype=np.uint8) if channel_number == 2: if self.enabled_and_selected: self.points = len(w.data.ch2) self.raw = np.frombuffer(w.data.ch2, dtype=np.uint8) self.calc_times_and_volts() def ds1000d(self, w, channel_number): """Interpret waveform data for 1000CD series scopes.""" self.time_scale = 1.0e-12 * w.header.time_scale self.time_offset = 1.0e-12 * w.header.time_offset if channel_number == 1: if self.enabled_and_selected: self.points = len(w.data.ch1) self.raw = np.frombuffer(w.data.ch1, dtype=np.uint8) if channel_number == 2: if self.enabled_and_selected: self.points = len(w.data.ch2) self.raw = np.frombuffer(w.data.ch2, dtype=np.uint8) self.calc_times_and_volts() def ds1000e(self, w, channel_number): """Interpret waveform data for 1000D and 1000E series scopes.""" self.roll_stop = w.header.roll_stop if channel_number == 1: self.time_offset = w.header.ch1_time_offset self.time_scale = w.header.ch1_time_scale if self.enabled_and_selected: self.points = len(w.data.ch1) self.raw = np.frombuffer(w.data.ch1, dtype=np.uint8) elif channel_number == 2: self.time_offset = w.header.ch2_time_offset self.time_scale = w.header.ch2_time_scale if self.enabled_and_selected: self.points = len(w.data.ch2) self.raw = np.frombuffer(w.data.ch2, dtype=np.uint8) self.calc_times_and_volts() def ds1000z(self, w, channel_number): """Interpret waveform for the Rigol DS1000Z series.""" self.time_scale = w.header.time_scale self.time_offset = w.header.time_offset self.points = w.header.points self.stride = w.header.stride self.firmware = w.preheader.firmware_version self.probe = w.header.ch[channel_number-1].probe_value self.coupling = w.header.ch[channel_number-1].coupling.name.upper() self.y_scale = w.header.ch[channel_number-1].y_scale self.y_offset = w.header.ch[channel_number-1].y_offset if self.enabled_and_selected: self.raw = _channel_bytes(channel_number, w) self.points = len(self.raw) self.calc_times_and_volts() def ds2000(self, w, channel_number): """Interpret waveform for the Rigol DS2000 series.""" self.time_offset = w.header.time_offset self.time_scale = w.header.time_scale self.points = w.header.storage_depth self.firmware = w.header.firmware_version self.unit = UnitEnum(w.header.ch[channel_number-1].unit_actual) self.coupling = w.header.ch[channel_number-1].coupling.name.upper() self.y_scale = -self.volt_scale self.y_offset = self.volt_offset if self.enabled_and_selected: if channel_number == 1: self.raw = np.frombuffer(w.header.raw_1, dtype=np.uint8) if channel_number == 2: self.raw = np.frombuffer(w.header.raw_2, dtype=np.uint8) if channel_number == 3: self.raw = np.frombuffer(w.header.raw_3, dtype=np.uint8) if channel_number == 4: self.raw = np.frombuffer(w.header.raw_4, dtype=np.uint8) self.calc_times_and_volts() def ds4000(self, w, channel_number): """Interpret waveform for the Rigol DS4000 series.""" self.time_offset = w.header.time_offset self.time_scale = w.header.time_scale self.points = w.header.points self.firmware = w.header.firmware_version self.coupling = w.header.ch[channel_number-1].coupling.name.upper() self.y_scale = -self.volt_scale self.y_offset = self.volt_offset if self.enabled_and_selected: if channel_number == 1: self.raw = np.frombuffer(w.header.raw_1, dtype=np.uint8) if channel_number == 2: self.raw = np.frombuffer(w.header.raw_2, dtype=np.uint8) if channel_number == 3: self.raw = np.frombuffer(w.header.raw_3, dtype=np.uint8) if channel_number == 4: self.raw = np.frombuffer(w.header.raw_4, dtype=np.uint8) self.calc_times_and_volts() def ds6000(self, w, channel_number): """Interpret waveform for the Rigol DS6000 series.""" self.time_offset = w.header.time_offset self.time_scale = w.header.time_scale self.points = w.header.points self.firmware = w.header.firmware_version self.coupling = w.header.ch[channel_number-1].coupling.name.upper() self.unit = w.header.ch[channel_number-1].unit if self.enabled_and_selected: if channel_number == 1: self.raw = np.array(w.header.raw_1, dtype=np.uint8) if channel_number == 2: self.raw = np.array(w.header.raw_2, dtype=np.uint8) if channel_number == 3: self.raw = np.array(w.header.raw_3, dtype=np.uint8) if channel_number == 4: self.raw = np.array(w.header.raw_4, dtype=np.uint8) self.calc_times_and_volts()

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null

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null

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b9e6a9be08cb7ae14c68608c944b95cbe6233b10

1,477

py

Python

configs/raubtierv2a/faster_rcnn_x101_64x4d_fpn_1x_raubtierv2a_nofreeze_4gpu.py

esf-bt2020/mmdetection

abc5fe060e0fcb716f845c85441be3741b22d3cf

[ "Apache-2.0" ]

null

configs/raubtierv2a/faster_rcnn_x101_64x4d_fpn_1x_raubtierv2a_nofreeze_4gpu.py

esf-bt2020/mmdetection

abc5fe060e0fcb716f845c85441be3741b22d3cf

[ "Apache-2.0" ]

null

configs/raubtierv2a/faster_rcnn_x101_64x4d_fpn_1x_raubtierv2a_nofreeze_4gpu.py

esf-bt2020/mmdetection

abc5fe060e0fcb716f845c85441be3741b22d3cf

[ "Apache-2.0" ]

null

_base_ = '../faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py' model = dict( backbone=dict( num_stages=4, #frozen_stages=4 ), roi_head=dict( bbox_head=dict( num_classes=3 ) ) ) dataset_type = 'COCODataset' classes = ('luchs', 'rotfuchs', 'wolf') data = dict( train=dict( img_prefix='raubtierv2a/train/', classes=classes, ann_file='raubtierv2a/train/_annotations.coco.json'), val=dict( img_prefix='raubtierv2a/valid/', classes=classes, ann_file='raubtierv2a/valid/_annotations.coco.json'), test=dict( img_prefix='raubtierv2a/test/', classes=classes, ann_file='raubtierv2a/test/_annotations.coco.json')) #optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) #original (8x2=16) optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) #(4x2=8) 4 GPUs #optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001) #(1x2=2) total_epochs=24 evaluation = dict(classwise=True, interval=1, metric='bbox') work_dir = '/media/storage1/projects/WilLiCam/checkpoint_workdir/raubtierv2a/faster_rcnn_x101_64x4d_fpn_1x_raubtierv2a_nofreeze_4gpu' #http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth load_from = 'checkpoints/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth'

26.375

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1,477

4.832512

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1,477

55

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1

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false

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null

0

null

0

1

0

b9ea32c16e86b4071267eb26a711d79f81eaea56

2,925

py

Python

xos/hpc_observer/steps/sync_originserver.py

wathsalav/xos

f6bcaa37a948ee41729236afe7fce0802e002404

[ "Apache-2.0" ]

null

xos/hpc_observer/steps/sync_originserver.py

wathsalav/xos

f6bcaa37a948ee41729236afe7fce0802e002404

[ "Apache-2.0" ]

null

xos/hpc_observer/steps/sync_originserver.py

wathsalav/xos

f6bcaa37a948ee41729236afe7fce0802e002404

[ "Apache-2.0" ]

null

import os import sys import base64 from django.db.models import F, Q from xos.config import Config from observer.syncstep import SyncStep from core.models import Service from hpc.models import ServiceProvider, ContentProvider, CDNPrefix, OriginServer from util.logger import Logger, logging # hpclibrary will be in steps/.. parentdir = os.path.join(os.path.dirname(__file__),"..") sys.path.insert(0,parentdir) from hpclib import HpcLibrary logger = Logger(level=logging.INFO) class SyncOriginServer(SyncStep, HpcLibrary): provides=[OriginServer] requested_interval=0 def __init__(self, **args): SyncStep.__init__(self, **args) HpcLibrary.__init__(self) def fetch_pending(self, deleted): #self.consistency_check() return SyncStep.fetch_pending(self, deleted) def consistency_check(self): # set to true if something changed result=False # sanity check to make sure our PS objects have CMI objects behind them all_ors_ids = [x["origin_server_id"] for x in self.client.onev.ListAll("OriginServer")] for ors in OriginServer.objects.all(): if (ors.origin_server_id is not None) and (ors.origin_server_id not in all_ors_ids): # we have an origin server ID, but it doesn't exist in the CMI # something went wrong # start over logger.info("origin server %s was not found on CMI" % ors.origin_server_id) ors.origin_server_id=None ors.save() result = True return result def sync_record(self, ors): logger.info("sync'ing origin server %s" % str(ors)) if (not ors.contentProvider) or (not ors.contentProvider.content_provider_id): return cpid = ors.contentProvider.content_provider_id # validation requires URL start with http:// url = ors.url if not url.startswith("http://"): url = "http://" + url ors_dict = {"authenticated_content": ors.authenticated, "zone_redirects": ors.redirects, "content_provider_id": cpid, "url": url, "service_type": "HyperCache", "caching_type": "Optimistic", "description": ors.description} #print os_dict if not ors.origin_server_id: id = self.client.onev.Create("OriginServer", ors_dict) ors.origin_server_id = id else: self.client.onev.Update("OriginServer", ors.origin_server_id, ors_dict) # ... something breaks (analytics) if the URL starts with http://, so we # change it in cob after we added it via onev. url = url[7:] self.client.cob.UpdateContent(ors.origin_server_id, {"url": url}) ors.silent = True ors.save() def delete_record(self, m): if m.origin_server_id is not None: self.client.onev.Delete("OriginServer", m.origin_server_id)

34.411765

229

0.654701

380

2,925

4.873684

0.381579

0.090713

0.073434

0.104752

0.024838

0

0.002275

0.248547

2,925

84

230

34.821429

0.840309

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0

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0

0.103171

0.00843

0

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false

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0.019231

0.403846

0

null

0

null

0

1

0

b9ea437d66df34d28efcf808ad16c896dadcac76

400

py

Python

main.py

aroxby/pixel-processor

9cfe260a085ced0883ce8b0a35c28020f4aa8737

[ "MIT" ]

null

main.py

aroxby/pixel-processor

9cfe260a085ced0883ce8b0a35c28020f4aa8737

[ "MIT" ]

null

main.py

aroxby/pixel-processor

9cfe260a085ced0883ce8b0a35c28020f4aa8737

[ "MIT" ]

null

#!/usr/bin/env python3 from PIL import Image def tranform(r, g, b): tmp = b b = g // 2 g = tmp r = r // 2 return r, g, b def main(): im = Image.open('blue-flames.jpg') input_pixels = im.getdata() output_pixels = tuple(tranform(*pixel) for pixel in input_pixels) im.putdata(output_pixels) im.save('green-flames.png') if __name__ == '__main__': main()

17.391304

69

0.6

62

400

3.677419

0.580645

0.105263

0.026316

0

0.010067

0.255

400

22

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18.181818

0.755034

0.0525

0

0.103175

0

1

0.133333

false

0

0.066667

0

0.266667

0

null

0

null

0

1

0

b9eab80495274dd2446a7b029f17be91df29a452

1,539

py

Python

scipy/weave/examples/swig2_example.py

lesserwhirls/scipy-cwt

ee673656d879d9356892621e23ed0ced3d358621

[ "BSD-3-Clause" ]

8

2015-10-07T00:37:32.000Z

2022-01-21T17:02:33.000Z

scipy/weave/examples/swig2_example.py

lesserwhirls/scipy-cwt

ee673656d879d9356892621e23ed0ced3d358621

[ "BSD-3-Clause" ]

null

scipy/weave/examples/swig2_example.py

lesserwhirls/scipy-cwt

ee673656d879d9356892621e23ed0ced3d358621

[ "BSD-3-Clause" ]

8

2015-05-09T14:23:57.000Z

2018-11-15T05:56:00.000Z

"""Simple example to show how to use weave.inline on SWIG2 wrapped objects. SWIG2 refers to SWIG versions >= 1.3. To run this example you must build the trivial SWIG2 extension called swig2_ext. To do this you need to do something like this:: $ swig -c++ -python -I. -o swig2_ext_wrap.cxx swig2_ext.i $ g++ -Wall -O2 -I/usr/include/python2.3 -fPIC -I. -c \ -o swig2_ext_wrap.os swig2_ext_wrap.cxx $ g++ -shared -o _swig2_ext.so swig2_ext_wrap.os \ -L/usr/lib/python2.3/config The files swig2_ext.i and swig2_ext.h are included in the same directory that contains this file. Note that weave's SWIG2 support works fine whether SWIG_COBJECT_TYPES are used or not. Author: Prabhu Ramachandran Copyright (c) 2004, Prabhu Ramachandran License: BSD Style. """ # Import our SWIG2 wrapped library import swig2_ext import scipy.weave as weave from scipy.weave import swig2_spec, converters # SWIG2 support is not enabled by default. We do this by adding the # swig2 converter to the default list of converters. converters.default.insert(0, swig2_spec.swig2_converter()) def test(): """Instantiate the SWIG wrapped object and then call its method from C++ using weave.inline """ a = swig2_ext.A() b = swig2_ext.foo() # This will be an APtr instance. b.thisown = 1 # Prevent memory leaks. code = """a->f(); b->f(); """ weave.inline(code, ['a', 'b'], include_dirs=['.'], headers=['"swig2_ext.h"'], verbose=1) if __name__ == "__main__": test()

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0

0.030204

0.204029

1,539

53

70

29.037736

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0

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0

1

0.066667

false

0

0.2

0

0.266667

0

null

0

null

0

1

0

b9eba9b75a6e45fee4cdfe3d81874f5e8476b939

1,951

py

Python

src/simplify.py

denghz/Probabilistic-Programming

fa505a75c4558e507fd3effd2737c63537bfe50d

[ "BSD-3-Clause" ]

null

src/simplify.py

denghz/Probabilistic-Programming

fa505a75c4558e507fd3effd2737c63537bfe50d

[ "BSD-3-Clause" ]

null

src/simplify.py

denghz/Probabilistic-Programming

fa505a75c4558e507fd3effd2737c63537bfe50d

[ "BSD-3-Clause" ]

null

from wolframclient.language.expression import WLSymbol from nnDiff import * def parseGlobalSymbol(s): if isinstance(s, numbers.Number): return s if isinstance(s, WLSymbol): if s.name == 'E': return 'E' else: return s.name[7:] def parse(exp): symbol = parseGlobalSymbol(exp) if symbol: return [symbol] else: f = str(exp.head) args = list(map(parse, exp.args)) res = [] if (f == "Power"): res1 = [] p = args[1][0] e = args[0] if e == ['E']: return ['Exp'] + args[1] if p < 0: res = ["Inv"] p = -p if p >= 2: p = p - 2 res1 = ["Times"] + e + e while p > 0: p = p - 1 res1 = ["Times"] + res1 + e return res + res1 else: return res + e else: if len(args) == 1: return [f] + args[0] elif len(args) >= 2: res = [f] + args[0] + args[1] args = args[2:] for arg in args: res = [f] + res + arg return res def simplify(exp): with WolframLanguageSession() as session: session.evaluate("Inv[zzz_] := 1/zzz") f = wlexpr(str(Func(exp))) getfreeVars = wlexpr("Reduce`FreeVariables") freeVariables = session.evaluate(getfreeVars(f)) ass = wl.Element(wl.Alternatives(freeVariables), wl.Reals) wmres = session.evaluate(wl.FullSimplify(f,ass)) print(wmres) res = parse(wmres) return res if __name__ == "__main__": exp = sys.argv[1:] if exp == []: exp = ["Sin", "x"] res = map(str,simplify(exp)) print(' '.join(res), file=sys.stderr)

27.097222

67

0.438237

212

1,951

3.990566

0.334906

0.023641

0.030733

0.033097

0

0.020814

0.433624

1,951

72

68

27.097222

0.744796

0

0.096774

0

0.038422

0

1

0.048387

false

0

0.032258

0

0.241935

0.032258

0

null

0

null

0

1

0

b9eda494aa9f90de7b3474adbd78e46927f9990c

406

py

Python

src/cart/forms.py

cbsBiram/xarala__ssr

863e1362c786daa752b942b796f7a015211d2f1b

[ "FSFAP" ]

null

src/cart/forms.py

cbsBiram/xarala__ssr

863e1362c786daa752b942b796f7a015211d2f1b

[ "FSFAP" ]

null

src/cart/forms.py

cbsBiram/xarala__ssr

863e1362c786daa752b942b796f7a015211d2f1b

[ "FSFAP" ]

null

from django import forms from django.utils.translation import gettext_lazy as _ COURSE_QUANTITY_CHOICES = [(i, str(i)) for i in range(1, 21)] class CartAddCourseForm(forms.Form): quantity = forms.TypedChoiceField( choices=COURSE_QUANTITY_CHOICES, coerce=int, label=_("Quantité") ) override = forms.BooleanField( required=False, initial=False, widget=forms.HiddenInput )

27.066667

72

0.726601

49

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0.693878

0.069444

0.145833

0

0.008955

0.174877

406

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73

29

0.850746

0

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0

1

0

false

0

0.2

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0.5

0

null

0

null

0

1

0

b9edd7dbf25e820fdbc6faa76fd63ef5d9d3ec94

1,090

py

Python

appengine/components/tests/datastore_utils_properties_test.py

pombreda/swarming

c70f311f3db8f25752c793a0d7b36cf537d95580

[ "Apache-2.0" ]

null

appengine/components/tests/datastore_utils_properties_test.py

pombreda/swarming

c70f311f3db8f25752c793a0d7b36cf537d95580

[ "Apache-2.0" ]

null

appengine/components/tests/datastore_utils_properties_test.py

pombreda/swarming

c70f311f3db8f25752c793a0d7b36cf537d95580

[ "Apache-2.0" ]

1

2021-12-06T03:37:36.000Z

#!/usr/bin/env python # Copyright 2014 The Swarming Authors. All rights reserved. # Use of this source code is governed by the Apache v2.0 license that can be # found in the LICENSE file. import sys import unittest import test_env test_env.setup_test_env() from google.appengine.ext import ndb from components.datastore_utils import properties from support import test_case class BP(ndb.Model): prop = properties.BytesComputedProperty(lambda _: '\x00') class DJP(ndb.Model): prop = properties.DeterministicJsonProperty(json_type=dict) class PropertiesTest(test_case.TestCase): def test_DeterministicJsonProperty(self): self.assertEqual({'a': 1}, DJP(prop={'a': 1}).prop) DJP(prop={'a': 1}).put() self.assertEqual({'a': 1}, DJP.query().get().prop) with self.assertRaises(TypeError): DJP(prop=[]) def test_BytesComputedProperty(self): self.assertEqual('\x00', BP().prop) BP().put() self.assertEqual('\x00', BP.query().get().prop) if __name__ == '__main__': if '-v' in sys.argv: unittest.TestCase.maxDiff = None unittest.main()

23.695652

76

0.713761

150

1,090

5.06

0.52

0.079051

0.031621

0.057971

0.052701

0

0.017279

0.150459

1,090

45

77

24.222222

0.802376

0.165138

0

0.028698

0

0.192308

1

0.076923

false

0

0.230769

0

0.5

0

null

0

null

0

1

0

b9ef252652f99c5c9feffaab6f06bdbb7fe7dd89

953

py

Python

covfefe/covfefe.py

fixator10/Trusty-cogs

3d47a63f562cb64eb44da6bb53cfe9f8324026e7

[ "MIT" ]

148

2017-04-23T19:57:50.000Z

2022-03-12T06:59:58.000Z

covfefe/covfefe.py

mina9999/Trusty-cogs

a47de7c233f3c1802effd29f4a86f8a9b0e2b34a

[ "MIT" ]

155

2018-01-01T13:27:45.000Z

2022-03-12T05:17:51.000Z

covfefe/covfefe.py

mina9999/Trusty-cogs

a47de7c233f3c1802effd29f4a86f8a9b0e2b34a

[ "MIT" ]

221

2017-04-02T00:26:08.000Z

2022-03-26T15:06:54.000Z

import re import discord from redbot.core import commands class Covfefe(commands.Cog): """ Convert almost any word into covfefe """ def __init__(self, bot): self.bot = bot async def covfefe(self, x, k="aeiouy])"): """ https://codegolf.stackexchange.com/a/123697 """ try: b, c, v = re.findall(f"(.*?[{k}([^{k}.*?([{k}", x)[0] return b + c + (("bcdfgkpstvz" + c)["pgtvkgbzdfs".find(c)] + v) * 2 except IndexError: return None async def red_delete_data_for_user(self, **kwargs): """ Nothing to delete """ return @commands.command() async def covefy(self, ctx, msg): """Convert almost any word into covfefe""" newword = await self.covfefe(msg) if newword is not None: await ctx.send(newword) else: await ctx.send("I cannot covfefeify that word")

24.435897

79

0.541448

115

953

4.417391

0.582609

0.047244

0.062992

0.07874

0.122047

0

0.012365

0.321091

953

38

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false

0

0.142857

0

0.380952

0

null

0

null

0

1

0

b9ef4b5c2209cb05949e60eccf8cd9158602e350

4,784

py

Python

exp_gqa/test.py

ronghanghu/gqa_single_hop_baseline

332d342da60dfefd40f2364d60215ed2f191aa2d

[ "BSD-2-Clause" ]

19

2019-08-19T18:09:26.000Z

2021-08-29T15:58:30.000Z

exp_gqa/test.py

ronghanghu/gqa_single_hop_baseline

332d342da60dfefd40f2364d60215ed2f191aa2d

[ "BSD-2-Clause" ]

1

2019-11-24T14:36:29.000Z

2019-12-11T08:33:12.000Z

exp_gqa/test.py

ronghanghu/gqa_single_hop_baseline

332d342da60dfefd40f2364d60215ed2f191aa2d

[ "BSD-2-Clause" ]

1

2019-10-30T05:55:52.000Z

import os import numpy as np import tensorflow as tf from models_gqa.model import Model from models_gqa.config import build_cfg_from_argparse from util.gqa_train.data_reader import DataReader import json # Load config cfg = build_cfg_from_argparse() # Start session os.environ["CUDA_VISIBLE_DEVICES"] = str(cfg.GPU_ID) sess = tf.Session(config=tf.ConfigProto( gpu_options=tf.GPUOptions(allow_growth=cfg.GPU_MEM_GROWTH))) # Data files imdb_file = cfg.IMDB_FILE % cfg.TEST.SPLIT_VQA scene_graph_file = cfg.SCENE_GRAPH_FILE % \ cfg.TEST.SPLIT_VQA.replace('_balanced', '').replace('_all', '') data_reader = DataReader( imdb_file, shuffle=False, one_pass=True, batch_size=cfg.TEST.BATCH_SIZE, T_encoder=cfg.T_ENCODER, vocab_question_file=cfg.VOCAB_QUESTION_FILE, vocab_answer_file=cfg.VOCAB_ANSWER_FILE, feature_type=cfg.FEAT_TYPE, spatial_feature_dir=cfg.SPATIAL_FEATURE_DIR, objects_feature_dir=cfg.OBJECTS_FEATURE_DIR, objects_max_num=cfg.W_FEAT, scene_graph_file=scene_graph_file, vocab_name_file=cfg.VOCAB_NAME_FILE, vocab_attr_file=cfg.VOCAB_ATTR_FILE, spatial_pos_enc_dim=cfg.SPATIAL_POS_ENC_DIM, bbox_tile_num=cfg.BBOX_TILE_NUM) num_vocab = data_reader.batch_loader.vocab_dict.num_vocab num_choices = data_reader.batch_loader.answer_dict.num_vocab # Inputs and model input_seq_batch = tf.placeholder(tf.int32, [None, None]) seq_length_batch = tf.placeholder(tf.int32, [None]) image_feat_batch = tf.placeholder( tf.float32, [None, cfg.H_FEAT, cfg.W_FEAT, cfg.D_FEAT]) image_valid_batch = tf.placeholder( tf.float32, [None, cfg.H_FEAT, cfg.W_FEAT]) model = Model( input_seq_batch, seq_length_batch, image_feat_batch, image_valid_batch, num_vocab=num_vocab, num_choices=num_choices, is_training=False) # Load snapshot if cfg.TEST.USE_EMA: ema = tf.train.ExponentialMovingAverage(decay=0.9) # decay doesn't matter var_names = { (ema.average_name(v) if v in model.params else v.op.name): v for v in tf.global_variables()} else: var_names = {v.op.name: v for v in tf.global_variables()} snapshot_file = cfg.TEST.SNAPSHOT_FILE % (cfg.EXP_NAME, cfg.TEST.ITER) print('loading model snapshot from %s' % snapshot_file) snapshot_saver = tf.train.Saver(var_names) snapshot_saver.restore(sess, snapshot_file) print('Done') # Write results result_dir = cfg.TEST.RESULT_DIR % (cfg.EXP_NAME, cfg.TEST.ITER) os.makedirs(result_dir, exist_ok=True) # Run test answer_correct, num_questions = 0, 0 if cfg.TEST.OUTPUT_VQA_EVAL_PRED: output_predictions = [] answer_word_list = data_reader.batch_loader.answer_dict.word_list pred_file = os.path.join( result_dir, 'gqa_eval_preds_%s_%s_%08d.json' % ( cfg.TEST.SPLIT_VQA, cfg.EXP_NAME, cfg.TEST.ITER)) for n_batch, batch in enumerate(data_reader.batches()): if 'answer_label_batch' not in batch: batch['answer_label_batch'] = -np.ones( len(batch['qid_list']), np.int32) if num_questions == 0: print('imdb has no answer labels. Using dummy labels.\n\n' '**The final accuracy will be zero (no labels provided)**\n') vqa_scores_value = sess.run(model.vqa_scores, feed_dict={ input_seq_batch: batch['input_seq_batch'], seq_length_batch: batch['seq_length_batch'], image_feat_batch: batch['image_feat_batch'], image_valid_batch: batch['image_valid_batch']}) # compute accuracy vqa_labels = batch['answer_label_batch'] vqa_predictions = np.argmax(vqa_scores_value, axis=1) answer_correct += np.sum(vqa_predictions == vqa_labels) num_questions += len(vqa_labels) accuracy = answer_correct / num_questions if n_batch % 20 == 0: print('exp: %s, iter = %d, accumulated accuracy on %s = %f (%d / %d)' % (cfg.EXP_NAME, cfg.TEST.ITER, cfg.TEST.SPLIT_VQA, accuracy, answer_correct, num_questions)) if cfg.TEST.OUTPUT_VQA_EVAL_PRED: output_predictions.extend([ {"questionId": qId, "prediction": answer_word_list[p]} for qId, p in zip(batch['qid_list'], vqa_predictions)]) with open(os.path.join( result_dir, 'vqa_results_%s.txt' % cfg.TEST.SPLIT_VQA), 'w') as f: print('\nexp: %s, iter = %d, final accuracy on %s = %f (%d / %d)' % (cfg.EXP_NAME, cfg.TEST.ITER, cfg.TEST.SPLIT_VQA, accuracy, answer_correct, num_questions)) print('exp: %s, iter = %d, final accuracy on %s = %f (%d / %d)' % (cfg.EXP_NAME, cfg.TEST.ITER, cfg.TEST.SPLIT_VQA, accuracy, answer_correct, num_questions), file=f) if cfg.TEST.OUTPUT_VQA_EVAL_PRED: with open(pred_file, 'w') as f: json.dump(output_predictions, f, indent=2) print('prediction file written to %s' % pred_file)

40.201681

79

0.713002

736

4,784

4.328804

0.256793

0.043942

0.026365

0.032957

0.315443

0.284683

0.205273

0.161017

0.134024

0

0.005542

0.170151

4,784

118

80

40.542373

0.796977

0.026756

0

0.082474

0

0.030928

0.125054

0.006457

0

1

0

false

0.010309

0.072165

0

0.072165

0

null

0

null

0

1

0

b9efb93e53325ce5948d495ecf3a99ce26893591

2,071

py

Python

extract_gear/armor_visitor.py

kamerons/dde-extract-gear

44464ae470bd5de6279d32e3587b469ce006ea42

[ "Apache-2.0" ]

null

extract_gear/armor_visitor.py

kamerons/dde-extract-gear

44464ae470bd5de6279d32e3587b469ce006ea42

[ "Apache-2.0" ]

null

extract_gear/armor_visitor.py

kamerons/dde-extract-gear

44464ae470bd5de6279d32e3587b469ce006ea42

[ "Apache-2.0" ]

null

class ArmorVisitor: def __init__(self, num_pages, first_page_col_start, first_page_row_start, last_page_row_start, last_page_col_end, last_page_row_end, num_col_page=5, num_row_page=3): self.num_pages = num_pages self.first_page_col_start = first_page_col_start self.first_page_row_start = first_page_row_start self.last_page_row_start = last_page_row_start self.last_page_col_end = last_page_col_end self.last_page_row_end = last_page_row_end self.num_col_page = num_col_page self.num_row_page = num_row_page def iterate(self, callback): for page_num in range(1, self.num_pages + 1): page = self.create_page(page_num) i = 0 for coord in page: callback(coord, page_num, i) i += 1 def create_page(self, page_num): if page_num == 1: last_col = self.num_col_page if self.num_pages > 1 else self.last_page_col_end last_row = self.num_row_page if self.num_pages > 1 else self.last_page_row_end page = Page(self.first_page_col_start, self.first_page_row_start, last_col, last_row, self.num_col_page) elif page_num == self.num_pages: page = Page(1, self.last_page_row_start, self.last_page_col_end, self.last_page_row_end, self.num_col_page) else: page = Page(1, 1, self.num_col_page, self.num_row_page, self.num_col_page) return page class Page: def __init__(self, start_col, start_row, last_col, last_row, num_col_page=5): self.start_col = start_col self.start_row = start_row self.last_col = last_col self.last_row = last_row self.num_col_page = num_col_page def __iter__(self): self.cur_row = self.start_row self.cur_col = self.start_col return self def __next__(self): position = (self.cur_row, self.cur_col) if self.cur_row > self.last_row or (self.cur_col > self.last_col and self.cur_row == self.last_row): raise StopIteration elif self.cur_col == self.num_col_page: self.cur_col = 1 self.cur_row += 1 else: self.cur_col += 1 return position

32.873016

110

0.718493

360

2,071

3.680556

0.105556

0.089811

0.090566

0.084528

0.490566

0.436981

0.315472

0.230943

0.199245

0.05283

0

0.00965

0.199421

2,071

62

111

33.403226

0.789505

0

0.08

0

1

0.12

false

0

0.22

0

null

0

null

0

1

0

b9f401385afbe018601c2bef20e53c9b587fb7df

485

py

Python

examples/test_scalar_field.py

gemini3d/pv-gemini

99dff15b43a2c93cbcb63d2f8946d425d0555ef3

[ "Apache-2.0" ]

null

examples/test_scalar_field.py

gemini3d/pv-gemini

99dff15b43a2c93cbcb63d2f8946d425d0555ef3

[ "Apache-2.0" ]

null

examples/test_scalar_field.py

gemini3d/pv-gemini

99dff15b43a2c93cbcb63d2f8946d425d0555ef3

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 """ example of 3D scalar field If you get this error, ParaView doesn't know your data file format: TypeError: TestFileReadability argument %Id: %V """ from pathlib import Path import argparse import paraview.simple as pvs p = argparse.ArgumentParser() p.add_argument("fn", help="data file to load with paraview OpenDataFile()") P = p.parse_args() fn = Path(P.fn).expanduser() if not fn.is_file(): raise FileNotFoundError(fn) pvs.OpenDataFile(str(fn))

20.208333

75

0.740206

74

485

4.810811

0.702703

0.044944

0

0.004819

0.14433

485

23

76

21.086957

0.853012

0.340206

0

0.153846

0

1

0

false

0

0.3

0

0.3

0

null

0

null

0

1

0

b9f4182f4b0683cbf4f51c72cef042f5acb55553

341

py

Python

src/cms/forms/languages/language_form.py

S10MC2015/cms-django

b08f2be60a9db6c8079ee923de2cd8912f550b12

[ "Apache-2.0" ]

null

src/cms/forms/languages/language_form.py

S10MC2015/cms-django

b08f2be60a9db6c8079ee923de2cd8912f550b12

[ "Apache-2.0" ]

null

src/cms/forms/languages/language_form.py

S10MC2015/cms-django

b08f2be60a9db6c8079ee923de2cd8912f550b12

[ "Apache-2.0" ]

null

from django import forms from ...models import Language class LanguageForm(forms.ModelForm): """ Form for creating and modifying language objects """ class Meta: model = Language fields = [ "code", "english_name", "native_name", "text_direction", ]

17.947368

52

0.548387

31

341

5.935484

0.774194

0

0.363636

341

18

53

18.944444

0.847926

0.140762

0

0.148014

0

1

0

false

0

0.181818

0

0.363636

0

null

0

null

0

1

0

b9f437d2e63f9838da4ffa0491804e95e149a773

1,482

py

Python

search/forms.py

gregneagle/sal

74c583fb1c1b33d3201b308b147376b3dcaca33f

[ "Apache-2.0" ]

2

2019-11-01T20:50:35.000Z

2021-01-13T22:02:55.000Z

search/forms.py

gregneagle/sal

74c583fb1c1b33d3201b308b147376b3dcaca33f

[ "Apache-2.0" ]

null

search/forms.py

gregneagle/sal

74c583fb1c1b33d3201b308b147376b3dcaca33f

[ "Apache-2.0" ]

null

from django import forms from .models import * from server.models import * class ChoiceFieldNoValidation(forms.ChoiceField): def validate(self, value): pass class SaveSearchForm(forms.ModelForm): class Meta: model = SavedSearch fields = ('name',) class SearchRowForm(forms.ModelForm): skip_fields = [ 'id', 'machine_group', 'report', 'activity', 'errors', 'warnings', 'install_log', 'puppet_errors', 'install_log_hash' ] search_fields = [] for f in Machine._meta.fields: if f.name not in skip_fields: add = (f.name,f.name,) search_fields.append(add) search_field = ChoiceFieldNoValidation(choices=sorted(search_fields)) and_or = ChoiceFieldNoValidation(choices=AND_OR_CHOICES) def __init__(self, *args, **kwargs): self.search_group = kwargs.pop('search_group', None) super(SearchRowForm, self).__init__(*args, **kwargs) try: search_group_count = self.search_group.searchrow_set.count() except: search_group_count = 0 if search_group_count == 0 and self.search_group: self.fields['and_or'] = ChoiceFieldNoValidation( initial='AND', widget=forms.HiddenInput() ) class Meta: model = SearchRow fields = ('search_models', 'search_field', 'and_or', 'operator','search_term',)

27.962264

87

0.609312

155

1,482

5.574194

0.412903

0.08912

0.052083

0.078704

0

0.001881

0.282726

1,482

52

88

28.5

0.810913

0

0.045455

0

0.106613

0

1

0.045455

false

0.022727

0.068182

0

0.318182

0

null

0

null

0

1

0

b9f67833672023bef782862284907976acb9371f

2,216

py

Python

newsparser.py

antoreep-jana/BBC-News-Analyzer

0a6e54ddf4baefa4532213c5e6f60e712ff3a1ca

[ "MIT" ]

1

2021-12-27T12:57:07.000Z

newsparser.py

antoreep-jana/BBC-News-Analyzer

0a6e54ddf4baefa4532213c5e6f60e712ff3a1ca

[ "MIT" ]

null

newsparser.py

antoreep-jana/BBC-News-Analyzer

0a6e54ddf4baefa4532213c5e6f60e712ff3a1ca

[ "MIT" ]

null

from bs4 import BeautifulSoup as bs import requests class BBC: def __init__(self, url:str): article = requests.get(url) self.soup = bs(article.content, "html.parser") #print(dir(self.soup)) #print(self.soup.h1.text) self.body = self.get_body() self.link = url self.title = self.get_title() self.author = self.get_author() self.images = self.get_images() self.date = self.get_date() #author = self.soup.find #date = self.soup #for img in imgs: # print(img['src']) paras = self.soup.find_all('div', {"class" : "ssrcss-17j9f6r-RichTextContainer e5tfeyi1"}) #for para in paras: # print(para.text) def get_body(self) -> list: #body = self.soup.find(property="articleBody") paras = self.soup.find_all('div', {"class" : "ssrcss-17j9f6r-RichTextContainer e5tfeyi1"}) #for para in paras: # print(para.text) return [p.text for p in paras] #return [p.text for p in body.find_all("p")] def get_title(self) -> str: #return self.soup.find(class_="story-body__h1").text return self.soup.h1.text def get_author(self) -> str: author = self.soup.find('p', {'class' : 'ssrcss-1rv0moy-Contributor e5xb54n2'}) return author.text.replace("BBC News", "") def get_images(self) -> list: imgs = self.soup.find_all('figure', {'class' : 'ssrcss-wpgbih-StyledFigure e34k3c23'}) imgs_lst = [] for img in imgs: try: if "blank_white_space" not in img.img['src']: imgs_lst.append(img.img['src'])#['div']['span']['span']['img']) except: pass return imgs_lst def get_date(self) -> str: date = self.soup.find_all('time')[0] return date['datetime'] parsed = BBC("https://www.bbc.co.uk/news/world-europe-49345912") #print(parsed.title) #print(parsed.link) #print(parsed.author) #print(parsed.date) #print(parsed.title) #print(parsed.body) #print(parsed.images) #print(parsed.body)

28.410256

98

0.564982

278

2,216

4.406475

0.291367

0.084898

0.078367

0.04898

0.223673

0.179592

0.151837

0

0.022872

0.289711

2,216

77

99

28.779221

0.7554

0.229242

0

0.057143

0

0.170326

0.068843

0

1

0.171429

false

0.028571

0.057143

0.028571

0.4

0

null

0

null

0

1

0

b9f73f41171ea9b93f4f79bc336c9fe6927dba89

2,044

py

Python

SIR_model-Copy.Caroline.1.py

Caroline-Odevall/final-project-team-18

fbf00ae4ec554dee9245a9834ff4108b3d339842

[ "MIT" ]

null

SIR_model-Copy.Caroline.1.py

Caroline-Odevall/final-project-team-18

fbf00ae4ec554dee9245a9834ff4108b3d339842

[ "MIT" ]

null

SIR_model-Copy.Caroline.1.py

Caroline-Odevall/final-project-team-18

fbf00ae4ec554dee9245a9834ff4108b3d339842

[ "MIT" ]

null

# In[42]: from scipy.integrate import odeint import numpy as np import matplotlib.pyplot as plt # In[43]: # describe the model def deriv(y, t, N, beta, gamma, delta): S, E, I, R = y dSdt = -beta * S * I / N # S(t) – susceptible (de som är mottagliga för infektion). dEdt = beta * S * I / N - gamma * E dIdt = delta * E - gamma * I # I(t) – infected (de som har pågående infektion) dRdt = gamma * I return dSdt, dEdt, dIdt, dRdt # In[44]: # describe the parameters N = 2283 #Totala befolkningen N=s(t)+I(t)+R(t) D = 4.0 #infections last four days gamma = 1.0 / D #Reoval rate (Hur många som tillfrisknar) delta = 1.0 / 5.0 #incubation period of five days R_0 = 2.5 #Reproduktionstalet beta = R_0 * gamma #r_0=beta/gamma. antal som smittas per infekterad och per tid (beror på virusets egenskaper samt hur vi beter oss). S0, E0, I0, R0 = N-1, 1, 0, 0 # initial conditions: one infected, rest susceptible #Rt = R0 * S(t)/Ntot* (1 – b). b = effekt av policy och beteendeförändringar # In[45]: t = np.linspace(0, 99, 100) # Grid of time points (in days) y0 = S0, E0, I0, R0 # Initial conditions vector # Integrate the SIR equations over the time grid, t. ret = odeint(deriv, y0, t, args=(N, beta, gamma, delta)) S, E, I, R = ret.T # In[46]: def plotsir(t, S, E, I, R): f, ax = plt.subplots(1,1,figsize=(10,4)) ax.plot(t, S, 'b', alpha=0.7, linewidth=2, label='Susceptible') ax.plot(t, E, 'y', alpha=0.7, linewidth=2, label='Exposed') ax.plot(t, I, 'r', alpha=0.7, linewidth=2, label='Infected') ax.plot(t, R, 'g', alpha=0.7, linewidth=2, label='Recovered') ax.set_xlabel('Time (days)') ax.yaxis.set_tick_params(length=0) ax.xaxis.set_tick_params(length=0) ax.grid(b=True, which='major', c='w', lw=2, ls='-') legend = ax.legend() legend.get_frame().set_alpha(0.5) for spine in ('top', 'right', 'bottom', 'left'): ax.spines[spine].set_visible(False) plt.savefig('Plot.png') plt.show(); # plot the graph # In[47]: plotsir(t, S, E, I, R) # In[ ]:

24.333333

137

0.630137

364

2,044

3.516484

0.436813

0.007813

0.009375

0.0125

0.151563

0.029688

0

0.04473

0.201566

2,044

83

138

24.626506

0.737745

0.354207

0

0.064241

0

1

0.052632

false

0

0.078947

0

0.157895

0

null

0

null

0

1

0

b9f8215f5040fa71b2646d52a053545a92c3fd12

1,681

py

Python

app/middleware/cache_headers.py

Niclnx/service-stac

ad9129a7130d09b2bed387d8e82575eb86fdfa7b

[ "BSD-3-Clause" ]

9

2020-08-17T11:01:48.000Z

2022-01-17T22:24:13.000Z

app/middleware/cache_headers.py

Niclnx/service-stac

ad9129a7130d09b2bed387d8e82575eb86fdfa7b

[ "BSD-3-Clause" ]

100

2020-08-14T05:56:40.000Z

2022-03-01T22:39:58.000Z

app/middleware/cache_headers.py

Niclnx/service-stac

ad9129a7130d09b2bed387d8e82575eb86fdfa7b

[ "BSD-3-Clause" ]

3

2020-09-02T14:01:07.000Z

2021-07-27T06:30:26.000Z

import logging import re from urllib.parse import urlparse from django.conf import settings from django.utils.cache import add_never_cache_headers from django.utils.cache import patch_cache_control from django.utils.cache import patch_response_headers logger = logging.getLogger(__name__) STAC_BASE = settings.STAC_BASE STAC_BASE_V = settings.STAC_BASE_V class CacheHeadersMiddleware: '''Middleware that adds appropriate cache headers to GET and HEAD methods. NOTE: /checker, /get-token, /metrics and /{healthcheck} endpoints are marked as never cache. ''' def __init__(self, get_response): self.get_response = get_response def __call__(self, request): # Code to be executed for each request before # the view (and later middleware) are called. response = self.get_response(request) # Code to be executed for each request/response after # the view is called. # match /xxx or /api/stac/xxx # f.ex. /metrics, /checker, /api/stac/{healthcheck}, /api/stac/get-token if re.match(fr'^(/{STAC_BASE})?/\w+$', request.path): add_never_cache_headers(response) elif ( request.method in ('GET', 'HEAD') and not request.path.startswith(urlparse(settings.STATIC_URL).path) ): logger.debug( "Patching cache headers for request %s %s", request.method, request.path, extra={"request": request} ) patch_response_headers(response, settings.CACHE_MIDDLEWARE_SECONDS) patch_cache_control(response, public=True) return response

32.960784

96

0.662701

207

1,681

5.188406

0.415459

0.037244

0.041899

0.055866

0.150838

0.126629

0.068901

0

0.250446

1,681

50

97

33.62

0.852381

0.252826

0

0.06068

0.01699

0

1

0.066667

false

0

0.233333

0

0.366667

0

null

0

null

0

1

0

b9f87264f50f9243a592053fcbe97aca0b8c2377

2,818

py

Python

mmdet/models/detectors/knowledge_distilling/kd_single_stage.py

anorthman/mmdetection

52e28154364f0e19d11c206bb357d88f29fc4a2d

[ "Apache-2.0" ]

5

2019-06-11T11:08:54.000Z

2021-03-25T10:06:01.000Z

mmdet/models/detectors/knowledge_distilling/kd_single_stage.py

anorthman/mmdetection

52e28154364f0e19d11c206bb357d88f29fc4a2d

[ "Apache-2.0" ]

null

mmdet/models/detectors/knowledge_distilling/kd_single_stage.py

anorthman/mmdetection

52e28154364f0e19d11c206bb357d88f29fc4a2d

[ "Apache-2.0" ]

1

2019-06-11T11:08:55.000Z

# author huangchuanhong import torch from mmcv.runner import load_checkpoint from ..base import BaseDetector from ..single_stage import SingleStageDetector from ...registry import DETECTORS from ...builder import build_detector @DETECTORS.register_module class KDSingleStageDetector(SingleStageDetector): def __init__(self, backbone, teacher, neck=None, bbox_head=None, train_cfg=None, test_cfg=None, pretrained=None): super(KDSingleStageDetector, self).__init__(backbone, neck=neck, bbox_head=bbox_head, train_cfg=train_cfg, test_cfg=test_cfg, pretrained=pretrained) self.teacher_detector = build_detector(teacher.model, train_cfg=None, test_cfg=test_cfg) load_checkpoint(self.teacher_detector, teacher.checkpoint) self.teacher_detector.eval() self.beta = train_cfg.teacher.beta def forward_train(self, img, img_metas, gt_bboxes, gt_labels, gt_bboxes_ignore=None, beta=1000.): feats = () backbone_feats = self.backbone(img) if self.train_cfg.teacher.backbone_at: for i in self.train_cfg.teacher.backbone_at_idxes: feats += (backbone_feats[i],) if self.with_neck: neck_feats = self.neck(backbone_feats) if self.train_cfg.teacher.neck_at: feats += neck_feats outs = self.bbox_head(neck_feats) else: outs = self.bbox_head(backbone_feats) with torch.no_grad(): t_feats = () t_backbone_feats = self.teacher_detector.backbone(img) if self.train_cfg.teacher.backbone_at: for i in self.train_cfg.teacher.backbone_at_idxes: t_feats += (t_backbone_feats[i],) if self.with_neck: t_neck_feats = self.teacher_detector.neck(t_backbone_feats) if self.train_cfg.teacher.neck_at: t_feats += t_neck_feats t_outs = self.teacher_detector.bbox_head(t_neck_feats) else: t_outs = self.teacher_detector.bbox_head(t_backbone_feats) loss_inputs = (feats,) + outs + (t_feats,) + t_outs + (gt_bboxes, gt_labels, img_metas, self.train_cfg) losses = self.bbox_head.loss( *loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) return losses

42.059701

112

0.551455

297

2,818

4.895623

0.218855

0.066025

0.091472

0.078404

0.296424

0.251719

0.213205

0.167813

0.112792

0

0.002279

0.377218

2,818

66

113

42.69697

0.826211

0.007452

0

0.16129

0

1

0.032258

false

0

0.096774

0

0.16129

0

null

0

null

0

1

0

b9f8cb65181ebad752b9a810d28cc601137f1877

4,518

py

Python

metaworld/envs/mujoco/sawyer_xyz/v2/sawyer_dial_turn_v2.py

yiwc/robotics-world

48efda3a8ea6741b35828b02860f45753252e376

[ "MIT" ]

681

2019-09-09T19:34:37.000Z

2022-03-31T12:17:58.000Z

metaworld/envs/mujoco/sawyer_xyz/v2/sawyer_dial_turn_v2.py

yiwc/robotics-world

48efda3a8ea6741b35828b02860f45753252e376

[ "MIT" ]

212

2019-09-18T14:43:44.000Z

2022-03-27T22:21:00.000Z

metaworld/envs/mujoco/sawyer_xyz/v2/sawyer_dial_turn_v2.py

yiwc/robotics-world

48efda3a8ea6741b35828b02860f45753252e376

[ "MIT" ]

157

2019-09-12T05:06:05.000Z

2022-03-29T14:47:24.000Z

import numpy as np from gym.spaces import Box from metaworld.envs import reward_utils from metaworld.envs.asset_path_utils import full_v2_path_for from metaworld.envs.mujoco.sawyer_xyz.sawyer_xyz_env import SawyerXYZEnv, _assert_task_is_set class SawyerDialTurnEnvV2(SawyerXYZEnv): TARGET_RADIUS = 0.07 def __init__(self): hand_low = (-0.5, 0.40, 0.05) hand_high = (0.5, 1, 0.5) obj_low = (-0.1, 0.7, 0.0) obj_high = (0.1, 0.8, 0.0) goal_low = (-0.1, 0.73, 0.0299) goal_high = (0.1, 0.83, 0.0301) super().__init__( self.model_name, hand_low=hand_low, hand_high=hand_high, ) self.init_config = { 'obj_init_pos': np.array([0, 0.7, 0.0]), 'hand_init_pos': np.array([0, 0.6, 0.2], dtype=np.float32), } self.goal = np.array([0., 0.73, 0.08]) self.obj_init_pos = self.init_config['obj_init_pos'] self.hand_init_pos = self.init_config['hand_init_pos'] self._random_reset_space = Box( np.array(obj_low), np.array(obj_high), ) self.goal_space = Box(np.array(goal_low), np.array(goal_high)) @property def model_name(self): return full_v2_path_for('sawyer_xyz/sawyer_dial.xml') @_assert_task_is_set def evaluate_state(self, obs, action): (reward, tcp_to_obj, _, target_to_obj, object_grasped, in_place) = self.compute_reward(action, obs) info = { 'success': float(target_to_obj <= self.TARGET_RADIUS), 'near_object': float(tcp_to_obj <= 0.01), 'grasp_success': 1., 'grasp_reward': object_grasped, 'in_place_reward': in_place, 'obj_to_target': target_to_obj, 'unscaled_reward': reward, } return reward, info def _get_pos_objects(self): dial_center = self.get_body_com('dial').copy() dial_angle_rad = self.data.get_joint_qpos('knob_Joint_1') offset = np.array([ np.sin(dial_angle_rad), -np.cos(dial_angle_rad), 0 ]) dial_radius = 0.05 offset *= dial_radius return dial_center + offset def _get_quat_objects(self): return self.sim.data.get_body_xquat('dial') def reset_model(self): self._reset_hand() self._target_pos = self.goal.copy() self.obj_init_pos = self.init_config['obj_init_pos'] self.prev_obs = self._get_curr_obs_combined_no_goal() if self.random_init: goal_pos = self._get_state_rand_vec() self.obj_init_pos = goal_pos[:3] final_pos = goal_pos.copy() + np.array([0, 0.03, 0.03]) self._target_pos = final_pos self.sim.model.body_pos[self.model.body_name2id('dial')] = self.obj_init_pos self.dial_push_position = self._get_pos_objects() + np.array([0.05, 0.02, 0.09]) return self._get_obs() def compute_reward(self, action, obs): obj = self._get_pos_objects() dial_push_position = self._get_pos_objects() + np.array([0.05, 0.02, 0.09]) tcp = self.tcp_center target = self._target_pos.copy() target_to_obj = (obj - target) target_to_obj = np.linalg.norm(target_to_obj) target_to_obj_init = (self.dial_push_position - target) target_to_obj_init = np.linalg.norm(target_to_obj_init) in_place = reward_utils.tolerance( target_to_obj, bounds=(0, self.TARGET_RADIUS), margin=abs(target_to_obj_init - self.TARGET_RADIUS), sigmoid='long_tail', ) dial_reach_radius = 0.005 tcp_to_obj = np.linalg.norm(dial_push_position - tcp) tcp_to_obj_init = np.linalg.norm(self.dial_push_position - self.init_tcp) reach = reward_utils.tolerance( tcp_to_obj, bounds=(0, dial_reach_radius), margin=abs(tcp_to_obj_init-dial_reach_radius), sigmoid='gaussian', ) gripper_closed = min(max(0, action[-1]), 1) reach = reward_utils.hamacher_product(reach, gripper_closed) tcp_opened = 0 object_grasped = reach reward = 10 * reward_utils.hamacher_product(reach, in_place) return (reward, tcp_to_obj, tcp_opened, target_to_obj, object_grasped, in_place)

31.816901

93

0.599823

628

4,518

3.941083

0.221338

0.038384

0.053333

0.028283

0.225859

0.149091

0.098586

0.073535

0

0.036772

0.28973

4,518

141

94

32.042553

0.734497

0

0.088496

0

0.047587

0.005755

0

0.017699

1

0.061947

false

0

0.044248

0.017699

0.176991

0

null

0

null

0

1

0

b9fa7c6bd7a253ee2a588381042c5dfd3d99cb96

2,560

py

Python

yezdi/parser/parser.py

ragsagar/yezdi

5b97bedc56d5af7f28b244a0d7c0c8259f643102

[ "MIT" ]

1

2021-04-27T20:07:42.000Z

yezdi/parser/parser.py

ragsagar/yezdi

5b97bedc56d5af7f28b244a0d7c0c8259f643102

[ "MIT" ]

null

yezdi/parser/parser.py

ragsagar/yezdi

5b97bedc56d5af7f28b244a0d7c0c8259f643102

[ "MIT" ]

null

from yezdi.lexer.token import TokenType from yezdi.parser.ast import Program, Statement, Participant, Title, LineStatement class Parser: def __init__(self, lexer): self.lexer = lexer self.current_token = None self.peek_token = None self.next_token() self.next_token() self.participants = {} def next_token(self): self.current_token, self.peek_token = self.peek_token, self.lexer.next_token() def parse_program(self): program = Program() while self.current_token.type != TokenType.EOF: statement = self.parse_statement() if statement: program.statements.append(statement) self.next_token() return program def parse_statement(self): if self.current_token.type == TokenType.IDENTIFIER: return self.parse_line_statement() elif self.current_token.type == TokenType.TITLE: return self.parse_title() return None def parse_line_statement(self): participant_literal = self.current_token.literal if not self.peek_token.type in [TokenType.SOLID_LINE, TokenType.DASHED_LINE]: return None self.next_token() participant = Participant(participant_literal) line = LineStatement(self.current_token.type) line.set_source(participant) if not self.expect_peek(TokenType.IDENTIFIER): return None target = Participant(self.current_token.literal) line.set_target(target) if not self.expect_peek(TokenType.COLON): return None if self.expect_peek(TokenType.IDENTIFIER): line.set_info(self.current_token.literal) if self.peek_token.type not in [TokenType.NEWLINE, TokenType.EOF]: return None statement = Statement(line) return statement def get_participant(self, value): if value in self.participants: return self.participants[value] else: participant = Participant(value) self.participants[value] = participant return participant def expect_peek(self, token_type): if self.peek_token.type == token_type: self.next_token() return True else: return False def parse_title(self): if not self.expect_peek(TokenType.IDENTIFIER): return None title = Title(self.current_token.literal) return Statement(title) class ParserError(Exception): pass

32

86

0.640625

288

2,560

5.517361

0.184028

0.069226

0.100692

0.050346

0.229704

0.078037

0.060415

0

0.280469

2,560

79

87

32.405063

0.862649

0

0.227273

0

1

0.121212

false

0.015152

0.030303

0

0.409091

0

null

0

null

0

1

0

b9fae34b418d8854a4b364f1044c114896456110

1,050

py

Python

scripts/check_categories.py

oberron/entolusis

209e1e245d8e501e5e6ea2f52dd5b0da7d886f5c

[ "MIT" ]

null

scripts/check_categories.py

oberron/entolusis

209e1e245d8e501e5e6ea2f52dd5b0da7d886f5c

[ "MIT" ]

null

scripts/check_categories.py

oberron/entolusis

209e1e245d8e501e5e6ea2f52dd5b0da7d886f5c

[ "MIT" ]

null

# list categories in category folder from os import walk from os.path import abspath,join, pardir categories_folder = abspath(join(__file__,pardir,pardir,"category")) post_folder = abspath(join(__file__,pardir,pardir,"_posts")) site_categories = [] for root,directories,files in walk(categories_folder): for f in files: site_categories.append(f.split(".md")[0]) site_categories = set(site_categories) for root,directories,files in walk(post_folder): for f in files: with open(join(root,f),'r',encoding="utf-8") as fi: lines = fi.readlines() for l in lines: if l.find("categories")==0: categories = l.split(":")[1] for c in [" ","[","]","\n"]: categories = categories.replace(c,"") categories=categories.split(",") if len(set(categories)-site_categories)>0: print(f,set(categories)-site_categories) break print("done")

36.206897

68

0.578095

124

1,050

4.741935

0.387097

0.142857

0.057823

0.071429

0.316327

0.258503

0.146259

0

0.00672

0.291429

1,050

29

69

36.206897

0.783602

0.032381

0

0.086957

0

0.04335

0

1

0

false

0

0.086957

0

0.086957

0

null

0

null

0

1

0

b9fb43e9d0e20574f25b444b461b284752a17b4c

5,311

py

Python

docsrc/makedoc.py

syoyo/soloud

cce88a2408a4b1e88ccbc75de9897b39bc3e7dda

[ "Libpng", "Zlib" ]

1

2019-11-25T11:32:09.000Z

docsrc/makedoc.py

syoyo/soloud

cce88a2408a4b1e88ccbc75de9897b39bc3e7dda

[ "Libpng", "Zlib" ]

null

docsrc/makedoc.py

syoyo/soloud

cce88a2408a4b1e88ccbc75de9897b39bc3e7dda

[ "Libpng", "Zlib" ]

null

#!/usr/bin/env python3 """ builds documentation files from multimarkdown (mmd) source to various formats, including the web site and pdf. """ import subprocess import glob import os import sys import time import shutil src = [ "intro.mmd", "downloads.mmd", "quickstart.mmd", "faq.mmd", "dirstruct.mmd", "premake.mmd", "legal.mmd", "concepts.mmd", "concepts3d.mmd", "voicemanagement.mmd", "examples.mmd", "foreign_interface.mmd", "c_api.mmd", "python_api.mmd", "ruby_api.mmd", "rpgmaker_api.mmd", "bmx_api.mmd", "gamemaker_api.mmd", "cs_api.mmd", "d_api.mmd", "codegen.mmd", "basics.mmd", "attributes.mmd", "faders.mmd", "voicegroups.mmd", "coremisc.mmd", "core3d.mmd", "audiosource.mmd", "newsoundsources.mmd", "wav.mmd", "wavstream.mmd", "speech.mmd", "sfxr.mmd", "modplug.mmd", "monotone.mmd", "tedsid.mmd", "vizsn.mmd", "vic.mmd", "filters.mmd", "biquadfilter.mmd", "echofilter.mmd", "lofifilter.mmd", "flangerfilter.mmd", "dcremovalfilter.mmd", "fftfilter.mmd", "bassboostfilter.mmd", "waveshaperfilter.mmd", "mixbus.mmd", "queue.mmd", "collider.mmd", "attenuator.mmd", "file.mmd", "backends.mmd" ] website_only = [ "downloads.mmd" ] unknown = 0 for file in glob.glob("*.mmd"): if file not in src: unknown = 1 print(file + " not included in docs!") if unknown: print("Add the new files to makedoc.py, soloud.tex and htmlpre.txt.") sys.exit() datestring = time.strftime("%Y%m%d") if not os.path.exists(datestring + "/web"): os.makedirs(datestring + "/web") if not os.path.exists("temp/"): os.makedirs("temp/") print("- -- --- -- - Generating single-file HTML docs") callp = ["pandoc", "-s", "-t", "html5", "-f", "markdown-smart", "--metadata", 'title="SoLoud ' + datestring + '"', "-H", "singlehtml_head.txt", "-B", "singlehtml_body.txt", "--toc", "--self-contained", "--default-image-extension=png", "-o", datestring + "/soloud_" + datestring + ".html"] for x in src: if x not in website_only: callp.append(x) subprocess.call(callp) print("- -- --- -- - Generating web site") for x in src: subprocess.call(["pandoc", "--template=html.pandoc", "-f", "markdown-smart", "--metadata", 'title="SoLoud ' + datestring + ' ' + x[:len(x)-4] + '"', "-B", "htmlpre.txt", "-A", "htmlpost.txt", "--default-image-extension=png", x, "-o", datestring + "/web/" + x[:len(x)-3]+"html.bak"]) with open(datestring + "/web/" + x[:len(x)-3]+"html", "w") as file_out: with open(datestring + "/web/" + x[:len(x)-3]+"html.bak", "r") as file_in: for line in file_in: file_out.write(line.replace('code>', 'code>\n').replace('::','::<wbr>').replace('\xc2','').replace('\xa0','')) if x == "intro.mmd": if os.path.isfile(datestring + "/web/index.html"): os.remove(datestring + "/web/index.html") os.rename(datestring + "/web/intro.html", datestring + "/web/index.html") print("- -- --- -- - Generating epub") callp = ["pandoc", "-N", "--toc", "--epub-cover-image=images/cover.png", "-t", "epub3", "--default-image-extension=png", "-f", "markdown-smart", "--css=epub.css", "--epub-metadata=metadata.xml", "-o", datestring + "/soloud_" + datestring + ".epub", "title.txt"] for x in src: if x not in website_only: callp.append(x) subprocess.call(callp) print("- -- --- -- - Converting epub -> mobi (kindlegen_output.txt)") with open('kindlegen_output.txt', 'w') as outfile: subprocess.call(["kindlegen", datestring + "/soloud_" + datestring + ".epub", "-c2"], stdout=outfile) print("- -- --- -- - Generating LaTex") for x in src: if x not in website_only: subprocess.call(["pandoc", "-t", "latex", "--listings", "--default-image-extension=pdf", "--top-level-division=chapter", x, "-o", "temp/" + x[:len(x)-3]+"tex.orig"]) with open("temp/" + x[:len(x)-3]+"tex", "w") as file_out: with open("temp/" + x[:len(x)-3]+"tex.orig", "r") as file_in: for line in file_in: file_out.write(line.replace('\\begin{longtable}[]{@{}ll@{}}', '\\begin{tabulary}{\\textwidth}{lJ}').replace('\\begin{longtable}[]{@{}lll@{}}', '\\begin{tabulary}{\\textwidth}{lJJ}').replace('\\begin{longtable}[]{@{}llll@{}}', '\\begin{tabulary}{\\textwidth}{lJJJ}').replace('\\endhead','').replace('\\end{longtable}','\\end{tabulary}')) print("- -- --- -- - Generating pdf (xelatex_output.txt)") with open('xelatex_output.txt', 'w') as outfile: subprocess.call(["xelatex", "SoLoud.tex"], stdout=outfile) print("- -- --- -- - Generating pdf pass 2..") subprocess.call(["xelatex", "SoLoud.tex"], stdout=outfile) shutil.move("SoLoud.pdf", datestring + "/soloud_" + datestring + ".pdf") print("- -- --- -- - Cleanup..") tempsuffix = ["aux", "toc", "out", "log", "lg", "4ct", "4tc", "idv", "tmp", "xdv", "xref", "bak"] for suffix in tempsuffix: for file in glob.glob("*."+suffix): os.remove(file) for file in glob.glob(datestring + "/web/*."+suffix): os.remove(file) for file in glob.glob("temp/*"): os.remove(file) os.rmdir("temp") print("- -- --- -- - Done - " + datestring)

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0.011742

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null

0

null

0

1

0

b9ff46cab163507c14f9b26bf086ce4979f54a2c

4,972

py

Python

tools/unidatadownload.py

henryiii/backrefs

ec82844098bc3bdc7bcaa61b32f80271e6a73da6

[ "MIT" ]

null

tools/unidatadownload.py

henryiii/backrefs

ec82844098bc3bdc7bcaa61b32f80271e6a73da6

[ "MIT" ]

null

tools/unidatadownload.py

henryiii/backrefs

ec82844098bc3bdc7bcaa61b32f80271e6a73da6

[ "MIT" ]

null

"""Download `Unicodedata` files.""" from __future__ import unicode_literals import os import zipfile import codecs from urllib.request import urlopen __version__ = '2.2.0' HOME = os.path.dirname(os.path.abspath(__file__)) def zip_unicode(output, version): """Zip the Unicode files.""" zipper = zipfile.ZipFile(os.path.join(output, 'unicodedata', '%s.zip' % version), 'w', zipfile.ZIP_DEFLATED) target = os.path.join(output, 'unicodedata', version) print('Zipping %s.zip...' % version) for root, dirs, files in os.walk(target): for file in files: if file.endswith('.txt'): zipper.write(os.path.join(root, file), arcname=file) def unzip_unicode(output, version): """Unzip the Unicode files.""" unzipper = zipfile.ZipFile(os.path.join(output, 'unicodedata', '%s.zip' % version)) target = os.path.join(output, 'unicodedata', version) print('Unzipping %s.zip...' % version) os.makedirs(target) for f in unzipper.namelist(): # Do I need backslash on windows? Or is it forward as well? unzipper.extract(f, target) def download_unicodedata(version, output=HOME, no_zip=False): """Download Unicode data scripts and blocks.""" ver = tuple([int(x) for x in version.split('.')]) files = [ 'UnicodeData.txt', 'Scripts.txt', 'Blocks.txt', 'PropList.txt', 'DerivedCoreProperties.txt', 'DerivedNormalizationProps.txt', 'CompositionExclusions.txt', 'PropertyValueAliases.txt', 'PropertyAliases.txt', 'EastAsianWidth.txt', 'LineBreak.txt', 'HangulSyllableType.txt', 'DerivedAge.txt', 'auxiliary/WordBreakProperty.txt', 'auxiliary/SentenceBreakProperty.txt', 'auxiliary/GraphemeBreakProperty.txt', 'extracted/DerivedDecompositionType.txt', 'extracted/DerivedNumericType.txt', 'extracted/DerivedNumericValues.txt', 'extracted/DerivedJoiningType.txt', 'extracted/DerivedJoiningGroup.txt', 'extracted/DerivedCombiningClass.txt', 'emoji/emoji-data.txt' ] files.append('ScriptExtensions.txt') files.append('IndicPositionalCategory.txt') files.append('IndicSyllabicCategory.txt') files.append('BidiBrackets.txt') if ver >= (11, 0, 0): files.append('VerticalOrientation.txt') http_url = 'http://www.unicode.org/Public/%s/ucd/' % version ftp_url = 'ftp://ftp.unicode.org/Public/%s/ucd/' % version destination = os.path.join(output, 'unicodedata', version) if not os.path.exists(destination): os.makedirs(destination) zip_data = not no_zip for f in files: file_location = os.path.join(destination, os.path.basename(f)) retrieved = False if not os.path.exists(file_location): for url in (ftp_url, http_url): furl = url + f try: print('Downloading: %s --> %s' % (furl, file_location)) response = urlopen(furl, timeout=30) data = response.read() except Exception: print('Failed: %s' % url) continue with codecs.open(file_location, 'w', encoding='utf-8') as uf: uf.write(data.decode('utf-8')) retrieved = True break if not retrieved: print('Failed to acquire all needed Unicode files!') break else: retrieved = True print('Skipping: found %s' % file_location) if not retrieved: zip_data = False break if zip_data and not os.path.exists(os.path.join(output, 'unicodedata', '%s.zip' % version)): zip_unicode(output, version) def get_unicodedata(version, output=HOME, no_zip=False): """Ensure we have Unicode data to generate Unicode tables.""" target = os.path.join(output, 'unicodedata', version) zip_target = os.path.join(output, 'unicodedata', '%s.zip' % version) if not os.path.exists(target) and os.path.exists(zip_target): unzip_unicode(output, version) # Download missing files if any. Zip if required. download_unicodedata(version, output, no_zip) if __name__ == '__main__': import argparse import unicodedata parser = argparse.ArgumentParser(prog='unidatadownload', description='Generate a unicode property table.') parser.add_argument('--version', action='version', version="%(prog)s " + __version__) parser.add_argument('--output', default=HOME, help='Output file.') parser.add_argument('--unicode-version', default=None, help='Force a specific Unicode version.') args = parser.parse_args() if args.unicode_version is None: version = unicodedata.unidata_version else: version = args.unicode_version get_unicodedata(version, output=args.output)

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112

0.627715

560

4,972

5.467857

0.310714

0.035271

0.032658

0.041803

0.173416

0.167864

0.126061

0.088178

0.033965

0

0.002934

0.245977

4,972

150

113

33.146667

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0.11602

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false

0

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0.055556

0

null

0

null

0

1

0

6a00c6e63b457a75c0424a247757123821cb24fb

1,230

py

Python

aspx2url/aspx2url.py

marcocucinato/aspx2url

985a0e51865bb7be15618155ff9844730c2eaaf6

[ "MIT" ]

null

aspx2url/aspx2url.py

marcocucinato/aspx2url

985a0e51865bb7be15618155ff9844730c2eaaf6

[ "MIT" ]

null

aspx2url/aspx2url.py

marcocucinato/aspx2url

985a0e51865bb7be15618155ff9844730c2eaaf6

[ "MIT" ]

null

from __future__ import print_function import re, sys, glob, getopt, os def usage(): print('aspx2url v1.0') print('Usage:') print(sys.argv[0]+' -d -h filename(s)') print('-d : Delete original file') print('-h : This help') def main(): try: opts, args = getopt.getopt(sys.argv[1:], "hd") except getopt.GetoptError as err: print(str(err)) usage() sys.exit(2) deleteOriginal = False for option,value in opts: if option == '-h': usage() sys.exit() elif option == '-d': deleteOriginal = True for origFilename in args: with open(origFilename, "r") as f: html_doc = f.read() prog = re.compile('\<mso\:URL.*?\>(.*?),.*?\<\/mso\:URL\>', re.M) result = prog.search(html_doc) url = result.group(1); filename = re.search('(.*?)\.aspx',origFilename).group(1) fullFilename = filename+'.url' with open(fullFilename, 'w') as out: out.write('[InternetShortcut]\n') out.write('URL='+url) out.write('\n') if deleteOriginal: os.remove(origFilename) if __name__ == '__main__': main()

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1,230

4.393103

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0.037677

0

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1,230

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0.735673

0

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0.030894

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0.052632

false

0

0.052632

0

0.105263

0.184211

0

null

0

null

0

1

0

6a00f65f8d9c6385beccc2cbd3c37ef660b0dc52

6,343

py

Python

tarentsocialwall/MongoDBClient.py

tarent/socialwall-backend

2f09b8ccdd62a15daaa281d6ff568cb6ef749ab6

[ "MIT" ]

null

tarentsocialwall/MongoDBClient.py

tarent/socialwall-backend

2f09b8ccdd62a15daaa281d6ff568cb6ef749ab6

[ "MIT" ]

null

tarentsocialwall/MongoDBClient.py

tarent/socialwall-backend

2f09b8ccdd62a15daaa281d6ff568cb6ef749ab6

[ "MIT" ]

2

2019-08-06T14:14:44.000Z

2019-08-06T14:21:19.000Z

import random from datetime import datetime from passlib.handlers.sha2_crypt import sha256_crypt from pymongo import MongoClient from pymongo.errors import ConnectionFailure from tarentsocialwall.SocialPost import SocialPost from tarentsocialwall.User import User from tarentsocialwall.Util import Util class MongoDBClient: __instance = None @staticmethod def getInstance(): """ Static access method. """ if MongoDBClient.__instance == None: MongoDBClient() client = None db = None random_social_post_list = None reset_counter = None def __init__(self, uri): # connect to MongoDB, change the << MONGODB URL >> to reflect your own connection string self.client = MongoClient(uri) self.db = self.client.socialPosts try: # The ismaster command is cheap and does not require auth. self.client.admin.command('ismaster') except ConnectionFailure: print("Server not available") if MongoDBClient.__instance != None: raise Exception("This class is a singleton!") else: MongoDBClient.__instance = self self.update_all_socialposts() # write social_post into mongo def write_social_post(self, social_post: SocialPost): existing_dict = None try: existing_dict = self.db.socialPosts.find_one({'externalId': social_post.externalId}) except Exception as ex: print(ex) existing_dict = None if existing_dict is None: self.db.socialPosts.insert_one(social_post.__dict__) else: update_identifier = {'externalId': social_post.externalId, 'source': social_post.source} self.db.socialPosts.replace_one(update_identifier, social_post.__dict__) return 0 # read random social_post from list def get_random_social_post(self) -> SocialPost: if len(self.random_social_post_list) == 0: return None else: if self.reset_counter >= len(self.random_social_post_list): # when we went through all posts once we reset counter and shuffle list # so we dont repeat the same circle of posts every time self.reset_counter = 0 random.shuffle(self.random_social_post_list) post = self.random_social_post_list[self.reset_counter] self.reset_counter = self.reset_counter + 1 print(post) if post is None: return None social_post = SocialPost() social_post.set_dictionary(post) return social_post # read custom social_post from mongo def get_custom_social_post(self): doc = list(self.db.socialPosts.aggregate([{'$match': {'source': 'custom post'}}])) print(list(doc)) if doc is None: return None social_post_list = [] for post in doc: custom_post_item = SocialPost() custom_post_item.set_dictionary(post) social_post_list.append(custom_post_item) return social_post_list def delete_post(self, external_id): removed = self.db.socialPosts.delete_one({'externalId': external_id}) print(removed) def write_access_token(self, access_token, source): existing_dict = self.db.storeAccessToken.find_one({'source': access_token}) if existing_dict is None: identifier = {'access_token': access_token, 'source': source} self.db.storeAccessToken.insert_one(identifier) else: update_identifier = {'access_token': access_token, 'source': source} self.db.storeAccessToken.replace_one(update_identifier, access_token) return 0 def read_access_token(self, source): existing_dict = self.db.storeAccessToken.find_one({'source': source}) return existing_dict def get_google_calendar_posts(self): timestamp_var = datetime.utcnow().timestamp() doc = list(self.db.socialPosts.aggregate([ {'$match': {'validFrom': {'$lte': timestamp_var}, 'validTo': {'$gte': timestamp_var}, 'source': 'Google calendar'}}, {'$sort': {'start': 1}} ])) if doc is None: return None social_post_list = [] for post in doc: custom_post_item = SocialPost() custom_post_item.set_dictionary(post) social_post_list.append(custom_post_item) return social_post_list def get_users(self): users_db = list(self.db.socialwall_users.find()) if users_db is None: return None users = [] for item in users_db: if item['username'] is not 'admin': user = User() user.set_dictionary(item) users.append(user) return users def read_user(self, username): return self.db.socialwall_users.find_one({'username': username}) def write_user(self, user: User): username_dict = self.db.socialwall_users.find_one({'username': user.username}) if username_dict is None: self.db.socialwall_users.insert_one(user.__dict__) else: update_identifier = {'username': user.username} self.db.socialwall_users.replace_one(update_identifier, user.__dict__) return 0 def delete_user(self, user: User): self.db.socialwall_users.delete_one({'username': user['username']}) def init_admin(self): random_string = Util.randomString() user = User() user.username = 'admin' user.password = sha256_crypt.hash(random_string) print("Admin password is '%s'" % random_string) user.firstname = 'admin' user.lastname = 'admin' self.write_user(user) #Get all valid social posts from db and shuffle them in random order def update_all_socialposts(self): timestamp = datetime.utcnow().timestamp() self.random_social_post_list = list(self.db.socialPosts.aggregate( [{'$match': {'validFrom': {'$lte': timestamp}, 'validTo': {'$gte': timestamp}}}])) random.shuffle(self.random_social_post_list) self.reset_counter = 0

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0.201094

0.073164

0.047557

0.036582

0.296316

0.267311

0.238045

0.195453

0.174549

0.117586

0

0.003274

0.277787

6,343

191

101

33.209424

0.832133

0.07189

0

0.282609

0

0.058233

0

1

0.108696

false

0.021739

0.057971

0.007246

0.311594

0.043478

0

null

0

null

0

1

0

6a0102385be6299942545100e581de23300db9a4

76,697

py

Python

src/mount_efs/__init__.py

Sodki/efs-utils

493d9ea0dde93b560519b184219f6f71e32a8fcf

[ "MIT" ]

null

src/mount_efs/__init__.py

Sodki/efs-utils

493d9ea0dde93b560519b184219f6f71e32a8fcf

[ "MIT" ]

null

src/mount_efs/__init__.py

Sodki/efs-utils

493d9ea0dde93b560519b184219f6f71e32a8fcf

[ "MIT" ]

12

2020-10-22T03:47:51.000Z

2022-03-19T18:09:59.000Z

#!/usr/bin/env python # # Copyright 2017-2018 Amazon.com, Inc. and its affiliates. All Rights Reserved. # # Licensed under the MIT License. See the LICENSE accompanying this file # for the specific language governing permissions and limitations under # the License. # # # Copy this script to /sbin/mount.efs and make sure it is executable. # # You will be able to mount an EFS file system by its short name, by adding it # to /etc/fstab. The syntax of an fstab entry is: # # [Device] [Mount Point] [File System Type] [Options] [Dump] [Pass] # # Add an entry like this: # # fs-deadbeef /mount_point efs _netdev 0 0 # # Using the 'efs' type will cause '/sbin/mount.efs' to be called by 'mount -a' # for this file system. The '_netdev' option tells the init system that the # 'efs' type is a networked file system type. This has been tested with systemd # (Amazon Linux 2, CentOS 7, RHEL 7, Debian 9, and Ubuntu 16.04), and upstart # (Amazon Linux 2017.09). # # Once there is an entry in fstab, the file system can be mounted with: # # sudo mount /mount_point # # The script will add recommended mount options, if not provided in fstab. import base64 import errno import hashlib import hmac import json import logging import os import pwd import random import re import socket import subprocess import sys import threading import time from contextlib import contextmanager from datetime import datetime, timedelta from logging.handlers import RotatingFileHandler try: import ConfigParser from ConfigParser import NoOptionError, NoSectionError except ImportError: from configparser import ConfigParser, NoOptionError, NoSectionError try: from urllib.parse import quote_plus except ImportError: from urllib import quote_plus try: from urllib2 import URLError, HTTPError, build_opener, urlopen, Request, HTTPHandler from urllib import urlencode except ImportError: from urllib.request import urlopen, Request from urllib.error import URLError, HTTPError from urllib.parse import urlencode try: import botocore.session from botocore.exceptions import ClientError, NoCredentialsError, EndpointConnectionError BOTOCORE_PRESENT = True except ImportError: BOTOCORE_PRESENT = False VERSION = '1.28.2' SERVICE = 'elasticfilesystem' CONFIG_FILE = '/etc/amazon/efs/efs-utils.conf' CONFIG_SECTION = 'mount' CLIENT_INFO_SECTION = 'client-info' CLIENT_SOURCE_STR_LEN_LIMIT = 100 CLOUDWATCH_LOG_SECTION = 'cloudwatch-log' DEFAULT_CLOUDWATCH_LOG_GROUP = '/aws/efs/utils' DEFAULT_RETENTION_DAYS = 14 # Cloudwatchlog agent dict includes cloudwatchlog botocore client, cloudwatchlog group name, cloudwatchlog stream name CLOUDWATCHLOG_AGENT = None LOG_DIR = '/var/log/amazon/efs' LOG_FILE = 'mount.log' STATE_FILE_DIR = '/var/run/efs' PRIVATE_KEY_FILE = '/etc/amazon/efs/privateKey.pem' DATE_ONLY_FORMAT = '%Y%m%d' SIGV4_DATETIME_FORMAT = '%Y%m%dT%H%M%SZ' CERT_DATETIME_FORMAT = '%y%m%d%H%M%SZ' AWS_CREDENTIALS_FILE = os.path.expanduser(os.path.join('~' + pwd.getpwuid(os.getuid()).pw_name, '.aws', 'credentials')) AWS_CONFIG_FILE = os.path.expanduser(os.path.join('~' + pwd.getpwuid(os.getuid()).pw_name, '.aws', 'config')) CA_CONFIG_BODY = """dir = %s RANDFILE = $dir/database/.rand [ ca ] default_ca = local_ca [ local_ca ] database = $dir/database/index.txt serial = $dir/database/serial private_key = %s cert = $dir/certificate.pem new_certs_dir = $dir/certs default_md = sha256 preserve = no policy = efsPolicy x509_extensions = v3_ca [ efsPolicy ] CN = supplied [ req ] prompt = no distinguished_name = req_distinguished_name [ req_distinguished_name ] CN = %s %s %s %s """ # SigV4 Auth ALGORITHM = 'AWS4-HMAC-SHA256' AWS4_REQUEST = 'aws4_request' HTTP_REQUEST_METHOD = 'GET' CANONICAL_URI = '/' CANONICAL_HEADERS_DICT = { 'host': '%s' } CANONICAL_HEADERS = '\n'.join(['%s:%s' % (k, v) for k, v in sorted(CANONICAL_HEADERS_DICT.items())]) SIGNED_HEADERS = ';'.join(CANONICAL_HEADERS_DICT.keys()) REQUEST_PAYLOAD = '' FS_ID_RE = re.compile('^(?P<fs_id>fs-[0-9a-f]+)$') EFS_FQDN_RE = re.compile(r'^(?P<fs_id>fs-[0-9a-f]+)\.efs\.(?P<region>[a-z0-9-]+)\.(?P<dns_name_suffix>[a-z0-9.]+)$') AP_ID_RE = re.compile('^fsap-[0-9a-f]{17}$') CREDENTIALS_KEYS = ['AccessKeyId', 'SecretAccessKey', 'Token'] ECS_URI_ENV = 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI' ECS_TASK_METADATA_API = 'http://169.254.170.2' WEB_IDENTITY_ROLE_ARN_ENV = 'AWS_ROLE_ARN' WEB_IDENTITY_TOKEN_FILE_ENV = 'AWS_WEB_IDENTITY_TOKEN_FILE' STS_ENDPOINT_URL = 'https://sts.amazonaws.com/' INSTANCE_METADATA_TOKEN_URL = 'http://169.254.169.254/latest/api/token' INSTANCE_METADATA_SERVICE_URL = 'http://169.254.169.254/latest/dynamic/instance-identity/document/' INSTANCE_IAM_URL = 'http://169.254.169.254/latest/meta-data/iam/security-credentials/' SECURITY_CREDS_ECS_URI_HELP_URL = 'https://docs.aws.amazon.com/AmazonECS/latest/developerguide/task-iam-roles.html' SECURITY_CREDS_WEBIDENTITY_HELP_URL = 'https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts.html' SECURITY_CREDS_IAM_ROLE_HELP_URL = 'https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/iam-roles-for-amazon-ec2.html' DEFAULT_STUNNEL_VERIFY_LEVEL = 2 DEFAULT_STUNNEL_CAFILE = '/etc/amazon/efs/efs-utils.crt' NOT_BEFORE_MINS = 15 NOT_AFTER_HOURS = 3 EFS_ONLY_OPTIONS = [ 'accesspoint', 'awscredsuri', 'awsprofile', 'cafile', 'iam', 'netns', 'noocsp', 'ocsp', 'tls', 'tlsport', 'verify' ] UNSUPPORTED_OPTIONS = [ 'capath' ] STUNNEL_GLOBAL_CONFIG = { 'fips': 'no', 'foreground': 'yes', 'socket': [ 'l:SO_REUSEADDR=yes', 'a:SO_BINDTODEVICE=lo', ], } STUNNEL_EFS_CONFIG = { 'client': 'yes', 'accept': '127.0.0.1:%s', 'connect': '%s:2049', 'sslVersion': 'TLSv1.2', 'renegotiation': 'no', 'TIMEOUTbusy': '20', 'TIMEOUTclose': '0', 'TIMEOUTidle': '70', 'delay': 'yes', } WATCHDOG_SERVICE = 'amazon-efs-mount-watchdog' SYSTEM_RELEASE_PATH = '/etc/system-release' OS_RELEASE_PATH = '/etc/os-release' RHEL8_RELEASE_NAME = 'Red Hat Enterprise Linux release 8' CENTOS8_RELEASE_NAME = 'CentOS Linux release 8' FEDORA_RELEASE_NAME = 'Fedora release' SUSE_RELEASE_NAME = 'openSUSE Leap' SKIP_NO_LIBWRAP_RELEASES = [RHEL8_RELEASE_NAME, CENTOS8_RELEASE_NAME, FEDORA_RELEASE_NAME, SUSE_RELEASE_NAME] def fatal_error(user_message, log_message=None, exit_code=1): if log_message is None: log_message = user_message sys.stderr.write('%s\n' % user_message) logging.error(log_message) publish_cloudwatch_log(CLOUDWATCHLOG_AGENT, 'Mount failed, %s' % log_message) sys.exit(exit_code) def get_target_region(config): def _fatal_error(message): fatal_error('Error retrieving region. Please set the "region" parameter in the efs-utils configuration file.', message) metadata_exception = 'Unknown error' try: return config.get(CONFIG_SECTION, 'region') except NoOptionError: pass try: return get_region_from_instance_metadata() except Exception as e: metadata_exception = e logging.warning('Region not found in config file and metadata service call failed, falling back ' 'to legacy "dns_name_format" check') try: region = get_region_from_legacy_dns_format(config) sys.stdout.write('Warning: region obtained from "dns_name_format" field. Please set the "region" ' 'parameter in the efs-utils configuration file.') return region except Exception: logging.warning('Legacy check for region in "dns_name_format" failed') _fatal_error(metadata_exception) def get_region_from_instance_metadata(): instance_identity = get_instance_identity_info_from_instance_metadata('region') if not instance_identity: raise Exception("Cannot retrieve region from instance_metadata") return instance_identity def get_instance_identity_info_from_instance_metadata(property): ec2_metadata_unsuccessful_resp = 'Unsuccessful retrieval of EC2 metadata at %s.' % INSTANCE_METADATA_SERVICE_URL ec2_metadata_url_error_msg = 'Unable to reach %s to retrieve EC2 instance metadata.' % INSTANCE_METADATA_SERVICE_URL instance_identity = url_request_helper(INSTANCE_METADATA_SERVICE_URL, ec2_metadata_unsuccessful_resp, ec2_metadata_url_error_msg, retry_with_new_header_token=True) if instance_identity: try: return instance_identity[property] except KeyError as e: logging.warning('%s not present in %s: %s' % (property, instance_identity, e)) except TypeError as e: logging.warning('response %s is not a json object: %s' % (instance_identity, e)) return None def get_region_from_legacy_dns_format(config): """ For backwards compatibility check dns_name_format to obtain the target region. This functionality should only be used if region is not present in the config file and metadata calls fail. """ dns_name_format = config.get(CONFIG_SECTION, 'dns_name_format') if '{region}' not in dns_name_format: split_dns_name_format = dns_name_format.split('.') if '{dns_name_suffix}' in dns_name_format: return split_dns_name_format[-2] elif 'amazonaws.com' in dns_name_format: return split_dns_name_format[-3] raise Exception('Region not found in dns_name_format') def get_aws_ec2_metadata_token(): try: opener = build_opener(HTTPHandler) request = Request(INSTANCE_METADATA_TOKEN_URL) request.add_header('X-aws-ec2-metadata-token-ttl-seconds', 21600) request.get_method = lambda: 'PUT' res = opener.open(request) return res.read() except NameError: headers = {'X-aws-ec2-metadata-token-ttl-seconds': 21600} req = Request(INSTANCE_METADATA_TOKEN_URL, headers=headers, method='PUT') res = urlopen(req) return res.read() def get_aws_security_credentials(use_iam, awsprofile=None, aws_creds_uri=None): """ Lookup AWS security credentials (access key ID and secret access key). Adapted credentials provider chain from: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html and https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/credentials.html """ if not use_iam: return None, None # attempt to lookup AWS security credentials through the credentials URI the ECS agent generated if aws_creds_uri: return get_aws_security_credentials_from_ecs(aws_creds_uri, True) # attempt to lookup AWS security credentials in AWS credentials file (~/.aws/credentials) # and configs file (~/.aws/config) with given awsprofile if awsprofile: return get_aws_security_credentials_from_awsprofile(awsprofile, True) # attempt to lookup AWS security credentials through AWS_CONTAINER_CREDENTIALS_RELATIVE_URI environment variable if ECS_URI_ENV in os.environ: credentials, credentials_source = get_aws_security_credentials_from_ecs(os.environ[ECS_URI_ENV], False) if credentials and credentials_source: return credentials, credentials_source # attempt to lookup AWS security credentials through AssumeRoleWithWebIdentity # (e.g. for IAM Role for Service Accounts (IRSA) approach on EKS) if WEB_IDENTITY_ROLE_ARN_ENV in os.environ and WEB_IDENTITY_TOKEN_FILE_ENV in os.environ: credentials, credentials_source = get_aws_security_credentials_from_webidentity( os.environ[WEB_IDENTITY_ROLE_ARN_ENV], os.environ[WEB_IDENTITY_TOKEN_FILE_ENV], False ) if credentials and credentials_source: return credentials, credentials_source # attempt to lookup AWS security credentials with IAM role name attached to instance # through IAM role name security credentials lookup uri iam_role_name = get_iam_role_name() if iam_role_name: credentials, credentials_source = get_aws_security_credentials_from_instance_metadata(iam_role_name) if credentials and credentials_source: return credentials, credentials_source error_msg = 'AWS Access Key ID and Secret Access Key are not found in AWS credentials file (%s), config file (%s), ' \ 'from ECS credentials relative uri, or from the instance security credentials service' % \ (AWS_CREDENTIALS_FILE, AWS_CONFIG_FILE) fatal_error(error_msg, error_msg) def get_aws_security_credentials_from_awsprofile(awsprofile, is_fatal=False): for file_path in [AWS_CREDENTIALS_FILE, AWS_CONFIG_FILE]: if os.path.exists(file_path): credentials = credentials_file_helper(file_path, awsprofile) if credentials['AccessKeyId']: return credentials, os.path.basename(file_path) + ':' + awsprofile # Fail if credentials cannot be fetched from the given awsprofile if is_fatal: log_message = 'AWS security credentials not found in %s or %s under named profile [%s]' % \ (AWS_CREDENTIALS_FILE, AWS_CONFIG_FILE, awsprofile) fatal_error(log_message) else: return None, None def get_aws_security_credentials_from_ecs(aws_creds_uri, is_fatal=False): ecs_uri = ECS_TASK_METADATA_API + aws_creds_uri ecs_unsuccessful_resp = 'Unsuccessful retrieval of AWS security credentials at %s.' % ecs_uri ecs_url_error_msg = 'Unable to reach %s to retrieve AWS security credentials. See %s for more info.' \ % (ecs_uri, SECURITY_CREDS_ECS_URI_HELP_URL) ecs_security_dict = url_request_helper(ecs_uri, ecs_unsuccessful_resp, ecs_url_error_msg) if ecs_security_dict and all(k in ecs_security_dict for k in CREDENTIALS_KEYS): return ecs_security_dict, 'ecs:' + aws_creds_uri # Fail if credentials cannot be fetched from the given aws_creds_uri if is_fatal: fatal_error(ecs_unsuccessful_resp, ecs_unsuccessful_resp) else: return None, None def get_aws_security_credentials_from_webidentity(role_arn, token_file, is_fatal=False): try: with open(token_file, 'r') as f: token = f.read() except Exception as e: if is_fatal: unsuccessful_resp = 'Error reading token file %s: %s' % (token_file, e) fatal_error(unsuccessful_resp, unsuccessful_resp) else: return None, None webidentity_url = STS_ENDPOINT_URL + '?' + urlencode({ 'Version': '2011-06-15', 'Action': 'AssumeRoleWithWebIdentity', 'RoleArn': role_arn, 'RoleSessionName': 'efs-mount-helper', 'WebIdentityToken': token }) unsuccessful_resp = 'Unsuccessful retrieval of AWS security credentials at %s.' % STS_ENDPOINT_URL url_error_msg = 'Unable to reach %s to retrieve AWS security credentials. See %s for more info.' % \ (STS_ENDPOINT_URL, SECURITY_CREDS_WEBIDENTITY_HELP_URL) resp = url_request_helper(webidentity_url, unsuccessful_resp, url_error_msg, headers={'Accept': 'application/json'}) if resp: creds = resp \ .get('AssumeRoleWithWebIdentityResponse', {}) \ .get('AssumeRoleWithWebIdentityResult', {}) \ .get('Credentials', {}) if all(k in creds for k in ['AccessKeyId', 'SecretAccessKey', 'SessionToken']): return { 'AccessKeyId': creds['AccessKeyId'], 'SecretAccessKey': creds['SecretAccessKey'], 'Token': creds['SessionToken'] }, 'webidentity:' + ','.join([role_arn, token_file]) # Fail if credentials cannot be fetched from the given aws_creds_uri if is_fatal: fatal_error(unsuccessful_resp, unsuccessful_resp) else: return None, None def get_aws_security_credentials_from_instance_metadata(iam_role_name): security_creds_lookup_url = INSTANCE_IAM_URL + iam_role_name unsuccessful_resp = 'Unsuccessful retrieval of AWS security credentials at %s.' % security_creds_lookup_url url_error_msg = 'Unable to reach %s to retrieve AWS security credentials. See %s for more info.' % \ (security_creds_lookup_url, SECURITY_CREDS_IAM_ROLE_HELP_URL) iam_security_dict = url_request_helper(security_creds_lookup_url, unsuccessful_resp, url_error_msg, retry_with_new_header_token=True) if iam_security_dict and all(k in iam_security_dict for k in CREDENTIALS_KEYS): return iam_security_dict, 'metadata:' else: return None, None def get_iam_role_name(): iam_role_unsuccessful_resp = 'Unsuccessful retrieval of IAM role name at %s.' % INSTANCE_IAM_URL iam_role_url_error_msg = 'Unable to reach %s to retrieve IAM role name. See %s for more info.' % \ (INSTANCE_IAM_URL, SECURITY_CREDS_IAM_ROLE_HELP_URL) iam_role_name = url_request_helper(INSTANCE_IAM_URL, iam_role_unsuccessful_resp, iam_role_url_error_msg, retry_with_new_header_token=True) return iam_role_name def credentials_file_helper(file_path, awsprofile): aws_credentials_configs = read_config(file_path) credentials = {'AccessKeyId': None, 'SecretAccessKey': None, 'Token': None} try: access_key = aws_credentials_configs.get(awsprofile, 'aws_access_key_id') secret_key = aws_credentials_configs.get(awsprofile, 'aws_secret_access_key') session_token = aws_credentials_configs.get(awsprofile, 'aws_session_token') credentials['AccessKeyId'] = access_key credentials['SecretAccessKey'] = secret_key credentials['Token'] = session_token except NoOptionError as e: if 'aws_access_key_id' in str(e) or 'aws_secret_access_key' in str(e): logging.debug('aws_access_key_id or aws_secret_access_key not found in %s under named profile [%s]', file_path, awsprofile) if 'aws_session_token' in str(e): logging.debug('aws_session_token not found in %s', file_path) credentials['AccessKeyId'] = aws_credentials_configs.get(awsprofile, 'aws_access_key_id') credentials['SecretAccessKey'] = aws_credentials_configs.get(awsprofile, 'aws_secret_access_key') except NoSectionError: logging.debug('No [%s] section found in config file %s', awsprofile, file_path) return credentials def get_aws_profile(options, use_iam): awsprofile = options.get('awsprofile') if not awsprofile and use_iam: for file_path in [AWS_CREDENTIALS_FILE, AWS_CONFIG_FILE]: aws_credentials_configs = read_config(file_path) # check if aws access key id is found under [default] section in current file and return 'default' if so try: access_key = aws_credentials_configs.get('default', 'aws_access_key_id') if access_key is not None: return 'default' except (NoSectionError, NoOptionError): continue return awsprofile def url_request_helper(url, unsuccessful_resp, url_error_msg, headers={}, retry_with_new_header_token=False): try: req = Request(url) for k, v in headers.items(): req.add_header(k, v) request_resp = urlopen(req, timeout=1) return get_resp_obj(request_resp, url, unsuccessful_resp) except HTTPError as e: # For instance enable with IMDSv2, Unauthorized 401 error will be thrown, # to retrieve metadata, the header should embeded with metadata token if e.code == 401 and retry_with_new_header_token: token = get_aws_ec2_metadata_token() req.add_header('X-aws-ec2-metadata-token', token) request_resp = urlopen(req, timeout=1) return get_resp_obj(request_resp, url, unsuccessful_resp) err_msg = 'Unable to reach the url at %s: status=%d, reason is %s' % (url, e.code, e.reason) except URLError as e: err_msg = 'Unable to reach the url at %s, reason is %s' % (url, e.reason) if err_msg: logging.debug('%s %s', url_error_msg, err_msg) return None def get_resp_obj(request_resp, url, unsuccessful_resp): if request_resp.getcode() != 200: logging.debug(unsuccessful_resp + ' %s: ResponseCode=%d', url, request_resp.getcode()) return None resp_body = request_resp.read() resp_body_type = type(resp_body) try: if resp_body_type is str: resp_dict = json.loads(resp_body) else: resp_dict = json.loads(resp_body.decode(request_resp.headers.get_content_charset() or 'us-ascii')) return resp_dict except ValueError as e: logging.info('ValueError parsing "%s" into json: %s. Returning response body.' % (str(resp_body), e)) return resp_body if resp_body_type is str else resp_body.decode('utf-8') def parse_options(options): opts = {} for o in options.split(','): if '=' in o: k, v = o.split('=') opts[k] = v else: opts[o] = None return opts def get_tls_port_range(config): lower_bound = config.getint(CONFIG_SECTION, 'port_range_lower_bound') upper_bound = config.getint(CONFIG_SECTION, 'port_range_upper_bound') if lower_bound >= upper_bound: fatal_error('Configuration option "port_range_upper_bound" defined as %d ' 'must be strictly greater than "port_range_lower_bound" defined as %d.' % (upper_bound, lower_bound)) return lower_bound, upper_bound def choose_tls_port(config, options): if 'tlsport' in options: ports_to_try = [int(options['tlsport'])] else: lower_bound, upper_bound = get_tls_port_range(config) tls_ports = list(range(lower_bound, upper_bound)) # Choose a random midpoint, and then try ports in-order from there mid = random.randrange(len(tls_ports)) ports_to_try = tls_ports[mid:] + tls_ports[:mid] assert len(tls_ports) == len(ports_to_try) sock = socket.socket() for tls_port in ports_to_try: try: sock.bind(('localhost', tls_port)) sock.close() return tls_port except socket.error: continue sock.close() if 'tlsport' in options: fatal_error('Specified port [%s] is unavailable. Try selecting a different port.' % options['tlsport']) else: fatal_error('Failed to locate an available port in the range [%d, %d], try specifying a different port range in %s' % (lower_bound, upper_bound, CONFIG_FILE)) def is_ocsp_enabled(config, options): if 'ocsp' in options: return True elif 'noocsp' in options: return False else: return config.getboolean(CONFIG_SECTION, 'stunnel_check_cert_validity') def get_mount_specific_filename(fs_id, mountpoint, tls_port): return '%s.%s.%d' % (fs_id, os.path.abspath(mountpoint).replace(os.sep, '.').lstrip('.'), tls_port) def serialize_stunnel_config(config, header=None): lines = [] if header: lines.append('[%s]' % header) for k, v in config.items(): if type(v) is list: for item in v: lines.append('%s = %s' % (k, item)) else: lines.append('%s = %s' % (k, v)) return lines def add_stunnel_ca_options(efs_config, config, options): if 'cafile' in options: stunnel_cafile = options['cafile'] else: try: stunnel_cafile = config.get(CONFIG_SECTION, 'stunnel_cafile') except NoOptionError: logging.debug('No CA file configured, using default CA file %s', DEFAULT_STUNNEL_CAFILE) stunnel_cafile = DEFAULT_STUNNEL_CAFILE if not os.path.exists(stunnel_cafile): fatal_error('Failed to find certificate authority file for verification', 'Failed to find CAfile "%s"' % stunnel_cafile) efs_config['CAfile'] = stunnel_cafile def is_stunnel_option_supported(stunnel_output, stunnel_option_name): supported = False for line in stunnel_output: if line.startswith(stunnel_option_name): supported = True break if not supported: logging.warning('stunnel does not support "%s"', stunnel_option_name) return supported def get_version_specific_stunnel_options(): stunnel_command = [_stunnel_bin(), '-help'] proc = subprocess.Popen(stunnel_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True) proc.wait() _, err = proc.communicate() stunnel_output = err.splitlines() check_host_supported = is_stunnel_option_supported(stunnel_output, b'checkHost') ocsp_aia_supported = is_stunnel_option_supported(stunnel_output, b'OCSPaia') return check_host_supported, ocsp_aia_supported def _stunnel_bin(): return find_command_path('stunnel', 'Please install it following the instructions at ' 'https://docs.aws.amazon.com/efs/latest/ug/using-amazon-efs-utils.html#upgrading-stunnel') def find_command_path(command, install_method): try: env_path = '/sbin:/usr/sbin:/usr/local/sbin:/root/bin:/usr/local/bin:/usr/bin:/bin' os.putenv('PATH', env_path) path = subprocess.check_output(['which', command]) except subprocess.CalledProcessError as e: fatal_error('Failed to locate %s in %s - %s' % (command, env_path, install_method), e) return path.strip().decode() def get_system_release_version(): try: with open(SYSTEM_RELEASE_PATH) as f: return f.read().strip() except IOError: logging.debug('Unable to read %s', SYSTEM_RELEASE_PATH) try: with open(OS_RELEASE_PATH) as f: for line in f: if 'PRETTY_NAME' in line: return line.split('=')[1].strip() except IOError: logging.debug('Unable to read %s', OS_RELEASE_PATH) return 'unknown' def write_stunnel_config_file(config, state_file_dir, fs_id, mountpoint, tls_port, dns_name, verify_level, ocsp_enabled, options, log_dir=LOG_DIR, cert_details=None): """ Serializes stunnel configuration to a file. Unfortunately this does not conform to Python's config file format, so we have to hand-serialize it. """ mount_filename = get_mount_specific_filename(fs_id, mountpoint, tls_port) global_config = dict(STUNNEL_GLOBAL_CONFIG) if config.getboolean(CONFIG_SECTION, 'stunnel_debug_enabled'): global_config['debug'] = 'debug' if config.has_option(CONFIG_SECTION, 'stunnel_logs_file'): global_config['output'] = config.get(CONFIG_SECTION, 'stunnel_logs_file').replace('{fs_id}', fs_id) else: global_config['output'] = os.path.join(log_dir, '%s.stunnel.log' % mount_filename) efs_config = dict(STUNNEL_EFS_CONFIG) efs_config['accept'] = efs_config['accept'] % tls_port efs_config['connect'] = efs_config['connect'] % dns_name efs_config['verify'] = verify_level if verify_level > 0: add_stunnel_ca_options(efs_config, config, options) if cert_details: efs_config['cert'] = cert_details['certificate'] efs_config['key'] = cert_details['privateKey'] check_host_supported, ocsp_aia_supported = get_version_specific_stunnel_options() tls_controls_message = 'WARNING: Your client lacks sufficient controls to properly enforce TLS. Please upgrade stunnel, ' \ 'or disable "%%s" in %s.\nSee %s for more detail.' % (CONFIG_FILE, 'https://docs.aws.amazon.com/console/efs/troubleshooting-tls') if config.getboolean(CONFIG_SECTION, 'stunnel_check_cert_hostname'): if check_host_supported: efs_config['checkHost'] = dns_name else: fatal_error(tls_controls_message % 'stunnel_check_cert_hostname') # Only use the config setting if the override is not set if ocsp_enabled: if ocsp_aia_supported: efs_config['OCSPaia'] = 'yes' else: fatal_error(tls_controls_message % 'stunnel_check_cert_validity') system_release_version = get_system_release_version() if not any(release in system_release_version for release in SKIP_NO_LIBWRAP_RELEASES): efs_config['libwrap'] = 'no' stunnel_config = '\n'.join(serialize_stunnel_config(global_config) + serialize_stunnel_config(efs_config, 'efs')) logging.debug('Writing stunnel configuration:\n%s', stunnel_config) stunnel_config_file = os.path.join(state_file_dir, 'stunnel-config.%s' % mount_filename) with open(stunnel_config_file, 'w') as f: f.write(stunnel_config) return stunnel_config_file def write_tls_tunnel_state_file(fs_id, mountpoint, tls_port, tunnel_pid, command, files, state_file_dir, cert_details=None): """ Return the name of the temporary file containing TLS tunnel state, prefixed with a '~'. This file needs to be renamed to a non-temporary version following a successful mount. """ state_file = '~' + get_mount_specific_filename(fs_id, mountpoint, tls_port) state = { 'pid': tunnel_pid, 'cmd': command, 'files': files, } if cert_details: state.update(cert_details) with open(os.path.join(state_file_dir, state_file), 'w') as f: json.dump(state, f) return state_file def test_tunnel_process(tunnel_proc, fs_id): tunnel_proc.poll() if tunnel_proc.returncode is not None: out, err = tunnel_proc.communicate() fatal_error('Failed to initialize TLS tunnel for %s' % fs_id, 'Failed to start TLS tunnel (errno=%d). stdout="%s" stderr="%s"' % (tunnel_proc.returncode, out.strip(), err.strip())) def poll_tunnel_process(tunnel_proc, fs_id, mount_completed): """ poll the tunnel process health every .5s during the mount attempt to fail fast if the tunnel dies - since this is not called from the main thread, if the tunnel fails, exit uncleanly with os._exit """ while not mount_completed.is_set(): try: test_tunnel_process(tunnel_proc, fs_id) except SystemExit as e: os._exit(e.code) mount_completed.wait(.5) def get_init_system(comm_file='/proc/1/comm'): init_system = 'unknown' try: with open(comm_file) as f: init_system = f.read().strip() except IOError: logging.warning('Unable to read %s', comm_file) logging.debug('Identified init system: %s', init_system) return init_system def check_network_target(fs_id): with open(os.devnull, 'w') as devnull: rc = subprocess.call(['systemctl', 'status', 'network.target'], stdout=devnull, stderr=devnull, close_fds=True) if rc != 0: fatal_error('Failed to mount %s because the network was not yet available, add "_netdev" to your mount options' % fs_id, exit_code=0) def check_network_status(fs_id, init_system): if init_system != 'systemd': logging.debug('Not testing network on non-systemd init systems') return check_network_target(fs_id) def start_watchdog(init_system): if init_system == 'init': proc = subprocess.Popen( ['/sbin/status', WATCHDOG_SERVICE], stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True) status, _ = proc.communicate() if 'stop' in status: with open(os.devnull, 'w') as devnull: subprocess.Popen(['/sbin/start', WATCHDOG_SERVICE], stdout=devnull, stderr=devnull, close_fds=True) elif 'start' in status: logging.debug('%s is already running', WATCHDOG_SERVICE) elif init_system == 'systemd': rc = subprocess.call(['systemctl', 'is-active', '--quiet', WATCHDOG_SERVICE], close_fds=True) if rc != 0: with open(os.devnull, 'w') as devnull: subprocess.Popen(['systemctl', 'start', WATCHDOG_SERVICE], stdout=devnull, stderr=devnull, close_fds=True) else: logging.debug('%s is already running', WATCHDOG_SERVICE) else: error_message = 'Could not start %s, unrecognized init system "%s"' % (WATCHDOG_SERVICE, init_system) sys.stderr.write('%s\n' % error_message) logging.warning(error_message) def create_required_directory(config, directory): mode = 0o750 try: mode_str = config.get(CONFIG_SECTION, 'state_file_dir_mode') try: mode = int(mode_str, 8) except ValueError: logging.warning('Bad state_file_dir_mode "%s" in config file "%s"', mode_str, CONFIG_FILE) except NoOptionError: pass try: os.makedirs(directory, mode) except OSError as e: if errno.EEXIST != e.errno or not os.path.isdir(directory): raise @contextmanager def bootstrap_tls(config, init_system, dns_name, fs_id, mountpoint, options, state_file_dir=STATE_FILE_DIR): tls_port = choose_tls_port(config, options) # override the tlsport option so that we can later override the port the NFS client uses to connect to stunnel. # if the user has specified tlsport=X at the command line this will just re-set tlsport to X. options['tlsport'] = tls_port use_iam = 'iam' in options ap_id = options.get('accesspoint') cert_details = {} security_credentials = None client_info = get_client_info(config) if use_iam: aws_creds_uri = options.get('awscredsuri') if aws_creds_uri: kwargs = {'aws_creds_uri': aws_creds_uri} else: kwargs = {'awsprofile': get_aws_profile(options, use_iam)} security_credentials, credentials_source = get_aws_security_credentials(use_iam, **kwargs) if credentials_source: cert_details['awsCredentialsMethod'] = credentials_source if ap_id: cert_details['accessPoint'] = ap_id # additional symbol appended to avoid naming collisions cert_details['mountStateDir'] = get_mount_specific_filename(fs_id, mountpoint, tls_port) + '+' # common name for certificate signing request is max 64 characters cert_details['commonName'] = socket.gethostname()[0:64] cert_details['region'] = get_target_region(config) cert_details['certificateCreationTime'] = create_certificate(config, cert_details['mountStateDir'], cert_details['commonName'], cert_details['region'], fs_id, security_credentials, ap_id, client_info, base_path=state_file_dir) cert_details['certificate'] = os.path.join(state_file_dir, cert_details['mountStateDir'], 'certificate.pem') cert_details['privateKey'] = get_private_key_path() cert_details['fsId'] = fs_id start_watchdog(init_system) if not os.path.exists(state_file_dir): create_required_directory(config, state_file_dir) verify_level = int(options.get('verify', DEFAULT_STUNNEL_VERIFY_LEVEL)) ocsp_enabled = is_ocsp_enabled(config, options) stunnel_config_file = write_stunnel_config_file(config, state_file_dir, fs_id, mountpoint, tls_port, dns_name, verify_level, ocsp_enabled, options, cert_details=cert_details) tunnel_args = [_stunnel_bin(), stunnel_config_file] if 'netns' in options: tunnel_args = ['nsenter', '--net=' + options['netns']] + tunnel_args # launch the tunnel in a process group so if it has any child processes, they can be killed easily by the mount watchdog logging.info('Starting TLS tunnel: "%s"', ' '.join(tunnel_args)) tunnel_proc = subprocess.Popen( tunnel_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, preexec_fn=os.setsid, close_fds=True) logging.info('Started TLS tunnel, pid: %d', tunnel_proc.pid) temp_tls_state_file = write_tls_tunnel_state_file(fs_id, mountpoint, tls_port, tunnel_proc.pid, tunnel_args, [stunnel_config_file], state_file_dir, cert_details=cert_details) try: yield tunnel_proc finally: os.rename(os.path.join(state_file_dir, temp_tls_state_file), os.path.join(state_file_dir, temp_tls_state_file[1:])) def get_nfs_mount_options(options): # If you change these options, update the man page as well at man/mount.efs.8 if 'nfsvers' not in options and 'vers' not in options: options['nfsvers'] = '4.1' if 'rsize' not in options: options['rsize'] = '1048576' if 'wsize' not in options: options['wsize'] = '1048576' if 'soft' not in options and 'hard' not in options: options['hard'] = None if 'timeo' not in options: options['timeo'] = '600' if 'retrans' not in options: options['retrans'] = '2' if 'noresvport' not in options: options['noresvport'] = None if 'tls' in options: options['port'] = options['tlsport'] def to_nfs_option(k, v): if v is None: return k return '%s=%s' % (str(k), str(v)) nfs_options = [to_nfs_option(k, v) for k, v in options.items() if k not in EFS_ONLY_OPTIONS] return ','.join(nfs_options) def mount_nfs(dns_name, path, mountpoint, options): if 'tls' in options: mount_path = '127.0.0.1:%s' % path else: mount_path = '%s:%s' % (dns_name, path) command = ['/sbin/mount.nfs4', mount_path, mountpoint, '-o', get_nfs_mount_options(options)] if 'netns' in options: command = ['nsenter', '--net=' + options['netns']] + command logging.info('Executing: "%s"', ' '.join(command)) proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True) out, err = proc.communicate() if proc.returncode == 0: message = 'Successfully mounted %s at %s' % (dns_name, mountpoint) logging.info(message) publish_cloudwatch_log(CLOUDWATCHLOG_AGENT, message) else: message = 'Failed to mount %s at %s: returncode=%d, stderr="%s"' % (dns_name, mountpoint, proc.returncode, err.strip()) fatal_error(err.strip(), message, proc.returncode) def usage(out, exit_code=1): out.write('Usage: mount.efs [--version] [-h|--help] <fsname> <mountpoint> [-o <options>]\n') sys.exit(exit_code) def parse_arguments_early_exit(args=None): """Parse arguments, checking for early exit conditions only""" if args is None: args = sys.argv if '-h' in args[1:] or '--help' in args[1:]: usage(out=sys.stdout, exit_code=0) if '--version' in args[1:]: sys.stdout.write('%s Version: %s\n' % (args[0], VERSION)) sys.exit(0) def parse_arguments(config, args=None): """Parse arguments, return (fsid, path, mountpoint, options)""" if args is None: args = sys.argv fsname = None mountpoint = None options = {} if len(args) > 1: fsname = args[1] if len(args) > 2: mountpoint = args[2] if len(args) > 4 and '-o' in args[:-1]: options_index = args.index('-o') + 1 options = parse_options(args[options_index]) if not fsname or not mountpoint: usage(out=sys.stderr) fs_id, path = match_device(config, fsname) return fs_id, path, mountpoint, options def get_client_info(config): client_info = {} # source key/value pair in config file if config.has_option(CLIENT_INFO_SECTION, 'source'): client_source = config.get(CLIENT_INFO_SECTION, 'source') if 0 < len(client_source) <= CLIENT_SOURCE_STR_LEN_LIMIT: client_info['source'] = client_source return client_info def create_certificate(config, mount_name, common_name, region, fs_id, security_credentials, ap_id, client_info, base_path=STATE_FILE_DIR): current_time = get_utc_now() tls_paths = tls_paths_dictionary(mount_name, base_path) certificate_config = os.path.join(tls_paths['mount_dir'], 'config.conf') certificate_signing_request = os.path.join(tls_paths['mount_dir'], 'request.csr') certificate = os.path.join(tls_paths['mount_dir'], 'certificate.pem') ca_dirs_check(config, tls_paths['database_dir'], tls_paths['certs_dir']) ca_supporting_files_check(tls_paths['index'], tls_paths['index_attr'], tls_paths['serial'], tls_paths['rand']) private_key = check_and_create_private_key(base_path) if security_credentials: public_key = os.path.join(tls_paths['mount_dir'], 'publicKey.pem') create_public_key(private_key, public_key) create_ca_conf(certificate_config, common_name, tls_paths['mount_dir'], private_key, current_time, region, fs_id, security_credentials, ap_id, client_info) create_certificate_signing_request(certificate_config, private_key, certificate_signing_request) not_before = get_certificate_timestamp(current_time, minutes=-NOT_BEFORE_MINS) not_after = get_certificate_timestamp(current_time, hours=NOT_AFTER_HOURS) cmd = 'openssl ca -startdate %s -enddate %s -selfsign -batch -notext -config %s -in %s -out %s' % \ (not_before, not_after, certificate_config, certificate_signing_request, certificate) subprocess_call(cmd, 'Failed to create self-signed client-side certificate') return current_time.strftime(CERT_DATETIME_FORMAT) def get_private_key_path(): """Wrapped for mocking purposes in unit tests""" return PRIVATE_KEY_FILE def check_and_create_private_key(base_path=STATE_FILE_DIR): # Creating RSA private keys is slow, so we will create one private key and allow mounts to share it. # This means, however, that we have to include a locking mechanism to ensure that the private key is # atomically created, as mounts occurring in parallel may try to create the key simultaneously. key = get_private_key_path() @contextmanager def open_lock_file(): lock_file = os.path.join(base_path, 'efs-utils-lock') f = os.open(lock_file, os.O_CREAT | os.O_DSYNC | os.O_EXCL | os.O_RDWR) try: lock_file_contents = 'PID: %s' % os.getpid() os.write(f, lock_file_contents.encode('utf-8')) yield f finally: os.close(f) os.remove(lock_file) def do_with_lock(function): while True: try: with open_lock_file(): return function() except OSError as e: if e.errno == errno.EEXIST: logging.info('Failed to take out private key creation lock, sleeping 50 ms') time.sleep(0.05) else: raise def generate_key(): if os.path.isfile(key): return cmd = 'openssl genpkey -algorithm RSA -out %s -pkeyopt rsa_keygen_bits:3072' % key subprocess_call(cmd, 'Failed to create private key') read_only_mode = 0o400 os.chmod(key, read_only_mode) do_with_lock(generate_key) return key def create_certificate_signing_request(config_path, private_key, csr_path): cmd = 'openssl req -new -config %s -key %s -out %s' % (config_path, private_key, csr_path) subprocess_call(cmd, 'Failed to create certificate signing request (csr)') def create_ca_conf(config_path, common_name, directory, private_key, date, region, fs_id, security_credentials, ap_id, client_info): """Populate ca/req configuration file with fresh configurations at every mount since SigV4 signature can change""" public_key_path = os.path.join(directory, 'publicKey.pem') ca_extension_body = ca_extension_builder(ap_id, security_credentials, fs_id, client_info) efs_client_auth_body = efs_client_auth_builder(public_key_path, security_credentials['AccessKeyId'], security_credentials['SecretAccessKey'], date, region, fs_id, security_credentials['Token']) if security_credentials else '' efs_client_info_body = efs_client_info_builder(client_info) if client_info else '' full_config_body = CA_CONFIG_BODY % (directory, private_key, common_name, ca_extension_body, efs_client_auth_body, efs_client_info_body) with open(config_path, 'w') as f: f.write(full_config_body) return full_config_body def ca_extension_builder(ap_id, security_credentials, fs_id, client_info): ca_extension_str = '[ v3_ca ]\nsubjectKeyIdentifier = hash' if ap_id: ca_extension_str += '\n1.3.6.1.4.1.4843.7.1 = ASN1:UTF8String:' + ap_id if security_credentials: ca_extension_str += '\n1.3.6.1.4.1.4843.7.2 = ASN1:SEQUENCE:efs_client_auth' ca_extension_str += '\n1.3.6.1.4.1.4843.7.3 = ASN1:UTF8String:' + fs_id if client_info: ca_extension_str += '\n1.3.6.1.4.1.4843.7.4 = ASN1:SEQUENCE:efs_client_info' return ca_extension_str def efs_client_auth_builder(public_key_path, access_key_id, secret_access_key, date, region, fs_id, session_token=None): public_key_hash = get_public_key_sha1(public_key_path) canonical_request = create_canonical_request(public_key_hash, date, access_key_id, region, fs_id, session_token) string_to_sign = create_string_to_sign(canonical_request, date, region) signature = calculate_signature(string_to_sign, date, secret_access_key, region) efs_client_auth_str = '[ efs_client_auth ]' efs_client_auth_str += '\naccessKeyId = UTF8String:' + access_key_id efs_client_auth_str += '\nsignature = OCTETSTRING:' + signature efs_client_auth_str += '\nsigv4DateTime = UTCTIME:' + date.strftime(CERT_DATETIME_FORMAT) if session_token: efs_client_auth_str += '\nsessionToken = EXPLICIT:0,UTF8String:' + session_token return efs_client_auth_str def efs_client_info_builder(client_info): efs_client_info_str = '[ efs_client_info ]' for key, value in client_info.items(): efs_client_info_str += '\n%s = UTF8String:%s' % (key, value) return efs_client_info_str def create_public_key(private_key, public_key): cmd = 'openssl rsa -in %s -outform PEM -pubout -out %s' % (private_key, public_key) subprocess_call(cmd, 'Failed to create public key') def subprocess_call(cmd, error_message): """Helper method to run shell openssl command and to handle response error messages""" retry_times = 3 for retry in range(retry_times): process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True) (output, err) = process.communicate() rc = process.poll() if rc != 0: logging.error('Command %s failed, rc=%s, stdout="%s", stderr="%s"' % (cmd, rc, output, err), exc_info=True) try: process.kill() except OSError: # Silently fail if the subprocess has exited already pass else: return output, err error_message = '%s, error is: %s' % (error_message, err) fatal_error(error_message, error_message) def ca_dirs_check(config, database_dir, certs_dir): """Check if mount's database and certs directories exist and if not, create directories (also create all intermediate directories if they don't exist).""" if not os.path.exists(database_dir): create_required_directory(config, database_dir) if not os.path.exists(certs_dir): create_required_directory(config, certs_dir) def ca_supporting_files_check(index_path, index_attr_path, serial_path, rand_path): """Recreate all supporting openssl ca and req files if they're not present in their respective directories""" if not os.path.isfile(index_path): open(index_path, 'w').close() if not os.path.isfile(index_attr_path): with open(index_attr_path, 'w+') as f: f.write('unique_subject = no') if not os.path.isfile(serial_path): with open(serial_path, 'w+') as f: f.write('00') if not os.path.isfile(rand_path): open(rand_path, 'w').close() def get_certificate_timestamp(current_time, **kwargs): updated_time = current_time + timedelta(**kwargs) return updated_time.strftime(CERT_DATETIME_FORMAT) def get_utc_now(): """ Wrapped for patching purposes in unit tests """ return datetime.utcnow() def assert_root(): if os.geteuid() != 0: sys.stderr.write('only root can run mount.efs\n') sys.exit(1) def read_config(config_file=CONFIG_FILE): try: p = ConfigParser.SafeConfigParser() except AttributeError: p = ConfigParser() p.read(config_file) return p def bootstrap_logging(config, log_dir=LOG_DIR): raw_level = config.get(CONFIG_SECTION, 'logging_level') levels = { 'debug': logging.DEBUG, 'info': logging.INFO, 'warning': logging.WARNING, 'error': logging.ERROR, 'critical': logging.CRITICAL } level = levels.get(raw_level.lower()) level_error = False if not level: # delay logging error about malformed log level until after logging is configured level_error = True level = logging.INFO max_bytes = config.getint(CONFIG_SECTION, 'logging_max_bytes') file_count = config.getint(CONFIG_SECTION, 'logging_file_count') handler = RotatingFileHandler(os.path.join(log_dir, LOG_FILE), maxBytes=max_bytes, backupCount=file_count) handler.setFormatter(logging.Formatter(fmt='%(asctime)s - %(levelname)s - %(message)s')) logger = logging.getLogger() logger.setLevel(level) logger.addHandler(handler) if level_error: logging.error('Malformed logging level "%s", setting logging level to %s', raw_level, level) def get_dns_name(config, fs_id): def _validate_replacement_field_count(format_str, expected_ct): if format_str.count('{') != expected_ct or format_str.count('}') != expected_ct: raise ValueError('DNS name format has an incorrect number of replacement fields') dns_name_format = config.get(CONFIG_SECTION, 'dns_name_format') if '{fs_id}' not in dns_name_format: raise ValueError('DNS name format must include {fs_id}') format_args = {'fs_id': fs_id} expected_replacement_field_ct = 1 if '{region}' in dns_name_format: expected_replacement_field_ct += 1 format_args['region'] = get_target_region(config) if '{dns_name_suffix}' in dns_name_format: expected_replacement_field_ct += 1 config_section = CONFIG_SECTION region = format_args.get('region') if region: region_specific_config_section = '%s.%s' % (CONFIG_SECTION, region) if config.has_section(region_specific_config_section): config_section = region_specific_config_section format_args['dns_name_suffix'] = config.get(config_section, 'dns_name_suffix') logging.debug("Using dns_name_suffix %s in config section [%s]", format_args.get('dns_name_suffix'), config_section) _validate_replacement_field_count(dns_name_format, expected_replacement_field_ct) dns_name = dns_name_format.format(**format_args) try: socket.gethostbyname(dns_name) except socket.gaierror: fatal_error('Failed to resolve "%s" - check that your file system ID is correct.\nSee %s for more detail.' % (dns_name, 'https://docs.aws.amazon.com/console/efs/mount-dns-name'), 'Failed to resolve "%s"' % dns_name) return dns_name def tls_paths_dictionary(mount_name, base_path=STATE_FILE_DIR): tls_dict = { 'mount_dir': os.path.join(base_path, mount_name), # every mount will have its own ca mode assets due to lack of multi-threading support in openssl 'database_dir': os.path.join(base_path, mount_name, 'database'), 'certs_dir': os.path.join(base_path, mount_name, 'certs'), 'index': os.path.join(base_path, mount_name, 'database/index.txt'), 'index_attr': os.path.join(base_path, mount_name, 'database/index.txt.attr'), 'serial': os.path.join(base_path, mount_name, 'database/serial'), 'rand': os.path.join(base_path, mount_name, 'database/.rand') } return tls_dict def get_public_key_sha1(public_key): # truncating public key to remove the header and footer '-----(BEGIN|END) PUBLIC KEY-----' with open(public_key, 'r') as f: lines = f.readlines() lines = lines[1:-1] key = ''.join(lines) key = bytearray(base64.b64decode(key)) # Parse the public key to pull out the actual key material by looking for the key BIT STRING # Example: # 0:d=0 hl=4 l= 418 cons: SEQUENCE # 4:d=1 hl=2 l= 13 cons: SEQUENCE # 6:d=2 hl=2 l= 9 prim: OBJECT :rsaEncryption # 17:d=2 hl=2 l= 0 prim: NULL # 19:d=1 hl=4 l= 399 prim: BIT STRING cmd = 'openssl asn1parse -inform PEM -in %s' % public_key output, err = subprocess_call(cmd, 'Unable to ASN1 parse public key file, %s, correctly' % public_key) key_line = '' for line in output.splitlines(): if 'BIT STRING' in line.decode('utf-8'): key_line = line.decode('utf-8') if not key_line: err_msg = 'Public key file, %s, is incorrectly formatted' % public_key fatal_error(err_msg, err_msg) key_line = key_line.replace(' ', '') # DER encoding TLV (Tag, Length, Value) # - the first octet (byte) is the tag (type) # - the next octets are the length - "definite form" # - the first octet always has the high order bit (8) set to 1 # - the remaining 127 bits are used to encode the number of octets that follow # - the following octets encode, as big-endian, the length (which may be 0) as a number of octets # - the remaining octets are the "value" aka content # # For a BIT STRING, the first octet of the value is used to signify the number of unused bits that exist in the last # content byte. Note that this is explicitly excluded from the SubjectKeyIdentifier hash, per # https://tools.ietf.org/html/rfc5280#section-4.2.1.2 # # Example: # 0382018f00...<subjectPublicKey> # - 03 - BIT STRING tag # - 82 - 2 length octets to follow (ignore high order bit) # - 018f - length of 399 # - 00 - no unused bits in the last content byte offset = int(key_line.split(':')[0]) key = key[offset:] num_length_octets = key[1] & 0b01111111 # Exclude the tag (1), length (1 + num_length_octets), and number of unused bits (1) offset = 1 + 1 + num_length_octets + 1 key = key[offset:] sha1 = hashlib.sha1() sha1.update(key) return sha1.hexdigest() def create_canonical_request(public_key_hash, date, access_key, region, fs_id, session_token=None): """ Create a Canonical Request - https://docs.aws.amazon.com/general/latest/gr/sigv4-create-canonical-request.html """ formatted_datetime = date.strftime(SIGV4_DATETIME_FORMAT) credential = quote_plus(access_key + '/' + get_credential_scope(date, region)) request = HTTP_REQUEST_METHOD + '\n' request += CANONICAL_URI + '\n' request += create_canonical_query_string(public_key_hash, credential, formatted_datetime, session_token) + '\n' request += CANONICAL_HEADERS % fs_id + '\n' request += SIGNED_HEADERS + '\n' sha256 = hashlib.sha256() sha256.update(REQUEST_PAYLOAD.encode()) request += sha256.hexdigest() return request def create_canonical_query_string(public_key_hash, credential, formatted_datetime, session_token=None): canonical_query_params = { 'Action': 'Connect', # Public key hash is included in canonical request to tie the signature to a specific key pair to avoid replay attacks 'PublicKeyHash': quote_plus(public_key_hash), 'X-Amz-Algorithm': ALGORITHM, 'X-Amz-Credential': credential, 'X-Amz-Date': quote_plus(formatted_datetime), 'X-Amz-Expires': 86400, 'X-Amz-SignedHeaders': SIGNED_HEADERS, } if session_token: canonical_query_params['X-Amz-Security-Token'] = quote_plus(session_token) # Cannot use urllib.urlencode because it replaces the %s's return '&'.join(['%s=%s' % (k, v) for k, v in sorted(canonical_query_params.items())]) def create_string_to_sign(canonical_request, date, region): """ Create a String to Sign - https://docs.aws.amazon.com/general/latest/gr/sigv4-create-string-to-sign.html """ string_to_sign = ALGORITHM + '\n' string_to_sign += date.strftime(SIGV4_DATETIME_FORMAT) + '\n' string_to_sign += get_credential_scope(date, region) + '\n' sha256 = hashlib.sha256() sha256.update(canonical_request.encode()) string_to_sign += sha256.hexdigest() return string_to_sign def calculate_signature(string_to_sign, date, secret_access_key, region): """ Calculate the Signature - https://docs.aws.amazon.com/general/latest/gr/sigv4-calculate-signature.html """ def _sign(key, msg): return hmac.new(key, msg.encode('utf-8'), hashlib.sha256) key_date = _sign(('AWS4' + secret_access_key).encode('utf-8'), date.strftime(DATE_ONLY_FORMAT)).digest() add_region = _sign(key_date, region).digest() add_service = _sign(add_region, SERVICE).digest() signing_key = _sign(add_service, 'aws4_request').digest() return _sign(signing_key, string_to_sign).hexdigest() def get_credential_scope(date, region): return '/'.join([date.strftime(DATE_ONLY_FORMAT), region, SERVICE, AWS4_REQUEST]) def match_device(config, device): """Return the EFS id and the remote path to mount""" try: remote, path = device.split(':', 1) except ValueError: remote = device path = '/' if FS_ID_RE.match(remote): return remote, path try: primary, secondaries, _ = socket.gethostbyname_ex(remote) hostnames = list(filter(lambda e: e is not None, [primary] + secondaries)) except socket.gaierror: create_default_cloudwatchlog_agent_if_not_exist(config) fatal_error( 'Failed to resolve "%s" - check that the specified DNS name is a CNAME record resolving to a valid EFS DNS ' 'name' % remote, 'Failed to resolve "%s"' % remote ) if not hostnames: create_default_cloudwatchlog_agent_if_not_exist(config) fatal_error( 'The specified domain name "%s" did not resolve to an EFS mount target' % remote ) for hostname in hostnames: efs_fqdn_match = EFS_FQDN_RE.match(hostname) if efs_fqdn_match: fs_id = efs_fqdn_match.group('fs_id') expected_dns_name = get_dns_name(config, fs_id) # check that the DNS name of the mount target matches exactly the DNS name the CNAME resolves to if hostname == expected_dns_name: return fs_id, path else: create_default_cloudwatchlog_agent_if_not_exist(config) fatal_error('The specified CNAME "%s" did not resolve to a valid DNS name for an EFS mount target. ' 'Please refer to the EFS documentation for mounting with DNS names for examples: %s' % (remote, 'https://docs.aws.amazon.com/efs/latest/ug/mounting-fs-mount-cmd-dns-name.html')) def is_nfs_mount(mountpoint): cmd = ['stat', '-f', '-L', '-c', '%T', mountpoint] p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True) output, _ = p.communicate() return output and 'nfs' in str(output) def mount_tls(config, init_system, dns_name, path, fs_id, mountpoint, options): if os.path.ismount(mountpoint) and is_nfs_mount(mountpoint): sys.stdout.write("%s is already mounted, please run 'mount' command to verify\n" % mountpoint) logging.warning("%s is already mounted, mount aborted" % mountpoint) return with bootstrap_tls(config, init_system, dns_name, fs_id, mountpoint, options) as tunnel_proc: mount_completed = threading.Event() t = threading.Thread(target=poll_tunnel_process, args=(tunnel_proc, fs_id, mount_completed)) t.daemon = True t.start() mount_nfs(dns_name, path, mountpoint, options) mount_completed.set() t.join() def check_unsupported_options(options): for unsupported_option in UNSUPPORTED_OPTIONS: if unsupported_option in options: warn_message = 'The "%s" option is not supported and has been ignored, as amazon-efs-utils relies on a built-in ' \ 'trust store.' % unsupported_option sys.stderr.write('WARN: %s\n' % warn_message) logging.warning(warn_message) del options[unsupported_option] def check_options_validity(options): if 'tls' in options: if 'port' in options: fatal_error('The "port" and "tls" options are mutually exclusive') if 'tlsport' in options: try: int(options['tlsport']) except ValueError: fatal_error('tlsport option [%s] is not an integer' % options['tlsport']) if 'ocsp' in options and 'noocsp' in options: fatal_error('The "ocsp" and "noocsp" options are mutually exclusive') if 'accesspoint' in options: if 'tls' not in options: fatal_error('The "tls" option is required when mounting via "accesspoint"') if not AP_ID_RE.match(options['accesspoint']): fatal_error('Access Point ID %s is malformed' % options['accesspoint']) if 'iam' in options and 'tls' not in options: fatal_error('The "tls" option is required when mounting via "iam"') if 'awsprofile' in options and 'iam' not in options: fatal_error('The "iam" option is required when mounting with named profile option, "awsprofile"') if 'awscredsuri' in options and 'iam' not in options: fatal_error('The "iam" option is required when mounting with "awscredsuri"') if 'awscredsuri' in options and 'awsprofile' in options: fatal_error('The "awscredsuri" and "awsprofile" options are mutually exclusive') def bootstrap_cloudwatch_logging(config, fs_id=None): if not check_if_cloudwatch_log_enabled(config): return None cloudwatchlog_client = get_botocore_client(config, 'logs') if not cloudwatchlog_client: return None cloudwatchlog_config = get_cloudwatchlog_config(config, fs_id) log_group_name = cloudwatchlog_config.get('log_group_name') log_stream_name = cloudwatchlog_config.get('log_stream_name') retention_days = cloudwatchlog_config.get('retention_days') group_creation_completed = create_cloudwatch_log_group(cloudwatchlog_client, log_group_name) if not group_creation_completed: return None put_retention_policy_completed = put_cloudwatch_log_retention_policy(cloudwatchlog_client, log_group_name, retention_days) if not put_retention_policy_completed: return None stream_creation_completed = create_cloudwatch_log_stream(cloudwatchlog_client, log_group_name, log_stream_name) if not stream_creation_completed: return None return { 'client': cloudwatchlog_client, 'log_group_name': log_group_name, 'log_stream_name': log_stream_name } def create_default_cloudwatchlog_agent_if_not_exist(config): if not check_if_cloudwatch_log_enabled(config): return None global CLOUDWATCHLOG_AGENT if not CLOUDWATCHLOG_AGENT: CLOUDWATCHLOG_AGENT = bootstrap_cloudwatch_logging(config) def get_botocore_client(config, service): if not BOTOCORE_PRESENT: logging.error('Failed to import botocore, please install botocore first.') return None session = botocore.session.get_session() region = get_target_region(config) iam_role_name = get_iam_role_name() if iam_role_name: credentials, _ = get_aws_security_credentials_from_instance_metadata(iam_role_name) if credentials: return session.create_client(service, aws_access_key_id=credentials['AccessKeyId'], aws_secret_access_key=credentials['SecretAccessKey'], aws_session_token=credentials['Token'], region_name=region) return session.create_client(service, region_name=region) def get_cloudwatchlog_config(config, fs_id=None): log_group_name = DEFAULT_CLOUDWATCH_LOG_GROUP if config.has_option(CLOUDWATCH_LOG_SECTION, 'log_group_name'): log_group_name = config.get(CLOUDWATCH_LOG_SECTION, 'log_group_name') retention_days = DEFAULT_RETENTION_DAYS if config.has_option(CLOUDWATCH_LOG_SECTION, 'retention_in_days'): retention_days = config.get(CLOUDWATCH_LOG_SECTION, 'retention_in_days') log_stream_name = get_cloudwatch_log_stream_name(fs_id) return { 'log_group_name': log_group_name, 'retention_days': int(retention_days), 'log_stream_name': log_stream_name } def get_cloudwatch_log_stream_name(fs_id=None): instance_id = get_instance_identity_info_from_instance_metadata('instanceId') if instance_id and fs_id: log_stream_name = '%s - %s - mount.log' % (fs_id, instance_id) elif instance_id: log_stream_name = '%s - mount.log' % (instance_id) elif fs_id: log_stream_name = '%s - mount.log' % (fs_id) else: log_stream_name = 'default - mount.log' return log_stream_name def check_if_cloudwatch_log_enabled(config): if config.has_option(CLOUDWATCH_LOG_SECTION, 'enabled'): return config.getboolean(CLOUDWATCH_LOG_SECTION, 'enabled') return False def cloudwatch_create_log_group_helper(cloudwatchlog_client, log_group_name): cloudwatchlog_client.create_log_group( logGroupName=log_group_name ) logging.info('Created cloudwatch log group %s' % log_group_name) def create_cloudwatch_log_group(cloudwatchlog_client, log_group_name): try: cloudwatch_create_log_group_helper(cloudwatchlog_client, log_group_name) except ClientError as e: exception = e.response['Error']['Code'] if exception == 'ResourceAlreadyExistsException': logging.debug('Log group %s already exist, %s' % (log_group_name, e.response)) return True elif exception == 'LimitExceededException': logging.error('Reached the maximum number of log groups that can be created, %s' % e.response) return False elif exception == 'OperationAbortedException': logging.debug('Multiple requests to update the same log group %s were in conflict, %s' % (log_group_name, e.response)) return False elif exception == 'InvalidParameterException': logging.error('Log group name %s is specified incorrectly, %s' % (log_group_name, e.response)) return False else: handle_general_botocore_exceptions(e) return False except NoCredentialsError as e: logging.warning('Credentials are not properly configured, %s' % e) return False except EndpointConnectionError as e: logging.warning('Could not connect to the endpoint, %s' % e) return False except Exception as e: logging.warning('Unknown error, %s.' % e) return False return True def cloudwatch_put_retention_policy_helper(cloudwatchlog_client, log_group_name, retention_days): cloudwatchlog_client.put_retention_policy( logGroupName=log_group_name, retentionInDays=retention_days ) logging.debug('Set cloudwatch log group retention days to %s' % retention_days) def put_cloudwatch_log_retention_policy(cloudwatchlog_client, log_group_name, retention_days): try: cloudwatch_put_retention_policy_helper(cloudwatchlog_client, log_group_name, retention_days) except ClientError as e: exception = e.response['Error']['Code'] if exception == 'ResourceNotFoundException': logging.error('Log group %s does not exist, %s' % (log_group_name, e.response)) return False elif exception == 'OperationAbortedException': logging.debug('Multiple requests to update the same log group %s were in conflict, %s' % (log_group_name, e.response)) return False elif exception == 'InvalidParameterException': logging.error('Either parameter log group name %s or retention in days %s is specified incorrectly, %s' % (log_group_name, retention_days, e.response)) return False else: handle_general_botocore_exceptions(e) return False except NoCredentialsError as e: logging.warning('Credentials are not properly configured, %s' % e) return False except EndpointConnectionError as e: logging.warning('Could not connect to the endpoint, %s' % e) return False except Exception as e: logging.warning('Unknown error, %s.' % e) return False return True def cloudwatch_create_log_stream_helper(cloudwatchlog_client, log_group_name, log_stream_name): cloudwatchlog_client.create_log_stream( logGroupName=log_group_name, logStreamName=log_stream_name ) logging.info('Created cloudwatch log stream %s in log group %s' % (log_stream_name, log_group_name)) def create_cloudwatch_log_stream(cloudwatchlog_client, log_group_name, log_stream_name): try: cloudwatch_create_log_stream_helper(cloudwatchlog_client, log_group_name, log_stream_name) except ClientError as e: exception = e.response['Error']['Code'] if exception == 'ResourceAlreadyExistsException': logging.debug('Log stream %s already exist in log group %s, %s' % (log_stream_name, log_group_name, e.response)) return True elif exception == 'InvalidParameterException': logging.error('Either parameter log group name %s or log stream name %s is specified incorrectly, %s' % (log_group_name, log_stream_name, e.response)) return False elif exception == 'ResourceNotFoundException': logging.error('Log group %s does not exist, %s' % (log_group_name, e.response)) return False else: handle_general_botocore_exceptions(e) return False except NoCredentialsError as e: logging.warning('Credentials are not properly configured, %s' % e) return False except EndpointConnectionError as e: logging.warning('Could not connect to the endpoint, %s' % e) return False except Exception as e: logging.warning('Unknown error, %s.' % e) return False return True def cloudwatch_put_log_events_helper(cloudwatchlog_agent, message, token=None): kwargs = { 'logGroupName': cloudwatchlog_agent.get('log_group_name'), 'logStreamName': cloudwatchlog_agent.get('log_stream_name'), 'logEvents': [ { 'timestamp': int(round(time.time() * 1000)), 'message': message } ] } if token: kwargs['sequenceToken'] = token cloudwatchlog_agent.get('client').put_log_events(**kwargs) def publish_cloudwatch_log(cloudwatchlog_agent, message): if not cloudwatchlog_agent or not cloudwatchlog_agent.get('client'): return False token = get_log_stream_next_token(cloudwatchlog_agent) try: cloudwatch_put_log_events_helper(cloudwatchlog_agent, message, token) except ClientError as e: exception = e.response['Error']['Code'] if exception == 'InvalidSequenceTokenException': logging.debug('The sequence token is not valid, %s' % e.response) return False elif exception == 'InvalidParameterException': logging.debug('One of the parameter to put log events is not valid, %s' % e.response) return False elif exception == 'DataAlreadyAcceptedException': logging.debug('The event %s was already logged, %s' % (message, e.response)) return False elif exception == 'UnrecognizedClientException': logging.debug('The most likely cause is an invalid AWS access key ID or secret Key, %s' % e.response) return False elif exception == 'ResourceNotFoundException': logging.error('Either log group %s or log stream %s does not exist, %s' % (cloudwatchlog_agent.get('log_group_name'), cloudwatchlog_agent.get('log_stream_name'), e.response)) return False else: logging.debug('Unexpected error: %s' % e) return False except NoCredentialsError as e: logging.warning('Credentials are not properly configured, %s' % e) return False except EndpointConnectionError as e: logging.warning('Could not connect to the endpoint, %s' % e) return False except Exception as e: logging.warning('Unknown error, %s.' % e) return False return True def cloudwatch_describe_log_streams_helper(cloudwatchlog_agent): return cloudwatchlog_agent.get('client').describe_log_streams( logGroupName=cloudwatchlog_agent.get('log_group_name'), logStreamNamePrefix=cloudwatchlog_agent.get('log_stream_name') ) def get_log_stream_next_token(cloudwatchlog_agent): try: response = cloudwatch_describe_log_streams_helper(cloudwatchlog_agent) except ClientError as e: exception = e.response['Error']['Code'] if exception == 'InvalidParameterException': logging.debug('Either parameter log group name %s or log stream name %s is specified incorrectly, %s' % (cloudwatchlog_agent.get('log_group_name'), cloudwatchlog_agent.get('log_stream_name'), e.response)) elif exception == 'ResourceNotFoundException': logging.debug('Either log group %s or log stream %s does not exist, %s' % (cloudwatchlog_agent.get('log_group_name'), cloudwatchlog_agent.get('log_stream_name'), e.response)) else: handle_general_botocore_exceptions(e) return None except NoCredentialsError as e: logging.warning('Credentials are not properly configured, %s' % e) return None except EndpointConnectionError as e: logging.warning('Could not connect to the endpoint, %s' % e) return None except Exception as e: logging.warning('Unknown error, %s' % e) return None try: log_stream = response['logStreams'][0] return log_stream.get('uploadSequenceToken') except (IndexError, TypeError, KeyError): pass return None def handle_general_botocore_exceptions(error): exception = error.response['Error']['Code'] if exception == 'ServiceUnavailableException': logging.debug('The service cannot complete the request, %s' % error.response) elif exception == 'AccessDeniedException': logging.debug('User is not authorized to perform the action, %s' % error.response) else: logging.debug('Unexpected error: %s' % error) def main(): parse_arguments_early_exit() assert_root() config = read_config() bootstrap_logging(config) fs_id, path, mountpoint, options = parse_arguments(config) logging.info('version=%s options=%s', VERSION, options) global CLOUDWATCHLOG_AGENT CLOUDWATCHLOG_AGENT = bootstrap_cloudwatch_logging(config, fs_id) check_unsupported_options(options) check_options_validity(options) init_system = get_init_system() check_network_status(fs_id, init_system) dns_name = get_dns_name(config, fs_id) if 'tls' in options: mount_tls(config, init_system, dns_name, path, fs_id, mountpoint, options) else: mount_nfs(dns_name, path, mountpoint, options) if '__main__' == __name__: main()

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130

0.680535

10,057

76,697

4.939147

0.107984

0.005234

0.010871

0.005818

0.40829

0.331186

0.284038

0.230407

0.202988

0.164556

0

0.007997

0.222316

76,697

1,988

131

38.57998

0.8248

0.092663

0

0.225897

0

0.014075

0.198127

0.023725

0

0.002111

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false

0.002815

0.024631

0.003519

0.194229

0

null

0

null

0

1

0

6a01fe7f065ff8fbb40e8cf44137b52463e1417c

1,010

py

Python

upcfcardsearch/c8.py

ProfessorSean/Kasutamaiza

7a69a69258f67bbb88bebbac6da4e6e1434947e6

[ "MIT" ]

null

upcfcardsearch/c8.py

ProfessorSean/Kasutamaiza

7a69a69258f67bbb88bebbac6da4e6e1434947e6

[ "MIT" ]

null

upcfcardsearch/c8.py

ProfessorSean/Kasutamaiza

7a69a69258f67bbb88bebbac6da4e6e1434947e6

[ "MIT" ]

null

import discord from discord.ext import commands from discord.utils import get class c8(commands.Cog, name="c8"): def __init__(self, bot: commands.Bot): self.bot = bot @commands.command(name='Sacrosanct_Devouring_Pyre', aliases=['c8']) async def example_embed(self, ctx): embed = discord.Embed(title='Sacrosanct Devouring Pyre', color=0xBC5A84) embed.set_thumbnail(url='https://www.duelingbook.com/images/custom-pics/2300000/2308475.jpg') embed.add_field(name='Status (Archetype)', value='Casual:3/Tournament:3', inline=True) embed.add_field(name='Type', value='Trap/Normal', inline=False) embed.add_field(name='Card Effect', value='Tribute 2 monsters, then target 2 monsters; destroy those targets. You can only activate 1 "Sacrosanct Devouring Pyre" per turn.', inline=False) embed.set_footer(text='Set Code: ANCF') await ctx.send(embed=embed) def setup(bot: commands.Bot): bot.add_cog(c8(bot))

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195

0.687129

138

1,010

4.934783

0.565217

0.048458

0.101322

0.07489

0

0.032767

0.184158

1,010

23

196

43.913043

0.793689

0

0.055556

0.323442

0.0455

0

0.007913

0

1

0.111111

false

0

0.166667

0

0.333333

0

null

0

null

0

1

0

6a023f8c8af70de4e0b8e937c5773e7da489fab5

2,627

py

Python

SVMmodel_withSKF.py

tameney22/DCI-Capstone

6f59541f16030bfa3f0a706fd9f0e4394e1ee974

[ "MIT" ]

null

SVMmodel_withSKF.py

tameney22/DCI-Capstone

6f59541f16030bfa3f0a706fd9f0e4394e1ee974

[ "MIT" ]

null

SVMmodel_withSKF.py

tameney22/DCI-Capstone

6f59541f16030bfa3f0a706fd9f0e4394e1ee974

[ "MIT" ]

null

""" This script is where the preprocessed data is used to train the SVM model to perform the classification. I am using Stratified K-Fold Cross Validation to prevent bias and/or any imbalance that could affect the model's accuracy. REFERENCE: https://medium.com/@bedigunjit/simple-guide-to-text-classification-nlp-using-svm-and-naive-bayes-with-python-421db3a72d34 """ import numpy as np import pandas as pd from sklearn import model_selection, svm from sklearn.metrics import accuracy_score from sklearn.preprocessing import LabelEncoder from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import StratifiedKFold # Open preproccessed csv df = pd.read_csv("preprocessed.csv", index_col=0) print(df.head()) print("SPLITTING TRAIN-TEST") x = df["Text"] y = df["PublicationTitle"] train_x, test_x, train_y, test_y = model_selection.train_test_split( df["Text"], df["PublicationTitle"], test_size=0.3) # Label encode the target variable to transform categorical data of string # type into numerical values the model can understand encoder = LabelEncoder() # train_y = encoder.fit_transform(train_y) # test_y = encoder.fit_transform(test_y) # Word vectorization # turning a collection of text documents into numerical feature vectors # We are using Term Frequency - Inverse Document tfidf_vect = TfidfVectorizer(max_features=5000) tfidf_vect.fit(df["Text"]) # train_x_tfidf = tfidf_vect.transform(train_x) # test_x_tfidf = tfidf_vect.transform(test_x) x_tfidf = tfidf_vect.transform(df["Text"]) y = encoder.fit_transform(y) # print(tfidf_vect.vocabulary_) # Fit the training dataset to the classifier print("TRAINING THE MODEL") SVM = svm.SVC(C=1.0, kernel='linear', degree=3, gamma='auto') skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=1) accuracies = [] fold = 1 for train_idx, test_idx in skf.split(x, y): print("Working on fold", fold) x_train_fold, x_test_fold = x_tfidf[train_idx], x_tfidf[test_idx] y_train_fold, y_test_fold = y[train_idx], y[test_idx] SVM.fit(x_train_fold, y_train_fold) acc = SVM.score(x_test_fold, y_test_fold) print("Acc", fold, ":", acc) accuracies.append(acc) fold += 1 print("ACCURACIES:", accuracies) print("Max Accuracy:", np.max(accuracies)) print("Min Accuracy:", np.min(accuracies)) print("Mean of Accuracies:", np.mean(accuracies)) print("STD of Accuracies:", np.std(accuracies)) # print("RUNNING TEST PREDICTIONS") # predictions = SVM.predict(test_x_tfidf) # # Calculate accuracy score # accuracy = accuracy_score(test_y, predictions) # print("Accuracy:", str(accuracy * 100) + "%")

31.650602

132

0.760183

396

2,627

4.876263

0.386364

0.027965

0.01709

0.031072

0.037286

0

0.011329

0.12638

2,627

82

133

32.036585

0.830065

0.412257

0

0.134957

0

1

0

false

0

0.184211

0

0.184211

0.263158

0

null

0

null

0

1

0

6a048666edf3e5d75a0ded13639990b1d6bed2e8

33,554

py

Python

src/consensus.py

dschwoerer/samscripts

caee697e96a0639b7a4f9db02f70f4fd92b39ef9

[ "MIT" ]

null

src/consensus.py

dschwoerer/samscripts

caee697e96a0639b7a4f9db02f70f4fd92b39ef9

[ "MIT" ]

null

src/consensus.py

dschwoerer/samscripts

caee697e96a0639b7a4f9db02f70f4fd92b39ef9

[ "MIT" ]

null

#! /usr/bin/env python # Copyright Ivan Sovic, 2015. www.sovic.org # # Creates a pileup from a given SAM/BAM file, and calls consensus bases (or variants). import os import sys import operator import subprocess def increase_in_dict(dict_counter, value): try: dict_counter[value] += 1 except: dict_counter[value] = 1 def process_mpileup_line( line, line_number, ret_variant_list, ret_vcf_list, ret_snp_count, ret_insertion_count, ret_deletion_count, ret_num_undercovered_bases, ret_num_called_bases, ret_num_correct_bases, ret_coverage_sum, coverage_threshold, verbose=False, ): # Split the line, and perform a sanity check. split_line = line.strip().split("\t") if len(split_line) < 5 or len(split_line) > 6: sys.stderr.write(line + "\n") return 0 ref_name = split_line[0] position = split_line[1] ref_base = split_line[2] coverage = split_line[3] original_bases = split_line[4] if len(split_line) == 6: qualities = split_line[5] bases = "" # Replace the '.' and ',' signs with the actual reference base. i = 0 while i < len(original_bases): if original_bases[i] == "." or original_bases[i] == ",": bases += ref_base else: bases += original_bases[i] i += 1 base_counts = {} insertion_count = 0 current_base_deletion_count = 0 deletion_count = 0 insertion_event_counts = {} deletion_event_counts = {} end_counts = 0 # print 'position: %s' % position; # print 'bases: "%s"' % bases; # print 'line_number: %d' % line_number; # print line; # print ''; # sys.stdout.flush(); i = 0 while i < len(bases): base = bases[i] if base == r"^": # This is the starting position of a read. It encodes two # symbols: '^' marking the read start and a char marking the # mapping quality of the read. # increase_in_dict(base_counts, bases[i + 1].upper()); i += 1 # Increase only by 1, because we have i += 1 down there. elif base == r"$": # This marks the end of a read. end_counts += 1 elif base == r"*": # This is a deletion, just count it. current_base_deletion_count += 1 elif base == r"-": # This marks the occurance of deletions. It is a composite object # consisting of: the special character '-', the number of the deleted bases # and the actual bases that are deleted (these bases follow the current position). # In our approach, we ignore this case, because we count deletions one by one # through the '*' character. # Get the number of bases that need to be skipped in the string. j = i + 1 while bases[j] in "0123456789": j += 1 num_bases = int(bases[(i + 1) : j]) skip_bases = (j - i) + num_bases - 1 deletion_count += 1 deletion = bases[j : (j + num_bases)].upper() increase_in_dict(deletion_event_counts, deletion) # Skip the length of the numeric entry plus the actual number of bases # that need to be skipped. i += skip_bases elif base == r"+": # This marks the occurance of an insertion. It is a composite object # consisting of: the special character '+', the number of the inserted bases # and the actual bases that are inserted (these bases follow the current position). # Similar to the deletion marking, but here we actually care about the bases, # and we need to make an allele aware count. # Get the number of bases that are inserted; j = i + 1 while bases[j] in "0123456789": j += 1 num_bases = int(bases[(i + 1) : j]) skip_bases = (j - i) + num_bases - 1 insertion_count += 1 insertion = bases[j : (j + num_bases)].upper() increase_in_dict(insertion_event_counts, insertion) i += skip_bases else: increase_in_dict(base_counts, bases[i].upper()) i += 1 # TODO: An additional problematic case, discovered this on 03.11.2014., when analyzing BWA-MEM's mpileup. # There are pileup bases that do not have any actual bases, but only the '*' symbols. How should this be handled properly? # Example line from the mpileup file: # gi|48994873|gb|U00096.2|_Escherichia_coli_str._K-12_substr._MG1655,_complete_genome 1938202 T 20 ******************** 8,2*#-;)$B>2$1&D- # I chose to handle them as undercovered bases. non_indel_coverage_current_base = int(coverage) - current_base_deletion_count if verbose == True: sys.stdout.write("%s\nbase_counts: %s\n" % (line.strip(), str(base_counts))) # EDIT: Previously I compared the total coverage of the current base with the coverage threshold. # However, the total coverage also accounts for the deletions denoted with the '*' sign, which I think # isn't relevant, as deletions are counted prior to occuring, and at that point is already decided if there is going # to be a deletion event. If we wound up at this base (i.e. this base didn't get skipped because of a deletion # consensus), then the deletions on this base are ignored. # if (int(coverage) < coverage_threshold or int(coverage) == current_base_deletion_count): # if (non_indel_coverage_current_base < coverage_threshold): if int(coverage) < coverage_threshold: ret_num_undercovered_bases[0] += 1 # ret_coverage_sum[0] += 0; ret_coverage_sum[0] += int(coverage) # TODO: Should I count total coverage of this base, or the non_indel_coverage_current_base? sorted_base_counts = [["A", 0], ["C", 0], ["T", 0], ["G", 0]] sorted_base_counts = sorted( list(base_counts.items()), key=operator.itemgetter(1) ) try: most_common_base_count = sorted_base_counts[-1][1] except Exception as e: most_common_base_count = 0 pass # variant_line = 'undercovered1\tpos = %s\tcoverage = %d\tnon_indel_cov_curr = %d\tmost_common_base_count = %d\tref_base = %s\tcons_base = %s\tbase_counts = %s\tinsertion_counts = %s\tdeletion_counts = %s\t%s' % (position, int(coverage), non_indel_coverage_current_base, most_common_base_count, ref_base, sorted_base_counts[-1][0], str(sorted_base_counts), str(insertion_event_counts), str(deletion_event_counts), line.strip()); # ret_variant_list.append(variant_line); variant_line = ( "undercovered1\tpos = %s\tref = %s\tcoverage = %d\tbase_counts = %s\tinsertion_counts = %s\tdeletion_counts = %s" % ( position, ref_name, int(coverage), str(sorted_base_counts), str(insertion_event_counts), str(deletion_event_counts), ) ) ret_variant_list.append(variant_line) ### VCF output ### qual = 1000 info = "DP=%s;TYPE=snp" % (coverage) ref_field = ref_base alt_field = "N" vcf_line = "%s\t%s\t.\t%s\t%s\t%d\tPASS\t%s" % ( ref_name, position, ref_field, alt_field, qual, info, ) ret_vcf_list.append(vcf_line) ################## else: ret_num_called_bases[0] += 1 ret_coverage_sum[0] += int(coverage) # TODO: Should I count total coverage of this base, or the non_indel_coverage_current_base? most_common_base_count = 0 ### Handling base consensus. sorted_base_counts = sorted( list(base_counts.items()), key=operator.itemgetter(1) ) try: most_common_base_count = sorted_base_counts[-1][1] except Exception as e: pass # sys.stderr.write(str(e) + '\n'); # sys.stderr.write('sorted_base_counts:\n'); # sys.stderr.write(str(sorted_base_counts) + '\n'); # sys.stderr.write('base_counts:\n'); # sys.stderr.write(str(base_counts) + '\n'); # sys.stderr.write('original_bases:\n'); # sys.stderr.write(str(original_bases) + '\n'); # sys.stderr.write('line:\n'); # sys.stderr.write(line.strip() + '\n'); # most_common_base_count = 0; # Allow for the case where there are multiple equally good choices. # In this case, we prefer the choice which is equal to the reference. is_good = False for base_count in sorted_base_counts: if base_count[1] == most_common_base_count: if base_count[0] == ref_base: is_good = True break if is_good == False: if len(sorted_base_counts) > 0: ret_snp_count[0] += 1 # ret_variant_list.append(line_number); variant_line = ( "SNP\tpos = %s\tref = %s\tcoverage = %d\tnon_indel_cov_curr = %d\tmost_common_base_count = %d\tref_base = %s\tcons_base = %s\tbase_counts = %s\tinsertion_counts = %s\tdeletion_counts = %s\t%s" % ( position, ref_name, int(coverage), non_indel_coverage_current_base, most_common_base_count, ref_base, ("{}") if (len(sorted_base_counts) == 0) else (str(sorted_base_counts[-1][0])), str(sorted_base_counts), str(insertion_event_counts), str(deletion_event_counts), line.strip(), ) ) ret_variant_list.append(variant_line) ### VCF output ### alt_base = ( ("{}") if (len(sorted_base_counts) == 0) else (str(sorted_base_counts[-1][0])) ) qual = 1000 info = "DP=%s;TYPE=snp" % (coverage) ref_field = ref_base alt_field = alt_base vcf_line = "%s\t%s\t.\t%s\t%s\t%d\tPASS\t%s" % ( ref_name, position, ref_field, alt_field, qual, info, ) ret_vcf_list.append(vcf_line) ################## else: sys.stderr.write( "\nWarning: a SNP was detected, but there were no bases in the sorted_base_counts!" ) variant_line = ( "SNP\tpos = %s\tref = %s\tcoverage = %d\tnon_indel_cov_curr = %d\tmost_common_base_count = %d\tref_base = %s\tcons_base = %s\tbase_counts = %s\tinsertion_counts = %s\tdeletion_counts = %s\t%s" % ( position, ref_name, int(coverage), non_indel_coverage_current_base, most_common_base_count, ref_base, ("{}") if (len(sorted_base_counts) == 0) else (str(sorted_base_counts[-1][0])), str(sorted_base_counts), str(insertion_event_counts), str(deletion_event_counts), line.strip(), ) ) sys.stderr.write("\n") else: ret_num_correct_bases[0] += 1 if verbose == True: sys.stdout.write("Reference base: %s\n" % (ref_base)) sys.stdout.write("Consensus base: %s\n\n" % (base_count[0])) # if (int(position) == 100000 or int(position) == 1000000 or int(position) == 2000000 or int(position) == 3000000 or int(position) == 4000000): # print '\nTEST\tpos = %s\tcoverage = %d\tnon_indel_cov_curr = %d\tmost_common_base_count = %d\tref_base = %s\tcons_base = %s\tbase_counts = %s\tinsertion_counts = %s\tdeletion_counts = %s\t%s\n' % (position, int(coverage), non_indel_coverage_current_base, most_common_base_count, ref_base, sorted_base_counts[-1][0], str(sorted_base_counts), str(insertion_event_counts), str(deletion_event_counts), line.strip()); ### Handling indel consensus. ### Put a different coverage threshold. Here we are interested even in the reads ### which had a '*' at the current position (because we don't know where it ends). non_indel_coverage_next_base = ( int(coverage) - end_counts - deletion_count - insertion_count ) if ( non_indel_coverage_next_base + deletion_count + insertion_count ) > coverage_threshold: # Sanity check, just to see if there actually were any insertions (to avoid index out of bounds error). # If there are insertions, get the most common one. if len(list(insertion_event_counts.keys())) > 0: sorted_insertion_counts = sorted( list(insertion_event_counts.items()), key=operator.itemgetter(1) ) most_common_insertion_count = sorted_insertion_counts[-1][1] most_common_insertion_length = len(sorted_insertion_counts[-1][0]) insertion_unique = ( True if ( sum( [ int(insertion_count[1] == most_common_insertion_count) for insertion_count in sorted_insertion_counts ] ) == 1 ) else False ) else: most_common_insertion_count = 0 most_common_insertion_length = 0 insertion_unique = False # Sanity check, just to see if there actually were any deletions (to avoid index out of bounds error). # If there are deletions, get the most common one. if len(list(deletion_event_counts.keys())) > 0: sorted_deletion_counts = sorted( list(deletion_event_counts.items()), key=operator.itemgetter(1) ) most_common_deletion_count = sorted_deletion_counts[-1][1] most_common_deletion_length = len(sorted_deletion_counts[-1][0]) deletion_unique = ( True if ( sum( [ int(deletion_count[1] == most_common_deletion_count) for deletion_count in sorted_deletion_counts ] ) == 1 ) else False ) else: most_common_deletion_count = 0 most_common_deletion_length = 0 deletion_unique = False if ( most_common_insertion_count > most_common_deletion_count and most_common_insertion_count > non_indel_coverage_next_base ): # In this case, insertions are a clear winner. if insertion_unique == True: # ret_insertion_count[0] += most_common_insertion_length; ret_insertion_count[0] += 1 ret_num_called_bases[0] += most_common_insertion_length # variant_line = 'insertion\t%d\t%s\t%s\t%s\t%s' % (most_common_insertion_count, str(sorted_base_counts), str(insertion_event_counts), str(deletion_event_counts), line.strip()); # ret_variant_list.append(variant_line); try: temp_sorted_bc = sorted_base_counts[-1][0] except: temp_sorted_bc = 0 indel_length = most_common_insertion_length variant_line = ( "ins\tpos = %s\tref = %s\tnon_indel_cov_next = %d\tnon_indel_cov_curr = %d\tmost_common_insertion_count = %d\tref_base = %s\tcons_base = %s\tbase_counts = %s\tinsertion_counts = %s\tdeletion_counts = %s\t%s" % ( position, ref_name, non_indel_coverage_next_base, non_indel_coverage_current_base, most_common_insertion_count, ref_base, temp_sorted_bc, str(sorted_base_counts), str(insertion_event_counts), str(deletion_event_counts), line.strip(), ) ) ret_variant_list.append(variant_line) ### Insertions in the VCF format specifies the position where a insertion occurs. The ref position should contain the base which is the same as ref, but the alt field contains the ref base + the insertion event. ### VCF output ### alt_base = ( ("{}") if (len(sorted_base_counts) == 0) else (str(sorted_base_counts[-1][0])) ) qual = 1000 info = "DP=%s;TYPE=ins" % (coverage) ref_field = ref_base alt_field = "%s%s" % (ref_base, sorted_insertion_counts[-1][0]) vcf_line = "%s\t%s\t.\t%s\t%s\t%d\tPASS\t%s" % ( ref_name, position, ref_field, alt_field, qual, info, ) ret_vcf_list.append(vcf_line) ################## elif ( most_common_deletion_count > most_common_insertion_count and most_common_deletion_count > non_indel_coverage_next_base ): # In this case, deletions are a clear winner. if deletion_unique == True: # ret_deletion_count[0] += most_common_deletion_length; ret_deletion_count[0] += 1 # variant_line = 'deletion\t%d\t%s\t%s\t%s\t%s' % (most_common_deletion_count, str(sorted_base_counts), str(insertion_event_counts), str(deletion_event_counts), line.strip()); # ret_variant_list.append(variant_line); # return most_common_deletion_length; variant_line = ( "del\tpos = %s\tref = %s\tnon_indel_cov_next = %d\tnon_indel_cov_curr = %d\tmost_common_deletion_count = %d\tref_base = %s\tcons_base = %s\tbase_counts = %s\tinsertion_counts = %s\tdeletion_counts = %s\t%s" % ( position, ref_name, non_indel_coverage_next_base, non_indel_coverage_current_base, most_common_deletion_count, ref_base, sorted_base_counts[-1][0], str(sorted_base_counts), str(insertion_event_counts), str(deletion_event_counts), line.strip(), ) ) ret_variant_list.append(variant_line) ### Deletions in the VCF format specifies the position where a deletion occurs, with the first base being non-deletion, and the following bases being a deletion event. ### VCF output ### alt_base = ( ("{}") if (len(sorted_base_counts) == 0) else (str(sorted_base_counts[-1][0])) ) qual = 1000 info = "DP=%s;TYPE=del" % (coverage) ref_field = "%s%s" % (ref_base, sorted_deletion_counts[-1][0]) alt_field = ref_base vcf_line = "%s\t%s\t.\t%s\t%s\t%d\tPASS\t%s" % ( ref_name, position, ref_field, alt_field, qual, info, ) ret_vcf_list.append(vcf_line) ################## return most_common_deletion_length else: # In this case, either the base count consensus wins, or the # insertion/deletion count is ambiguous. pass return 0 def process_mpileup( alignments_path, reference_path, mpileup_path, coverage_threshold, output_prefix, thread_id=0, bed_position="", ): fp = None try: fp = open(mpileup_path, "r") except IOError: sys.stderr.write( 'ERROR: Could not open file "%s" for reading!\n' % mpileup_path ) return None ret_variant_list = [] ret_vcf_list = [] ret_snp_count = [0] ret_insertion_count = [0] ret_deletion_count = [0] ret_num_undercovered_bases = [0] ret_num_called_bases = [0] ret_num_correct_bases = [0] ret_coverage_sum = [0] # lines = fp.readlines(); fp_variant = None fp_vcf = None if output_prefix != "": if not os.path.exists(os.path.dirname(output_prefix)): os.makedirs(os.path.dirname(output_prefix)) variant_file = "%s-cov_%d.variant.csv" % (output_prefix, coverage_threshold) fp_variant = open(variant_file, "w") vcf_file = "%s-cov_%d.variant.vcf" % (output_prefix, coverage_threshold) fp_vcf = open(vcf_file, "w") fp_vcf.write("##fileformat=VCFv4.0\n") fp_vcf.write("##fileDate=20150409\n") fp_vcf.write("##source=%s\n" % (" ".join(sys.argv))) fp_vcf.write("##reference=%s\n" % reference_path) fp_vcf.write('##INFO=<ID=DP,Number=1,Type=Integer,Description="Raw Depth">\n') fp_vcf.write( '##INFO=<ID=TYPE,Number=A,Type=String,Description="Type of each allele (snp, ins, del, mnp, complex)">\n' ) fp_vcf.write( '##INFO=<ID=AF,Number=1,Type=Float,Description="Allele Frequency">\n' ) fp_vcf.write( '##INFO=<ID=SB,Number=1,Type=Integer,Description="Phred-scaled strand bias at this position">\n' ) fp_vcf.write( '##INFO=<ID=DP4,Number=4,Type=Integer,Description="Counts for ref-forward bases, ref-reverse, alt-forward and alt-reverse bases">\n' ) fp_vcf.write( '##INFO=<ID=INDEL,Number=0,Type=Flag,Description="Indicates that the variant is an INDEL.">\n' ) fp_vcf.write( '##INFO=<ID=CONSVAR,Number=0,Type=Flag,Description="Indicates that the variant is a consensus variant (as opposed to a low frequency variant).">\n' ) fp_vcf.write( '##INFO=<ID=HRUN,Number=1,Type=Integer,Description="Homopolymer length to the right of report indel position">\n' ) fp_vcf.write("#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n") fp_vcf.flush() use_bed = False bed_chromosome = "" bed_pos_start = 0 # bed_pos_end = len(lines); bed_pos_end = -1 if bed_position != "": bed_split = bed_position.split(":") if len(bed_split) != 2: use_bed = False else: bed_chromosome = bed_split[0] bed_pos_split = bed_split[1].split("-") if len(bed_pos_split) != 2: use_bed = False else: bed_pos_start = int(bed_pos_split[0]) bed_pos_end = int(bed_pos_split[1]) use_bed = True sys.stderr.write("Using location specified through commandline:\n") sys.stderr.write('\tChromosome: "%s"\n' % bed_chromosome) sys.stderr.write("\tStart: %d\n" % bed_pos_start) sys.stderr.write("\tEnd: %d\n\n" % bed_pos_end) # i = 0; i = 0 if (use_bed == False) else max((bed_pos_start - 10), 0) j = 0 # while (i < bed_pos_end): # len(lines)): num_bases_to_skip = 0 for line in fp: # line = lines[i]; if num_bases_to_skip > 0: num_bases_to_skip -= 1 continue if use_bed == True: line_split = line.strip().split("\t") if len(line_split) > 2 and line_split[0] == bed_chromosome: current_pos = int(line_split[1]) if current_pos < bed_pos_start or current_pos >= bed_pos_end: i += 1 j += 1 continue else: # print line_split[0]; # print bed_chromosome; i += 1 j += 1 continue if thread_id == 0: if (j % 1000) == 0: sys.stderr.write( "\r[%d] snps = %d, insertions = %d, deletions = %d, undercovered = %d, coverage = %.2f" % ( i, ret_snp_count[0], ret_insertion_count[0], ret_deletion_count[0], ret_num_undercovered_bases[0], (float(ret_coverage_sum[0]) / float((i + 1))), ) ) sys.stderr.flush() variant_list_length = len(ret_variant_list) vcf_list_length = len(ret_vcf_list) num_bases_to_skip = process_mpileup_line( line, i, ret_variant_list, ret_vcf_list, ret_snp_count, ret_insertion_count, ret_deletion_count, ret_num_undercovered_bases, ret_num_called_bases, ret_num_correct_bases, ret_coverage_sum, coverage_threshold, verbose=use_bed, ) if len(ret_variant_list) > variant_list_length and fp_variant != None: fp_variant.write("\n".join(ret_variant_list[variant_list_length:]) + "\n") fp_variant.flush() if len(ret_vcf_list) > vcf_list_length and fp_vcf != None: fp_vcf.write("\n".join(ret_vcf_list[vcf_list_length:]) + "\n") fp_vcf.flush() i += num_bases_to_skip i += 1 j += 1 # if (i > 10000): # break; fp.close() sys.stderr.write("\n") if fp_variant != None: fp_variant.close() if fp_vcf != None: fp_vcf.close() summary_lines = "" summary_lines += "alignments_file: %s\n" % alignments_path summary_lines += "mpileup_file: %s\n" % mpileup_path summary_lines += "coverage_threshold: %d\n" % coverage_threshold summary_lines += "snp_count: %d\n" % ret_snp_count[0] summary_lines += "insertion_count: %d\n" % ret_insertion_count[0] summary_lines += "deletion_count: %d\n" % ret_deletion_count[0] summary_lines += "num_undercovered_bases: %d\n" % ret_num_undercovered_bases[0] summary_lines += "num_called_bases: %d\n" % ret_num_called_bases[0] summary_lines += "num_correct_bases: %d\n" % ret_num_correct_bases[0] summary_lines += "average_coverage: %.2f\n" % ( (float(ret_coverage_sum[0]) / float((i + 1))) ) sys.stderr.write(summary_lines + "\n") sys.stderr.write("\n") if output_prefix != "": # summary_file = output_prefix + '.conssum'; summary_file = "%s-cov_%d.variant.sum" % (output_prefix, coverage_threshold) try: fp_sum = open(summary_file, "w") fp_sum.write(summary_lines) fp_sum.close() return summary_file except IOError: sys.stderr.write( 'ERROR: Could not open file "%s" for writing!\n' % (summary_file) ) return None return None def main( alignments_path, reference_path, coverage_threshold, output_prefix, thread_id=0, bed_position="", ): # Sanity checking the existence of the file, and the correctness of its extension. # Also, if input file is a SAM file, then convert it to a sorted BAM. alignments_path_bam = alignments_path if os.path.exists(alignments_path) == False: sys.stderr.write('ERROR: File "%s" does not exist!\n' % alignments_path) return if alignments_path.endswith("sam"): # Determine the path where the new BAM file will be generated. dir_name = os.path.dirname(alignments_path) if dir_name == "": dir_name = "." alignments_path_bam = ( dir_name + "/" + os.path.splitext(os.path.basename(alignments_path))[0] + ".bam" ) alignments_path_bam_exists = os.path.exists(alignments_path_bam) # Check if a BAM file with the given name already exists. if alignments_path_bam_exists == False or ( alignments_path_bam_exists == True and os.path.getmtime(alignments_path) > os.path.getmtime(alignments_path_bam) ): # Convert the SAM file to a sorted BAM file. command = "samtools view -bS %s | samtools sort - %s" % ( alignments_path, os.path.splitext(alignments_path_bam)[0], ) sys.stderr.write(command + "\n") subprocess.call(command, shell="True") # Create the BAM index file. command = "samtools index %s %s.bai" % ( alignments_path_bam, alignments_path_bam, ) subprocess.call(command, shell="True") elif alignments_path.endswith("bam") == False: sys.stderr.write( 'ERROR: File extension needs to be either .sam or .bam! Input file path: "%s".\n' % alignments_path ) return # Convert the sorted BAM file to a mpileup file if it doesn't exist yet. mpileup_path = "%s.mpileup" % alignments_path_bam mpileup_exists = os.path.exists(mpileup_path) if mpileup_exists == False or ( mpileup_exists == True and os.path.getmtime(alignments_path) > os.path.getmtime(mpileup_path) ): command = "samtools mpileup -B -d 1000000 -Q 0 -A -f %s %s > %s.mpileup" % ( reference_path, alignments_path_bam, alignments_path_bam, ) subprocess.call(command, shell="True") sys.stderr.write('Processing file "%s"...\n' % alignments_path) sys.stderr.write('Reference file "%s"...\n' % reference_path) sys.stderr.write("Coverage threshold: %d\n" % coverage_threshold) summary_file = process_mpileup( alignments_path, reference_path, ("%s.mpileup" % alignments_path_bam), coverage_threshold, output_prefix, thread_id, bed_position, ) def CollectSummaries( sam_files, prefix_for_intermediate_results, collective_output_file ): fp_collect = None try: fp_collect = open(collective_output_file, "w") except IOError: sys.stderr.write( 'ERROR: Could not open file "%s" for writing!\n' % collective_output_file ) return for sam_file in sam_files: summary_file = prefix_for_intermediate_results + ".sum" try: fp_sum = open(summary_file, "r") lines = fp_sum.readlines() fp_sum.close() except IOError: sys.stderr.write( 'ERROR: Could not open file "%s" for reading!\n' % summary_file ) continue fp_collect.write("".join(lines) + "\n") fp_collect.close() if __name__ == "__main__": # if (len(sys.argv) < 5): # sys.stderr.write('Usage:\n'); # sys.stderr.write('\t%s <reference_file_path> coverage_threshold <collective_output_file> <{sb}am_file_1> [<{sb}am_file_2> <{sb}am_file_3> ...]\n' % sys.argv[0]); # sys.stderr.write('\t(If <collective_output_file> is equal to "-", no files will be written to disk.)\n'); # exit(1); if len(sys.argv) < 5: sys.stderr.write("Usage:\n") sys.stderr.write( "\t%s <reference_file_path> coverage_threshold <output_prefix> <{sb}am_file_> [position]\n" % sys.argv[0] ) sys.stderr.write( '\t(If <collective_output_file> is equal to "-", no files will be written to disk.)\n' ) sys.stderr.write( '\tPosition parameter is a string specifying "chromosome:start-end"\n\n' ) exit(1) reference_file = sys.argv[1] coverage_threshold = int(sys.argv[2]) output_prefix = sys.argv[3] sam_file = sys.argv[4] bed_position = "" if len(sys.argv) > 5: bed_position = sys.argv[5] # sys.stderr.write('bed_position: "%s"\n\n' % bed_position); processes = [] if output_prefix == "-": output_prefix = os.path.splitext(sam_file)[0] main(sam_file, reference_file, coverage_threshold, output_prefix, 0, bed_position) # if (output_prefix != '-'): # CollectSummaries([sam_file], output_prefix, output_prefix + '.variant.sum');

39.755924

436

0.550933

4,042

33,554

4.313953

0.114547

0.023513

0.03051

0.016345

0.535413

0.445375

0.385158

0.3453

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33,554

843

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0.216695

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0

0.029412

0

null

0

null

0

1

0

6a049ff78a91de998072b637d1639d25a433a194

5,867

py

Python

web/addons/account_payment/wizard/account_payment_populate_statement.py

diogocs1/comps

63df07f6cf21c41e4527c06e2d0499f23f4322e7

[ "Apache-2.0" ]

null

web/addons/account_payment/wizard/account_payment_populate_statement.py

diogocs1/comps

63df07f6cf21c41e4527c06e2d0499f23f4322e7

[ "Apache-2.0" ]

null

web/addons/account_payment/wizard/account_payment_populate_statement.py

diogocs1/comps

63df07f6cf21c41e4527c06e2d0499f23f4322e7

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- ############################################################################## # # OpenERP, Open Source Management Solution # Copyright (C) 2004-2010 Tiny SPRL (<http://tiny.be>). # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # ############################################################################## import time from lxml import etree from openerp.osv import fields, osv class account_payment_populate_statement(osv.osv_memory): _name = "account.payment.populate.statement" _description = "Account Payment Populate Statement" _columns = { 'lines': fields.many2many('payment.line', 'payment_line_rel_', 'payment_id', 'line_id', 'Payment Lines') } def fields_view_get(self, cr, uid, view_id=None, view_type='form', context=None, toolbar=False, submenu=False): line_obj = self.pool.get('payment.line') res = super(account_payment_populate_statement, self).fields_view_get(cr, uid, view_id=view_id, view_type=view_type, context=context, toolbar=toolbar, submenu=False) line_ids = line_obj.search(cr, uid, [ ('move_line_id.reconcile_id', '=', False), ('bank_statement_line_id', '=', False), ('move_line_id.state','=','valid')]) line_ids.extend(line_obj.search(cr, uid, [ ('move_line_id.reconcile_id', '=', False), ('order_id.mode', '=', False), ('move_line_id.state','=','valid')])) domain = '[("id", "in", '+ str(line_ids)+')]' doc = etree.XML(res['arch']) nodes = doc.xpath("//field[@name='lines']") for node in nodes: node.set('domain', domain) res['arch'] = etree.tostring(doc) return res def populate_statement(self, cr, uid, ids, context=None): line_obj = self.pool.get('payment.line') statement_obj = self.pool.get('account.bank.statement') statement_line_obj = self.pool.get('account.bank.statement.line') currency_obj = self.pool.get('res.currency') voucher_obj = self.pool.get('account.voucher') voucher_line_obj = self.pool.get('account.voucher.line') move_line_obj = self.pool.get('account.move.line') if context is None: context = {} data = self.read(cr, uid, ids, context=context)[0] line_ids = data['lines'] if not line_ids: return {'type': 'ir.actions.act_window_close'} statement = statement_obj.browse(cr, uid, context['active_id'], context=context) for line in line_obj.browse(cr, uid, line_ids, context=context): ctx = context.copy() ctx['date'] = line.ml_maturity_date # was value_date earlier,but this field exists no more now amount = currency_obj.compute(cr, uid, line.currency.id, statement.currency.id, line.amount_currency, context=ctx) if not line.move_line_id.id: continue context = dict(context, move_line_ids=[line.move_line_id.id]) result = voucher_obj.onchange_partner_id(cr, uid, [], partner_id=line.partner_id.id, journal_id=statement.journal_id.id, amount=abs(amount), currency_id= statement.currency.id, ttype='payment', date=line.ml_maturity_date, context=context) if line.move_line_id: voucher_res = { 'type': 'payment', 'name': line.name, 'partner_id': line.partner_id.id, 'journal_id': statement.journal_id.id, 'account_id': result['value'].get('account_id', statement.journal_id.default_credit_account_id.id), 'company_id': statement.company_id.id, 'currency_id': statement.currency.id, 'date': line.date or time.strftime('%Y-%m-%d'), 'amount': abs(amount), 'period_id': statement.period_id.id, } voucher_id = voucher_obj.create(cr, uid, voucher_res, context=context) voucher_line_dict = {} for line_dict in result['value']['line_cr_ids'] + result['value']['line_dr_ids']: move_line = move_line_obj.browse(cr, uid, line_dict['move_line_id'], context) if line.move_line_id.move_id.id == move_line.move_id.id: voucher_line_dict = line_dict if voucher_line_dict: voucher_line_dict.update({'voucher_id': voucher_id}) voucher_line_obj.create(cr, uid, voucher_line_dict, context=context) st_line_id = statement_line_obj.create(cr, uid, { 'name': line.order_id.reference or '?', 'amount': - amount, 'partner_id': line.partner_id.id, 'statement_id': statement.id, 'ref': line.communication, }, context=context) line_obj.write(cr, uid, [line.id], {'bank_statement_line_id': st_line_id}) return {'type': 'ir.actions.act_window_close'} # vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:

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723

5,867

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0.26971

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0.222389

0.136624

0.058381

0

0.003465

0.262144

5,867

119

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0.764149

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0

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0

0.160677

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0

1

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false

0

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0

0.146341

0

null

0

null

0

1

0

6a051324d6c23235da009880d6bcb0d30ed4d8dc

315

py

Python

2-Python-Fundamentals (Jan 2021)/Course-Exercises-and-Exams/08-Text-Processing/01_Lab/02-Repeat-Strings.py

karolinanikolova/SoftUni-Software-Engineering

7891924956598b11a1e30e2c220457c85c40f064

[ "MIT" ]

null

2-Python-Fundamentals (Jan 2021)/Course-Exercises-and-Exams/08-Text-Processing/01_Lab/02-Repeat-Strings.py

karolinanikolova/SoftUni-Software-Engineering

7891924956598b11a1e30e2c220457c85c40f064

[ "MIT" ]

null

2-Python-Fundamentals (Jan 2021)/Course-Exercises-and-Exams/08-Text-Processing/01_Lab/02-Repeat-Strings.py

karolinanikolova/SoftUni-Software-Engineering

7891924956598b11a1e30e2c220457c85c40f064

[ "MIT" ]

null

# 2. Repeat Strings # Write a Program That Reads a list of strings. Each string is repeated N times, where N is the length of the string. Print the concatenated string. strings = input().split() output_string = "" for string in strings: N = len(string) output_string += string * N print(output_string)

22.5

148

0.71746

49

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0.55102

0.161435

0

0.003984

0.203175

315

13

149

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0.884462

0.520635

0

1

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false

0

0.166667

0

null

0

null

0

1

0

6a0724ca0ed93e378a29473e0b6b5911cc4be4e6

944

py

Python

algorithm/dfs/boj_1260.py

ruslanlvivsky/python-algorithm

2b49bed33cd0e95b8a1e758008191f4392b3f667

[ "MIT" ]

3

2021-07-18T14:40:24.000Z

2021-08-14T18:08:13.000Z

algorithm/dfs/boj_1260.py

jinsuSang/python-algorithm

524849a0a7e71034d329fef63c4f384930334177

[ "MIT" ]

null

algorithm/dfs/boj_1260.py

jinsuSang/python-algorithm

524849a0a7e71034d329fef63c4f384930334177

[ "MIT" ]

null

def dfs(V): print(V, end=' ') visited[V] = True for n in graph[V]: if not visited[n]: dfs(n) def dfs_s(V): stack = [V] visited[V] = True while stack: now = stack.pop() print(now, end=' ') for n in graph[now]: if not visited[n]: stack.append(n) visited[n] = True def bfs(V): visited[V] = True queue = [V] while queue: now = queue.pop(0) print(now, end=' ') for n in graph[now]: if not visited[n]: queue.append(n) visited[n] = True N, M, V = map(int, input().strip().split()) visited = [False] * (N + 1) graph = [[] for _ in range(N + 1)] for i in range(M): a, b = map(int, input().strip().split()) graph[a].append(b) graph[b].append(a) for i in range(1, N + 1): graph[i].sort() dfs(V) visited = [False] * (N + 1) print() bfs(V)

19.265306

44

0.470339

141

944

3.134752

0.248227

0.090498

0.081448

0.074661

0.352941

0.171946

0

0.009901

0.358051

944

48

45

19.666667

0.719472

0

0.358974

0

0.003178

0

1

0.076923

false

0

0.076923

0.102564

0

null

0

null

0

1

0

6a07aa532405a92d53e9ed5f46dcbcbd7a845cfa

634

py

Python

redirector.py

UKPLab/DiGAT

b044648a6c79428872a778908d3a8a689f0ac3e6

[ "Apache-2.0" ]

8

2016-06-22T17:02:45.000Z

2020-11-16T23:46:13.000Z

redirector.py

UKPLab/DiGAT

b044648a6c79428872a778908d3a8a689f0ac3e6

[ "Apache-2.0" ]

null

redirector.py

UKPLab/DiGAT

b044648a6c79428872a778908d3a8a689f0ac3e6

[ "Apache-2.0" ]

1

2019-02-25T04:40:04.000Z

from google.appengine.ext import webapp from google.appengine.ext.webapp.util import run_wsgi_app __author__ = "Artem Vovk, Roland Kluge, and Christian Kirschner" __copyright__ = "Copyright 2013-2015 UKP TU Darmstadt" __credits__ = ["Artem Vovk", "Roland Kluge", "Christian Kirschner"] __license__ = "ASL" class Redirector(webapp.RequestHandler): def get(self): self.redirect("/argunit/home") def post(self): self.redirect("/argunit/home") application = webapp.WSGIApplication( [('/.*', Redirector)], debug=True) def main(): run_wsgi_app(application) if __name__ == "__main__": main()

22.642857

67

0.705047

74

634

5.662162

0.594595

0.047733

0.090692

0.105012

0.128878

0

0.015152

0.167192

634

27

68

23.481481

0.778409

0

0.111111

0

0.26183

0

1

0.166667

false

0

0.111111

0

0.333333

0

null

0

null

0

1

0

6a0b98cc37e3d3bfecf8eba880eba829290a251c

1,862

py

Python

deepgp_dsvi/demos/step_function.py

dks28/Deep-Gaussian-Process

a7aace43e78aae81468849aee7d172742e6ecf86

[ "MIT" ]

21

2020-03-07T15:40:13.000Z

2021-11-05T07:49:24.000Z

deepgp_dsvi/demos/step_function.py

dks28/Deep-Gaussian-Process

a7aace43e78aae81468849aee7d172742e6ecf86

[ "MIT" ]

3

2021-02-03T13:32:45.000Z

2021-07-17T16:07:06.000Z

src/demos/step_function.py

FelixOpolka/Deep-Gaussian-Process

40181f210d7b09863c321d1a90335be77233df80

[ "MIT" ]

2

2020-08-10T14:02:28.000Z

2020-12-28T16:03:09.000Z

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from gpflow.kernels import White, RBF from gpflow.likelihoods import Gaussian from deep_gp import DeepGP np.random.seed(0) tf.random.set_seed(0) def get_data(): Ns = 300 Xs = np.linspace(-0.5, 1.5, Ns)[:, None] N, M = 50, 25 X = np.random.uniform(0, 1, N)[:, None] Z = np.random.uniform(0, 1, M)[:, None] f_step = lambda x: 0. if x < 0.5 else 1. Y = np.reshape([f_step(x) for x in X], X.shape) + np.random.randn( *X.shape) * 1e-2 return Xs, X, Y, Z def make_deep_GP(num_layers, X, Y, Z): kernels = [] layer_sizes = [] for l in range(num_layers): kernel = RBF(lengthscales=0.2, variance=1.0) + White(variance=1e-5) kernels.append(kernel) layer_sizes.append(1) dgp = DeepGP(X, Y, Z, kernels, layer_sizes, Gaussian(), num_samples=100) # init hidden layers to be near deterministic for layer in dgp.layers[:-1]: layer.q_sqrt.assign(layer.q_sqrt * 1e-5) return dgp if __name__ == '__main__': Xs, X_train, Y_train, Z = get_data() dgp = make_deep_GP(3, X_train, Y_train, Z) optimizer = tf.optimizers.Adam(learning_rate=0.01, epsilon=1e-08) for _ in range(1500): with tf.GradientTape(watch_accessed_variables=False) as tape: tape.watch(dgp.trainable_variables) objective = -dgp.elbo((X_train, Y_train)) gradients = tape.gradient(objective, dgp.trainable_variables) optimizer.apply_gradients(zip(gradients, dgp.trainable_variables)) print(f"ELBO: {-objective.numpy()}") samples, _, _ = dgp.predict_all_layers(Xs, num_samples=50, full_cov=True) plt.plot(Xs, samples[-1].numpy()[:, :, 0].T, color='r', alpha=0.3) plt.title('Deep Gaussian Process') plt.scatter(X_train, Y_train) plt.show()

31.033333

77

0.645005

295

1,862

3.908475

0.40339

0.027754

0.024284

0.041631

0.08673

0.034692

0

0.036301

0.215897

1,862

60

78

31.033333

0.753425

0.023093

0

0.030803

0

1

0.044444

false

0

0.133333

0

0.222222

0.022222

0

null

0

null

0

1

0

6a0bd26d528523a33d941c1d0799a814a2b95dcf

5,343

py

Python

metaspace/engine/sm/engine/annotation_lithops/moldb_pipeline.py

METASPACE2020/METASPACE

e1acd9a409f84a78eed7ca9713258c09b0e137ca

[ "Apache-2.0" ]

32

2018-08-13T15:49:42.000Z

2022-01-17T18:32:19.000Z

metaspace/engine/sm/engine/annotation_lithops/moldb_pipeline.py

METASPACE2020/METASPACE

e1acd9a409f84a78eed7ca9713258c09b0e137ca

[ "Apache-2.0" ]

624

2018-07-02T15:18:22.000Z

2022-03-30T08:10:35.000Z

metaspace/engine/sm/engine/annotation_lithops/moldb_pipeline.py

METASPACE2020/METASPACE

e1acd9a409f84a78eed7ca9713258c09b0e137ca

[ "Apache-2.0" ]

6

2021-01-10T22:24:30.000Z

2022-03-16T19:14:37.000Z

from __future__ import annotations import json import logging from contextlib import contextmanager, ExitStack from typing import List, Dict import pandas as pd from lithops.storage import Storage from lithops.storage.utils import CloudObject, StorageNoSuchKeyError from sm.engine.annotation_lithops.build_moldb import ( build_moldb, InputMolDb, DbFDRData, ) from sm.engine.annotation_lithops.calculate_centroids import ( calculate_centroids, validate_centroids, ) from sm.engine.annotation_lithops.executor import Executor from sm.engine.annotation_lithops.io import ( CObj, save_cobj, iter_cobjects_with_prefetch, deserialize, ) from sm.engine.annotation_lithops.utils import jsonhash from sm.engine.utils.db_mutex import DBMutex from sm.engine.ds_config import DSConfig from sm.engine.annotation.isocalc_wrapper import IsocalcWrapper logger = logging.getLogger('annotation-pipeline') class CentroidsCacheEntry: def __init__( self, executor: Executor, sm_storage: Dict, ds_config: DSConfig, moldbs: List[InputMolDb] ): ds_hash_params = ds_config.copy() self.ds_config = { **ds_hash_params, # type: ignore # https://github.com/python/mypy/issues/4122 # Include the `targeted` value of databases so that a new cache entry is made if # someone manually changes that field 'databases': [(moldb['id'], moldb['targeted']) for moldb in moldbs], } # Remove database_ids as it may be in a different order to moldbs del self.ds_config['database_ids'] self.ds_hash = jsonhash(self.ds_config) self.executor = executor self.storage = executor.storage self.bucket, raw_prefix = sm_storage['centroids'] self.prefix = f"{raw_prefix}/{self.ds_hash}" self.config_key = f'{self.prefix}/ds_config.json' self.meta_key = f'{self.prefix}/meta' @contextmanager def lock(self): with DBMutex().lock(self.ds_hash, timeout=3600): yield def load(self): try: db_data_cobjs, peaks_cobjs = deserialize( self.storage.get_object(self.bucket, self.meta_key) ) return db_data_cobjs, peaks_cobjs except StorageNoSuchKeyError: return None def save(self, db_data_cobjs: List[CObj[DbFDRData]], peaks_cobjs: List[CObj[pd.DataFrame]]): def batch_copy(src_cobjs: List[CloudObject], dest_prefix: str, *, storage: Storage): # If Lithops' storage supported Copy Object operations, this could be easily optimized. # Not sure if it's worth the effort yet result_cobjs = [] for i, data in enumerate(iter_cobjects_with_prefetch(storage, src_cobjs)): dest_key = f'{dest_prefix}/{i:06}' result_cobjs.append(storage.put_cloudobject(data, dest_bucket, dest_key)) return result_cobjs dest_bucket = self.bucket # Copy cobjs to the cache dir new_db_data_cobjs, new_peaks_cobjs = self.executor.map( batch_copy, [(db_data_cobjs, f'{self.prefix}/db_data'), (peaks_cobjs, f'{self.prefix}/peaks')], runtime_memory=1024, ) # Save config in case it's needed for debugging self.storage.put_cloudobject( json.dumps(self.ds_config, indent=4), self.bucket, self.config_key ) # Save list of cobjects. This list would be easy to reconstruct by listing keys, but # saving a separate object as the last step of the process is helpful to confirm that # the cache item is complete, and didn't partially fail to copy. save_cobj(self.storage, (new_db_data_cobjs, new_peaks_cobjs), self.bucket, self.meta_key) return new_db_data_cobjs, new_peaks_cobjs def clear(self): keys = self.storage.list_keys(self.bucket, self.prefix) if keys: logger.info(f'Clearing centroids cache {self.prefix}') self.storage.delete_objects(self.bucket, keys) def get_moldb_centroids( executor: Executor, sm_storage: Dict, ds_config: DSConfig, moldbs: List[InputMolDb], debug_validate=False, use_cache=True, use_db_mutex=True, ): moldb_cache = CentroidsCacheEntry(executor, sm_storage, ds_config, moldbs) with ExitStack() as stack: if use_db_mutex: stack.enter_context(moldb_cache.lock()) if use_cache: cached_val = moldb_cache.load() else: cached_val = None moldb_cache.clear() if cached_val: db_data_cobjs, peaks_cobjs = cached_val logger.info( f'Loaded {len(db_data_cobjs)} DBs, {len(peaks_cobjs)} peak segms from cache' ) else: formula_cobjs, db_data_cobjs = build_moldb(executor, ds_config, moldbs) isocalc_wrapper = IsocalcWrapper(ds_config) peaks_cobjs = calculate_centroids(executor, formula_cobjs, isocalc_wrapper) if debug_validate: validate_centroids(executor, peaks_cobjs) moldb_cache.save(db_data_cobjs, peaks_cobjs) logger.info(f'Saved {len(db_data_cobjs)} DBs, {len(peaks_cobjs)} peak segms to cache') return db_data_cobjs, peaks_cobjs

36.59589

99

0.668351

685

5,343

4.989781

0.290511

0.024576

0.041837

0.038619

0.176419

0.115565

0.086893

0.078994

0.060854

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0.248362

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145

100

36.848276

0.847361

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0.256637

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null

0

null

0

1

0

6a0dc9555ac01260e856ab868bd3c294497c065f

2,830

py

Python

gui/main_window/node_editor/items/connector_top_item.py

anglebinbin/Barista-tool

2d51507fb3566881923f0b273127f59d23ed317f

[ "MIT" ]

1

2020-02-11T19:05:17.000Z

gui/main_window/node_editor/items/connector_top_item.py

anglebinbin/Barista-tool

2d51507fb3566881923f0b273127f59d23ed317f

[ "MIT" ]

null

gui/main_window/node_editor/items/connector_top_item.py

anglebinbin/Barista-tool

2d51507fb3566881923f0b273127f59d23ed317f

[ "MIT" ]

null

from PyQt5.QtWidgets import QMenu from gui.main_window.node_editor.items.connector_item import ConnectorItem class ConnectorTopItem(ConnectorItem): """ Class to provide top connector functionality """ def __init__(self, index, nodeItem, nodeEditor, parent=None): super(ConnectorTopItem, self).__init__(index, nodeItem, nodeEditor, parent) def isTopConnector(self): """ Returns whether the connector is a top connector (implementation for parent class) """ return True def isInPlace(self): """ Returns whether the connector is connected to a in-place working layer A top connector is in place if any connected bottom connector is in place. (implementation for parent class) """ for connection in self._connections: if connection.getIsInPlace(): return True return False def getConnectedNodes(self): """ Returns a list of node items, connected to this connector (implementation for parent class) """ nodes = list() # for each connection get the node connected to the bottom of the connection for connection in self._connections: connectionsBottomConnector = connection.getBottomConnector() if connectionsBottomConnector is not None: nodes.append(connectionsBottomConnector.getNodeItem()) return nodes def addConnection(self, connection): """ Adds a connection to the connector and sets the start of the connection to this connectors position (implementation for parent class) """ self._connections.append(connection) connection.setStart(self.scenePos()) def updateConnectionPositions(self): """ Updates the connected connections, sets the start of all connected connections to this connectors position (implementation for parent class) """ for connection in self._connections: connection.setStart(self.scenePos()) def contextMenuEvent(self, event): """ Context menu for the top connector """ contextMenu = QMenu() renameTop = contextMenu.addAction("Change name") disconnectTop = contextMenu.addAction("Disconnect") if self.getConnectionCount() == 0: disconnectTop.setEnabled(False) removeTop = contextMenu.addAction("Remove") action = contextMenu.exec_(event.screenPos()) if action is not None: if action == removeTop: self._nodeEditor.tryToRemoveTopBlob(self._nodeItem.getLayerID(), self._index) elif action == renameTop: self._nodeEditor.tryToRenameTopBlob(self) elif action == disconnectTop: self._nodeEditor.disconnectTopBlob(self._nodeItem.getLayerID(), self._index)

44.21875

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289

2,830

6.519031

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0.35

0

null

0

null

0

1

0

6a0e7a4577ac3f9f8b9fd994210704a26f91ee39

2,606

py

Python

api/src/opentrons/protocol_engine/commands/thermocycler/open_lid.py

Opentrons/protocol_framework

ebbd6b2fe984edd6ecfcbf1dbe040db7f7356b9f

[ "Apache-2.0" ]

null

api/src/opentrons/protocol_engine/commands/thermocycler/open_lid.py

Opentrons/protocol_framework

ebbd6b2fe984edd6ecfcbf1dbe040db7f7356b9f

[ "Apache-2.0" ]

null

api/src/opentrons/protocol_engine/commands/thermocycler/open_lid.py

Opentrons/protocol_framework

ebbd6b2fe984edd6ecfcbf1dbe040db7f7356b9f

[ "Apache-2.0" ]

null

"""Command models to open a Thermocycler's lid.""" from __future__ import annotations from typing import Optional, TYPE_CHECKING from typing_extensions import Literal, Type from pydantic import BaseModel, Field from ..command import AbstractCommandImpl, BaseCommand, BaseCommandCreate from opentrons.protocol_engine.types import MotorAxis if TYPE_CHECKING: from opentrons.protocol_engine.state import StateView from opentrons.protocol_engine.execution import EquipmentHandler, MovementHandler OpenLidCommandType = Literal["thermocycler/openLid"] class OpenLidParams(BaseModel): """Input parameters to open a Thermocycler's lid.""" moduleId: str = Field(..., description="Unique ID of the Thermocycler.") class OpenLidResult(BaseModel): """Result data from opening a Thermocycler's lid.""" class OpenLidImpl(AbstractCommandImpl[OpenLidParams, OpenLidResult]): """Execution implementation of a Thermocycler's open lid command.""" def __init__( self, state_view: StateView, equipment: EquipmentHandler, movement: MovementHandler, **unused_dependencies: object, ) -> None: self._state_view = state_view self._equipment = equipment self._movement = movement async def execute(self, params: OpenLidParams) -> OpenLidResult: """Open a Thermocycler's lid.""" thermocycler_state = self._state_view.modules.get_thermocycler_module_substate( params.moduleId ) thermocycler_hardware = self._equipment.get_module_hardware_api( thermocycler_state.module_id ) # move the pipettes and gantry over the trash # do not home plunger axes because pipettes may be holding liquid await self._movement.home( [ MotorAxis.X, MotorAxis.Y, MotorAxis.RIGHT_Z, MotorAxis.LEFT_Z, ] ) if thermocycler_hardware is not None: await thermocycler_hardware.open() return OpenLidResult() class OpenLid(BaseCommand[OpenLidParams, OpenLidResult]): """A command to open a Thermocycler's lid.""" commandType: OpenLidCommandType = "thermocycler/openLid" params: OpenLidParams result: Optional[OpenLidResult] _ImplementationCls: Type[OpenLidImpl] = OpenLidImpl class OpenLidCreate(BaseCommandCreate[OpenLidParams]): """A request to open a Thermocycler's lid.""" commandType: OpenLidCommandType = "thermocycler/openLid" params: OpenLidParams _CommandCls: Type[OpenLid] = OpenLid

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87

0.699156

263

2,606

6.771863

0.387833

0.051095

0.055025

0.057271

0.138686

0.126895

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0

0.224482

2,606

85

88

30.658824

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0.149655

0

0.078431

0

0.041822

0

1

0.019608

false

0

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0

0.45098

0

null

0

null

0

1

0

6a11d7dca909e3885ae2dbc3bc1e2d0a99547ada

3,901

py

Python

scripts/randomize_sw2_seed.py

epichoxha/nanodump

3a269ed427b474a701197e13ce40cb1daf803a82

[ "Apache-2.0" ]

null

scripts/randomize_sw2_seed.py

epichoxha/nanodump

3a269ed427b474a701197e13ce40cb1daf803a82

[ "Apache-2.0" ]

null

scripts/randomize_sw2_seed.py

epichoxha/nanodump

3a269ed427b474a701197e13ce40cb1daf803a82

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import os import re import glob import random import struct def get_old_seed(): with open('include/syscalls.h') as f: code = f.read() match = re.search(r'#define SW2_SEED (0x[a-fA-F0-9]{8})', code) assert match is not None, 'SW2_SEED not found!' return match.group(1) def replace_seed(old_seed, new_seed): with open('include/syscalls.h') as f: code = f.read() code = code.replace( f'#define SW2_SEED {old_seed}', f'#define SW2_SEED 0x{new_seed:08X}', 1 ) with open('include/syscalls.h', 'w') as f: f.write(code) def get_function_hash(seed, function_name, is_syscall=True): function_hash = seed function_name = function_name.replace('_', '') if is_syscall and function_name[:2] == 'Nt': function_name = 'Zw' + function_name[2:] name = function_name + '\0' ror8 = lambda v: ((v >> 8) & (2 ** 32 - 1)) | ((v << 24) & (2 ** 32 - 1)) for segment in [s for s in [name[i:i + 2] for i in range(len(name))] if len(s) == 2]: partial_name_short = struct.unpack('<H', segment.encode())[0] function_hash ^= partial_name_short + ror8(function_hash) return function_hash def replace_syscall_hashes(seed): with open('source/syscalls.c') as f: code = f.read() regex = re.compile(r'__declspec$naked$ NTSTATUS (Nt[^(]+)') syscall_names = re.findall(regex, code) syscall_names = set(syscall_names) syscall_definitions = code.split('#elif defined(__GNUC__)')[3] for syscall_name in syscall_names: regex = re.compile('NTSTATUS ' + syscall_name + '\\(.*?"mov ecx, (0x[A-Fa-f0-9]{8})', re.DOTALL) match = re.search(regex, syscall_definitions) assert match is not None, f'hash of syscall {syscall_name} not found!' old_hash = match.group(1) new_hash = get_function_hash(seed, syscall_name) print(f'{syscall_name} -> {old_hash} - 0x{new_hash:08X}') code = code.replace( old_hash, f'0x{new_hash:08X}' ) with open('source/syscalls.c', 'w') as f: f.write(code) with open('source/syscalls-asm.asm') as f: code = f.read() for syscall_name in syscall_names: regex = re.compile(syscall_name + ' PROC.*?mov ecx, 0([A-Fa-f0-9]{8})h', re.DOTALL) match = re.search(regex, code) assert match is not None, f'hash of syscall {syscall_name} not found!' old_hash = match.group(1) new_hash = get_function_hash(seed, syscall_name) code = code.replace( f'0{old_hash}h', f'0{new_hash:08X}h', 1 ) with open('source/syscalls-asm.asm', 'w') as f: f.write(code) def replace_dinvoke_hashes(seed): for header_file in glob.glob("include/**/*.h", recursive=True): with open(header_file) as f: code = f.read() regex = re.compile(r'#define (\w+)_SW2_HASH (0x[a-fA-F0-9]{8})') matches = re.findall(regex, code) for function_name, old_hash in matches: new_hash = get_function_hash(seed, function_name, is_syscall=False) code = code.replace( f'#define {function_name}_SW2_HASH {old_hash}', f'#define {function_name}_SW2_HASH 0x{new_hash:08X}', 1 ) if matches: with open(header_file, 'w') as f: f.write(code) def main(): new_seed = random.randint(2 ** 28, 2 ** 32 - 1) #new_seed = 0x1337c0de old_seed = get_old_seed() replace_seed(old_seed, new_seed) replace_syscall_hashes(new_seed) replace_dinvoke_hashes(new_seed) if os.name == 'nt': print('done! recompile with:\nnmake -f Makefile.msvc') else: print('done! recompile with:\nmake -f Makefile.mingw') if __name__ == '__main__': main()

32.508333

104

0.600103

568

3,901

3.922535

0.207746

0.059246

0.015709

0.017953

0.461849

0.380162

0.253591

0.2307

0.194794

0.132855

0

0.027586

0.256601

3,901

119

105

32.781513

0.74069

0.016406

0

0.242105

0

0.213876

0.024517

0

0.031579

1

0.063158

false

0

0.052632

0

0.136842

0.031579

0

null

0

null

0

1

0

6a11fa8d863a9e5b451bd2a7ef2241aafe768509

1,289

py

Python

checker/checker/executer.py

grimpy/hexa-a

556e9a2a70758bf9c7d70f91776d361b40524c78

[ "Apache-2.0" ]

3

2018-02-05T11:43:04.000Z

2019-02-22T18:11:55.000Z

checker/checker/executer.py

grimpy/hexa-a

556e9a2a70758bf9c7d70f91776d361b40524c78

[ "Apache-2.0" ]

4

2019-03-26T09:51:43.000Z

2019-03-31T06:41:14.000Z

checker/checker/executer.py

grimpy/hexa-a

556e9a2a70758bf9c7d70f91776d361b40524c78

[ "Apache-2.0" ]

1

2019-03-03T20:55:21.000Z

from subprocess import run, PIPE, TimeoutExpired, CompletedProcess from codes import exitcodes def _error_decode(response): stderr = "" if response.returncode: if response.returncode < 0: errmsg = exitcodes.get(abs(response.returncode), "Unknown Error") if isinstance(errmsg, dict): errmsg = errmsg["descr"] else: errmsg = response.stderr stderr = "Exit code ({}): {}".format(abs(response.returncode), errmsg) return response.returncode, stderr def execute(cmd, workdir=None, timeout=60): cmd = ["/bin/bash", "-c", cmd] try: response = run( cmd, stderr=PIPE, stdout=PIPE, cwd=workdir, timeout=timeout, universal_newlines=True, ) except TimeoutExpired: response = CompletedProcess( args=cmd, returncode=124, stderr="Timeout" ) except: response = CompletedProcess( args=cmd, returncode=-1, stderr="Internal Checker Error" ) response.stdout = "" if not response.stdout else str(response.stdout) response.returncode, response.stderr = _error_decode(response) return response

30.690476

78

0.577967

121

1,289

6.115702

0.429752

0.145946

0.051351

0.083784

0.110811

0

0.008037

0.324282

1,289

42

79

30.690476

0.841561

0

0.102564

0

0.058915

0

1

0.051282

false

0

0.051282

0

0.153846

0

null

0

null

0

1

0

6a124e6043f5f93ce124eed73efc4b8488512375

1,739

py

Python

pfm/pf_command/update.py

takahi-i/pfm

224ca961ca43f50bd877789e2d8659ae838d517f

[ "MIT" ]

9

2018-01-06T05:44:43.000Z

2020-06-24T00:15:16.000Z

pfm/pf_command/update.py

takahi-i/pfm

224ca961ca43f50bd877789e2d8659ae838d517f

[ "MIT" ]

27

2018-01-06T09:29:48.000Z

2020-04-10T16:11:59.000Z

pfm/pf_command/update.py

takahi-i/pfm

224ca961ca43f50bd877789e2d8659ae838d517f

[ "MIT" ]

1

2018-01-09T01:33:42.000Z

import json from pfm.pf_command.base import BaseCommand from pfm.util.log import logger class UpdateCommand(BaseCommand): def __init__(self, name, forward_type, remote_host, remote_port, local_port, ssh_server, server_port, login_user, config): super(UpdateCommand, self).__init__(config) self.name = name self.forward_type = forward_type self.remote_host = remote_host self.remote_port = remote_port self.local_port = local_port self.ssh_server = ssh_server self.server_port = server_port self.login_user = login_user def run(self): f = open(self.config_path, 'r') targets = json.load(f) if self.name in targets: target = targets[self.name] self.update(target) else: logger.warn("Port forward setting named " + self.name + "is not registered") # write the target f = open(self.config_path, 'w') f.write(json.dumps(targets, indent=4)) f.close() def update(self, target): if self.forward_type is not None: target["type"] = self.forward_type if self.remote_host is not None: target["remote_host"] = self.remote_host if self.remote_port is not None: target["remote_port"] = self.remote_port if self.local_port is not None: target["local_port"] = self.local_port if self.ssh_server is not None: target["ssh_server"] = self.ssh_server if self.server_port is not None: target["server_port"] = self.server_port if self.login_user is not None: target["login_user"] = self.login_user

34.78

88

0.617021

229

1,739

4.458515

0.240175

0.047013

0.061704

0.10284

0.119491

0

0.000815

0.294422

1,739

49

89

35.489796

0.831296

0.009201

0

0.06566

0

1

0.071429

false

0

0.071429

0

0.166667

0

null

0

null

0

1

0

6a12692597c07586454530c9bcf5baae61076b3f

7,499

py

Python

tests/atfork/test_atfork.py

luciferliu/xTools

324ef1388be13ece0d952e3929eb685212d573f1

[ "Apache-2.0" ]

null

tests/atfork/test_atfork.py

luciferliu/xTools

324ef1388be13ece0d952e3929eb685212d573f1

[ "Apache-2.0" ]

null

tests/atfork/test_atfork.py

luciferliu/xTools

324ef1388be13ece0d952e3929eb685212d573f1

[ "Apache-2.0" ]

null

#!/usr/bin/python # # Copyright 2009 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Licensed to the PSF under a Contributor Agreement. # # Author: Gregory P. Smith <greg@krypto.org> """Tests for atfork.""" import os import sys import importlib from xTool.compat import StringIO import traceback import unittest from xTool import atfork class AtforkTest(unittest.TestCase): def setUp(self): atfork.monkeypatch_os_fork_functions() self.calls = [] self.orig_stderr = sys.stderr self.assertFalse( atfork._fork_lock.locked(), "atfork._fork_lock not released by an earlier test!", ) # Unregister calls registered by earlier tests. atfork._prepare_call_list = [] atfork._parent_call_list = [] atfork._child_call_list = [] def tearDown(self): # Un-monkeypatch the os module. ook. global os importlib.reload(os) sys.stderr = self.orig_stderr def _pre(self): self.calls.append(self._pre) def _parent(self): self.calls.append(self._parent) def _child(self): self.calls.append(self._child) def _other(self): self.calls.append(self._other) def _raise_pre(self): self._pre() raise RuntimeError("This as the first parent error expected.") def _raise_parent(self): self._parent() raise RuntimeError("This as the second parent error expected.") def _raise_child(self): self._child() raise RuntimeError("This child error is expected.") def _assert_expected_parent_stderr(self, error_msg): self.assertTrue(("first parent error" in error_msg), error_msg) self.assertTrue(("second parent error" in error_msg), error_msg) self.assertTrue( (error_msg.index("first parent") < error_msg.index("second parent")), "first and second errors out of order in:\n%r" % error_msg, ) self.assertEqual(2, error_msg.count("RuntimeError:")) def _assert_expected_child_stderr(self, error_msg): self.assertTrue("child error is expected" in error_msg) self.assertEqual(1, error_msg.count("RuntimeError:"), error_msg) def test_monkeypatching(self): if not hasattr(os, "fork"): return # Nothing to test on this platform. self.assertTrue(callable(atfork._orig_os_fork)) self.assertTrue(callable(atfork._orig_os_forkpty)) # The os module was patched, these should not be equal. self.assertNotEqual(atfork._orig_os_fork, os.fork) self.assertNotEqual(atfork._orig_os_forkpty, os.forkpty) # These are the wrapped versions we patched in. self.assertEqual(atfork.os_fork_wrapper, os.fork) self.assertEqual(atfork.os_forkpty_wrapper, os.forkpty) def test_register_atfork_calls(self): # Test with both positional and keyword arguments as well as None. atfork.atfork(self._pre, self._parent, self._child) atfork.atfork(prepare=self._pre) atfork.atfork(parent=self._parent) atfork.atfork(child=self._child) self.assertEqual([self._pre] * 2, atfork._prepare_call_list) self.assertEqual([self._parent] * 2, atfork._parent_call_list) self.assertEqual([self._child] * 2, atfork._child_call_list) if __debug__: self.assertRaises(AssertionError, atfork.atfork, 1, 2, 3) def test_call_atfork_list(self): self.assertEqual([], atfork._call_atfork_list([])) self.assertEqual([], atfork._call_atfork_list([self._pre])) def raise_something(): raise RuntimeError() errors = atfork._call_atfork_list([raise_something] * 2) self.assertEqual(2, len(errors)) for exc_info in errors: self.assertEqual(RuntimeError, exc_info[0]) def _test_a_fork_wrapper(self, fork_func): sys.stderr = StringIO() # restored in tearDown atfork.atfork(self._raise_pre, self._raise_parent, self._raise_child) atfork.atfork(self._other, self._other, self._other) pid = fork_func() if pid == 0: try: try: self.assertEqual( [self._pre, self._other, self._child, self._other], self.calls ) self.assertFalse(atfork._fork_lock.locked()) self._assert_expected_child_stderr(sys.stderr.getvalue()) except BaseException: try: traceback.print_exc() self.orig_stderr.write(sys.stderr.getvalue()) finally: os._exit(1) finally: os._exit(0) else: self.assertEqual( [self._pre, self._other, self._parent, self._other], self.calls ) self.assertFalse(atfork._fork_lock.locked()) self.assertEqual(0, os.waitpid(pid, 0)[1], "error in child") self._assert_expected_parent_stderr(sys.stderr.getvalue()) def test_os_fork_wrapper(self): self._test_a_fork_wrapper(os.fork) def test_os_forkpty_wrapper(self): self._test_a_fork_wrapper(lambda: os.forkpty()[0]) def _test_fork_failure(self, orig_fork_attrname, fork_wrapper): def failing_fork(): raise OSError(0, "testing a fork failure") atfork.atfork(self._pre, self._parent, self._child) orig_orig_fork = getattr(atfork, orig_fork_attrname) try: setattr(atfork, orig_fork_attrname, failing_fork) try: pid = fork_wrapper() if pid == 0: # This should never happen but do this just in case. os._exit(0) except OSError: self.assertEqual([self._pre, self._parent], self.calls) else: self.fail("Fork failed to fail!") finally: setattr(atfork, orig_fork_attrname, orig_orig_fork) def test_fork_wrapper_failure(self): self._test_fork_failure("_orig_os_fork", atfork.os_fork_wrapper) def test_forkpty_wrapper_failure(self): self._test_fork_failure("_orig_os_forkpty", atfork.os_forkpty_wrapper) def test_multiple_monkeypatch_safe(self): self.assertNotEqual(atfork._orig_os_fork, atfork.os_fork_wrapper) self.assertNotEqual(atfork._orig_os_forkpty, atfork.os_forkpty_wrapper) atfork.monkeypatch_os_fork_functions() self.assertNotEqual(atfork._orig_os_fork, atfork.os_fork_wrapper) self.assertNotEqual(atfork._orig_os_forkpty, atfork.os_forkpty_wrapper) atfork.monkeypatch_os_fork_functions() self.assertNotEqual(atfork._orig_os_fork, atfork.os_fork_wrapper) self.assertNotEqual(atfork._orig_os_forkpty, atfork.os_forkpty_wrapper) if __name__ == "__main__": unittest.main()

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86

0.648887

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4.971922

0.222462

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0.026064

0.048653

0.356212

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0.221546

0.165508

0.149001

0.111642

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0.005204

0.256834

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37.308458

0.820922

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0.006993

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null

0

null

0

1

0

6a131e98cf16cdcab3785e1e0af7a922aba56c50

2,213

py

Python

IO/files/handling.py

brendano257/Zugspitze-Schneefernerhaus

64bb86ece2eec147f2a7fb412f87ff2313388753

[ "MIT" ]

null

IO/files/handling.py

brendano257/Zugspitze-Schneefernerhaus

64bb86ece2eec147f2a7fb412f87ff2313388753

[ "MIT" ]

null

IO/files/handling.py

brendano257/Zugspitze-Schneefernerhaus

64bb86ece2eec147f2a7fb412f87ff2313388753

[ "MIT" ]

null

import os from pathlib import Path __all__ = ['list_files_recur', 'scan_and_create_dir_tree', 'get_all_data_files', 'get_subsubdirs'] def list_files_recur(path): """ Cheater function that wraps path.rglob(). :param Path path: path to list recursively :return list: list of Path objects """ files = [] for file in path.rglob('*'): files.append(file) return files def scan_and_create_dir_tree(path, file=True): """ Creates all the necessary directories for the file at the end of path to be created. When specified with a filepath to a file or folder, it creates directories until the path is valid. :param Path path: must end with a filename, else the final directory won't be created :param bool file: Boolean, does the given path end with a file? If not, path.parts[-1] will be created :return None: """ parts = path.parts path_to_check = Path(parts[0]) for i in range(1, len(parts)): if not path_to_check.exists(): path_to_check.mkdir() path_to_check = path_to_check / parts[i] if file: pass else: if not path_to_check.exists(): path_to_check.mkdir() def get_all_data_files(path, filetype): """ Recursively search the given directory for .xxx files. :param Path path: Path to search :param str filetype: str, ".type" of file to search for :return list: list of file-like Path objects """ files = list_files_recur(path) files[:] = [file for file in files if filetype in file.name] return files def get_subsubdirs(path): """ Get the second-level subdirectories of the given path. If given path 'a/b', a sample return would be ['a/b/c/d', 'a/b/c/d2', 'a/b/c/etc'] :param str path: :return list: list containing Path instances for all paths found two levels below the supplied path """ leveltwo_subdirs = [] immediate_subdirs = [os.scandir(subdir) for subdir in os.scandir(path) if Path(subdir).is_dir()] for scan in immediate_subdirs: for subdir in scan: leveltwo_subdirs.append(Path(subdir)) if Path(subdir).is_dir() else None return leveltwo_subdirs

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0.66742

338

2,213

4.230769

0.319527

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0.022378

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0.847296

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0.290323

0

null

0

null

0

1

0

6a139742e2452134cace4ac02e78a8badeceb098

2,617

py

Python

tools/mo/openvino/tools/mo/ops/detection_output_onnx.py

ryanloney/openvino-1

4e0a740eb3ee31062ba0df88fcf438564f67edb7

[ "Apache-2.0" ]

1,127

2018-10-15T14:36:58.000Z

2020-04-20T09:29:44.000Z

tools/mo/openvino/tools/mo/ops/detection_output_onnx.py

ryanloney/openvino-1

4e0a740eb3ee31062ba0df88fcf438564f67edb7

[ "Apache-2.0" ]

439

2018-10-20T04:40:35.000Z

2020-04-19T05:56:25.000Z

tools/mo/openvino/tools/mo/ops/detection_output_onnx.py

ryanloney/openvino-1

4e0a740eb3ee31062ba0df88fcf438564f67edb7

[ "Apache-2.0" ]

414

2018-10-17T05:53:46.000Z

2020-04-16T17:29:53.000Z

# Copyright (C) 2018-2022 Intel Corporation # SPDX-License-Identifier: Apache-2.0 import numpy as np from openvino.tools.mo.front.common.partial_infer.utils import dynamic_dimension_value, shape_array, set_input_shapes from openvino.tools.mo.ops.op import Op class ExperimentalDetectronDetectionOutput(Op): op = 'ExperimentalDetectronDetectionOutput' enabled = True def __init__(self, graph, attrs): mandatory_props = dict( type=self.op, op=self.op, version='opset6', infer=self.infer, reverse_infer=self.reverse_infer, type_infer=self.type_infer, in_ports_count=4, out_ports_count=3, ) super().__init__(graph, mandatory_props, attrs) def backend_attrs(self): return [ ('class_agnostic_box_regression', lambda node: str(bool(node['class_agnostic_box_regression'])).lower()), 'max_detections_per_image', 'nms_threshold', 'num_classes', 'post_nms_count', 'score_threshold', 'max_delta_log_wh', ('deltas_weights', lambda node: ','.join(map(str, node['deltas_weights'])))] @staticmethod def infer(node): rois_num = node.max_detections_per_image # boxes node.out_port(0).data.set_shape([rois_num, 4]) # classes, scores, batch indices # We use range(1, 1 + max(node.out_ports().keys())) instead of range(1, 3), because there are incorrectly # generated models where ExperimentalDetectronDetectionOutput has 4 outputs. for port_ind in range(1, 1 + max(node.out_ports().keys())): if not node.out_port(port_ind).disconnected(): node.out_port(port_ind).data.set_shape([rois_num]) @staticmethod def type_infer(node): in_data_type = node.in_port(0).get_data_type() node.out_port(0).set_data_type(in_data_type) node.out_port(1).set_data_type(np.int32) # the second output contains class indices node.out_port(2).set_data_type(in_data_type) if node.is_out_port_connected(3): node.out_port(3).set_data_type(np.int32) # the fourth output contains batch indices @staticmethod def reverse_infer(node): set_input_shapes(node, shape_array([dynamic_dimension_value, 4]), shape_array([dynamic_dimension_value, node['num_classes'] * 4]), shape_array([dynamic_dimension_value, node['num_classes']]), shape_array([1, 3]))

39.059701

117

0.635078

328

2,617

4.762195

0.375

0.040333

0.049296

0.049936

0.232394

0.145967

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0.058899

0

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2,617

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0.02

0.24

0

null

0

null

0

1

0

6a139aa59f68903a8a744250e0c92696c28eb301

2,046

py

Python

driver.py

FahimMahmudJoy/Physionet_2019_Sepsis

d31bec40aa0359071bfaff1a4d72569c5731a04e

[ "BSD-2-Clause" ]

1

2019-06-26T19:38:33.000Z

driver.py

FahimMahmudJoy/Physionet_2019_Sepsis

d31bec40aa0359071bfaff1a4d72569c5731a04e

[ "BSD-2-Clause" ]

null

driver.py

FahimMahmudJoy/Physionet_2019_Sepsis

d31bec40aa0359071bfaff1a4d72569c5731a04e

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python import numpy as np, os, sys from get_sepsis_score import load_sepsis_model, get_sepsis_score def load_challenge_data(file): with open(file, 'r') as f: header = f.readline().strip() column_names = header.split('|') data = np.loadtxt(f, delimiter='|') # Ignore SepsisLabel column if present. if column_names[-1] == 'SepsisLabel': column_names = column_names[:-1] data = data[:, :-1] return data def save_challenge_predictions(file, scores, labels): with open(file, 'w') as f: f.write('PredictedProbability|PredictedLabel\n') for (s, l) in zip(scores, labels): f.write('%g|%d\n' % (s, l)) if __name__ == '__main__': # Parse arguments. if len(sys.argv) != 3: raise Exception('Include the input and output directories as arguments, e.g., python driver.py input output.') input_directory = sys.argv[1] output_directory = sys.argv[2] # Find files. files = [] for f in os.listdir(input_directory): if os.path.isfile(os.path.join(input_directory, f)) and not f.lower().startswith('.') and f.lower().endswith('psv'): files.append(f) if not os.path.isdir(output_directory): os.mkdir(output_directory) # Load model. model = load_sepsis_model() print(model) # Iterate over files. for f in files: # Load data. input_file = os.path.join(input_directory, f) data = load_challenge_data(input_file) # print(type(data)) # Make predictions. num_rows = len(data) scores = np.zeros(num_rows) labels = np.zeros(num_rows) for t in range(num_rows): current_data = data[:t+1] current_score, current_label = get_sepsis_score(current_data, model) scores[t] = current_score labels[t] = current_label # Save results. output_file = os.path.join(output_directory, f) save_challenge_predictions(output_file, scores, labels)

30.537313

124

0.623167

274

2,046

4.463504

0.368613

0.02453

0.034342

0.017989

0.040883

0

0.004596

0.255621

2,046

66

125

31

0.798424

0.091398

0

0.087615

0.020011

0

1

0.047619

false

0

0.047619

0

0.119048

0.02381

0

null

0

null

0

1

0

6a139fa7954e69a2e28f61ebd4a2c8e7028fb83e

2,589

py

Python

src/LspRuntimeMonitor.py

TafsirGna/ClspGeneticAlgorithm

25184afbbd52773b8aed2e268ae98dd9656cacda

[ "MIT" ]

null

src/LspRuntimeMonitor.py

TafsirGna/ClspGeneticAlgorithm

25184afbbd52773b8aed2e268ae98dd9656cacda

[ "MIT" ]

null

src/LspRuntimeMonitor.py

TafsirGna/ClspGeneticAlgorithm

25184afbbd52773b8aed2e268ae98dd9656cacda

[ "MIT" ]

null

#!/usr/bin/python3.5 # -*-coding: utf-8 -* from collections import defaultdict from threading import Thread from time import perf_counter, time from LspLibrary import bcolors import time import matplotlib.pyplot as plt class LspRuntimeMonitor: """ """ clockStart = None clockEnd = None mutation_strategy = "simple_mutation" popsData = defaultdict(lambda: None) outputString = "" outputFilePath = "data/output/output.txt" verbose = False running = True def __init__(self) -> None: """ """ pass @classmethod def duration(cls): """ """ return f"{cls.clockEnd - cls.clockStart} second(s)" @classmethod def started(cls): """ """ cls.running = True LspRuntimeMonitor.clockStart = perf_counter() print(f"{bcolors.OKGREEN}Processing input data.{bcolors.ENDC}") # Thread(cls.waitingAnimation()) @classmethod def ended(cls): """ """ cls.running = False LspRuntimeMonitor.clockEnd = perf_counter() @classmethod def output(cls, output): """ """ cls.outputString += output if cls.verbose: print(output) @classmethod def saveOutput(cls): """ """ f = open(cls.outputFilePath, "w") f.write(cls.outputString) f.close() @classmethod def report(cls): """ """ # Duration durationStatement = cls.duration() cls.output(durationStatement) # Saving all generated output to a default file cls.saveOutput() cls.plotData() @classmethod def plotData(cls): """ """ print('-----------------------------------------') print(cls.popsData) data = list(cls.popsData.values())[0] # Plots # Plotting the evolution of the minimal cost over generations plt.plot(list(range(len(data["max"]))), data["max"]) plt.ylabel("Population maximal cost") plt.show() # Plotting the evolution of the minimal cost over generations plt.plot(list(range(len(data["min"]))), data["min"]) plt.ylabel("Population minimal cost") plt.show() @classmethod def waitingAnimation(cls): """ """ animation = "|/-\\" idx = 0 # while thing_not_complete(): while cls.running: print(animation[idx % len(animation)], end="\r") idx += 1 time.sleep(0.1)

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250

2,589

5.604

0.428

0.079943

0.018558

0.031406

0.105639

0

0.00453

0.317883

2,589

118

72

21.940678

0.788788

0.107764

0

0.15625

0

0.11039

0.041744

0

1

0.140625

false

0.015625

0.09375

0

0.390625

0.078125

0

null

0

null

0

1

0

6a152a32efa9784006230b4163868ce2479ff3ba

20,737

py

Python

methylcheck/predict/sex.py

FoxoTech/methylcheck

881d14d78e6086aab184716e0b79cdf87e9be8bf

[ "MIT" ]

null

methylcheck/predict/sex.py

FoxoTech/methylcheck

881d14d78e6086aab184716e0b79cdf87e9be8bf

[ "MIT" ]

11

2021-04-08T16:14:54.000Z

2022-03-09T00:22:13.000Z

methylcheck/predict/sex.py

FoxoTech/methylcheck

881d14d78e6086aab184716e0b79cdf87e9be8bf

[ "MIT" ]

1

2022-02-10T09:06:45.000Z

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from pathlib import Path #app import methylcheck # uses .load; get_sex uses methylprep models too and detect_array() import logging LOGGER = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def _get_copy_number(meth,unmeth): """function to return copy number. requires dataframes of methylated and unmethylated values. can be raw OR corrected""" # minfi R version: # log2(getMeth(object) + getUnmeth(object)) return np.log2(meth+unmeth) def get_sex(data_source, array_type=None, verbose=False, plot=False, save=False, on_lambda=False, median_cutoff= -2, include_probe_failure_percent=True, poobah_cutoff=20, custom_label=None, return_fig=False, return_labels=False): """This will calculate and predict the sex of each sample. inputs: ======= the "data_source" can be any one of: path -- to a folder with csv data that contains processed sample data path -- to a folder with the 'meth_values.pkl' and 'unmeth_values.pkl' dataframes path -- to a folder also containing samplesheet pkl and poobah_values.pkl, if you want to compare predicted sex with actual sex. data_containers -- object created from methylprep.run_pipeline() or methylcheck.load(path, 'meth') tuple of (meth, unmeth) dataframes array_type (string) enum: {'27k','450k','epic','epic+','mouse'} if not specified, it will load the data from data_source and determine the array for you. median_cutoff the minimum difference in the medians of X and Y probe copy numbers to assign male or female (copied from the minfi sex predict function) include_probe_failure_percent: True: includes poobah percent per sample as column in the output table and on the plot. Note: you must supply a 'path' as data_source to include poobah in plots. poobah_cutoff The maximum percent of sample probes that can fail before the sample fails. Default is 20 (percent) Has no effect if `include_probe_failure_percent` is False. plot True: creates a plot, with option to `save` as image or `return_fig`. save True: saves the plot, if plot is True return_fig If True, returns a pyplot figure instead of a dataframe. Default is False. Note: return_fig will not show a plot on screen. return_labels: (requires plot == True) When using poobah_cutoff, the figure only includes A-Z,1...N labels on samples on plot to make it easier to read. So to get what sample_ids these labels correspond to, you can rerun the function with return_labels=True and it will skip plotting and just return a dictionary with sample_ids and these labels, to embed in a PDF report if you like. custom_label: Option to provide a dictionary with keys as sample_ids and values as labels to apply to samples. e.g. add more data about samples to the multi-dimensional QC plot while providing a filepath is the easiest way, you can also pass in a data_containers object, a list of data_containers containing raw meth/unmeth values, instead. This object is produced by methylprep.run_pipeline, or by using methylcheck.load(filepath, format='meth') and lets you customize the import if your files were not prepared using methylprep (non-standand CSV columns, for example) If a `poobah_values.pkl` file can be found in path, the dataframe returned will also include percent of probes for X and Y chromosomes that failed quality control, and warn the user if any did. This feature won't work if a containers object or tuple of dataframes is passed in, instead of a path. Note: ~90% of Y probes should fail if the sample is female. That chromosome is missing.""" allowed_array_types = {'27k','450k','epic','epic+','mouse'} try: from methylprep.files import Manifest from methylprep.models import ArrayType except ImportError: raise ImportError("This function requires methylprep to be installed (pip3 install `methylprep`)") (data_source_type, data_source) = methylcheck.load_processed._data_source_type(data_source) # data_source_type is one of {'path', 'container', 'control', 'meth_unmeth_tuple'} poobah=None if data_source_type in ('path'): # this will look for saved pickles first, then csvs or parsing the containers (which are both slower) # the saved pickles function isn't working for batches yet. try: meth, unmeth = methylcheck.qc_plot._get_data( data_containers=None, path=data_source, compare=False, noob=False, verbose=False) except Exception as e: meth, unmeth = methylcheck.qc_plot._get_data( data_containers=None, path=data_source, compare=False, noob=True, verbose=False) if include_probe_failure_percent == True and Path(data_source,'poobah_values.pkl').expanduser().exists(): poobah = pd.read_pickle(Path(data_source,'poobah_values.pkl').expanduser()) elif data_source_type in ('container'): # this will look for saved pickles first, then csvs or parsing the containers (which are both slower) # the saved pickles function isn't working for batches yet. meth, unmeth = methylcheck.qc_plot._get_data( data_containers=data_source, path=None, compare=False, noob=False, verbose=False) elif data_source_type == 'meth_unmeth_tuple': (meth, unmeth) = data_source if len(meth) != len(unmeth): raise ValueError(f"WARNING: probe count mismatch: meth {len(meth)} -- unmeth {len(unmeth)}") if array_type == None: # get list of X any Y probes - using .methylprep_manifest_files (or MANIFEST_DIR_PATH_LAMBDA) and auto-detected array here array_type = ArrayType(methylcheck.detect_array(meth, on_lambda=on_lambda)) elif isinstance(array_type,str): if array_type in allowed_array_types: array_type = ArrayType(array_type) else: raise ValueError(f"Your array_type must be one of these: {allowed_array_types} or None.") if verbose: LOGGER.debug(array_type) LOGGER.setLevel(logging.WARNING) manifest = Manifest(array_type, on_lambda=on_lambda, verbose=verbose)._Manifest__data_frame # 'custom', '27k', '450k', 'epic', 'epic+' LOGGER.setLevel(logging.INFO) x_probes = manifest.index[manifest['CHR']=='X'] y_probes = manifest.index[manifest['CHR']=='Y'] if verbose: LOGGER.info(f"Found {len(x_probes)} X and {len(y_probes)} Y probes") # dataframes of meth and unmeth values for the sex chromosomes x_meth = meth[meth.index.isin(x_probes)] x_unmeth = unmeth[unmeth.index.isin(x_probes)] y_meth = meth[meth.index.isin(y_probes)] y_unmeth = unmeth[unmeth.index.isin(y_probes)] # create empty dataframe for output output = pd.DataFrame(index=[s for s in meth.columns], columns=['x_median','y_median','predicted_sex']) # get median values for each sex chromosome for each sample x_med = _get_copy_number(x_meth,x_unmeth).median() y_med = _get_copy_number(y_meth,y_unmeth).median() # populate output dataframe with values output['x_median'] = output.index.map(x_med) output['y_median'] = output.index.map(y_med) # compute difference median_difference = output['y_median'] - output['x_median'] # median cutoff - can be manipulated by user --- default = -2 --- used to predict sex sex0 = ['F' if x < median_cutoff else 'M' for x in median_difference] # NOTE for testing: GSE85566/GPL13534 (N=120) has 4 samples that are predicted as wrong sex when using -2, but work at -0.5. # populate dataframe with predicted sex output['predicted_sex'] = sex0 output = output.round(1) # if poobah_df exists, calculate percent X and Y probes that failed sample_failure_percent = {} # % of ALL probes in sample, not just X or Y if include_probe_failure_percent == True and isinstance(poobah, pd.DataFrame): p_value_cutoff = 0.05 X_col = [] Y_col = [] failed_samples = [] for column in poobah.columns: sample_failure_percent[column] = round(100*len(poobah[column][poobah[column] >= p_value_cutoff].index) / len(poobah.index),1) failed_probe_names = poobah[column][poobah[column] >= p_value_cutoff].index failed_x_probe_names = list(set(failed_probe_names) & set(x_probes)) failed_y_probe_names = list(set(failed_probe_names) & set(y_probes)) X_percent = round(100*len(failed_x_probe_names)/poobah.index.isin(list(x_probes)).sum(),1) Y_percent = round(100*len(failed_y_probe_names)/poobah.index.isin(list(y_probes)).sum(),1) X_col.append(X_percent) Y_col.append(Y_percent) if X_percent > 10: failed_samples.append(column) output['X_fail_percent'] = X_col #output.index.map(X_col) output['Y_fail_percent'] = Y_col #output.index.map(Y_col) if failed_samples != []: LOGGER.warning(f"{len(failed_samples)} samples had >10% of X probes fail p-value probe detection. Predictions for these may be unreliable:") LOGGER.warning(f"{failed_samples}") if data_source_type in ('path'): output = _fetch_actual_sex_from_sample_sheet_meta_data(data_source, output) if plot == True: fig = _plot_predicted_sex(data=output, # 'x_median', 'y_median', 'predicted_sex', 'X_fail_percent', 'Y_fail_percent' sample_failure_percent=sample_failure_percent, median_cutoff=median_cutoff, include_probe_failure_percent=include_probe_failure_percent, verbose=verbose, save=save, poobah_cutoff=poobah_cutoff, custom_label=custom_label, data_source_type=data_source_type, data_source=data_source, return_fig=return_fig, return_labels=return_labels, ) if return_labels: return fig # these are a lookup dictionary of labels if return_fig: return fig return output def _plot_predicted_sex(data=pd.DataFrame(), sample_failure_percent={}, median_cutoff= -2, include_probe_failure_percent=True, verbose=False, save=False, poobah_cutoff=20, #% custom_label=None, data_source_type=None, data_source=None, return_fig=False, return_labels=False): """ data columns: ['x_median', 'y_median', 'predicted_sex', 'X_fail_percent', 'Y_fail_percent'] - color is sex, pink or blue - marker circle size will be larger and more faded if poobah values are worse, smaller and darker if low variance. Like a probability cloud. - sample text is (ID, delta age) - sex mismatches are X, matched samples are circles (if samplesheet contains actual sex data) - omits labels for samples that have LOW failure rates, but shows IDs when failed - adds legend of sketchy samples and labels - show delta age on labels (using custom column dict) - unit tests with custom label and without, and check that controls_report still works with this function - save_fig - return_labels, returns a lookup dict instead of plot if there is a "custom_label" dict passed in, such as (actual_age - predicted_age), it simply adds those this label to the marker text labels. Dicts must match the data DF index. """ if sample_failure_percent != {} and set(sample_failure_percent.keys()) == set(data.index): data['sample_failure_percent'] = pd.Series(sample_failure_percent) else: LOGGER.warning("sample_failure_percent index did not align with output data index") #sns.set_theme(style="white") show_mismatches = None if 'sex_matches' not in data.columns else "sex_matches" if show_mismatches: data["sex_matches"] = data["sex_matches"].map({0:"Mismatch", 1:"Match"}) show_failure = None if 'sample_failure_percent' not in data.columns else "sample_failure_percent" sample_sizes = (20, 600) if show_failure: # avoid sizing dots with narrow range; gives false impression of bad samples. poobah_range = data["sample_failure_percent"].max() - data["sample_failure_percent"].min() if poobah_range < poobah_cutoff/2: show_failure = None sample_sizes = (40,40) custom_palette = sns.set_palette(sns.color_palette(['#FE6E89','#0671B7'])) # if only one sex, make sure male is blue; female is pink # if hasattr(output, 'actual_sex') and set(output.actual_sex) == set('M') # if first value to be plotted is male, change palette if hasattr(data, 'predicted_sex') and list(data.predicted_sex)[0] == 'M': custom_palette = sns.set_palette(sns.color_palette(['#0671B7','#FE6E89'])) fig = sns.relplot(data=data, x='x_median', y='y_median', hue="predicted_sex", size=show_failure, style=show_mismatches, sizes=sample_sizes, alpha=.5, palette=custom_palette, height=8, aspect=1.34) ax = fig.axes[0,0] fig.fig.subplots_adjust(top=.95) # for zoomed-in plots with few points close together, set the min scale to be at least 2 units. yscale = plt.gca().get_ylim() xscale = plt.gca().get_xlim() if abs(yscale[1]-yscale[0]) < 2.0: ax.set_xlim(xmin=xscale[0]-1, xmax=xscale[1]+1) ax.set_ylim(ymin=yscale[0]-1, ymax=yscale[1]+1) label_lookup = {index_val: chr(i+65) if (i <= 26) else str(i-26) for i,index_val in enumerate(data.index)} for idx,row in data.iterrows(): if "sample_failure_percent" in row and row['sample_failure_percent'] > poobah_cutoff: label = f"{label_lookup[idx]}, {custom_label.get(idx)}" if isinstance(custom_label, dict) and custom_label.get(idx) else label_lookup[idx] ax.text(row['x_median'], row['y_median'], label, horizontalalignment='center', fontsize=10, color='darkred') else: label = f"{custom_label.get(idx)}" if isinstance(custom_label, dict) else None if label: ax.text(row['x_median']+0.05, row['y_median']+0.05, label, horizontalalignment='center', fontsize=10, color='grey') if return_labels: plt.close() # release memory return label_lookup if "sample_failure_percent" in data.columns: N_failed = len(data[data['sample_failure_percent'] > poobah_cutoff].index) N_total = len(data['sample_failure_percent'].index) ax.set_title(f"{N_failed} of {N_total} samples failed poobah, with at least {poobah_cutoff}% of probes failing") else: ax.set_title(f"Predicted sex based on matching X and Y probes.") if save: filepath = 'predicted_sexes.png' if data_source_type != 'path' else Path(data_source,'predicted_sexes.png').expanduser() plt.savefig(filepath, bbox_inches="tight") if return_fig: return fig plt.show() def _fetch_actual_sex_from_sample_sheet_meta_data(filepath, output): """output is a dataframe with Sample_ID in the index. This adds actual_sex as a column and returns it.""" # controls_report() does the same thing, and only calls get_sex() with the minimum of data to be fast, because these are already loaded. Just passes in meth/unmeth data # Sample sheet should have 'M' or 'F' in column to match predicted sex. # merge actual sex into processed output, if available file_patterns = { 'sample_sheet_meta_data.pkl': 'meta', '*_meta_data.pkl': 'meta', '*samplesheet*.csv': 'meta', '*sample_sheet*.csv': 'meta', } loaded_files = {} for file_pattern in file_patterns: for filename in Path(filepath).expanduser().rglob(file_pattern): if '.pkl' in filename.suffixes: loaded_files['meta'] = pd.read_pickle(filename) break if '.csv' in filename.suffixes: loaded_files['meta'] = pd.read_csv(filename) break if len(loaded_files) == 1: # methylprep v1.5.4-6 was creating meta_data files with two Sample_ID columns. Check and fix here: # methylcheck 0.7.9 / prep 1.6.0 meta_data lacking Sample_ID when sample_sheet uses alt column names and gets replaced. if any(loaded_files['meta'].columns.duplicated()): loaded_files['meta'] = loaded_files['meta'].loc[:, ~loaded_files['meta'].columns.duplicated()] LOGGER.info("Removed a duplicate Sample_ID column in samplesheet") if 'Sample_ID' in loaded_files['meta'].columns: loaded_files['meta'] = loaded_files['meta'].set_index('Sample_ID') elif 'Sentrix_ID' in loaded_files['meta'].columns and 'Sentrix_Position' in loaded_files['meta'].columns: loaded_files['meta']['Sample_ID'] = loaded_files['meta']['Sentrix_ID'].astype(str) + '_' + loaded_files['meta']['Sentrix_Position'].astype(str) loaded_files['meta'] = loaded_files['meta'].set_index('Sample_ID') else: raise ValueError("Your sample sheet must have a Sample_ID column, or (Sentrix_ID and Sentrix_Position) columns.") # fixing case of the relevant column renamed_column = None if ('Gender' in loaded_files['meta'].columns or 'Sex' in loaded_files['meta'].columns): if 'Gender' in loaded_files['meta'].columns: renamed_column = 'Gender' elif 'Sex' in loaded_files['meta'].columns: renamed_column = 'Sex' else: renamed_columns = {col:(col.title() if col.lower() in ('sex','gender') else col) for col in loaded_files['meta'].columns} loaded_files['meta'] = loaded_files['meta'].rename(columns=renamed_columns) if 'Gender' in renamed_columns.values(): renamed_column = 'Gender' elif 'Sex' in renamed_columns.values(): renamed_column = 'Sex' if renamed_column is not None: # next, ensure samplesheet Sex/Gender (Male/Female) are recoded as M/F; controls_report() does NOT do this step, but should. sex_values = set(loaded_files['meta'][renamed_column].unique()) #print('sex_values', sex_values) if not sex_values.issubset(set(['M','F'])): # subset, because samples might only contain one sex if 'Male' in sex_values or 'Female' in sex_values: loaded_files['meta'][renamed_column] = loaded_files['meta'][renamed_column].map({'Male':'M', 'Female':'F'}) elif 'male' in sex_values or 'female' in sex_values: loaded_files['meta'][renamed_column] = loaded_files['meta'][renamed_column].map({'male':'M', 'female':'F'}) elif 'MALE' in sex_values or 'FEMALE' in sex_values: loaded_files['meta'][renamed_column] = loaded_files['meta'][renamed_column].map({'MALE':'M', 'FEMALE':'F'}) elif 'm' in sex_values or 'f' in sex_values: loaded_files['meta'][renamed_column] = loaded_files['meta'][renamed_column].map({'m':'M', 'f':'F'}) else: raise ValueError(f"Cannot compare with predicted sex because actual sexes listed in your samplesheet are not understood (expecting M or F): (found {sex_values})") output['actual_sex'] = None output['sex_matches'] = None for row in output.itertuples(): try: actual_sex = str(loaded_files['meta'].loc[row.Index].get(renamed_column)) except KeyError: if 'Sample_ID' in output.columns: LOGGER.warning("Sample_ID was another column in your output DataFrame; Set that to the index when you pass it in.") raise KeyError("Could not read actual sex from meta data to compare.") if isinstance(actual_sex, pd.Series): LOGGER.warning(f"Multiple samples matched actual sex for {row.Index}, because Sample_ID repeats in sample sheets. Only using first match, so matches may not be accurate.") actual_sex = actual_sex[0] if hasattr(row,'predicted_sex'): sex_matches = 1 if actual_sex.upper() == str(row.predicted_sex).upper() else 0 else: sex_matches = np.nan output.loc[row.Index, 'actual_sex'] = actual_sex output.loc[row.Index, 'sex_matches'] = sex_matches else: pass # no Sex/Gender column found in samplesheet return output

53.583979

191

0.672711

2,931

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0.090957

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null

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null

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1

0

6a164cca97745158870c1da7ad0a330912380e28

2,504

py

Python

tests/test_basics.py

sirosen/git-fortune

69ef3e18506aa67fdc812854f1588828ea4e7448

[ "ECL-2.0", "Apache-2.0" ]

null

tests/test_basics.py

sirosen/git-fortune

69ef3e18506aa67fdc812854f1588828ea4e7448

[ "ECL-2.0", "Apache-2.0" ]

null

tests/test_basics.py

sirosen/git-fortune

69ef3e18506aa67fdc812854f1588828ea4e7448

[ "ECL-2.0", "Apache-2.0" ]

null

import subprocess from git_fortune._compat import fix_line_endings from git_fortune.version import __version__ def test_help(capfd): subprocess.check_call(["git-fortune", "-h"]) captured = capfd.readouterr() assert ( fix_line_endings( """ A fortune-like command for showing git tips Invoke it as 'git-fortune' or 'git fortune' """ ) in captured.out ) def test_version(capfd): subprocess.check_call(["git-fortune", "--version"]) captured = capfd.readouterr() assert "git-fortune {}".format(__version__) in captured.out def test_tip_boxformat(capfd): subprocess.check_call(["git-fortune", "--id", "3"]) tip3boxbody = fix_line_endings( """\ +-------------------------------------------------------------------------------+ | GIT TIP #3 | | | | `git log --graph` can show you a tree-like representation of the git history. | | | | Try adding in `--oneline --decorate --all`. | | | +-------------------------------------------------------------------------------+ """ ) captured = capfd.readouterr() assert captured.out == tip3boxbody def test_tip_plainformat(capfd): subprocess.check_call(["git-fortune", "--format", "plain", "--id", "1"]) tip1plainbody = fix_line_endings( "Modify your last commit before pushing with `git commit --amend`.\n" ) captured = capfd.readouterr() assert captured.out == tip1plainbody def test_noargs(capfd): """just make sure it doesn't crashfail""" subprocess.check_call(["git-fortune"]) captured = capfd.readouterr() assert "GIT TIP #" in captured.out # from the box format def test_category(capfd): """just make sure it doesn't crashfail""" subprocess.check_call(["git-fortune", "--category", "diff"]) captured = capfd.readouterr() assert "GIT TIP #" in captured.out # from the box format def test_category_and_id_mutex(capfd): ret = subprocess.call(["git-fortune", "--category", "diff", "--id", "3"]) assert ret == 2 captured = capfd.readouterr() assert "" == captured.out assert "argument --id: not allowed with argument --category" in captured.err

33.386667

81

0.527157

251

2,504

5.111554

0.334661

0.093531

0.076383

0.158223

0.506625

0.420889

0.221356

0

0.005093

0.294329

2,504

74

82

33.837838

0.720996

0.044728

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0.214286

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0.166667

false

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0.071429

0

0.238095

0

null

0

null

0

1

0

6a167dd5d92960139223aa44954c2cb6cacf4375

2,487

py

Python

configs/keypoints/faster_rcnn_r50_fpn_keypoints.py

VGrondin/CBNetV2_mask_remote

b27246af5081d5395db3c3105d32226de05fcd13

[ "Apache-2.0" ]

null

configs/keypoints/faster_rcnn_r50_fpn_keypoints.py

VGrondin/CBNetV2_mask_remote

b27246af5081d5395db3c3105d32226de05fcd13

[ "Apache-2.0" ]

null

configs/keypoints/faster_rcnn_r50_fpn_keypoints.py

VGrondin/CBNetV2_mask_remote

b27246af5081d5395db3c3105d32226de05fcd13

[ "Apache-2.0" ]

null

_base_ = [ '../_base_/models/faster_rcnn_r50_fpn.py' ] model = dict( type='FasterRCNN', # pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch'), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0]), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0)), roi_head=dict( # type='StandardRoIHead', _delete_=True, type='KeypointRoIHead', output_heatmaps=False, # keypoint_head=dict( # type='HRNetKeypointHead', # num_convs=8, # in_channels=256, # features_size=[256, 256, 256, 256], # conv_out_channels=512, # num_keypoints=5, # loss_keypoint=dict(type='MSELoss', loss_weight=50.0)), keypoint_decoder=dict(type='HeatmapDecodeOneKeypoint', upscale=4), bbox_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), out_channels=256, featmap_strides=[4, 8, 16, 32]), bbox_head=dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=80, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0., 0., 0., 0.], target_stds=[0.1, 0.1, 0.2, 0.2]), reg_class_agnostic=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0))) ) #optimizer = dict(lr=0.002) #lr_config = dict(step=[40, 55]) #total_epochs = 60

32.298701

77

0.542421

292

2,487

4.386986

0.39726

0.118657

0.01171

0.01249

0.259953

0.235753

0.229508

0.165496

0

0.083284

0.31926

2,487

76

78

32.723684

0.673361

0.148372

0

0.166667

0

0.119297

0.029943

0

1

0

false

0

null

0

null

0

1

0

6a168cae49b57ce434a41c7070da071ca4734fc0

3,232

py

Python

maskrcnn_benchmark/layers/roi_align_rotated_3d.py

picwoon/As_built_BIM

9e6b81e2fd8904f5afd013e21d2db45456c138d5

[ "MIT" ]

2

2020-03-05T06:39:03.000Z

2020-03-31T12:08:04.000Z

maskrcnn_benchmark/layers/roi_align_rotated_3d.py

picwoon/As_built_BIM

9e6b81e2fd8904f5afd013e21d2db45456c138d5

[ "MIT" ]

null

maskrcnn_benchmark/layers/roi_align_rotated_3d.py

picwoon/As_built_BIM

9e6b81e2fd8904f5afd013e21d2db45456c138d5

[ "MIT" ]

1

2021-09-24T13:17:40.000Z

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import torch, math from torch import nn from torch.autograd import Function from torch.autograd.function import once_differentiable from torch.nn.modules.utils import _pair from SparseConvNet.sparseconvnet.tools_3d_2d import sparse_3d_to_dense_2d import _C class _ROIAlignRotated3D(Function): @staticmethod def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio): ctx.save_for_backward(roi) ctx.output_size = _pair(output_size) ctx.spatial_scale = spatial_scale ctx.sampling_ratio = sampling_ratio ctx.input_shape = input.size() # input: [4, 256, 304, 200, 7] # roi: [171, 8] # spatial_scale: 0.25 # output_size: [7,7,7] # sampling_ratio: 2 output = _C.roi_align_rotated_3d_forward( input, roi, spatial_scale, output_size[0], output_size[1], output_size[2], sampling_ratio ) # [171, 256, 7, 7] return output @staticmethod @once_differentiable def backward(ctx, grad_output): rois, = ctx.saved_tensors output_size = ctx.output_size spatial_scale = ctx.spatial_scale sampling_ratio = ctx.sampling_ratio bs, ch, h, w, zsize = ctx.input_shape grad_input = _C.roi_align_rotated_3d_backward( grad_output, rois, spatial_scale, output_size[0], output_size[1], output_size[2], bs, ch, h, w, zsize, sampling_ratio, ) return grad_input, None, None, None, None roi_align_rotated_3d = _ROIAlignRotated3D.apply class ROIAlignRotated3D(nn.Module): def __init__(self, output_size, spatial_scale, sampling_ratio): ''' output_size:[pooled_height, pooled_width] spatial_scale: size_of_map/size_of_original_image sampling_ratio: how many points to use for bilinear_interpolate ''' super(ROIAlignRotated3D, self).__init__() self.output_size = output_size # (7,7,7) self.spatial_scale = spatial_scale # 0.25 self.sampling_ratio = sampling_ratio # 2 def forward(self, input_s3d, rois_3d): ''' input0: sparse 3d tensor rois_3d: 3d box, xyz order is same as input0, yaw unit is rad, anti-clock wise is positive input: [batch_size, feature, h, w] rois: [n,5] [batch_ind, center_w, center_h, roi_width, roi_height, theta] theta unit: degree, anti-clock wise is positive Note: the order of w and h inside of input and rois is different. ''' input_d3d = sparse_3d_to_dense_2d(input_s3d) output = roi_align_rotated_3d( input_d3d, rois_3d, self.output_size, self.spatial_scale, self.sampling_ratio ) return output def __repr__(self): tmpstr = self.__class__.__name__ + "(" tmpstr += "output_size=" + str(self.output_size) tmpstr += ", spatial_scale=" + str(self.spatial_scale) tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) tmpstr += ")" return tmpstr

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0

null

0

null

0

1

0

6a16ef74b6b87e7acddaab1f4ea03a7e48da5422

8,360

py

Python

src/model/utils/utils.py

J-CITY/METADATA-EXTRACTOR

6bc01a7e4b74a3156c07efc2c80d5519c325dd53

[ "Apache-2.0" ]

null

src/model/utils/utils.py

J-CITY/METADATA-EXTRACTOR

6bc01a7e4b74a3156c07efc2c80d5519c325dd53

[ "Apache-2.0" ]

null

src/model/utils/utils.py

J-CITY/METADATA-EXTRACTOR

6bc01a7e4b74a3156c07efc2c80d5519c325dd53

[ "Apache-2.0" ]

null

import numpy as np import os from .logger import printLog UNK = "$UNK$" NUM = "$NUM$" NONE = "O" class ParrotIOError(Exception): def __init__(self, filename): message = "ERROR: Can not find file {}.".format(filename) super(ParrotIOError, self).__init__(message) # Class that iterates over CoNLL Dataset class CoNLLDataset(object): def __init__(self, filename, processingWord=None, processingTag=None, maxIter=None): self.filename = filename self.processingWord = processingWord # function that takes a word as input self.processingTag = processingTag # function that takes a tag as input self.maxIter = maxIter # max number of sentences to yield self.length = None def __iter__(self): niter = 0 with open(self.filename, encoding='utf-8') as f: words, tags = [], [] for line in f: line = line.strip() # delete spaces in start and end if (len(line) == 0 or line.startswith("-DOCSTART-")): if len(words) != 0: niter += 1 if self.maxIter is not None and niter > self.maxIter: break yield words, tags words, tags = [], [] else: ls = line.split(' ') word, tag = ls[0],ls[-1] if self.processingWord is not None: word = self.processingWord(word) if self.processingTag is not None: tag = self.processingTag(tag) words += [word] tags += [tag] def __len__(self): if self.length is None: self.length = 0 for _ in self: self.length += 1 return self.length #Create a dictionary from dataset def getDictionary(datasets): printLog("Building dictionary: ") dictWords = set() dictTags = set() for dataset in datasets: for words, tags in dataset: dictWords.update(words) dictTags.update(tags) printLog("DONE: " + str(len(dictWords)) + " size") return dictWords, dictTags def getCharDictionary(dataset): dictChar = set() for words, _ in dataset: for word in words: dictChar.update(word) return dictChar #filename - path wo file with vectors def getGloveDictionary(filename): printLog("Building dictionary") dictGlove = set() with open(filename, encoding='utf-8') as f: for line in f: word = line.strip().split(' ')[0] dictGlove.add(word) printLog("DONE: "+ str(len(dictGlove)) +" tokens") return dictGlove def saveDictionary(dictionary, filename): printLog("SAVE") with open(filename, "w", encoding='utf-8') as f: for i, word in enumerate(dictionary): if i != len(dictionary) - 1: f.write("{}\n".format(word)) else: f.write(word) def loadDictionary(filename): try: d = dict() with open(filename, encoding='utf-8') as f: for idx, word in enumerate(f): word = word.strip() d[word] = idx except IOError: raise ParrotIOError(filename) return d def exportCompactGloveVectors(dictionary, gloveFilename, trimmedFilename, dim): embeddings = np.zeros([len(dictionary), dim]) with open(gloveFilename, encoding='utf-8') as f: for line in f: line = line.strip().split(' ') word = line[0] if word in dictionary: embedding = [float(x) for x in line[1:]] #glove coords wordID = dictionary[word] embeddings[wordID] = np.asarray(embedding) np.savez_compressed(trimmedFilename, embeddings=embeddings) # store glove matrix def getCompactGloveVectors(filename): try: with np.load(filename) as data: return data["embeddings"] except IOError: raise ParrotIOError(filename) def getProcessingWord(dictWords=None, dictChars=None, lowercase=False, chars=False, allowUNK=True): def f(word): # char ids for word if (dictChars is not None) and (chars == True): charIDs = [] for char in word: if (char in dictChars): charIDs.append(dictChars[char]) if lowercase: word = word.lower() if word.isdigit(): word = NUM # word id if (dictWords is not None): if word in dictWords: word = dictWords[word] elif allowUNK: word = dictWords[UNK] else: raise Exception("Unknow tag.") if (dictChars is not None) and (chars == True): # chars ids and word id return charIDs, word # word id return word return f def _padSequences(sequences, padtok, maxLength): sequencePadded, sequenceLength = [], [] for seq in sequences: seq = list(seq) seq_ = seq[:maxLength] + [padtok]*max(maxLength - len(seq), 0) sequencePadded += [seq_] sequenceLength += [min(len(seq), maxLength)] # all sublist have same length return sequencePadded, sequenceLength def padSequences(sequences, padtok, nlevels=1): if nlevels == 1: maxLength = max(map(lambda x : len(x), sequences)) sequencePadded, sequenceLength = _padSequences(sequences, padtok, maxLength) elif nlevels == 2: maxLengthWord = max([max(map(lambda x: len(x), seq)) for seq in sequences]) sequencePadded, sequenceLength = [], [] for seq in sequences: # all words are same length sp, sl = _padSequences(seq, padtok, maxLengthWord) sequencePadded += [sp] sequenceLength += [sl] maxLengthSentence = max(map(lambda x : len(x), sequences)) sequencePadded, _ = _padSequences(sequencePadded, [padtok]*maxLengthWord, maxLengthSentence) sequenceLength, _ = _padSequences(sequenceLength, 0, maxLengthSentence) return sequencePadded, sequenceLength def minibatches(data, minibatchSize): x_batch, y_batch = [], [] for (x, y) in data: if len(x_batch) == minibatchSize: yield x_batch, y_batch x_batch, y_batch = [], [] if type(x[0]) == tuple: x = zip(*x) x_batch += [x] y_batch += [y] if len(x_batch) != 0: yield x_batch, y_batch def getChunkType(tok, idxToTag): tagName = idxToTag[tok] tagClass = tagName.split('-')[0] tagType = tagName.split('-')[-1] return tagClass, tagType def getChunks(seq, tags): """Given a sequence of tags, group entities and their position Args: seq: [4, 4, 0, 0, ...] sequence of labels tags: dict["O"] = 4 Returns: list of (chunkType, chunkStart, chunkEnd) Example: seq = [4, 5, 0, 3] tags = {"B-PER": 4, "I-PER": 5, "B-LOC": 3} result = [("PER", 0, 2), ("LOC", 3, 4)] """ default = tags[NONE] idxToTag = {idx: tag for tag, idx in tags.items()} chunks = [] chunkType, chunkStart = None, None for i, tok in enumerate(seq): # End of a chunk 1 if tok == default and chunkType is not None: # Add a chunk. chunk = (chunkType, chunkStart, i) chunks.append(chunk) chunkType, chunkStart = None, None # End of a chunk + start of a chunk! elif tok != default: tokChunkClass, tokChunkType = getChunkType(tok, idxToTag) if chunkType is None: chunkType, chunkStart = tokChunkType, i elif tokChunkType != chunkType or tokChunkClass == "B": chunk = (chunkType, chunkStart, i) chunks.append(chunk) chunkType, chunkStart = tokChunkType, i else: pass # end condition if chunkType is not None: chunk = (chunkType, chunkStart, len(seq)) chunks.append(chunk) return chunks

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null

0

null

0

1

0

6a177f73dcbbd6c1d2721285cc1b7c72b4784fb1

2,781

py

Python

discordbot/economy/currencies.py

minhhoang1023/GamestonkTerminal

195dc19b491052df080178c0cc6a9d535a91a704

[ "MIT" ]

1

2022-02-18T04:02:52.000Z

discordbot/economy/currencies.py

minhhoang1023/GamestonkTerminal

195dc19b491052df080178c0cc6a9d535a91a704

[ "MIT" ]

null

discordbot/economy/currencies.py

minhhoang1023/GamestonkTerminal

195dc19b491052df080178c0cc6a9d535a91a704

[ "MIT" ]

null

import os import df2img import disnake import pandas as pd from PIL import Image import discordbot.config_discordbot as cfg from discordbot.config_discordbot import logger from discordbot.helpers import autocrop_image from gamestonk_terminal.economy import wsj_model async def currencies_command(ctx): """Currencies overview [Wall St. Journal]""" try: # Debug user input if cfg.DEBUG: logger.debug("econ-currencies") # Retrieve data df = wsj_model.global_currencies() df = pd.DataFrame.from_dict(df) # Check for argument if df.empty: raise Exception("No available data found") df["Last"] = pd.to_numeric(df["Last"].astype(float)) df["Chng"] = pd.to_numeric(df["Chng"].astype(float)) df["%Chng"] = pd.to_numeric(df["%Chng"].astype(float)) formats = {"Last": "{:.2f}", "Chng": "{:.2f}", "%Chng": "{:.2f}%"} for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 df = df.fillna("") df.set_index(" ", inplace=True) # Debug user output if cfg.DEBUG: logger.debug(df.to_string()) df = df[ [ "Last", "Chng", "%Chng", ] ] dindex = len(df.index) fig = df2img.plot_dataframe( df, fig_size=(800, (40 + (40 * dindex))), col_width=[8, 3, 3], tbl_cells=dict( align="left", height=35, ), template="plotly_dark", font=dict( family="Consolas", size=20, ), paper_bgcolor="rgba(0, 0, 0, 0)", ) imagefile = "econ-currencies.png" df2img.save_dataframe(fig=fig, filename=imagefile) image = Image.open(imagefile) image = autocrop_image(image, 0) image.save(imagefile, "PNG", quality=100) image = disnake.File(imagefile) title = "Economy: [WSJ] Currencies" embed = disnake.Embed(title=title, colour=cfg.COLOR) embed.set_image(url=f"attachment://{imagefile}") embed.set_author( name=cfg.AUTHOR_NAME, icon_url=cfg.AUTHOR_ICON_URL, ) os.remove(imagefile) await ctx.send(embed=embed, file=image) except Exception as e: embed = disnake.Embed( title="ERROR Economy: [WSJ] Currencies", colour=cfg.COLOR, description=e, ) embed.set_author( name=cfg.AUTHOR_NAME, icon_url=cfg.AUTHOR_ICON_URL, ) await ctx.send(embed=embed, delete_after=30.0)

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null

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null

0

1

0

6a17d1c656acfd1f8102ff27381a0764e4f0a027

3,276

py

Python

aiovectortiler/config_handler.py

shongololo/aiovectortiler

cfd0008d5ac05baee52a24264f991946324f5a42

[ "MIT" ]

4

2016-07-24T20:39:40.000Z

2018-12-26T06:43:35.000Z

aiovectortiler/config_handler.py

songololo/aiovectortiler

cfd0008d5ac05baee52a24264f991946324f5a42

[ "MIT" ]

7

2016-08-10T16:27:39.000Z

2018-10-13T13:16:24.000Z

aiovectortiler/config_handler.py

songololo/aiovectortiler

cfd0008d5ac05baee52a24264f991946324f5a42

[ "MIT" ]

3

2016-08-09T03:12:24.000Z

2016-11-08T01:17:29.000Z

import os import yaml import logging logger = logging.getLogger(__name__) class Configs: server = None recipes = {} DB = None plugins = None @classmethod def init_server_configs(cls, server_configs): with open(server_configs) as s_c: cls.server = yaml.load(s_c.read()) @classmethod def init_layer_recipes(cls, recipe_configs): recipe_name = None if '/' in recipe_configs: recipe_name = os.path.normpath(recipe_configs).split('/')[-1] # for windows elif '\\' in recipe_configs: recipe_name = os.path.normpath(recipe_configs).split('\\')[-1] if recipe_name[-4:] == '.yml': recipe_name = recipe_name[:-4] elif recipe_name[-5:] == '.yaml': recipe_name = recipe_name[:-5] else: raise FileExistsError('File in layer recipes folder does not have a YAML extension: {0}'.format(recipe_configs)) with open(recipe_configs) as r_c: load_recipe = yaml.load(r_c.read()) cls.recipes[recipe_name] = Recipe(load_recipe) # add the recipe name based on the file name # this is needed by the tilejson query cls.recipes[recipe_name].name = recipe_name logger.info('Adding layer: {0}'.format(recipe_name)) ''' Plugins.load() Plugins.hook('before_load', config=Configs) def load_recipe(data): name = data.get('name', 'default') if name in RECIPES: raise ValueError('Recipe with name {} already exist'.format(name)) data['name'] = name RECIPES[name] = Recipe(data) if len(RECIPES) == 1 and name != 'default': RECIPES['default'] = RECIPES[data['name']] for recipe in Configs.layers: with Path(recipe).open() as f: load_recipe(yaml.load(f.read())) Plugins.hook('load', config=config, recipes=RECIPES) ''' # the following model structures for recipes / layers / queries allows searching up the chain # for attributes. If not found in the root recipes level then it will check the server configs. class Recipe(dict): def __init__(self, data): super().__init__(data) self.load_layers(data['layers']) def load_layers(self, layers): self.layers = {} for layer in layers: self.layers[layer['name']] = Layer(self, layer) def __getattr__(self, attr): return self.get(attr, Configs.server.get(attr, None)) class Layer(dict): def __init__(self, recipe, layer_data): self.recipe = recipe super().__init__(layer_data) self.load_queries(layer_data['queries']) def load_queries(self, queries): self.queries = [] for query in queries: self.queries.append(Query(self, query)) def __getattr__(self, attr): return self.get(attr, getattr(self.recipe, attr)) @property def id(self): return '{0}:{1}'.format(self.recipe.name, self.name) @property def description(self): return self.get('description', 'no description provided') class Query(dict): def __init__(self, layer, data): self.layer = layer super().__init__(data) def __getattr__(self, attr): return self.get(attr, getattr(self.layer, attr))

28.99115

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4.695238

0.25

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0.03499

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0

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false

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0

null

0

null

0

1

0

6a17e7c4a91ac2e9483c7bdc29806cbac3d7a40c

13,237

py

Python

t2vretrieval/models/mlmatch.py

Roc-Ng/HANet

e679703e9e725205424d87f750358fb4f62ceec5

[ "MIT" ]

34

2021-07-26T12:22:05.000Z

2022-03-08T03:49:33.000Z

t2vretrieval/models/mlmatch.py

hexiangteng/HANet

31d37ccad9c56ff9422cb4eb9d32e79e7b9bc831

[ "MIT" ]

null

t2vretrieval/models/mlmatch.py

hexiangteng/HANet

31d37ccad9c56ff9422cb4eb9d32e79e7b9bc831

[ "MIT" ]

3

2021-08-03T06:00:26.000Z

2021-12-27T03:26:12.000Z

import numpy as np import torch import framework.ops import t2vretrieval.encoders.mlsent import t2vretrieval.encoders.mlvideo import t2vretrieval.models.globalmatch from t2vretrieval.models.criterion import cosine_sim from t2vretrieval.models.globalmatch import VISENC, TXTENC class RoleGraphMatchModelConfig(t2vretrieval.models.globalmatch.GlobalMatchModelConfig): def __init__(self): super().__init__() self.num_verbs = 4 self.num_nouns = 6 self.attn_fusion = 'embed' # sim, embed self.simattn_sigma = 4 self.hard_topk = 1 self.max_violation = True self.loss_weights = None ## this config will be covered by model.json due to the functions of load and load_from_dict self.subcfgs[VISENC] = t2vretrieval.encoders.mlvideo.MultilevelEncoderConfig() self.subcfgs[TXTENC] = t2vretrieval.encoders.mlsent.RoleGraphEncoderConfig() class RoleGraphMatchModel(t2vretrieval.models.globalmatch.GlobalMatchModel): def build_submods(self): return { VISENC: t2vretrieval.encoders.mlvideo.MultilevelEncoder(self.config.subcfgs[VISENC]), TXTENC: t2vretrieval.encoders.mlsent.RoleGraphEncoder(self.config.subcfgs[TXTENC]) } def forward_video_embed(self, batch_data): vid_fts = torch.FloatTensor(batch_data['attn_fts']).to(self.device) vid_lens = torch.LongTensor(batch_data['attn_lens']).to(self.device) # (batch, max_vis_len, dim_embed) vid_sent_embeds, vid_verb_embeds, vid_noun_embeds, local_sent_embeds, logits, max_len = self.submods[VISENC](vid_fts, vid_lens) return { 'vid_sent_embeds': vid_sent_embeds, 'vid_verb_embeds': vid_verb_embeds, 'vid_noun_embeds': vid_noun_embeds, 'local_vid_embeds': local_sent_embeds, 'vid_lens': vid_lens, 'max_len': max_len, 'logits': logits, } def forward_text_embed(self, batch_data): sent_ids = torch.LongTensor(batch_data['sent_ids']).to(self.device) ## sentence sent_lens = torch.LongTensor(batch_data['sent_lens']).to(self.device) ## length verb_masks = torch.BoolTensor(batch_data['verb_masks']).to(self.device) ## batch*nv*max_sen_len noun_masks = torch.BoolTensor(batch_data['noun_masks']).to(self.device) node_roles = torch.LongTensor(batch_data['node_roles']).to(self.device) ## batch*(n_v+n_n) rel_edges = torch.FloatTensor(batch_data['rel_edges']).to(self.device) ## batch*(1+n_v+n_n)*(1+n_v+n_n) verb_lens = torch.sum(verb_masks, 2) noun_lens = torch.sum(noun_masks, 2) # sent_embeds: (batch, dim_embed) # verb_embeds, noun_embeds: (batch, num_xxx, dim_embed) sent_embeds, verb_embeds, noun_embeds, local_sent_embeds, sent_logits = self.submods[TXTENC]( sent_ids, sent_lens, verb_masks, noun_masks, node_roles, rel_edges) return { 'sent_embeds': sent_embeds, 'sent_lens': sent_lens, 'verb_embeds': verb_embeds, 'verb_lens': verb_lens, 'noun_embeds': noun_embeds, 'noun_lens': noun_lens, 'sent_logits': sent_logits, 'local_sent_embeds': local_sent_embeds, } def generate_phrase_scores(self, vid_embeds, vid_masks, phrase_embeds, phrase_masks, mask_flag=False): '''Args: - vid_embeds: (batch, num_frames, embed_size) - vid_masks: (batch, num_frames) - phrase_embeds: (batch, num_phrases, embed_size) - phrase_masks: (batch, num_phrases) ''' batch_vids, num_frames, _ = vid_embeds.size() vid_pad_masks = (vid_masks == 0).unsqueeze(1).unsqueeze(3) batch_phrases, num_phrases, dim_embed = phrase_embeds.size() # compute component-wise similarity vid_2d_embeds = vid_embeds.view(-1, dim_embed) phrase_2d_embeds = phrase_embeds.view(-1, dim_embed) # size = (batch_vids, batch_phrases, num_frames, num_phrases) ground_sims = cosine_sim(vid_2d_embeds, phrase_2d_embeds).view( batch_vids, num_frames, batch_phrases, num_phrases).transpose(1, 2) ### if mask_flag: vid_attn_per_word = ground_sims.masked_fill(vid_pad_masks, 0) ############## else: vid_attn_per_word = ground_sims vid_attn_per_word[vid_attn_per_word < 0] = 0 vid_attn_per_word = framework.ops.l2norm(vid_attn_per_word, dim=2) if mask_flag: vid_attn_per_word = vid_attn_per_word.masked_fill(vid_pad_masks, -1e18) ################# vid_attn_per_word = torch.softmax(self.config.simattn_sigma * vid_attn_per_word, dim=2) if self.config.attn_fusion == 'embed': vid_attned_embeds = torch.einsum('abcd,ace->abde', vid_attn_per_word, vid_embeds) word_attn_sims = torch.einsum('abde,bde->abd', framework.ops.l2norm(vid_attned_embeds), framework.ops.l2norm(phrase_embeds)) elif self.config.attn_fusion == 'sim': # (batch_vids, batch_phrases, num_phrases) word_attn_sims = torch.sum(ground_sims * vid_attn_per_word, dim=2) # sum: (batch_vid, batch_phrases) phrase_scores = torch.sum(word_attn_sims * phrase_masks.float().unsqueeze(0), 2) \ / torch.sum(phrase_masks, 1).float().unsqueeze(0).clamp(min=1) return phrase_scores def generate_scores(self, **kwargs): ##### shared ##### vid_lens = kwargs['vid_lens'] # (batch, ) num_frames = int(kwargs['max_len'])###########################kwargs['vid_verb_embeds'].size(1) vid_masks = framework.ops.sequence_mask(vid_lens, num_frames, inverse=False) # batch*max_len ##### sentence-level scores ##### sent_scores = cosine_sim(kwargs['vid_sent_embeds'], kwargs['sent_embeds']) ####################################################### # concept scores use jaccard similarity concept_verb_scores = self.jaccard_sim(kwargs['logits'][0], kwargs['sent_logits'][0]) concept_noun_scores = self.jaccard_sim(kwargs['logits'][1], kwargs['sent_logits'][1]) ####################################################### ##### verb-level scores ##### vid_verb_embeds = kwargs['vid_verb_embeds'] # (batch, num_frames, dim_embed) verb_embeds = kwargs['verb_embeds'] # (batch, num_verbs, dim_embed) verb_lens = kwargs['verb_lens'] # (batch, num_verbs) local_vid_embeds =kwargs['local_vid_embeds'] local_sent_embeds = kwargs['local_sent_embeds'] verb_masks = framework.ops.sequence_mask(torch.sum(verb_lens > 0, 1).long(), self.config.num_verbs, inverse=False) # sum: (batch_vids, batch_sents) verb_scores = self.generate_phrase_scores(vid_verb_embeds, vid_masks, verb_embeds, verb_masks) ind_verb_scores = self.generate_phrase_scores(local_vid_embeds[0], vid_masks, local_sent_embeds[0], verb_masks, True) ##### noun-level scores ##### vid_noun_embeds = kwargs['vid_noun_embeds'] # (batch, num_frames, dim_embed) noun_embeds = kwargs['noun_embeds'] # (batch, num_nouns, dim_embed) noun_lens = kwargs['noun_lens'] # (batch, num_nouns) noun_masks = framework.ops.sequence_mask(torch.sum(noun_lens > 0, 1).long(), self.config.num_nouns, inverse=False) # sum: (batch_vids, batch_sents) noun_scores = self.generate_phrase_scores(vid_noun_embeds, vid_masks, noun_embeds, noun_masks) ind_noun_scores = self.generate_phrase_scores(local_vid_embeds[1], vid_masks, local_sent_embeds[1], noun_masks, True) return sent_scores, verb_scores, noun_scores, concept_verb_scores, concept_noun_scores, ind_verb_scores, ind_noun_scores def jaccard_sim(self, im, s): im_bs = im.size(0) s_bs = s.size(0) im = im.unsqueeze(1).expand(-1, s_bs, -1) s = s.unsqueeze(0).expand(im_bs, -1, -1) intersection = torch.min(im, s).sum(-1) union = torch.max(im, s).sum(-1) score = intersection / union return score def forward_loss(self, batch_data, step=None): enc_outs = self.forward_video_embed(batch_data) cap_enc_outs = self.forward_text_embed(batch_data) enc_outs.update(cap_enc_outs) sent_scores, verb_scores, noun_scores, concept_verb_scores, concept_noun_scores, local_verb_scores, local_noun_scores = self.generate_scores(**enc_outs) scores = (sent_scores + verb_scores + noun_scores + local_verb_scores + local_noun_scores) / 5 scores2 = (concept_verb_scores + concept_noun_scores) / 2 sent_loss = self.criterion(sent_scores) verb_loss = self.criterion(verb_scores) noun_loss = self.criterion(noun_scores) eta = 0.1 mu = 0.01 concept_verb_loss = 0.5*self.criterion(concept_verb_scores) concept_noun_loss = 0.5*self.criterion(concept_noun_scores) concept_loss = eta*self.criterion(scores2) verb_concept_label = torch.FloatTensor(batch_data['verb_concept_label']).to(self.device) noun_concept_label = torch.FloatTensor(batch_data['noun_concept_label']).to(self.device) verb_concept_mask = torch.FloatTensor(batch_data['verb_concept_mask']).to(self.device) noun_concept_mask = torch.FloatTensor(batch_data['noun_concept_mask']).to(self.device) v_mask_sum = torch.sum(verb_concept_mask, dim=1) n_mask_sum = torch.sum(noun_concept_mask, dim=1) vbce_loss = torch.sum(verb_concept_mask*self.criterion_bce(enc_outs['logits'][0], verb_concept_label), dim=1) vbce_loss = mu*torch.mean(vbce_loss/v_mask_sum) nbce_loss = torch.sum(noun_concept_mask*self.criterion_bce(enc_outs['logits'][1], noun_concept_label), dim=1) nbce_loss = mu*torch.mean(nbce_loss/n_mask_sum) vbce_sent_loss = torch.sum(verb_concept_mask*self.criterion_bce(enc_outs['sent_logits'][0], verb_concept_label), dim=1) vbce_sent_loss = mu*torch.mean(vbce_sent_loss/v_mask_sum) nbce_sent_loss = torch.sum(noun_concept_mask*self.criterion_bce(enc_outs['sent_logits'][1], noun_concept_label), dim=1) nbce_sent_loss = mu*torch.mean(nbce_sent_loss/n_mask_sum) fusion_loss = self.criterion(scores) if self.config.loss_weights is None: loss = fusion_loss + 1*(vbce_loss+nbce_loss) + 1*(vbce_sent_loss+nbce_sent_loss) + concept_loss else: loss = self.config.loss_weights[0] * fusion_loss + \ self.config.loss_weights[1] * sent_loss + \ self.config.loss_weights[2] * verb_loss + \ self.config.loss_weights[3] * noun_loss + \ vbce_loss + nbce_loss if step is not None and self.config.monitor_iter > 0 and step % self.config.monitor_iter == 0: neg_scores = scores.masked_fill(torch.eye(len(scores), dtype=torch.bool).to(self.device), -1e10) self.print_fn('\tstep %d: pos mean scores %.2f, hard neg mean scores i2t %.2f, t2i %.2f'%( step, torch.mean(torch.diag(scores)), torch.mean(torch.max(neg_scores, 1)[0]), torch.mean(torch.max(neg_scores, 0)[0]))) self.print_fn('\tstep %d: sent_loss %.4f, verb_loss %.4f, noun_loss %.4f, fusion_loss %.4f'%( step, sent_loss.data.item(), verb_loss.data.item(), noun_loss.data.item(), fusion_loss.data.item())) self.print_fn('\tstep %d: vbce_loss %.4f, nbce_loss %.4f'%(step, vbce_loss.item(), nbce_loss.item())) self.print_fn('\tstep %d: vbce_sent_loss %.4f, nbce_sent_loss %.4f'%(step, vbce_sent_loss.item(), nbce_sent_loss.item())) self.print_fn('\tstep %d: sim_loss %.4f, vsim_loss %.4f, nsim_loss %.4f'%(step, concept_loss.item(), concept_verb_loss.item(), concept_noun_loss.item())) return loss def evaluate_scores(self, tst_reader): K = self.config.subcfgs[VISENC].num_levels K = K + 4 assert K == 7, 'Note that this error indicates losing other scores!' vid_names, all_scores = [], [[] for _ in range(K)] cap_names = tst_reader.dataset.captions for vid_data in tst_reader: vid_names.extend(vid_data['names']) vid_enc_outs = self.forward_video_embed(vid_data) for k in range(K): all_scores[k].append([]) ijj = 0 for cap_data in tst_reader.dataset.iterate_over_captions(self.config.tst_batch_size): cap_enc_outs = self.forward_text_embed(cap_data) cap_enc_outs.update(vid_enc_outs) indv_scores = self.generate_scores(**cap_enc_outs) for k in range(K): all_scores[k][-1].append(indv_scores[k].data.cpu().numpy()) ijj += 0 for k in range(K): all_scores[k][-1] = np.concatenate(all_scores[k][-1], axis=1) for k in range(K): all_scores[k] = np.concatenate(all_scores[k], axis=0) # (n_img, n_cap) all_scores = np.array(all_scores) # (k, n_img, n_cap) return vid_names, cap_names, all_scores def evaluate(self, tst_reader, return_outs=False): vid_names, cap_names, scores = self.evaluate_scores(tst_reader) i2t_gts = [] for vid_name in vid_names: i2t_gts.append([]) for i, cap_name in enumerate(cap_names): if cap_name in tst_reader.dataset.ref_captions[vid_name]: i2t_gts[-1].append(i) t2i_gts = {} for i, t_gts in enumerate(i2t_gts): for t_gt in t_gts: t2i_gts.setdefault(t_gt, []) t2i_gts[t_gt].append(i) idx = [0, 1, 2, 5, 6] fused_scores = (np.mean(scores[idx], 0) + np.mean(scores[3:5], 0))/2 metrics = self.calculate_metrics(fused_scores, i2t_gts, t2i_gts) if return_outs: outs = { 'vid_names': vid_names, 'cap_names': cap_names, 'scores': scores, } return metrics, outs else: return metrics

46.939716

156

0.694568

1,936

13,237

4.410641

0.129132

0.02108

0.018269

0.019674

0.338564

0.231994

0.146973

0.093688

0.046141

0.038412

0

0.01392

0.169676

13,237

281

157

47.106762

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false

0

0.037037

0.00463

0.138889

0.023148

0

null

0

null

0

1

0

6a190e5eb1440e6a01fc6f170da74507f39571ac

6,295

py

Python

dronesym-python/flask-api/src/dronepool.py

dilinade/DroneSym

30073bd31343bc27c6b8d72e48b4e06ced0c5fe6

[ "Apache-2.0" ]

1

2019-03-24T23:50:07.000Z

dronesym-python/flask-api/src/dronepool.py

dilinade/DroneSym

30073bd31343bc27c6b8d72e48b4e06ced0c5fe6

[ "Apache-2.0" ]

null

dronesym-python/flask-api/src/dronepool.py

dilinade/DroneSym

30073bd31343bc27c6b8d72e48b4e06ced0c5fe6

[ "Apache-2.0" ]

null

#DronePool module which handles interaction with SITLs from dronekit import Vehicle, VehicleMode, connect from dronekit_sitl import SITL from threading import Lock import node, time import mavparser import threadrunner drone_pool = {} instance_count = 0 env_test = False q = None mq = None lock = Lock() class Sim(SITL, object): def __init__(self, instance=1, home=None): super(Sim, self).download("copter", "3.3", verbose=not env_test) self.instance = instance if home: self.home = home else: self.home = {"lat":6.9271, "lon":79.8612, "alt": 1} self.p = None return def connection_string(self): return super(Sim, self).connection_string()[:-4] + str(5760 + self.instance * 10) def launch(self): home_str = str(self.home['lat']) + ',' + str(self.home['lon']) + ',0,353' super(Sim, self).launch(["--instance", str(self.instance), "--home", home_str], await_ready=True, verbose=not env_test) def get_sitl_status(self): return { 'id': self.instance, 'home': self.home } def initialize(): global q, mq, instance_count q = threadrunner.q mq = threadrunner.mq drones = node.get_drones()['drones'] if not drones: return for drone_id in drones: if drone_id not in list(drone_pool.keys()): drone = node.get_drone_by_id(drone_id) location = drone['location'] q.put((create_new_drone, { "db_key" : drone_id, "home" : location })) if 'status' in list(drone.keys()) and drone['status'] == 'FLYING': q.put((resume_flight, { "drone_id" : drone_id })) def resume_flight(kwargs): drone_id = kwargs.get("drone_id", None) drone = node.get_drone_by_id(drone_id) waypoints = [] for wp in sorted(drone['waypoints']): waypoints.append(drone['waypoints'][wp]) next_waypoint = waypoints.index(drone['waypoint']) print (next_waypoint) q.put((takeoff_drone, { "drone_id" : drone_id, "waypoints" : waypoints[next_waypoint:] })) def create_new_drone(kwargs): global instance_count instance_count += 1 home = kwargs.get("home", None) db_key = kwargs.get("db_key", None) retries = 3 drone = Sim(instance_count, home) drone.launch() while retries > 0: try: drone_conn = connect(drone.connection_string(), wait_ready=True) break except: print ("Retrying...") retries -= 1 drone_pool[db_key] = drone_conn res = { "status" : "OK", "id" : db_key } return res def remove_drone(kwargs): drone_id = kwargs.get("drone_id", None) if drone_id not in drone_pool: return { "status" : "ERROR", "msg" : "Drone instance not found" } drone = drone_pool[drone_id] if drone.mode == VehicleMode('AUTO'): return { "status" : "ERROR", "msg" : "Drone in operation" } del drone_pool[drone_id] return { "status" : "OK", "id" : drone_id } def run_mission(drone, target_height, waypoints): while True: print(("Reaching target alt : " + str(drone.location.global_relative_frame.alt))) if drone.location.global_relative_frame.alt >= target_height * 0.9: break print ('target alt reached') mavparser.create_mission(drone, waypoints) print ('mission acquired') drone.mode = VehicleMode('AUTO') print ('initiating sequence') print ('in mission') def attach_listener(kwargs): attr = kwargs.get('attr', None) fn = kwargs.get('fn', None) attach_fn = kwargs.get('attach_fn', None) if not fn == None and not attr == None and not attach_fn == None: attach_fn(attr, fn) def takeoff_drone(kwargs): global q drone_id = kwargs.get("drone_id", None) target_height = kwargs.get("target_height", 10) waypoints = kwargs.get("waypoints", None) try: drone = drone_pool[drone_id] except: raise drone.initialize() drone.mode = VehicleMode('GUIDED') drone.armed = True while not drone.armed: time.sleep(1) drone.simple_takeoff(target_height) print (waypoints) if waypoints: run_mission(drone, target_height, waypoints) def detach_event_listeners(drone, value, status): drone.remove_attribute_listener('location', update_location) drone.remove_attribute_listener('airspeed', update_airspeed) drone.remove_attribute_listener('attitude', udpate_attitude) drone.remove_attribute_listener('heading', update_heading) node.update_drone(drone_id, { "location" : {"lat": value.global_relative_frame.lat, "lon": value.global_relative_frame.lon, "alt": value.global_relative_frame.alt}, "status": status}) return def update_location(self, attr_name, value): node.update_drone(drone_id, { "location" : {"lat": value.global_relative_frame.lat, "lon": value.global_relative_frame.lon, "alt": value.global_relative_frame.alt}, "status": "FLYING"}) command_len = len(drone.commands) wp_len = len(waypoints) if command_len >= wp_len : diff = command_len - wp_len next_wp = max(drone.commands.__next__ - diff, 0) % len(waypoints) waypoint = waypoints[next_wp] # print "df: " + `diff` # print next_wp node.update_drone(drone_id, { "waypoint" : waypoint }) if drone.mode == VehicleMode('LAND') and drone.location.global_relative_frame.alt <= 0.1: detach_event_listeners(drone, value, "HALTED") return if drone.commands.__next__ == len(drone.commands): detach_event_listeners(drone, value, "FINISHED") return def update_airspeed(self, attr_name, value): node.update_drone(drone_id, {"airspeed": value}) def udpate_attitude(self, attr_name, value): node.update_drone(drone_id, { "pitch": value.pitch, 'roll': value.roll, 'yaw': value.yaw }) def update_heading(self, attr_name, value): node.update_drone(drone_id, { "heading": value }) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'location', "fn" : update_location })) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'airspeed', "fn" : update_airspeed })) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'attitude', "fn" : udpate_attitude })) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'heading', "fn" : update_heading })) print ('took off') return True def land_drone(kwargs): drone_id = kwargs.get("drone_id", None) try: drone = drone_pool[drone_id] except: raise if not drone.armed: return False cmds = drone.commands cmds.wait_ready() cmds.clear() drone.mode = VehicleMode('LAND') print((drone.mode)) return True

27.133621

187

0.707705

884

6,295

4.825792

0.186652

0.047586

0.040084

0.028129

0.296296

0.246835

0.205345

0.199015

0.178153

0.11158

0

0.007268

0.147577

6,295

231

188

27.251082

0.787738

0.014138

0

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0

0.107045

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false

0

0.037037

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0.061728

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null

0

null

0

1

0

6a19dea1f3bc079f6c50613369f0699df82e34cf

2,365

py

Python

Problemset/longest-string-chain/longest-string-chain.py

KivenCkl/LeetCode

fcc97c66f8154a5d20c2aca86120cb37b9d2d83d

[ "MIT" ]

7

2019-05-08T03:41:05.000Z

2020-12-22T12:39:43.000Z

Problemset/longest-string-chain/longest-string-chain.py

Yuziquan/LeetCode

303fc1c8af847f783c4020bd731b28b72ed92a35

[ "MIT" ]

1

2021-07-19T03:48:35.000Z

Problemset/longest-string-chain/longest-string-chain.py

Yuziquan/LeetCode

303fc1c8af847f783c4020bd731b28b72ed92a35

[ "MIT" ]

7

2019-05-10T20:43:20.000Z

2021-02-22T03:47:35.000Z

# @Title: 最长字符串链 (Longest String Chain) # @Author: KivenC # @Date: 2019-05-26 20:35:25 # @Runtime: 144 ms # @Memory: 13.3 MB class Solution: # # way 1 # def longestStrChain(self, words: List[str]) -> int: # # 动态规划 # # dp[i] = max(dp[i], dp[j] + 1) (0 <= j < i 且 words[j] 是 words[i] 的前身) # length = len(words) # if length < 2: # return length # dp = [1 for _ in range(length)] # words.sort(key=len) # 按字符串长度递增排序 # for i in range(1, length): # if i >= 1 and words[i] == words[i - 1]: # 去重 # continue # for j in range(i - 1, -1, -1): # if len(words[i]) - len(words[j]) > 1: # 剪枝 # break # if len(words[i]) == len(words[j]): # continue # if self.isPre(words[j], words[i]): # dp[i] = max(dp[i], dp[j] + 1) # return max(dp) # def isPre(self, word1: str, word2: str) -> bool: # # 判断 word1 是否是 word2 的前身 # # 双指针 # # i, j, length1, length2 = 0, 0, len(word1), len(word2) # # while i < length1 and j < length2: # # if word1[i] == word2[j]: # # i += 1 # # j += 1 # # if length2 - length1 == 1 and i == length1: # # return True # # return False # # word2 去除任意一个位置的字符后与 word1 进行比对 # if len(word1) + 1 != len(word2): # return False # for i in range(len(word2)): # if word2[: i] + word2[i + 1:] == word1: # return True # return False # way 2 def longestStrChain(self, words: List[str]) -> int: import collections length = len(words) if length < 2: return length pool = collections.defaultdict(list) # 将字符串按照其长度进行分组 dp = {} for word in words: pool[len(word)].append(word) for key in sorted(pool.keys()): if key - 1 not in pool: continue for word in pool[key]: for j in range(key): tmp = word[: j] + word[j + 1:] if tmp in pool[key - 1]: dp[word] = max(dp.get(word, 1), dp.get(tmp, 1) + 1) return max(dp.values()) if dp else 1

33.785714

81

0.442283

299

2,365

3.494983

0.284281

0.03445

0.042105

0.051675

0.200957

0.091866

0

0.052364

0.418605

2,365

69

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34.275362

0.707636

0.647357

0

1

0.052632

false

0

0.052632

0

0.263158

0

null

0

null

0

1

0

6a19e8bf83375a817e65cca3fb4f7daafac8434e

21,107

py

Python

IKFK Builder/IKFK_Builder.py

ssimbox/ssimbox-rigTools

824bc3b90c42ab54d01b4b0007f00e7cc2f2f08c

[ "MIT" ]

1

2021-01-19T13:36:42.000Z

IKFK Builder/IKFK_Builder.py

ssimbox/sbx-autorig

824bc3b90c42ab54d01b4b0007f00e7cc2f2f08c

[ "MIT" ]

2

2021-03-29T22:15:08.000Z

2021-03-29T22:17:37.000Z

IKFK Builder/IKFK_Builder.py

ssimbox/ssimbox-rigTools

824bc3b90c42ab54d01b4b0007f00e7cc2f2f08c

[ "MIT" ]

null

from ctrlUI_lib import createClav2, createSphere import maya.cmds as cmds import maya.OpenMaya as om from functools import partial def duplicateChain(*args): global ogChain global chainLen global switcherLoc global side global controllerColor global clavCheckbox global rigGrp, ctrlGrp ogRootchain = cmds.ls(sl = True, type = "joint")[0] ogChain = cmds.listRelatives(ogRootchain, ad = True, type = "joint") ogChain.append(ogRootchain) ogChain.reverse() side = ogRootchain[0:2] # Initialize input from UI scaleController = cmds.intField(scaleField_UI, q=1, v=1) blendCheckbox = cmds.checkBox(blendCheckbox_UI, q=1, v=1) constraintCheckBox = cmds.checkBox(constraintCheckBox_UI, q=1, v=1) chainMenu = cmds.optionMenu("chainMenu_UI", q=1, v=1) clavCheckbox = cmds.checkBox(clavCheckbox_UI, q=1, v=0) if side == "l_": controllerColor = rgb=(0, 0, 255) elif side == "r_": controllerColor = rgb=(255, 0, 0) if chainMenu == "Leg": chainLen = 5 else: #this is totally unscalable but for now it's ok chainLen = 3 #suffix for the new chains newJointList = ["_ik", "_fk", "_scale"] for newJoint in newJointList: for i in range(chainLen): if blendCheckbox == 0 and constraintCheckBox == 0: cmds.error("pls, select one relation type") break newJointName = ogChain[i] + newJoint #create a joint, copy their position and freeze transform cmds.joint(n = newJointName) cmds.matchTransform(newJointName, ogChain[i]) cmds.makeIdentity(newJointName, a = 1, t = 0, r = 1, s = 0) #deselect to make the two different hierarchies cmds.select(cl = 1) cmds.parent((ogChain[0] + "_ik"), world = True) cmds.setAttr(ogChain[0] + "_ik.visibility", 0) cmds.setAttr(ogChain[0] + "_fk.visibility", 0) # Create a locator used for switching IK/FK mode and snap it between two joints switcherLoc = cmds.spaceLocator(n=side + chainMenu + "_ikfk_Switch") switcherLocGrp = cmds.group(em=1, n=switcherLoc[0] + "_grp") cmds.color(switcherLoc, rgb=(255, 255, 0)) #yellow cmds.delete(cmds.pointConstraint(switcherLoc, switcherLocGrp)) cmds.parent(switcherLoc, switcherLocGrp) cmds.delete(cmds.pointConstraint(ogChain[1], ogChain[2], switcherLocGrp)) cmds.addAttr(switcherLoc, ln="FKIK_Mode", at="short", min=0, max=1, k=1, r=1) cmds.move(0,0,-12, switcherLocGrp, r=1) #IMPROVE THIS SHIT cmds.parentConstraint(ogChain[1], switcherLocGrp, mo=1) #remove .t, .r, .s and .v from the channelbox for coord in ["X", "Y", "Z"]: cmds.setAttr(switcherLoc[0] + ".translate" + coord, k=0, l=1) cmds.setAttr(switcherLoc[0] + ".rotate" + coord, k=0, l=1) cmds.setAttr(switcherLoc[0] + ".scale" + coord, k=0, l=1) cmds.setAttr(switcherLoc[0] + ".visibility", k=0, l=1) # Create hierarchy groups rigGrp = cmds.group(em=1, n= side + chainMenu + "_rig_grp") ctrlGrp = cmds.group(em=1, n= side + chainMenu + "_ctrl_grp") cmds.delete(cmds.parentConstraint(ogChain[0], rigGrp)) cmds.delete(cmds.parentConstraint(ogChain[0], ctrlGrp)) cmds.parent(ctrlGrp, rigGrp) # Execute if blendCheckbox == 1: blendNodeFunc(scaleController, chainMenu) if constraintCheckBox == 1: constraintFunc(scaleController, chainMenu) if clavCheckbox == 1: clavSel(scaleController) else: cmds.parent(ogChain[0] + "_ik", ogChain[0] + "_fk", ctrlGrp) cmds.parent(ogChain[0] + "_fk_anim_grp", ctrlGrp) cmds.parent(switcherLocGrp, rigGrp) def clavSel(scaleClav): # Select clavicle Joint moving up and put it at the top of the chain clavJoint = cmds.pickWalk(ogChain[0], d="up")[0] #ogChain.insert(0, clavJoint) clavController = createClav2(clavJoint + "_anim") # Import coordinates from ctrlUI_lib cmds.delete(cmds.pointConstraint(clavJoint, clavController)) # Create offset group, FDH and move up clavControllerGrp = cmds.group(n=clavController + "_grp", em=1) cmds.delete(cmds.parentConstraint(clavJoint, clavControllerGrp)) cmds.parent(clavController, clavControllerGrp) fixedScale = scaleClav/4 cmds.scale(fixedScale, fixedScale, fixedScale, clavController) cmds.makeIdentity(clavController, a=1) cmds.move(0,10,0, clavControllerGrp, ws=1, r=1) cmds.color(clavController, rgb=controllerColor) # Move pivots on clavicle joint piv = cmds.xform(clavJoint, q=True, ws=True, t=True) cmds.xform(clavController, ws=True, piv=piv) cmds.xform(clavControllerGrp, ws=True, piv=piv) cmds.orientConstraint(clavController, clavJoint) # Parent ik and fk chain under clavicle controller cmds.parent((ogChain[0]+"_fk_anim_grp"),(ogChain[0] + "_ik"), (ogChain[0] + "_fk"), clavController) cmds.parent(clavControllerGrp, ctrlGrp) def visCheck(vis): if vis == "Arm": asd = True if vis == "Leg": asd = False cmds.checkBox(clavCheckbox_UI, e=1, vis=asd, v=asd) # Buttons +1 and +3 count = 0 def addOneUnit(*args): global count count = count + 1 cmds.intField(scaleField_UI, v=1+count, e=1) def addThreeUnit(*args): global count count = count + 3 cmds.intField(scaleField_UI, v=1+count, e=1) def blendNodeFunc(scaleController, selectChain): # Create some blendColors node with the same name of the joint for x in range(chainLen): blendColorsNode = cmds.createNode("blendColors", n = ogChain[x] + "_blend") # Connect FK and IK chains into blendColors channels and then connect the output to the original joint chain cmds.connectAttr((ogChain[x] + "_ik.rotate"), blendColorsNode + ".color1") cmds.connectAttr((ogChain[x] + "_fk.rotate"), blendColorsNode + ".color2") cmds.connectAttr((blendColorsNode + ".output"), (ogChain[x] + ".rotate" )) cmds.connectAttr(switcherLoc[0]+".FKIK_Mode", blendColorsNode + ".blender") ikChainBuild(scaleController, selectChain) fkControllerCreator(scaleController, selectChain) def constraintFunc(scaleController, selectChain): # Create some blendColors node with the same name of the joint for x in range(chainLen): # Setup orient constraints cmds.parentConstraint((ogChain[x] + "_ik"), ogChain[x]) cmds.parentConstraint((ogChain[x] + "_fk"), ogChain[x]) # Setup SDK naming convention sdkDriver = switcherLoc[0] + ".FKIK_Mode" ikSdkDriven = ogChain[x] + "_parentConstraint1." + ogChain[x] + "_ikW0" fkSdkDriven = ogChain[x] + "_parentConstraint1." + ogChain[x] + "_fkW1" # Setup SDK cmds.setAttr(sdkDriver, 0) cmds.setDrivenKeyframe(ikSdkDriven, cd=sdkDriver, v=0, dv=0) cmds.setDrivenKeyframe(fkSdkDriven, cd=sdkDriver, v=1, dv=0) cmds.setAttr(sdkDriver, 1) cmds.setDrivenKeyframe(ikSdkDriven, cd=sdkDriver, v=1, dv=1) cmds.setDrivenKeyframe(fkSdkDriven, cd=sdkDriver, v=0, dv=1) ikChainBuild(scaleController, selectChain) fkControllerCreator(scaleController, selectChain) def fkControllerCreator(fkSize, legOrArm): orientController = cmds.optionMenu("UI_orientControllerMenu", q=1, v=1) # Create controllers and group offsets # Change rotation, color for y in range(chainLen): anim_group = cmds.group(em=1, n=ogChain[y] + "_fk_anim_grp") fk_controller = cmds.circle(n=ogChain[y] + "_fk_anim")[0] # If not [0] it'll warn some stuff related to Maya underworld # Set scale cmds.scale(fkSize, fkSize, fkSize, fk_controller) cmds.matchTransform(anim_group, ogChain[y]) cmds.delete(cmds.parentConstraint(ogChain[y], fk_controller)) cmds.parent(fk_controller, anim_group) # Set controller orientation based on second axis if orientController == "x": cmds.rotate(90,0,0, fk_controller) if orientController == "y": cmds.rotate(0,90,0, fk_controller) if orientController == "z": cmds.rotate(0,0,90, fk_controller) # Freeze transform, delete history and set color cmds.makeIdentity(fk_controller, a = 1, t = 1, r = 1, s = 0) cmds.delete(fk_controller, ch = 1) cmds.color(fk_controller, rgb=controllerColor) # Set SDK visibility sdkDriver = switcherLoc[0] + ".FKIK_Mode" cmds.setAttr(sdkDriver, 1) cmds.setDrivenKeyframe(ogChain[0] + "_fk_anim_grp.visibility", cd=sdkDriver, v=1, dv=0) cmds.setAttr(sdkDriver, 0) cmds.setDrivenKeyframe(ogChain[0] + "_fk_anim_grp.visibility", cd=sdkDriver, v=0, dv=1) # Lock .t and .s attributes #for x in ["X", "Y", "Z"]: #cmds.setAttr(fk_controller + ".translate" + x, k=0, l=1) #cmds.setAttr(fk_controller + ".scale" + x, k=0, l=1) # Create ordered hierarchy for x in reversed(range(chainLen)): if x == 0: continue cmds.parent(ogChain[x] + "_fk_anim_grp", ogChain[x-1] + "_fk_anim") # Set orientConstraint _anim controllers with _fk hierarchy for x in range(chainLen): cmds.parentConstraint(ogChain[x] + "_fk_anim", ogChain[x] + "_fk") # If leg chain is selected delete toe controller, else not if legOrArm == "Leg": if x == (chainLen-1): cmds.delete(ogChain[chainLen-1] + "_fk_anim_grp") def ikChainBuild(scaleIK, HandleName): masterIkHandle = cmds.ikHandle(sj=ogChain[0] + "_ik", ee=ogChain[2] + "_ik", sol="ikRPsolver", n=side + HandleName + "_ikHandle") cmds.setAttr(masterIkHandle[0] + ".visibility", 0) if HandleName == "Arm": #print ("scaleController", scaleField_UI) armIk(scaleIK, masterIkHandle, HandleName) else: #print ("scaleController", scaleField_UI) legIK(scaleIK, masterIkHandle, HandleName) def armIk(armIkScale, armikHandle, pvName): ikHandJoint = cmds.joint(n=side + "hand_ik") cmds.delete(cmds.parentConstraint(ogChain[2] + "_ik", ikHandJoint)) cmds.makeIdentity(ikHandJoint, a = 1, t = 1, r = 1, s = 0) if side == "l_": cmds.move(10,0,0, ikHandJoint, r=1, os=1) else: cmds.move(-10,0,0, ikHandJoint, r=1, os=1) cmds.parent(ikHandJoint, ogChain[2] + "_ik") handikHandle = cmds.ikHandle(sj=ogChain[2] + "_ik", ee=ikHandJoint, n=side + "hand_ikHandle", sol="ikSCsolver") cmds.parent(handikHandle[0], armikHandle[0]) #create IK controller ---> CUBE crvIkCube = cmds.curve(d=1, p=[(-0.5, 0.5, -0.5), (0.5, 0.5, -0.5), (0.5, 0.5, 0.5), (-0.5, 0.5, 0.5), (-0.5, -0.5, 0.5), (-0.5, -0.5, -0.5), (-0.5, 0.5, -0.5), (-0.5, 0.5, 0.5), (-0.5, -0.5, 0.5), (0.5, -0.5, 0.5), (0.5, 0.5, 0.5), (0.5, 0.5, -0.5), (0.5, -0.5, -0.5), (0.5, -0.5, 0.5), (0.5, -0.5, -0.5), (-0.5, -0.5, -0.5)], k=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5], n=side + "hand_ik_anim" ) # Rename shape node shapeList = cmds.listRelatives(crvIkCube, s = True) cmds.rename(shapeList, crvIkCube + "Shape") crvIkCubeGrp = cmds.group(n=crvIkCube + "_grp") cmds.delete(cmds.parentConstraint(ogChain[2] + "_ik", crvIkCubeGrp)) cmds.color(crvIkCube, rgb=controllerColor) cmds.scale(armIkScale, armIkScale, armIkScale, crvIkCubeGrp) cmds.parent(armikHandle[0], crvIkCube) pvController = createSphere(nome= side+pvName+"_PV") findPoleVector(loc=pvController, targetHandle=armikHandle[0]) cmds.addAttr(pvController, at="enum", enumName = "------", ln="Attributes", k=1, r=1) cmds.addAttr(pvController, ln="Follow", k=1, r=1, min=0, max=1) cmds.addAttr(pvController, ln="Follow_Clav_Hand", k=1, r=1, min=0, max=1, dv=0.5) # Parent ikController and PV under _rig_GRP cmds.parent(crvIkCubeGrp, pvController + "_grp" ,rigGrp) #set SDK visibility sdkDriver = switcherLoc[0] + ".FKIK_Mode" cmds.setAttr(sdkDriver, 0) cmds.setDrivenKeyframe(crvIkCubeGrp + ".visibility", cd=sdkDriver, v=0, dv=0) cmds.setDrivenKeyframe(pvController + "_grp.visibility", cd=sdkDriver, v=0, dv=0) cmds.setAttr(sdkDriver, 1) cmds.setDrivenKeyframe(crvIkCubeGrp + ".visibility", cd=sdkDriver, v=1, dv=1) cmds.setDrivenKeyframe(pvController + "_grp.visibility", cd=sdkDriver, v=1, dv=1) def legIK(ikFootScale, legikHandle, pvName): ballikHandle = cmds.ikHandle(sj=ogChain[2] + "_ik", ee=ogChain[3] + "_ik", sol="ikSCsolver", n=side + "ball_ikHandle") toeikHandle = cmds.ikHandle(sj=ogChain[3] + "_ik", ee=ogChain[4] + "_ik", sol="ikSCsolver", n=side + "toe_ikHandle") # Create and place ik controller ikFootControl = cmds.curve(d=2, p=[(0.997, 0, 1.789), (0, 0, 2.39), (-0.997,0,1.789), (-1.108, 0, 0), (-0.784, 0,-2.5), (0, 0,-3), (0.784, 0, -2.5), (1.108, 0, 0), (0.997, 0, 1.789), (0, 0, 2.39)], k=[0,1,2,3,4,5,6,7,8,9,10], n=side + "leg_anim_ik") # Rename shape node shapeList = cmds.listRelatives(ikFootControl, s = True) cmds.rename(shapeList, ikFootControl + "Shape") ikFootControlGrp = cmds.group(em=1, n=ikFootControl + "_grp") cmds.parent(ikFootControl, ikFootControlGrp) # Set size, freeze transform, create offset group and color cmds.scale(ikFootScale, ikFootScale, ikFootScale, ikFootControlGrp) cmds.move(0,-3.2,0, ikFootControl, r=1) cmds.makeIdentity(ikFootControl, a = 1, t = 1, r = 1, s = 1) cmds.delete(ikFootControl, ch = 1) cmds.delete(cmds.pointConstraint(ogChain[3] + "_ik", ikFootControlGrp)) cmds.color(ikFootControl, rgb=controllerColor) # pivot snapping on ankle joint piv = cmds.xform(ogChain[2], q=True, ws=True, t=True) cmds.xform(ikFootControl, ws=True, piv=piv) cmds.parent(ballikHandle[0], toeikHandle[0], legikHandle[0], ikFootControl) #---------- Making Pole Vector -------------# # Pole Vector controller ---> Sphere pvController = createSphere(nome= side+pvName+"_PV") findPoleVector(loc=pvController, targetHandle=legikHandle[0]) cmds.addAttr(pvController, ln="Follow", k=1, r=1, min=0, max=1) cmds.addAttr(pvController, ln="Follow_Leg_Foot", k=1, r=1, min=0, max=1, dv=0.5) # Create attributes on ikController cmds.addAttr(ikFootControl, at="enum",enumName = "------", ln="Attributes", k=1, r=1) cmds.addAttr(ikFootControl, ln="Twist", k=1, r=1) cmds.addAttr(ikFootControl, ln="Lateral_Roll", k=1, r=1) for bone in ["Ankle", "Ball", "Toe_Tap"]: cmds.addAttr(ikFootControl, at="enum", enumName = "------", ln=bone, k=1, r=1) for coord in ["X", "Y", "Z"]: cmds.addAttr(ikFootControl, ln=bone+coord, k=1, r=1) # Parent ikController and PV under _rig_GRP cmds.parent(ikFootControlGrp, pvController + "_grp" ,rigGrp) # Set SDK visibility sdkDriver = switcherLoc[0] + ".FKIK_Mode" cmds.setAttr(sdkDriver, 0) cmds.setDrivenKeyframe(ikFootControlGrp + ".visibility", cd=sdkDriver, v=0, dv=0) cmds.setDrivenKeyframe(pvController + "_grp.visibility", cd=sdkDriver, v=0, dv=0) cmds.setAttr(sdkDriver, 1) cmds.setDrivenKeyframe(ikFootControlGrp + ".visibility", cd=sdkDriver, v=1, dv=1) cmds.setDrivenKeyframe(pvController + "_grp.visibility", cd=sdkDriver, v=1, dv=1) def findPoleVector(loc, targetHandle): # This func is kinda black magic # All credits to https://vimeo.com/66015036 start = cmds.xform(ogChain[0], q=1, ws=1, t=1) mid = cmds.xform(ogChain[1], q=1, ws=1, t=1) end = cmds.xform(ogChain[2], q=1, ws=1, t=1) startV = om.MVector(start[0], start[1], start[2]) midV = om.MVector(mid[0], mid[1], mid[2]) endV = om.MVector(end[0], end[1], end[2]) startEnd = endV - startV startMid = midV - startV dotP = startMid * startEnd proj = float(dotP) / float(startEnd.length()) startEndN = startEnd.normal() projV = startEndN * proj arrowV = startMid - projV arrowV*= 10 #distance from joint finalV = arrowV + midV cmds.xform(loc, ws=1, t=(finalV.x, finalV.y ,finalV.z)) locGrp = cmds.group(em=1, n=loc + "_grp") #snap, parent offsetGrp, set color and then make Constraint cmds.delete(cmds.pointConstraint(loc, locGrp)) cmds.parent(loc, locGrp) cmds.makeIdentity(loc, a=1, t=1, r=1, s=1) cmds.color(loc, rgb=controllerColor) cmds.poleVectorConstraint(loc, targetHandle) def showUI(): global chainMenu_UI global scaleField_UI global orientControllerMenu global constraintCheckBox_UI global blendCheckbox_UI global plusOne_UI global plusThree_UI global clavCheckbox_UI if cmds.window("switchModeUI", ex = 1): cmds.deleteUI("switchModeUI") myWin = cmds.window("switchModeUI", t="IKFK Builder", w=300, h=300, s=1) mainLayout = cmds.formLayout(nd=50) # Useful in selecting which chain: Leg or Arm? chainMenu_UI = cmds.optionMenu("chainMenu_UI", l="Which chain?", cc=visCheck) cmds.menuItem(l="Leg") cmds.menuItem(l="Arm") constraintCheckBox_UI = cmds.checkBox(label = "orientConsts+SDK Mode", v=0, cc= lambda state: (cmds.checkBox(blendCheckbox_UI, e=1, en=state-1))) blendCheckbox_UI = cmds.checkBox(label = "blendColor Mode", v=0, cc= lambda state: (cmds.checkBox(constraintCheckBox_UI, e=1, en=state-1))) clavCheckbox_UI = cmds.checkBox(l="Clavicle", vis=0) # Useful in orienting FK controllers as the user wishes. Maybe this can be improved orientControllerMenu = cmds.optionMenu("UI_orientControllerMenu", l="What's the secondary axis") cmds.menuItem(l="x") cmds.menuItem(l="y") cmds.menuItem(l="z") # Scale the UI becase you'll never know scaleControllerText = cmds.text(l="Controllers size") scaleField_UI = cmds.intField(en=10, v=1, min=1) plusOne_UI = cmds.button(l="+1", c=addOneUnit) plusThree_UI = cmds.button(l="+3", c=addThreeUnit) separator01 = cmds.separator(h=5) separator02 = cmds.separator(h=5) # execButton = cmds.button(l="Duplicate Chain", c=partial(duplicateChain, blendNodeFunc, constraintFunc)) cmds.formLayout(mainLayout, e=1, attachForm = [ (chainMenu_UI, "left", 8), (chainMenu_UI, "top", 5), (chainMenu_UI, "right", 80), (clavCheckbox_UI, "top", 7), (blendCheckbox_UI, "left", 5), (separator01, "left", 1), (separator01, "right", 2), #-------------------- (scaleField_UI, "right", 65), (scaleField_UI, "left", 5), (plusOne_UI, "right", 5), (plusThree_UI, "right", 5), (scaleControllerText, "left", 5), (separator02, "left", 1), (separator02, "right", 2), #-------------------- (orientControllerMenu, "left", 8), (orientControllerMenu, "top", 5), #-------------------- (execButton, "bottom", 5), (execButton, "left", 5), (execButton, "right", 5), ], attachControl = [(clavCheckbox_UI, "left", 10, chainMenu_UI), (constraintCheckBox_UI, "top", 5, chainMenu_UI), (blendCheckbox_UI, "top", 5, chainMenu_UI), (separator01, "top", 5, constraintCheckBox_UI), (scaleField_UI, "top", 5, separator01), (scaleControllerText, "top", 8, separator01), (plusOne_UI, "top", 4, separator01), (plusThree_UI, "top", 4, separator01), (separator02, "top", 6, scaleField_UI), (orientControllerMenu, "top", 6, separator02), ], attachPosition = [#(clavCheckbox_UI, "right", 0, 10), (constraintCheckBox_UI, "left", 0, 26), (blendCheckbox_UI, "right", 10, 24), (scaleControllerText, "left", 5, 0), (scaleField_UI, "left", 110, 0), #(scaleField_UI, "right",0, 40), (plusOne_UI, "right", 0, 45), (plusThree_UI, "right", 0, 49) ] ) cmds.showWindow(myWin) showUI()

41.386275

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0.607713

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0.166987

0.007908

0.01115

0.014866

0.319706

0.25431

0.223312

0.178475

0.157204

0.133797

0

0.038685

0.250486

21,107

510

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41.386275

0.760683

0.115791

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1

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0.052147

0

null

0

null

0

1

0

6a1cf3b76d95e590eb1efa6bc9673c121f9d7242

5,128

py

Python

pipng/imagescale-q-m.py

nwiizo/joke

808c4c998cc7f5b7f6f3fb5a3ce421588a70c087

[ "MIT" ]

1

2017-01-11T06:12:24.000Z

pipng/imagescale-q-m.py

ShuyaMotouchi/joke

808c4c998cc7f5b7f6f3fb5a3ce421588a70c087

[ "MIT" ]

null

pipng/imagescale-q-m.py

ShuyaMotouchi/joke

808c4c998cc7f5b7f6f3fb5a3ce421588a70c087

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright © 2012-13 Qtrac Ltd. All rights reserved. # This program or module is free software: you can redistribute it # and/or modify it under the terms of the GNU General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. It is provided for # educational purposes and is distributed in the hope that it will be # useful, but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. import argparse import collections import math import multiprocessing import os import sys import Image import Qtrac Result = collections.namedtuple("Result", "copied scaled name") Summary = collections.namedtuple("Summary", "todo copied scaled canceled") def main(): size, smooth, source, target, concurrency = handle_commandline() Qtrac.report("starting...") summary = scale(size, smooth, source, target, concurrency) summarize(summary, concurrency) def handle_commandline(): parser = argparse.ArgumentParser() parser.add_argument("-c", "--concurrency", type=int, default=multiprocessing.cpu_count(), help="specify the concurrency (for debugging and " "timing) [default: %(default)d]") parser.add_argument("-s", "--size", default=400, type=int, help="make a scaled image that fits the given dimension " "[default: %(default)d]") parser.add_argument("-S", "--smooth", action="store_true", help="use smooth scaling (slow but good for text)") parser.add_argument("source", help="the directory containing the original .xpm images") parser.add_argument("target", help="the directory for the scaled .xpm images") args = parser.parse_args() source = os.path.abspath(args.source) target = os.path.abspath(args.target) if source == target: args.error("source and target must be different") if not os.path.exists(args.target): os.makedirs(target) return args.size, args.smooth, source, target, args.concurrency def scale(size, smooth, source, target, concurrency): canceled = False jobs = multiprocessing.JoinableQueue() results = multiprocessing.Queue() create_processes(size, smooth, jobs, results, concurrency) todo = add_jobs(source, target, jobs) try: jobs.join() except KeyboardInterrupt: # May not work on Windows Qtrac.report("canceling...") canceled = True copied = scaled = 0 while not results.empty(): # Safe because all jobs have finished result = results.get_nowait() copied += result.copied scaled += result.scaled return Summary(todo, copied, scaled, canceled) def create_processes(size, smooth, jobs, results, concurrency): for _ in range(concurrency): process = multiprocessing.Process(target=worker, args=(size, smooth, jobs, results)) process.daemon = True process.start() def worker(size, smooth, jobs, results): while True: try: sourceImage, targetImage = jobs.get() try: result = scale_one(size, smooth, sourceImage, targetImage) Qtrac.report("{} {}".format("copied" if result.copied else "scaled", os.path.basename(result.name))) results.put(result) except Image.Error as err: Qtrac.report(str(err), True) finally: jobs.task_done() def add_jobs(source, target, jobs): for todo, name in enumerate(os.listdir(source), start=1): sourceImage = os.path.join(source, name) targetImage = os.path.join(target, name) jobs.put((sourceImage, targetImage)) return todo def scale_one(size, smooth, sourceImage, targetImage): oldImage = Image.from_file(sourceImage) if oldImage.width <= size and oldImage.height <= size: oldImage.save(targetImage) return Result(1, 0, targetImage) else: if smooth: scale = min(size / oldImage.width, size / oldImage.height) newImage = oldImage.scale(scale) else: stride = int(math.ceil(max(oldImage.width / size, oldImage.height / size))) newImage = oldImage.subsample(stride) newImage.save(targetImage) return Result(0, 1, targetImage) def summarize(summary, concurrency): message = "copied {} scaled {} ".format(summary.copied, summary.scaled) difference = summary.todo - (summary.copied + summary.scaled) if difference: message += "skipped {} ".format(difference) message += "using {} processes".format(concurrency) if summary.canceled: message += " [canceled]" Qtrac.report(message) print() if __name__ == "__main__": main()

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5.517949

0.353846

0.027881

0.026332

0.026022

0.178748

0.118959

0.049566

0

0.004491

0.261895

5,128

138

77

37.15942

0.848085

0.12617

0

0.046729

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false

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0

null

0

null

0

1

0

6a1f0af3de00ce3a7fdb8765f1bbb9115dd67f60

35,122

py

Python

test/integration_test.py

NoopDog/azul

37614eff627888065c7b0a277b3137b8a587ed51

[ "Apache-2.0" ]

null

test/integration_test.py

NoopDog/azul

37614eff627888065c7b0a277b3137b8a587ed51

[ "Apache-2.0" ]

null

test/integration_test.py

NoopDog/azul

37614eff627888065c7b0a277b3137b8a587ed51

[ "Apache-2.0" ]

null

from abc import ( ABCMeta, ) from concurrent.futures.thread import ( ThreadPoolExecutor, ) from contextlib import ( contextmanager, ) import csv from functools import ( lru_cache, ) import gzip from io import ( BytesIO, TextIOWrapper, ) import json import logging import os import random import re import sys import threading import time from typing import ( AbstractSet, Any, Dict, IO, List, Mapping, Optional, Sequence, Tuple, cast, ) import unittest from unittest import ( mock, ) import uuid from zipfile import ( ZipFile, ) import attr import chalice.cli from furl import ( furl, ) from google.cloud import ( storage, ) from google.oauth2 import ( service_account, ) from hca.dss import ( DSSClient, ) from hca.util import ( SwaggerAPIException, ) from humancellatlas.data.metadata.helpers.dss import ( download_bundle_metadata, ) from more_itertools import ( first, one, ) from openapi_spec_validator import ( validate_spec, ) import requests from azul import ( CatalogName, cached_property, config, drs, ) from azul.azulclient import ( AzulClient, AzulClientNotificationError, ) from azul.drs import ( AccessMethod, ) import azul.dss from azul.es import ( ESClientFactory, ) from azul.indexer import ( BundleFQID, ) from azul.indexer.index_service import ( IndexService, ) from azul.logging import ( configure_test_logging, ) from azul.modules import ( load_app_module, ) from azul.portal_service import ( PortalService, ) from azul.requests import ( requests_session_with_retry_after, ) from azul.types import ( JSON, ) from azul_test_case import ( AlwaysTearDownTestCase, AzulTestCase, ) log = logging.getLogger(__name__) # noinspection PyPep8Naming def setUpModule(): configure_test_logging(log) class IntegrationTestCase(AzulTestCase, metaclass=ABCMeta): bundle_uuid_prefix: str = '' @cached_property def azul_client(self): return AzulClient(prefix=self.bundle_uuid_prefix) class IndexingIntegrationTest(IntegrationTestCase, AlwaysTearDownTestCase): prefix_length = 2 max_bundles = 64 min_timeout = 20 * 60 @classmethod def setUpClass(cls) -> None: super().setUpClass() cls.bundle_uuid_prefix = ''.join([ str(random.choice('abcdef0123456789')) for _ in range(cls.prefix_length) ]) def setUp(self) -> None: super().setUp() self.pruning_seed = random.randint(0, sys.maxsize) @contextmanager def subTest(self, msg: Any = None, **params: Any): log.info('Beginning sub-test [%s] %r', msg, params) with super().subTest(msg, **params): try: yield except BaseException: log.info('Failed sub-test [%s] %r', msg, params) raise else: log.info('Successful sub-test [%s] %r', msg, params) def test(self): @attr.s(auto_attribs=True, kw_only=True) class Catalog: name: CatalogName notifications: Mapping[BundleFQID, JSON] @property def num_bundles(self): return len(self.notifications) @property def bundle_fqids(self) -> AbstractSet[BundleFQID]: return self.notifications.keys() def notifications_with_duplicates(self) -> List[JSON]: num_duplicates = self.num_bundles // 2 notifications = list(self.notifications.values()) # Index some bundles again to test that we handle duplicate additions. # Note: random.choices() may pick the same element multiple times so # some notifications will end up being sent three or more times. notifications.extend(random.choices(notifications, k=num_duplicates)) return notifications def _wait_for_indexer(): num_bundles = sum(catalog.num_bundles for catalog in catalogs) self.azul_client.wait_for_indexer(num_expected_bundles=num_bundles, min_timeout=self.min_timeout) # For faster modify-deploy-test cycles, set `delete` to False and run # test once. Then also set `index` to False. Subsequent runs will use # catalogs from first run. Don't commit changes to these two lines. index = True delete = True if index: self._reset_indexer() catalogs: List[Catalog] = [ Catalog(name=catalog, notifications=self._prepare_notifications(catalog) if index else {}) for catalog in config.integration_test_catalogs ] if index: for catalog in catalogs: log.info('Starting integration test for catalog %r with %i bundles from prefix %r.', catalog, catalog.num_bundles, self.bundle_uuid_prefix) self.azul_client.index(catalog=catalog.name, notifications=catalog.notifications_with_duplicates()) _wait_for_indexer() for catalog in catalogs: self._assert_catalog_complete(catalog=catalog.name, entity_type='files', bundle_fqids=catalog.bundle_fqids) for catalog in catalogs: self._test_manifest(catalog.name) self._test_dos_and_drs(catalog.name) self._test_repository_files(catalog.name) if index and delete: for catalog in catalogs: self.azul_client.index(catalog=catalog.name, notifications=catalog.notifications_with_duplicates(), delete=True) _wait_for_indexer() for catalog in catalogs: self._assert_catalog_empty(catalog.name) self._test_other_endpoints() def _reset_indexer(self): # While it's OK to erase the integration test catalog, the queues are # shared by all catalogs and we can't afford to trash them in a stable # deployment like production. self.azul_client.reset_indexer(catalogs=config.integration_test_catalogs, # Can't purge the queues in stable deployment as # they may contain work for non-IT catalogs. purge_queues=not config.is_stable_deployment(), delete_indices=True, create_indices=True) def _test_other_endpoints(self): service_paths = ( '/', '/openapi', '/version', '/index/summary', '/index/files/order', ) service_routes = ( (config.service_endpoint(), path) for path in service_paths ) health_endpoints = ( config.service_endpoint(), config.indexer_endpoint() ) health_paths = ( '', # default keys for lambda '/', # all keys '/basic', '/elasticsearch', '/queues', '/progress', '/api_endpoints', '/other_lambdas' ) health_routes = ( (endpoint, '/health' + path) for endpoint in health_endpoints for path in health_paths ) for endpoint, path in (*service_routes, *health_routes): with self.subTest('other_endpoints', endpoint=endpoint, path=path): self._check_endpoint(endpoint, path) def _test_manifest(self, catalog: CatalogName): for format_, validator, attempts in [ (None, self._check_manifest, 1), ('compact', self._check_manifest, 1), ('full', self._check_manifest, 3), ('terra.bdbag', self._check_terra_bdbag, 1) ]: with self.subTest('manifest', catalog=catalog, format=format_, attempts=attempts): assert attempts > 0 params = dict(catalog=catalog) if format_ is not None: params['format'] = format_ for attempt in range(attempts): start = time.time() response = self._check_endpoint(config.service_endpoint(), '/manifest/files', params) log.info('Request %i/%i took %.3fs to execute.', attempt + 1, attempts, time.time() - start) validator(catalog, response) @lru_cache(maxsize=None) def _get_one_file_uuid(self, catalog: CatalogName) -> str: filters = {'fileFormat': {'is': ['fastq.gz', 'fastq']}} response = self._check_endpoint(endpoint=config.service_endpoint(), path='/index/files', query=dict(catalog=catalog, filters=json.dumps(filters), size=1, order='asc', sort='fileSize')) hits = json.loads(response) return one(one(hits['hits'])['files'])['uuid'] def _test_dos_and_drs(self, catalog: CatalogName): if config.is_dss_enabled(catalog) and config.dss_direct_access: file_uuid = self._get_one_file_uuid(catalog) self._test_dos(catalog, file_uuid) self._test_drs(catalog, file_uuid) @cached_property def _requests(self) -> requests.Session: return requests_session_with_retry_after() def _check_endpoint(self, endpoint: str, path: str, query: Optional[Mapping[str, Any]] = None) -> bytes: query = {} if query is None else {k: str(v) for k, v in query.items()} url = furl(endpoint, path=path, query=query) return self._get_url_content(url.url) def _get_url_content(self, url: str) -> bytes: return self._get_url(url).content def _get_url(self, url: str, allow_redirects=True) -> requests.Response: log.info('GET %s', url) response = self._requests.get(url, allow_redirects=allow_redirects) expected_statuses = (200,) if allow_redirects else (200, 301, 302) self._assertResponseStatus(response, expected_statuses) return response def _assertResponseStatus(self, response: requests.Response, expected_statuses: Tuple[int, ...] = (200,)): self.assertIn(response.status_code, expected_statuses, (response.reason, response.content)) def _check_manifest(self, _catalog: CatalogName, response: bytes): self.__check_manifest(BytesIO(response), 'bundle_uuid') def _check_terra_bdbag(self, catalog: CatalogName, response: bytes): with ZipFile(BytesIO(response)) as zip_fh: data_path = os.path.join(os.path.dirname(first(zip_fh.namelist())), 'data') file_path = os.path.join(data_path, 'participants.tsv') with zip_fh.open(file_path) as file: rows = self.__check_manifest(file, 'bundle_uuid') for row in rows: # Terra doesn't allow colons in this column, but they may # exist in versions indexed by TDR self.assertNotIn(':', row['entity:participant_id']) suffix = '__file_drs_uri' header, *rows = rows prefixes = [ c[:-len(suffix)] for c in header.keys() if c.endswith(suffix) ] size, drs_uri, name = min( ( int(row[prefix + '__file_size']), row[prefix + suffix], row[prefix + '__file_name'], ) for row in rows for prefix in prefixes if row[prefix + suffix] ) log.info('Resolving %r (%r) from catalog %r (%i bytes)', drs_uri, name, catalog, size) plugin = self.azul_client.repository_plugin(catalog) drs_client = plugin.drs_client() access = drs_client.get_object(drs_uri, access_method=AccessMethod.https) self.assertIsNone(access.headers) self.assertEqual('https', furl(access.url).scheme) # Try HEAD first because it's more efficient, fall back to GET if the # DRS implementations prohibits it, like Azul's DRS proxy of DSS. for method in ('HEAD', 'GET'): log.info('%s %s', method, access.url) # For DSS, any HTTP client should do but for TDR we need to use an # authenticated client. TDR does return a Bearer token in the `headers` # part of the DRS response but we know that this token is the same as # the one we're making the DRS request with. response = drs_client.http_client.request(method, access.url) if response.status != 403: break self.assertEqual(200, response.status, response.data) self.assertEqual(size, int(response.headers['Content-Length'])) def __check_manifest(self, file: IO[bytes], uuid_field_name: str) -> List[Mapping[str, str]]: text = TextIOWrapper(file) reader = csv.DictReader(text, delimiter='\t') rows = list(reader) log.info(f'Manifest contains {len(rows)} rows.') self.assertGreater(len(rows), 0) self.assertIn(uuid_field_name, reader.fieldnames) bundle_uuid = rows[0][uuid_field_name] self.assertEqual(bundle_uuid, str(uuid.UUID(bundle_uuid))) return rows def _test_repository_files(self, catalog: str): with self.subTest('repository_files', catalog=catalog): file_uuid = self._get_one_file_uuid(catalog) response = self._check_endpoint(endpoint=config.service_endpoint(), path=f'/fetch/repository/files/{file_uuid}', query=dict(catalog=catalog)) response = json.loads(response) while response['Status'] != 302: self.assertEqual(301, response['Status']) response = self._get_url(response['Location']).json() content = self._get_url_content(response['Location']) self._validate_fastq_content(content) def _test_drs(self, catalog: CatalogName, file_uuid: str): repository_plugin = self.azul_client.repository_plugin(catalog) drs = repository_plugin.drs_client() for access_method in AccessMethod: with self.subTest('drs', catalog=catalog, access_method=AccessMethod.https): log.info('Resolving file %r with DRS using %r', file_uuid, access_method) drs_uri = f'drs://{config.api_lambda_domain("service")}/{file_uuid}' access = drs.get_object(drs_uri, access_method=access_method) self.assertIsNone(access.headers) if access.method is AccessMethod.https: content = self._get_url_content(access.url) elif access.method is AccessMethod.gs: content = self._get_gs_url_content(access.url) else: self.fail(access_method) self._validate_fastq_content(content) def _test_dos(self, catalog: CatalogName, file_uuid: str): with self.subTest('dos', catalog=catalog): log.info('Resolving file %s with DOS', file_uuid) response = self._check_endpoint(config.service_endpoint(), path=drs.dos_object_url_path(file_uuid), query=dict(catalog=catalog)) json_data = json.loads(response)['data_object'] file_url = first(json_data['urls'])['url'] while True: response = self._get_url(file_url, allow_redirects=False) # We handle redirects ourselves so we can log each request if response.status_code in (301, 302): file_url = response.headers['Location'] try: retry_after = response.headers['Retry-After'] except KeyError: pass else: time.sleep(int(retry_after)) else: break self._assertResponseStatus(response) self._validate_fastq_content(response.content) def _get_gs_url_content(self, url: str) -> bytes: self.assertTrue(url.startswith('gs://')) path = os.environ['GOOGLE_APPLICATION_CREDENTIALS'] credentials = service_account.Credentials.from_service_account_file(path) storage_client = storage.Client(credentials=credentials) content = BytesIO() storage_client.download_blob_to_file(url, content) return content.getvalue() def _validate_fastq_content(self, content: bytes): # Check signature of FASTQ file. with gzip.open(BytesIO(content)) as buf: fastq = buf.read(1024 * 1024) lines = fastq.splitlines() # Assert first character of first and third line of file (see https://en.wikipedia.org/wiki/FASTQ_format). self.assertTrue(lines[0].startswith(b'@')) self.assertTrue(lines[2].startswith(b'+')) def _prepare_notifications(self, catalog: CatalogName) -> Dict[BundleFQID, JSON]: bundle_fqids = self.azul_client.list_bundles(catalog) bundle_fqids = self._prune_test_bundles(catalog, bundle_fqids, self.max_bundles) return { bundle_fqid: self.azul_client.synthesize_notification(catalog, bundle_fqid) for bundle_fqid in bundle_fqids } def _prune_test_bundles(self, catalog: CatalogName, bundle_fqids: Sequence[BundleFQID], max_bundles: int ) -> List[BundleFQID]: seed = self.pruning_seed log.info('Selecting %i bundles with projects, out of %i candidates, using random seed %i.', max_bundles, len(bundle_fqids), seed) random_ = random.Random(x=seed) # The same seed should give same random order so we need to have a # deterministic order in the input list. bundle_fqids = sorted(bundle_fqids) random_.shuffle(bundle_fqids) # Pick bundles off of the randomly ordered input until we have the # desired number of bundles with project metadata. filtered_bundle_fqids = [] for bundle_fqid in bundle_fqids: if len(filtered_bundle_fqids) < max_bundles: if self.azul_client.bundle_has_project_json(catalog, bundle_fqid): filtered_bundle_fqids.append(bundle_fqid) else: break return filtered_bundle_fqids def _assert_catalog_complete(self, catalog: CatalogName, entity_type: str, bundle_fqids: AbstractSet[BundleFQID]) -> None: with self.subTest('catalog_complete', catalog=catalog): expected_fqids = set(self.azul_client.filter_obsolete_bundle_versions(bundle_fqids)) obsolete_fqids = bundle_fqids - expected_fqids if obsolete_fqids: log.debug('Ignoring obsolete bundle versions %r', obsolete_fqids) num_bundles = len(expected_fqids) timeout = 600 indexed_fqids = set() log.debug('Expecting bundles %s ', sorted(expected_fqids)) retries = 0 deadline = time.time() + timeout while True: hits = self._get_entities(catalog, entity_type) indexed_fqids.update( BundleFQID(bundle['bundleUuid'], bundle['bundleVersion']) for hit in hits for bundle in hit.get('bundles', []) ) log.info('Detected %i of %i bundles in %i hits for entity type %s on try #%i.', len(indexed_fqids), num_bundles, len(hits), entity_type, retries) if len(indexed_fqids) == num_bundles: log.info('Found the expected %i bundles.', num_bundles) break elif len(indexed_fqids) > num_bundles: log.error('Found %i bundles, more than the expected %i.', len(indexed_fqids), num_bundles) break elif time.time() > deadline: log.error('Only found %i of %i bundles in under %i seconds.', len(indexed_fqids), num_bundles, timeout) break else: retries += 1 time.sleep(5) self.assertSetEqual(indexed_fqids, expected_fqids) entity_types = ['files', 'projects', 'samples', 'bundles'] def _assert_catalog_empty(self, catalog: CatalogName): for entity_type in self.entity_types: with self.subTest('catalog_empty', catalog=catalog, entity_type=entity_type): hits = self._get_entities(catalog, entity_type) self.assertEqual([], [hit['entryId'] for hit in hits]) def _get_entities(self, catalog: CatalogName, entity_type): entities = [] size = 100 params = dict(catalog=catalog, size=str(size)) url = furl(url=config.service_endpoint(), path=('index', entity_type), query_params=params ).url while True: response = self._get_url(url) body = response.json() hits = body['hits'] entities.extend(hits) url = body['pagination']['next'] if url is None: break return entities def _assert_indices_exist(self, catalog: CatalogName): """ Aside from checking that all indices exist this method also asserts that we can instantiate a local ES client pointing at a real, remote ES domain. """ es_client = ESClientFactory.get() service = IndexService() for index_name in service.index_names(catalog): self.assertTrue(es_client.indices.exists(index_name)) class AzulClientIntegrationTest(IntegrationTestCase): def test_azul_client_error_handling(self): invalid_notification = {} notifications = [invalid_notification] self.assertRaises(AzulClientNotificationError, self.azul_client.index, first(config.integration_test_catalogs), notifications) class PortalRegistrationIntegrationTest(IntegrationTestCase): # FIXME: Re-enable once overloading of S3 API is resolved # https://github.com/DataBiosphere/azul/issues/2399 @unittest.skipIf(True or config.is_main_deployment(), 'Test would pollute portal DB') def test_concurrent_portal_db_crud(self): """ Use multithreading to simulate multiple users simultaneously modifying the portals database. """ # Currently takes about 50 seconds and creates a 25 kb db file. n_threads = 10 n_tasks = n_threads * 10 n_ops = 5 portal_service = PortalService() entry_format = 'task={};op={}' def run(thread_count): for op_count in range(n_ops): mock_entry = cast(JSON, { "portal_id": "foo", "integrations": [ { "integration_id": "bar", "entity_type": "project", "integration_type": "get", "entity_ids": ["baz"] } ], "mock-count": entry_format.format(thread_count, op_count) }) portal_service._crud(lambda db: list(db) + [mock_entry]) old_db = portal_service.read() with ThreadPoolExecutor(max_workers=n_threads) as executor: futures = [executor.submit(run, i) for i in range(n_tasks)] self.assertTrue(all(f.result() is None for f in futures)) new_db = portal_service.read() old_entries = [portal for portal in new_db if 'mock-count' not in portal] self.assertEqual(old_entries, old_db) mock_counts = [portal['mock-count'] for portal in new_db if 'mock-count' in portal] self.assertEqual(len(mock_counts), len(set(mock_counts))) self.assertEqual(set(mock_counts), {entry_format.format(i, j) for i in range(n_tasks) for j in range(n_ops)}) # Reset to pre-test state. portal_service.overwrite(old_db) class OpenAPIIntegrationTest(AzulTestCase): def test_openapi(self): service = config.service_endpoint() response = requests.get(service + '/') self.assertEqual(response.status_code, 200) self.assertEqual(response.headers['content-type'], 'text/html') self.assertGreater(len(response.content), 0) # validate OpenAPI spec response = requests.get(service + '/openapi') response.raise_for_status() spec = response.json() validate_spec(spec) class DSSIntegrationTest(AzulTestCase): def test_patched_dss_client(self): query = { "query": { "bool": { "must_not": [ { "term": { "admin_deleted": True } } ], "must": [ { "exists": { "field": "files.project_json" } }, { "range": { "manifest.version": { "gte": "2019-04-01" } } } ] } } } self.maxDiff = None for direct in {config.dss_direct_access, False}: for replica in 'aws', 'gcp': if direct: with self._failing_s3_get_object(): dss_client = azul.dss.direct_access_client() self._test_dss_client(direct, query, dss_client, replica, fallback=True) dss_client = azul.dss.direct_access_client() self._test_dss_client(direct, query, dss_client, replica, fallback=False) else: dss_client = azul.dss.client() self._test_dss_client(direct, query, dss_client, replica, fallback=False) class SpecialError(Exception): pass def _failing_s3_get_object(self): def make_mock(**kwargs): original = kwargs['spec'] def mock_boto3_client(service, *args, **kwargs): if service == 's3': mock_s3 = mock.MagicMock() mock_s3.get_object.side_effect = self.SpecialError() return mock_s3 else: return original(service, *args, **kwargs) return mock_boto3_client return mock.patch('azul.deployment.aws.client', spec=True, new_callable=make_mock) def _test_dss_client(self, direct: bool, query: JSON, dss_client: DSSClient, replica: str, fallback: bool): with self.subTest(direct=direct, replica=replica, fallback=fallback): response = dss_client.post_search(es_query=query, replica=replica, per_page=10) bundle_uuid, _, bundle_version = response['results'][0]['bundle_fqid'].partition('.') with mock.patch('azul.dss.logger') as captured_log: _, manifest, metadata = download_bundle_metadata(client=dss_client, replica=replica, uuid=bundle_uuid, version=bundle_version, num_workers=config.num_dss_workers) log.info('Captured log calls: %r', captured_log.mock_calls) self.assertGreater(len(metadata), 0) self.assertGreater(set(f['name'] for f in manifest), set(metadata.keys())) for f in manifest: self.assertIn('s3_etag', f) # Extract the log method name and the first three words of log # message logged. Note that the PyCharm debugger will call # certain dunder methods on the variable, leading to failed # assertions. actual = [(m, ' '.join(re.split(r'[\s,]', a[0])[:3])) for m, a, k in captured_log.mock_calls] if direct: if replica == 'aws': if fallback: expected = [ ('debug', 'Loading bundle %s'), ('debug', 'Loading object %s'), ('warning', 'Error accessing bundle'), ('warning', 'Failed getting bundle') ] + [ ('debug', 'Loading file %s'), ('debug', 'Loading object %s'), ('warning', 'Error accessing file'), ('warning', 'Failed getting file') ] * len(metadata) else: expected = [ ('debug', 'Loading bundle %s'), ('debug', 'Loading object %s') ] + [ ('debug', 'Loading file %s'), ('debug', 'Loading object %s'), # file ('debug', 'Loading object %s') # blob ] * len(metadata) else: # On `gcp` the precondition check fails right away, preventing any attempts of direct access expected = [ ('warning', 'Failed getting bundle') ] + [ ('warning', 'Failed getting file') ] * len(metadata) else: expected = [] self.assertSequenceEqual(sorted(expected), sorted(actual)) def test_get_file_fail(self): for direct in {config.dss_direct_access, False}: with self.subTest(direct=direct): dss_client = azul.dss.direct_access_client() if direct else azul.dss.client() with self.assertRaises(SwaggerAPIException) as e: dss_client.get_file(uuid='acafefed-beef-4bad-babe-feedfa11afe1', version='2018-11-19T232756.056947Z', replica='aws') self.assertEqual(e.exception.reason, 'not_found') def test_mini_dss_failures(self): uuid = 'acafefed-beef-4bad-babe-feedfa11afe1' version = '2018-11-19T232756.056947Z' with self._failing_s3_get_object(): mini_dss = azul.dss.MiniDSS(config.dss_endpoint) with self.assertRaises(self.SpecialError): mini_dss._get_file_object(uuid, version) with self.assertRaises(KeyError): mini_dss._get_blob_key({}) with self.assertRaises(self.SpecialError): mini_dss._get_blob('/blobs/foo', {'content-type': 'application/json'}) with self.assertRaises(self.SpecialError): mini_dss.get_bundle(uuid, version, 'aws') with self.assertRaises(self.SpecialError): mini_dss.get_file(uuid, version, 'aws') with self.assertRaises(self.SpecialError): mini_dss.get_native_file_url(uuid, version, 'aws') class AzulChaliceLocalIntegrationTest(AzulTestCase): url = furl(scheme='http', host='127.0.0.1', port=8000) server = None server_thread = None @classmethod def setUpClass(cls) -> None: super().setUpClass() app_module = load_app_module('service') app_dir = os.path.dirname(app_module.__file__) factory = chalice.cli.factory.CLIFactory(app_dir) config = factory.create_config_obj() cls.server = factory.create_local_server(app_obj=app_module.app, config=config, host=cls.url.host, port=cls.url.port) cls.server_thread = threading.Thread(target=cls.server.serve_forever) cls.server_thread.start() @classmethod def tearDownClass(cls) -> None: cls.server.shutdown() cls.server_thread.join() super().tearDownClass() def test_local_chalice_health_endpoint(self): url = self.url.copy().set(path='health').url response = requests.get(url) self.assertEqual(200, response.status_code) catalog = first(config.integration_test_catalogs.keys()) def test_local_chalice_index_endpoints(self): url = self.url.copy().set(path='index/files', query=dict(catalog=self.catalog)).url response = requests.get(url) self.assertEqual(200, response.status_code) def test_local_filtered_index_endpoints(self): filters = {'genusSpecies': {'is': ['Homo sapiens']}} url = self.url.copy().set(path='index/files', query=dict(filters=json.dumps(filters), catalog=self.catalog)).url response = requests.get(url) self.assertEqual(200, response.status_code)

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