ytzi/the-stack-dedup-python-filtered-all · Datasets at Hugging Face

5d3f7ba06d8f30ec1f43524b350975670db0b280

1,597

py

Python

tests/gmprocess/waveform_processing/adjust_highpass_ridder_test.py

usgs/groundmotion-processing-

ed188e2bb1dcd9b17433ef4677874eac654fdd16

[ "Unlicense" ]

null

tests/gmprocess/waveform_processing/adjust_highpass_ridder_test.py

usgs/groundmotion-processing-

ed188e2bb1dcd9b17433ef4677874eac654fdd16

[ "Unlicense" ]

null

tests/gmprocess/waveform_processing/adjust_highpass_ridder_test.py

usgs/groundmotion-processing-

ed188e2bb1dcd9b17433ef4677874eac654fdd16

[ "Unlicense" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import os if __name__ == "__main__": os.environ["CALLED_FROM_PYTEST"] = "True" test_auto_fchp()

27.067797

76

0.634314

#!/usr/bin/env python # -*- coding: utf-8 -*- import os import numpy as np from gmprocess.core.streamcollection import StreamCollection from gmprocess.io.read import read_data from gmprocess.utils.test_utils import read_data_dir from gmprocess.waveform_processing.adjust_highpass_ridder import ridder_fchp from gmprocess.utils.config import get_config def test_auto_fchp(): data_files, origin = read_data_dir("geonet", "us1000778i", "*.V1A") data_files.sort() streams = [] for f in data_files: streams += read_data(f) sc = StreamCollection(streams) output_fchp = [] config = get_config() config["integration"]["frequency"] = True for st in sc: for tr in st: tr.setParameter( "corner_frequencies", {"type": "constant", "highpass": 0.001, "lowpass": 20}, ) tmp_st = ridder_fchp(st, config=config) for tr in tmp_st: initial_corners = tr.getParameter("corner_frequencies") output_fchp.append(initial_corners["highpass"]) target_fchp = np.array( [ 0.021345158261480087, 0.022839239726168643, 0.02482398434993213, 0.01399481102242619, 0.026850167635921275, 0.004817661513765862, 0.008204101694236587, 0.006429246474225982, 0.004237087327289796, ] ) np.testing.assert_allclose(output_fchp, target_fchp, atol=1e-7) if __name__ == "__main__": os.environ["CALLED_FROM_PYTEST"] = "True" test_auto_fchp()

0

1,124

0

164

155

8314dc9023ae51350cf14c4fc6f29ae2621bb8d7

952

py

Python

supperfeed/importer.py

corydodt/SupperFeed

2980e3f6d287f56c6eade06cfe57870d9796c5ea

[ "MIT" ]

2

2015-10-28T23:53:29.000Z

2018-02-27T12:39:54.000Z

supperfeed/importer.py

corydodt/SupperFeed

2980e3f6d287f56c6eade06cfe57870d9796c5ea

[ "MIT" ]

null

supperfeed/importer.py

corydodt/SupperFeed

2980e3f6d287f56c6eade06cfe57870d9796c5ea

[ "MIT" ]

null

""" Import recipes from URLs to our database """ from txpx.process import LineGlueProtocol LineGlueProtocol.MAX_LENGTH=10000

24.410256

77

0.658613

""" Import recipes from URLs to our database """ import re import json from txpx import background, EchoProcess from txpx.process import LineGlueProtocol from supperfeed.build import Recipe LineGlueProtocol.MAX_LENGTH=10000 class ImportProcess(EchoProcess): """ Import a recipe by loading the json data dumped by the downloader process """ def __init__(self, *a, **kw): EchoProcess.__init__(self, *a, **kw) self.linebuf = [] def outLineReceived(self, line): if re.match(r'^/\*+/$', line): return self.finished() self.linebuf.append(line) def finished(self): data = json.loads('\n'.join(self.linebuf)) recipe = Recipe.fromLoadedData(data) recipe.save() self.linebuf[:] = [] def importRecipe(url): d = background(['recipeschema', url], proto=ImportProcess) d.addCallback(lambda ok: Recipe.objects(importedFrom=url).first()) return d

0

529

0

148

0

11

136

30f5d5f62a940e9c9c56d93b2735b45ae0a23f7e

759

py

Python

src/my_project/medium_problems/from1to50/group_people_give_group_size.py

ivan1016017/LeetCodeAlgorithmProblems

f617f30201fb1cd53e32de35084fdeb88ef36023

[ "MIT" ]

null

src/my_project/medium_problems/from1to50/group_people_give_group_size.py

ivan1016017/LeetCodeAlgorithmProblems

f617f30201fb1cd53e32de35084fdeb88ef36023

[ "MIT" ]

1

2021-09-22T12:26:14.000Z

src/my_project/medium_problems/from1to50/group_people_give_group_size.py

ivan1016017/LeetCodeAlgorithmProblems

454284b76634cc34ed41f7fa30d857403cedf1bf

[ "MIT" ]

null

solution = Solution() print(solution.groupThePeople(groupSizes = [3,3,3,3,3,1,3]))

29.192308

71

0.546772

from typing import List class Solution: def groupThePeople(self, groupSizes: List[int]) -> List[List[int]]: final_groups=[] count_dict = {} for ind, val in enumerate(groupSizes): # print(count_dict) if val in count_dict.keys(): if len(count_dict[val]) < val: count_dict[val].append(ind) else: final_groups.append(count_dict[val]) count_dict[val] = [ind] else: count_dict[val] = [ind] for key in count_dict.keys(): final_groups.append(count_dict[key]) return final_groups solution = Solution() print(solution.groupThePeople(groupSizes = [3,3,3,3,3,1,3]))

0

626

0

2

45

dbd8f6bc8f256424cc38bdbd062b914922bea024

1,011

py

Python

tests/bidirectional_lut_test.py

pnarvor/nephelae_simulation

7b3f3a2c2aaa49324f8b09a6ab62819c280efa4c

[ "BSD-3-Clause" ]

null

tests/bidirectional_lut_test.py

pnarvor/nephelae_simulation

7b3f3a2c2aaa49324f8b09a6ab62819c280efa4c

[ "BSD-3-Clause" ]

null

tests/bidirectional_lut_test.py

pnarvor/nephelae_simulation

7b3f3a2c2aaa49324f8b09a6ab62819c280efa4c

[ "BSD-3-Clause" ]

null

#! /usr/bin/python3 import sys sys.path.append('../') import numpy as np import matplotlib.pyplot as plt import matplotlib.cm as cm from netCDF4 import MFDataset import mesonh_probe as cdf """ test file for periodiccontainer and netcdfinterface types - arguments : mesonh (netcdf) files to open """ mesonhfiles = sys.argv[slice(1,len(sys.argv))] atm = MFDataset(mesonhfiles) lut = cdf.BiDirectionalLUT(atm.variables['VLEV'][:,0,0]) lin = cdf.BiDirectionalLinear(atm.variables['S_N_direction'][:]) plot1, axes1 = plt.subplots(1,2) x = np.linspace(0,160,1000) axes1[0].plot(x, lut.to_output_space(np.linspace(0,160,1000))) x = np.linspace(0.005,3.95,1000) axes1[1].plot(x, lut.to_input_space(np.linspace(0.005,3.95,1000))) plot1, axes1 = plt.subplots(1,2) x = np.linspace(0,160,1000) axes1[0].plot(x, lin.to_output_space(np.linspace(0,700,1000))) x = np.linspace(0.005,3.95,1000) axes1[1].plot(x, lin.to_input_space(np.linspace(-1,5,1000))) plt.show(block=False)

25.923077

66

0.732938

#! /usr/bin/python3 import sys import os sys.path.append('../') import numpy as np import matplotlib.pyplot as plt import imageio import matplotlib.cm as cm import time from netCDF4 import MFDataset import mesonh_probe as cdf """ test file for periodiccontainer and netcdfinterface types - arguments : mesonh (netcdf) files to open """ mesonhfiles = sys.argv[slice(1,len(sys.argv))] atm = MFDataset(mesonhfiles) lut = cdf.BiDirectionalLUT(atm.variables['VLEV'][:,0,0]) lin = cdf.BiDirectionalLinear(atm.variables['S_N_direction'][:]) plot1, axes1 = plt.subplots(1,2) x = np.linspace(0,160,1000) axes1[0].plot(x, lut.to_output_space(np.linspace(0,160,1000))) x = np.linspace(0.005,3.95,1000) axes1[1].plot(x, lut.to_input_space(np.linspace(0.005,3.95,1000))) plot1, axes1 = plt.subplots(1,2) x = np.linspace(0,160,1000) axes1[0].plot(x, lin.to_output_space(np.linspace(0,700,1000))) x = np.linspace(0.005,3.95,1000) axes1[1].plot(x, lin.to_input_space(np.linspace(-1,5,1000))) plt.show(block=False)

0

-29

66

5c0bef4089d7fb266a9c29954e06a567e8ce9a6d

800

py

Python

constant/constant.py

ZxbMsl160918/covid19-vaccin

7ebf3fa1de45fdaec8108e79ff6e090400cde9eb

[ "Apache-2.0" ]

null

constant/constant.py

ZxbMsl160918/covid19-vaccin

7ebf3fa1de45fdaec8108e79ff6e090400cde9eb

[ "Apache-2.0" ]

null

constant/constant.py

ZxbMsl160918/covid19-vaccin

7ebf3fa1de45fdaec8108e79ff6e090400cde9eb

[ "Apache-2.0" ]

null

# RESPONSE_OK = 200 # URL URLS = { # "hostUrl": "https://m.r.umiaohealth.com/", # POST "vaccinationAddress": "/InstitutionMedicineStock/GetBykeyword_InstitutionMedicineStock", # "hospitalTimeRange": "/Reservation/GetByWorkDate_Rsv_TimeRange", # urlGET "secVaccination": "/Reservation/Reservation_Create", # childId "childId": "/Adult/Index", # "userMsg": "/Home/My" } # AREAS = [ "", "", "", "", "", "", "", "", "", "", "" ] # VACCINE_TYPES = { "veroCell": 5601, # Vero "adenovirusVector": 5602 # # etc... } # SEC_TYPE = VACCINE_TYPES["veroCell"]

18.181818

92

0.6075

# 请求成功 RESPONSE_OK = 200 # 请求所需的URL地址 URLS = { # 主机地址 "hostUrl": "https://m.r.umiaohealth.com/", # 获取疫苗接种列表地址；POST "vaccinationAddress": "/InstitutionMedicineStock/GetBykeyword_InstitutionMedicineStock", # 获取某个社区医院的某一天可预约的时间段 "hospitalTimeRange": "/Reservation/GetByWorkDate_Rsv_TimeRange", # 执行疫苗预约请求 url；GET "secVaccination": "/Reservation/Reservation_Create", # 获取 childId "childId": "/Adult/Index", # 获取用户信息 "userMsg": "/Home/My" } # 区域名称 AREAS = [ "天河区", "白云区", "黄埔区", "荔湾区", "越秀区", "海珠区", "番禺区", "花都区", "南沙区", "增城区", "从化区" ] # 所有疫苗类型 VACCINE_TYPES = { "veroCell": 5601, # 新冠疫苗（Vero细胞） "adenovirusVector": 5602 # 新冠疫苗（腺病毒载体） # etc... } # 需要预约的疫苗类型 SEC_TYPE = VACCINE_TYPES["veroCell"]

399

0

5ebd8f1c512b1380b449a67ec585f3905c6bceac

9,220

py

Python

personal_context_builder/gensim_hdp.py

InternetOfUs/personal-context-builder

89e7388d622bc0efbf708542566fdcdca667a4e5

[ "Apache-2.0" ]

null

personal_context_builder/gensim_hdp.py

InternetOfUs/personal-context-builder

89e7388d622bc0efbf708542566fdcdca667a4e5

[ "Apache-2.0" ]

null

personal_context_builder/gensim_hdp.py

InternetOfUs/personal-context-builder

89e7388d622bc0efbf708542566fdcdca667a4e5

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2011 Radim Rehurek <[email protected]> # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html """Scikit learn interface for :class:`~gensim.models.hdpmodel.HdpModel`. Follows scikit-learn API conventions to facilitate using gensim along with scikit-learn. Examples -------- .. sourcecode:: pycon >>> from gensim.test.utils import common_dictionary, common_corpus >>> from gensim.sklearn_api import HdpTransformer >>> >>> # Lets extract the distribution of each document in topics >>> model = HdpTransformer(id2word=common_dictionary) >>> distr = model.fit_transform(common_corpus) """

42.100457

119

0.61974

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2011 Radim Rehurek <[email protected]> # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html """Scikit learn interface for :class:`~gensim.models.hdpmodel.HdpModel`. Follows scikit-learn API conventions to facilitate using gensim along with scikit-learn. Examples -------- .. sourcecode:: pycon >>> from gensim.test.utils import common_dictionary, common_corpus >>> from gensim.sklearn_api import HdpTransformer >>> >>> # Lets extract the distribution of each document in topics >>> model = HdpTransformer(id2word=common_dictionary) >>> distr = model.fit_transform(common_corpus) """ import numpy as np from gensim import matutils # type: ignore from gensim import models # type: ignore from scipy import sparse # type: ignore from sklearn.base import BaseEstimator, TransformerMixin # type: ignore from sklearn.exceptions import NotFittedError # type: ignore class HdpTransformer(TransformerMixin, BaseEstimator): """Base HDP module, wraps :class:`~gensim.models.hdpmodel.HdpModel`. The inner workings of this class heavily depends on `Wang, Paisley, Blei: "Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)" <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_. """ def __init__( self, id2word, max_chunks=None, max_time=None, chunksize=256, kappa=1.0, tau=64.0, K=15, T=150, alpha=1, gamma=1, eta=0.01, scale=1.0, var_converge=0.0001, outputdir=None, random_state=None, ): """ Parameters ---------- id2word : :class:`~gensim.corpora.dictionary.Dictionary`, optional Mapping between a words ID and the word itself in the vocabulary. max_chunks : int, optional Upper bound on how many chunks to process.It wraps around corpus beginning in another corpus pass, if there are not enough chunks in the corpus. max_time : int, optional Upper bound on time in seconds for which model will be trained. chunksize : int, optional Number of documents to be processed by the model in each mini-batch. kappa : float, optional Learning rate, see `Wang, Paisley, Blei: "Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)" <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_. tau : float, optional Slow down parameter, see `Wang, Paisley, Blei: "Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)" <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_. K : int, optional Second level truncation level, see `Wang, Paisley, Blei: "Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)" <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_. T : int, optional Top level truncation level, see `Wang, Paisley, Blei: "Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)" <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_. alpha : int, optional Second level concentration, see `Wang, Paisley, Blei: "Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)" <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_. gamma : int, optional First level concentration, see `Wang, Paisley, Blei: "Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)" <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_. eta : float, optional The topic Dirichlet, see `Wang, Paisley, Blei: "Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)" <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_. scale : float, optional Weights information from the mini-chunk of corpus to calculate rhot. var_converge : float, optional Lower bound on the right side of convergence. Used when updating variational parameters for a single document. outputdir : str, optional Path to a directory where topic and options information will be stored. random_state : int, optional Seed used to create a :class:`~np.random.RandomState`. Useful for obtaining reproducible results. """ self.gensim_model = None self.id2word = id2word self.max_chunks = max_chunks self.max_time = max_time self.chunksize = chunksize self.kappa = kappa self.tau = tau self.K = K self.T = T self.alpha = alpha self.gamma = gamma self.eta = eta self.scale = scale self.var_converge = var_converge self.outputdir = outputdir self.random_state = random_state def fit(self, X, y=None): """Fit the model according to the given training data. Parameters ---------- X : {iterable of list of (int, number), scipy.sparse matrix} A collection of documents in BOW format used for training the model. Returns ------- :class:`~gensim.sklearn_api.hdp.HdpTransformer` The trained model. """ if sparse.issparse(X): corpus = matutils.Sparse2Corpus(sparse=X, documents_columns=False) else: corpus = X self.gensim_model = models.HdpModel( corpus=corpus, id2word=self.id2word, max_chunks=self.max_chunks, max_time=self.max_time, chunksize=self.chunksize, kappa=self.kappa, tau=self.tau, K=self.K, T=self.T, alpha=self.alpha, gamma=self.gamma, eta=self.eta, scale=self.scale, var_converge=self.var_converge, outputdir=self.outputdir, random_state=self.random_state, ) return self def transform(self, docs): """Infer a matrix of topic distribution for the given document bow, where a_ij indicates (topic_i, topic_probability_j). Parameters ---------- docs : {iterable of list of (int, number), list of (int, number)} Document or sequence of documents in BOW format. Returns ------- numpy.ndarray of shape [`len(docs), num_topics`] Topic distribution for `docs`. """ if self.gensim_model is None: raise NotFittedError( "This model has not been fitted yet. Call 'fit' with appropriate arguments before using this method." ) # The input as array of array if isinstance(docs[0], tuple): docs = [docs] distribution, max_num_topics = [], 0 for doc in docs: topicd = self.gensim_model[doc] distribution.append(topicd) max_num_topics = max(max_num_topics, max(topic[0] for topic in topicd) + 1) # returning dense representation for compatibility with sklearn # but we should go back to sparse representation in the future distribution = [matutils.sparse2full(t, max_num_topics) for t in distribution] return np.reshape(np.array(distribution), (len(docs), max_num_topics)) def partial_fit(self, X): """Train model over a potentially incomplete set of documents. Uses the parameters set in the constructor. This method can be used in two ways: * On an unfitted model in which case the model is initialized and trained on `X`. * On an already fitted model in which case the model is **updated** by `X`. Parameters ---------- X : {iterable of list of (int, number), scipy.sparse matrix} A collection of documents in BOW format used for training the model. Returns ------- :class:`~gensim.sklearn_api.hdp.HdpTransformer` The trained model. """ if sparse.issparse(X): X = matutils.Sparse2Corpus(sparse=X, documents_columns=False) if self.gensim_model is None: self.gensim_model = models.HdpModel( id2word=self.id2word, max_chunks=self.max_chunks, max_time=self.max_time, chunksize=self.chunksize, kappa=self.kappa, tau=self.tau, K=self.K, T=self.T, alpha=self.alpha, gamma=self.gamma, eta=self.eta, scale=self.scale, var_converge=self.var_converge, outputdir=self.outputdir, random_state=self.random_state, ) self.gensim_model.update(corpus=X) return self

0

8,216

0

69

235

b582f1040cc1af3be284ad67fe9c371e838dde5d

2,060

py

Python

zaqar-8.0.0/zaqar/common/policies/claims.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

97

2015-01-02T09:35:23.000Z

2022-03-25T00:38:45.000Z

zaqar-8.0.0/zaqar/common/policies/claims.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

5

2019-08-14T06:46:03.000Z

2021-12-13T20:01:25.000Z

zaqar-8.0.0/zaqar/common/policies/claims.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

44

2015-01-28T03:01:28.000Z

2021-05-13T18:55:19.000Z

# 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 oslo_policy import policy from zaqar.common.policies import base CLAIMS = 'claims:%s' rules = [ policy.DocumentedRuleDefault( name=CLAIMS % 'create', check_str=base.UNPROTECTED, description='Claims a set of messages from the specified queue.', operations=[ { 'path': '/v2/queues/{queue_name}/claims', 'method': 'POST' } ] ), policy.DocumentedRuleDefault( name=CLAIMS % 'get', check_str=base.UNPROTECTED, description='Queries the specified claim for the specified queue.', operations=[ { 'path': '/v2/queues/{queue_name}/claims/{claim_id}', 'method': 'GET' } ] ), policy.DocumentedRuleDefault( name=CLAIMS % 'delete', check_str=base.UNPROTECTED, description='Releases the specified claim for the specified queue.', operations=[ { 'path': '/v2/queues/{queue_name}/claims/{claim_id}', 'method': 'DELETE' } ] ), policy.DocumentedRuleDefault( name=CLAIMS % 'update', check_str=base.UNPROTECTED, description='Updates the specified claim for the specified queue.', operations=[ { 'path': '/v2/queues/{queue_name}/claims/{claim_id}', 'method': 'PATCH' } ] ) ]

29.428571

76

0.591748

# 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 oslo_policy import policy from zaqar.common.policies import base CLAIMS = 'claims:%s' rules = [ policy.DocumentedRuleDefault( name=CLAIMS % 'create', check_str=base.UNPROTECTED, description='Claims a set of messages from the specified queue.', operations=[ { 'path': '/v2/queues/{queue_name}/claims', 'method': 'POST' } ] ), policy.DocumentedRuleDefault( name=CLAIMS % 'get', check_str=base.UNPROTECTED, description='Queries the specified claim for the specified queue.', operations=[ { 'path': '/v2/queues/{queue_name}/claims/{claim_id}', 'method': 'GET' } ] ), policy.DocumentedRuleDefault( name=CLAIMS % 'delete', check_str=base.UNPROTECTED, description='Releases the specified claim for the specified queue.', operations=[ { 'path': '/v2/queues/{queue_name}/claims/{claim_id}', 'method': 'DELETE' } ] ), policy.DocumentedRuleDefault( name=CLAIMS % 'update', check_str=base.UNPROTECTED, description='Updates the specified claim for the specified queue.', operations=[ { 'path': '/v2/queues/{queue_name}/claims/{claim_id}', 'method': 'PATCH' } ] ) ] def list_rules(): return rules

0

13

0

23

48f845ebbaca4afd27efda9fdf590cf268429691

2,139

py

Python

bot.py

boyuan12/MLH-Helper

efa98907b89bae2ef1f1c8b551075356b61ad741

[ "MIT" ]

null

bot.py

boyuan12/MLH-Helper

efa98907b89bae2ef1f1c8b551075356b61ad741

[ "MIT" ]

null

bot.py

boyuan12/MLH-Helper

efa98907b89bae2ef1f1c8b551075356b61ad741

[ "MIT" ]

null

import discord import discord.utils client = discord.Client() SECRET_KEY="secretkey" BASE_URL="http://0.0.0.0:1234" client.run("")

40.358491

198

0.625993

import discord import discord.utils from discord.ext import commands import requests import random client = discord.Client() SECRET_KEY="secretkey" BASE_URL="http://0.0.0.0:1234" @client.event async def on_ready(): print('We have logged in as {0.user}'.format(client)) @client.event async def on_message(message): id = client.user.id if str(id) in message.content: # get the question resp = str(message.content).split(f"<@!{str(id)}> ")[1] if resp == "checkin": await message.channel.send(f"Welcome! Please go ahead and go to {BASE_URL}/{message.author.id}. When you finished, please tag me and say finished, and I will send you more information!") elif resp == "attendees" and "mlh" in [y.name.lower() for y in message.author.roles]: curr = requests.get("https://mlh-events.now.sh/na-2020").json()[0]["name"] csv_file = requests.get(f"{BASE_URL}/api/generate/{curr}/{SECRET_KEY}").json()["url"] channel = await message.author.create_dm() await channel.send(f"Here's the file link to download: {csv_file}") elif resp == "attendees" and "mlh" not in [y.name.lower() for y in message.author.roles]: await message.channel.send(f"Oops, looks like you don't have permission to use this command!") elif resp == "finished": resp = requests.get(f"{BASE_URL}/api/current_hack/{message.author.id}").json() if resp["hack"] in [hack.name for hack in message.guild.roles] and resp["hack"] not in [y.name.lower() for y in message.author.roles]: role = discord.utils.get(message.guild.roles, name=resp["hack"]) user = message.author await user.add_roles(role) else: guild = message.guild await guild.create_role(name=resp["hack"], colour=discord.Colour(0x00FF00)) role = discord.utils.get(message.guild.roles, name=resp["hack"]) user = message.author await user.add_roles(role) await message.channel.send(resp["resp"]) client.run("")

0

1,896

0

-3

112

f968e78a0a396c803ccb0a25d591d668dacf68bf

553

py

Python

django_pathfinder_statcrunch/urls.py

porowns/django-pathfinder-statcrunch

4a31dd014b6e1c27b3e70ae88ca5762841ce72db

[ "MIT" ]

null

django_pathfinder_statcrunch/urls.py

porowns/django-pathfinder-statcrunch

4a31dd014b6e1c27b3e70ae88ca5762841ce72db

[ "MIT" ]

null

django_pathfinder_statcrunch/urls.py

porowns/django-pathfinder-statcrunch

4a31dd014b6e1c27b3e70ae88ca5762841ce72db

[ "MIT" ]

null

from django.urls import path from . import views # SSO urlpatterns = [ path('reports/', views.list_reports, name="django-pathfinder-statcrunch-list-reports"), path('reports/<int:pk>/', views.view_report, name="django-pathfinder-statcrunch-view-report"), path('reports/<int:pk>/refresh/', views.refresh_report, name="django-pathfinder-statcrunch-view-report-refresh"), ]

34.5625

67

0.735986

from django.contrib.staticfiles.urls import staticfiles_urlpatterns from django.conf import settings from django.contrib import admin from django.urls import path, re_path from . import views # SSO urlpatterns = [ path('reports/', views.list_reports, name="django-pathfinder-statcrunch-list-reports"), path('reports/<int:pk>/', views.view_report, name="django-pathfinder-statcrunch-view-report"), path('reports/<int:pk>/refresh/', views.refresh_report, name="django-pathfinder-statcrunch-view-report-refresh"), ]

0

77

66

0ed78fe0d10f673e40978db5dbae120f7d215015

222

py

Python

adventofcode/2021/1/2.py

jan25/code_sorted

f405fd0898f72eb3d5428f9e10aefb4a009d5089

[ "Unlicense" ]

2

2018-01-18T11:01:36.000Z

2021-12-20T18:14:48.000Z

adventofcode/2021/1/2.py

jan25/code_sorted

f405fd0898f72eb3d5428f9e10aefb4a009d5089

[ "Unlicense" ]

null

adventofcode/2021/1/2.py

jan25/code_sorted

f405fd0898f72eb3d5428f9e10aefb4a009d5089

[ "Unlicense" ]

null

import fileinput nums = list(map(int, fileinput.input())) print(sum(inc for inc in gen()))

17.076923

56

0.540541

import fileinput nums = list(map(int, fileinput.input())) def gen(): for i in range(1, len(nums) - 2): if sum(nums[i:i + 3]) > sum(nums[i - 1: i + 2]): yield 1 print(sum(inc for inc in gen()))

0

104

0

23

2f907545d59c9d2bffc0a13955bd9ba29dc05554

477

py

Python

openapi-server/python-flask/openapi_server/controllers/health_check_service_controller.py

michilu/proto-api

aca02aaa11064e87462ab34674c0c4974cf70372

[ "Apache-2.0" ]

null

openapi-server/python-flask/openapi_server/controllers/health_check_service_controller.py

michilu/proto-api

aca02aaa11064e87462ab34674c0c4974cf70372

[ "Apache-2.0" ]

1

2020-07-15T09:50:06.000Z

openapi-server/python-flask/openapi_server/controllers/health_check_service_controller.py

michilu/proto-openapi

aca02aaa11064e87462ab34674c0c4974cf70372

[ "Apache-2.0" ]

null

def health_check_service_health_check(): # noqa: E501 """health_check_service_health_check # noqa: E501 :rtype: V1HealthCheckServiceHealthCheckResponse """ return 'do some magic!'

26.5

133

0.802935

import connexion import six from openapi_server.models.runtime_error import RuntimeError # noqa: E501 from openapi_server.models.v1_health_check_service_health_check_response import V1HealthCheckServiceHealthCheckResponse # noqa: E501 from openapi_server import util def health_check_service_health_check(): # noqa: E501 """health_check_service_health_check # noqa: E501 :rtype: V1HealthCheckServiceHealthCheckResponse """ return 'do some magic!'

0

131

139

887fea9a7f42da7c5675403aa05cab40094c6fb6

1,067

py

Python

src/model/synapses/tensor_backend/VoltageJump.py

Fassial/pku-intern

4463e7d5a5844c8002f7e3d01b4fadc3a20e2038

[ "MIT" ]

null

src/model/synapses/tensor_backend/VoltageJump.py

Fassial/pku-intern

4463e7d5a5844c8002f7e3d01b4fadc3a20e2038

[ "MIT" ]

null

src/model/synapses/tensor_backend/VoltageJump.py

Fassial/pku-intern

4463e7d5a5844c8002f7e3d01b4fadc3a20e2038

[ "MIT" ]

null

""" Created on 12:39, June. 4th, 2021 Author: fassial Filename: VoltageJump.py """ __all__ = [ "VoltageJump", ]

25.404762

79

0.582943

""" Created on 12:39, June. 4th, 2021 Author: fassial Filename: VoltageJump.py """ import brainpy as bp __all__ = [ "VoltageJump", ] class VoltageJump(bp.TwoEndConn): target_backend = "general" def __init__(self, pre, post, conn, weight = 1., delay = 0., **kwargs ): # init params self.weight = weight self.delay = delay # init connections self.conn = conn(pre.size, post.size) self.conn_mat = self.conn.requires("conn_mat") self.size = bp.ops.shape(self.conn_mat) # init vars self.w = bp.ops.ones(self.size) * self.weight self.Isyn = self.register_constant_delay("Isyn", size = self.size, delay_time = self.delay ) # init super super(VoltageJump, self).__init__(pre = pre, post = post, **kwargs) def update(self, _t): # set Isyn & post.V Isyn = self.w * bp.ops.unsqueeze(self.pre.spike, 1) * self.conn_mat self.post.V += bp.ops.sum(Isyn * (1. - self.post.refractory), axis = 0)

0

905

0

-1

45

2356a07736148deea7e9a4b60909d1ff37a28c82

845

py

Python

clahe_and_augmentation/parseConfig.py

RandomeName745/DD2424---Project-Covid-19

2e1e647e841eeb00760daecb58effeba3ca237c4

[ "MIT" ]

null

clahe_and_augmentation/parseConfig.py

RandomeName745/DD2424---Project-Covid-19

2e1e647e841eeb00760daecb58effeba3ca237c4

[ "MIT" ]

null

clahe_and_augmentation/parseConfig.py

RandomeName745/DD2424---Project-Covid-19

2e1e647e841eeb00760daecb58effeba3ca237c4

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun May 10 10:31:44 2020 @author: alex """

29.137931

85

0.654438

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun May 10 10:31:44 2020 @author: alex """ import argparse from clodsa.utils.conf import Conf def parseConfig(configfile): ap = argparse.ArgumentParser() ap.add_argument("-c", "--conf", required=True, help="path to configuration file") args = vars(ap.parse_args(args=["-c",configfile])) config = {} conf = Conf(args["conf"]) config["problem"] = conf["problem"] config["annotationMode"] = conf["annotation_mode"] config["outputMode"] = conf["output_mode"] config["generationMode"] = conf["generation_mode"] config["inputPath"] = conf["input_path"] # parameters = conf["parameters"] config["outputPath"] = conf["output_path"] config["augmentationTechniques"] = conf["augmentation_techniques"] return config

0

666

0

7

67

d6844ad5af8ac8dd3731b4be397175e5d6c05d9f

3,563

py

Python

clientui/vstrm_server.py

cbk914/BlackMamba

826d5e2994368006cad09acaaa7c6bfa047891b5

[ "MIT" ]

902

2021-02-09T09:42:57.000Z

2022-03-26T09:28:03.000Z

clientui/vstrm_server.py

cbk914/BlackMamba

826d5e2994368006cad09acaaa7c6bfa047891b5

[ "MIT" ]

11

2021-02-12T16:46:51.000Z

2021-12-20T21:12:14.000Z

clientui/vstrm_server.py

cbk914/BlackMamba

826d5e2994368006cad09acaaa7c6bfa047891b5

[ "MIT" ]

126

2021-02-09T12:16:50.000Z

2022-02-25T04:19:18.000Z

#################################################################################### # BLACKMAMBA BY: LOSEYS (https://github.com/loseys) # # QT GUI INTERFACE BY: WANDERSON M.PIMENTA (https://github.com/Wanderson-Magalhaes) # ORIGINAL QT GUI: https://github.com/Wanderson-Magalhaes/Simple_PySide_Base #################################################################################### """ Video streaming server. """ import sys from os import environ environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1' try: SERVER_IP = sys.argv[1] PORT_VIDEO = sys.argv[2] except: SERVER_IP = 0 PORT_VIDEO = 0 if __name__ == "__main__": start_stream()

27.407692

91

0.572271

#################################################################################### # BLACKMAMBA BY: LOSEYS (https://github.com/loseys) # # QT GUI INTERFACE BY: WANDERSON M.PIMENTA (https://github.com/Wanderson-Magalhaes) # ORIGINAL QT GUI: https://github.com/Wanderson-Magalhaes/Simple_PySide_Base #################################################################################### """ Video streaming server. """ import sys import socket from os import environ environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1' import pygame from zlib import decompress from cryptography.fernet import Fernet try: SERVER_IP = sys.argv[1] PORT_VIDEO = sys.argv[2] except: SERVER_IP = 0 PORT_VIDEO = 0 def crypt(msg, key): command = str(msg) command = bytes(command, encoding='utf8') cipher_suite = Fernet(key) encoded_text = cipher_suite.encrypt(command) return encoded_text def decrypt(msg, key): cipher_suite = Fernet(key) decoded_text_f = cipher_suite.decrypt(msg) return decoded_text_f def recvall(conn, length): try: buf = b'' while len(buf) < length: data = conn.recv(length - len(buf)) if not data: return data buf += data return buf except: pass def start_stream(host=str(SERVER_IP), port=int(PORT_VIDEO)): sock = socket.socket() sock.bind((host, port)) print("Listening ....") sock.settimeout(15.0) sock.listen(5) try: conn, addr = sock.accept() except: print('socket.timeout: timed out') return print("Accepted ....", addr) client_resolution = (conn.recv(50).decode()) client_resolution = str(client_resolution).split(',') CLIENT_WIDTH = int(client_resolution[0]) CLIENT_HEIGHT = int(client_resolution[1]) with open('bin/profile/vstream_size.txt', 'r') as f: scsize = f.read() f.close() try: scsize = scsize.split(':') SERVER_WIDTH = int(scsize[0]) SERVER_HEIGHT = int( scsize[1]) except: SERVER_WIDTH = 1000 SERVER_HEIGHT = 600 pygame.init() pygame.display.set_caption('BlackMamba') programIcon = pygame.image.load('icons/others/icon_3.png') pygame.display.set_icon(programIcon) screen = pygame.display.set_mode((SERVER_WIDTH, SERVER_HEIGHT)) clock = pygame.time.Clock() watching = True try: while watching: for event in pygame.event.get(): if event.type == pygame.QUIT: watching = False break # Retreive the size of the pixels length, the pixels length and pixels try: size_len = int.from_bytes(conn.recv(1), byteorder='big') size = int.from_bytes(conn.recv(size_len), byteorder='big') pixels = decompress(recvall(conn, size)) # Create the Surface from raw pixels img = pygame.image.fromstring(pixels, (CLIENT_WIDTH, CLIENT_HEIGHT), 'RGB') # resize the client image to match the server's screen dimensions scaled_img = pygame.transform.scale(img, (SERVER_WIDTH, SERVER_HEIGHT)) # Display the picture screen.blit(scaled_img, (0, 0)) pygame.display.flip() #clock.tick(60) clock.tick(120) except: break finally: pygame.quit() sock.close() if __name__ == "__main__": start_stream()

0

2,719

0

7

181

58f8d59cc2c4eb8e7df9a61a133e9fc628033ad5

4,775

py

Python

Unit3SC_0205/northwind.py

TemsyChen/DS-Unit-3-Sprint-2-SQL-and-Databases

cba1e0f5476f5e7a13e10ad450474a565d302b33

[ "MIT" ]

null

Unit3SC_0205/northwind.py

TemsyChen/DS-Unit-3-Sprint-2-SQL-and-Databases

cba1e0f5476f5e7a13e10ad450474a565d302b33

[ "MIT" ]

null

Unit3SC_0205/northwind.py

TemsyChen/DS-Unit-3-Sprint-2-SQL-and-Databases

cba1e0f5476f5e7a13e10ad450474a565d302b33

[ "MIT" ]

null

import sqlite3 # Connect to the sqlite3 file connection = sqlite3.connect("northwind_small.sqlite3") cursor = connection.cursor() # Queries # `expensive_items`: What are the ten most expensive items (per unit price) in the database? price_query = f""" SELECT UnitPrice, ProductName FROM product ORDER BY UnitPrice DESC LIMIT 10;""" expensive_items = cursor.execute(price_query).fetchall() print("Expensive items:", expensive_items) # Expensive items: [(263.5, 'Cte de Blaye'), (123.79, 'Thringer Rostbratwurst'), # (97, 'Mishi Kobe Niku'), (81, "Sir Rodney's Marmalade"), (62.5, 'Carnarvon Tigers'), # (55, 'Raclette Courdavault'), (53, 'Manjimup Dried Apples'), (49.3, 'Tarte au sucre'), # (46, 'Ipoh Coffee'), (45.6, 'Rssle Sauerkraut')] # `avg_hire_age`: What is the average age of an employee at the time of their hiring? # ONLY RAN THIS THE FIRST TIME, then commented it out # add_age_column = f""" # ALTER TABLE Employee # ADD age INT AS (hiredate - birthdate) # """ # cursor.execute(add_age_column) avghire_query = f"""SELECT AVG(age) from employee""" avg_hire_age = cursor.execute(avghire_query).fetchone()[0] print("Average hire age:", avg_hire_age) # Average hire age: 37.22222222222222 # (*Stretch*) `avg_age_by_city`: How does the average age of employee at hire vary by city? avg_by_city_query = f"""SELECT AVG(age), city FROM employee GROUP BY city """ avg_age_by_city = cursor.execute(avg_by_city_query).fetchall() print("Average age by city:", avg_age_by_city) # Average age by city: [(29.0, 'Kirkland'), (32.5, 'London'), # (56.0, 'Redmond'), (40.0, 'Seattle'), (40.0, 'Tacoma')] # - `ten_most_expensive`: What are the ten most expensive items (per unit price) in the database # *and* their suppliers? # COMMENTING OUT AFTER RUNNING ONCE # suppliers_prices_table = f"""CREATE TABLE suppliers_prices AS # SELECT Product.ProductName, Product.UnitPrice, Supplier.CompanyName # FROM Product # LEFT JOIN Supplier ON Product.SupplierId = Supplier.Id # """ # cursor.execute(suppliers_prices_table) # insertion_query = f"""SELECT Product.ProductName, Product.UnitPrice, Supplier.CompanyName # FROM Product # LEFT JOIN Supplier ON Product.SupplierId = Supplier.Id""" # cursor.execute(insertion_query) price_supplier_query = f"""SELECT unitprice, companyname FROM suppliers_prices ORDER BY unitprice DESC LIMIT 10; """ price_supplier_topten = cursor.execute(price_supplier_query).fetchall() print("Top most expensive items and their suppliers:", price_supplier_topten) # Top most expensive items and their suppliers: [(263.5, 'Aux # joyeux ecclsiastiques'), (123.79, 'Plutzer Lebensmittelgromrkte AG'), # (97, 'Tokyo Traders'), (81, 'Specialty Biscuits, Ltd.'), # (62.5, 'Pavlova, Ltd.'), (55, 'Gai pturage'), (53, "G'day, Mate"), # (49.3, "Forts d'rables"), (46, 'Leka Trading'), (45.6, 'Plutzer Lebensmittelgromrkte AG')] # - `largest_category`: What is the largest category (by number of unique products in it)? largest_category_query = f"""SELECT CategoryId, COUNT(DISTINCT ProductName) FROM Product GROUP BY CategoryId ORDER BY COUNT(DISTINCT ProductName) DESC""" largest_category = cursor.execute(largest_category_query).fetchone()[0] print("Largest category:", largest_category) # Largest category: 3 # - (*Stretch*) `most_territories`: Who's the employee with the most territories? # Use `TerritoryId` (not name, region, or other fields) as the unique # identifier for territories. # COMMENT OUT AFTER RUNNING ONCE # employee_territory_table = f"""CREATE TABLE employee_territory AS # SELECT Employee.FirstName, Employee.LastName, # EmployeeTerritory.EmployeeId, EmployeeTerritory.TerritoryId # FROM Employee # JOIN EmployeeTerritory ON Employee.Id = EmployeeTerritory.EmployeeId;""" # cursor.execute(employee_territory_table) territory_query = f"""SELECT COUNT(DISTINCT TerritoryId), FirstName, LastName, EmployeeId from employee_territory GROUP BY EmployeeId ORDER BY COUNT(DISTINCT TerritoryId) DESC""" employee_territory = cursor.execute(territory_query).fetchone() print("Which employee has the most territory?", employee_territory) # Which employee has the most territory? (10, 'Robert', 'King', 7) connection.commit() connection.close()

45.47619

113

0.660314

import sqlite3 # Connect to the sqlite3 file connection = sqlite3.connect("northwind_small.sqlite3") cursor = connection.cursor() # Queries # `expensive_items`: What are the ten most expensive items (per unit price) in the database? price_query = f""" SELECT UnitPrice, ProductName FROM product ORDER BY UnitPrice DESC LIMIT 10;""" expensive_items = cursor.execute(price_query).fetchall() print("Expensive items:", expensive_items) # Expensive items: [(263.5, 'Côte de Blaye'), (123.79, 'Thüringer Rostbratwurst'), # (97, 'Mishi Kobe Niku'), (81, "Sir Rodney's Marmalade"), (62.5, 'Carnarvon Tigers'), # (55, 'Raclette Courdavault'), (53, 'Manjimup Dried Apples'), (49.3, 'Tarte au sucre'), # (46, 'Ipoh Coffee'), (45.6, 'Rössle Sauerkraut')] # `avg_hire_age`: What is the average age of an employee at the time of their hiring? # ONLY RAN THIS THE FIRST TIME, then commented it out # add_age_column = f""" # ALTER TABLE Employee # ADD age INT AS (hiredate - birthdate) # """ # cursor.execute(add_age_column) avghire_query = f"""SELECT AVG(age) from employee""" avg_hire_age = cursor.execute(avghire_query).fetchone()[0] print("Average hire age:", avg_hire_age) # Average hire age: 37.22222222222222 # (*Stretch*) `avg_age_by_city`: How does the average age of employee at hire vary by city? avg_by_city_query = f"""SELECT AVG(age), city FROM employee GROUP BY city """ avg_age_by_city = cursor.execute(avg_by_city_query).fetchall() print("Average age by city:", avg_age_by_city) # Average age by city: [(29.0, 'Kirkland'), (32.5, 'London'), # (56.0, 'Redmond'), (40.0, 'Seattle'), (40.0, 'Tacoma')] # - `ten_most_expensive`: What are the ten most expensive items (per unit price) in the database # *and* their suppliers? # COMMENTING OUT AFTER RUNNING ONCE # suppliers_prices_table = f"""CREATE TABLE suppliers_prices AS # SELECT Product.ProductName, Product.UnitPrice, Supplier.CompanyName # FROM Product # LEFT JOIN Supplier ON Product.SupplierId = Supplier.Id # """ # cursor.execute(suppliers_prices_table) # insertion_query = f"""SELECT Product.ProductName, Product.UnitPrice, Supplier.CompanyName # FROM Product # LEFT JOIN Supplier ON Product.SupplierId = Supplier.Id""" # cursor.execute(insertion_query) price_supplier_query = f"""SELECT unitprice, companyname FROM suppliers_prices ORDER BY unitprice DESC LIMIT 10; """ price_supplier_topten = cursor.execute(price_supplier_query).fetchall() print("Top most expensive items and their suppliers:", price_supplier_topten) # Top most expensive items and their suppliers: [(263.5, 'Aux # joyeux ecclésiastiques'), (123.79, 'Plutzer Lebensmittelgroßmärkte AG'), # (97, 'Tokyo Traders'), (81, 'Specialty Biscuits, Ltd.'), # (62.5, 'Pavlova, Ltd.'), (55, 'Gai pâturage'), (53, "G'day, Mate"), # (49.3, "Forêts d'érables"), (46, 'Leka Trading'), (45.6, 'Plutzer Lebensmittelgroßmärkte AG')] # - `largest_category`: What is the largest category (by number of unique products in it)? largest_category_query = f"""SELECT CategoryId, COUNT(DISTINCT ProductName) FROM Product GROUP BY CategoryId ORDER BY COUNT(DISTINCT ProductName) DESC""" largest_category = cursor.execute(largest_category_query).fetchone()[0] print("Largest category:", largest_category) # Largest category: 3 # - (*Stretch*) `most_territories`: Who's the employee with the most territories? # Use `TerritoryId` (not name, region, or other fields) as the unique # identifier for territories. # COMMENT OUT AFTER RUNNING ONCE # employee_territory_table = f"""CREATE TABLE employee_territory AS # SELECT Employee.FirstName, Employee.LastName, # EmployeeTerritory.EmployeeId, EmployeeTerritory.TerritoryId # FROM Employee # JOIN EmployeeTerritory ON Employee.Id = EmployeeTerritory.EmployeeId;""" # cursor.execute(employee_territory_table) territory_query = f"""SELECT COUNT(DISTINCT TerritoryId), FirstName, LastName, EmployeeId from employee_territory GROUP BY EmployeeId ORDER BY COUNT(DISTINCT TerritoryId) DESC""" employee_territory = cursor.execute(territory_query).fetchone() print("Which employee has the most territory?", employee_territory) # Which employee has the most territory? (10, 'Robert', 'King', 7) connection.commit() connection.close()

22

0

1c787236266f19826db1b1ea01fce5c806ce4267

8,541

py

Python

lib/model/complete_net.py

chensjtu/poxture

f6abea1216c987f0e4c628b250054d764eaecf2e

[ "Apache-2.0" ]

null

lib/model/complete_net.py

chensjtu/poxture

f6abea1216c987f0e4c628b250054d764eaecf2e

[ "Apache-2.0" ]

null

lib/model/complete_net.py

chensjtu/poxture

f6abea1216c987f0e4c628b250054d764eaecf2e

[ "Apache-2.0" ]

null

import torch.nn as nn # from .DepthNormalizer import DepthNormalizer # from iPERCore.models.networks.criterions import VGGLoss

44.717277

164

0.619482

import torch import torch.nn as nn import torch.nn.functional as F import functools from .SurfaceClassifier import conv1_1, period_loss # from .DepthNormalizer import DepthNormalizer from ..net_util import * # from iPERCore.models.networks.criterions import VGGLoss from lib.model.Models import NestedUNet import numpy as np class Pint_Model(nn.Module): def __init__(self, opt): super(Pint_Model, self).__init__() self.period_loss = period_loss() self.feat_uv_error = nn.SmoothL1Loss() # A feature with B uvmap self.opt = opt self.NUnet = NestedUNet(in_ch=3, out_ch=3) norm_type = get_norm_layer(norm_type=opt.norm_color) self.image_filter = ResnetFilter(opt, norm_layer=norm_type) # self.conv = conv1_1(input_layers=256, output_layers=16) init_net(self) def filter(self, images): ''' Filter the input images store all intermediate features. :param images: [B, C, H, W] input images ''' self.im_feat = self.image_filter(images) def forward(self, uv_A, uv_B, part_uv_B, index): ''' this function is made for pint total train. ''' complete_feat = self.NUnet(uv_A) complete_feat_B = self.NUnet(uv_B) # im_feat = self.image_filter(uv_A) # B C H W for 512 uv_B, B 256 128 128 # complete_feat = self.conv(im_feat) # B 16 128 128 -> b 16 512 512 [0:3] loss # im_feat_B = self.image_filter(uv_B) # complete_feat_B = self.conv(im_feat_B) # A_feat = F.interpolate(complete_feat[:,0:3,:,:], scale_factor=4, mode='bilinear', align_corners=True) # in this param, A_feat means complete feature. # part_uv_B.requires_grad=True # to make uvb as one leaf # A_feat = complete_feat[:,0:3,:,:] # part_uv_B = F.interpolate(part_uv_B, scale_factor=0.25, mode='bilinear', align_corners=True) A_vis_feat = complete_feat[index==1] B_vis_uv = part_uv_B[index==1] loss1 = self.feat_uv_error(A_vis_feat, B_vis_uv.detach()) # loss2 = self.vgg_loss(complete_feat[:,:3], complete_feat_B[:,:3].detach()) # loss2 = self.period_loss(complete_feat, complete_feat_B.detach()) loss2=0 return complete_feat, complete_feat_B, loss1, loss2 # def pint_forward(self, uv_A, uv_B): # ''' # this function is made for pint total train. # ''' # im_feat = self.image_filter(uv_A) # B C H W for 512 uv_B, B 256 128 128 # self.complete_feat = self.conv(im_feat) # B 16 128 128 -> b 16 512 512 [0:3] loss # im_feat_B = self.image_filter(uv_B.squeeze(1)) # complete_feat_B = self.conv(im_feat_B) # A_feat = F.interpolate(self.complete_feat[:,0:3,:,:], scale_factor=4, mode='bilinear', align_corners=True) # in this param, A_feat means complete feature. # uv_B_feat = uv_B.squeeze(1).expand_as(A_feat) # uv_B_feat.requires_grad=True # to make uvb as one leaf # A_vis_feat = A_feat[uv_B_feat != 0.0] # B_vis_uv = uv_B_feat[uv_B_feat != 0.0] # loss_content = self.feat_uv_error(A_vis_feat, B_vis_uv) * 100 # loss_content1 = self.feat_uv_error(A_feat, uv_A)*100 # # loss_feat = self.error_term(self.complete_feat, complete_feat_B) # return A_feat, A_vis_feat, B_vis_uv, self.complete_feat, complete_feat_B, loss_content+loss_content1 class ResnetBlock(nn.Module): """Define a Resnet block""" def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias, last=False): """Initialize the Resnet block A resnet block is a conv block with skip connections We construct a conv block with build_conv_block function, and implement skip connections in <forward> function. Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf """ super(ResnetBlock, self).__init__() self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias, last) def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias, last=False): """Construct a convolutional block. Parameters: dim (int) -- the number of channels in the conv layer. padding_type (str) -- the name of padding layer: reflect | replicate | zero norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers. use_bias (bool) -- if the conv layer uses bias or not Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU)) """ conv_block = [] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)] if use_dropout: conv_block += [nn.Dropout(0.5)] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) if last: conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias)] else: conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)] return nn.Sequential(*conv_block) def forward(self, x): """Forward function (with skip connections)""" out = x + self.conv_block(x) # add skip connections return out class ResnetFilter(nn.Module): """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations. We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style) """ def __init__(self, opt, input_nc=3, output_nc=256, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'): """Construct a Resnet-based generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images ngf (int) -- the number of filters in the last conv layer norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers n_blocks (int) -- the number of ResNet blocks padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero """ assert (n_blocks >= 0) super(ResnetFilter, self).__init__() if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias), norm_layer(ngf), nn.ReLU(True)] n_downsampling = 2 for i in range(n_downsampling): # add downsampling layers mult = 2 ** i model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias), norm_layer(ngf * mult * 2), nn.ReLU(True)] mult = 2 ** n_downsampling for i in range(n_blocks): # add ResNet blocks if i == n_blocks - 1: model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias, last=True)] else: model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)] if opt.use_tanh: model += [nn.Tanh()] self.model = nn.Sequential(*model) def forward(self, input): """Standard forward""" return self.model(input)

0

8,146

0

44

223

91b66cd5922aac62b6769b7d844bbbf6732242a0

13,831

py

Python

ActiveCell_RealMorphology_Burst_Inh_cluster.py

EilamLeleo/burst

538dbf6845f4de5519c0392368d611d1e54608e1

[ "MIT" ]

null

ActiveCell_RealMorphology_Burst_Inh_cluster.py

EilamLeleo/burst

538dbf6845f4de5519c0392368d611d1e54608e1

[ "MIT" ]

null

ActiveCell_RealMorphology_Burst_Inh_cluster.py

EilamLeleo/burst

538dbf6845f4de5519c0392368d611d1e54608e1

[ "MIT" ]

null

#!/usr/lib/python-exec/python2.7/python import os import sys os.chdir('C:/Users/Leleo/Documents/Active Cell Real Morphology/') from neuron import h from neuron import gui #%% import numpy as np import time import math import cPickle as pickle #%% sk = False if sk==True: from sklearn import decomposition from sklearn import cluster from sklearn import linear_model from sklearn import ensemble from sklearn import cross_validation #%% h.load_file('nrngui.hoc') h.load_file("import3d.hoc") cvode = h.CVode() cvode.active(0) morphologyFilename = "morphologies/cell1.asc" #morphologyFilename = "morphologies/cell2.asc" #morphologyFilename = "morphologies/cell3.asc" #biophysicalModelFilename = "L5PCbiophys1.hoc" #biophysicalModelFilename = "L5PCbiophys2.hoc" #biophysicalModelFilename = "L5PCbiophys3.hoc" #biophysicalModelFilename = "L5PCbiophys4.hoc" #biophysicalModelFilename = "L5PCbiophys5.hoc" biophysicalModelFilename = "L5PCbiophys5b.hoc" #biophysicalModelTemplateFilename = "L5PCtemplate.hoc" biophysicalModelTemplateFilename = "L5PCtemplate_2.hoc" #%% h.load_file(biophysicalModelFilename) h.load_file(biophysicalModelTemplateFilename) L5PC = h.L5PCtemplate(morphologyFilename) h.celsius = 34 #%% set dendritic VDCC g=0 #secs = h.allsec VDCC_g = 1 if VDCC_g==0: for sec in h.allsec(): if hasattr(sec, 'gCa_HVAbar_Ca_HVA'): sec.gCa_HVAbar_Ca_HVA = 0 #%% helper functions #%% create length-weighted random section list #%% add some random NMDA synapses and plot a somatic trace just to see all things are alive and kicking #%% run simulation on some parameter pair, plot the space L5PC = h.L5PCtemplate(morphologyFilename) name = 'inh_secdt_meds62_exc60dt0sd0num15' #saveDir = '/ems/elsc-labs/segev-i/eilam.goldenberg/Documents/coincidence/wgh1/'+name+'/' saveDir = 'C:/Users/Leleo/Documents/coincidence/wgh1/'+name+'/' if not os.path.exists(saveDir): os.makedirs(saveDir) try: randomSeed = int(sys.argv[1]) print 'random seed selected by user - %d' %(randomSeed) except: randomSeed = np.random.randint(100000) print 'randomly chose seed - %d' %(randomSeed) np.random.seed(randomSeed) #ind = 1 #a = np.linspace(-50,-25,num=6),np.linspace(-20,20,num=21),np.linspace(25,100,num=16) ApicalBasalInterval = [0]#np.linspace(-10,10,num=11) #[x for xs in a for x in xs] numBasal = 50 #35 #np.linspace(0,200,num=81) numApical = 30 #np.linspace(0,20,num=11)#50,num=21)# numInh = 20 #0 #numOblique = 40-numApical #totalSyn = [20,50,100,200,400,600,800]#[80,120,150,180]#np.linspace(0,200,num=5)#41) partApical = 2 #[5,10,20,50,100,200,500]#[i for i in np.linspace(10,100,num=10)]+[200,300,400,500]#np.logspace(0,7,num=29,base=2) medSegment = [0,36,60,63]#[36]+[i for i in np.linspace(60,65,num=6)]#37,44,num=8)] ##40#60 # #secInh = [60[0.5],60[1],61[0],62[0],63[0],64[0],67[0]] #optimal planned inh at prox junc #secInh = [60[1],61[0],63[1]] #encapsulating inh for partApi=20 #random.choice(secInh) treeTime = 0 #0.1*np.logspace(3,10,num=22,base=2) numExperiments = 20 spks = [[0 for i in range(len(ApicalBasalInterval))] for j in range(len(medSegment))]#*4)] frqs = [[0 for i in range(len(ApicalBasalInterval))] for j in range(len(medSegment))]#*4)] #trc = [[[] for i in range(len(ApicalBasalInterval))] for j in range(len(medSegment))]#*4)] i = 0 j = 0 start = time.time() for ApiBasInd in ApicalBasalInterval:#treeT in treeTime:# print "Running for interval: %s [ms]" % (int(ApiBasInd))#treeTime: %.2f [ms]" % (treeT)# #for numB in numBasal:#totalS in totalSyn:# # print "Running for %s basal synapses" % (int(numB)) # for partApi in partApical: for medS in medSegment: # for numA in numApical:#np.linspace(0,totalS,num=41):# print "Running for inhibition in sec: %s" % (int(medS)) #partApi=%s" % (float(partApi)) # # numA = int(totalS*0.4) spks[j][i],frqs[j][i] = runSim(L5PC,ApiBasInd,treeTime,numBasal,numInh,numApical,medS,partApical,numExperiments) j = j+1 j = 0 i = i+1 pickle.dump(spks,open(saveDir+name+'_spks'+str(randomSeed)+".npy","wb"),protocol=2) pickle.dump(frqs,open(saveDir+name+'_frqs'+str(randomSeed)+".npy","wb"),protocol=2) print "Saved as ", saveDir+name+'_spks'+str(randomSeed)+".npy" print "Total running time was: ", (time.time()-start)/3600, "hours" #saveDir = '/ems/elsc-labs/segev-i/eilam.goldenberg/Documents/concidence/' #pickle.dump(spks1,open(saveDir+'dt_treet_30tot_hires_spks',"wb"),protocol=2) #pickle.dump(frqs1,open(saveDir+'dt_treet_30tot_hires_frqs',"wb"),protocol=2)

41.041543

150

0.670161

#!/usr/lib/python-exec/python2.7/python import os import sys os.chdir('C:/Users/Leleo/Documents/Active Cell Real Morphology/') from neuron import h from neuron import gui #%% import numpy as np import time import math import cPickle as pickle #%% sk = False if sk==True: from sklearn import decomposition from sklearn import cluster from sklearn import linear_model from sklearn import ensemble from sklearn import cross_validation #%% h.load_file('nrngui.hoc') h.load_file("import3d.hoc") cvode = h.CVode() cvode.active(0) morphologyFilename = "morphologies/cell1.asc" #morphologyFilename = "morphologies/cell2.asc" #morphologyFilename = "morphologies/cell3.asc" #biophysicalModelFilename = "L5PCbiophys1.hoc" #biophysicalModelFilename = "L5PCbiophys2.hoc" #biophysicalModelFilename = "L5PCbiophys3.hoc" #biophysicalModelFilename = "L5PCbiophys4.hoc" #biophysicalModelFilename = "L5PCbiophys5.hoc" biophysicalModelFilename = "L5PCbiophys5b.hoc" #biophysicalModelTemplateFilename = "L5PCtemplate.hoc" biophysicalModelTemplateFilename = "L5PCtemplate_2.hoc" #%% h.load_file(biophysicalModelFilename) h.load_file(biophysicalModelTemplateFilename) L5PC = h.L5PCtemplate(morphologyFilename) h.celsius = 34 #%% set dendritic VDCC g=0 #secs = h.allsec VDCC_g = 1 if VDCC_g==0: for sec in h.allsec(): if hasattr(sec, 'gCa_HVAbar_Ca_HVA'): sec.gCa_HVAbar_Ca_HVA = 0 #%% helper functions def Add_NMDA_SingleSynapticEventToSegment(segment, activationTime, synapseWeight, exc_inh): # synapse = h.ProbAMPANMDA2(segment) # synapse = h.ProbAMPANMDA_EMS(segLoc,sec=section) if exc_inh==0: # inhibitory synapse = h.ProbGABAAB_EMS(segment) #GABAA/B synapse.tau_r_GABAA = 0.2 synapse.tau_d_GABAA = 8 synapse.tau_r_GABAB = 3.5 synapse.tau_d_GABAB = 260.9 # synapse.gmax = .001 synapse.e_GABAA = -80 synapse.e_GABAB = -97 synapse.GABAB_ratio = 0.0 # synapse.Use = 1 # synapse.u0 = 0 # synapse.Dep = 0 # synapse.Fac = 0 else: # excitatory synapse = h.ProbAMPANMDA2(segment) synapse.gmax = .0004 # synapse = h.ProbAMPANMDA_EMS(segLoc,sec=section) synapse.Use = 1.0 synapse.Dep = 0 synapse.Fac = 0 netStimulation = h.NetStim() netStimulation.number = 1 netStimulation.start = activationTime netConnection = h.NetCon(netStimulation,synapse) netConnection.delay = 0 netConnection.weight[0] = synapseWeight return netStimulation,netConnection,synapse #%% create length-weighted random section list def randSecWeight(obj,medSeg,part,num): allLen = [] for i in range(len(obj)): allLen.append(obj[i].L) randSecList = [0 for i in range(num)] h.distance(sec=obj[medSeg]) # define distance measure from medSeg # draw from cumulative length a seg for syn x = np.sum(allLen[:medSeg])+(np.random.rand(num)-0.5)*np.sum(allLen)/part j=0 farbug=0 while j<num: # redraw boundary crossers if x[j]<0 or x[j]>np.sum(allLen): x[j] = np.sum(allLen[:medSeg])+(np.random.rand()-0.5)*np.sum(allLen)/part continue # find sec for i in range(len(obj)): if x[j]<np.sum(allLen[:i+1]): randSecList[j]=i break # check that sec is sufficiently close to medseg if h.distance(obj[randSecList[j]](1))>sum(allLen)/part and farbug<5:#obj[medSeg].L+obj[randSecList[j]].L:# x[j] = np.sum(allLen[:medSeg])+(np.random.rand()-0.5)*np.sum(allLen)/part farbug+=1 continue j+=1 farbug=0 return randSecList #%% add some random NMDA synapses and plot a somatic trace just to see all things are alive and kicking def runSim(cell,ApiBasInt,treeT,numBas,numApi,partApi,medSeg,inSec,numExp): simulationTime = 400 silentTimeAtStart = 100 delayTime = 200 silentTimeAtEnd = 100 origNumSamplesPerMS = 40 #20 # was 20!!! totalSimDuration = simulationTime + silentTimeAtStart + silentTimeAtEnd listOfSomaTraces = [] spikes = [] numSpikes = 0 numSpikesPerExp = [0]*numExp freq = [0]*numExp for experiment in range(numExp): startTime = time.time() listOfRandBasalSectionInds = randSecWeight(cell.dend,44,1,int(numBas))#np.random.randint(0,len(cell.dend),int(numBas)) listOfRandApicalSectionInds = randSecWeight(cell.apic,62,20,20)#int(numApi)) #medSeg + np.random.randint(-distance,distance,int(numApi)) if partApi>15: listOfRandInhSectionInds = randSecWeight(cell.apic,medSeg,partApi,numApi) else: listOfRandInhSectionInds = randSecWeight(cell.apic,medSeg,partApi,numApi) # listOfRandApicalSectionInds = randSecWeight(cell.apic,np.random.randint(37,78),partApi,10)#int(numApi)) # listOfRandObliqueSectionInds = np.random.randint(0,len(cell.apic)/partApi,0)#int(40-numApi)) #obliques listOfBasalSections = [cell.dend[x] for x in listOfRandBasalSectionInds] listOfApicalSections = [cell.apic[x] for x in listOfRandApicalSectionInds] listOfInhSections = [cell.apic[x] for x in listOfRandInhSectionInds] # listOfObliqueSections = [cell.apic[x] for x in listOfRandObliqueSectionInds] # listOfSections = listOfApicalSections + listOfBasalSections listOfRandBasalLocationsInSection = np.random.rand(len(listOfRandBasalSectionInds)) listOfRandApicalLocationsInSection = np.random.rand(len(listOfRandApicalSectionInds)) # listOfRandInhLocationsInSection = float(inSec)/4 + 0.25*np.random.rand(len(listOfRandInhSectionInds)) if partApi>30: listOfRandInhLocationsInSection = [1]*numApi #min(1,float(7440)/partApi/cell.apic[medSeg].L)*np.random.rand(len(listOfRandInhSectionInds)) else: listOfRandInhLocationsInSection = np.random.rand(len(listOfRandInhSectionInds)) # listOfRandObliqueLocationsInSection = np.random.rand(len(listOfRandObliqueSectionInds)) # listOfSegLocs = list(listOfRandApicalLocationsInSection) + list(listOfRandBasalLocationsInSection) listOfEvents = [] for k, section in enumerate(listOfApicalSections): eventTime = silentTimeAtStart + 100*np.random.normal(0,1) listOfEvents.append(Add_NMDA_SingleSynapticEventToSegment(section(listOfRandApicalLocationsInSection[k]), eventTime, 1, 1)) for k, section in enumerate(listOfBasalSections): eventTime = silentTimeAtStart + 100*np.random.normal(0,1) #simulationTime/2*np.random.rand(1)[0] listOfEvents.append(Add_NMDA_SingleSynapticEventToSegment(section(listOfRandBasalLocationsInSection[k]), eventTime, 1, 1)) for k, section in enumerate(listOfInhSections): eventTime = silentTimeAtStart + 100*np.random.normal(0,1) #simulationTime/2*np.random.rand(1)[0] listOfEvents.append(Add_NMDA_SingleSynapticEventToSegment(section(listOfRandInhLocationsInSection[k]), eventTime, 1, 0)) for k, section in enumerate(listOfApicalSections): eventTime = silentTimeAtStart + delayTime + treeT*np.random.normal(0,1) #gauss(0.5,0.2) listOfEvents.append(Add_NMDA_SingleSynapticEventToSegment(section(listOfRandApicalLocationsInSection[k]), eventTime, 1, 1)) for k, section in enumerate(listOfBasalSections): eventTime = silentTimeAtStart + delayTime + treeT*np.random.normal(0,1) #simulationTime/2*np.random.rand(1)[0] listOfEvents.append(Add_NMDA_SingleSynapticEventToSegment(section(listOfRandBasalLocationsInSection[k]), eventTime, 1, 1)) for k, section in enumerate(listOfInhSections): eventTime = silentTimeAtStart + delayTime + ApiBasInt + treeT*np.random.normal(0,1) #simulationTime/2*np.random.rand(1)[0] listOfEvents.append(Add_NMDA_SingleSynapticEventToSegment(section(listOfRandInhLocationsInSection[k]), eventTime, 1, 0)) #add obliques # for k, section in enumerate(listOfObliqueSections): # eventTime = silentTimeAtStart + delayTime + treeT*np.random.normal(1,0.2) #simulationTime/2*np.random.rand(1)[0] # listOfEvents.append(Add_NMDA_SingleSynapticEventToSegment(section(listOfRandObliqueLocationsInSection[k]), eventTime, 2)) ##%% run the simulation h.dt = 0.025 recTime = h.Vector() recTime.record(h._ref_t) recVoltage = h.Vector() recVoltage.record(cell.soma[0](0.5)._ref_v) cvode.cache_efficient(1) h.finitialize(-76) stopTime = totalSimDuration neuron.run(stopTime) # plot the trace origRecordingTime = np.array(recTime.to_python()) # ugly workaround to recTime.as_numpy() origSomaVoltage = np.array(recVoltage.to_python()) # ugly workaround to recVoltage.as_numpy() recordingTime = np.arange(0,totalSimDuration,1.0/origNumSamplesPerMS) somaVoltage = np.interp(recordingTime, origRecordingTime, origSomaVoltage) listOfSomaTraces.append(somaVoltage) origSpikes = [] tempSpikes = 0 k = (silentTimeAtStart+delayTime-50)*origNumSamplesPerMS #int(np.min([0,ApiBasInt])) while k < (totalSimDuration-silentTimeAtEnd)*origNumSamplesPerMS: if somaVoltage[k]>-10: tempTime = float(k)/origNumSamplesPerMS if tempSpikes>0 and tempTime-origSpikes[-1]>20: break origSpikes.append(tempTime) # numSpikesPerExp[experiment] = tempSpikes + 1 numSpikes = numSpikes + 1 tempSpikes += 1 # numSpikesPerExp[experiment] k = k+origNumSamplesPerMS*3 else: k = k+5 # was 1 before # spikes = [] spikes.append(origSpikes) if tempSpikes>1: freq[experiment] = tempSpikes/(origSpikes[-1]-origSpikes[-tempSpikes]) # plt.figure() # plt.plot(recordingTime, somaVoltage) # plt.xlabel('Time [ms]'); plt.ylabel('Voltage [mV]') # plt.axis(xmin=0, xmax=stopTime, ymin=min(somaVoltage)-5, ymax=max(somaVoltage)+5) #listOfEvents = [] if (experiment+1)%10==0 or (time.time()-startTime)/60>5 or numExp<5: print "Dt %s treeTime %s exp. # %s took %.3f minutes" % (ApiBasInt,treeT,experiment+1, (time.time()-startTime)/60) print "Mean no. of spikes: %s" % (float(numSpikes)/numExp) return float(numSpikes)/numExp,np.mean(freq)#, listOfSomaTraces, recordingTime #%% run simulation on some parameter pair, plot the space L5PC = h.L5PCtemplate(morphologyFilename) name = 'inh_secdt_meds62_exc60dt0sd0num15' #saveDir = '/ems/elsc-labs/segev-i/eilam.goldenberg/Documents/coincidence/wgh1/'+name+'/' saveDir = 'C:/Users/Leleo/Documents/coincidence/wgh1/'+name+'/' if not os.path.exists(saveDir): os.makedirs(saveDir) try: randomSeed = int(sys.argv[1]) print 'random seed selected by user - %d' %(randomSeed) except: randomSeed = np.random.randint(100000) print 'randomly chose seed - %d' %(randomSeed) np.random.seed(randomSeed) #ind = 1 #a = np.linspace(-50,-25,num=6),np.linspace(-20,20,num=21),np.linspace(25,100,num=16) ApicalBasalInterval = [0]#np.linspace(-10,10,num=11) #[x for xs in a for x in xs] numBasal = 50 #35 #np.linspace(0,200,num=81) numApical = 30 #np.linspace(0,20,num=11)#50,num=21)# numInh = 20 #0 #numOblique = 40-numApical #totalSyn = [20,50,100,200,400,600,800]#[80,120,150,180]#np.linspace(0,200,num=5)#41) partApical = 2 #[5,10,20,50,100,200,500]#[i for i in np.linspace(10,100,num=10)]+[200,300,400,500]#np.logspace(0,7,num=29,base=2) medSegment = [0,36,60,63]#[36]+[i for i in np.linspace(60,65,num=6)]#37,44,num=8)] ##40#60 # #secInh = [60[0.5],60[1],61[0],62[0],63[0],64[0],67[0]] #optimal planned inh at prox junc #secInh = [60[1],61[0],63[1]] #encapsulating inh for partApi=20 #random.choice(secInh) treeTime = 0 #0.1*np.logspace(3,10,num=22,base=2) numExperiments = 20 spks = [[0 for i in range(len(ApicalBasalInterval))] for j in range(len(medSegment))]#*4)] frqs = [[0 for i in range(len(ApicalBasalInterval))] for j in range(len(medSegment))]#*4)] #trc = [[[] for i in range(len(ApicalBasalInterval))] for j in range(len(medSegment))]#*4)] i = 0 j = 0 start = time.time() for ApiBasInd in ApicalBasalInterval:#treeT in treeTime:# print "Running for interval: %s [ms]" % (int(ApiBasInd))#treeTime: %.2f [ms]" % (treeT)# #for numB in numBasal:#totalS in totalSyn:# # print "Running for %s basal synapses" % (int(numB)) # for partApi in partApical: for medS in medSegment: # for numA in numApical:#np.linspace(0,totalS,num=41):# print "Running for inhibition in sec: %s" % (int(medS)) #partApi=%s" % (float(partApi)) # # numA = int(totalS*0.4) spks[j][i],frqs[j][i] = runSim(L5PC,ApiBasInd,treeTime,numBasal,numInh,numApical,medS,partApical,numExperiments) j = j+1 j = 0 i = i+1 pickle.dump(spks,open(saveDir+name+'_spks'+str(randomSeed)+".npy","wb"),protocol=2) pickle.dump(frqs,open(saveDir+name+'_frqs'+str(randomSeed)+".npy","wb"),protocol=2) print "Saved as ", saveDir+name+'_spks'+str(randomSeed)+".npy" print "Total running time was: ", (time.time()-start)/3600, "hours" #saveDir = '/ems/elsc-labs/segev-i/eilam.goldenberg/Documents/concidence/' #pickle.dump(spks1,open(saveDir+'dt_treet_30tot_hires_spks',"wb"),protocol=2) #pickle.dump(frqs1,open(saveDir+'dt_treet_30tot_hires_frqs',"wb"),protocol=2)

0

9,194

0

69

ec42f79f6e9bebd0d6cc7d934779f60d52434f4f

1,263

py

Python

sharesansar/driver.py

prajwal-stha/web-scrapers

bfcc5a065e859c69f1a9a2065c9c857b22af42c0

[ "MIT" ]

null

sharesansar/driver.py

prajwal-stha/web-scrapers

bfcc5a065e859c69f1a9a2065c9c857b22af42c0

[ "MIT" ]

null

sharesansar/driver.py

prajwal-stha/web-scrapers

bfcc5a065e859c69f1a9a2065c9c857b22af42c0

[ "MIT" ]

null

from selenium import webdriver import time from datetime import date from selenium.webdriver.common.keys import Keys from scrape_table_all import scrape_table from return_dates import return_dates # Open the link PATH = "/Users/prajwalshrestha/Desktop/PythonApp/thesis/web-scrapers/sharesansar/chromedriver" browser = webdriver.Chrome(PATH) browser.maximize_window() browser.get("https://www.sharesansar.com/today-share-price") # Select the type of data to scrape searchBar = browser.find_element_by_id('sector') browser.implicitly_wait(20) # Select Commercial Bank searchBar.send_keys('Commercial Bank') sdate = date(2020, 3, 23) edate = date(2021, 5, 13) dates = return_dates(sdate, edate) for day in dates: # Enter the date date_box = browser.find_elements_by_id('fromdate') date_box[0].clear() date_box[0].send_keys(day) # Click Search searchBar = browser.find_element_by_id('btn_todayshareprice_submit') searchBar.click() time.sleep(3) # Needed for this sites searchBar.send_keys(Keys.ENTER) # Wait for data to show up longer wait time ensures data has loaded before scraping begins time.sleep(8) # Scrape the table html = browser.page_source scrape_table(data=html, date=day) browser.close()

30.071429

94

0.760095

from selenium import webdriver import time from datetime import date from selenium.webdriver.common.keys import Keys from scrape_table_all import scrape_table from return_dates import return_dates # Open the link PATH = "/Users/prajwalshrestha/Desktop/PythonApp/thesis/web-scrapers/sharesansar/chromedriver" browser = webdriver.Chrome(PATH) browser.maximize_window() browser.get("https://www.sharesansar.com/today-share-price") # Select the type of data to scrape searchBar = browser.find_element_by_id('sector') browser.implicitly_wait(20) # Select Commercial Bank searchBar.send_keys('Commercial Bank') sdate = date(2020, 3, 23) edate = date(2021, 5, 13) dates = return_dates(sdate, edate) for day in dates: # Enter the date date_box = browser.find_elements_by_id('fromdate') date_box[0].clear() date_box[0].send_keys(day) # Click Search searchBar = browser.find_element_by_id('btn_todayshareprice_submit') searchBar.click() time.sleep(3) # Needed for this sites searchBar.send_keys(Keys.ENTER) # Wait for data to show up longer wait time ensures data has loaded before scraping begins time.sleep(8) # Scrape the table html = browser.page_source scrape_table(data=html, date=day) browser.close()

0

b8886f82232200a3b8d715152e899cad7d68af4a

2,038

py

Python

ots/main/urls.py

rashikbuksh/Optimal-Transportation-System

18c6d5341c6d3ecbb1c8fcfba8e46ca2ba493882

[ "MIT" ]

3

2021-12-01T15:56:42.000Z

2021-12-23T15:49:48.000Z

ots/main/urls.py

rashikbuksh/Optimal-Transportation-System

18c6d5341c6d3ecbb1c8fcfba8e46ca2ba493882

[ "MIT" ]

null

ots/main/urls.py

rashikbuksh/Optimal-Transportation-System

18c6d5341c6d3ecbb1c8fcfba8e46ca2ba493882

[ "MIT" ]

2

2021-09-24T19:49:28.000Z

2021-12-22T10:25:38.000Z

from django.conf.urls import url from django.urls import path from . import views app_name = 'articles' urlpatterns = [ url(r'^$', views.homepage, name="list"), url(r'^about/$', views.about, name="about"), url(r'^contact/$', views.contact, name="contact"), url(r'^bolaka/$', views.bolaka, name="bolaka"), url(r'^offers_page/$', views.offers, name="offers_page"), url(r'^bolakareview/$', views.bolakareview, name="bolakareview"), url(r'^ticket/$', views.ticket, name="ticket"), path('deletebalaka/<str:pk>/$', views.deletebalaka, name="deletebalaka"), url(r'^ticket_page/$', views.ticket_page, name="ticket_page"), # Air url(r'^Air_Biman_Bangladesh/$', views.Air_Biman_Bangladesh, name="Air_Biman_Bangladesh"), url(r'^Air_Novoair/$', views.Air_Novoair, name="Air_Novoair"), url(r'^Air_US_Bangla/$', views.Air_US_Bangla, name="Air_US_Bangla"), # Bus url(r'^Bus_Akash/$', views.Bus_Akash, name="Bus_Akash"), url(r'^Bus_Alif/$', views.Bus_Alif, name="Bus_Alif"), url(r'^Bus_Anabil/$', views.Bus_Anabil, name="Bus_Anabil"), url(r'^Bus_BRTC/$', views.Bus_BRTC, name="Bus_BRTC"), url(r'^Bus_Green_Dhaka/$', views.Bus_Green_Dhaka, name="Bus_Green_Dhaka"), url(r'^Bus_Raida/$', views.Bus_Raida, name="Bus_Raida"), url(r'^Bus_Skyline/$', views.Bus_Skyline, name="Bus_Skyline"), url(r'^Bus_Supravat/$', views.Bus_Supravat, name="Bus_Supravat"), url(r'^Bus_VIP/$', views.Bus_VIP, name="Bus_VIP"), # Train url(r'^Train_Chitra_Express/$', views.Train_Chitra_Express, name="Train_Chitra_Express"), url(r'^Train_Ekota_Express/$', views.Train_Ekota_Express, name="Train_Ekota_Express"), url(r'^Train_Mahanagar_Godhuli/$', views.Train_Mahanagar_Godhuli, name="Train_Mahanagar_Godhuli"), url(r'^Train_Suborno_Express/$', views.Train_Suborno_Express, name="Train_Suborno_Express"), url(r'^Train_Tista_Express/$', views.Train_Tista_Express, name="Train_Tista_Express"), url(r'^(?P<slug>[\w-]+)/$', views.homepage, name="list"), ]

44.304348

102

0.686948

from django.conf.urls import url from django.urls import path from . import views app_name = 'articles' urlpatterns = [ url(r'^$', views.homepage, name="list"), url(r'^about/$', views.about, name="about"), url(r'^contact/$', views.contact, name="contact"), url(r'^bolaka/$', views.bolaka, name="bolaka"), url(r'^offers_page/$', views.offers, name="offers_page"), url(r'^bolakareview/$', views.bolakareview, name="bolakareview"), url(r'^ticket/$', views.ticket, name="ticket"), path('deletebalaka/<str:pk>/$', views.deletebalaka, name="deletebalaka"), url(r'^ticket_page/$', views.ticket_page, name="ticket_page"), # Air url(r'^Air_Biman_Bangladesh/$', views.Air_Biman_Bangladesh, name="Air_Biman_Bangladesh"), url(r'^Air_Novoair/$', views.Air_Novoair, name="Air_Novoair"), url(r'^Air_US_Bangla/$', views.Air_US_Bangla, name="Air_US_Bangla"), # Bus url(r'^Bus_Akash/$', views.Bus_Akash, name="Bus_Akash"), url(r'^Bus_Alif/$', views.Bus_Alif, name="Bus_Alif"), url(r'^Bus_Anabil/$', views.Bus_Anabil, name="Bus_Anabil"), url(r'^Bus_BRTC/$', views.Bus_BRTC, name="Bus_BRTC"), url(r'^Bus_Green_Dhaka/$', views.Bus_Green_Dhaka, name="Bus_Green_Dhaka"), url(r'^Bus_Raida/$', views.Bus_Raida, name="Bus_Raida"), url(r'^Bus_Skyline/$', views.Bus_Skyline, name="Bus_Skyline"), url(r'^Bus_Supravat/$', views.Bus_Supravat, name="Bus_Supravat"), url(r'^Bus_VIP/$', views.Bus_VIP, name="Bus_VIP"), # Train url(r'^Train_Chitra_Express/$', views.Train_Chitra_Express, name="Train_Chitra_Express"), url(r'^Train_Ekota_Express/$', views.Train_Ekota_Express, name="Train_Ekota_Express"), url(r'^Train_Mahanagar_Godhuli/$', views.Train_Mahanagar_Godhuli, name="Train_Mahanagar_Godhuli"), url(r'^Train_Suborno_Express/$', views.Train_Suborno_Express, name="Train_Suborno_Express"), url(r'^Train_Tista_Express/$', views.Train_Tista_Express, name="Train_Tista_Express"), url(r'^(?P<slug>[\w-]+)/$', views.homepage, name="list"), ]

0

b81f72c1a629494935d113e4876d1627760656f2

53

py

Python

bana/OpenMayaFX/__init__.py

christophercrouzet/bana

8087df05ba9844b4d78d3c4699948ca61cf7621d

[ "MIT" ]

24

2017-01-11T15:57:46.000Z

2020-09-23T06:18:30.000Z

bana/OpenMayaFX/__init__.py

christophercrouzet/bana

8087df05ba9844b4d78d3c4699948ca61cf7621d

[ "MIT" ]

null

bana/OpenMayaFX/__init__.py

christophercrouzet/bana

8087df05ba9844b4d78d3c4699948ca61cf7621d

[ "MIT" ]

2

2017-03-06T23:52:08.000Z

2020-09-23T06:19:03.000Z

"""Extensions for the ``maya.OpenMayaFX`` module."""

26.5

52

0.679245

"""Extensions for the ``maya.OpenMayaFX`` module."""

0

8b0fe30871875ffa461d0b5d638a14149f7fd68f

378

py

Python

apps/metadata/users/models.py

DiegoCorrea/ouvidoMusical

e8bdb993e2c6ef2fe4a78e844bc60be2738a5ba5

[ "MIT" ]

1

2021-10-06T19:35:48.000Z

apps/metadata/users/models.py

DiegoCorrea/ouvido_musical-Back

e8bdb993e2c6ef2fe4a78e844bc60be2738a5ba5

[ "MIT" ]

null

apps/metadata/users/models.py

DiegoCorrea/ouvido_musical-Back

e8bdb993e2c6ef2fe4a78e844bc60be2738a5ba5

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals

17.181818

39

0.589947

# -*- coding: utf-8 -*- from __future__ import unicode_literals import uuid from django.db import models class User(models.Model): id = models.CharField( max_length=255, unique=True, db_index=True, primary_key=True, default=uuid.uuid1().hex ) def as_json(self): return dict( user_id=self.id )

0

247

0

-3

69

1477ad88726678c8460cc5fe89ba40da27efa1cb

626

py

Python

ansible/collections/ansible_collections/nhsd/apigee/plugins/filter/filters.py

uk-gov-mirror/nhsdigital.api-management-utils

4ee5489f7ce7595c371e2f4e83fc0c753308905a

[ "MIT" ]

null

ansible/collections/ansible_collections/nhsd/apigee/plugins/filter/filters.py

uk-gov-mirror/nhsdigital.api-management-utils

4ee5489f7ce7595c371e2f4e83fc0c753308905a

[ "MIT" ]

20

2020-05-27T15:00:31.000Z

2021-09-13T11:38:58.000Z

ansible/collections/ansible_collections/nhsd/apigee/plugins/filter/filters.py

uk-gov-mirror/nhsdigital.api-management-utils

4ee5489f7ce7595c371e2f4e83fc0c753308905a

[ "MIT" ]

3

2021-04-11T07:31:36.000Z

2022-01-24T11:23:18.000Z

from ansible_collections.nhsd.apigee.plugins.module_utils import constants def org_from_env(environment) -> str: """Get nhsd apigee organization name from environment name.""" for org, envs in constants.APIGEE_ORG_TO_ENV.items(): if environment in envs: return org valid_envs = [] for v in constants.APIGEE_ORG_TO_ENV.values(): valid_envs = valid_envs + v raise ValueError(f"Unknown environment {environment}, valid environments are {valid_envs}")

28.454545

95

0.683706

from ansible_collections.nhsd.apigee.plugins.module_utils import constants def org_from_env(environment) -> str: """Get nhsd apigee organization name from environment name.""" for org, envs in constants.APIGEE_ORG_TO_ENV.items(): if environment in envs: return org valid_envs = [] for v in constants.APIGEE_ORG_TO_ENV.values(): valid_envs = valid_envs + v raise ValueError(f"Unknown environment {environment}, valid environments are {valid_envs}") class FilterModule: @staticmethod def filters(): return { 'org_from_env': org_from_env }

0

79

0

-2

0

50

9821d58fd217247d13910323dd73ffca785b11ca

11,055

py

Python

IPython/deathrow/astyle.py

dchichkov/ipython

8096bb8640ee7e7c5ebdf3f428fe69cd390e1cd4

[ "BSD-3-Clause-Clear" ]

11

2019-03-20T07:38:35.000Z

2021-06-18T09:42:46.000Z

IPython/deathrow/astyle.py

dchichkov/ipython

8096bb8640ee7e7c5ebdf3f428fe69cd390e1cd4

[ "BSD-3-Clause-Clear" ]

3

2015-04-01T13:14:57.000Z

2015-05-26T16:01:37.000Z

IPython/deathrow/astyle.py

dchichkov/ipython

8096bb8640ee7e7c5ebdf3f428fe69cd390e1cd4

[ "BSD-3-Clause-Clear" ]

5

2019-06-29T03:13:02.000Z

2020-04-23T04:47:11.000Z

""" ``astyle`` provides classes for adding style (foreground and background color; bold; blink; etc.) to terminal and curses output. """ import os try: import curses except ImportError: curses = None COLOR_BLACK = 0 COLOR_RED = 1 COLOR_GREEN = 2 COLOR_YELLOW = 3 COLOR_BLUE = 4 COLOR_MAGENTA = 5 COLOR_CYAN = 6 COLOR_WHITE = 7 A_BLINK = 1<<0 # Blinking text A_BOLD = 1<<1 # Extra bright or bold text A_DIM = 1<<2 # Half bright text A_REVERSE = 1<<3 # Reverse-video text A_STANDOUT = 1<<4 # The best highlighting mode available A_UNDERLINE = 1<<5 # Underlined text def switchstyle(s1, s2): """ Return the ANSI escape sequence needed to switch from style ``s1`` to style ``s2``. """ attrmask = (A_BLINK|A_BOLD|A_UNDERLINE|A_REVERSE) a1 = s1.attrs & attrmask a2 = s2.attrs & attrmask args = [] if s1 != s2: # do we have to get rid of the bold/underline/blink bit? # (can only be done by a reset) # use reset when our target color is the default color # (this is shorter than 37;40) if (a1 & ~a2 or s2==style_default): args.append("0") s1 = style_default a1 = 0 # now we know that old and new color have the same boldness, # or the new color is bold and the old isn't, # i.e. we only might have to switch bold on, not off if not (a1 & A_BOLD) and (a2 & A_BOLD): args.append("1") # Fix underline if not (a1 & A_UNDERLINE) and (a2 & A_UNDERLINE): args.append("4") # Fix blink if not (a1 & A_BLINK) and (a2 & A_BLINK): args.append("5") # Fix reverse if not (a1 & A_REVERSE) and (a2 & A_REVERSE): args.append("7") # Fix foreground color if s1.fg != s2.fg: args.append("3%d" % s2.fg) # Finally fix the background color if s1.bg != s2.bg: args.append("4%d" % s2.bg) if args: return "\033[%sm" % ";".join(args) return "" try: import ipipe except ImportError: pass else: ipipe.xrepr.when_type(Text)(xrepr_astyle_text) def streamstyle(stream, styled=None): """ If ``styled`` is ``None``, return whether ``stream`` refers to a terminal. If this can't be determined (either because ``stream`` doesn't refer to a real OS file, or because you're on Windows) return ``False``. If ``styled`` is not ``None`` ``styled`` will be returned unchanged. """ if styled is None: try: styled = os.isatty(stream.fileno()) except (KeyboardInterrupt, SystemExit): raise except Exception: styled = False return styled def write(stream, styled, *texts): """ Write ``texts`` to ``stream``. """ text = Text(*texts) text.write(stream, streamstyle(stream, styled)) def writeln(stream, styled, *texts): """ Write ``texts`` to ``stream`` and finish with a line feed. """ write(stream, styled, *texts) stream.write("\n") stdout = stdout() stderr = stderr() if curses is not None: # This is probably just range(8) COLOR2CURSES = [ COLOR_BLACK, COLOR_RED, COLOR_GREEN, COLOR_YELLOW, COLOR_BLUE, COLOR_MAGENTA, COLOR_CYAN, COLOR_WHITE, ] A2CURSES = { A_BLINK: curses.A_BLINK, A_BOLD: curses.A_BOLD, A_DIM: curses.A_DIM, A_REVERSE: curses.A_REVERSE, A_STANDOUT: curses.A_STANDOUT, A_UNDERLINE: curses.A_UNDERLINE, } # default style style_default = Style.fromstr("white:black") # Styles for datatypes style_type_none = Style.fromstr("magenta:black") style_type_bool = Style.fromstr("magenta:black") style_type_number = Style.fromstr("yellow:black") style_type_datetime = Style.fromstr("magenta:black") style_type_type = Style.fromstr("cyan:black") # Style for URLs and file/directory names style_url = Style.fromstr("green:black") style_dir = Style.fromstr("cyan:black") style_file = Style.fromstr("green:black") # Style for ellipsis (when an output has been shortened style_ellisis = Style.fromstr("red:black") # Style for displaying exceptions style_error = Style.fromstr("red:black") # Style for displaying non-existing attributes style_nodata = Style.fromstr("red:black")

27.568579

92

0.569064

""" ``astyle`` provides classes for adding style (foreground and background color; bold; blink; etc.) to terminal and curses output. """ import sys, os try: import curses except ImportError: curses = None COLOR_BLACK = 0 COLOR_RED = 1 COLOR_GREEN = 2 COLOR_YELLOW = 3 COLOR_BLUE = 4 COLOR_MAGENTA = 5 COLOR_CYAN = 6 COLOR_WHITE = 7 A_BLINK = 1<<0 # Blinking text A_BOLD = 1<<1 # Extra bright or bold text A_DIM = 1<<2 # Half bright text A_REVERSE = 1<<3 # Reverse-video text A_STANDOUT = 1<<4 # The best highlighting mode available A_UNDERLINE = 1<<5 # Underlined text class Style(object): """ Store foreground color, background color and attribute (bold, underlined etc.). """ __slots__ = ("fg", "bg", "attrs") COLORNAMES = { "black": COLOR_BLACK, "red": COLOR_RED, "green": COLOR_GREEN, "yellow": COLOR_YELLOW, "blue": COLOR_BLUE, "magenta": COLOR_MAGENTA, "cyan": COLOR_CYAN, "white": COLOR_WHITE, } ATTRNAMES = { "blink": A_BLINK, "bold": A_BOLD, "dim": A_DIM, "reverse": A_REVERSE, "standout": A_STANDOUT, "underline": A_UNDERLINE, } def __init__(self, fg, bg, attrs=0): """ Create a ``Style`` object with ``fg`` as the foreground color, ``bg`` as the background color and ``attrs`` as the attributes. Examples: >>> Style(COLOR_RED, COLOR_BLACK) <Style fg=red bg=black attrs=0> >>> Style(COLOR_YELLOW, COLOR_BLUE, A_BOLD|A_UNDERLINE) <Style fg=yellow bg=blue attrs=bold|underline> """ self.fg = fg self.bg = bg self.attrs = attrs def __call__(self, *args): text = Text() for arg in args: if isinstance(arg, Text): text.extend(arg) else: text.append((self, arg)) return text def __eq__(self, other): return self.fg == other.fg and self.bg == other.bg and self.attrs == other.attrs def __neq__(self, other): return self.fg != other.fg or self.bg != other.bg or self.attrs != other.attrs def __repr__(self): color2name = ("black", "red", "green", "yellow", "blue", "magenta", "cyan", "white") attrs2name = ("blink", "bold", "dim", "reverse", "standout", "underline") return "<%s fg=%s bg=%s attrs=%s>" % ( self.__class__.__name__, color2name[self.fg], color2name[self.bg], "|".join([attrs2name[b] for b in xrange(6) if self.attrs&(1<<b)]) or 0) def fromstr(cls, value): """ Create a ``Style`` object from a string. The format looks like this: ``"red:black:bold|blink"``. """ # defaults fg = COLOR_WHITE bg = COLOR_BLACK attrs = 0 parts = value.split(":") if len(parts) > 0: fg = cls.COLORNAMES[parts[0].lower()] if len(parts) > 1: bg = cls.COLORNAMES[parts[1].lower()] if len(parts) > 2: for strattr in parts[2].split("|"): attrs |= cls.ATTRNAMES[strattr.lower()] return cls(fg, bg, attrs) fromstr = classmethod(fromstr) def fromenv(cls, name, default): """ Create a ``Style`` from an environment variable named ``name`` (using ``default`` if the environment variable doesn't exist). """ return cls.fromstr(os.environ.get(name, default)) fromenv = classmethod(fromenv) def switchstyle(s1, s2): """ Return the ANSI escape sequence needed to switch from style ``s1`` to style ``s2``. """ attrmask = (A_BLINK|A_BOLD|A_UNDERLINE|A_REVERSE) a1 = s1.attrs & attrmask a2 = s2.attrs & attrmask args = [] if s1 != s2: # do we have to get rid of the bold/underline/blink bit? # (can only be done by a reset) # use reset when our target color is the default color # (this is shorter than 37;40) if (a1 & ~a2 or s2==style_default): args.append("0") s1 = style_default a1 = 0 # now we know that old and new color have the same boldness, # or the new color is bold and the old isn't, # i.e. we only might have to switch bold on, not off if not (a1 & A_BOLD) and (a2 & A_BOLD): args.append("1") # Fix underline if not (a1 & A_UNDERLINE) and (a2 & A_UNDERLINE): args.append("4") # Fix blink if not (a1 & A_BLINK) and (a2 & A_BLINK): args.append("5") # Fix reverse if not (a1 & A_REVERSE) and (a2 & A_REVERSE): args.append("7") # Fix foreground color if s1.fg != s2.fg: args.append("3%d" % s2.fg) # Finally fix the background color if s1.bg != s2.bg: args.append("4%d" % s2.bg) if args: return "\033[%sm" % ";".join(args) return "" class Text(list): """ A colored string. A ``Text`` object is a sequence, the sequence items will be ``(style, string)`` tuples. """ def __init__(self, *args): list.__init__(self) self.append(*args) def __repr__(self): return "%s.%s(%s)" % ( self.__class__.__module__, self.__class__.__name__, list.__repr__(self)[1:-1]) def append(self, *args): for arg in args: if isinstance(arg, Text): self.extend(arg) elif isinstance(arg, tuple): # must be (style, string) list.append(self, arg) elif isinstance(arg, unicode): list.append(self, (style_default, arg)) else: list.append(self, (style_default, str(arg))) def insert(self, index, *args): self[index:index] = Text(*args) def __add__(self, other): new = Text() new.append(self) new.append(other) return new def __iadd__(self, other): self.append(other) return self def format(self, styled=True): """ This generator yields the strings that will make up the final colorized string. """ if styled: oldstyle = style_default for (style, string) in self: if not isinstance(style, (int, long)): switch = switchstyle(oldstyle, style) if switch: yield switch if string: yield string oldstyle = style switch = switchstyle(oldstyle, style_default) if switch: yield switch else: for (style, string) in self: if not isinstance(style, (int, long)): yield string def string(self, styled=True): """ Return the resulting string (with escape sequences, if ``styled`` is true). """ return "".join(self.format(styled)) def __str__(self): """ Return ``self`` as a string (without ANSI escape sequences). """ return self.string(False) def write(self, stream, styled=True): """ Write ``self`` to the output stream ``stream`` (with escape sequences, if ``styled`` is true). """ for part in self.format(styled): stream.write(part) try: import ipipe except ImportError: pass else: def xrepr_astyle_text(self, mode="default"): yield (-1, True) for info in self: yield info ipipe.xrepr.when_type(Text)(xrepr_astyle_text) def streamstyle(stream, styled=None): """ If ``styled`` is ``None``, return whether ``stream`` refers to a terminal. If this can't be determined (either because ``stream`` doesn't refer to a real OS file, or because you're on Windows) return ``False``. If ``styled`` is not ``None`` ``styled`` will be returned unchanged. """ if styled is None: try: styled = os.isatty(stream.fileno()) except (KeyboardInterrupt, SystemExit): raise except Exception: styled = False return styled def write(stream, styled, *texts): """ Write ``texts`` to ``stream``. """ text = Text(*texts) text.write(stream, streamstyle(stream, styled)) def writeln(stream, styled, *texts): """ Write ``texts`` to ``stream`` and finish with a line feed. """ write(stream, styled, *texts) stream.write("\n") class Stream(object): """ Stream wrapper that adds color output. """ def __init__(self, stream, styled=None): self.stream = stream self.styled = streamstyle(stream, styled) def write(self, *texts): write(self.stream, self.styled, *texts) def writeln(self, *texts): writeln(self.stream, self.styled, *texts) def __getattr__(self, name): return getattr(self.stream, name) class stdout(object): """ Stream wrapper for ``sys.stdout`` that adds color output. """ def write(self, *texts): write(sys.stdout, None, *texts) def writeln(self, *texts): writeln(sys.stdout, None, *texts) def __getattr__(self, name): return getattr(sys.stdout, name) stdout = stdout() class stderr(object): """ Stream wrapper for ``sys.stderr`` that adds color output. """ def write(self, *texts): write(sys.stderr, None, *texts) def writeln(self, *texts): writeln(sys.stderr, None, *texts) def __getattr__(self, name): return getattr(sys.stdout, name) stderr = stderr() if curses is not None: # This is probably just range(8) COLOR2CURSES = [ COLOR_BLACK, COLOR_RED, COLOR_GREEN, COLOR_YELLOW, COLOR_BLUE, COLOR_MAGENTA, COLOR_CYAN, COLOR_WHITE, ] A2CURSES = { A_BLINK: curses.A_BLINK, A_BOLD: curses.A_BOLD, A_DIM: curses.A_DIM, A_REVERSE: curses.A_REVERSE, A_STANDOUT: curses.A_STANDOUT, A_UNDERLINE: curses.A_UNDERLINE, } # default style style_default = Style.fromstr("white:black") # Styles for datatypes style_type_none = Style.fromstr("magenta:black") style_type_bool = Style.fromstr("magenta:black") style_type_number = Style.fromstr("yellow:black") style_type_datetime = Style.fromstr("magenta:black") style_type_type = Style.fromstr("cyan:black") # Style for URLs and file/directory names style_url = Style.fromstr("green:black") style_dir = Style.fromstr("cyan:black") style_file = Style.fromstr("green:black") # Style for ellipsis (when an output has been shortened style_ellisis = Style.fromstr("red:black") # Style for displaying exceptions style_error = Style.fromstr("red:black") # Style for displaying non-existing attributes style_nodata = Style.fromstr("red:black")

0

6,404

97

0

5

141

0ba81da5ce04ec487462a1396a9e594fbd6ac8ba

853

py

Python

0000_examples/gelsight/tst.py

Photon26/wrs-main_0614

c0d0e38deac9785e9c382305f65f3ac5f221787d

[ "MIT" ]

null

0000_examples/gelsight/tst.py

Photon26/wrs-main_0614

c0d0e38deac9785e9c382305f65f3ac5f221787d

[ "MIT" ]

null

0000_examples/gelsight/tst.py

Photon26/wrs-main_0614

c0d0e38deac9785e9c382305f65f3ac5f221787d

[ "MIT" ]

null

import robot_sim.robots.ur3_dual.ur3_dual as ur3d import rbt_con.force_control as ur3dx # import robot_con.ur.ur3_dual_x as ur3dx import visualization.panda.world as wd import modeling.geometric_model as gm import numpy as np ur_dual_x = ur3dx.UR3DualX(lft_robot_ip='10.2.0.50', rgt_robot_ip='10.2.0.51', pc_ip='10.2.0.100') base = wd.World(cam_pos=[2,1,3], lookat_pos=[0,0,1.1]) gm.gen_frame().attach_to(base) robot_s = ur3d.UR3Dual() jnt = ur_dual_x.get_jnt_values("lft_arm") robot_s.fk(component_name="lft_arm",jnt_values= np.array(jnt)) robot_meshmodel = robot_s.gen_meshmodel(toggle_tcpcs=True) robot_meshmodel.attach_to(base) base.run()

32.807692

98

0.805393

import pickle import robot_sim.robots.ur3_dual.ur3_dual as ur3d import rbt_con.force_control as ur3dx # import robot_con.ur.ur3_dual_x as ur3dx import visualization.panda.world as wd import modeling.geometric_model as gm import motion.optimization_based.incremental_nik as inik import numpy as np import modeling.collision_model as cm import cv2 import img_to_depth as itd import time import motion.probabilistic.rrt_connect as rrtc ur_dual_x = ur3dx.UR3DualX(lft_robot_ip='10.2.0.50', rgt_robot_ip='10.2.0.51', pc_ip='10.2.0.100') base = wd.World(cam_pos=[2,1,3], lookat_pos=[0,0,1.1]) gm.gen_frame().attach_to(base) robot_s = ur3d.UR3Dual() jnt = ur_dual_x.get_jnt_values("lft_arm") robot_s.fk(component_name="lft_arm",jnt_values= np.array(jnt)) robot_meshmodel = robot_s.gen_meshmodel(toggle_tcpcs=True) robot_meshmodel.attach_to(base) base.run()

0

53

154

3fd17c06364dbbcfbf61a1c852f8a863c6286b83

4,656

py

Python

galaxy_model/spiral_arms/three_kpc.py

K-Monty/galaxy-model

9515d7c87c31e68338466d2044d8e9c679bf8648

[ "MIT" ]

null

galaxy_model/spiral_arms/three_kpc.py

K-Monty/galaxy-model

9515d7c87c31e68338466d2044d8e9c679bf8648

[ "MIT" ]

null

galaxy_model/spiral_arms/three_kpc.py

K-Monty/galaxy-model

9515d7c87c31e68338466d2044d8e9c679bf8648

[ "MIT" ]

null

""" This module use SpiralArm superclass, with some modifications, to create 3-kpc arm. """

40.486957

78

0.597938

""" This module use SpiralArm superclass, with some modifications, to create 3-kpc arm. """ from shapely.geometry.polygon import Polygon from descartes import PolygonPatch from .spiral_parameters import Three_Kpc from . import spiral_property as spiral_eq from .spiral_arm_superclass import SpiralArm class ThreeKpcArm(SpiralArm): def __init__(self): self.params = Three_Kpc self._color = 'yellow' self.tuning_window = 3 self._spine_r_kpc, self.x_spine, self.y_spine, self._B_spine, \ self._width_kpc = \ self.spine_radii_coords_b_range_and_width_with_smoothing() self._poly_coords_inner, self._poly_coords_outer = self._poly_coords() self._polygon_near = Polygon(self._poly_coords_inner) self._polygon_far = Polygon(self._poly_coords_outer) self.polypatch_near = PolygonPatch(self._polygon_near, color=self._color, alpha=0.2) self.polypatch_far = PolygonPatch(self._polygon_far, color=self._color, alpha=0.2) def __repr__(self): return "ThreeKpc" def _radii_factory(self, B): return self._spine_radius_at_B_and_psi( B, self.params['B-kink'], self.params['psi'], self.params['R-kink']) def _width_factory(self, B, r): return spiral_eq.CylinderSize.width_kpc( self.params['w-kink'], r, self.params['R-kink']) + 0.1 def spine_radii_coords_b_range_and_width_with_smoothing(self): r_spine_near = [] x_spine_near = [] y_spine_near = [] width_kpc_near = [] B_list_near = [x for x in range(self.params['B-begin-near'], self.params['B-end-near'])] r_spine_far = [] x_spine_far = [] y_spine_far = [] width_kpc_far = [] B_list_far = [x for x in range(self.params['B-begin-far'], self.params['B-end-far'])] for B_near in B_list_near: r_near = self._radii_factory(B_near) w_near = self._width_factory(B_near, r_near) r_spine_near.append(r_near) width_kpc_near.append(w_near) for B_far in B_list_far: r_far = self._radii_factory(B_far) w_far = self._width_factory(B_far, r_far) r_spine_far.append(r_far) width_kpc_far.append(w_far) for B_near, r_near in zip(B_list_near, r_spine_near): cartesian_coords = spiral_eq.polar_to_cartesian(r_near, B_near) x_spine_near.append(cartesian_coords[0]) y_spine_near.append(cartesian_coords[1]) for B_far, r_far in zip(B_list_far, r_spine_far): cartesian_coords = spiral_eq.polar_to_cartesian(r_far, B_far) x_spine_far.append(cartesian_coords[0]) y_spine_far.append(cartesian_coords[1]) r_spine = [r_spine_near, r_spine_far] x_spine = [x_spine_near, x_spine_far] y_spine = [y_spine_near, y_spine_far] B_list = [B_list_near, B_list_far] width_kpc = [width_kpc_near, width_kpc_far] return (r_spine, x_spine, y_spine, B_list, width_kpc) def _poly_coords(self): x_border_inner_near, y_border_inner_near, x_border_outer_near, \ y_border_outer_near = \ self._border_coords(self.x_spine[0], self.y_spine[0], self._B_spine[0], self._width_kpc[0]) x_border_inner_far, y_border_inner_far, x_border_outer_far, \ y_border_outer_far = \ self._border_coords(self.x_spine[1], self.y_spine[1], self._B_spine[1], self._width_kpc[1]) x_poly_edge_coords_near = x_border_inner_near \ + x_border_outer_near[::-1] y_poly_edge_coords_near = y_border_inner_near \ + y_border_outer_near[::-1] poly_edge_coords_near = [xy for xy in zip(x_poly_edge_coords_near, y_poly_edge_coords_near)] x_poly_edge_coords_far = x_border_inner_far + x_border_outer_far[::-1] y_poly_edge_coords_far = y_border_inner_far + y_border_outer_far[::-1] poly_edge_coords_far = [xy for xy in zip(x_poly_edge_coords_far, y_poly_edge_coords_far)] return poly_edge_coords_near, poly_edge_coords_far

0

4,328

0

99

135

dbeff05f1c61b09708b8a95665c71d6764ac49fd

2,045

py

Python

rbi/rbi_budgets_scraper.py

cbgaindia/scrappers

f8aab1d0f0a52007d8f0ab94e7ea38047e6e46a9

[ "MIT" ]

null

rbi/rbi_budgets_scraper.py

cbgaindia/scrappers

f8aab1d0f0a52007d8f0ab94e7ea38047e6e46a9

[ "MIT" ]

null

rbi/rbi_budgets_scraper.py

cbgaindia/scrappers

f8aab1d0f0a52007d8f0ab94e7ea38047e6e46a9

[ "MIT" ]

null

'Code for scrapping RBI Data' import logging from logging.config import fileConfig fileConfig('scrappers/logging_config.ini') logger = logging.getLogger() OUT_FOLDER = "rbi" if __name__ == '__main__': obj = RBIBudgetScraper() for year in range(2002,2015): year = str(year) url1 = "https://www.rbi.org.in/scripts/AnnualPublications.aspx?head=Handbook%20of%20Statistics%20on%20Indian%20Economy" url2 = "https://rbi.org.in/Scripts/AnnualPublications.aspx?head=State+Finances+%3a+A+Study+of+Budgets" obj.fetch_docs_for_year(url1, year) obj.fetch_docs_for_year(url2, year)

44.456522

127

0.630318

'Code for scrapping RBI Data' from datetime import date from lxml import etree import logging from logging.config import fileConfig from scrappers.scrapping_utils import ScrappingUtils fileConfig('scrappers/logging_config.ini') logger = logging.getLogger() OUT_FOLDER = "rbi" class RBIBudgetScraper(ScrappingUtils): def fetch_docs_for_year(self, url, year=None): '''Fetches all documents for a budget year ''' if not year: current_year = date.today().year year = "%s" % (current_year) page_dom = self.get_page_dom(url) title = self.get_text_from_element(page_dom, xpath="//h2[@class='page_title']/text()") download_dir = "%s/%s/%s" % (OUT_FOLDER, year, title) file_dir = download_dir for node in page_dom.xpath("//table[@class='tablebg']/tr"): node_title = self.get_text_from_element(node, xpath="./td[@class='tableheader']//text()") if node_title: file_dir = "%s/%s" % (download_dir, node_title) continue node_title = self.get_text_from_element(node, xpath="./td[@style]//text()") file_path = "%s/%s" % (file_dir, node_title) file_link = node.xpath("./td[2]/a[@target]/@href") if file_link: self.fetch_and_save_file(file_link[0].replace('http://', 'https://'), file_path + ".xls") file_link = node.xpath("./td[3]/a[@target]/@href") if file_link: self.fetch_and_save_file(file_link[0].replace('http://', 'https://'), file_path + ".pdf") if __name__ == '__main__': obj = RBIBudgetScraper() for year in range(2002,2015): year = str(year) url1 = "https://www.rbi.org.in/scripts/AnnualPublications.aspx?head=Handbook%20of%20Statistics%20on%20Indian%20Economy" url2 = "https://rbi.org.in/Scripts/AnnualPublications.aspx?head=State+Finances+%3a+A+Study+of+Budgets" obj.fetch_docs_for_year(url1, year) obj.fetch_docs_for_year(url2, year)

0

1,292

0

36

99

30b7600879ed7470a3a4e7671d503041ddaee708

404

py

Python

0x11-python-network_1/2-post_email.py

Nahi-Terefe/alx-higher_level_programming

c67a78a6f79e853918963971f8352979e7691541

[ "MIT" ]

null

0x11-python-network_1/2-post_email.py

Nahi-Terefe/alx-higher_level_programming

c67a78a6f79e853918963971f8352979e7691541

[ "MIT" ]

null

0x11-python-network_1/2-post_email.py

Nahi-Terefe/alx-higher_level_programming

c67a78a6f79e853918963971f8352979e7691541

[ "MIT" ]

null

#!/usr/bin/python3 """ post email """ import urllib.request import urllib.parse import sys if __name__ == "__main__": value = {'email': sys.argv[2]} data = urllib.parse.urlencode(value) data = data.encode('utf-8') req = urllib.request.Request(sys.argv[1], data) with urllib.request.urlopen(req) as response: res = response.read().decode(encoding='UTF-8') print(res)

25.25

54

0.65099

#!/usr/bin/python3 """ post email """ import urllib.request import urllib.parse import sys if __name__ == "__main__": value = {'email': sys.argv[2]} data = urllib.parse.urlencode(value) data = data.encode('utf-8') req = urllib.request.Request(sys.argv[1], data) with urllib.request.urlopen(req) as response: res = response.read().decode(encoding='UTF-8') print(res)

0

98dd68e4820e061630336d4d9dc6896eb5df6fb1

2,534

py

Python

modules/selection.py

psp-codes/reduced-decoy-ensemble

096926d8d44a6d7bdbf9c49dd52fb83ff86e8b3f

[ "MIT" ]

2

2020-04-22T04:16:03.000Z

2020-08-19T13:50:20.000Z

modules/selection.py

psp-codes/reduced-decoy-ensemble

096926d8d44a6d7bdbf9c49dd52fb83ff86e8b3f

[ "MIT" ]

null

modules/selection.py

psp-codes/reduced-decoy-ensemble

096926d8d44a6d7bdbf9c49dd52fb83ff86e8b3f

[ "MIT" ]

1

2020-08-19T13:50:26.000Z

# selection.py # since: 10/2018 # Developed by: Shehu Lab """Module for selecting next generation from current generation. This module provides methods to select next generation from current generation. Available Functions: - truncation: Selects next generation via elitism truncation selection. """ def truncation(parent_population, child_population, parents_scores, children_scores, elitism_rate): """Selects next generation using elitism truncation selection. This function implements truncation selection while ensuring elitism to select a specific number of members for the next generation. Args: parent_population: A list containing members of parent population. child_population: A list containing members of offspring population. parents_scores: A list containing scores of each member of the parent population. The format is: [member 1 score, member 2 score, ....] The order of members has to be consistent with parent_population argument. children_scores: A list containing scores of each member of the offspring population. The format is: [member 1 score, member 2 score, ....] The order of members has to be consistent with child_population argument. elitism_rate: A float indicating the elitism percentage. Returns: A list of members for the next generation of population. """ population_size = len(parent_population) population_indices = list(range(population_size)) sorted_parents_indices = [x for _, x in sorted(zip( parents_scores, population_indices ))] sorted_parents_scores = sorted(parents_scores) # Slice parent population using elitism rate slice_index = int(population_size * elitism_rate) selected_parents_indices = sorted_parents_indices[:slice_index] selected_parents = [parent_population[i] for i in selected_parents_indices] combined_population = selected_parents + child_population combined_scores = sorted_parents_scores[:slice_index] + children_scores combined_population_indices = list(range(len(combined_population))) sorted_population_indices = [x for _, x in sorted(zip( combined_scores, combined_population_indices ))] selected_population_indices = sorted_population_indices[:population_size] # Truncate and return return [combined_population[i] for i in selected_population_indices]

37.820896

79

0.723757

# selection.py # since: 10/2018 # Developed by: Shehu Lab """Module for selecting next generation from current generation. This module provides methods to select next generation from current generation. Available Functions: - truncation: Selects next generation via elitism truncation selection. """ def truncation(parent_population, child_population, parents_scores, children_scores, elitism_rate): """Selects next generation using elitism truncation selection. This function implements truncation selection while ensuring elitism to select a specific number of members for the next generation. Args: parent_population: A list containing members of parent population. child_population: A list containing members of offspring population. parents_scores: A list containing scores of each member of the parent population. The format is: [member 1 score, member 2 score, ....] The order of members has to be consistent with parent_population argument. children_scores: A list containing scores of each member of the offspring population. The format is: [member 1 score, member 2 score, ....] The order of members has to be consistent with child_population argument. elitism_rate: A float indicating the elitism percentage. Returns: A list of members for the next generation of population. """ population_size = len(parent_population) population_indices = list(range(population_size)) sorted_parents_indices = [x for _, x in sorted(zip( parents_scores, population_indices ))] sorted_parents_scores = sorted(parents_scores) # Slice parent population using elitism rate slice_index = int(population_size * elitism_rate) selected_parents_indices = sorted_parents_indices[:slice_index] selected_parents = [parent_population[i] for i in selected_parents_indices] combined_population = selected_parents + child_population combined_scores = sorted_parents_scores[:slice_index] + children_scores combined_population_indices = list(range(len(combined_population))) sorted_population_indices = [x for _, x in sorted(zip( combined_scores, combined_population_indices ))] selected_population_indices = sorted_population_indices[:population_size] # Truncate and return return [combined_population[i] for i in selected_population_indices]

0

3f46e12fb5d861255b9732050d691bdff492592b

129

py

Python

Desafios/desafio030.py

VanessaCML/python

56133b9000ba89154f37038e11a3c2d1aa6d1094

[ "MIT" ]

null

Desafios/desafio030.py

VanessaCML/python

56133b9000ba89154f37038e11a3c2d1aa6d1094

[ "MIT" ]

null

Desafios/desafio030.py

VanessaCML/python

56133b9000ba89154f37038e11a3c2d1aa6d1094

[ "MIT" ]

null

n = int(input('Digite um nmero: ')) if n % 2 == 0: print(f'O nmero {n} par.') else: print(f'O nmero {n} mpar.')

18.428571

36

0.542636

n = int(input('Digite um número: ')) if n % 2 == 0: print(f'O número {n} é par.') else: print(f'O número {n} é ímpar.')

12

0

be760d7ba6e881b8660d24ef1857485b859c9f5b

1,487

py

Python

sorted_insert_position.py

UPstartDeveloper/Problem_Solving_Practice

bd61333b3b056e82a94297e02bc05a17552e3496

[ "MIT" ]

null

sorted_insert_position.py

UPstartDeveloper/Problem_Solving_Practice

bd61333b3b056e82a94297e02bc05a17552e3496

[ "MIT" ]

null

sorted_insert_position.py

UPstartDeveloper/Problem_Solving_Practice

bd61333b3b056e82a94297e02bc05a17552e3496

[ "MIT" ]

null

import math def find_index(sorted_list, target): """Finds the index where the target value is expected in a sorted list.""" def binary_search(low_index, hi_index): """Searches for a value in a list, throwing away half each call""" # locate the middle index mid_index = math.ceil((low_index + hi_index) / 2) # obtain values from all three indices low_val, mid_val, high_val = ( sorted_list[low_index], sorted_list[mid_index], sorted_list[hi_index], ) # Base case: the target value is found if mid_val == target: return mid_index # target value not found: elif mid_val > target: # if target lies "before" the array if low_index == hi_index: # return the 0 index return mid_index # otherwise search the lower half of the array return binary_search(low_index, mid_index - 1) elif mid_val < target: # if target lies "after" the last value if low_index == hi_index: return mid_index + 1 # otherwise search the larger half of the array return binary_search(mid_index + 1, hi_index) # store the array length ARRAY_LENGTH = len(sorted_list) # execute binary search on the array return binary_search(0, ARRAY_LENGTH - 1) if __name__ == "__main__": print(find_index([1, 3, 5, 6], 5))

34.581395

78

0.597848

import math def find_index(sorted_list, target): """Finds the index where the target value is expected in a sorted list.""" def binary_search(low_index, hi_index): """Searches for a value in a list, throwing away half each call""" # locate the middle index mid_index = math.ceil((low_index + hi_index) / 2) # obtain values from all three indices low_val, mid_val, high_val = ( sorted_list[low_index], sorted_list[mid_index], sorted_list[hi_index], ) # Base case: the target value is found if mid_val == target: return mid_index # target value not found: elif mid_val > target: # if target lies "before" the array if low_index == hi_index: # return the 0 index return mid_index # otherwise search the lower half of the array return binary_search(low_index, mid_index - 1) elif mid_val < target: # if target lies "after" the last value if low_index == hi_index: return mid_index + 1 # otherwise search the larger half of the array return binary_search(mid_index + 1, hi_index) # store the array length ARRAY_LENGTH = len(sorted_list) # execute binary search on the array return binary_search(0, ARRAY_LENGTH - 1) if __name__ == "__main__": print(find_index([1, 3, 5, 6], 5))

0

b4bd721ab4429c8eb5ef6b2554f6ed76ab1c49a3

404

py

Python

2021/Day_8/AoCSignals.py

ArrowThunder/AoC

f4649115fc83b989745c83251a85710e76eb0368

[ "MIT" ]

1

2021-12-04T12:44:51.000Z

2021/Day_8/AoCSignals.py

ArrowThunder/AoC2021

f4649115fc83b989745c83251a85710e76eb0368

[ "MIT" ]

null

2021/Day_8/AoCSignals.py

ArrowThunder/AoC2021

f4649115fc83b989745c83251a85710e76eb0368

[ "MIT" ]

null

with open('input.txt') as file: total = 0 for line in file: inputs, outputs = parse_line(line) for code in outputs: if len(code) == 2 or len(code) == 3 or len(code) == 4 or len(code) == 7: total += 1 print(total)

28.857143

84

0.549505

def parse_line(line): line = line.split('|') inputs = line[0].split() outputs = line[1].split() return inputs, outputs with open('input.txt') as file: total = 0 for line in file: inputs, outputs = parse_line(line) for code in outputs: if len(code) == 2 or len(code) == 3 or len(code) == 4 or len(code) == 7: total += 1 print(total)

0

113

0

22

13d4af942e0928ac2e926dad1c9830ea003345d6

1,885

py

Python

4.Graph_pangenome/1.construction_graph_genome/02_scripts/prepareAugmentFiles.py

YaoZhou89/TGG

b9b30f6a1bf365895c39cb6fa4dddf0588d3c5dd

[ "MIT" ]

7

2022-01-19T14:17:23.000Z

2022-02-08T12:17:39.000Z

4.Graph_pangenome/1.construction_graph_genome/02_scripts/prepareAugmentFiles.py

YaoZhou89/TGG

b9b30f6a1bf365895c39cb6fa4dddf0588d3c5dd

[ "MIT" ]

null

4.Graph_pangenome/1.construction_graph_genome/02_scripts/prepareAugmentFiles.py

YaoZhou89/TGG

b9b30f6a1bf365895c39cb6fa4dddf0588d3c5dd

[ "MIT" ]

null

import vcf import argparse from pyfaidx import Fasta from Bio import SeqIO from Bio.SeqRecord import SeqRecord from Bio.Seq import MutableSeq parser = argparse.ArgumentParser(description='Make fasta for each variant to align/augment.') parser.add_argument('-v', help='the input VCF file.', required=True) parser.add_argument('-r', help='the reference FASTA file.', required=True) parser.add_argument('-s', help='the output FASTA file with SV sequence to align/augment', required=True) parser.add_argument('-f', default=50000, type=int, help='the flank size. Default 50000.') args = parser.parse_args() # get chromosome length ref = Fasta(args.r) # read vcf vcfi = open(args.v, 'r') vcf_reader = vcf.Reader(vcfi) fa_outf = open(args.s, 'w') tail_buff = 1000 # tail buffer: no sequence extracted from a buffer at the chunk tails to ensure they stay untouched for record in vcf_reader: chr_len = len(ref[record.CHROM]) # retrieve alt allele with flanks # left flank sequence fl1_e = record.POS - 1 if fl1_e < tail_buff: l1_s = tail_buff / 2 else: fl1_s = fl1_e - args.f fl1_s = max(0, fl1_s) + tail_buff fl1_seq = ref[record.CHROM][fl1_s:fl1_e] fl1_seq = fl1_seq.seq # Get flank 2 sequence fl2_s = record.POS + len(record.REF) - 1 if fl2_s > chr_len - tail_buff: fl2_e = (chr_len + fl2_s)/2 else: fl2_e = fl2_s + args.f fl2_e = min(fl2_e, len(ref[record.CHROM])) - tail_buff fl2_seq = ref[record.CHROM][int(fl2_s):int(fl2_e)] fl2_seq = fl2_seq.seq # Fasta record oseq = fl1_seq + str(record.ALT[0]) + fl2_seq svid = '{}_{}_{}_{}'.format(record.CHROM, int(fl1_s), int(fl2_e), record.ID) orec = SeqRecord(MutableSeq(oseq.upper()), id=svid, description='') SeqIO.write(orec, fa_outf, "fasta") fa_outf.close() vcfi.close()

35.566038

116

0.669496

import vcf import argparse from pyfaidx import Fasta from Bio import SeqIO from Bio.SeqRecord import SeqRecord from Bio.Seq import MutableSeq parser = argparse.ArgumentParser(description='Make fasta for each variant to align/augment.') parser.add_argument('-v', help='the input VCF file.', required=True) parser.add_argument('-r', help='the reference FASTA file.', required=True) parser.add_argument('-s', help='the output FASTA file with SV sequence to align/augment', required=True) parser.add_argument('-f', default=50000, type=int, help='the flank size. Default 50000.') args = parser.parse_args() # get chromosome length ref = Fasta(args.r) # read vcf vcfi = open(args.v, 'r') vcf_reader = vcf.Reader(vcfi) fa_outf = open(args.s, 'w') tail_buff = 1000 # tail buffer: no sequence extracted from a buffer at the chunk tails to ensure they stay untouched for record in vcf_reader: chr_len = len(ref[record.CHROM]) # retrieve alt allele with flanks # left flank sequence fl1_e = record.POS - 1 if fl1_e < tail_buff: l1_s = tail_buff / 2 else: fl1_s = fl1_e - args.f fl1_s = max(0, fl1_s) + tail_buff fl1_seq = ref[record.CHROM][fl1_s:fl1_e] fl1_seq = fl1_seq.seq # Get flank 2 sequence fl2_s = record.POS + len(record.REF) - 1 if fl2_s > chr_len - tail_buff: fl2_e = (chr_len + fl2_s)/2 else: fl2_e = fl2_s + args.f fl2_e = min(fl2_e, len(ref[record.CHROM])) - tail_buff fl2_seq = ref[record.CHROM][int(fl2_s):int(fl2_e)] fl2_seq = fl2_seq.seq # Fasta record oseq = fl1_seq + str(record.ALT[0]) + fl2_seq svid = '{}_{}_{}_{}'.format(record.CHROM, int(fl1_s), int(fl2_e), record.ID) orec = SeqRecord(MutableSeq(oseq.upper()), id=svid, description='') SeqIO.write(orec, fa_outf, "fasta") fa_outf.close() vcfi.close()

0

878b92fc40120b934c575d753a800a9538d9a14d

22,851

py

Python

gateware/daqnet/ethernet/rmii.py

dvdfreitag/daqnet

6a84185d2cf35d99dd620d1e09b4df7fb0630784

[ "MIT" ]

null

gateware/daqnet/ethernet/rmii.py

dvdfreitag/daqnet

6a84185d2cf35d99dd620d1e09b4df7fb0630784

[ "MIT" ]

null

gateware/daqnet/ethernet/rmii.py

dvdfreitag/daqnet

6a84185d2cf35d99dd620d1e09b4df7fb0630784

[ "MIT" ]

null

30.921516

78

0.495427

""" Ethernet RMII Interface Copyright 2018-2019 Adam Greig Released under the MIT license; see LICENSE for details. """ from nmigen import Elaboratable, Module, Signal, Cat from .crc import CRC32 from .mac_address_match import MACAddressMatch class RMIIRx(Elaboratable): """ RMII receive module Receives incoming packets and saves them to a memory. Validates incoming frame check sequence and only asserts `rx_valid` when an entire valid packet has been saved to the port. This module must be run in the RMII ref_clk domain, and the memory port and inputs and outputs must also be in that clock domain. Parameters: * `mac_addr`: 6-byte MAC address (list of ints) Ports: * `write_port`: a write-capable memory port, 8 bits wide by 2048, running in the RMII ref_clk domain Pins: * `crs_dv`: RMII carrier sense/data valid * `rxd0`: RMII receive data 0 * `rxd1`: RMII receive data 1 Outputs: * `rx_valid`: pulsed when a valid packet is in memory * `rx_offset`: n-bit start address of received packet * `rx_len`: 11-bit length of received packet """ def __init__(self, mac_addr, write_port, crs_dv, rxd0, rxd1): # Outputs self.rx_valid = Signal() self.rx_offset = Signal(write_port.addr.width) self.rx_len = Signal(11) # Store arguments self.mac_addr = mac_addr self.write_port = write_port self.crs_dv = crs_dv self.rxd0 = rxd0 self.rxd1 = rxd1 def elaborate(self, platform): m = Module() m.submodules.crc = crc = CRC32() m.submodules.mac_match = mac_match = MACAddressMatch(self.mac_addr) m.submodules.rxbyte = rxbyte = RMIIRxByte( self.crs_dv, self.rxd0, self.rxd1) adr = Signal(self.write_port.addr.width) with m.FSM() as fsm: m.d.comb += [ self.write_port.addr.eq(adr), self.write_port.data.eq(rxbyte.data), self.write_port.en.eq(rxbyte.data_valid), crc.data.eq(rxbyte.data), crc.data_valid.eq(rxbyte.data_valid), crc.reset.eq(fsm.ongoing("IDLE")), mac_match.data.eq(rxbyte.data), mac_match.data_valid.eq(rxbyte.data_valid), mac_match.reset.eq(fsm.ongoing("IDLE")), ] # Idle until we see data valid with m.State("IDLE"): m.d.sync += self.rx_len.eq(0) m.d.sync += self.rx_valid.eq(0) with m.If(rxbyte.dv): m.d.sync += self.rx_offset.eq(adr) m.next = "DATA" # Save incoming data to memory with m.State("DATA"): with m.If(rxbyte.data_valid): m.d.sync += adr.eq(adr + 1) m.d.sync += self.rx_len.eq(self.rx_len + 1) with m.Elif(~rxbyte.dv): m.next = "EOF" with m.State("EOF"): with m.If(crc.crc_match & mac_match.mac_match): m.d.sync += self.rx_valid.eq(1) m.next = "IDLE" return m class RMIIRxByte(Elaboratable): """ RMII Receive Byte De-muxer Handles receiving a byte dibit-by-dibit. This submodule must be in the RMII ref_clk clock domain, and its outputs are likewise in that domain. Pins: * `crs_dv`: Data valid, input * `rxd0`: RX data 0, input * `rxd1`: RX data 1, input Outputs: * `data`: 8-bit wide output data * `data_valid`: Asserted for one cycle when `data` is valid * `dv`: RMII Data valid recovered signal * `crs`: RMII Carrier sense recovered signal """ def __init__(self, crs_dv, rxd0, rxd1): # Outputs self.data = Signal(8) self.data_valid = Signal() self.dv = Signal() self.crs = Signal() self.crs_dv = crs_dv self.rxd0 = rxd0 self.rxd1 = rxd1 def elaborate(self, platform): m = Module() # Sample RMII signals on rising edge of ref_clk crs_dv_reg = Signal() rxd_reg = Signal(2) m.d.sync += [ crs_dv_reg.eq(self.crs_dv), rxd_reg.eq(Cat(self.rxd0, self.rxd1)), ] with m.FSM(): with m.State("IDLE"): m.d.sync += [ self.crs.eq(0), self.dv.eq(0), self.data_valid.eq(0), ] with m.If(crs_dv_reg & (rxd_reg == 0b01)): m.next = "PREAMBLE_SFD" with m.State("PREAMBLE_SFD"): m.d.sync += [ self.crs.eq(1), self.dv.eq(1), self.data_valid.eq(0), ] with m.If(rxd_reg == 0b11): m.next = "NIBBLE1" with m.Elif(rxd_reg != 0b01): m.next = "IDLE" with m.State("NIBBLE1"): m.d.sync += [ self.data[0:2].eq(rxd_reg), self.data_valid.eq(0), ] with m.If(self.dv): m.d.sync += self.crs.eq(crs_dv_reg) m.next = "NIBBLE2" with m.Else(): m.next = "IDLE" with m.State("NIBBLE2"): m.d.sync += [ self.data[2:4].eq(rxd_reg), self.data_valid.eq(0), ] with m.If(self.dv): m.d.sync += self.dv.eq(crs_dv_reg) m.next = "NIBBLE3" with m.Else(): m.next = "IDLE" with m.State("NIBBLE3"): m.d.sync += [ self.data[4:6].eq(rxd_reg), self.data_valid.eq(0), ] with m.If(self.dv): m.d.sync += self.crs.eq(crs_dv_reg) m.next = "NIBBLE4" with m.Else(): m.next = "IDLE" with m.State("NIBBLE4"): m.d.sync += [ self.data[6:8].eq(rxd_reg), self.data_valid.eq(0), ] with m.If(self.dv): m.d.sync += [ self.dv.eq(crs_dv_reg), self.data_valid.eq(1), ] m.next = "NIBBLE1" with m.Else(): m.d.sync += self.data_valid.eq(1), m.next = "IDLE" return m class RMIITx(Elaboratable): """ RMII transmit module Transmits outgoing packets from a memory. Adds preamble, start of frame delimiter, and frame check sequence (CRC32) automatically. This module must be run in the RMII ref_clk domain, and the memory port and inputs and outputs must also be in that clock domain. Ports: * `read_port`: a read memory port, 8 bits wide by 2048, running in the RMII ref_clk domain Pins: * `txen`: RMII transmit enable * `txd0`: RMII transmit data 0 * `txd1`: RMII transmit data 1 Inputs: * `tx_start`: Pulse high to begin transmission of a packet * `tx_offset`: n-bit address offset of packet to transmit * `tx_len`: 11-bit length of packet to transmit Outputs: * `tx_ready`: Asserted while ready to transmit a new packet """ def __init__(self, read_port, txen, txd0, txd1): # Inputs self.tx_start = Signal() self.tx_offset = Signal(read_port.addr.width) self.tx_len = Signal(11) # Outputs self.tx_ready = Signal() self.read_port = read_port self.txen = txen self.txd0 = txd0 self.txd1 = txd1 def elaborate(self, platform): m = Module() # Transmit byte counter tx_idx = Signal(self.read_port.addr.width) # Transmit length latch tx_len = Signal(11) # Transmit offset latch tx_offset = Signal(self.read_port.addr.width) m.submodules.crc = crc = CRC32() m.submodules.txbyte = txbyte = RMIITxByte( self.txen, self.txd0, self.txd1) with m.FSM() as fsm: m.d.comb += [ self.read_port.addr.eq(tx_idx + tx_offset), crc.data.eq(txbyte.data), crc.reset.eq(fsm.ongoing("IDLE")), crc.data_valid.eq( (fsm.ongoing("DATA") | fsm.ongoing("PAD")) & txbyte.ready), self.tx_ready.eq(fsm.ongoing("IDLE")), txbyte.data_valid.eq( ~(fsm.ongoing("IDLE") | fsm.ongoing("IPG"))), ] with m.State("IDLE"): m.d.comb += txbyte.data.eq(0) m.d.sync += [ tx_idx.eq(0), tx_offset.eq(self.tx_offset), tx_len.eq(self.tx_len), ] with m.If(self.tx_start): m.next = "PREAMBLE" with m.State("PREAMBLE"): m.d.comb += txbyte.data.eq(0x55) with m.If(txbyte.ready): with m.If(tx_idx == 6): m.d.sync += tx_idx.eq(0) m.next = "SFD" with m.Else(): m.d.sync += tx_idx.eq(tx_idx + 1) with m.State("SFD"): m.d.comb += txbyte.data.eq(0xD5) with m.If(txbyte.ready): m.next = "DATA" with m.State("DATA"): m.d.comb += txbyte.data.eq(self.read_port.data) with m.If(txbyte.ready): m.d.sync += tx_idx.eq(tx_idx + 1) with m.If(tx_idx == tx_len - 1): with m.If(tx_len < 60): m.next = "PAD" with m.Else(): m.next = "FCS1" with m.State("PAD"): m.d.comb += txbyte.data.eq(0x00) with m.If(txbyte.ready): m.d.sync += tx_idx.eq(tx_idx + 1) with m.If(tx_idx == 59): m.next = "FCS1" with m.State("FCS1"): m.d.comb += txbyte.data.eq(crc.crc_out[0:8]) with m.If(txbyte.ready): m.next = "FCS2" with m.State("FCS2"): m.d.comb += txbyte.data.eq(crc.crc_out[8:16]) with m.If(txbyte.ready): m.next = "FCS3" with m.State("FCS3"): m.d.comb += txbyte.data.eq(crc.crc_out[16:24]) with m.If(txbyte.ready): m.next = "FCS4" with m.State("FCS4"): m.d.comb += txbyte.data.eq(crc.crc_out[24:32]) with m.If(txbyte.ready): m.d.sync += tx_idx.eq(0) m.next = "IPG" with m.State("IPG"): m.d.sync += tx_idx.eq(tx_idx + 1) with m.If(tx_idx == 48): m.next = "IDLE" return m class RMIITxByte(Elaboratable): """ RMII Transmit Byte Muxer Handles transmitting a byte dibit-by-dibit. This submodule must be in the RMII ref_clk clock domain, and its inputs and outputs are likewise in that domain. Pins: * `txen`: RMII Transmit enable * `txd0`: TMII Transmit data 0 * `txd1`: TMII Transmit data 1 Inputs: * `data`: 8-bit wide data to transmit. Latched internally so you may update it to the next word after asserting `data_valid`. * `data_valid`: Assert while valid data is present at `data`. Outputs: * `ready`: Asserted when ready to receive new data. This is asserted while the final dibit is being transmitted so that new data can be produced on the next clock cycle. """ def __init__(self, txen, txd0, txd1): # Inputs self.data = Signal(8) self.data_valid = Signal() # Outputs self.ready = Signal() self.txen = txen self.txd0 = txd0 self.txd1 = txd1 def elaborate(self, platform): m = Module() # Register input data on the data_valid signal data_reg = Signal(8) with m.FSM() as fsm: m.d.comb += [ self.ready.eq(fsm.ongoing("IDLE") | fsm.ongoing("NIBBLE4")), self.txen.eq(~fsm.ongoing("IDLE")), ] with m.State("IDLE"): m.d.comb += [ self.txd0.eq(0), self.txd1.eq(0), ] m.d.sync += data_reg.eq(self.data) with m.If(self.data_valid): m.next = "NIBBLE1" with m.State("NIBBLE1"): m.d.comb += [ self.txd0.eq(data_reg[0]), self.txd1.eq(data_reg[1]), ] m.next = "NIBBLE2" with m.State("NIBBLE2"): m.d.comb += [ self.txd0.eq(data_reg[2]), self.txd1.eq(data_reg[3]), ] m.next = "NIBBLE3" with m.State("NIBBLE3"): m.d.comb += [ self.txd0.eq(data_reg[4]), self.txd1.eq(data_reg[5]), ] m.next = "NIBBLE4" with m.State("NIBBLE4"): m.d.comb += [ self.txd0.eq(data_reg[6]), self.txd1.eq(data_reg[7]), ] m.d.sync += data_reg.eq(self.data) with m.If(self.data_valid): m.next = "NIBBLE1" with m.Else(): m.next = "IDLE" return m def test_rmii_rx(): import random from nmigen.back import pysim from nmigen import Memory crs_dv = Signal() rxd0 = Signal() rxd1 = Signal() mem = Memory(width=8, depth=128) mem_port = mem.write_port() mac_addr = [random.randint(0, 255) for _ in range(6)] rmii_rx = RMIIRx(mac_addr, mem_port, crs_dv, rxd0, rxd1) def testbench(): def tx_packet(): yield (crs_dv.eq(1)) # Preamble for _ in range(random.randint(10, 40)): yield (rxd0.eq(1)) yield (rxd1.eq(0)) yield # SFD yield (rxd0.eq(1)) yield (rxd1.eq(1)) yield # Data for txbyte in txbytes: for dibit in range(0, 8, 2): yield (rxd0.eq((txbyte >> (dibit + 0)) & 1)) yield (rxd1.eq((txbyte >> (dibit + 1)) & 1)) yield yield (crs_dv.eq(0)) # Finish clocking for _ in range(6): yield for _ in range(10): yield txbytes = [ 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xF0, 0xDE, 0xF1, 0x38, 0x89, 0x40, 0x08, 0x00, 0x45, 0x00, 0x00, 0x54, 0x00, 0x00, 0x40, 0x00, 0x40, 0x01, 0xB6, 0xD0, 0xC0, 0xA8, 0x01, 0x88, 0xC0, 0xA8, 0x01, 0x00, 0x08, 0x00, 0x0D, 0xD9, 0x12, 0x1E, 0x00, 0x07, 0x3B, 0x3E, 0x0C, 0x5C, 0x00, 0x00, 0x00, 0x00, 0x13, 0x03, 0x0F, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x57, 0x6F, 0x72, 0x6C, 0x64, 0x48, 0x65, 0x6C, 0x6C, 0x6F, 0x20, 0x57, 0x6F, 0x72, 0x6C, 0x64, 0x48, 0x65, 0x6C, 0x6C, 0x6F, 0x20, 0x57, 0x6F, 0x72, 0x6C, 0x64, 0x48, 0x65, 0x6C, 0x6C, 0x6F, 0x20, 0x57, 0x6F, 0x72, 0x6C, 0x64, 0x48, 0x52, 0x32, 0x1F, 0x9E ] # Transmit first packet yield from tx_packet() # Check packet was received assert (yield rmii_rx.rx_valid) assert (yield rmii_rx.rx_len) == 102 assert (yield rmii_rx.rx_offset) == 0 mem_contents = [] for idx in range(102): mem_contents.append((yield mem[idx])) assert mem_contents == txbytes # Pause (inter-frame gap) for _ in range(20): yield assert (yield rmii_rx.rx_valid) == 0 # Transmit a second packet yield from tx_packet() # Check packet was received assert (yield rmii_rx.rx_valid) assert (yield rmii_rx.rx_len) == 102 assert (yield rmii_rx.rx_offset) == 102 mem_contents = [] for idx in range(102): mem_contents.append((yield mem[(102+idx) % 128])) assert mem_contents == txbytes yield mod = Module() mod.submodules += rmii_rx, mem_port vcdf = open("rmii_rx.vcd", "w") with pysim.Simulator(mod, vcd_file=vcdf) as sim: sim.add_clock(1/50e6) sim.add_sync_process(testbench()) sim.run() def test_rmii_rx_byte(): import random from nmigen.back import pysim crs_dv = Signal() rxd0 = Signal() rxd1 = Signal() rmii_rx_byte = RMIIRxByte(crs_dv, rxd0, rxd1) def testbench(): for _ in range(10): yield txbytes = [random.randint(0, 255) for _ in range(8)] rxbytes = [] yield (crs_dv.eq(1)) # Preamble for _ in range(random.randint(10, 40)): yield (rxd0.eq(1)) yield (rxd1.eq(0)) yield # SFD yield (rxd0.eq(1)) yield (rxd1.eq(1)) yield # Data (except last two bytes), with CRS=1 DV=1 for txbyte in txbytes[:-2]: for dibit in range(0, 8, 2): yield (rxd0.eq((txbyte >> (dibit + 0)) & 1)) yield (rxd1.eq((txbyte >> (dibit + 1)) & 1)) yield if (yield rmii_rx_byte.data_valid): rxbytes.append((yield rmii_rx_byte.data)) # Data (last two bytes), with CRS=0 DV=1 for txbyte in txbytes[-2:]: for dibit in range(0, 8, 2): yield (rxd0.eq((txbyte >> (dibit + 0)) & 1)) yield (rxd1.eq((txbyte >> (dibit + 1)) & 1)) if dibit in (0, 4): # CRS=0 yield (crs_dv.eq(0)) else: # DV=1 yield (crs_dv.eq(1)) yield if (yield rmii_rx_byte.data_valid): rxbytes.append((yield rmii_rx_byte.data)) yield (crs_dv.eq(0)) for _ in range(10): yield if (yield rmii_rx_byte.data_valid): rxbytes.append((yield rmii_rx_byte.data)) assert rxbytes == txbytes vcdf = open("rmii_rx_byte.vcd", "w") with pysim.Simulator(rmii_rx_byte, vcd_file=vcdf) as sim: sim.add_clock(1/50e6) sim.add_sync_process(testbench()) sim.run() def test_rmii_tx(): from nmigen.back import pysim from nmigen import Memory txen = Signal() txd0 = Signal() txd1 = Signal() txbytes = [ 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x02, 0x44, 0x4e, 0x30, 0x76, 0x9e, 0x08, 0x06, 0x00, 0x01, 0x08, 0x00, 0x06, 0x04, 0x00, 0x01, 0x02, 0x44, 0x4e, 0x30, 0x76, 0x9e, 0xc0, 0xa8, 0x02, 0xc8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0xa8, 0x02, 0xc8 ] preamblebytes = [0x55, 0x55, 0x55, 0x55, 0x55, 0x55, 0x55, 0xD5] padbytes = [0x00] * (60 - len(txbytes)) crcbytes = [0x44, 0x5E, 0xB4, 0xD2] txnibbles = [] rxnibbles = [] for txbyte in preamblebytes + txbytes + padbytes + crcbytes: txnibbles += [ (txbyte & 0b11), ((txbyte >> 2) & 0b11), ((txbyte >> 4) & 0b11), ((txbyte >> 6) & 0b11), ] # Put the transmit bytes into memory at some offset, and fill the rest of # memory with all-1s (to ensure we're not relying on memory being zeroed). txbytes_zp = txbytes + [0xFF]*(128 - len(txbytes)) txoffset = 120 txbytes_mem = txbytes_zp[-txoffset:] + txbytes_zp[:-txoffset] mem = Memory(width=8, depth=128, init=txbytes_mem) mem_port = mem.read_port() rmii_tx = RMIITx(mem_port, txen, txd0, txd1) def testbench(): for _ in range(10): yield yield (rmii_tx.tx_start.eq(1)) yield (rmii_tx.tx_offset.eq(txoffset)) yield (rmii_tx.tx_len.eq(len(txbytes))) yield yield (rmii_tx.tx_start.eq(0)) yield (rmii_tx.tx_offset.eq(0)) yield (rmii_tx.tx_len.eq(0)) for _ in range((len(txbytes) + 12) * 4 + 120): if (yield txen): rxnibbles.append((yield txd0) | ((yield txd1) << 1)) yield print(len(txnibbles), len(rxnibbles)) print(txnibbles) print(rxnibbles) assert txnibbles == rxnibbles mod = Module() mod.submodules += rmii_tx, mem_port vcdf = open("rmii_tx.vcd", "w") with pysim.Simulator(mod, vcd_file=vcdf) as sim: sim.add_clock(1/50e6) sim.add_sync_process(testbench()) sim.run() def test_rmii_tx_byte(): import random from nmigen.back import pysim txen = Signal() txd0 = Signal() txd1 = Signal() rmii_tx_byte = RMIITxByte(txen, txd0, txd1) data = rmii_tx_byte.data data_valid = rmii_tx_byte.data_valid def testbench(): for _ in range(10): yield txbytes = [random.randint(0, 255) for _ in range(8)] txnibbles = [] rxnibbles = [] yield (data_valid.eq(1)) for txbyte in txbytes: txnibbles += [ (txbyte & 0b11), ((txbyte >> 2) & 0b11), ((txbyte >> 4) & 0b11), ((txbyte >> 6) & 0b11), ] yield (data.eq(txbyte)) yield rxnibbles.append((yield txd0) | ((yield txd1) << 1)) yield rxnibbles.append((yield txd0) | ((yield txd1) << 1)) yield rxnibbles.append((yield txd0) | ((yield txd1) << 1)) yield rxnibbles.append((yield txd0) | ((yield txd1) << 1)) yield (data_valid.eq(0)) yield rxnibbles.append((yield txd0) | ((yield txd1) << 1)) rxnibbles = rxnibbles[1:] assert txnibbles == rxnibbles for _ in range(10): yield vcdf = open("rmii_tx_byte.vcd", "w") with pysim.Simulator(rmii_tx_byte, vcd_file=vcdf) as sim: sim.add_clock(1/50e6) sim.add_sync_process(testbench()) sim.run()

0

13,801

8,613

0

57

251

d60d3beec1a8bf5f5b1156875db9e3b65d35b8d6

536

py

Python

cogs/roll.py

morozoffnor/govnoed_grisha_rewritten

6a34336cede03a081954479f998d5a8162e1a31d

[ "Apache-2.0" ]

null

cogs/roll.py

morozoffnor/govnoed_grisha_rewritten

6a34336cede03a081954479f998d5a8162e1a31d

[ "Apache-2.0" ]

null

cogs/roll.py

morozoffnor/govnoed_grisha_rewritten

6a34336cede03a081954479f998d5a8162e1a31d

[ "Apache-2.0" ]

null

import sys sys.path.insert(1, '../functions')

23.304348

73

0.697761

import discord from discord.ext import commands import random import sys sys.path.insert(1, '../functions') from functions.cmd_print import cmd_print class Roll(commands.Cog): def __init__(self, client): self.client = client @commands.command() async def roll(self, ctx, *, number=100): generatedNumber = random.randrange(1, number, 1) await ctx.send(generatedNumber) await cmd_print('debug', f'Generated number - {generatedNumber}') def setup(client): client.add_cog(Roll(client))

0

215

0

93

0

30

0

16

134

e3ae88a557bc39da15fb4fb98b0be693a0a7911c

377

py

Python

scripts/aggregation_test_script.py

michaelfaerber/Agnos

b4b6ff9cdca9090fb426f1fc2cead8e5ef4ad9bf

[ "MIT" ]

null

scripts/aggregation_test_script.py

michaelfaerber/Agnos

b4b6ff9cdca9090fb426f1fc2cead8e5ef4ad9bf

[ "MIT" ]

3

2021-12-10T01:22:05.000Z

2021-12-14T21:33:16.000Z

scripts/aggregation_test_script.py

michaelfaerber/Agnos

b4b6ff9cdca9090fb426f1fc2cead8e5ef4ad9bf

[ "MIT" ]

null

sentences = [['a', 'b', 'c'], ['a', 'd','e']] default_val = '' entity_embeddings_dict = {} entity_embeddings_dict = {sentence[0]: doThis(sentence) + entity_embeddings_dict.get(sentence[0], default_val) \ for sentence in sentences } print(entity_embeddings_dict)

25.133333

112

0.681698

sentences = [['a', 'b', 'c'], ['a', 'd','e']] def doThis(sentence): ret = None for x in sentence: if ret is None: ret = x else: ret += x return ret default_val = '' entity_embeddings_dict = {} entity_embeddings_dict = {sentence[0]: doThis(sentence) + entity_embeddings_dict.get(sentence[0], default_val) \ for sentence in sentences } print(entity_embeddings_dict)

0

93

0

22

bd662dc3d2fdbefbc6e614c5d255badccde8b474

5,481

py

Python

scripts/mod_grav/plot_limits.py

charlesblakemore/opt_lev_analysis

704f174e9860907de349688ed82b5812bbb07c2d

[ "MIT" ]

null

scripts/mod_grav/plot_limits.py

charlesblakemore/opt_lev_analysis

704f174e9860907de349688ed82b5812bbb07c2d

[ "MIT" ]

null

scripts/mod_grav/plot_limits.py

charlesblakemore/opt_lev_analysis

704f174e9860907de349688ed82b5812bbb07c2d

[ "MIT" ]

1

2019-11-27T19:10:25.000Z

import numpy as np import matplotlib.pyplot as plt import grav_util_3 as gu import bead_util as bu import warnings warnings.filterwarnings("ignore") theory_data_dir = '/data/grav_sim_data/2um_spacing_data/' data_dirs = [#'/data/20180625/bead1/grav_data/shield/X50-75um_Z15-25um_17Hz', \ #'/data/20180625/bead1/grav_data/shield/X50-75um_Z15-25um_17Hz_elec-term', \ #\ #'/data/20180704/bead1/grav_data/shield', \ #'/data/20180704/bead1/grav_data/shield_1s_1h', \ #'/data/20180704/bead1/grav_data/shield2', \ #'/data/20180704/bead1/grav_data/shield3', \ #'/data/20180704/bead1/grav_data/shield4', \ '/data/20180704/no_bead/grav_data/shield', \ #\ #'/data/20180808/bead4/grav_data/shield1' ] fit_type = 'Gaussian' #fit_type = 'Planar' p0_bead_dict = {'20180625': [19.0,40.0,20.0], \ '20180704': [18.7,40.0,20.0], \ '20180808': [18,40.0,23.0] \ } load_agg = True harms = [1,2,3,4,5,6] #opt_ext = 'TEST' opt_ext = '_6harm-full' if fit_type == 'Gaussian': data_ind = 2 err_ind = 4 if fit_type == 'Planar': data_ind = 0 err_ind = 1 for ddir in data_dirs: print() parts = ddir.split('/') date = parts[2] p0_bead = p0_bead_dict[date] nobead = ('no_bead' in parts) or ('nobead' in parts) or ('no-bead' in parts) if nobead: opt_ext += '_NO-BEAD' agg_path = '/processed_data/aggdat/' + date + '_' + parts[-1] + opt_ext + '.agg' alpha_arr_path = '/processed_data/alpha_arrs/' + date + '_' + parts[-1] + opt_ext + '.arr' lambda_path = alpha_arr_path[:-4] + '_lambdas.arr' if load_agg: print(agg_path) agg_dat = gu.AggregateData([], p0_bead=p0_bead, harms=harms) agg_dat.load(agg_path) agg_dat.reload_grav_funcs() #agg_dat.fit_alpha_xyz_vs_alldim(weight_planar=False, plot=False, plot_hists=True) alpha_arr = agg_dat.alpha_xyz_best_fit lambdas = agg_dat.lambdas np.save(open(alpha_arr_path, 'wb'), alpha_arr) np.save(open(lambda_path, 'wb'), agg_dat.lambdas) else: alpha_arr = np.load(open(alpha_arr_path, 'rb')) lambdas = np.load(open(lambda_path, 'rb')) Ncomp = alpha_arr.shape[-2] comp_colors = bu.get_color_map(Ncomp, cmap='viridis') alpha_w = np.sum(alpha_arr[:,0:2,:,data_ind]*alpha_arr[:,0:2,:,err_ind]**(-2), axis=1) / \ np.sum(alpha_arr[:,0:2,:,err_ind]**(-2), axis=1) #alpha_w = np.sum(alpha_arr[:,0:2,:,2], axis=1) * 0.5 errs_x = np.zeros_like(alpha_arr[:,0,0,0]) N = 0 for ind in range(Ncomp - 1): errs_x += alpha_w[:,ind+1]**2 N += 1 errs_x = np.sqrt(errs_x / N) sigma_alpha_w = 1.0 / np.sqrt( np.sum(alpha_arr[:,:2,:,3]**(-2), axis=1) ) N_w = np.sum(alpha_arr[:,:2,:,7], axis=1) plt.figure(1) if nobead: plt.title(date + '_' + 'no-bead' + ': Result of %s Fitting' % fit_type, fontsize=16) else: plt.title(date + '_' + parts[-1] + ': Result of %s Fitting' % fit_type, fontsize=16) plt.loglog(lambdas, np.abs(alpha_w[:,0]), lw=4, \ label='Template basis vector') plt.loglog(lambdas, errs_x, '--', lw=2, \ label='Quadrature sum of other vectors') plt.loglog(gu.limitdata[:,0], gu.limitdata[:,1], '--', label=gu.limitlab, \ linewidth=3, color='r') plt.loglog(gu.limitdata2[:,0], gu.limitdata2[:,1], '--', label=gu.limitlab2, \ linewidth=3, color='k') plt.xlabel('Length Scale: $\lambda$ [m]') plt.ylabel('Strength: |$\\alpha$| [arb]') plt.xlim(1e-7, 1e-3) plt.ylim(1e4, 1e14) plt.legend() plt.grid() plt.show() for ind in range(Ncomp): fig2 = plt.figure(2) plt.title("%s fit for Basis Vector: %i" % (fit_type, ind)) plt.loglog(lambdas, np.abs(alpha_arr[:,0,ind,data_ind]), \ color=comp_colors[ind], ls='--', label='$\\alpha_x$') plt.loglog(lambdas, np.abs(alpha_arr[:,0,ind,err_ind]), \ color=comp_colors[ind], ls='--', label='$\sigma_{\\alpha_x}$', \ alpha=0.5) plt.loglog(lambdas, np.abs(alpha_w[:,ind]), \ color=comp_colors[ind], ls='-', lw=3, label='Weighted mean') plt.loglog(lambdas, np.abs(alpha_arr[:,1,ind,data_ind]), \ color=comp_colors[ind], ls='-.', label='$\\alpha_y$') plt.loglog(lambdas, np.abs(alpha_arr[:,1,ind,err_ind]), \ color=comp_colors[ind], ls='-.', label='$\sigma_{\\alpha_y}$', \ alpha=0.5) plt.xlabel('Length Scale: $\lambda$ [m]') plt.ylabel('Strength: |$\\alpha$| [arb]') plt.xlim(1e-6, 1e-3) plt.ylim(1e6, 1e15) plt.legend() plt.grid() fig_title = '/home/charles/plots/' + date + '/' + parts[-1] + '/' \ + date + '_' + parts[-1] + '_%s-fit_comp%i.png' % (fit_type, ind) fig2.savefig(fig_title) plt.close(fig2) #plt.show() #for fig_num in [1,2,3]: # plt.figure(fig_num) # plt.xlabel('Length Scale: $\lambda$ [m]') # plt.ylabel('Strength: |$\\alpha$| [arb]') # plt.legend() # plt.grid() #plt.show()

31.142045

94

0.560482

import dill as pickle import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import grav_util_3 as gu import bead_util as bu import configuration as config import warnings warnings.filterwarnings("ignore") theory_data_dir = '/data/grav_sim_data/2um_spacing_data/' data_dirs = [#'/data/20180625/bead1/grav_data/shield/X50-75um_Z15-25um_17Hz', \ #'/data/20180625/bead1/grav_data/shield/X50-75um_Z15-25um_17Hz_elec-term', \ #\ #'/data/20180704/bead1/grav_data/shield', \ #'/data/20180704/bead1/grav_data/shield_1s_1h', \ #'/data/20180704/bead1/grav_data/shield2', \ #'/data/20180704/bead1/grav_data/shield3', \ #'/data/20180704/bead1/grav_data/shield4', \ '/data/20180704/no_bead/grav_data/shield', \ #\ #'/data/20180808/bead4/grav_data/shield1' ] fit_type = 'Gaussian' #fit_type = 'Planar' p0_bead_dict = {'20180625': [19.0,40.0,20.0], \ '20180704': [18.7,40.0,20.0], \ '20180808': [18,40.0,23.0] \ } load_agg = True harms = [1,2,3,4,5,6] #opt_ext = 'TEST' opt_ext = '_6harm-full' if fit_type == 'Gaussian': data_ind = 2 err_ind = 4 if fit_type == 'Planar': data_ind = 0 err_ind = 1 for ddir in data_dirs: print() parts = ddir.split('/') date = parts[2] p0_bead = p0_bead_dict[date] nobead = ('no_bead' in parts) or ('nobead' in parts) or ('no-bead' in parts) if nobead: opt_ext += '_NO-BEAD' agg_path = '/processed_data/aggdat/' + date + '_' + parts[-1] + opt_ext + '.agg' alpha_arr_path = '/processed_data/alpha_arrs/' + date + '_' + parts[-1] + opt_ext + '.arr' lambda_path = alpha_arr_path[:-4] + '_lambdas.arr' if load_agg: print(agg_path) agg_dat = gu.AggregateData([], p0_bead=p0_bead, harms=harms) agg_dat.load(agg_path) agg_dat.reload_grav_funcs() #agg_dat.fit_alpha_xyz_vs_alldim(weight_planar=False, plot=False, plot_hists=True) alpha_arr = agg_dat.alpha_xyz_best_fit lambdas = agg_dat.lambdas np.save(open(alpha_arr_path, 'wb'), alpha_arr) np.save(open(lambda_path, 'wb'), agg_dat.lambdas) else: alpha_arr = np.load(open(alpha_arr_path, 'rb')) lambdas = np.load(open(lambda_path, 'rb')) Ncomp = alpha_arr.shape[-2] comp_colors = bu.get_color_map(Ncomp, cmap='viridis') alpha_w = np.sum(alpha_arr[:,0:2,:,data_ind]*alpha_arr[:,0:2,:,err_ind]**(-2), axis=1) / \ np.sum(alpha_arr[:,0:2,:,err_ind]**(-2), axis=1) #alpha_w = np.sum(alpha_arr[:,0:2,:,2], axis=1) * 0.5 errs_x = np.zeros_like(alpha_arr[:,0,0,0]) N = 0 for ind in range(Ncomp - 1): errs_x += alpha_w[:,ind+1]**2 N += 1 errs_x = np.sqrt(errs_x / N) sigma_alpha_w = 1.0 / np.sqrt( np.sum(alpha_arr[:,:2,:,3]**(-2), axis=1) ) N_w = np.sum(alpha_arr[:,:2,:,7], axis=1) plt.figure(1) if nobead: plt.title(date + '_' + 'no-bead' + ': Result of %s Fitting' % fit_type, fontsize=16) else: plt.title(date + '_' + parts[-1] + ': Result of %s Fitting' % fit_type, fontsize=16) plt.loglog(lambdas, np.abs(alpha_w[:,0]), lw=4, \ label='Template basis vector') plt.loglog(lambdas, errs_x, '--', lw=2, \ label='Quadrature sum of other vectors') plt.loglog(gu.limitdata[:,0], gu.limitdata[:,1], '--', label=gu.limitlab, \ linewidth=3, color='r') plt.loglog(gu.limitdata2[:,0], gu.limitdata2[:,1], '--', label=gu.limitlab2, \ linewidth=3, color='k') plt.xlabel('Length Scale: $\lambda$ [m]') plt.ylabel('Strength: |$\\alpha$| [arb]') plt.xlim(1e-7, 1e-3) plt.ylim(1e4, 1e14) plt.legend() plt.grid() plt.show() for ind in range(Ncomp): fig2 = plt.figure(2) plt.title("%s fit for Basis Vector: %i" % (fit_type, ind)) plt.loglog(lambdas, np.abs(alpha_arr[:,0,ind,data_ind]), \ color=comp_colors[ind], ls='--', label='$\\alpha_x$') plt.loglog(lambdas, np.abs(alpha_arr[:,0,ind,err_ind]), \ color=comp_colors[ind], ls='--', label='$\sigma_{\\alpha_x}$', \ alpha=0.5) plt.loglog(lambdas, np.abs(alpha_w[:,ind]), \ color=comp_colors[ind], ls='-', lw=3, label='Weighted mean') plt.loglog(lambdas, np.abs(alpha_arr[:,1,ind,data_ind]), \ color=comp_colors[ind], ls='-.', label='$\\alpha_y$') plt.loglog(lambdas, np.abs(alpha_arr[:,1,ind,err_ind]), \ color=comp_colors[ind], ls='-.', label='$\sigma_{\\alpha_y}$', \ alpha=0.5) plt.xlabel('Length Scale: $\lambda$ [m]') plt.ylabel('Strength: |$\\alpha$| [arb]') plt.xlim(1e-6, 1e-3) plt.ylim(1e6, 1e15) plt.legend() plt.grid() fig_title = '/home/charles/plots/' + date + '/' + parts[-1] + '/' \ + date + '_' + parts[-1] + '_%s-fit_comp%i.png' % (fit_type, ind) fig2.savefig(fig_title) plt.close(fig2) #plt.show() #for fig_num in [1,2,3]: # plt.figure(fig_num) # plt.xlabel('Length Scale: $\lambda$ [m]') # plt.ylabel('Strength: |$\\alpha$| [arb]') # plt.legend() # plt.grid() #plt.show()

0

27

67

6a7f23c80626351657198207fd0b08ed99b96da9

1,095

py

Python

Intro to us.py

DarshPro/Minecraft-in-python

3a52a60e9a36107252aafc971b3a32fc84b135df

[ "MIT" ]

1

2021-03-04T15:42:36.000Z

Intro to us.py

DarshPro/Minecraft-in-python

3a52a60e9a36107252aafc971b3a32fc84b135df

[ "MIT" ]

null

Intro to us.py

DarshPro/Minecraft-in-python

3a52a60e9a36107252aafc971b3a32fc84b135df

[ "MIT" ]

null

# Test Cube # Test button # update is run every frame # basic window app = Ursina() # basic cube cube = Entity(model='quad', color=color.orange, scale = (2,5), position = (5,1)) # quad with texture #sans_image = load_texture('Sans.png') #sans = Entity(model = 'quad', texture = sans_image) #sans = Entity(model = 'quad', texture = 'Sans.png') # creating a block properly test = Test_cube() # creating a button btn = Test_button() punch_sound = Audio('assets/punch', loop=False, autoplay=False) app.run()

20.277778

80

0.660274

from ursina import * # Test Cube class Test_cube(Entity): def __init__(self): super().__init__( parent = scene, model = 'cube', texture = 'white_cube', rotation = Vec3(45,45,45)) # Test button class Test_button(Button): def __init__(self,scale = 0.1): super().__init__( parent = scene, model = 'cube', texture = 'brick', color = color.white, highlight_color = color.red, pressed_color = color.lime) def input(self,key): if self.hovered: if key == 'left mouse down': punch_sound.play() # update is run every frame def update(): #print('test') if held_keys['a']: cube.x -= 1 * time.dt # basic window app = Ursina() # basic cube cube = Entity(model='quad', color=color.orange, scale = (2,5), position = (5,1)) # quad with texture #sans_image = load_texture('Sans.png') #sans = Entity(model = 'quad', texture = sans_image) #sans = Entity(model = 'quad', texture = 'Sans.png') # creating a block properly test = Test_cube() # creating a button btn = Test_button() punch_sound = Audio('assets/punch', loop=False, autoplay=False) app.run()

0

441

0

52

0

-1

88

dc32ad09fb9c4eae33279bcdb6ba48542e65e16b

20,087

py

Python

Python/utils/rvs/expressions.py

sgiguere/Fairness-Gaurantees-under-Demographic-Shift

d081307d34cde75ca74e07ddbe059e8273095aee

[ "MIT" ]

1

2022-03-22T20:13:02.000Z

Python/utils/rvs/expressions.py

sgiguere/Fairness-Gaurantees-under-Demographic-Shift

d081307d34cde75ca74e07ddbe059e8273095aee

[ "MIT" ]

null

Python/utils/rvs/expressions.py

sgiguere/Fairness-Gaurantees-under-Demographic-Shift

d081307d34cde75ca74e07ddbe059e8273095aee

[ "MIT" ]

null

def parse_value(value): ''' Attempts to interpret <value> as a number. ''' if isinstance(value, str): try: value = int(value) except ValueError: value = float(value) return value

29.980597

129

0.697366

import numpy as np from copy import copy, deepcopy from utils.rvs.utils import COMPARATOR_NEGATIONS def get_constant_name(counter={'c':0}): name = 'c%d' % counter['c'] counter['c'] += 1 return name def get_variable_name(counter={'v':0}): name = 'v%d' % counter['v'] counter['v'] += 1 return name def get_expression_name(counter={'e':0}): name = 'e%d' % counter['c'] counter['e'] += 1 return name class Expression(): def __init__(self): self.trivial_bounds = None self._terms = [] def __eq__(self, E): return isinstance(E, self.__class__) and all([ T==_T for (T,_T) in zip(self._terms,E._terms)]) class CommutativeExpression(Expression): def __init__(self): super().__init__() def __eq__(self,E): if not(isinstance(E, self.__class__)): return False terms, _terms = copy(self._terms), copy(E._terms) try: for term in terms: _terms.remove(term) except ValueError: return False return len(_terms) == 0 class NoncommutativeExpression(Expression): def __init__(self): super().__init__() def __eq__(self,E): return isinstance(E, self.__class__) and all([ T==_T for (T,_T) in zip(self._terms,E._terms) ]) class SingleTermExpression(): pass class SampleSet(Expression): def __init__(self, expression, condition=None): super().__init__() self.expression = expression self.condition = condition class ConstantExpression(Expression, SingleTermExpression): def __init__(self, name, value): super().__init__() self.name = get_constant_name() self.value = value def __repr__(self): return str(self.value) def __eq__(self, E): return isinstance(E,self.__class__) and self.value == E.value class VariableExpression(Expression, SingleTermExpression): def __init__(self, name): super().__init__() if name.startswith('#'): self.name = name[1:] self._special = 'index' else: self.name = name self._special = None def __repr__(self): return self.name def __eq__(self, E): return isinstance(E,self.__class__) and self.name == E.name and self._special == E._special class SampleSet(Expression, SingleTermExpression): def __init__(self, expression, condition=None): super().__init__() name = '%r' % expression if not(condition is None): name += '|%r' % condition self.name = '[%s]' % name self.expression = expression self.condition = condition def __repr__(self): return self.name def __eq__(self, E): return isinstance(E,self.__class__) and (self.expression == E.expression) and (self.condition == E.condition) class ExpectedValue(Expression, SingleTermExpression): def __init__(self, sample_set, is_func=None, is_expr=None): super().__init__() if is_func is None: self.name = 'E%s' % sample_set.name else: self.name = 'E{%s(%s)}%s' % (is_func, is_expr.name, sample_set.name) # self.name = 'E{%s}%s' % () + sample_set.name self.sample_set = sample_set self._is_func = is_func self._is_expr = is_expr def __repr__(self): return self.name def __eq__(self, E): if not(isinstance(E,self.__class__)): return False if not(self.sample_set == E.sample_set): return False if self._is_func is None and E._is_func is None: return True else: return (self._is_func == E._is_func) and (self._is_expr == E._is_expr) class ComparatorExpression(VariableExpression): def __init__(self, term1, comp, term2): name = '%r %s %r' % (term1, comp, term2) super().__init__(name) self.variable = term1 self.comparator = comp self.value = term2 class NegativeExpression(NoncommutativeExpression, SingleTermExpression): def __init__(self, expression): super().__init__() self._terms = [expression] def __repr__(self): if isinstance(self._terms[0], SumExpression): return '-(%r)' % self._terms[0] return '-%r' % self._terms[0] def __eq__(self, E): if isinstance(E,self.__class__) and (self._terms[0]==E._terms[0]): return True if isinstance(E, SumExpression): return E == self return False class NotExpression(NoncommutativeExpression, SingleTermExpression): def __init__(self, expression): super().__init__() self._terms = [expression] def __repr__(self): return '~(%r)' % self._terms[0] def __eq__(self, E): if isinstance(E,self.__class__) and (self._terms[0]==E._terms[0]): return True return False class AbsExpression(NoncommutativeExpression, SingleTermExpression): def __init__(self, expression): super().__init__() self._terms = [expression] def __repr__(self): return '|%r|' % self._terms[0] def __eq__(self, E): return isinstance(E,self.__class__) and (self._terms[0]==E._terms[0]) class FractionExpression(NoncommutativeExpression): def __init__(self, num, den): super().__init__() self._terms = [num, den] def __repr__(self): num, den = self._terms num_str = '(%r)'%num if isinstance(num, SumExpression) else '%r'%num den_str = '%r'%den if isinstance(den, SingleTermExpression) else '(%r)'%den return '%s/%s' % (num_str, den_str) def __eq__(self, E): return isinstance(E, self.__class__) and (self._terms[0]==E._terms[0]) and (self._terms[1]==E._terms[1]) class SumExpression(CommutativeExpression): def __init__(self, expressions): super().__init__() self._terms = list(expressions) def __repr__(self): string = '%r' % self._terms[0] for t in self._terms[1:]: string += '%r'%t if isinstance(t, NegativeExpression) else '+%r'%t return string def __eq__(self, E): if super().__eq__(E): return True if isinstance(E, NegativeExpression): return E == RVFuncs.negative(SumExpression([ RVFuncs.negative(e) for e in self._terms ])) return False class AndExpression(CommutativeExpression): def __init__(self, comparisons): super().__init__() self._terms = list(comparisons) self.name = ','.join('%s'%c.name for c in comparisons) def __repr__(self): return ','.join([('(%r)' % t) if isinstance(t,OrExpression) else ('%r' % t) for t in self._terms]) def __eq__(self, E): return super().__eq__(E) class OrExpression(CommutativeExpression): def __init__(self, comparisons): super().__init__() self._terms = list(comparisons) self.name = '||'.join('%s'%c.name for c in comparisons) def __repr__(self): return '||'.join([('(%r)' % t) if isinstance(t,AndExpression) else ('%r' % t) for t in self._terms]) def __eq__(self, E): return super().__eq__(E) class ProductExpression(CommutativeExpression): def __init__(self, expressions): super().__init__() self._terms = list(expressions) def __repr__(self): string = '(%r)'%self._terms[0] if (isinstance(self._terms[0], SumExpression) and len(self._terms) > 1) else '%r'%self._terms[0] for t in self._terms[1:]: string += '*(%r)'%t if isinstance(t, SumExpression) else '*%r'%t return string class MaxExpression(CommutativeExpression, SingleTermExpression): def __init__(self, expressions): super().__init__() self._terms = list(expressions) def __repr__(self): return 'MAX{%s}' % ', '.join([ '%r'%t for t in self._terms ]) class MaxRecipExpression(Expression, SingleTermExpression): def __init__(self, expression): super().__init__() self._terms = [expression] def __repr__(self): return 'MAX{%s, %s}' % (self._terms[0], RVFuncs.fraction(RVFuncs.constant(1), self._terms[0])) class NANMaxExpression(CommutativeExpression, SingleTermExpression): def __init__(self, expressions): super().__init__() self._terms = list(expressions) def __repr__(self): return 'NANMAX{%s}' % ', '.join([ '%r'%t for t in self._terms ]) def safesum(a, b): a_inf, a_nan = np.isinf(a), np.isnan(a) b_inf, b_nan = np.isinf(b), np.isnan(b) if (a_nan or b_nan): return np.nan if a_inf and b_inf and (np.sign(a) != np.sign(b)): return np.nan return a + b def safeprod(a, b): a_inf, a_nan = np.isinf(a), np.isnan(a) b_inf, b_nan = np.isinf(b), np.isnan(b) if (a_nan or b_nan): return np.nan if (a_inf and b==0) or (b_inf and a==0): return 0.0 return a * b def safediv(a, b): a_inf, a_nan = np.isinf(a), np.isnan(a) b_inf, b_nan = np.isinf(b), np.isnan(b) if (a_nan or b_nan) or (a_inf and b_inf): return np.nan if (b==0): return np.nan return a / b def parse_value(value): ''' Attempts to interpret <value> as a number. ''' if isinstance(value, str): try: value = int(value) except ValueError: value = float(value) return value class RVFuncs(): @staticmethod def constant(value_raw): value = parse_value(value_raw) return ConstantExpression('c', value) @staticmethod def variable(name): return VariableExpression(name) @staticmethod def comparator_variable(term1, comp, term2): return ComparatorExpression(term1, comp, term2) @staticmethod def sample_set(variable, condition=None): return SampleSet(variable, condition) @staticmethod def expected_value(sampleset, is_func=None, is_expr=None): return ExpectedValue(sampleset, is_func=is_func, is_expr=is_expr) @staticmethod def negative(e): ''' Returns the negative of <e>, reducing nested negatives. ''' n_negatives = 1 while isinstance(e, NegativeExpression): e = e._terms[0] n_negatives += 1 if isinstance(e, ConstantExpression): return RVFuncs.constant(-e.value if (n_negatives % 2 == 1) else e.value) return NegativeExpression(e) if (n_negatives % 2 == 1) else e @staticmethod def logical_not(e): n_nots = 1 while isinstance(e, NotExpression): e = e._terms[0] n_nots += 1 if (n_nots % 2 == 0): return e if isinstance(e, ComparatorExpression): return ComparatorExpression(e.variable, COMPARATOR_NEGATIONS[e.comparator], e.value) return NotExpression(e) @staticmethod def sum(*expressions): ''' Returns the sum of <expressions>, factoring out constants and shared factors. ''' # Aggregate terms that are sums themselves exps = [] for e in expressions: if isinstance(e, SumExpression): exps.extend(e._terms) else: exps.append(e) expressions = exps # Aggregate terms that are constants cval = 0 exps = [] for e in expressions: if isinstance(e, ConstantExpression): cval += e.value elif isinstance(e, NegativeExpression) and isinstance(e._terms[0], ConstantExpression): cval -= e._terms[0].value else: exps.append(e) if cval != 0 or len(exps) == 0: const = RVFuncs.constant(cval) exps = [ const, *exps] expressions = exps if len(expressions) == 1: return expressions[0] # Check if all terms share a common denominator and factor it out def split_as_fraction(e): if isinstance(e, FractionExpression): return [e._terms[0], e._terms[1]] elif isinstance(e, NegativeExpression) and isinstance(e._terms[0],FractionExpression): return [RVFuncs.negative(e._terms[0]._terms[0]), e._terms[0]._terms[1]] return [e, None] nums, dens = zip(*[ split_as_fraction(e) for e in exps ]) if all([ not(dens[0] is None) and d==dens[0] for d in dens ]): exps = nums common_den = dens[0] else: common_den = None # Check if any terms have shared product factors and factor them out def extract_unsigned_terms(e): if isinstance(e, NegativeExpression) or isinstance(e, FractionExpression): return extract_unsigned_terms(e._terms[0]) if isinstance(e, ProductExpression): return e._terms return [e] def remove_terms(e, terms): if isinstance(e, NegativeExpression): return RVFuncs.negative(remove_terms(e._terms[0], terms)) if isinstance(e, FractionExpression): return RVFuncs.fraction(remove_terms(e._terms[0], terms), e._terms[1]) if isinstance(e, ProductExpression): remaining = e._terms.copy() for t in terms: remaining.remove(t) return RVFuncs.product(*remaining) if len(remaining) > 0 else RVFuncs.constant(1) return RVFuncs.constant(1) if len(terms) > 0 else e has_negative = [ isinstance(e,NegativeExpression) for e in exps ] unsigned_terms = [ extract_unsigned_terms(e) for e in exps ] unsigned_terms_tmp = deepcopy(unsigned_terms) shared_terms = [] for st in unsigned_terms[0]: if isinstance(st, ConstantExpression) and (st.value == 1): continue if all([ (st in terms) for terms in unsigned_terms_tmp[1:] ]): shared_terms.append(st) for terms in unsigned_terms_tmp: terms.remove(st) if len(shared_terms) > 0: remainder = RVFuncs.sum(*[ remove_terms(e, shared_terms) for e in exps ]) else: remainder = SumExpression(exps) # Return the product of the common factor and the remainder sum if len(shared_terms) > 0 and common_den is None: common_factor = RVFuncs.product(*shared_terms) return RVFuncs.product(common_factor, remainder) elif len(shared_terms) > 0: common_factor = RVFuncs.fraction(RVFuncs.product(*shared_terms), common_den) return RVFuncs.product(common_factor, remainder) return remainder @staticmethod def diff(e0, e1): return RVFuncs.sum(e0, RVFuncs.negative(e1)) @staticmethod def max(*expressions): if len(expressions) == 1: return expressions[0] exps = [] for e in expressions: if isinstance(e, MaxExpression): exps.extend(e._terms) else: exps.append(e) if len(expressions) == 2: e1, e2 = expressions # If the max *happens* to be Max(E, 1/E) for some E, reduce to a MaxRecip if e1 == RVFuncs.fraction(RVFuncs.constant(1), e2): return MaxRecipExpression(e1) # If the max *happens* to be Max(E, -E) for some E, reduce to Abs elif e1 == RVFuncs.negative(e2): return AbsExpression(e1) return MaxExpression(exps) def nanmax(*expressions): if len(expressions) == 1: return expressions[0] exps = [] for e in expressions: if isinstance(e, MaxExpression): exps.extend(e._terms) else: exps.append(e) return NANMaxExpression(exps) @staticmethod def min(*expressions): if len(expressions) == 1: return expressions[0] exps = [] for e in expressions: if isinstance(e, MaxExpression): exps.extend(e._terms) else: exps.append(e) # Convert to a negative max exps = [ RVFuncs.negative(e) for e in exps ] return RVFuncs.negative(RVFuncs.max(*exps)) @staticmethod def abs(e): if isinstance(e, NegativeExpression): e = e._terms[0] return AbsExpression(e) @staticmethod def pow(e, c): return e # fix @staticmethod def logical_and(expressions): events = [] for e in expressions: if isinstance(e, AndExpression): events.extend(e._terms) else: events.append(e) return AndExpression(events) @staticmethod def logical_or(expressions): events = [] for e in expressions: if isinstance(e, OrExpression): events.extend(e._terms) else: events.append(e) return OrExpression(events) @staticmethod def product(*expressions): # Strip negatives from input expressions n_negatives = 0 exps = [] for e in expressions: if isinstance(e, NegativeExpression): exps.append(e._terms[0]) n_negatives += 1 else: exps.append(e) expressions = exps # Remove and input expressions that are a constant 1 exps = [] for e in expressions: if not(isinstance(e, ConstantExpression) and (e.value == 1)): exps.append(e) expressions = exps # # If there is only one input expression remaining, just return it # if len(expressions) == 1: # return RVFuncs.negative(expressions[0]) if n_negatives % 2 == 1 else expressions[0] # If any of the input expressions are a constant equal to 0, return 0 if any([ isinstance(e,ConstantExpression) and (e.value==0) for e in expressions ]): return RVFuncs.constant(0) # Aggregate input expressions that are products or fractions num_exps = [] den_exps = [] for e in expressions: if isinstance(e, ProductExpression): num_exps.extend(e._terms) elif isinstance(e, FractionExpression): num_exps.append(e._terms[0]) den_exps.append(e._terms[1]) else: num_exps.append(e) if len(den_exps) > 0: # We have a fraction num = RVFuncs.product(*num_exps) if len(num_exps) > 1 else num_exps[0] den = RVFuncs.product(*den_exps) if len(den_exps) > 1 else den_exps[0] expr = RVFuncs.fraction(num, den) else: # We have a non-fraction product # Aggregate constants cval = 1 _exps = [] for e in num_exps: if isinstance(e, ConstantExpression): cval = safeprod(cval, e.value) else: _exps.append(e) if len(_exps) == 0: expr = RVFuncs.constant(cval) elif cval != 1: _exps.append(RVFuncs.constant(cval)) expr = ProductExpression(_exps) elif len(_exps) > 1: expr = ProductExpression(_exps) else: expr = _exps[0] return expr if (n_negatives % 2 == 0) else RVFuncs.negative(expr) @staticmethod def fraction(num, den): ''' Process the numerator and denominator to produce a reduced expression of one of the following forms, in this priority: Constant or Variable Negative(Product(PositiveConstant, Fraction)) Product(PositiveConstant, Fraction) Negative(Fraction). Assumes that num and den are already processed into Negative(Product(Constant, Expression)) form. ''' # Simplify negative signs in the numerator/denominator n_negatives = 0 if isinstance(num, NegativeExpression): num = num._terms[0] n_negatives += 1 if isinstance(den, NegativeExpression): den = den._terms[0] n_negatives += 1 # Remove any constants in front of the numerator or denominator num_val = 1 den_val = 1 if isinstance(num, ProductExpression) and isinstance(num._terms[0], ConstantExpression): num_val = num._terms[0].value num = RVFuncs.product(*num._terms[1:]) if len(num._terms) > 1 else RVFuncs.constant(1) if isinstance(den, ProductExpression) and isinstance(den._terms[0], ConstantExpression): den_val = den._terms[0].value den = RVFuncs.product(*den._terms[1:]) if len(den._terms) > 1 else RVFuncs.constant(1) cval = safediv(num_val, den_val) if cval < 0: n_negatives += 1 cval = -cval # Aggregate terms in the numerator/denominator if one or both are already a fraction if isinstance(num, FractionExpression) and isinstance(den, FractionExpression): _num = RVFuncs.product(num._terms[0], den._terms[1]) _den = RVFuncs.product(num._terms[1], den._terms[0]) num, den = _num, _den elif isinstance(num, FractionExpression): _num = num._terms[0] _den = RVFuncs.product(num._terms[1], den) num, den = _num, _den elif isinstance(den, FractionExpression): _num = RVFuncs.product(den._terms[1], num) _den = den._terms[0] num, den = _num, _den # Remove terms in products that are present in both the numerator and denominator expr = None if num == den: expr = RVFuncs.constant(1) elif isinstance(den, ConstantExpression) and den.value == 1: expr = num elif isinstance(num, ProductExpression) and isinstance(den, ProductExpression): nterms, dterms = copy(num._terms), copy(den._terms) for term in nterms: if term in den._terms: num._terms.remove(term) den._terms.remove(term) num = RVFuncs.constant(1) if len(num._terms) == 0 else RVFuncs.product(*num._terms) den = RVFuncs.constant(1) if len(den._terms) == 0 else RVFuncs.product(*den._terms) if isinstance(num, ConstantExpression) and isinstance(den, ConstantExpression): expr = RVFuncs.constant(safediv(num.value, den.value)) elif isinstance(num, ProductExpression) and isinstance(den, SingleTermExpression): if den in num._terms: num._terms.remove(den) expr = RVFuncs.product(*num._terms) elif isinstance(den, ProductExpression) and isinstance(num, SingleTermExpression): if num in den._terms: den._terms.remove(num) den = RVFuncs.product(*den._terms) if isinstance(den, ConstantExpression): print(safediv(1,den.value), RVFuncs.constant(safediv(1,den.value)).value) expr = RVFuncs.constant(safediv(1,den.value)) else: expr = FractionExpression(RVFuncs.constant(1), RVFuncs.product(*den._terms)) if expr is None: expr = FractionExpression(num, den) # Add a constant scaling factor if it is not 1 if cval != 1: constant = RVFuncs.constant(cval) expr = RVFuncs.product(constant, expr) return RVFuncs.negative(expr) if n_negatives % 2 == 1 else expr

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aa30c170932818c11a70361a198633d3d3e3f96b

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Python

docs/conf.py

Qiskit/qiskit-aqt-provider

276b10bde45027e0d33c80a6942887f7de0204da

[ "Apache-2.0" ]

5

2019-10-21T02:57:22.000Z

2020-04-09T00:03:42.000Z

docs/conf.py

Qiskit/qiskit-aqt-provider

276b10bde45027e0d33c80a6942887f7de0204da

[ "Apache-2.0" ]

8

2019-09-25T19:48:34.000Z

2020-02-27T16:30:41.000Z

docs/conf.py

Qiskit/qiskit-aqt-provider

276b10bde45027e0d33c80a6942887f7de0204da

[ "Apache-2.0" ]

10

2019-09-25T18:47:44.000Z

2020-06-05T17:45:35.000Z

# This code is part of Qiskit. # # (C) Copyright IBM 2018. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. # pylint: disable=invalid-name """ Sphinx documentation builder """ project = 'Qiskit AQT Provider' copyright = '2021, Qiskit and AQT development teams' # pylint: disable=redefined-builtin author = 'Qiskit and AQT development teams' # The short X.Y version version = '0.5.0' # The full version, including alpha/beta/rc tags release = '0.5.0' extensions = [ 'sphinx.ext.napoleon', 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.extlinks', 'jupyter_sphinx', ] templates_path = ["_templates"] html_static_path = ['_static'] html_css_files = [] autosummary_generate = True autosummary_generate_overwrite = False autoclass_content = "both" numfig = True numfig_format = { 'table': 'Table %s' } language = None exclude_patterns = ['_build', '**.ipynb_checkpoints'] pygments_style = 'colorful' add_module_names = False modindex_common_prefix = ['qiskit_aqt.'] html_theme = 'qiskit_sphinx_theme' html_last_updated_fmt = '%Y/%m/%d' html_theme_options = { 'logo_only': True, 'display_version': True, 'prev_next_buttons_location': 'bottom', 'style_external_links': True, }

24.397059

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# This code is part of Qiskit. # # (C) Copyright IBM 2018. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. # pylint: disable=invalid-name """ Sphinx documentation builder """ project = 'Qiskit AQT Provider' copyright = '2021, Qiskit and AQT development teams' # pylint: disable=redefined-builtin author = 'Qiskit and AQT development teams' # The short X.Y version version = '0.5.0' # The full version, including alpha/beta/rc tags release = '0.5.0' extensions = [ 'sphinx.ext.napoleon', 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.extlinks', 'jupyter_sphinx', ] templates_path = ["_templates"] html_static_path = ['_static'] html_css_files = [] autosummary_generate = True autosummary_generate_overwrite = False autoclass_content = "both" numfig = True numfig_format = { 'table': 'Table %s' } language = None exclude_patterns = ['_build', '**.ipynb_checkpoints'] pygments_style = 'colorful' add_module_names = False modindex_common_prefix = ['qiskit_aqt.'] html_theme = 'qiskit_sphinx_theme' html_last_updated_fmt = '%Y/%m/%d' html_theme_options = { 'logo_only': True, 'display_version': True, 'prev_next_buttons_location': 'bottom', 'style_external_links': True, }

0

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448

py

Python

backend/api/ImageSearch/migrations/0009_imagemetadata_image_fpath.py

eth-library-lab/open-image-search

7be76cd4b7730dd76623e15f034f1c337ab99f84

[ "MIT" ]

5

2021-06-14T10:49:52.000Z

2022-02-16T15:56:49.000Z

backend/api/ImageSearch/migrations/0009_imagemetadata_image_fpath.py

eth-library-lab/open-image-search

7be76cd4b7730dd76623e15f034f1c337ab99f84

[ "MIT" ]

11

2021-06-11T16:12:49.000Z

2021-12-03T16:41:13.000Z

backend/api/ImageSearch/migrations/0009_imagemetadata_image_fpath.py

eth-library-lab/open-image-search

7be76cd4b7730dd76623e15f034f1c337ab99f84

[ "MIT" ]

null

# Generated by Django 3.1.2 on 2021-09-20 13:36

23.578947

98

0.629464

# Generated by Django 3.1.2 on 2021-09-20 13:36 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('ImageSearch', '0008_auto_20210916_0909'), ] operations = [ migrations.AddField( model_name='imagemetadata', name='image_fpath', field=models.CharField(max_length=300, null=True, verbose_name='local path to image'), ), ]

0

334

0

19

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244d8e1d804d4ab001743ae24cbdc613ee9ca42f

9,566

py

Python

test_interest.py

castacks/interestingness

b614818ab11dcc15c5fe6b55fe993882add3e8e6

[ "BSD-3-Clause" ]

1

2021-07-20T14:58:36.000Z

test_interest.py

castacks/interestingness

b614818ab11dcc15c5fe6b55fe993882add3e8e6

[ "BSD-3-Clause" ]

null

test_interest.py

castacks/interestingness

b614818ab11dcc15c5fe6b55fe993882add3e8e6

[ "BSD-3-Clause" ]

1

2021-04-17T08:25:05.000Z

#!/usr/bin/env python3 # Copyright <2019> <Chen Wang [https://chenwang.site], Carnegie Mellon University> # Redistribution and use in source and binary forms, with or without modification, are # permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright notice, this list of # conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice, this list # of conditions and the following disclaimer in the documentation and/or other materials # provided with the distribution. # 3. Neither the name of the copyright holder nor the names of its contributors may be # used to endorse or promote products derived from this software without specific prior # written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES # OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT # SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED # TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; # OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH # DAMAGE. import os import time import torch import os.path import argparse import torch.nn as nn import torch.optim as optim import torch.utils.data as Data import torchvision.transforms as transforms from dataset import DroneFilming, SubTF, PersonalVideo from torchutil import count_parameters, Timer, MovAvg from torchutil import ConvLoss, CorrelationLoss, FiveSplit2d if __name__ == "__main__": # Arguements parser = argparse.ArgumentParser(description='Test Interestingness Networks') parser.add_argument("--data-root", type=str, default='/data/datasets', help="dataset root folder") parser.add_argument("--model-save", type=str, default='saves/ae.pt.SubTF.n1000.mse', help="read model") parser.add_argument("--test-data", type=int, default=2, help='test data ID.') parser.add_argument("--seed", type=int, default=0, help='Random seed.') parser.add_argument("--crop-size", type=int, default=320, help='crop size') parser.add_argument("--num-interest", type=int, default=10, help='loss compute by grid') parser.add_argument("--skip-frames", type=int, default=1, help='number of skip frame') parser.add_argument("--window-size", type=int, default=1, help='smooth window size >=1') parser.add_argument('--dataset', type=str, default='SubTF', help='dataset type (SubTF, DroneFilming') parser.add_argument('--save-flag', type=str, default='n1000', help='save name flag') parser.add_argument("--rr", type=float, default=5, help="reading rate") parser.add_argument("--wr", type=float, default=5, help="writing rate") parser.add_argument('--debug', dest='debug', action='store_true') parser.add_argument('--drawbox', dest='drawbox', action='store_true') parser.set_defaults(debug=False) parser.set_defaults(drawbox=False) args = parser.parse_args(); print(args) torch.manual_seed(args.seed) os.makedirs('results', exist_ok=True) if args.debug is True and not os.path.exists('images/%s-%d'%(args.dataset,args.test_data)): os.makedirs('images/%s-%d'%(args.dataset,args.test_data)) transform = transforms.Compose([ # transforms.CenterCrop(args.crop_size), transforms.Resize((args.crop_size,args.crop_size)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) timer = Timer() test_name = '%s-%d-%s-%s'%(args.dataset, args.test_data, time.strftime('%Y-%m-%d-%H:%M:%S'), args.save_flag) if args.dataset == 'DroneFilming': test_data = DroneFilming(root=args.data_root, train=False, test_data=args.test_data, transform=transform) elif args.dataset == 'SubTF': test_data = SubTF(root=args.data_root, train=False, test_data=args.test_data, transform=transform) elif args.dataset == 'PersonalVideo': test_data = PersonalVideo(root=args.data_root, train=False, test_data=args.test_data, transform=transform) test_loader = Data.DataLoader(dataset=test_data, batch_size=1, shuffle=False) net = torch.load(args.model_save) net.set_train(False) net.memory.set_learning_rate(rr=args.rr, wr=args.wr) interest = Interest(args.num_interest, 'results/%s.txt'%(test_name)) movavg = MovAvg(args.window_size) if torch.cuda.is_available(): net = net.cuda() drawbox = ConvLoss(input_size=args.crop_size, kernel_size=args.crop_size//2, stride=args.crop_size//4) criterion = CorrelationLoss(args.crop_size//2, reduce=False, accept_translation=False) fivecrop = FiveSplit2d(args.crop_size//2) print('number of parameters:', count_parameters(net)) val_loss = performance(test_loader, net) print('Done.')

44.493023

137

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#!/usr/bin/env python3 # Copyright <2019> <Chen Wang [https://chenwang.site], Carnegie Mellon University> # Redistribution and use in source and binary forms, with or without modification, are # permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright notice, this list of # conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice, this list # of conditions and the following disclaimer in the documentation and/or other materials # provided with the distribution. # 3. Neither the name of the copyright holder nor the names of its contributors may be # used to endorse or promote products derived from this software without specific prior # written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES # OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT # SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED # TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; # OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH # DAMAGE. import os import cv2 import copy import time import math import torch import os.path import argparse import torchvision import numpy as np import torch.nn as nn import torch.optim as optim from torchvision import models import torch.utils.data as Data from torch.autograd import Variable from torch.nn import functional as F from torchvision.models.vgg import VGG import torchvision.transforms as transforms from torchvision.datasets import CocoDetection from torch.optim.lr_scheduler import ReduceLROnPlateau from interestingness import AE, VAE, AutoEncoder, Interestingness from dataset import ImageData, Dronefilm, DroneFilming, SubT, SubTF, PersonalVideo from torchutil import count_parameters, show_batch, show_batch_origin, Timer, MovAvg from torchutil import ConvLoss, CosineLoss, CorrelationLoss, Split2d, Merge2d, PearsonLoss, FiveSplit2d class Interest(): ''' Maintain top K interests ''' def __init__(self, K, filename): self.K = K self.interests = [] self.filename = filename f = open(self.filename, 'w') f.close() def add_interest(self, tensor, loss, batch_idx, visualize_window=None): f = open(self.filename, 'a+') f.write("%d %f\n" % (batch_idx, loss)) f.close() self.interests.append((loss, tensor, batch_idx)) self.interests.sort(key=self._sort_loss, reverse=True) self._maintain() interests = np.concatenate([self.interests[i][1] for i in range(len(self.interests))], axis=1) if visualize_window is not None: cv2.imshow(visualize_window, interests) return interests def _sort_loss(self, val): return val[0] def _maintain(self): if len(self.interests) > self.K: self.interests = self.interests[:self.K] def performance(loader, net): test_loss, time_use = 0, 0 with torch.no_grad(): for batch_idx, inputs in enumerate(loader): if batch_idx % args.skip_frames !=0: continue if torch.cuda.is_available(): inputs = inputs.cuda() timer.tic() inputs = Variable(inputs) outputs, loss = net(inputs) loss = movavg.append(loss) time_use += timer.end() if args.drawbox is True: drawbox(inputs, outputs) test_loss += loss.item() frame = show_batch_box(inputs, batch_idx, loss.item()) top_interests = interest.add_interest(frame, loss, batch_idx, visualize_window='Top Interests') if args.debug is True: image = show_batch(torch.cat([outputs], dim=0), 'reconstruction') recon = show_batch(torch.cat([(inputs-outputs).abs()], dim=0), 'difference') cv2.imwrite('images/%s-%d/%s-interestingness-%06d.png'%(args.dataset,args.test_data,args.save_flag,batch_idx), frame*255) cv2.imwrite('images/%s-%d/%s-reconstruction-%06d.png'%(args.dataset,args.test_data,args.save_flag,batch_idx), image*255) cv2.imwrite('images/%s-%d/%s-difference-%06d.png'%(args.dataset,args.test_data,args.save_flag,batch_idx), recon*255) print('batch_idx:', batch_idx, 'loss:%.6f'%(loss.item())) print("Total time using: %.2f seconds, %.2f ms/frame"%(time_use, 1000*time_use/(batch_idx+1))) cv2.imwrite('results/%s.png'%(test_name), 255*top_interests) return test_loss/(batch_idx+1) def level_height(bar, ranges=[0.02, 0.08]): h = min(max(0,(bar-ranges[0])/(ranges[1]-ranges[0])),1) return (np.tanh(np.tan(math.pi/2*(2*h-1))-0.8)+1)/2 def boxbar(height, bar, ranges=[0.02, 0.08], threshold=[0.05, 0.06]): width = 15 box = np.zeros((height,width,3), np.uint8) h = level_height(bar, ranges) x1, y1 = 0, int((1-h)*height) x2, y2 = int(width), int(height) cv2.rectangle(box,(x1,y1),(x2,y2),(0,1,0),-1) for i in threshold: x1, y1 = 0, int((1.0-i/ranges[1])*height) x2, y2 = width, int((1.0-i/ranges[1])*height) cv2.line(box,(x1, y1), (x2, y2), (1,0,0), 3) return box def show_batch_box(batch, batch_idx, loss, box_id=None, show_now=True): min_v = torch.min(batch) range_v = torch.max(batch) - min_v if range_v > 0: batch = (batch - min_v) / range_v else: batch = torch.zeros(batch.size()) grid = torchvision.utils.make_grid(batch).cpu() img = grid.numpy()[::-1].transpose((1, 2, 0)) box = boxbar(grid.size(-2), loss, threshold=[]) frame = np.hstack([img, box]) if show_now: cv2.imshow('interestingness', frame) cv2.waitKey(1) return frame if __name__ == "__main__": # Arguements parser = argparse.ArgumentParser(description='Test Interestingness Networks') parser.add_argument("--data-root", type=str, default='/data/datasets', help="dataset root folder") parser.add_argument("--model-save", type=str, default='saves/ae.pt.SubTF.n1000.mse', help="read model") parser.add_argument("--test-data", type=int, default=2, help='test data ID.') parser.add_argument("--seed", type=int, default=0, help='Random seed.') parser.add_argument("--crop-size", type=int, default=320, help='crop size') parser.add_argument("--num-interest", type=int, default=10, help='loss compute by grid') parser.add_argument("--skip-frames", type=int, default=1, help='number of skip frame') parser.add_argument("--window-size", type=int, default=1, help='smooth window size >=1') parser.add_argument('--dataset', type=str, default='SubTF', help='dataset type (SubTF, DroneFilming') parser.add_argument('--save-flag', type=str, default='n1000', help='save name flag') parser.add_argument("--rr", type=float, default=5, help="reading rate") parser.add_argument("--wr", type=float, default=5, help="writing rate") parser.add_argument('--debug', dest='debug', action='store_true') parser.add_argument('--drawbox', dest='drawbox', action='store_true') parser.set_defaults(debug=False) parser.set_defaults(drawbox=False) args = parser.parse_args(); print(args) torch.manual_seed(args.seed) os.makedirs('results', exist_ok=True) if args.debug is True and not os.path.exists('images/%s-%d'%(args.dataset,args.test_data)): os.makedirs('images/%s-%d'%(args.dataset,args.test_data)) transform = transforms.Compose([ # transforms.CenterCrop(args.crop_size), transforms.Resize((args.crop_size,args.crop_size)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) timer = Timer() test_name = '%s-%d-%s-%s'%(args.dataset, args.test_data, time.strftime('%Y-%m-%d-%H:%M:%S'), args.save_flag) if args.dataset == 'DroneFilming': test_data = DroneFilming(root=args.data_root, train=False, test_data=args.test_data, transform=transform) elif args.dataset == 'SubTF': test_data = SubTF(root=args.data_root, train=False, test_data=args.test_data, transform=transform) elif args.dataset == 'PersonalVideo': test_data = PersonalVideo(root=args.data_root, train=False, test_data=args.test_data, transform=transform) test_loader = Data.DataLoader(dataset=test_data, batch_size=1, shuffle=False) net = torch.load(args.model_save) net.set_train(False) net.memory.set_learning_rate(rr=args.rr, wr=args.wr) interest = Interest(args.num_interest, 'results/%s.txt'%(test_name)) movavg = MovAvg(args.window_size) if torch.cuda.is_available(): net = net.cuda() drawbox = ConvLoss(input_size=args.crop_size, kernel_size=args.crop_size//2, stride=args.crop_size//4) criterion = CorrelationLoss(args.crop_size//2, reduce=False, accept_translation=False) fivecrop = FiveSplit2d(args.crop_size//2) print('number of parameters:', count_parameters(net)) val_loss = performance(test_loader, net) print('Done.')

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9a70fd2b8f51d98c7016e821a8b8f28b97a4e005

108,582

py

Python

src/eclipse_jdt/final_results/preliminary_experiment/plot_results.py

Ericsson/oss-automatic-bug-assignment

f4965babd0491118713d7b19bd7ddd30fa39254f

[ "MIT" ]

3

2018-09-25T02:29:54.000Z

2020-02-12T12:35:55.000Z

src/eclipse_jdt/final_results/preliminary_experiment/plot_results.py

dbreddyAI/oss-automatic-bug-assignment

f4965babd0491118713d7b19bd7ddd30fa39254f

[ "MIT" ]

null

src/eclipse_jdt/final_results/preliminary_experiment/plot_results.py

dbreddyAI/oss-automatic-bug-assignment

f4965babd0491118713d7b19bd7ddd30fa39254f

[ "MIT" ]

3

2017-10-26T13:50:21.000Z

2019-12-17T03:40:11.000Z

import matplotlib.pyplot as plt def plot_learning_curve(title, computed_score, train_sizes, \ train_scores_mean, train_scores_std, test_scores_mean, \ test_scores_std): """Generate a plot of the test and training learning curves. Parameters ---------- title: string Contains the title of the chart. computed_score: string Contains the name of the computed score. train_sizes: a one dimension numpy.ndarray An array containing the various sizes of the training set for which the scores have been computed. train_scores_mean: a one dimension numpy.ndarray An array containing the various means of the scores related to each element in train_sizes. These scores should have been computed on the training set. train_scores_std: a one dimension numpy.ndarray An array containing the various standard deviations of the scores related to each element in train_sizes. These scores should have been computed on the training set. test_scores_mean: a one dimension numpy.ndarray An array containing the various means of the scores related to each element in train_sizes. These scores should have been computed on the test set. test_scores_std: a one dimension numpy.ndarray An array containing the various standard deviations of the scores related to each element in train_sizes. These scores should have been computed on the test set. ylim: tuple, shape (ymin, ymax), optional Defines minimum and maximum yvalues plotted. """ fig = plt.figure(figsize=(20.0, 12.5)) plt.title(title, size=31) plt.xlim(xmin=0, xmax=25000) plt.ylim(ymin=0.0, ymax=1.0) plt.xlabel("Training examples", size=28) plt.ylabel(computed_score.capitalize(), size=28) plt.grid(linewidth=3) plt.fill_between(train_sizes, train_scores_mean - \ train_scores_std, train_scores_mean + train_scores_std, \ alpha=0.3, color="r") plt.fill_between(train_sizes, test_scores_mean - \ test_scores_std, test_scores_mean + test_scores_std, \ alpha=0.3, color="g") plt.plot(train_sizes, train_scores_mean, 'o-', color="r", \ label="Training {}".format(computed_score), \ linewidth=5.0, markersize=13.0) plt.plot(train_sizes, test_scores_mean, 'o-', color="g", \ label="Test {}".format(computed_score), \ linewidth=5.0, markersize=13.0) plt.legend(loc="best", prop={'size': 26}) plt.tick_params(axis='both', which='major', labelsize=22) return fig if __name__ == "__main__": main()

236.562092

26,139

0.795611

from numpy import array import os import inspect import matplotlib.pyplot as plt def plot_learning_curve(title, computed_score, train_sizes, \ train_scores_mean, train_scores_std, test_scores_mean, \ test_scores_std): """Generate a plot of the test and training learning curves. Parameters ---------- title: string Contains the title of the chart. computed_score: string Contains the name of the computed score. train_sizes: a one dimension numpy.ndarray An array containing the various sizes of the training set for which the scores have been computed. train_scores_mean: a one dimension numpy.ndarray An array containing the various means of the scores related to each element in train_sizes. These scores should have been computed on the training set. train_scores_std: a one dimension numpy.ndarray An array containing the various standard deviations of the scores related to each element in train_sizes. These scores should have been computed on the training set. test_scores_mean: a one dimension numpy.ndarray An array containing the various means of the scores related to each element in train_sizes. These scores should have been computed on the test set. test_scores_std: a one dimension numpy.ndarray An array containing the various standard deviations of the scores related to each element in train_sizes. These scores should have been computed on the test set. ylim: tuple, shape (ymin, ymax), optional Defines minimum and maximum yvalues plotted. """ fig = plt.figure(figsize=(20.0, 12.5)) plt.title(title, size=31) plt.xlim(xmin=0, xmax=25000) plt.ylim(ymin=0.0, ymax=1.0) plt.xlabel("Training examples", size=28) plt.ylabel(computed_score.capitalize(), size=28) plt.grid(linewidth=3) plt.fill_between(train_sizes, train_scores_mean - \ train_scores_std, train_scores_mean + train_scores_std, \ alpha=0.3, color="r") plt.fill_between(train_sizes, test_scores_mean - \ test_scores_std, test_scores_mean + test_scores_std, \ alpha=0.3, color="g") plt.plot(train_sizes, train_scores_mean, 'o-', color="r", \ label="Training {}".format(computed_score), \ linewidth=5.0, markersize=13.0) plt.plot(train_sizes, test_scores_mean, 'o-', color="g", \ label="Test {}".format(computed_score), \ linewidth=5.0, markersize=13.0) plt.legend(loc="best", prop={'size': 26}) plt.tick_params(axis='both', which='major', labelsize=22) return fig def main(): current_dir = os.path.dirname(os.path.abspath( \ inspect.getfile(inspect.currentframe()))) incremental_train_accuracy_per_size_4_folds = {18336: [0.82422556719022688], 12224: [0.8534031413612565, 0.84031413612565442], 6112: [0.90068717277486909, 0.88890706806282727, 0.88219895287958117]} incremental_test_accuracy_per_size_4_folds = {18336: [0.094210009813542689], 12224: [0.098135426889106966, 0.224967277486911], 6112: [0.10091593065096501, 0.23707460732984292, 0.24803664921465968]} incremental_train_sizes_4_folds = array([ 6112, 12224, 18336]) incremental_train_scores_mean_4_folds = array([ 0.89059773, 0.84685864, 0.82422557]) incremental_train_scores_std_4_folds = array([ 0.00764187, 0.0065445, 0. ]) incremental_test_scores_mean_4_folds = array([ 0.1953424, 0.16155135, 0.09421001]) incremental_test_scores_std_4_folds = array([ 0.0669194, 0.06341593, 0. ]) incremental_train_accuracy_per_size_6_folds = {20375: [0.82012269938650306], 16300: [0.83558282208588952, 0.82993865030674852], 12225: [0.85824130879345606, 0.84932515337423309, 0.84040899795501023], 8150: [0.88981595092024535, 0.88110429447852756, 0.86932515337423311, 0.86687116564417177], 4075: [0.92122699386503071, 0.91533742331288348, 0.90625766871165647, 0.90134969325153369, 0.89447852760736202]} incremental_test_accuracy_per_size_6_folds = {20375: [0.054969325153374236], 16300: [0.056441717791411043, 0.21398773006134969], 12225: [0.05865030674846626, 0.22331288343558281, 0.24957055214723928], 8150: [0.063803680981595098, 0.23018404907975459, 0.26085889570552145, 0.27558282208588958], 4075: [0.075828220858895706, 0.22012269938650306, 0.25840490797546012, 0.2723926380368098, 0.23484662576687115]} incremental_train_sizes_6_folds = array([ 4075, 8150, 12225, 16300, 20375]) incremental_train_scores_mean_6_folds = array([ 0.90773006, 0.87677914, 0.84932515, 0.83276074, 0.8201227 ]) incremental_train_scores_std_6_folds = array([ 0.00957621, 0.00925196, 0.00728001, 0.00282209, 0. ]) incremental_test_scores_mean_6_folds = array([ 0.21231902, 0.20760736, 0.17717791, 0.13521472, 0.05496933]) incremental_test_scores_std_6_folds = array([ 0.07061286, 0.08462505, 0.08449442, 0.07877301, 0. ]) incremental_train_accuracy_per_size_8_folds = {21392: [0.81740837696335078], 18336: [0.82869764397905754, 0.82422556719022688], 15280: [0.84332460732984293, 0.83776178010471203, 0.83082460732984298], 12224: [0.86338350785340312, 0.8534031413612565, 0.84710405759162299, 0.84031413612565442], 9168: [0.88568935427574169, 0.87565445026178013, 0.87041884816753923, 0.85907504363001741, 0.8586387434554974], 6112: [0.90085078534031415, 0.90068717277486909, 0.89725130890052351, 0.88890706806282727, 0.88334424083769636, 0.88219895287958117], 3056: [0.91819371727748689, 0.9240837696335078, 0.92702879581151831, 0.91852094240837701, 0.91819371727748689, 0.92833769633507857, 0.90150523560209428]} incremental_test_accuracy_per_size_8_folds = {21392: [0.039241334205362979], 18336: [0.039568345323741004, 0.17604712041884818], 15280: [0.043819489862655332, 0.17702879581151831, 0.2486910994764398], 12224: [0.048724656638325703, 0.18160994764397906, 0.25327225130890052, 0.26897905759162305], 9168: [0.055591890124264222, 0.18422774869109948, 0.26734293193717279, 0.27945026178010474, 0.30268324607329844], 6112: [0.072596468279921514, 0.18259162303664922, 0.27192408376963351, 0.28435863874345552, 0.29286649214659688, 0.26145287958115182], 3056: [0.11118378024852844, 0.1806282722513089, 0.2581806282722513, 0.27290575916230364, 0.27748691099476441, 0.26897905759162305, 0.25785340314136124]} incremental_train_sizes_8_folds = array([ 3056, 6112, 9168, 12224, 15280, 18336, 21392]) incremental_train_scores_mean_8_folds = array([ 0.91940912, 0.89220659, 0.86989529, 0.85105121, 0.83730366, 0.82646161, 0.81740838]) incremental_train_scores_std_8_folds = array([ 0.00831456, 0.00776394, 0.01026335, 0.00849238, 0.00511337, 0.00223604, 0. ]) incremental_test_scores_mean_8_folds = array([ 0.23245969, 0.2276317, 0.21785922, 0.18814648, 0.15651313, 0.10780773, 0.03924133]) incremental_test_scores_std_8_folds = array([ 0.05818413, 0.07814916, 0.09044222, 0.0869719, 0.08488723, 0.06823939, 0. ]) incremental_train_accuracy_per_size_10_folds = {22005: [0.81799591002044991], 19560: [0.82617586912065444, 0.82249488752556232], 17115: [0.83651767455448434, 0.83102541630148996, 0.82810400233713122], 14670: [0.84935241990456711, 0.84226312201772324, 0.83974096796182685, 0.83278800272665299], 12225: [0.86633946830265851, 0.85758691206543969, 0.85251533742331287, 0.84621676891615538, 0.84040899795501023], 9780: [0.88548057259713697, 0.87525562372188137, 0.87300613496932511, 0.86124744376278117, 0.85429447852760731, 0.8535787321063395], 7335: [0.89788684389911388, 0.89570552147239269, 0.88943421949556922, 0.88657123381049763, 0.87607361963190189, 0.87075664621676896, 0.87457396046353097], 4890: [0.91942740286298563, 0.90817995910020455, 0.9122699386503067, 0.90817995910020455, 0.90122699386503069, 0.88568507157464216, 0.90081799591002043, 0.88670756646216764], 2445: [0.92842535787321068, 0.93006134969325149, 0.93660531697341509, 0.93047034764826175, 0.93987730061349695, 0.91574642126789363, 0.91983640081799589, 0.9235173824130879, 0.91002044989775055]} incremental_test_accuracy_per_size_10_folds = {22005: [0.11820040899795502], 19560: [0.11738241308793455, 0.095296523517382409], 17115: [0.12106339468302658, 0.10061349693251534, 0.25439672801635993], 14670: [0.12269938650306748, 0.10224948875255624, 0.2593047034764826, 0.23640081799591003], 12225: [0.12719836400817996, 0.11615541922290389, 0.25807770961145193, 0.23885480572597137, 0.29284253578732106], 9780: [0.14069529652351739, 0.11942740286298568, 0.26625766871165646, 0.24498977505112474, 0.30224948875255625, 0.31533742331288345], 7335: [0.16196319018404909, 0.12392638036809817, 0.2593047034764826, 0.2560327198364008, 0.31083844580777098, 0.31124744376278118, 0.2523517382413088], 4890: [0.17995910020449898, 0.1329243353783231, 0.2523517382413088, 0.26339468302658486, 0.29734151329243352, 0.29979550102249491, 0.26134969325153373, 0.26871165644171779], 2445: [0.19386503067484662, 0.13006134969325153, 0.2392638036809816, 0.24498977505112474, 0.29202453987730059, 0.27034764826175867, 0.24130879345603273, 0.24989775051124744, 0.25071574642126787]} incremental_train_sizes_10_folds = array([ 2445, 4890, 7335, 9780, 12225, 14670, 17115, 19560, 22005]) incremental_train_scores_mean_10_folds = array([ 0.92606226, 0.90281186, 0.88442886, 0.86714383, 0.8526135, 0.84103613, 0.83188236, 0.82433538, 0.81799591]) incremental_train_scores_std_10_folds = array([ 0.00914095, 0.0110811, 0.00994113, 0.01169251, 0.00897791, 0.005924, 0.00348791, 0.00184049, 0. ]) incremental_test_scores_mean_10_folds = array([ 0.23471938, 0.24447853, 0.23938066, 0.23149284, 0.20662577, 0.1801636, 0.15869121, 0.10633947, 0.11820041]) incremental_test_scores_std_10_folds = array([ 0.04451149, 0.05448996, 0.06594014, 0.07553149, 0.07157226, 0.06855415, 0.06818705, 0.01104294, 0. ]) incremental_train_accuracy_per_size_15_folds = {22820: [0.81849255039439084], 21190: [0.82298253893345918, 0.81727229825389336], 19560: [0.83052147239263807, 0.82264826175869121, 0.82249488752556232], 17930: [0.83736754043502515, 0.830117122141662, 0.82861126603457891, 0.82381483547127721], 16300: [0.84699386503067486, 0.83619631901840485, 0.83668711656441719, 0.83073619631901841, 0.82993865030674852], 14670: [0.85405589638718471, 0.8477164280845263, 0.84226312201772324, 0.83871847307430125, 0.83653715064758005, 0.83278800272665299], 13040: [0.86618098159509205, 0.85552147239263798, 0.85421779141104293, 0.84739263803680986, 0.84631901840490797, 0.84072085889570547, 0.8377300613496933], 11410: [0.87765118317265556, 0.86958808063102544, 0.86126205083260299, 0.86038562664329532, 0.85723049956178787, 0.85021910604732687, 0.84469763365468886, 0.84522348816827342], 9780: [0.88588957055214723, 0.87924335378323104, 0.87525562372188137, 0.87044989775051129, 0.87157464212678937, 0.86124744376278117, 0.8563394683026585, 0.85685071574642124, 0.8535787321063395], 8150: [0.89447852760736202, 0.89042944785276079, 0.88858895705521468, 0.88638036809815945, 0.88110429447852756, 0.87754601226993867, 0.87067484662576689, 0.86907975460122699, 0.86453987730061355, 0.86687116564417177], 6520: [0.90567484662576692, 0.90000000000000002, 0.89892638036809813, 0.89984662576687113, 0.89309815950920246, 0.89401840490797546, 0.88696319018404912, 0.88312883435582823, 0.87423312883435578, 0.87944785276073623, 0.87898773006134967], 4890: [0.91390593047034763, 0.91451942740286296, 0.90817995910020455, 0.91267893660531696, 0.90899795501022496, 0.90817995910020455, 0.90061349693251536, 0.9002044989775051, 0.88568507157464216, 0.89836400817995909, 0.90040899795501017, 0.88670756646216764], 3260: [0.91932515337423315, 0.92638036809815949, 0.92699386503067482, 0.92791411042944782, 0.91748466257668715, 0.92392638036809815, 0.93006134969325149, 0.91625766871165648, 0.90490797546012269, 0.90674846625766869, 0.91901840490797548, 0.90889570552147236, 0.89785276073619635], 1630: [0.93496932515337428, 0.94049079754601228, 0.94601226993865029, 0.94969325153374229, 0.93619631901840494, 0.92883435582822083, 0.93803680981595094, 0.94907975460122695, 0.91717791411042948, 0.92699386503067482, 0.9319018404907975, 0.94723926380368095, 0.91533742331288348, 0.92638036809815949]} incremental_test_accuracy_per_size_15_folds = {22820: [0.15276073619631902], 21190: [0.150920245398773, 0.060122699386503067], 19560: [0.15337423312883436, 0.059509202453987733, 0.12331288343558282], 17930: [0.15214723926380369, 0.058282208588957052, 0.13190184049079753, 0.2607361963190184], 16300: [0.15398773006134969, 0.061963190184049083, 0.13190184049079753, 0.27116564417177913, 0.25398773006134967], 14670: [0.15460122699386503, 0.066257668711656448, 0.13374233128834356, 0.27116564417177913, 0.25398773006134967, 0.2411042944785276], 13040: [0.15766871165644172, 0.068711656441717797, 0.14294478527607363, 0.27300613496932513, 0.25398773006134967, 0.24049079754601227, 0.29877300613496932], 11410: [0.16809815950920245, 0.066871165644171782, 0.15337423312883436, 0.26625766871165646, 0.26380368098159507, 0.2460122699386503, 0.30858895705521472, 0.33128834355828218], 9780: [0.17668711656441718, 0.073619631901840496, 0.15398773006134969, 0.27484662576687119, 0.26503067484662579, 0.25644171779141106, 0.30613496932515338, 0.32822085889570551, 0.31411042944785278], 8150: [0.18466257668711655, 0.078527607361963195, 0.16073619631901839, 0.26687116564417179, 0.27423312883435585, 0.26932515337423313, 0.31226993865030672, 0.33742331288343558, 0.31963190184049078, 0.26748466257668713], 6520: [0.19570552147239265, 0.095092024539877307, 0.16809815950920245, 0.2588957055214724, 0.26993865030674846, 0.26564417177914113, 0.30797546012269938, 0.33680981595092024, 0.31963190184049078, 0.27361963190184047, 0.27975460122699386], 4890: [0.20429447852760735, 0.11288343558282209, 0.17055214723926379, 0.26503067484662579, 0.26319018404907973, 0.2822085889570552, 0.30184049079754599, 0.32699386503067485, 0.30306748466257671, 0.28650306748466259, 0.27607361963190186, 0.26625766871165646], 3260: [0.21717791411042944, 0.12208588957055215, 0.16380368098159509, 0.25950920245398773, 0.252760736196319, 0.27300613496932513, 0.29815950920245399, 0.33128834355828218, 0.29509202453987732, 0.27975460122699386, 0.28343558282208586, 0.26503067484662579, 0.27300613496932513], 1630: [0.23803680981595093, 0.15889570552147239, 0.15705521472392639, 0.23251533742331287, 0.25521472392638039, 0.25582822085889573, 0.28159509202453986, 0.31901840490797545, 0.2570552147239264, 0.26196319018404907, 0.26319018404907973, 0.23558282208588957, 0.25214723926380367, 0.25950920245398773]} incremental_train_sizes_15_folds = array([ 1630, 3260, 4890, 6520, 8150, 9780, 11410, 13040, 14670, 16300, 17930, 19560, 21190, 22820]) incremental_train_scores_mean_15_folds = array([ 0.93488168, 0.91736668, 0.90320382, 0.8903932, 0.87896933, 0.86782549, 0.85828221, 0.84972612, 0.84201318, 0.83611043, 0.82997769, 0.82522154, 0.82012742, 0.81849255]) incremental_train_scores_std_15_folds = array([ 0.01074397, 0.00967823, 0.00931587, 0.00992714, 0.01023275, 0.01070537, 0.01082517, 0.00899664, 0.00711293, 0.00609528, 0.00485997, 0.00374814, 0.00285512, 0. ]) incremental_test_scores_mean_15_folds = array([ 0.2448291, 0.25493157, 0.25490798, 0.25192415, 0.24711656, 0.23878664, 0.22553681, 0.20508326, 0.18680982, 0.17460123, 0.15076687, 0.11206544, 0.10552147, 0.15276074]) incremental_test_scores_std_15_folds = array([ 0.04097555, 0.05470141, 0.05920192, 0.06843836, 0.07712113, 0.08085383, 0.08314464, 0.07722247, 0.07412044, 0.07818233, 0.07246458, 0.03913685, 0.04539877, 0. ]) incremental_train_accuracy_per_size_25_folds = {23472: [0.81799591002044991], 22494: [0.8191517738063484, 0.81821819151773811], 21516: [0.824595649749024, 0.82041271611823763, 0.81692693809258221], 20538: [0.82753919563735512, 0.82520206446586819, 0.81892102444249681, 0.81862888304606096], 19560: [0.83195296523517381, 0.82878323108384455, 0.82356850715746421, 0.82014314928425358, 0.82249488752556232], 18582: [0.8366698955978904, 0.83397911957808635, 0.82741362608976432, 0.82692928640619956, 0.82348509310085027, 0.82396943278441503], 17604: [0.84003635537377863, 0.83702567598273114, 0.83242444898886614, 0.83094751192910699, 0.82969779595546467, 0.826630311292888, 0.82509656896159966], 16626: [0.84620473956453746, 0.84187417298207623, 0.83585949717310237, 0.83549861662456393, 0.8338145073980513, 0.83297245278479493, 0.82828100565379525, 0.8286418862023337], 15648: [0.85090746421267893, 0.84809560327198363, 0.84189672801635995, 0.83876533742331283, 0.83825408997955009, 0.83640081799591004, 0.8339723926380368, 0.83167177914110424, 0.83032975460122704], 14670: [0.85821404226312203, 0.85262440354464897, 0.84723926380368098, 0.84464894342194952, 0.84226312201772324, 0.84137695978186777, 0.83749147920927058, 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0.86242796058746984, 0.85833798103736758, 0.85489868005205427, 0.85015802193716306, 0.84960029745305821, 0.84690462911321807, 0.84987915969511063], 9780: [0.88486707566462164, 0.88926380368098157, 0.88159509202453989, 0.87770961145194271, 0.87525562372188137, 0.87259713701431496, 0.87157464212678937, 0.87044989775051129, 0.86543967280163603, 0.86124744376278117, 0.85664621676891617, 0.85521472392638032, 0.85644171779141109, 0.85419222903885483, 0.8535787321063395], 8802: [0.88559418314019545, 0.89036582594864799, 0.88798000454442172, 0.88479890933878669, 0.8827539195637355, 0.87991365598727567, 0.87855032947057488, 0.87832310838445804, 0.87502840263576465, 0.86980231765507843, 0.86639400136332656, 0.86321290615769142, 0.86332651670074978, 0.8626448534423995, 0.85810043172006367, 0.86332651670074978], 7824: [0.89187116564417179, 0.89570552147239269, 0.89327709611451944, 0.89199897750511248, 0.89059304703476483, 0.88905930470347649, 0.88854805725971375, 0.8880368098159509, 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0.92706203135650989, 0.93626448534423989, 0.92842535787321068, 0.91990456714383095, 0.92603953646898429, 0.91922290388548056, 0.93456032719836402, 0.92160872528970683, 0.91717791411042948, 0.91240627130197682, 0.91717791411042948, 0.91342876618950242, 0.92058623040218135, 0.92808452624403548, 0.91615541922290389, 0.9035446489434219, 0.90558963871847309], 1956: [0.90286298568507162, 0.93762781186094069, 0.93098159509202449, 0.93711656441717794, 0.93813905930470343, 0.93865030674846628, 0.94734151329243355, 0.93762781186094069, 0.93302658486707568, 0.92944785276073616, 0.92484662576687116, 0.93404907975460127, 0.94529652351738236, 0.92331288343558282, 0.91666666666666663, 0.93456032719836402, 0.92995910020449901, 0.92586912065439675, 0.93404907975460127, 0.93660531697341509, 0.91922290388548056, 0.91768916155419222, 0.9253578732106339], 978: [0.89059304703476483, 0.95194274028629855, 0.94887525562372188, 0.95194274028629855, 0.93762781186094069, 0.94478527607361962, 0.95910020449897748, 0.95501022494887522, 0.9253578732106339, 0.95092024539877296, 0.93456032719836402, 0.9468302658486708, 0.95705521472392641, 0.9468302658486708, 0.91411042944785281, 0.94478527607361962, 0.93149284253578735, 0.94069529652351735, 0.93456032719836402, 0.96319018404907975, 0.93865030674846628, 0.93558282208588961, 0.93149284253578735, 0.92126789366053174]} incremental_test_accuracy_per_size_25_folds = {23472: [0.10633946830265849], 22494: [0.10531697341513292, 0.22903885480572597], 21516: [0.10736196319018405, 0.22392638036809817, 0.070552147239263799], 20538: [0.10736196319018405, 0.22699386503067484, 0.07259713701431493, 0.13905930470347649], 19560: [0.10531697341513292, 0.22903885480572597, 0.068507157464212681, 0.13803680981595093, 0.1492842535787321], 18582: [0.10736196319018405, 0.22290388548057261, 0.06646216768916155, 0.14519427402862986, 0.15235173824130879, 0.26687116564417179], 17604: [0.11349693251533742, 0.22903885480572597, 0.06646216768916155, 0.14519427402862986, 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0.17484662576687116, 0.27505112474437626, 0.29652351738241312, 0.2658486707566462, 0.31901840490797545, 0.23517382413087934, 0.28629856850715746, 0.34049079754601225], 11736: [0.1196319018404908, 0.24028629856850717, 0.075664621676891614, 0.15337423312883436, 0.18609406952965235, 0.26993865030674846, 0.28732106339468305, 0.26278118609406953, 0.31186094069529652, 0.22699386503067484, 0.28936605316973413, 0.34049079754601225, 0.34049079754601225], 10758: [0.130879345603272, 0.2607361963190184, 0.078732106339468297, 0.15848670756646216, 0.18711656441717792, 0.27607361963190186, 0.28936605316973413, 0.26278118609406953, 0.32106339468302658, 0.22801635991820041, 0.29141104294478526, 0.34458077709611451, 0.34151329243353784, 0.32617586912065438], 9780: [0.1329243353783231, 0.27709611451942739, 0.079754601226993863, 0.15439672801635992, 0.18813905930470348, 0.28527607361963192, 0.29141104294478526, 0.27198364008179959, 0.32515337423312884, 0.24335378323108384, 0.29243353783231085, 0.34662576687116564, 0.34458077709611451, 0.33435582822085891, 0.32515337423312884], 8802: [0.13190184049079753, 0.28629856850715746, 0.078732106339468297, 0.16564417177914109, 0.19325153374233128, 0.28118609406952966, 0.28834355828220859, 0.28834355828220859, 0.32924335378323111, 0.25153374233128833, 0.28834355828220859, 0.35276073619631904, 0.34253578732106338, 0.31697341513292432, 0.33333333333333331, 0.28323108384458079], 7824: [0.13496932515337423, 0.29550102249488752, 0.085889570552147243, 0.16871165644171779, 0.19836400817995911, 0.27300613496932513, 0.29243353783231085, 0.27811860940695299, 0.32822085889570551, 0.26482617586912066, 0.29447852760736198, 0.35787321063394684, 0.34867075664621677, 0.33128834355828218, 0.32310838445807771, 0.2822085889570552, 0.28732106339468305], 6846: [0.13701431492842536, 0.29447852760736198, 0.096114519427402859, 0.16973415132924335, 0.19836400817995911, 0.27402862985685073, 0.28527607361963192, 0.27402862985685073, 0.32719836400817998, 0.25153374233128833, 0.29345603271983639, 0.34969325153374231, 0.34969325153374231, 0.33026584867075665, 0.32617586912065438, 0.29243353783231085, 0.29550102249488752, 0.29141104294478526], 5868: [0.14723926380368099, 0.30572597137014312, 0.097137014314928424, 0.17177914110429449, 0.20449897750511248, 0.26380368098159507, 0.28118609406952966, 0.27607361963190186, 0.32515337423312884, 0.27300613496932513, 0.29754601226993865, 0.34049079754601225, 0.34969325153374231, 0.32310838445807771, 0.32924335378323111, 0.29243353783231085, 0.28629856850715746, 0.30572597137014312, 0.27096114519427406], 4890: [0.15132924335378323, 0.31697341513292432, 0.11247443762781185, 0.17791411042944785, 0.21370143149284254, 0.26278118609406953, 0.28732106339468305, 0.2822085889570552, 0.32310838445807771, 0.27709611451942739, 0.28732106339468305, 0.35378323108384457, 0.32924335378323111, 0.31799591002044991, 0.31288343558282211, 0.28834355828220859, 0.29447852760736198, 0.31083844580777098, 0.27198364008179959, 0.27505112474437626], 3912: [0.15746421267893659, 0.32515337423312884, 0.12678936605316973, 0.20040899795501022, 0.21267893660531698, 0.25664621676891614, 0.28834355828220859, 0.26993865030674846, 0.30981595092024539, 0.28118609406952966, 0.28016359918200406, 0.34969325153374231, 0.33946830265848671, 0.30981595092024539, 0.31492842535787319, 0.28425357873210633, 0.29141104294478526, 0.31186094069529652, 0.2822085889570552, 0.27811860940695299, 0.30163599182004092], 2934: [0.17586912065439672, 0.33435582822085891, 0.14314928425357873, 0.21676891615541921, 0.19938650306748465, 0.25255623721881393, 0.28732106339468305, 0.27096114519427406, 0.29959100204498978, 0.27709611451942739, 0.26380368098159507, 0.35378323108384457, 0.34049079754601225, 0.29038854805725972, 0.30470347648261759, 0.27096114519427406, 0.2658486707566462, 0.32208588957055212, 0.28834355828220859, 0.26278118609406953, 0.31492842535787319, 0.25869120654396727], 1956: [0.19325153374233128, 0.34253578732106338, 0.15848670756646216, 0.24130879345603273, 0.17382413087934559, 0.22290388548057261, 0.31390593047034765, 0.26482617586912066, 0.30674846625766872, 0.2822085889570552, 0.26789366053169733, 0.34049079754601225, 0.33231083844580778, 0.28936605316973413, 0.28016359918200406, 0.28425357873210633, 0.25766871165644173, 0.30470347648261759, 0.28527607361963192, 0.24335378323108384, 0.27198364008179959, 0.26482617586912066, 0.29243353783231085], 978: [0.19529652351738241, 0.34253578732106338, 0.18507157464212678, 0.24233128834355827, 0.21370143149284254, 0.20961145194274028, 0.27811860940695299, 0.27505112474437626, 0.25869120654396727, 0.2556237218813906, 0.2310838445807771, 0.29447852760736198, 0.29038854805725972, 0.27198364008179959, 0.24846625766871167, 0.24539877300613497, 0.20143149284253578, 0.25869120654396727, 0.25255623721881393, 0.26993865030674846, 0.18916155419222905, 0.22903885480572597, 0.28323108384458079, 0.23415132924335377]} incremental_train_sizes_25_folds = array([ 978, 1956, 2934, 3912, 4890, 5868, 6846, 7824, 8802, 9780, 10758, 11736, 12714, 13692, 14670, 15648, 16626, 17604, 18582, 19560, 20538, 21516, 22494, 23472]) incremental_train_scores_mean_25_folds = array([ 0.93988582, 0.93044812, 0.9208496, 0.91140812, 0.90287321, 0.89537366, 0.88779336, 0.881556, 0.87438224, 0.86840491, 0.86273338, 0.85664753, 0.8515285, 0.84671872, 0.84274029, 0.83892155, 0.83539336, 0.8316941, 0.82874108, 0.82538855, 0.82257279, 0.8206451, 0.81868498, 0.81799591]) incremental_train_scores_std_25_folds = array([ 0.01574382, 0.00986344, 0.00941513, 0.0086523, 0.00859881, 0.00843904, 0.0094164, 0.00978435, 0.0102025, 0.01149665, 0.01132038, 0.011076, 0.01026567, 0.00910232, 0.00777419, 0.00661961, 0.00574061, 0.00496077, 0.00492852, 0.00433164, 0.00388806, 0.00313505, 0.00046679, 0. ]) incremental_test_scores_mean_25_folds = array([ 0.24816803, 0.27020539, 0.27244841, 0.27485636, 0.27234151, 0.27058444, 0.26868893, 0.26735234, 0.26322853, 0.2595092, 0.24978089, 0.23879188, 0.2305726, 0.21649005, 0.20593047, 0.20529425, 0.19171779, 0.18112767, 0.16019087, 0.13803681, 0.13650307, 0.13394683, 0.16717791, 0.10633947]) incremental_test_scores_std_25_folds = array([ 0.03709919, 0.0474123, 0.05100164, 0.0554582, 0.06067133, 0.06643791, 0.07039766, 0.07419473, 0.0772398, 0.08042228, 0.07961067, 0.08014752, 0.08023328, 0.07686982, 0.0755778, 0.07961578, 0.07611923, 0.07668308, 0.06730022, 0.05350297, 0.05728938, 0.06537574, 0.06186094, 0. ]) incremental_train_accuracy_per_size_50_folds = {23961: [0.81770376862401406], 23472: [0.81842194955691894, 0.81799591002044991], 22983: [0.81877909759387368, 0.81821346212417878, 0.81890962885611107], 22494: [0.82177469547434867, 0.8191517738063484, 0.819196230105806, 0.81821819151773811], 22005: [0.82394910247670983, 0.82140422631220178, 0.81972279027493755, 0.81904112701658716, 0.81799591002044991], 21516: [0.82594348391894401, 0.824595649749024, 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0.13905930470347649, 0.15950920245398773, 0.24539877300613497, 0.22085889570552147, 0.23517382413087934, 0.25357873210633947, 0.17382413087934559, 0.39672801635991822]} incremental_train_sizes_50_folds = array([ 489, 978, 1467, 1956, 2445, 2934, 3423, 3912, 4401, 4890, 5379, 5868, 6357, 6846, 7335, 7824, 8313, 8802, 9291, 9780, 10269, 10758, 11247, 11736, 12225, 12714, 13203, 13692, 14181, 14670, 15159, 15648, 16137, 16626, 17115, 17604, 18093, 18582, 19071, 19560, 20049, 20538, 21027, 21516, 22005, 22494, 22983, 23472, 23961]) incremental_train_scores_mean_50_folds = array([ 0.94641292, 0.94018405, 0.93508245, 0.92998133, 0.92539877, 0.92040807, 0.91610107, 0.91143247, 0.90711645, 0.903318, 0.89938555, 0.89571898, 0.89192165, 0.88847908, 0.88510274, 0.8819883, 0.87861655, 0.87507101, 0.87178365, 0.8690559, 0.86611193, 0.8634239, 0.86069042, 0.85786338, 0.85518855, 0.85250511, 0.85012629, 0.84780429, 0.84546392, 0.84356169, 0.84149767, 0.83968132, 0.83789902, 0.83605873, 0.83428571, 0.83245285, 0.83075052, 0.82938238, 0.8276965, 0.82601738, 0.82459641, 0.82349182, 0.82237803, 0.82158239, 0.82042263, 0.81958522, 0.81863406, 0.81820893, 0.81770377]) incremental_train_scores_std_50_folds = array([ 0.01849963, 0.01378789, 0.01221196, 0.01036566, 0.00951653, 0.0095293, 0.00920113, 0.00879358, 0.00817018, 0.00829866, 0.00816914, 0.00840314, 0.00921439, 0.00955778, 0.00966554, 0.01008684, 0.01065165, 0.0106473, 0.0110683, 0.01155044, 0.01137906, 0.01131299, 0.01164905, 0.01127409, 0.01067386, 0.01048885, 0.00997217, 0.00943623, 0.00849798, 0.00799113, 0.00751208, 0.00688614, 0.00636693, 0.00622729, 0.00592422, 0.00536307, 0.00523293, 0.00500458, 0.0051211, 0.00461855, 0.00442537, 0.00428366, 0.00362497, 0.0031117, 0.00208263, 0.00132304, 0.00030215, 0.00021302, 0. ]) incremental_test_scores_mean_50_folds = array([ 0.24009849, 0.26921438, 0.28260018, 0.28949942, 0.29202454, 0.29280535, 0.2946212, 0.29418639, 0.29457828, 0.29335378, 0.28850087, 0.28796685, 0.28740397, 0.28732106, 0.28425358, 0.28317094, 0.28282828, 0.27952454, 0.27653539, 0.27484663, 0.26937452, 0.26701724, 0.26168295, 0.25727544, 0.25071575, 0.24710293, 0.24290922, 0.23545269, 0.23001266, 0.22617587, 0.22354967, 0.22438082, 0.21953567, 0.2101227, 0.20804363, 0.20333041, 0.19207173, 0.18404908, 0.17159323, 0.16462168, 0.16950693, 0.1658998, 0.16681274, 0.16768916, 0.18241309, 0.18711656, 0.1724608, 0.12372188, 0.09611452]) incremental_test_scores_std_50_folds = array([ 0.04942445, 0.04132467, 0.04251402, 0.04883771, 0.04769812, 0.04882399, 0.05244483, 0.05212685, 0.05434039, 0.05685099, 0.05870295, 0.06312129, 0.06397167, 0.06475891, 0.06619849, 0.06858608, 0.07052225, 0.07211568, 0.07247594, 0.07426551, 0.0754153, 0.07650368, 0.08005695, 0.07839793, 0.07575411, 0.07573291, 0.0747032, 0.07206771, 0.07047588, 0.07223881, 0.07240794, 0.07394336, 0.0739362, 0.07114794, 0.07094068, 0.07254348, 0.06833713, 0.06574783, 0.05170119, 0.05388267, 0.05510474, 0.05699836, 0.05972988, 0.06344963, 0.06136878, 0.06931128, 0.07251949, 0.02760736, 0. ]) normal_train_accuracy_per_size_4_folds = {18336: [0.82422556719022688], 12224: [0.8534031413612565], 6112: [0.90068717277486909]} normal_test_accuracy_per_size_4_folds = {18336: [0.094210009813542689], 12224: [0.098135426889106966], 6112: [0.10091593065096501]} normal_train_sizes_4_folds = array([ 6112, 12224, 18336]) normal_train_scores_mean_4_folds = array([ 0.90068717, 0.85340314, 0.82422557]) normal_train_scores_std_4_folds = array([ 0., 0., 0.]) normal_test_scores_mean_4_folds = array([ 0.10091593, 0.09813543, 0.09421001]) normal_test_scores_std_4_folds = array([ 0., 0., 0.]) normal_train_accuracy_per_size_6_folds = {20375: [0.82012269938650306], 16300: [0.83558282208588952], 12225: [0.85824130879345606], 8150: [0.88981595092024535], 4075: [0.92122699386503071]} normal_test_accuracy_per_size_6_folds = {20375: [0.054969325153374236], 16300: [0.056441717791411043], 12225: [0.05865030674846626], 8150: [0.063803680981595098], 4075: [0.075828220858895706]} normal_train_sizes_6_folds = array([ 4075, 8150, 12225, 16300, 20375]) normal_train_scores_mean_6_folds = array([ 0.92122699, 0.88981595, 0.85824131, 0.83558282, 0.8201227 ]) normal_train_scores_std_6_folds = array([ 0., 0., 0., 0., 0.]) normal_test_scores_mean_6_folds = array([ 0.07582822, 0.06380368, 0.05865031, 0.05644172, 0.05496933]) normal_test_scores_std_6_folds = array([ 0., 0., 0., 0., 0.]) normal_train_accuracy_per_size_8_folds = {21392: [0.81740837696335078], 18336: [0.82869764397905754], 15280: [0.84332460732984293], 12224: [0.86338350785340312], 9168: [0.88568935427574169], 6112: [0.90085078534031415], 3056: [0.91819371727748689]} normal_test_accuracy_per_size_8_folds = {21392: [0.039241334205362979], 18336: [0.039568345323741004], 15280: [0.043819489862655332], 12224: [0.048724656638325703], 9168: [0.055591890124264222], 6112: [0.072596468279921514], 3056: [0.11118378024852844]} normal_train_sizes_8_folds = array([ 3056, 6112, 9168, 12224, 15280, 18336, 21392]) normal_train_scores_mean_8_folds = array([ 0.91819372, 0.90085079, 0.88568935, 0.86338351, 0.84332461, 0.82869764, 0.81740838]) normal_train_scores_std_8_folds = array([ 0., 0., 0., 0., 0., 0., 0.]) normal_test_scores_mean_8_folds = array([ 0.11118378, 0.07259647, 0.05559189, 0.04872466, 0.04381949, 0.03956835, 0.03924133]) normal_test_scores_std_8_folds = array([ 0., 0., 0., 0., 0., 0., 0.]) normal_train_accuracy_per_size_10_folds = {22005: [0.81799591002044991], 19560: [0.82617586912065444], 17115: [0.83651767455448434], 14670: [0.84935241990456711], 12225: [0.86633946830265851], 9780: [0.88548057259713697], 7335: [0.89788684389911388], 4890: [0.91942740286298563], 2445: [0.92842535787321068]} normal_test_accuracy_per_size_10_folds = {22005: [0.11820040899795502], 19560: [0.11738241308793455], 17115: [0.12106339468302658], 14670: [0.12269938650306748], 12225: [0.12719836400817996], 9780: [0.14069529652351739], 7335: [0.16196319018404909], 4890: [0.17995910020449898], 2445: [0.19386503067484662]} normal_train_sizes_10_folds = array([ 2445, 4890, 7335, 9780, 12225, 14670, 17115, 19560, 22005]) normal_train_scores_mean_10_folds = array([ 0.92842536, 0.9194274, 0.89788684, 0.88548057, 0.86633947, 0.84935242, 0.83651767, 0.82617587, 0.81799591]) normal_train_scores_std_10_folds = array([ 0., 0., 0., 0., 0., 0., 0., 0., 0.]) normal_test_scores_mean_10_folds = array([ 0.19386503, 0.1799591, 0.16196319, 0.1406953, 0.12719836, 0.12269939, 0.12106339, 0.11738241, 0.11820041]) normal_test_scores_std_10_folds = array([ 0., 0., 0., 0., 0., 0., 0., 0., 0.]) normal_train_accuracy_per_size_15_folds = {22820: [0.81849255039439084], 21190: [0.82298253893345918], 19560: [0.83052147239263807], 17930: [0.83736754043502515], 16300: [0.84699386503067486], 14670: [0.85405589638718471], 13040: [0.86618098159509205], 11410: [0.87765118317265556], 9780: [0.88588957055214723], 8150: [0.89447852760736202], 6520: [0.90567484662576692], 4890: [0.91390593047034763], 3260: [0.91932515337423315], 1630: [0.93496932515337428]} normal_test_accuracy_per_size_15_folds = {22820: [0.15276073619631902], 21190: [0.150920245398773], 19560: [0.15337423312883436], 17930: [0.15214723926380369], 16300: [0.15398773006134969], 14670: [0.15460122699386503], 13040: [0.15766871165644172], 11410: [0.16809815950920245], 9780: [0.17668711656441718], 8150: [0.18466257668711655], 6520: [0.19570552147239265], 4890: [0.20429447852760735], 3260: [0.21717791411042944], 1630: [0.23803680981595093]} normal_train_sizes_15_folds = array([ 1630, 3260, 4890, 6520, 8150, 9780, 11410, 13040, 14670, 16300, 17930, 19560, 21190, 22820]) normal_train_scores_mean_15_folds = array([ 0.93496933, 0.91932515, 0.91390593, 0.90567485, 0.89447853, 0.88588957, 0.87765118, 0.86618098, 0.8540559, 0.84699387, 0.83736754, 0.83052147, 0.82298254, 0.81849255]) normal_train_scores_std_15_folds = array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) normal_test_scores_mean_15_folds = array([ 0.23803681, 0.21717791, 0.20429448, 0.19570552, 0.18466258, 0.17668712, 0.16809816, 0.15766871, 0.15460123, 0.15398773, 0.15214724, 0.15337423, 0.15092025, 0.15276074]) normal_test_scores_std_15_folds = array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) normal_train_accuracy_per_size_25_folds = {23472: [0.81799591002044991], 22494: [0.8191517738063484], 21516: [0.824595649749024], 20538: [0.82753919563735512], 19560: [0.83195296523517381], 18582: [0.8366698955978904], 17604: [0.84003635537377863], 16626: [0.84620473956453746], 15648: [0.85090746421267893], 14670: [0.85821404226312203], 13692: [0.86240140227870288], 12714: [0.871558911436212], 11736: [0.87585207907293794], 10758: [0.88213422569250788], 9780: [0.88486707566462164], 8802: [0.88559418314019545], 7824: [0.89187116564417179], 6846: [0.89789658194566169], 5868: [0.90184049079754602], 4890: [0.89959100204498976], 3912: [0.90030674846625769], 2934: [0.90149965916837083], 1956: [0.90286298568507162], 978: [0.89059304703476483]} normal_test_accuracy_per_size_25_folds = {23472: [0.10633946830265849], 22494: [0.10531697341513292], 21516: [0.10736196319018405], 20538: [0.10736196319018405], 19560: [0.10531697341513292], 18582: [0.10736196319018405], 17604: [0.11349693251533742], 16626: [0.11042944785276074], 15648: [0.1165644171779141], 14670: [0.11349693251533742], 13692: [0.11451942740286299], 12714: [0.11758691206543967], 11736: [0.1196319018404908], 10758: [0.130879345603272], 9780: [0.1329243353783231], 8802: [0.13190184049079753], 7824: [0.13496932515337423], 6846: [0.13701431492842536], 5868: [0.14723926380368099], 4890: [0.15132924335378323], 3912: [0.15746421267893659], 2934: [0.17586912065439672], 1956: [0.19325153374233128], 978: [0.19529652351738241]} normal_train_sizes_25_folds = array([ 978, 1956, 2934, 3912, 4890, 5868, 6846, 7824, 8802, 9780, 10758, 11736, 12714, 13692, 14670, 15648, 16626, 17604, 18582, 19560, 20538, 21516, 22494, 23472]) normal_train_scores_mean_25_folds = array([ 0.89059305, 0.90286299, 0.90149966, 0.90030675, 0.899591, 0.90184049, 0.89789658, 0.89187117, 0.88559418, 0.88486708, 0.88213423, 0.87585208, 0.87155891, 0.8624014, 0.85821404, 0.85090746, 0.84620474, 0.84003636, 0.8366699, 0.83195297, 0.8275392, 0.82459565, 0.81915177, 0.81799591]) normal_train_scores_std_25_folds = array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) normal_test_scores_mean_25_folds = array([ 0.19529652, 0.19325153, 0.17586912, 0.15746421, 0.15132924, 0.14723926, 0.13701431, 0.13496933, 0.13190184, 0.13292434, 0.13087935, 0.1196319, 0.11758691, 0.11451943, 0.11349693, 0.11656442, 0.11042945, 0.11349693, 0.10736196, 0.10531697, 0.10736196, 0.10736196, 0.10531697, 0.10633947]) normal_test_scores_std_25_folds = array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) normal_train_accuracy_per_size_50_folds = {23961: [0.81770376862401406], 23472: [0.81842194955691894], 22983: [0.81877909759387368], 22494: [0.82177469547434867], 22005: [0.82394910247670983], 21516: [0.82594348391894401], 21027: [0.8270319113520711], 20538: [0.82958418541240631], 20049: [0.8316624270537184], 19560: [0.83333333333333337], 19071: [0.83682030307797184], 18582: [0.83785383704660421], 18093: [0.84115403747305584], 17604: [0.84350147693705979], 17115: [0.84691790826760149], 16626: [0.84885119692048594], 16137: [0.85077771580839068], 15648: [0.8539749488752556], 15159: [0.85744442245530705], 14670: [0.8597818677573279], 14181: [0.86150483040688242], 13692: [0.86634531113058721], 13203: [0.87002953874119515], 12714: [0.8728173666823974], 12225: [0.87427402862985681], 11736: [0.87857873210633952], 11247: [0.88094603005245842], 10758: [0.87999628183677259], 10269: [0.88207225630538511], 9780: [0.88466257668711656], 9291: [0.884834786352384], 8802: [0.88604862531242901], 8313: [0.89125466137375198], 7824: [0.89391615541922287], 7335: [0.89747784594410362], 6846: [0.90037978381536665], 6357: [0.89948088721094854], 5868: [0.8979209270620313], 5379: [0.8947759806655512], 4890: [0.89427402862985683], 4401: [0.89502385821404229], 3912: [0.89570552147239269], 3423: [0.89862693543675143], 2934: [0.89604635310156777], 2445: [0.89775051124744376], 1956: [0.89570552147239269], 1467: [0.89229720518064082], 978: [0.91104294478527603], 489: [0.91820040899795496]} normal_test_accuracy_per_size_50_folds = {23961: [0.096114519427402859], 23472: [0.096114519427402859], 22983: [0.096114519427402859], 22494: [0.096114519427402859], 22005: [0.09815950920245399], 21516: [0.096114519427402859], 21027: [0.09815950920245399], 20538: [0.096114519427402859], 20049: [0.096114519427402859], 19560: [0.09202453987730061], 19071: [0.096114519427402859], 18582: [0.094069529652351741], 18093: [0.096114519427402859], 17604: [0.09815950920245399], 17115: [0.096114519427402859], 16626: [0.09202453987730061], 16137: [0.094069529652351741], 15648: [0.09202453987730061], 15159: [0.096114519427402859], 14670: [0.094069529652351741], 14181: [0.096114519427402859], 13692: [0.10020449897750511], 13203: [0.09815950920245399], 12714: [0.10224948875255624], 12225: [0.10224948875255624], 11736: [0.10224948875255624], 11247: [0.10224948875255624], 10758: [0.10633946830265849], 10269: [0.10838445807770961], 9780: [0.10838445807770961], 9291: [0.11042944785276074], 8802: [0.11451942740286299], 8313: [0.1165644171779141], 7824: [0.1165644171779141], 7335: [0.12269938650306748], 6846: [0.12883435582822086], 6357: [0.1329243353783231], 5868: [0.1411042944785276], 5379: [0.14314928425357873], 4890: [0.15132924335378323], 4401: [0.15337423312883436], 3912: [0.17791411042944785], 3423: [0.19631901840490798], 2934: [0.19222903885480572], 2445: [0.19222903885480572], 1956: [0.20245398773006135], 1467: [0.21063394683026584], 978: [0.22085889570552147], 489: [0.21472392638036811]} normal_train_sizes_50_folds = array([ 489, 978, 1467, 1956, 2445, 2934, 3423, 3912, 4401, 4890, 5379, 5868, 6357, 6846, 7335, 7824, 8313, 8802, 9291, 9780, 10269, 10758, 11247, 11736, 12225, 12714, 13203, 13692, 14181, 14670, 15159, 15648, 16137, 16626, 17115, 17604, 18093, 18582, 19071, 19560, 20049, 20538, 21027, 21516, 22005, 22494, 22983, 23472, 23961]) normal_train_scores_mean_50_folds = array([ 0.91820041, 0.91104294, 0.89229721, 0.89570552, 0.89775051, 0.89604635, 0.89862694, 0.89570552, 0.89502386, 0.89427403, 0.89477598, 0.89792093, 0.89948089, 0.90037978, 0.89747785, 0.89391616, 0.89125466, 0.88604863, 0.88483479, 0.88466258, 0.88207226, 0.87999628, 0.88094603, 0.87857873, 0.87427403, 0.87281737, 0.87002954, 0.86634531, 0.86150483, 0.85978187, 0.85744442, 0.85397495, 0.85077772, 0.8488512, 0.84691791, 0.84350148, 0.84115404, 0.83785384, 0.8368203, 0.83333333, 0.83166243, 0.82958419, 0.82703191, 0.82594348, 0.8239491, 0.8217747, 0.8187791, 0.81842195, 0.81770377]) normal_train_scores_std_50_folds = array([ 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., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) normal_test_scores_mean_50_folds = array([ 0.21472393, 0.2208589, 0.21063395, 0.20245399, 0.19222904, 0.19222904, 0.19631902, 0.17791411, 0.15337423, 0.15132924, 0.14314928, 0.14110429, 0.13292434, 0.12883436, 0.12269939, 0.11656442, 0.11656442, 0.11451943, 0.11042945, 0.10838446, 0.10838446, 0.10633947, 0.10224949, 0.10224949, 0.10224949, 0.10224949, 0.09815951, 0.1002045, 0.09611452, 0.09406953, 0.09611452, 0.09202454, 0.09406953, 0.09202454, 0.09611452, 0.09815951, 0.09611452, 0.09406953, 0.09611452, 0.09202454, 0.09611452, 0.09611452, 0.09815951, 0.09611452, 0.09815951, 0.09611452, 0.09611452, 0.09611452, 0.09611452]) normal_test_scores_std_50_folds = array([ 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., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) data = { "incremental": { 4: { "train_accuracy_per_size": incremental_train_accuracy_per_size_4_folds, "test_accuracy_per_size": incremental_test_accuracy_per_size_4_folds, "train_sizes": incremental_train_sizes_4_folds, "train_scores_mean": incremental_train_scores_mean_4_folds, "train_scores_std": incremental_train_scores_std_4_folds, "test_scores_mean": incremental_test_scores_mean_4_folds, "test_scores_std": incremental_test_scores_std_4_folds }, 6: { "train_accuracy_per_size": incremental_train_accuracy_per_size_6_folds, "test_accuracy_per_size": incremental_test_accuracy_per_size_6_folds, "train_sizes": incremental_train_sizes_6_folds, "train_scores_mean": incremental_train_scores_mean_6_folds, "train_scores_std": incremental_train_scores_std_6_folds, "test_scores_mean": incremental_test_scores_mean_6_folds, "test_scores_std": incremental_test_scores_std_6_folds }, 8: { "train_accuracy_per_size": incremental_train_accuracy_per_size_8_folds, "test_accuracy_per_size": incremental_test_accuracy_per_size_8_folds, "train_sizes": incremental_train_sizes_8_folds, "train_scores_mean": incremental_train_scores_mean_8_folds, "train_scores_std": incremental_train_scores_std_8_folds, "test_scores_mean": incremental_test_scores_mean_8_folds, "test_scores_std": incremental_test_scores_std_8_folds }, 10: { "train_accuracy_per_size": incremental_train_accuracy_per_size_10_folds, "test_accuracy_per_size": incremental_test_accuracy_per_size_10_folds, "train_sizes": incremental_train_sizes_10_folds, "train_scores_mean": incremental_train_scores_mean_10_folds, "train_scores_std": incremental_train_scores_std_10_folds, "test_scores_mean": incremental_test_scores_mean_10_folds, "test_scores_std": incremental_test_scores_std_10_folds }, 15: { "train_accuracy_per_size": incremental_train_accuracy_per_size_15_folds, "test_accuracy_per_size": incremental_test_accuracy_per_size_15_folds, "train_sizes": incremental_train_sizes_15_folds, "train_scores_mean": incremental_train_scores_mean_15_folds, "train_scores_std": incremental_train_scores_std_15_folds, "test_scores_mean": incremental_test_scores_mean_15_folds, "test_scores_std": incremental_test_scores_std_15_folds }, 25: { "train_accuracy_per_size": incremental_train_accuracy_per_size_25_folds, "test_accuracy_per_size": incremental_test_accuracy_per_size_25_folds, "train_sizes": incremental_train_sizes_25_folds, "train_scores_mean": incremental_train_scores_mean_25_folds, "train_scores_std": incremental_train_scores_std_25_folds, "test_scores_mean": incremental_test_scores_mean_25_folds, "test_scores_std": incremental_test_scores_std_25_folds }, 50: { "train_accuracy_per_size": incremental_train_accuracy_per_size_50_folds, "test_accuracy_per_size": incremental_test_accuracy_per_size_50_folds, "train_sizes": incremental_train_sizes_50_folds, "train_scores_mean": incremental_train_scores_mean_50_folds, "train_scores_std": incremental_train_scores_std_50_folds, "test_scores_mean": incremental_test_scores_mean_50_folds, "test_scores_std": incremental_test_scores_std_50_folds } }, "normal": { 4: { "train_accuracy_per_size": normal_train_accuracy_per_size_4_folds, "test_accuracy_per_size": normal_test_accuracy_per_size_4_folds, "train_sizes": normal_train_sizes_4_folds, "train_scores_mean": normal_train_scores_mean_4_folds, "train_scores_std": normal_train_scores_std_4_folds, "test_scores_mean": normal_test_scores_mean_4_folds, "test_scores_std": normal_test_scores_std_4_folds }, 6: { "train_accuracy_per_size": normal_train_accuracy_per_size_6_folds, "test_accuracy_per_size": normal_test_accuracy_per_size_6_folds, "train_sizes": normal_train_sizes_6_folds, "train_scores_mean": normal_train_scores_mean_6_folds, "train_scores_std": normal_train_scores_std_6_folds, "test_scores_mean": normal_test_scores_mean_6_folds, "test_scores_std": normal_test_scores_std_6_folds }, 8: { "train_accuracy_per_size": normal_train_accuracy_per_size_8_folds, "test_accuracy_per_size": normal_test_accuracy_per_size_8_folds, "train_sizes": normal_train_sizes_8_folds, "train_scores_mean": normal_train_scores_mean_8_folds, "train_scores_std": normal_train_scores_std_8_folds, "test_scores_mean": normal_test_scores_mean_8_folds, "test_scores_std": normal_test_scores_std_8_folds }, 10: { "train_accuracy_per_size": normal_train_accuracy_per_size_10_folds, "test_accuracy_per_size": normal_test_accuracy_per_size_10_folds, "train_sizes": normal_train_sizes_10_folds, "train_scores_mean": normal_train_scores_mean_10_folds, "train_scores_std": normal_train_scores_std_10_folds, "test_scores_mean": normal_test_scores_mean_10_folds, "test_scores_std": normal_test_scores_std_10_folds }, 15: { "train_accuracy_per_size": normal_train_accuracy_per_size_15_folds, "test_accuracy_per_size": normal_test_accuracy_per_size_15_folds, "train_sizes": normal_train_sizes_15_folds, "train_scores_mean": normal_train_scores_mean_15_folds, "train_scores_std": normal_train_scores_std_15_folds, "test_scores_mean": normal_test_scores_mean_15_folds, "test_scores_std": normal_test_scores_std_15_folds }, 25: { "train_accuracy_per_size": normal_train_accuracy_per_size_25_folds, "test_accuracy_per_size": normal_test_accuracy_per_size_25_folds, "train_sizes": normal_train_sizes_25_folds, "train_scores_mean": normal_train_scores_mean_25_folds, "train_scores_std": normal_train_scores_std_25_folds, "test_scores_mean": normal_test_scores_mean_25_folds, "test_scores_std": normal_test_scores_std_25_folds }, 50: { "train_accuracy_per_size": normal_train_accuracy_per_size_50_folds, "test_accuracy_per_size": normal_test_accuracy_per_size_50_folds, "train_sizes": normal_train_sizes_50_folds, "train_scores_mean": normal_train_scores_mean_50_folds, "train_scores_std": normal_train_scores_std_50_folds, "test_scores_mean": normal_test_scores_mean_50_folds, "test_scores_std": normal_test_scores_std_50_folds } } } for key, value in data.items(): # print("{}: {}".format(key, value)) # Debug for subKey, subValue in value.items(): # print("{}: {}".format(subKey, subValue)) # Debug # Then, we plot the aforementioned learning curves title = "Learning Curves (Linear SVM without tuning, " + \ key + \ " approach, {} folds)".format(subKey) fig = plot_learning_curve(title, "accuracy", \ subValue["train_sizes"], \ subValue["train_scores_mean"], \ subValue["train_scores_std"], \ subValue["test_scores_mean"], \ subValue["test_scores_std"]) name_file = "{}_learning_curves_{}_folds.png".format( \ key, subKey) # save_file = None if not save_file \ # else os.path.join(current_dir, name_file) save_file = os.path.join(current_dir, name_file) if save_file: plt.savefig(save_file, bbox_inches="tight") plt.close(fig) else: plt.show() if __name__ == "__main__": main()

0

105,829

0

-17

89

cf398266bba39f44a35399478e373fb7c8895c96

735

py

Python

pycopyql/cli.py

elazar/pycopyql

4c8384b847fcd9ef2811c12375fc5e9e63094b3e

[ "MIT" ]

1

2018-08-02T18:42:34.000Z

pycopyql/cli.py

elazar/pycopyql

4c8384b847fcd9ef2811c12375fc5e9e63094b3e

[ "MIT" ]

null

pycopyql/cli.py

elazar/pycopyql

4c8384b847fcd9ef2811c12375fc5e9e63094b3e

[ "MIT" ]

null

from .args import get_args from .config import get_config, get_connection_config, get_engine, get_meta from .query import query from .export import get_exporter def main(): """ Provides a CLI entrypoint to access a database and export a subset of its data in a specified format. """ args = get_args() config = get_config(args.config) connection_config = get_connection_config(config, args.connection) engine = get_engine(connection_config) export = get_exporter(args.format, config['exporters']) connection = engine.connect() meta = get_meta(engine) resolver = connection_config['resolver'] data = query(connection, meta, resolver, args.query) export(meta, data, args.output)

31.956522

77

0.726531

from .args import get_args from .config import get_config, get_connection_config, get_engine, get_meta from .query import query from .export import get_exporter def main(): """ Provides a CLI entrypoint to access a database and export a subset of its data in a specified format. """ args = get_args() config = get_config(args.config) connection_config = get_connection_config(config, args.connection) engine = get_engine(connection_config) export = get_exporter(args.format, config['exporters']) connection = engine.connect() meta = get_meta(engine) resolver = connection_config['resolver'] data = query(connection, meta, resolver, args.query) export(meta, data, args.output)

0

aeb45e871f3a30dab24f8b244ee9dacf414f769a

258

py

Python

cride/circles/urls.py

daecazu/platziride

79782770d05e71823c7eb27fec76a3870c737689

[ "MIT" ]

null

cride/circles/urls.py

daecazu/platziride

79782770d05e71823c7eb27fec76a3870c737689

[ "MIT" ]

2

2020-03-03T20:29:18.000Z

2020-03-03T20:29:19.000Z

cride/circles/urls.py

daecazu/platziride

79782770d05e71823c7eb27fec76a3870c737689

[ "MIT" ]

null

"""Circles URLs""" # Django from django.urls import path # Views from cride.circles.views import list_circles from cride.circles.views import create_circle urlpatterns = [ path ('circles/', list_circles), path ('circles/create/', create_circle), ]

19.846154

45

0.732558

"""Circles URLs""" # Django from django.urls import path # Views from cride.circles.views import list_circles from cride.circles.views import create_circle urlpatterns = [ path ('circles/', list_circles), path ('circles/create/', create_circle), ]

0

4cbc553ee05900bc18bb23ac67ed68a847770fd8

150

py

Python

billing/__init__.py

xprilion/django-customer-billing

82f8147d74ff62e84d9e57465b4d521434c48e49

[ "MIT" ]

null

billing/__init__.py

xprilion/django-customer-billing

82f8147d74ff62e84d9e57465b4d521434c48e49

[ "MIT" ]

null

billing/__init__.py

xprilion/django-customer-billing

82f8147d74ff62e84d9e57465b4d521434c48e49

[ "MIT" ]

1

2020-06-25T22:55:48.000Z

30

60

0.726667

0

2a6cfbfefd1436bb7d9f94ae6539e54bf036f4d7

48,716

py

Python

ShotgunORM/SgFields.py

jonykalavera/python-shotgunorm

3b0a2b433030815631588ff709c8ffd3e9660476

[ "BSD-3-Clause" ]

null

ShotgunORM/SgFields.py

jonykalavera/python-shotgunorm

3b0a2b433030815631588ff709c8ffd3e9660476

[ "BSD-3-Clause" ]

null

ShotgunORM/SgFields.py

jonykalavera/python-shotgunorm

3b0a2b433030815631588ff709c8ffd3e9660476

[ "BSD-3-Clause" ]

null

# Copyright (c) 2013, Nathan Dunsworth - NFXPlugins # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the NFXPlugins nor the names of its contributors # may be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL NFXPLUGINS BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # __all__ = [ 'SgFieldCheckbox', 'SgFieldColor', 'SgFieldColor2', 'SgFieldDate', 'SgFieldDateTime', 'SgFieldEntity', 'SgFieldEntityMulti', 'SgFieldFloat', 'SgFieldID', 'SgFieldImage', 'SgFieldInt', 'SgFieldSelectionList', 'SgFieldTagList', 'SgFieldText', 'SgFieldType', 'SgFieldUrl' ] # Python imports # This module imports import ShotgunORM # Register the fields. ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_CHECKBOX, SgFieldCheckbox) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_COLOR, SgFieldColor) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_COLOR2, SgFieldColor2) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_DATE, SgFieldDate) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_DATE_TIME, SgFieldDateTime) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_ENTITY, SgFieldEntity) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_FLOAT, SgFieldFloat) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_IMAGE, SgFieldImage) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_INT, SgFieldInt) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_LIST, SgFieldSelectionList) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_MULTI_ENTITY, SgFieldEntityMulti) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_SERIALIZABLE, SgFieldSerializable) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_STATUS_LIST, SgFieldSelectionList) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_SUMMARY, SgFieldSummary) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_TAG_LIST, SgFieldTagList) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_TEXT, SgFieldText) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_URL, SgFieldUrl) ################################################################################ # # Custom fields # ################################################################################

24.956967

132

0.59812

# Copyright (c) 2013, Nathan Dunsworth - NFXPlugins # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the NFXPlugins nor the names of its contributors # may be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL NFXPLUGINS BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # __all__ = [ 'SgFieldCheckbox', 'SgFieldColor', 'SgFieldColor2', 'SgFieldDate', 'SgFieldDateTime', 'SgFieldEntity', 'SgFieldEntityMulti', 'SgFieldFloat', 'SgFieldID', 'SgFieldImage', 'SgFieldInt', 'SgFieldSelectionList', 'SgFieldTagList', 'SgFieldText', 'SgFieldType', 'SgFieldUrl' ] # Python imports import copy import datetime import os import re import threading import urllib2 import webbrowser # This module imports import ShotgunORM class SgFieldCheckbox(ShotgunORM.SgField): ''' Entity field that stores a bool value for a checkbox. ''' def _fromFieldData(self, sgData): try: sgData = bool(sgData) except: raise TypeError('%s invalid value type "%s", expected a bool' % (self, type(sgData).__name__)) if self._value == sgData: return False self._value = sgData return True def returnType(self): return self.RETURN_TYPE_CHECKBOX def _setValue(self, sgData): try: sgData = bool(sgData) except: raise TypeError('%s invalid value type "%s", expected a bool' % (self, type(sgData).__name__)) if self._value == sgData: return False self._value = sgData return True class SgFieldColor(ShotgunORM.SgField): ''' Entity field that stores a list of 3 ints that represent a rgb color 0-255. Example: [128, 128, 128] ''' REGEXP_COLOR = re.compile(r'(\d+,\d+,\d+)') def _fromFieldData(self, sgData): if sgData == None: result = self._value == sgData if not result: self._value = None return result try: if not self.REGEXP_COLOR.match(sgData): raise ValueError('invalid value %s' % sgData) except Exception, e: ShotgunORM.LoggerField.error('%(field)s: %(error)s', { 'field': self, 'error': e }) raise ValueError('%s invalid data from Shotgun "%s", expected a list of ints' % (self, sgData)) if self._value == sgData: return False self._value = sgData return True def returnType(self): return self.RETURN_TYPE_COLOR def _setValue(self, sgData): if sgData == None: result = self._value == sgData if not result: self._value = sgData return result try: if isinstance(sgData, str): if not self.REGEXP_COLOR.match(sgData): raise ValueError('invalid value %s' % sgData) else: if len(sgData != 3): raise ValueError('invalid value %s' % sgData) sgData = '%d,%d,%d' % (sgData[0], sgData[1], sgData[2]) except: raise TypeError('%s invalid value "%s", expected a list of three ints' % (self, sgData)) if self._value == sgData: return False self._value = sgData return True def _Value(self): if self._value == None: return None result = [] for i in self._value.split(','): result.append(int(i)) return result class SgFieldColor2(ShotgunORM.SgField): ''' Entity field that stores a list of 3 ints that represent a rgb color 0-255. Fix the color return value for Task and Phase Entities color field. Task and Phase Entities can have their color field set to a value that points to the color field of the pipeline step or project they belong to. Brilliant engineering to still call the return type "color" and not differentiate the two I know right? ''' REGEXP_COLOR = re.compile(r'(\d+,\d+,\d+)') REGEXP_TASK_COLOR = re.compile(r'(\d+,\d+,\d+)|(pipeline_step)') REGEXP_PHASE_COLOR = re.compile(r'(\d+,\d+,\d+)|(project)') def __init__(self, name, label=None, sgFieldSchemaInfo=None): super(SgFieldColor2, self).__init__(name, label=label, sgFieldSchemaInfo=sgFieldSchemaInfo) self._regexp = self.REGEXP_COLOR self._linkString = None self._linkField = None def _fromFieldData(self, sgData): if sgData == None: result = self._value == sgData if not result: self._value = None return result if not self._regexp.match(sgData): raise ValueError('%s invalid color value "%s", expected format is "255,255,255" or "%s"' % (self, sgData, self._linkString)) if self._value == sgData: return False self._value = sgData return True def returnType(self): return self.RETURN_TYPE_COLOR2 def _setValue(self, sgData): if sgData == None: result = self._value != None self._value = None return result if isinstance(sgData, str): if not self._regexp.match(sgData): raise ValueError('%s invalid color value "%s", expected format is "255,255,255" or "%s"' % (self, sgData, self._linkString)) else: if not isinstance(sgData, (tuple, list)): raise TypeError('%s invalid value type "%s", expected a list' % (self, type(sgData).__name__)) if len(sgData) != 3: raise ValueError('%s list len is not 3' % self) newData = [] try: sgData = '%d,%d,%d' % tuple(sgData) except: raise ValueError('%s invalid color values %s' % (self, sgData)) if self._value == sgData: return False self._value = sgData return True def linkField(self): ''' Returns the link field this color field can possibly link to. ''' return self._linkField def parentChanged(self): ''' ''' parent = self.parentEntity() if parent == None: return pType = parent.schemaInfo().name() if pType == 'Task': self._regexp = self.REGEXP_TASK_COLOR self._linkString = 'pipeline_step' self._linkField = 'step' elif pType == 'Phase': self._regexp = self.REGEXP_PHASE_COLOR self._linkString = 'project' self._linkField= 'project' else: self._regexp = self.REGEXP_COLOR def value(self, linkEvaluate=True): ''' Args: * (bool) linkEvaluate: When True and the color field is a link to another Entity's color field the value of the linked color field will be returned. If linkEvaluate is False a string may be returned instead of a list. ''' result = super(SgFieldColor2, self).value() if result == None: return None if not linkEvaluate and result == self._linkString: return result parent = self.parentEntity() if parent == None: if result == self._linkString: return None newResult = [] for i in result.split(','): newResult.append(int(i)) if result == self._linkString: linkObj = self.parentEntity()[self._linkField] if linkObj == None: return None return linkObj['color'] else: newResult = [] for i in result.split(','): newResult.append(int(i)) class SgFieldDate(ShotgunORM.SgField): ''' Entity field that stores a date string Example: "1980-01-30". ''' REGEXP = re.compile(r'^\d{4}-\d{2}-\d{2}') def _fromFieldData(self, sgData): if sgData != None: sgData = str(sgData) if not self.REGEXP.match(sgData): raise ValueError('%s invalid date string from Shotgun "%s"' % (self, sgData)) if self._value == sgData: return False self._value = sgData return True def returnType(self): return self.RETURN_TYPE_DATE def _setValue(self, sgData): if sgData != None: if not isinstance(sgData, (str, unicode)): raise TypeError('%s invalid type "%s", expected a string' % (self, type(sgData).__name__)) sgData = str(sgData) if not self.REGEXP.match(sgData): raise ValueError('%s invalid date string "%s"' % (self, sgData)) if self._value == sgData: return False self._value = sgData return True class SgFieldDateTime(ShotgunORM.SgField): ''' Entity field that stores a python datetime object. ''' def _fromFieldData(self, sgData): if sgData != None: sgData = datetime.datetime(*sgData.timetuple()[:6], tzinfo=sgData.tzinfo) if self._value == sgData: return False self._value = sgData return True def returnType(self): return self.RETURN_TYPE_DATE_TIME def _setValue(self, sgData): if sgData != None: if not isinstance(sgData, datetime.datetime): raise TypeError('%s invalid type "%s", expected a datetime obj' % (self, type(sgData).__name__)) sgData = datetime.datetime(*sgData.timetuple()[:6], tzinfo=sgData.tzinfo) if self._value == sgData: return False self._value = sgData return True def _toFieldData(self): result = self._value if result == None: return result return datetime.datetime(*result.timetuple()[:6], tzinfo=result.tzinfo) def _Value(self): return self._toFieldData() class SgFieldEntity(ShotgunORM.SgField): ''' Entity field that stores a link to another Entity. ''' ############################################################################## # # IMPORTANT!!!! # # Any changes to _fromFieldData, _setValue, _toFieldData, value functions # should also be applied to the SgUserFieldAbstractEntity class. # ############################################################################## def _fromFieldData(self, sgData): if sgData == None: result = self._value != None self._value = None return result try: newValue = { 'type': sgData['type'], 'id': sgData['id'] } # This fixes the two Entities as their name field is only available when # returned as another Entities field value. if newValue['type'] in ['AppWelcome', 'Banner'] and sgData.has_key('name'): newValue['name'] = sgData['name'] except Exception, e: ShotgunORM.LoggerField.error('%(field)s: %(error)s', { 'field': self, 'error': e }) raise ValueError('%s invalid data from Shotgun "%s", expected a Shotgun formated Entity dict' % (self, sgData)) if newValue == self._value: return False parent = self.parentEntity() self._value = newValue return True def returnType(self): return self.RETURN_TYPE_ENTITY def _setValue(self, sgData): if sgData == None: result = self._value != None self._value = None return result if not isinstance(sgData, ShotgunORM.SgEntity): raise TypeError('%s invalid value type "%s", expected a SgEntity' % (self, type(sgData).__name__)) valueTypes = self.valueTypes() if valueTypes != None: if len(valueTypes) > 0: if not sgData.type in valueTypes: raise ValueError('not a valid value Entiy type: %s, valid=%s' % (sgData.type, valueTypes)) if sgData['id'] == None: raise RuntimeError('can not set field value to a Entity that has not been created in Shotgun yet') parent = self.parentEntity() if parent == None: raise RuntimeError('field does not have a parent') connection = parent.connection() # Lord knows you shouldn't do this but if you build it people will try! if connection.url() != sgData.connection().url(): raise ValueError('%s passed an Entity from another url' % self) if self._value == sgData: return False self._value = sgData.toEntityFieldData() return True def _toFieldData(self): if self._value == None: return None return dict(self._value) def value(self, sgSyncFields=None): ''' Returns the fields value as a Entity object. Args: * (list) sgSyncFields: List of field names to populate the returned Entity with. ''' value = super(SgFieldEntity, self).value() parent = self.parentEntity() if value == None or parent == None: return None connection = parent.connection() if isinstance(sgSyncFields, dict): sgSyncFields = sgSyncFields.get(parent.type, None) elif isinstance(sgSyncFields, str): sgSyncFields = [sgSyncFields] if sgSyncFields == None: sgSyncFields = connection.defaultEntityQueryFields(value['type']) if len(sgSyncFields) <= 0: sgSyncFields = None else: pullFields = set(sgSyncFields) extraFields = [] if 'all' in pullFields: pullFields.remove('all') extraFields = parent.fieldNames() if 'default' in pullFields: pullFields.remove('default') elif 'default' in pullFields: pullFields.remove('default') extraFields = connection.defaultEntityQueryFields(value['type']) pullFields.update(extraFields) if len(pullFields) >= 1: sgSyncFields = list(pullFields) else: sgSyncFields = None result = connection._createEntity( value['type'], value, sgSyncFields=sgSyncFields ) return result class SgFieldEntityMulti(ShotgunORM.SgField): ''' Entity field that stores a list of links to other Entities. Example: [Entity01, Entity02, ...] ''' ############################################################################## # # IMPORTANT!!!! # # Any changes to _fromFieldData, _setValue, _toFieldData, value functions # should also be applied to the SgUserFieldAbstractMultiEntity class. # ############################################################################## def _fromFieldData(self, sgData): if isinstance(sgData, (tuple, set)): sgData = list(sgData) if sgData in [None, []]: result = self._value in [None, []] if result: self._value = self.defaultValue() return result newValue = [] try: for i in sgData: e = { 'type': i['type'], 'id': i['id'] } if e in newValue: continue # This fixes the two Entities as their name field is only available when # returned as another Entities field value. if e['type'] in ['AppWelcome', 'Banner'] and i.has_key('name'): e['name'] = i['name'] newValue.append(e) except Exception, e: ShotgunORM.LoggerField.error('%(field)s: %(error)s', { 'field': self, 'error': e }) raise ValueError('%s invalid data from Shotgun "%s", expected a Shotgun formated Entity dict' % (self, sgData)) if self._value == newValue: return False self._value = newValue return True def returnType(self): return self.RETURN_TYPE_MULTI_ENTITY def _setValue(self, sgData): if isinstance(sgData, (tuple, set)): sgData = list(sgData) if sgData in [None, []]: result = self._value in [None, []] if result: self._value = self.defaultValue() return result if isinstance(sgData, ShotgunORM.SgEntity): sgData = [sgData] elif not isinstance(sgData, list): raise TypeError('%s invalid value type "%s", expected a SgEntity or list' % (self, type(sgData).__name__)) else: for i in sgData: if not isinstance(i, ShotgunORM.SgEntity): raise TypeError('%s invalid value type "%s", expected a SgEntity' % (self, type(i).__name__)) valueTypes = self.valueTypes() if valueTypes != None: if len(valueTypes) > 0: for i in sgData: if not i.type in valueTypes: raise ValueError('not a valid value type: %s, valid=%s' % (i.type, valueTypes)) parent = self.parentEntity() newValue = [] if parent == None: for i in sgData: if i['id'] == None: raise RuntimeError('can not set field value to a SgEntity that has not been created in Shotgun yet') edata = i.toEntityFieldData() if edata in newValue: continue newValue.append(edata) else: connection = parent.connection() for i in sgData: if i['id'] == None: raise RuntimeError('can not set field value to a SgEntity that has not been created in Shotgun yet') # Lord knows you shouldn't do this but if you build it people will try! if connection.url() != i.connection().url(): raise ValueError('%s passed an Entity from another url' % self) edata = i.toEntityFieldData() if edata in newValue: continue newValue.append(edata) if self._value == newValue: return False self._value = newValue return True def _toFieldData(self): if self._value == None: return None result = [] for i in self._value: result.append(dict(i)) return result def value(self, sgSyncFields=None): ''' Returns the fields value as a list of Entity objects. Args: * (dict) sgSyncFields: Dict of entity types and field names to populate the returned Entities with. ''' result = super(SgFieldEntityMulti, self).value() if result in [None, []]: return result parent = self.parentEntity() if parent == None: return copy.deepcopy(result) connection = parent.connection() schema = connection.schema() tmp = [] qEng = connection.queryEngine() qEng.block() try: for i in result: t = i['type'] iSyncFields = None if sgSyncFields != None: if sgSyncFields.has_key(t): iFields = sgSyncFields[t] if iFields == None: iSyncFields = connection.defaultEntityQueryFields(t) if len(iSyncFields) <= 0: iSyncFields = None else: pullFields = [] if isinstance(iFields, str): pullFields = set([iFields]) else: pullFields = set(iFields) extraFields = [] if 'all' in pullFields: pullFields.remove('all') extraFields = schema.entityInfo(t).fieldNames() if 'default' in pullFields: pullFields.remove('default') elif 'default' in pullFields: pullFields.remove('default') extraFields = connection.defaultEntityQueryFields(t) pullFields.update(extraFields) if len(pullFields) >= 1: iSyncFields = list(pullFields) else: iSyncFields = None else: iSyncFields = connection.defaultEntityQueryFields(t) if len(iSyncFields) <= 0: iSyncFields = None else: iSyncFields = connection.defaultEntityQueryFields(t) entity = connection._createEntity(t, i, sgSyncFields=iSyncFields) tmp.append(entity) finally: qEng.unblock() return tmp class SgFieldFloat(ShotgunORM.SgField): ''' Entity field that stores a float. ''' def _fromFieldData(self, sgData): if sgData != None: try: sgData = float(sgData) except: raise ValueError('%s invalid data from Shotgun "%s", expected a float' % (self, sgData)) if self._value == sgData: return False self._value = sgData return True def returnType(self): return self.RETURN_TYPE_FLOAT def _setValue(self, sgData): if sgData != None: try: sgData = float(sgData) except: raise TypeError('%s invalid value type "%s", expected a float' % (self, type(sgData).__name__)) if self._value == sgData: return False self._value = sgData return True class SgFieldInt(ShotgunORM.SgField): ''' Entity field that stores an integer. ''' def _fromFieldData(self, sgData): if sgData != None: try: sgData = int(sgData) except: raise ValueError('%s invalid data from Shotgun "%s", expected a int' % (self, sgData)) if self._value == sgData: return False self._value = sgData return True def returnType(self): return self.RETURN_TYPE_INT def _setValue(self, sgData): if sgData != None: try: sgData = int(sgData) except: raise TypeError('%s invalid value type "%s", expected a int' % (self, type(sgData).__name__)) if self._value == sgData: return False self._value = sgData return True class SgFieldSelectionList(ShotgunORM.SgField): ''' Entity field that stores a text string that is from a list selection. The field may contain a list of valid values which when the field is set are compared and an Exception thrown when the value is not a valid one. ''' def _fromFieldData(self, sgData): if sgData == None: result = self._value == sgData if not result: self._value = None return result if self._value == sgData: return False self._value = sgData return True def returnType(self): return self.RETURN_TYPE_LIST def _setValue(self, sgData): if sgData == None: result = self._value == sgData if result: self._value = None return result if not isinstance(sgData, (str, unicode)): raise TypeError('%s invalid type "%s", expected a string' % (self, type(sgData).__name__)) sgData = str(sgData) if self._value == sgData: return False validValues = self.validValues() if len(validValues) > 0: if not sgData in validValues: raise ValueError('%s invalid value "%s"' % (self, sgData)) self._value = sgData return True class SgFieldSerializable(ShotgunORM.SgField): ''' Entity field that stores serializable data. ''' def _fromFieldData(self, sgData): if sgData in [None, {}]: result = self._value in [None, {}] if result: self._value = None return result if not isinstance(sgData, dict): raise ValueError('%s invalid data from Shotgun "%s", expected a dict' % (self, sgData)) if self._value == sgData: return False sgData = copy.deepcopy(sgData) self._value = sgData return True def returnType(self): return self.RETURN_TYPE_SERIALIZABLE def _setValue(self, sgData): if sgData == None: result = self._value == sgData if result: self._value = None return result if not isinstance(sgData, dict): raise TypeError('%s invalid value type "%s", expected a dict' % (self, type(sgData).__name__)) if self._value == sgData: return False sgData = copy.deepcopy(sgData) self._value = sgData return True def _toFieldData(self): if self._value == None: return None return copy.deepcopy(self._value) def _Value(self): return self._toFieldData() class SgFieldSummary(ShotgunORM.SgField): ''' Entity field that returns an Entity or list of Entities based on a search expression. Summary fields. ''' DATE_REGEXP = re.compile(r'(\d{4})-(\d{2})-(\d{2}) (\d{2}):(\d{2}):(\d{2}) UTC') def __init__(self, name, label=None, sgFieldSchemaInfo=None): super(SgFieldSummary, self).__init__(name, label=label, sgFieldSchemaInfo=sgFieldSchemaInfo) self.__buildLock = threading.Lock() summaryInfo = self.schemaInfo().summaryInfo() if summaryInfo == None: raise RuntimeError('invalid field schema info for summary info') self._entityType = summaryInfo['entity_type'] self._filtersRaw = summaryInfo['filters'] self._summaryType = summaryInfo['summary_type'] self._summaryField = summaryInfo['summary_field'] self._summaryValue = summaryInfo['summary_value'] self._searchFilter = None def _buildLogicalOp(self, conditions, info): ''' Builds the logical operator search pattern and returns it. ''' result = [] parent = self.parentEntity() connection = parent.connection() for c in conditions: if c.has_key('logical_operator'): logicalOp = { 'conditions': self._buildLogicalOp(c['conditions'], info), 'logical_operator': c['logical_operator'] } result.append(logicalOp) else: newValues = [] cInfo = info.fieldInfo(c['path']) cType = cInfo.returnType() ######################################################################## # # Date and Date Time fields # ######################################################################## if cType in [ShotgunORM.SgField.RETURN_TYPE_DATE, ShotgunORM.SgField.RETURN_TYPE_DATE_TIME]: # http://stackoverflow.com/a/13287083 def utc_to_local(utc_dt): # get integer timestamp to avoid precision lost timestamp = calendar.timegm(utc_dt.timetuple()) local_dt = datetime.fromtimestamp(timestamp) assert utc_dt.resolution >= timedelta(microseconds=1) return local_dt.replace(microsecond=utc_dt.microsecond) for v in c['values']: if isinstance(v, dict): if v.has_key('relative_day'): time = datetime.time(*v['time']) date = datetime.date.today() rd = v['relative_day'] if rd == 'tomorrow': date = date.replace(day=date.day + 1) elif rd == 'yesterday': date = date.replace(day=date.day - 1) dt = datetime.datetime.combine(date, time) # Relative day calcs use utc time! dt.replace(tzinfo=None) newValues.append(dt) else: newValues.append(v) elif isinstance(v, str): search = DATE_REGEXP.match(v) if search: time = datetime.time(search.group(4), search.group(5), search.group(6)) date = datetime.date(search.group(1), search.group(2), search.group(3)) dt = datetime.datetime.combine(date, time) dt.replace(tzinfo=None) newValues.append(utc_to_local(dt)) else: newValues.append(v) ######################################################################## # # Entity and Multi-Entity fields # ######################################################################## elif cType in [ShotgunORM.SgField.RETURN_TYPE_ENTITY, ShotgunORM.SgField.RETURN_TYPE_MULTI_ENTITY]: for v in c['values']: if v['name'] == 'Current %s' % parent.type: newValues.append(parent.toEntityFieldData()) elif v['name'] == 'Me': login = os.getenv('USERNAME') user = connection.findOne('HumanUser', [['login', 'is', login]], ['login']) if user == None: raise RuntimError('summary field unable to find user "%s" in Shotgun' % login) newValues.append(user.toEntityFieldData()) else: newValues.append(v) else: # Do nothing newValues = c['values'] c['values'] = newValues del c['active'] result.append(c) return result def _buildSearchFilter(self): ''' ''' opsRaw = copy.deepcopy(self._filtersRaw) logicalOps = { 'conditions': self._buildLogicalOp( opsRaw['conditions'], self.parentEntity().connection().schema().entityInfo(self.entityType()) ), 'logical_operator': opsRaw['logical_operator'] } self._searchFilter = logicalOps def _fromFieldData(self, sgData): ''' Always return False for summary fields, they can not be set. ''' if self._value == sgData: return False self._value = sgData return True def returnType(self): return self.RETURN_TYPE_SUMMARY def _toFieldData(self): result = self._value if result == None: return None if isinstance(result, dict): return copy.deepcopy(result) return result def entityType(self): ''' Returns the type of Entity the summary field will return. ''' return self._entityType def hasCommit(self): ''' Always returns False for summary fields. ''' return False def _invalidate(self): ''' Deletes the search filter so its built again. ''' self._searchFilter = None def isEditable(self): ''' Always return False for summary fields. ''' return False def isQueryable(self): ''' Even though summary fields can be queried from Shotgun return False. ''' return False def setHasCommit(self, valid): ''' Summary fields can't be committed, always returns False. ''' return False def setHasSyncUpdate(self, valid): ''' Summary fields cant be queried so thus they can not be background pulled. Always returns False. ''' return False def _setValue(self, value): ''' Always return False for summary fields, they can not be set. ''' return False def _valueSg(self): parent = self.parentEntity() if parent == None or not parent.exists(): return None connection = parent.connection() with self.__buildLock: if self._searchFilter == None: self._buildSearchFilter() searchExp = self._searchFilter result = None ############################################################################ # # Single record # ############################################################################ if self._summaryType == 'single_record': order = [ { 'field_name': self._summaryValue['column'], 'direction': self._summaryValue['direction'] } ] result = connection._sg_find_one(self.entityType(), searchExp, order=order) ############################################################################ # # Status percentage and list # ############################################################################ elif self._summaryType.startswith('status_'): sgSearch = connection.find(self.entityType(), searchExp, fields=[self._summaryField]) if self._summaryType == 'status_percentage': if len(sgSearch) <= 0: result = 0 else: validCount = 0 for e in sgSearch: value = e.field(self._summaryField).value() if value == self._summaryValue: validCount += 1 if validCount <= 0: result = 0.0 else: result = float(validCount) / len(sgSearch) elif self._summaryType == 'status_list': if len(sgSearch) <= 0: result = 'ip' else: value = sgSearch[0].field(self._summaryField).value() for e in sgSearch[1:]: v = e.field(self._summaryField).value() if v != value: # I have no clue why Shotgun always defaults this result to ip # but whatevs yo. value = 'ip' break result = value ############################################################################ # # Record count # ############################################################################ elif self._summaryType == 'record_count': # Dont use the orm for this search, waste to build the classes when all # we are doing is getting a len on the search result. sgSearch = connection._sg_find(self.entityType(), searchExp) result = len(sgSearch) elif self._summaryType == 'count': searchExp = { 'conditions': [ searchExp, { #'active': 'true', 'path': self._summaryField, 'relation': 'is_not', 'values': [None] } ], 'logical_operator': 'and' } # Dont use the orm for this search, waste to build the classes when all # we are doing is getting a len on the search result. sgSearch = connection._sg_find(self.entityType(), searchExp, fields=[]) result = len(sgSearch) ############################################################################ # # Sum # ############################################################################ elif self._summaryType == 'sum': sgSearch = connection.find(self.entityType(), searchExp, fields=[self._summaryField]) if len(sgSearch) <= 0: result = 0 else: value = 0 for e in sgSearch: v = e.field(self._summaryField).value() if v != None: value += v result = value ############################################################################ # # Min # ############################################################################ elif self._summaryType == 'min': sgSearch = connection.find(self.entityType(), searchExp, fields=[self._summaryField]) if len(sgSearch) <= 0: result = None else: value = sgSearch[0].field(self._summaryField).value() for e in sgSearch[1:]: v = e.field(self._summaryField).value() if v != None: value = min(v, value) result = value ############################################################################ # # Max # ############################################################################ elif self._summaryType == 'max': sgSearch = connection.find(self.entityType(), searchExp, fields=[self._summaryField]) if len(sgSearch) <= 0: result = None else: value = sgSearch[0].field(self._summaryField).value() for e in sgSearch[1:]: v = e.field(self._summaryField).value() if v != None: value = max(v, value) result = value ############################################################################ # # Average # ############################################################################ elif self._summaryType == 'avg': sgSearch = connection.find(self.entityType(), searchExp, fields=[self._summaryField]) if len(sgSearch) <= 0: result = 0 else: value = sgSearch[0].field(self._summaryField).value() for e in sgSearch[1:]: v = e.field(self._summaryField).value() if v != None: value += v value = float(value) / len(sgSearch) result = value ############################################################################ # # Percentage # ############################################################################ elif self._summaryType == 'percentage': sgSearch = connection.find(self.entityType(), searchExp, fields=[self._summaryField]) if len(sgSearch) <= 0: result = 0 else: value = 0 for e in sgSearch: if e.field(self._summaryField).value() == self._summaryValue: value += 1 if value >= 1: value = float(value) / len(sgSearch) result = value return result def _Value(self): if self._value == None: return None if self._summaryType == 'single_record': parent = self.parentEntity() if parent == None: return copy.deepcopy(self._value) connection = parent.connection() return connection._createEntity(self._value['type'], self._value) return copy.deepcopy(self._value) class SgFieldTagList(ShotgunORM.SgField): ''' Entity field that stores a list of strings. The field may contain a list of valid values which when the field is set are compared and an Exception thrown when the value is not a valid one. ''' def _fromFieldData(self, sgData): if isinstance(sgData, (tuple, set)): sgData = list(sgData) if sgData in [None, []]: result = self._value in [None, []] if result: self._value = self.defaultValue() return result for i in sgData: if not isinstance(i, str): raise TypeError('%s invalid type "%s" in value "%s", expected a string' % (self, type(i).__name__, sgData)) sgData = list(set(sgData)) validValues = self.validValues() if len(validValues) > 0: for i in sgData: if not i in validValues: ValueError('%s invalid value "%s", valid %s' % (self, i, validValues)) if self._value == sgData: return False self._value = sgData return True def returnType(self): return self.RETURN_TYPE_TAG_LIST def _setValue(self, sgData): if isinstance(sgData, (tuple, set)): sgData = list(sgData) if sgData in [None, []]: result = self._value in [None, []] if result: self._value = self.defaultValue() return result for i in sgData: if not isinstance(i, str): raise TypeError('%s invalid type "%s" in value "%s", expected a string' % (self, type(i).__name__, sgData)) sgData = list(set(sgData)) validValues = self.validValues() if len(validValues) > 0: for i in sgData: if not i in validValues: ValueError('%s invalid value "%s", valid %s' % (self, i, validValues)) if self._value == sgData: return False self._value = sgData return True def _toFieldData(self): result = self._value if result == None: return None return list(result) def _Value(self): return self._toFieldData() class SgFieldText(ShotgunORM.SgField): ''' Entity field that stores a str. ''' def _fromFieldData(self, sgData): if self._value == sgData: return False self._value = str(sgData) return True def returnType(self): return self.RETURN_TYPE_TEXT def _setValue(self, sgData): if sgData != None: if not isinstance(sgData, (str, unicode)): raise TypeError('%s invalid value type "%s", expected a str' % (self, type(sgData).__name__)) sgData = str(sgData) if self._value == sgData: return False self._value = sgData return True class SgFieldImage(SgFieldText): ''' See SgFieldText. ''' def downloadThumbnail(self, path): ''' Downloads the image to the specified path. ''' url = self.value() if url == None or url == '': raise ValueError('%s value is empty' % self) if os.path.exists(path) and os.path.isdir(path): raise OSError('output path "%s" is a directory' % path) try: data = urllib2.urlopen(url) f = open(path, 'w') f.write(data.read()) f.close() except Exception, e: ShotgunORM.LoggerField.error('%(field)s: %(error)s', { 'field': self, 'error': e }) raise RuntimeError('%s an error occured while downloading the file' % self) return True def openInBrowser(self): ''' Opens the image in a web-browser ''' url = self.value() if url == None: url = '' webbrowser.open(url) def returnType(self): return self.RETURN_TYPE_IMAGE def uploadThumbnail(self, path): ''' Uploads the specified image file and sets it as the Entities thumbnail. Returns the Attachment id. ''' parent = self.parentEntity() if not parent.exists(): raise RuntimeError('parent entity does not exist') with self: if self.hasCommit(): raise RuntimeError('can not upload a new thumbnail while the image field has an un-commited update') parent = self.parentEntity() if parent == None or not parent.exist(): raise RuntimeError('parent entity does not exists') sgconnection = parent.connection().connection() with ShotgunORM.SHOTGUN_API_LOCK: sgResult = sgconnection.upload_thumbnail(parent.type, parent['id'], path) parent.sync([self.name()]) return sgResult def uploadFilmstripThumbnail(self, path): ''' Uploads the specified image file and sets it as the Entities flimstrip thumbnail. Returns the Attachment id. Note: This function is only valid for Version Entities. ''' with self: if self.hasCommit(): raise RuntimeError('can not upload a new thumbnail while the image field has an un-commited update') parent = self.parentEntity() if not parent.type == 'Version': raise RuntimeError('only valid on Version Entities') if parent == None or not parent.exist(): raise RuntimeError('parent entity does not exists') sgconnection = parent.connection().connection() sgResult = sgconnection.upload_filmstrip_thumbnail(parent.type, parent['id'], path) parent.sync([self.name()]) return sgResult class SgFieldUrl(ShotgunORM.SgField): ''' Entity field that stores a url. Example URL: { 'content_type': 'image/jpeg', 'link_type': 'upload', 'name': 'bob.jpg', 'url': 'http://www.owned.com/bob.jpg' } Example Local: { 'content_type': 'image/jpeg', 'link_type': 'local', 'name': 'bob.jpg', 'local_storage': 'c:/temp/bob.jpg' } ''' def _fromFieldData(self, sgData): result = {} if sgData == None: result = self._value == None if not result: self._value = None return result if not isinstance(sgData, dict): raise TypeError('%s invalid sgData "%s", expected a dict or string' % (self, sgData)) try: result['link_type'] = sgData['link_type'].lower() if result['link_type'] in ['upload', 'web']: result['url'] = sgData['url'] else: result['local_storage'] = sgData['local_storage'] result['name'] = sgData['name'] result['content_type'] = sgData.get('content_type', None) except Exception, e: ShotgunORM.LoggerField.warn(e) raise TypeError('%s invalid sgData dict "%s"' % (self, sgData)) if not result['link_type'] in ['local', 'upload', 'web']: raise ValueError('%s invalid link_type "%s"' % (self, result['link_type'])) if self._value == result: return False self._value = result return True def returnType(self): return self.RETURN_TYPE_URL def setValue(self, sgData): return self.fromFieldData(sgData) def _toFieldData(self): if self._value == None: return None return copy.deepcopy(self._value) def _Value(self): return self._toFieldData() def url(self, openInBrowser=False): ''' Returns the url value. When the arg "openInBrowser" is set to True then the returned URL will also be opened in the operating systems default web-browser. ''' data = self.value() result = '' if data == None: result = '' else: try: result = data['url'] except: pass if openInBrowser: webbrowser.open(url) return result # Register the fields. ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_CHECKBOX, SgFieldCheckbox) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_COLOR, SgFieldColor) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_COLOR2, SgFieldColor2) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_DATE, SgFieldDate) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_DATE_TIME, SgFieldDateTime) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_ENTITY, SgFieldEntity) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_FLOAT, SgFieldFloat) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_IMAGE, SgFieldImage) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_INT, SgFieldInt) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_LIST, SgFieldSelectionList) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_MULTI_ENTITY, SgFieldEntityMulti) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_SERIALIZABLE, SgFieldSerializable) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_STATUS_LIST, SgFieldSelectionList) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_SUMMARY, SgFieldSummary) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_TAG_LIST, SgFieldTagList) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_TEXT, SgFieldText) ShotgunORM.SgField.registerFieldClass(ShotgunORM.SgField.RETURN_TYPE_URL, SgFieldUrl) ################################################################################ # # Custom fields # ################################################################################ class SgFieldID(SgFieldInt): ''' Field that returns the parent Entities Type. ''' # Do not allow the field to lock, no point in it. def __enter__(self): pass def __exit__(self, exc_type, exc_value, traceback): return False def __init__(self, parentEntity, sgFieldSchemaInfo): super(SgFieldID, self).__init__(None, None, sgFieldSchemaInfo) self._SgField__setParentEntity(parentEntity) self._SgField__valid = True def invalidate(self): ''' Does nothing for ID fields. ''' return False def isCacheable(self): ''' Always returns False for ID fields. ''' return False def setHasSyncUpdate(self, valid): ''' Always returns False for ID fields. ''' return False def setValid(self, valid): ''' Always returns False for ID fields. ''' return False def setValueFromShotgun(self): ''' Always returns False for ID fields. ''' return False def validate(self, forReal=False, force=False): ''' Always returns False for ID fields. ''' return False def value(self): ''' Returns the value of the ID field. ''' return self._value def _valueSg(self): ''' Returns the value of the ID field. For ID fields this will never query Shotgun. ''' return self._value class SgFieldType(SgFieldText): ''' Field that returns the parent Entities Type. ''' # Do not allow the field to lock, no point in it. def __enter__(self): pass def __exit__(self, exc_type, exc_value, traceback): return False def __init__(self, parentEntity, sgFieldSchemaInfo): super(SgFieldType, self).__init__(None, None, sgFieldSchemaInfo) self._SgField__setParentEntity(parentEntity) self._SgField__valid = True def invalidate(self): ''' Always returns False for Type fields. ''' return False def isCacheable(self): ''' Always returns False for Type fields. ''' return False def setHasSyncUpdate(self, valid): ''' Always returns False for Type fields. ''' return False def setValid(self, valid): ''' Always returns False for Type fields. ''' return False def setValueFromShotgun(self): ''' Always returns False for Type fields. ''' return False def validate(self, forReal=False, force=False): ''' Always returns False for Type fields. ''' return False def value(self): ''' Returns the Entity type the field belongs to. ''' return self._value def _valueSg(self): ''' Returns the Entity type the field belongs to. For Type fields this will never query Shotgun. ''' return self._value

0

44,478

0

-56

568

b5961ce174a5e07a6544a64153caec0e5af3facd

3,763

py

Python

src/day16.py

blu3r4y/AdventOfCode2018

5ef6ee251f9184e0f66657d0eb8b5b129a6f93e5

[ "MIT" ]

2

2019-01-02T22:57:13.000Z

2019-05-07T23:13:25.000Z

src/day16.py

blu3r4y/AdventOfCode2018

5ef6ee251f9184e0f66657d0eb8b5b129a6f93e5

[ "MIT" ]

null

src/day16.py

blu3r4y/AdventOfCode2018

5ef6ee251f9184e0f66657d0eb8b5b129a6f93e5

[ "MIT" ]

1

2021-12-06T12:38:26.000Z

# Advent of Code 2018, Day 16 # (c) blu3r4y from collections import namedtuple OPERATIONS = ['addr', 'addi', 'mulr', 'muli', 'banr', 'bani', 'borr', 'bori', 'setr', 'seti', 'gtir', 'gtri', 'gtrr', 'eqir', 'eqri', 'eqrr'] Observation = namedtuple("Observation", ["instruction", "before", "after"]) if __name__ == "__main__": print(part1(_parse(open(r"../assets/day16.txt").readlines())[0])) print(part2(*_parse(open(r"../assets/day16.txt").readlines())))

28.08209

79

0.549296

# Advent of Code 2018, Day 16 # (c) blu3r4y from collections import namedtuple from parse import parse OPERATIONS = ['addr', 'addi', 'mulr', 'muli', 'banr', 'bani', 'borr', 'bori', 'setr', 'seti', 'gtir', 'gtri', 'gtrr', 'eqir', 'eqri', 'eqrr'] Observation = namedtuple("Observation", ["instruction", "before", "after"]) def part1(observations): three_or_more = 0 for obsv in observations: # execute all possible candidates num_matches = 0 for op in OPERATIONS: if obsv.after == execute(obsv.instruction, obsv.before, op): num_matches += 1 # count observations with three or more possible operations if num_matches >= 3: three_or_more += 1 return three_or_more def part2(observations, program): # store possible candidates for every opcode operations = {i: set(OPERATIONS) for i in range(len(OPERATIONS))} for obsv in observations: matching_operations = set() opcode = obsv.instruction[0] # execute all possible candidates for op in operations[opcode]: if obsv.after == execute(obsv.instruction, obsv.before, op): matching_operations.add(op) # keep only the matching operations operations[opcode] = matching_operations # if we uniquely identified an operation ... if len(matching_operations) == 1: unique_op = next(iter(matching_operations)) # ... remove it from the other mappings for key in set(operations.keys()) - {opcode}: operations[key].discard(unique_op) # map set values to scalar operations = {i: ops.pop() for i, ops in operations.items()} # interpret the program reg = [0, 0, 0, 0] for instruction in program: reg = execute(instruction, reg, operations[instruction[0]]) return reg[0] def execute(instruction, reg, op): _, a, b, c = instruction reg = list(reg) # copy register if op == 'addr': reg[c] = reg[a] + reg[b] elif op == 'addi': reg[c] = reg[a] + b elif op == 'mulr': reg[c] = reg[a] * reg[b] elif op == 'muli': reg[c] = reg[a] * b elif op == 'banr': reg[c] = reg[a] & reg[b] elif op == 'bani': reg[c] = reg[a] & b elif op == 'borr': reg[c] = reg[a] | reg[b] elif op == 'bori': reg[c] = reg[a] | b elif op == 'setr': reg[c] = reg[a] elif op == 'seti': reg[c] = a elif op == 'gtir': reg[c] = 1 if a > reg[b] else 0 elif op == 'gtri': reg[c] = 1 if reg[a] > b else 0 elif op == 'gtrr': reg[c] = 1 if reg[a] > reg[b] else 0 elif op == 'eqir': reg[c] = 1 if a == reg[b] else 0 elif op == 'eqri': reg[c] = 1 if reg[a] == b else 0 elif op == 'eqrr': reg[c] = 1 if reg[a] == reg[b] else 0 return reg def _parse(lines): observations, program, i = [], [], 0 # parse observations while i < len(lines): before = parse("Before: [{:d}, {:d}, {:d}, {:d}]", lines[i].strip()) instruction = parse("{:d} {:d} {:d} {:d}", lines[i + 1].strip()) after = parse("After: [{:d}, {:d}, {:d}, {:d}]", lines[i + 2].strip()) i += 4 if not (before and after and instruction): break observations.append(Observation([*instruction], [*before], [*after])) # parse program for line in lines[i - 2:]: program.append(list(map(int, line.strip().split(' ')))) return observations, program if __name__ == "__main__": print(part1(_parse(open(r"../assets/day16.txt").readlines())[0])) print(part2(*_parse(open(r"../assets/day16.txt").readlines())))

0

3,161

0

2

115

d31ca228a7c49cb870496d2465087e6fff372030

2,685

py

Python

programming_languages_classification/test.py

contimatteo/Programming-Languages-Classification

34ccf1bd403f55226ed5131d57265df45d314b6f

[ "MIT" ]

1

2022-03-18T12:54:52.000Z

programming_languages_classification/test.py

contimatteo/Programming-Languages-Classification

34ccf1bd403f55226ed5131d57265df45d314b6f

[ "MIT" ]

5

2021-11-10T19:58:25.000Z

2022-03-19T18:17:41.000Z

programming_languages_classification/test.py

contimatteo/programming-language-classifier

60847ab91cff4dc20ded1a024d272c75956194a0

[ "MIT" ]

null

from keras.models import load_model import os import json from utils import FileManager ## global dictionary global model dictionaryUrl = os.path.join(FileManager.getRootUrl(), 'tmp/wordindex.json') dictionary = json.loads(FileManager.readFile(dictionaryUrl)) modelUrl = os.path.join(FileManager.getRootUrl(), 'tmp/code_model.h5') model = load_model(modelUrl) ## ## if __name__ == "__main__": main()

29.833333

106

0.633147

from keras.models import load_model import keras.preprocessing.text as kpt from keras.preprocessing.sequence import pad_sequences import sys import os import json import numpy as np from utils import ConfigurationManager, FileManager ## global dictionary global model dictionaryUrl = os.path.join(FileManager.getRootUrl(), 'tmp/wordindex.json') dictionary = json.loads(FileManager.readFile(dictionaryUrl)) modelUrl = os.path.join(FileManager.getRootUrl(), 'tmp/code_model.h5') model = load_model(modelUrl) def convert_text_to_index_array(text): # one really important thing that `text_to_word_sequence` does # is make all texts the same length -- in this case, the length # of the longest text in the set. wordvec = [] for word in kpt.text_to_word_sequence(text): if word in dictionary: if dictionary[word] <= 100000: wordvec.append([dictionary[word]]) else: wordvec.append([0]) else: wordvec.append([0]) return wordvec ## def main(): data = {"success": False} languages = ConfigurationManager.getLanguages() matched = 0 totalExamples = 0 for languageFolder in FileManager.getLanguagesFolders(FileManager.datasets['testing']['url']): language = str(languageFolder.name).lower() for exampleFolder in FileManager.getExamplesFolders(languageFolder.path): totalExamples += 1 X_test = [] originalFileContent = FileManager.readFile(FileManager.getOriginalFileUrl(exampleFolder.path)) code_snip = originalFileContent # print(code_snip, file=sys.stdout) word_vec = convert_text_to_index_array(code_snip) X_test.append(word_vec) X_test = pad_sequences(X_test, maxlen=100) # print(X_test[0].reshape(1,X_test.shape[1]), file=sys.stdout) y_prob = model.predict(X_test[0].reshape(1, X_test.shape[1]), batch_size=1, verbose=2)[0] a = np.array(y_prob) idx = np.argmax(a) if str(languages[idx]) == language: matched += 1 # data["predictions"] = [] # for i in range(len(languages)): # # print(languages[i], file=sys.stdout) # r = {"label": languages[i], "probability": format(y_prob[i] * 100, '.2f')} # data["predictions"].append(r) print('') print('') print('totalExamples = ' + str(totalExamples)) print('matched = ' + str(matched)) print('matched / totalExamples = ' + str(matched / totalExamples)) print('') print('') ## if __name__ == "__main__": main()

0

2,077

0

58

134

2e93f597b4ad68b69e4599e8fc30321be3c05d7a

31,071

py

Python

src/Animate/Scripts.py

henkjannl/py-animate

dbc93c8a264ef008954901ea76286331ad1737ee

[ "MIT" ]

null

src/Animate/Scripts.py

henkjannl/py-animate

dbc93c8a264ef008954901ea76286331ad1737ee

[ "MIT" ]

null

src/Animate/Scripts.py

henkjannl/py-animate

dbc93c8a264ef008954901ea76286331ad1737ee

[ "MIT" ]

null

#import time import logging LOG_FILENAME = '__logfile.txt' logging.basicConfig(filename=LOG_FILENAME,level=logging.DEBUG)

41.931174

186

0.491101

import pandas as pd from PIL import Image # www.pythonware.com/library/pil/handbook from PIL import ImageFont, ImageDraw, ImageEnhance from PIL import ImageFilter import os #import time import logging from Animate.Items import * from Animate.Properties import * from Animate.Constants import * LOG_FILENAME = '__logfile.txt' logging.basicConfig(filename=LOG_FILENAME,level=logging.DEBUG) def SelectFont(Directories, Fonts): for Font in Fonts: for Path in Directories: try: FontName=os.path.join(Path,Font) SelectedFont = ImageFont.truetype(FontName, 20) return FontName except: logging.debug('%s not successful' % FontName ) print('All attempts to load fonts failed') def isNumber(somePandasValue): if pd.isnull(somePandasValue): return False elif isinstance(somePandasValue, int): return True elif isinstance(somePandasValue, float): return True else: return False def isString(somePandasValue): if pd.isnull(somePandasValue): return False elif isinstance(somePandasValue, str): return True else: return False class Script(): def __init__(self, FileName, SheetName, ScriptList): logging.debug(' Script.__init__(%s, %s)' % (FileName, SheetName) ) self.FileName = FileName self.SheetName = SheetName self.ScriptList = ScriptList self.IsCanvas = False self.FirstImage = True self.ImageDir = 'Pictures' self.FirstFrame = 0 # Allows the processing of a subset of frames self.LastFrame = -1 self.FramesPerSecond = 10 self.ShowTime = False # Display the time in each frame self.Movie = False # Can be overridden by filename of movie self.AnimatedGIF = False # Can be overridden by filename of animated gif self.MaxTime = 0 # Largest time, retrieved from the parser self.TimeOffset = 0.0 # Script, assembly or canvas can be run with an offset to the global time self.Width = 800 # Width of the output image self.Height = 600 # Height of the output image self.Items = ItemDict() # Dictionary of items # List of (time, item, back/front) tuples self.Zbuffer = [] self.ZbufferIndex = 0 # List of Items self.Zorder = [] # Picture that was processed last self.Picture = False self.PictureFrame = -1 def ParseScript(self, FileName, SheetName): logging.debug(' Script.ParseScript(%s, %s)' % (FileName, SheetName)) # Open excel file with frame data df = pd.read_excel(FileName, sheet_name=SheetName, header=None) print(' - parsing script %s' % SheetName) for Row in range(df.shape[0]): # A row contains valid data if the first cell contains a number if isNumber(df.loc[Row,0]): time = df.loc[Row,0] command = df.loc[Row,1].upper().strip() if self.MaxTime<time: self.MaxTime=time if command == 'WIDTH': # Determine the width of the output frames assert isNumber(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a number" % (command, Row+1, SheetName) self.Width = int(df.loc[Row,2]) elif command == 'HEIGHT': # Determine the height of the output frames assert isNumber(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a number" % (command, Row+1, SheetName) self.Height = int(df.loc[Row,2]) elif command == 'FRAMESPERSECOND': # Sets the number of frames per second for the whole movie assert isNumber(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a number in column C" % (command, Row+1, SheetName) assert (df.loc[Row,2] >0 ), \ "Frames per second in sheet %s at row %d should be larger than 0" % (SheetName, Row+1) self.FramesPerSecond = df.loc[Row,2] elif command == 'FIRSTFRAME': # Determine the first frame to be processed, # if not all frames must be processed. For debugging assert isNumber(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a number in column C" % (command, Row+1, SheetName) self.FirstFrame = int(df.loc[Row,2]) elif command == 'LASTFRAME': # Determine the last frame to be processed, # if not all frames must be processed. For debugging assert isNumber(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a number in column C" % (command, Row+1, SheetName) self.LastFrame = int(df.loc[Row,2]) elif command == 'SHOWTIME': # Write the time in the lower left corner of the frames, for debug purposes self.ShowTime = True elif command == 'HIDETIME': # Do not write the time self.ShowTime = False elif command == 'MOVIE': # Sets the number of frames per second for the whole movie assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a filename for the movie" % (command, Row+1, SheetName) self.Movie= df.loc[Row,2] print(" - movie {movie} will be created after generating the frames".format(movie=self.Movie)) elif command == 'ANIMATEDGIF': # Sets the number of frames per second for the whole movie assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a filename for the animated gif" % (command, Row+1, SheetName) self.AnimatedGIF= df.loc[Row,2] print("- animated GIF {gif} will be created after generating the frames".format(gif=self.AnimatedGIF)) elif command == 'TABLE': # Do not create a new script object, but import the commands in the current script assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string for the table name" % (command, Row+1, SheetName) sheetname = df.loc[Row,2].strip() self.ParseTable(self.FileName, sheetname) elif command == 'SCRIPT': # Do not create a new script object, but import the commands in the current script assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string for the script name" % (command, Row+1, SheetName) sheetname = df.loc[Row,2].strip() self.ParseScript(self.FileName, sheetname) elif command == 'ASSEMBLY': # Create a new script object and use the image created by this # script as feed for this item assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string for the assembly name" % (command, Row+1, SheetName) assert isString(df.loc[Row,3]), \ "%s at row %d of sheet %s expects a string for the sheet name" % (command, Row+1, SheetName) itemname = df.loc[Row,2].upper().strip() sheetname = df.loc[Row,3] # If the script is not yet in the list, create it if not sheetname in self.ScriptList: NewScript = Script(FileName, sheetname, self.ScriptList) self.ScriptList[sheetname] = NewScript NewScript.ParseScript(FileName, sheetname) # Assign the script to the # ToDo: Implement item type directly # ToDo: Implement change of script as function of time self.Items[itemname].AddScript( time, sheetname ) elif command == 'CANVAS': # A canvas is an assembly of which the background is not reset for a new frame assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string for the item tag" % (command, Row+1, SheetName) assert isString(df.loc[Row,3]), \ "%s at row %d of sheet %s expects a string for the sheet name" % (command, Row+1, SheetName) itemname = df.loc[Row,2].upper().strip() sheetname = df.loc[Row,3] # If the script is not yet in the list, create it if not sheetname in self.ScriptList: NewScript = Script(FileName, sheetname, self.ScriptList) NewScript.IsCanvas = True self.ScriptList[sheetname] = NewScript NewScript.ParseScript(FileName, sheetname) # Assign the script to the # ToDo: Implement item type directly # ToDo: Implement change of script as function of time self.Items[itemname].AddCanvas( time, sheetname ) elif command == 'IMAGE': # Assign a new filename for an image item assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string for the item name" % (command, Row+1, SheetName) assert isString(df.loc[Row,3]), \ "%s at row %d of sheet %s expects a string for the filename" % (command, Row+1, SheetName) itemname = df.loc[Row,2].upper().strip() filename = os.path.join(self.ImageDir, df.loc[Row,3]) assert os.path.isfile(filename), \ "%s at row %d could not find file %s" % (command, Row+1, filename) self.Items[itemname].AddImage( time, filename ) elif command == 'MASK': # Assign a new filename for a mask item assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string for the item tag" % (command, Row+1, SheetName) assert isString(df.loc[Row,3]), \ "%s at row %d of sheet %s expects a string for the filename" % (command, Row+1, SheetName) itemname = df.loc[Row,2].upper().strip() filename = os.path.join(self.ImageDir, df.loc[Row,3]) assert os.path.isfile(filename), \ "%s at row %d could not find file %s" % (command, Row+1, filename) self.Items[itemname].AddMask( time, filename ) elif command == 'TEXT': # Assign a new title for a text item assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string in column C" % (command, Row+1, SheetName) assert isString(df.loc[Row,3]), \ "%s at row %d of sheet %s expects a string in column D" % (command, Row+1, SheetName) itemname = df.loc[Row,2].upper().strip() title = df.loc[Row,3] self.Items[itemname].AddText( time, title ) elif command in ['XPOS', 'YPOS', 'XPOLE', 'YPOLE', 'XSCALE', 'YSCALE', 'ROTATION', 'TIMEOFFSET', 'TEXTSIZE', 'OPACITY']: # Set a new x position assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects an item name in column C" % (command, Row+1, SheetName) assert isNumber(df.loc[Row,3]), \ "%s at row %d of sheet %s expects a number in column D" % (command, Row+1, SheetName) itemname = df.loc[Row,2].upper().strip() value = df.loc[Row,3] self.Items[itemname].Properties[command].Append(time, value) elif command in ['XMOVE', 'YMOVE', 'SXMOVE', 'SYMOVE', 'RMOVE', 'OMOVE']: # Determine linear or cycloid movement assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string in column C" % (command, Row+1, SheetName) assert isString(df.loc[Row,3]), \ "%s at row %d of sheet %s expects a string in column D" % (command, Row+1, SheetName) itemname = df.loc[Row,2].upper().strip() move = df.loc[Row,3].strip().upper() if move in CheckMove: self.Items[itemname].Properties[command].Append(time, CheckMove[move]) else: print("Did not recognize type of movement on row %d." % (Row+1)) elif command in ['TEXTCOLOR', 'FONT']: # Set a new text color assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string in column C" % (command, Row+1, SheetName) assert isString(df.loc[Row,3]), \ "%s at row %d of sheet %s expects a string in column D" % (command, Row+1, SheetName) itemname = df.loc[Row,2].upper().strip() textcolor = df.loc[Row,3].strip() self.Items[itemname].Properties[command].Append(time, textcolor) elif command == 'BRINGTOFRONT': # Bring the item to front at this time position assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string in column C" % (command, Row+1, SheetName) itemname = df.loc[Row,2].upper().strip() self.Zbuffer.append( ( time, itemname, FRONT) ) elif command == 'SENDTOBACK': # Send the item to back at this time position assert isString(df.loc[Row,2]), \ "%s at row %d of sheet %s expects a string in column C" % (command, Row+1, SheetName) itemname = df.loc[Row,2].upper().strip() self.Zbuffer.append( ( time, itemname, BACK) ) else: print("Command %s not recognized on row %d." % (command, Row+1)) def ParseTable(self, FileName, SheetName): logging.debug(' Script.ParseTable(%s, %s)' % (FileName, SheetName)) # Open excel file with frame data df = pd.read_excel(FileName, sheet_name=SheetName, header=None) # Investigate which data each column contains print(' - parsing table %s' % SheetName) for Row in range(2, df.shape[0]): # Only process rows with a time in the first column if isNumber(df.loc[Row,0]): time = df.loc[Row,0] # Increase time if the table exceeds the maximum if self.MaxTime<time: self.MaxTime=time for Col in range(1, df.shape[1]): # Only process columns with an existing object in the first row and a command in the second row if isString(df.loc[0,Col]) and isString(df.loc[1,Col]) and\ len(df.loc[0,Col])>0 and len(df.loc[1,Col])>0: itemname = df.loc[0,Col].upper().strip() command = df.loc[1,Col].upper().strip() # Only process items that have already been created in another script if itemname in self.Items: item = self.Items[itemname] if command == 'IMAGE': if item.ItemType == IT_IMAGE: # Assign a new filename for an image item if isString(df.loc[Row,Col]): filename = os.path.join(self.ImageDir, df.loc[Row,Col]) assert os.path.isfile(filename), \ "%s at row %d could not find file %s" % (command, Row+1, filename) self.Items[itemname].AddImage( time, filename ) elif command == 'MASK': if self.Items[item].ItemType == IT_MASK: # Assign a new filename for an image item if isString(df.loc[Row,Col]): filename = os.path.join(self.ImageDir, df.loc[Row,Col]) assert os.path.isfile(filename), \ "%s at row %d could not find file %s" % (command, Row+1, filename) self.Items[itemname].AddMask( time, filename ) elif command == 'TEXT': if item.ItemType == IT_TEXT: # Assign a new title for a text item if isString(df.loc[Row,Col]): text = df.loc[Row,Col] self.Items[itemname].AddText( time, text ) elif command in ['XPOS', 'YPOS', 'XPOLE', 'YPOLE', 'XSCALE', 'YSCALE', 'ROTATION', 'TIMEOFFSET', 'TEXTSIZE', 'OPACITY']: # Set a new float property if isNumber(df.loc[Row,Col]): val = df.loc[Row,Col] self.Items[itemname].Properties[command].Append(time, val) elif command in ['XMOVE', 'YMOVE', 'SXMOVE', 'SYMOVE', 'RMOVE', 'OMOVE']: # Determine type of movement if isString(df.loc[Row,Col]): move = df.loc[Row,Col].strip().upper() if move in CheckMove: self.Items[itemname].Properties[command].Append(time, CheckMove[move]) else: print("Did not recognize type of movement on row %d." % (Row+1)) elif command in ['TEXTCOLOR', 'FONT']: if isString(df.loc[Row,Col]): textcolor = df.loc[Row,Col].strip() self.Items[itemname].Properties[command].Append(time, textcolor) else: print('Command: ', command) print('Column: ', Col+1) print("Command %s not recognized on col %d." % (command, Col+1)) def StandardChecks(self): print(' - checking script %s which has %d items' % (self.SheetName, len(self.Items) )) # Do some standard checks after parsing OK = True self.TimeOffsetUsed=False for i in self.Items.values(): i.StandardChecks() if i.TimeOffsetUsed: self.TimeOffsetUsed=True if (i.ItemType == IT_IMAGE): if len(i.Properties['IMAGE'].Sequence)==0: print('ERROR: %s has NO images' % i.ItemName) OK=False else: for time, filename in i.Properties['IMAGE'].Sequence: if not os.path.isfile(filename): print('Image not found: %s at tim %.3f' % (filename, time)) OK = False if (i.ItemType == IT_MASK): if len(i.Properties['MASK'].Sequence)==0: print('ERROR: %s has NO mask' % i.ItemName) OK=False else: for time, filename in i.Properties['MASK'].Sequence: if not os.path.isfile(filename): print('Mask not found: %s at tim %.3f' % (filename, time)) OK = False if (i.ItemType == IT_TEXT): if len(i.Properties['TEXT'].Sequence)==0: print('ERROR: %s has NO lines of text' % i.ItemName) OK=False return OK def Deploy(self, MaxTime): logging.debug('') logging.debug('* DEPLOYING SCRIPT %s' % self.SheetName) for item in self.Items.values(): item.Deploy(MaxTime) if not self.Zbuffer: # The Zbuffer has no items because the user did not specify # any BRINGTOFRONT or SENDTOBACK commands # Get the name of a random item itemname = list(self.Items.keys())[0] self.Zbuffer.append( ( 0, itemname, FRONT) ) self.Zbuffer.sort() time, item, direction = self.Zbuffer[-1] self.Zbuffer.append( (MaxTime, item, direction) ) self.Zbuffer.sort() # Determine the order of the items at time = 0 self.ZbufferIndex = 0 # list() means we create a copy self.Zorder = list(self.Items.keys()) def GetPicture(self, Time, Frame): # If exactly the same image was calculated before, # use that image #if Frame != self.PictureFrame and not self.TimeOffsetUsed: if True: logging.debug('') logging.debug('* SCRIPT %s IS GENERATING FRAME %.5d at time %.2f' % (self.SheetName, Frame, Time )) # Start with a transparent image if (not self.IsCanvas) or self.FirstImage: self.Picture = Image.new("RGBA", (self.Width, self.Height), (255,0,0,0) ) self.FirstImage=False # Determine the Z-order at the desired time while True: time, item, direction = self.Zbuffer[self.ZbufferIndex] if item not in self.Zorder: print('Z-order failure: item %s not in script %s' % (item, self.SheetName) ) self.Zorder.remove(item) if direction == FRONT: self.Zorder.append(item) else: self.Zorder.insert(0, item) if (self.Zbuffer[self.ZbufferIndex+1][0])>Time: break else: self.ZbufferIndex+=1 ItemPicture = Image.new("RGBA", (self.Width, self.Height), (255,0,0,0) ) # Draw each item for itemname in self.Zorder: Item = self.Items[itemname] move = Item.Properties['OMOVE' ].Value(Time) opacity = Item.Properties['OPACITY' ].Value(Time, move) move = Item.Properties['XMOVE' ].Value(Time) xpos = Item.Properties['XPOS' ].Value(Time, move) move = Item.Properties['YMOVE' ].Value(Time) ypos = Item.Properties['YPOS' ].Value(Time, move) move = Item.Properties['SXMOVE' ].Value(Time) sx = Item.Properties['XSCALE' ].Value(Time, move) move = Item.Properties['SYMOVE' ].Value(Time) sy = Item.Properties['YSCALE' ].Value(Time, move) move = Item.Properties['RMOVE' ].Value(Time) rot = Item.Properties['ROTATION'].Value(Time, move) try: logging.debug(' - Item %s:%s xpos= %.2f ypos= %.2f xscale= %.3f yscale= %.3f rot= %.3f opacity= %.3f' % (self.SheetName, itemname, xpos, ypos, sx, sy, rot, opacity)) except: print('opacity', opacity) print('xpos', xpos) print('ypos', ypos) print('sx', sx) print('sy', sy) print('rot', rot) if opacity>0: if Item.ItemType == IT_ASSY: script = Item.Properties['SCRIPT'].Value(Time) logging.debug(' - Assembly %s:%s requests an image from script %s' % (self.SheetName, itemname, script)) if script in self.ScriptList: dt=Item.Properties['TIMEOFFSET'].Value(Time, LINEAR) ItemPicture = self.ScriptList[script].GetPicture(Time-dt, Frame) else: logging.debug(' Script %s not in scriptlist!!:'% (script)) ItemPicture = Image.new("RGBA", (self.Width, self.Height), (255,0,0,0) ) logging.debug(' Assembly %s continues:'% (self.SheetName)) if Item.ItemType == IT_CANVAS: script = Item.Properties['SCRIPT'].Value(Time) logging.debug(' - Canvas %s:%s requests an image from script %s' % (self.SheetName, itemname, script)) if script in self.ScriptList: dt=Item.Properties['TIMEOFFSET'].Value(Time, LINEAR) ItemPicture = self.ScriptList[script].GetPicture(Time-dt, Frame) else: ItemPicture = Image.new("RGBA", (self.Width, self.Height), (255,0,0,0) ) elif Item.ItemType == IT_IMAGE: image = Item.Properties['IMAGE'].Value(Time) if Item.PrevImageName != image: Item.LoadedImage = Image.open(image).convert("RGBA") Item.PrevImageName = image ItemPicture = Item.LoadedImage elif Item.ItemType == IT_MASK: image = Item.Properties['MASK'].Value(Time) logging.debug('Line 585 mask is %s' % image) if Item.PrevImageName != image: Item.LoadedImage = Image.open(image).convert("RGBA") Item.PrevImageName = image ItemPicture = Item.LoadedImage elif Item.ItemType == IT_TEXT: ItemPicture = Image.new("RGBA", (self.Width, self.Height), (255,0,0,0) ) text = Item.Properties['TEXT' ].Value(Time) textsize = int(Item.Properties['TEXTSIZE' ].Value(Time, LINEAR)) textcolor = Item.Properties['TEXTCOLOR'].Value(Time) fontname = Item.Properties['FONT' ].Value(Time) Directories = [ 'C:\\WINDOWS\\Fonts\\' ] Fonts = [fontname, 'calibri.ttf', 'YanoneKaffeesatz-Regular.ttf', 'ARIALN.TTF', 'verdana.ttf', 'YanoneKaffeesatz-Light.ttf'] Face = ImageFont.truetype(SelectFont(Directories, Fonts), textsize) Draw = ImageDraw.Draw(ItemPicture) Draw.text( (0,0), text, fill=textcolor, font=Face) # Retrieve the general properties move = Item.Properties['XMOVE' ].Value(Time) xpos = Item.Properties['XPOS' ].Value(Time, move) move = Item.Properties['YMOVE' ].Value(Time) ypos = Item.Properties['YPOS' ].Value(Time, move) move = Item.Properties['SXMOVE' ].Value(Time) sx = Item.Properties['XSCALE' ].Value(Time, move) xpole = Item.Properties['XPOLE' ].Value(Time, move) move = Item.Properties['SYMOVE' ].Value(Time) sy = Item.Properties['YSCALE' ].Value(Time, move) ypole = Item.Properties['YPOLE' ].Value(Time, move) move = Item.Properties['RMOVE' ].Value(Time) rot = Item.Properties['ROTATION'].Value(Time, move) fi = math.pi/180*rot sinfi = math.sin(fi) cosfi = math.cos(fi) w,h = ItemPicture.size # Resize and rotate the ItemPicture try: ItemPicture=ItemPicture.resize( (int(sx*w+0.5), int(sy*h+0.5) ), Image.ANTIALIAS) ItemPicture=ItemPicture.rotate(rot, expand=1) except: print('ERROR Script 663: Item %s:%s sx= %.2f sy= %.2f' % (self.SheetName, itemname, sx, sy)) break wr,hr = ItemPicture.size xt = xpos + xpole - ypole*sy*sinfi - xpole*sx*cosfi +0.5*w*sx*cosfi +0.5*h*sy*sinfi -0.5*wr yt = ypos + ypole - ypole*sy*cosfi + xpole*sx*sinfi -0.5*w*sx*sinfi +0.5*h*sy*cosfi -0.5*hr Mask = ItemPicture.convert("RGBA") Mask = Image.blend(Image.new(ItemPicture.mode, ItemPicture.size, 0), ItemPicture, opacity) if Item.ItemType != IT_MASK: # Item is picture, assembly or canvas self.Picture.paste( ItemPicture, (int(xt),int(yt)), Mask ) else: # Item is mask logging.debug(' - Applying mask for %s' % itemname) # Start with a clean image with transparent background CleanImage = Image.new("RGBA", (self.Width, self.Height), (0,0,0,0) ) # Use the mask rotated and translated Mask = Image.new("L", (self.Width, self.Height), 0 ) Mask.paste( ItemPicture, (int(xt),int(yt))) # Copy the image as-is with rotation and translation set to zero CleanImage.paste( self.Picture, (0,0), Mask ) self.Picture = CleanImage.copy() self.PictureFrame = Frame return self.Picture.copy()

0

29,838

0

747

0

48

313

da91ac6418297ed4e02987a76d7e459a1c8dc944

2,510

py

Python

DatasetHandler/FileHelperFunc.py

previtus/MGR-Project-Code

1126215059eb3f731dcf78ec24d9a480e73abce6

[ "MIT" ]

null

DatasetHandler/FileHelperFunc.py

previtus/MGR-Project-Code

1126215059eb3f731dcf78ec24d9a480e73abce6

[ "MIT" ]

null

DatasetHandler/FileHelperFunc.py

previtus/MGR-Project-Code

1126215059eb3f731dcf78ec24d9a480e73abce6

[ "MIT" ]

null

import os def get_project_folder(): ''' Gives us the path to MGR-Project-Code from a list of allowed folders. :return: ''' PATH_ALTERNATIVES = ['/home/ekmek/Project II/MGR-Project-Code/', '/storage/brno2/home/previtus/MGR-Project-Code/', '/home/ekmek/Vitek/MGR-Project-Code/'] ABS_PATH_TO_PRJ = use_path_which_exists(PATH_ALTERNATIVES) return ABS_PATH_TO_PRJ def get_geojson_path(): ''' Gives us the path directly to attractivity_previtus_data_1_edges.geojson from a list of allowed paths :return: ''' folders = ['/home/ekmek/Desktop/Project II/graph_new_data/', '/home/ekmek/Vitek/graph_new_data/', '/storage/brno2/home/previtus/important_files/'] folder = use_path_which_exists(folders) return folder+'attractivity_previtus_data_1_edges.geojson' def use_path_which_exists(list_of_possible_paths): ''' From a list of possible paths choose the one which exists. :param list_of_possible_paths: possible paths :return: working path ''' used_path = '' for path in list_of_possible_paths: if os.path.exists(path): used_path = path if used_path == '': print "Error, cannot locate the path of project, will likely fail!" return used_path def file_exists(fname): ''' Does file exist, returns boolean.''' return os.path.isfile(fname) def get_folder_from_file(fname): ''' Get folder name from path to a file.''' return os.path.dirname(fname) + '/' def folder_exists(directory): ''' Does folder with this name exist, returns boolean''' return os.path.exists(directory) def make_folder_ifItDoesntExist(directory): ''' Make a new directory, if it didn't previously exist.''' if not os.path.exists(directory): os.makedirs(directory) import shutil, errno def copy_folder(src, dst): ''' Copy and paste folders. Used for dataset augmentation.''' try: shutil.copytree(src, dst) except OSError as exc: # python >2.5 if exc.errno == errno.ENOTDIR: shutil.copy(src, dst) else: raise def copy_file(src, dst): ''' Copy and paste file.''' try: shutil.copy(src, dst) except OSError as exc: raise import hashlib def md5(fname): ''' Get md5 hash of a file.''' hash_md5 = hashlib.md5() with open(fname, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): hash_md5.update(chunk) return hash_md5.hexdigest()

30.240964

157

0.661753

import os def get_project_folder(): ''' Gives us the path to MGR-Project-Code from a list of allowed folders. :return: ''' PATH_ALTERNATIVES = ['/home/ekmek/Project II/MGR-Project-Code/', '/storage/brno2/home/previtus/MGR-Project-Code/', '/home/ekmek/Vitek/MGR-Project-Code/'] ABS_PATH_TO_PRJ = use_path_which_exists(PATH_ALTERNATIVES) return ABS_PATH_TO_PRJ def get_geojson_path(): ''' Gives us the path directly to attractivity_previtus_data_1_edges.geojson from a list of allowed paths :return: ''' folders = ['/home/ekmek/Desktop/Project II/graph_new_data/', '/home/ekmek/Vitek/graph_new_data/', '/storage/brno2/home/previtus/important_files/'] folder = use_path_which_exists(folders) return folder+'attractivity_previtus_data_1_edges.geojson' def use_path_which_exists(list_of_possible_paths): ''' From a list of possible paths choose the one which exists. :param list_of_possible_paths: possible paths :return: working path ''' used_path = '' for path in list_of_possible_paths: if os.path.exists(path): used_path = path if used_path == '': print "Error, cannot locate the path of project, will likely fail!" return used_path def file_exists(fname): ''' Does file exist, returns boolean.''' return os.path.isfile(fname) def get_folder_from_file(fname): ''' Get folder name from path to a file.''' return os.path.dirname(fname) + '/' def folder_exists(directory): ''' Does folder with this name exist, returns boolean''' return os.path.exists(directory) def make_folder_ifItDoesntExist(directory): ''' Make a new directory, if it didn't previously exist.''' if not os.path.exists(directory): os.makedirs(directory) import shutil, errno def copy_folder(src, dst): ''' Copy and paste folders. Used for dataset augmentation.''' try: shutil.copytree(src, dst) except OSError as exc: # python >2.5 if exc.errno == errno.ENOTDIR: shutil.copy(src, dst) else: raise def copy_file(src, dst): ''' Copy and paste file.''' try: shutil.copy(src, dst) except OSError as exc: raise import hashlib def md5(fname): ''' Get md5 hash of a file.''' hash_md5 = hashlib.md5() with open(fname, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): hash_md5.update(chunk) return hash_md5.hexdigest()

0

ca53a879e6d706ed4d4875884504a13f91f27b99

1,575

py

Python

trajectory_executor/Parameters.py

WPI-MMR/trajectory_generator

61a3c61d37e674cfa81ec4e67fd56bb825e56ab8

[ "MIT" ]

null

trajectory_executor/Parameters.py

WPI-MMR/trajectory_generator

61a3c61d37e674cfa81ec4e67fd56bb825e56ab8

[ "MIT" ]

null

trajectory_executor/Parameters.py

WPI-MMR/trajectory_generator

61a3c61d37e674cfa81ec4e67fd56bb825e56ab8

[ "MIT" ]

null

# from sympy import * # Robot Chassis Parameters l = 370 #hip to hip length of the robot b = 210.1 #hip to hip breadth of the robot h = 44 #height of the robot ## Leg Type 1: Rear ''' Variable name convention as follows: The first number represents the length and the second number represents the Leg Type l11 is the hip to knee length of Leg Type 1 l21 is the knee to ankle length of Leg Type 1 l22 is the knee to ankle length of Leg Type 2 and so on... ''' # Defining lengths and offsets l11 = 160 #hip to knee length l21 = 160 #knee to ankle length l3 = 39 #ankle to toe length d1 = 37 #hip offset d2 = 12.95 #knee offset ''' Variable name convention as follows: The first number represents the angle and the second number represents the Leg # theta11 is the hip rotation angle of Leg 1 theta21 is the knee roation angle of Leg 1 theta31 is the ankle roation angle of Leg 1 theta14 is the hip rotation angle of Leg 4 and so on... ''' # theta11, alpha11, theta21, alpha21, theta31, alpha31 = symbols("theta11 alpha11 theta21 alpha21 theta31 alpha31") # theta14, alpha14, theta24, alpha24, theta34, alpha34 = symbols("theta14 alpha14 theta24 alpha24 theta34 alpha34") ## Leg Type 2: Front # Defining lengths and offsets l12 = 160 #hip to knee length l22 = 173.5 #knee to ankle length # theta12, alpha12, theta22, alpha22 = symbols("theta12 alpha12 theta22 alpha22") # theta13, alpha13, theta23, alpha23 = symbols("theta13 alpha13 theta23 alpha23")

28.125

116

0.690794

# from sympy import * # Robot Chassis Parameters l = 370 #hip to hip length of the robot b = 210.1 #hip to hip breadth of the robot h = 44 #height of the robot ## Leg Type 1: Rear ''' Variable name convention as follows: The first number represents the length and the second number represents the Leg Type l11 is the hip to knee length of Leg Type 1 l21 is the knee to ankle length of Leg Type 1 l22 is the knee to ankle length of Leg Type 2 and so on... ''' # Defining lengths and offsets l11 = 160 #hip to knee length l21 = 160 #knee to ankle length l3 = 39 #ankle to toe length d1 = 37 #hip offset d2 = 12.95 #knee offset ''' Variable name convention as follows: The first number represents the angle and the second number represents the Leg # theta11 is the hip rotation angle of Leg 1 theta21 is the knee roation angle of Leg 1 theta31 is the ankle roation angle of Leg 1 theta14 is the hip rotation angle of Leg 4 and so on... ''' # theta11, alpha11, theta21, alpha21, theta31, alpha31 = symbols("theta11 alpha11 theta21 alpha21 theta31 alpha31") # theta14, alpha14, theta24, alpha24, theta34, alpha34 = symbols("theta14 alpha14 theta24 alpha24 theta34 alpha34") ## Leg Type 2: Front # Defining lengths and offsets l12 = 160 #hip to knee length l22 = 173.5 #knee to ankle length # theta12, alpha12, theta22, alpha22 = symbols("theta12 alpha12 theta22 alpha22") # theta13, alpha13, theta23, alpha23 = symbols("theta13 alpha13 theta23 alpha23")

0

41e9c7b6bd0603988abf0a0263e4f16fab3ff22b

716

py

Python

plbmng/lib/ssh_map.py

xxMAKMAKxx/plbmng

64bbe70424801092c7429d5e73ecaf5466b6c437

[ "MIT" ]

null

plbmng/lib/ssh_map.py

xxMAKMAKxx/plbmng

64bbe70424801092c7429d5e73ecaf5466b6c437

[ "MIT" ]

null

plbmng/lib/ssh_map.py

xxMAKMAKxx/plbmng

64bbe70424801092c7429d5e73ecaf5466b6c437

[ "MIT" ]

null

import folium import csv def main(): """ Creates a map of nodes with available SSH connection.\n :return: map_ssh.html file """ map_ssh = folium.Map(location=[45.523, -122.675], zoom_start=2) with open('lib/base_data.txt') as tsv: for row in csv.reader(tsv, delimiter='\t'): name = row[0] try: x = float(row[1]) y = float(row[2]) print(" %s " % name) folium.Marker([x, y], popup=name).add_to(map_ssh) except ValueError: pass map_ssh.save('map_ssh.html') if __name__ == "__main__": main()

23.866667

65

0.523743

import folium import csv from folium.plugins import MarkerCluster def main(): """ Creates a map of nodes with available SSH connection.\n :return: map_ssh.html file """ map_ssh = folium.Map(location=[45.523, -122.675], zoom_start=2) with open('lib/base_data.txt') as tsv: for row in csv.reader(tsv, delimiter='\t'): name = row[0] try: x = float(row[1]) y = float(row[2]) print(" %s " % name) folium.Marker([x, y], popup=name).add_to(map_ssh) except ValueError: pass map_ssh.save('map_ssh.html') if __name__ == "__main__": main()

0

19

22

2bb73ddf9d4ab92638002a38558adbe2794f6be1

146

py

Python

day08/part2.py

mtn/advent15

b23bcf5761363596336d5361218c52db0b078793

[ "MIT" ]

null

day08/part2.py

mtn/advent15

b23bcf5761363596336d5361218c52db0b078793

[ "MIT" ]

null

day08/part2.py

mtn/advent15

b23bcf5761363596336d5361218c52db0b078793

[ "MIT" ]

null

#!/usr/bin/env python3 ans = 0 with open("input.txt") as f: for line in f: ans += 2 + line.count("\\") + line.count("\"") print(ans)

18.25

54

0.541096

#!/usr/bin/env python3 ans = 0 with open("input.txt") as f: for line in f: ans += 2 + line.count("\\") + line.count("\"") print(ans)

0

b39083aacc0b8fea019f95a80c7e48ff65c4cb4a

818

py

Python

opttrack/lib/ui/obs_menu.py

aisthesis/opttrack

17e0c7740ea43e0f07166e30d689b106d0319d0b

[ "MIT" ]

null

opttrack/lib/ui/obs_menu.py

aisthesis/opttrack

17e0c7740ea43e0f07166e30d689b106d0319d0b

[ "MIT" ]

2

2016-03-30T02:50:31.000Z

2016-03-30T16:18:23.000Z

opttrack/lib/ui/obs_menu.py

aisthesis/opttrack

17e0c7740ea43e0f07166e30d689b106d0319d0b

[ "MIT" ]

null

24.787879

67

0.665037

""" Copyright (c) 2015 Marshall Farrier license http://opensource.org/licenses/MIT lib/ui/edit_menu.py Content for interactive editor """ from functools import partial from .obs_handlers import ObsHandlers from .menu import Menu from .spread_selector import SpreadSelector class ObsMenu(Menu): def __init__(self, logger, tz): super(ObsMenu, self).__init__(logger, tz=tz) self.spread_sel = SpreadSelector() self._handlers = ObsHandlers(self.logger, self.tz) overrides = {'main': {'desc': 'Quit', 'do': lambda: False}} self._menus = {'main': self.spread_sel.get(overrides, dgb=partial(self.handlers.obs, 'dgb'))} @property def menus(self): return self._menus @property def handlers(self): return self._handlers

0

78

0

440

0

47

113

c2dee6d1aa40a7f38a96156bda231d31db4d330b

1,307

py

Python

nsr/utils/majority_vote.py

GaoSida/Neural-SampleRank

8b4a7a40cc34bff608f19d3f7eb64bda76669c5b

[ "MIT" ]

2

2020-11-17T18:41:05.000Z

2021-08-12T14:40:56.000Z

nsr/utils/majority_vote.py

GaoSida/Neural-SampleRank

8b4a7a40cc34bff608f19d3f7eb64bda76669c5b

[ "MIT" ]

null

nsr/utils/majority_vote.py

GaoSida/Neural-SampleRank

8b4a7a40cc34bff608f19d3f7eb64bda76669c5b

[ "MIT" ]

null

"""A helper for running inference callable multiple times, and ensemble the predictions with a simple majority vote. """ from typing import Callable def majority_vote_ensemble(eval_func: Callable, num_runs: int): """ Args: eval_func: call without argument to get a prediction or a list of predictions. num_runs: how many times to run the eval_func to get the predictions Returns: a prediction or a list of predictions after majority vote. """ if num_runs == 1: return eval_func() all_predictions = [eval_func() for _ in range(num_runs)] if not isinstance(all_predictions[0][0], list): # eval func gives single prediction return _vote(all_predictions) else: # eval func gives a list of predictions results = list() for i in range(len(all_predictions[0])): results.append(_vote([pred_list[i] for pred_list in all_predictions])) return results

33.512821

76

0.638868

"""A helper for running inference callable multiple times, and ensemble the predictions with a simple majority vote. """ from typing import Callable from collections import Counter def majority_vote_ensemble(eval_func: Callable, num_runs: int): """ Args: eval_func: call without argument to get a prediction or a list of predictions. num_runs: how many times to run the eval_func to get the predictions Returns: a prediction or a list of predictions after majority vote. """ if num_runs == 1: return eval_func() def _vote(prediction_list): results = list() for i in range(len(prediction_list[0])): votes = Counter([pred[i] for pred in prediction_list]) results.append(votes.most_common(1)[0][0]) return results all_predictions = [eval_func() for _ in range(num_runs)] if not isinstance(all_predictions[0][0], list): # eval func gives single prediction return _vote(all_predictions) else: # eval func gives a list of predictions results = list() for i in range(len(all_predictions[0])): results.append(_vote([pred_list[i] for pred_list in all_predictions])) return results

0

225

0

10

53

5ded5a7640fb3ff4dbd2ec961b2d0176566b38b3

12,872

py

Python

postgres-appliance/major_upgrade/pg_upgrade.py

OlleLarsson/spilo

a347a9453a2deef33d968261096c1a328d9a2d87

[ "Apache-2.0" ]

null

postgres-appliance/major_upgrade/pg_upgrade.py

OlleLarsson/spilo

a347a9453a2deef33d968261096c1a328d9a2d87

[ "Apache-2.0" ]

null

postgres-appliance/major_upgrade/pg_upgrade.py

OlleLarsson/spilo

a347a9453a2deef33d968261096c1a328d9a2d87

[ "Apache-2.0" ]

null

import logging logger = logging.getLogger(__name__)

43.931741

118

0.64582

import logging import os import shutil import subprocess import psutil from patroni.postgresql import Postgresql logger = logging.getLogger(__name__) class _PostgresqlUpgrade(Postgresql): _INCOMPATIBLE_EXTENSIONS = ('amcheck_next',) def adjust_shared_preload_libraries(self, version): from spilo_commons import adjust_extensions shared_preload_libraries = self.config.get('parameters').get('shared_preload_libraries') self._old_config_values['shared_preload_libraries'] = shared_preload_libraries if shared_preload_libraries: self.config.get('parameters')['shared_preload_libraries'] =\ adjust_extensions(shared_preload_libraries, version) def no_bg_mon(self): shared_preload_libraries = self.config.get('parameters').get('shared_preload_libraries') if shared_preload_libraries: tmp = filter(lambda a: a != "bg_mon", map(lambda a: a.strip(), shared_preload_libraries.split(","))) self.config.get('parameters')['shared_preload_libraries'] = ",".join(tmp) def restore_shared_preload_libraries(self): if getattr(self, '_old_shared_preload_libraries'): self.config.get('parameters')['shared_preload_libraries'] = self._old_shared_preload_libraries return True def start_old_cluster(self, config, version): self.set_bin_dir(version) version = float(version) config[config['method']]['command'] = 'true' if version < 9.5: # 9.4 and older don't have recovery_target_action action = config[config['method']].get('recovery_target_action') config[config['method']]['pause_at_recovery_target'] = str(action == 'pause').lower() # make sure we don't archive wals from the old version self._old_config_values = {'archive_mode': self.config.get('parameters').get('archive_mode')} self.config.get('parameters')['archive_mode'] = 'off' # and don't load shared_preload_libraries which don't exist in the old version self.adjust_shared_preload_libraries(version) return self.bootstrap.bootstrap(config) def get_cluster_version(self): with open(self._version_file) as f: return f.read().strip() def set_bin_dir(self, version): from spilo_commons import get_bin_dir self._old_bin_dir = self._bin_dir self._bin_dir = get_bin_dir(version) @property def local_conn_kwargs(self): conn_kwargs = self.config.local_connect_kwargs conn_kwargs['options'] = '-c synchronous_commit=local -c statement_timeout=0 -c search_path=' conn_kwargs.pop('connect_timeout', None) return conn_kwargs def _get_all_databases(self): return [d[0] for d in self.query('SELECT datname FROM pg_catalog.pg_database WHERE datallowconn')] def drop_possibly_incompatible_extensions(self): from patroni.postgresql.connection import get_connection_cursor logger.info('Dropping extensions from the cluster which could be incompatible') conn_kwargs = self.local_conn_kwargs for d in self._get_all_databases(): conn_kwargs['database'] = d with get_connection_cursor(**conn_kwargs) as cur: for ext in self._INCOMPATIBLE_EXTENSIONS: logger.info('Executing "DROP EXTENSION IF EXISTS %s" in the database="%s"', ext, d) cur.execute("DROP EXTENSION IF EXISTS {0}".format(ext)) def drop_possibly_incompatible_objects(self): from patroni.postgresql.connection import get_connection_cursor logger.info('Dropping objects from the cluster which could be incompatible') conn_kwargs = self.local_conn_kwargs for d in self._get_all_databases(): conn_kwargs['database'] = d with get_connection_cursor(**conn_kwargs) as cur: logger.info('Executing "DROP FUNCTION metric_helpers.pg_stat_statements" in the database="%s"', d) cur.execute("DROP FUNCTION IF EXISTS metric_helpers.pg_stat_statements(boolean) CASCADE") for ext in ('pg_stat_kcache', 'pg_stat_statements') + self._INCOMPATIBLE_EXTENSIONS: logger.info('Executing "DROP EXTENSION IF EXISTS %s" in the database="%s"', ext, d) cur.execute("DROP EXTENSION IF EXISTS {0}".format(ext)) if d[0] == 'postgres': logger.info('Executing "DROP TABLE postgres_log CASCADE" in the database=postgres') cur.execute('DROP TABLE IF EXISTS public.postgres_log CASCADE') cur.execute("SELECT oid::regclass FROM pg_catalog.pg_class WHERE relpersistence = 'u'") for unlogged in cur.fetchall(): logger.info('Truncating unlogged table %s', unlogged[0]) try: cur.execute('TRUNCATE {0}'.format(unlogged[0])) except Exception as e: logger.error('Failed: %r', e) def update_extensions(self): from patroni.postgresql.connection import get_connection_cursor conn_kwargs = self.local_conn_kwargs for d in self._get_all_databases(): conn_kwargs['database'] = d with get_connection_cursor(**conn_kwargs) as cur: cur.execute('SELECT quote_ident(extname) FROM pg_catalog.pg_extension') for extname in cur.fetchall(): query = 'ALTER EXTENSION {0} UPDATE'.format(extname[0]) logger.info("Executing '%s' in the database=%s", query, d) try: cur.execute(query) except Exception as e: logger.error('Failed: %r', e) @staticmethod def remove_new_data(d): if d.endswith('_new') and os.path.isdir(d): shutil.rmtree(d) def cleanup_new_pgdata(self): if getattr(self, '_new_data_dir', None): self.remove_new_data(self._new_data_dir) def cleanup_old_pgdata(self): if os.path.exists(self._old_data_dir): logger.info('Removing %s', self._old_data_dir) shutil.rmtree(self._old_data_dir) return True def switch_pgdata(self): self._old_data_dir = self._data_dir + '_old' self.cleanup_old_pgdata() os.rename(self._data_dir, self._old_data_dir) if getattr(self, '_new_data_dir', None): os.rename(self._new_data_dir, self._data_dir) self.configure_server_parameters() return True def switch_back_pgdata(self): if os.path.exists(self._data_dir): self._new_data_dir = self._data_dir + '_new' self.cleanup_new_pgdata() os.rename(self._data_dir, self._new_data_dir) os.rename(self._old_data_dir, self._data_dir) def pg_upgrade(self, check=False): upgrade_dir = self._data_dir + '_upgrade' if os.path.exists(upgrade_dir) and os.path.isdir(upgrade_dir): shutil.rmtree(upgrade_dir) os.makedirs(upgrade_dir) old_cwd = os.getcwd() os.chdir(upgrade_dir) pg_upgrade_args = ['-k', '-j', str(psutil.cpu_count()), '-b', self._old_bin_dir, '-B', self._bin_dir, '-d', self._data_dir, '-D', self._new_data_dir, '-O', "-c timescaledb.restoring='on'"] if 'username' in self.config.superuser: pg_upgrade_args += ['-U', self.config.superuser['username']] if check: pg_upgrade_args += ['--check'] else: self.config.write_postgresql_conf() logger.info('Executing pg_upgrade%s', (' --check' if check else '')) if subprocess.call([self.pgcommand('pg_upgrade')] + pg_upgrade_args) == 0: os.chdir(old_cwd) shutil.rmtree(upgrade_dir) return True def prepare_new_pgdata(self, version): from spilo_commons import append_extentions locale = self.query('SHOW lc_collate').fetchone()[0] encoding = self.query('SHOW server_encoding').fetchone()[0] initdb_config = [{'locale': locale}, {'encoding': encoding}] if self.query("SELECT current_setting('data_checksums')::bool").fetchone()[0]: initdb_config.append('data-checksums') logger.info('initdb config: %s', initdb_config) self._new_data_dir = os.path.abspath(self._data_dir) self._old_data_dir = self._new_data_dir + '_old' self._data_dir = self._new_data_dir + '_new' self.remove_new_data(self._data_dir) old_postgresql_conf = self.config._postgresql_conf self.config._postgresql_conf = os.path.join(self._data_dir, 'postgresql.conf') old_version_file = self._version_file self._version_file = os.path.join(self._data_dir, 'PG_VERSION') self.set_bin_dir(version) # shared_preload_libraries for the old cluster, cleaned from incompatible/missing libs old_shared_preload_libraries = self.config.get('parameters').get('shared_preload_libraries') # restore original values of archive_mode and shared_preload_libraries if getattr(self, '_old_config_values', None): for name, value in self._old_config_values.items(): if value is None: self.config.get('parameters').pop(name) else: self.config.get('parameters')[name] = value # for the new version we maybe need to add some libs to the shared_preload_libraries shared_preload_libraries = self.config.get('parameters').get('shared_preload_libraries') if shared_preload_libraries: self._old_shared_preload_libraries = self.config.get('parameters')['shared_preload_libraries'] =\ append_extentions(shared_preload_libraries, float(version)) self.no_bg_mon() if not self.bootstrap._initdb(initdb_config): return False self.bootstrap._running_custom_bootstrap = False # Copy old configs. XXX: some parameters might be incompatible! for f in os.listdir(self._new_data_dir): if f.startswith('postgresql.') or f.startswith('pg_hba.conf') or f == 'patroni.dynamic.json': shutil.copy(os.path.join(self._new_data_dir, f), os.path.join(self._data_dir, f)) self.config.write_postgresql_conf() self._new_data_dir, self._data_dir = self._data_dir, self._new_data_dir self.config._postgresql_conf = old_postgresql_conf self._version_file = old_version_file if old_shared_preload_libraries: self.config.get('parameters')['shared_preload_libraries'] = old_shared_preload_libraries self.no_bg_mon() self.configure_server_parameters() return True def do_upgrade(self): return self.pg_upgrade() and self.restore_shared_preload_libraries()\ and self.switch_pgdata() and self.cleanup_old_pgdata() def analyze(self, in_stages=False): vacuumdb_args = ['--analyze-in-stages'] if in_stages else [] logger.info('Rebuilding statistics (vacuumdb%s)', (' ' + vacuumdb_args[0] if in_stages else '')) if 'username' in self.config.superuser: vacuumdb_args += ['-U', self.config.superuser['username']] vacuumdb_args += ['-Z', '-j'] # vacuumdb is processing databases sequantially, while we better do them in parallel, # because it will help with the case when there are multiple databases in the same cluster. single_worker_dbs = ('postgres', 'template1') databases = self._get_all_databases() db_count = len([d for d in databases if d not in single_worker_dbs]) # calculate concurrency per database, except always existing "single_worker_dbs" (they'll get always 1 worker) concurrency = str(max(1, int(psutil.cpu_count()/max(1, db_count)))) procs = [] for d in databases: j = '1' if d in single_worker_dbs else concurrency try: procs.append(subprocess.Popen([self.pgcommand('vacuumdb')] + vacuumdb_args + [j, '-d', d])) except Exception: pass for proc in procs: try: proc.wait() except Exception: pass def PostgresqlUpgrade(config): config['postgresql'].update({'callbacks': {}, 'pg_ctl_timeout': 3600*24*7}) # avoid unnecessary interactions with PGDATA and postgres is_running = _PostgresqlUpgrade.is_running _PostgresqlUpgrade.is_running = lambda s: False try: return _PostgresqlUpgrade(config['postgresql']) finally: _PostgresqlUpgrade.is_running = is_running

0

355

0

11,937

0

380

0

-12

157

efecd1c0949806cb18bbe2451a6a6c7bb21dc209

46

py

Python

code/lib/tosca/__init__.py

superfluidity/RDCL3D

3c5717941bd4046aa1be178e9004db1dc1c469a0

[ "Apache-2.0" ]

8

2017-03-13T16:34:28.000Z

2021-11-16T11:35:56.000Z

code/lib/tosca/__init__.py

superfluidity/RDCL3D

3c5717941bd4046aa1be178e9004db1dc1c469a0

[ "Apache-2.0" ]

null

code/lib/tosca/__init__.py

superfluidity/RDCL3D

3c5717941bd4046aa1be178e9004db1dc1c469a0

[ "Apache-2.0" ]

3

2017-03-28T09:26:40.000Z

2020-12-08T14:16:12.000Z

__all__ = ["tosca_parser", "tosca_rdcl_graph"]

46

0.76087

__all__ = ["tosca_parser", "tosca_rdcl_graph"]

0

68097eba15392e818a32df460c2104fd3ca64819

4,729

py

Python

examples/resources/aws/subnet.py

cfeenstra67/statey

6d127ed48265e2e072fbb26486458a4b28a333ec

[ "MIT" ]

4

2021-02-16T19:34:38.000Z

2022-01-31T16:44:14.000Z

examples/resources/aws/subnet.py

cfeenstra67/statey

6d127ed48265e2e072fbb26486458a4b28a333ec

[ "MIT" ]

null

examples/resources/aws/subnet.py

cfeenstra67/statey

6d127ed48265e2e072fbb26486458a4b28a333ec

[ "MIT" ]

null

from typing import Optional import statey as st SubnetConfigType = st.Struct[ "vpc_id" : st.String, "cidr_block" : st.String, # Optional args "ipv6_cidr_block" : ~st.String, "map_public_ip_on_launch" : st.Boolean(default=False), "assign_ipv6_address_on_creation" : st.Boolean(default=False), # Missing: tags ] SubnetType = st.Struct[ "vpc_id" : st.String, "cidr_block" : st.String, "ipv6_association_id" : ~st.String, "ipv6_cidr_block" : ~st.String, "map_public_ip_on_launch" : st.Boolean, "assign_ipv6_address_on_creation" : st.Boolean, # Missing: tags "id" : st.String, "owner_id" : st.Integer, ] subnet_resource = st.MachineResource("aws_subnet", SubnetMachine) Subnet = subnet_resource.s RESOURCES = [subnet_resource] def register(registry: Optional["Registry"] = None) -> None: """ Register resources in this module """ if registry is None: registry = st.registry for resource in RESOURCES: registry.register(resource)

31.317881

85

0.60055

import asyncio import contextlib from typing import Dict, Any, Optional import aioboto3 import botocore import statey as st SubnetConfigType = st.Struct[ "vpc_id" : st.String, "cidr_block" : st.String, # Optional args "ipv6_cidr_block" : ~st.String, "map_public_ip_on_launch" : st.Boolean(default=False), "assign_ipv6_address_on_creation" : st.Boolean(default=False), # Missing: tags ] SubnetType = st.Struct[ "vpc_id" : st.String, "cidr_block" : st.String, "ipv6_association_id" : ~st.String, "ipv6_cidr_block" : ~st.String, "map_public_ip_on_launch" : st.Boolean, "assign_ipv6_address_on_creation" : st.Boolean, # Missing: tags "id" : st.String, "owner_id" : st.Integer, ] class SubnetMachine(st.SimpleMachine): """ Maching representing an AWS subnet """ UP = st.State("UP", SubnetConfigType, SubnetType) @contextlib.asynccontextmanager async def resource_ctx(self): async with aioboto3.resource("ec2") as ec2: yield ec2 @contextlib.asynccontextmanager async def client_ctx(self): async with aioboto3.client("ec2") as client: yield client @staticmethod async def convert_instance(subnet: "Subnet") -> Dict[str, Any]: out = {"id": subnet.id} ipv6_associations = [] ( out["owner_id"], out["cidr_block"], # ipv6_associations, out["map_public_ip_on_launch"], out["assign_ipv6_address_on_creation"], out["vpc_id"], ) = await asyncio.gather( subnet.owner_id, subnet.cidr_block, # subnet.ipv6_cidr_block_assocation_set, subnet.map_public_ip_on_launch, subnet.assign_ipv6_address_on_creation, subnet.vpc_id, ) if ipv6_associations: association = ipv6_associations[0] out["ipv6_association_id"] = association["AssociationId"] out["ipv6_cidr_block"] = association["Ipv6CidrBlock"] else: out["ipv6_association_id"] = None out["ipv6_cidr_block"] = None return out async def refresh_state(self, data: Any) -> Optional[Any]: async with self.resource_ctx() as ec2: instance = await ec2.Subnet(data["id"]) try: await instance.load() except botocore.exceptions.ClientError: return None return await self.convert_instance(instance) async def create_task(self, config: SubnetConfigType) -> SubnetType: """ Create a new subnet """ async with self.resource_ctx() as ec2, self.client_ctx() as client: kws = {"CidrBlock": config["cidr_block"], "VpcId": config["vpc_id"]} if config["ipv6_cidr_block"] is not None: kws["Ipv6CidrBlock"] = config["ipv6_cidr_block"] subnet = await ec2.create_subnet(**kws) yield await self.convert_instance(subnet) map_public_ip_on_launch = await subnet.map_public_ip_on_launch if map_public_ip_on_launch != config["map_public_ip_on_launch"]: await client.modify_subnet_attribute( MapPublicIpOnLaunch={"Value": config["map_public_ip_on_launch"]}, SubnetId=subnet.id, ) await subnet.load() yield await self.convert_instance(subnet) assign_ipv6_address_on_creation = ( await subnet.assign_ipv6_address_on_creation ) if ( assign_ipv6_address_on_creation != config["assign_ipv6_address_on_creation"] ): await client.modify_subnet_attribute( AssignIpv6AddressOnCreation={ "Value": config["assign_ipv6_address_on_creation"] }, SubnetId=subnet.id, ) await subnet.load() yield await self.convert_instance(subnet) async def delete_task(self, current: SubnetType) -> st.EmptyType: """ Delete the subnet """ async with self.resource_ctx() as ec2: subnet = await ec2.Subnet(current["id"]) await subnet.delete() subnet_resource = st.MachineResource("aws_subnet", SubnetMachine) Subnet = subnet_resource.s RESOURCES = [subnet_resource] def register(registry: Optional["Registry"] = None) -> None: """ Register resources in this module """ if registry is None: registry = st.registry for resource in RESOURCES: registry.register(resource)

0

1,201

2,102

289

0

-12

112

194ab0dc74cd18f13ee2868097d1372c6db981b3

7,181

py

Python

archive/_s3.py

zpz/upathlib

5bf7013be244c5f1b276e0b0ac1b9d7637666ceb

[ "MIT" ]

null

archive/_s3.py

zpz/upathlib

5bf7013be244c5f1b276e0b0ac1b9d7637666ceb

[ "MIT" ]

19

2021-07-08T06:42:31.000Z

2021-10-15T09:07:17.000Z

archive/_s3.py

zpz/upathlib

5bf7013be244c5f1b276e0b0ac1b9d7637666ceb

[ "MIT" ]

null

import logging # This module requires a directory `.aws/` containing credentials in the home directory, # or environment variables `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY`. logger = logging.getLogger(__name__)

32.640909

98

0.569002

import logging from pathlib import Path import time import boto3 # This module requires a directory `.aws/` containing credentials in the home directory, # or environment variables `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY`. logger = logging.getLogger(__name__) def _get_client(): return boto3.session.Session().client('s3') def _has_key(s3_client, bucket: str, key: str) -> bool: response = s3_client.list_objects_v2(Bucket=bucket, Prefix=key) for obj in response.get('Contents', []): if obj['Key'] == key: return True return False def _delete_key(s3_client, bucket: str, key: str) -> None: s3_client.delete_object(Bucket=bucket, Key=key) def has_key(bucket: str, key: str) -> bool: return _has_key(_get_client(), bucket, key) def delete_key(bucket: str, key: str) -> None: return _delete_key(_get_client(), bucket, key) class Bucket: def __init__(self, bucket): for header in ('s3://', 's3n://'): if bucket.startswith(header): bucket = bucket[len(header):] break if '/' in bucket: bucket = bucket[: bucket.find('/')] self._bucket = boto3.resource('s3').Bucket(bucket) @property def name(self): return self._bucket.name def _remove_bucket_key(self, key): for header in ('s3://', 's3n://'): if key.startswith(header): assert key.startswith(header + self.name + '/') key = key[(len(header) + len(self.name) + 1):] return key def upload(self, local_file: str, s3_key: str) -> None: ''' Upload a single file to S3. `local_file`: path to local file. `s3_key`: S3 'key'. Example: suppose current bucket is s3://my-org, with local_file: /home/zepu/work/data/xyz/memo.txt s3_key: mysurvey/memo --> remote file: s3://my-org/mysurvey/memo Existing file with the same name with be overwritten. ''' local_file = Path(local_file) if not local_file.is_file(): raise Exception('a file name is expected') data = open(local_file, 'rb') s3_key = self._remove_bucket_key(s3_key) self._bucket.put_object(Key=s3_key, Body=data) def upload_tree(self, local_path: str, s3_path: str, pattern: str = '**/*') -> None: ''' `local_path`: directory whose content will be uploaded. If `local_path` contains a trailing `/`, then no part of this path name becomes part of the remote name; otherwise, the final node in this path name becomes the leading segment of the remote name. `pattern`: '*' (everything directly under `local_path`), '**/*' (everything recursively under `local_path`), '*.py' (every Python module directly under `local_path`), '**/*.py' (every Python module recursively under `local_path`), etc. Example: suppose current bucket is s3://my-org, with local_path: /home/me/work/data/xyz, containing .../xyz/a.txt, .../xyz/b.txt, ../xyz/zyx/aa.txt) s3_path: dataset1 s3_name: '**/*' --> remote files: s3://my-org/dataset1/xyz/a.txt s3://my-org/dataset1/xyz/b.txt s3://my-org/dataset1/xyz/zyx/aa.txt local_path: /home/me/work/data/xyz/ (note the trailing '/') --> remote files: s3://my-org/dataset1/a.txt s3://my-org/dataset1/b.txt s3://my-org/dataset1/zyx/aa.txt ''' with_root = not local_path.endswith('/') local_path = Path(local_path) if not local_path.is_dir(): raise Exception('a directory name is expected') nodes = [v for v in local_path.glob(pattern) if v.is_file()] s3_path = self._remove_bucket_key(s3_path) for node in nodes: key = node.relative_to(local_path) if with_root: key = local_path.name / key key = s3_path / key self.upload(node, str(key)) def download(self, s3_key: str, local_file: str = None) -> None: s3_key = self._remove_bucket_key(s3_key) if local_file is None: local_file = str(Path(s3_key).name) self._bucket.download_file(s3_key, local_file) def download_tree(self, s3_path: str, local_path: str = None) -> None: s3_path = self._remove_bucket_key(s3_path) raise NotImplementedError def ls(self, key, recursive: bool = False): # List object names directly or recursively named like `key*`. # If `key` is `abc/def/`, # then `abc/def/123/45` will return as `123/45` # # If `key` is `abc/def`, # then `abc/defgh/45` will return as `defgh/45`; # `abc/def/gh` will return as `/gh`. # # So if you know `key` is a `directory`, then it's a good idea to # include the trailing `/` in `key`. key = self._remove_bucket_key(key) z = self._bucket.objects.filter(Prefix=key) if key.endswith('/'): key_len = len(key) else: key_len = key.rfind('/') + 1 if recursive: return (v.key[key_len:] for v in z) # this is a generator, b/c there can be many, many elements else: keys = set() for v in z: vv = v.key[key_len:] idx = vv.find('/') if idx >= 0: vv = vv[: idx] keys.add(vv) return sorted(list(keys)) def has(self, key: str) -> bool: key = self._remove_bucket_key(key) if not hasattr(self, '_s3'): self._s3 = _get_client() return _has_key(self._s3, self._bucket.name, key) def delete(self, key: str) -> None: key = self._remove_bucket_key(key) if not hasattr(self, '_s3'): self._s3 = _get_client() _delete_key(self._s3, self._bucket.name, key) def delete_tree(self, s3_path: str) -> int: s3_path = self._remove_bucket_key(s3_path) n = 0 while True: nn = self._delete_tree(s3_path) if nn == 0: break n = max(n, nn) time.sleep(0.5) return n def _delete_tree(self, s3_path: str) -> int: ''' Return the number of objects deleted. After this operation, the 'folder' `s3_path` is also gone. TODO: this is not the fastest way to do it. ''' assert s3_path.endswith('/') n = 0 for k in self.ls(s3_path, recursive=True): kk = s3_path + k self.delete(kk) n += 1 return n def reduce_boto_logging(): import boto3.s3.transfer assert boto3.s3.transfer # silence pyflakes for name in logging.Logger.manager.loggerDict.keys(): if name.startswith('boto') or name.startswith('urllib3') or name.startswith('s3transfer'): logging.getLogger(name).setLevel(logging.ERROR)

0

41

0

5,902

0

798

0

-16

228

75f81d84c2a063746a49d48076491405182c7fc8

10,799

py

Python

storitch/handlers/store.py

thomaserlang/storitch

dbcf97af547d9cb1ae5c3994654e8db03e43a253

[ "MIT" ]

null

storitch/handlers/store.py

thomaserlang/storitch

dbcf97af547d9cb1ae5c3994654e8db03e43a253

[ "MIT" ]

1

2022-03-03T00:35:08.000Z

storitch/handlers/store.py

thomaserlang/storitch

dbcf97af547d9cb1ae5c3994654e8db03e43a253

[ "MIT" ]

null

import os from wand import image def thumbnail(path: str) -> bool: ''' Specify the path and add a "@" followed by the arguments. This allows us to easily get the original file, make the changes, save the file with the full path, so the server never has to do the operation again, as long as the arguments are precisely the same. Arguments can be specified as followed: SXx - Width, keeps aspect ratio SYx - Height, keeps aspect ration. Ignored if SX is specified. ROTATEx - Number of degrees you wise to rotate the image. Supports negative numbers. RESx - Resolution, used for PDF files, the higher the number, the better the quality. PAGEx - Page index in the PDF document. The file format can be specified by ending the path with E.g. .jpg, .png, .tiff, etc. The arguments can be separated with _ or just don't separate them. Works either way. Example: /foo/14bc...@SX1024_ROTATE90.png Resizes the image to a width of 1024, rotates it 90 degrees and converts it to a PNG file. :param path: str ''' p = path.split('@') if len(p) != 2: return False if os.path.exists(path): return True size_match, rotate_match, resolution_match, \ page_match, format_match = __parse_arguments(p[1]) # a specific page in a PDF document if page_match and page_match.group(1) != None: page = '[{}]'.format(page_match.group(1)) else: # Prevent a dicom file or pdf file from extracting multiple images page = '[0]' o = { 'filename': p[0]+page } if resolution_match and resolution_match.group(1) != None: o['resolution'] = int(resolution_match.group(1)) with image.Image(**o) as img: if size_match: # resize, keep aspect ratio if size_match.group(1) != None:# width img.transform(resize=size_match.group(1)) elif size_match.group(2) != None:# height img.transform(resize='x'+size_match.group(2)) if rotate_match: if rotate_match.group(1) != None: img.rotate(int(rotate_match.group(1))) if format_match: img.format = format_match.group(1) img.save(filename=path) return True

32.429429

96

0.567738

from typing import Union, Dict, List, Any, Tuple, Optional import json, tempfile, os, logging, re, shutil, mimetypes, good from tornado import httpclient, web, queues from storitch import utils, config from storitch.decorators import run_on_executor from wand import image, exceptions class Base_handler(web.RequestHandler): def write_object(self, data: Union[Dict, List]) -> None: self.set_json_headers() self.write(json.dumps(data)) def set_json_headers(self) -> None: self.set_header('Cache-Control', 'no-cache, must-revalidate') self.set_header('Expires', 'Sat, 26 Jul 1997 05:00:00 GMT') self.set_header('Content-Type', 'application/json') def write_error(self, status_code: int, **kwargs) -> None: self.set_json_headers() error = {'error': 'Unknown error'} if 'exc_info' in kwargs: error['error'] = str(kwargs['exc_info'][1]) self.set_status(status_code) self.write_object(error) @run_on_executor def move_to_permanent_store(self, temp_path: str, filename: str) -> Dict[str, Any]: return move_to_permanent_store(temp_path, filename) def get_content_type(self, path: str) -> str: # From: https://www.tornadoweb.org/en/stable/_modules/tornado/web.html#StaticFileHandler mime_type, encoding = mimetypes.guess_type(path) # per RFC 6713, use the appropriate type for a gzip compressed file if encoding == "gzip": return "application/gzip" # As of 2015-07-21 there is no bzip2 encoding defined at # http://www.iana.org/assignments/media-types/media-types.xhtml # So for that (and any other encoding), use octet-stream. elif encoding is not None: return "application/octet-stream" elif mime_type is not None: return mime_type # if mime_type not detected, use application/octet-stream else: return "application/octet-stream" class Multipart_handler(Base_handler): async def post(self) -> None: if 'multipart/form-data' not in self.request.headers.get('Content-Type').lower(): raise web.HTTPError(400, 'Content-Type must be multipart/form-data, was: {}'.format( self.request.headers.get('Content-Type') ) ) if not self.request.files: raise web.HTTPError(400, 'No files uploaded') self.set_status(201) results = [] for n in self.request.files: for f in self.request.files[n]: temp_path = await self.save_body(f['body']) f['body'] = None r = await self.move_to_permanent_store(temp_path, f['filename']) results.append(r) self.write_object(results) @run_on_executor def save_body(self, body: bytes) -> str: with tempfile.NamedTemporaryFile(delete=False, prefix='storitch-') as t: t.write(body) return t.name @web.stream_request_body class Session_handler(Base_handler): __schema__ = good.Schema({ 'finished': good.Boolean(), 'filename': good.All(str, good.Length(min=1, max=255)), good.Optional('session'): str, }) def prepare(self) -> None: if 'application/octet-stream' not in self.request.headers.get('Content-Type').lower(): raise web.HTTPError(400, 'Content-Type must be application/octet-stream, was: {}'.format( self.request.headers.get('Content-Type') ) ) j = self.request.headers.get('storitch-json', None) if not j: raise web.HTTPError(400, 'Header: storitch-json must be set') data = json.loads(j) self.h_finished = data['finished'] self.h_filename = data['filename'] self.h_session = data.get('session') if not self.h_session: self.h_session = self.new_session() self.temp_path = os.path.join( tempfile.gettempdir(), self.h_session ) if not os.path.isfile(self.temp_path): raise web.HTTPError(400, 'Session unknown') self.file = open(self.temp_path, 'ab') def validate_json(self, data: Dict[str, Any]) -> Union[Dict[str, Any], List]: try: return self.__schema__(data) except good.MultipleInvalid as ee: data = [] for e in ee: data.append( '{}: {}'.format( '.'.join(str(x) for x in e.path), e.message, ) ) raise web.HTTPError(400,' - '.join(d for d in data)) except good.Invalid as e: raise web.HTTPError(400, '{}: {}'.format( '.'.join(str(x) for x in e.path), e.message, )) async def data_received(self, chunk: bytes) -> None: self.file.write(chunk) async def put(self) -> None: self.file.close() if self.h_finished: r = await self.move_to_permanent_store(self.temp_path, self.h_filename) self.write_object(r) else: self.write_object({ 'session': self.h_session, }) def new_session(self) -> str: with tempfile.NamedTemporaryFile(delete=False, prefix='storitch-') as t: return os.path.basename(t.name) class Thumbnail_handler(Base_handler): async def get(self, hash_: Optional[str] = None) -> None: if not hash_ or len(hash_) < 64: raise web.HTTPError(404, 'Please specify a file hash') path = os.path.abspath(os.path.join( os.path.realpath(config['store_path']), utils.path_from_hash(hash_), hash_ )) if '@' in hash_: path = await self.thumbnail(path) if not path: self.write('Failed to create the thumbnail') self.set_header('Content-Type', self.get_content_type(path)) with open(path, 'rb') as f: while True: d = f.read(16384) if not d: break self.write(d) @run_on_executor def thumbnail(self, path: str) -> str: if thumbnail(path): return path def move_to_permanent_store(temp_path: str, filename: str) -> Dict[str, Any]: hash_ = utils.file_sha256(temp_path) path = os.path.abspath(os.path.join( os.path.realpath(config['store_path']), utils.path_from_hash(hash_), )) if not os.path.exists(path): os.makedirs(path, mode=0o755) path = os.path.join(path, hash_) if not os.path.exists(path): shutil.move(temp_path, path) os.chmod(path, 0o755) else: os.remove(temp_path) extra = { 'type': 'file', } d = os.path.splitext(filename) if len(d) == 2: ext = d[1] if ext.lower() in config['image_exts']: wh = image_width_high(path) if wh: wh['type'] = 'image' if wh: extra.update(wh) return { 'stored': True, 'filesize': os.stat(path).st_size, 'hash': hash_, 'filename': filename, **extra } def image_width_high(path) -> Optional[Dict[str, int]]: try: with image.Image(filename=path) as img: return { 'width': img.width, 'height': img.height, } except (ValueError, exceptions.MissingDelegateError): return None def thumbnail(path: str) -> bool: ''' Specify the path and add a "@" followed by the arguments. This allows us to easily get the original file, make the changes, save the file with the full path, so the server never has to do the operation again, as long as the arguments are precisely the same. Arguments can be specified as followed: SXx - Width, keeps aspect ratio SYx - Height, keeps aspect ration. Ignored if SX is specified. ROTATEx - Number of degrees you wise to rotate the image. Supports negative numbers. RESx - Resolution, used for PDF files, the higher the number, the better the quality. PAGEx - Page index in the PDF document. The file format can be specified by ending the path with E.g. .jpg, .png, .tiff, etc. The arguments can be separated with _ or just don't separate them. Works either way. Example: /foo/14bc...@SX1024_ROTATE90.png Resizes the image to a width of 1024, rotates it 90 degrees and converts it to a PNG file. :param path: str ''' p = path.split('@') if len(p) != 2: return False if os.path.exists(path): return True size_match, rotate_match, resolution_match, \ page_match, format_match = __parse_arguments(p[1]) # a specific page in a PDF document if page_match and page_match.group(1) != None: page = '[{}]'.format(page_match.group(1)) else: # Prevent a dicom file or pdf file from extracting multiple images page = '[0]' o = { 'filename': p[0]+page } if resolution_match and resolution_match.group(1) != None: o['resolution'] = int(resolution_match.group(1)) with image.Image(**o) as img: if size_match: # resize, keep aspect ratio if size_match.group(1) != None:# width img.transform(resize=size_match.group(1)) elif size_match.group(2) != None:# height img.transform(resize='x'+size_match.group(2)) if rotate_match: if rotate_match.group(1) != None: img.rotate(int(rotate_match.group(1))) if format_match: img.format = format_match.group(1) img.save(filename=path) return True def __parse_arguments(arguments: str) -> Tuple[str, str, str, str, str]: size_match = re.search( 'SX(\d+)|SY(\d+)', arguments, re.I ) rotate_match = re.search( 'ROTATE(-?\d+)', arguments, re.I ) resolution_match = re.search( 'RES(\d+)', arguments, re.I ) page_match = re.search( 'PAGE(\d+)', arguments, re.I ) format_match = re.search( '\.([a-z0-9]{2,5})', arguments, re.I ) return ( size_match, rotate_match, resolution_match, page_match, format_match, )

0

2,866

1,489

1,572

0

1,877

0

164

357

b0fbc439e2b9764f97c049f14ced20df3b6321a9

7,052

py

Python

ddf_library/functions/graph_lib/page_rank.py

eubr-bigsea/Compss-Python

09ab7c474c8badc9932de3e1148f62ffba16b0b2

[ "Apache-2.0" ]

3

2017-08-22T11:32:02.000Z

2021-08-09T09:35:51.000Z

ddf_library/functions/graph_lib/page_rank.py

eubr-bigsea/Compss-Python

09ab7c474c8badc9932de3e1148f62ffba16b0b2

[ "Apache-2.0" ]

null

ddf_library/functions/graph_lib/page_rank.py

eubr-bigsea/Compss-Python

09ab7c474c8badc9932de3e1148f62ffba16b0b2

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- __author__ = "Lucas Miguel S Ponce" __email__ = "[email protected]" from ddf_library.utils import generate_info, create_stage_files, save_stage_file from pycompss.api.api import compss_wait_on import numpy as np __all__ = ['PageRank'] # TODO: this algorithm can be optimized def _pagerank_split(result, nfrag): """Split the list of vertex into nfrag parts. Note: the list of unique vertex and their ranks must be fit in memory. """ result = compss_wait_on(result) result = np.array_split(result, nfrag) outfiles = create_stage_files(nfrag) info = [0] * nfrag for f, table in enumerate(result): save_stage_file(outfiles[f], table) info[f] = generate_info(table, f) return outfiles, info

29.630252

78

0.620108

#!/usr/bin/env python3 # -*- coding: utf-8 -*- __author__ = "Lucas Miguel S Ponce" __email__ = "[email protected]" from ddf_library.bases.metadata import Status, OPTGroup from ddf_library.bases.context_base import ContextBase from ddf_library.ddf import DDF from ddf_library.bases.ddf_model import ModelDDF from ddf_library.utils import generate_info, read_stage_file, \ create_stage_files, save_stage_file from pycompss.api.api import compss_wait_on, compss_delete_object from pycompss.api.task import task from pycompss.functions.reduce import merge_reduce from pycompss.api.parameter import FILE_IN, COLLECTION_IN import pandas as pd import numpy as np __all__ = ['PageRank'] # TODO: this algorithm can be optimized class PageRank(ModelDDF): # noinspection PyUnresolvedReferences """ PageRank is one of the methods Google uses to determine a page's relevance or importance. The idea that Page Rank brought up was that, the importance of any web page can be judged by looking at the pages that link to it. PageRank can be utilized in others domains. For example, may also be used as a methodology to measure the apparent impact of a community. .. note: This parallel implementation assumes that the list of unique vertex can be fit in memory. :Example: >>> pr = PageRank(damping_factor=0.85) >>> ddf2 = pr.transform(ddf1, inlink_col='col1', outlink_col='col2') """ def __init__(self, damping_factor=0.85, max_iters=100): """ :param damping_factor: Default damping factor is 0.85; :param max_iters: Maximum number of iterations (default is 100). """ super(PageRank, self).__init__() self.inlink_col = None self.outlink_col = None self.max_iters = max_iters self.damping_factor = damping_factor def transform(self, data, outlink_col, inlink_col): """ Generates the PageRank's result. :param data: DDF :param outlink_col: Out-link vertex; :param inlink_col: In-link vertex; :return: DDF with Vertex and Rank columns """ df, nfrag, tmp = self._ddf_initial_setup(data) self.inlink_col = inlink_col self.outlink_col = outlink_col col1 = 'Vertex' col2 = 'Rank' """ Load all URL's from the data and initialize their neighbors. Initialize each page’s rank to 1.0. """ adj_list = [{} for _ in range(nfrag)] rank_list = [{} for _ in range(nfrag)] counts_in = [{} for _ in range(nfrag)] for i in range(nfrag): adj_list[i], rank_list[i], counts_in[i] = \ _pr_create_adjlist(df[i], inlink_col, outlink_col) counts_in = merge_reduce(_merge_counts, counts_in) for i in range(nfrag): adj_list[i] = _pr_update_adjlist(adj_list[i], counts_in) compss_delete_object(counts_in) for iteration in range(self.max_iters): """Calculate the partial contribution of each vertex.""" contributions = [_calc_contribuitions(adj_list[i], rank_list[i]) for i in range(nfrag)] merged_c = merge_reduce(_merge_counts, contributions) """Update each vertex rank in the fragment.""" rank_list = [_update_rank(rank_list[i], merged_c, self.damping_factor) for i in range(nfrag)] merged_table = merge_ranks(rank_list, col1, col2) result, info = _pagerank_split(merged_table, nfrag) new_state_uuid = ContextBase\ .ddf_add_task(self.name, status=Status.STATUS_COMPLETED, opt=OPTGroup.OPT_OTHER, info_data=info, parent=[tmp.last_uuid], result=result, function=self.transform, parameters=data) return DDF(last_uuid=new_state_uuid) @task(returns=3, data=FILE_IN) def _pr_create_adjlist(data, inlink, outlink): cols = [outlink, inlink] adj = {} ranks = {} data = read_stage_file(data, cols=cols) for link in data[cols].to_numpy(): v_out, v_in = link # Generate a partial adjacency list. if v_out in adj: adj[v_out][0].append(v_in) adj[v_out][1] += 1 else: adj[v_out] = [[v_in], 1] # Generate a partial rank list of each vertex. if v_out not in ranks: ranks[v_out] = 1.0 # Rank, contributions, main if v_in not in ranks: ranks[v_in] = 1.0 # Generate a partial list of frequency of each vertex. counts_in = {} for v_out in adj: counts_in[v_out] = adj[v_out][1] return adj, ranks, counts_in @task(returns=1) def _merge_counts(counts1, counts2): """ Merge the frequency of each vertex. .. note:: It assumes that the frequency list can be fitted in memory. """ for v_out in counts2: if v_out in counts1: counts1[v_out] += counts2[v_out] else: counts1[v_out] = counts2[v_out] return counts1 @task(returns=1) def _pr_update_adjlist(adj1, counts_in): """Update the frequency of vertex in each fragment.""" for key in adj1: adj1[key][1] = counts_in[key] return adj1 @task(returns=1) def _calc_contribuitions(adj, ranks): """Calculate the partial contribution of each vertex.""" contrib = {} for key in adj: urls = adj[key][0] num_neighbors = adj[key][1] rank = ranks[key] for url in urls: if url not in contrib: # out = contrib contrib[url] = rank/num_neighbors else: contrib[url] += rank/num_neighbors return contrib @task(returns=1) def _update_rank(ranks, contrib, factor): """Update the rank of each vertex in the fragment.""" bo = 1.0 - factor for key in contrib: if key in ranks: ranks[key] = bo + factor*contrib[key] return ranks @task(returns=1, dfs=COLLECTION_IN) def merge_ranks(dfs, c1, c2): """Create the final result. Merge and remove duplicates vertex.""" dfs = [pd.DataFrame(ranks.items(), columns=[c1, c2]) for ranks in dfs] dfs = pd.concat(dfs, ignore_index=True)\ .drop_duplicates(ignore_index=True)\ .sort_values(['Rank'], ascending=False, ignore_index=True) return dfs def _pagerank_split(result, nfrag): """Split the list of vertex into nfrag parts. Note: the list of unique vertex and their ranks must be fit in memory. """ result = compss_wait_on(result) result = np.array_split(result, nfrag) outfiles = create_stage_files(nfrag) info = [0] * nfrag for f, table in enumerate(result): save_stage_file(outfiles[f], table) info[f] = generate_info(table, f) return outfiles, info

3

2,368

0

3,314

0

225

339

faeba7f3ca3382662de94211ec44a097d3c7ac9f

1,390

py

Python

UART_py/tela_serial.py

Rodrigo98Matos/Interface-Grafca-Serial

b996655e7376229856116ec3d150f3210a82cb4d

[ "MIT" ]

null

UART_py/tela_serial.py

Rodrigo98Matos/Interface-Grafca-Serial

b996655e7376229856116ec3d150f3210a82cb4d

[ "MIT" ]

null

UART_py/tela_serial.py

Rodrigo98Matos/Interface-Grafca-Serial

b996655e7376229856116ec3d150f3210a82cb4d

[ "MIT" ]

null

from uart_serial import uart arduino = uart()

37.567568

117

0.538129

import PySimpleGUI as sg from uart_serial import uart arduino = uart() class tela: def __init__(self, portas): #Layout sg.theme('Black') layout = [ [sg.Text('Porta:',size=(7,0)),sg.Combo(values=(portas),key='porta')], [sg.Text('Baudrate:',size=(7,0)),sg.Combo(values=([9600,115200]),key='baudrate')], [sg.Checkbox('Dados da Missão',key='dados_missao'),sg.Checkbox('Beacon',key='beacon')], [sg.Button('Continuar')], ] """layout = [ [sg.Text('Nome',size=(5,0)),sg.Input(size=(15,0),key='nome')], [sg.Text('Idade',size=(5,0)),sg.Input(size=(5,0),key='idade')], [sg.Text('Email:')], [sg.Checkbox('Gmail',key='gmail'),sg.Checkbox('Hotmail',key='hotmail'),sg.Checkbox('Yahoo',key='yahoo')], [sg.Radio('Sim','email',key='email_sim'),sg.Radio('Não','email',key='email_nao')], [sg.Button('Enviar dados')], [sg.Output(size=(30,20))] ]""" #Janela self.janela = sg.Window("dados do Usuário").layout(layout) #Extrair os dados da tela self.button, self.values = self.janela.Read() def iniciar(self): while True: self.button, self.values = self.janela.Read() if self.button == sg.WIN_CLOSED: break print(self.values)

6

0

1,291

0

3

45

a747752e784483f13e0672fa7ef44261d743dd9f

403

py

Python

babybuddy/migrations/0017_promocode_max_usage_per_account.py

amcquistan/babyasst

310a7948f06b71ae0d62593a3b5932abfd4eb444

[ "BSD-2-Clause" ]

null

babybuddy/migrations/0017_promocode_max_usage_per_account.py

amcquistan/babyasst

310a7948f06b71ae0d62593a3b5932abfd4eb444

[ "BSD-2-Clause" ]

null

babybuddy/migrations/0017_promocode_max_usage_per_account.py

amcquistan/babyasst

310a7948f06b71ae0d62593a3b5932abfd4eb444

[ "BSD-2-Clause" ]

null

# Generated by Django 2.2.6 on 2019-11-27 20:28

21.210526

49

0.615385

# Generated by Django 2.2.6 on 2019-11-27 20:28 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('babybuddy', '0016_auto_20191127_1424'), ] operations = [ migrations.AddField( model_name='promocode', name='max_usage_per_account', field=models.IntegerField(default=1), ), ]

0

289

0

19

46

838a777e045278ea92893c031457352439926ec4

1,897

py

Python

transact/models.py

BrilliantGrant/Transaction-application

988fdbd6ed8a1fea9ca8366eeb2b30275b727836

[ "MIT", "Unlicense" ]

null

transact/models.py

BrilliantGrant/Transaction-application

988fdbd6ed8a1fea9ca8366eeb2b30275b727836

[ "MIT", "Unlicense" ]

null

transact/models.py

BrilliantGrant/Transaction-application

988fdbd6ed8a1fea9ca8366eeb2b30275b727836

[ "MIT", "Unlicense" ]

null

# Create your models here.

25.293333

72

0.710596

from django.contrib.auth.models import User from tinymce.models import HTMLField from django.db import models # Create your models here. class Pic(models.Model): pic = models.ImageField(upload_to = "pics/",null = True) user = models.ForeignKey(User,null=True) pic_name = models.CharField(max_length = 30,null = True) likes = models.IntegerField(default=0) pic_caption = models.TextField(null = True) pub_date = models.DateTimeField(auto_now_add=True,null=True) # profile = models.ForeignKey(Profile, null=True) comments = models.IntegerField(default=0) def __str__(self): return self.pic_name def delete_pic(self): self.delete() def save_pic(self): self.save() def update_caption(self,new_caption): self.pic_caption = new_caption self.save() @classmethod def get_pics_by_user(cls,id): sent_pics = Pic.objects.filter(user_id=id) return sent_pics @classmethod def get_pics_by_id(cls,id): fetched_pic = Pic.objects.get(id = id) return fetched_pic class Meta: ordering = ['-pub_date'] def __str__(self): return self.user.username def save_profile(self): self.save() class Profile(models.Model): username = models.CharField(default='User',max_length=30) email = models.CharField(default='email',max_length=30) profile_pic = models.ImageField(upload_to = "profile/",null=True) Phone_number = models.CharField(max_length =30) Amount = models.CharField(max_length =30) withdraw = models.CharField(max_length =30) Balance = models.CharField(max_length =30) def __str__(self): return self.username def delete_profile(self): self.delete() def save_profile(self): self.save() @classmethod def search_profile(cls,search_term): got_profiles = cls.objects.filter(first_name__icontains = search_term) return got_profiles

0

323

0

1,390

0

44

111

4a30bc154d6f294fba0d9fd2e54096f76bfb7a5f

553

py

Python

migrations/versions/6564c80d1598_.py

realtimclemans/SafetyHealthDotCloud

c7eca52f3e6519de34b05ba573a5778423c2dae2

[ "MIT" ]

null

migrations/versions/6564c80d1598_.py

realtimclemans/SafetyHealthDotCloud

c7eca52f3e6519de34b05ba573a5778423c2dae2

[ "MIT" ]

1

2021-02-15T15:58:54.000Z

migrations/versions/6564c80d1598_.py

realtimclemans/SafetyHealthDotCloud

c7eca52f3e6519de34b05ba573a5778423c2dae2

[ "MIT" ]

null

"""empty message Revision ID: 6564c80d1598 Revises: c3c2dc9000d3 Create Date: 2021-06-19 17:03:45.811885 """ # revision identifiers, used by Alembic. revision = '6564c80d1598' down_revision = 'c3c2dc9000d3' branch_labels = None depends_on = None

19.068966

65

0.687161

"""empty message Revision ID: 6564c80d1598 Revises: c3c2dc9000d3 Create Date: 2021-06-19 17:03:45.811885 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '6564c80d1598' down_revision = 'c3c2dc9000d3' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### pass # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### pass # ### end Alembic commands ###

0

208

0

3

90

5eb6460889a29c993a99192a3b46f1a9dae54de9

1,181

py

Python

setup.py

tizz98/xl

4534a1792f878964fedd87432c438ab6364ece49

[ "MIT" ]

1

2018-03-30T17:36:41.000Z

setup.py

tizz98/xl

4534a1792f878964fedd87432c438ab6364ece49

[ "MIT" ]

null

setup.py

tizz98/xl

4534a1792f878964fedd87432c438ab6364ece49

[ "MIT" ]

null

#!/usr/bin/env python try: from setuptools import setup except ImportError: from distutils.core import setup try: import pypandoc long_description = pypandoc.convert('README.md', 'rst') except (IOError, ImportError): with open('README.md') as f: long_description = f.read() from xl import __str_version__, __author__ setup( name='xl', version=__str_version__, description='A nice way of generating excel formulas in python.', long_description=long_description, url='https://github.com/tizz98/xl', download_url='https://github.com/tizz98/xl/tarball/%s' % ( __str_version__ ), author=__author__, author_email='[email protected]', license='MIT', packages=['xl'], keywords='xl excel formulas formula formulae', zip_safe=True, classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Topic :: Software Development :: Libraries', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', ], )

27.465116

69

0.647756

#!/usr/bin/env python try: from setuptools import setup except ImportError: from distutils.core import setup try: import pypandoc long_description = pypandoc.convert('README.md', 'rst') except (IOError, ImportError): with open('README.md') as f: long_description = f.read() from xl import __str_version__, __author__ setup( name='xl', version=__str_version__, description='A nice way of generating excel formulas in python.', long_description=long_description, url='https://github.com/tizz98/xl', download_url='https://github.com/tizz98/xl/tarball/%s' % ( __str_version__ ), author=__author__, author_email='[email protected]', license='MIT', packages=['xl'], keywords='xl excel formulas formula formulae', zip_safe=True, classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Topic :: Software Development :: Libraries', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', ], )

0

91c554ac6c77dd73935b5f3788cc38b6a16bd729

420

py

Python

GroupCondition.py

simplymanas/python-learning

75bc99c0dce211fd1bce5f6ce1155e0f4c71d7d0

[ "Apache-2.0" ]

4

2020-08-18T05:29:38.000Z

2021-03-13T19:01:10.000Z

GroupCondition.py

simplymanas/python-learning

75bc99c0dce211fd1bce5f6ce1155e0f4c71d7d0

[ "Apache-2.0" ]

null

GroupCondition.py

simplymanas/python-learning

75bc99c0dce211fd1bce5f6ce1155e0f4c71d7d0

[ "Apache-2.0" ]

1

2020-08-29T12:57:17.000Z

# all, any for group condition check # Manas Dash # 22th July 2020 # think of any (or) and all (and) as series of logical or and and operators healthy_percentage = 100 have_money = 0 no_of_friends = 5 mental_happiness = [ healthy_percentage > 50, have_money > 0, no_of_friends >= 1 ] if all(mental_happiness): print('happiness inside') if any(mental_happiness): print('happiness outside') # happiness outside

17.5

75

0.738095

# all, any for group condition check # Manas Dash # 22th July 2020 # think of any (or) and all (and) as series of logical or and and operators healthy_percentage = 100 have_money = 0 no_of_friends = 5 mental_happiness = [ healthy_percentage > 50, have_money > 0, no_of_friends >= 1 ] if all(mental_happiness): print('happiness inside') if any(mental_happiness): print('happiness outside') # happiness outside

0

41559822f3cd5754bbcded35318328ac3c23e9ab

12,012

py

Python

zx64c/ast.py

khrynczenko/zx64c

5a95bef1dff281266ea3f0d0bfd63d27ab5e9965

[ "Apache-2.0" ]

null

zx64c/ast.py

khrynczenko/zx64c

5a95bef1dff281266ea3f0d0bfd63d27ab5e9965

[ "Apache-2.0" ]

null

zx64c/ast.py

khrynczenko/zx64c

5a95bef1dff281266ea3f0d0bfd63d27ab5e9965

[ "Apache-2.0" ]

null

from __future__ import annotations from typing import TypeVar T = TypeVar("T")

24.217742

88

0.635614

from __future__ import annotations import abc from typing import List, Text, TypeVar, Generic from abc import ABC from functools import singledispatchmethod from dataclasses import dataclass from zx64c.types import Type, Callable T = TypeVar("T") class AstVisitor(ABC, Generic[T]): @abc.abstractmethod def visit_program(self, node: Program) -> T: pass @abc.abstractmethod def visit_function(self, node: Function) -> T: pass @abc.abstractmethod def visit_block(self, node: Block) -> T: pass @abc.abstractmethod def visit_if(self, node: Block) -> T: pass @abc.abstractmethod def visit_print(self, node: Print) -> T: pass @abc.abstractmethod def visit_let(self, node: Assignment) -> T: pass @abc.abstractmethod def visit_return(self, node: Return) -> T: pass @abc.abstractmethod def visit_assignment(self, node: Assignment) -> T: pass @abc.abstractmethod def visit_equal(self, node: Equal) -> T: pass @abc.abstractmethod def visit_not_equal(self, node: NotEqual) -> T: pass @abc.abstractmethod def visit_addition(self, node: Addition) -> T: pass @abc.abstractmethod def visit_subtraction(self, node: Subtraction) -> T: pass @abc.abstractmethod def visit_negation(self, node: Negation) -> T: pass @abc.abstractmethod def visit_function_call(self, node: FunctionCall) -> T: pass @abc.abstractmethod def visit_identifier(self, node: Identifier) -> T: pass @abc.abstractmethod def visit_unsignedint(self, node: Unsignedint) -> T: pass @abc.abstractmethod def visit_bool(self, node: Bool) -> T: pass class SourceContext: def __init__(self, line: int, column: int): self._line = line self._column = column def __eq__(self, rhs: SourceContext) -> bool: return self._line == rhs._line and self._column == rhs._column @property def line(self) -> int: return self._line @property def column(self) -> int: return self._column class Ast(ABC): def __init__(self, context: SourceContext): self._context = context @property def context(self) -> SourceContext: return self._context @abc.abstractmethod def visit(self, v: AstVisitor[T]) -> T: pass @abc.abstractmethod def __eq__(self, rhs: Ast) -> bool: pass class SjasmplusSnapshotProgram(Ast): def __init__(self, program: Program, source_name: Text): super().__init__(program.context) self.program = program self.source_name = source_name @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_program(self) @SjasmplusSnapshotProgram.__eq__.register def __eq__(self, rhs: SjasmplusSnapshotProgram) -> bool: return self.program == rhs.program and self.source_name == rhs._source_name class Program(Ast): def __init__(self, functions: List[Function], context: SourceContext): super().__init__(context) self.functions = functions @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_program(self) @Program.__eq__.register def _(self, rhs: Program) -> bool: return self.functions == rhs.functions and self.context == rhs.context @dataclass class Parameter: name: str type_id: Type class Function(Ast): def __init__( self, name: str, parameters: List[Parameter], return_type: Type, code_block: Block, context: SourceContext, ): super().__init__(context) self.name = name self.parameters = parameters self.return_type = return_type self.code_block = code_block self.type = Callable(return_type, [p.type_id for p in parameters]) @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_function(self) @Function.__eq__.register def _(self, rhs: Function) -> bool: return ( self.name == rhs.name and self.parameters == rhs.parameters and self.return_type == rhs.return_type and self.code_block == rhs.code_block ) class Block(Ast): def __init__(self, statements: [Ast], context: SourceContext): super().__init__(context) self.statements = statements @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_block(self) @Block.__eq__.register def _(self, rhs: Block) -> bool: return self.statements == rhs.statements class If(Ast): def __init__(self, condition: Ast, consequence: Ast, context: SourceContext): super().__init__(context) self.condition = condition self.consequence = consequence @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_if(self) @If.__eq__.register def _(self, rhs: If) -> bool: return self.condition == rhs.condition and self.consequence == rhs.consequence class Print(Ast): def __init__(self, expression: Ast, context: SourceContext): super().__init__(context) self.expression = expression @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_print(self) @Print.__eq__.register def _(self, rhs: Print) -> bool: return self.expression == rhs.expression and self.context == rhs.context class Let(Ast): def __init__(self, name: str, var_type: Type, rhs: Ast, context: SourceContext): super().__init__(context) self.name = name self.var_type = var_type self.rhs = rhs @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_let(self) @Let.__eq__.register def _(self, rhs: Let) -> bool: return ( self.name == rhs.name and self.var_type == rhs.var_type and self.rhs == rhs.rhs and self.context == rhs.context ) class Assignment(Ast): def __init__(self, name: str, rhs: Ast, context: SourceContext): super().__init__(context) self.name = name self.rhs = rhs @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_assignment(self) @Assignment.__eq__.register def _(self, rhs: Assignment) -> bool: return self.name == rhs.name and self.rhs == rhs.rhs and self.context == rhs.context class Return(Ast): def __init__(self, expr: Ast, context: SourceContext): super().__init__(context) self.expr = expr @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_return(self) @Return.__eq__.register def _(self, rhs: Return) -> bool: return self.expr == rhs.expr class Equal(Ast): def __init__(self, lhs: Ast, rhs: Ast, context: SourceContext): super().__init__(context) self.lhs = lhs self.rhs = rhs @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_equal(self) @Equal.__eq__.register def _(self, rhs: Equal) -> bool: return self.lhs == rhs.lhs and self.rhs == rhs.rhs and self.context == rhs.context class NotEqual(Ast): def __init__(self, lhs: Ast, rhs: Ast, context: SourceContext): super().__init__(context) self.lhs = lhs self.rhs = rhs @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_not_equal(self) @NotEqual.__eq__.register def _(self, rhs: NotEqual) -> bool: return self.lhs == rhs.lhs and self.rhs == rhs.rhs and self.context == rhs.context class Addition(Ast): def __init__(self, lhs: Ast, rhs: Ast, context: SourceContext): super().__init__(context) self.lhs = lhs self.rhs = rhs @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_addition(self) @Addition.__eq__.register def _(self, rhs: Addition) -> bool: return self.lhs == rhs.lhs and self.rhs == rhs.rhs and self.context == rhs.context class Subtraction(Ast): def __init__(self, lhs: Ast, rhs: Ast, context: SourceContext): super().__init__(context) self.lhs = lhs self.rhs = rhs @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_subtraction(self) @Subtraction.__eq__.register def _(self, rhs: Subtraction) -> bool: return self.lhs == rhs.lhs and self.rhs == rhs.rhs and self.context == rhs.context class Negation(Ast): def __init__(self, expression: Ast, context: SourceContext): super().__init__(context) self.expression = expression @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_negation(self) @Negation.__eq__.register def _(self, rhs: Negation) -> bool: return self.expression == rhs.expression and self.context == rhs.context class FunctionCall(Ast): def __init__( self, function_name: str, arguments: List[Ast], context: SourceContext ): super().__init__(context) self.function_name = function_name self.arguments = arguments @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_function_call(self) @FunctionCall.__eq__.register def _(self, rhs: FunctionCall) -> bool: return ( self.function_name == rhs.function_name and self.arguments == rhs.arguments and self.context == rhs.context ) class Identifier(Ast): def __init__(self, value: int, context: SourceContext): super().__init__(context) self.value = value @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_identifier(self) @Identifier.__eq__.register def _(self, rhs: Identifier) -> bool: return ( isinstance(rhs, Identifier) and self.value == rhs.value and self.context == self.context ) class Unsignedint(Ast): def __init__(self, value: int, context: SourceContext): super().__init__(context) self.value = value @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_unsignedint(self) @Unsignedint.__eq__.register def _(self, rhs: Unsignedint) -> bool: return ( isinstance(rhs, Unsignedint) and self.value == rhs.value and self.context == self.context ) class Bool(Ast): def __init__(self, value: bool, context: SourceContext): super().__init__(context) self.value = value @singledispatchmethod def __eq__(self, rhs: Ast) -> bool: return False def visit(self, v: AstVisitor[T]) -> T: return v.visit_bool(self) @Bool.__eq__.register def _(self, rhs: Bool) -> bool: return ( isinstance(rhs, Bool) and self.value == rhs.value and self.context == self.context )

0

4,957

0

5,386

0

58

1,490

12d6ccc9bc22866f30ca1c766583f034776a1025

4,946

py

Python

src/lib/recorder.py

l-maia/viseron

d762be93db74f780db13ac332bf8673c41592aa9

[ "MIT" ]

null

src/lib/recorder.py

l-maia/viseron

d762be93db74f780db13ac332bf8673c41592aa9

[ "MIT" ]

null

src/lib/recorder.py

l-maia/viseron

d762be93db74f780db13ac332bf8673c41592aa9

[ "MIT" ]

null

import logging LOGGER = logging.getLogger(__name__)

35.84058

88

0.641326

import datetime import logging import os from threading import Thread import cv2 from lib.cleanup import SegmentCleanup from lib.helpers import draw_objects from lib.mqtt.camera import MQTTCamera from lib.segments import Segments LOGGER = logging.getLogger(__name__) class FFMPEGRecorder: def __init__(self, config, detection_lock, mqtt_queue): self._logger = logging.getLogger(__name__ + "." + config.camera.name_slug) if getattr(config.recorder.logging, "level", None): self._logger.setLevel(config.recorder.logging.level) elif getattr(config.camera.logging, "level", None): self._logger.setLevel(config.camera.logging.level) self._logger.debug("Initializing ffmpeg recorder") self.config = config self._mqtt_queue = mqtt_queue self.is_recording = False self.last_recording_start = None self.last_recording_end = None self._event_start = None self._event_end = None self._recording_name = None segments_folder = os.path.join( config.recorder.segments_folder, config.camera.name ) self.create_directory(segments_folder) self._segmenter = Segments( self._logger, config, segments_folder, detection_lock ) self._segment_cleanup = SegmentCleanup(config) self._mqtt_devices = {} if self.config.recorder.thumbnail.send_to_mqtt: self._mqtt_devices["latest_thumbnail"] = MQTTCamera( config, mqtt_queue, object_id="latest_thumbnail" ) def on_connect(self, client): for device in self._mqtt_devices.values(): device.on_connect(client) def subfolder_name(self, today): return ( f"{today.year:04}-{today.month:02}-{today.day:02}/{self.config.camera.name}" ) def create_thumbnail(self, file_name, frame, objects, resolution): draw_objects( frame.decoded_frame_umat_rgb, objects, resolution, ) cv2.imwrite(file_name, frame.decoded_frame_umat_rgb) if self.config.recorder.thumbnail.save_to_disk: thumbnail_folder = os.path.join( self.config.recorder.folder, "thumbnails", self.config.camera.name ) self.create_directory(thumbnail_folder) self._logger.debug(f"Saving thumbnail in {thumbnail_folder}") if not cv2.imwrite( os.path.join(thumbnail_folder, "latest_thumbnail.jpg"), frame.decoded_frame_umat_rgb, ): self._logger.error("Failed saving thumbnail to disk") if self.config.recorder.thumbnail.send_to_mqtt and self._mqtt_devices: ret, jpg = cv2.imencode(".jpg", frame.decoded_frame_umat_rgb) if ret: self._mqtt_devices["latest_thumbnail"].publish(jpg.tobytes()) def create_directory(self, path): try: if not os.path.isdir(path): self._logger.debug(f"Creating folder {path}") os.makedirs(path) except FileExistsError: pass def start_recording(self, frame, objects, resolution): self._logger.info("Starting recorder") self.is_recording = True self._segment_cleanup.pause() now = datetime.datetime.now() self.last_recording_start = now.isoformat() self.last_recording_end = None self._event_start = int(now.timestamp()) if self.config.recorder.folder is None: self._logger.error("Output directory is not specified") return # Create filename now = datetime.datetime.now() video_name = f"{now.strftime('%H:%M:%S')}.{self.config.recorder.extension}" thumbnail_name = f"{now.strftime('%H:%M:%S')}.jpg" # Create foldername subfolder = self.subfolder_name(now) full_path = os.path.join(self.config.recorder.folder, subfolder) self.create_directory(full_path) if frame: self.create_thumbnail( os.path.join(full_path, thumbnail_name), frame, objects, resolution ) self._recording_name = os.path.join(full_path, video_name) def concat_segments(self): self._segmenter.concat_segments( self._event_start - self.config.recorder.lookback, self._event_end, self._recording_name, ) # Dont resume cleanup if new recording started during encoding if not self.is_recording: self._segment_cleanup.resume() def stop_recording(self): self._logger.info("Stopping recorder") self.is_recording = False now = datetime.datetime.now() self.last_recording_end = now.isoformat() self._event_end = int(now.timestamp()) concat_thread = Thread(target=self.concat_segments) concat_thread.start()

0

4,652

0

39

201

6ad5c56d611d041bd4e20428bfb9dda30e760ae2

810

py

Python

src/USEFUL/basic_examples/example_setdefault.py

binxiangni/Python-and-Algorithms-and-Data-Structures

d2c082d261a68b06f533703867ae8a90ac7f4df1

[ "MIT" ]

5

2017-08-03T06:33:49.000Z

2021-08-06T13:20:57.000Z

src/USEFUL/basic_examples/example_setdefault.py

ritahu/Python-and-Algorithms-and-Data-Structures

d2c082d261a68b06f533703867ae8a90ac7f4df1

[ "MIT" ]

null

src/USEFUL/basic_examples/example_setdefault.py

ritahu/Python-and-Algorithms-and-Data-Structures

d2c082d261a68b06f533703867ae8a90ac7f4df1

[ "MIT" ]

6

2017-04-27T13:30:49.000Z

2020-11-01T20:28:55.000Z

#!/usr/bin/env python __author__ = "bt3" if __name__ == '__main__': test_setdef()

20.769231

51

0.549383

#!/usr/bin/env python __author__ = "bt3" def usual_dict(dict_data): newdata = {} for k, v in dict_data: if k in newdata: newdata[k].append(v) else: newdata[k] = [v] return newdata def setdefault_dict(dict_data): newdata = {} for k, v in dict_data: newdata.setdefault(k, []).append(v) return newdata def test_setdef(module_name='this module'): dict_data = (('key1', 'value1'), ('key1', 'value2'), ('key2', 'value3'), ('key2', 'value4'), ('key2', 'value5'),) print(usual_dict(dict_data)) print(setdefault_dict(dict_data)) s = 'Tests in {name} have {con}!' print(s.format(name=module_name, con='passed')) if __name__ == '__main__': test_setdef()

0

648

0

69

aa3156cdaa41c2efd05f58391fef51ddc65a1c89

423

py

Python

examples/PyObjC/pbplugin/PyTestPlugin.py

flupke/py2app

8eb6c618f9c63d6ac970fb145a7f7782b71bcb4d

[ "MIT" ]

81

2015-11-29T12:17:39.000Z

2021-08-02T07:06:51.000Z

examples/PyObjC/pbplugin/PyTestPlugin.py

flupke/py2app

8eb6c618f9c63d6ac970fb145a7f7782b71bcb4d

[ "MIT" ]

11

2016-10-23T16:34:10.000Z

2022-01-30T05:45:54.000Z

examples/PyObjC/pbplugin/PyTestPlugin.py

flupke/py2app

8eb6c618f9c63d6ac970fb145a7f7782b71bcb4d

[ "MIT" ]

21

2016-01-25T18:46:31.000Z

2021-01-08T17:38:03.000Z

import objc import sys print "PyTestPlugin", __name__ print u"[inside] currentBundle %r" % (objc.currentBundle(),)

23.5

60

0.664303

from Foundation import * import objc import sys class PyTestPlugin(NSObject): def init(self): self = super(PyTestPlugin, self).init() print 'class load!!' print "Hello from py2app" print "frozen", repr(getattr(sys, "frozen", None)) return self class PyTestPlugin2(NSObject): pass print "PyTestPlugin", __name__ print u"[inside] currentBundle %r" % (objc.currentBundle(),)

0

236

0

3

68

4daa46c2152e35f2d6fed9c1e7f117f7a7694955

439

py

Python

tests/grammar/grammars/simple.py

AlexandreH/securify2

2d2ba0e1c20cdda550120ecdc1a7164db9b90e3c

[ "Apache-2.0" ]

258

2020-01-23T16:58:38.000Z

2022-03-31T17:29:25.000Z

tests/grammar/grammars/simple.py

sirhashalot/securify2

6852707449577add14bafce8e304946b3490a977

[ "Apache-2.0" ]

34

2020-01-30T06:11:58.000Z

2022-02-27T07:53:17.000Z

tests/grammar/grammars/simple.py

sirhashalot/securify2

6852707449577add14bafce8e304946b3490a977

[ "Apache-2.0" ]

66

2020-01-28T09:23:05.000Z

2022-03-22T09:01:43.000Z

from __future__ import annotations

12.911765

60

0.738041

from __future__ import annotations from typing import Sequence, Union, Optional from securify.grammar import abstract_production, production @abstract_production class Base: pass @abstract_production class AOrC(Base): pass @production class A(AOrC, Base): optional: Optional[Base] @production class B(Base): seq: Sequence[AOrC] @production class C(AOrC, Base): single: B @production class E(Base): pass

0

154

0

62

184

ab7e4c8ae6107856ac778d397edffb130f2bed1a

2,490

py

Python

includes/vars.py

jerseyshawn/cf-vcap-vars

26effac112b500271e2f5ed298f0e6ab50bd7c4e

[ "MIT" ]

null

includes/vars.py

jerseyshawn/cf-vcap-vars

26effac112b500271e2f5ed298f0e6ab50bd7c4e

[ "MIT" ]

null

includes/vars.py

jerseyshawn/cf-vcap-vars

26effac112b500271e2f5ed298f0e6ab50bd7c4e

[ "MIT" ]

null

#!/usr/bin/env python3

35.571429

83

0.485944

#!/usr/bin/env python3 from os import environ from json import dumps class CloudFoundry: def __init__(self, **kwargs): self.__CF_VARIABLES__ = {'CF_INSTANCE_ADDR': '', 'CF_INSTANCE_GUID': '', 'CF_INSTANCE_INDEX': '', 'CF_INSTANCE_INTERNAL_IP': '', 'CF_INSTANCE_IP': '', 'CF_INSTANCE_PORT': '', 'CF_INSTANCE_PORTS': [{}], 'CF_STACK': '', 'DATABASE_URL': '', 'HOME': '', 'INSTANCE_GUID': '', 'INSTANCE_INDEX': '', 'LANG': '', 'MEMORY_LIMIT': '', 'PATH': '', 'PORT': '', 'PWD': '', 'TMPDIR': '', 'USER': '', 'VCAP_APP_HOST': '', 'VCAP_APP_PORT': '', 'VCAP_APPLICATION': {}, 'VCAP_SERVICES': {}} if kwargs.get('testing'): self.load_testing_data(**kwargs) else: self.set_cf_variables(**kwargs) def load_testing_data(self, **kwargs): pass def set_cf_variables(self, **kwargs): variables = kwargs.get('variables', None) if isinstance(variables, str): cf_variables = [variable.upper() for variable in variables.split(',')] elif isinstance(variables, list): cf_variables = [variable.upper() for variable in variables] else: cf_variables = self.__CF_VARIABLES__ for cf_variable in cf_variables: # found in env if cf_variable in environ: setattr(self,str(cf_variable).lower(),environ[cf_variable]) # not in env, but a known cf var elif cf_variable in self.__CF_VARIABLES__: setattr(self, str(cf_variable).lower(), self.__CF_VARIABLES__[cf_variable]) # not in env and not defaulted else: setattr(self, str(cf_variable).lower(), '') def get_cf_variables(self, **kwargs): variables = {} for variable in sorted(self.__CF_VARIABLES__): variable = variable.lower() if hasattr(self, variable): variables[variable] = getattr(self,variable) print(dumps(variables, indent=4)) return(variables)

0

2,394

0

2

68

f6bbb564e37b6b680c5c92655011416fe930bcbf

2,043

py

Python

vega/__init__.py

zjzh/vega

aa6e7b8c69024262fc483ee06113b4d1bd5156d8

[ "Apache-2.0" ]

null

vega/__init__.py

zjzh/vega

aa6e7b8c69024262fc483ee06113b4d1bd5156d8

[ "Apache-2.0" ]

null

vega/__init__.py

zjzh/vega

aa6e7b8c69024262fc483ee06113b4d1bd5156d8

[ "Apache-2.0" ]

null

# -*- coding:utf-8 -*- # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Vega's methods.""" __all__ = [ "set_backend", "is_cpu_device", "is_gpu_device", "is_npu_device", "is_ms_backend", "is_tf_backend", "is_torch_backend", "get_devices", "ClassFactory", "ClassType", "FileOps", "run", "init_cluster_args", "module_existed", "TrialAgent", "get_network", "get_dataset", "get_trainer", "get_quota", ] __version__ = "1.8.0" import sys if sys.version_info < (3, 6): sys.exit('Sorry, Python < 3.6 is not supported.') from .common.backend_register import set_backend, is_cpu_device, is_gpu_device, is_npu_device, is_ms_backend, is_tf_backend, is_torch_backend, get_devices from .common.class_factory import ClassFactory, ClassType from .common.file_ops import FileOps from .core import run, init_cluster_args, module_existed from .trainer.trial_agent import TrialAgent def get_network(name, **kwargs): """Return network.""" return ClassFactory.get_cls(ClassType.NETWORK, name)(**kwargs) def get_dataset(name, **kwargs): """Return dataset.""" return ClassFactory.get_cls(ClassType.DATASET, name)(**kwargs) def get_trainer(name="Trainer", **kwargs): """Return trainer.""" return ClassFactory.get_cls(ClassType.TRAINER, name)(**kwargs) def get_quota(**kwargs): """Return quota.""" return ClassFactory.get_cls(ClassType.QUOTA, "Quota")(**kwargs)

28.375

96

0.714146

# -*- coding:utf-8 -*- # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Vega's methods.""" __all__ = [ "set_backend", "is_cpu_device", "is_gpu_device", "is_npu_device", "is_ms_backend", "is_tf_backend", "is_torch_backend", "get_devices", "ClassFactory", "ClassType", "FileOps", "run", "init_cluster_args", "module_existed", "TrialAgent", "get_network", "get_dataset", "get_trainer", "get_quota", ] __version__ = "1.8.0" import sys if sys.version_info < (3, 6): sys.exit('Sorry, Python < 3.6 is not supported.') from .common.backend_register import set_backend, is_cpu_device, is_gpu_device, is_npu_device, \ is_ms_backend, is_tf_backend, is_torch_backend, get_devices from .common.class_factory import ClassFactory, ClassType from .common.file_ops import FileOps from .core import run, init_cluster_args, module_existed from .trainer.trial_agent import TrialAgent from . import quota def get_network(name, **kwargs): """Return network.""" return ClassFactory.get_cls(ClassType.NETWORK, name)(**kwargs) def get_dataset(name, **kwargs): """Return dataset.""" return ClassFactory.get_cls(ClassType.DATASET, name)(**kwargs) def get_trainer(name="Trainer", **kwargs): """Return trainer.""" return ClassFactory.get_cls(ClassType.TRAINER, name)(**kwargs) def get_quota(**kwargs): """Return quota.""" return ClassFactory.get_cls(ClassType.QUOTA, "Quota")(**kwargs)

0

4

22

a3fd214bd3ac94e9556d5163c8b69ad52bfd9956

13,033

py

Python

colour/models/tests/test_cie_lab.py

MaxSchambach/colour

3f3685d616fda4be58cec20bc1e16194805d7e2d

[ "BSD-3-Clause" ]

null

colour/models/tests/test_cie_lab.py

MaxSchambach/colour

3f3685d616fda4be58cec20bc1e16194805d7e2d

[ "BSD-3-Clause" ]

null

colour/models/tests/test_cie_lab.py

MaxSchambach/colour

3f3685d616fda4be58cec20bc1e16194805d7e2d

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Defines unit tests for :mod:`colour.models.cie_lab` module. """ from __future__ import division, unicode_literals import unittest __author__ = 'Colour Developers' __copyright__ = 'Copyright (C) 2013-2019 - Colour Developers' __license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause' __maintainer__ = 'Colour Developers' __email__ = '[email protected]' __status__ = 'Production' __all__ = [ 'TestXYZ_to_Lab', 'TestLab_to_XYZ', 'TestLab_to_LCHab', 'TestLCHab_to_Lab' ] if __name__ == '__main__': unittest.main()

34.028721

79

0.58559

# -*- coding: utf-8 -*- """ Defines unit tests for :mod:`colour.models.cie_lab` module. """ from __future__ import division, unicode_literals import numpy as np import unittest from itertools import permutations from colour.models import XYZ_to_Lab, Lab_to_XYZ, Lab_to_LCHab, LCHab_to_Lab from colour.utilities import domain_range_scale, ignore_numpy_errors __author__ = 'Colour Developers' __copyright__ = 'Copyright (C) 2013-2019 - Colour Developers' __license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause' __maintainer__ = 'Colour Developers' __email__ = '[email protected]' __status__ = 'Production' __all__ = [ 'TestXYZ_to_Lab', 'TestLab_to_XYZ', 'TestLab_to_LCHab', 'TestLCHab_to_Lab' ] class TestXYZ_to_Lab(unittest.TestCase): """ Defines :func:`colour.models.cie_lab.XYZ_to_Lab` definition unit tests methods. """ def test_XYZ_to_Lab(self): """ Tests :func:`colour.models.cie_lab.XYZ_to_Lab` definition. """ np.testing.assert_almost_equal( XYZ_to_Lab(np.array([0.20654008, 0.12197225, 0.05136952])), np.array([41.52787529, 52.63858304, 26.92317922]), decimal=7) np.testing.assert_almost_equal( XYZ_to_Lab(np.array([0.14222010, 0.23042768, 0.10495772])), np.array([55.11636304, -41.08791787, 30.91825778]), decimal=7) np.testing.assert_almost_equal( XYZ_to_Lab(np.array([0.07818780, 0.06157201, 0.28099326])), np.array([29.80565520, 20.01830466, -48.34913874]), decimal=7) np.testing.assert_almost_equal( XYZ_to_Lab( np.array([0.20654008, 0.12197225, 0.05136952]), np.array([0.44757, 0.40745])), np.array([41.52787529, 38.48089305, -5.73295122]), decimal=7) np.testing.assert_almost_equal( XYZ_to_Lab( np.array([0.20654008, 0.12197225, 0.05136952]), np.array([0.34570, 0.35850])), np.array([41.52787529, 51.19354174, 19.91843098]), decimal=7) np.testing.assert_almost_equal( XYZ_to_Lab( np.array([0.20654008, 0.12197225, 0.05136952]), np.array([0.34570, 0.35850, 1.00000])), np.array([41.52787529, 51.19354174, 19.91843098]), decimal=7) def test_n_dimensional_XYZ_to_Lab(self): """ Tests :func:`colour.models.cie_lab.XYZ_to_Lab` definition n-dimensional support. """ XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) illuminant = np.array([0.31270, 0.32900]) Lab = XYZ_to_Lab(XYZ, illuminant) XYZ = np.tile(XYZ, (6, 1)) Lab = np.tile(Lab, (6, 1)) np.testing.assert_almost_equal( XYZ_to_Lab(XYZ, illuminant), Lab, decimal=7) illuminant = np.tile(illuminant, (6, 1)) np.testing.assert_almost_equal( XYZ_to_Lab(XYZ, illuminant), Lab, decimal=7) XYZ = np.reshape(XYZ, (2, 3, 3)) illuminant = np.reshape(illuminant, (2, 3, 2)) Lab = np.reshape(Lab, (2, 3, 3)) np.testing.assert_almost_equal( XYZ_to_Lab(XYZ, illuminant), Lab, decimal=7) def test_domain_range_scale_XYZ_to_Lab(self): """ Tests :func:`colour.models.cie_lab.XYZ_to_Lab` definition domain and range scale support. """ XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) illuminant = np.array([0.31270, 0.32900]) Lab = XYZ_to_Lab(XYZ, illuminant) d_r = (('reference', 1, 1), (1, 1, 0.01), (100, 100, 1)) for scale, factor_a, factor_b in d_r: with domain_range_scale(scale): np.testing.assert_almost_equal( XYZ_to_Lab(XYZ * factor_a, illuminant), Lab * factor_b, decimal=7) @ignore_numpy_errors def test_nan_XYZ_to_Lab(self): """ Tests :func:`colour.models.cie_lab.XYZ_to_Lab` definition nan support. """ cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan] cases = set(permutations(cases * 3, r=3)) for case in cases: XYZ = np.array(case) illuminant = np.array(case[0:2]) XYZ_to_Lab(XYZ, illuminant) class TestLab_to_XYZ(unittest.TestCase): """ Defines :func:`colour.models.cie_lab.Lab_to_XYZ` definition unit tests methods. """ def test_Lab_to_XYZ(self): """ Tests :func:`colour.models.cie_lab.Lab_to_XYZ` definition. """ np.testing.assert_almost_equal( Lab_to_XYZ(np.array([41.52787529, 52.63858304, 26.92317922])), np.array([0.20654008, 0.12197225, 0.05136952]), decimal=7) np.testing.assert_almost_equal( Lab_to_XYZ(np.array([55.11636304, -41.08791787, 30.91825778])), np.array([0.14222010, 0.23042768, 0.10495772]), decimal=7) np.testing.assert_almost_equal( Lab_to_XYZ(np.array([29.80565520, 20.01830466, -48.34913874])), np.array([0.07818780, 0.06157201, 0.28099326]), decimal=7) np.testing.assert_almost_equal( Lab_to_XYZ( np.array([41.52787529, 38.48089305, -5.73295122]), np.array([0.44757, 0.40745])), np.array([0.20654008, 0.12197225, 0.05136952]), decimal=7) np.testing.assert_almost_equal( Lab_to_XYZ( np.array([41.52787529, 51.19354174, 19.91843098]), np.array([0.34570, 0.35850])), np.array([0.20654008, 0.12197225, 0.05136952]), decimal=7) np.testing.assert_almost_equal( Lab_to_XYZ( np.array([41.52787529, 51.19354174, 19.91843098]), np.array([0.34570, 0.35850, 1.00000])), np.array([0.20654008, 0.12197225, 0.05136952]), decimal=7) def test_n_dimensional_Lab_to_XYZ(self): """ Tests :func:`colour.models.cie_lab.Lab_to_XYZ` definition n-dimensional support. """ Lab = np.array([41.52787529, 52.63858304, 26.92317922]) illuminant = np.array([0.31270, 0.32900]) XYZ = Lab_to_XYZ(Lab, illuminant) Lab = np.tile(Lab, (6, 1)) XYZ = np.tile(XYZ, (6, 1)) np.testing.assert_almost_equal( Lab_to_XYZ(Lab, illuminant), XYZ, decimal=7) illuminant = np.tile(illuminant, (6, 1)) np.testing.assert_almost_equal( Lab_to_XYZ(Lab, illuminant), XYZ, decimal=7) Lab = np.reshape(Lab, (2, 3, 3)) illuminant = np.reshape(illuminant, (2, 3, 2)) XYZ = np.reshape(XYZ, (2, 3, 3)) np.testing.assert_almost_equal( Lab_to_XYZ(Lab, illuminant), XYZ, decimal=7) def test_domain_range_scale_Lab_to_XYZ(self): """ Tests :func:`colour.models.cie_lab.Lab_to_XYZ` definition domain and range scale support. """ Lab = np.array([41.52787529, 52.63858304, 26.92317922]) illuminant = np.array([0.31270, 0.32900]) XYZ = Lab_to_XYZ(Lab, illuminant) d_r = (('reference', 1, 1), (1, 0.01, 1), (100, 1, 100)) for scale, factor_a, factor_b in d_r: with domain_range_scale(scale): np.testing.assert_almost_equal( Lab_to_XYZ(Lab * factor_a, illuminant), XYZ * factor_b, decimal=7) @ignore_numpy_errors def test_nan_Lab_to_XYZ(self): """ Tests :func:`colour.models.cie_lab.Lab_to_XYZ` definition nan support. """ cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan] cases = set(permutations(cases * 3, r=3)) for case in cases: Lab = np.array(case) illuminant = np.array(case[0:2]) Lab_to_XYZ(Lab, illuminant) class TestLab_to_LCHab(unittest.TestCase): """ Defines :func:`colour.models.cie_lab.Lab_to_LCHab` definition unit tests methods. """ def test_Lab_to_LCHab(self): """ Tests :func:`colour.models.cie_lab.Lab_to_LCHab` definition. """ np.testing.assert_almost_equal( Lab_to_LCHab(np.array([41.52787529, 52.63858304, 26.92317922])), np.array([41.52787529, 59.12425901, 27.08848784]), decimal=7) np.testing.assert_almost_equal( Lab_to_LCHab(np.array([55.11636304, -41.08791787, 30.91825778])), np.array([55.11636304, 51.42135412, 143.03889556]), decimal=7) np.testing.assert_almost_equal( Lab_to_LCHab(np.array([29.80565520, 20.01830466, -48.34913874])), np.array([29.80565520, 52.32945383, 292.49133666]), decimal=7) def test_n_dimensional_Lab_to_LCHab(self): """ Tests :func:`colour.models.cie_lab.Lab_to_LCHab` definition n-dimensional arrays support. """ Lab = np.array([41.52787529, 52.63858304, 26.92317922]) LCHab = Lab_to_LCHab(Lab) Lab = np.tile(Lab, (6, 1)) LCHab = np.tile(LCHab, (6, 1)) np.testing.assert_almost_equal(Lab_to_LCHab(Lab), LCHab, decimal=7) Lab = np.reshape(Lab, (2, 3, 3)) LCHab = np.reshape(LCHab, (2, 3, 3)) np.testing.assert_almost_equal(Lab_to_LCHab(Lab), LCHab, decimal=7) def test_domain_range_scale_Lab_to_LCHab(self): """ Tests :func:`colour.models.cie_lab.Lab_to_LCHab` definition domain and range scale support. """ Lab = np.array([41.52787529, 52.63858304, 26.92317922]) LCHab = Lab_to_LCHab(Lab) d_r = (('reference', 1, 1), (1, 0.01, np.array([0.01, 0.01, 1 / 360])), (100, 1, np.array([1, 1, 1 / 3.6]))) for scale, factor_a, factor_b in d_r: with domain_range_scale(scale): np.testing.assert_almost_equal( Lab_to_LCHab(Lab * factor_a), LCHab * factor_b, decimal=7) @ignore_numpy_errors def test_nan_Lab_to_LCHab(self): """ Tests :func:`colour.models.cie_lab.Lab_to_LCHab` definition nan support. """ cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan] cases = set(permutations(cases * 3, r=3)) for case in cases: Lab = np.array(case) Lab_to_LCHab(Lab) class TestLCHab_to_Lab(unittest.TestCase): """ Defines :func:`colour.models.cie_lab.LCHab_to_Lab` definition unit tests methods. """ def test_LCHab_to_Lab(self): """ Tests :func:`colour.models.cie_lab.LCHab_to_Lab` definition. """ np.testing.assert_almost_equal( LCHab_to_Lab(np.array([41.52787529, 59.12425901, 27.08848784])), np.array([41.52787529, 52.63858304, 26.92317922]), decimal=7) np.testing.assert_almost_equal( LCHab_to_Lab(np.array([55.11636304, 51.42135412, 143.03889556])), np.array([55.11636304, -41.08791787, 30.91825778]), decimal=7) np.testing.assert_almost_equal( LCHab_to_Lab(np.array([29.80565520, 52.32945383, 292.49133666])), np.array([29.80565520, 20.01830466, -48.34913874]), decimal=7) def test_n_dimensional_LCHab_to_Lab(self): """ Tests :func:`colour.models.cie_lab.LCHab_to_Lab` definition n-dimensional arrays support. """ LCHab = np.array([41.52787529, 59.12425901, 27.08848784]) Lab = LCHab_to_Lab(LCHab) LCHab = np.tile(LCHab, (6, 1)) Lab = np.tile(Lab, (6, 1)) np.testing.assert_almost_equal(LCHab_to_Lab(LCHab), Lab, decimal=7) LCHab = np.reshape(LCHab, (2, 3, 3)) Lab = np.reshape(Lab, (2, 3, 3)) np.testing.assert_almost_equal(LCHab_to_Lab(LCHab), Lab, decimal=7) def test_domain_range_scale_LCHab_to_Lab(self): """ Tests :func:`colour.models.cie_lab.LCHab_to_Lab` definition domain and range scale support. """ LCHab = np.array([41.52787529, 59.12425901, 27.08848784]) Lab = LCHab_to_Lab(LCHab) d_r = (('reference', 1, 1), (1, np.array([0.01, 0.01, 1 / 360]), 0.01), (100, np.array([1, 1, 1 / 3.6]), 1)) for scale, factor_a, factor_b in d_r: with domain_range_scale(scale): np.testing.assert_almost_equal( LCHab_to_Lab(LCHab * factor_a), Lab * factor_b, decimal=7) @ignore_numpy_errors def test_nan_LCHab_to_Lab(self): """ Tests :func:`colour.models.cie_lab.LCHab_to_Lab` definition nan support. """ cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan] cases = set(permutations(cases * 3, r=3)) for case in cases: LCHab = np.array(case) LCHab_to_Lab(LCHab) if __name__ == '__main__': unittest.main()

0

1,482

0

10,662

0

112

182

84c76c41a480dae4b33646b4f0e6c9ccbdced4c9

17,510

py

Python

letype_extractor.py

olzama/neural-supertagging

340a9b3eaf6427e5ec475cd03bc6f4b3d4891ba4

[ "MIT" ]

null

letype_extractor.py

olzama/neural-supertagging

340a9b3eaf6427e5ec475cd03bc6f4b3d4891ba4

[ "MIT" ]

null

letype_extractor.py

olzama/neural-supertagging

340a9b3eaf6427e5ec475cd03bc6f4b3d4891ba4

[ "MIT" ]

null

import sys, pathlib import pickle from datetime import datetime CONTEXT_WINDOW = 2 DEV = ['ws212', 'ecpa'] TEST = ['cb', 'ecpr', 'jhk', 'jhu', 'tgk', 'tgu', 'psk', 'psu', #'rondane', 'vm32', 'ws213', 'ws214', 'petet', 'wsj23'] IGNORE = ['ntucle', 'omw', 'wlb03', 'wnb03'] NONTRAIN = DEV + TEST + IGNORE if __name__ == "__main__": args = sys.argv[1:] dt_str = '-'.join(str(datetime.now()).split()).replace(':','.') run_id = sys.argv[3] + dt_str if len(sys.argv) > 3: autoreg = sys.argv[4] == 'autoreg' out_dir = './output/' + run_id pathlib.Path(out_dir).mkdir(parents=True, exist_ok=False) le = LexTypeExtractor() le.parse_lexicons(args[0]) le.stats['total lextypes'] = len(le.lextypes) if autoreg: le.process_testsuites_autoreg(args[1],le.lextypes,out_dir) else: le.process_testsuites_nonautoreg(args[1],le.lextypes,out_dir) with open(out_dir + '/lextypes','wb') as f: lextypes = set([str(v) for v in list(le.lextypes.values())]) pickle.dump(lextypes,f)

46.693333

116

0.525871

from delphin import tdl, itsdb from delphin.tokens import YYTokenLattice import glob, sys, pathlib import json, pickle import numpy as np from collections import OrderedDict import pos_map from datetime import datetime CONTEXT_WINDOW = 2 DEV = ['ws212', 'ecpa'] TEST = ['cb', 'ecpr', 'jhk', 'jhu', 'tgk', 'tgu', 'psk', 'psu', #'rondane', 'vm32', 'ws213', 'ws214', 'petet', 'wsj23'] IGNORE = ['ntucle', 'omw', 'wlb03', 'wnb03'] NONTRAIN = DEV + TEST + IGNORE class LexTypeExtractor: def __init__(self): self.stats = {'corpora': [], 'failed corpora': [], 'tokens': {}, 'total lextypes': 0} def parse_lexicons(self,lexicons): lextypes = {} # mapping of lexical entry IDs to types for lexicon in glob.iglob(lexicons+'**'): for event, obj, lineno in tdl.iterparse(lexicon): if event == 'TypeDefinition': lextypes[obj.identifier] = obj.supertypes[0] # assume exactly 1 self.lextypes = lextypes def read_testsuites(self,path): max_sen_length = 0 corpus_size = 0 data = {'train':{'by corpus':[], 'by length': {}}, 'test':{'by corpus':[], 'by length': {}}, 'dev':{'by corpus':[], 'by length': {}}} print('Reading test suite files into pydelphin objects...') n = 0 for idx in ['train','dev','test']: t = 0 for i, tsuite in enumerate(sorted(glob.iglob(path + idx + '/**'))): n += 1 ts = itsdb.TestSuite(tsuite) if idx == 'train': message = "A nontrain dataset {} is being added as training data!".format(ts.path.stem) assert ts.path.stem not in NONTRAIN, message data[idx]['by corpus'].append({'name':ts.path.stem}) items = list(ts.processed_items()) data[idx]['by corpus'][i]['sentences'] = {} data[idx]['by corpus'][i]['tokens-tags'] = [] corpus_size += len(items) for response in items: if len(response['results']) > 0: deriv = response.result(0).derivation() terminals = deriv.terminals() t += len(terminals) p_input = response['p-input'] p_tokens = response['p-tokens'] terminals_tok_tags = self.map_lattice_to_input(p_input, p_tokens, deriv) if len(terminals) not in data[idx]['by corpus'][i]['sentences']: data[idx]['by corpus'][i]['sentences'][len(terminals)] = [] data[idx]['by corpus'][i]['sentences'][len(terminals)].append(terminals_tok_tags) data[idx]['by corpus'][i]['tokens-tags'].append(terminals_tok_tags) if len(terminals) > max_sen_length: max_sen_length = len(terminals) print('All raw {} tokens: {}'.format(idx,t)) t1 = 0 t2 = 0 if idx == 'train': all_sentences = {} for ts in data[idx]['by corpus']: t1 += self.org_sen_by_length(all_sentences, ts) for l in all_sentences: for s in all_sentences[l]: t2 += len(s) data[idx]['by length'] = OrderedDict(sorted(all_sentences.items())) else: for ts in data[idx]['by corpus']: all_sentences = {} t1 += self.org_sen_by_length(all_sentences, ts) data[idx]['by length'][ts['name']] = OrderedDict(sorted(all_sentences.items())) for ts in data[idx]['by length']: for l in data[idx]['by length'][ts]: for s in data[idx]['by length'][ts][l]: t2 += len(s) print('Added {} {} tokens to the by-corpus table'.format(t1,idx)) print('Added {} {} tokens to the by-length table'.format(t2,idx)) return max_sen_length, corpus_size, n+1, data def org_sen_by_length(self, all_sentences, ts): n = 0 for l in ts['sentences']: for s in ts['sentences'][l]: n += len(s) if l not in all_sentences: all_sentences[l] = [] all_sentences[l] += ts['sentences'][l] return n def process_testsuites_autoreg(self,testsuites,lextypes, out_dir): max_sen_length, corpus_size, num_ts, data = self.read_testsuites(testsuites) tables_by_len = {'train':{},'dev':{},'test':{}} for k in ['train','dev','test']: pathlib.Path(out_dir + '/labeled-data/' + k).mkdir(parents=True, exist_ok=False) all_tokens = 0 test = k in ['dev','test'] if test: for corpus in data[k]['by length']: all_tokens += self.process_table(data, k, lextypes, tables_by_len, test, corpus) else: all_tokens += self.process_table(data, k, lextypes, tables_by_len, test) print('Total PROCESSED {} tokens: {}'.format(k, all_tokens)) def process_testsuites_nonautoreg(self,testsuites,lextypes, out_dir): pos_mapper = pos_map.Pos_mapper('./pos-map.txt') max_sen_length, corpus_size, num_ts, data = self.read_testsuites(testsuites) for k in ['train','dev','test']: is_devtest_data = k in ['dev','test'] pathlib.Path(out_dir + '/labeled-data/' + k).mkdir(parents=True, exist_ok=False) if is_devtest_data: for corpus in data[k]['by corpus']: x,y = self.process_corpus(lextypes,corpus,pos_mapper) data_table['ft'] = x data_table['lt'] = y with open(out_dir + '/labeled-data/' + k + '/' + corpus['name'], 'wb') as f: pickle.dump(data_table, f) else: data_table = {'ft':[],'lt':[]} for corpus in data[k]['by corpus']: x, y = self.process_corpus(lextypes,corpus,pos_mapper) data_table['ft'] += x data_table['lt'] += y with open(out_dir + '/labeled-data/train/train' , 'wb') as f: pickle.dump(data_table, f) def process_corpus(self, lextypes, corpus,pos_mapper): data = [] y = [] for sen in corpus['tokens-tags']: tokens, labels, pos_tags, autoregress_labels = \ self.get_tokens_labels(sen, CONTEXT_WINDOW, lextypes, pos_mapper, False) for k, t in enumerate(tokens): if k < CONTEXT_WINDOW or k >= len(tokens) - CONTEXT_WINDOW: continue y.append(labels[k]) data.append(self.get_context(t, tokens, pos_tags, k, CONTEXT_WINDOW)) return data, y def process_table(self, data, k, lextypes, tables_by_len, test, corpus=None): n = 0 table = data[k]['by length'] if not test else data[k]['by length'][corpus] for sen_len in table: tables_by_len[k][sen_len] = {} autoregress_table = np.array([[{}] * len(table[sen_len]) for i in range(sen_len)]) labels_table = np.array([[{}] * len(table[sen_len]) for i in range(sen_len)]) # print("Processing sentences of length {}".format(sen_len)) n += self.process_length(lextypes, table[sen_len], autoregress_table, labels_table, test=test) tables_by_len[k][sen_len]['ft'] = autoregress_table tables_by_len[k][sen_len]['lt'] = labels_table if test: with open(out_dir + '/labeled-data/' + k + '/' + corpus, 'wb') as f: pickle.dump(tables_by_len[k], f) else: with open(out_dir + '/labeled-data/train/train' , 'wb') as f: pickle.dump(tables_by_len[k], f) return n ''' Assume a numpy table coming in. Get e.g. tokens 2 through 5 in sentences 4 and 5, for the test suite #20 in the data. ''' def get_table_portion(self, ts_info, table, ts_num, token_range, sentence_range): ts_column = ts_info[ts_num]['column'] tokens = sum(ts_info[ts_num]['sentences'][sentence_range[0]:sentence_range[1]]) return table[token_range[0]:token_range[1],ts_column:ts_column+tokens] def process_testsuite(self, lextypes, logf, tsuite, autoregress_table, labels_table, start): print("Processing " + tsuite['name']) logf.write("Processing " + tsuite['name'] + '\n') pairs = [] contexts = [] y = [] ys = [] pos_mapper = pos_map.Pos_mapper('./pos-map.txt') # do this for every test suite to count unknowns in each for sentence_len in tsuite['sentences']: items = tsuite['sentences'][sentence_len] for j, lst_of_terminals in enumerate(items): contexts.append([]) #if j % 100 == 0: # print("Processing item {} out of {}...".format(j, len(items))) tokens,labels,pos_tags,autoregress_labels = \ self.get_tokens_labels(tsuite['tokens-tags'][j],CONTEXT_WINDOW, lextypes,pos_mapper,test=False) ys.append(labels[CONTEXT_WINDOW:CONTEXT_WINDOW*-1]) for k, t in enumerate(tokens): if k < CONTEXT_WINDOW or k >= len(tokens) - CONTEXT_WINDOW: continue pairs.append((t, labels[k])) y.append(labels[k]) contexts[j].append(self.get_context(t, tokens, pos_tags, k, CONTEXT_WINDOW)) autoregress_table[k-CONTEXT_WINDOW][start+j] = \ self.get_autoregress_context(tokens,pos_tags,autoregress_labels, k,CONTEXT_WINDOW) labels_table[k-CONTEXT_WINDOW][start+j] = labels[k] pairs.append(('--EOS--','--EOS--')) # sentence separator y.append('\n') # sentence separator self.write_output(contexts, pairs, tsuite['name']) return ys def process_length(self, lextypes, items, autoregress_table, labels_table,test): y = [] ys = [] all_tokens = 0 pos_mapper = pos_map.Pos_mapper('./pos-map.txt') # do this for every test suite to count unknowns in each for j, lst_of_terminals in enumerate(items): #if j % 100 == 0: # print("Processing item {} out of {}...".format(j, len(items))) tokens,labels,pos_tags,autoregress_labels = \ self.get_tokens_labels(lst_of_terminals,CONTEXT_WINDOW, lextypes,pos_mapper,test) ys.append(labels[CONTEXT_WINDOW:CONTEXT_WINDOW*-1]) for k, t in enumerate(tokens): if k < CONTEXT_WINDOW or k >= len(tokens) - CONTEXT_WINDOW: continue y.append(labels[k]) autoregress_table[k-CONTEXT_WINDOW][j] = \ self.get_autoregress_context(tokens,pos_tags,autoregress_labels, k,CONTEXT_WINDOW) labels_table[k-CONTEXT_WINDOW][j] = labels[k] all_tokens += 1 y.append('\n') # sentence separator return all_tokens def map_lattice_to_input(self, p_input, p_tokens, deriv): yy_lattice = YYTokenLattice.from_string(p_tokens) yy_input = YYTokenLattice.from_string(p_input) terminals_toks_postags = [] for t in deriv.terminals(): toks_pos_tags = [] for ttok in t.tokens: span = None pos_probs = {} for lat_tok in yy_lattice.tokens: if lat_tok.id == ttok.id: span = lat_tok.lnk.data break for i,in_tok in enumerate(yy_input.tokens): if in_tok.lnk.data[0] == span[0]: for pos, p in in_tok.pos: if pos not in pos_probs: pos_probs[pos] = [] pos_probs[pos].append(float(p)) if in_tok.lnk.data[1] != span[1]: cur_tok = in_tok while cur_tok.lnk.data[1] != span[1]: next_tok = yy_input.tokens[i+1] i += 1 for pos, p in next_tok.pos: if pos not in pos_probs: pos_probs[pos] = [] pos_probs[pos].append(float(p)) cur_tok = next_tok else: break toks_pos_tags.append((ttok, pos_probs)) terminals_toks_postags.append((t,toks_pos_tags)) return terminals_toks_postags def write_output(self, contexts, pairs, ts_name): for d in ['train/','test/','dev/', 'ignore/']: for pd in ['simple/','by-corpus/contexts/','by-corpus/true_labels/']: pathlib.Path('./output/' + pd + d).mkdir(parents=True, exist_ok=True) true_labels = [] suf = 'train/' if ts_name in IGNORE: suf = 'ignore/' if ts_name in TEST: suf = 'test/' elif ts_name in DEV: suf = 'dev/' with open('./output/simple/' + suf + ts_name, 'w') as f: for form, letype in pairs: if not letype=='--EOS--': true_labels.append(str(letype)) str_pair = f'{form}\t{letype}' f.write(str_pair + '\n') else: f.write('\n') # sentence separator true_labels.append('\n') # sentence separator with open('./output/by-corpus/true_labels/' + suf + ts_name, 'w') as f: for tl in true_labels: f.write(tl) if tl != '\n': f.write('\n') with open('./output/by-corpus/contexts/' + suf + ts_name, 'w') as f: f.write(json.dumps(contexts)) def get_context(self, t, tokens, pos_tags, i, window): context = {'w': t, 'pos': pos_tags[i]} for j in range(1,window+1): prev_tok = tokens[i-j] prev_pos = pos_tags[i-j] next_tok = tokens[i+j] next_pos = pos_tags[i+j] context['w-' + str(j)] = prev_tok context['w+' + str(j)] = next_tok context['pos-' + str(j)] = prev_pos context['pos+' + str(j)] = next_pos return context def get_autoregress_context(self,tokens,pos_tags,predicted_labels, k,window): context = {'w':tokens[k],'pos':pos_tags[k]} for i in range(1,window+1): context['w-' + str(i)] = tokens[k-i] context['w+' + str(i)] = tokens[k+i] context['pos-' + str(i)] = pos_tags[k-i] context['pos+' + str(i)] = pos_tags[k+i] context['tag-' + str(i)] = predicted_labels[k-i] # Will be None or FAKE in test mode return context def get_tokens_labels(self, terms_and_tokens_tags, context_window, lextypes,pos_mapper, test): tokens = [] labels = [] pos_tags = [] previous_tags = [] for i,(terminal, toks_tags) in enumerate(terms_and_tokens_tags): letype = str(lextypes.get(terminal.parent.entity, "<UNK>")) tokens.append(terminal.form) labels.append(letype) pos_tags.append(self.get_pos_tag(toks_tags, pos_mapper)) if test: previous_tags.append(None) else: previous_tags.append(letype) for i in range(1,1+context_window): tokens.insert(0, 'FAKE-' + str(i)) labels.insert(0, 'FAKE-' + str(i)) pos_tags.insert(0,'FAKE-' + str(i)) previous_tags.insert(0, 'FAKE-' + str(i)) tokens.append('FAKE+' + str(i)) labels.append('FAKE+' + str(i)) pos_tags.append('FAKE+' + str(i)) return tokens, labels, pos_tags, previous_tags def get_pos_tag(self,tokens_tags, pos_mapper): tag = '' for tt in tokens_tags: pos_probs = tt[1] for pos in pos_probs: tag = tag + '+' + pos tag = tag.strip('+') if '+' in tag: tag = pos_mapper.map_tag(tag) return tag if __name__ == "__main__": args = sys.argv[1:] dt_str = '-'.join(str(datetime.now()).split()).replace(':','.') run_id = sys.argv[3] + dt_str if len(sys.argv) > 3: autoreg = sys.argv[4] == 'autoreg' out_dir = './output/' + run_id pathlib.Path(out_dir).mkdir(parents=True, exist_ok=False) le = LexTypeExtractor() le.parse_lexicons(args[0]) le.stats['total lextypes'] = len(le.lextypes) if autoreg: le.process_testsuites_autoreg(args[1],le.lextypes,out_dir) else: le.process_testsuites_nonautoreg(args[1],le.lextypes,out_dir) with open(out_dir + '/lextypes','wb') as f: lextypes = set([str(v) for v in list(le.lextypes.values())]) pickle.dump(lextypes,f)

0

16,271

0

45

133

e415bab977b01817df0d4c4b2e45aacf11aa8fbf

2,294

py

Python

homeassistant/components/environment_canada/camera.py

mtarjoianu/core

44e9146463ac505eb3d1c0651ad126cb25c28a54

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

homeassistant/components/environment_canada/camera.py

mtarjoianu/core

44e9146463ac505eb3d1c0651ad126cb25c28a54

[ "Apache-2.0" ]

24,710

2016-04-13T08:27:26.000Z

2020-03-02T12:59:13.000Z

homeassistant/components/environment_canada/camera.py

mtarjoianu/core

44e9146463ac505eb3d1c0651ad126cb25c28a54

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Support for the Environment Canada radar imagery.""" from __future__ import annotations import voluptuous as vol SERVICE_SET_RADAR_TYPE = "set_radar_type" SET_RADAR_TYPE_SCHEMA = { vol.Required("radar_type"): vol.In(["Auto", "Rain", "Snow"]), }

33.246377

79

0.722319

"""Support for the Environment Canada radar imagery.""" from __future__ import annotations import voluptuous as vol from homeassistant.components.camera import Camera from homeassistant.config_entries import ConfigEntry from homeassistant.core import HomeAssistant from homeassistant.helpers.entity_platform import ( AddEntitiesCallback, async_get_current_platform, ) from homeassistant.helpers.update_coordinator import CoordinatorEntity from .const import ATTR_OBSERVATION_TIME, DOMAIN SERVICE_SET_RADAR_TYPE = "set_radar_type" SET_RADAR_TYPE_SCHEMA = { vol.Required("radar_type"): vol.In(["Auto", "Rain", "Snow"]), } async def async_setup_entry( hass: HomeAssistant, config_entry: ConfigEntry, async_add_entities: AddEntitiesCallback, ) -> None: """Add a weather entity from a config_entry.""" coordinator = hass.data[DOMAIN][config_entry.entry_id]["radar_coordinator"] async_add_entities([ECCamera(coordinator)]) platform = async_get_current_platform() platform.async_register_entity_service( SERVICE_SET_RADAR_TYPE, SET_RADAR_TYPE_SCHEMA, "async_set_radar_type", ) class ECCamera(CoordinatorEntity, Camera): """Implementation of an Environment Canada radar camera.""" def __init__(self, coordinator): """Initialize the camera.""" super().__init__(coordinator) Camera.__init__(self) self.radar_object = coordinator.ec_data self._attr_name = f"{coordinator.config_entry.title} Radar" self._attr_unique_id = f"{coordinator.config_entry.unique_id}-radar" self._attr_attribution = self.radar_object.metadata["attribution"] self._attr_entity_registry_enabled_default = False self.content_type = "image/gif" def camera_image( self, width: int | None = None, height: int | None = None ) -> bytes | None: """Return bytes of camera image.""" self._attr_extra_state_attributes = { ATTR_OBSERVATION_TIME: self.radar_object.timestamp, } return self.radar_object.image async def async_set_radar_type(self, radar_type: str): """Set the type of radar to retrieve.""" self.radar_object.precip_type = radar_type.lower() await self.radar_object.update()

0

671

939

0

248

180

dfd8028393ae8ae7d4bfcfe8f9c74276b8f956f7

308

py

Python

v1/chapter6/4-readingCsvDict.py

QTYResources/python-scraping

d7afe25a012fb5d079ee42372c7fce94b9494b9f

[ "MIT" ]

null

v1/chapter6/4-readingCsvDict.py

QTYResources/python-scraping

d7afe25a012fb5d079ee42372c7fce94b9494b9f

[ "MIT" ]

null

v1/chapter6/4-readingCsvDict.py

QTYResources/python-scraping

d7afe25a012fb5d079ee42372c7fce94b9494b9f

[ "MIT" ]

null

from urllib.request import urlopen from io import StringIO import csv data = urlopen("http://pythonscraping.com/files/MontyPythonAlbums.csv").read().decode('ascii', 'ignore') dataFile = StringIO(data) dictReader = csv.DictReader(dataFile) print(dictReader.fieldnames) for row in dictReader: print(row)

25.666667

104

0.775974

from urllib.request import urlopen from io import StringIO import csv data = urlopen("http://pythonscraping.com/files/MontyPythonAlbums.csv").read().decode('ascii', 'ignore') dataFile = StringIO(data) dictReader = csv.DictReader(dataFile) print(dictReader.fieldnames) for row in dictReader: print(row)

0

02ae809d3645a6053bab6f39633b7e2d90bf2e2e

741

py

Python

hash-array-string/1.8-zero-matrix/main.py

digoreis/code-interview

e2250c39b0fc9b6a8f0bc151b4f796d17cdce3e3

[ "MIT" ]

null

hash-array-string/1.8-zero-matrix/main.py

digoreis/code-interview

e2250c39b0fc9b6a8f0bc151b4f796d17cdce3e3

[ "MIT" ]

null

hash-array-string/1.8-zero-matrix/main.py

digoreis/code-interview

e2250c39b0fc9b6a8f0bc151b4f796d17cdce3e3

[ "MIT" ]

null

# Write an algorithm such that if an element in MxN matrix is 0, it's entire row and column are set to 0. matrix = [[1,1,1,1],[1,1,1,1],[1,1,0,1],[1,1,1,1],[1,1,1,0]] matrixZero(matrix) print('\n'.join(['\t'.join([str(cell) for cell in row]) for row in matrix]))

26.464286

105

0.562753

# Write an algorithm such that if an element in MxN matrix is 0, it's entire row and column are set to 0. def rowZero(matrix, row): for i in range(len(matrix[row])): matrix[row][i] = 0 def columnZero(matrix, column): for i in range(len(matrix)): matrix[i][column] = 0 def matrixZero(matrix): points = [] for i in range(len(matrix)): for j in range(len(matrix[0])): if matrix[i][j] == 0: points.append((i,j)) for p in points: rowZero(matrix, p[0]) columnZero(matrix, p[1]) matrix = [[1,1,1,1],[1,1,1,1],[1,1,0,1],[1,1,1,1],[1,1,1,0]] matrixZero(matrix) print('\n'.join(['\t'.join([str(cell) for cell in row]) for row in matrix]))

0

393

0

68

d805c677ed9537d580479c240741257bc4c84e5c

4,725

py

Python

src/Class/shadowedrice.py

Jonathan-Browning/Shadowed-Rician-Fading-Python

c1faa061c4d2a253bd1fe7098edc0e21740cb3ea

[ "MIT" ]

2

2021-02-23T15:49:47.000Z

2021-04-24T01:32:42.000Z

src/Class/shadowedrice.py

Jonathan-Browning/Shadowed-Rician-Fading-Python

c1faa061c4d2a253bd1fe7098edc0e21740cb3ea

[ "MIT" ]

null

src/Class/shadowedrice.py

Jonathan-Browning/Shadowed-Rician-Fading-Python

c1faa061c4d2a253bd1fe7098edc0e21740cb3ea

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Nov 4 18:55:46 2020 @author: Jonathan Browning """

36.346154

151

0.572275

# -*- coding: utf-8 -*- """ Created on Wed Nov 4 18:55:46 2020 @author: Jonathan Browning """ import numpy as np from scipy.stats import gaussian_kde as kdf from scipy import special as sp class ShadowedRice: numSamples = 2*(10**6) # the number of samples used in the simulation r = np.linspace(0, 6, 6000) # theoretical envelope PDF x axes theta = np.linspace(-np.pi, np.pi, 6000) # theoretical phase PDF x axes def __init__(self, K, m, r_hat, phi): # user input checks and assigns value self.K = self.input_Check(K, "K", 0.001, 50) self.m = self.input_Check(m, "m", 0.001, 50) self.r_hat = self.input_Check(r_hat, "\hat{r}", 0.5, 2.5) self.phi = self.input_Check(phi, "\phi", -np.pi, np.pi) # simulating and theri densities self.multipathFading = self.complex_Multipath_Fading() self.xdataEnv, self.ydataEnv = self.envelope_Density() self.xdataPh, self.ydataPh = self.phase_Density() # theoretical PDFs calculated self.envelopeProbability = self.envelope_PDF() self.phaseProbability = self.phase_PDF() def input_Check(self, data, inputName, lower, upper): # input_Check checks the user inputs # has a value been entered if data == "": raise ValueError(" ".join((inputName, "must have a numeric value"))) # incase of an non-numeric input try: data = float(data) except: raise ValueError(" ".join((inputName, "must have a numeric value"))) # data must be within the range if data < lower or data > upper: raise ValueError(" ".join((inputName, f"must be in the range [{lower:.2f}, {upper:.2f}]"))) return data def calculate_Means(self): # calculate_means calculates the means of the complex Gaussians representing the # in-phase and quadrature components p = np.sqrt(self.K / (1+self.K)) * self.r_hat * np.cos(self.phi) q = np.sqrt(self.K / (1+self.K)) * self.r_hat * np.sin(self.phi) return p, q def scattered_Component(self): # scattered_Component calculates the power of the scattered signal component sigma = self.r_hat / np.sqrt( 2 * (1+self.K) ) return sigma def generate_Gaussians(self, mean, sigma): # generate_Gaussians generates the Gaussian random variables gaussians = np.random.default_rng().normal(mean, sigma, self.numSamples) return gaussians def complex_Multipath_Fading(self): # complex_Multipath_Fading generates the complex fading random variables p, q = self.calculate_Means() sigma = self.scattered_Component() xi = np.sqrt(np.random.gamma(self.m, 1/self.m, self.numSamples)) multipathFading = self.generate_Gaussians(xi*p, sigma) + (1j*self.generate_Gaussians(xi*q, sigma)) return multipathFading def envelope_PDF(self): # envelope_PDF calculates the theoretical envelope PDF PDF = 2 * (1+self.K) * self.r *(self.m**(self.m)) / (self.r_hat**(2)*(self.m+self.K)**(self.m)) \ * np.exp(- ((1+self.K) * self.r**(2)) / self.r_hat**(2)) \ * sp.hyp1f1(self.m, 1, self.r**(2)*self.K*(self.K+1)/(self.r_hat**(2)*(self.K+self.m))) return PDF def phase_PDF(self): # phase_PDF calculates the theoretical phase PDF PDF = (self.m**self.m * np.sqrt(self.K)/(2 * np.sqrt(np.pi) * (self.K + self.m)**(self.m +1/2))) \ * ( np.sqrt((self.K +self.m)/(np.pi*self.K)) * sp.hyp2f1(self.m, 1, 1/2, (self.K*(np.cos(self.theta - self.phi))**(2))/(self.K +self.m)) \ + ((sp.gamma(self.m+1/2) / sp.gamma(self.m))*np.cos(self.theta-self.phi) \ * (1- (self.K*(np.cos(self.theta - self.phi))**(2)) / (self.K +self.m))**(-self.m-1/2))) return PDF def envelope_Density(self): # envelope_Density finds the envelope PDF of the simulated random variables R = np.sqrt((np.real(self.multipathFading))**2 + (np.imag(self.multipathFading))**2) kde = kdf(R) x = np.linspace(R.min(), R.max(), 100) p = kde(x) return x, p def phase_Density(self): # phase_Density finds the phase PDF of the simulated random variables R = np.angle(self.multipathFading) kde = kdf(R) x = np.linspace(R.min(), R.max(), 100) p = kde(x) return x, p

0

4,503

0

29

90

c00b03a6c58efa0a53f7586ea8d163bb92f588f1

1,063

py

Python

src/ggrc_workflows/migrations/versions/20170925135632_3ebe14ae9547_set_empty_next_cycle_start_date.py

HLD/ggrc-core

9bdc0fc6ca9e252f4919db682d80e360d5581eb4

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

null

src/ggrc_workflows/migrations/versions/20170925135632_3ebe14ae9547_set_empty_next_cycle_start_date.py

HLD/ggrc-core

9bdc0fc6ca9e252f4919db682d80e360d5581eb4

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

10

2018-07-06T00:04:23.000Z

2021-02-26T21:13:20.000Z

src/ggrc_workflows/migrations/versions/20170925135632_3ebe14ae9547_set_empty_next_cycle_start_date.py

HLD/ggrc-core

9bdc0fc6ca9e252f4919db682d80e360d5581eb4

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

1

2017-11-11T22:16:56.000Z

# Copyright (C) 2017 Google Inc. # Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file> """ Set empty next_cycle_start_date Create Date: 2017-09-25 13:56:32.087965 """ # disable Invalid constant name pylint warning for mandatory Alembic variables. # pylint: disable=invalid-name from alembic import op # revision identifiers, used by Alembic. revision = '3ebe14ae9547' down_revision = '4991c5731711' def upgrade(): """Upgrade database schema and/or data, creating a new revision.""" op.execute("UPDATE workflows, ( " "SELECT w.id " "FROM workflows AS w " "LEFT JOIN task_groups AS tg ON tg.workflow_id = w.id " "LEFT JOIN task_group_tasks AS t ON t.task_group_id = tg.id " "WHERE t.id IS NULL AND w.next_cycle_start_date IS NOT NULL " ") AS t " "SET workflows.next_cycle_start_date = NULL " "WHERE workflows.id = t.id;") def downgrade(): """Downgrade database schema and/or data back to the previous revision.""" pass

29.527778

79

0.664158

# Copyright (C) 2017 Google Inc. # Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file> """ Set empty next_cycle_start_date Create Date: 2017-09-25 13:56:32.087965 """ # disable Invalid constant name pylint warning for mandatory Alembic variables. # pylint: disable=invalid-name from alembic import op # revision identifiers, used by Alembic. revision = '3ebe14ae9547' down_revision = '4991c5731711' def upgrade(): """Upgrade database schema and/or data, creating a new revision.""" op.execute("UPDATE workflows, ( " "SELECT w.id " "FROM workflows AS w " "LEFT JOIN task_groups AS tg ON tg.workflow_id = w.id " "LEFT JOIN task_group_tasks AS t ON t.task_group_id = tg.id " "WHERE t.id IS NULL AND w.next_cycle_start_date IS NOT NULL " ") AS t " "SET workflows.next_cycle_start_date = NULL " "WHERE workflows.id = t.id;") def downgrade(): """Downgrade database schema and/or data back to the previous revision.""" pass

0

a9dd6bbf97a596cde44dca1d194056908053fcb0

29,435

py

Python

test/test_madx.py

odidev/cpymad

7b58d013a669d0973c233743e05fa205257233dd

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

22

2015-05-27T13:45:55.000Z

2022-03-03T15:43:47.000Z

test/test_madx.py

odidev/cpymad

7b58d013a669d0973c233743e05fa205257233dd

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

102

2015-01-23T18:21:29.000Z

2022-02-28T17:07:26.000Z

test/test_madx.py

odidev/cpymad

7b58d013a669d0973c233743e05fa205257233dd

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

18

2015-01-24T12:43:57.000Z

2021-11-23T08:29:57.000Z

""" Tests for the :class:`cpymad.madx.Madx` API. """ import os from pytest import raises from cpymad.madx import Madx SEQU = """ ! constants QP_K1 = 2; ! elements qp: quadrupole, k1:=QP_K1, l=1; sb: sbend, l=2, angle=3.14/4; dr: drift, l=1; ! sequences s1: sequence, l=8, refer=center; dr, at=0.5; ! dr[1] ~ betx_full1[1] qp, at=1.5; dr, at=2.5; ! dr[2] ~ betx_full1[3] ~ betx_range[0] qp, at=3.5; ! ~ betx_full1[4] ~ betx_range[1] dr, at=4.5; sb, at=6.0; ! ~ betx_range[3] dr, at=7.5; endsequence; s2: sequence, l=3, refer=entry; qp1: qp, at=0, k1=3; qp2: qp, at=1, l=2; endsequence; """ def normalize(path): """Normalize path name to eliminate different spellings of the same path. This is needed for path comparisons in tests, especially on windows where pathes are case insensitive and allow a multitude of spellings.""" return os.path.normcase(os.path.normpath(path)) def test_version(mad): """Check that the Madx.version attribute can be used as expected.""" version = mad.version # check format: major, minor, micro = map(int, version.release.split('.')) # We need at least MAD-X 5.05.00: assert (major, minor, micro) >= (5, 5, 0) # check format: year, month, day = map(int, version.date.split('.')) assert (year, month, day) >= (2019, 5, 10) assert 1 <= month <= 12 assert 1 <= day <= 31 assert str(version).startswith( 'MAD-X {}'.format(version.release)) # TODO: We need to fix this on windows, but for now, I just need it to # pass so that the CI builds the release... def test_command_log(): """Check that the command log contains all input commands.""" # create a new Madx instance that uses the history feature: history_filename = '_test_madx.madx.tmp' try: # feed some input lines and compare with history file: lines = """ l = 5; f = 200; fodo: sequence, refer=entry, l=100; QF: quadrupole, l=5, at= 0, k1= 1/(f*l); QD: quadrupole, l=5, at=50, k1=-1/(f*l); endsequence; beam, particle=proton, energy=2; use, sequence=fodo; """.splitlines() lines = [line.strip() for line in lines if line.strip()] with Madx(command_log=history_filename) as mad: for line in lines: mad.input(line) with open(history_filename) as history_file: history = history_file.read() assert history.strip() == '\n'.join(lines).strip() finally: # remove history file os.remove(history_filename) def test_append_semicolon(): """Check that semicolon is automatically appended to input() text.""" # Regression test for #73 log = [] with Madx(command_log=log.append) as mad: mad.input('a = 0') mad.input('b = 1') assert log == ['a = 0;', 'b = 1;'] assert mad.globals.a == 0 assert mad.globals.b == 1 # def test_sequence_get_expanded_elements(): def test_crash(mad): """Check that a RuntimeError is raised in case MAD-X crashes.""" assert bool(mad) # a.t.m. MAD-X crashes on this input, because the L (length) # parametere is missing: raises(RuntimeError, mad.input, 'XXX: sequence;') assert not bool(mad)

29.114738

77

0.592526

""" Tests for the :class:`cpymad.madx.Madx` API. """ import os import sys import numpy as np from numpy.testing import assert_allclose, assert_equal from pytest import approx, fixture, mark, raises import cpymad from cpymad.madx import Madx, Sequence, metadata @fixture def mad(): with Madx(prompt='X:> ') as mad: yield mad @fixture def lib(mad): return mad._libmadx SEQU = """ ! constants QP_K1 = 2; ! elements qp: quadrupole, k1:=QP_K1, l=1; sb: sbend, l=2, angle=3.14/4; dr: drift, l=1; ! sequences s1: sequence, l=8, refer=center; dr, at=0.5; ! dr[1] ~ betx_full1[1] qp, at=1.5; dr, at=2.5; ! dr[2] ~ betx_full1[3] ~ betx_range[0] qp, at=3.5; ! ~ betx_full1[4] ~ betx_range[1] dr, at=4.5; sb, at=6.0; ! ~ betx_range[3] dr, at=7.5; endsequence; s2: sequence, l=3, refer=entry; qp1: qp, at=0, k1=3; qp2: qp, at=1, l=2; endsequence; """ def normalize(path): """Normalize path name to eliminate different spellings of the same path. This is needed for path comparisons in tests, especially on windows where pathes are case insensitive and allow a multitude of spellings.""" return os.path.normcase(os.path.normpath(path)) def test_copyright(): notice = cpymad.get_copyright_notice() assert isinstance(notice, type(u"")) def test_version(mad): """Check that the Madx.version attribute can be used as expected.""" version = mad.version # check format: major, minor, micro = map(int, version.release.split('.')) # We need at least MAD-X 5.05.00: assert (major, minor, micro) >= (5, 5, 0) # check format: year, month, day = map(int, version.date.split('.')) assert (year, month, day) >= (2019, 5, 10) assert 1 <= month <= 12 assert 1 <= day <= 31 assert str(version).startswith( 'MAD-X {}'.format(version.release)) def test_metadata(mad): version = mad.version assert metadata.__version__ == version.release assert isinstance(metadata.get_copyright_notice(), type(u"")) def test_independent_instances(): # Check independence by defining a variable differently in each # instance: with Madx(prompt='X1:> ') as mad1, Madx(prompt='X2:> ') as mad2: mad1.input('ANSWER=42;') mad2.input('ANSWER=43;') assert mad1.eval('ANSWER') == 42 assert mad2.eval('ANSWER') == 43 # TODO: We need to fix this on windows, but for now, I just need it to # pass so that the CI builds the release... @mark.xfail( sys.platform != 'linux', reason='Output is sometimes garbled on MacOS and windows.', ) def test_streamreader(): output = [] with Madx(stdout=output.append) as m: assert len(output) == 1 assert b'+++++++++++++++++++++++++++++++++' in output[0] assert b'+ Support: [email protected],' in output[0] assert b'+ Release date: ' in output[0] assert b'+ Execution date: ' in output[0] # assert b'+ Support: [email protected], ', output[1] m.input('foo = 3;') assert len(output) == 1 m.input('foo = 3;') assert len(output) == 2 assert output[1] == b'++++++ info: foo redefined\n' assert len(output) == 3 assert b'+ MAD-X finished normally ' in output[2] def test_quit(mad): mad.quit() assert mad._process.returncode is not None assert not bool(mad) with raises(RuntimeError): mad.input(';') @mark.xfail( sys.platform != 'linux', reason='Output is sometimes garbled on MacOS and windows.', ) def test_context_manager(): output = [] with Madx(stdout=output.append) as m: m.input('foo = 3;') assert m.globals.foo == 3 assert b'+ MAD-X finished normally ' in output[-1] assert not bool(m) with raises(RuntimeError): m.input(';') def test_command_log(): """Check that the command log contains all input commands.""" # create a new Madx instance that uses the history feature: history_filename = '_test_madx.madx.tmp' try: # feed some input lines and compare with history file: lines = """ l = 5; f = 200; fodo: sequence, refer=entry, l=100; QF: quadrupole, l=5, at= 0, k1= 1/(f*l); QD: quadrupole, l=5, at=50, k1=-1/(f*l); endsequence; beam, particle=proton, energy=2; use, sequence=fodo; """.splitlines() lines = [line.strip() for line in lines if line.strip()] with Madx(command_log=history_filename) as mad: for line in lines: mad.input(line) with open(history_filename) as history_file: history = history_file.read() assert history.strip() == '\n'.join(lines).strip() finally: # remove history file os.remove(history_filename) def test_append_semicolon(): """Check that semicolon is automatically appended to input() text.""" # Regression test for #73 log = [] with Madx(command_log=log.append) as mad: mad.input('a = 0') mad.input('b = 1') assert log == ['a = 0;', 'b = 1;'] assert mad.globals.a == 0 assert mad.globals.b == 1 def test_call_and_chdir(mad): folder = os.path.abspath(os.path.dirname(__file__)) parent = os.path.dirname(folder) getcwd = mad._libmadx.getcwd g = mad.globals mad.chdir(folder) assert normalize(getcwd()) == normalize(folder) mad.call('answer_42.madx') assert g.answer == 42 with mad.chdir('..'): assert normalize(getcwd()) == normalize(parent) mad.call('test/answer_43.madx') assert g.answer == 43 mad.call('test/answer_call42.madx', True) assert g.answer == 42 assert normalize(getcwd()) == normalize(folder) mad.call('answer_43.madx') assert g.answer == 43 mad.chdir('..') assert normalize(getcwd()) == normalize(parent) def _check_twiss(mad, seq_name): beam = 'ex=1, ey=2, particle=electron, sequence={0};'.format(seq_name) mad.command.beam(beam) mad.use(seq_name) initial = dict(alfx=0.5, alfy=1.5, betx=2.5, bety=3.5) twiss = mad.twiss(sequence=seq_name, **initial) # Check initial values: assert twiss['alfx'][0] == approx(initial['alfx']) assert twiss['alfy'][0] == approx(initial['alfy']) assert twiss['betx'][0] == approx(initial['betx']) assert twiss['bety'][0] == approx(initial['bety']) assert twiss.summary['ex'] == approx(1) assert twiss.summary['ey'] == approx(2) # Check that keys are all lowercase: for k in twiss: assert k == k.lower() for k in twiss.summary: assert k == k.lower() def test_error(mad): mad.input(""" seq: sequence, l=1; endsequence; beam; use, sequence=seq; """) # Errors in MAD-X must not crash, but return False instead: assert not mad.input('twiss;') assert mad.input('twiss, betx=1, bety=1;') def test_twiss_1(mad): mad.input(SEQU) _check_twiss(mad, 's1') # s1 can be computed at start _check_twiss(mad, 's1') # s1 can be computed multiple times _check_twiss(mad, 's2') # s2 can be computed after s1 def test_twiss_2(mad): mad.input(SEQU) _check_twiss(mad, 's2') # s2 can be computed at start _check_twiss(mad, 's1') # s1 can be computed after s2 def test_twiss_with_range(mad): beam = 'ex=1, ey=2, particle=electron, sequence=s1;' mad.input(SEQU) mad.command.beam(beam) mad.use('s1') params = dict(alfx=0.5, alfy=1.5, betx=2.5, bety=3.5, sequence='s1') # Compute TWISS on full sequence, then on a sub-range, then again on # the full sequence. This checks that none of the range selections # have side-effects on each other: betx_full1 = mad.twiss(**params)['betx'] betx_range = mad.twiss(range=('dr[2]', 'sb'), **params)['betx'] betx_full2 = mad.twiss(**params)['betx'] # Check that the results have the expected lengths: assert len(betx_full1) == 9 assert len(betx_range) == 4 assert len(betx_full2) == 9 # Check numeric results. Since the first 3 elements of range and full # sequence are identical, equal results are expected. And non-equal # results afterwards. assert betx_range[0] == approx(betx_full1[1]) # dr:2, dr:1 assert betx_range[1] == approx(betx_full1[2]) # qp:2, qp:1 assert betx_range[2] == approx(betx_full1[3]) # dr:3, dr:2 assert betx_range[3] != approx(betx_full1[4]) # sb, qp:2 def test_range_row_api(mad): beam = 'ex=1, ey=2, particle=electron, sequence=s1;' mad.input(SEQU) mad.command.beam(beam) mad.use('s1') params = dict(alfx=0.5, alfy=1.5, betx=2.5, bety=3.5, sequence='s1') tab = mad.twiss(range=('dr[2]', 'sb'), **params) assert tab.range == ('dr[2]', 'sb') assert 'betx' in tab def test_survey(mad): mad.input(SEQU) mad.beam() mad.use('s1') tab = mad.survey() assert tab._name == 'survey' assert 'x' in tab assert 'y' in tab assert 'z' in tab assert 'theta' in tab assert 'phi' in tab assert 'psi' in tab assert tab.x[-1] < -1 assert tab.y == approx(0) assert tab.z[-1] > 7 def test_match(mad): beam = 'ex=1, ey=2, particle=electron, sequence=s2;' mad.input(SEQU) mad.command.beam(beam) mad.use('s2') params = dict(alfx=0.5, alfy=1.5, betx=2.5, bety=3.5, sequence='s2') mad.match(constraints=[dict(range='s1$end', betx=2.0)], weight={'betx': 2}, vary=['qp2->k1'], **params) twiss = mad.twiss(**params) val = twiss.betx[-1] assert val == approx(2.0, rel=1e-2) def test_verbose(mad): mad.verbose(False) assert mad.options.echo is False assert mad.options.info is False mad.verbose(True) assert mad.options.echo is True assert mad.options.info is True def test_active_sequence(mad): mad.input(SEQU) mad.command.beam('ex=1, ey=2, particle=electron, sequence=s1;') mad.use('s1') assert mad.sequence() == 's1' mad.beam() mad.use('s2') assert mad.sequence().name == 's2' def test_get_sequence(mad): mad.input(SEQU) with raises(KeyError): mad.sequence['sN'] s1 = mad.sequence['s1'] assert s1.name == 's1' seqs = mad.sequence assert set(seqs) == {'s1', 's2'} def test_eval(mad): mad.input(SEQU) assert mad.eval(True) is True assert mad.eval(13) == 13 assert mad.eval(1.3) == 1.3 assert mad.eval([2, True, 'QP_K1']) == [2, True, 2.0] assert mad.eval("1/QP_K1") == approx(0.5) def test_eval_functions(mad): assert mad.eval("sin(1.0)") == approx(np.sin(1.0)) assert mad.eval("cos(1.0)") == approx(np.cos(1.0)) mad.input(""" mqf.k1 = 0.3037241107; mqd.k1 = -0.3037241107; fodo: sequence, l=10, refer=entry; mqf: quadrupole, at=0, l=1, k1:=mqf.k1; dff: drift, at=1, l=4; mqd: quadrupole, at=5, l=1, k1:=mqd.k1; dfd: drift, at=6, l=4; endsequence; beam; use, sequence=fodo; twiss, sequence=fodo, x=0.1; """) elems = mad.sequence.fodo.expanded_elements twiss = mad.table.twiss mad.input("mqf_x = table(twiss, mqf, x);") assert mad.eval("table(twiss, mqf, x)") \ == twiss.row(elems.index('mqf')).x \ == mad.globals.mqf_x def test_globals(mad): g = mad.globals # Membership: assert 'FOO' not in g # Setting values: g['FOO'] = 2 assert 'FOO' in g assert g['FOO'] == 2 assert mad.eval('FOO') == 2 # Re-setting values: g['FOO'] = 3 assert mad.eval('FOO') == 3 # Setting expressions: g['BAR'] = '3*foo' assert mad.eval('BAR') == 9 g['FOO'] = 4 assert mad.eval('BAR') == 12 assert g.defs.bar == "3*foo" assert g.cmdpar.bar.definition == "3*foo" # attribute access: g.bar = 42 assert g.defs.bar == 42 assert g.cmdpar.bar.definition == 42 assert g.BAR == 42 # repr assert "'bar': 42.0" in str(g) with raises(NotImplementedError): del g['bar'] with raises(NotImplementedError): del g.bar assert g.bar == 42 # still there assert 'bar' in list(g) assert 'foo' in list(g) # assert list(g) == list(g.defs) # assert list(g) == list(g.cmdpar) assert len(g) == len(list(g)) assert len(g.defs) == len(list(g.defs)) assert len(g.cmdpar) == len(list(g.cmdpar)) def test_elements(mad): mad.input(SEQU) assert 'sb' in mad.elements assert 'sb' in list(mad.elements) assert 'foobar' not in mad.elements assert mad.elements['sb']['angle'] == approx(3.14/4) idx = mad.elements.index('qp1') elem = mad.elements[idx] assert elem['k1'] == 3 def test_sequence_map(mad): mad.input(SEQU) seq = mad.sequence assert len(seq) == 2 assert set(seq) == {'s1', 's2'} assert 's1' in seq assert 's3' not in seq assert hasattr(seq, 's1') assert not hasattr(seq, 's3') assert seq.s1.name == 's1' assert seq.s2.name == 's2' with raises(AttributeError): seq.s3 def test_table_map(mad): mad.input(SEQU) mad.beam() mad.use('s2') mad.survey(sequence='s2') tab = mad.table assert 'survey' in list(tab) assert 'survey' in tab assert 'foobar' not in tab assert len(tab) == len(list(tab)) with raises(AttributeError): tab.foobar def test_sequence(mad): mad.input(SEQU) s1 = mad.sequence.s1 assert str(s1) == '<Sequence: s1>' assert s1 == mad.sequence.s1 assert s1 == 's1' assert s1 != mad.sequence.s2 assert s1 != 's2' with raises(RuntimeError): s1.beam with raises(RuntimeError): s1.twiss_table with raises(RuntimeError): s1.twiss_table_name assert not s1.has_beam assert not s1.is_expanded s1.expand() assert s1.has_beam assert s1.is_expanded s1.expand() # idempotent assert s1.has_beam assert s1.is_expanded initial = dict(alfx=0.5, alfy=1.5, betx=2.5, bety=3.5) mad.twiss(sequence='s1', sectormap=True, table='my_twiss', **initial) # Now works: assert s1.beam.particle == 'positron' assert s1.twiss_table_name == 'my_twiss' assert s1.twiss_table.betx[0] == 2.5 assert s1.element_names() == [ 's1$start', 'dr', 'qp', 'dr[2]', 'qp[2]', 'dr[3]', 'sb', 'dr[4]', 's1$end', ] assert s1.expanded_element_names() == s1.element_names() assert len(s1.element_names()) == len(s1.element_positions()) assert s1.element_positions() == [ 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 7.0, 8.0] assert s1.expanded_element_positions() == [ 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 7.0, 8.0] assert s1.elements[0].name == 's1$start' assert s1.elements[-1].name == 's1$end' assert s1.elements[-1].index == len(s1.elements)-1 assert s1.elements[3].index == 3 assert s1.elements.index('#s') == 0 assert s1.elements.index('#e') == len(s1.elements)-1 assert s1.elements.index('sb') == 6 assert s1.length == 8.0 def test_sequence_get_elements_s1(mad): mad.input(SEQU) s1 = mad.sequence.s1.elements qp1 = s1['qp[1]'] qp2 = s1['qp[2]'] sb1 = s1['sb[1]'] assert s1.index('qp') < s1.index('qp[2]') assert s1.index('qp[2]') < s1.index('sb') assert qp1['at'] == approx(1.5) assert qp2['at'] == approx(3.5) assert sb1['at'] == approx(6) assert qp1.position == approx(1) assert qp2.position == approx(3) assert sb1.position == approx(5) assert qp1['l'] == approx(1) assert qp2['l'] == approx(1) assert sb1['l'] == approx(2) assert float(qp1['k1']) == approx(2) assert float(qp2['k1']) == approx(2) assert float(sb1['angle']) == approx(3.14/4) assert qp1.cmdpar.k1.expr.lower() == "qp_k1" def test_sequence_get_elements_s2(mad): mad.input(SEQU) s2 = mad.sequence.s2.elements qp1 = s2['qp1[1]'] qp2 = s2['qp2[1]'] assert s2.index('qp1') < s2.index('qp2') assert qp1['at'] == approx(0) assert qp2['at'] == approx(1) assert qp1['l'] == approx(1) assert qp2['l'] == approx(2) assert float(qp1['k1']) == approx(3) assert float(qp2['k1']) == approx(2) # def test_sequence_get_expanded_elements(): def test_crash(mad): """Check that a RuntimeError is raised in case MAD-X crashes.""" assert bool(mad) # a.t.m. MAD-X crashes on this input, because the L (length) # parametere is missing: raises(RuntimeError, mad.input, 'XXX: sequence;') assert not bool(mad) def test_sequence_elements(mad): mad.input(SEQU) elements = mad.sequence['s1'].elements iqp2 = elements.index('qp[2]') qp1 = elements['qp[1]'] qp2 = elements[iqp2] assert qp1['at'] == approx(1.5) assert qp2['at'] == approx(3.5) assert qp1.position == approx(1) assert qp2.position == approx(3) assert iqp2 == elements.at(3.1) def test_sequence_expanded_elements(mad): beam = 'ex=1, ey=2, particle=electron, sequence=s1;' mad.input(SEQU) mad.command.beam(beam) mad.use('s1') elements = mad.sequence['s1'].expanded_elements iqp2 = elements.index('qp[2]') qp1 = elements['qp[1]'] qp2 = elements[iqp2] assert qp1['at'] == approx(1.5) assert qp2['at'] == approx(3.5) assert qp1.position == approx(1) assert qp2.position == approx(3) assert iqp2 == elements.at(3.1) def test_element_inform(mad): beam = 'ex=1, ey=2, particle=electron, sequence=s1;' mad.input(SEQU) mad.command.beam(beam) mad.use('s1') elem = mad.sequence.s1.expanded_elements['qp'] assert { name for name in elem if elem.cmdpar[name].inform } == {'k1', 'l', 'at'} def test_table(mad): beam = 'ex=1, ey=2, particle=electron, sequence=s1;' mad.input(SEQU) mad.command.beam(beam) mad.use('s1') initial = dict(alfx=0.5, alfy=1.5, betx=2.5, bety=3.5) twiss = mad.twiss(sequence='s1', sectormap=True, **initial) sector = mad.table.sectortable assert str(twiss).startswith("<Table 'twiss': ") assert str(sector).startswith("<Table 'sectortable': ") assert 'betx' in twiss assert 't111' in sector assert 't111' not in twiss assert 'betx' not in sector assert len(twiss) == len(list(twiss)) assert set(twiss) == set(twiss[0]) assert twiss.s[5] == twiss[5].s assert twiss.s[-1] == twiss[-1].s copy = twiss.copy() assert copy['betx'] == approx(twiss.betx) assert set(copy) == set(twiss) copy = twiss.copy(['betx']) assert set(copy) == {'betx'} ALL = slice(None) assert sector.tmat(0).shape == (6, 6, 6) assert_allclose(sector.tmat(ALL)[0, 0, 0, :], sector.t111) assert_allclose(sector.tmat(ALL)[1, 5, 3, :], sector.t264) assert_allclose(sector.tmat(ALL)[3, 0, 3, :], sector.t414) assert_allclose(sector.tmat(ALL)[4, 4, 4, :], sector.t555) assert_allclose(sector.rmat(ALL)[0, 0, :], sector.r11) assert_allclose(sector.rmat(ALL)[1, 5, :], sector.r26) assert_allclose(sector.rmat(ALL)[3, 0, :], sector.r41) assert_allclose(sector.rmat(ALL)[4, 4, :], sector.r55) assert_allclose(sector.kvec(ALL)[0, :], sector.k1) assert_allclose(sector.kvec(ALL)[1, :], sector.k2) assert_allclose(sector.kvec(ALL)[3, :], sector.k4) assert_allclose(sector.kvec(ALL)[4, :], sector.k5) r = mad.sectortable()[:, :6, :6] k = mad.sectortable()[:, 6, :6] t = mad.sectortable2() num_elems = len(mad.sequence.s1.elements) assert t.shape == (num_elems, 6, 6, 6) assert r.shape == (num_elems, 6, 6) assert k.shape == (num_elems, 6) assert_allclose(t[:, 0, 0, 0], sector.t111) assert_allclose(t[:, 1, 5, 3], sector.t264) assert_allclose(t[:, 3, 0, 3], sector.t414) assert_allclose(t[:, 4, 4, 4], sector.t555) assert_allclose(r[:, 0, 0], sector.r11) assert_allclose(r[:, 1, 5], sector.r26) assert_allclose(r[:, 3, 0], sector.r41) assert_allclose(r[:, 4, 4], sector.r55) assert_allclose(k[:, 0], sector.k1) assert_allclose(k[:, 1], sector.k2) assert_allclose(k[:, 3], sector.k4) assert_allclose(k[:, 4], sector.k5) def test_selected_columns(mad, lib): mad.input(SEQU) mad.command.beam() mad.use('s1') mad.select(flag='twiss', column=['s', 'x', 'y']) table = mad.twiss(sequence='s1', betx=1, bety=1) assert set(table) > {'s', 'x', 'y', 'betx', 'bety'} assert set(table.copy()) > {'s', 'x', 'y', 'betx', 'bety'} assert table.selected_columns() == ['s', 'x', 'y'] assert table.selection().col_names() == ['s', 'x', 'y'] assert table.selection().copy().keys() == {'s', 'x', 'y'} mad.select(flag='twiss', clear=True) mad.select(flag='twiss', column=['betx', 'bety']) lib.apply_table_selections('twiss') table = mad.table.twiss assert set(table) > {'s', 'x', 'y', 'betx', 'bety'} assert set(table.copy()) > {'s', 'x', 'y', 'betx', 'bety'} assert table.selected_columns() == ['betx', 'bety'] assert table.selection().col_names() == ['betx', 'bety'] assert table.selection().copy().keys() == {'betx', 'bety'} def test_table_selected_rows(mad, lib): mad.input(SEQU) mad.command.beam() mad.use('s1') def check_selection(table, name): assert_equal( table.column(name, rows='selected'), table[name][table.selected_rows()]) assert_equal( table.column(name, rows='selected'), table.selection()[name]) mad.select(flag='twiss', class_='quadrupole') table = mad.twiss(sequence='s1', betx=1, bety=1) assert table.selected_rows() == [2, 4] check_selection(table, 'alfx') check_selection(table, 'alfy') check_selection(table, 'betx') check_selection(table, 'bety') mad.select(flag='twiss', clear=True) mad.select(flag='twiss', class_='drift') lib.apply_table_selections('twiss') table = mad.table.twiss assert table.selected_rows() == [1, 3, 5, 7] check_selection(table, 'alfx') check_selection(table, 'alfy') check_selection(table, 'betx') check_selection(table, 'bety') def test_table_selected_rows_mask(mad, lib): mad.input(SEQU) mad.command.beam() mad.use('s1') mad.select(flag='twiss', class_='quadrupole') table = mad.twiss(sequence='s1', betx=1, bety=1) mask = lib.get_table_selected_rows_mask('twiss') assert mask.shape == (len(mad.sequence.s1.expanded_elements), ) assert_equal(mask.nonzero(), (table.selected_rows(), )) def test_attr(mad): assert hasattr(mad, 'constraint') assert hasattr(mad, 'constraint_') assert hasattr(mad, 'global_') assert not hasattr(mad, 'foobar') assert not hasattr(mad, '_constraint') def test_expr(mad): g = mad.globals vars = mad.expr_vars g.foo = 1 g.bar = 2 assert set(vars('foo')) == {'foo'} assert set(vars('(foo) * sin(2*pi*bar)')) == {'foo', 'bar'} def test_command(mad): mad.input(SEQU) twiss = mad.command.twiss sbend = mad.elements.sb clone = sbend.clone('foobar', angle="pi/5", l=1) assert 'betx=0' in str(twiss) assert 'angle=' in str(sbend) assert 'tilt' in sbend assert sbend.tilt == 0 assert len(sbend) == len(list(sbend)) assert 'tilt' in list(sbend) assert clone.name == 'foobar' assert clone.base_type.name == 'sbend' assert clone.parent.name == 'sb' assert clone.defs.angle == 'pi / 5' assert clone.angle == approx(0.6283185307179586) assert len(clone) == len(sbend) assert 'angle=0.628' in str(clone) assert 'tilt' not in str(clone) clone.angle = 0.125 clone = mad.elements.foobar # need to update cache assert clone.angle == 0.125 assert len(twiss) == len(list(twiss)) assert 'betx' in list(twiss) assert clone.angle != approx(clone.parent.angle) del clone.angle clone = mad.elements.foobar # need to update cache assert clone.angle == clone.parent.angle with raises(AttributeError): clone.missing_attribute with raises(NotImplementedError): del twiss['betx'] with raises(NotImplementedError): del clone.base_type.angle def test_array_attribute(mad): mad.globals.nine = 9 clone = mad.elements.multipole.clone('foo', knl=[0, 'nine/3', 4]) knl = clone.knl assert knl[0] == 0 assert knl[1] == 3 assert knl[2] == 4 assert len(knl) == 3 assert list(knl) == [0.0, 3.0, 4.0] assert str(knl) == '[0.0, 3.0, 4.0]' knl[1] = '3*nine' assert mad.elements.foo.defs.knl[1] == '3 * nine' assert mad.elements.foo.knl[1] == 27 def test_array_attribute_comparison(mad): mad.globals.nine = 9 foo = mad.elements.multipole.clone('foo', knl=[0, 5, 10]) bar_eq = mad.elements.multipole.clone('bar_eq', knl=[0, 5, 10]) bar_gt = mad.elements.multipole.clone('bar_gt', knl=[0, 6, 10]) bar_lt = mad.elements.multipole.clone('bar_lt', knl=[0, 5, 'nine']) knl = foo.knl knl_eq = bar_eq.knl knl_gt = bar_gt.knl knl_lt = bar_lt.knl assert knl == knl_eq assert not (knl == knl_gt) assert not (knl == knl_lt) assert not (knl < knl_eq) assert knl < knl_gt assert not (knl < knl_lt) assert knl <= knl_eq assert knl <= knl_gt assert not (knl <= knl_lt) assert not (knl > knl_eq) assert not (knl > knl_gt) assert knl > knl_lt assert knl >= knl_eq assert not (knl >= knl_gt) assert knl >= knl_lt def test_command_map(mad): command = mad.command assert 'match' in command assert 'sbend' in command assert 'foooo' not in command assert 'match' in list(command) assert len(command) == len(list(command)) assert 'match' in str(command) assert 'sbend' in str(command) assert 'sbend' in mad.base_types assert 'match' not in mad.base_types def test_comments(mad): var = mad.globals mad('x = 1; ! x = 2;') assert var.x == 1 mad('x = 2; // x = 3;') assert var.x == 2 mad('x = 3; /* x = 4; */') assert var.x == 3 mad('/* x = 3; */ x = 4;') assert var.x == 4 mad('x = 5; ! /* */ x = 6;') assert var.x == 5 mad('x = 5; /* ! */ x = 6;') assert var.x == 6 def test_multiline_input(mad): var = mad.globals mad(''' x = 1; y = 2; ''') assert var.x, 1 assert var.y, 2 mad(''' x = /* 3; y =*/ 4; ''') assert var.x == 4 assert var.y == 2 mad(''' x = 1; /* */ x = 2; */ if (x == 1) { x = 3; } ''') assert var.x == 2 mad(''' x = 1; /* x = 2; */ if (x == 1) { x = 3; } ''') assert var.x == 3 def test_errors(mad): mad.input(SEQU) mad.beam() mad.use(sequence='s1') mad.select(flag='error', range='qp') dkn = [1e-6, 2e-6, 3e-6] dks = [4e-6, 5e-6, 6e-6] mad.efcomp(dkn=dkn, dks=dks) mad.ealign(dx=1e-3, dy=-4e-3) fd = mad.sequence['s1'].expanded_elements['qp'].field_errors al = mad.sequence['s1'].expanded_elements['qp'].align_errors expected_dkn = np.hstack((dkn, np.zeros(len(fd.dkn) - len(dkn)))) expected_dks = np.hstack((dks, np.zeros(len(fd.dks) - len(dks)))) assert_allclose(fd.dkn, expected_dkn) assert_allclose(fd.dks, expected_dks) assert_allclose(al.dx, 1e-3) assert_allclose(al.dy, -4e-3) def test_subsequence(mad): mad.input(""" d1: RBEND, l=0.1, angle=0.1; seq1: sequence, l=0.1; d1.1: d1, at=0.05; endsequence; seq2: sequence, l=0.2; seq1, at=0.05; seq1, at=0.15; endsequence; """) seq2 = mad.sequence.seq2 assert isinstance(seq2.elements['seq1'], Sequence) assert seq2.elements['seq1'].name == 'seq1' assert seq2.elements['seq1'].element_names() == \ mad.sequence.seq1.element_names() def test_dframe_after_use(mad): mad.input(""" mqf.k1 = 0.3037241107; mqd.k1 = -0.3037241107; fodo: sequence, l=10, refer=entry; mqf: quadrupole, at=0, l=1, k1:=mqf.k1; dff: drift, at=1, l=4; mqd: quadrupole, at=5, l=1, k1:=mqd.k1; dfd: drift, at=6, l=4; endsequence; beam; use, sequence=fodo; twiss, sequence=fodo, x=0.1; """) index = ['#s', 'mqf', 'dff', 'mqd', 'dfd', '#e'] names = ['fodo$start', 'mqf', 'dff', 'mqd', 'dfd', 'fodo$end'] twiss = mad.table.twiss assert index == twiss.row_names() assert index == twiss.dframe().index.tolist() assert names == twiss.dframe(index='name').index.tolist() mad.use(sequence='fodo') twiss = mad.table.twiss # Should still work: assert names == twiss.dframe(index='name').index.tolist() # The following assert demonstrates the current behaviour and is # meant to detect if the MAD-X implementation changes. It may lead # to crashes or change in the future. In that case, please remove # this line. It does not represent desired behaviour! assert mad.table.twiss.row_names() == \ ['#s', '#e', 'dfd', 'mqd', 'dff', 'mqf']

0

1,239

0

23,589

0

55

1,170

beca016de282d8ad828e46810f9fa27aac015a7f

4,852

py

Python

geneticpython/engines/single_objective/single_objective_engine.py

ngocjr7/geneticpython

4b4157523ce13b3da56cef61282cb0a984cd317b

[ "MIT" ]

null

geneticpython/engines/single_objective/single_objective_engine.py

ngocjr7/geneticpython

4b4157523ce13b3da56cef61282cb0a984cd317b

[ "MIT" ]

null

geneticpython/engines/single_objective/single_objective_engine.py

ngocjr7/geneticpython

4b4157523ce13b3da56cef61282cb0a984cd317b

[ "MIT" ]

null

""" File: single_objective_engine.py Author: ngocjr7 Email: [email protected] Github: https://github.com/ngocjr7 Description: """ from __future__ import absolute_import

38.816

83

0.557708

""" File: single_objective_engine.py Author: ngocjr7 Email: [email protected] Github: https://github.com/ngocjr7 Description: """ from __future__ import absolute_import from typing import List, Union, Callable from functools import wraps from collections import OrderedDict from ..geneticengine import GeneticEngine from ...core.population import Population from ...core.operators import Selection, Crossover, Mutation, Replacement from ...core.individual import Individual from ...callbacks import Callback, CallbackList from ...callbacks import History import math class SingleObjectiveEngine(GeneticEngine): def __init__(self, population: Population, objective: Callable[[Individual], Union[float, int]] = None, selection: Selection = None, selection_size: int = None, crossover: Crossover = None, mutation: Mutation = None, replacement: Replacement = None, callbacks: List[Callback] = None, generations: int = 100, random_state: int = None): callback_list = CallbackList( callbacks, add_history=True, add_progbar=True) super(SingleObjectiveEngine, self).__init__(population=population, objective=objective, selection=selection, selection_size=selection_size, crossover=crossover, mutation=mutation, replacement=replacement, callbacks=callback_list, generations=generations, random_state=random_state) def get_best_indv(self) -> Individual: best_indv = min(self.population.individuals, key=lambda indv: indv._objective) return best_indv.clone() def _update_metrics(self): self.metrics = self.metrics or OrderedDict() self.metrics['best_objective'] = self.get_best_indv().objective def _update_logs(self, logs): logs = logs or {} logs.update(self.metrics or OrderedDict()) return logs def compute_objectives(self, population: List[Individual]) -> List[Individual]: ret = list() # compute objectives for indv in population: if self.objective is None: raise ValueError(f"Engine has no registered objective functions") indv._coefficient = self.coefficient indv._objective = self.objective(indv) ret.append(indv) return ret def minimize_objective(self, fn): """ register objective function """ @wraps(fn) def _fn_minimization_with_objective_check(indv): ''' A wrapper function for objective function with objective value check. ''' # Check indv type. if not isinstance(indv, Individual): raise TypeError( 'indv\'s class must be subclass of IndividualBase') # Check objective. objective = float(fn(indv)) is_invalid = not isinstance( objective, (float, int)) or (math.isnan(objective)) if is_invalid: msg = 'objective value(value: {}, type: {}) is invalid' msg = msg.format(objective, type(objective)) raise ValueError(msg) return objective self.objective = _fn_minimization_with_objective_check self.coefficient = 1 def maximize_objective(self, fn): """ register maximization of objective function """ @wraps(fn) def _fn_maximization_with_objective_check(indv): ''' A wrapper function for objective function with objective value check. ''' # Check indv type. if not isinstance(indv, Individual): raise TypeError( 'indv\'s class must be subclass of IndividualBase') # Check objective. objective = float(fn(indv)) is_invalid = not isinstance( objective, (float, int)) or (math.isnan(objective)) if is_invalid: msg = 'objective value(value: {}, type: {}) is invalid' msg = msg.format(objective, type(objective)) raise ValueError(msg) return -objective self.objective = _fn_maximization_with_objective_check self.coefficient = -1

0

1,507

0

2,750

0

178

245

6c1a2218b3975b6c65e1c36ce24d867d86a06bee

517

py

Python

timing.py

darkless456/Python

1ba37d028e4a818ccfffc18682c1bac15554e3ac

[ "MIT" ]

null

timing.py

darkless456/Python

1ba37d028e4a818ccfffc18682c1bac15554e3ac

[ "MIT" ]

null

timing.py

darkless456/Python

1ba37d028e4a818ccfffc18682c1bac15554e3ac

[ "MIT" ]

null

# timing.py import datetime today = datetime.date.today() yesterday = today - datetime.timedelta(days = 1) tomorrow = today + datetime.timedelta(days = 1) print(yesterday, today, tomorrow) # ------------------------- ''' last_friday = datetime.date.today() oneday = datetime.timedelta(days = 1) while last_friday.weekday() != calendar.FRIDAY : last_friday = oneday print(last_friday.strftime('%A, %d-%b-%Y')) ''' t = datetime.datetime(2012,9,3,21,30) k = datetime.date.today() print(t, '\n', k)

19.884615

48

0.659574

# timing.py import datetime, calendar today = datetime.date.today() yesterday = today - datetime.timedelta(days = 1) tomorrow = today + datetime.timedelta(days = 1) print(yesterday, today, tomorrow) # ------------------------- ''' last_friday = datetime.date.today() oneday = datetime.timedelta(days = 1) while last_friday.weekday() != calendar.FRIDAY : last_friday = oneday print(last_friday.strftime('%A, %d-%b-%Y')) ''' t = datetime.datetime(2012,9,3,21,30) k = datetime.date.today() print(t, '\n', k)

0

10

0

1676722d3f346db563fa9c4d25ad5528e4cd54fa

25,385

py

Python

manticore/core/smtlib/visitors.py

Srinivas11789/manticore

af3c6aada811833864efaccef7477f14e9b5e0dd

[ "Apache-2.0" ]

null

manticore/core/smtlib/visitors.py

Srinivas11789/manticore

af3c6aada811833864efaccef7477f14e9b5e0dd

[ "Apache-2.0" ]

null

manticore/core/smtlib/visitors.py

Srinivas11789/manticore

af3c6aada811833864efaccef7477f14e9b5e0dd

[ "Apache-2.0" ]

null

from manticore.utils.helpers import CacheDict import logging logger = logging.getLogger(__name__) constant_folder_simplifier_cache = CacheDict(max_size=150000, flush_perc=25) arithmetic_simplifier_cache = CacheDict(max_size=150000, flush_perc=25)

34.869505

167

0.593146

from manticore.utils.helpers import CacheDict from .expression import * from functools import lru_cache import logging import operator logger = logging.getLogger(__name__) class Visitor(object): ''' Class/Type Visitor Inherit your class visitor from this one and get called on a different visiting function for each type of expression. It will call the first implemented method for the __mro__ class order. For example for a BitVecAdd it will try visit_BitVecAdd() if not defined then it will try with visit_BitVecOperation() if not defined then it will try with visit_BitVec() if not defined then it will try with visit_Operation() if not defined then it will try with visit_Expression() Other class named visitors are: visit_Constant() visit_Variable() visit_Operation() visit_BitVec() visit_Bool() visit_Array() ''' def __init__(self, cache=None, **kwargs): super().__init__() self._stack = [] self._cache = {} if cache is None else cache def push(self, value): assert value is not None self._stack.append(value) def pop(self): if len(self._stack) == 0: return None result = self._stack.pop() return result @property def result(self): assert len(self._stack) == 1 return self._stack[-1] def _method(self, expression, *args): #Special case. Need to get the unsleeved version of the array if isinstance(expression, ArrayProxy): expression = expression.array assert expression.__class__.__mro__[-1] is object for cls in expression.__class__.__mro__: sort = cls.__name__ methodname = 'visit_%s' % sort if hasattr(self, methodname): value = getattr(self, methodname)(expression, *args) if value is not None: assert isinstance(value, Expression) return value return self._rebuild(expression, args) def visit(self, node, use_fixed_point=False): ''' The entry point of the visitor. The exploration algorithm is a DFS post-order traversal The implementation used two stacks instead of a recursion The final result is store in self.result :param node: Node to explore :type node: Expression :param use_fixed_point: if True, it runs _methods until a fixed point is found :type use_fixed_point: Bool ''' cache = self._cache visited = set() stack = [] stack.append(node) while stack: node = stack.pop() if node in cache: self.push(cache[node]) elif isinstance(node, Operation): if node in visited: operands = [self.pop() for _ in range(len(node.operands))] value = self._method(node, *operands) visited.remove(node) self.push(value) cache[node] = value else: visited.add(node) stack.append(node) stack.extend(node.operands) else: self.push(self._method(node)) if use_fixed_point: old_value = None new_value = self.pop() while old_value is not new_value: self.visit(new_value) old_value = new_value new_value = self.pop() self.push(new_value) @staticmethod def _rebuild(expression, operands): if isinstance(expression, Constant): return expression if isinstance(expression, Operation): if any(x is not y for x, y in zip(expression.operands, operands)): import copy aux = copy.copy(expression) aux._operands = operands return aux return expression class Translator(Visitor): ''' Simple visitor to translate an expression into something else ''' def _method(self, expression, *args): #Special case. Need to get the unsleeved version of the array if isinstance(expression, ArrayProxy): expression = expression.array assert expression.__class__.__mro__[-1] is object for cls in expression.__class__.__mro__: sort = cls.__name__ methodname = 'visit_{:s}'.format(sort) if hasattr(self, methodname): value = getattr(self, methodname)(expression, *args) if value is not None: return value raise Exception("No translation for this {}".format(expression)) class GetDeclarations(Visitor): ''' Simple visitor to collect all variables in an expression or set of expressions ''' def __init__(self, **kwargs): super().__init__(**kwargs) self.variables = set() def visit_Variable(self, expression): self.variables.add(expression) @property def result(self): return self.variables class GetDepth(Translator): ''' Simple visitor to collect all variables in an expression or set of expressions ''' def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def visit_Expression(self, expression): return 1 def visit_Operation(self, expression, *operands): return 1 + max(operands) def get_depth(exp): visitor = GetDepth() visitor.visit(exp) return visitor.result class PrettyPrinter(Visitor): def __init__(self, depth=None, **kwargs): super().__init__(**kwargs) self.output = '' self.indent = 0 self.depth = depth def _print(self, s, e=None): self.output += ' ' * self.indent + str(s) # + '(%016x)'%hash(e) self.output += '\n' def visit(self, expression): ''' Overload Visitor.visit because: - We need a pre-order traversal - We use a recursion as it makes it easier to keep track of the indentation ''' self._method(expression) def _method(self, expression, *args): ''' Overload Visitor._method because we want to stop to iterate over the visit_ functions as soon as a valid visit_ function is found ''' assert expression.__class__.__mro__[-1] is object for cls in expression.__class__.__mro__: sort = cls.__name__ methodname = 'visit_%s' % sort method = getattr(self, methodname, None) if method is not None: method(expression, *args) return return def visit_Operation(self, expression, *operands): self._print(expression.__class__.__name__, expression) self.indent += 2 if self.depth is None or self.indent < self.depth * 2: for o in expression.operands: self.visit(o) else: self._print('...') self.indent -= 2 return '' def visit_BitVecExtract(self, expression): self._print(expression.__class__.__name__ + '{%d:%d}' % (expression.begining, expression.end), expression) self.indent += 2 if self.depth is None or self.indent < self.depth * 2: for o in expression.operands: self.visit(o) else: self._print('...') self.indent -= 2 return '' def visit_Constant(self, expression): self._print(expression.value) return '' def visit_Variable(self, expression): self._print(expression.name) return '' @property def result(self): return self.output def pretty_print(expression, **kwargs): if not isinstance(expression, Expression): return str(expression) pp = PrettyPrinter(**kwargs) pp.visit(expression) return pp.result class ConstantFolderSimplifier(Visitor): def __init__(self, **kw): super().__init__(**kw) operations = {BitVecAdd: operator.__add__, BitVecSub: operator.__sub__, BitVecMul: operator.__mul__, BitVecDiv: operator.__truediv__, BitVecShiftLeft: operator.__lshift__, BitVecShiftRight: operator.__rshift__, BitVecAnd: operator.__and__, BitVecOr: operator.__or__, BitVecXor: operator.__xor__, BitVecNot: operator.__not__, BitVecNeg: operator.__invert__, LessThan: operator.__lt__, LessOrEqual: operator.__le__, Equal: operator.__eq__, GreaterThan: operator.__gt__, GreaterOrEqual: operator.__ge__, BoolAnd: operator.__and__, BoolOr: operator.__or__, BoolNot: operator.__not__} def visit_BitVecConcat(self, expression, *operands): if all(isinstance(o, Constant) for o in operands): result = 0 for o in operands: result <<= o.size result |= o.value return BitVecConstant(expression.size, result, taint=expression.taint) def visit_BitVecZeroExtend(self, expression, *operands): if all(isinstance(o, Constant) for o in operands): return BitVecConstant(expression.size, operands[0].value, taint=expression.taint) def visit_BitVecSignExtend(self, expression, *operands): if expression.extend == 0: return operands[0] def visit_BitVecExtract(self, expression, *operands): if all(isinstance(o, Constant) for o in expression.operands): value = expression.operands[0].value begining = expression.begining end = expression.end value = value >> begining mask = 2**(end - begining + 1) - 1 value = value & mask return BitVecConstant(expression.size, value, taint=expression.taint) def visit_BoolAnd(self, expression, a, b): if isinstance(a, Constant) and a.value == True: return b if isinstance(b, Constant) and b.value == True: return a def visit_BoolOr(self, expression, a, b): if isinstance(a, Constant) and a.value == False: return b if isinstance(b, Constant) and b.value == False: return a def visit_Operation(self, expression, *operands): ''' constant folding, if all operands of an expression are a Constant do the math ''' operation = self.operations.get(type(expression), None) if operation is not None and \ all(isinstance(o, Constant) for o in operands): value = operation(*(x.value for x in operands)) if isinstance(expression, BitVec): return BitVecConstant(expression.size, value, taint=expression.taint) else: isinstance(expression, Bool) return BoolConstant(value, taint=expression.taint) else: if any(operands[i] is not expression.operands[i] for i in range(len(operands))): expression = self._rebuild(expression, operands) return expression constant_folder_simplifier_cache = CacheDict(max_size=150000, flush_perc=25) @lru_cache(maxsize=128) def constant_folder(expression): global constant_folder_simplifier_cache simp = ConstantFolderSimplifier(cache=constant_folder_simplifier_cache) simp.visit(expression, use_fixed_point=True) return simp.result class ArithmeticSimplifier(Visitor): def __init__(self, parent=None, **kw): super().__init__(**kw) @staticmethod def _same_constant(a, b): return isinstance(a, Constant) and\ isinstance(b, Constant) and\ a.value == b.value or a is b @staticmethod def _changed(expression, operands): if isinstance(expression, Constant) and len(operands) > 0: return True arity = len(operands) return any(operands[i] is not expression.operands[i] for i in range(arity)) def visit_Operation(self, expression, *operands): ''' constant folding, if all operands of an expression are a Constant do the math ''' if all(isinstance(o, Constant) for o in operands): expression = constant_folder(expression) if self._changed(expression, operands): expression = self._rebuild(expression, operands) return expression def visit_BitVecZeroExtend(self, expression, *operands): if self._changed(expression, operands): return BitVecZeroExtend(expression.size, *operands, taint=expression.taint) else: return expression def visit_BitVecITE(self, expression, *operands): if isinstance(expression.operands[0], Constant): if expression.operands[0].value: result = expression.operands[1] else: result = expression.operands[2] import copy result = copy.copy(result) result._taint |= expression.operands[0].taint return result if self._changed(expression, operands): return BitVecITE(expression.size, *operands, taint=expression.taint) def visit_BitVecExtract(self, expression, *operands): ''' extract(sizeof(a), 0)(a) ==> a extract(16, 0)( concat(a,b,c,d) ) => concat(c, d) extract(m,M)(and/or/xor a b ) => and/or/xor((extract(m,M) a) (extract(m,M) a) ''' op = expression.operands[0] begining = expression.begining end = expression.end # extract(sizeof(a), 0)(a) ==> a if begining == 0 and end + 1 == op.size: return op elif isinstance(op, BitVecConcat): new_operands = [] bitcount = 0 for item in reversed(op.operands): if begining >= item.size: begining -= item.size else: if bitcount < expression.size: new_operands.append(item) bitcount += item.size if begining != expression.begining: return BitVecExtract(BitVecConcat(sum([x.size for x in new_operands]), *reversed(new_operands)), begining, expression.size, taint=expression.taint) if isinstance(op, (BitVecAnd, BitVecOr, BitVecXor)): bitoperand_a, bitoperand_b = op.operands return op.__class__(BitVecExtract(bitoperand_a, begining, expression.size), BitVecExtract(bitoperand_b, begining, expression.size), taint=expression.taint) def visit_BitVecAdd(self, expression, *operands): ''' a + 0 ==> a 0 + a ==> a ''' left = expression.operands[0] right = expression.operands[1] if isinstance(right, BitVecConstant): if right.value == 0: return left if isinstance(left, BitVecConstant): if left.value == 0: return right def visit_BitVecSub(self, expression, *operands): ''' a - 0 ==> 0 (a + b) - b ==> a (b + a) - b ==> a ''' left = expression.operands[0] right = expression.operands[1] if isinstance(left, BitVecAdd): if self._same_constant(left.operands[0], right): return left.operands[1] elif self._same_constant(left.operands[1], right): return left.operands[0] def visit_BitVecOr(self, expression, *operands): ''' a | 0 => a 0 | a => a 0xffffffff & a => 0xffffffff a & 0xffffffff => 0xffffffff ''' left = expression.operands[0] right = expression.operands[1] if isinstance(right, BitVecConstant): if right.value == 0: return left elif right.value == left.mask: return right elif isinstance(left, BitVecOr): left_left = left.operands[0] left_right = left.operands[1] if isinstance(right, Constant): return BitVecOr(left_left, (left_right | right), taint=expression.taint) elif isinstance(left, BitVecConstant): return BitVecOr(right, left, taint=expression.taint) def visit_BitVecAnd(self, expression, *operands): ''' ct & x => x & ct move constants to the right a & 0 => 0 remove zero a & 0xffffffff => a remove full mask (b & ct2) & ct => b & (ct&ct2) associative property (a & (b | c) => a&b | a&c distribute over | ''' left = expression.operands[0] right = expression.operands[1] if isinstance(right, BitVecConstant): if right.value == 0: return right elif right.value == right.mask: return left elif isinstance(left, BitVecAnd): left_left = left.operands[0] left_right = left.operands[1] if isinstance(right, Constant): return BitVecAnd(left_left, left_right & right, taint=expression.taint) elif isinstance(left, BitVecOr): left_left = left.operands[0] left_right = left.operands[1] return BitVecOr(right & left_left, right & left_right, taint=expression.taint) elif isinstance(left, BitVecConstant): return BitVecAnd(right, left, taint=expression.taint) def visit_BitVecShiftLeft(self, expression, *operands): ''' a << 0 => a remove zero a << ct => 0 if ct > sizeof(a) remove big constant shift ''' left = expression.operands[0] right = expression.operands[1] if isinstance(right, BitVecConstant): if right.value == 0: return left elif right.value >= right.size: return left def visit_ArraySelect(self, expression, *operands): ''' ArraySelect (ArrayStore((ArrayStore(x0,v0) ...),xn, vn), x0) -> v0 ''' arr, index = operands if isinstance(arr, ArrayVariable): return if isinstance(index, BitVecConstant): ival = index.value # props are slow and using them in tight loops should be avoided, esp when they offer no additional validation # arr._operands[1] = arr.index, arr._operands[0] = arr.array while isinstance(arr, ArrayStore) and isinstance(arr._operands[1], BitVecConstant) and arr._operands[1]._value != ival: arr = arr._operands[0] # arr.array if isinstance(index, BitVecConstant) and isinstance(arr, ArrayStore) and isinstance(arr.index, BitVecConstant) and arr.index.value == index.value: return arr.value else: if arr is not expression.array: return arr.select(index) def visit_Expression(self, expression, *operands): assert len(operands) == 0 assert not isinstance(expression, Operation) return expression arithmetic_simplifier_cache = CacheDict(max_size=150000, flush_perc=25) @lru_cache(maxsize=128) def arithmetic_simplify(expression): global arithmetic_simplifier_cache simp = ArithmeticSimplifier(cache=arithmetic_simplifier_cache) simp.visit(expression, use_fixed_point=True) return simp.result def to_constant(expression): value = arithmetic_simplify(expression) if isinstance(value, Constant): return value.value elif isinstance(value, Array): if value.index_max: ba = bytearray() for i in range(value.index_max): value_i = simplify(value[i]) if not isinstance(value_i, Constant): break ba.append(value_i.value) else: return ba return value @lru_cache(maxsize=128) def simplify(expression): expression = constant_folder(expression) expression = arithmetic_simplify(expression) return expression class TranslatorSmtlib(Translator): ''' Simple visitor to translate an expression to its smtlib representation ''' unique = 0 def __init__(self, use_bindings=False, *args, **kw): assert 'bindings' not in kw super().__init__(*args, **kw) self.use_bindings = use_bindings self._bindings_cache = {} self._bindings = [] def _add_binding(self, expression, smtlib): if not self.use_bindings or len(smtlib) <= 10: return smtlib if smtlib in self._bindings_cache: return self._bindings_cache[smtlib] TranslatorSmtlib.unique += 1 name = 'a_%d' % TranslatorSmtlib.unique self._bindings.append((name, expression, smtlib)) self._bindings_cache[expression] = name return name @property def bindings(self): return self._bindings translation_table = { BoolNot: 'not', BoolEq: '=', BoolAnd: 'and', BoolOr: 'or', BoolXor: 'xor', BoolITE: 'ite', BitVecAdd: 'bvadd', BitVecSub: 'bvsub', BitVecMul: 'bvmul', BitVecDiv: 'bvsdiv', BitVecUnsignedDiv: 'bvudiv', BitVecMod: 'bvsmod', BitVecRem: 'bvsrem', BitVecUnsignedRem: 'bvurem', BitVecShiftLeft: 'bvshl', BitVecShiftRight: 'bvlshr', BitVecArithmeticShiftLeft: 'bvashl', BitVecArithmeticShiftRight: 'bvashr', BitVecAnd: 'bvand', BitVecOr: 'bvor', BitVecXor: 'bvxor', BitVecNot: 'bvnot', BitVecNeg: 'bvneg', LessThan: 'bvslt', LessOrEqual: 'bvsle', Equal: '=', GreaterThan: 'bvsgt', GreaterOrEqual: 'bvsge', UnsignedLessThan: 'bvult', UnsignedLessOrEqual: 'bvule', UnsignedGreaterThan: 'bvugt', UnsignedGreaterOrEqual: 'bvuge', BitVecSignExtend: '(_ sign_extend %d)', BitVecZeroExtend: '(_ zero_extend %d)', BitVecExtract: '(_ extract %d %d)', BitVecConcat: 'concat', BitVecITE: 'ite', ArrayStore: 'store', ArraySelect: 'select', } def visit_BitVecConstant(self, expression): assert isinstance(expression, BitVecConstant) if expression.size == 1: return '#' + bin(expression.value & expression.mask)[1:] else: return '#x%0*x' % (int(expression.size / 4), expression.value & expression.mask) def visit_BoolConstant(self, expression): return expression.value and 'true' or 'false' def visit_Variable(self, expression): return expression.name def visit_ArraySelect(self, expression, *operands): array_smt, index_smt = operands if isinstance(expression.array, ArrayStore): array_smt = self._add_binding(expression.array, array_smt) return '(select %s %s)' % (array_smt, index_smt) def visit_Operation(self, expression, *operands): operation = self.translation_table[type(expression)] if isinstance(expression, (BitVecSignExtend, BitVecZeroExtend)): operation = operation % expression.extend elif isinstance(expression, BitVecExtract): operation = operation % (expression.end, expression.begining) operands = [self._add_binding(*x) for x in zip(expression.operands, operands)] return '(%s %s)' % (operation, ' '.join(operands)) @property def results(self): raise Exception("NOOO") @property def result(self): output = super().result if self.use_bindings: for name, expr, smtlib in reversed(self._bindings): output = '( let ((%s %s)) %s )' % (name, smtlib, output) return output def translate_to_smtlib(expression, **kwargs): translator = TranslatorSmtlib(**kwargs) translator.visit(expression) return translator.result class Replace(Visitor): ''' Simple visitor to replaces expressions ''' def __init__(self, bindings=None, **kwargs): super().__init__(**kwargs) if bindings is None: raise ValueError("bindings needed in replace") self._replace_bindings = bindings def visit_Variable(self, expression): if expression in self._replace_bindings: return self._replace_bindings[expression] return expression def replace(expression, bindings): if not bindings: return expression visitor = Replace(bindings) visitor.visit(expression, use_fixed_point=True) result_expression = visitor.result #for var in get_variables(result_expression): # assert var not in bindings return result_expression def get_variables(expression): visitor = GetDeclarations() visitor.visit(expression) return visitor.result

0

1,842

0

21,533

0

1,253

0

8

480

34a65f614b2aed9614eeb0a853f10c891d51443b

280

py

Python

account/urls.py

Wizock/CRUD-master

07fbf3c64610a8725724fc934e66c6be35690cc9

[ "CC0-1.0" ]

1

2022-03-13T09:50:04.000Z

account/urls.py

Wizock/TodoButBetter

07fbf3c64610a8725724fc934e66c6be35690cc9

[ "CC0-1.0" ]

null

account/urls.py

Wizock/TodoButBetter

07fbf3c64610a8725724fc934e66c6be35690cc9

[ "CC0-1.0" ]

null

from django.urls import path urlpatterns = [ path(r'user/<str:usr>/', accountView), path('register_/', register_), ]

23.333333

61

0.739286

from django import urls from django.conf.urls import include, url from django.contrib.auth.forms import AdminPasswordChangeForm from django.urls import path from .views import * urlpatterns = [ path(r'user/<str:usr>/', accountView), path('register_/', register_), ]

0

61

88

eb106290ccfcc64601c7996ff5449b815f8ad55c

1,020

py

Python

pyaibot.py

linsicheng20060818/PythonExercises

dff362b066de54186d8e2a71f0fb6b8fcb1c8f2a

[ "MIT" ]

2

2019-01-05T13:34:08.000Z

2019-01-06T05:33:17.000Z

pyaibot.py

linsicheng20060818/PythonExercises

dff362b066de54186d8e2a71f0fb6b8fcb1c8f2a

[ "MIT" ]

null

pyaibot.py

linsicheng20060818/PythonExercises

dff362b066de54186d8e2a71f0fb6b8fcb1c8f2a

[ "MIT" ]

null

# -*- coding: utf-8 -*- """try: import jieba except: print("please install jieba first.") input("press any key to continue") quit()"""

25.5

73

0.435294

# -*- coding: utf-8 -*- """try: import jieba except: print("please install jieba first.") input("press any key to continue") quit()""" def chchat(a): import jieba v=False #if a=="quit" or a=="exit" or a=="退出" or a=="再见": # import os # exit()#Error list1=jieba.lcut(a)#jieba分词 #print(list1)#Debug i=0 b="" if list1[i]=="你好": return(a) else: for i in range(len(list1)): if list1[i]=="你": list1[i]="我" elif list1[i]=="我": list1[i]="你" elif list1[i]=="几": import random v=True '''for r in range(len(ni)):#'你'换成'我' list1[r]="我" for i in range(len(wo)):#'我'换成'你' list1[i]="你"''' for i in range(len(list1)): b=b+list1[i] if v==True: return(random.randint(-10,2000)) else: return((b.replace("吗","").replace("?","!")).replace("？","！"))

78

0

822

0

22

040bfae6c7070cefcd380adace083b08384a141a

391

py

Python

map_annotate_app/admin.py

tushar-agarwal/WikiNearby

0cc10bdeb1cb0728a6405808cc25f2d9e65dcb95

[ "MIT" ]

2

2018-03-20T21:30:35.000Z

2019-03-19T04:58:42.000Z

map_annotate_app/admin.py

tushar-agarwal/map_annotate

0cc10bdeb1cb0728a6405808cc25f2d9e65dcb95

[ "MIT" ]

2

2016-08-21T13:21:51.000Z

2016-09-07T10:01:24.000Z

map_annotate_app/admin.py

tushar-agarwal/WikiNearby

0cc10bdeb1cb0728a6405808cc25f2d9e65dcb95

[ "MIT" ]

2

2016-10-06T13:47:24.000Z

2017-02-13T23:10:12.000Z

""" This is the C{admin.py} file for C{map_annotate_app}. For more details, see the documentation for C{map_annotate_app}. """ from django.contrib import admin from .models import Crime from .models import CrimeType from .models import Location from .models import Sansad admin.site.register(Location) admin.site.register(Crime) admin.site.register(CrimeType) admin.site.register(Sansad)

23

64

0.792839

""" This is the C{admin.py} file for C{map_annotate_app}. For more details, see the documentation for C{map_annotate_app}. """ from django.contrib import admin from .models import Crime from .models import CrimeType from .models import Location from .models import Sansad admin.site.register(Location) admin.site.register(Crime) admin.site.register(CrimeType) admin.site.register(Sansad)

0

81256977bc9b0ff7623132bef93f3ba3fe7872ae

4,444

py

Python

parsers.py

ekiwi/parsers

1837fe8c76b813da7befeee99668ab59b51aaefa

[ "BSD-2-Clause" ]

null

parsers.py

ekiwi/parsers

1837fe8c76b813da7befeee99668ab59b51aaefa

[ "BSD-2-Clause" ]

null

parsers.py

ekiwi/parsers

1837fe8c76b813da7befeee99668ab59b51aaefa

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright 2018, University of California, Berkeley # author: Kevin Laeufer <[email protected]> if __name__ == "__main__": g = Grammar() S, B, D, E, F = non_term = g.non_terminal('S', 'B', 'D', 'E', 'F') u, v, w, x, y, z = term = g.terminal('u', 'v', 'w', 'x', 'y', 'z') g.r(S, [u, B, D, z]) g.r(B, [B, v]) g.r(B, [w]) g.r(D, [E, F]) g.r(E, [y]) g.r(E, []) g.r(F, [x]) g.r(F, []) for nt in non_term: print(f"FIRST({nt}): {g.first(nt)}") print() for nt in non_term: print(f"FOLLOW({nt}): {g.follow(nt)}") print() print(g.ll_one(check_conflicts=False))

22.789744

81

0.633213

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright 2018, University of California, Berkeley # author: Kevin Laeufer <[email protected]> from collections import defaultdict class Symbol: def __init__(self, name, bold=False): self.name = name self._bold = bold def __str__(self): if self._bold: return f"\033[1m{self.name}\033[0m" else: return f"{self.name}" def __repr__(self): return self.name class NonTerminal(Symbol): def __init__(self, name): super().__init__(name, bold=True) class Terminal(Symbol): def __init__(self, name): super().__init__(name) class Epsilon(Terminal): def __init__(self): super().__init__("ε") class Rule: def __init__(self, lhs, rhs): assert isinstance(lhs, NonTerminal) assert isinstance(rhs, list) assert all(isinstance(sym, Symbol) for sym in rhs) self.lhs = lhs self.rhs = rhs def __getitem__(self, item): if item not in {0, 1}: raise IndexError(item) if item == 0: return self.lhs else: return self.rhs def __str__(self): return f"{self.lhs} -> {''.join(str(sym) for sym in self.rhs)}" def __repr__(self): return str(self) class Grammar: def __init__(self): self._syms = [Epsilon(), Terminal('$')] self._rules = [] self._root = None def _make_syms(self, Type, names): syms = [Type(name) for name in names] self._syms += syms return syms def non_terminal(self, *names): return self._make_syms(NonTerminal, names) def terminal(self, *names): return self._make_syms(Terminal, names) def epsilon(self): return self._syms[0] def eof(self): return self._syms[1] def r(self, lhs, rhs): if len(rhs) < 1: rhs = [self.epsilon()] if self._root is None: self._root = lhs # by convention self._rules.append(Rule(lhs, rhs)) def first(self, sym): assert isinstance(sym, Symbol), f"{sym} : {type(sym)}" #print(f"FIRST({sym})") if isinstance(sym, Terminal): return {sym} _first = set() for lhs, rhs in self._rules: if lhs != sym: continue annullable = True for s in rhs: if s == sym: annullable = False break s_first = self.first(s) s_annullable = self.epsilon() in s_first _first = _first | (s_first - {self.epsilon()}) if not s_annullable: annullable = False break if annullable: _first |= {self.epsilon()} return _first def follow(self, non_term): assert isinstance(non_term, NonTerminal) _follow = set() for lhs, rhs in self._rules: if non_term not in rhs: continue for ii, sym in enumerate(rhs): if sym != non_term: continue # scan following symbols followed_by_annulable = True for ff in rhs[ii+1:]: _first = self.first(ff) _follow |= (_first - {self.epsilon()}) if self.epsilon() not in _first: followed_by_annulable = False break if followed_by_annulable: _follow |= self.follow(lhs) if non_term == self._root: _follow |= {self.eof()} return _follow def ll_one(self, check_conflicts=False): non_terms = [s for s in self._syms if isinstance(s, NonTerminal)] table = defaultdict(dict) for nt in non_terms: terms = self.first(nt) if self.epsilon() in terms: terms = (terms - {self.epsilon()}) | self.follow(nt) # pick rule: for tt in terms: applicable_rules = [] for rule in self._rules: if rule.lhs != nt: continue # scan rhs annullable = True for sym in rule.rhs: s_first = self.first(sym) if tt in s_first: applicable_rules.append(rule) break if not self.epsilon() in s_first: annullable = False break if annullable and tt in self.follow(nt): applicable_rules.append(rule) if check_conflicts: if len(applicable_rules) > 1: raise RuntimeError(f"Found multiple applicable rules for ({nt}, {tt}):\n" + '\n'.join(str(r) for r in applicable_rules)) table[nt][tt] = applicable_rules return dict(table) if __name__ == "__main__": g = Grammar() S, B, D, E, F = non_term = g.non_terminal('S', 'B', 'D', 'E', 'F') u, v, w, x, y, z = term = g.terminal('u', 'v', 'w', 'x', 'y', 'z') g.r(S, [u, B, D, z]) g.r(B, [B, v]) g.r(B, [w]) g.r(D, [E, F]) g.r(E, [y]) g.r(E, []) g.r(F, [x]) g.r(F, []) for nt in non_term: print(f"FIRST({nt}): {g.first(nt)}") print() for nt in non_term: print(f"FOLLOW({nt}): {g.follow(nt)}") print() print(g.ll_one(check_conflicts=False))

2

0

3,631

0

14

161

e6d68e135afb09552ac2f3d818b48fe79807d853

1,387

py

Python

Server.py

louis103/Python-Chat-Application

5212360194236daf5888d296fd71ed92303d7f94

[ "MIT" ]

1

2021-11-22T20:04:16.000Z

Server.py

louis103/Python-Chat-Application

5212360194236daf5888d296fd71ed92303d7f94

[ "MIT" ]

null

Server.py

louis103/Python-Chat-Application

5212360194236daf5888d296fd71ed92303d7f94

[ "MIT" ]

null

import socket HOST = "127.0.0.1" PORT = 9999 server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server.bind((HOST, PORT)) server.listen() clients = [] nicknames = [] # broadcast func # handle func # receive func print("******Server is running******") receive()

23.508475

72

0.581831

import socket, threading HOST = "127.0.0.1" PORT = 9999 server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server.bind((HOST, PORT)) server.listen() clients = [] nicknames = [] # broadcast func def broadcast(message): for client in clients: client.send(message) # handle func def handle(client): while True: try: message = client.recv(2048) print(f"{nicknames[clients.index(client)]} says {message}") broadcast(message) except: index = clients.index(client) clients.remove(client) nickname = nicknames[index] nicknames.remove(nickname) break # receive func def receive(): while True: client,address = server.accept() print(f"Connected with {str(address)}!") client.send("NICKNAME".encode("utf-8")) nickname = client.recv(2048).decode("utf-8") nicknames.append(nickname) clients.append(client) print(f"Nickname of new client is {nickname}") broadcast(f"{nickname} joined the chat!\n".encode("utf-8")) client.send("You Have Connected to the server".encode("utf-8")) thread = threading.Thread(target=handle,args=(client,)) thread.start() print("******Server is running******") receive()

0

999

0

11

69

d2d464639fd7c2110b4c254cb34f59661eddfc5e

18,069

py

Python

pypsi/core.py

Rudedog9d/pypsi

38dda442b21b8deb569d61076ab0a19c0e78edc8

[ "0BSD" ]

null

pypsi/core.py

Rudedog9d/pypsi

38dda442b21b8deb569d61076ab0a19c0e78edc8

[ "0BSD" ]

null

pypsi/core.py

Rudedog9d/pypsi

38dda442b21b8deb569d61076ab0a19c0e78edc8

[ "0BSD" ]

null

# # Copyright (c) 2015, Adam Meily <[email protected]> # Pypsi - https://github.com/ameily/pypsi # # Permission to use, copy, modify, and/or distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. # ''' Base classes for developing pluggable commands and plugins. ''' import sys from pypsi.ansi import AnsiCode from pypsi.format import get_lines, wrap_line def pypsi_print(*args, sep=' ', end='\n', file=None, flush=True, width=None, wrap=True, wrap_prefix=None, replace_errors=True): ''' Wraps the functionality of the Python builtin `print` function. The :meth:`pypsi.shell.Shell.bootstrap` overrides the Python :meth:`print` function with :meth:`pypsi_print`. :param str sep: string to print between arguments :param str end: string to print at the end of the output :param file file: output stream, if this is :const:`None`, the default is :data:`sys.stdout` :param bool flush: whether to flush the output stream :param int width: override the stream's width :param bool wrap: whether to word wrap the output :param str wrap_prefix: prefix string to print prior to every new line that is wrapped :param bool replace_errors: replace invalid character points with the '?' character ''' file = file or sys.stdout last = len(args) - 1 def write_safe(data): ''' Write the input str to the file and, if an encoding error occurs and replace_errors is ``True``, remove invalid code points and print again. ''' try: file.write(data) except UnicodeEncodeError: if replace_errors: enc = getattr(file, 'encoding', sys.getdefaultencoding()) file.write(data.encode(enc, errors='replace').decode(enc)) else: raise if wrap and hasattr(file, 'width') and file.width: width = width or file.width parts = [] for arg in args: if isinstance(arg, str): parts.append(arg) elif arg is None: parts.append('') elif isinstance(arg, AnsiCode): if file.isatty(): parts.append(str(arg)) elif arg.s is not None: parts.append(str(arg.s)) else: parts.append(str(arg)) txt = sep.join(parts) for (line, endl) in get_lines(txt): if line: first = True wrapno = 0 for wrapped in wrap_line(line, width, wrap_prefix=wrap_prefix): if not wrapped: continue wrapno += 1 if not first: file.write('\n') else: first = False write_safe(wrapped) if not line or endl: file.write('\n') else: last = len(args) - 1 for (i, arg) in enumerate(args): write_safe(str(arg)) if sep and i != last: write_safe(sep) if end: write_safe(end) if flush: file.flush()

38.363057

96

0.625989

# # Copyright (c) 2015, Adam Meily <[email protected]> # Pypsi - https://github.com/ameily/pypsi # # Permission to use, copy, modify, and/or distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. # ''' Base classes for developing pluggable commands and plugins. ''' import argparse import sys from pypsi.ansi import AnsiCodes, AnsiCode from pypsi.format import get_lines, wrap_line class Plugin(object): ''' A plugin is an object that is able to modify a :py:class:`pypsi.shell.Shell` object's behavior. Whereas a command can be execute from user input, the `Plugin` class does not contain a `run()` function. ''' def __init__(self, preprocess=None, postprocess=None): ''' Constructor can take two parameters: `preprocess` and `postprocess` These values determine where the plugin resides inside of the preprocess and postprocess list. This list, inside of :class:`pypsi.shell.Shell`, is iterated sequentially, from most priority to least. So, the highest priority value is 0, which means it will be the first plugin to run, and the lowest value is 100, which means it will be the last plugin to run. If either value is `None`, the plugin is not added to the processing list. For example, if this plugin only provides a preprocessing functionality, then postprocess should be set to :const:`None`. :param int preprocess: the preprocess priority :param int postprocess: the postprocess priority ''' self.preprocess = preprocess self.postprocess = postprocess def setup(self, shell): # pylint: disable=unused-argument ''' Called after the plugin has been registered to the active shell. :param pypsi.shell.Shell shell: the active shell :returns int: 0 on success, -1 on failure ''' return 0 def on_input(self, shell, line): # pylint: disable=unused-argument ''' Called after input from the user has been received. The return value is the preprocessed line. This means that modifying the line argument will not populate back. If this function does no preprocessing, return line unmodified. :param pypsi.shell.Shell shell: the active shell :param str line: the current input statement string :returns str: the preprocessed line ''' return line def on_tokenize(self, shell, tokens, origin): # pylint: disable=unused-argument ''' Called after an input string has been tokenized. If this function performs no preprocessing, return the tokens unmodified. :param pypsi.shell.Shell shell: the active shell :param list tokens: the list of :class:`pypsi.cmdline.Token` objects :param str origin: the origin of the input, can be either 'input' if received from a call to `input()` or 'prompt' if the input is the prompt to display to the user :returns list: the list of preprocessed :class:`pypsi.cmdline.Token` objects ''' return tokens def on_input_canceled(self, shell): # pylint: disable=unused-argument ''' Called when the user can canceled entering a statement via SIGINT (Ctrl+C). :param pypsi.shell.Shell shell: the active shell :returns int: 0 on success, -1 on error ''' return 0 def on_statement_finished(self, shell, rc): # pylint: disable=unused-argument ''' Called when a statement has been completely executed. :param pypsi.shell.Shell shell: the active shell :returns int: 0 on success, -1 on error ''' return 0 class Command(object): ''' A pluggable command that users can execute. All commands need to derive from this class. When a command is executed by a user, the command's :meth:`run` method will be called. The return value of the :meth:`run` method is used when processing forthcoming commands in the active statement. The return value must be an :class:`int` and follows the Unix standard: 0 on success, less than 0 on error, and greater than 0 given invalid input or incorrect usage. Each command has a topic associated with it. This topic can be referenced by commands such as :class:`pypsi.commands.help.HelpCommand` to categorize commands in help messages. A command can be used as a fallback handler by implementing the :meth:`fallback` method. This is similar to the :meth:`run` method, except that is accepts one more argument: the command name to execute that wasn't found by the shell. The return value of :meth:`fallback` holds the same purpose as the return value of :meth:`run`. By the time :meth:`run` is called, the system streams have been updated to point to the current file streams issued in the statement. For example, if the statement redirects standard out (:attr:`sys.stdout`) to a file, the destination file is automatically opened and :attr:`sys.stdout` is redirected to the opened file stream. Once the command has complete execution, the redirected stream is automatically closed and :attr:`sys.stdout` is set to its original stream. ''' def __init__(self, name, usage=None, brief=None, topic=None, pipe='str'): ''' :param str name: the name of the command which the user will reference in the shell :param str usage: the usage message to be displayed to the user :param str brief: a brief description of the command :param str topic: the topic that this command belongs to :param str pipe: the type of data that will be read from and written to any pipes ''' self.name = name self.usage = usage or '' self.brief = brief or '' self.topic = topic or '' self.pipe = pipe or 'str' def complete(self, shell, args, prefix): # pylint: disable=unused-argument ''' Called when the user attempts a tab-completion action for this command. :param pypsi.shell.Shell shell: the active shell :param list args: the list of arguments, the last one containing the cursor position :param str prefix: the prefix that all items returned must start with :returns list: the list of strings that could complete the current action ''' return [] def usage_error(self, shell, *args): ''' Display an error message that indicates incorrect usage of this command. After the error is displayed, the usage is printed. :param pypsi.shell.Shell shell: the active shell :param args: list of strings that are the error message ''' self.error(shell, *args) print(AnsiCodes.yellow, self.usage, AnsiCodes.reset, sep='') def error(self, shell, *args): # pylint: disable=unused-argument ''' Display an error message to the user. :param pypsi.shell.Shell shell: the active shell :param args: the error message to display ''' msg = "{}: {}".format(self.name, ''.join([str(a) for a in args])) print(AnsiCodes.red, msg, AnsiCodes.reset, file=sys.stderr, sep='') def run(self, shell, args): ''' Execute the command. All commands need to implement this method. :param pypsi.shell.Shell shell: the active shell :param list args: list of string arguments :returns int: 0 on success, less than 0 on error, and greater than 0 on invalid usage ''' raise NotImplementedError() def setup(self, shell): # pylint: disable=unused-argument ''' Called when the plugin has been registered to the active shell. :param pypsi.shell.Shell shell: the active shell :returns int: 0 on success, -1 on error ''' return 0 def fallback(self, shell, name, args): # pylint: disable=unused-argument ''' Called when this command was set as the fallback command. The only difference between this and :meth:`run` is that this method accepts the command name that was entered by the user. :param pypsi.shell.Shell shell: the active shell :param str name: the name of the command to run :param list args: arguments :returns int: 0 on success, less than 0 on error, and greater than 0 on invalid usage ''' return None class CommandShortCircuit(Exception): ''' Exception raised when the user enter invalid arguments or requests usage information via the -h and --help flags. ''' def __init__(self, code): ''' :param int code: the code the command should return ''' super().__init__(code) self.code = code class PypsiArgParser(argparse.ArgumentParser): ''' Customized :class:`argparse.ArgumentParser` for use in pypsi. This class slightly modifies the base ArgumentParser so that the following occurs: - The whole program does not exit on printing the help message or bad arguments - Any error messages are intercepted and printed on the active shell's error stream - Adds the option to provide callbacks for tab-completing options and parameters ''' def __init__(self, *args, **kwargs): #: Store callback functions for positional parameters self._pos_completers = [] #: Store callback functions for optional arguments with values self._op_completers = {} #: If a positional argument can be specified more than once, # store it's callback here and return it multiple times self._repeating_cb = None super().__init__(*args, **kwargs) def exit(self, status=0, message=None): if message: print(AnsiCodes.red, message, AnsiCodes.reset, file=sys.stderr, sep='') raise CommandShortCircuit(status) def print_usage(self, file=None): f = file or sys.stderr print(AnsiCodes.yellow, self.format_usage(), AnsiCodes.reset, sep='', file=f) def print_help(self, file=None): f = file or sys.stderr print(AnsiCodes.yellow, self.format_help(), AnsiCodes.reset, sep='', file=f) def get_options(self): ''' :return: All optional arguments (ex, '-v'/'--verbose') ''' return list(self._op_completers.keys()) def get_option_completer(self, option): ''' Returns the callback for the specified optional argument, Or None if one was not specified. :param str option: The Option :return function: The callback function or None ''' return self._op_completers.get(option, None) def has_value(self, arg): ''' Check if the optional argument has a value associated with it. :param str arg: Optional argument to check :return: True if arg has a value, false otherwise ''' # pylint: disable=protected-access # _option_string_actions is a dictionary containing all of the optional # arguments and the argparse action they should perform. Currently, the # only two actions that store a value are _AppendAction/_StoreAction. # These represent the value passed to 'action' in add_argument: # parser.add_argument('-l', '--long', action='store') action = self._option_string_actions.get(arg, None) return isinstance(action, (argparse._AppendAction, argparse._StoreAction)) def get_positional_completer(self, pos): ''' Get the callback for a positional parameter :param pos: index of the parameter - first param's index = 0 :return: The callback if it exists, else None ''' try: return self._pos_completers[pos] except IndexError: if self._repeating_cb: # A positional parameter is set to repeat return self._repeating_cb return None def get_positional_arg_index(self, args): ''' Get the positional index of a cursor, based on optional arguments and positional arguments :param list args: List of str arguments from the Command Line :return: ''' index = 0 for token in args: if token in self._option_string_actions: # Token is an optional argument ( ex, '-v' / '--verbose' ) if self.has_value(token): # Optional Argument has a value associated with it, so # reduce index to not count it's value as a pos param index -= 1 else: # Is a positional param or value for an optional argument index += 1 # return zero-based index return index - 1 def add_argument(self, *args, completer=None, **kwargs): # pylint: disable=arguments-differ ''' Override add_argument function of argparse.ArgumentParser to handle callback functions. :param args: Positional arguments to pass up to argparse :param function completer: Optional callback function for argument :param kwargs: Keywork arguments to pass up to argparse :return: ''' cb = completer nargs = kwargs.get('nargs', None) chars = self.prefix_chars if not args or len(args) == 1 and args[0][0] not in chars: # If no positional args are supplied or only one is supplied and # it doesn't look like an option string, parse a positional # argument ( from argparse ) if nargs and nargs in ('+', '*', argparse.REMAINDER): # Positional param can repeat # Currently only stores the last repeating completer specified self._repeating_cb = cb self._pos_completers.append(cb) else: # Add an optional argument for arg in args: self._op_completers[arg] = cb # Call argparse.add_argument() return super().add_argument(*args, **kwargs) def error(self, message): print(AnsiCodes.red, self.prog, ": error: ", message, AnsiCodes.reset, sep='', file=sys.stderr) self.print_usage() self.exit(1) def pypsi_print(*args, sep=' ', end='\n', file=None, flush=True, width=None, wrap=True, wrap_prefix=None, replace_errors=True): ''' Wraps the functionality of the Python builtin `print` function. The :meth:`pypsi.shell.Shell.bootstrap` overrides the Python :meth:`print` function with :meth:`pypsi_print`. :param str sep: string to print between arguments :param str end: string to print at the end of the output :param file file: output stream, if this is :const:`None`, the default is :data:`sys.stdout` :param bool flush: whether to flush the output stream :param int width: override the stream's width :param bool wrap: whether to word wrap the output :param str wrap_prefix: prefix string to print prior to every new line that is wrapped :param bool replace_errors: replace invalid character points with the '?' character ''' file = file or sys.stdout last = len(args) - 1 def write_safe(data): ''' Write the input str to the file and, if an encoding error occurs and replace_errors is ``True``, remove invalid code points and print again. ''' try: file.write(data) except UnicodeEncodeError: if replace_errors: enc = getattr(file, 'encoding', sys.getdefaultencoding()) file.write(data.encode(enc, errors='replace').decode(enc)) else: raise if wrap and hasattr(file, 'width') and file.width: width = width or file.width parts = [] for arg in args: if isinstance(arg, str): parts.append(arg) elif arg is None: parts.append('') elif isinstance(arg, AnsiCode): if file.isatty(): parts.append(str(arg)) elif arg.s is not None: parts.append(str(arg.s)) else: parts.append(str(arg)) txt = sep.join(parts) for (line, endl) in get_lines(txt): if line: first = True wrapno = 0 for wrapped in wrap_line(line, width, wrap_prefix=wrap_prefix): if not wrapped: continue wrapno += 1 if not first: file.write('\n') else: first = False write_safe(wrapped) if not line or endl: file.write('\n') else: last = len(args) - 1 for (i, arg) in enumerate(args): write_safe(str(arg)) if sep and i != last: write_safe(sep) if end: write_safe(end) if flush: file.flush()

0

14,110

0

5

115

5050f96a5b09f087a43bfbf366927f7c8ded0262

687

py

Python

2020/6/main.py

klrkdekira/adventofcode

8384d919093712c95b707b8e3f293dbfba22be74

[ "BSD-2-Clause" ]

1

2020-12-01T08:41:55.000Z

2020/6/main.py

klrkdekira/adventofcode

8384d919093712c95b707b8e3f293dbfba22be74

[ "BSD-2-Clause" ]

null

2020/6/main.py

klrkdekira/adventofcode

8384d919093712c95b707b8e3f293dbfba22be74

[ "BSD-2-Clause" ]

null

QUESTIONS = ['a', 'b', 'c', 'x', 'y', 'z'] if __name__ == '__main__': with open('input') as file: groups = [] group = [] for row in file: row = row.strip() if not row: groups.append(group) group = [] continue group.append(row) groups.append(group) print(sum(map(anyone, groups))) print(sum(map(everyone, groups)))

24.535714

66

0.519651

QUESTIONS = ['a', 'b', 'c', 'x', 'y', 'z'] def anyone(group): answers = [] for person in group: answers.extend(person) return len(set(answers)) def everyone(group): answers = set.intersection(*(set(person) for person in group)) return len(answers) if __name__ == '__main__': with open('input') as file: groups = [] group = [] for row in file: row = row.strip() if not row: groups.append(group) group = [] continue group.append(row) groups.append(group) print(sum(map(anyone, groups))) print(sum(map(everyone, groups)))

0

189

0

46

62d60dcf7dc46a76d9c2e17fa4e8e062fa646f12

9,440

py

Python

frille-lang/lib/python3.6/site-packages/pathy/gcs.py

frillecode/CDS-spring-2021-language

a0b2116044cd20d4a34b98f23bd2663256c90c5d

[ "MIT" ]

null

frille-lang/lib/python3.6/site-packages/pathy/gcs.py

frillecode/CDS-spring-2021-language

a0b2116044cd20d4a34b98f23bd2663256c90c5d

[ "MIT" ]

null

frille-lang/lib/python3.6/site-packages/pathy/gcs.py

frillecode/CDS-spring-2021-language

a0b2116044cd20d4a34b98f23bd2663256c90c5d

[ "MIT" ]

null

from typing import Any try: from google.api_core import exceptions as gcs_errors # type:ignore from google.auth.exceptions import DefaultCredentialsError # type:ignore from google.cloud.storage import Blob as GCSNativeBlob # type:ignore from google.cloud.storage import Bucket as GCSNativeBucket # type:ignore from google.cloud.storage import Client as GCSNativeClient # type:ignore has_gcs = True except ImportError: GCSNativeBlob = Any DefaultCredentialsError = BaseException gcs_errors = Any GCSNativeBucket = Any GCSNativeClient = Any has_gcs = False _MISSING_DEPS = """You are using the GCS functionality of Pathy without having the required dependencies installed. Please try installing them: pip install pathy[gcs] """

34.327273

86

0.584322

from dataclasses import dataclass from typing import Any, Dict, Generator, List, Optional from .base import ( Blob, Bucket, BucketClient, BucketEntry, ClientError, PathyScanDir, PurePathy, ) try: from google.api_core import exceptions as gcs_errors # type:ignore from google.auth.exceptions import DefaultCredentialsError # type:ignore from google.cloud.storage import Blob as GCSNativeBlob # type:ignore from google.cloud.storage import Bucket as GCSNativeBucket # type:ignore from google.cloud.storage import Client as GCSNativeClient # type:ignore has_gcs = True except ImportError: GCSNativeBlob = Any DefaultCredentialsError = BaseException gcs_errors = Any GCSNativeBucket = Any GCSNativeClient = Any has_gcs = False _MISSING_DEPS = """You are using the GCS functionality of Pathy without having the required dependencies installed. Please try installing them: pip install pathy[gcs] """ class BucketEntryGCS(BucketEntry["BucketGCS", GCSNativeBlob]): ... @dataclass class BlobGCS(Blob[GCSNativeBucket, GCSNativeBlob]): def delete(self) -> None: self.raw.delete() def exists(self) -> bool: return self.raw.exists() @dataclass class BucketGCS(Bucket): name: str bucket: GCSNativeBucket def get_blob(self, blob_name: str) -> Optional[BlobGCS]: assert isinstance( blob_name, str ), f"expected str blob name, but found: {type(blob_name)}" native_blob = None try: native_blob = self.bucket.get_blob(blob_name) except gcs_errors.ClientError: pass if native_blob is None: return None return BlobGCS( bucket=self.bucket, owner=native_blob.owner, name=native_blob.name, raw=native_blob, size=native_blob.size, updated=int(native_blob.updated.timestamp()), ) def copy_blob( # type:ignore[override] self, blob: BlobGCS, target: "BucketGCS", name: str ) -> Optional[BlobGCS]: assert blob.raw is not None, "raw storage.Blob instance required" native_blob = self.bucket.copy_blob(blob.raw, target.bucket, name) if native_blob is None: return None return BlobGCS( bucket=self.bucket, owner=native_blob.owner, name=native_blob.name, raw=native_blob, size=native_blob.size, updated=int(native_blob.updated.timestamp()), ) def delete_blob(self, blob: BlobGCS) -> None: # type:ignore[override] return self.bucket.delete_blob(blob.name) def delete_blobs(self, blobs: List[BlobGCS]) -> None: # type:ignore[override] return self.bucket.delete_blobs(blobs) def exists(self) -> bool: try: return self.bucket.exists() except gcs_errors.ClientError: return False class BucketClientGCS(BucketClient): client: Optional[GCSNativeClient] @property def client_params(self) -> Any: return dict(client=self.client) def __init__(self, **kwargs: Any) -> None: self.recreate(**kwargs) def recreate(self, **kwargs: Any) -> None: creds = kwargs["credentials"] if "credentials" in kwargs else None if creds is not None: kwargs["project"] = creds.project_id try: self.client = GCSNativeClient(**kwargs) except TypeError: # TypeError is raised if the imports for GCSNativeClient fail and are # assigned to Any, which is not callable. self.client = None def make_uri(self, path: PurePathy) -> str: return str(path) def create_bucket(self, path: PurePathy) -> Bucket: assert self.client is not None, _MISSING_DEPS return self.client.create_bucket(path.root) def delete_bucket(self, path: PurePathy) -> None: assert self.client is not None, _MISSING_DEPS bucket = self.client.get_bucket(path.root) bucket.delete() def exists(self, path: PurePathy) -> bool: # Because we want all the parents of a valid blob (e.g. "directory" in # "directory/foo.file") to return True, we enumerate the blobs with a prefix # and compare the object names to see if they match a substring of the path key_name = str(path.key) try: for obj in self.list_blobs(path): if obj.name == key_name: return True if obj.name.startswith(key_name + path._flavour.sep): return True except gcs_errors.ClientError: return False return False def lookup_bucket(self, path: PurePathy) -> Optional[BucketGCS]: assert self.client is not None, _MISSING_DEPS try: native_bucket = self.client.bucket(path.root) if native_bucket is not None: return BucketGCS(str(path.root), bucket=native_bucket) except gcs_errors.ClientError as err: print(err) return None def get_bucket(self, path: PurePathy) -> BucketGCS: assert self.client is not None, _MISSING_DEPS try: native_bucket = self.client.bucket(path.root) if native_bucket is not None: return BucketGCS(str(path.root), bucket=native_bucket) raise FileNotFoundError(f"Bucket {path.root} does not exist!") except gcs_errors.ClientError as e: raise ClientError(message=e.message, code=e.code) def list_buckets( self, **kwargs: Dict[str, Any] ) -> Generator[GCSNativeBucket, None, None]: assert self.client is not None, _MISSING_DEPS return self.client.list_buckets(**kwargs) # type:ignore def scandir( # type:ignore[override] self, path: Optional[PurePathy] = None, prefix: Optional[str] = None, delimiter: Optional[str] = None, ) -> PathyScanDir: return _GCSScanDir(client=self, path=path, prefix=prefix, delimiter=delimiter) def list_blobs( self, path: PurePathy, prefix: Optional[str] = None, delimiter: Optional[str] = None, include_dirs: bool = False, ) -> Generator[BlobGCS, None, None]: assert self.client is not None, _MISSING_DEPS continuation_token = None bucket = self.lookup_bucket(path) if bucket is None: return while True: if continuation_token: response = self.client.list_blobs( path.root, prefix=prefix, delimiter=delimiter, page_token=continuation_token, ) else: response = self.client.list_blobs( path.root, prefix=prefix, delimiter=delimiter ) for page in response.pages: for item in page: yield BlobGCS( bucket=bucket, owner=item.owner, name=item.name, raw=item, size=item.size, updated=item.updated.timestamp(), ) if response.next_page_token is None: break continuation_token = response.next_page_token class _GCSScanDir(PathyScanDir): _client: BucketClientGCS def scandir(self) -> Generator[BucketEntryGCS, None, None]: assert self._client.client is not None, _MISSING_DEPS continuation_token = None if self._path is None or not self._path.root: gcs_bucket: GCSNativeBucket for gcs_bucket in self._client.client.list_buckets(): yield BucketEntryGCS(gcs_bucket.name, is_dir=True, raw=None) return sep = self._path._flavour.sep bucket = self._client.lookup_bucket(self._path) if bucket is None: return while True: if continuation_token: response = self._client.client.list_blobs( bucket.name, prefix=self._prefix, delimiter=sep, page_token=continuation_token, ) else: response = self._client.client.list_blobs( bucket.name, prefix=self._prefix, delimiter=sep ) for page in response.pages: for folder in list(page.prefixes): full_name = folder[:-1] if folder.endswith(sep) else folder name = full_name.split(sep)[-1] if name: yield BucketEntryGCS(name, is_dir=True, raw=None) for item in page: name = item.name.split(sep)[-1] if name: yield BucketEntryGCS( name=name, is_dir=False, size=item.size, last_modified=item.updated.timestamp(), raw=item, ) if response.next_page_token is None: break continuation_token = response.next_page_token

0

1,938

0

6,396

0

152

160

3deebfeffce2abe1ba44b1052c91bfb62a647fb4

1,750

py

Python

project/both.py

mahmoudabuelnaga/baby-names-scraping

44ded037a4c24306123c4da749e32575eee4afc6

[ "MIT" ]

null

project/both.py

mahmoudabuelnaga/baby-names-scraping

44ded037a4c24306123c4da749e32575eee4afc6

[ "MIT" ]

null

project/both.py

mahmoudabuelnaga/baby-names-scraping

44ded037a4c24306123c4da749e32575eee4afc6

[ "MIT" ]

null

# -*- coding: utf-8 -*- from bs4 import BeautifulSoup import csv import requests links = [] items = [] for i in range(1,38): endpoint = f"https://baby.webteb.com/baby-names/%D8%A7%D8%B3%D9%85%D8%A7%D8%A1-%D8%A7%D9%88%D9%84%D8%A7%D8%AF-%D9%88%D8%A8%D9%86%D8%A7%D8%AA?pageindex={i}" get_response = requests.get(endpoint) # print(get_response.content) soup = BeautifulSoup(get_response.content, 'lxml') # print(soup.prettify()) section = soup.find('div', {'class':'page-section'}) for li in section.find_all('li'): links.append(li.a['href']) print(f'{i}', li.a['href']) for i, link in zip(range(1,len(links)+1), links): url = f"https://baby.webteb.com{link}" get_response = requests.get(url) soup = BeautifulSoup(get_response.content, 'lxml') content = soup.find('div', {'class':'section name'}) section1 = content.find('div', {'class':'section'}) name_detail = content.find('div', {'class':'name-details'}) section2 = name_detail.find('div', {'class':'section'}) span = section2.find('span', {'class':'latin'}) item = {} if content.h1.text: item['arabic_name'] = content.h1.text if section1.p.text: item['meaning'] = section1.p.text if span.text: item['english_name'] = span.text print(i, content.h1.text, section1.p.text, span.text) items.append(item) filename = '/home/naga/dev/babyNamesScraping/project/both.csv' with open(filename, 'w', newline='') as f: w = csv.DictWriter(f, fieldnames=['arabic_name','meaning', 'english_name'], extrasaction='ignore' , delimiter = ';') w.writeheader() print(items) for item in items: w.writerow(item) print(item)

29.166667

159

0.630857

# -*- coding: utf-8 -*- from time import sleep from bs4 import BeautifulSoup import csv import requests links = [] items = [] for i in range(1,38): endpoint = f"https://baby.webteb.com/baby-names/%D8%A7%D8%B3%D9%85%D8%A7%D8%A1-%D8%A7%D9%88%D9%84%D8%A7%D8%AF-%D9%88%D8%A8%D9%86%D8%A7%D8%AA?pageindex={i}" get_response = requests.get(endpoint) # print(get_response.content) soup = BeautifulSoup(get_response.content, 'lxml') # print(soup.prettify()) section = soup.find('div', {'class':'page-section'}) for li in section.find_all('li'): links.append(li.a['href']) print(f'{i}', li.a['href']) for i, link in zip(range(1,len(links)+1), links): url = f"https://baby.webteb.com{link}" get_response = requests.get(url) soup = BeautifulSoup(get_response.content, 'lxml') content = soup.find('div', {'class':'section name'}) section1 = content.find('div', {'class':'section'}) name_detail = content.find('div', {'class':'name-details'}) section2 = name_detail.find('div', {'class':'section'}) span = section2.find('span', {'class':'latin'}) item = {} if content.h1.text: item['arabic_name'] = content.h1.text if section1.p.text: item['meaning'] = section1.p.text if span.text: item['english_name'] = span.text print(i, content.h1.text, section1.p.text, span.text) items.append(item) filename = '/home/naga/dev/babyNamesScraping/project/both.csv' with open(filename, 'w', newline='') as f: w = csv.DictWriter(f, fieldnames=['arabic_name','meaning', 'english_name'], extrasaction='ignore' , delimiter = ';') w.writeheader() print(items) for item in items: w.writerow(item) print(item)

0

1

22

bb2ad701ba189d46d6b8954c67ceae977de8da75

4,318

py

Python

sde/solvers.py

d-l-fernandes/ito_general

8a9889fa13e5893e923c8d32fd1c94d22aec84d9

[ "MIT" ]

null

sde/solvers.py

d-l-fernandes/ito_general

8a9889fa13e5893e923c8d32fd1c94d22aec84d9

[ "MIT" ]

null

sde/solvers.py

d-l-fernandes/ito_general

8a9889fa13e5893e923c8d32fd1c94d22aec84d9

[ "MIT" ]

null

import jax.numpy as jnp from absl import flags Array = jnp.ndarray flags.DEFINE_enum("solver", "strong_3_halfs", ["euler_maruyama", "strong_3_halfs"], "Solver to use.") FLAGS = flags.FLAGS solvers_dict = { "euler_maruyama": EulerMaruyamaSolver, "strong_3_halfs": Strong3HalfsSolver }

40.35514

117

0.619268

from typing import Tuple import haiku as hk import jax.numpy as jnp import numpyro from absl import flags from numpyro.distributions.continuous import MultivariateNormal from sde import drifts, diffusions Array = jnp.ndarray flags.DEFINE_enum("solver", "strong_3_halfs", ["euler_maruyama", "strong_3_halfs"], "Solver to use.") FLAGS = flags.FLAGS class BaseSolver: def __init__(self, delta_t: float, beta_dims: int, drift: drifts.BaseDrift, diffusion: diffusions.BaseDiffusion): self.delta_t = delta_t self.beta_dims = beta_dims self.drift = drift self.diffusion = diffusion def __call__(self, x_0: Array, time: float) -> Array: raise NotImplementedError class EulerMaruyamaSolver(BaseSolver): def __init__(self, delta_t: float, beta_dims: int, drift: drifts.BaseDrift, diffusion: diffusions.BaseDiffusion): super().__init__(delta_t, beta_dims, drift, diffusion) def __call__(self, x_0: Array, time: float) -> Array: rng_beta = hk.next_rng_key() delta_beta = numpyro.sample("delta_beta", MultivariateNormal( loc=jnp.zeros(self.beta_dims), scale_tril=jnp.sqrt(self.delta_t) * jnp.eye(self.beta_dims)), rng_key=rng_beta) drift = self.drift(x_0, time) diff = self.diffusion(x_0, time) x_1 = x_0 + drift * self.delta_t + jnp.matmul(diff, delta_beta) return x_1 class Strong3HalfsSolver(BaseSolver): def __init__(self, delta_t: float, beta_dims: int, drift: drifts.BaseDrift, diffusion: diffusions.BaseDiffusion): super().__init__(delta_t, beta_dims, drift, diffusion) def __call__(self, x_0: Array, time: float) -> Array: rng_beta = hk.next_rng_key() # Vector of zeros beta_mean_vector = jnp.zeros((self.beta_dims*2, )) # Covariance matrix for the betas and gammas beta_covariance_top_left = self.delta_t ** 3 / 3 * jnp.eye(self.beta_dims) beta_covariance_top_right = self.delta_t ** 2 / 2 * jnp.eye(self.beta_dims) beta_covariance_bottom_right = self.delta_t * jnp.eye(self.beta_dims) beta_covariance_top = jnp.concatenate((beta_covariance_top_left, beta_covariance_top_right), axis=1) beta_covariance_bottom = jnp.concatenate((beta_covariance_top_right, beta_covariance_bottom_right), axis=1) beta_covariance = jnp.concatenate((beta_covariance_top, beta_covariance_bottom), axis=0) delta_gamma_beta = numpyro.sample("delta_gamma_beta", MultivariateNormal(loc=beta_mean_vector, covariance_matrix=beta_covariance), rng_key=rng_beta) delta_gamma = delta_gamma_beta[:self.beta_dims] delta_beta = delta_gamma_beta[self.beta_dims:] drift_0 = self.drift(x_0, time) diff = self.diffusion(x_0, time) diff_plus = self.diffusion(x_0, time + self.delta_t) init_x_1 = x_0 + drift_0 * self.delta_t + jnp.matmul(diff, delta_beta) init_x_1 += 1. / self.delta_t * jnp.matmul(diff_plus - diff, delta_beta * self.delta_t - delta_gamma) def scan_fn(carry, s): x_1 = carry x_0_plus = \ x_0 + drift_0 * self.delta_t / self.beta_dims + \ diff[:, s] * jnp.sqrt(self.delta_t) x_0_minus = \ x_0 + drift_0 * self.delta_t / self.beta_dims - \ diff[:, s] * jnp.sqrt(self.delta_t) drift_0_plus = self.drift(x_0_plus, time + self.delta_t) drift_0_minus = self.drift(x_0_minus, time + self.delta_t) x_1 += 0.25 * self.delta_t * (drift_0_plus + drift_0_minus) x_1 -= 0.5 * drift_0 * self.delta_t x_1 += \ 1. / (2 * jnp.sqrt(self.delta_t)) * (drift_0_plus - drift_0_minus) * delta_gamma[s] return x_1, None final_x_1, _ = hk.scan(scan_fn, init_x_1, jnp.arange(self.beta_dims)) return final_x_1 solvers_dict = { "euler_maruyama": EulerMaruyamaSolver, "strong_3_halfs": Strong3HalfsSolver }

0

3,790

0

48

181

79cfe256477332ba59823cac9001633a38f29bc4

5,767

py

Python

pysnmp/JUNIPER-SONET-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

11

2021-02-02T16:27:16.000Z

2021-08-31T06:22:49.000Z

pysnmp/JUNIPER-SONET-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

75

2021-02-24T17:30:31.000Z

2021-12-08T00:01:18.000Z

pysnmp/JUNIPER-SONET-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

10

2019-04-30T05:51:36.000Z

2022-02-16T03:33:41.000Z

# # PySNMP MIB module JUNIPER-SONET-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/JUNIPER-SONET-MIB # Produced by pysmi-0.3.4 at Mon Apr 29 19:50:16 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # OctetString, ObjectIdentifier, Integer = mibBuilder.importSymbols("ASN1", "OctetString", "ObjectIdentifier", "Integer") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueRangeConstraint, SingleValueConstraint, ConstraintsUnion, ConstraintsIntersection, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "SingleValueConstraint", "ConstraintsUnion", "ConstraintsIntersection", "ValueSizeConstraint") ifIndex, ifDescr = mibBuilder.importSymbols("IF-MIB", "ifIndex", "ifDescr") jnxMibs, jnxSonetNotifications = mibBuilder.importSymbols("JUNIPER-SMI", "jnxMibs", "jnxSonetNotifications") NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance") MibIdentifier, Gauge32, iso, ObjectIdentity, TimeTicks, ModuleIdentity, Integer32, Counter32, Unsigned32, MibScalar, MibTable, MibTableRow, MibTableColumn, NotificationType, Counter64, IpAddress, Bits = mibBuilder.importSymbols("SNMPv2-SMI", "MibIdentifier", "Gauge32", "iso", "ObjectIdentity", "TimeTicks", "ModuleIdentity", "Integer32", "Counter32", "Unsigned32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "NotificationType", "Counter64", "IpAddress", "Bits") DisplayString, DateAndTime, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "DateAndTime", "TextualConvention") jnxSonet = ModuleIdentity((1, 3, 6, 1, 4, 1, 2636, 3, 20)) jnxSonet.setRevisions(('2002-12-12 00:00', '2002-08-08 00:00',)) if mibBuilder.loadTexts: jnxSonet.setLastUpdated('200307182154Z') if mibBuilder.loadTexts: jnxSonet.setOrganization('Juniper Networks, Inc.') jnxSonetAlarms = MibIdentifier((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1)) jnxSonetAlarmTable = MibTable((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1), ) if mibBuilder.loadTexts: jnxSonetAlarmTable.setStatus('current') jnxSonetAlarmEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex")) if mibBuilder.loadTexts: jnxSonetAlarmEntry.setStatus('current') jnxSonetCurrentAlarms = MibTableColumn((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1, 1), JnxSonetAlarmId()).setMaxAccess("readonly") if mibBuilder.loadTexts: jnxSonetCurrentAlarms.setStatus('current') jnxSonetLastAlarmId = MibTableColumn((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1, 2), JnxSonetAlarmId()).setMaxAccess("readonly") if mibBuilder.loadTexts: jnxSonetLastAlarmId.setStatus('current') jnxSonetLastAlarmTime = MibTableColumn((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1, 3), TimeTicks()).setMaxAccess("readonly") if mibBuilder.loadTexts: jnxSonetLastAlarmTime.setStatus('current') jnxSonetLastAlarmDate = MibTableColumn((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1, 4), DateAndTime()).setMaxAccess("readonly") if mibBuilder.loadTexts: jnxSonetLastAlarmDate.setStatus('current') jnxSonetLastAlarmEvent = MibTableColumn((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("none", 1), ("set", 2), ("cleared", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: jnxSonetLastAlarmEvent.setStatus('current') jnxSonetNotificationPrefix = MibIdentifier((1, 3, 6, 1, 4, 1, 2636, 4, 6, 0)) jnxSonetAlarmSet = NotificationType((1, 3, 6, 1, 4, 1, 2636, 4, 6, 0, 1)).setObjects(("IF-MIB", "ifDescr"), ("JUNIPER-SONET-MIB", "jnxSonetLastAlarmId"), ("JUNIPER-SONET-MIB", "jnxSonetCurrentAlarms"), ("JUNIPER-SONET-MIB", "jnxSonetLastAlarmDate")) if mibBuilder.loadTexts: jnxSonetAlarmSet.setStatus('current') jnxSonetAlarmCleared = NotificationType((1, 3, 6, 1, 4, 1, 2636, 4, 6, 0, 2)).setObjects(("IF-MIB", "ifDescr"), ("JUNIPER-SONET-MIB", "jnxSonetLastAlarmId"), ("JUNIPER-SONET-MIB", "jnxSonetCurrentAlarms"), ("JUNIPER-SONET-MIB", "jnxSonetLastAlarmDate")) if mibBuilder.loadTexts: jnxSonetAlarmCleared.setStatus('current') mibBuilder.exportSymbols("JUNIPER-SONET-MIB", jnxSonetAlarms=jnxSonetAlarms, jnxSonetCurrentAlarms=jnxSonetCurrentAlarms, jnxSonetLastAlarmTime=jnxSonetLastAlarmTime, jnxSonetAlarmTable=jnxSonetAlarmTable, JnxSonetAlarmId=JnxSonetAlarmId, jnxSonetLastAlarmEvent=jnxSonetLastAlarmEvent, jnxSonetAlarmSet=jnxSonetAlarmSet, PYSNMP_MODULE_ID=jnxSonet, jnxSonetNotificationPrefix=jnxSonetNotificationPrefix, jnxSonetAlarmCleared=jnxSonetAlarmCleared, jnxSonetAlarmEntry=jnxSonetAlarmEntry, jnxSonet=jnxSonet, jnxSonetLastAlarmId=jnxSonetLastAlarmId, jnxSonetLastAlarmDate=jnxSonetLastAlarmDate)

128.155556

904

0.752211

# # PySNMP MIB module JUNIPER-SONET-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/JUNIPER-SONET-MIB # Produced by pysmi-0.3.4 at Mon Apr 29 19:50:16 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # OctetString, ObjectIdentifier, Integer = mibBuilder.importSymbols("ASN1", "OctetString", "ObjectIdentifier", "Integer") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueRangeConstraint, SingleValueConstraint, ConstraintsUnion, ConstraintsIntersection, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "SingleValueConstraint", "ConstraintsUnion", "ConstraintsIntersection", "ValueSizeConstraint") ifIndex, ifDescr = mibBuilder.importSymbols("IF-MIB", "ifIndex", "ifDescr") jnxMibs, jnxSonetNotifications = mibBuilder.importSymbols("JUNIPER-SMI", "jnxMibs", "jnxSonetNotifications") NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance") MibIdentifier, Gauge32, iso, ObjectIdentity, TimeTicks, ModuleIdentity, Integer32, Counter32, Unsigned32, MibScalar, MibTable, MibTableRow, MibTableColumn, NotificationType, Counter64, IpAddress, Bits = mibBuilder.importSymbols("SNMPv2-SMI", "MibIdentifier", "Gauge32", "iso", "ObjectIdentity", "TimeTicks", "ModuleIdentity", "Integer32", "Counter32", "Unsigned32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "NotificationType", "Counter64", "IpAddress", "Bits") DisplayString, DateAndTime, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "DateAndTime", "TextualConvention") jnxSonet = ModuleIdentity((1, 3, 6, 1, 4, 1, 2636, 3, 20)) jnxSonet.setRevisions(('2002-12-12 00:00', '2002-08-08 00:00',)) if mibBuilder.loadTexts: jnxSonet.setLastUpdated('200307182154Z') if mibBuilder.loadTexts: jnxSonet.setOrganization('Juniper Networks, Inc.') class JnxSonetAlarmId(TextualConvention, Bits): status = 'current' namedValues = NamedValues(("sonetLolAlarm", 0), ("sonetPllAlarm", 1), ("sonetLofAlarm", 2), ("sonetLosAlarm", 3), ("sonetSefAlarm", 4), ("sonetLaisAlarm", 5), ("sonetPaisAlarm", 6), ("sonetLopAlarm", 7), ("sonetBerrSdAlarm", 8), ("sonetBerrSfAlarm", 9), ("sonetLrdiAlarm", 10), ("sonetPrdiAlarm", 11), ("sonetReiAlarm", 12), ("sonetUneqAlarm", 13), ("sonetPmisAlarm", 14), ("sonetLocAlarm", 15), ("sonetVaisAlarm", 16), ("sonetVlopAlarm", 17), ("sonetVrdiAlarm", 18), ("sonetVuneqAlarm", 19), ("sonetVmisAlarm", 20), ("sonetVlocAlarm", 21), ("sdhLolAlarm", 22), ("sdhPllAlarm", 23), ("sdhLofAlarm", 24), ("sdhLosAlarm", 25), ("sdhOofAlarm", 26), ("sdhMsAisAlarm", 27), ("sdhHpAisAlarm", 28), ("sdhLopAlarm", 29), ("sdhBerrSdAlarm", 30), ("sdhBerrSfAlarm", 31), ("sdhMsFerfAlarm", 32), ("sdhHpFerfAlarm", 33), ("sdhMsFebeAlarm", 34), ("sdhHpUneqAlarm", 35), ("sdhHpMisAlarm", 36), ("sdhLocAlarm", 37)) jnxSonetAlarms = MibIdentifier((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1)) jnxSonetAlarmTable = MibTable((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1), ) if mibBuilder.loadTexts: jnxSonetAlarmTable.setStatus('current') jnxSonetAlarmEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex")) if mibBuilder.loadTexts: jnxSonetAlarmEntry.setStatus('current') jnxSonetCurrentAlarms = MibTableColumn((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1, 1), JnxSonetAlarmId()).setMaxAccess("readonly") if mibBuilder.loadTexts: jnxSonetCurrentAlarms.setStatus('current') jnxSonetLastAlarmId = MibTableColumn((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1, 2), JnxSonetAlarmId()).setMaxAccess("readonly") if mibBuilder.loadTexts: jnxSonetLastAlarmId.setStatus('current') jnxSonetLastAlarmTime = MibTableColumn((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1, 3), TimeTicks()).setMaxAccess("readonly") if mibBuilder.loadTexts: jnxSonetLastAlarmTime.setStatus('current') jnxSonetLastAlarmDate = MibTableColumn((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1, 4), DateAndTime()).setMaxAccess("readonly") if mibBuilder.loadTexts: jnxSonetLastAlarmDate.setStatus('current') jnxSonetLastAlarmEvent = MibTableColumn((1, 3, 6, 1, 4, 1, 2636, 3, 20, 1, 1, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("none", 1), ("set", 2), ("cleared", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: jnxSonetLastAlarmEvent.setStatus('current') jnxSonetNotificationPrefix = MibIdentifier((1, 3, 6, 1, 4, 1, 2636, 4, 6, 0)) jnxSonetAlarmSet = NotificationType((1, 3, 6, 1, 4, 1, 2636, 4, 6, 0, 1)).setObjects(("IF-MIB", "ifDescr"), ("JUNIPER-SONET-MIB", "jnxSonetLastAlarmId"), ("JUNIPER-SONET-MIB", "jnxSonetCurrentAlarms"), ("JUNIPER-SONET-MIB", "jnxSonetLastAlarmDate")) if mibBuilder.loadTexts: jnxSonetAlarmSet.setStatus('current') jnxSonetAlarmCleared = NotificationType((1, 3, 6, 1, 4, 1, 2636, 4, 6, 0, 2)).setObjects(("IF-MIB", "ifDescr"), ("JUNIPER-SONET-MIB", "jnxSonetLastAlarmId"), ("JUNIPER-SONET-MIB", "jnxSonetCurrentAlarms"), ("JUNIPER-SONET-MIB", "jnxSonetLastAlarmDate")) if mibBuilder.loadTexts: jnxSonetAlarmCleared.setStatus('current') mibBuilder.exportSymbols("JUNIPER-SONET-MIB", jnxSonetAlarms=jnxSonetAlarms, jnxSonetCurrentAlarms=jnxSonetCurrentAlarms, jnxSonetLastAlarmTime=jnxSonetLastAlarmTime, jnxSonetAlarmTable=jnxSonetAlarmTable, JnxSonetAlarmId=JnxSonetAlarmId, jnxSonetLastAlarmEvent=jnxSonetLastAlarmEvent, jnxSonetAlarmSet=jnxSonetAlarmSet, PYSNMP_MODULE_ID=jnxSonet, jnxSonetNotificationPrefix=jnxSonetNotificationPrefix, jnxSonetAlarmCleared=jnxSonetAlarmCleared, jnxSonetAlarmEntry=jnxSonetAlarmEntry, jnxSonet=jnxSonet, jnxSonetLastAlarmId=jnxSonetLastAlarmId, jnxSonetLastAlarmDate=jnxSonetLastAlarmDate)

0

954

0

22

74a36f9de503409718965b9b6bc829fa35d95202

190

py

Python

dev.py

LCBRU/batch_demographics

e516e958091fd74dad00b1705431ac030e3c4503

[ "MIT" ]

null

dev.py

LCBRU/batch_demographics

e516e958091fd74dad00b1705431ac030e3c4503

[ "MIT" ]

null

dev.py

LCBRU/batch_demographics

e516e958091fd74dad00b1705431ac030e3c4503

[ "MIT" ]

null

#!/usr/bin/env python from batch_demographics import create_app from config import DevConfig app = create_app(DevConfig()) if __name__ == "__main__": app.run(host="0.0.0.0", port=8000)

23.75

41

0.736842

#!/usr/bin/env python from batch_demographics import create_app from config import DevConfig app = create_app(DevConfig()) if __name__ == "__main__": app.run(host="0.0.0.0", port=8000)

0

dc71d49cfcda3d4e87c6a2b9fa01e89c155ee69a

151

py

Python

widgetProject/widgetApp/admin.py

cs-fullstack-2019-spring/django-fields-widgets-cw-rsalcido

4b19595867ee38396d0a80bfa0adcd0cb9811d23

[ "Apache-2.0" ]

null

widgetProject/widgetApp/admin.py

cs-fullstack-2019-spring/django-fields-widgets-cw-rsalcido

4b19595867ee38396d0a80bfa0adcd0cb9811d23

[ "Apache-2.0" ]

null

widgetProject/widgetApp/admin.py

cs-fullstack-2019-spring/django-fields-widgets-cw-rsalcido

4b19595867ee38396d0a80bfa0adcd0cb9811d23

[ "Apache-2.0" ]

null

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

21.571429

32

0.801325

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

0

0430b1b3554d1367b14b734250b34ede8b260068

337

py

Python

main.py

pooyapooya/rizpardazande

818721a3daac1385daf71ac508ad00bf153cbf0b

[ "MIT" ]

null

main.py

pooyapooya/rizpardazande

818721a3daac1385daf71ac508ad00bf153cbf0b

[ "MIT" ]

null

main.py

pooyapooya/rizpardazande

818721a3daac1385daf71ac508ad00bf153cbf0b

[ "MIT" ]

null

from easygui.boxes.choice_box import choicebox from phase1 import phase1 from phase2 import phase2 __author__ = 'po0ya' choices = [ 'Phase1', 'Phase2' ] choice = choicebox(msg='Please select project phase:', choices=choices) if choice == choices[0]: phase1() else: phase2()

17.736842

71

0.724036

from easygui.boxes.choice_box import choicebox from easygui.boxes.text_box import textbox from phase1 import phase1 from phase2 import phase2 __author__ = 'po0ya' choices = [ 'Phase1', 'Phase2' ] choice = choicebox(msg='Please select project phase:', choices=choices) if choice == choices[0]: phase1() else: phase2()

0

21

22

4471ff0d57c1cc3ec8a60aec1f93edea9763dd0c

2,984

py

Python

saws/data_util.py

Pangeam/saws

5aba511e72bf5feb35eb44be82fbdf805dfe3553

[ "Apache-2.0" ]

5,358

2015-09-18T19:16:11.000Z

2022-03-31T20:40:51.000Z

saws/data_util.py

Pangeam/saws

5aba511e72bf5feb35eb44be82fbdf805dfe3553

[ "Apache-2.0" ]

112

2015-09-10T10:53:57.000Z

2022-03-03T09:32:29.000Z

saws/data_util.py

Pangeam/saws

5aba511e72bf5feb35eb44be82fbdf805dfe3553

[ "Apache-2.0" ]

333

2015-09-18T19:16:13.000Z

2022-03-06T17:27:54.000Z

# -*- coding: utf-8 -*- # Copyright 2015 Donne Martin. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://www.apache.org/licenses/LICENSE-2.0 # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. from __future__ import unicode_literals from __future__ import print_function try: from collections import OrderedDict except:

35.52381

77

0.610925

# -*- coding: utf-8 -*- # Copyright 2015 Donne Martin. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://www.apache.org/licenses/LICENSE-2.0 # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. from __future__ import unicode_literals from __future__ import print_function import re try: from collections import OrderedDict except: from ordereddict import OrderedDict class DataUtil(object): """Utility class to read from the data folder. Attributes: * None. """ def create_header_to_type_map(self, headers, data_type): """Creates a dict mapping headers to ResourceTypes. Headers are the resource headers as they appear in the RESOURCES.txt. Headers are mapped to their corresponding ResourceType. Args: * headers: A string that represents the header. * data_type: An Enum specifying the data type. Returns: An OrderedDict mapping headers to ResourceTypes. """ command_types = [] for item in data_type: if item != data_type.NUM_TYPES: command_types.append(item) return OrderedDict(zip(headers, command_types)) def get_data(self, data_file_path, header_to_type_map, data_type): """Gets all data from the specified data file. Args: * data_file_path: A string representing the full file path of the data file. * header_to_type_map: A dictionary mapping the data header labels to the data types. * data_type: An Enum specifying the data type. Returns: A list, where each element is a list of completions for each data_type """ data_lists = [[] for x in range(data_type.NUM_TYPES.value)] with open(data_file_path) as f: for line in f: line = re.sub('\n', '', line) parsing_header = False # Check if we are reading in a data header to determine # which set of data we are parsing for key, value in header_to_type_map.items(): if key in line: data_type = value parsing_header = True break if not parsing_header: # Store the data in its associated list if line.strip() != '': data_lists[data_type.value].append(line) for data_list in data_lists: data_list.sort() return data_lists

0

2,200

0

2

71