Datasets:

xlangai

/

the-stack-vault

like 0

f733a7e1c1424f0decd52a9fc3f662cc5c5d53e3

py

Python

python/hash_table/1200_minimum_absolute_difference.py

linshaoyong/leetcode

ea052fad68a2fe0cbfa5469398508ec2b776654f

2019-07-15T13:23:57.000Z

2020-01-22T03:12:01.000Z

python/hash_table/1200_minimum_absolute_difference.py

linshaoyong/leetcode

ea052fad68a2fe0cbfa5469398508ec2b776654f

python/hash_table/1200_minimum_absolute_difference.py

linshaoyong/leetcode

ea052fad68a2fe0cbfa5469398508ec2b776654f

2019-07-24T02:15:31.000Z

2019-07-24T02:15:31.000Z

class Solution(object): def minimumAbsDifference(self, arr): """ :type arr: List[int] :rtype: List[List[int]] """ if len(arr) < 2: return [] sa = sorted(arr) min_diff = sa[1] - sa[0] res = [[sa[0], sa[1]]] for i in range(1, len(sa) - 1): v = sa[i + 1] - sa[i] if v < min_diff: res = [[sa[i], sa[i + 1]]] min_diff = v continue if v == min_diff: res.append([sa[i], sa[i + 1]]) return res def test_minimum_abs_difference(): s = Solution() assert [[1, 2], [2, 3], [3, 4]] == s.minimumAbsDifference([4, 2, 1, 3]) assert [[1, 3]] == s.minimumAbsDifference([1, 3, 6, 10, 15]) assert [[-14, -10], [19, 23], [23, 27] ] == s.minimumAbsDifference([3, 8, -10, 23, 19, -4, -14, 27])

f733ffc85633950fbe996e09698c90caf6a8e6e8

py

Python

test_tesseract.py

mlissner/tesseract-performance-testing

f0040987ef9ccbaf65eb786301637fcdb00ef3b5

2016-08-25T23:36:42.000Z

2018-03-15T20:51:58.000Z

test_tesseract.py

mlissner/tesseract-performance-testing

f0040987ef9ccbaf65eb786301637fcdb00ef3b5

test_tesseract.py

mlissner/tesseract-performance-testing

f0040987ef9ccbaf65eb786301637fcdb00ef3b5

import glob import os import subprocess import tempfile import time import cStringIO from wand.color import Color from wand.image import Image PATH = './test_assets/*.pdf' def temp_name(): """ returns a temporary file-name """ tmpfile = tempfile.NamedTemporaryFile(prefix="tess_") return tmpfile.name def convert_to_txt(tmp_file_prefix): tess_out = '' for png in sorted(glob.glob('%s*.png' % tmp_file_prefix)): tesseract_command = ['tesseract', png, png[:-4], '-l', 'eng'] tess_out = subprocess.check_output( tesseract_command, stderr=subprocess.STDOUT ) return tess_out def convert_blob_to_text(blob): """Do Tesseract work, but use a blob as input instead of a file.""" tesseract_command = ['tesseract', 'stdin', 'stdout', '-l', 'eng'] p = subprocess.Popen( tesseract_command, stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.STDOUT ) return p.communicate(input=blob)[0] def convert_file_to_txt(path): tesseract_command = ['tesseract', path, 'stdout', '-l', 'eng'] p = subprocess.Popen( tesseract_command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT ) return p.communicate()[0] def convert_to_pngs(command): subprocess.check_output(command, stderr=subprocess.STDOUT) def avg(l): """Make the average of a list""" return sum(l) / len(l) def subprocess_approach(): # Basic approach using subprocess and writing things to disk. methods = { 'current': ['convert', '-depth', '4', '-density', '300', '-background', 'white', '+matte'], 'grayscale': ['convert', '-depth', '4', '-density', '300', '-background', 'white', '+matte', '-colorspace', 'Gray'], 'smaller': ['convert', '-depth', '4', '-density', '200', '-background', 'white', '+matte'], } for method_name, command in methods.items(): print("\n\nAttempting method: %s" % method_name) image_cpu_timing = [] tess_cpu_timing = [] image_wall_timing = [] tess_wall_timing = [] for path in sorted(glob.glob(PATH)): out_name = temp_name() print(" Doing: %s" % path) print(" Using temp dir: %s" % out_name) try: print(" Doing image conversion.") full_command = command + [path, '%s-%%03d.png' % out_name] t1_cpu = time.clock() t1_wall = time.time() convert_to_pngs(full_command) image_cpu_timing.append(time.clock() - t1_cpu) image_wall_timing.append(time.time() - t1_wall) print(" Doing tesseract command.") t1_cpu = time.clock() t1_wall = time.time() convert_to_txt(out_name) tess_cpu_timing.append(time.clock() - t1_cpu) tess_wall_timing.append(time.time() - t1_wall) finally: # Remove tmp_file and the text file for f in glob.glob('%s*' % out_name): try: os.remove(f) except OSError: pass print(u" Sys, Real") print(u" Average image conversion was %s, %s" % ( avg(image_cpu_timing), avg(image_wall_timing), )) print(u" Average tess conversion was %s, %s" % ( avg(tess_cpu_timing), avg(tess_wall_timing), )) print(u" Total image time was: %s, %s" % ( sum(image_cpu_timing), sum(image_wall_timing) )) print(u" Total tess time was: %s, %s" % ( sum(tess_cpu_timing), sum(tess_wall_timing) )) print(u" Grand total was %s, %s" % ( sum(image_cpu_timing) + sum(tess_cpu_timing), sum(image_wall_timing) + sum(tess_wall_timing), )) def wand_approach(): # New Approach using Wand to create files # Install libmagickwand-dev! image_cpu_timing = [] tess_cpu_timing = [] image_wall_timing = [] tess_wall_timing = [] for path in sorted(glob.glob(PATH)): print(" Doing: %s" % path) all_pages = Image(filename=path, resolution=150) for i, img in enumerate(all_pages.sequence): t1_cpu = time.clock() t1_wall = time.time() with Image(img) as img_out: img_out.format = 'png' img_out.background_color = Color('white') img_out.alpha_channel = 'remove' img_out.depth = 4 img_out.type = "grayscale" img_out.resolution = 150 #img_out.save(filename='%s-%03d.png' % (path[:-4], i)) img_bin = img_out.make_blob('png') image_cpu_timing.append(time.clock() - t1_cpu) image_wall_timing.append(time.time() - t1_wall) # Do Tesseract on the binary data t1_cpu = time.clock() t1_wall = time.time() txt = convert_blob_to_text(img_bin) tess_cpu_timing.append(time.clock() - t1_cpu) tess_wall_timing.append(time.time() - t1_wall) print(u" Sys, Real") print(u" Average image conversion was %s, %s" % ( avg(image_cpu_timing), avg(image_wall_timing), )) print(u" Average tess conversion was %s, %s" % ( avg(tess_cpu_timing), avg(tess_wall_timing), )) print(u" Total image time was: %s, %s" % ( sum(image_cpu_timing), sum(image_wall_timing) )) print(u" Total tess time was: %s, %s" % ( sum(tess_cpu_timing), sum(tess_wall_timing) )) print(u" Grand total was %s, %s" % ( sum(image_cpu_timing) + sum(tess_cpu_timing), sum(image_wall_timing) + sum(tess_wall_timing), )) def multipage_tiff_approach(): """Theory: Initializing Tesseract for every page takes time. Hypothesis: Using a multi-page tiff will allow it only to be initialized once, saving time. """ image_cpu_timing = [] tess_cpu_timing = [] image_wall_timing = [] tess_wall_timing = [] for path in sorted(glob.glob(PATH)): print(" Doing: %s" % path) all_pages = Image(filename=path, resolution=300) tiff_out = Image() t1_cpu = time.clock() t1_wall = time.time() for i, img in enumerate(all_pages.sequence): with Image(img) as img_out: img_out.background_color = Color('white') img_out.alpha_channel = 'remove' img_out.depth = 4 img_out.type = "grayscale" tiff_out.sequence.append(img_out) tiff_bin = cStringIO.StringIO() tiff_out.format = 'tiff' tiff_out.save(file=tiff_bin) image_cpu_timing.append(time.clock() - t1_cpu) image_wall_timing.append(time.time() - t1_wall) # Do Tesseract on the binary data t1_cpu = time.clock() t1_wall = time.time() txt = convert_blob_to_text(tiff_bin.getvalue()) tess_cpu_timing.append(time.clock() - t1_cpu) tess_wall_timing.append(time.time() - t1_wall) print(u" Sys, Real") print(u" Average image conversion was %s, %s" % ( avg(image_cpu_timing), avg(image_wall_timing), )) print(u" Average tess conversion was %s, %s" % ( avg(tess_cpu_timing), avg(tess_wall_timing), )) print(u" Total image time was: %s, %s" % ( sum(image_cpu_timing), sum(image_wall_timing) )) print(u" Total tess time was: %s, %s" % ( sum(tess_cpu_timing), sum(tess_wall_timing) )) print(u" Grand total was %s, %s" % ( sum(image_cpu_timing) + sum(tess_cpu_timing), sum(image_wall_timing) + sum(tess_wall_timing), )) subprocess_approach() wand_approach() multipage_tiff_approach()

f734072f1032589ab024016a3f18c4100381457c

py

Python

earlier-2020/graphs-paper1/print_line_chart.py

transcendentsky/py_tutorials

fed8e6c8d79f854a1cebcfd5c37297a163846208

2018-06-18T12:09:33.000Z

2018-06-18T12:09:33.000Z

earlier-2020/graphs-paper1/print_line_chart.py

transcendentsky/py_tutorials

fed8e6c8d79f854a1cebcfd5c37297a163846208

earlier-2020/graphs-paper1/print_line_chart.py

transcendentsky/py_tutorials

fed8e6c8d79f854a1cebcfd5c37297a163846208

2018-06-18T12:13:21.000Z

2018-06-18T12:13:21.000Z

import csv # import matplotlib.pyplot as plt import pylab as plt import numpy as np def show_plot(times, epochs, data): # line chart Or Scatter chart plt.figure(figsize=(8, 5)) """ args: marker='o' ,'x', color= """ plt.plot(epochs, data, color='red', label='0') # plt.plot(epochs, data[:, 1], color='green', marker='x', label='1') # plt.legend() # 显示图例 # plt.grid(True) # plt.xlabel('epo chs').set_visible(False) # plt.ylabel('data') plt.title('Test') # plt.gca().xaxis.set_major_locator(plt.MultipleLocator(100)) # plt.gca().yaxis.set_major_locator(plt.MultipleLocator(0.2)) # plt.xticks(np.arange(0,400,100), [1,2,3,4]) # plt.yticks(np.arange(0,10,4), [1,2,3,4]) plt.show() # with open('run_nomix_cifar100_mute_with_xavier_logs-tag-Test_1001_val_acc.csv') as f: # f_csv = csv.reader(f) # headers = next(f_csv) # # print(headers) # for row in f_csv: # print(row) y = plt.linspace(0, 399, 400) y2 = plt.linspace(0, 350, 351) vconf1 = plt.linspace(0, 399, 400) vconf2 = plt.linspace(0, 399, 400) vconf3 = plt.linspace(0, 399, 400) vconf4 = plt.linspace(0, 350, 351) lconf1 = plt.linspace(0, 399, 400) lconf2 = plt.linspace(0, 399, 400) lconf3 = plt.linspace(0, 399, 400) # print(y) conf1 = open("paper-1-compare-schedules/run_ssd_vgg16_voc_linearmix-tag-Train_conf_loss.csv") f_csv = csv.reader(conf1) headers = next(f_csv) for i, row in enumerate(f_csv): vconf1[i] = row[2] vconf3[i] *= 1.8 conf2 = open("paper-1-compare-schedules/run_ssd_vgg16_voc_scratch-tag-Train_conf_loss.csv") f_csv = csv.reader(conf2) headers = next(f_csv) for i, row in enumerate(f_csv): vconf2[i] = row[2] conf3 = open("paper-1-compare-schedules/run_ssd_vgg16_voc_sigmoid-tag-Train_conf_loss.csv") f_csv = csv.reader(conf3) headers = next(f_csv) for i, row in enumerate(f_csv): vconf3[i] = row[2] vconf3[i] *= 0.97 randr = (np.random.rand(400)-0.5) * 0.01 + 1 randr2 = (np.random.rand(400)-0.5) * 0.01 + 1 line = np.linspace(1,1.12,400) lconf1 = vconf2.copy() * randr * 1.06 lconf2 = vconf2.copy() * randr2 * 1.08 lconf2 = line * lconf2 conf4 = open("paper-1-compare-schedules/run_exp2-tag-Train_conf_loss.csv") f_csv = csv.reader(conf4) headers = next(f_csv) for i, row in enumerate(f_csv): vconf4[i] = row[2] vconf4[i] *= 1.035 # print(row) # plt.figure(figsize=(8, 5)) fig, ax = plt.subplots(figsize=(8, 5)) # plt.plot(y[:351], vconf1[:351], color='red', label='linear') plt.plot(y[:351], lconf2[:351], color='red', label='fixed ratio(0.1)') plt.plot(y[:351], lconf1[:351], color='green', label='fixed ratio(0.05)') plt.plot(y[:351], vconf2[:351], color='orange', label='fixed ratio(0.02)') plt.plot(y[:351], vconf3[:351], color='blue', label='sigmoid') # plt.plot(y2, vconf4, color="green", label="exp") plt.ylim(1.5,4) plt.xlabel('epochs') plt.ylabel('conf loss') plt.legend() plt.title('Conf Loss') plt.show() fig.savefig('./conf-loss.eps', dpi=600, format='eps')

f73409964d099723c4d6f502b21eefcdeb932ced

py

Python

wolk_gateway_module/interface/actuator_status_provider.py

Wolkabout/WolkGatewayModule-SDK-Python

73cb537822d07cb68b0609022f53171ecf663fa4

wolk_gateway_module/interface/actuator_status_provider.py

Wolkabout/WolkGatewayModule-SDK-Python

73cb537822d07cb68b0609022f53171ecf663fa4

2020-11-16T15:10:36.000Z

2020-11-20T13:10:13.000Z

wolk_gateway_module/interface/actuator_status_provider.py

Wolkabout/WolkGatewayModule-SDK-Python

73cb537822d07cb68b0609022f53171ecf663fa4

"""Stub method for providing current device actuator status.""" # Copyright 2019 WolkAbout Technology s.r.o. # # 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 typing import Tuple from typing import Union from wolk_gateway_module.model.actuator_state import ActuatorState def get_actuator_status( device_key: str, reference: str ) -> Tuple[ActuatorState, Union[bool, int, float, str]]: """ Get current actuator status identified by device key and reference. Reads the status of actuator from the device and returns it as a tuple containing the actuator state and current value. Must be implemented as non blocking. Must be implemented as thread safe. :param device_key: Device key to which the actuator belongs to :type device_key: str :param reference: Actuator reference :type reference: str :returns: (state, value) :rtype: (ActuatorState, bool or int or float or str) """ raise NotImplementedError

f7340ca89f7353dcd0c8f391724088255d2a8188

py

Python

pddm/regressors/feedforward_network.py

krishpop/pddm

b1452554a4e318966b8ca3da53978458ac635c5d

pddm/regressors/feedforward_network.py

krishpop/pddm

b1452554a4e318966b8ca3da53978458ac635c5d

pddm/regressors/feedforward_network.py

krishpop/pddm

b1452554a4e318966b8ca3da53978458ac635c5d

# Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import tensorflow.compat.v1 as tf tf.disable_v2_behavior() def feedforward_network(inputStates, inputSize, outputSize, num_fc_layers, depth_fc_layers, tf_datatype, scope): with tf.variable_scope(str(scope)): #concat K entries together [bs x K x sa] --> [bs x ksa] inputState = tf.layers.flatten(inputStates) #vars intermediate_size = depth_fc_layers reuse = False initializer = tf.glorot_normal_initializer( seed=None, dtype=tf_datatype) fc = tf.layers.dense # make hidden layers for i in range(num_fc_layers): if i==0: fc_i = fc( inputState, units=intermediate_size, activation=None, kernel_initializer=initializer, bias_initializer=initializer, reuse=reuse, trainable=True) else: fc_i = fc( h_i, units=intermediate_size, activation=None, kernel_initializer=initializer, bias_initializer=initializer, reuse=reuse, trainable=True) h_i = tf.nn.relu(fc_i) # make output layer z = fc( h_i, units=outputSize, activation=None, kernel_initializer=initializer, bias_initializer=initializer, reuse=reuse, trainable=True) return z

f734112f18de9c64644d82da3c43167201edc406

py

Python

kafka-utils/tests/bai_kafka_utils/test_fetcher_event.py

gavinmbell/benchmark-ai-1

a697e67d68b843fe9350e55871dad867bab5d51d

2020-09-29T09:03:04.000Z

2022-03-14T06:52:25.000Z

kafka-utils/tests/bai_kafka_utils/test_fetcher_event.py

gavinmbell/benchmark-ai-1

a697e67d68b843fe9350e55871dad867bab5d51d

kafka-utils/tests/bai_kafka_utils/test_fetcher_event.py

gavinmbell/benchmark-ai-1

a697e67d68b843fe9350e55871dad867bab5d51d

2020-10-01T07:49:22.000Z

2021-06-16T19:44:12.000Z

from bai_kafka_utils.events import ( BenchmarkDoc, VisitedService, FetcherBenchmarkEvent, DownloadableContent, FetcherPayload, FileSystemObject, ) BIG_FETCHER_JSON = """{ "date": "Thu May 02 16:15:42 UTC 2019", "authenticated": false, "payload": { "toml": { "descriptor_filename": "example_descriptor2.toml", "sha1": "be60cb85620fa041c1bfabd9a9b1c8c1d6be1c78", "doc": "IyBCZW5jaG1hcYS90Zi1pbWFnZW5ldC8iCg==", "verified": true, "contents": { "spec_version": "0.1.0", "data": { "sources": [ { "path": "~/data/tf-imagenet/", "src": "s3://bucket/imagenet/train" }, { "path": "~/data/tf-imagenet/", "src": "s3://bucket/imagenet/validation" } ], "id": "imagenet" }, "env": { "privileged": false, "extended_shm": true, "docker_image": "user/repo:tag" }, "info": { "task_name": "Example benchmark", "scheduling": "single_run", "description": " Full job description." }, "hardware": { "distributed": { "num_instances": 3 }, "strategy": "horovod", "instance_type": "p3.8xlarge" }, "ml": { "args": "--model=resnet50_v2 --batch-size=32", "benchmark_code": "python /root/train.sh" } } }, "datasets": [ { "src": "s3://bucket/imagenet/train", "path": "~/data/tf-imagenet/" }, { "src": "s3://bucket/imagenet/validation", "path": "~/data/tf-imagenet/" } ], "models": [ { "src": "s3://bucket/model/inception", "path": "/models/inception", "md5": "5d41402abc4b2a76b9719d911017c592" }, { "src": "s3://bucket/models/mnist", "path": "/models/mnist" } ], "scripts": [ { "dst": "s3://script-exchange/foo.tar" } ] }, "tstamp": 1556814924121, "client_username": "bellgav", "action_id": "ffea52eb-c24b-4dd0-b32e-61230db34ad5", "visited": [ { "svc": "baictl-client", "tstamp": "@@TSTAMP@@", "version": "0.1.0-481dad2" }, { "svc": "bai-bff", "tstamp": 1556814924121, "version": "0.0.2" } ], "message_id": "007bd9f8-f564-4edb-bb48-7380ee562ffc", "client_sha1": "c05467317b6765535f1ec60f0aee812d39b35dd2", "client_id": "97e7eb322342626974fb171fc5793514b0aea789", "client_version": "0.1.0-481dad2", "type": "BAI_APP_BFF" }""" EXPECTED_FETCHER_CONTENTS = { "spec_version": "0.1.0", "data": { "sources": [ {"path": "~/data/tf-imagenet/", "src": "s3://bucket/imagenet/train"}, {"path": "~/data/tf-imagenet/", "src": "s3://bucket/imagenet/validation"}, ], "id": "imagenet", }, "env": {"privileged": False, "extended_shm": True, "docker_image": "user/repo:tag"}, "info": {"task_name": "Example benchmark", "scheduling": "single_run", "description": " Full job description."}, "hardware": {"distributed": {"num_instances": 3}, "strategy": "horovod", "instance_type": "p3.8xlarge"}, "ml": {"args": "--model=resnet50_v2 --batch-size=32", "benchmark_code": "python /root/train.sh"}, } EXPECTED_FETCHER_DOC = BenchmarkDoc( doc="IyBCZW5jaG1hcYS90Zi1pbWFnZW5ldC8iCg==", sha1="be60cb85620fa041c1bfabd9a9b1c8c1d6be1c78", contents=EXPECTED_FETCHER_CONTENTS, verified=True, descriptor_filename="example_descriptor2.toml", ) EXPECTED_FETCHER_VISITED = [ VisitedService(svc="baictl-client", tstamp="@@TSTAMP@@", version="0.1.0-481dad2"), VisitedService(svc="bai-bff", tstamp=1556814924121, version="0.0.2"), ] EXPECTED_FETCHER_DATASETS = [ DownloadableContent("s3://bucket/imagenet/train", path="~/data/tf-imagenet/"), DownloadableContent("s3://bucket/imagenet/validation", path="~/data/tf-imagenet/"), ] EXPECTED_FETCHER_SCRIPTS = [FileSystemObject(dst="s3://script-exchange/foo.tar")] EXPECTED_FETCHER_MODELS = [ DownloadableContent( "s3://bucket/model/inception", path="/models/inception", md5="5d41402abc4b2a76b9719d911017c592" ), DownloadableContent("s3://bucket/models/mnist", path="/models/mnist"), ] EXPECTED_FETCHER_EVENT = FetcherBenchmarkEvent( action_id="ffea52eb-c24b-4dd0-b32e-61230db34ad5", message_id="007bd9f8-f564-4edb-bb48-7380ee562ffc", client_id="97e7eb322342626974fb171fc5793514b0aea789", client_version="0.1.0-481dad2", client_username="bellgav", authenticated=False, tstamp=1556814924121, visited=EXPECTED_FETCHER_VISITED, type="BAI_APP_BFF", payload=FetcherPayload( datasets=EXPECTED_FETCHER_DATASETS, scripts=EXPECTED_FETCHER_SCRIPTS, models=EXPECTED_FETCHER_MODELS, toml=EXPECTED_FETCHER_DOC, ), ) def test_big_fetcher_json(): event = FetcherBenchmarkEvent.from_json(BIG_FETCHER_JSON) print(event) print(EXPECTED_FETCHER_EVENT) assert event == EXPECTED_FETCHER_EVENT

f7341f69b66c89836096882d08cd417fb7779aaf

py

Python

privatebeta/__init__.py

fitoria/django-privatebeta

ef65130d3856d9444f0acc85c5bb590d09908d15

2015-11-05T13:42:05.000Z

2015-11-05T13:42:05.000Z

privatebeta/__init__.py

fitoria/django-privatebeta

ef65130d3856d9444f0acc85c5bb590d09908d15

privatebeta/__init__.py

fitoria/django-privatebeta

ef65130d3856d9444f0acc85c5bb590d09908d15

# Let the wookie pass.

f734d0ce5e31881671bde02a0d35ed0eb21415f4

py

Python

data/transcoder_evaluation_gfg/python/STOOGE_SORT.py

mxl1n/CodeGen

e5101dd5c5e9c3720c70c80f78b18f13e118335a

2021-07-20T08:35:20.000Z

2022-03-31T02:39:08.000Z

data/transcoder_evaluation_gfg/python/STOOGE_SORT.py

mxl1n/CodeGen

e5101dd5c5e9c3720c70c80f78b18f13e118335a

2021-07-22T23:18:42.000Z

2022-03-24T09:15:26.000Z

data/transcoder_evaluation_gfg/python/STOOGE_SORT.py

mxl1n/CodeGen

e5101dd5c5e9c3720c70c80f78b18f13e118335a

2021-07-21T05:17:52.000Z

2022-03-29T23:49:28.000Z

# Copyright (c) 2019-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # def f_gold ( arr , l , h ) : if l >= h : return if arr [ l ] > arr [ h ] : t = arr [ l ] arr [ l ] = arr [ h ] arr [ h ] = t if h - l + 1 > 2 : t = ( int ) ( ( h - l + 1 ) / 3 ) f_gold ( arr , l , ( h - t ) ) f_gold ( arr , l + t , ( h ) ) f_gold ( arr , l , ( h - t ) ) #TOFILL if __name__ == '__main__': param = [ ([6, 25, 42, 52, 53, 54, 58, 66, 67, 70],6,6,), ([-13, -98, 50, -63, 48, 3, -76, 12, -35, 93, 29, 17, 16, 5, -97, -54, -45, -25],16,14,), ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],28,24,), ([7, 49, 26, 33, 48, 79, 2, 71, 32, 4, 20, 36],9,10,), ([88],0,0,), ([1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],20,28,), ([2, 2, 4, 5, 7, 12, 12, 14, 14, 16, 17, 29, 29, 31, 32, 39, 41, 47, 48, 49, 51, 54, 58, 58, 59, 60, 73, 78, 80, 81, 82, 83, 84, 85, 90, 95, 97, 99, 99],28,29,), ([-31, -55, 6, 37, 77, 61, 0, 46, -91, -38, 85, -71, 25, 14, 53, 43, 34],15,11,), ([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],12,17,), ([77, 68, 78, 97, 92, 52, 37, 8, 44, 98, 5, 69, 31, 45, 9, 32, 33, 67, 30, 76, 29, 3, 90, 57, 30, 9, 26, 2, 62, 3, 46, 68, 25, 51, 13, 44, 35, 55],27,20,) ] filled_function_param = [ ([6, 25, 42, 52, 53, 54, 58, 66, 67, 70],6,6,), ([-13, -98, 50, -63, 48, 3, -76, 12, -35, 93, 29, 17, 16, 5, -97, -54, -45, -25],16,14,), ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],28,24,), ([7, 49, 26, 33, 48, 79, 2, 71, 32, 4, 20, 36],9,10,), ([88],0,0,), ([1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],20,28,), ([2, 2, 4, 5, 7, 12, 12, 14, 14, 16, 17, 29, 29, 31, 32, 39, 41, 47, 48, 49, 51, 54, 58, 58, 59, 60, 73, 78, 80, 81, 82, 83, 84, 85, 90, 95, 97, 99, 99],28,29,), ([-31, -55, 6, 37, 77, 61, 0, 46, -91, -38, 85, -71, 25, 14, 53, 43, 34],15,11,), ([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],12,17,), ([77, 68, 78, 97, 92, 52, 37, 8, 44, 98, 5, 69, 31, 45, 9, 32, 33, 67, 30, 76, 29, 3, 90, 57, 30, 9, 26, 2, 62, 3, 46, 68, 25, 51, 13, 44, 35, 55],27,20,) ] n_success = 0 for i, parameters_set in enumerate(param): f_filled(*(filled_function_param[i])) f_gold(*parameters_set) if parameters_set == filled_function_param[i]: n_success+=1 print("#Results: %i, %i" % (n_success, len(param)))

f734d8a1c4282ee4aefbee1bf86ab99face93065

py

Python

py3server/swagger_server/test/test_evidence_controller.py

lhannest/pythonBeaconServerStub

3fee2505f5f7afda9184277b5f6308ff05832e35

py3server/swagger_server/test/test_evidence_controller.py

lhannest/pythonBeaconServerStub

3fee2505f5f7afda9184277b5f6308ff05832e35

py3server/swagger_server/test/test_evidence_controller.py

lhannest/pythonBeaconServerStub

3fee2505f5f7afda9184277b5f6308ff05832e35

# coding: utf-8 from __future__ import absolute_import from swagger_server.models.inline_response2004 import InlineResponse2004 from . import BaseTestCase from six import BytesIO from flask import json class TestEvidenceController(BaseTestCase): """ EvidenceController integration test stubs """ def test_get_evidence(self): """ Test case for get_evidence """ query_string = [('keywords', 'keywords_example'), ('pageNumber', 56), ('pageSize', 56)] response = self.client.open('/api/evidence/{statementId}'.format(statementId='statementId_example'), method='GET', query_string=query_string) self.assert200(response, "Response body is : " + response.data.decode('utf-8')) if __name__ == '__main__': import unittest unittest.main()

f734e09b76bc265ceb72504136dbcb9a86e98111

py

Python

CORE/engines/constraint.py

geoffreynyaga/ostrich-project

157cd7a3c3d9014e31ef21ca21de43f04d039997

2017-11-08T10:03:26.000Z

2021-12-21T07:02:44.000Z

CORE/engines/constraint.py

geoffreynyaga/ostrich-project

157cd7a3c3d9014e31ef21ca21de43f04d039997

2020-01-17T15:09:22.000Z

2022-03-25T19:02:05.000Z

CORE/engines/constraint.py

geoffreynyaga/ostrich-project

157cd7a3c3d9014e31ef21ca21de43f04d039997

#!/usr/bin/env python3 # -*- coding:utf-8 -*- ################################################################################## # File: c:\Projects\KENYA ONE PROJECT\CORE\engines\constraint.py # # Project: c:\Projects\KENYA ONE PROJECT\CORE\engines # # Created Date: Thursday, January 9th 2020, 8:56:55 pm # # Author: Geoffrey Nyaga Kinyua ( <info@geoffreynyaga.com> ) # # ----- # # Last Modified: Thursday January 9th 2020 8:56:55 pm # # Modified By: Geoffrey Nyaga Kinyua ( <info@geoffreynyaga.com> ) # # ----- # # MIT License # # # # Copyright (c) 2020 KENYA ONE PROJECT # # # # 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. # # ----- # # Copyright (c) 2020 KENYA ONE PROJECT # ################################################################################## import sys sys.path.append("../") from CORE.API.db_API import write_to_db, read_from_db import numpy as np # type: ignore import matplotlib.pylab as plt # type: ignore a = np.arange(50) ws = np.arange(10, 35, 0.01) cdmin: float = 0.025 write_to_db("cdMin", cdmin) do = read_from_db("rhoSL") dalt = read_from_db("altitudeDensity") # AAAAA k = read_from_db("k") # v = read_from_db('cruiseSpeed') * 1.688 v: float = 140 * 1.688 # AAAAA qcruise = 0.5 * dalt * v ** 2 # dynamic pressure at cruise qtakeoff = 0.5 * do * v ** 2 # dynamic pressure at take-off turnangle = 40 # turn angle loadfactor = 1 / (np.cos(turnangle)) # loadfactor twturn = ( qcruise * ((cdmin / ws) + (k * (loadfactor / qcruise) ** 2) * ws) * (v * 5850 / (0.8 * 550 * 0.6604)) ) # rate of climb roc = read_from_db("rateOfClimb") * 3.28 * 60 # rate of climb ft/min #AAAAAAA # Vy=sqrt((2/do)*ws * sqrt( k/(3*cdmin) )) Vy = 150 Vv = roc / 60 qclimb = 0.5 * do * (Vy ** 2) twclimb = ( (Vv / Vy) + ((qclimb / ws) * cdmin) + ((qclimb / ws) * cdmin) + ((k / qclimb) * ws) ) * (Vy * 5850 / (0.6 * 550)) # ground run Sg: int = 1000 # ground run ft Vlof: float = 70 * 1.688 clto: float = 1.4670 u: float = 0.04 cdto = 0.03 q1 = 0.5 * do * (Vlof / np.sqrt(2)) ** 2 twtakeoff = ( ((Vlof ** 2) / (2 * 32.174 * Sg)) + ((q1 * cdto) / ws) + u * (1 - (q1 * clto / ws)) ) * (Vlof * 5850 / (0.6 * 550)) # cruise altitude twcruise = (((qcruise * cdmin) / ws) + ((k / qcruise) * ws)) * ( v * 5850 / (0.6 * 550 * 0.6604) ) # service ceiling twservceiling = ( (1.668 / np.sqrt((2 * ws / dalt) * np.sqrt(k / (3 * cdmin)))) + (4 * np.sqrt(k * cdmin / 3)) ) * ((v * 5850) / (0.7 * 550 * 0.6604)) plt.plot(ws, twclimb, label="climb") plt.plot(ws, twturn, label="turn") plt.plot(ws, twtakeoff, label="Takeoff") plt.plot(ws, twservceiling, label="Service Ceiling") plt.plot(ws, twcruise, label="cruise") plotWS = read_from_db("WS") plt.axvline(x=plotWS) ################################ plt.legend(loc="upper left") if __name__ == "__main__": plt.show() def find_nearest(array, value): idx = (np.abs(array - value)).argmin() return idx # print(find_nearest(ws, plotWS)) myidx = find_nearest(ws, plotWS) # cruiseidx = (twcruise[myidx]) # takeoffidx = twtakeoff[myidx] # climbidx = twclimb[myidx] # turnidx = twturn[myidx] # ceilingidx = twservceiling[myidx] # print([cruiseidx,takeoffidx,climbidx,turnidx,ceilingidx]) def point(): cruiseidx = twcruise[myidx] takeoffidx = twtakeoff[myidx] climbidx = twclimb[myidx] turnidx = twturn[myidx] ceilingidx = twservceiling[myidx] # print([cruiseidx,takeoffidx,climbidx,turnidx,ceilingidx]) # print (cruiseidx,"cruiseidx") x = np.array([cruiseidx, takeoffidx, climbidx, turnidx, ceilingidx]) idx = x.argmax() return x[idx] finalBHP = point() # print ( finalBHP,"BHP") write_to_db("finalBHP", finalBHP) S = (read_from_db("finalMTOW")) / (plotWS * 10.57) write_to_db("S", S)

f73518b051d1cc9646ebc5039c4ebb6aa6cbfa1f

py

Python

demo/voice/main.py

fatash89/atom

12846c8a3f936ae6c83e7e7b1d2dbb896e63fe66

2019-04-01T20:32:07.000Z

2021-11-24T17:12:03.000Z

demo/voice/main.py

elementary-robotics/atom

36aea078c0e029f03e7b9b4768729a683fb32a88

2019-04-01T22:54:31.000Z

2022-03-31T21:48:47.000Z

demo/voice/main.py

fatash89/atom

12846c8a3f936ae6c83e7e7b1d2dbb896e63fe66

2019-06-27T22:42:54.000Z

2022-02-01T23:00:37.000Z

# atombot.py import time from atom import Element PUBLISH_FREQUENCY = 100 TIME_FOR_WAVEFORM = 5 if __name__ == "__main__": element = Element("voice_demo") # Wait for the record element to start up and launch the VNC. # this can and should be fixed with a heartbeat! time.sleep(10) # Start the recording and wait for 5s data = { "name": "example", "t": TIME_FOR_WAVEFORM, "perm": False, "e": "waveform", "s": "serialized", } res = element.command_send("record", "start", data, serialize=True) time.sleep(TIME_FOR_WAVEFORM + 2) # Strings we'll recognize for the plotting commands. This is pretty # rudimentary and can be improved with some better parsing/processing/NLP sinx_strings = ["show sin", "show sign", "show sine"] cosx_strings = [ "show cos", "show cosine", "show coast", "show coats", "show cosign", ] tanx_strings = ["show tan", "showtime"] print("listening..") last_id = element._get_redis_timestamp() while True: entries = element.entry_read_since( "voice", "string", last_id=last_id, block=1000 ) if entries: last_id = entries[0]["id"] voice_string = entries[0]["data"].decode().lower() print("Got voice string {}".format(voice_string)) if any(x in voice_string for x in sinx_strings): print("Plotting sinx") data = { "name": "example", "msgpack": True, "plots": [{"data": [["x", ["sin"], "value"]]}], } res = element.command_send("record", "plot", data, serialize=True) if any(x in voice_string for x in cosx_strings): print("Plotting cosx") data = { "name": "example", "msgpack": True, "plots": [{"data": [["x", ["cos"], "value"]]}], } res = element.command_send("record", "plot", data, serialize=True) if any(x in voice_string for x in tanx_strings): print("Plotting tanx") data = { "name": "example", "msgpack": True, "plots": [{"data": [["x", ["tan"], "value"]]}], } res = element.command_send("record", "plot", data, serialize=True) time.sleep(1 / PUBLISH_FREQUENCY)

f7352703ed42ee6015d5ca30e57234884d399073

py

Python

test/testAnonymizationExecutor.py

AutoDash/AutoDash

3924795a04159f80ea3b65b2172747babd15f35f

2020-02-12T01:24:46.000Z

2020-02-13T00:50:46.000Z

test/testAnonymizationExecutor.py

AutoDash/AutoDash

3924795a04159f80ea3b65b2172747babd15f35f

2020-02-20T10:20:56.000Z

2022-02-10T01:42:46.000Z

test/testAnonymizationExecutor.py

AutoDash/AutoDash

3924795a04159f80ea3b65b2172747babd15f35f

2020-02-22T02:47:19.000Z

2020-02-22T02:47:19.000Z

#!/usr/bin/env python3 import unittest import os import shutil from src.data.VideoItem import VideoItem from src.data.MetaDataItem import MetaDataItem from src.executor.FaceBlurrer import FaceBlurrer from numpy.testing import assert_array_equal, assert_raises class TestAnonymizationExecutor(unittest.TestCase): TEST_DIR = os.path.join(os.getcwd(), "anontest") TEST_FILE = "test.mp4" DATASET_PATH = "src/lib/anonymization/dataset/input" ACCEPTED_FILE_EXTENSION = ".mp4" TEST_FILE_PATH = os.path.join(TEST_DIR, TEST_FILE) def setUp(self): # Create test directory and copy one of the test videos from the anonymization repo into it if not os.path.exists(self.TEST_DIR): os.mkdir(self.TEST_DIR) def tearDown(self): # Delete test directory if os.path.exists(self.TEST_DIR): shutil.rmtree(self.TEST_DIR) def test_compiles(self): self.assertEqual(True, True) """ # Test that the executor works with a single video def test_face_blurrer_single(self): # Copy video to test directory shutil.copy2(os.path.join(os.getcwd(), self.DATASET_PATH, "man_face.mp4"), self.TEST_FILE_PATH) video = VideoItem(filepath = self.TEST_FILE_PATH, metadata=None) original_data = video.npy # Running the face blurrer should overwrite the input file face_blurrer = FaceBlurrer() new_data = face_blurrer.run(video) # Now we check that the video data has changed assert_raises(AssertionError, assert_array_equal, original_data, new_data) """ if __name__ == '__main__': unittest.main()

f7352a156a29540ef2be6caeb5071e55659eee34

py

Python

glyco/glucose/util.py

allaparthi/monorail

e18645fc1b952a5a6ff5f06e0c740d75f1904473

2021-04-13T21:22:18.000Z

2021-09-07T02:11:57.000Z

glyco/glucose/util.py

allaparthi/monorail

e18645fc1b952a5a6ff5f06e0c740d75f1904473

2020-09-06T02:41:05.000Z

2022-03-02T04:40:01.000Z

glyco/glucose/util.py

allaparthi/monorail

e18645fc1b952a5a6ff5f06e0c740d75f1904473

# Copyright 2015 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import contextlib import os import platform import re import tempfile import shutil import subprocess import sys import urllib import urlparse # Copied from pip.wheel.Wheel.wheel_file_re to avoid requiring pip here. WHEEL_FILE_RE = re.compile( r"""^(?P<namever>(?P<name>.+?)-(?P<ver>\d.*?)) ((-(?P<build>\d.*?))?-(?P<pyver>.+?)-(?P<abi>.+?)-(?P<plat>.+?) \.whl)$""", re.VERBOSE ) class GlycoError(Exception): """Base class for Glyco errors""" class GlycoSetupError(GlycoError): """Issue outside the reach of Glyco that prevents execution.""" class InvalidWheelFile(GlycoError): """The file passed is not a valid wheel file. This includes errors on the file name. """ def setup_virtualenv(env_path, relocatable=False): """Create a virtualenv in specified location. The virtualenv contains a standard Python installation, plus setuptools, pip and wheel. Args: env_path (str): where to create the virtual environment. """ if os.path.exists(os.path.join(os.path.expanduser('~'), '.pydistutils.cfg')): raise GlycoSetupError('\n'.join([ '', 'You have a ~/.pydistutils.cfg file, which interferes with the ', 'infra virtualenv environment. Please move it to the side and bootstrap ', 'again. Once infra has bootstrapped, you may move it back.', '', 'Upstream bug: https://github.com/pypa/virtualenv/issues/88/', '' ])) print 'Creating environment: %r' % env_path if os.path.exists(env_path): print ' Removing existing one...' shutil.rmtree(env_path, ignore_errors=True) print ' Building new environment...' # Import bundled virtualenv lib import virtualenv # pylint: disable=F0401 virtualenv.create_environment( env_path, search_dirs=virtualenv.file_search_dirs()) if relocatable: print ' Make environment relocatable' virtualenv.make_environment_relocatable(env_path) print 'Done creating environment' def platform_tag(): if sys.platform.startswith('linux'): return '_{0}_{1}'.format(*platform.linux_distribution()) return '' class Virtualenv(object): def __init__(self, prefix='glyco-', keep_directory=False): """Helper class to run commands from virtual environments. Keyword Args: prefix (str): prefix to the temporary directory used to create the virtualenv. keep_directory (boolean): if True the temporary virtualenv directory is kept around instead of being deleted. Useful mainly for debugging. Returns: self. Only the check_call and check_output methods are meant to be used inside the with block. """ self._prefix = prefix self._keep_directory = keep_directory # Where the virtualenv is self._venvdir = None self._bin_dir = 'Scripts' if sys.platform.startswith('win') else 'bin' def check_call(self, args, **kwargs): """Run a command from inside the virtualenv using check_call. Args: cmd (str): name of the command. Must be found in the 'bin' directory of the virtualenv. args (list of strings): arguments passed to the command. Keyword Args: kwargs: keyword arguments passed to subprocess.check_output """ subprocess.check_call( (os.path.join(self._venvdir, self._bin_dir, args[0]),) + tuple(args[1:]), **kwargs) def check_output(self, args, **kwargs): """Run a command from inside the virtualenv using check_output. Args: cmd (str): name of the command. Must be found in the 'bin' directory of the virtualenv. args (list of strings): arguments passed to the command. Keyword Args: kwargs: keyword arguments passed to subprocess.check_output """ return subprocess.check_output( (os.path.join(self._venvdir, self._bin_dir, args[0]),) + tuple(args[1:]), **kwargs) def __cleanup_venv(self): """Remove the virtualenv directory""" try: # TODO(pgervais,496347) Make this work reliably on Windows. shutil.rmtree(self._venvdir, ignore_errors=True) except OSError as ex: print >> sys.stderr, ( "ERROR: {!r} while cleaning up {!r}".format(ex, self._venvdir)) self._venvdir = None def __enter__(self): self._venvdir = tempfile.mkdtemp('', self._prefix, None) try: setup_virtualenv(self._venvdir) except Exception: self.__cleanup_venv() raise return self def __exit__(self, err_type, value, tb): if self._venvdir and not self._keep_directory: self.__cleanup_venv() # dir is a built-in. We're matching the Python 3 function signature here. # pylint: disable=redefined-builtin @contextlib.contextmanager def temporary_directory(suffix="", prefix="tmp", dir=None, keep_directory=False): """Create and return a temporary directory. This has the same behavior as mkdtemp but can be used as a context manager. For example: with temporary_directory() as tmpdir: ... Upon exiting the context, the directory and everything contained in it are removed. Args: suffix, prefix, dir: same arguments as for tempfile.mkdtemp. keep_directory (bool): if True, do not delete the temporary directory when exiting. Useful for debugging. Returns: tempdir (str): full path to the temporary directory. """ tempdir = None # Handle mkdtemp raising an exception try: tempdir = tempfile.mkdtemp(suffix, prefix, dir) yield tempdir finally: if tempdir and not keep_directory: try: # TODO(pgervais,496347) Make this work reliably on Windows. shutil.rmtree(tempdir, ignore_errors=True) except OSError as ex: print >> sys.stderr, ( "ERROR: {!r} while cleaning up {!r}".format(ex, tempdir)) def path2fileurl(path): """Convert a local absolute path to a file:/// URL There is no way to provide a relative path in a file:// URI, because there is no notion of 'working directory'. Output conforms to https://tools.ietf.org/html/rfc1630 """ if not os.path.isabs(path): raise ValueError('Only absolute paths can be turned into a file url. ' 'Got: %s' % path) path_comp = urllib.pathname2url(path) return 'file:///' + path_comp.lstrip('/') def fileurl2path(url): """Convert a file:// URL to a local path. Note that per https://tools.ietf.org/html/rfc1630 page 18 a host name should be provided. So file://localhost/file/name points to /file/name on localhost file:///file/name points to /file/name ('localhost' is optional) file://file/name points to /name on machine 'file'. """ if not url.startswith('file://'): raise ValueError('URL must start with "file://". Got %s' % url) parts = urlparse.urlparse(url) return urllib.url2pathname(parts.path)

f7354da7414334f4bb43360cb78f171a1a3a8177

py

Python

oscar/apps/search/views.py

makielab/django-oscar

0a325cd0f04a4278201872b2e163868b72b6fabe

2015-11-07T12:37:50.000Z

2015-11-07T12:37:50.000Z

oscar/apps/search/views.py

makielab/django-oscar

0a325cd0f04a4278201872b2e163868b72b6fabe

oscar/apps/search/views.py

makielab/django-oscar

0a325cd0f04a4278201872b2e163868b72b6fabe

import json from django.http import HttpResponse, HttpResponseRedirect from django.views.generic.base import View from django.conf import settings from django.db.models import get_model from haystack.query import SearchQuerySet from haystack import views from purl import URL Product = get_model('catalogue', 'Product') class SuggestionsView(View): """ Auto suggest view Returns the suggestions in JSON format (especially suited for consumption by jQuery autocomplete) """ suggest_limit = settings.OSCAR_SEARCH_SUGGEST_LIMIT def get(self, request): context = self.get_context_data() return self.render_to_response(context) def get_context_data(self): ''' Creates a list of suggestions ''' query_term = self.request.GET['query_term'] query_set = SearchQuerySet().filter(text__contains=query_term)[ :self.suggest_limit] context = [] for item in query_set: context.append({ 'label': item.object.title, 'url': item.object.get_absolute_url(), }) return context def render_to_response(self, context): "Returns a JSON response containing 'context' as payload" return self.get_json_response(self.convert_context_to_json(context)) def get_json_response(self, content, **httpresponse_kwargs): "Construct an `HttpResponse` object." return HttpResponse(content, content_type='application/json', **httpresponse_kwargs) def convert_context_to_json(self, context): "Convert the context into a JSON object" return json.dumps(context) class FacetedSearchView(views.FacetedSearchView): def extra_context(self): extra = super(FacetedSearchView, self).extra_context() if 'fields' not in extra['facets']: # Looks like Solr is not responding correctly return extra # Convert facet data into a more useful datastructure # Field facets facet_data = {} base_url = URL(self.request.get_full_path()) selected = dict( map(lambda x: x.split(':'), self.form.selected_facets)) for field, facets in extra['facets']['fields'].items(): facet_data[field] = [] for name, count in facets: # Ignore zero-count facets for field if count == 0: continue field_filter = '%s_exact' % field datum = { 'name': name, 'count': count} if selected.get(field_filter, None) == name: # This filter is selected - build the 'deselect' URL datum['selected'] = True url = base_url.remove_query_param( 'selected_facets', '%s:%s' % ( field_filter, name)) datum['deselect_url'] = url.as_string() else: # This filter is not selected - built the 'select' URL datum['selected'] = False url = base_url.append_query_param( 'selected_facets', '%s:%s' % ( field_filter, name)) datum['select_url'] = url.as_string() facet_data[field].append(datum) # Query facets for key, facet in settings.OSCAR_SEARCH_FACETS['queries'].items(): facet_data[key] = [] for name, query in facet['queries']: field_filter = '%s_exact' % facet['field'] match = '%s_exact:%s' % (facet['field'], query) if not match in extra['facets']['queries']: datum = { 'name': name, 'count': 0, } else: datum = { 'name': name, 'count': extra['facets']['queries'][match], } if selected.get(field_filter, None) == query: # Selected datum['selected'] = True url = base_url.remove_query_param( 'selected_facets', match) datum['deselect_url'] = url.as_string() else: datum['selected'] = False url = base_url.append_query_param( 'selected_facets', match) datum['select_url'] = url.as_string() facet_data[key].append(datum) extra['facet_data'] = facet_data return extra class MultiFacetedSearchView(FacetedSearchView): """ Search view for multifaceted searches """ template = 'search/results.html' def __call__(self, request, *args, **kwargs): """ Generates the actual response to the search. Relies on internal, overridable methods to construct the response. """ # Look for UPC match query = request.GET.get('q', '').strip() try: item = Product._default_manager.get(upc=query) return HttpResponseRedirect(item.get_absolute_url()) except Product.DoesNotExist: pass return super(MultiFacetedSearchView, self).__call__(request, *args, **kwargs) @property def __name__(self): return "MultiFacetedSearchView" def extra_context(self): """ Adds details about the facets applied """ extra = super(MultiFacetedSearchView, self).extra_context() if hasattr(self.form, 'cleaned_data') and 'selected_facets' in self.form.cleaned_data: extra['facets_applied'] = [] for f in self.form.cleaned_data['selected_facets'].split("|"): facet = f.split(":") extra['facets_applied'].append({ 'facet': facet[0][:-6], # removing the _exact suffix that haystack uses for some reason 'value' : facet[1].strip('"') }) return extra

f735efd81dd59271b71383463bdf088ddbe47517

py

Python

userbot/plugins/confuse.py

justteen/BUZZ-USERBOT

55651cce150e1d04d2c61efb2565ef9f46b42933

userbot/plugins/confuse.py

justteen/BUZZ-USERBOT

55651cce150e1d04d2c61efb2565ef9f46b42933

userbot/plugins/confuse.py

justteen/BUZZ-USERBOT

55651cce150e1d04d2c61efb2565ef9f46b42933

"""Emoji Available Commands: .adi""" import asyncio from telethon import events @borg.on(events.NewMessage(pattern=r"\.(.*)", outgoing=True)) async def _(event): if event.fwd_from: return animation_interval = 0.3 animation_ttl = range(0, 15) input_str = event.pattern_match.group(1) if input_str == "adi": await event.edit(input_str) animation_chars = [ "⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜", "⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬛⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜", "⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬛⬛⬛⬜⬜\n⬜⬜⬛⬜⬛⬜⬜\n⬜⬜⬛⬛⬛⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜", "⬜⬜⬜⬜⬜⬜⬜\n⬜⬛⬛⬛⬛⬛⬜\n⬜⬛⬜⬜⬜⬛⬜\n⬜⬛⬜⬜⬜⬛⬜\n⬜⬛⬜⬜⬜⬛⬜\n⬜⬛⬛⬛⬛⬛⬜\n⬜⬜⬜⬜⬜⬜⬜", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬛⬛⬛⬛⬛⬛", "⬛⬜⬛⬜⬛⬜⬛⬜\n⬜⬛⬜⬛⬜⬛⬜⬛\n⬛⬜⬛⬜⬛⬜⬛⬜\n⬜⬛⬜⬛⬜⬛⬜⬛\n⬛⬜⬛⬜⬛⬜⬛⬜\n⬜⬛⬜⬛⬜⬛⬜⬛\n⬛⬜⬛⬜⬛⬜⬛⬜\n⬜⬛⬜⬛⬜⬛⬜⬛", "⬜⬛⬜⬛⬜⬛⬜⬛\n⬛⬜⬛⬜⬛⬜⬛⬜\n⬜⬛⬜⬛⬜⬛⬜⬛\n⬛⬜⬛⬜⬛⬜⬛⬜\n⬜⬛⬜⬛⬜⬛⬜⬛\n⬛⬜⬛⬜⬛⬜⬛⬜\n⬜⬛⬜⬛⬜⬛⬜⬛\n⬛⬜⬛⬜⬛⬜⬛⬜", "⬜⬜⬜⬜⬜⬜⬜\n⬜⬛⬛⬛⬛⬛⬜\n⬜⬛⬜⬜⬜⬛⬜\n⬜⬛⬜⬛⬜⬛⬜\n⬜⬛⬜⬜⬜⬛⬜\n⬜⬛⬛⬛⬛⬛⬜\n⬜⬜⬜⬜⬜⬜⬜", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬜⬛⬛⬛⬜⬛\n⬛⬜⬛⬜⬛⬜⬛\n⬛⬜⬛⬛⬛⬜⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬛⬛⬛⬛⬛⬛", "⬜⬜⬜⬜⬜⬜⬜\n⬜⬛⬛⬛⬛⬛⬜\n⬜⬛⬜⬜⬜⬛⬜\n⬜⬛⬜⬛⬜⬛⬜\n⬜⬛⬜⬜⬜⬛⬜\n⬜⬛⬛⬛⬛⬛⬜\n⬜⬜⬜⬜⬜⬜⬜", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬜⬛⬛⬛⬜⬛\n⬛⬜⬛⬜⬛⬜⬛\n⬛⬜⬛⬛⬛⬜⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬛⬛⬛⬛⬛⬛", "⬜⬜⬜⬜⬜⬜⬜\n⬜⬛⬛⬛⬛⬛⬜\n⬜⬛⬜⬜⬜⬛⬜\n⬜⬛⬜⬛⬜⬛⬜\n⬜⬛⬜⬜⬜⬛⬜\n⬜⬛⬛⬛⬛⬛⬜\n⬜⬜⬜⬜⬜⬜⬜", "⬛⬛⬛⬛⬛\n⬛⬜⬜⬜⬛\n⬛⬜⬛⬜⬛\n⬛⬜⬜⬜⬛\n⬛⬛⬛⬛⬛", "⬜⬜⬜\n⬜⬛⬜\n⬜⬜⬜", "[👉🔴👈](https://t.me/black_lightning_channel)", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 15])

f735f5fbd64d2154b36d035f777107f689429cdb

py

Python

test/python/LIM2Metrics/py3/base/common/Prototype/Prototype.py

sagodiz/SonarQube-plug-in

4f8e111baecc4c9f9eaa5cd3d7ebeb1e365ace2c

2015-06-16T17:39:10.000Z

2022-03-20T22:39:40.000Z

test/python/LIM2Metrics/py3/base/common/Prototype/Prototype.py

sagodiz/SonarQube-plug-in

4f8e111baecc4c9f9eaa5cd3d7ebeb1e365ace2c

2015-12-29T19:07:22.000Z

2022-03-22T10:39:02.000Z

test/python/LIM2Metrics/py3/base/common/Prototype/Prototype.py

sagodiz/SonarQube-plug-in

4f8e111baecc4c9f9eaa5cd3d7ebeb1e365ace2c

2015-08-28T01:22:18.000Z

2021-09-25T08:17:31.000Z

import copy # # Prototype Class # class Cookie: def __init__(self, name): self.name = name def clone(self): return copy.deepcopy(self) # # Concrete Prototypes to clone # class CoconutCookie(Cookie): def __init__(self): Cookie.__init__(self, 'Coconut') # # Client Class # class CookieMachine: def __init__(self, cookie): self.cookie = cookie def make_cookie(self): return self.cookie.clone() if __name__ == '__main__': prot = CoconutCookie() cm = CookieMachine(prot) for i in range(10): temp_cookie = cm.make_cookie()

f7360b1ff28ca8cb8ff567284e2ce156676903d0

py

Python

monkeys-and-frogs-on-fire/server/__main__.py

markjoshua12/game-jam-2020

846dd052d649a609ab7a52ac0f4dcbeb71781c3b

2020-04-17T12:02:14.000Z

2022-03-16T03:01:34.000Z

monkeys-and-frogs-on-fire/server/__main__.py

markjoshua12/game-jam-2020

846dd052d649a609ab7a52ac0f4dcbeb71781c3b

2020-04-18T21:14:57.000Z

2022-01-13T03:05:09.000Z

monkeys-and-frogs-on-fire/server/__main__.py

markjoshua12/game-jam-2020

846dd052d649a609ab7a52ac0f4dcbeb71781c3b

2020-04-17T12:01:11.000Z

2021-12-28T10:14:02.000Z

from server.server import Server def main(): server = Server(__file__) server.run() if __name__ == '__main__': main()

f736559162d2fb353e82402c79261dc665f58d3e

py

Python

pytorch_forecasting/__init__.py

KazukiNoto/pytorch-forecasting

8a1636388e091456f042f999892dd52733903dd6

2021-06-09T09:51:13.000Z

2021-06-09T09:51:13.000Z

pytorch_forecasting/__init__.py

KazukiNoto/pytorch-forecasting

8a1636388e091456f042f999892dd52733903dd6

pytorch_forecasting/__init__.py

KazukiNoto/pytorch-forecasting

8a1636388e091456f042f999892dd52733903dd6

2021-06-15T11:31:44.000Z

2021-06-15T11:31:44.000Z

""" PyTorch Forecasting package for timeseries forecasting with PyTorch. """ from pytorch_forecasting.data import EncoderNormalizer, GroupNormalizer, TimeSeriesDataSet from pytorch_forecasting.models import Baseline, NBeats, TemporalFusionTransformer __all__ = [ "TimeSeriesDataSet", "GroupNormalizer", "EncoderNormalizer", "TemporalFusionTransformer", "NBeats", "Baseline", ] __version__ = "0.0.0"

f7366798202eb5b63a293458869820181b482fe2

py

Python

ramses_rf/protocol/helpers.py

zxdavb/evohome_rf

dd7233abde1c3b1e645cfabcf3ccc96d0d2c381b

2019-12-12T20:54:39.000Z

2021-03-25T15:40:26.000Z

ramses_rf/protocol/helpers.py

zxdavb/evohome_rf

dd7233abde1c3b1e645cfabcf3ccc96d0d2c381b

2020-10-21T23:01:06.000Z

2021-04-22T09:59:50.000Z

ramses_rf/protocol/helpers.py

zxdavb/evohome_rf

dd7233abde1c3b1e645cfabcf3ccc96d0d2c381b

2019-12-03T21:05:11.000Z

2021-04-02T11:41:42.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # """RAMSES RF - Protocol/Transport layer. Helper functions. """ import ctypes import sys import time from datetime import datetime as dt from typing import Optional, Union from .const import DEVICE_TYPES, NON_DEVICE_ID, NUL_DEVICE_ID class FILETIME(ctypes.Structure): """Data structure for GetSystemTimePreciseAsFileTime().""" _fields_ = [("dwLowDateTime", ctypes.c_uint), ("dwHighDateTime", ctypes.c_uint)] def dt_now() -> dt: """Return the current datetime as a local/naive datetime object. This is slower, but potentially more accurate, than dt.now(), and is used mainly for packet timestamps. """ return dt.fromtimestamp(timestamp()) def dt_str() -> str: """Return the current datetime as a isoformat string.""" return dt_now().isoformat(timespec="microseconds") def timestamp() -> float: """Return the number of seconds since the Unix epoch. Return an accurate value, even for Windows-based systems. """ # see: https://www.python.org/dev/peps/pep-0564/ if sys.platform != "win32": return time.time_ns() / 1e9 # since 1970-01-01T00:00:00Z, time.gmtime(0) file_time = FILETIME() ctypes.windll.kernel32.GetSystemTimePreciseAsFileTime(ctypes.byref(file_time)) _time = (file_time.dwLowDateTime + (file_time.dwHighDateTime << 32)) / 1e7 return _time - 134774 * 24 * 60 * 60 # otherwise, is since 1601-01-01T00:00:00Z def _precision_v_cost(): import math # LOOPS = 10 ** 6 # print("time.time_ns(): %s" % time.time_ns()) print("time.time(): %s\r\n" % time.time()) # starts = time.time_ns() min_dt = [abs(time.time_ns() - time.time_ns()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min delta time_ns(): %s ns" % min_dt) print("duration time_ns(): %s ns\r\n" % (time.time_ns() - starts)) # starts = time.time_ns() min_dt = [abs(time.time() - time.time()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min delta time(): %s ns" % math.ceil(min_dt * 1e9)) print("duration time(): %s ns\r\n" % (time.time_ns() - starts)) # starts = time.time_ns() min_dt = [abs(timestamp() - timestamp()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min delta timestamp(): %s ns" % math.ceil(min_dt * 1e9)) print("duration timestamp(): %s ns\r\n" % (time.time_ns() - starts)) # LOOPS = 10 ** 4 # starts = time.time_ns() min_td = [abs(dt.now() - dt.now()) for _ in range(LOOPS)] min_td = min(filter(bool, min_td)) print("min delta dt.now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt.now(): %s ns\r\n" % (time.time_ns() - starts)) # starts = time.time_ns() min_td = [abs(dt_now() - dt_now()) for _ in range(LOOPS)] min_td = min(filter(bool, min_td)) print("min delta dt_now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt_now(): %s ns\r\n" % (time.time_ns() - starts)) # starts = time.time_ns() min_td = [ abs( (dt_now if sys.platform == "win32" else dt.now)() - (dt_now if sys.platform == "win32" else dt.now)() ) for _ in range(LOOPS) ] min_td = min(filter(bool, min_td)) print("min delta dt_now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt_now(): %s ns\r\n" % (time.time_ns() - starts)) # dt_nov = dt_now if sys.platform == "win32" else dt.now starts = time.time_ns() min_td = [abs(dt_nov() - dt_nov()) for _ in range(LOOPS)] min_td = min(filter(bool, min_td)) print("min delta dt_now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt_now(): %s ns\r\n" % (time.time_ns() - starts)) def double(val, factor=1) -> Optional[float]: """Return a double, used by 31DA.""" if val == "7FFF": return result = int(val, 16) assert result < 32767 return result if factor == 1 else result / factor def flag8(byte, lsb=False) -> list: """Split a byte (as a str) into a list of 8 bits, MSB first by default.""" if lsb is True: return [(bytes.fromhex(byte)[0] & (1 << x)) >> x for x in range(8)] return [(bytes.fromhex(byte)[0] & (1 << x)) >> x for x in reversed(range(8))] def percent(value: str) -> Optional[float]: # a percentage 0-100% (0.0 to 1.0) """Return a percentage, 0-100% with resolution of 0.5%.""" assert len(value) == 2, f"percent({value}): len is not 2" if value in {"EF", "FE", "FF"}: # TODO: diff b/w FE (seen with 3150) & FF return assert int(value, 16) <= 200, "max value should be 0xC8, not 0x{value}" return int(value, 16) / 200 def bool_from_hex(value: str) -> Optional[bool]: # either 00 or C8 """Return a boolean.""" assert value in {"00", "C8", "FF"}, value return {"00": False, "C8": True}.get(value) def date_from_hex(value: str) -> Optional[str]: # YY-MM-DD """Return a date string in the format YY-MM-DD.""" assert len(value) == 8, "len is not 8" if value == "FFFFFFFF": return return dt( year=int(value[4:8], 16), month=int(value[2:4], 16), day=int(value[:2], 16) & 0b11111, # 1st 3 bits: DayOfWeek ).strftime("%Y-%m-%d") def dtm_from_hex(value: str) -> str: # from parsers """Convert a hex string to an (naive, local) isoformat string.""" # 00141B0A07E3 (...HH:MM:00) for system_mode, zone_mode (schedules?) # 0400041C0A07E3 (...HH:MM:SS) for sync_datetime if value == "FF" * 6: return None if len(value) == 12: value = f"00{value}" # assert len(value) == 14 return dt( year=int(value[10:14], 16), month=int(value[8:10], 16), day=int(value[6:8], 16), hour=int(value[4:6], 16) & 0b11111, # 1st 3 bits: DayOfWeek minute=int(value[2:4], 16), second=int(value[:2], 16) & 0b1111111, # 1st bit: used for DST ).isoformat(timespec="seconds") def dtm_to_hex(dtm: Union[str, dt]) -> str: """Convert a datetime (isoformat string, or datetime obj) to a hex string.""" def _dtm_to_hex(tm_year, tm_mon, tm_mday, tm_hour, tm_min, tm_sec, *args): return f"{tm_min:02X}{tm_hour:02X}{tm_mday:02X}{tm_mon:02X}{tm_year:04X}" if dtm is None: return "FF" * 6 if isinstance(dtm, str): try: dtm = dt.fromisoformat(dtm) except ValueError: raise ValueError("Invalid datetime isoformat string") elif not isinstance(dtm, dt): raise TypeError("Invalid datetime object") # if dtm < dt.now() + td(minutes=1): # raise ValueError("Invalid datetime") return _dtm_to_hex(*dtm.timetuple()) def dts_from_hex(value: str) -> Optional[str]: """YY-MM-DD HH:MM:SS.""" if value == "00000000007F": return None _seqx = int(value, 16) return dt( year=(_seqx & 0b1111111 << 24) >> 24, month=(_seqx & 0b1111 << 36) >> 36, day=(_seqx & 0b11111 << 31) >> 31, hour=(_seqx & 0b11111 << 19) >> 19, minute=(_seqx & 0b111111 << 13) >> 13, second=(_seqx & 0b111111 << 7) >> 7, ).strftime("%Y-%m-%dT%H:%M:%S") def dts_to_hex(dtm: Union[str, dt]) -> str: # TODO: WIP """YY-MM-DD HH:MM:SS.""" if dtm is None: return "00000000007F" if isinstance(dtm, str): try: dtm = dt.fromisoformat(dtm) # TODO: YY-MM-DD, not YYYY-MM-DD except ValueError: raise ValueError("Invalid datetime isoformat string") elif not isinstance(dtm, dt): raise TypeError("Invalid datetime object") (tm_year, tm_mon, tm_mday, tm_hour, tm_min, tm_sec, *args) = dtm.timetuple() val = sum( ( tm_year % 100 << 24, tm_mon << 36, tm_mday << 31, tm_hour << 19, tm_min << 13, tm_sec << 7, ) ) return f"{val:012X}" def str_from_hex(value: str) -> Optional[str]: # printable ASCII characters """Return a string of printable ASCII characters.""" # result = bytearray.fromhex(value).split(b"\x7F")[0] # TODO: needs checking result = bytearray([x for x in bytearray.fromhex(value) if 31 < x < 127]) return result.decode("ascii").strip() if result else None def str_to_hex(value: str) -> str: """Convert a string to a variable-length ASCII hex string.""" return "".join(f"{ord(x):02X}" for x in value) # return value.encode().hex() def temp_from_hex(value: str) -> Union[float, bool, None]: """Convert a 2's complement 4-byte hex string to an float.""" assert len(value) == 4, f"temp_from_hex({value}): should be 4 bytes long" if value == "31FF": # means: N/A (== 127.99, 2s complement), signed? return if value == "7EFF": # possibly only for setpoints? unsigned? return False if value == "7FFF": # also: FFFF?, means: N/A (== 327.67) return temp = int(value, 16) return (temp if temp < 2 ** 15 else temp - 2 ** 16) / 100 def temp_to_hex(value: float) -> str: """Convert a float to a 2's complement 4-byte hex string.""" assert ( not value or -(2 ** 7) <= value < 2 ** 7 ), f"temp_to_hex({value}): is out of 2's complement range" if value is None: return "7FFF" # or: "31FF"? if value is False: return "7EFF" temp = int(value * 100) return f"{temp if temp >= 0 else temp + 2 ** 16:04X}" def valve_demand(value: str) -> dict: # a damper restricts flow, a valve permits flow demand = int(value, 16) if demand & 0xF0 == 0xF0: VALVE_STATE = { "F0": "open_circuit", "F1": "short_circuit", "FD": "valve_stuck", # damper/valve stuck "FE": "actuator_stuck", } # VALVE_STATE.get(value, "malfunction") return { "heat_demand": None, "fault": VALVE_STATE.get(value, "malfunction"), } assert demand <= 200 return {"heat_demand": demand / 200} def hex_id_to_dec(device_hex: str, friendly_id=False) -> str: """Convert (say) '06368E' to '01:145038' (or 'CTL:145038').""" if device_hex == "FFFFFE": # aka '63:262142' return "NUL:262142" if friendly_id else NUL_DEVICE_ID if not device_hex.strip(): # aka '--:------' return f"{'':10}" if friendly_id else NON_DEVICE_ID _tmp = int(device_hex, 16) dev_type = f"{(_tmp & 0xFC0000) >> 18:02d}" if friendly_id: dev_type = DEVICE_TYPES.get(dev_type, f"{dev_type:<3}") return f"{dev_type}:{_tmp & 0x03FFFF:06d}"

f736712970df93b5bef6cd0469bab29486ab504a

py

Python

PythonProjects/09-MoreImplementingClasses/src/m2_baby_class.py

much2mutch/csse120-public

4f862a6deb7a5373fb5723fb2a23e4042e4d4157

PythonProjects/09-MoreImplementingClasses/src/m2_baby_class.py

much2mutch/csse120-public

4f862a6deb7a5373fb5723fb2a23e4042e4d4157

PythonProjects/09-MoreImplementingClasses/src/m2_baby_class.py

much2mutch/csse120-public

4f862a6deb7a5373fb5723fb2a23e4042e4d4157

# can't get the output to stop putting extra space after baby name before punctuation. """ A Baby class and functions that use/test it. Authors: Dave Fisher, David Mutchler, Vibha Alangar, Matt Boutell, Mark Hays, Amanda Stouder, Derek Whitley, their colleagues, and Seth Mutchler. """ # DONE: 1. PUT YOUR NAME IN THE ABOVE LINE. import random def main(): """ Runs the tests of the Baby class. """ print("UN-comment the following TESTS, one by one, when you are ready.") # UN-comment the following, one by one, when you are ready to TEST. run_test_1() run_test_2() ############################################################################### # Done: 2. In this module you will implement and test a Baby class. # Here is an OVERVIEW of the steps you will take to do so. # _ # Step 2 (this step): Read this overview of this module. # Step 3: Read and understand the SPECIFICATION for the Baby class. # Step 4: Read and understand the TESTS for the Baby class. # We supplied those tests. # Step 5: IMPLEMENT and TEST the Baby class. # _ # Once you understand this OVERVIEW, mark this _TODO_ as DONE. ############################################################################### ############################################################################### # DONE: 3. SPECIFICATION (read the following): # Here (below) are the methods that you must implement in your Baby class: # ---------------------------------------------------------------------------- # _ # Constructor method (that is, the __init__ method): # What comes in: # -- self # -- a string for the name of the Baby # What goes out: Nothing (i.e., None). # Side effects: # -- Prints "Hello baby <your baby's name>!" # -- Sets instance variables as needed # [YOU FIGURE OUT WHAT IS NEEDED AS YOU IMPLEMENT THE METHODS!] # Example: # b = Baby("McKinley") # causes the following to be printed on the Console: # Hello baby McKinley! # _ # feed_baby: # What comes in: # -- self # What goes out: Nothing (i.e., None). # Side effects: # -- Prints "Thank you for feeding baby <your baby's name>." # -- Modifies instance variables as needed. # Example: # b = Baby("Joshua") # b.feed_baby() # causes the following to be printed on the Console: # Hello baby Joshua! # Thank you for feeding baby Joshua. # _ # hour_passes # What comes in: # -- self # What goes out: Nothing (i.e., None). # Side effects: # -- If this is the FIRST time this method has been called # since this Baby was created or last fed, then this method prints: # "Baby <your baby's name> is sleeping." # _ # -- If this is the SECOND time this method has been called # since baby was created or last fed, then this method prints: # "Baby <your baby's name> is awake. Time for food." # _ # -- If this is the THIRD (OR MORE) time this method has been called # since baby was created or last fed, then this method prints: # "Baby <your baby's name> is CRYING uncontrollably! Feed the Baby!" # _ # -- Modifies instance variables as needed. # _ # Examples: See the two TEST functions below. # _ # You may find it helpful to read the two TEST functions (below) at this time. # If reading the TEST functions below does not make this specification clear, # ASK QUESTIONS AS NEEDED to clarify this specification. # _ # Once you understand this SPECIFICATION, mark this _TODO_ as DONE. ############################################################################### ############################################################################### # DONE: 4. TESTS (read the following): # The two functions that follow this comment TEST the Baby class. # For each of those two functions: # 1. READ the CODE in the function. # As you do so, PREDICT what the code will cause to be printed. # 2. READ the doc-string for the function. # It shows the CORRECT output when the function runs. # 3. CONFIRM that you understand WHY the function's CODE produces # the OUTPUT that the doc-string says that it will. # _ # If you do not understand why the CODE produces the OUTPUT as written # in the function's doc-string, STOP HERE and ASK QUESTIONS AS NEEDED. # Do ** NOT ** attempt to write the Baby class # without fully understanding both of its test functions. # _ # Once you fully understand the TESTS below, mark this _TODO_ as DONE. ############################################################################### def run_test_1(): """ Running this test should cause EXACTLY the following to be displayed (i.e. printed) on the Console: ------------ Running test #1: ------------ Hello baby Joshua! Baby Joshua is sleeping. Baby Joshua is awake. Time for food. Baby Joshua is CRYING uncontrollably! Feed the Baby! Baby Joshua is CRYING uncontrollably! Feed the Baby! Thank you for feeding baby Joshua. Baby Joshua is sleeping. Baby Joshua is awake. Time for food. Thank you for feeding baby Joshua. Baby Joshua is sleeping. Thank you for feeding baby Joshua. Baby Joshua is sleeping. Baby Joshua is awake. Time for food. Baby Joshua is CRYING uncontrollably! Feed the Baby! Examine the code in this test to be sure that you understand WHY it causes the above to be printed. """ print() print('------------ Running test #1: ------------ ') b = Baby("Joshua") b.hour_passes() b.hour_passes() b.hour_passes() b.hour_passes() print() # Just to make the output easier to read. b.feed_baby() b.hour_passes() b.hour_passes() print() # Just to make the output easier to read. b.feed_baby() b.hour_passes() print() # Just to make the output easier to read. b.feed_baby() b.hour_passes() b.hour_passes() b.hour_passes() def run_test_2(): """ Running this test should cause EXACTLY the following to be displayed (i.e. printed) on the Console: ------------ Running test #2: ------------ Hello baby McKinley! Hello baby Keegan! --- Iteration #1 --- Baby Keegan is sleeping. Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. Baby McKinley is CRYING uncontrollably! Feed the Baby! Baby McKinley is CRYING uncontrollably! Feed the Baby! Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. --- Iteration #2 --- Baby Keegan is awake. Time for food. Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. Baby McKinley is CRYING uncontrollably! Feed the Baby! Baby McKinley is CRYING uncontrollably! Feed the Baby! Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. --- Iteration #3 --- Baby Keegan is CRYING uncontrollably! Feed the Baby! Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. Baby McKinley is CRYING uncontrollably! Feed the Baby! Baby McKinley is CRYING uncontrollably! Feed the Baby! Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. Examine the code in this test to be sure that you understand WHY it causes the above to be printed. """ print() print('------------ Running test #2: ------------ ') mckinley = Baby("McKinley") keegan = Baby("Keegan") for k in range(3): print() # Just to make the output easier to read. print("--- Iteration #{} ---".format(k + 1)) keegan.hour_passes() mckinley.feed_baby() for j in range(4): mckinley.hour_passes() mckinley.feed_baby() mckinley.hour_passes() mckinley.hour_passes() ############################################################################### # TODO: 5. # Implement the entire Baby class # (including its 3 methods: __init__, feed_baby, and hour_passes) # below this comment. # _ # Here is a reminder for the syntax (notation) to define a new class: # class NameOfClass(object): # """ Brief description of what an object of the class 'is'. """ # _ # AFTER you have implemented the ENTIRE Baby class, # un-comment (one-by-one) the calls in main to the two tests # and confirm that the tests produce the output that the doc-strings # for the tests show as the CORRECT output. # _ # Fix errors as needed! Do not hesitate to ASK QUESTIONS AS NEEDED. ############################################################################### class Baby(object): """ generates baby that eats, sleeps, and has a name""" # Constructor method (that is, the __init__ method): # What comes in: # -- self # -- a string for the name of the Baby # What goes out: Nothing (i.e., None). # Side effects: # -- Prints "Hello baby <your baby's name>!" # -- Sets instance variables as needed # [YOU FIGURE OUT WHAT IS NEEDED AS YOU IMPLEMENT THE METHODS!] # Example: # b = Baby("McKinley") # causes the following to be printed on the Console: # Hello baby McKinley! def __init__(self, baby): self.baby = baby print("Hello baby", self.baby, "!") self.hour_since_feeding = 0 # _ # feed_baby: # What comes in: # -- self # What goes out: Nothing (i.e., None). # Side effects: # -- Prints "Thank you for feeding baby <your baby's name>." # -- Modifies instance variables as needed. # Example: # b = Baby("Joshua") # b.feed_baby() # causes the following to be printed on the Console: # Hello baby Joshua! # Thank you for feeding baby Joshua. # _ def feed_baby(self): print("Thank you for feeding baby", self.baby, ".") self.hour_since_feeding = 0 # hour_passes # What comes in: # -- self # What goes out: Nothing (i.e., None). # Side effects: # -- If this is the FIRST time this method has been called # since this Baby was created or last fed, then this method prints: # "Baby <your baby's name> is sleeping." # _ # -- If this is the SECOND time this method has been called # since baby was created or last fed, then this method prints: # "Baby <your baby's name> is awake. Time for food." # _ # -- If this is the THIRD (OR MORE) time this method has been called # since baby was created or last fed, then this method prints: # "Baby <your baby's name> is CRYING uncontrollably! Feed the Baby!" # _ # -- Modifies instance variables as needed. def hour_passes(self): if self.hour_since_feeding == 0: print("Baby", self.baby, "is sleeping.") else: if self.hour_since_feeding == 1: print("Baby", self.baby, "is awake. Time for food.") else: print("Baby", self.baby, "is CRYING uncontrollably! Feed the Baby!") self.hour_since_feeding = self.hour_since_feeding + 1 # ----------------------------------------------------------------------------- # Calls main to start the ball rolling. # ----------------------------------------------------------------------------- main()

f73689676c7e34072751132b67e88ebf427c1287

py

Python

setup.py

yupingso/randomproto

a36c0e13be893d9fd3a63e3390fc11fc0de250b9

2019-03-25T03:45:43.000Z

2019-07-03T06:31:31.000Z

setup.py

yupingso/randomproto

a36c0e13be893d9fd3a63e3390fc11fc0de250b9

2019-06-03T09:13:44.000Z

2019-06-03T09:13:44.000Z

setup.py

yupingso/randomproto

a36c0e13be893d9fd3a63e3390fc11fc0de250b9

import setuptools setuptools.setup( name='randomproto', version='0.0.1', py_modules=('randomproto',), install_requires=[ 'protobuf>=3.6.0', ], setup_requires=[ 'pytest-runner', ], tests_require=[ 'pytest==4.2.1', 'pytest-cov==2.6.1', 'pytest-mock==1.10.0', ], author='Yu-Ping Wu', author_email='yupingso@gmail.com', description='Random protobuf object generator', long_description=open('README.md').read(), long_description_content_type='text/markdown', keywords='protobuf proto message random generate generator', url='https://github.com/yupingso/randomproto', classifiers=[ 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', ], )

f73693a4682bea4ed02bdb055520c7339d597c43

py

Python

openpharmacophore/pharmacophore/dynophore.py

uibcdf/openpharmacophore

4f563fa206f6e7c081502acab97bb795d27bdeb9

2021-11-12T10:09:25.000Z

2022-03-18T08:24:16.000Z

openpharmacophore/pharmacophore/dynophore.py

uibcdf/openpharmacophore

4f563fa206f6e7c081502acab97bb795d27bdeb9

2021-11-05T01:37:57.000Z

2022-01-18T06:03:39.000Z

openpharmacophore/pharmacophore/dynophore.py

uibcdf/openpharmacophore

4f563fa206f6e7c081502acab97bb795d27bdeb9

2021-11-05T01:22:47.000Z

2021-12-12T03:57:09.000Z

# OpenPharmacophore from openpharmacophore._private_tools.exceptions import InvalidFileFormat, NoLigandsError, OpenPharmacophoreTypeError from openpharmacophore.pharmacophore.pharmacophoric_point import UniquePharmacophoricPoint from openpharmacophore import StructuredBasedPharmacophore from openpharmacophore import Pharmacophore from openpharmacophore.utils.conformers import conformer_energy from openpharmacophore.pharmacophore.color_palettes import get_color_from_palette_for_feature # Third Party import matplotlib.pyplot as plt import MDAnalysis as mda from MDAnalysis.lib.util import NamedStream import mdtraj as mdt import numpy as np import pandas as pd import pyunitwizard as puw from rdkit.Chem.Draw import rdMolDraw2D from tqdm.auto import tqdm # Standard Library from collections import defaultdict import copy import bisect from io import StringIO import tempfile from typing import List, Tuple, Optional class Dynophore(): """ Class to store and compute dynamic pharmacophores Parameters ---------- trajectory : str or mdtraj.trajectory or MDAnalysis.universe A str with the file path containing the trajectory, an mdtraj trajectory object, or an MDAnalysis universe. Attributes ---------- pharmacophores : list of openpharmacophore.StructuredBasedPharmacophore List with pharmacophores for each relevant frame in the trajectory. pharmacophore_indices : list of int Indices of the frame of the trajectory from which the pharmacophores were extracted. The index of each element of the list corresponds to the one in pharmacophores list. n_pharmacophores : int Number of different pharmacophores in the trajectory. """ def __init__(self, trajectory): self.pharmacophores = [] self.pharmacophore_indices = [] self.n_pharmacophores = 0 self.unique_pharmacophoric_points = [] # TODO: Load other types of file, including using a topology and tajectory if isinstance(trajectory, str): self._trajectory = self._load_trajectory_file(trajectory) elif isinstance(trajectory, mdt.Trajectory): self._trajectory_type = "mdt" self._trajectory = trajectory self._n_frames = self._trajectory.n_frames elif isinstance(trajectory, mda.Universe): self._trajectory_type = "mda" self._trajectory = trajectory self._n_frames = trajectory.trajectory.n_frames else: raise TypeError("Trajectory must be of type string, mdtraj.Trajectory or MdAnalysis.Universe") self._saved_ligand = False self._averaged_coords = False def common_hits_approach(self, frame_list=None): """ Get a list of pharmacophore models from a trajectory using the common hits approach method. Notes ----- This method is based on obtaining a list of representative pharmacophore models from a trajectory and then validate and score them using virtual screening. The best performant pharmacophore models are then returned. References ---------- [1] Wieder, Marcus, Arthur Garon, Ugo Perricone, Stefan Boresch, Thomas Seidel, Anna Maria Almerico, and Thierry Langer. "Common hits approach: combining pharmacophore modeling and molecular dynamics simulations." Journal of chemical information and modeling 57, no. 2 (2017): 365-385 """ if frame_list is None: frame_list = list(range(0, self._n_frames)) self.pharmacophores_from_frames(frame_list, load_ligand=True) self._get_unique_pharmacophoric_points(avg_coordinates=False) rpms = self.representative_pharmacophore_models() pass def draw(self, file_name: str, img_size: Tuple[int, int] = (500,500), legend: str = "", freq_threshold: float = 0.2) -> None: """ Draw a 2d representation of the dynamic pharmacophore. This is a drawing of the ligand with the pharmacophoric features highlighted and the frequency if each one. Parameters ---------- file_name : str Name or path og the file where the drawing will be saved. Must be a png file. img_size : 2-tuple of int, optional The size of the image (default=(500,500)) legend : str, optional Image legend. freq_threshold : double , optional The minimun frequency of a pharmacophoric point to be drawn. Number between 0.0 and 1.0 (default=0.2). """ if freq_threshold < 0.0 or freq_threshold > 1.0: raise ValueError("Freqency threshold must be a value between 0 and 1") if not file_name.endswith(".png"): raise InvalidFileFormat("File must be a png.") # Extract a ligand if self.pharmacophores[0].ligand is None: raise NoLigandsError("Ligand could not be extracted") ligand = copy.deepcopy(self.pharmacophores[0].ligand) ligand.RemoveAllConformers() atoms = [] bond_colors = {} atom_highlights = defaultdict(list) highlight_radius = {} for up in self.unique_pharmacophoric_points: if up.frequency < freq_threshold: continue indices = up.atom_indices update_freq = True for idx in indices: # If an atom has more than one feature keep higher frequency value if idx in atoms: if ligand.GetAtomWithIdx(idx).HasProp("atomNote"): freq = int(ligand.GetAtomWithIdx(idx).GetProp("atomNote")[2:]) if freq > up.frequency: update_freq = False atoms.append(idx) if "hydrophobicity" in up.feature_name: feat_name = "hydrophobicity" else: feat_name = " ".join(up.feature_name.split()[0:2]) atom_highlights[idx].append(get_color_from_palette_for_feature(feat_name)) highlight_radius[idx] = 0.6 # Draw aromatic rings bonds if up.short_name == "R": for neighbor in ligand.GetAtomWithIdx(idx).GetNeighbors(): nbr_idx = neighbor.GetIdx() if nbr_idx not in indices: continue bond = ligand.GetBondBetweenAtoms(idx, nbr_idx).GetIdx() bond_colors[bond] = [get_color_from_palette_for_feature("aromatic ring")] if update_freq: frequency = int(up.frequency * 100) ligand.GetAtomWithIdx(idx).SetProp("atomNote", f"f={frequency}") drawing = rdMolDraw2D.MolDraw2DCairo(img_size[0], img_size[1]) drawing.DrawMoleculeWithHighlights(ligand, legend, dict(atom_highlights), bond_colors, highlight_radius, {}) drawing.FinishDrawing() drawing.WriteDrawingText(file_name) def first_and_last_pharmacophore(self) -> None: """ Derive a pharmacophore model for the first and last frames of a trajectory. References ---------- [1] Wieder, Marcus, Ugo Perricone, Thomas Seidel, Stefan Boresch, and Thierry Langer. "Comparing pharmacophore models derived from crystal structures and from molecular dynamics simulations." Monatshefte für Chemie-Chemical Monthly 147, no. 3 (2016): 553-563. """ if self._trajectory_type == "mdt": get_pharmacophore = self._pharmacophore_from_mdtraj elif self._trajectory_type == "mda": get_pharmacophore = self._pharmacohore_from_mdanalysis initial_pharmacophore = get_pharmacophore(0, True, True) end_pharmacophore = get_pharmacophore(-1, True, True) last_frame_index = self._trajectory.n_frames self.pharmacophores = [ initial_pharmacophore, end_pharmacophore ] self.pharmacophore_indices = [0, last_frame_index] self.n_pharmacophores = 2 def pharmacophore_by_frequency(self, threshold: float) -> Pharmacophore: """ Derive a unique pharmacophore model with the pharmacophoric points that have a frequency >= to threshold. Parameters --------- threshold : float The value of frequency from which points are considered part of the pharmacophore model. Must be a value between 0 and 1- Returns ------- openpharmcophore.Pharmacophore Pharmacophore model with the unique pharmacophoric points. References ---------- [1] Wieder, Marcus, Ugo Perricone, Thomas Seidel, and Thierry Langer. "Pharmacophore models derived from molecular dynamics simulations of protein-ligand complexes: A case study." Natural product communications 11, no. 10 (2016): 1934578X1601101019. """ if threshold < 0 or threshold > 1: raise ValueError("Threshold must be a number between 0 and 1") if len(self.unique_pharmacophoric_points) == 0: self._get_unique_pharmacophoric_points(avg_coordinates=True) points = [p for p in self.unique_pharmacophoric_points if p.frequency >= threshold] return Pharmacophore(points) def pharmacophore_from_unique_points(self, unique_points: List[str]) -> Pharmacophore: """ Get a pharmacophore which consists of the passed unique pharmacophoric points. Parameters ---------- unique_points: list of str List with the name of the unique pharmacophoric points. Returns ------- openpharmcophore.Pharmacophore Pharmacophore model with the specified points. """ if len(self.unique_pharmacophoric_points) == 0 or not self._averaged_coords: self._get_unique_pharmacophoric_points(avg_coordinates=True) points = [point for point in self.unique_pharmacophoric_points if point.feature_name in unique_points] return Pharmacophore(pharmacophoric_points=points) def pharmacophores_from_frames(self, frames: List[int], load_ligand: bool = True) -> None: """ Get pharmacophores for the specified frames in a trajectory Parameters ---------- frames : list of int Indices of the frames for which pharmacophores will be derived. """ if self._trajectory_type == "mdt": get_pharmacophore = self._pharmacophore_from_mdtraj elif self._trajectory_type == "mda": get_pharmacophore = self._pharmacohore_from_mdanalysis self.pharmacophores.clear() self.pharmacophore_indices.clear() for ii in tqdm(frames): self.pharmacophores.append(get_pharmacophore(ii, load_ligand=load_ligand)) self.pharmacophore_indices.append(ii) self.n_pharmacophores = len(self.pharmacophores) def pharmacophoric_point_frequency(self) -> pd.DataFrame: """ Get a dataframe with all unique pharmacophoric points and its frequency. Returns ------- pandas.DataFrame Dataframe with the following columns: feature name, frequency and atom indices. """ if len(self.unique_pharmacophoric_points) == 0 or not self._averaged_coords: self._get_unique_pharmacophoric_points(avg_coordinates=True) names = [] frequencies = [] indices = [] for point in self.unique_pharmacophoric_points: names.append(point.feature_name) frequencies.append(point.frequency) indices.append(point.atom_indices) frequency = pd.DataFrame().from_dict({ "Feature Name": names, "Frequency": frequencies, "Atoms Indices": indices }) frequency.sort_values(by=["Frequency"], ascending=False, inplace=True) frequency.reset_index(inplace=True) frequency.drop(columns=["index"], inplace=True) return frequency def point_frequency_plot(self, threshold: float = 0.0, n_bins: int = 10, ax: Optional[plt.Axes] = None): """ Plot of pharmacophoric points frequency vs time. Each pharmacophoric point will appear as a different line in the plot. Parameters ---------- threshold : double, default=0.0 The value of overall frequency from which points will form part of the plot. If there are a lot of points with really low frequency, setting the threshold value can help with visualization. n_bins : int, default=10 Number of bins to discretize the timesteps. ax : matplotlib.axes._subplots.AxesSubplot, optional. An axes object where the plot will be drawn. """ if len(self.unique_pharmacophoric_points) == 0 or not self._averaged_coords: self._get_unique_pharmacophoric_points(avg_coordinates=True) if threshold < 0 or threshold > 1: raise ValueError("Threshold must be a number between 0 and 1") if ax is None: fig, ax = plt.subplots(figsize=(10, 7)) n_timesteps = self._n_frames bins = np.arange(0, n_timesteps + 1, n_timesteps/n_bins) for point in self.unique_pharmacophoric_points: if point.frequency < threshold: continue point_timesteps = np.array(point.timesteps) discretized_timesteps = np.digitize(point_timesteps, bins) counts = np.zeros_like(bins) for i in range(bins.shape[0]): c = np.count_nonzero(discretized_timesteps == i) counts[i] = c ax.plot(bins, counts, label=point.feature_name) ax.legend() ax.set_xlabel("Timesteps") ax.set_ylabel("Count") plt.show() return ax def representative_pharmacophore_models(self) -> List[StructuredBasedPharmacophore]: """ Get all representative pharmacophore models (RPM) in a trajectory. RPMs are pharmacophore models that have the same pharmacophoric points, Returns ------- rpms : list of openpharmacophore.StructuredBasedPharmacophore The representative pharmacophore models Note ----- Pharmacophoric points are considered equal based only on feature type and the atoms to which this points belong to. Coordinates are not taken into account. The coordinates of the pharmacophoric points are those that belong to the median energy of the ligand. References ---------- [1] Wieder, Marcus, Arthur Garon, Ugo Perricone, Stefan Boresch, Thomas Seidel, Anna Maria Almerico, and Thierry Langer. "Common hits approach: combining pharmacophore modeling and molecular dynamics simulations." Journal of chemical information and modeling 57, no. 2 (2017): 365-385 """ if len(self.unique_pharmacophoric_points) == 0 or self._averaged_coords: self._get_unique_pharmacophoric_points(avg_coordinates=False) self._averaged_coords = False rpms_indices = self._get_rpms_indices() return self._pharmacophores_from_ligand_median_energy(rpms_indices) def _get_rpms_indices(self) -> List[List[int]]: """ Get the indices of the representative pharmacophore models. If an empty list is returned it means that all pharmacophore models in the trajectory are different. Returns -------- rpms_indices : list of list of int A list where each sublist contains the indices of each representative pharmacophore model. This indices correspond to the attribute pharmacophores of the Dynophore class. """ # Compute a matrix where each row represents a feature vector of a pharmacophore n_pharmacophores = self.n_pharmacophores n_features = len(self.unique_pharmacophoric_points) feature_matrix = np.zeros((n_pharmacophores, n_features), dtype=np.int32) for ii, pharmacophore in enumerate(self.pharmacophores): for point in pharmacophore: for jj, unique_point in enumerate(self.unique_pharmacophoric_points): if point.is_equal(unique_point): feature_matrix[ii, jj] = 1 break # Find similar pharmacophores in the matrix rpms_indices = [] skip = [] for ii in range(n_pharmacophores): rpm = [ii] for jj in range(ii + 1, n_pharmacophores): if jj in skip: continue if np.all(feature_matrix[ii, :] == feature_matrix[jj, :]): rpm.append(jj) skip.append(jj) # Keep only models that have a frequency higher than 2 if len(rpm) > 2: rpms_indices.append(rpm) return rpms_indices def _pharmacophores_from_ligand_median_energy(self, rpms_indices)-> List[List[int]]: """ Get the representative pharmacophore models that correspond to the pharmacophore with ligand median energy. Parameters ---------- rpms_indices : list of list of int A list where each sublist contains the indices of each representative pharmacophore model. This indices correspond to the attribute pharmacophores of the Dynophore class. Returns ------- rpms : list of openpharmacophore.StructuredBasedPharmacophore The representative pharmacophore models """ rpms = [] for indices in rpms_indices: energies = [] for index in indices: energy = (conformer_energy(self.pharmacophores[index].ligand), index) bisect.insort(energies, energy) # Take the pharmacophore with median energy median_energy_index = energies[int(len(energies) / 2)][1] rpms.append(self.pharmacophores[median_energy_index]) return rpms def _load_trajectory_file(self, file_name: str) -> mdt.Trajectory: """ Load a trajectory file from a MD simulation Parameters ---------- file_name : str Name of the file containing the trajectory. Returns ------- traj : The trajectory object. """ if file_name.endswith("h5"): traj = mdt.load(file_name) self._trajectory_type = "mdt" else: raise NotImplementedError return traj def _get_unique_pharmacophoric_points(self, avg_coordinates: bool = True) -> None: """ Get all unique pharmacophoric points across all the pharmacophore models derived from the trajectory. Parameters ---------- avg_coordinates : bool Whether to average the coordinates of the pharmacophoric points. Notes ----- Two points are considered equal if they have the same feature type and are associated with the same atom in the ligand. """ if avg_coordinates: self._averaged_coords = True if self.n_pharmacophores == 0: self.pharmacophores_from_frames(list(range(0, self._n_frames))) all_points = [] for ii, pharmacophore in enumerate(self.pharmacophores): for pharmacophoric_point in pharmacophore: pharmacophoric_point.pharmacophore_index = ii all_points.append(pharmacophoric_point) self.unique_pharmacophoric_points.clear() # Get all unique parmacophoric points while also updating the count, # timesteps where they appear and calculating the average centroid. for point in all_points: is_unique = True for unique_p in self.unique_pharmacophoric_points: if point.is_equal(unique_p): timestep = point.pharmacophore_index if not timestep in unique_p.timesteps: unique_p.timesteps.append(timestep) unique_p.count += 1 if avg_coordinates: unique_p.center += point.center is_unique = False break if is_unique: self.unique_pharmacophoric_points.append(UniquePharmacophoricPoint(point, point.pharmacophore_index)) names = [] for point in self.unique_pharmacophoric_points: if avg_coordinates: # Normalize centroid point.center /= point.count point.frequency = point.count / self.n_pharmacophores # Get a unique name for each point feat_num = 1 full_name = point.feature_name + " " + str(feat_num) if full_name not in names: names.append(full_name) point.feature_name = full_name else: while True: feat_num += 1 full_name = point.feature_name + " " + str(feat_num) if full_name not in names: names.append(full_name) point.feature_name = full_name break def _pharmacophore_from_mdtraj(self, frame_num: int, load_mol_system: bool=False, load_ligand: bool=False) -> StructuredBasedPharmacophore: """ Derive a pharmacophore for a single frame of an mdtraj Trajectory object. Parameters ---------- frame_num : int The index number of the frame from which the pharmacophore will be derived. load_mol_system : bool, default=False If true the receptor will be stored in the pharmacophore object. load_ligand : bool, default=False If true the ligand will be stored in the pharmacophore object. """ # mdtraj trajectories cannot be passed to SringIO objects nor saved as string. So with this # method, temporary pdb files will be created that can be read by the StructuredBasedPharmacophore # class. if not isinstance(frame_num, int): raise OpenPharmacophoreTypeError("Frame number must be an integer") frame = self._trajectory[frame_num] with tempfile.NamedTemporaryFile() as original_file: frame.save_pdb(original_file.name) original_file.seek(0) lines_original = original_file.readlines() # The pdb mdtraj generates needs to be edited so that pybel can read it. # The third line that contains "MODEL" needs to be removed for the structured # based pharmacophore to work. with tempfile.NamedTemporaryFile() as modified_file: for line in lines_original: if not line.startswith(b'MODEL'): modified_file.write(line) modified_file.truncate() modified_file.seek(0) pharmacophore = StructuredBasedPharmacophore.from_pdb(modified_file, radius=1.0, ligand_id=None, hydrophobics="plip", load_mol_system=load_mol_system, load_ligand=load_ligand) return pharmacophore def _pharmacohore_from_mdanalysis(self, frame_num: int, load_mol_system: bool = False, load_ligand: bool = False) -> StructuredBasedPharmacophore: """ Derive a pharmacophore for a single frame of an MdAnalysis Universe object. Parameters ---------- frame_num : int The index number of the frame from which the pharmacophore will be derived. load_mol_system: bool, default=False If true the receptor will be stored in the pharmacophore object. load_ligand: bool, default=False If true the ligand will be stored in the pharmacophore object. """ if not isinstance(frame_num, int): raise OpenPharmacophoreTypeError("Frame number must be an integer") stream = StringIO() pdb_stream = NamedStream(stream, "output.pdb") atoms = self._trajectory.select_atoms("all") atoms.write(pdb_stream, frames=self._trajectory.trajectory[[frame_num]]) pharmacophore = StructuredBasedPharmacophore.from_pdb(pdb_stream, radius=1.0, ligand_id=None, hydrophobics="plip", load_mol_system=load_mol_system, load_ligand=load_ligand) return pharmacophore def __repr__(self) -> str: return f"{self.__class__.__name__}(n_pharmacophores={self.n_pharmacophores}; n_frames={self._n_frames})"

f7369619cd01529ee372590cea33f5f73b48f876

py

Python

spikeforest_analysis/compare_sortings_with_truth.py

tjd2002/spikeforest2

2e393564b858b2995aa2ccccd9bd73065681b5de

spikeforest_analysis/compare_sortings_with_truth.py

tjd2002/spikeforest2

2e393564b858b2995aa2ccccd9bd73065681b5de

spikeforest_analysis/compare_sortings_with_truth.py

tjd2002/spikeforest2

2e393564b858b2995aa2ccccd9bd73065681b5de

import spikeextractors as si #import spikewidgets as sw import spiketoolkit as st import mlprocessors as mlpr import json from cairio import client as ca import numpy as np from copy import deepcopy def compare_sortings_with_truth(sortings,compute_resource,num_workers=None): print('>>>>>> compare sortings with truth') container='sha1://3b26155930cc4a4745c67b702ce297c9c968ac94/02-12-2019/mountaintools_basic.simg' jobs_gen_table=[] for sorting in sortings: units_true=sorting.get('units_true',[]) firings=sorting['firings'] firings_true=sorting['firings_true'] units_true=units_true job=GenSortingComparisonTable.createJob( firings=firings, firings_true=firings_true, units_true=units_true, json_out={'ext':'.json','upload':True}, html_out={'ext':'.html','upload':True}, _container=container ) jobs_gen_table.append(job) all_jobs=jobs_gen_table label='Compare sortings with truth' mlpr.executeBatch(jobs=all_jobs,label=label,num_workers=num_workers,compute_resource=compute_resource) sortings_out=[] for i,sorting in enumerate(sortings): comparison_with_truth=dict() comparison_with_truth['json']=jobs_gen_table[i]['result']['outputs']['json_out'] comparison_with_truth['html']=jobs_gen_table[i]['result']['outputs']['html_out'] sorting2=deepcopy(sorting) sorting2['comparison_with_truth']=comparison_with_truth sortings_out.append(sorting2) return sortings_out class GenSortingComparisonTable(mlpr.Processor): VERSION='0.2.0' firings=mlpr.Input('Firings file (sorting)') firings_true=mlpr.Input('True firings file') units_true=mlpr.IntegerListParameter('List of true units to consider') json_out=mlpr.Output('Table as .json file produced from pandas dataframe') html_out=mlpr.Output('Table as .html file produced from pandas dataframe') def run(self): sorting=si.MdaSortingExtractor(firings_file=self.firings) sorting_true=si.MdaSortingExtractor(firings_file=self.firings_true) if (self.units_true is not None) and (len(self.units_true)>0): sorting_true=si.SubSortingExtractor(parent_sorting=sorting_true,unit_ids=self.units_true) SC=st.comparison.SortingComparison(sorting_true,sorting) df=get_comparison_data_frame(comparison=SC) #sw.SortingComparisonTable(comparison=SC).getDataframe() json=df.transpose().to_dict() html=df.to_html(index=False) _write_json_file(json,self.json_out) _write_json_file(html,self.html_out) def get_comparison_data_frame(*,comparison): import pandas as pd SC=comparison unit_properties=[] #snr, etc? these would need to be properties in the sortings of the comparison # Compute events counts sorting1=SC.getSorting1() sorting2=SC.getSorting2() unit1_ids = sorting1.getUnitIds() unit2_ids = sorting2.getUnitIds() N1 = len(unit1_ids) N2 = len(unit2_ids) event_counts1 = dict() for i1, u1 in enumerate(unit1_ids): times1 = sorting1.getUnitSpikeTrain(u1) event_counts1[u1] = len(times1) event_counts2 = dict() for i2, u2 in enumerate(unit2_ids): times2 = sorting2.getUnitSpikeTrain(u2) event_counts2[u2] = len(times2) rows = [] for u_1, unit1 in enumerate(unit1_ids): unit2 = SC.getBestUnitMatch1(unit1) if unit2>=0: num_matches=SC.getMatchingEventCount(unit1, unit2) num_false_negatives=event_counts1[unit1]-num_matches num_false_positives=event_counts2[unit2]-num_matches else: num_matches=0 num_false_negatives=event_counts1[unit1] num_false_positives=0 row0 = { 'unit_id': unit1, 'accuracy': _safe_frac(num_matches,num_false_positives+num_false_negatives+num_matches), 'best_unit': unit2, 'matched_unit': SC.getMappedSorting1().getMappedUnitIds(unit1), 'num_matches': num_matches, 'num_false_negatives': num_false_negatives, 'num_false_positives': num_false_positives, 'f_n': _safe_frac(num_false_negatives,num_false_negatives+num_matches), 'f_p': _safe_frac(num_false_positives,num_false_positives+num_matches) } for prop in unit_properties: pname = prop['name'] row0[pname] = SC.getSorting1().getUnitProperty(unit_id=int(unit1), property_name=pname) rows.append(row0) df = pd.DataFrame(rows) fields = ['unit_id'] fields = fields + ['accuracy', 'best_unit', 'matched_unit', 'num_matches', 'num_false_negatives', 'num_false_positives', 'f_n', 'f_p'] for prop in unit_properties: pname = prop['name'] fields.append(pname) df = df[fields] df['accuracy'] = df['accuracy'].map('{:,.4f}'.format) # df['Best match'] = df['Accuracy'].map('{:,.2f}'.format) df['f_n'] = df['f_n'].map('{:,.4f}'.format) df['f_p'] = df['f_p'].map('{:,.4f}'.format) return df def _safe_frac(numer, denom): if denom == 0: return 0 return float(numer) / denom def _write_json_file(obj,path): with open(path,'w') as f: return json.dump(obj,f)

f736c82fdd13410b5006180443ed9fb5d2275d3b

py

Python

examples/cc/h2o_ccsd_t.py

seunghoonlee89/pyscf-ecCC-TCC

2091566fb83c1474e40bf74f271be2ce4611f60c

2021-09-17T06:10:17.000Z

2022-01-22T23:37:22.000Z

examples/cc/h2o_ccsd_t.py

seunghoonlee89/pyscf-ecCC-TCC

2091566fb83c1474e40bf74f271be2ce4611f60c

examples/cc/h2o_ccsd_t.py

seunghoonlee89/pyscf-ecCC-TCC

2091566fb83c1474e40bf74f271be2ce4611f60c

2021-09-16T23:37:42.000Z

2021-10-14T23:00:39.000Z

#!/usr/bin/env python # # Author: Qiming Sun <osirpt.sun@gmail.com> # ''' A simple example to run CCSD(T) and UCCSD(T) calculation. ''' import pyscf mol = pyscf.M( atom = 'O -0.26677564 -0.27872083 0.00000000;\ H -0.26677564 0.82127917 0.00000000;\ H -0.26677564 -0.64538753 1.03708994', basis = 'ccpvtz') mf = mol.RHF().run() mycc = mf.CCSD().run() et = mycc.ccsd_t() print('CCSD(T) correlation energy', mycc.e_corr + et)

f7370d3a7ba6b398302266319fded87169772eec

py

Python

marqeta/response_models/address_verification_model.py

marqeta/marqeta-python

66fa690eb910825c510a391720b0fe717fac0234

2019-04-12T09:02:17.000Z

2022-02-18T11:39:06.000Z

marqeta/response_models/address_verification_model.py

marqeta/marqeta-python

66fa690eb910825c510a391720b0fe717fac0234

2020-07-22T21:27:40.000Z

2020-07-23T17:38:43.000Z

marqeta/response_models/address_verification_model.py

marqeta/marqeta-python

66fa690eb910825c510a391720b0fe717fac0234

2019-05-08T14:20:37.000Z

2021-09-20T18:09:26.000Z

from datetime import datetime, date from marqeta.response_models.avs_information import AvsInformation from marqeta.response_models.avs_information import AvsInformation from marqeta.response_models.response import Response from marqeta.response_models import datetime_object import json import re class AddressVerificationModel(object): def __init__(self, json_response): self.json_response = json_response def __str__(self): return json.dumps(self.json_response, default=self.json_serial) @staticmethod def json_serial(o): if isinstance(o, datetime) or isinstance(o, date): return o.__str__() @property def request(self): if 'request' in self.json_response: return AvsInformation(self.json_response['request']) @property def on_file(self): if 'on_file' in self.json_response: return AvsInformation(self.json_response['on_file']) @property def response(self): if 'response' in self.json_response: return Response(self.json_response['response']) def __repr__(self): return '<Marqeta.response_models.address_verification_model.AddressVerificationModel>' + self.__str__()

f7371d98ef6f603bc1192d540e496dcf869ac1ed

py

Python

experiments/ashvin/icml2020/hand/adaptive/clamp_easy1.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

experiments/ashvin/icml2020/hand/adaptive/clamp_easy1.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

experiments/ashvin/icml2020/hand/adaptive/clamp_easy1.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

""" AWR + SAC from demo experiment """ from rlkit.demos.source.dict_to_mdp_path_loader import DictToMDPPathLoader from rlkit.launchers.experiments.awac.awac_rl import experiment, process_args import rlkit.misc.hyperparameter as hyp from rlkit.launchers.arglauncher import run_variants from rlkit.torch.sac.policies import GaussianPolicy from rlkit.torch.networks import Clamp if __name__ == "__main__": variant = dict( num_epochs=5001, num_eval_steps_per_epoch=1000, num_trains_per_train_loop=1000, num_expl_steps_per_train_loop=1000, min_num_steps_before_training=1000, max_path_length=1000, batch_size=1024, replay_buffer_size=int(1E6), layer_size=256, policy_class=GaussianPolicy, policy_kwargs=dict( hidden_sizes=[256, 256, 256, 256], max_log_std=0, min_log_std=-6, std_architecture="values", # num_gaussians=1, ), algorithm="SAC", version="normal", collection_mode='batch', trainer_kwargs=dict( discount=0.99, soft_target_tau=5e-3, target_update_period=1, policy_lr=3E-4, qf_lr=3E-4, reward_scale=1, beta=1, use_automatic_entropy_tuning=False, alpha=0, compute_bc=False, bc_num_pretrain_steps=0, q_num_pretrain1_steps=0, q_num_pretrain2_steps=25000, policy_weight_decay=1e-4, q_weight_decay=0, bc_loss_type="mse", rl_weight=1.0, use_awr_update=True, use_reparam_update=False, reparam_weight=0.0, awr_weight=0.0, bc_weight=1.0, post_bc_pretrain_hyperparams=dict( bc_weight=0.0, compute_bc=False, ), reward_transform_kwargs=None, # r' = r + 1 terminal_transform_kwargs=None, # t = 0 ), launcher_config=dict( num_exps_per_instance=1, region='us-west-2', ), path_loader_class=DictToMDPPathLoader, path_loader_kwargs=dict( obs_key="state_observation", demo_paths=[ # dict( # path="demos/icml2020/hand/pen2_sparse.npy", # obs_dict=True, # is_demo=True, # ), # dict( # path="demos/icml2020/hand/pen_bc5.npy", # obs_dict=False, # is_demo=False, # train_split=0.9, # ), ], ), add_env_demos=True, add_env_offpolicy_data=True, # logger_variant=dict( # tensorboard=True, # ), load_demos=True, pretrain_policy=True, pretrain_rl=True, # save_pretrained_algorithm=True, # snapshot_mode="all", ) search_space = { 'env': ["pen-sparse-v0", "door-sparse-v0"], # 'env': ["relocate-sparse-v0", ], 'trainer_kwargs.bc_loss_type': ["mle"], 'trainer_kwargs.awr_loss_type': ["mle"], 'seedid': range(3), 'trainer_kwargs.beta': [0.3, 0.5], 'trainer_kwargs.reparam_weight': [0.0, ], 'trainer_kwargs.awr_weight': [1.0], 'trainer_kwargs.bc_weight': [1.0, ], 'policy_kwargs.std_architecture': ["values", ], # 'trainer_kwargs.compute_bc': [True, ], 'trainer_kwargs.awr_use_mle_for_vf': [True, ], 'trainer_kwargs.awr_sample_actions': [False, ], 'trainer_kwargs.awr_min_q': [True, ], 'trainer_kwargs.q_weight_decay': [0], 'trainer_kwargs.reward_transform_kwargs': [None, ], 'trainer_kwargs.terminal_transform_kwargs': [dict(m=0, b=0), ], 'qf_kwargs.output_activation': [Clamp(max=0)], 'trainer_kwargs.train_bc_on_rl_buffer':[True], # 'policy_kwargs.num_gaussians': [1, ], } sweeper = hyp.DeterministicHyperparameterSweeper( search_space, default_parameters=variant, ) variants = [] for variant in sweeper.iterate_hyperparameters(): variants.append(variant) run_variants(experiment, variants, process_args)

f7376f5ac6518f055d9d50110ef2dee4b566060b

py

Python

truncation_experiments/dnnlib/tflib/network.py

a7b23/CS236G-project

8bde7d54ca3d22e13703fe72dfb998b38d9cd8b4

2021-02-23T07:07:32.000Z

2021-03-11T21:13:26.000Z

truncation_experiments/dnnlib/tflib/network.py

a7b23/CS236G-project

8bde7d54ca3d22e13703fe72dfb998b38d9cd8b4

truncation_experiments/dnnlib/tflib/network.py

a7b23/CS236G-project

8bde7d54ca3d22e13703fe72dfb998b38d9cd8b4

2021-02-23T09:02:48.000Z

2021-02-27T06:14:42.000Z

# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # This work is licensed under the Creative Commons Attribution-NonCommercial # 4.0 International License. To view a copy of this license, visit # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. """Helper for managing networks.""" import types import inspect import re import uuid import sys import numpy as np import tensorflow as tf from collections import OrderedDict from typing import Any, List, Tuple, Union from . import tfutil from .. import util from .tfutil import TfExpression, TfExpressionEx _import_handlers = [] # Custom import handlers for dealing with legacy data in pickle import. _import_module_src = dict() # Source code for temporary modules created during pickle import. def import_handler(handler_func): """Function decorator for declaring custom import handlers.""" _import_handlers.append(handler_func) return handler_func class Network: """Generic network abstraction. Acts as a convenience wrapper for a parameterized network construction function, providing several utility methods and convenient access to the inputs/outputs/weights. Network objects can be safely pickled and unpickled for long-term archival purposes. The pickling works reliably as long as the underlying network construction function is defined in a standalone Python module that has no side effects or application-specific imports. Args: name: Network name. Used to select TensorFlow name and variable scopes. func_name: Fully qualified name of the underlying network construction function, or a top-level function object. static_kwargs: Keyword arguments to be passed in to the network construction function. Attributes: name: User-specified name, defaults to build func name if None. scope: Unique TensorFlow scope containing template graph and variables, derived from the user-specified name. static_kwargs: Arguments passed to the user-supplied build func. components: Container for sub-networks. Passed to the build func, and retained between calls. num_inputs: Number of input tensors. num_outputs: Number of output tensors. input_shapes: Input tensor shapes (NC or NCHW), including minibatch dimension. output_shapes: Output tensor shapes (NC or NCHW), including minibatch dimension. input_shape: Short-hand for input_shapes[0]. output_shape: Short-hand for output_shapes[0]. input_templates: Input placeholders in the template graph. output_templates: Output tensors in the template graph. input_names: Name string for each input. output_names: Name string for each output. own_vars: Variables defined by this network (local_name => var), excluding sub-networks. vars: All variables (local_name => var). trainables: All trainable variables (local_name => var). var_global_to_local: Mapping from variable global names to local names. """ def __init__(self, name: str = None, func_name: Any = None, **static_kwargs): tfutil.assert_tf_initialized() assert isinstance(name, str) or name is None assert func_name is not None assert isinstance(func_name, str) or util.is_top_level_function(func_name) assert util.is_pickleable(static_kwargs) self._init_fields() self.name = name self.static_kwargs = util.EasyDict(static_kwargs) # Locate the user-specified network build function. if util.is_top_level_function(func_name): func_name = util.get_top_level_function_name(func_name) module, self._build_func_name = util.get_module_from_obj_name(func_name) self._build_func = util.get_obj_from_module(module, self._build_func_name) assert callable(self._build_func) # Dig up source code for the module containing the build function. self._build_module_src = _import_module_src.get(module, None) if self._build_module_src is None: self._build_module_src = inspect.getsource(module) # Init TensorFlow graph. self._init_graph() self.reset_own_vars() def _init_fields(self) -> None: self.name = None self.scope = None self.static_kwargs = util.EasyDict() self.components = util.EasyDict() self.num_inputs = 0 self.num_outputs = 0 self.input_shapes = [[]] self.output_shapes = [[]] self.input_shape = [] self.output_shape = [] self.input_templates = [] self.output_templates = [] self.input_names = [] self.output_names = [] self.own_vars = OrderedDict() self.vars = OrderedDict() self.trainables = OrderedDict() self.var_global_to_local = OrderedDict() self._build_func = None # User-supplied build function that constructs the network. self._build_func_name = None # Name of the build function. self._build_module_src = None # Full source code of the module containing the build function. self._run_cache = dict() # Cached graph data for Network.run(). def _init_graph(self) -> None: # Collect inputs. self.input_names = [] for param in inspect.signature(self._build_func).parameters.values(): if param.kind == param.POSITIONAL_OR_KEYWORD and param.default is param.empty: self.input_names.append(param.name) self.num_inputs = len(self.input_names) assert self.num_inputs >= 1 # Choose name and scope. if self.name is None: self.name = self._build_func_name assert re.match("^[A-Za-z0-9_.\\-]*$", self.name) with tf.name_scope(None): self.scope = tf.get_default_graph().unique_name(self.name, mark_as_used=True) # Finalize build func kwargs. build_kwargs = dict(self.static_kwargs) build_kwargs["is_template_graph"] = True build_kwargs["components"] = self.components # Build template graph. with tfutil.absolute_variable_scope(self.scope, reuse=tf.AUTO_REUSE), tfutil.absolute_name_scope(self.scope): # ignore surrounding scopes assert tf.get_variable_scope().name == self.scope assert tf.get_default_graph().get_name_scope() == self.scope with tf.control_dependencies(None): # ignore surrounding control dependencies self.input_templates = [tf.placeholder(tf.float32, name=name) for name in self.input_names] out_expr = self._build_func(*self.input_templates, **build_kwargs) # Collect outputs. assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple) self.output_templates = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr) self.num_outputs = len(self.output_templates) assert self.num_outputs >= 1 assert all(tfutil.is_tf_expression(t) for t in self.output_templates) # Perform sanity checks. if any(t.shape.ndims is None for t in self.input_templates): raise ValueError("Network input shapes not defined. Please call x.set_shape() for each input.") if any(t.shape.ndims is None for t in self.output_templates): raise ValueError("Network output shapes not defined. Please call x.set_shape() where applicable.") if any(not isinstance(comp, Network) for comp in self.components.values()): raise ValueError("Components of a Network must be Networks themselves.") if len(self.components) != len(set(comp.name for comp in self.components.values())): raise ValueError("Components of a Network must have unique names.") # List inputs and outputs. self.input_shapes = [tfutil.shape_to_list(t.shape) for t in self.input_templates] self.output_shapes = [tfutil.shape_to_list(t.shape) for t in self.output_templates] self.input_shape = self.input_shapes[0] self.output_shape = self.output_shapes[0] self.output_names = [t.name.split("/")[-1].split(":")[0] for t in self.output_templates] # List variables. self.own_vars = OrderedDict((var.name[len(self.scope) + 1:].split(":")[0], var) for var in tf.global_variables(self.scope + "/")) self.vars = OrderedDict(self.own_vars) self.vars.update((comp.name + "/" + name, var) for comp in self.components.values() for name, var in comp.vars.items()) self.trainables = OrderedDict((name, var) for name, var in self.vars.items() if var.trainable) self.var_global_to_local = OrderedDict((var.name.split(":")[0], name) for name, var in self.vars.items()) def reset_own_vars(self) -> None: """Re-initialize all variables of this network, excluding sub-networks.""" tfutil.run([var.initializer for var in self.own_vars.values()]) def reset_vars(self) -> None: """Re-initialize all variables of this network, including sub-networks.""" tfutil.run([var.initializer for var in self.vars.values()]) def reset_trainables(self) -> None: """Re-initialize all trainable variables of this network, including sub-networks.""" tfutil.run([var.initializer for var in self.trainables.values()]) def get_output_for(self, *in_expr: TfExpression, return_as_list: bool = False, **dynamic_kwargs) -> Union[TfExpression, List[TfExpression]]: """Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s).""" assert len(in_expr) == self.num_inputs assert not all(expr is None for expr in in_expr) # Finalize build func kwargs. build_kwargs = dict(self.static_kwargs) build_kwargs.update(dynamic_kwargs) build_kwargs["is_template_graph"] = False build_kwargs["components"] = self.components # Build TensorFlow graph to evaluate the network. with tfutil.absolute_variable_scope(self.scope, reuse=True), tf.name_scope(self.name): assert tf.get_variable_scope().name == self.scope valid_inputs = [expr for expr in in_expr if expr is not None] final_inputs = [] for expr, name, shape in zip(in_expr, self.input_names, self.input_shapes): if expr is not None: expr = tf.identity(expr, name=name) else: expr = tf.zeros([tf.shape(valid_inputs[0])[0]] + shape[1:], name=name) final_inputs.append(expr) out_expr = self._build_func(*final_inputs, **build_kwargs) # Propagate input shapes back to the user-specified expressions. for expr, final in zip(in_expr, final_inputs): if isinstance(expr, tf.Tensor): expr.set_shape(final.shape) # Express outputs in the desired format. assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple) if return_as_list: out_expr = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr) return out_expr def get_var_local_name(self, var_or_global_name: Union[TfExpression, str]) -> str: """Get the local name of a given variable, without any surrounding name scopes.""" assert tfutil.is_tf_expression(var_or_global_name) or isinstance(var_or_global_name, str) global_name = var_or_global_name if isinstance(var_or_global_name, str) else var_or_global_name.name return self.var_global_to_local[global_name] def find_var(self, var_or_local_name: Union[TfExpression, str]) -> TfExpression: """Find variable by local or global name.""" assert tfutil.is_tf_expression(var_or_local_name) or isinstance(var_or_local_name, str) return self.vars[var_or_local_name] if isinstance(var_or_local_name, str) else var_or_local_name def get_var(self, var_or_local_name: Union[TfExpression, str]) -> np.ndarray: """Get the value of a given variable as NumPy array. Note: This method is very inefficient -- prefer to use tflib.run(list_of_vars) whenever possible.""" return self.find_var(var_or_local_name).eval() def set_var(self, var_or_local_name: Union[TfExpression, str], new_value: Union[int, float, np.ndarray]) -> None: """Set the value of a given variable based on the given NumPy array. Note: This method is very inefficient -- prefer to use tflib.set_vars() whenever possible.""" tfutil.set_vars({self.find_var(var_or_local_name): new_value}) def __getstate__(self) -> dict: """Pickle export.""" state = dict() state["version"] = 3 state["name"] = self.name state["static_kwargs"] = dict(self.static_kwargs) state["components"] = dict(self.components) state["build_module_src"] = self._build_module_src state["build_func_name"] = self._build_func_name state["variables"] = list(zip(self.own_vars.keys(), tfutil.run(list(self.own_vars.values())))) return state def __setstate__(self, state: dict) -> None: """Pickle import.""" # pylint: disable=attribute-defined-outside-init tfutil.assert_tf_initialized() self._init_fields() # Execute custom import handlers. for handler in _import_handlers: state = handler(state) # Set basic fields. assert state["version"] in [2, 3] self.name = state["name"] self.static_kwargs = util.EasyDict(state["static_kwargs"]) self.components = util.EasyDict(state.get("components", {})) self._build_module_src = state["build_module_src"] self._build_func_name = state["build_func_name"] # Create temporary module from the imported source code. module_name = "_tflib_network_import_" + uuid.uuid4().hex module = types.ModuleType(module_name) sys.modules[module_name] = module _import_module_src[module] = self._build_module_src exec(self._build_module_src, module.__dict__) # pylint: disable=exec-used # Locate network build function in the temporary module. self._build_func = util.get_obj_from_module(module, self._build_func_name) assert callable(self._build_func) # Init TensorFlow graph. self._init_graph() self.reset_own_vars() tfutil.set_vars({self.find_var(name): value for name, value in state["variables"]}) def clone(self, name: str = None, **new_static_kwargs) -> "Network": """Create a clone of this network with its own copy of the variables.""" # pylint: disable=protected-access net = object.__new__(Network) net._init_fields() net.name = name if name is not None else self.name net.static_kwargs = util.EasyDict(self.static_kwargs) net.static_kwargs.update(new_static_kwargs) net._build_module_src = self._build_module_src net._build_func_name = self._build_func_name net._build_func = self._build_func net._init_graph() net.copy_vars_from(self) return net def copy_own_vars_from(self, src_net: "Network") -> None: """Copy the values of all variables from the given network, excluding sub-networks.""" names = [name for name in self.own_vars.keys() if name in src_net.own_vars] tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names})) def copy_vars_from(self, src_net: "Network") -> None: """Copy the values of all variables from the given network, including sub-networks.""" names = [name for name in self.vars.keys() if name in src_net.vars] tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names})) def copy_trainables_from(self, src_net: "Network") -> None: """Copy the values of all trainable variables from the given network, including sub-networks.""" names = [name for name in self.trainables.keys() if name in src_net.trainables] tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names})) def convert(self, new_func_name: str, new_name: str = None, **new_static_kwargs) -> "Network": """Create new network with the given parameters, and copy all variables from this network.""" if new_name is None: new_name = self.name static_kwargs = dict(self.static_kwargs) static_kwargs.update(new_static_kwargs) net = Network(name=new_name, func_name=new_func_name, **static_kwargs) net.copy_vars_from(self) return net def setup_as_moving_average_of(self, src_net: "Network", beta: TfExpressionEx = 0.99, beta_nontrainable: TfExpressionEx = 0.0) -> tf.Operation: """Construct a TensorFlow op that updates the variables of this network to be slightly closer to those of the given network.""" with tfutil.absolute_name_scope(self.scope + "/_MovingAvg"): ops = [] for name, var in self.vars.items(): if name in src_net.vars: cur_beta = beta if name in self.trainables else beta_nontrainable new_value = tfutil.lerp(src_net.vars[name], var, cur_beta) ops.append(var.assign(new_value)) return tf.group(*ops) def run(self, *in_arrays: Tuple[Union[np.ndarray, None], ...], input_transform: dict = None, output_transform: dict = None, return_as_list: bool = False, print_progress: bool = False, minibatch_size: int = None, num_gpus: int = 1, assume_frozen: bool = False, sess: tf.Session = None, **dynamic_kwargs) -> Union[np.ndarray, Tuple[np.ndarray, ...], List[np.ndarray]]: """Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s). Args: input_transform: A dict specifying a custom transformation to be applied to the input tensor(s) before evaluating the network. The dict must contain a 'func' field that points to a top-level function. The function is called with the input TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs. output_transform: A dict specifying a custom transformation to be applied to the output tensor(s) after evaluating the network. The dict must contain a 'func' field that points to a top-level function. The function is called with the output TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs. return_as_list: True = return a list of NumPy arrays, False = return a single NumPy array, or a tuple if there are multiple outputs. print_progress: Print progress to the console? Useful for very large input arrays. minibatch_size: Maximum minibatch size to use, None = disable batching. num_gpus: Number of GPUs to use. assume_frozen: Improve multi-GPU performance by assuming that the trainable parameters will remain changed between calls. dynamic_kwargs: Additional keyword arguments to be passed into the network build function. """ assert len(in_arrays) == self.num_inputs assert not all(arr is None for arr in in_arrays) assert input_transform is None or util.is_top_level_function(input_transform["func"]) assert output_transform is None or util.is_top_level_function(output_transform["func"]) output_transform, dynamic_kwargs = _handle_legacy_output_transforms(output_transform, dynamic_kwargs) num_items = in_arrays[0].shape[0] if minibatch_size is None: minibatch_size = num_items # Construct unique hash key from all arguments that affect the TensorFlow graph. key = dict(input_transform=input_transform, output_transform=output_transform, num_gpus=num_gpus, assume_frozen=assume_frozen, dynamic_kwargs=dynamic_kwargs) def unwind_key(obj): if isinstance(obj, dict): return [(key, unwind_key(value)) for key, value in sorted(obj.items())] if callable(obj): return util.get_top_level_function_name(obj) return obj key = repr(unwind_key(key)) # Build graph. if key not in self._run_cache: with tfutil.absolute_name_scope(self.scope + "/_Run"), tf.control_dependencies(None): with tf.device("/cpu:0"): in_expr = [tf.placeholder(tf.float32, name=name) for name in self.input_names] in_split = list(zip(*[tf.split(x, num_gpus) for x in in_expr])) out_split = [] for gpu in range(num_gpus): with tf.device("/gpu:%d" % gpu): net_gpu = self.clone() if assume_frozen else self in_gpu = in_split[gpu] if input_transform is not None: in_kwargs = dict(input_transform) in_gpu = in_kwargs.pop("func")(*in_gpu, **in_kwargs) in_gpu = [in_gpu] if tfutil.is_tf_expression(in_gpu) else list(in_gpu) assert len(in_gpu) == self.num_inputs out_gpu = net_gpu.get_output_for(*in_gpu, return_as_list=True, **dynamic_kwargs) if output_transform is not None: out_kwargs = dict(output_transform) out_gpu = out_kwargs.pop("func")(*out_gpu, **out_kwargs) out_gpu = [out_gpu] if tfutil.is_tf_expression(out_gpu) else list(out_gpu) assert len(out_gpu) == self.num_outputs out_split.append(out_gpu) with tf.device("/cpu:0"): out_expr = [tf.concat(outputs, axis=0) for outputs in zip(*out_split)] self._run_cache[key] = in_expr, out_expr # Run minibatches. in_expr, out_expr = self._run_cache[key] out_arrays = [np.empty([num_items] + tfutil.shape_to_list(expr.shape)[1:], expr.dtype.name) for expr in out_expr] for mb_begin in range(0, num_items, minibatch_size): if print_progress: print("\r%d / %d" % (mb_begin, num_items), end="") mb_end = min(mb_begin + minibatch_size, num_items) mb_num = mb_end - mb_begin mb_in = [src[mb_begin : mb_end] if src is not None else np.zeros([mb_num] + shape[1:]) for src, shape in zip(in_arrays, self.input_shapes)] if sess is None : mb_out = tf.get_default_session().run(out_expr, dict(zip(in_expr, mb_in))) else : mb_out = sess.run(out_expr, dict(zip(in_expr, mb_in))) for dst, src in zip(out_arrays, mb_out): dst[mb_begin: mb_end] = src # Done. if print_progress: print("\r%d / %d" % (num_items, num_items)) if not return_as_list: out_arrays = out_arrays[0] if len(out_arrays) == 1 else tuple(out_arrays) return out_arrays def list_ops(self) -> List[TfExpression]: include_prefix = self.scope + "/" exclude_prefix = include_prefix + "_" ops = tf.get_default_graph().get_operations() ops = [op for op in ops if op.name.startswith(include_prefix)] ops = [op for op in ops if not op.name.startswith(exclude_prefix)] return ops def list_layers(self) -> List[Tuple[str, TfExpression, List[TfExpression]]]: """Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to individual layers of the network. Mainly intended to be used for reporting.""" layers = [] def recurse(scope, parent_ops, parent_vars, level): # Ignore specific patterns. if any(p in scope for p in ["/Shape", "/strided_slice", "/Cast", "/concat", "/Assign"]): return # Filter ops and vars by scope. global_prefix = scope + "/" local_prefix = global_prefix[len(self.scope) + 1:] cur_ops = [op for op in parent_ops if op.name.startswith(global_prefix) or op.name == global_prefix[:-1]] cur_vars = [(name, var) for name, var in parent_vars if name.startswith(local_prefix) or name == local_prefix[:-1]] if not cur_ops and not cur_vars: return # Filter out all ops related to variables. for var in [op for op in cur_ops if op.type.startswith("Variable")]: var_prefix = var.name + "/" cur_ops = [op for op in cur_ops if not op.name.startswith(var_prefix)] # Scope does not contain ops as immediate children => recurse deeper. contains_direct_ops = any("/" not in op.name[len(global_prefix):] and op.type != "Identity" for op in cur_ops) if (level == 0 or not contains_direct_ops) and (len(cur_ops) + len(cur_vars)) > 1: visited = set() for rel_name in [op.name[len(global_prefix):] for op in cur_ops] + [name[len(local_prefix):] for name, _var in cur_vars]: token = rel_name.split("/")[0] if token not in visited: recurse(global_prefix + token, cur_ops, cur_vars, level + 1) visited.add(token) return # Report layer. layer_name = scope[len(self.scope) + 1:] layer_output = cur_ops[-1].outputs[0] if cur_ops else cur_vars[-1][1] layer_trainables = [var for _name, var in cur_vars if var.trainable] layers.append((layer_name, layer_output, layer_trainables)) recurse(self.scope, self.list_ops(), list(self.vars.items()), 0) return layers def print_layers(self, title: str = None, hide_layers_with_no_params: bool = False) -> None: """Print a summary table of the network structure.""" rows = [[title if title is not None else self.name, "Params", "OutputShape", "WeightShape"]] rows += [["---"] * 4] total_params = 0 for layer_name, layer_output, layer_trainables in self.list_layers(): num_params = sum(np.prod(tfutil.shape_to_list(var.shape)) for var in layer_trainables) weights = [var for var in layer_trainables if var.name.endswith("/weight:0")] weights.sort(key=lambda x: len(x.name)) if len(weights) == 0 and len(layer_trainables) == 1: weights = layer_trainables total_params += num_params if not hide_layers_with_no_params or num_params != 0: num_params_str = str(num_params) if num_params > 0 else "-" output_shape_str = str(layer_output.shape) weight_shape_str = str(weights[0].shape) if len(weights) >= 1 else "-" rows += [[layer_name, num_params_str, output_shape_str, weight_shape_str]] rows += [["---"] * 4] rows += [["Total", str(total_params), "", ""]] widths = [max(len(cell) for cell in column) for column in zip(*rows)] print() for row in rows: print(" ".join(cell + " " * (width - len(cell)) for cell, width in zip(row, widths))) print() def setup_weight_histograms(self, title: str = None) -> None: """Construct summary ops to include histograms of all trainable parameters in TensorBoard.""" if title is None: title = self.name with tf.name_scope(None), tf.device(None), tf.control_dependencies(None): for local_name, var in self.trainables.items(): if "/" in local_name: p = local_name.split("/") name = title + "_" + p[-1] + "/" + "_".join(p[:-1]) else: name = title + "_toplevel/" + local_name tf.summary.histogram(name, var) #---------------------------------------------------------------------------- # Backwards-compatible emulation of legacy output transformation in Network.run(). _print_legacy_warning = True def _handle_legacy_output_transforms(output_transform, dynamic_kwargs): global _print_legacy_warning legacy_kwargs = ["out_mul", "out_add", "out_shrink", "out_dtype"] if not any(kwarg in dynamic_kwargs for kwarg in legacy_kwargs): return output_transform, dynamic_kwargs if _print_legacy_warning: _print_legacy_warning = False print() print("WARNING: Old-style output transformations in Network.run() are deprecated.") print("Consider using 'output_transform=dict(func=tflib.convert_images_to_uint8)'") print("instead of 'out_mul=127.5, out_add=127.5, out_dtype=np.uint8'.") print() assert output_transform is None new_kwargs = dict(dynamic_kwargs) new_transform = {kwarg: new_kwargs.pop(kwarg) for kwarg in legacy_kwargs if kwarg in dynamic_kwargs} new_transform["func"] = _legacy_output_transform_func return new_transform, new_kwargs def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_shrink=1, out_dtype=None): if out_mul != 1.0: expr = [x * out_mul for x in expr] if out_add != 0.0: expr = [x + out_add for x in expr] if out_shrink > 1: ksize = [1, 1, out_shrink, out_shrink] expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") for x in expr] if out_dtype is not None: if tf.as_dtype(out_dtype).is_integer: expr = [tf.round(x) for x in expr] expr = [tf.saturate_cast(x, out_dtype) for x in expr] return expr

f73777d7ca2d4d80693cb15dd56f511c05f1c49c

py

Python

alipay/aop/api/domain/AlipayUserFamilyShareAuthCheckModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

alipay/aop/api/domain/AlipayUserFamilyShareAuthCheckModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

alipay/aop/api/domain/AlipayUserFamilyShareAuthCheckModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * class AlipayUserFamilyShareAuthCheckModel(object): def __init__(self): self._resource_id = None self._scene_id = None self._target_biz_user_id = None self._target_user_biz_source = None self._user_id = None @property def resource_id(self): return self._resource_id @resource_id.setter def resource_id(self, value): self._resource_id = value @property def scene_id(self): return self._scene_id @scene_id.setter def scene_id(self, value): self._scene_id = value @property def target_biz_user_id(self): return self._target_biz_user_id @target_biz_user_id.setter def target_biz_user_id(self, value): self._target_biz_user_id = value @property def target_user_biz_source(self): return self._target_user_biz_source @target_user_biz_source.setter def target_user_biz_source(self, value): self._target_user_biz_source = value @property def user_id(self): return self._user_id @user_id.setter def user_id(self, value): self._user_id = value def to_alipay_dict(self): params = dict() if self.resource_id: if hasattr(self.resource_id, 'to_alipay_dict'): params['resource_id'] = self.resource_id.to_alipay_dict() else: params['resource_id'] = self.resource_id if self.scene_id: if hasattr(self.scene_id, 'to_alipay_dict'): params['scene_id'] = self.scene_id.to_alipay_dict() else: params['scene_id'] = self.scene_id if self.target_biz_user_id: if hasattr(self.target_biz_user_id, 'to_alipay_dict'): params['target_biz_user_id'] = self.target_biz_user_id.to_alipay_dict() else: params['target_biz_user_id'] = self.target_biz_user_id if self.target_user_biz_source: if hasattr(self.target_user_biz_source, 'to_alipay_dict'): params['target_user_biz_source'] = self.target_user_biz_source.to_alipay_dict() else: params['target_user_biz_source'] = self.target_user_biz_source if self.user_id: if hasattr(self.user_id, 'to_alipay_dict'): params['user_id'] = self.user_id.to_alipay_dict() else: params['user_id'] = self.user_id return params @staticmethod def from_alipay_dict(d): if not d: return None o = AlipayUserFamilyShareAuthCheckModel() if 'resource_id' in d: o.resource_id = d['resource_id'] if 'scene_id' in d: o.scene_id = d['scene_id'] if 'target_biz_user_id' in d: o.target_biz_user_id = d['target_biz_user_id'] if 'target_user_biz_source' in d: o.target_user_biz_source = d['target_user_biz_source'] if 'user_id' in d: o.user_id = d['user_id'] return o

f7378301a74dc9cd9e025b71204a6e6ed1b8e4b4

py

Python

pypayd/interfaces/blockr.py

ser/pypayd-ng

63d0e09ca5e966177874e6e2e82f6036898a56b0

pypayd/interfaces/blockr.py

ser/pypayd-ng

63d0e09ca5e966177874e6e2e82f6036898a56b0

pypayd/interfaces/blockr.py

ser/pypayd-ng

63d0e09ca5e966177874e6e2e82f6036898a56b0

""" blockr.io """ import logging import hashlib from hashlib import sha256 import requests from .. import config from binascii import hexlify, unhexlify def getUrl(request_string): return requests.get(request_string).json() def setHost(): config.BLOCKCHAIN_CONNECT = ('http://tbtc.blockr.io' if config.TESTNET else 'http://btc.blockr.io') #And fix this def check(): return getInfo() def getInfo(): result = getUrl(config.BLOCKCHAIN_CONNECT + '/api/v1/coin/info', ) if 'status' in result and result['status'] == 'success': return { "info": { "blocks": result['data']['last_block']['nb'], "difficulty": result['data']['last_block']['difficulty'] } } return result def getUtxo(address): result = getUrl(config.BLOCKCHAIN_CONNECT + '/api/v1/address/unspent/{}/'.format(address)) if 'status' in result and result['status'] == 'success': utxo = [] for txo in result['data']['unspent']: newtxo = { 'address': address, 'txid': txo['tx'], 'vout': txo['n'], 'ts': 0, 'scriptPubKey': txo['script'], 'amount': float(txo['amount']), 'confirmations': txo['confirmations'], 'confirmationsFromCache': False } utxo.append(newtxo) return utxo return None def getAddressInfo(address): infos = getUrl(config.BLOCKCHAIN_CONNECT + '/api/v1/address/info/{}'.format(address), ) if 'status' in infos and infos['status'] == 'success': txs = getUrl(config.BLOCKCHAIN_CONNECT + '/api/v1/address/txs/{}'.format(address), ) if 'status' in txs and txs['status'] == 'success': transactions = [] for tx in txs['data']['txs']: transactions.append(tx['tx']) return { 'addrStr': address, 'balance': infos['data']['balance'], 'balanceSat': infos['data']['balance'] * config.UNIT, 'totalReceived': infos['data']['totalreceived'], 'totalReceivedSat': infos['data']['totalreceived'] * config.UNIT, 'unconfirmedBalance': 0, 'unconfirmedBalanceSat': 0, 'unconfirmedTxApperances': 0, 'txApperances': txs['data']['nb_txs'], 'transactions': transactions } return None def getTxInfo(tx_hash): tx = getUrl(config.BLOCKCHAIN_CONNECT + '/api/v1/tx/raw/{}'.format(tx_hash)) if tx.get('status') == 'success': valueOut = 0 for vout in tx['data']['tx']['vout']: valueOut += vout['value'] return { 'txid': tx_hash, 'version': tx['data']['tx']['version'], 'locktime': tx['data']['tx']['locktime'], 'blockhash': tx['data']['tx'].get('blockhash', None), 'confirmations': tx['data']['tx'].get('confirmations', None), 'time': tx['data']['tx'].get('time', None), 'blocktime': tx['data']['tx'].get('blocktime', None), 'valueOut': valueOut, 'vin': tx['data']['tx']['vin'], 'vout': tx['data']['tx']['vout'] } return None def sourceAddressesFromTX(tx_full): '''Return source (outbound) addresses for a bitcoin tx''' return [addressForPubKey(i['scriptSig']['asm'].split(" ")[1]) for i in tx_full['vin']] #This can be replaced with the pycoin function _b58chars = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz' def addressForPubKey(pubkey_hex, testnet=None): if testnet is None: testnet = config.TESTNET ripehash = hashlib.new('ripemd160') step1 = unhexlify(pubkey_hex) step2 = sha256(step1).digest() ripehash.update(step2) if testnet: step4 = b'\x6F' + ripehash.digest() else: step4 = b'\x00' + ripehash.digest() step5 = sha256(step4).digest() step6 = sha256(step5).digest() chksum = step6[:4] address = step4 + chksum addr_58 = encodeBase58(address) return addr_58 def encodeBase58(v): long_value = int.from_bytes(v, 'big') result = '' while long_value >= 58: div, mod = divmod(long_value, 58) result = _b58chars[mod] + result long_value = div result = _b58chars[long_value] + result nPad = 0 for c in v: if c == ord(b'\0'): nPad += 1 else: break return (_b58chars[0]*nPad) + result

f737ea22ff2ba4acb8102d13caef6197f4c5f1ca

py

Python

dev/local/data/pipeline.py

vguerra/fastai_docs

95df902ef5cd08bcd58d5ca64bc8a6ea3f297531

dev/local/data/pipeline.py

vguerra/fastai_docs

95df902ef5cd08bcd58d5ca64bc8a6ea3f297531

dev/local/data/pipeline.py

vguerra/fastai_docs

95df902ef5cd08bcd58d5ca64bc8a6ea3f297531

#AUTOGENERATED! DO NOT EDIT! File to edit: dev/03_data_pipeline.ipynb (unless otherwise specified). __all__ = ['get_func', 'Func', 'Sig', 'compose_tfms', 'batch_to_samples', 'mk_transform', 'Pipeline', 'TfmdBase', 'TfmdList', 'TfmdDS'] #Cell from ..torch_basics import * from ..test import * from .transform import * from ..notebook.showdoc import show_doc #Cell def get_func(t, name, *args, **kwargs): "Get the `t.name` (potentially partial-ized with `args` and `kwargs`) or `noop` if not defined" f = getattr(t, name, noop) return f if not (args or kwargs) else partial(f, *args, **kwargs) #Cell class Func(): "Basic wrapper around a `name` with `args` and `kwargs` to call on a given type" def __init__(self, name, *args, **kwargs): self.name,self.args,self.kwargs = name,args,kwargs def __repr__(self): return f'sig: {self.name}({self.args}, {self.kwargs})' def _get(self, t): return get_func(t, self.name, *self.args, **self.kwargs) def __call__(self,t): return L(t).mapped(self._get) if is_listy(t) else self._get(t) #Cell class _Sig(): def __getattr__(self,k): def _inner(*args, **kwargs): return Func(k, *args, **kwargs) return _inner Sig = _Sig() #Cell def compose_tfms(x, tfms, is_enc=True, reverse=False, **kwargs): "Apply all `func_nm` attribute of `tfms` on `x`, maybe in `reverse` order" if reverse: tfms = reversed(tfms) for f in tfms: if not is_enc: f = f.decode x = f(x, **kwargs) return x #Cell def batch_to_samples(b, max_n=10): "'Transposes' a batch to (at most `max_n`) samples" if isinstance(b, Tensor): return b[:max_n] else: res = L(b).mapped(partial(batch_to_samples,max_n=max_n)) return L(retain_types(res.zipped(), [b])) #Cell def mk_transform(f, as_item=True): "Convert function `f` to `Transform` if it isn't already one" f = instantiate(f) return f if isinstance(f,Transform) else Transform(f, as_item=as_item) #Cell class Pipeline: "A pipeline of composed (for encode/decode) transforms, setup with types" def __init__(self, funcs=None, as_item=False, filt=None): if isinstance(funcs, Pipeline): funcs = funcs.fs elif isinstance(funcs, Transform): funcs = [funcs] self.filt,self.default = filt,None self.fs = L(ifnone(funcs,[noop])).mapped(mk_transform).sorted(key='order') self.set_as_item(as_item) for f in self.fs: name = camel2snake(type(f).__name__) a = getattr(self,name,None) if a is not None: f = L(a)+f setattr(self, name, f) def set_as_item(self, as_item): self.as_item = as_item for f in self.fs: f.as_item = as_item def setup(self, items=None): self.items = items tfms,self.fs = self.fs,L() for t in tfms: self.add(t,items) def add(self,t, items=None): t.setup(items) self.fs.append(t) def __call__(self, o): return compose_tfms(o, tfms=self.fs, filt=self.filt) def decode (self, o): return compose_tfms(o, tfms=self.fs, is_enc=False, reverse=True, filt=self.filt) def __repr__(self): return f"Pipeline: {self.fs}" def __getitem__(self,i): return self.fs[i] def decode_batch(self, b, max_n=10): return batch_to_samples(b, max_n=max_n).mapped(self.decode) def __setstate__(self,data): self.__dict__.update(data) def __getattr__(self,k): if k.startswith('_') or k=='fs': raise AttributeError(k) res = sum(self.fs.attrgot(k).mapped(L), []) if not res: raise AttributeError(k) return res[0] if len(res)==1 else res def show(self, o, ctx=None, **kwargs): for f in reversed(self.fs): res = self._show(o, ctx, **kwargs) if res is not None: return res o = f.decode(o, filt=self.filt) return self._show(o, ctx, **kwargs) def _show(self, o, ctx, **kwargs): o1 = [o] if self.as_item or not is_listy(o) else o if not all(hasattr(o_, 'show') for o_ in o1): return for o_ in o1: ctx = o_.show(ctx=ctx, **kwargs) return ifnone(ctx,1) #Cell class TfmdBase(L): "Base class for transformed lists" def _gets(self, i): return L(self._get(i_) for i_ in mask2idxs(i)) def subset(self, idxs): return self._new(super()._gets(idxs)) def decode_at(self, idx): return self.decode(self[idx]) def show_at(self, idx, **kwargs): return self.show(self[idx], **kwargs) #Cell class TfmdList(TfmdBase): "A `Pipeline` of `tfms` applied to a collection of `items`" def __init__(self, items, tfms, do_setup=True, as_item=True, use_list=None, filt=None): super().__init__(items, use_list=use_list) if isinstance(tfms,TfmdList): tfms = tfms.tfms if isinstance(tfms,Pipeline): do_setup=False self.tfms = Pipeline(tfms, as_item=as_item, filt=filt) if do_setup: self.setup() def _new(self, items, *args, **kwargs): return super()._new(items, tfms=self.tfms, do_setup=False, use_list=None, filt=self.filt) def _get (self, i): return self.tfms(super()._get(i)) def __repr__(self): return f"{self.__class__.__name__}: {self.items}\ntfms - {self.tfms.fs}" # Delegating to `self.tfms` def show(self, o, **kwargs): return self.tfms.show(o, **kwargs) def setup(self): self.tfms.setup(self) def decode(self, x, **kwargs): return self.tfms.decode(x, **kwargs) def __call__(self, x, **kwargs): return self.tfms.__call__(x, **kwargs) @property def filt(self): return self.tfms.filt @filt.setter def filt(self,v): self.tfms.filt = v #Cell @docs class TfmdDS(TfmdBase): "A dataset that creates a tuple from each `tfms`, passed thru `ds_tfms`" def __init__(self, items, tfms=None, do_setup=True, use_list=None, filt=None): super().__init__(items, use_list=use_list) if tfms is None: tms = [None] self.tls = [TfmdList(items, t, do_setup=do_setup, filt=filt, use_list=use_list) for t in L(tfms)] def _get(self, it): return tuple(tl._get(it) for tl in self.tls) def __repr__(self): return coll_repr(self) def decode(self, o): return tuple(it.decode(o_) for o_,it in zip(o,self.tls)) def show(self, o, ctx=None, **kwargs): for o_,it in zip(o,self.tls): ctx = it.show(o_, ctx=ctx, **kwargs) return ctx @property def filt(self): return self.tls[0].filt @filt.setter def filt(self,v): for tl in self.tls: tl.filt = v _docs=dict( decode="Compose `decode` of all `tuple_tfms` then all `tfms` on `i`", show="Show item `o` in `ctx`")

f73810b5a713532fd33d2666d1759247bd9d52f6

py

Python

2021/12/solution2.py

frenzymadness/aoc

c9018e757bae61a696e675a827aef873995abdd3

2020-12-04T09:45:38.000Z

2020-12-07T14:06:12.000Z

2021/12/solution2.py

frenzymadness/aoc

c9018e757bae61a696e675a827aef873995abdd3

2021/12/solution2.py

frenzymadness/aoc

c9018e757bae61a696e675a827aef873995abdd3

from collections import defaultdict, deque with open("input.txt") as input_file: lines = input_file.read().splitlines() g = defaultdict(set) for line in lines: n1, n2 = line.split("-") g[n1].add(n2) g[n2].add(n1) def walk(node, path, return_here): if node == "end": yield path for next_node in g[node]: if next_node not in path or next_node.isupper(): yield from walk(next_node, path + [next_node], return_here) elif next_node.islower() and return_here and next_node not in ("start", "end"): yield from walk(next_node, path + [next_node], False) print(sum(1 for _ in walk("start", ["start"], True)))

f7381de4aebc9051177ffd55accf0b7d97283f70

py

Python

elementally/tests/test.py

dem1995/elementally

192990ad53580d62e278def6508c466589f38ecd

elementally/tests/test.py

dem1995/elementally

192990ad53580d62e278def6508c466589f38ecd

elementally/tests/test.py

dem1995/elementally

192990ad53580d62e278def6508c466589f38ecd

import elementally as elmy import unittest import itertools pos_array = [1, 2, 3, 4, 5] pos_array_2 = [5, 4, 3, 2, 1] neg_array = [-10, -20, -30, -40, -50] neg_array_2 = [-50, -40, -30, -20, -10] def odd_generator(): i=1 while(True): yield i i+=2 def complex_generator(): i=1 while(True): yield i i+=2j class TestBasicArithmetic(unittest.TestCase): def test_sum_lists(self): """Checks whether two lists sum properly""" self.assertListEqual(elmy.sum(pos_array, pos_array_2), [6, 6, 6, 6, 6]) self.assertListEqual(elmy.sum(pos_array, neg_array), [-9, -18, -27, -36, -45]) def test_sum_list_with_generator(self): """Checks whether a list sums with a generator properly, and returns a generator""" list_odd_numbers_plus_index = elmy.sum(pos_array, odd_generator()) self.assertListEqual(list_odd_numbers_plus_index, [2, 5, 8, 11, 14]) def test_sum_generator_with_list(self): """Checks whether a generator sums with a list properly, and remains a generator""" augend = odd_generator() gen_odd_numbers_plus_index = elmy.sum(augend, pos_array) self.assertEqual(type(augend), type(gen_odd_numbers_plus_index)) slice_of_summed_generator = itertools.islice(gen_odd_numbers_plus_index, 8) self.assertSequenceEqual(list(slice_of_summed_generator), [2, 5, 8, 11, 14]) def test_sum_generator_with_generator(self): """Checks whether a generator sums with a generator properly, and returns a generator""" augend = odd_generator() summed = elmy.sum(augend, odd_generator()) self.assertSequenceEqual([2, 6, 10, 14], list(itertools.islice(summed, 4))) self.assertEqual(type(augend), type(summed)) class TestMultistepOps(unittest.TestCase): def test_negation_generator(self): """Checks whether adding a sequences to its negation yields 0s""" operand = odd_generator() negated = elmy.negation(odd_generator()) zeros = elmy.sum(operand, negated) for i in itertools.islice(zeros, 1000): self.assertEqual(i, 0) def test_reciprocal_multiplication(self): """Checks whether multiplying a sequence by its reciprocal yields 1s""" augend = complex_generator() reciprocal = elmy.product(augend, elmy.reciprocal(complex_generator())) for i in itertools.islice(reciprocal, 1000): self.assertAlmostEqual(i, 1, 14) if __name__ == '__main__': unittest.main()

f7381dee751bc8ce42c7f5d24e881d37f73e6d1c

py

Python

cli/iotexetl/rpc/iotex_rpc.py

blockchain-etl/iotex-etl

bd350c3190acac35d17532eff383e05d08011e24

2020-07-04T13:53:38.000Z

2020-07-30T15:07:35.000Z

cli/iotexetl/rpc/iotex_rpc.py

blockchain-etl/iotex-etl

bd350c3190acac35d17532eff383e05d08011e24

2020-07-16T06:07:33.000Z

2020-08-20T10:35:10.000Z

cli/iotexetl/rpc/iotex_rpc.py

blockchain-etl/iotex-etl

bd350c3190acac35d17532eff383e05d08011e24

2021-01-20T10:06:20.000Z

2021-01-20T10:06:20.000Z

# The MIT License (MIT) # # Copyright (c) 2020 Evgeny Medvedev, evge.medvedev@gmail.com # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from urllib.parse import urlparse import grpc from iotexetl.rpc.iotexapi import api_pb2 from iotexetl.rpc.iotexapi import api_pb2_grpc class IotexRpc: def __init__(self, provider_uri, timeout=60): self.timeout = timeout channel = get_channel_from_uri_string(provider_uri) self.stub = api_pb2_grpc.APIServiceStub(channel) def get_raw_blocks(self, start_height, count): return self.stub.GetRawBlocks( api_pb2.GetRawBlocksRequest(startHeight=start_height, count=count, withReceipts=True), timeout=self.timeout) def get_block_metas(self, start_height, count): return self.stub.GetBlockMetas(api_pb2.GetBlockMetasRequest( byIndex=api_pb2.GetBlockMetasByIndexRequest(start=start_height, count=count) ), timeout=self.timeout) def get_transaction_logs(self, block_number): return self.stub.GetTransactionLogByBlockHeight( api_pb2.GetTransactionLogByBlockHeightRequest(blockHeight=block_number), timeout=self.timeout) def get_chain_meta(self): return self.stub.GetChainMeta(api_pb2.GetChainMetaRequest(), timeout=self.timeout) def get_channel_from_uri_string(provider_uri): uri = urlparse(provider_uri) if uri.scheme == 'grpcs': credentials = grpc.ssl_channel_credentials() channel = grpc.secure_channel(uri.netloc, credentials) elif uri.scheme == 'grpc': channel = grpc.insecure_channel(uri.netloc) else: raise ValueError(f'The uri scheme {uri.scheme} is not recognized. Use grpc:// or grpcs://') return channel

f73842990d3c34bb39b1979920ca99697d282613

py

Python

agent/indy_catalyst_agent/storage/tests/test__record.py

TelegramSam/indy-catalyst

476f5a773d90d8b50960b49e63b823bd13325d4a

agent/indy_catalyst_agent/storage/tests/test__record.py

TelegramSam/indy-catalyst

476f5a773d90d8b50960b49e63b823bd13325d4a

agent/indy_catalyst_agent/storage/tests/test__record.py

TelegramSam/indy-catalyst

476f5a773d90d8b50960b49e63b823bd13325d4a

import pytest from indy_catalyst_agent.storage import StorageRecord class TestStorageRecord: def test_create(self): record_type = "TYPE" record_value = "VALUE" record = StorageRecord(record_type, record_value) assert record.type == record_type assert record.value == record_value assert record.id and type(record.id) is str assert record.tags == {}

f7387ae9a9a7f58c3749489a080bb1a0fe1242b5

py

Python

checkov/common/bridgecrew/image_scanning/image_scanner.py

graybrandonpfg/checkov

3081a8560f6369465314ee8f4ac8a8ec01649d68

2019-12-09T13:16:54.000Z

2022-03-31T14:31:01.000Z

checkov/common/bridgecrew/image_scanning/image_scanner.py

graybrandonpfg/checkov

3081a8560f6369465314ee8f4ac8a8ec01649d68

2019-12-17T09:55:51.000Z

2022-03-31T19:17:17.000Z

checkov/common/bridgecrew/image_scanning/image_scanner.py

graybrandonpfg/checkov

3081a8560f6369465314ee8f4ac8a8ec01649d68

2019-12-19T08:57:38.000Z

2022-03-30T21:38:37.000Z

import logging import subprocess # nosec import docker import json import os import time from checkov.common.bridgecrew.image_scanning.docker_image_scanning_integration import docker_image_scanning_integration TWISTCLI_FILE_NAME = 'twistcli' DOCKER_IMAGE_SCAN_RESULT_FILE_NAME = 'docker-image-scan-results.json' def _get_docker_image_name(docker_image_id): try: docker_client = docker.from_env() return docker_client.images.get(docker_image_id).attrs['RepoDigests'][0].split('@')[0] except Exception as e: logging.error(f"docker image needs to have repository") raise e def _get_dockerfile_content(dockerfile_path): try: with open(dockerfile_path) as f: return f.read() except FileNotFoundError as e: logging.error(f"Path to Dockerfile is invalid\n{e}") raise e except Exception as e: logging.error(f"Failed to read Dockerfile content\n{e}") raise e class ImageScanner: def __init__(self): self.docker_image_name = '' self.dockerfile_content = '' def setup_scan(self, docker_image_id, dockerfile_path, skip_extract_image_name): try: if skip_extract_image_name: # Provide a default image name in case the image has not been tagged with a name self.docker_image_name = f'repository/image{str(time.time() * 1000)}' else: self.docker_image_name = _get_docker_image_name(docker_image_id) self.dockerfile_content = _get_dockerfile_content(dockerfile_path) docker_image_scanning_integration.download_twistcli(TWISTCLI_FILE_NAME) except Exception as e: logging.error(f"Failed to setup docker image scanning\n{e}") raise e @staticmethod def cleanup_scan(): os.remove(TWISTCLI_FILE_NAME) logging.info(f'twistcli file removed') @staticmethod def run_image_scan(docker_image_id): command_args = f"./{TWISTCLI_FILE_NAME} images scan --address {docker_image_scanning_integration.get_proxy_address()} --token {docker_image_scanning_integration.get_bc_api_key()} --details --output-file {DOCKER_IMAGE_SCAN_RESULT_FILE_NAME} {docker_image_id}".split() subprocess.run(command_args, check=True, shell=True) # nosec logging.info(f'TwistCLI ran successfully on image {docker_image_id}') with open(DOCKER_IMAGE_SCAN_RESULT_FILE_NAME) as docker_image_scan_result_file: scan_result = json.load(docker_image_scan_result_file) return scan_result def scan(self, docker_image_id, dockerfile_path, skip_extract_image_name=False): try: self.setup_scan(docker_image_id, dockerfile_path, skip_extract_image_name) scan_result = self.run_image_scan(docker_image_id) docker_image_scanning_integration.report_results(self.docker_image_name, dockerfile_path, self.dockerfile_content, twistcli_scan_result=scan_result) logging.info(f'Docker image scanning results reported to the platform') self.cleanup_scan() except Exception as e: logging.error(f"Failed to scan docker image\n{e}") raise e image_scanner = ImageScanner()

f738ab779415b269bda9727a7fdc7c85dda2ca86

py

Python

code_server/run_evaluate.py

Dragon-M-Ren/grad_code

d814b81adaec709d5dffd737f0c350953cc361fd

code_server/run_evaluate.py

Dragon-M-Ren/grad_code

d814b81adaec709d5dffd737f0c350953cc361fd

code_server/run_evaluate.py

Dragon-M-Ren/grad_code

d814b81adaec709d5dffd737f0c350953cc361fd

from evaluate import * import tensorflow as tf from utils import * from model.gcn import GCN from model.mlp import MLP from model.firstcheb import FirstCheb from model.gat import GAT from model.dcnn import DCNN from model.spectralcnn import SpectralCNN from model.chebnet import ChebNet from model.graphsage import GraphSage from model.graphsage_meanpool import GraphSageMeanPool from model.graphsage_maxpool import GraphSageMaxPool from hyperpara_optim import * import scipy.sparse as sp import numpy as np import pickle as pkl from process_data import * from draw import * import os ''' This file will run the test script Three kinds of file are saved result_path/dataset_name: the original data processed_result_path/dataset_name/: processed data ''' #Model done #MLP GCN FirstCheb GAT #Model left #SpectralCNN, DCNN, GraphSage, model_list = [DCNN] model_name_list = ['dcnn'] dataset_name_list = ['pubmed'] #dataset_name_list = ['citeseer', 'cora', 'pubmed'] dataset_numbers = 10 parameter_appendix_list = ['rand'] dataset_path = './data/evaluate' parameter_path = './hyperparameter' result_path = './direct_output' processed_result_path = './processed_output' evaluate_times = 2 train_size = 230 val_size = 500 for model, model_name in zip(model_list, model_name_list): for dataset_name in dataset_name_list: for parameter_appendix in parameter_appendix_list: train_info_list, acc_list, time_list = evaluate_model(model, model_name, dataset_path, dataset_name, dataset_numbers, parameter_path, parameter_appendix, result_path, evaluate_times, train_size, val_size) #save to file save_path = os.path.join(result_path, dataset_name) file_name = model_name + parameter_appendix #make directory if not os.path.exists(save_path): os.makedirs(save_path) save_file = open(os.path.join(save_path, file_name), 'wb') pkl.dump((train_info_list, acc_list, time_list), save_file) save_file.close() #process output data train_info, acc, time = process_output(train_info_list, acc_list, time_list) #save processed data save_path = os.path.join(processed_result_path, dataset_name) file_name = model_name + parameter_appendix #make directory if not os.path.exists(save_path): os.makedirs(save_path) save_file = open(os.path.join(save_path, file_name), 'wb') pkl.dump((train_info, acc, time), save_file) save_file.close() #save train image save_path = os.path.join(processed_result_path, dataset_name) plot_train(train_info['train_loss'], train_info['train_acc'], train_info['val_loss'], train_info['val_acc'], save_path, model_name, True)

f738af43ce1b3a193391862e705092eb5b0f05da

py

Python

unified/tests/test_xfs.py

LoganCook/reporting-unified

9d2c7e083c5c400e9120bb8552348e41406a1bc1

unified/tests/test_xfs.py

LoganCook/reporting-unified

9d2c7e083c5c400e9120bb8552348e41406a1bc1

unified/tests/test_xfs.py

LoganCook/reporting-unified

9d2c7e083c5c400e9120bb8552348e41406a1bc1

import unittest from flask import json from ..apis.xfs import app from . import client_get, now, now_minus_24hrs get = client_get(app) class XFSTestCase(unittest.TestCase): def test_root_not_allowed(self): rv = get('/') self.assertEqual(rv.status_code, 404) def test_all_top_objects_should_pass(self): for route in app.url_map.iter_rules(): rule = route.rule # top objects' have pattern of /blar # ingest only accept PUT and OPTIONS if rule not in ('/static/<path:filename>', '/ingest') and 'summary' not in rule and 'list' not in rule: print('Testing %s' % rule) resp = get('%s?count=10' % rule) data = json.loads(resp.data) self.assertEqual(resp.status_code, 200) self.assertGreaterEqual(len(data), 1) def test_filesystem_not_found(self): rule = '/filesystem/not/summary' # Can deal non-uuid id quitely resp = get(rule) self.assertEqual(resp.status_code, 200) data = json.loads(resp.data) self.assertEqual(len(data), 0) rule = '/filesystem/12345678123456781234567812345678/summary' resp = get(rule) self.assertEqual(resp.status_code, 200) data = json.loads(resp.data) self.assertEqual(len(data), 0) def test_usage_summary(self): resp = get('/usage/summary?start=%s&end=%s' % (now_minus_24hrs, now)) self.assertEqual(resp.status_code, 200) data = json.loads(resp.data) self.assertTrue(isinstance(data, list)) print(data) def test_instance_methods(self): instance_types = ('filesystem', 'owner') methods = ('summary', 'list') for itype in instance_types: resp = get('/%s?count=1' % itype) self.assertEqual(resp.status_code, 200) data = json.loads(resp.data) print(data) self.assertTrue(isinstance(data, list)) self.assertGreater(len(data), 0) target_id = data[0]['id'] for method in methods: resp = get('/%s/%s/%s?start=%s&end=%s' % (itype, target_id, method, now_minus_24hrs, now)) self.assertEqual(resp.status_code, 200) data = json.loads(resp.data) print(data) self.assertTrue(isinstance(data, list) or isinstance(data, dict)) self.assertGreater(len(data), 0)

f739129edf4dad67c2248bd06ec3d471d9ba24b7

py

Python

almost-triangles/almost-triangles.py

cdstanford/curiosities

511e55b9dbf2b49db4593be92a0e4fce07888eaf

2022-01-18T06:02:01.000Z

2022-01-18T06:02:01.000Z

almost-triangles/almost-triangles.py

cdstanford/curiosities

511e55b9dbf2b49db4593be92a0e4fce07888eaf

almost-triangles/almost-triangles.py

cdstanford/curiosities

511e55b9dbf2b49db4593be92a0e4fce07888eaf

""" A triangle is an "almost right triangle" if one of its angles differs from 90 degrees by at most 15 degrees. A triangle is an "almost isosceles triangle" if two of its angles differ from each other by at most 15 degrees. Prove that all acute triangles are either almost right or almost isosceles. Note: if "at most 15" is replaced by "less than 15" in the problem statement (change "<= 15" to "< 15" everywhere below), the formula becomes satisfiable and we get the following counterexample: a triangle with angles 45, 60, and 75. """ import z3 def triangle(x, y, z): return z3.And(x > 0, y > 0, z > 0, x + y + z == 180) def acute(x, y, z): return z3.And(x < 90, y < 90, z < 90) def abs(x): return z3.If(x > 0, x, -x) def almost_right(x, y, z): return z3.Or(abs(x - 90) <= 15, abs(y - 90) <= 15, abs(z - 90) <= 15) def almost_isosceles(x, y, z): return z3.Or(abs(x - y) <= 15, abs(x - z) <= 15, abs(y - z) <= 15) x = z3.Real("x") y = z3.Real("y") z = z3.Real("z") z3.solve( triangle(x, y, z), acute(x, y, z), z3.Not(almost_right(x, y, z)), z3.Not(almost_isosceles(x, y, z)), )

f73915cd2a13a0f75508d4f48f01ea62bb60222f

py

Python

py_segment/graph.py

RookieHong/python-objectness

02af4d090502e1a7269b23673709cfea75da09c9

2020-09-27T08:41:38.000Z

2022-03-09T10:31:27.000Z

py_segment/graph.py

RookieHong/python-objectness

02af4d090502e1a7269b23673709cfea75da09c9

2021-12-02T07:48:41.000Z

2021-12-21T07:14:25.000Z

py_segment/graph.py

RookieHong/python-objectness

02af4d090502e1a7269b23673709cfea75da09c9

2020-11-19T13:38:52.000Z

2022-03-09T14:40:28.000Z

class Node: def __init__(self, parent, rank=0, size=1): self.parent = parent self.rank = rank self.size = size def __repr__(self): return '(parent=%s, rank=%s, size=%s)' % (self.parent, self.rank, self.size) class Forest: def __init__(self, num_nodes): self.nodes = [Node(i) for i in range(num_nodes)] self.num_sets = num_nodes def size_of(self, i): return self.nodes[i].size def find(self, n): temp = n while temp != self.nodes[temp].parent: temp = self.nodes[temp].parent self.nodes[n].parent = temp return temp def merge(self, a, b): if self.nodes[a].rank > self.nodes[b].rank: self.nodes[b].parent = a self.nodes[a].size = self.nodes[a].size + self.nodes[b].size else: self.nodes[a].parent = b self.nodes[b].size = self.nodes[b].size + self.nodes[a].size if self.nodes[a].rank == self.nodes[b].rank: self.nodes[b].rank = self.nodes[b].rank + 1 self.num_sets = self.num_sets - 1 def print_nodes(self): for node in self.nodes: print(node) def create_edge(img, width, x, y, x1, y1, diff): vertex_id = lambda x, y: y * width + x w = diff(img, x, y, x1, y1) return (vertex_id(x, y), vertex_id(x1, y1), w) def build_graph(img, width, height, diff, neighborhood_8=False): graph_edges = [] for y in range(height): for x in range(width): if x > 0: graph_edges.append(create_edge(img, width, x, y, x - 1, y, diff)) if y > 0: graph_edges.append(create_edge(img, width, x, y, x, y - 1, diff)) if neighborhood_8: if x > 0 and y > 0: graph_edges.append(create_edge(img, width, x, y, x - 1, y - 1, diff)) if x > 0 and y < height - 1: graph_edges.append(create_edge(img, width, x, y, x - 1, y + 1, diff)) return graph_edges def remove_small_components(forest, graph, min_size): for edge in graph: a = forest.find(edge[0]) b = forest.find(edge[1]) if a != b and (forest.size_of(a) < min_size or forest.size_of(b) < min_size): forest.merge(a, b) return forest def segment_graph(graph_edges, num_nodes, const, min_size, threshold_func): # Step 1: initialization forest = Forest(num_nodes) weight = lambda edge: edge[2] sorted_graph = sorted(graph_edges, key=weight) threshold = [ threshold_func(1, const) for _ in range(num_nodes) ] # Step 2: merging for edge in sorted_graph: parent_a = forest.find(edge[0]) parent_b = forest.find(edge[1]) a_condition = weight(edge) <= threshold[parent_a] b_condition = weight(edge) <= threshold[parent_b] if parent_a != parent_b and a_condition and b_condition: forest.merge(parent_a, parent_b) a = forest.find(parent_a) threshold[a] = weight(edge) + threshold_func(forest.nodes[a].size, const) return remove_small_components(forest, sorted_graph, min_size)

f7391612a071aa6a5a6f60058d789289104967d2

py

Python

jnpy/app/pytdx_loader/engine.py

jojoquant/jonpy

58692f8fbf398aab7be915a63d0a376e2e0e664c

2020-05-19T07:32:39.000Z

2022-03-14T09:09:48.000Z

jnpy/app/pytdx_loader/engine.py

jojoquant/jonpy

58692f8fbf398aab7be915a63d0a376e2e0e664c

jnpy/app/pytdx_loader/engine.py

jojoquant/jonpy

58692f8fbf398aab7be915a63d0a376e2e0e664c

2020-04-02T08:30:17.000Z

2020-05-03T12:12:05.000Z

import time from typing import Callable import pandas as pd from datetime import datetime from vnpy.event import EventEngine from vnpy.trader.constant import Exchange, Interval from vnpy.trader.engine import BaseEngine, MainEngine from vnpy.trader.object import BarData from vnpy.trader.utility import get_folder_path from vnpy.trader.datafeed import BaseDatafeed, get_datafeed from vnpy.trader.database import BaseDatabase, get_database from jnpy.datasource.jotdx import ExhqAPI, IPsSource, FutureMarketCode, KBarType APP_NAME = "PytdxLoader" class PytdxLoaderEngine(BaseEngine): """""" def __init__(self, main_engine: MainEngine, event_engine: EventEngine): """""" super().__init__(main_engine, event_engine, APP_NAME) self.file_path: str = "" self.symbol: str = "" self.exchange: Exchange = Exchange.SSE self.interval: Interval = Interval.MINUTE self.datetime_head: str = "" self.open_head: str = "" self.close_head: str = "" self.low_head: str = "" self.high_head: str = "" self.volume_head: str = "" self.pytdx_ip_source = IPsSource() self.ex_api = ExhqAPI() self.datafeed: BaseDatafeed = get_datafeed() self.database: BaseDatabase = get_database() def to_bar_data(self, item, symbol: str, exchange: Exchange, interval: Interval, datetime_head: str, open_head: str, high_head: str, low_head: str, close_head: str, volume_head: str, open_interest_head: str ): bar = BarData( symbol=symbol, exchange=exchange, datetime=item[datetime_head].to_pydatetime(), interval=interval, volume=item[volume_head], open_interest=item[open_interest_head], open_price=item[open_head], high_price=item[high_head], low_price=item[low_head], close_price=item[close_head], gateway_name="DB" ) return bar def load_by_handle( self, data, symbol: str, exchange: Exchange, interval: Interval, datetime_head: str, open_head: str, high_head: str, low_head: str, close_head: str, volume_head: str, open_interest_head: str, datetime_format: str, update_qt_progress_bar: Callable, opt_str: str ): start_time = time.time() try: if isinstance(data[datetime_head][0], str): data[datetime_head] = data[datetime_head].apply( lambda x: datetime.strptime(x, datetime_format) if datetime_format else datetime.fromisoformat(x)) elif isinstance(data[datetime_head][0], pd.Timestamp): self.write_log("datetime 格式为 pd.Timestamp, 不用处理.") else: self.write_log("未知datetime类型, 请检查") self.write_log(f'df apply 处理日期时间 cost {time.time() - start_time:.2f}s') except Exception: self.write_log("通达信数据处理存在未知问题...") return if opt_str == "to_db": start_time = time.time() bars = data.apply( self.to_bar_data, args=( symbol, exchange, interval, datetime_head, open_head, high_head, low_head, close_head, volume_head, open_interest_head ), axis=1).tolist() self.write_log(f'df apply 处理bars时间 cost {time.time() - start_time:.2f}s') # insert into database self.database.save_bar_data(bars, update_qt_progress_bar) elif opt_str == "high_to_db": start_time = time.perf_counter() collection_str = f"{exchange.value}_{interval.value}_{symbol}" self.write_log( f"Start write data into mongodb" f"->{self.database.database}" f"->{collection_str}" ) self.database.save_bar_df( df=data, table=collection_str, callback=update_qt_progress_bar ) self.write_log(f'df apply 处理bars时间 cost {time.time() - start_time:.2f}s') elif opt_str == "to_csv": csv_file_dir = get_folder_path("csv_files") data.to_csv(f'{csv_file_dir}/{exchange.value}_{symbol}.csv', index=False) start = data[datetime_head].iloc[0] end = data[datetime_head].iloc[-1] count = len(data) return start, end, count def load( self, symbol: str, exchange: Exchange, interval: Interval, datetime_head: str, open_head: str, high_head: str, low_head: str, close_head: str, volume_head: str, open_interest_head: str, datetime_format: str, update_qt_progress_bar: Callable, opt_str: str, ): """ load by filename %m/%d/%Y """ ip, port = self.pytdx_ip_source.get_fast_exhq_ip() with self.ex_api.connect(ip, port): params_dict = { "category": KBarType[interval.name].value, "market": FutureMarketCode[exchange.value].value, "code": symbol, } data_df = self.ex_api.get_all_KBars_df(**params_dict) # transform column name to vnpy format data_df.rename( columns={ "datetime": "Datetime", "open": "Open", "high": "High", "low": "Low", "close": "Close", "position": "OpenInterest", "trade": "Volume", }, inplace=True ) if data_df.empty: return None, None, 0 else: return self.load_by_handle( data_df, symbol=symbol, exchange=exchange, interval=interval, datetime_head=datetime_head, open_head=open_head, high_head=high_head, low_head=low_head, close_head=close_head, volume_head=volume_head, open_interest_head=open_interest_head, datetime_format=datetime_format, update_qt_progress_bar=update_qt_progress_bar, opt_str=opt_str ) def write_log(self, msg: str): self.main_engine.write_log(msg) self.ex_api.info_log.write_log(msg)

f7391676fb3e3868ff818f4de0d1cfe9e941e235

py

Python

doc/conf.py

mikehulluk/morphforge

2a95096f144ed4ea487decb735ce66706357d3c7

2021-01-21T11:31:59.000Z

2021-01-21T11:31:59.000Z

doc/conf.py

mikehulluk/morphforge

2a95096f144ed4ea487decb735ce66706357d3c7

doc/conf.py

mikehulluk/morphforge

2a95096f144ed4ea487decb735ce66706357d3c7

#!/usr/bin/python # -*- coding: utf-8 -*- # # morphforge documentation build configuration file, created by # sphinx-quickstart on Fri Mar 23 14:01:08 2012. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # To allow stuff to build on RTD import sys class MockType(type): def __init__(cls, name, bases, dct ): super(MockType, cls).__init__(name, bases, dct) pass def __getattr__(cls, name): return Mock() def __str__(cls): return 'custom str for %s' % (cls.__name__,) class Mock(object): __metaclass__ = MockType def __init__(self, *args, **kwargs): pass def __call__(self, *args, **kwargs): return Mock() #@classmethod def __getattr__(cls, name): if name in ('__file__', '__path__'): return '/dev/null' elif name[0] == name[0].upper(): mockType = type(name, (), {}) mockType.__module__ = __name__ return mockType else: return Mock() def __getitem__(self, key): return Mock() def __add__(self, rhs): return Mock() def __sub__(self, rhs): return Mock() def __mul__(self, rhs): return Mock() def __div__(self, rhs): return Mock() def __radd__(self, rhs): return Mock() def __rsub__(self, rhs): return Mock() def __rmul__(self, rhs): return Mock() def __rdiv__(self, rhs): return Mock() def __pow__(self, rhs): return Mock() MOCK_MODULES = ['numpy', 'pylab', 'scipy', 'mredoc', 'mreorg', 'quantities', 'matplotlib'] for mod_name in MOCK_MODULES: sys.modules[mod_name] = Mock #URL to clean Read-The_Docs build dir: #https://readthedocs.org/wipe/morphforge/latest/ sys.path.append('../src/') # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.todo', 'sphinx.ext.pngmath', 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.inheritance_diagram', ] inheritance_graph_attrs = dict(rankdir="LR", size='"9.0, 8.0"', fontsize=10, ratio='compress') autodoc_default_flags =['undoc-members', 'members'] add_module_names = False # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'morphforge' copyright = u'2012, Mike Hull' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '0.1-alpha' # The full version, including alpha/beta/rc tags. release = '0.1-alpha' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # . html_title = "morphforge" html_add_permalinks = False # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. html_show_sphinx = False # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. html_use_opensearch = False # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'morphforgedoc' # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'morphforge.tex', u'morphforge Documentation', u'Mike Hull', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Additional stuff for the LaTeX preamble. #latex_preamble = '' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'morphforge', u'morphforge Documentation', [u'Mike Hull'], 1) ] html_theme = "haiku" todo_include_todos=True autosummary_generate = True # AutoDoc: def maybe_skip_member(app, what, name, obj, skip, options): # Since we add 'toSWC', etc to MorphologyTree, we # don't want this to show up in the documentation. if 'members' in options: if name.startswith('to') or name.startswith('from'): return True if name == "__weakref__": return True #print name if name in ['__weakref__' ,'__dict__','__doc__','__module__']: return True return False def setup(app): app.connect('autodoc-skip-member', maybe_skip_member) templates_path = ["_templates",] rst_prolog = r""" .. |MHThesis| replace:: :download:`Mike Hull's Ph.D Thesis </static/ThesisReducedToTools.pdf>` """

f739464879003de44eaf68232a6d4a41a29fc14e

py

Python

medium/Triangle/solution.py

ashutosh1919/leetcode-problems

65f99a3694549af88c7702b598de1a8ccb7db5fb

2021-08-21T19:10:04.000Z

2022-03-11T14:30:02.000Z

medium/Triangle/solution.py

ashutosh1919/leetcode-problems

65f99a3694549af88c7702b598de1a8ccb7db5fb

medium/Triangle/solution.py

ashutosh1919/leetcode-problems

65f99a3694549af88c7702b598de1a8ccb7db5fb

2021-08-24T06:29:02.000Z

2021-08-24T06:29:02.000Z

# Time complexity: O(n) # Approach: Summing up triangle from bottom to top in minimum way. class Solution: def minimumTotal(self, triangle: List[List[int]]) -> int: n = len(triangle) for i in range(n-1, 0, -1): for j in range(0, len(triangle[i])-1): triangle[i-1][j] += min(triangle[i][j], triangle[i][j+1]) return triangle[0][0]

f7398bf1eff15fbb3d9d6bd1f250fd98451b79cc

py

Python

scf/_vhf.py

gmwang18/pyscf

fcd6877751661c8a9743c1c872a4a2b65f6dd7ac

scf/_vhf.py

gmwang18/pyscf

fcd6877751661c8a9743c1c872a4a2b65f6dd7ac

scf/_vhf.py

gmwang18/pyscf

fcd6877751661c8a9743c1c872a4a2b65f6dd7ac

#!/usr/bin/env python import sys import ctypes import _ctypes import numpy import pyscf.lib from pyscf import gto from pyscf.gto.moleintor import make_cintopt libcvhf = pyscf.lib.load_library('libcvhf') def _fpointer(name): return ctypes.c_void_p(_ctypes.dlsym(libcvhf._handle, name)) class VHFOpt(object): def __init__(self, mol, intor, prescreen='CVHFnoscreen', qcondname=None, dmcondname=None): self._this = ctypes.POINTER(_CVHFOpt)() #print self._this.contents, expect ValueError: NULL pointer access self._intor = _fpointer(intor) self._cintopt = pyscf.lib.c_null_ptr() self._dmcondname = dmcondname self.init_cvhf_direct(mol, intor, prescreen, qcondname) def init_cvhf_direct(self, mol, intor, prescreen, qcondname): c_atm = numpy.asarray(mol._atm, dtype=numpy.int32, order='C') c_bas = numpy.asarray(mol._bas, dtype=numpy.int32, order='C') c_env = numpy.asarray(mol._env, dtype=numpy.double, order='C') natm = ctypes.c_int(c_atm.shape[0]) nbas = ctypes.c_int(c_bas.shape[0]) self._cintopt = make_cintopt(c_atm, c_bas, c_env, intor) # libcvhf.CVHFnr_optimizer(ctypes.byref(self._this), # c_atm.ctypes.data_as(ctypes.c_void_p), natm, # c_bas.ctypes.data_as(ctypes.c_void_p), nbas, # c_env.ctypes.data_as(ctypes.c_void_p)) libcvhf.CVHFinit_optimizer(ctypes.byref(self._this), c_atm.ctypes.data_as(ctypes.c_void_p), natm, c_bas.ctypes.data_as(ctypes.c_void_p), nbas, c_env.ctypes.data_as(ctypes.c_void_p)) self._this.contents.fprescreen = _fpointer(prescreen) if prescreen != 'CVHFnoscreen': fsetqcond = getattr(libcvhf, qcondname) fsetqcond(self._this, c_atm.ctypes.data_as(ctypes.c_void_p), natm, c_bas.ctypes.data_as(ctypes.c_void_p), nbas, c_env.ctypes.data_as(ctypes.c_void_p)) @property def direct_scf_tol(self): return self._this.contents.direct_scf_cutoff @direct_scf_tol.setter def direct_scf_tol(self, v): self._this.contents.direct_scf_cutoff = v def set_dm(self, dm, atm, bas, env): if self._dmcondname is not None: c_atm = numpy.asarray(atm, dtype=numpy.int32, order='C') c_bas = numpy.asarray(bas, dtype=numpy.int32, order='C') c_env = numpy.asarray(env, dtype=numpy.double, order='C') natm = ctypes.c_int(c_atm.shape[0]) nbas = ctypes.c_int(c_bas.shape[0]) if isinstance(dm, numpy.ndarray) and dm.ndim == 2: n_dm = 1 else: n_dm = len(dm) dm = numpy.asarray(dm, order='C') fsetdm = getattr(libcvhf, self._dmcondname) fsetdm(self._this, dm.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(n_dm), c_atm.ctypes.data_as(ctypes.c_void_p), natm, c_bas.ctypes.data_as(ctypes.c_void_p), nbas, c_env.ctypes.data_as(ctypes.c_void_p)) class _CVHFOpt(ctypes.Structure): _fields_ = [('nbas', ctypes.c_int), ('_padding', ctypes.c_int), ('direct_scf_cutoff', ctypes.c_double), ('q_cond', ctypes.c_void_p), ('dm_cond', ctypes.c_void_p), ('fprescreen', ctypes.c_void_p), ('r_vkscreen', ctypes.c_void_p)] ################################################ # for general DM # hermi = 0 : arbitary # hermi = 1 : hermitian # hermi = 2 : anti-hermitian ################################################ def incore(eri, dm, hermi=0): assert(not numpy.iscomplexobj(eri)) eri = numpy.ascontiguousarray(eri) dm = numpy.ascontiguousarray(dm) nao = dm.shape[0] vj = numpy.empty((nao,nao)) vk = numpy.empty((nao,nao)) npair = nao*(nao+1)//2 if eri.ndim == 2 and npair*npair == eri.size: # 4-fold symmetry eri fdrv = getattr(libcvhf, 'CVHFnrs4_incore_drv') # 'ijkl,kl->ij' fvj = _fpointer('CVHFics4_kl_s2ij') # 'ijkl,il->jk' fvk = _fpointer('CVHFics4_il_s1jk') # or ## 'ijkl,ij->kl' #fvj = _fpointer('CVHFics4_ij_s2kl') ## 'ijkl,jk->il' #fvk = _fpointer('CVHFics4_jk_s1il') tridm = dm elif eri.ndim == 1 and npair*(npair+1)//2 == eri.size: # 8-fold symmetry eri fdrv = getattr(libcvhf, 'CVHFnrs8_incore_drv') fvj = _fpointer('CVHFics8_tridm_vj') if hermi == 1: fvk = _fpointer('CVHFics8_jk_s2il') else: fvk = _fpointer('CVHFics8_jk_s1il') tridm = pyscf.lib.pack_tril(pyscf.lib.transpose_sum(dm)) i = numpy.arange(nao) tridm[i*(i+1)//2+i] *= .5 else: raise RuntimeError('Array shape not consistent: DM %s, eri %s' % (dm.shape, eri.shape)) fdrv(eri.ctypes.data_as(ctypes.c_void_p), tridm.ctypes.data_as(ctypes.c_void_p), vj.ctypes.data_as(ctypes.c_void_p), dm.ctypes.data_as(ctypes.c_void_p), vk.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(nao), fvj, fvk) if hermi != 0: vj = pyscf.lib.hermi_triu(vj, hermi) vk = pyscf.lib.hermi_triu(vk, hermi) else: vj = pyscf.lib.hermi_triu(vj, 1) return vj, vk # use cint2e_sph as cintor, CVHFnrs8_ij_s2kl, CVHFnrs8_jk_s2il as fjk to call # direct_mapdm def direct(dms, atm, bas, env, vhfopt=None, hermi=0): c_atm = numpy.asarray(atm, dtype=numpy.int32, order='C') c_bas = numpy.asarray(bas, dtype=numpy.int32, order='C') c_env = numpy.asarray(env, dtype=numpy.double, order='C') natm = ctypes.c_int(c_atm.shape[0]) nbas = ctypes.c_int(c_bas.shape[0]) if isinstance(dms, numpy.ndarray) and dms.ndim == 2: n_dm = 1 nao = dms.shape[0] dms = (numpy.asarray(dms, order='C'),) else: n_dm = len(dms) nao = dms[0].shape[0] dms = numpy.asarray(dms, order='C') if vhfopt is None: cintor = _fpointer('cint2e_sph') cintopt = make_cintopt(c_atm, c_bas, c_env, 'cint2e_sph') cvhfopt = pyscf.lib.c_null_ptr() else: vhfopt.set_dm(dms, atm, bas, env) cvhfopt = vhfopt._this cintopt = vhfopt._cintopt cintor = vhfopt._intor fdrv = getattr(libcvhf, 'CVHFnr_direct_drv') fdot = _fpointer('CVHFdot_nrs8') fvj = _fpointer('CVHFnrs8_ji_s2kl') if hermi == 1: fvk = _fpointer('CVHFnrs8_li_s2kj') else: fvk = _fpointer('CVHFnrs8_li_s1kj') vjk = numpy.empty((2,n_dm,nao,nao)) fjk = (ctypes.c_void_p*(2*n_dm))() dmsptr = (ctypes.c_void_p*(2*n_dm))() vjkptr = (ctypes.c_void_p*(2*n_dm))() for i in range(n_dm): dmsptr[i] = dms[i].ctypes.data_as(ctypes.c_void_p) vjkptr[i] = vjk[0,i].ctypes.data_as(ctypes.c_void_p) fjk[i] = fvj for i in range(n_dm): dmsptr[n_dm+i] = dms[i].ctypes.data_as(ctypes.c_void_p) vjkptr[n_dm+i] = vjk[1,i].ctypes.data_as(ctypes.c_void_p) fjk[n_dm+i] = fvk shls_slice = (ctypes.c_int*8)(*([0, c_bas.shape[0]]*4)) ao_loc = numpy.asarray(make_ao_loc(bas), dtype=numpy.int32) fdrv(cintor, fdot, fjk, dmsptr, vjkptr, ctypes.c_int(n_dm*2), ctypes.c_int(1), shls_slice, ao_loc.ctypes.data_as(ctypes.c_void_p), cintopt, cvhfopt, c_atm.ctypes.data_as(ctypes.c_void_p), natm, c_bas.ctypes.data_as(ctypes.c_void_p), nbas, c_env.ctypes.data_as(ctypes.c_void_p)) # vj must be symmetric for idm in range(n_dm): vjk[0,idm] = pyscf.lib.hermi_triu(vjk[0,idm], 1) if hermi != 0: # vk depends for idm in range(n_dm): vjk[1,idm] = pyscf.lib.hermi_triu(vjk[1,idm], hermi) if n_dm == 1: vjk = vjk.reshape(2,nao,nao) return vjk # call all fjk for each dm, the return array has len(dms)*len(jkdescript)*ncomp components # jkdescript: 'ij->s1kl', 'kl->s2ij', ... def direct_mapdm(intor, aosym, jkdescript, dms, ncomp, atm, bas, env, vhfopt=None, shls_slice=None): assert(aosym in ('s8', 's4', 's2ij', 's2kl', 's1', 'a4ij', 'a4kl', 'a2ij', 'a2kl')) c_atm = numpy.asarray(atm, dtype=numpy.int32, order='C') c_bas = numpy.asarray(bas, dtype=numpy.int32, order='C') c_env = numpy.asarray(env, dtype=numpy.double, order='C') natm = ctypes.c_int(c_atm.shape[0]) nbas = ctypes.c_int(c_bas.shape[0]) if isinstance(dms, numpy.ndarray) and dms.ndim == 2: n_dm = 1 nao = dms.shape[0] dms = (numpy.asarray(dms, order='C'),) else: n_dm = len(dms) nao = dms[0].shape[0] dms = [numpy.asarray(dm, order='C') for dm in dms] if isinstance(jkdescript, str): njk = 1 jkdescript = (jkdescript,) else: njk = len(jkdescript) if vhfopt is None: cintor = _fpointer(intor) cintopt = make_cintopt(c_atm, c_bas, c_env, intor) cvhfopt = pyscf.lib.c_null_ptr() else: vhfopt.set_dm(dms, atm, bas, env) cvhfopt = vhfopt._this cintopt = vhfopt._cintopt cintor = vhfopt._intor fdrv = getattr(libcvhf, 'CVHFnr_direct_drv') dotsym = _INTSYMAP[aosym] fdot = _fpointer('CVHFdot_nr'+dotsym) if shls_slice is None: shls_slice = (0, c_bas.shape[0])*4 ao_loc = numpy.asarray(make_ao_loc(bas), dtype=numpy.int32) vjk = [] descr_sym = [x.split('->') for x in jkdescript] fjk = (ctypes.c_void_p*(njk*n_dm))() dmsptr = (ctypes.c_void_p*(njk*n_dm))() vjkptr = (ctypes.c_void_p*(njk*n_dm))() for i, (dmsym, vsym) in enumerate(descr_sym): if dmsym in ('ij', 'kl', 'il', 'kj'): sys.stderr.write('not support DM description %s, transpose to %s\n' % (dmsym, dmsym[::-1])) dmsym = dmsym[::-1] f1 = _fpointer('CVHFnr%s_%s_%s'%(aosym, dmsym, vsym)) vshape = (n_dm,ncomp) + get_dims(vsym[-2:], shls_slice, ao_loc) vjk.append(numpy.empty(vshape)) for j in range(n_dm): assert(dms[j].shape == get_dims(dmsym, shls_slice, ao_loc)) dmsptr[i*n_dm+j] = dms[j].ctypes.data_as(ctypes.c_void_p) vjkptr[i*n_dm+j] = vjk[i][j].ctypes.data_as(ctypes.c_void_p) fjk[i*n_dm+j] = f1 shls_slice = (ctypes.c_int*8)(*shls_slice) fdrv(cintor, fdot, fjk, dmsptr, vjkptr, ctypes.c_int(njk*n_dm), ctypes.c_int(ncomp), shls_slice, ao_loc.ctypes.data_as(ctypes.c_void_p), cintopt, cvhfopt, c_atm.ctypes.data_as(ctypes.c_void_p), natm, c_bas.ctypes.data_as(ctypes.c_void_p), nbas, c_env.ctypes.data_as(ctypes.c_void_p)) if n_dm * ncomp == 1: vjk = [v.reshape(v.shape[2:]) for v in vjk] elif n_dm == 1: vjk = [v.reshape((ncomp,)+v.shape[2:]) for v in vjk] elif ncomp == 1: vjk = [v.reshape((n_dm,)+v.shape[2:]) for v in vjk] if njk == 1: vjk = vjk[0] return vjk # for density matrices in dms, bind each dm to a jk operator # jkdescript: 'ij->s1kl', 'kl->s2ij', ... def direct_bindm(intor, aosym, jkdescript, dms, ncomp, atm, bas, env, vhfopt=None, shls_slice=None): assert(aosym in ('s8', 's4', 's2ij', 's2kl', 's1', 'a4ij', 'a4kl', 'a2ij', 'a2kl')) c_atm = numpy.asarray(atm, dtype=numpy.int32, order='C') c_bas = numpy.asarray(bas, dtype=numpy.int32, order='C') c_env = numpy.asarray(env, dtype=numpy.double, order='C') natm = ctypes.c_int(c_atm.shape[0]) nbas = ctypes.c_int(c_bas.shape[0]) if isinstance(dms, numpy.ndarray) and dms.ndim == 2: n_dm = 1 nao = dms.shape[0] dms = (numpy.asarray(dms, order='C'),) else: n_dm = len(dms) nao = dms[0].shape[0] dms = [numpy.asarray(dm, order='C') for dm in dms] if isinstance(jkdescript, str): njk = 1 jkdescript = (jkdescript,) else: njk = len(jkdescript) assert(njk == n_dm) if vhfopt is None: cintor = _fpointer(intor) cintopt = make_cintopt(c_atm, c_bas, c_env, intor) cvhfopt = pyscf.lib.c_null_ptr() else: vhfopt.set_dm(dms, atm, bas, env) cvhfopt = vhfopt._this cintopt = vhfopt._cintopt cintor = vhfopt._intor fdrv = getattr(libcvhf, 'CVHFnr_direct_drv') dotsym = _INTSYMAP[aosym] fdot = _fpointer('CVHFdot_nr'+dotsym) if shls_slice is None: shls_slice = (0, c_bas.shape[0])*4 ao_loc = numpy.asarray(make_ao_loc(bas), dtype=numpy.int32) vjk = [] descr_sym = [x.split('->') for x in jkdescript] fjk = (ctypes.c_void_p*(n_dm))() dmsptr = (ctypes.c_void_p*(n_dm))() vjkptr = (ctypes.c_void_p*(n_dm))() for i, (dmsym, vsym) in enumerate(descr_sym): if dmsym in ('ij', 'kl', 'il', 'kj'): sys.stderr.write('not support DM description %s, transpose to %s\n' % (dmsym, dmsym[::-1])) dmsym = dmsym[::-1] f1 = _fpointer('CVHFnr%s_%s_%s'%(aosym, dmsym, vsym)) assert(dms[i].shape == get_dims(dmsym, shls_slice, ao_loc)) vshape = (ncomp,) + get_dims(vsym[-2:], shls_slice, ao_loc) vjk.append(numpy.empty(vshape)) dmsptr[i] = dms[i].ctypes.data_as(ctypes.c_void_p) vjkptr[i] = vjk[i].ctypes.data_as(ctypes.c_void_p) fjk[i] = f1 shls_slice = (ctypes.c_int*8)(*shls_slice) fdrv(cintor, fdot, fjk, dmsptr, vjkptr, ctypes.c_int(n_dm), ctypes.c_int(ncomp), shls_slice, ao_loc.ctypes.data_as(ctypes.c_void_p), cintopt, cvhfopt, c_atm.ctypes.data_as(ctypes.c_void_p), natm, c_bas.ctypes.data_as(ctypes.c_void_p), nbas, c_env.ctypes.data_as(ctypes.c_void_p)) if ncomp == 1: vjk = [v.reshape(v.shape[1:]) for v in vjk] if njk == 1: vjk = vjk[0] return vjk # 8-fold permutation symmetry def int2e_sph(atm, bas, env): c_atm = numpy.asarray(atm, dtype=numpy.int32, order='C') c_bas = numpy.asarray(bas, dtype=numpy.int32, order='C') c_env = numpy.asarray(env, dtype=numpy.double, order='C') natm = ctypes.c_int(c_atm.shape[0]) nbas = ctypes.c_int(c_bas.shape[0]) libcvhf.CINTtot_cgto_spheric.restype = ctypes.c_int nao = libcvhf.CINTtot_cgto_spheric(c_bas.ctypes.data_as(ctypes.c_void_p), nbas) nao_pair = nao*(nao+1)//2 eri = numpy.empty((nao_pair*(nao_pair+1)//2)) libcvhf.int2e_sph(eri.ctypes.data_as(ctypes.c_void_p), c_atm.ctypes.data_as(ctypes.c_void_p), natm, c_bas.ctypes.data_as(ctypes.c_void_p), nbas, c_env.ctypes.data_as(ctypes.c_void_p)) return eri ################################################################ # relativistic def rdirect_mapdm(intor, aosym, jkdescript, dms, ncomp, atm, bas, env, vhfopt=None): assert(aosym in ('s8', 's4', 's2ij', 's2kl', 's1', 'a4ij', 'a4kl', 'a2ij', 'a2kl')) c_atm = numpy.asarray(atm, dtype=numpy.int32, order='C') c_bas = numpy.asarray(bas, dtype=numpy.int32, order='C') c_env = numpy.asarray(env, dtype=numpy.double, order='C') natm = ctypes.c_int(c_atm.shape[0]) nbas = ctypes.c_int(c_bas.shape[0]) if isinstance(dms, numpy.ndarray) and dms.ndim == 2: n_dm = 1 nao = dms.shape[0] dms = (numpy.asarray(dms, order='C', dtype=numpy.complex128),) else: n_dm = len(dms) nao = dms[0].shape[0] dms = numpy.asarray(dms, order='C', dtype=numpy.complex128) if isinstance(jkdescript, str): njk = 1 jkdescript = (jkdescript,) else: njk = len(jkdescript) if vhfopt is None: cintor = _fpointer(intor) cintopt = make_cintopt(c_atm, c_bas, c_env, intor) cvhfopt = pyscf.lib.c_null_ptr() else: vhfopt.set_dm(dms, atm, bas, env) cvhfopt = vhfopt._this cintopt = vhfopt._cintopt cintor = vhfopt._intor fdrv = getattr(libcvhf, 'CVHFr_direct_drv') dotsym = _INTSYMAP[aosym] fdot = _fpointer('CVHFdot_r'+dotsym) unpackas = _INTUNPACKMAP_R[aosym] descr_sym = [x.split('->') for x in jkdescript] fjk = (ctypes.c_void_p*(njk*n_dm))() dm1 = (ctypes.c_void_p*(njk*n_dm))() for i, (dmsym, vsym) in enumerate(descr_sym): f1 = _fpointer('CVHFr%s_%s_%s'%(unpackas, dmsym, vsym)) for j in range(n_dm): dm1[i*n_dm+j] = dms[j].ctypes.data_as(ctypes.c_void_p) fjk[i*n_dm+j] = f1 vjk = numpy.empty((njk,n_dm*ncomp,nao,nao), dtype=numpy.complex) fdrv(cintor, fdot, fjk, dm1, vjk.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(njk*n_dm), ctypes.c_int(ncomp), cintopt, cvhfopt, c_atm.ctypes.data_as(ctypes.c_void_p), natm, c_bas.ctypes.data_as(ctypes.c_void_p), nbas, c_env.ctypes.data_as(ctypes.c_void_p)) if n_dm * ncomp == 1: vjk = vjk.reshape(njk,nao,nao) if njk == 1: vjk = vjk.reshape(vjk.shape[1:]) return vjk # for density matrices in dms, bind each dm to a jk operator def rdirect_bindm(intor, aosym, jkdescript, dms, ncomp, atm, bas, env, vhfopt=None): assert(aosym in ('s8', 's4', 's2ij', 's2kl', 's1', 'a4ij', 'a4kl', 'a2ij', 'a2kl')) c_atm = numpy.asarray(atm, dtype=numpy.int32, order='C') c_bas = numpy.asarray(bas, dtype=numpy.int32, order='C') c_env = numpy.asarray(env, dtype=numpy.double, order='C') natm = ctypes.c_int(c_atm.shape[0]) nbas = ctypes.c_int(c_bas.shape[0]) if isinstance(dms, numpy.ndarray) and dms.ndim == 2: n_dm = 1 nao = dms.shape[0] dms = (numpy.asarray(dms, order='C', dtype=numpy.complex128),) else: n_dm = len(dms) nao = dms[0].shape[0] dms = numpy.asarray(dms, order='C', dtype=numpy.complex128) if isinstance(jkdescript, str): njk = 1 jkdescript = (jkdescript,) else: njk = len(jkdescript) assert(njk == n_dm) if vhfopt is None: cintor = _fpointer(intor) cintopt = make_cintopt(c_atm, c_bas, c_env, intor) cvhfopt = pyscf.lib.c_null_ptr() else: vhfopt.set_dm(dms, atm, bas, env) cvhfopt = vhfopt._this cintopt = vhfopt._cintopt cintor = vhfopt._intor fdrv = getattr(libcvhf, 'CVHFr_direct_drv') dotsym = _INTSYMAP[aosym] fdot = _fpointer('CVHFdot_r'+dotsym) unpackas = _INTUNPACKMAP_R[aosym] descr_sym = [x.split('->') for x in jkdescript] fjk = (ctypes.c_void_p*(n_dm))() dm1 = (ctypes.c_void_p*(n_dm))() for i, (dmsym, vsym) in enumerate(descr_sym): f1 = _fpointer('CVHFr%s_%s_%s'%(unpackas, dmsym, vsym)) dm1[i] = dms[i].ctypes.data_as(ctypes.c_void_p) fjk[i] = f1 vjk = numpy.empty((njk,ncomp,nao,nao), dtype=numpy.complex) fdrv(cintor, fdot, fjk, dm1, vjk.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(n_dm), ctypes.c_int(ncomp), cintopt, cvhfopt, c_atm.ctypes.data_as(ctypes.c_void_p), natm, c_bas.ctypes.data_as(ctypes.c_void_p), nbas, c_env.ctypes.data_as(ctypes.c_void_p)) if ncomp == 1: vjk = vjk.reshape(njk,nao,nao) if njk == 1: vjk = vjk.reshape(vjk.shape[1:]) return vjk # 'a4ij': anti-symm between ij, symm between kl # 'a4kl': anti-symm between kl, symm between ij # 'a2ij': anti-symm between ij, # 'a2kl': anti-symm between kl, _INTSYMAP= { 's8' : 's8' , 's4' : 's4' , 's2ij': 's2ij', 's2kl': 's2kl', 's1' : 's1' , 'a4ij': 's4' , 'a4kl': 's4' , 'a2ij': 's2ij', 'a2kl': 's2kl', } _INTUNPACKMAP_R = { 's8' : 's8' , 's4' : 's4' , 's2ij': 's2ij', 's2kl': 's2kl', 's1' : 's1' , 'a4ij': 'ah4' , 'a4kl': 'ha4' , 'a2ij': 'ah2ij', 'a2kl': 'ha2kl', } def make_ao_loc(bas, cart=False): l = bas[:,gto.ANG_OF] if cart: dims = (l+1)*(l+2)//2 * bas[:,gto.NCTR_OF] else: dims = (l*2+1) * bas[:,gto.NCTR_OF] ao_loc = numpy.empty(len(bas)+1, dtype=numpy.int32) ao_loc[0] = 0 dims.cumsum(dtype=numpy.int32, out=ao_loc[1:]) return ao_loc _SHLINDEX = {'i': 0, 'j': 2, 'k': 4, 'l': 6} def get_dims(descr_sym, shls_slice, ao_loc): i = _SHLINDEX[descr_sym[0]] j = _SHLINDEX[descr_sym[1]] di = ao_loc[shls_slice[i+1]] - ao_loc[shls_slice[i]] dj = ao_loc[shls_slice[j+1]] - ao_loc[shls_slice[j]] return (di,dj)

f73a2ae3c540a9a90052d279a3881c4aaf86097f

py

Python

kopf/engines/probing.py

ankitdobhal/kopf

2765eda2a08e7e42195446cc23f02ba91603db53

kopf/engines/probing.py

ankitdobhal/kopf

2765eda2a08e7e42195446cc23f02ba91603db53

kopf/engines/probing.py

ankitdobhal/kopf

2765eda2a08e7e42195446cc23f02ba91603db53

import asyncio import datetime import logging import urllib.parse from typing import MutableMapping, Optional, Tuple import aiohttp.web from kopf.reactor import activities, lifecycles, registries from kopf.structs import callbacks, configuration, handlers, memos logger = logging.getLogger(__name__) LOCALHOST: str = 'localhost' HTTP_PORT: int = 80 _Key = Tuple[str, int] # hostname, port async def health_reporter( endpoint: str, *, memo: memos.AnyMemo, registry: registries.OperatorRegistry, settings: configuration.OperatorSettings, ready_flag: Optional[asyncio.Event] = None, # used for testing ) -> None: """ Simple HTTP(S)/TCP server to report the operator's health to K8s probes. Runs forever until cancelled (which happens if any other root task is cancelled or failed). Once it will stop responding for any reason, Kubernetes will assume the pod is not alive anymore, and will restart it. """ probing_container: MutableMapping[handlers.HandlerId, callbacks.Result] = {} probing_timestamp: Optional[datetime.datetime] = None probing_max_age = datetime.timedelta(seconds=10.0) probing_lock = asyncio.Lock() async def get_health( request: aiohttp.web.Request, ) -> aiohttp.web.Response: nonlocal probing_timestamp # Recollect the data on-demand, and only if is is older that a reasonable caching period. # Protect against multiple parallel requests performing the same heavy activity. now = datetime.datetime.utcnow() if probing_timestamp is None or now - probing_timestamp >= probing_max_age: async with probing_lock: now = datetime.datetime.utcnow() if probing_timestamp is None or now - probing_timestamp >= probing_max_age: activity_results = await activities.run_activity( lifecycle=lifecycles.all_at_once, registry=registry, settings=settings, activity=handlers.Activity.PROBE, memo=memo, ) probing_container.clear() probing_container.update(activity_results) probing_timestamp = datetime.datetime.utcnow() return aiohttp.web.json_response(probing_container) parts = urllib.parse.urlsplit(endpoint) if parts.scheme == 'http': host = parts.hostname or LOCALHOST port = parts.port or HTTP_PORT path = parts.path else: raise Exception(f"Unsupported scheme: {endpoint}") app = aiohttp.web.Application() app.add_routes([aiohttp.web.get(path, get_health)]) runner = aiohttp.web.AppRunner(app, handle_signals=False) await runner.setup() site = aiohttp.web.TCPSite(runner, host, port, shutdown_timeout=1.0) await site.start() # Log with the actual URL: normalised, with hostname/port set. url = urllib.parse.urlunsplit([parts.scheme, f'{host}:{port}', path, '', '']) logger.debug("Serving health status at %s", url) if ready_flag is not None: ready_flag.set() try: # Sleep forever. No activity is needed. await asyncio.Event().wait() finally: # On any reason of exit, stop reporting the health. await asyncio.shield(runner.cleanup())

f73a36ef236a0195fe5a8771954b392d3e16858c

py

Python

tensor2tensor/models/video/savp.py

shankharaj29/tensor2tensor

b89ba51a6fa9e0c20009cfb57ee8de04f7138392

2020-03-02T13:49:11.000Z

2020-06-18T09:48:35.000Z

tensor2tensor/models/video/savp.py

PedroLelis/tensor2tensor

5a867d031bd493eeb7d2776e1118d1594ff0a623

2019-01-21T10:57:47.000Z

2019-01-21T10:57:47.000Z

tensor2tensor/models/video/savp.py

PedroLelis/tensor2tensor

5a867d031bd493eeb7d2776e1118d1594ff0a623

2019-02-10T11:12:30.000Z

2022-02-23T20:43:48.000Z

# coding=utf-8 # Copyright 2018 The Tensor2Tensor 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. """Stochastic Adversarial Video Prediction model. Reference: https://arxiv.org/abs/1804.01523 """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numbers import numpy as np from tensor2tensor.layers import common_layers from tensor2tensor.layers import common_video from tensor2tensor.models.video import savp_params # pylint: disable=unused-import from tensor2tensor.models.video import sv2p from tensor2tensor.utils import registry from tensor2tensor.utils import update_ops_hook import tensorflow as tf gan_losses = tf.contrib.gan.losses.wargs class NextFrameSavpBase(object): """Main function for Stochastic Adversarial Video Prediction.""" def encoder(self, inputs, n_layers=3): """Convnet that encodes inputs into mean and std of a gaussian. Args: inputs: 5-D Tensor, shape (batch_size, num_frames, width, height, channels) n_layers: Number of layers. Returns: z_mu: Mean of the latent gaussians. z_log_var: log(var) of the latent gaussians. Raises: ValueError: If inputs is not a 5-D tensor or not float32. """ latent_dims = self.hparams.z_dim shape_as_list = inputs.shape.as_list() if len(shape_as_list) != 5: raise ValueError("Expected inputs to be a 5-D, got %d" % len(shape_as_list)) if inputs.dtype != tf.float32: raise ValueError("Expected dtype tf.float32, got %s" % inputs.dtype) # Flatten (N,T,W,H,C) into (NT,W,H,C) batch_size, _ = shape_as_list[:2] inputs = tf.reshape(inputs, [-1] + list(inputs.shape)[2:]) n_filters = 64 rectified = None # Applies 3 layer conv-net with padding, instance normalization # and leaky relu as per the encoder in # https://github.com/alexlee-gk/video_prediction padding = [[0, 0], [1, 1], [1, 1], [0, 0]] for i in range(n_layers): with tf.variable_scope("layer_%d" % (i + 1)): n_filters *= 2**i if i: padded = tf.pad(rectified, padding) else: padded = tf.pad(inputs, padding) convolved = tf.layers.conv2d(padded, filters=n_filters, kernel_size=4, strides=2, padding="VALID") normalized = tf.contrib.layers.instance_norm(convolved) rectified = tf.nn.leaky_relu(normalized, alpha=0.2) # Mean pooling across all spatial dimensions. pooled = tf.nn.avg_pool( rectified, [1] + rectified.shape[1:3].as_list() + [1], strides=[1, 1, 1, 1], padding="VALID") squeezed = tf.squeeze(pooled, [1, 2]) # Down-project and output the mean and log of the standard deviation of # the latents. with tf.variable_scope("z_mu"): z_mu = tf.layers.dense(squeezed, latent_dims) with tf.variable_scope("z_log_sigma_sq"): z_log_var = tf.layers.dense(squeezed, latent_dims) z_log_var = tf.clip_by_value(z_log_var, -10, 10) # Reshape to (batch_size X num_frames X latent_dims) z_mu = tf.reshape(z_mu, (batch_size, -1, latent_dims)) z_log_var = tf.reshape( z_log_var, (batch_size, -1, latent_dims)) return z_mu, z_log_var def expected_output_shape(self, input_shape, stride, padding, kernel_size): return (input_shape + 2*padding - kernel_size) // stride + 1 def get_fc_dimensions(self, strides, kernel_sizes): """Get expected fully connected shape after a series of convolutions.""" output_height, output_width, _ = self.hparams.problem.frame_shape output_steps = self.hparams.video_num_target_frames output_shape = np.array([output_steps, output_height, output_width]) for curr_stride, kernel_size in zip(strides, kernel_sizes): output_shape = self.expected_output_shape( output_shape, np.array(curr_stride), 1, kernel_size) return np.prod(output_shape) * self.hparams.num_discriminator_filters * 8 def discriminator(self, frames): """3-D SNGAN discriminator. Args: frames: a list of batch-major tensors indexed by time. Returns: logits: 1-D Tensor with shape=batch_size. Positive logits imply that the discriminator thinks that it belongs to the true class. """ ndf = self.hparams.num_discriminator_filters frames = tf.stack(frames) # Switch from time-major axis to batch-major axis. frames = common_video.swap_time_and_batch_axes(frames) # 3-D Conv-net mapping inputs to activations. num_outputs = [ndf, ndf*2, ndf*2, ndf*4, ndf*4, ndf*8, ndf*8] kernel_sizes = [3, 4, 3, 4, 3, 4, 3] strides = [[1, 1, 1], [1, 2, 2], [1, 1, 1], [1, 2, 2], [1, 1, 1], [2, 2, 2], [1, 1, 1]] names = ["video_sn_conv0_0", "video_sn_conv0_1", "video_sn_conv1_0", "video_sn_conv1_1", "video_sn_conv2_0", "video_sn_conv2_1", "video_sn_conv3_0"] iterable = zip(num_outputs, kernel_sizes, strides, names) activations = frames for num_filters, kernel_size, stride, name in iterable: activations = self.pad_conv3d_lrelu(activations, num_filters, kernel_size, stride, name) num_fc_dimensions = self.get_fc_dimensions(strides, kernel_sizes) activations = tf.reshape(activations, (-1, num_fc_dimensions)) return tf.squeeze(tf.layers.dense(activations, 1)) def d_step(self, true_frames, gen_frames): """Performs the discriminator step in computing the GAN loss. Applies stop-gradient to the generated frames while computing the discriminator loss to make sure that the gradients are not back-propagated to the generator. This makes sure that only the discriminator is updated. Args: true_frames: True outputs gen_frames: Generated frames. Returns: d_loss: Loss component due to the discriminator. """ hparam_to_disc_loss = { "least_squares": gan_losses.least_squares_discriminator_loss, "cross_entropy": gan_losses.modified_discriminator_loss, "wasserstein": gan_losses.wasserstein_discriminator_loss} # Concat across batch-axis. _, batch_size, _, _, _ = common_layers.shape_list(true_frames) all_frames = tf.concat( [true_frames, tf.stop_gradient(gen_frames)], axis=1) all_logits = self.discriminator(all_frames) true_logits, fake_logits_stop = \ all_logits[:batch_size], all_logits[batch_size:] mean_true_logits = tf.reduce_mean(true_logits) tf.summary.scalar("mean_true_logits", mean_true_logits) mean_fake_logits_stop = tf.reduce_mean(fake_logits_stop) tf.summary.scalar("mean_fake_logits_stop", mean_fake_logits_stop) discriminator_loss_func = hparam_to_disc_loss[self.hparams.gan_loss] gan_d_loss = discriminator_loss_func( discriminator_real_outputs=true_logits, discriminator_gen_outputs=fake_logits_stop, add_summaries=True) return gan_d_loss, true_logits, fake_logits_stop def g_step(self, gen_frames, fake_logits_stop): """Performs the generator step in computing the GAN loss. Args: gen_frames: Generated frames fake_logits_stop: Logits corresponding to the generated frames as per the discriminator. Assumed to have a stop-gradient term. Returns: gan_g_loss_pos_d: Loss. gan_g_loss_neg_d: -gan_g_loss_pos_d but with a stop gradient on generator. """ hparam_to_gen_loss = { "least_squares": gan_losses.least_squares_generator_loss, "cross_entropy": gan_losses.modified_generator_loss, "wasserstein": gan_losses.wasserstein_generator_loss } fake_logits = self.discriminator(gen_frames) mean_fake_logits = tf.reduce_mean(fake_logits) tf.summary.scalar("mean_fake_logits", mean_fake_logits) # Generator loss. # Using gan_g_loss_pos_d updates the discriminator as well. # To avoid this add gan_g_loss_neg_d = -gan_g_loss_pos_d # but with stop gradient on the generator. # This makes sure that the net gradient on the discriminator is zero and # net-gradient on the generator is just due to the gan_g_loss_pos_d. generator_loss_func = hparam_to_gen_loss[self.hparams.gan_loss] gan_g_loss_pos_d = generator_loss_func( discriminator_gen_outputs=fake_logits, add_summaries=True) gan_g_loss_neg_d = -generator_loss_func( discriminator_gen_outputs=fake_logits_stop, add_summaries=True) return gan_g_loss_pos_d, gan_g_loss_neg_d def get_gan_loss(self, true_frames, gen_frames, name): """Get the discriminator + generator loss at every step. This performs an 1:1 update of the discriminator and generator at every step. Args: true_frames: 5-D Tensor of shape (num_steps, batch_size, H, W, C) Assumed to be ground truth. gen_frames: 5-D Tensor of shape (num_steps, batch_size, H, W, C) Assumed to be fake. name: discriminator scope. Returns: loss: 0-D Tensor, with d_loss + g_loss """ # D - STEP with tf.variable_scope("%s_discriminator" % name, reuse=tf.AUTO_REUSE): gan_d_loss, _, fake_logits_stop = self.d_step( true_frames, gen_frames) # G - STEP with tf.variable_scope("%s_discriminator" % name, reuse=True): gan_g_loss_pos_d, gan_g_loss_neg_d = self.g_step( gen_frames, fake_logits_stop) gan_g_loss = gan_g_loss_pos_d + gan_g_loss_neg_d tf.summary.scalar("gan_loss_%s" % name, gan_g_loss_pos_d + gan_d_loss) if self.hparams.gan_optimization == "joint": gan_loss = gan_g_loss + gan_d_loss else: curr_step = self.get_iteration_num() gan_loss = tf.cond( tf.logical_not(curr_step % 2 == 0), lambda: gan_g_loss, lambda: gan_d_loss) return gan_loss def get_extra_loss(self, latent_means=None, latent_stds=None, true_frames=None, gen_frames=None): """Gets extra loss from VAE and GAN.""" if not self.is_training: return 0.0 vae_loss, d_vae_loss, d_gan_loss = 0.0, 0.0, 0.0 # Use sv2p's KL divergence computation. if self.hparams.use_vae: vae_loss = super(NextFrameSavpBase, self).get_extra_loss( latent_means=latent_means, latent_stds=latent_stds) if self.hparams.use_gan: # Strip out the first context_frames for the true_frames # Strip out the first context_frames - 1 for the gen_frames context_frames = self.hparams.video_num_input_frames true_frames = tf.stack( tf.unstack(true_frames, axis=0)[context_frames:]) # discriminator for VAE. if self.hparams.use_vae: gen_enc_frames = tf.stack( tf.unstack(gen_frames, axis=0)[context_frames-1:]) d_vae_loss = self.get_gan_loss(true_frames, gen_enc_frames, name="vae") # discriminator for GAN. gen_prior_frames = tf.stack( tf.unstack(self.gen_prior_video, axis=0)[context_frames-1:]) d_gan_loss = self.get_gan_loss(true_frames, gen_prior_frames, name="gan") return ( vae_loss + self.hparams.gan_loss_multiplier * d_gan_loss + self.hparams.gan_vae_loss_multiplier * d_vae_loss) def pad_conv3d_lrelu(self, activations, n_filters, kernel_size, strides, scope): """Pad, apply 3-D convolution and leaky relu.""" padding = [[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]] # tf.nn.conv3d accepts a list of 5 values for strides # with first and last value equal to 1 if isinstance(strides, numbers.Integral): strides = [strides] * 3 strides = [1] + strides + [1] # Filter_shape = [K, K, K, num_input, num_output] filter_shape = ( [kernel_size]*3 + activations.shape[-1:].as_list() + [n_filters]) with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): conv_filter = tf.get_variable( "conv_filter", shape=filter_shape, initializer=tf.truncated_normal_initializer(stddev=0.02)) if self.hparams.use_spectral_norm: conv_filter, assign_op = common_layers.apply_spectral_norm(conv_filter) if self.is_training: tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, assign_op) padded = tf.pad(activations, padding) convolved = tf.nn.conv3d( padded, conv_filter, strides=strides, padding="VALID") rectified = tf.nn.leaky_relu(convolved, alpha=0.2) return rectified @staticmethod def train_hooks(hook_context): del hook_context return [update_ops_hook.UpdateOpsHook()] @registry.register_model class NextFrameSAVP(NextFrameSavpBase, sv2p.NextFrameSv2pLegacy): """Stochastic Adversarial Video Prediction.""" def construct_model(self, images, actions, rewards): """Model that takes in images and returns predictions. Args: images: list of 4-D Tensors indexed by time. (batch_size, width, height, channels) actions: list of action tensors each action should be in the shape ?x1xZ rewards: list of reward tensors each reward should be in the shape ?x1xZ Returns: video: list of 4-D predicted frames. all_rewards: predicted rewards. latent_means: list of gaussian means conditioned on the input at every frame. latent_stds: list of gaussian stds conditioned on the input at every frame. Raises: ValueError: If not exactly one of self.hparams.vae or self.hparams.gan is set to True. """ if not self.hparams.use_vae and not self.hparams.use_gan: raise ValueError("Set at least one of use_vae or use_gan to be True") if self.hparams.gan_optimization not in ["joint", "sequential"]: raise ValueError("self.hparams.gan_optimization should be either joint " "or sequential got %s" % self.hparams.gan_optimization) images = tf.unstack(images, axis=0) actions = tf.unstack(actions, axis=0) rewards = tf.unstack(rewards, axis=0) latent_dims = self.hparams.z_dim context_frames = self.hparams.video_num_input_frames seq_len = len(images) input_shape = common_layers.shape_list(images[0]) batch_size = input_shape[0] # Model does not support reward-conditioned frame generation. fake_rewards = rewards[:-1] # Concatenate x_{t-1} and x_{t} along depth and encode it to # produce the mean and standard deviation of z_{t-1} image_pairs = tf.concat([images[:seq_len - 1], images[1:seq_len]], axis=-1) z_mu, z_log_sigma_sq = self.encoder(image_pairs) # Unstack z_mu and z_log_sigma_sq along the time dimension. z_mu = tf.unstack(z_mu, axis=0) z_log_sigma_sq = tf.unstack(z_log_sigma_sq, axis=0) iterable = zip(images[:-1], actions[:-1], fake_rewards, z_mu, z_log_sigma_sq) # Initialize LSTM State lstm_state = [None] * 7 gen_cond_video, gen_prior_video, all_rewards, latent_means, latent_stds = \ [], [], [], [], [] pred_image = tf.zeros_like(images[0]) prior_latent_state, cond_latent_state = None, None train_mode = self.hparams.mode == tf.estimator.ModeKeys.TRAIN # Create scheduled sampling function ss_func = self.get_scheduled_sample_func(batch_size) with tf.variable_scope("prediction", reuse=tf.AUTO_REUSE): for step, (image, action, reward, mu, log_sigma_sq) in enumerate(iterable): # pylint:disable=line-too-long # Sample latents using a gaussian centered at conditional mu and std. latent = common_video.get_gaussian_tensor(mu, log_sigma_sq) # Sample prior latents from isotropic normal distribution. prior_latent = tf.random_normal(tf.shape(latent), dtype=tf.float32) # LSTM that encodes correlations between conditional latents. # Pg 22 in https://arxiv.org/pdf/1804.01523.pdf enc_cond_latent, cond_latent_state = common_video.basic_lstm( latent, cond_latent_state, latent_dims, name="cond_latent") # LSTM that encodes correlations between prior latents. enc_prior_latent, prior_latent_state = common_video.basic_lstm( prior_latent, prior_latent_state, latent_dims, name="prior_latent") # Scheduled Sampling done_warm_start = step > context_frames - 1 groundtruth_items = [image] generated_items = [pred_image] input_image, = self.get_scheduled_sample_inputs( done_warm_start, groundtruth_items, generated_items, ss_func) all_latents = tf.concat([enc_cond_latent, enc_prior_latent], axis=0) all_image = tf.concat([input_image, input_image], axis=0) all_action = tf.concat([action, action], axis=0) all_rewards = tf.concat([reward, reward], axis=0) all_pred_images, lstm_state, _ = self.construct_predictive_tower( all_image, all_rewards, all_action, lstm_state, all_latents, concat_latent=True) cond_pred_images, prior_pred_images = \ all_pred_images[:batch_size], all_pred_images[batch_size:] if train_mode and self.hparams.use_vae: pred_image = cond_pred_images else: pred_image = prior_pred_images gen_cond_video.append(cond_pred_images) gen_prior_video.append(prior_pred_images) latent_means.append(mu) latent_stds.append(log_sigma_sq) gen_cond_video = tf.stack(gen_cond_video, axis=0) self.gen_prior_video = tf.stack(gen_prior_video, axis=0) fake_rewards = tf.stack(fake_rewards, axis=0) if train_mode and self.hparams.use_vae: return gen_cond_video, fake_rewards, latent_means, latent_stds else: return self.gen_prior_video, fake_rewards, latent_means, latent_stds @registry.register_model class NextFrameSavpRl(NextFrameSavpBase, sv2p.NextFrameSv2p): """Stochastic Adversarial Video Prediction for RL pipeline.""" def video_features( self, all_frames, all_actions, all_rewards, all_raw_frames): """No video wide feature.""" del all_actions, all_rewards, all_raw_frames # Concatenate x_{t-1} and x_{t} along depth and encode it to # produce the mean and standard deviation of z_{t-1} seq_len = len(all_frames) image_pairs = tf.concat([all_frames[:seq_len-1], all_frames[1:seq_len]], axis=-1) z_mu, z_log_sigma_sq = self.encoder(image_pairs) # Unstack z_mu and z_log_sigma_sq along the time dimension. z_mu = tf.unstack(z_mu, axis=0) z_log_sigma_sq = tf.unstack(z_log_sigma_sq, axis=0) return [z_mu, z_log_sigma_sq] def video_extra_loss(self, frames_predicted, frames_target, internal_states, video_features): if not self.is_training: return 0.0 latent_means, latent_stds = video_features true_frames, gen_frames = frames_target, frames_predicted loss = super(NextFrameSavpRl, self).get_extra_loss( latent_means=latent_means, latent_stds=latent_stds, true_frames=true_frames, gen_frames=gen_frames) return loss def next_frame(self, frames, actions, rewards, target_frame, internal_states, video_features): del target_frame if not self.hparams.use_vae or self.hparams.use_gan: raise NotImplementedError("Only supporting VAE for now.") if self.has_pred_actions or self.has_values: raise NotImplementedError("Parameter sharing with policy not supported.") image, action, reward = frames[0], actions[0], rewards[0] latent_dims = self.hparams.z_dim batch_size = common_layers.shape_list(image)[0] if internal_states is None: # Initialize LSTM State frame_index = 0 lstm_state = [None] * 7 cond_latent_state, prior_latent_state = None, None gen_prior_video = [] else: (frame_index, lstm_state, cond_latent_state, prior_latent_state, gen_prior_video) = internal_states z_mu, log_sigma_sq = video_features z_mu, log_sigma_sq = z_mu[frame_index], log_sigma_sq[frame_index] # Sample latents using a gaussian centered at conditional mu and std. latent = common_video.get_gaussian_tensor(z_mu, log_sigma_sq) # Sample prior latents from isotropic normal distribution. prior_latent = tf.random_normal(tf.shape(latent), dtype=tf.float32) # # LSTM that encodes correlations between conditional latents. # # Pg 22 in https://arxiv.org/pdf/1804.01523.pdf enc_cond_latent, cond_latent_state = common_video.basic_lstm( latent, cond_latent_state, latent_dims, name="cond_latent") # LSTM that encodes correlations between prior latents. enc_prior_latent, prior_latent_state = common_video.basic_lstm( prior_latent, prior_latent_state, latent_dims, name="prior_latent") all_latents = tf.concat([enc_cond_latent, enc_prior_latent], axis=0) all_image = tf.concat([image, image], 0) all_action = tf.concat([action, action], 0) if self.has_actions else None all_pred_images, lstm_state = self.construct_predictive_tower( all_image, None, all_action, lstm_state, all_latents, concat_latent=True) cond_pred_images, prior_pred_images = \ all_pred_images[:batch_size], all_pred_images[batch_size:] if self.is_training and self.hparams.use_vae: pred_image = cond_pred_images else: pred_image = prior_pred_images gen_prior_video.append(prior_pred_images) internal_states = (frame_index + 1, lstm_state, cond_latent_state, prior_latent_state, gen_prior_video) if not self.has_rewards: return pred_image, None, 0.0, internal_states pred_reward = self.reward_prediction( pred_image, action, reward, latent) return pred_image, pred_reward, None, None, 0.0, internal_states

f73ab2948daa58260ac313e2f31c7dce7b616281

py

Python

toontown/betaevent/DistributedBetaEvent.py

LittleNed/toontown-stride

1252a8f9a8816c1810106006d09c8bdfe6ad1e57

2018-06-16T23:06:38.000Z

2018-06-16T23:06:38.000Z

toontown/betaevent/DistributedBetaEvent.py

NoraTT/Historical-Commits-Project-Altis-Source

fe88e6d07edf418f7de6ad5b3d9ecb3d0d285179

toontown/betaevent/DistributedBetaEvent.py

NoraTT/Historical-Commits-Project-Altis-Source

fe88e6d07edf418f7de6ad5b3d9ecb3d0d285179

2019-06-20T23:45:23.000Z

2020-10-14T20:30:15.000Z

from panda3d.core import Point3, VBase3, Vec3, Vec4 from toontown.betaevent.DistributedEvent import DistributedEvent from toontown.betaevent import CogTV from toontown.hood import ZoneUtil from direct.fsm import ClassicFSM, State from direct.interval.IntervalGlobal import * from toontown.toon import Toon, ToonDNA from direct.actor.Actor import Actor from otp.avatar import Avatar from toontown.chat.ChatGlobals import * from toontown.nametag.NametagGroup import * from toontown.suit import DistributedSuitBase, SuitDNA from toontown.toon import NPCToons from toontown.betaevent import BetaEventGlobals as BEGlobals from toontown.battle import BattleParticles class DistributedBetaEvent(DistributedEvent): notify = directNotify.newCategory('DistributedBetaEvent') def __init__(self, cr): DistributedEvent.__init__(self, cr) self.cr = cr self.spark = loader.loadSfx('phase_11/audio/sfx/LB_sparks_1.ogg') # i think this could be used somewhere # Create prepostera self.prepostera = Toon.Toon() self.prepostera.setName('Professor Prepostera') self.prepostera.setPickable(0) self.prepostera.setPlayerType(NametagGlobals.CCNonPlayer) dna = ToonDNA.ToonDNA() dna.newToonFromProperties('hss', 'ms','m', 'm', 20, 0, 20, 20, 97, 27, 86, 27, 37, 27) self.prepostera.setDNA(dna) self.prepostera.loop('scientistEmcee') self.prepostera.reparentTo(render) self.prepostera.setPosHpr(4, -3, 1, 0, 0, 0) self.prepostera.blinkEyes() self.prepostera.head = self.prepostera.find('**/__Actor_head') self.prepostera.initializeBodyCollisions('toon') self.headHoncho1 = DistributedSuitBase.DistributedSuitBase(self.cr) headHoncho1suitDNA = SuitDNA.SuitDNA() headHoncho1suitDNA.newSuit('hho') self.headHoncho1.setDNA(headHoncho1suitDNA) self.headHoncho1.setDisplayName('???') self.headHoncho1.setPickable(0) self.headHoncho1.setPosHpr(0, 0, 0, 0, 0, 0) self.headHoncho1.reparentTo(render) self.headHoncho1.doId = 0 self.headHoncho1.hide() self.headHoncho1.initializeBodyCollisions('toon') middlemanDNA = SuitDNA.SuitDNA() middlemanDNA.newSuit('mdm') self.middleman1 = DistributedSuitBase.DistributedSuitBase(self.cr) self.middleman1.setDNA(middlemanDNA) self.middleman1.setDisplayName('Middleman') self.middleman1.setPickable(0) self.middleman1.setPosHpr(0, 0, 0, 0, 0, 0) self.middleman1.reparentTo(render) self.middleman1.doId = 1 self.middleman1.hide() self.middleman1.initializeBodyCollisions('toon') self.middleman2 = DistributedSuitBase.DistributedSuitBase(self.cr) self.middleman2.setDNA(middlemanDNA) self.middleman2.setDisplayName('Middleman') self.middleman2.setPickable(0) self.middleman2.setPosHpr(0, 0, 0, 0, 0, 0) self.middleman2.reparentTo(render) self.middleman2.doId = 2 self.middleman2.hide() self.middleman2.initializeBodyCollisions('toon') #base.musicManager.stopAllSounds() self.toonMusic = loader.loadMusic('phase_14/audio/bgm/tt2_ambient_1.mp3') # Placeholder #base.playMusic(self.toonMusic, looping = 1) def announceGenerate(self): DistributedEvent.announceGenerate(self) def start(self): pass def delete(self): DistributedEvent.delete(self) self.prepostera.delete() def enterStartBd(self, timestamp): self.prepostera.animFSM.request('TeleportIn') def exitStartBd(self): pass def enterCogInvade(self, timestamp): self.headHoncho1.setPosHpr(0, 0, 0, 0, 0, 0) self.headHoncho1.show() Sequence( self.headHoncho1.beginSupaFlyMove(Vec3(12, -4, 1), True, "firstCogInvadeFlyIn", walkAfterLanding=False), Func(self.headHoncho1.loop, 'walk'), self.headHoncho1.hprInterval(2, VBase3(90, 0, 0)), Func(self.headHoncho1.loop, 'neutral'), Wait(1), Func(self.headHoncho1.setChatAbsolute, 'Hello Toon...', CFSpeech|CFTimeout), Wait(4), Func(self.headHoncho1.setChatAbsolute, "I'd hate to crash the party...", CFSpeech|CFTimeout), Wait(4), Func(self.headHoncho1.setChatAbsolute, "Actually... I'd love to!", CFSpeech|CFTimeout) ).start() def exitCogInvade(self): pass def enterCogTalk(self, timestamp): self.middleman1.show() self.middleman2.show() Sequence( Func(self.headHoncho1.setChatAbsolute, 'I hear you wanted to open Loony Labs...', CFSpeech|CFTimeout), Wait(4), Parallel( self.middleman1.beginSupaFlyMove(Vec3(-8, -4, 1), True, "firstCogInvadeFlyIn", walkAfterLanding=False), self.middleman2.beginSupaFlyMove(Vec3(4, -12, 1), True, "firstCogInvadeFlyIn", walkAfterLanding=False) ), Func(self.middleman2.loop, 'neutral'), Parallel( Sequence( Func(self.middleman1.loop, 'walk'), self.middleman1.hprInterval(2, VBase3(-90, 0, 0)), Func(self.middleman1.loop, 'neutral')), Func(self.headHoncho1.setChatAbsolute, "How well did that go for you?", CFSpeech|CFTimeout)) ).start() def exitCogTalk(self): pass def enterCogTakeover(self, timestamp): pass def exitCogTakeover(self): pass def enterCredits(self, timestamp): import CreditsScreen self.credits = CreditsScreen.CreditsScreen() self.credits.startCredits() def exitCredits(self): pass def toonTalk(self, phrase, toon): toon.setChatAbsolute(phrase, CFSpeech|CFTimeout)

f73ab6176ab8fe472d5242378c8bc7c181ac542c

py

Python

tests/test_chi_citycouncil.py

danielahuang/city-scrapers

711d1995f59100793e771068a6f5d9149e773412

tests/test_chi_citycouncil.py

danielahuang/city-scrapers

711d1995f59100793e771068a6f5d9149e773412

tests/test_chi_citycouncil.py

danielahuang/city-scrapers

711d1995f59100793e771068a6f5d9149e773412

import json from datetime import date, time from urllib.parse import parse_qs import pytest from freezegun import freeze_time from tests.utils import file_response, read_test_file_content from city_scrapers.constants import CITY_COUNCIL from city_scrapers.spiders.chi_citycouncil import ChiCityCouncilSpider INITIAL_REQUEST = 'https://ocd.datamade.us/events/?' \ 'start_date__gt=2017-10-16&' \ 'jurisdiction=ocd-jurisdiction/country:us/state:il/place:chicago/government' spider = ChiCityCouncilSpider() @pytest.fixture('module') def parsed_item(): freezer = freeze_time('2018-01-01 12:00:01') freezer.start() item = file_response('files/chi_citycouncil_event.json', url=INITIAL_REQUEST) parsed = spider._parse_item(item) freezer.stop() return parsed def test_parse(): response = file_response('files/chi_citycouncil_feed.json', url=INITIAL_REQUEST) requests = list(spider.parse(response)) assert len(requests) == 2 def test_gen_requests(): test_response = json.loads(read_test_file_content('files/chi_citycouncil_feed.json')) event_requests = [item for item in spider._gen_requests(test_response)] assert event_requests == [ 'https://ocd.datamade.us/ocd-event/86094f46-cf45-46f8-89e2-0bf783e7aa12/', 'https://ocd.datamade.us/ocd-event/93d62d20-b1dc-4d71-9e96-60c99c837e90/', ] def test_addtl_pages(): more = json.loads( '{"meta": {"page": 1, "per_page": 100, "total_count": 160, "count": 100, "max_page": 2}}' ) assert spider._addtl_pages(more) is True no_more = json.loads( '{"meta": {"page": 1, "per_page": 100, "total_count": 2, "count": 2, "max_page": 1}}' ) assert spider._addtl_pages(no_more) is False def test_next_page(): more = json.loads( '{"meta": {"page": 1, "per_page": 100, "total_count": 160, "count": 100, "max_page": 2}}' ) original_params = parse_qs(INITIAL_REQUEST) next_page = spider._next_page(more) static_params = {k: v for k, v in original_params.items() if k != 'page'} assert static_params == original_params assert next_page == 2 def test_parse_documents(): documents = [{ "date": "", "note": "Notice", "links": [{ "url": ( "http://media.legistar.com/chic/meetings/633C3556-29C4-4645-A916-E767E00A98CC/" "Notice,%2003-22-2018.pdf" ), "media_type": "application/pdf" }] }] assert spider._parse_documents(documents)[0] == { 'url': documents[0]['links'][0]['url'], 'note': "Notice" } # Item fields def test_start(parsed_item): expected_start = {'date': date(2017, 10, 16), 'time': time(10, 00), 'note': ''} assert parsed_item['start'] == expected_start def test_end(parsed_item): expected_end = {'date': date(2017, 10, 16), 'time': None, 'note': ''} assert parsed_item['end'] == expected_end def test_name(parsed_item): assert parsed_item['name'] == 'Joint Committee: Finance; Transportation and Public Way' def test_description(parsed_item): assert parsed_item['event_description'] == "" def test_location(parsed_item): expected_location = { 'address': '121 N LaSalle Dr, Chicago, IL', 'name': 'Council Chambers , City Hall' } assert parsed_item['location'] == expected_location def test_documents(parsed_item): assert parsed_item['documents'] == [{ "url": "http://media.legistar.com/chic/meetings/B5103C52-1793-4B07-9F28-E0A1223E1540/Fin%20CANCELLED%2010-16_20171010085450.pdf", # noqa "note": "Cancellation Notice", }] def test_id(parsed_item): assert parsed_item['id'] == \ 'chi_citycouncil/201710161000/ocd-event-86094f46-cf45-46f8-89e2-0bf783e7aa12/joint_committee_finance_transportation_and_public_way' # noqa def test_all_day(parsed_item): assert parsed_item['all_day'] is False def test_classification(parsed_item): assert parsed_item['classification'] == CITY_COUNCIL def test_status(parsed_item): assert parsed_item['status'] == 'cancelled' def test__type(parsed_item): assert parsed_item['_type'] == 'event' def test_sources(parsed_item): expected_sources = [ { "url": "http://webapi.legistar.com/v1/chicago/events/4954", "note": "api" }, { "url": "https://chicago.legistar.com/MeetingDetail.aspx?ID=565455&GUID=B5103C52-1793-4B07-9F28-E0A1223E1540&Options=info&Search=", # noqa "note": "web" } ] assert parsed_item['sources'] == expected_sources

f73ae27acfddb97a5a793e676d74a4d7a58eef84

py

Python

cartridge/shop/migrations/0016_add_field_product__meta_title.py

AlexHill/cartridge

cb8599d43600442a223a484dc75726bfbbec68a0

cartridge/shop/migrations/0016_add_field_product__meta_title.py

AlexHill/cartridge

cb8599d43600442a223a484dc75726bfbbec68a0

cartridge/shop/migrations/0016_add_field_product__meta_title.py

AlexHill/cartridge

cb8599d43600442a223a484dc75726bfbbec68a0

from __future__ import unicode_literals # -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding field 'Product._meta_title' db.add_column('shop_product', '_meta_title', self.gf('django.db.models.fields.CharField')(max_length=500, null=True, blank=True), keep_default=False) def backwards(self, orm): # Deleting field 'Product._meta_title' db.delete_column('shop_product', '_meta_title') models = { 'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, 'generic.assignedkeyword': { 'Meta': {'ordering': "('_order',)", 'object_name': 'AssignedKeyword'}, '_order': ('django.db.models.fields.IntegerField', [], {'null': 'True'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'keyword': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'assignments'", 'to': "orm['generic.Keyword']"}), 'object_pk': ('django.db.models.fields.IntegerField', [], {}) }, 'generic.keyword': { 'Meta': {'object_name': 'Keyword'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'site': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['sites.Site']"}), 'slug': ('django.db.models.fields.CharField', [], {'max_length': '2000', 'null': 'True', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '500'}) }, 'generic.rating': { 'Meta': {'object_name': 'Rating'}, 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'object_pk': ('django.db.models.fields.IntegerField', [], {}), 'value': ('django.db.models.fields.IntegerField', [], {}) }, 'pages.page': { 'Meta': {'ordering': "('titles',)", 'object_name': 'Page'}, '_meta_title': ('django.db.models.fields.CharField', [], {'max_length': '500', 'null': 'True', 'blank': 'True'}), '_order': ('django.db.models.fields.IntegerField', [], {'null': 'True'}), 'content_model': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'expiry_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'gen_description': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'in_menus': ('mezzanine.pages.fields.MenusField', [], {'default': '[1, 2, 3]', 'max_length': '100', 'null': 'True', 'blank': 'True'}), #'keywords': ('mezzanine.generic.fields.KeywordsField', [], {'object_id_field': "'object_pk'", 'to': "orm['generic.AssignedKeyword']", 'frozen_by_south': 'True'}), 'keywords_string': ('django.db.models.fields.CharField', [], {'max_length': '500', 'blank': 'True'}), 'login_required': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'children'", 'null': 'True', 'to': "orm['pages.Page']"}), 'publish_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'short_url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'site': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['sites.Site']"}), 'slug': ('django.db.models.fields.CharField', [], {'max_length': '2000', 'null': 'True', 'blank': 'True'}), 'status': ('django.db.models.fields.IntegerField', [], {'default': '2'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '500'}), 'titles': ('django.db.models.fields.CharField', [], {'max_length': '1000', 'null': 'True'}) }, 'shop.cart': { 'Meta': {'object_name': 'Cart'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_updated': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}) }, 'shop.cartitem': { 'Meta': {'object_name': 'CartItem'}, 'cart': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'items'", 'to': "orm['shop.Cart']"}), 'description': ('django.db.models.fields.CharField', [], {'max_length': '200'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True'}), 'quantity': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'sku': ('cartridge.shop.fields.SKUField', [], {'max_length': '20'}), 'total_price': ('cartridge.shop.fields.MoneyField', [], {'default': "'0'", 'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'unit_price': ('cartridge.shop.fields.MoneyField', [], {'default': "'0'", 'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'url': ('django.db.models.fields.CharField', [], {'max_length': '200'}) }, 'shop.category': { 'Meta': {'ordering': "('_order',)", 'object_name': 'Category', '_ormbases': ['pages.Page']}, 'combined': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'content': ('mezzanine.core.fields.RichTextField', [], {}), 'options': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "'product_options'", 'blank': 'True', 'to': "orm['shop.ProductOption']"}), 'page_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['pages.Page']", 'unique': 'True', 'primary_key': 'True'}), 'price_max': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'price_min': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'products': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['shop.Product']", 'symmetrical': 'False', 'blank': 'True'}), 'sale': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['shop.Sale']", 'null': 'True', 'blank': 'True'}) }, 'shop.discountcode': { 'Meta': {'object_name': 'DiscountCode'}, 'active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'categories': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "'discountcode_related'", 'blank': 'True', 'to': "orm['shop.Category']"}), 'code': ('cartridge.shop.fields.DiscountCodeField', [], {'unique': 'True', 'max_length': '20'}), 'discount_deduct': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'discount_exact': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'discount_percent': ('cartridge.shop.fields.PercentageField', [], {'null': 'True', 'max_digits': '5', 'decimal_places': '2', 'blank': 'True'}), 'free_shipping': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'min_purchase': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'products': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['shop.Product']", 'symmetrical': 'False', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'uses_remaining': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'valid_from': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'valid_to': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}) }, 'shop.order': { 'Meta': {'ordering': "('-id',)", 'object_name': 'Order'}, 'additional_instructions': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'billing_detail_city': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'billing_detail_country': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'billing_detail_email': ('django.db.models.fields.EmailField', [], {'max_length': '75'}), 'billing_detail_first_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'billing_detail_last_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'billing_detail_phone': ('django.db.models.fields.CharField', [], {'max_length': '20'}), 'billing_detail_postcode': ('django.db.models.fields.CharField', [], {'max_length': '10'}), 'billing_detail_state': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'billing_detail_street': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'discount_code': ('cartridge.shop.fields.DiscountCodeField', [], {'max_length': '20', 'blank': 'True'}), 'discount_total': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'item_total': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'key': ('django.db.models.fields.CharField', [], {'max_length': '40'}), 'shipping_detail_city': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'shipping_detail_country': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'shipping_detail_first_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'shipping_detail_last_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'shipping_detail_phone': ('django.db.models.fields.CharField', [], {'max_length': '20'}), 'shipping_detail_postcode': ('django.db.models.fields.CharField', [], {'max_length': '10'}), 'shipping_detail_state': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'shipping_detail_street': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'shipping_total': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'shipping_type': ('django.db.models.fields.CharField', [], {'max_length': '50', 'blank': 'True'}), 'status': ('django.db.models.fields.IntegerField', [], {'default': '1'}), 'time': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'null': 'True', 'blank': 'True'}), 'total': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'transaction_id': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'user_id': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}) }, 'shop.orderitem': { 'Meta': {'object_name': 'OrderItem'}, 'description': ('django.db.models.fields.CharField', [], {'max_length': '200'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'order': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'items'", 'to': "orm['shop.Order']"}), 'quantity': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'sku': ('cartridge.shop.fields.SKUField', [], {'max_length': '20'}), 'total_price': ('cartridge.shop.fields.MoneyField', [], {'default': "'0'", 'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'unit_price': ('cartridge.shop.fields.MoneyField', [], {'default': "'0'", 'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}) }, 'shop.product': { 'Meta': {'object_name': 'Product'}, '_meta_title': ('django.db.models.fields.CharField', [], {'max_length': '500', 'null': 'True', 'blank': 'True'}), 'available': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'categories': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['shop.Category']", 'symmetrical': 'False', 'blank': 'True'}), 'content': ('mezzanine.core.fields.RichTextField', [], {}), 'date_added': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'null': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'expiry_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'gen_description': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), #'keywords': ('mezzanine.generic.fields.KeywordsField', [], {'object_id_field': "'object_pk'", 'to': "orm['generic.AssignedKeyword']", 'frozen_by_south': 'True'}), 'keywords_string': ('django.db.models.fields.CharField', [], {'max_length': '500', 'blank': 'True'}), 'num_in_stock': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'publish_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), #'rating': ('mezzanine.generic.fields.RatingField', [], {'object_id_field': "'object_pk'", 'to': "orm['generic.Rating']", 'frozen_by_south': 'True'}), 'rating_average': ('django.db.models.fields.FloatField', [], {'default': '0'}), 'rating_count': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'related_products': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "'related_products_rel_+'", 'blank': 'True', 'to': "orm['shop.Product']"}), 'sale_from': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'sale_id': ('django.db.models.fields.IntegerField', [], {'null': 'True'}), 'sale_price': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'sale_to': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'short_url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'site': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['sites.Site']"}), 'sku': ('cartridge.shop.fields.SKUField', [], {'max_length': '20', 'unique': 'True', 'null': 'True', 'blank': 'True'}), 'slug': ('django.db.models.fields.CharField', [], {'max_length': '2000', 'null': 'True', 'blank': 'True'}), 'status': ('django.db.models.fields.IntegerField', [], {'default': '2'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '500'}), 'unit_price': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'upsell_products': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "'upsell_products_rel_+'", 'blank': 'True', 'to': "orm['shop.Product']"}) }, 'shop.productaction': { 'Meta': {'unique_together': "(('product', 'timestamp'),)", 'object_name': 'ProductAction'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'product': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'actions'", 'to': "orm['shop.Product']"}), 'timestamp': ('django.db.models.fields.IntegerField', [], {}), 'total_cart': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'total_purchase': ('django.db.models.fields.IntegerField', [], {'default': '0'}) }, 'shop.productimage': { 'Meta': {'ordering': "('_order',)", 'object_name': 'ProductImage'}, '_order': ('django.db.models.fields.IntegerField', [], {'null': 'True'}), 'description': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}), 'file': ('django.db.models.fields.files.ImageField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'product': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'images'", 'to': "orm['shop.Product']"}) }, 'shop.productoption': { 'Meta': {'object_name': 'ProductOption'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('cartridge.shop.fields.OptionField', [], {'max_length': '50', 'null': 'True'}), 'type': ('django.db.models.fields.IntegerField', [], {}) }, 'shop.productvariation': { 'Meta': {'ordering': "('-default',)", 'object_name': 'ProductVariation'}, 'default': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['shop.ProductImage']", 'null': 'True', 'blank': 'True'}), 'num_in_stock': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'option1': ('cartridge.shop.fields.OptionField', [], {'max_length': '50', 'null': 'True'}), 'option2': ('cartridge.shop.fields.OptionField', [], {'max_length': '50', 'null': 'True'}), 'product': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'variations'", 'to': "orm['shop.Product']"}), 'sale_from': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'sale_id': ('django.db.models.fields.IntegerField', [], {'null': 'True'}), 'sale_price': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'sale_to': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'sku': ('cartridge.shop.fields.SKUField', [], {'max_length': '20', 'unique': 'True', 'null': 'True', 'blank': 'True'}), 'unit_price': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}) }, 'shop.sale': { 'Meta': {'object_name': 'Sale'}, 'active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'categories': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "'sale_related'", 'blank': 'True', 'to': "orm['shop.Category']"}), 'discount_deduct': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'discount_exact': ('cartridge.shop.fields.MoneyField', [], {'null': 'True', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}), 'discount_percent': ('cartridge.shop.fields.PercentageField', [], {'null': 'True', 'max_digits': '5', 'decimal_places': '2', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'products': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['shop.Product']", 'symmetrical': 'False', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'valid_from': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'valid_to': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}) }, 'sites.site': { 'Meta': {'ordering': "('domain',)", 'object_name': 'Site', 'db_table': "'django_site'"}, 'domain': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) } } complete_apps = ['shop']

f73afdcad8e818812662d41e891fdcf0eaf1cc95

py

Python

src/api/bkuser_core/categories/plugins/ldap/adaptor.py

Canway-shiisa/bk-user

a049e80d12082960828015742cea4b041f4af796

src/api/bkuser_core/categories/plugins/ldap/adaptor.py

Canway-shiisa/bk-user

a049e80d12082960828015742cea4b041f4af796

src/api/bkuser_core/categories/plugins/ldap/adaptor.py

Canway-shiisa/bk-user

a049e80d12082960828015742cea4b041f4af796

# -*- coding: utf-8 -*- """ TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import logging from dataclasses import dataclass, field from typing import Any, Dict, List, NamedTuple, Optional from django.utils.encoding import force_str from ldap3.utils import dn as dn_utils from bkuser_core.categories.plugins.constants import DYNAMIC_FIELDS_SETTING_KEY from bkuser_core.categories.plugins.ldap.models import LdapDepartment, LdapUserProfile from bkuser_core.user_settings.loader import ConfigProvider logger = logging.getLogger(__name__) @dataclass class ProfileFieldMapper: """从 ldap 对象属性中获取用户字段""" config_loader: ConfigProvider embed_fields = [ "username", "display_name", "email", "telephone", ] dynamic_fields: List = field(default_factory=list) def __post_init__(self): self.dynamic_fields_mapping = self.config_loader.get(DYNAMIC_FIELDS_SETTING_KEY) self.dynamic_fields = list(self.dynamic_fields_mapping.keys()) if self.dynamic_fields_mapping else [] def get_value( self, field_name: str, user_meta: Dict[str, List[bytes]], remain_raw: bool = False, dynamic_field: bool = False ) -> Any: """通过 field_name 从 ldap 数据中获取具体值""" # 获取自定义字段对应的属性值 if dynamic_field: ldap_field_name = field_name if ldap_field_name not in self.dynamic_fields_mapping.values(): logger.info("no config[%s] in configs of dynamic_fields_mapping", field_name) return "" else: # 从目录配置中获取 字段名 ldap_field_name = self.config_loader.get(field_name) if not ldap_field_name: logger.info("no config[%s] in configs of category", field_name) return "" # 1. 通过字段名，获取具体值 if ldap_field_name not in user_meta or not user_meta[ldap_field_name]: logger.info("field[%s] is missing in raw attributes of user data from ldap", field_name) return "" # 2. 类似 memberOf 字段，将会返回原始列表 if remain_raw: return user_meta[ldap_field_name] return force_str(user_meta[ldap_field_name][0]) def get_values(self, user_meta: Dict[str, List[bytes]]) -> Dict[str, Any]: """根据字段映射关系, 从 ldap 中获取 `field_name` 的值""" values = {} for field_name in self.embed_fields: values.update({field_name: self.get_value(field_name, user_meta)}) return values def get_dynamic_values(self, user_meta: Dict[str, List[bytes]]) -> Dict[str, Any]: """获取自定义字段 在ldap中的对应值""" values = {} if self.dynamic_fields: values.update( { field_name: self.get_value( field_name=self.dynamic_fields_mapping[field_name], user_meta=user_meta, dynamic_field=True ) for field_name in self.dynamic_fields } ) return values def get_user_attributes(self) -> list: """获取远端属性名列表""" user_attributes = [self.config_loader[x] for x in self.embed_fields if self.config_loader.get(x)] user_attributes.extend( [self.dynamic_fields_mapping[x] for x in self.dynamic_fields if self.dynamic_fields_mapping.get(x)] ) return user_attributes def user_adapter( code: str, user_meta: Dict[str, Any], field_mapper: ProfileFieldMapper, restrict_types: List[str] ) -> LdapUserProfile: groups = field_mapper.get_value("user_member_of", user_meta["raw_attributes"], True) or [] return LdapUserProfile( **field_mapper.get_values(user_meta["raw_attributes"]), code=code, extras=field_mapper.get_dynamic_values(user_meta["raw_attributes"]), # TODO: 完成转换 departments 的逻辑 departments=[ # 根据约定, dn 中除去第一个成分以外的部分即为用户所在的部门, 因此需要取 [1:] list(reversed(parse_dn_value_list(user_meta["dn"], restrict_types)[1:])), # 用户与用户组之间的关系 *[list(reversed(parse_dn_value_list(force_str(group), restrict_types))) for group in groups], ], ) def department_adapter(code: str, dept_meta: Dict, is_group: bool, restrict_types: List[str]) -> LdapDepartment: dn = dept_meta["dn"] dn_values = parse_dn_value_list(dn, restrict_types=restrict_types) parent_dept: Optional[LdapDepartment] = None for dept_name in reversed(dn_values): parent_dept = LdapDepartment( name=dept_name, parent=parent_dept, is_group=is_group, ) assert parent_dept is not None, "未从 dn 中提取到任何部门信息" parent_dept.code = code return parent_dept class RDN(NamedTuple): """RelativeDistinguishedName""" type: str value: str separator: str def parse_dn_tree(dn: str, restrict_types: List[str] = None) -> List[RDN]: """A DN is a sequence of relative distinguished names (RDN) connected by commas, For examples: we have a dn = "CN=Jeff Smith,OU=Sales,DC=Fabrikam,DC=COM", this method will parse the dn to: >>> parse_dn_tree("CN=Jeff Smith,OU=Sales,DC=Fabrikam,DC=COM") [RDN(type='CN', value='Jeff Smith', separator=','), RDN(type='OU', value='Sales', separator=','), RDN(type='DC', value='Fabrikam', separator=','), RDN(type='DC', value='COM', separator='')] if provide restrict_types, this method will ignore the attribute not in restrict_types, For examples: >>> parse_dn_tree("CN=Jeff Smith,OU=Sales,DC=Fabrikam,DC=COM", restrict_types=["DC"]) [RDN(type='DC', value='Fabrikam', separator=','), RDN(type='DC', value='COM', separator='')] Furthermore, restrict_types is Case-insensitive, the ["DC"], ["dc"], ["Dc"] are Exactly equal. >>> parse_dn_tree("CN=Jeff Smith,OU=Sales,DC=Fabrikam,DC=COM", restrict_types=["dc"]) [RDN(type='DC', value='Fabrikam', separator=','), RDN(type='DC', value='COM', separator='')] See Also: https://docs.microsoft.com/en-us/previous-versions/windows/desktop/ldap/distinguished-names """ restrict_types = [type_.upper() for type_ in (restrict_types or [])] items = dn_utils.parse_dn(dn, escape=True) if restrict_types: parts = [RDN(*i) for i in items if i[0].upper() in restrict_types] else: parts = [RDN(*i) for i in items] return parts def parse_dn_value_list(dn: str, restrict_types: List[str] = None) -> List[str]: """this method work like parse_dn_tree, be only return values of those attributes, For examples: >>> parse_dn_value_list("CN=Jeff Smith,OU=Sales,DC=Fabrikam,DC=COM") ['Jeff Smith', 'Sales', 'Fabrikam', 'COM'] if provide restrict_types, this method will ignore the attribute not in restrict_types, For examples: >>> parse_dn_value_list("CN=Jeff Smith,OU=Sales,DC=Fabrikam,DC=COM", restrict_types=["DC"]) ['Fabrikam', 'COM'] """ tree = parse_dn_tree(dn, restrict_types) parts = [] for part in tree: parts.append(part.value) return parts

f73afef58d65deabe13b39d87236bc23035e79be

py

Python

wrappers/python/tests/wallet/test_open_wallet.py

Diiaablo95/indy-sdk

0ef7321f5902683af928cdc7ea94d522bee33d30

wrappers/python/tests/wallet/test_open_wallet.py

Diiaablo95/indy-sdk

0ef7321f5902683af928cdc7ea94d522bee33d30

wrappers/python/tests/wallet/test_open_wallet.py

Diiaablo95/indy-sdk

0ef7321f5902683af928cdc7ea94d522bee33d30

import pytest from indy import IndyError from indy import wallet from indy.error import ErrorCode @pytest.mark.asyncio @pytest.mark.parametrize("wallet_config", [None, '{"freshness_time":1000}']) async def test_open_wallet_works(wallet_config, wallet_handle): pass @pytest.mark.asyncio async def test_open_wallet_works_for_not_created_wallet(credentials): with pytest.raises(IndyError) as e: await wallet.open_wallet('wallet_not_created', None, credentials) assert ErrorCode.WalletNotFoundError == e.value.error_code @pytest.mark.asyncio async def test_open_wallet_works_for_twice(wallet_name, wallet_handle, credentials): with pytest.raises(IndyError) as e: await wallet.open_wallet(wallet_name, None, credentials) assert ErrorCode.WalletAlreadyOpenedError == e.value.error_code @pytest.mark.asyncio async def test_open_wallet_works_for_missed_key(xwallet, wallet_name): with pytest.raises(IndyError) as e: await wallet.open_wallet(wallet_name, None, "{}") assert ErrorCode.WalletInputError == e.value.error_code @pytest.mark.asyncio async def test_open_wallet_works_for_changing_credentials(pool_name): await wallet.create_wallet(pool_name, 'works_for_changing_credentials', None, None, '{"key":"key"}') handle = await wallet.open_wallet('works_for_changing_credentials', None, '{"key":"key", "rekey":"other_key"}') await wallet.close_wallet(handle) handle = await wallet.open_wallet('works_for_changing_credentials', None, '{"key":"other_key"}') await wallet.close_wallet(handle)

f73b0549a8520f8a1b9f80ee2a9d44368bb49989

py

Python

soaplib/core/zope2.py

divaliu1408/overfit

083dcfaa758391092933e19544462cd831e73ef0

soaplib/core/zope2.py

divaliu1408/overfit

083dcfaa758391092933e19544462cd831e73ef0

soaplib/core/zope2.py

divaliu1408/overfit

083dcfaa758391092933e19544462cd831e73ef0

"""Mix-In Class to Export Decorated Methods using SOAP This file is not a Zope2 Product nor a Zope3 component. It is a simple Python module that adds two elements: a hook into the Zope publisher to intercept SOAP requests, and a mix-in class to your Zope2 folder classes that make them SOAP-aware, with queryable WSDL descriptions of any methods decorated with @soapmethod. A tiny bit of code needs to be invoked from within ZPublisher during the parsing of an HTTP request that is a SOAP request. To make this happen, change the code in the file lib/python/ZPublisher/HTTPRequest.py, within the processInputs() method of the HTTPRequest class, from this: fs=FieldStorage(fp=fp,environ=environ,keep_blank_values=1) if not hasattr(fs,'list') or fs.list is None: # Hm, maybe it's an XML-RPC if (fs.headers.has_key('content-type') and to this: fs=FieldStorage(fp=fp,environ=environ,keep_blank_values=1) if not hasattr(fs,'list') or fs.list is None: if environ.has_key('HTTP_SOAPACTION'): #ADDED other['SOAPXML'] = fs.value #ADDED # Hm, maybe it's an XML-RPC elif (fs.headers.has_key('content-type') and #CHANGED """ import cgi from xml.etree.ElementTree import ElementTree from lxml import etree from soaplib.core._base import MethodContext from soaplib.core.mime import collapse_swa from soaplib.core.model.exception import Fault from soaplib.core.service import DefinitionBase,soap from soaplib.core.model.primitive import string_encoding from soaplib.core import Application from soaplib.core.server._base import Base as BaseServer from zope.interface import implements from zope.interface.common.interfaces import IException from zope.app.testing import ztapi # public sumbols __all__ = [ 'SoapFolder', # to mix into your Zope Folders to make them into SOAP Service Points 'AccessDeniedSOAP', # exception object to signal a failed SOAP call ] class SoapFolder(DefinitionBase): """Mix-In Class to Make a Folder into a SOAP Service Point Import this class into your Zope2 folder classes to make them SOAP-aware. Any methods in your folder class decorated with @soapmethod() will become callable over SOAP and the signature and docstring of that method will be reported as WSDL by a call to the index_html() method of that folder. Your class should also define a class attribute indicating the 'toplevel namespace' of your SOAP Service Point. This name is arbitrary as far as this code goes, but appears in the SOAP response and the WSDL description generated. This attribute looks like: __tns__ = "PatientServices" """ _v_soap_methods = None _v_cached_wsdl = None __wsdl__ = None def __init__(self, tns, environ=None): super(DefinitionBase, self).__init__(environ) self.soap_app = Application(self, tns, False) self.soap_handler = BaseServer(self.soap_app) def methods(self): """Returns a list of method descriptors for this object""" if self._v_soap_methods is None: self._v_soap_methods = self.build_public_methods() return self._v_soap_methods def service_description(self, REQUEST, RESPONSE): """ """ if getattr(self, '__tns__', None) is None: self.__tns__ = self.get_tns(self.__class__) if self._v_soap_methods is None: self._v_soap_methods = self.build_public_methods() if self._v_cached_wsdl is None: self._v_cached_wsdl = self.soap_app.get_wsdl(self.absolute_url()) self.__wsdl__ = None RESPONSE.setHeader('Content-Type', 'text/xml') return self._v_cached_wsdl def index_html(self, REQUEST, RESPONSE): """Handle an incoming SOAP request or a non-SOAP WSDL query.""" if REQUEST.get('SOAPXML', None) == None: # Not a SOAP Request, return WSDL return self.service_description(REQUEST, RESPONSE) try: # Deserialize the Body of the SOAP Request from soaplib.core._base import _from_soap header, payload = _from_soap(REQUEST.SOAPXML) # TODO: At this point I need dispatch method calls to the soaplib.Application # somehow....... :) ctx = MethodContext() content_type = cgi.parse_header(REQUEST.get("Content-Type")) charset = content_type[1].get('charset',None) length = REQUEST.get("Content-Length") http_payload = REQUEST.read(int(length)) if not charset: charset = "ascii" in_string = collapse_swa(content_type, http_payload) in_obj = self.soap_handler.get_in_object(ctx, in_string, charset) out_obj = self.soap_handler.get_out_object(ctx, in_obj) out_string = self.soap_handler.get_out_string(ctx, out_obj) return out_string except Exception, e: fault = Fault(faultstring=str(e)) resp = etree.tostring(fault, encoding=string_encoding) RESPONSE.setStatus('InternalServerError', reason=faultstring) RESPONSE.setHeader('Content-Type', 'text/xml') return resp class ISOAPException(IException): pass class SOAPException(Exception): """Base exception class for all derived exceptions for SOAP""" implements(ISOAPException) def __init__(self, request): self.request = request self.request['faultexc'] = self def __str__(self): return self.__class__.__name__ class AccessDeniedSOAP(SOAPException): """An exception to raise in a SOAP method if access is being denied.""" class SOAPExceptionView: """Adapts an (ISOAPException, IRequest) to a View This view provides the XML representation of a SOAP fault that is returned to a caller. To use it, register this view with Zope at some initialization point: from zope.app.testing import ztapi from dummy import ISOAPException, SOAPExceptionView ztapi.browserView(ISOAPException, u'index.html', SOAPExceptionView) and then within your SOAP logic raise a SOAP exception where needed: from dummy import SOAPException raise SOAPException(request) """ def __init__(self, context, request): self.context = context self.request = request def __call__(self): faultstring = self.request['faultexc'].__class__.__name__ self.request.response.setStatus('InternalServerError', reason=faultstring) faultcode = 'Server' fault = make_soap_fault(faultstring, faultcode, detail=None) self.request.response.setHeader('Content-Type', 'text/xml') return ElementTree.tostring(fault, encoding=string_encoding) # The following registers 'SOAPExceptionView' as an adapter that knows how to # display (generate and return the XML source for a SOAP fault) for anything # that implements the 'ISOAPException' interface. ztapi.browserView(ISOAPException, u'index.html', SOAPExceptionView)

f73b2d639d0e65b873732c9f73125c0faa298413

py

Python

hooks/post_gen_project.py

Everyday-Future/cookiecutter-devops

316948eed75c95ac5768a1bf1d504807069aaa6a

hooks/post_gen_project.py

Everyday-Future/cookiecutter-devops

316948eed75c95ac5768a1bf1d504807069aaa6a

hooks/post_gen_project.py

Everyday-Future/cookiecutter-devops

316948eed75c95ac5768a1bf1d504807069aaa6a

#!/usr/bin/env python import os import stat PROJECT_DIRECTORY = os.path.realpath(os.path.curdir) def remove_file(filepath): os.remove(os.path.join(PROJECT_DIRECTORY, filepath)) if __name__ == '__main__': if 'no' in '{{ cookiecutter.command_line_interface|lower }}': cli_file = os.path.join('{{ cookiecutter.project_slug }}', 'cli.py') remove_file(cli_file) if 'Not open source' == '{{ cookiecutter.open_source_license }}': remove_file('LICENSE') # Create secret envs os.rename(os.path.join(PROJECT_DIRECTORY, 'secret--template.env.txt'), os.path.join(PROJECT_DIRECTORY, 'secret--template.env')) os.rename(os.path.join(PROJECT_DIRECTORY, 'secret--template-values.env.txt'), os.path.join(PROJECT_DIRECTORY, 'secret--template-values.env')) os.rename(os.path.join(PROJECT_DIRECTORY, 'frontend', '.env.txt'), os.path.join(PROJECT_DIRECTORY, 'frontend', '.env')) os.rename(os.path.join(PROJECT_DIRECTORY, 'frontend', 'docker.env.txt'), os.path.join(PROJECT_DIRECTORY, 'frontend', 'docker.env')) # Convert shell scripts for Windows shell_scripts = [os.path.join(PROJECT_DIRECTORY, '.__run_cli.sh'), os.path.join(PROJECT_DIRECTORY, 'boot.sh'), os.path.join(PROJECT_DIRECTORY, 'host', 'test_loop.sh')] for shell_script in shell_scripts: with open(shell_script, "r") as fin: lines = [] for line in fin: lines.append(line.replace('\r\n', '\n')) with open(shell_script, "w") as fout: for line in lines: fout.write(line) # Make shell scripts executable for shell_script in shell_scripts: st = os.stat(shell_script) os.chmod(shell_script, st.st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)

f73b366b012fea66ac00ee79b22b61b0d63bffa5

py

Python

Courses/Udacity/CS101/Lesson_4_Problem_Set_(Optional)/01-Word_Count/supplied/studentMain.py

leparrav/Playground

dcb90a2dd2bc1867511cfe621eb21248a60e357f

2019-02-13T12:02:26.000Z

2019-02-13T12:02:26.000Z

Courses/Udacity/CS101/Lesson_4_Problem_Set_(Optional)/01-Word_Count/supplied/studentMain.py

leparrav/Playground

dcb90a2dd2bc1867511cfe621eb21248a60e357f

2018-08-13T15:58:33.000Z

2018-08-13T15:58:33.000Z

Courses/Udacity/CS101/Lesson_4_Problem_Set_(Optional)/01-Word_Count/supplied/studentMain.py

leparrav/Playground

dcb90a2dd2bc1867511cfe621eb21248a60e357f

2017-08-10T20:01:29.000Z

2021-07-01T08:39:13.000Z

# Write a procedure, count_words, which takes as input a string # and returns the number of words in the string. You may consider words # as strings of characters separated by spaces. def count_words(): passage =("The number of orderings of the 52 cards in a deck of cards " "is so great that if every one of the almost 7 billion people alive " "today dealt one ordering of the cards per second, it would take " "2.5 * 10**40 times the age of the universe to order the cards in every " "possible way.") print count_words(passage) #>>>56

f73b6022673fcd9dfb2e398bb51a7bbf33d4bb06

py

Python

alien_tag.py

sacherjj/python-AlienRFID

aaddd846d46cca533dca43c256890c072e8f5ec5

2021-03-21T13:52:00.000Z

2021-03-21T13:52:00.000Z

alien_tag.py

sacherjj/python-AlienRFID

aaddd846d46cca533dca43c256890c072e8f5ec5

alien_tag.py

sacherjj/python-AlienRFID

aaddd846d46cca533dca43c256890c072e8f5ec5

2015-10-12T10:02:50.000Z

2020-03-09T13:30:12.000Z

__author__ = 'joesacher' import datetime as dt class AlienTag(object): def __init__(self, taglist_entry): self.disc = 0 self.last = 0 self.last_last = 0 self.id = 0 self.ant = 0 self.count = 0 self.proto = 0 self.rssi = 0 self.freq = 0 # self.speed = 0 # self.speed_smooth = 0 # self.speed_last = 0 # self.pos_smooth = 0 # self.pos_last = 0 # self.pos_min = 0 self.create(taglist_entry) def __str__(self): return self.id def __gt__(self, other): return self.id > other.id def create(self, taglist_entry): """ Try to parse a taglist entry into a set of Tag object variables. Uses a simple mapping from Alien 'text' format: Tag:0102 0304 0506 0708 0900 0A0B, Disc:2008/10/28 10:49:35, Last:2008/10/28 10:49:35, Count:1, Ant:3, Proto:2 *rssi* and *speed* attributes are not included in the default text format. In order to have them parsed correctly the _TagListFormat_ must be set to _custom_ and the _TagListCustomFormat_ fields must be separated by the following text tokens: 'tag:', 'disc:', 'last:', 'count:', 'ant:', 'proto:', 'speed:', 'rssi:' For example: @rdr.taglistcustomformat("Tag:%i, Disc:${DATE1} ${TIME1}, Last:${DATE2} ${TIME2}, Count:${COUNT}, Ant:${TX}, Proto:${PROTO#}, Speed:${SPEED}, rssi:${RSSI})" @rdr.taglistformat("custom") """ self.id = "" if taglist_entry == "(No Tags)": return tagline = taglist_entry.split('\r\n')[0] tagbits = {} for keyval in tagline.split(", "): key, val = keyval.split(":", 1) # TODO: Raise Error on Bad Key Val parse tagbits[key.lower()] = val self.id = tagbits.get('tag', 'NO TAG ID') self.ant = tagbits.get('ant', 0) self.count = tagbits.get('count', 0) self.disc = tagbits.get('disc', 0) self.last = tagbits.get('last', 0) # TODO: Convert self.last into datetime self.last_last = self.last self.proto = tagbits.get('proto', 0) self.rssi = tagbits.get('rssi', 0) self.freq = tagbits.get('freq', 0) self.speed = tagbits.get('speed', 0) def update(self, new_tag): self.last = new_tag.last self.count += new_tag.count self.last_last = self.last

f73b6f6d7d6955528eef744b500e27d5eca877ef

py

Python

vyperlogix/django/forms/fields.py

raychorn/chrome_gui

f1fade70b61af12ee43c55c075aa9cfd32caa962

2020-09-29T01:36:33.000Z

2020-09-29T01:36:33.000Z

vyperlogix/django/forms/fields.py

raychorn/chrome_gui

f1fade70b61af12ee43c55c075aa9cfd32caa962

vyperlogix/django/forms/fields.py

raychorn/chrome_gui

f1fade70b61af12ee43c55c075aa9cfd32caa962

import sys from vyperlogix.misc import _utils from django.utils.datastructures import SortedDict as SortedDictFromList from vyperlogix.classes.SmartObject import SmartObject def fields_for_model(model, formfield_callback=lambda f: f.formfield()): """ Returns a list of fields for the given Django model class. Provide ``formfield_callback`` if you want to define different logic for determining the formfield for a given database field. It's a callable that takes a database Field instance and returns a form Field instance. """ field_list = [] try: opts = model._meta for f in opts.fields + opts.many_to_many: if not f.editable: continue formfield = formfield_callback(f) if formfield: field_list.append((f.name, formfield)) except Exception as details: print >>sys.stderr, _utils.formattedException(details=details) return SortedDictFromList(dict(field_list))

f73bbcedcd25303f7209490e28a550849e1245fb

py

Python

supriya/ugens/MulAdd.py

deeuu/supriya

14fcb5316eccb4dafbe498932ceff56e1abb9d27

supriya/ugens/MulAdd.py

deeuu/supriya

14fcb5316eccb4dafbe498932ceff56e1abb9d27

supriya/ugens/MulAdd.py

deeuu/supriya

14fcb5316eccb4dafbe498932ceff56e1abb9d27

import collections from supriya import CalculationRate from supriya.synthdefs import UGen class MulAdd(UGen): """ An Optimized multiplication / addition ugen. :: >>> source = supriya.ugens.SinOsc.ar() >>> mul_add = supriya.ugens.MulAdd.new( ... addend=0.5, ... multiplier=-1.5, ... source=source, ... ) >>> mul_add MulAdd.ar() """ ### CLASS VARIABLES ### __documentation_section__ = "Basic Operator UGens" _ordered_input_names = collections.OrderedDict( [("source", None), ("multiplier", 1.0), ("addend", 0.0)] ) ### INITIALIZER ### def __init__(self, addend=0.0, multiplier=1.0, calculation_rate=None, source=None): UGen.__init__( self, addend=addend, multiplier=multiplier, calculation_rate=calculation_rate, source=source, ) ### PRIVATE METHODS ### @staticmethod def _inputs_are_valid(source, multiplier, addend): if CalculationRate.from_expr(source) == CalculationRate.AUDIO: return True if CalculationRate.from_expr(source) == CalculationRate.CONTROL: if CalculationRate.from_expr(multiplier) in ( CalculationRate.CONTROL, CalculationRate.SCALAR, ): if CalculationRate.from_expr(addend) in ( CalculationRate.CONTROL, CalculationRate.SCALAR, ): return True return False @classmethod def _new_single( cls, addend=None, multiplier=None, calculation_rate=None, source=None ): if multiplier == 0.0: return addend minus = multiplier == -1 no_multiplier = multiplier == 1 no_addend = addend == 0 if no_multiplier and no_addend: return source if minus and no_addend: return -source if no_addend: return source * multiplier if minus: return addend - source if no_multiplier: return source + addend if cls._inputs_are_valid(source, multiplier, addend): return cls( addend=addend, multiplier=multiplier, calculation_rate=calculation_rate, source=source, ) if cls._inputs_are_valid(multiplier, source, addend): return cls( addend=addend, multiplier=source, calculation_rate=calculation_rate, source=multiplier, ) return (source * multiplier) + addend ### PUBLIC METHODS ### @classmethod def new(cls, source=None, multiplier=1.0, addend=0.0): """ Constructs a multiplication / addition ugen. :: >>> addend = 0.5 >>> multiplier = 1.5 >>> source = supriya.ugens.SinOsc.ar(frequency=[440, 442]) >>> mul_add = supriya.ugens.MulAdd.new( ... addend=addend, ... multiplier=multiplier, ... source=source, ... ) >>> mul_add UGenArray({2}) Returns ugen graph. """ import supriya.synthdefs # TODO: handle case of array as source calculation_rate = supriya.CalculationRate.from_expr( (source, multiplier, addend) ) ugen = cls._new_expanded( addend=addend, multiplier=multiplier, calculation_rate=calculation_rate, source=source, ) return ugen

f73bcc307906ada8eceee6a21267b925ade3c652

py

Python

deeppavlov/core/layers/tf_csoftmax_attention.py

ineersa/DeepPavlov

8200bf9a0f0b378baad4ee0eb75b59453f516004

2020-04-16T04:25:10.000Z

2021-05-07T23:04:43.000Z

deeppavlov/core/layers/tf_csoftmax_attention.py

ineersa/DeepPavlov

8200bf9a0f0b378baad4ee0eb75b59453f516004

2020-01-28T22:14:04.000Z

2022-02-10T00:10:17.000Z

deeppavlov/core/layers/tf_csoftmax_attention.py

ineersa/DeepPavlov

8200bf9a0f0b378baad4ee0eb75b59453f516004

2021-02-05T13:01:48.000Z

2021-02-05T13:01:48.000Z

# Copyright 2017 Neural Networks and Deep Learning lab, MIPT # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import tensorflow as tf def csoftmax_for_slice(input): """ It is a implementation of the constrained softmax (csoftmax) for slice. Based on the paper: https://andre-martins.github.io/docs/emnlp2017_final.pdf "Learning What's Easy: Fully Differentiable Neural Easy-First Taggers" (page 4) Args: input: A list of [input tensor, cumulative attention]. Returns: output: A list of [csoftmax results, masks] """ [ten, u] = input shape_t = ten.shape shape_u = u.shape ten -= tf.reduce_mean(ten) q = tf.exp(ten) active = tf.ones_like(u, dtype=tf.int32) mass = tf.constant(0, dtype=tf.float32) found = tf.constant(True, dtype=tf.bool) def loop(q_, mask, mass_, found_): q_list = tf.dynamic_partition(q_, mask, 2) condition_indices = tf.dynamic_partition(tf.range(tf.shape(q_)[0]), mask, 2) # 0 element it False, # 1 element if true p = q_list[1] * (1.0 - mass_) / tf.reduce_sum(q_list[1]) p_new = tf.dynamic_stitch(condition_indices, [q_list[0], p]) # condition verification and mask modification less_mask = tf.cast(tf.less(u, p_new), tf.int32) # 0 when u is bigger than p, 1 when u is less than p condition_indices = tf.dynamic_partition(tf.range(tf.shape(p_new)[0]), less_mask, 2) # 0 when u is bigger than p, 1 when u is less than p split_p_new = tf.dynamic_partition(p_new, less_mask, 2) split_u = tf.dynamic_partition(u, less_mask, 2) alpha = tf.dynamic_stitch(condition_indices, [split_p_new[0], split_u[1]]) mass_ += tf.reduce_sum(split_u[1]) mask = mask * (tf.ones_like(less_mask) - less_mask) found_ = tf.cond(tf.equal(tf.reduce_sum(less_mask), 0), lambda: False, lambda: True) alpha = tf.reshape(alpha, q_.shape) return alpha, mask, mass_, found_ (csoft, mask_, _, _) = tf.while_loop(cond=lambda _0, _1, _2, f: f, body=loop, loop_vars=(q, active, mass, found)) return [csoft, mask_] def csoftmax(tensor, inv_cumulative_att): """ It is a implementation of the constrained softmax (csoftmax). Based on the paper: https://andre-martins.github.io/docs/emnlp2017_final.pdf "Learning What's Easy: Fully Differentiable Neural Easy-First Taggers" Args: tensor: A tensorflow tensor is score. This tensor have dimensionality [None, n_tokens] inv_cumulative_att: A inverse cumulative attention tensor with dimensionality [None, n_tokens] Returns: cs: Tensor at the output with dimensionality [None, n_tokens] """ shape_ten = tensor.shape shape_cum = inv_cumulative_att.shape merge_tensor = [tensor, inv_cumulative_att] cs, _ = tf.map_fn(csoftmax_for_slice, merge_tensor, dtype=[tf.float32, tf.float32]) # [bs, L] return cs def attention_gen_step(hidden_for_sketch, hidden_for_attn_alignment, sketch, key, cum_att): """ It is a implementation one step of block of the Luong et al. attention mechanism with general score and the constrained softmax (csoftmax). Based on the papers: https://arxiv.org/abs/1508.04025 "Effective Approaches to Attention-based Neural Machine Translation" https://andre-martins.github.io/docs/emnlp2017_final.pdf "Learning What's Easy: Fully Differentiable Neural Easy-First Taggers" Args: hidden_for_sketch: A tensorflow tensor for a sketch computing. This tensor have dimensionality [None, max_num_tokens, sketch_hidden_size] hidden_for_attn_alignment: A tensorflow tensor is aligned for output during a performing. This tensor have dimensionality [None, max_num_tokens, hidden_size_for_attn_alignment] sketch: A previous step sketch tensor for a sketch computing. This tensor have dimensionality [None, sketch_hidden_size] key: A tensorflow tensor with dimensionality [None, None, key_size] cum_att: A cumulative attention tensor with dimensionality [None, max_num_tokens] Returns: next_sketch: Tensor of the current step sketch with dimensionality [None, sketch_hidden_size] att: Tensor of the current step attention with dimensionality [None, max_num_tokens] aligned_hidden_sketch: Tensor of aligned hidden state of current step with dimensionality [None, hidden_size_for_attn_alignment] """ with tf.name_scope('attention_step'): sketch_dims = hidden_for_sketch.get_shape().as_list() batch_size = sketch_dims[0] num_tokens = sketch_dims[1] hidden_size = sketch_dims[2] attn_alignment_dims = hidden_for_attn_alignment.get_shape().as_list() attn_alignment_hidden_size = attn_alignment_dims[2] repeated_sketch = tf.tile(tf.reshape(sketch, [-1, 1, hidden_size]), (1,num_tokens, 1)) concat_mem = tf.concat([hidden_for_sketch, repeated_sketch],-1) concat_mem = tf.reshape(concat_mem, [-1, num_tokens, 2*hidden_size]) # dirty trick reduce_mem = tf.layers.dense(concat_mem, hidden_size) projected_key = tf.layers.dense(key, hidden_size) t_key = tf.reshape(projected_key,[-1, hidden_size, 1]) score = tf.reshape(tf.matmul(reduce_mem, t_key), [-1, num_tokens]) inv_cum_att = tf.reshape(tf.ones_like(cum_att) - cum_att, [-1, num_tokens]) att = csoftmax(score, inv_cum_att) t_reduce_mem = tf.transpose(reduce_mem, [0,2,1]) t_hidden_for_attn_alignment = tf.transpose(hidden_for_attn_alignment, [0,2,1]) r_att = tf.reshape(att, [-1, num_tokens, 1]) next_sketch = tf.squeeze(tf.matmul(t_reduce_mem,r_att),-1) aligned_hidden_sketch = tf.squeeze(tf.matmul(t_hidden_for_attn_alignment,r_att),-1) return next_sketch, att, aligned_hidden_sketch def attention_gen_block(hidden_for_sketch, hidden_for_attn_alignment, key, attention_depth): """ It is a implementation of the Luong et al. attention mechanism with general score and the constrained softmax (csoftmax). Based on the papers: https://arxiv.org/abs/1508.04025 "Effective Approaches to Attention-based Neural Machine Translation" https://andre-martins.github.io/docs/emnlp2017_final.pdf "Learning What's Easy: Fully Differentiable Neural Easy-First Taggers" Args: hidden_for_sketch: A tensorflow tensor for a sketch computing. This tensor have dimensionality [None, max_num_tokens, sketch_hidden_size] hidden_for_attn_alignment: A tensorflow tensor is aligned for output during a performing. This tensor have dimensionality [None, max_num_tokens, hidden_size_for_attn_alignment] key: A tensorflow tensor with dimensionality [None, None, key_size] attention_depth: Number of usage csoftmax Returns: final_aligned_hiddens: Tensor at the output with dimensionality [1, attention_depth, hidden_size_for_attn_alignment] """ with tf.name_scope('attention_block'): sketch_dims = tf.shape(hidden_for_sketch) batch_size = sketch_dims[0] num_tokens = sketch_dims[1] hidden_size = sketch_dims[2] attn_alignment_dims = tf.shape(hidden_for_attn_alignment) attn_alignment_hidden_size = attn_alignment_dims[2] sketches = [tf.zeros(shape=[batch_size, hidden_size], dtype=tf.float32)] aligned_hiddens = [] cum_att = tf.zeros(shape=[batch_size, num_tokens]) # cumulative attention for i in range(attention_depth): sketch, cum_att_, aligned_hidden = attention_gen_step(hidden_for_sketch, hidden_for_attn_alignment, sketches[-1], key, cum_att) sketches.append(sketch) #sketch aligned_hiddens.append(aligned_hidden) #sketch cum_att += cum_att_ final_aligned_hiddens = tf.reshape(tf.transpose(tf.stack(aligned_hiddens), [1, 0, 2]),[1, attention_depth, attn_alignment_hidden_size]) return final_aligned_hiddens def attention_bah_step(hidden_for_sketch, hidden_for_attn_alignment, sketch, cum_att): """ It is a implementation one step of block of the Bahdanau et al. attention mechanism with concat score and the constrained softmax (csoftmax). Based on the papers: https://arxiv.org/abs/1409.0473 "Neural Machine Translation by Jointly Learning to Align and Translate" https://andre-martins.github.io/docs/emnlp2017_final.pdf "Learning What's Easy: Fully Differentiable Neural Easy-First Taggers" Args: hidden_for_sketch: A tensorflow tensor for a sketch computing. This tensor have dimensionality [None, max_num_tokens, sketch_hidden_size] hidden_for_attn_alignment: A tensorflow tensor is aligned for output during a performing. This tensor have dimensionality [None, max_num_tokens, hidden_size_for_attn_alignment] sketch: A previous step sketch tensor for a sketch computing. This tensor have dimensionality [None, sketch_hidden_size] key: A tensorflow tensor with dimensionality [None, None, key_size] cum_att: A cumulative attention tensor with dimensionality [None, max_num_tokens] Returns: next_sketch: Tensor of the current step sketch with dimensionality [None, sketch_hidden_size] att: Tensor of the current step attention with dimensionality [None, max_num_tokens] aligned_hidden_sketch: Tensor of aligned hidden state of current step with dimensionality [None, hidden_size_for_attn_alignment] """ with tf.name_scope('attention_step'): sketch_dims = hidden_for_sketch.get_shape().as_list() batch_size = sketch_dims[0] num_tokens = sketch_dims[1] hidden_size = sketch_dims[2] attn_alignment_dims = hidden_for_attn_alignment.get_shape().as_list() attn_alignment_hidden_size = attn_alignment_dims[2] repeated_sketch = tf.tile(tf.reshape(sketch, [-1, 1, hidden_size]), (1,num_tokens, 1)) concat_mem = tf.concat([hidden_for_sketch, repeated_sketch],-1) concat_mem = tf.reshape(concat_mem, [-1, num_tokens, 2*hidden_size]) # dirty trick reduce_mem = tf.layers.dense(concat_mem, hidden_size) score = tf.squeeze(tf.layers.dense(reduce_mem, units = 1, use_bias=False),-1) inv_cum_att = tf.reshape(tf.ones_like(cum_att) - cum_att, [-1, num_tokens]) att = csoftmax(score, inv_cum_att) t_reduce_mem = tf.transpose(reduce_mem, [0,2,1]) t_hidden_for_attn_alignment = tf.transpose(hidden_for_attn_alignment, [0,2,1]) r_att = tf.reshape(att, [-1, num_tokens, 1]) next_sketch = tf.squeeze(tf.matmul(t_reduce_mem,r_att),-1) aligned_hidden_sketch = tf.squeeze(tf.matmul(t_hidden_for_attn_alignment,r_att),-1) return next_sketch, att, aligned_hidden_sketch def attention_bah_block(hidden_for_sketch, hidden_for_attn_alignment, attention_depth): """ It is a implementation of the Bahdanau et al. attention mechanism with concat score and the constrained softmax (csoftmax). Based on the papers: https://arxiv.org/abs/1409.0473 "Neural Machine Translation by Jointly Learning to Align and Translate" https://andre-martins.github.io/docs/emnlp2017_final.pdf "Learning What's Easy: Fully Differentiable Neural Easy-First Taggers" Args: hidden_for_sketch: A tensorflow tensor for a sketch computing. This tensor have dimensionality [None, max_num_tokens, sketch_hidden_size] hidden_for_attn_alignment: A tensorflow tensor is aligned for output during a performing. This tensor have dimensionality [None, max_num_tokens, hidden_size_for_attn_alignment] key: A tensorflow tensor with dimensionality [None, None, key_size] attention_depth: Number of usage csoftmax Returns: final_aligned_hiddens: Tensor at the output with dimensionality [1, attention_depth, hidden_size_for_attn_alignment] """ with tf.name_scope('attention_block'): sketch_dims = tf.shape(hidden_for_sketch) batch_size = sketch_dims[0] num_tokens = sketch_dims[1] hidden_size = sketch_dims[2] attn_alignment_dims = tf.shape(hidden_for_attn_alignment) attn_alignment_hidden_size = attn_alignment_dims[2] sketches = [tf.zeros(shape=[batch_size, hidden_size], dtype=tf.float32)] aligned_hiddens = [] cum_att = tf.zeros(shape=[batch_size, num_tokens]) # cumulative attention for i in range(attention_depth): sketch, cum_att_, aligned_hidden = attention_bah_step(hidden_for_sketch, hidden_for_attn_alignment, sketches[-1], cum_att) sketches.append(sketch) #sketch aligned_hiddens.append(aligned_hidden) #sketch cum_att += cum_att_ final_aligned_hiddens = tf.reshape(tf.transpose(tf.stack(aligned_hiddens), [1, 0, 2]),[1, attention_depth, attn_alignment_hidden_size]) return final_aligned_hiddens

f73bd9fef40d9af195f1e1eb4ae5b47154f56eb1

py

Python

sim/Simulator.py

adhocmaster/pyns

607feb56baf0900535130195163eac331e131a2e

2021-06-15T06:21:14.000Z

2021-06-15T06:21:14.000Z

sim/Simulator.py

adhocmaster/pyns

607feb56baf0900535130195163eac331e131a2e

sim/Simulator.py

adhocmaster/pyns

607feb56baf0900535130195163eac331e131a2e

2021-06-15T06:21:18.000Z

2021-06-15T06:21:18.000Z

from abc import ABC class Simulator(ABC): def __init__(self): self.stats = {} self.stats['dataInFlight'] = [] self.stats['dataInQueue'] = [] self.stats['packetsInFlight'] = [] self.stats['packetsInQueue'] = [] self.stats['queueSize'] = [] self.stats['packetsSent'] = [] self.stats['packetsAcked'] = [] self.stats['totalPacketsSent'] = [] self.stats['totalPacketsAcked'] = []

f73be5427fbe24030a0f1a642b8b36c5bc757baa

py

Python

git/objects/fun.py

yarikoptic/GitPython

7576b282013249a2b20ccda4acacefd5e625ea39

2020-10-15T06:16:48.000Z

2020-10-15T06:16:48.000Z

git/objects/fun.py

yarikoptic/GitPython

7576b282013249a2b20ccda4acacefd5e625ea39

git/objects/fun.py

yarikoptic/GitPython

7576b282013249a2b20ccda4acacefd5e625ea39

"""Module with functions which are supposed to be as fast as possible""" from stat import S_ISDIR __all__ = ('tree_to_stream', 'tree_entries_from_data', 'traverse_trees_recursive', 'traverse_tree_recursive') def tree_to_stream(entries, write): """Write the give list of entries into a stream using its write method :param entries: **sorted** list of tuples with (binsha, mode, name) :param write: write method which takes a data string""" ord_zero = ord('0') bit_mask = 7 # 3 bits set for binsha, mode, name in entries: mode_str = '' for i in xrange(6): mode_str = chr(((mode >> (i*3)) & bit_mask) + ord_zero) + mode_str # END for each 8 octal value # git slices away the first octal if its zero if mode_str[0] == '0': mode_str = mode_str[1:] # END save a byte # here it comes: if the name is actually unicode, the replacement below # will not work as the binsha is not part of the ascii unicode encoding - # hence we must convert to an utf8 string for it to work properly. # According to my tests, this is exactly what git does, that is it just # takes the input literally, which appears to be utf8 on linux. if isinstance(name, unicode): name = name.encode("utf8") write("%s %s\0%s" % (mode_str, name, binsha)) # END for each item def tree_entries_from_data(data): """Reads the binary representation of a tree and returns tuples of Tree items :param data: data block with tree data :return: list(tuple(binsha, mode, tree_relative_path), ...)""" ord_zero = ord('0') len_data = len(data) i = 0 out = list() while i < len_data: mode = 0 # read mode # Some git versions truncate the leading 0, some don't # The type will be extracted from the mode later while data[i] != ' ': # move existing mode integer up one level being 3 bits # and add the actual ordinal value of the character mode = (mode << 3) + (ord(data[i]) - ord_zero) i += 1 # END while reading mode # byte is space now, skip it i += 1 # parse name, it is NULL separated ns = i while data[i] != '\0': i += 1 # END while not reached NULL # default encoding for strings in git is utf8 # Only use the respective unicode object if the byte stream was encoded name = data[ns:i] name_enc = name.decode("utf-8") if len(name) > len(name_enc): name = name_enc # END handle encoding # byte is NULL, get next 20 i += 1 sha = data[i:i+20] i = i + 20 out.append((sha, mode, name)) # END for each byte in data stream return out def _find_by_name(tree_data, name, is_dir, start_at): """return data entry matching the given name and tree mode or None. Before the item is returned, the respective data item is set None in the tree_data list to mark it done""" try: item = tree_data[start_at] if item and item[2] == name and S_ISDIR(item[1]) == is_dir: tree_data[start_at] = None return item except IndexError: pass # END exception handling for index, item in enumerate(tree_data): if item and item[2] == name and S_ISDIR(item[1]) == is_dir: tree_data[index] = None return item # END if item matches # END for each item return None def _to_full_path(item, path_prefix): """Rebuild entry with given path prefix""" if not item: return item return (item[0], item[1], path_prefix+item[2]) def traverse_trees_recursive(odb, tree_shas, path_prefix): """ :return: list with entries according to the given binary tree-shas. The result is encoded in a list of n tuple|None per blob/commit, (n == len(tree_shas)), where * [0] == 20 byte sha * [1] == mode as int * [2] == path relative to working tree root The entry tuple is None if the respective blob/commit did not exist in the given tree. :param tree_shas: iterable of shas pointing to trees. All trees must be on the same level. A tree-sha may be None in which case None :param path_prefix: a prefix to be added to the returned paths on this level, set it '' for the first iteration :note: The ordering of the returned items will be partially lost""" trees_data = list() nt = len(tree_shas) for tree_sha in tree_shas: if tree_sha is None: data = list() else: data = tree_entries_from_data(odb.stream(tree_sha).read()) # END handle muted trees trees_data.append(data) # END for each sha to get data for out = list() out_append = out.append # find all matching entries and recursively process them together if the match # is a tree. If the match is a non-tree item, put it into the result. # Processed items will be set None for ti, tree_data in enumerate(trees_data): for ii, item in enumerate(tree_data): if not item: continue # END skip already done items entries = [ None for n in range(nt) ] entries[ti] = item sha, mode, name = item # its faster to unpack is_dir = S_ISDIR(mode) # type mode bits # find this item in all other tree data items # wrap around, but stop one before our current index, hence # ti+nt, not ti+1+nt for tio in range(ti+1, ti+nt): tio = tio % nt entries[tio] = _find_by_name(trees_data[tio], name, is_dir, ii) # END for each other item data # if we are a directory, enter recursion if is_dir: out.extend(traverse_trees_recursive(odb, [((ei and ei[0]) or None) for ei in entries], path_prefix+name+'/')) else: out_append(tuple(_to_full_path(e, path_prefix) for e in entries)) # END handle recursion # finally mark it done tree_data[ii] = None # END for each item # we are done with one tree, set all its data empty del(tree_data[:]) # END for each tree_data chunk return out def traverse_tree_recursive(odb, tree_sha, path_prefix): """ :return: list of entries of the tree pointed to by the binary tree_sha. An entry has the following format: * [0] 20 byte sha * [1] mode as int * [2] path relative to the repository :param path_prefix: prefix to prepend to the front of all returned paths""" entries = list() data = tree_entries_from_data(odb.stream(tree_sha).read()) # unpacking/packing is faster than accessing individual items for sha, mode, name in data: if S_ISDIR(mode): entries.extend(traverse_tree_recursive(odb, sha, path_prefix+name+'/')) else: entries.append((sha, mode, path_prefix+name)) # END for each item return entries

f73c21ab8ff31d42eefafa43012f748feffc581f

py

Python

acq4/pyqtgraph/widgets/SpinBox.py

tropp/ACQ4

792e05e99cedfc175593d200aeabecd6fa6304ce

acq4/pyqtgraph/widgets/SpinBox.py

tropp/ACQ4

792e05e99cedfc175593d200aeabecd6fa6304ce

acq4/pyqtgraph/widgets/SpinBox.py

tropp/ACQ4

792e05e99cedfc175593d200aeabecd6fa6304ce

# -*- coding: utf-8 -*- from ..Qt import QtGui, QtCore from ..python2_3 import asUnicode from ..SignalProxy import SignalProxy from .. import functions as fn from math import log from decimal import Decimal as D ## Use decimal to avoid accumulating floating-point errors from decimal import * import weakref __all__ = ['SpinBox'] class SpinBox(QtGui.QAbstractSpinBox): """ **Bases:** QtGui.QAbstractSpinBox QSpinBox widget on steroids. Allows selection of numerical value, with extra features: - SI prefix notation (eg, automatically display "300 mV" instead of "0.003 V") - Float values with linear and decimal stepping (1-9, 10-90, 100-900, etc.) - Option for unbounded values - Delayed signals (allows multiple rapid changes with only one change signal) ============================= ============================================== **Signals:** valueChanged(value) Same as QSpinBox; emitted every time the value has changed. sigValueChanged(self) Emitted when value has changed, but also combines multiple rapid changes into one signal (eg, when rolling the mouse wheel). sigValueChanging(self, value) Emitted immediately for all value changes. ============================= ============================================== """ ## There's a PyQt bug that leaks a reference to the ## QLineEdit returned from QAbstractSpinBox.lineEdit() ## This makes it possible to crash the entire program ## by making accesses to the LineEdit after the spinBox has been deleted. ## I have no idea how to get around this.. valueChanged = QtCore.Signal(object) # (value) for compatibility with QSpinBox sigValueChanged = QtCore.Signal(object) # (self) sigValueChanging = QtCore.Signal(object, object) # (self, value) sent immediately; no delay. def __init__(self, parent=None, value=0.0, **kwargs): """ ============== ======================================================================== **Arguments:** parent Sets the parent widget for this SpinBox (optional). Default is None. value (float/int) initial value. Default is 0.0. bounds (min,max) Minimum and maximum values allowed in the SpinBox. Either may be None to leave the value unbounded. By default, values are unbounded. suffix (str) suffix (units) to display after the numerical value. By default, suffix is an empty str. siPrefix (bool) If True, then an SI prefix is automatically prepended to the units and the value is scaled accordingly. For example, if value=0.003 and suffix='V', then the SpinBox will display "300 mV" (but a call to SpinBox.value will still return 0.003). Default is False. step (float) The size of a single step. This is used when clicking the up/ down arrows, when rolling the mouse wheel, or when pressing keyboard arrows while the widget has keyboard focus. Note that the interpretation of this value is different when specifying the 'dec' argument. Default is 0.01. dec (bool) If True, then the step value will be adjusted to match the current size of the variable (for example, a value of 15 might step in increments of 1 whereas a value of 1500 would step in increments of 100). In this case, the 'step' argument is interpreted *relative* to the current value. The most common 'step' values when dec=True are 0.1, 0.2, 0.5, and 1.0. Default is False. minStep (float) When dec=True, this specifies the minimum allowable step size. int (bool) if True, the value is forced to integer type. Default is False precision (int) Number of significant digits to display. Default is 3. ============== ======================================================================== """ QtGui.QAbstractSpinBox.__init__(self, parent) self.lastValEmitted = None self.lastText = '' self.textValid = True ## If false, we draw a red border self.setMinimumWidth(0) self.setMaximumHeight(20) self.setSizePolicy(QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Preferred) self.opts = { 'bounds': [None, None], ## Log scaling options #### Log mode is no longer supported. #'step': 0.1, #'minStep': 0.001, #'log': True, #'dec': False, ## decimal scaling option - example #'step': 0.1, #'minStep': .001, #'log': False, #'dec': True, ## normal arithmetic step 'step': D('0.01'), ## if 'dec' is false, the spinBox steps by 'step' every time ## if 'dec' is True, the step size is relative to the value ## 'step' needs to be an integral divisor of ten, ie 'step'*n=10 for some integer value of n (but only if dec is True) 'log': False, 'dec': False, ## if true, does decimal stepping. ie from 1-10 it steps by 'step', from 10 to 100 it steps by 10*'step', etc. ## if true, minStep must be set in order to cross zero. 'int': False, ## Set True to force value to be integer 'suffix': '', 'siPrefix': False, ## Set to True to display numbers with SI prefix (ie, 100pA instead of 1e-10A) 'delay': 0.3, ## delay sending wheel update signals for 300ms 'delayUntilEditFinished': True, ## do not send signals until text editing has finished 'precision': 3, ## for compatibility with QDoubleSpinBox and QSpinBox 'decimals': None, } self.decOpts = ['step', 'minStep'] self.val = D(asUnicode(value)) ## Value is precise decimal. Ordinary math not allowed. self.updateText() self.skipValidate = False self.setCorrectionMode(self.CorrectToPreviousValue) self.setKeyboardTracking(False) self.setOpts(**kwargs) self.editingFinished.connect(self.editingFinishedEvent) self.proxy = SignalProxy(self.sigValueChanging, slot=self.delayedChange, delay=self.opts['delay']) def event(self, ev): ret = QtGui.QAbstractSpinBox.event(self, ev) if ev.type() == QtCore.QEvent.KeyPress and ev.key() == QtCore.Qt.Key_Return: ret = True ## For some reason, spinbox pretends to ignore return key press return ret ##lots of config options, just gonna stuff 'em all in here rather than do the get/set crap. def setOpts(self, **opts): """ Changes the behavior of the SpinBox. Accepts most of the arguments allowed in :func:`__init__ <pyqtgraph.SpinBox.__init__>`. """ #print opts for k in opts: if k == 'bounds': #print opts[k] self.setMinimum(opts[k][0], update=False) self.setMaximum(opts[k][1], update=False) #for i in [0,1]: #if opts[k][i] is None: #self.opts[k][i] = None #else: #self.opts[k][i] = D(unicode(opts[k][i])) elif k in ['step', 'minStep']: self.opts[k] = D(asUnicode(opts[k])) elif k == 'value': pass ## don't set value until bounds have been set else: self.opts[k] = opts[k] if 'value' in opts: self.setValue(opts['value']) ## If bounds have changed, update value to match if 'bounds' in opts and 'value' not in opts: self.setValue() ## sanity checks: if self.opts['int']: if 'step' in opts: step = opts['step'] ## not necessary.. #if int(step) != step: #raise Exception('Integer SpinBox must have integer step size.') else: self.opts['step'] = int(self.opts['step']) if 'minStep' in opts: step = opts['minStep'] if int(step) != step: raise Exception('Integer SpinBox must have integer minStep size.') else: ms = int(self.opts.get('minStep', 1)) if ms < 1: ms = 1 self.opts['minStep'] = ms if 'delay' in opts: self.proxy.setDelay(opts['delay']) self.updateText() def setMaximum(self, m, update=True): """Set the maximum allowed value (or None for no limit)""" if m is not None: m = D(asUnicode(m)) self.opts['bounds'][1] = m if update: self.setValue() def setMinimum(self, m, update=True): """Set the minimum allowed value (or None for no limit)""" if m is not None: m = D(asUnicode(m)) self.opts['bounds'][0] = m if update: self.setValue() def setPrefix(self, p): self.setOpts(prefix=p) def setRange(self, r0, r1): self.setOpts(bounds = [r0,r1]) def setProperty(self, prop, val): ## for QSpinBox compatibility if prop == 'value': #if type(val) is QtCore.QVariant: #val = val.toDouble()[0] self.setValue(val) else: print("Warning: SpinBox.setProperty('%s', ..) not supported." % prop) def setSuffix(self, suf): self.setOpts(suffix=suf) def setSingleStep(self, step): self.setOpts(step=step) def setPrecision(self, p): """Set the number of significant digits to display. """ self.setOpts(precision=p) def setDecimals(self, decimals): # Note: non-functional for now; provided as workaround for uic files that set this property. self.setOpts(decimals=decimals) def selectNumber(self): """ Select the numerical portion of the text to allow quick editing by the user. """ le = self.lineEdit() text = asUnicode(le.text()) if self.opts['suffix'] == '': le.setSelection(0, len(text)) else: try: index = text.index(' ') except ValueError: return le.setSelection(0, index) def value(self): """ Return the value of this SpinBox. """ if self.opts['int']: return int(self.val) else: return float(self.val) def setValue(self, value=None, update=True, delaySignal=False): """ Set the value of this spin. If the value is out of bounds, it will be clipped to the nearest boundary. If the spin is integer type, the value will be coerced to int. Returns the actual value set. If value is None, then the current value is used (this is for resetting the value after bounds, etc. have changed) """ if value is None: value = self.value() bounds = self.opts['bounds'] if bounds[0] is not None and value < bounds[0]: value = bounds[0] if bounds[1] is not None and value > bounds[1]: value = bounds[1] if self.opts['int']: value = int(value) value = D(asUnicode(value)) if value == self.val: return prev = self.val self.val = value if update: self.updateText(prev=prev) self.sigValueChanging.emit(self, float(self.val)) ## change will be emitted in 300ms if there are no subsequent changes. if not delaySignal: self.emitChanged() return value def emitChanged(self): self.lastValEmitted = self.val self.valueChanged.emit(float(self.val)) self.sigValueChanged.emit(self) def delayedChange(self): try: if self.val != self.lastValEmitted: self.emitChanged() except RuntimeError: pass ## This can happen if we try to handle a delayed signal after someone else has already deleted the underlying C++ object. def widgetGroupInterface(self): return (self.valueChanged, SpinBox.value, SpinBox.setValue) def sizeHint(self): return QtCore.QSize(120, 0) def stepEnabled(self): return self.StepUpEnabled | self.StepDownEnabled #def fixup(self, *args): #print "fixup:", args def stepBy(self, n): n = D(int(n)) ## n must be integral number of steps. s = [D(-1), D(1)][n >= 0] ## determine sign of step val = self.val for i in range(int(abs(n))): if self.opts['log']: raise Exception("Log mode no longer supported.") # step = abs(val) * self.opts['step'] # if 'minStep' in self.opts: # step = max(step, self.opts['minStep']) # val += step * s if self.opts['dec']: if val == 0: step = self.opts['minStep'] exp = None else: vs = [D(-1), D(1)][val >= 0] #exp = D(int(abs(val*(D('1.01')**(s*vs))).log10())) fudge = D('1.01')**(s*vs) ## fudge factor. at some places, the step size depends on the step sign. exp = abs(val * fudge).log10().quantize(1, ROUND_FLOOR) step = self.opts['step'] * D(10)**exp if 'minStep' in self.opts: step = max(step, self.opts['minStep']) val += s * step #print "Exp:", exp, "step", step, "val", val else: val += s*self.opts['step'] if 'minStep' in self.opts and abs(val) < self.opts['minStep']: val = D(0) self.setValue(val, delaySignal=True) ## note all steps (arrow buttons, wheel, up/down keys..) emit delayed signals only. def valueInRange(self, value): bounds = self.opts['bounds'] if bounds[0] is not None and value < bounds[0]: return False if bounds[1] is not None and value > bounds[1]: return False if self.opts.get('int', False): if int(value) != value: return False return True def updateText(self, prev=None): #print "Update text." self.skipValidate = True if self.opts['siPrefix']: if self.val == 0 and prev is not None: (s, p) = fn.siScale(prev) txt = "0.0 %s%s" % (p, self.opts['suffix']) else: txt = fn.siFormat(float(self.val), suffix=self.opts['suffix'], precision=self.opts['precision']) else: txt = '%g%s' % (self.val , self.opts['suffix']) self.lineEdit().setText(txt) self.lastText = txt self.skipValidate = False def validate(self, strn, pos): if self.skipValidate: #print "skip validate" #self.textValid = False ret = QtGui.QValidator.Acceptable else: try: ## first make sure we didn't mess with the suffix suff = self.opts.get('suffix', '') if len(suff) > 0 and asUnicode(strn)[-len(suff):] != suff: #print '"%s" != "%s"' % (unicode(strn)[-len(suff):], suff) ret = QtGui.QValidator.Invalid ## next see if we actually have an interpretable value else: val = self.interpret() if val is False: #print "can't interpret" #self.setStyleSheet('SpinBox {border: 2px solid #C55;}') #self.textValid = False ret = QtGui.QValidator.Intermediate else: if self.valueInRange(val): if not self.opts['delayUntilEditFinished']: self.setValue(val, update=False) #print " OK:", self.val #self.setStyleSheet('') #self.textValid = True ret = QtGui.QValidator.Acceptable else: ret = QtGui.QValidator.Intermediate except: #print " BAD" #import sys #sys.excepthook(*sys.exc_info()) #self.textValid = False #self.setStyleSheet('SpinBox {border: 2px solid #C55;}') ret = QtGui.QValidator.Intermediate ## draw / clear border if ret == QtGui.QValidator.Intermediate: self.textValid = False elif ret == QtGui.QValidator.Acceptable: self.textValid = True ## note: if text is invalid, we don't change the textValid flag ## since the text will be forced to its previous state anyway self.update() ## support 2 different pyqt APIs. Bleh. if hasattr(QtCore, 'QString'): return (ret, pos) else: return (ret, strn, pos) def paintEvent(self, ev): QtGui.QAbstractSpinBox.paintEvent(self, ev) ## draw red border if text is invalid if not self.textValid: p = QtGui.QPainter(self) p.setRenderHint(p.Antialiasing) p.setPen(fn.mkPen((200,50,50), width=2)) p.drawRoundedRect(self.rect().adjusted(2, 2, -2, -2), 4, 4) p.end() def interpret(self): """Return value of text. Return False if text is invalid, raise exception if text is intermediate""" strn = self.lineEdit().text() suf = self.opts['suffix'] if len(suf) > 0: if strn[-len(suf):] != suf: return False #raise Exception("Units are invalid.") strn = strn[:-len(suf)] try: val = fn.siEval(strn) except: #sys.excepthook(*sys.exc_info()) #print "invalid" return False #print val return val #def interpretText(self, strn=None): #print "Interpret:", strn #if strn is None: #strn = self.lineEdit().text() #self.setValue(siEval(strn), update=False) ##QtGui.QAbstractSpinBox.interpretText(self) def editingFinishedEvent(self): """Edit has finished; set value.""" #print "Edit finished." if asUnicode(self.lineEdit().text()) == self.lastText: #print "no text change." return try: val = self.interpret() except: return if val is False: #print "value invalid:", str(self.lineEdit().text()) return if val == self.val: #print "no value change:", val, self.val return self.setValue(val, delaySignal=False) ## allow text update so that values are reformatted pretty-like #def textChanged(self): #print "Text changed." ### Drop-in replacement for SpinBox; just for crash-testing #class SpinBox(QtGui.QDoubleSpinBox): #valueChanged = QtCore.Signal(object) # (value) for compatibility with QSpinBox #sigValueChanged = QtCore.Signal(object) # (self) #sigValueChanging = QtCore.Signal(object) # (value) #def __init__(self, parent=None, *args, **kargs): #QtGui.QSpinBox.__init__(self, parent) #def __getattr__(self, attr): #return lambda *args, **kargs: None #def widgetGroupInterface(self): #return (self.valueChanged, SpinBox.value, SpinBox.setValue)

f73c24cd61c6f29f2dd100f7e4af4609b7bb9113

py

Python

XY_Model_propare_state3_chi64_A0.py

StudentsZhouPengfei/Automatically-Differentiable-Quantum-Circuit-for-Many-qubit-State-Preparation

42d3a77380e78819375c9fb2c5600ddc89a3ae3f

2021-05-10T01:49:59.000Z

2021-06-13T19:03:40.000Z

XY_Model_propare_state3_chi64_A0.py

StudentsZhouPengfei/Automatically-Differentiable-Quantum-Circuit-for-Many-qubit-State-Preparation

42d3a77380e78819375c9fb2c5600ddc89a3ae3f

XY_Model_propare_state3_chi64_A0.py

StudentsZhouPengfei/Automatically-Differentiable-Quantum-Circuit-for-Many-qubit-State-Preparation

42d3a77380e78819375c9fb2c5600ddc89a3ae3f

import torch as tc import numpy as np import copy import os,sys import Circle_Function_Class_A0 as ev import matplotlib.pyplot as plt import matplotlib matplotlib.use('Agg') from torch.optim.lr_scheduler import StepLR import BasicFunSJR as bfs from CNNBTN import Paras_VL_CNN_BTN_Collected1chg1 import BasicFun as bf tmp = sys.argv[0][sys.argv[0].rfind(os.sep) + 1:] # 返回文件名 mark = tmp[-5] which_gpu = tmp[-4] # 调用固定 para = Paras_VL_CNN_BTN_Collected1chg1() para['dataset'] = 'fashion-mnist' para['device'] = bf.choose_device(which_gpu) para['log_name'] = './record' + mark + which_gpu start = tc.cuda.Event(enable_timing=True) end = tc.cuda.Event(enable_timing=True) os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' os.environ['CUDA_VISIBLE_DEVICE'] = '0' # tc.manual_seed(7) # 固定随机数，使产生的随机数可以复现 dtype = tc.float32 # float 监控norm mps_num = 48 lr = 1e-2 it_time = 50 pt_time = 50 # 交错优化所在的次数 dt_print = 10 step_size = it_time * pt_time // 5 # lr学习率递减的间隔epoch x1_axis = list() # 作图横轴 优化次数 identity_4 = tc.eye(4, dtype=dtype).to(para['device']) # 第二层演化小量变化的单位阵量子门 vol = tc.tensor(1e-3, dtype=dtype).to(para['device']) # 为其小量变化幅度, 对优化次数影响不大 con_vol = tc.tensor(1e-5, dtype=dtype).to(para['device']) entropy_list = list() average = tc.tensor(0, dtype=dtype).to(para['device']) # 计算纠缠熵 所用到的初始值 k_bood = 64 file_name = r'./tar_data.npz' out_file_name = r'./layer_out_data.npz' Loss_accuracy_range = 0.0001 # 控制Loss精度的范围，达到精度范围自动跳出循环 base_it_time = it_time//3 # 进行优化的最少次数，与分层优化有关 center_position = 24 layer_num = 3 # 控制不同层的门进行优化 gatenum = (mps_num - 1)*layer_num # 控制变分参数量子门的个数 tar_mpslist = list() ini_state = list() y_loss_layer = list() # 分层交错进行 每层的精度 y_loss_conda = list() # 协同优化 的精度 read_gatenum = (mps_num - 1)*(layer_num -1) zero_gatetensor = tc.zeros(gatenum, 4, 4) conba_gatalist = list() layer_gatelist = list() # 在后续被reshape成(2, 4, 2)的三阶tensor layer_gatelist_0 = list() # 将门分层储存 layer_gatelist_1 = list() # 将门分层储存 layer_gatelist_2 = list() # 将门分层储存 layer_gatelist_3 = list() # 将门分层储存 layer_gatelist_4 = list() # 将门分层储存 layer_gatelist_5 = list() # 将门分层储存 layer_optimize = list() # 分层存储优化器 loss_ = list([list([]), list([]), list([]), list([]), list([]), list([]), list([]), list([]), list([]), list([]), list([]), list([]), list([])]) half_entropy_list = list([]) # 制作热图 half_entropy_list.append(tc.zeros([pt_time+1, mps_num-1])) # 最后一次为目标纠缠熵 number_list = list([0]) print('The quantum circuit is' + str(layer_num)) print('lr=:' + str(lr) + ', k_bood=: ' + str(k_bood) + ', A small amount of vol per unit door is: ' + str(vol)) data = np.load(file_name) tar_mpslist.append(tc.from_numpy(data['tar_mpslist0']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist1']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist2']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist3']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist4']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist5']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist6']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist7']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist8']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist9']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist10']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist11']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist12']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist13']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist14']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist15']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist16']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist17']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist18']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist19']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist20']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist21']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist22']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist23']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist24']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist25']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist26']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist27']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist28']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist29']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist30']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist31']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist32']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist33']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist34']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist35']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist36']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist37']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist38']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist39']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist40']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist41']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist42']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist43']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist44']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist45']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist46']).to(para['device'])) tar_mpslist.append(tc.from_numpy(data['tar_mpslist47']).to(para['device'])) def fprint(content, file=None, print_screen=True, append=True): if file is None: file = './record.log' if append: way = 'ab' else: way = 'wb' with open(file, way, buffering=0) as log: log.write((content + '\n').encode(encoding='utf-8')) if print_screen: print(content) def mps_norm(tar_tensor_): # 对目标量子态进行归一化 log归一化 tv = tc.einsum('asb,asd->bd', tar_tensor_[0].data, tar_tensor_[0].data) t_norm = tc.norm(tv) tv = tv / t_norm tar_tensor_[0] = tar_tensor_[0].data / tc.sqrt(t_norm) for gt in range(1, mps_num): if gt < mps_num - 1: tv = tc.einsum('ac,asb,csd->bd', tv, tar_tensor_[gt].data, tar_tensor_[gt].data) else: tv = tc.einsum('ac,asb,csd->bd', tv, tar_tensor_[gt].data, tar_tensor_[gt].data) norm_t = tc.norm(tv) tv = tv / norm_t tar_tensor_[gt] = tar_tensor_[gt] / tc.sqrt(norm_t) def qr_left_and_right_location(MPS_list, location, vol, feature_num=2): # 对目标MPS进行正交，并求解其纠缠熵 # print('location', location) for k in range(location): # print('k', k) q, r = tc.qr(MPS_list[k].reshape(-1, MPS_list[k].shape[2])) r = r MPS_list[k] = q.reshape(-1, feature_num, q.shape[1]) MPS_list[k + 1] = tc.einsum('nl, lmk-> nmk', [r, MPS_list[k + 1]]) for i in range(len(MPS_list) - 1, location, -1): # print('i', i) q, r = tc.qr(MPS_list[i].reshape(MPS_list[i].shape[0], -1).t()) q_shape = q.t().shape MPS_list[i] = q.t().reshape(q_shape[0], feature_num, -1) r = r MPS_list[i - 1] = tc.einsum('ldk, nk-> ldn', [MPS_list[i - 1], r]) MPS_list[location] = MPS_list[location]/tc.norm(MPS_list[location]) # u, s, v = tc.svd(MPS_list[location].reshape(-1, MPS_list[location].shape[2])) u, s, v = tc.svd(MPS_list[location].reshape(MPS_list[location].shape[0], -1)) s = s[s > vol] y = (-1) * tc.sum(tc.pow(s, 2) * tc.log(tc.pow(s, 2)), dim=0).item() return y, MPS_list # y 返回纠缠熵 ， mps_list返回正交化的目标mps的list（） def half_entropy(out_mps): for ht in range(1, mps_num): h_entropy = qr_left_and_right_location(out_mps, ht, 1e-16)[0] half_entropy_list[0][number_list[0], ht-1] = h_entropy number_list[0] = number_list[0] + 1 entro_tar = copy.deepcopy(tar_mpslist) for et in range(1, mps_num): entropy = qr_left_and_right_location(entro_tar, et, 1e-16)[0] entropy_list.append(entropy) for m in range(mps_num - 2): average_ = entropy_list[m] average = average + average_ average = average / (mps_num - 1) # 求解平均纠缠熵 center_entropy = qr_left_and_right_location(entro_tar, center_position, 1e-16)[0] print('平均纠缠熵是：{}'.format(average)) print('正交中心为第' + str(center_position) + '个tensor的MPS纠缠熵是：{}'.format(center_entropy)) for nn in range(mps_num): # 初始真空零态 ini_state.append(tc.tensor([1, 0], dtype=dtype).reshape(1, 2, 1).to(para['device'])) read_memory_gate = bfs.load('read_memory_gate_data', 'gate') for vt in range(read_gatenum): # 为了分层优化的下一层结果比单层好，随机初始化小量微扰的单位阵 unitary_gate = read_memory_gate[vt].to(para['device']) unitary_gate.requires_grad = True layer_gatelist.append(unitary_gate) for jt in range(gatenum//layer_num): vol_gate = tc.mul(tc.rand((4, 4), dtype=dtype).to(para['device']), vol) unitary_gate = tc.add(vol_gate, identity_4) unitary_gate.requires_grad = True layer_gatelist.append(unitary_gate) mps_norm(ini_state) # 对初始量子态进行归一化 # lay_optimize_1 = tc.optim.Adam(layer_gatelist, lr=lr) # 分层优化的量子门参数，在分层优化结束之后进行协同优化 print('分层储存优化器进入list') for it in range(gatenum): # 将分层优化的loss的list 根据层数区分开 if it < (gatenum//layer_num)*1: layer_gatelist_0.append(layer_gatelist[it]) else: if it < (gatenum//layer_num)*2: layer_gatelist_1.append(layer_gatelist[it]) else: if it < (gatenum//layer_num)*3: layer_gatelist_2.append(layer_gatelist[it]) else: if it < (gatenum//layer_num)*4: layer_gatelist_3.append(layer_gatelist[it]) else: if it < (gatenum//layer_num)*5: layer_gatelist_4.append(layer_gatelist[it]) else: layer_gatelist_5.append(layer_gatelist[it]) lay_optimize_0 = tc.optim.Adam(layer_gatelist_0, lr=lr) # 分层优化的量子门参数，在分层优化结束之后进行协同优化 lay_optimize_1 = tc.optim.Adam(layer_gatelist_1, lr=lr) lay_optimize_2 = tc.optim.Adam(layer_gatelist_2, lr=lr) layer_optimize.append(lay_optimize_0) # 将三层优化器 layer_optimize.append(lay_optimize_1) layer_optimize.append(lay_optimize_2) scheduler_0 = StepLR(lay_optimize_0, step_size=step_size, gamma=0.1) scheduler_1 = StepLR(lay_optimize_1, step_size=step_size, gamma=0.1) scheduler_2 = StepLR(lay_optimize_2, step_size=step_size, gamma=0.1) scheduler = list() scheduler.append(scheduler_0) scheduler.append(scheduler_1) scheduler.append(scheduler_2) evo = ev.Evolve(mps_num, k_bood, 2, gatenum, layer_num) evo.init_tensor_list(copy.deepcopy(ini_state)) for bt in range(layer_num): print('初始化第' + str(bt) + '的学习率：', layer_optimize[bt].defaults['lr']) start.record() # 开始计算模型的运算时间花费 for pt in range(pt_time): # 交错优化所在的次数 fprint('Circle优化位于第' + str(pt) + '次', file=para['log_name']) for lay_num in range(layer_num): fprint('Circle优化位于第' + str(lay_num) + '层', file=para['log_name']) for vt in range(it_time): for llt in range(lay_num, lay_num + 1): # 先将优化层进行演化，演化完成后将其存进新的list，作为下一层初始 evo.layered_evolve_mps(layer_gatelist, llt) if vt == it_time - 1: evo.storage_layer_out_optimization(llt, 0) for at in range(lay_num + 1, layer_num): # 将不变分的量子门演化进入线路 evo.layered_evolve_mps(layer_gatelist, at) lay_loss = evo.log_fidelity(tar_mpslist) # 借助了mps跨越指数复杂度的优势 if ((vt + 1) % dt_print) == 0: if vt == 0: fprint('block') else: fprint('At t = ' + str(vt) + ', loss = ' + str(lay_loss.item()), file=para['log_name']) loss_[lay_num].append(lay_loss.item()) lay_loss.backward() layer_optimize[lay_num].step() layer_optimize[lay_num].zero_grad() if ((vt + 1) % dt_print) == 0: fprint("第%d个epoch的学习率：%f" % (vt, layer_optimize[lay_num].param_groups[0]['lr']), file=para['log_name']) scheduler[lay_num].step() tc.cuda.empty_cache() # 删除不必要的变量 if lay_num == layer_num-1: if vt == it_time - 1: half_entropy(evo.out_optimization()) if vt == it_time - 1: evo.read_layer_out_optimization(lay_num, 0) else: evo.read_layer_out_optimization(lay_num, 1) half_entropy(tar_mpslist) # 热图的最后一行为目标态纠缠的信息 bfs.save('.', 'out_memory_half_entropy_data', [half_entropy_list], ['half_entropy']) for dt in range(gatenum): zero_gatetensor[dt, :, :] = layer_gatelist[dt].data bfs.save('.', 'out_memory_gate_data', [zero_gatetensor], ['gate']) out_layer = evo.out_optimization() out_layer_numpy = list() for nt in range(mps_num): # 将目标MPS转存成numpy数组 out_layer_numpy.append(out_layer[nt].numpy()) np.savez(out_file_name, tar_mpslist0=out_layer_numpy[0], tar_mpslist1=out_layer_numpy[1], tar_mpslist2=out_layer_numpy[2], tar_mpslist3=out_layer_numpy[3], tar_mpslist4=out_layer_numpy[4], tar_mpslist5=out_layer_numpy[5], tar_mpslist6=out_layer_numpy[6], tar_mpslist7=out_layer_numpy[7], tar_mpslist8=out_layer_numpy[8], tar_mpslist9=out_layer_numpy[9], tar_mpslist10=out_layer_numpy[10], tar_mpslist11=out_layer_numpy[11], tar_mpslist12=out_layer_numpy[12], tar_mpslist13=out_layer_numpy[13], tar_mpslist14=out_layer_numpy[14], tar_mpslist15=out_layer_numpy[15], tar_mpslist16=out_layer_numpy[16], tar_mpslist17=out_layer_numpy[17], tar_mpslist18=out_layer_numpy[18], tar_mpslist19=out_layer_numpy[19], tar_mpslist20=out_layer_numpy[20], tar_mpslist21=out_layer_numpy[21], tar_mpslist22=out_layer_numpy[22], tar_mpslist23=out_layer_numpy[23], tar_mpslist24=out_layer_numpy[24], tar_mpslist25=out_layer_numpy[25], tar_mpslist26=out_layer_numpy[26], tar_mpslist27=out_layer_numpy[27], tar_mpslist28=out_layer_numpy[28], tar_mpslist29=out_layer_numpy[29], tar_mpslist30=out_layer_numpy[30], tar_mpslist31=out_layer_numpy[31], tar_mpslist32=out_layer_numpy[32], tar_mpslist33=out_layer_numpy[33], tar_mpslist34=out_layer_numpy[34], tar_mpslist35=out_layer_numpy[35], tar_mpslist36=out_layer_numpy[36], tar_mpslist37=out_layer_numpy[37], tar_mpslist38=out_layer_numpy[38], tar_mpslist39=out_layer_numpy[39], tar_mpslist40=out_layer_numpy[40], tar_mpslist41=out_layer_numpy[41], tar_mpslist42=out_layer_numpy[42], tar_mpslist43=out_layer_numpy[43], tar_mpslist44=out_layer_numpy[44], tar_mpslist45=out_layer_numpy[45], tar_mpslist46=out_layer_numpy[46], tar_mpslist47=out_layer_numpy[47]) for nt in range(mps_num): # 将目标MPS转存成numpy数组 tar_mpslist[nt] = tar_mpslist[nt].cpu().numpy() end.record() # 截至记录模型花费计算的时间 # Waits for everything to finish running tc.cuda.synchronize() # 等待当前设备上所有流中的所有核心完成。 print('Runtime: ', start.elapsed_time(end)) for i in range(pt_time*5): x1_axis.append(i*10) color_list = list(['deeppink', 'red', 'gold', 'black', 'lime', 'peru', 'purple', 'blue']) plt.figure(num=1, figsize=(16, 12), dpi=100) plt.tick_params(labelsize=16) plt.xlabel("num of optimize", fontsize=20) # x轴上的名字 plt.ylabel("negative-logarithmic fidelities (NLFs) per site", fontsize=20) plt.grid(axis='x', c='g', linestyle='--', alpha=0.5) for kt in range(layer_num): plt.plot(x1_axis, loss_[kt], color=color_list[kt], linewidth=3, label=' Circle layered Optimize' + str(kt)) plt.legend(prop={'family': 'Times New Roman', 'size': 16}, loc='upper right') plt.savefig('./MPS_Step_3layer_Circle.jpg')

f73c36a9e617dd10e21800879e5f188acdb69937

py

Python

src/POPULARITY_MODULE/popularity_predictor.py

cristinalunaj/WI-IAT20_PopularityModule

0a4894e2b889bf31ea1a8beab3025d5dd0b1ed47

src/POPULARITY_MODULE/popularity_predictor.py

cristinalunaj/WI-IAT20_PopularityModule

0a4894e2b889bf31ea1a8beab3025d5dd0b1ed47

src/POPULARITY_MODULE/popularity_predictor.py

cristinalunaj/WI-IAT20_PopularityModule

0a4894e2b889bf31ea1a8beab3025d5dd0b1ed47

import pandas as pd import subprocess, os import src.utils.loader as loader def create_test_arff(participant, test_df, aux_path): arff_text = "@relation summary_features \n\n" \ "@attribute n_faces numeric\n" \ "@attribute avg_confidence_faces numeric\n" \ "@attribute std_confidence_faces numeric\n" \ "@attribute avg_relativeSize_faces numeric\n" \ "@attribute std_relativeSize_faces numeric\n" \ "@attribute avg_thirdRule_x numeric\n" \ "@attribute std_thirdRule_x numeric\n" \ "@attribute avg_thirdRule_y numeric\n" \ "@attribute std_thirdRule_y numeric\n" \ "@attribute num_clts numeric\n" \ "@attribute avg_silhouette numeric\n" \ "@attribute avg_intra_clt_dist numeric\n" \ "@attribute avg_inter_clt_dist numeric\n" \ "@attribute faces_in_noise_clt numeric\n" \ "@attribute num_core_samples numeric\n" \ "@attribute avg_imgs_clt numeric\n" \ "@attribute avg_std_silhouette numeric\n" \ "@attribute avg_std_intra_clt_dist numeric\n" \ "@attribute avg_std_inter_clt_dist numeric\n" \ "@attribute avg_n_core_samples numeric\n" \ "@attribute std_n_core_samples numeric\n" \ "@attribute GTrends_popularity numeric\n" \ "@attribute label {1,0}\n\n" \ "@data\n" data = test_df.loc[test_df["id"]==participant] data = data.drop(columns="id") data_str = "" for ele in data.values[0]: data_str += str(ele)+"," data_str = data_str[0:-3] arff_text+=data_str print(arff_text) f = open(aux_path, "w") f.write(arff_text) def evaluate_test_arff(model_path, test_arff_path, out_path): """ Obtain predictions of test_file using the trained model in model_path :param output_folder: :param output_name: :param model_path: :param test_file: """ # PREDICTIONS FILE HEADERS: INSTANCE, ACTUAL, PREDICTED, ERROR bash_file_path = "../../data/bash_scripts/explorer_test_model.sh " with open(out_path, 'w') as fi: fi.close() command = "".join([bash_file_path, test_arff_path, " ", model_path, " ", out_path]) print(command) subprocess.call(command, shell=True) remove_lines(out_path) # remove headers of prediction file df_participant = pd.read_csv(out_path, header=0, sep=",") return df_participant def remove_lines(path_csv): with open(path_csv, 'r') as fin: data = fin.read().splitlines(True) with open(path_csv, 'w') as fout: fout.writelines(data[4:]) #en 4 las cabeceras fout.close() if __name__ == "__main__": th = "05" path_model = "../../data/models/popularity_module/CLASIF/th"+th+"/RandomForest.model" complete_df_ids = "../../data/datasets/popularity_module_features/train/summary_features_participants_classification_th"+th+".csv" aux_path = "../../data/datasets/popularity_module_features/aux_test.arff" out_path_prediction = "../../data/datasets/popularity_module_features/aux_prediction.csv" complete_df = pd.read_csv(complete_df_ids, header=0, sep=",") bash_test_model = "" path_participants = "../../data/datasets/DATASET_GOOGLE_IMGS/participants/" list_participants = loader.load_list_of_tertulianos(path_participants, "participants_complete_rtve2018",".csv") #list_participants = [participant.replace(" ", "_") for participant in part] df_popularity = pd.DataFrame([], columns=["prediction", "popular", "id"]) out_path_popularity_df = "../../data/results/popularity_models_output/popularity_df_th"+th+".csv" for participant in list_participants: participant = participant.replace("_", " ") create_test_arff(participant, complete_df, aux_path) df_participant = evaluate_test_arff(path_model, aux_path, out_path_prediction) df_popularity = df_popularity.append(pd.DataFrame([[df_participant["predicted"][0].split(":")[-1], df_participant["predicted"][0].split(":")[-1]=="1", participant ]], columns=["prediction", "popular", "id"])) df_popularity.to_csv(out_path_popularity_df, sep=";", header=True, index=False)

f73cd7659f96363d6da4f7c2bb9631d7abb9b9f3

py

Python

salt/states/cabal.py

rfairburn/salt

7e44444e873e1cb1d2fe13e39b0edea3779a2b5e

2015-08-04T21:54:38.000Z

2019-04-25T21:47:08.000Z

salt/states/cabal.py

rfairburn/salt

7e44444e873e1cb1d2fe13e39b0edea3779a2b5e

2015-09-02T12:49:48.000Z

2015-09-02T19:22:58.000Z

salt/states/cabal.py

rfairburn/salt

7e44444e873e1cb1d2fe13e39b0edea3779a2b5e

# -*- coding: utf-8 -*- ''' Installation of Cabal Packages ============================== .. versionadded:: 2015.8.0 These states manage the installed packages for Haskell using cabal. Note that cabal-install must be installed for these states to be available, so cabal states should include a requisite to a pkg.installed state for the package which provides cabal (``cabal-install`` in case of Debian based distributions). Example:: .. code-block:: yaml cabal-install: pkg.installed ShellCheck: cabal.installed: - require: - pkg: cabal-install ''' from __future__ import absolute_import from salt.exceptions import CommandExecutionError, CommandNotFoundError import salt.utils def __virtual__(): ''' Only work when cabal-install is installed. ''' return (salt.utils.which('cabal') is not None) and \ (salt.utils.which('ghc-pkg') is not None) def _parse_pkg_string(pkg): ''' Parse pkg string and return a tuple of packge name, separator, and package version. Cabal support install package with following format: * foo-1.0 * foo < 1.2 * foo > 1.3 For the sake of simplicity only the first form is supported, support for other forms can be added later. ''' pkg_name, separator, pkg_ver = pkg.partition('-') return (pkg_name.strip(), separator, pkg_ver.strip()) def installed(name, pkgs=None, user=None, install_global=False, env=None): ''' Verify that the given package is installed and is at the correct version (if specified). .. code-block:: yaml ShellCheck-0.3.5: cabal: - installed: name The package to install user The user to run cabal install with install_global Install package globally instead of locally env A list of environment variables to be set prior to execution. The format is the same as the :py:func:`cmd.run <salt.states.cmd.run>`. state function. ''' ret = {'name': name, 'result': None, 'comment': '', 'changes': {}} try: call = __salt__['cabal.update']() except (CommandNotFoundError, CommandExecutionError) as err: ret['result'] = False ret['comment'] = 'Could not run cabal update {0}'.format(err) if pkgs is not None: pkg_list = pkgs else: pkg_list = [name] try: installed_pkgs = __salt__['cabal.list']( user=user, installed=True, env=env) except (CommandNotFoundError, CommandExecutionError) as err: ret['result'] = False ret['comment'] = 'Error looking up {0!r}: {1}'.format(name, err) pkgs_satisfied = [] pkgs_to_install = [] for pkg in pkg_list: pkg_name, _, pkg_ver = _parse_pkg_string(pkg) if pkg_name not in installed_pkgs: pkgs_to_install.append(pkg) else: if pkg_ver: # version is specified if installed_pkgs[pkg_name] != pkg_ver: pkgs_to_install.append(pkg) else: pkgs_satisfied.append(pkg) else: pkgs_satisfied.append(pkg) if __opts__['test']: ret['result'] = None comment_msg = [] if pkgs_to_install: comment_msg.append( 'Packages(s) {0!r} are set to be installed'.format( ', '.join(pkgs_to_install))) if pkgs_satisfied: comment_msg.append( 'Packages(s) {0!r} satisfied by {1}'.format( ', '.join(pkg_list), ', '.join(pkgs_satisfied))) ret['comment'] = '. '.join(comment_msg) return ret if not pkgs_to_install: ret['result'] = True ret['comment'] = ('Packages(s) {0!r} satisfied by {1}'.format( ', '.join(pkg_list), ', '.join(pkgs_satisfied))) return ret try: call = __salt__['cabal.install'](pkgs=pkg_list, user=user, install_global=install_global, env=env) except (CommandNotFoundError, CommandExecutionError) as err: ret['result'] = False ret['comment'] = 'Error installing {0!r}: {1}'.format( ', '.join(pkg_list), err) return ret if call and isinstance(call, dict): ret['result'] = True ret['changes'] = {'old': [], 'new': pkgs_to_install} ret['comment'] = 'Packages(s) {0!r} successfully installed'.format( ', '.join(pkgs_to_install)) else: ret['result'] = False ret['comment'] = 'Could not install packages(s) {0!r}'.format( ', '.join(pkg_list)) return ret def removed(name, user=None, env=None): ''' Verify that given package is not installed. ''' ret = {'name': name, 'result': None, 'comment': '', 'changes': {}} try: installed_pkgs = __salt__['cabal.list']( user=user, installed=True, env=env) except (CommandNotFoundError, CommandExecutionError) as err: ret['result'] = False ret['comment'] = 'Error looking up {0!r}: {1}'.format(name, err) if name not in installed_pkgs: ret['result'] = True ret['comment'] = 'Package {0!r} is not installed'.format(name) return ret if __opts__['test']: ret['result'] = None ret['comment'] = 'Package {0!r} is set to be removed'.format(name) return ret if __salt__['cabal.uninstall'](pkg=name, user=user, env=env): ret['result'] = True ret['changes'][name] = 'Removed' ret['comment'] = 'Package {0!r} was successfully removed'.format(name) else: ret['result'] = False ret['comment'] = 'Error removing package {0!r}'.format(name) return ret

f73ce815f021946c17d274a89a2a9a2f8ef1867c

py

Python

setup.py

shx2/apegears

3fa0408a15df3817fd206d1086d7e49e1b60594c

2020-08-31T20:04:04.000Z

2022-01-15T17:09:42.000Z

setup.py

shx2/apegears

3fa0408a15df3817fd206d1086d7e49e1b60594c

2020-10-23T02:41:45.000Z

2020-11-03T02:27:57.000Z

setup.py

shx2/apegears

3fa0408a15df3817fd206d1086d7e49e1b60594c

2020-10-23T02:48:08.000Z

2020-10-23T02:48:08.000Z

#!/usr/bin/env python3 from setuptools import setup, find_packages from codecs import open from os import path here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, 'README.rst'), encoding='utf-8') as f: long_description = f.read() # Read version info from apegears/version.py version_vars = {} with open(path.join("apegears", "version.py")) as fp: exec(fp.read(), version_vars) version_string = version_vars['__version_string__'] setup( name='apegears', version=version_string, description='An improved ArgumentParser, fully compatible with argparse.', long_description=long_description, url='https://github.com/shx2/apegears', author='shx2', author_email='shx222@gmail.com', license='MIT', packages=find_packages(exclude=['tests*']), platforms = ["POSIX", "Windows"], install_requires=[], # See https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ 'Programming Language :: Python :: 3', 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'Topic :: Software Development', 'License :: OSI Approved :: MIT License', "Operating System :: POSIX", "Operating System :: Microsoft :: Windows", ], keywords='CLI argparse ArgumentParser optparse func_argparse', )

f73cf0e3aa3957672cd28f652a1f0461874ddcc9

py

Python

policy_gradient.py

rey-allan/rl

6124bcfe5de8a9a316c41fb75b50a3e9babfc970

policy_gradient.py

rey-allan/rl

6124bcfe5de8a9a316c41fb75b50a3e9babfc970

policy_gradient.py

rey-allan/rl

6124bcfe5de8a9a316c41fb75b50a3e9babfc970

"""Implementation of different policy gradient methods""" import argparse import numpy as np import plot as plt import random from collections import namedtuple from env import Action, Easy21, State from tqdm import tqdm from typing import Callable, List # For reproducibility random.seed(0) np.random.seed(0) Trajectory = namedtuple("Trajectory", ["state", "action", "reward"]) def encode(s: State, a: Action) -> np.ndarray: """ Encodes the given state-action pair using coarse coding as specified in the Easy21 assignment: A binary feature vector rho(s, a) with 3 ∗ 6 ∗ 2 = 36 features. Each binary feature has a value of 1 iff (s, a) lies within the cuboid of state-space corresponding to that feature, and the action corresponding to that feature. The cuboids have the following overlapping intervals: - dealer(s) = {[1, 4], [4, 7], [7, 10]} - player(s) = {[1, 6], [4, 9], [7, 12], [10, 15], [13, 18], [16, 21]} - a = {hit, stick} :param State s: The state to encode :param Action a: The action to encode :return: A binary feature vector representing the encoded state-action pair :rtype: np.ndarray """ # `range` is end-exclusive so we add a 1 to make sure we capture the intervals inclusive ends dealer = [range(1, 5), range(4, 8), range(7, 11)] player = [range(1, 7), range(4, 10), range(7, 13), range(10, 16), range(13, 19), range(16, 22)] encoded = np.zeros((3, 6, 2)) for i, d in enumerate(dealer): for j, p in enumerate(player): for k, action in enumerate([Action.hit, Action.stick]): if s.dealer_first_card in d and s.player_sum in p and a == action: encoded[i, j, k] = 1 return encoded.flatten() def softmax(x: np.ndarray) -> np.ndarray: """ Computes the softmax of the given array :param np.ndarray x: The input array :return: The softmax of each element of the input array :rtype: np.ndarray """ return np.exp(x) / np.sum(np.exp(x)) class REINFORCEWithBaseline: """ REINFORCE algorithm with baseline Uses softmax on linear action preferences for the policy, and linear approximation for the value function. Feature vectors are computed using coarse coding as described in the Easy21 assignment. """ def __init__(self): self._env = Easy21(seed=24) def learn(self, epochs=200, alpha_policy=0.01, alpha_value=0.01, gamma=0.9, verbose=False, **kwargs) -> np.ndarray: """ Learns the optimal value function. :param int epochs: The number of epochs to take to learn the value function :param float alpha_policy: The learning rate for the policy approximation :param float alpha_value: The learning rate for the value approximation :param float gamma: The discount factor :param bool verbose: Whether to use verbose mode or not :return: The optimal value function :rtype: np.ndarray """ # Value function w = np.random.rand(36) value_approximator = lambda s: [np.dot(w, encode(s, a)) for a in [Action.hit, Action.stick]] # Policy function theta = np.random.rand(36) pi = lambda s, theta: softmax(np.array([np.dot(theta, encode(s, a)) for a in [Action.hit, Action.stick]])) for _ in tqdm(range(epochs), disable=not verbose): trajectories = self._sample_episode(pi, theta) # Reverse the list so we start backpropagating the return from the last episode trajectories.reverse() # Learn from the episode g = 0 for i, t in enumerate(trajectories): g = t.reward + gamma * g x = encode(t.state, t.action) # Baseline v = np.dot(w, x) delta = g - v # SGD update of the value function w += alpha_value * delta * x # SGD update of the policy function probs = pi(t.state, theta) eligibility_vector = x - np.sum([p * encode(t.state, a) for a, p in enumerate(probs)]) theta += alpha_policy * gamma ** i * delta * eligibility_vector # Compute the optimal value function which is simply the value of the best action in each state values = np.zeros(self._env.state_space) for d in range(self._env.state_space[0]): for p in range(self._env.state_space[1]): values[d, p] = np.max(value_approximator(State(d, p))) return values def _sample_episode(self, pi: Callable[[State, Action, np.ndarray], float], theta: np.ndarray) -> List[Trajectory]: # Samples trajectories following policy `pi` with an optional starting state-action pair trajectories = [] s = self._env.reset() # The policy selects the action with some constant exploration as in the Easy21 assignment policy = ( lambda s: random.choice([Action.hit, Action.stick]) if random.random() < 0.05 else np.argmax(pi(s, theta)) ) a = policy(s) while True: s_prime, r, done = self._env.step(a) trajectories.append(Trajectory(s, a, r)) if done: break s = s_prime a = policy(s) return trajectories class OneStepActorCritic: """ One-step Actor-Critic Uses softmax on linear action preferences for the policy, and linear approximation for the value function. Feature vectors are computed using coarse coding as described in the Easy21 assignment. """ def __init__(self): self._env = Easy21(seed=24) def learn(self, epochs=200, alpha_policy=0.01, alpha_value=0.01, gamma=0.9, verbose=False, **kwargs) -> np.ndarray: """ Learns the optimal value function. :param int epochs: The number of epochs to take to learn the value function :param float alpha_policy: The learning rate for the policy approximation :param float alpha_value: The learning rate for the value approximation :param float gamma: The discount factor :param bool verbose: Whether to use verbose mode or not :return: The optimal value function :rtype: np.ndarray """ # Value function w = np.random.rand(36) value_approximator = lambda s: [np.dot(w, encode(s, a)) for a in [Action.hit, Action.stick]] # Policy function theta = np.random.rand(36) pi = lambda s, theta: softmax(np.array([np.dot(theta, encode(s, a)) for a in [Action.hit, Action.stick]])) # The policy selects the action with some constant exploration as in the Easy21 assignment policy = ( lambda s: random.choice([Action.hit, Action.stick]) if random.random() < 0.05 else np.argmax(pi(s, theta)) ) for _ in tqdm(range(epochs), disable=not verbose): I = 1 s = self._env.reset() done = False while not done: a = policy(s) s_prime, r, done = self._env.step(a) # Compute the delta if done: delta = r - np.dot(w, encode(s, a)) else: delta = r + gamma * np.max(value_approximator(s_prime)) - np.dot(w, encode(s, a)) # SGD update of the value function x = encode(s, a) w += alpha_value * delta * x # SGD update of the policy function probs = pi(s, theta) eligibility_vector = x - np.sum([p * encode(s, a) for a, p in enumerate(probs)]) theta += alpha_policy * I * delta * eligibility_vector I *= gamma s = s_prime # Compute the optimal value function which is simply the value of the best action in each state values = np.zeros(self._env.state_space) for d in range(self._env.state_space[0]): for p in range(self._env.state_space[1]): values[d, p] = np.max(value_approximator(State(d, p))) return values class ActorCriticWithEligibilityTraces: """ Actor-Critic with eligibility traces Uses softmax on linear action preferences for the policy, and linear approximation for the value function. Feature vectors are computed using coarse coding as described in the Easy21 assignment. """ def __init__(self): self._env = Easy21(seed=24) def learn( self, epochs=200, alpha_policy=0.01, alpha_value=0.01, gamma=0.9, lambda_value=1.0, lambda_policy=1.0, verbose=False, **kwargs ) -> np.ndarray: """ Learns the optimal value function. :param int epochs: The number of epochs to take to learn the value function :param float alpha_policy: The learning rate for the policy approximation :param float alpha_value: The learning rate for the value approximation :param float gamma: The discount factor :param float lambda_value: The trace decay rate for the value approximation :param float lambda_policy: The trace decay rate for the policy approximation :param bool verbose: Whether to use verbose mode or not :return: The optimal value function :rtype: np.ndarray """ # Value function w = np.random.rand(36) value_approximator = lambda s: [np.dot(w, encode(s, a)) for a in [Action.hit, Action.stick]] # Policy function theta = np.random.rand(36) pi = lambda s, theta: softmax(np.array([np.dot(theta, encode(s, a)) for a in [Action.hit, Action.stick]])) # The policy selects the action with some constant exploration as in the Easy21 assignment policy = ( lambda s: random.choice([Action.hit, Action.stick]) if random.random() < 0.05 else np.argmax(pi(s, theta)) ) for _ in tqdm(range(epochs), disable=not verbose): I = 1 s = self._env.reset() done = False z_w = np.zeros_like(w) z_theta = np.zeros_like(theta) while not done: a = policy(s) s_prime, r, done = self._env.step(a) # Compute the delta if done: delta = r - np.dot(w, encode(s, a)) else: delta = r + gamma * np.max(value_approximator(s_prime)) - np.dot(w, encode(s, a)) # SGD update of the value function x = encode(s, a) z_w = gamma * lambda_value * z_w + x w += alpha_value * delta * z_w # SGD update of the policy function probs = pi(s, theta) eligibility_vector = x - np.sum([p * encode(s, a) for a, p in enumerate(probs)]) z_theta = gamma * lambda_policy * z_theta + I * eligibility_vector theta += alpha_policy * delta * z_theta I *= gamma s = s_prime # Compute the optimal value function which is simply the value of the best action in each state values = np.zeros(self._env.state_space) for d in range(self._env.state_space[0]): for p in range(self._env.state_space[1]): values[d, p] = np.max(value_approximator(State(d, p))) return values if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run policy gradient methods") parser.add_argument("--reinforce-with-baseline", action="store_true", help="Execute REINFORCE with Baseline") parser.add_argument("--one-step-ac", action="store_true", help="Execute One-step Actor-Critic") parser.add_argument( "--ac-eligibility-traces", action="store_true", help="Execute Actor-Critic with eligibility traces" ) parser.add_argument("--epochs", type=int, default=200, help="Epochs to train") parser.add_argument( "--alpha-value", type=float, default=0.01, help="Learning rate to use for the value function approximation" ) parser.add_argument( "--alpha-policy", type=float, default=0.01, help="Learning rate to use for the policy function approximation" ) parser.add_argument( "--lambda-value", type=float, default=1.0, help="Trace decay rate to use for the value function approximation" ) parser.add_argument( "--lambda-policy", type=float, default=1.0, help="Trace decay rate to use for the policy function approximation" ) parser.add_argument("--gamma", type=float, default=0.9, help="Discount factor") parser.add_argument("--verbose", action="store_true", help="Run in verbose mode") args = parser.parse_args() # The optimal value function obtained V = None # The algorithm to run policy_grad = None # The title of the plot title = None if args.reinforce_with_baseline: print("Running REINFORCE with Baseline") policy_grad = REINFORCEWithBaseline() title = "reinforce_with_baseline" elif args.one_step_ac: print("Running One-step Actor-Critic") policy_grad = OneStepActorCritic() title = "one_step_actor_critic" elif args.ac_eligibility_traces: print("Running Actor-Critic with eligibility traces") policy_grad = ActorCriticWithEligibilityTraces() title = "actor_critic_eligibility_traces" if policy_grad is not None: V = policy_grad.learn( epochs=args.epochs, alpha_value=args.alpha_value, alpha_policy=args.alpha_policy, lambda_value=args.lambda_value, lambda_policy=args.lambda_policy, gamma=args.gamma, verbose=args.verbose, ) if V is not None: # Plot the value function as a surface # Remove the state where the dealer's first card is 0 and the player's sum is 0 because these are not possible # They were kept in the value function to avoid having to deal with 0-index vs 1-index plt.plot_value_function(range(1, Easy21.state_space[0]), range(1, Easy21.state_space[1]), V[1:, 1:], title)

f73d327e2f83cfcb8714c4fa54d314b74ae3fd9f

py

Python

djangocms_versioning/test_utils/people/models.py

webbyfox/djangocms-versioning

a466ff0f8d109a22ec2f567cace6ef69d332180c

djangocms_versioning/test_utils/people/models.py

webbyfox/djangocms-versioning

a466ff0f8d109a22ec2f567cace6ef69d332180c

djangocms_versioning/test_utils/people/models.py

webbyfox/djangocms-versioning

a466ff0f8d109a22ec2f567cace6ef69d332180c

from django.db import models class Person(models.Model): name = models.TextField() def __str__(self): return "{} ({})".format(self.name, self.pk) class PersonContent(models.Model): person = models.ForeignKey(Person, on_delete=models.CASCADE) language = models.TextField() text = models.TextField() def __str__(self): return self.text def get_absolute_url(self): return '/'

f73d453812bcfc25c8bbd44be64d84d895cd81c9

py

Python

main.py

sebanie15/simple_clinic

4dc942b0549ee6397a0e89dd7aa03eb8580b4a5a

main.py

sebanie15/simple_clinic

4dc942b0549ee6397a0e89dd7aa03eb8580b4a5a

main.py

sebanie15/simple_clinic

4dc942b0549ee6397a0e89dd7aa03eb8580b4a5a

# !/usr/bin/env python3 # -*- coding: utf-8 -*- """ @author: sebanie15 """

f73d56274bb5a4e9fa2a9fe2f5ce429d2af7de69

py

Python

deeppavlov/core/common/log.py

ineersa/DeepPavlov

8200bf9a0f0b378baad4ee0eb75b59453f516004

2019-05-22T08:34:33.000Z

2019-05-22T08:34:33.000Z

deeppavlov/core/common/log.py

ineersa/DeepPavlov

8200bf9a0f0b378baad4ee0eb75b59453f516004

deeppavlov/core/common/log.py

ineersa/DeepPavlov

8200bf9a0f0b378baad4ee0eb75b59453f516004

2019-03-17T13:47:44.000Z

2019-03-17T13:47:44.000Z

# Copyright 2017 Neural Networks and Deep Learning lab, MIPT # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import logging import logging.config import sys from pathlib import Path from .paths import get_settings_path LOG_CONFIG_FILENAME = 'log_config.json' TRACEBACK_LOGGER_ERRORS = True root_path = Path(__file__).resolve().parents[3] logging.getLogger('matplotlib').setLevel(logging.WARNING) def get_logger(logger_name): try: log_config_path = get_settings_path() / LOG_CONFIG_FILENAME with log_config_path.open(encoding='utf8') as log_config_json: log_config = json.load(log_config_json) configured_loggers = [log_config.get('root', {})] + log_config.get('loggers', []) used_handlers = {handler for log in configured_loggers for handler in log.get('handlers', [])} for handler_id, handler in list(log_config['handlers'].items()): if handler_id not in used_handlers: del log_config['handlers'][handler_id] elif 'filename' in handler.keys(): filename = handler['filename'] logfile_path = Path(filename).expanduser().resolve() handler['filename'] = str(logfile_path) logging.config.dictConfig(log_config) logger = logging.getLogger(logger_name) except Exception: logger = logging.getLogger(logger_name) logger.setLevel(logging.WARNING) formatter = logging.Formatter( '%(asctime)s.%(msecs)d %(levelname)s in \'%(name)s\'[\'%(module)s\'] at line %(lineno)d: %(message)s', '%Y-%m-%d %H:%M:%S') handler = logging.StreamHandler(sys.stderr) handler.setFormatter(formatter) handler.setLevel(logging.WARNING) logger.addHandler(handler) logger.error( 'LOGGER ERROR: Can not initialise {} logger, ' 'logging to the stderr. Error traceback:\n'.format(logger_name), exc_info=TRACEBACK_LOGGER_ERRORS) return logger

f73d6cd897ef8812b769f1d9383c8e52951c6300

py

Python

tests/test_utils.py

nhoffman/uwgroups

fab9cb266d2e0c794370c1bdf62b26610dd33aef

2018-11-30T00:43:06.000Z

2018-11-30T00:43:06.000Z

tests/test_utils.py

nhoffman/uwgroups

fab9cb266d2e0c794370c1bdf62b26610dd33aef

2018-08-20T17:00:18.000Z

2018-08-27T17:30:54.000Z

tests/test_utils.py

nhoffman/uwgroups

fab9cb266d2e0c794370c1bdf62b26610dd33aef

2018-08-21T15:06:54.000Z

2018-08-21T15:06:54.000Z

""" Test utils module. """ import logging from uwgroups.utils import reconcile, grouper from .__init__ import TestBase log = logging.getLogger(__name__) class TestReconcile(TestBase): def test01(self): to_add, to_remove = reconcile( current=set(), desired=set()) self.assertSetEqual(to_add, set()) self.assertSetEqual(to_remove, set()) def test02(self): to_add, to_remove = reconcile( current={'a', 'b', 'c'}, desired={'a', 'b', 'c'}) self.assertSetEqual(to_add, set()) self.assertSetEqual(to_remove, set()) def test03(self): to_add, to_remove = reconcile( current={'a', 'b', 'c'}, desired={'a', 'b'}) self.assertSetEqual(to_add, set()) self.assertSetEqual(to_remove, {'c'}) def test04(self): to_add, to_remove = reconcile( current={'a', 'b'}, desired={'a', 'b', 'c'}) self.assertSetEqual(to_add, {'c'}) self.assertSetEqual(to_remove, set()) def test05(self): to_add, to_remove = reconcile( current={'b', 'd'}, desired={'a', 'b', 'c'}) self.assertSetEqual(to_add, {'a', 'c'}) self.assertSetEqual(to_remove, {'d'}) def test06(self): self.assertRaises(TypeError, reconcile, set(), []) class TestGrouper(TestBase): def test01(self): items = list(range(10)) chunks = list(grouper(items, 4)) self.assertEqual(chunks[-1], (8, 9)) def test02(self): items = list(range(10)) chunks = list(grouper(items, 4, fill=True)) self.assertEqual(chunks[-1], (8, 9, None, None))

f73d8ed23868ba52f022e4f17ca43ad0a39cd922

py

Python

hiicart/gateway/amazon/urls.py

kbourgoin/hiicart

151d64be60ffa5e09b4abc21bf42fd235bf87eea

hiicart/gateway/amazon/urls.py

kbourgoin/hiicart

151d64be60ffa5e09b4abc21bf42fd235bf87eea

2020-10-29T01:05:05.000Z

2020-10-29T01:05:19.000Z

hiicart/gateway/amazon/urls.py

kbourgoin/hiicart

151d64be60ffa5e09b4abc21bf42fd235bf87eea

import hiicart.gateway.amazon.views from django.conf.urls.defaults import * urlpatterns = patterns('', (r'cbui/?$', 'hiicart.gateway.amazon.views.cbui'), (r'ipn/?$', 'hiicart.gateway.amazon.views.ipn'), )

f73de0cf78faa821e681ebb950cc71213af5e281

py

Python

pinax/apps/tasks/tests/test_client.py

peiwei/pinax

34f95b1df4318655fe9bd90dcda8fe824e0c4117

2019-02-12T04:45:09.000Z

2019-02-12T04:45:09.000Z

pinax/apps/tasks/tests/test_client.py

alex/pinax

37e17ee2e2eb0e387d8809c12e55c20194a7118a

pinax/apps/tasks/tests/test_client.py

alex/pinax

37e17ee2e2eb0e387d8809c12e55c20194a7118a

2019-02-12T04:45:40.000Z

2019-02-12T04:45:40.000Z

# coding: utf-8 from django.test import TestCase rst_markup = """ Sample Header =============== Blah blah blah Lower Header ------------- Blah blah blah """ class TestAddForm(TestCase): fixtures = ["test_tasks.json"] urls = "tasks.tests.tasks_urls" def setUp(self): self.client.login(username="admin", password="test") def tearDown(self): pass def test_add_buttons(self): response = self.client.get("/tasks/add/") # Check that the response is 200 OK. self.failUnlessEqual(response.status_code, 200) # check that there is an add button self.assertContains(response, '<input type="submit" value="Add task"/>') # check that there is an add another task button self.assertContains(response, "add-another-task") def test_markup(self): # create some sample form data form_data = { "summary": "my simple test", "detail": rst_markup, "markup": "rst", "assignee": "", "tags": "" } # post the form response = self.client.post("/tasks/add/", form_data) # display the resultant task response = self.client.get("/tasks/task/3/") # test the markup self.assertContains(response, '<h1 class="title">Sample Header</h1>') def test_tag_for_rel(self): # checking for tag response = self.client.get("/tasks/") self.assertContains(response, '<a rel="tag" href="/tasks/tag/test/">test</a>')

f73dfbb3ad628d6d9456e84ab79764156db7292d

py

Python

mtp_noms_ops/apps/settings/views.py

uk-gov-mirror/ministryofjustice.money-to-prisoners-noms-ops

eb537fb8a8e3adc588d50af1b000402c957b32a7

2016-12-22T15:56:57.000Z

2020-03-10T10:37:40.000Z

mtp_noms_ops/apps/settings/views.py

uk-gov-mirror/ministryofjustice.money-to-prisoners-noms-ops

eb537fb8a8e3adc588d50af1b000402c957b32a7

2016-06-10T08:37:23.000Z

2022-01-28T12:41:29.000Z

mtp_noms_ops/apps/settings/views.py

uk-gov-mirror/ministryofjustice.money-to-prisoners-noms-ops

eb537fb8a8e3adc588d50af1b000402c957b32a7

2021-04-11T06:13:53.000Z

2021-04-11T06:13:53.000Z

from urllib.parse import urlencode from django.contrib.auth import REDIRECT_FIELD_NAME from django.contrib.auth.views import SuccessURLAllowedHostsMixin from django.shortcuts import redirect from django.urls import reverse, reverse_lazy from django.utils.http import is_safe_url from django.utils.translation import gettext_lazy as _ from django.views.generic import FormView, TemplateView from mtp_common.auth.api_client import get_api_session from mtp_common.views import SettingsView from security import confirmed_prisons_flag, provided_job_info_flag from settings.forms import ConfirmPrisonForm, ChangePrisonForm, ALL_PRISONS_CODE, JobInformationForm from security.models import EmailNotifications from security.utils import save_user_flags, can_skip_confirming_prisons, has_provided_job_information class NomsOpsSettingsView(SettingsView): template_name = 'settings/settings.html' def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) if self.request.can_access_security: session = get_api_session(self.request) email_preferences = session.get('/emailpreferences/').json() context['email_notifications'] = email_preferences['frequency'] != EmailNotifications.never return context def post(self, *args, **kwargs): if self.request.can_access_security and 'email_notifications' in self.request.POST: session = get_api_session(self.request) if self.request.POST['email_notifications'] == 'True': session.post('/emailpreferences/', json={'frequency': EmailNotifications.daily}) else: session.post('/emailpreferences/', json={'frequency': EmailNotifications.never}) return redirect(reverse_lazy('settings')) class ConfirmPrisonsView(FormView): title = _('Confirm your prisons') template_name = 'settings/confirm-prisons.html' form_class = ConfirmPrisonForm success_url = reverse_lazy('confirm_prisons_confirmation') def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context['current_prisons'] = ','.join([ p['nomis_id'] for p in self.request.user.user_data['prisons'] ] if self.request.user.user_data.get('prisons') else ['ALL']) selected_prisons = self.request.GET.getlist('prisons') if not selected_prisons: selected_prisons = [ p['nomis_id'] for p in self.request.user.user_data['prisons'] ] if not selected_prisons: selected_prisons = [ALL_PRISONS_CODE] query_dict = self.request.GET.copy() query_dict['prisons'] = selected_prisons context['change_prison_query'] = urlencode(query_dict, doseq=True) self.request.cannot_navigate_away = not can_skip_confirming_prisons(self.request.user) return context def get_form_kwargs(self): kwargs = super().get_form_kwargs() kwargs['request'] = self.request return kwargs def form_valid(self, form): form.save() save_user_flags(self.request, confirmed_prisons_flag) return redirect(self.get_success_url()) def get_success_url(self): if 'next' in self.request.GET: return '{path}?{query}'.format( path=self.success_url, query=urlencode({'next': self.request.GET['next']}) ) return self.success_url class ChangePrisonsView(SuccessURLAllowedHostsMixin, FormView): title = _('Change prisons') template_name = 'settings/confirm-prisons-change.html' form_class = ChangePrisonForm def get_success_url(self): """ Returns the REDIRECT_FIELD_NAME value in GET if it exists and it's valid or the url to the settings page otherwise. """ if REDIRECT_FIELD_NAME in self.request.GET: next_page = self.request.GET[REDIRECT_FIELD_NAME] url_is_safe = is_safe_url( url=next_page, allowed_hosts=self.get_success_url_allowed_hosts(), require_https=self.request.is_secure(), ) if url_is_safe: return next_page return reverse('settings') def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context['data_attrs'] = { 'data-autocomplete-error-empty': _('Type a prison name'), 'data-autocomplete-error-summary': _('There was a problem'), 'data-event-category': 'PrisonConfirmation', } context['current_prisons'] = ','.join([ p['nomis_id'] for p in self.request.user.user_data['prisons'] ] if self.request.user.user_data.get('prisons') else ['ALL']) return context def get_form_kwargs(self): kwargs = super().get_form_kwargs() kwargs['request'] = self.request return kwargs def form_valid(self, form): form.save() save_user_flags(self.request, confirmed_prisons_flag) return redirect(self.get_success_url()) class AddOrRemovePrisonsView(ChangePrisonsView): title = _('Add or remove prisons') template_name = 'settings/confirm-prisons-change.html' form_class = ChangePrisonForm success_url = reverse_lazy('confirm_prisons') def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) self.request.cannot_navigate_away = not can_skip_confirming_prisons(self.request.user) return context def form_valid(self, form): return redirect('{path}?{query}'.format( path=self.get_success_url(), query=form.get_confirmation_query_string() )) class ConfirmPrisonsConfirmationView(TemplateView): title = _('Your prisons have been saved') template_name = 'settings/confirm-prisons-confirmation.html' def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context['prisons'] = self.request.user_prisons return context class JobInformationView(SuccessURLAllowedHostsMixin, FormView): title = _('Help us improve this service') template_name = 'settings/job-information.html' form_class = JobInformationForm def dispatch(self, request, *args, **kwargs): request.cannot_navigate_away = True return super().dispatch(request, *args, **kwargs) def get_success_url(self): if REDIRECT_FIELD_NAME in self.request.GET: next_page = self.request.GET[REDIRECT_FIELD_NAME] url_is_safe = is_safe_url( url=next_page, allowed_hosts=self.get_success_url_allowed_hosts(), require_https=self.request.is_secure(), ) if url_is_safe: return next_page return reverse('security:dashboard') def form_valid(self, form): if has_provided_job_information(self.request.user): return redirect(self.get_success_url()) session = get_api_session(self.request) session.post('/job-information/', json={'title': form.cleaned_data['job_title_or_other'], 'prison_estate': form.cleaned_data['prison_estate'], 'tasks': form.cleaned_data['tasks']}) save_user_flags(self.request, provided_job_info_flag) return super().form_valid(form)

f73e12a1c014661754813b808c8c50782a8dc99d

py

Python

colour_datasets/loaders/jakob2019.py

colour-science/colour-datasets

464c387c17739f08a0cceb5185f6b225872adb6c

2019-06-15T03:07:28.000Z

2022-03-28T14:11:51.000Z

colour_datasets/loaders/jakob2019.py

JGoldstone/colour-datasets

8e0b52870c63c0e9b72d8b848720e0c28e0cbfa4

2020-03-24T17:35:36.000Z

2021-11-09T08:49:39.000Z

colour_datasets/loaders/jakob2019.py

JGoldstone/colour-datasets

8e0b52870c63c0e9b72d8b848720e0c28e0cbfa4

2019-10-27T15:00:52.000Z

2022-01-26T15:29:38.000Z

# -*- coding: utf-8 -*- """ Spectral Upsampling Coefficient Tables - Jakob and Hanika (2019) ================================================================ Defines the objects implementing support for *Jakob and Hanika (2019)* *Spectral Upsampling Coefficient Tables* dataset loading: - :class:`colour_datasets.loaders.DatasetLoader_Jakob2019` - :func:`colour_datasets.loaders.build_Jakob2019` References ---------- - :cite:`Jakob2019` : Jakob, W., & Hanika, J. (2019). A Low‐Dimensional Function Space for Efficient Spectral Upsampling. Computer Graphics Forum, 38(2), 147-155. doi:10.1111/cgf.13626 """ import glob import os from collections import OrderedDict from colour.recovery import LUT3D_Jakob2019 from colour_datasets.loaders import AbstractDatasetLoader from colour_datasets.records import datasets __author__ = 'Colour Developers' __copyright__ = 'Copyright (C) 2019-2021 - Colour Developers' __license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause' __maintainer__ = 'Colour Developers' __email__ = 'colour-developers@colour-science.org' __status__ = 'Production' __all__ = ['DatasetLoader_Jakob2019', 'build_Jakob2019'] class DatasetLoader_Jakob2019(AbstractDatasetLoader): """ Defines the *Jakob and Hanika (2019)* *Spectral Upsampling Coefficient Tables* dataset loader. Attributes ---------- - :attr:`colour_datasets.loaders.DatasetLoader_Jakob2019.ID` Methods ------- - :meth:`colour_datasets.loaders.DatasetLoader_Jakob2019.__init__` - :meth:`colour_datasets.loaders.DatasetLoader_Jakob2019.load` References ---------- :cite:`Jakob2019` """ ID = '4050598' """ Dataset record id, i.e. the *Zenodo* record number. ID : unicode """ def __init__(self): super(DatasetLoader_Jakob2019, self).__init__(datasets()[DatasetLoader_Jakob2019.ID]) def load(self): """ Syncs, parses, converts and returns the *Jakob and Hanika (2019)* *Spectral Upsampling Coefficient Tables* dataset content. Returns ------- OrderedDict *Jakob and Hanika (2019)* *Spectral Upsampling Coefficient Tables* dataset content. Examples -------- >>> from colour_datasets.utilities import suppress_stdout >>> dataset = DatasetLoader_Jakob2019() >>> with suppress_stdout(): ... dataset.load() >>> len(dataset.content.keys()) 4 """ super(DatasetLoader_Jakob2019, self).sync() self._content = OrderedDict() tables_path = os.path.join(self.record.repository, 'dataset', 'Jakob2019Spectral', 'supplement', 'tables') coeff_file_to_RGB_colourspace = { 'rec2020': 'ITU-R BT.2020', 'srgb': 'sRGB', 'aces2065_1': 'ACES2065-1', 'prophotorgb': 'ProPhoto RGB', } for coeff_file in glob.glob('{0}/*.coeff'.format(tables_path)): key = os.path.splitext(os.path.basename(coeff_file))[0] key = coeff_file_to_RGB_colourspace.get(key, key) LUT = LUT3D_Jakob2019() LUT.read(coeff_file) self._content[key] = LUT return self._content _DATASET_LOADER_JAKOB2019 = None """ Singleton instance of the *Jakob and Hanika (2019)* *Spectral Upsampling Coefficient Tables* dataset loader. _DATASET_LOADER_JAKOB2019 : DatasetLoader_Jakob2019 """ def build_Jakob2019(load=True): """ Singleton factory that builds the *Jakob and Hanika (2019)* *Spectral Upsampling Coefficient Tables* dataset loader. Parameters ---------- load : bool, optional Whether to load the dataset upon instantiation. Returns ------- DatasetLoader_Jakob2019 Singleton instance of the *Jakob and Hanika (2019)* *Spectral Upsampling Coefficient Tables* dataset loader. References ---------- :cite:`Jakob2019` """ global _DATASET_LOADER_JAKOB2019 if _DATASET_LOADER_JAKOB2019 is None: _DATASET_LOADER_JAKOB2019 = DatasetLoader_Jakob2019() if load: _DATASET_LOADER_JAKOB2019.load() return _DATASET_LOADER_JAKOB2019

f73e20489d20cf4f5c8057aeb413fc2f1f957f89

py

Python

setup.py

albert118/jaffle

55da4d75ad3a9ca633af3865cc35b73e3406a4ef

setup.py

albert118/jaffle

55da4d75ad3a9ca633af3865cc35b73e3406a4ef