Reset23/the-stack-v2-filtered-python · Datasets at Hugging Face

6552dea2d2667854202895aec4f0df5259855cbc

b0f6dbd92c368bd68fa1aafd67fdde9c323ab1be

/config.py

578b0ee4e0b9ed526e8784e67ae9a7c91b5a685d

[ "Apache-2.0" ]

permissive

niezhongliang/InsightFace-v3

ac62cff7d4aeb957fac9189ccca26976f9a045e9

e10cefec3bf0c465c92c42980ecbdb32eacc6dd5

refs/heads/master

2020-09-15T20:36:16.087481

2019-11-23T00:23:46

null

0

null

UTF-8

Python

false

1,130

py

import logging import os import torch device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # sets device for model and PyTorch tensors # Model parameters image_w = 112 image_h = 112 channel = 3 emb_size = 512 # Training parameters num_workers = 8 # for data-loading; right now, only 1 works with h5py grad_clip = 5. # clip gradients at an absolute value of print_freq = 100 # print training/validation stats every __ batches checkpoint = None # path to checkpoint, None if none # Data parameters num_classes = 85164 num_samples = 3804846 DATA_DIR = 'data' faces_ms1m_folder = 'data/faces_ms1m_112x112' path_imgidx = os.path.join(faces_ms1m_folder, 'train.idx') path_imgrec = os.path.join(faces_ms1m_folder, 'train.rec') IMG_DIR = 'data/images' pickle_file = 'data/faces_ms1m_112x112.pickle' def get_logger(): logger = logging.getLogger() handler = logging.StreamHandler() formatter = logging.Formatter("%(asctime)s %(levelname)s \t%(message)s") handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(logging.INFO) return logger logger = get_logger()

[ "liuyang12@focusmedia.cn" ]

liuyang12@focusmedia.cn

f9460bdd828edd3892ba9506260ad360ad7bfbad

ef3a7391b0a5c5d8e276355e97cbe4de621d500c

/venv/Lib/site-packages/thinc/neural/train.py

1a0492b1e6ef38288d5f82838d0e13063fc3efe1

[ "MIT", "Apache-2.0" ]

permissive

countBMB/BenjiRepo

143f6da5d198ea6f06404b4559e1f4528b71b3eb

79d882263baaf2a11654ca67d2e5593074d36dfa

refs/heads/master

2022-12-11T07:37:04.807143

2019-12-25T11:26:29

230,090,428

1

Apache-2.0

2022-12-08T03:21:09

2019-12-25T11:05:59

Python

UTF-8

Python

false

1,862

py

# coding: utf8 from __future__ import unicode_literals import numpy.random from tqdm import tqdm from .optimizers import Adam, linear_decay class Trainer(object): def __init__(self, model, **cfg): self.ops = model.ops self.model = model self.L2 = cfg.get("L2", 0.0) self.optimizer = Adam(model.ops, 0.001, decay=0.0, eps=1e-8, L2=self.L2) self.batch_size = cfg.get("batch_size", 128) self.nb_epoch = cfg.get("nb_epoch", 20) self.i = 0 self.dropout = cfg.get("dropout", 0.0) self.dropout_decay = cfg.get("dropout_decay", 0.0) self.each_epoch = [] def __enter__(self): return self, self.optimizer def __exit__(self, exc_type, exc_val, exc_tb): self.model.use_params(self.optimizer.averages) def iterate(self, train_X, train_y, progress_bar=True): orig_dropout = self.dropout for i in range(self.nb_epoch): indices = numpy.arange(len(train_X)) numpy.random.shuffle(indices) indices = self.ops.asarray(indices) j = 0 with tqdm(total=indices.shape[0], leave=False) as pbar: while j < indices.shape[0]: slice_ = indices[j : j + self.batch_size] X = _take_slice(train_X, slice_) y = _take_slice(train_y, slice_) yield X, y self.dropout = linear_decay(orig_dropout, self.dropout_decay, j) j += self.batch_size if progress_bar: pbar.update(self.batch_size) for func in self.each_epoch: func() def _take_slice(data, slice_): if isinstance(data, list) or isinstance(data, tuple): return [data[int(i)] for i in slice_] else: return data[slice_]

[ "bengmen92@gmail.com" ]

bengmen92@gmail.com

85a20a5685d762ddec4eeda36978c63036c74206

6a01a9287a4c23c7f11b7c5399cfb96bbe42eba8

/python/scripts/make_id_table_with_diff_expr.py

314e25e631430921796b32ad7d8d52c104d61aff

[ "MIT" ]

permissive

xguse/gmm-to-gff-transcripts-vs-snps

3c25bf2752aee76174d5dab92060fe7269caf99f

75337135ab8ff6d840af3cfccfe6404a06777a54

refs/heads/master

2021-01-19T01:50:33.473897

2016-08-02T20:31:18

54,731,430

0

null

UTF-8

Python

false

4,455

py

"""Describe here what this rule accomplishes.""" import pandas as pd import numpy as np # Settings edger_results_labels = snakemake.params.edger_results_labels cufflinks_results_labels = snakemake.params.cufflinks_results_labels # input edger_results = snakemake.input.edger_results cufflinks_results = snakemake.input.cufflinks_results ids_no_diff_expr = snakemake.input.ids_no_diff_expr #output ids_with_diff_expr = snakemake.output.ids_with_diff_expr def load_and_filter_diff_expr_data(path,ids,comparison,program,fdr_thresh): """Return new dataframe that has standardized and filtered the DE input tables. `path` (str): location of input file `ids` (dataframe): with the following columns - tcons_id - xloc_id - gene_id_external - gene_id_internal `comparison` (str): describe the RNA-seq analysis run ('midgut', 'salivary gland', etc) `program` (str): one of ['edger', 'cufflinks'] `fdr_thresh` (float): defining multiple testing significance threshold above which DE tests should NOT be reported """ column_conversions = {'edger': {'Gene_Name': 'gene_id_external', 'Gene_ID': 'xloc_id', 'logFC': 'lg2_fc', 'PValue': 'p', 'FDR': 'fdr'}, 'cufflinks': {'gene': 'gene_id_external', 'gene_id': 'xloc_id', 'log2.fold_change.': 'lg2_fc', 'p_value': 'p', 'q_value': 'fdr'}, } keep_columns = ["de_id", "xloc_id", "tcons_id","gene_id_external","gene_id_internal","lg2_fc","p","fdr","comparison","program"] de_id_program_map = {'edger': 'EDGR', 'cufflinks': 'CUFF', } # Load df = pd.read_csv(path, sep='\t') # Convert Columns df = df.rename(columns=column_conversions[program]) # Make missing fdr values NaN df['fdr'] = df.fdr.apply(lambda i: np.nan if i == '-' else i) # Filter for fdr df = df.query(""" fdr <= 0.05 """).copy() # Add Columns df['program'] = program df['comparison'] = comparison df['de_id'] = generate_de_ids(df=df, de_type=de_id_program_map[program], type_mod='|{comparison}'.format(comparison=comparison), nlen=7) # Join external and internal IDS df = pd.merge(left=df, right=ids_no_diff_expr, how='left', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False).fillna('-') # Retain only needed columns df = df[keep_columns] # Return dataframe return df.copy() def generate_de_ids(df,de_type,type_mod='',nlen=7): """Generate unique tracking IDs for each statistical test of diff expr.""" template = '{de}{mod}_{{0:0{nlen}d}}'.format(de=de_type, mod=type_mod, nlen=nlen) return [template.format(n) for n in range(1,len(df)+1)] ids_no_diff_expr = pd.read_csv(ids_no_diff_expr) table_list = [] # Load EDGER DE reults for name, path in zip(edger_results_labels, edger_results): df = load_and_filter_diff_expr_data(path=path, ids=ids_no_diff_expr, comparison=name, program='edger', fdr_thresh=0.05) table_list.append(df) # Load CUFFLINKS DE reults for name, path in zip(cufflinks_results_labels, cufflinks_results): df = load_and_filter_diff_expr_data(path=path, ids=ids_no_diff_expr, comparison=name, program='cufflinks', fdr_thresh=0.05) table_list.append(df) # Concat all result files into single dataframe combined = pd.concat(objs=table_list, axis=0) # Write out the resulting dataframe combined.to_csv(path_or_buf=ids_with_diff_expr, sep=',', header=True, index=False,)

[ "wadunn83@gmail.com" ]

wadunn83@gmail.com

f258f81afafb2186624f0028d7416f7aca37869d

3114430ce15c18281117459e26eea4b774e3998a

/day4/accounts/models.py

1fd9d1bf8a13f354846f792bd07b42ea810b5486

[ "MIT" ]

permissive

Joseamica/Easily-written-Django

c02e7333e84ca2257b7b8bfae3f6732898c5000a

0b746638751702c453db9490fe29ef6d34e4a3bc

refs/heads/master

2021-05-27T20:25:41.341149

2014-05-25T08:25:53

null

0

null

UTF-8

Python

false

445

py

from django.db import models from django.contrib.auth.models import User import hashlib # Create your models here. class Account(models.Model): user = models.OneToOneField(User) def gravatar_url(self): return "http://www.gravatar.com/avatar/%s?s=50" % hashlib.md5(self.user.email).hexdigest() def __unicode__(self): return self.user User.profile = property(lambda u: UserProfile.objects.get_or_create(user=u)[0])

[ "carpedm20@gmail.com" ]

carpedm20@gmail.com

538fc3e6a7b554c75a45025f802bf9fb341dae19

d6e287bbba11be4906e599d1362c9ef89c4fb9de

/modules/utils/datasets/__init__.py

53671ef9604559f6da0848293411281007d9f83b

[ "MIT" ]

permissive

bityangke/WSDDN.pytorch-1

67d52f158238f2d5b234ddefeb7f05f06bf6b123

9a67323c80566cacc762c68021824aa80a82c524

refs/heads/master

2022-11-15T00:22:44.903418

2020-07-06T13:43:22

null

0

null

UTF-8

Python

false

201

py

'''import all''' from .Sampler import GroupSampler from .VOCDataset import VOCDataset from .Builder import buildDataloader '''define alll''' __all__ = ['GroupSampler', 'VOCDataset', 'buildDataloader']

[ "1159254961@qq.com" ]

1159254961@qq.com

826f60594002015e659cc80aca283bfe601d0b98

0c958692bb3abf99ecbd03bd75a605b202d4da5a

/CRAB/MuNu/synch/2014ocbr24/synchThree.py

0bc6084ab362fd99b029e74554cc6bfc9b96b5f1

[]

no_license

tmrhombus/UWAnalysis

a9ed18a7ba8726522c8d98fbdc018c77d80c5cc5

eb9e0794e1b847f36c660a55d3631176a39148e2

refs/heads/master

2021-01-23T20:46:41.578341

2017-05-01T08:26:57

10,620,824

0

null

2014-10-21T11:21:16

2013-06-11T12:19:43

Python

UTF-8

Python

false

4,427

py

cut = 'C1_data_2014ocbr23_m12e10_smrGenNu_clnMu' #cut = 'C3_tt_2014ocbr23_m12e10_smrGenNu_clnMu' andreas_events = set([line.strip() for line in open('./comp/%s_and.txt'%(cut))]) jelenas_events = set([line.strip() for line in open('./comp/%s_jel.txt'%(cut))]) toms_events = set([line.strip() for line in open('./comp/%s_tom.txt'%(cut))]) just_andrea = [] just_jelena = [] just_tom = [] t_noJ = [] t_noA = [] j_noT = [] j_noA = [] a_noT = [] a_noJ = [] t_j = [] a_t = [] j_a = [] t_j_a = [] #runover = set([line.strip() for line in open('./comp/badevents.txt')]) runover = set([]) for toms_event in toms_events: tj = False ta = False for jelenas_event in jelenas_events: if long(toms_event) == long(jelenas_event): tj = True break for andreas_event in andreas_events: if long(toms_event) == long(andreas_event): ta = True break if tj == False and ta == False: just_tom.append(toms_event) runover.add(toms_event) if tj == False: t_noJ.append(toms_event) runover.add(toms_event) if ta == False: t_noA.append(toms_event) runover.add(toms_event) if tj == True and ta == True: t_j_a.append(toms_event) if tj == True: t_j.append(toms_event) if ta == True: a_t.append(toms_event) for andreas_event in andreas_events: at = False aj = False for toms_event in toms_events: if long(andreas_event) == long(toms_event): at = True break for jelenas_event in jelenas_events: if long(andreas_event) == long(jelenas_event): aj = True break if at == False and aj == False: just_andrea.append(andreas_event) runover.add(andreas_event) if at == False: a_noT.append(andreas_event) runover.add(andreas_event) if aj == False: a_noJ.append(andreas_event) runover.add(andreas_event) if aj == True: j_a.append(andreas_event) for jelenas_event in jelenas_events: ja = False jt = False for andreas_event in andreas_events: if long(andreas_event) == long(jelenas_event): ja = True break for toms_event in toms_events: if long(toms_event) == long(jelenas_event): jt = True if ja == False and jt == False: just_jelena.append(jelenas_event) runover.add(jelenas_event) if ja == False: j_noA.append(jelenas_event) runover.add(jelenas_event) if jt == False: j_noT.append(jelenas_event) runover.add(jelenas_event) print( "http://www.hep.wisc.edu/~tperry/wbb/synch/2014ocbr24/%s_comp.log"%(cut)) log = open('/afs/hep.wisc.edu/home/tperry/www/wbb/synch/2014ocbr24/%s_comp.log'%(cut),'w') log.write("Andreas Events: %s\n"%len(andreas_events)) log.write("Jelenas Events: %s\n"%len(jelenas_events)) log.write("Toms Events: %s\n"%len(toms_events )) log.write("All Three: %s\n\n"%len(t_j_a)) log.write(" Tom Has, Jelena Doesn't (%s)\n"%len(t_noJ)) for e in t_noJ: log.write(" "+e) log.write("\n\n") log.write(" Jelena Has, Tom Doesn't (%s)\n"%len(j_noT)) for e in j_noT: log.write(" "+e) log.write("\n\n") log.write("====================================================================\n\n") log.write(" Tom Has, Andrea Doesn't (%s)\n"%len(t_noA)) for e in t_noA: log.write(" "+e) log.write("\n\n") log.write(" Andrea Has, Tom Doesn't (%s)\n"%len(a_noT)) for e in a_noT: log.write(" "+e) log.write("\n\n") log.write("====================================================================\n\n") log.write(" Jelena Has, Andrea Doesn't (%s)\n"%len(j_noA)) for e in j_noA: log.write(" "+e) log.write("\n\n") log.write(" Andrea Has, Jelena Doesn't (%s)\n"%len(a_noJ)) for e in a_noJ: log.write(" "+e) log.write("\n\n") log.write("We All Have %s\n"%len(t_j_a)) for e in t_j_a: log.write(e+" ") log.write("\n\n") log.write("Tom Has %s\n"%len(toms_events)) for e in toms_events: log.write(e+" ") log.write("\n\n") log.write("Jelena Has %s\n"%len(jelenas_events)) for e in jelenas_events: log.write(e+" ") log.write("\n\n") log.write("Andreas Has %s\n"%len(andreas_events)) for e in andreas_events: log.write(e+" ") log.write("\n\n") log.write("Run Over\n") log.write("eventsToProcess = cms.untracked.VEventRange(") bades = [] for e in set(runover): bades.append("'1:%s'"%e) badlist = ",".join(bades) log.write("%s)"%(badlist)) log.write("\n\n") log.write("eventsToProcess = cms.untracked.VEventRange(") badet = [] for e in set(runover): badet.append("'1:%s-1:%s'"%(e,e)) badliss = ",".join(badet) log.write("%s)"%(badliss)) log.write("\n\n") #lob = open('./comp/badevents.txt','a') #for e in set(runover): lob.write("%s\n"%(e))

[ "tperry@cern.ch" ]

tperry@cern.ch

6d8ce22c751efd861956be268dafc8c2f00f3fbd

c0acf82a18b8e90cd38afedb02e45e53425a067e

/pyecharts/custom/overlap.py

e2cdd57622347e115a2fe03fcdc86c1ef34f05fd

[ "MIT" ]

permissive

caideyang/pyecharts

66b61d0400ea15b25ef7fb90f7305647343eea3a

c13f2fecece566359b2c881705bf96337c42ce40

refs/heads/master

2021-01-22T13:48:00.474761

2017-08-18T07:09:53

100,685,801

1

0

null

2017-08-18T07:31:13

null

UTF-8

Python

false

1,609

py

#!/usr/bin/env python # coding=utf-8 class Overlap(object): def __init__(self): self._chart = None def add(self, chart): """ :param chart: chart instance :return: """ if self._chart is None: self._chart = chart else: self.__custom(self.__get_series(chart)) def __get_series(self, chart): """ Get chart series data :param chart: chart instance :return: """ return ( chart._option.get('legend')[0].get('data'), chart._option.get('series'), ) def __custom(self, series): """ Appends the data for the series of the chart type :param series: series data """ _name, _series = series for n in _name: self._chart._option.get('legend')[0].get('data').append(n) for s in _series: self._chart._option.get('series').append(s) def render(self, path="render.html"): """ :param path: :return: """ self._chart.render(path) def render_embed(self): """ :return: """ return self._chart.render_embed() def show_config(self): """ :return: """ import pprint return pprint.pprint(self._chart._option) @property def chart(self): """ :return: """ return self._chart def _repr_html_(self): """ :return: """ return self._chart._repr_html_()

[ "chenjiandongx@qq.com" ]

chenjiandongx@qq.com

003d9d838b7372a3284b30915aec63707830d821

ef20884169d10ec9ac4d1d3b77ee35245d248294

/practice/deep-learning-from-scratch-2/np_random_choice.py

9360dbb41512575e33d9d1d800f8a11e55fdeec2

[]

no_license

heaven324/Deeplearning

64016671879cdf1742eff6f374cfb640cfc708ae

a7a8d590fa13f53348f83f8c808538affbc7b3e8

refs/heads/master

2023-05-05T08:54:27.888155

2021-05-22T08:25:47

188,010,607

1

0

null

UTF-8

Python

false

727

py

import numpy as np print(np.random.choice(10)) # 5 print(np.random.choice(10)) # 9 # words에서 하나만 무작위로 샘플링 words = ['you', 'say', 'goodbye', 'I', 'hello', '.'] print(np.random.choice(words)) # 5개만 무작위로 샘플링(중복 있음) print(np.random.choice(words, size = 5)) # 5개만 무작위로 샘플링(중복 없음) print(np.random.choice(words, size = 5, replace = False)) # 확률분포에 따라 샘플링 p = [0.5, 0.1, 0.05, 0.2, 0.05, 0.1] print(np.random.choice(words, p = p)) # 0.75제곱을 하는 이유( 빈도가 낮은 단어의 확률을 살짝 높이기 위해서) p = [0.7, 0.29, 0.01] new_p = np.power(p, 0.75) print(new_p) new_p /= np.sum(new_p) print(new_p)

[ "wjdtjdgh2005@gmail.com" ]

wjdtjdgh2005@gmail.com

778459e47142827e3629b6af6b3dbfc2ccc5d25e

ce990be34e8759efb96b890d9676da313fd2d9b4

/tests/python/contrib/test_ethosu/cascader/test_plan.py

ddc40b49ac8a8de119af6b9b19d208ef745f4899

[ "BSD-3-Clause", "LicenseRef-scancode-unknown-license-reference", "Unlicense", "Zlib", "MIT", "Apache-2.0", "BSD-2-Clause" ]

permissive

tmoreau89/tvm

291c0b1beb13503e18b1e45f135aaf334660b68d

8136173a631bf6c7274d26285349225fcf6e495f

refs/heads/master

2022-11-23T08:36:24.853648

2022-11-21T07:36:57

119,757,672

5

1

Apache-2.0

2019-03-22T23:06:53

2018-01-31T23:41:33

Python

UTF-8

Python

false

7,708

py

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import tvm.contrib.ethosu.cascader as cs import pytest def test_plan(DRAM, SRAM): subgraph = cs.TESubgraph([], None) part = cs.InlinePart( subgraph, [ cs.Propagator( [[1, 0, 0], [0, 1, 0], [0, 0, 1]], [0, 0], ), ], ) tensor_1 = cs.Tensor([10, 10], "uint8") tensor_2 = cs.Tensor([10, 10], "uint8") part.set_input(0, tensor_1) part.set_output(tensor_2) tensor_1.add_consumer(part) tensor_2.add_producer(part) output_stripe_config = cs.StripeConfig( shape=[5, 5], extent=[10, 10], strides=[5, 5], order=[1, 2], stripes=[2, 2], offset=[0, 0], ) tensor_config_out = cs.TensorConfig( tensor=tensor_2, home_region=DRAM, state=cs.TensorConfigState.BOUNDARY, buffer_mode=cs.BufferMode.RECOMPUTE, stripe_configs=[output_stripe_config], copy_tensor=False, ) input_stripe_config = part.calculate_input_stripe_configs(output_stripe_config)[0] tensor_config_in = cs.TensorConfig( tensor=tensor_1, home_region=DRAM, state=cs.TensorConfigState.INTERIOR, buffer_mode=cs.BufferMode.ROLLING, stripe_configs=[input_stripe_config], copy_tensor=False, ) tensor_configs = {tensor_1: tensor_config_in, tensor_2: tensor_config_out} open_configs = frozenset([tensor_config_in]) part_group = frozenset([part]) interior_region = SRAM memory_usage = 100 cycles = 20 plan = cs.Plan( tensor_configs=tensor_configs, open_configs=open_configs, output_config=tensor_config_out, part_group=part_group, interior_region=interior_region, memory_usage=memory_usage, cycles=cycles, ) assert plan.tensor_configs == tensor_configs assert plan.open_configs == open_configs assert plan.output_config == tensor_config_out assert plan.part_group == part_group assert plan.interior_region == interior_region assert plan.memory_usage == memory_usage assert plan.cycles == cycles def test_plan_merge(DRAM, SRAM): subgraph = cs.TESubgraph([], None) part_1 = cs.InlinePart( subgraph, [ cs.Propagator( [[2, 0, 0], [0, 2, 0], [0, 0, 1]], [0, 0], ), ], ) part_2 = cs.InlinePart( subgraph, [ cs.Propagator( [[1, 0, 0], [0, 1, 0], [0, 0, 1]], [0, 0], ), cs.Propagator( [[0, 0, 6], [0, 0, 6], [0, 0, 1]], [0, 0], ), cs.Propagator( [[1, 0], [0, 1]], [0], ), ], ) tensor_1 = cs.Tensor([20, 20], "uint8") tensor_2 = cs.Tensor([10, 10], "uint8") tensor_3 = cs.Tensor([6, 6], "uint8") tensor_4 = cs.Tensor([10], "uint8") tensor_5 = cs.Tensor([10, 10], "uint8") part_1.set_input(0, tensor_1) part_1.set_output(tensor_2) tensor_1.add_consumer(part_1) tensor_2.add_producer(part_1) part_2.set_input(0, tensor_2) part_2.set_input(1, tensor_3) part_2.set_input(2, tensor_4) part_2.set_output(tensor_5) tensor_2.add_consumer(part_2) tensor_3.add_consumer(part_2) tensor_4.add_consumer(part_2) tensor_5.add_producer(part_2) output_stripe_config = cs.StripeConfig( shape=[5, 5], extent=[10, 10], strides=[5, 5], order=[1, 2], stripes=[2, 2], offset=[0, 0], ) tensor_config_5 = cs.TensorConfig( tensor=tensor_5, home_region=DRAM, state=cs.TensorConfigState.BOUNDARY, buffer_mode=cs.BufferMode.RECOMPUTE, stripe_configs=[output_stripe_config], copy_tensor=False, ) input_stripe_configs = part_2.calculate_input_stripe_configs(output_stripe_config) tensor_config_4 = cs.TensorConfig( tensor=tensor_4, home_region=DRAM, state=cs.TensorConfigState.BOUNDARY, buffer_mode=cs.BufferMode.RECOMPUTE, stripe_configs=[input_stripe_configs[2]], copy_tensor=False, ) tensor_config_3 = cs.TensorConfig( tensor=tensor_3, home_region=SRAM, state=cs.TensorConfigState.INTERIOR, buffer_mode=cs.BufferMode.RECOMPUTE, stripe_configs=[input_stripe_configs[1]], copy_tensor=False, ) tensor_config_2 = cs.TensorConfig( tensor=tensor_2, home_region=SRAM, state=cs.TensorConfigState.INTERIOR, buffer_mode=cs.BufferMode.ROLLING, stripe_configs=[input_stripe_configs[0]], copy_tensor=False, ) input_stripe_config = part_1.calculate_input_stripe_configs(input_stripe_configs[0])[0] tensor_config_1 = cs.TensorConfig( tensor=tensor_1, home_region=DRAM, state=cs.TensorConfigState.BOUNDARY, buffer_mode=cs.BufferMode.ROLLING, stripe_configs=[input_stripe_config], copy_tensor=False, ) tensor_configs = {tensor_1: tensor_config_1, tensor_2: tensor_config_2} open_configs = frozenset([tensor_config_2]) part_group = frozenset([part_1]) interior_region = SRAM memory_usage = 100 cycles = 20 plan_1 = cs.Plan( tensor_configs=tensor_configs, open_configs=open_configs, output_config=tensor_config_2, part_group=part_group, interior_region=interior_region, memory_usage=memory_usage, cycles=cycles, ) tensor_configs = { tensor_2: tensor_config_2, tensor_3: tensor_config_3, tensor_4: tensor_config_4, tensor_5: tensor_config_5, } open_configs = frozenset([tensor_config_2, tensor_config_3]) part_group = frozenset([part_2]) interior_region = SRAM memory_usage = 200 cycles = 30 plan_2 = cs.Plan( tensor_configs=tensor_configs, open_configs=open_configs, output_config=tensor_config_5, part_group=part_group, interior_region=interior_region, memory_usage=memory_usage, cycles=cycles, ) merged_plan = plan_1.merge(plan_2) assert merged_plan.tensor_configs == { tensor_1: tensor_config_1, tensor_2: tensor_config_2, tensor_3: tensor_config_3, tensor_4: tensor_config_4, tensor_5: tensor_config_5, } assert merged_plan.open_configs == frozenset([tensor_config_3]) assert merged_plan.output_config == tensor_config_5 assert merged_plan.part_group == frozenset([part_1, part_2]) assert merged_plan.interior_region == interior_region assert merged_plan.memory_usage == plan_1.memory_usage + plan_2.memory_usage assert merged_plan.cycles == plan_1.cycles + plan_2.cycles if __name__ == "__main__": pytest.main([__file__])

[ "noreply@github.com" ]

tmoreau89.noreply@github.com

b57aa04bb1157d20423de65671bee218d8715f6d

730b92e439dbb013950b8bbf417cfde1bb40f8b9

/Python/Add-Binary.py

8b8be13ae529418ef8672901ffeb760e078c1eb4

[]

no_license

yuede/Lintcode

fdbca5984c2860c8b532b5f4d99bce400b0b26d0

d40b7ca1c03af7005cc78b26b877a769ca0ab723

refs/heads/master

2021-01-13T04:14:32.754210

2015-08-22T13:15:54

null

0

null

UTF-8

Python

false

833

py

class Solution: # @param {string} a a number # @param {string} b a number # @return {string} the result def addBinary(self, a, b): # Write your code here pa = len(a) - 1 pb = len(b) - 1 s = "" d = 0 while pa >= 0 and pb >= 0: cur = d + int(a[pa]) + int(b[pb]) pa -= 1 pb -= 1 s += str(cur % 2) d = cur / 2 while pa >= 0: cur = d + int(a[pa]) pa -= 1 s += str(cur % 2) d = cur / 2 while pb >= 0: cur = d + int(b[pb]) pb -= 1 s += str(cur % 2) d = cur / 2 if d > 0: s += str(d) rs = "" for i in range(len(s)): rs += s[len(s) - 1 - i] return rs

[ "jiangyi0425@gmail.com" ]

jiangyi0425@gmail.com

97a5797d6b970d29dbea2c4c90e09131f13ca91c

e5efada3529d94875455c4230c8dabe27fb72a89

/apps/search/migrations/0015_advancedsearchpage_simplesearchpage.py

74a14dceeeef2ab60fb56655bb00ed68b2a72af6

[]

no_license

alexmon1989/uma

d8c321fb0ec9b1a9039b1c83aeaaff774f657416

5dea579d634eeb1c8103c21157299b33ca5590f0

refs/heads/master

2023-08-03T04:31:13.598577

2023-07-22T18:17:13

154,835,498

0

null

2023-03-02T11:20:54

2018-10-26T13:02:12

Nunjucks

UTF-8

Python

false

1,712

py

# Generated by Django 2.1.3 on 2019-10-10 13:38 import ckeditor_uploader.fields from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('search', '0014_auto_20190719_1155'), ] operations = [ migrations.CreateModel( name='AdvancedSearchPage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('description_uk', ckeditor_uploader.fields.RichTextUploadingField(verbose_name='Опис сторінки (укр.)')), ('description_en', ckeditor_uploader.fields.RichTextUploadingField(verbose_name='Опис сторінки (англ.)')), ], options={ 'verbose_name': 'Сторінка розширенного пошуку', 'verbose_name_plural': 'Сторінка розширенного пошуку', }, ), migrations.CreateModel( name='SimpleSearchPage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('description_uk', ckeditor_uploader.fields.RichTextUploadingField(verbose_name='Опис сторінки (укр.)')), ('description_en', ckeditor_uploader.fields.RichTextUploadingField(verbose_name='Опис сторінки (англ.)')), ], options={ 'verbose_name': 'Сторінка простого пошуку', 'verbose_name_plural': 'Сторінка простого пошуку', }, ), ]

[ "alex.mon1989@gmail.com" ]

alex.mon1989@gmail.com

52980438ee437a5977680307d4b13bd673f3b1a3

6d7a67be5c2aa1bcebdcfd5bec855c0172c8f01f

/convert_weight.py

55566963e7439f9fb4e9649bdd289f5114337916

[ "MIT", "LicenseRef-scancode-unknown-license-reference", "BSD-2-Clause", "Apache-2.0" ]

permissive

JWHennessey/stylegan2-pytorch-1

19184e1713b9bcfce6404fb6d19478f1dbcc56ec

88852e3695d3ffd9281787690c3f8796dc1e225a

refs/heads/master

2020-12-11T17:17:04.082956

2020-01-14T18:44:39

233,909,977

0

NOASSERTION

2020-01-14T18:37:33

2020-01-14T18:37:32

null

UTF-8

Python

false

6,849

py

import argparse import os import sys import pickle import math import torch import numpy as np from torchvision import utils from model import Generator, Discriminator def convert_modconv(vars, source_name, target_name, flip=False): weight = vars[source_name + '/weight'].value().eval() mod_weight = vars[source_name + '/mod_weight'].value().eval() mod_bias = vars[source_name + '/mod_bias'].value().eval() noise = vars[source_name + '/noise_strength'].value().eval() bias = vars[source_name + '/bias'].value().eval() dic = { 'conv.weight': np.expand_dims(weight.transpose((3, 2, 0, 1)), 0), 'conv.modulation.weight': mod_weight.transpose((1, 0)), 'conv.modulation.bias': mod_bias + 1, 'noise.weight': np.array([noise]), 'activate.bias': bias, } dic_torch = {} for k, v in dic.items(): dic_torch[target_name + '.' + k] = torch.from_numpy(v) if flip: dic_torch[target_name + '.conv.weight'] = torch.flip( dic_torch[target_name + '.conv.weight'], [3, 4] ) return dic_torch def convert_conv(vars, source_name, target_name, bias=True, start=0): weight = vars[source_name + '/weight'].value().eval() dic = {'weight': weight.transpose((3, 2, 0, 1))} if bias: dic['bias'] = vars[source_name + '/bias'].value().eval() dic_torch = {} dic_torch[target_name + f'.{start}.weight'] = torch.from_numpy(dic['weight']) if bias: dic_torch[target_name + f'.{start + 1}.bias'] = torch.from_numpy(dic['bias']) return dic_torch def convert_torgb(vars, source_name, target_name): weight = vars[source_name + '/weight'].value().eval() mod_weight = vars[source_name + '/mod_weight'].value().eval() mod_bias = vars[source_name + '/mod_bias'].value().eval() bias = vars[source_name + '/bias'].value().eval() dic = { 'conv.weight': np.expand_dims(weight.transpose((3, 2, 0, 1)), 0), 'conv.modulation.weight': mod_weight.transpose((1, 0)), 'conv.modulation.bias': mod_bias + 1, 'bias': bias.reshape((1, 3, 1, 1)), } dic_torch = {} for k, v in dic.items(): dic_torch[target_name + '.' + k] = torch.from_numpy(v) return dic_torch def convert_dense(vars, source_name, target_name): weight = vars[source_name + '/weight'].value().eval() bias = vars[source_name + '/bias'].value().eval() dic = {'weight': weight.transpose((1, 0)), 'bias': bias} dic_torch = {} for k, v in dic.items(): dic_torch[target_name + '.' + k] = torch.from_numpy(v) return dic_torch def update(state_dict, new): for k, v in new.items(): if k not in state_dict: raise KeyError(k + ' is not found') if v.shape != state_dict[k].shape: raise ValueError(f'Shape mismatch: {v.shape} vs {state_dict[k].shape}') state_dict[k] = v def discriminator_fill_statedict(statedict, vars, size): log_size = int(math.log(size, 2)) update(statedict, convert_conv(vars, f'{size}x{size}/FromRGB', 'convs.0')) conv_i = 1 for i in range(log_size - 2, 0, -1): reso = 4 * 2 ** i update(statedict, convert_conv(vars, f'{reso}x{reso}/Conv0', f'convs.{conv_i}.conv1')) update(statedict, convert_conv(vars, f'{reso}x{reso}/Conv1_down', f'convs.{conv_i}.conv2', start=1)) update(statedict, convert_conv(vars, f'{reso}x{reso}/Skip', f'convs.{conv_i}.skip', start=1, bias=False)) conv_i += 1 update(statedict, convert_conv(vars, f'4x4/Conv', 'final_conv')) update(statedict, convert_dense(vars, f'4x4/Dense0', 'final_linear.0')) update(statedict, convert_dense(vars, f'Output', 'final_linear.1')) return statedict def fill_statedict(state_dict, vars, size): log_size = int(math.log(size, 2)) for i in range(8): update(state_dict, convert_dense(vars, f'G_mapping/Dense{i}', f'style.{i + 1}')) update( state_dict, { 'input.input': torch.from_numpy( vars['G_synthesis/4x4/Const/const'].value().eval() ) }, ) update(state_dict, convert_torgb(vars, 'G_synthesis/4x4/ToRGB', 'to_rgb1')) for i in range(log_size - 2): reso = 4 * 2 ** (i + 1) update( state_dict, convert_torgb(vars, f'G_synthesis/{reso}x{reso}/ToRGB', f'to_rgbs.{i}'), ) update(state_dict, convert_modconv(vars, 'G_synthesis/4x4/Conv', 'conv1')) conv_i = 0 for i in range(log_size - 2): reso = 4 * 2 ** (i + 1) update( state_dict, convert_modconv( vars, f'G_synthesis/{reso}x{reso}/Conv0_up', f'convs.{conv_i}', flip=True, ), ) update( state_dict, convert_modconv( vars, f'G_synthesis/{reso}x{reso}/Conv1', f'convs.{conv_i + 1}' ), ) conv_i += 2 return state_dict if __name__ == '__main__': device = 'cuda' parser = argparse.ArgumentParser() parser.add_argument('--repo', type=str, required=True) parser.add_argument('--gen', action='store_true') parser.add_argument('--disc', action='store_true') parser.add_argument('path', metavar='PATH') args = parser.parse_args() sys.path.append(args.repo) from dnnlib import tflib tflib.init_tf() with open(args.path, 'rb') as f: generator, discriminator, g_ema = pickle.load(f) size = g_ema.output_shape[2] g = Generator(size, 512, 8) state_dict = g.state_dict() state_dict = fill_statedict(state_dict, g_ema.vars, size) g.load_state_dict(state_dict) latent_avg = torch.from_numpy(g_ema.vars['dlatent_avg'].value().eval()) ckpt = {'g_ema': state_dict, 'latent_avg': latent_avg} if args.gen: g_train = Generator(size, 512, 8) g_train_state = g_train.state_dict() g_train_state = fill_statedict(g_train_state, generator.vars, size) ckpt['g'] = g_train_state if args.disc: disc = Discriminator(size) d_state = disc.state_dict() d_state = discriminator_fill_statedict(d_state, discriminator.vars, size) ckpt['d'] = d_state name = os.path.splitext(os.path.basename(args.path))[0] torch.save(ckpt, name + '.pt') batch_size = {256: 16, 512: 9, 1024: 4} n_sample = batch_size.get(size, 25) g = g.to(device) x = torch.randn(n_sample, 512).to(device) with torch.no_grad(): img, _ = g([x], truncation=0.5, truncation_latent=latent_avg.to(device)) utils.save_image( img, name + '.png', nrow=int(n_sample ** 0.5), normalize=True, range=(-1, 1) )

[ "kim.seonghyeon@snu.ac.kr" ]

kim.seonghyeon@snu.ac.kr

782a5e2a11fe39696a75f0f5a033a5af024cc786

f8ffac4fa0dbe27316fa443a16df8a3f1f5cff05

/Python/Counting_Valleys.py

db3c7a3eda8be589ae74a986fadb83c8e44b2c00

[]

no_license

ankitniranjan/HackerrankSolutions

e27073f9837787a8af7a0157d95612028c07c974

e110c72d3b137cf4c5cef6e91f58a17452c54c08

refs/heads/master

2023-03-16T19:06:17.805307

2021-03-09T16:28:39

292,994,949

0

null

UTF-8

Python

false

528

py

import math import os import random import re import sys # Complete the countingValleys function below. def countingValleys(n, s): level=valley=0 for i in range(n): if(s[i]=='U'): level+=1 if(level==0): valley+=1 else: level-=1 return valley if __name__ == '__main__': fptr = open(os.environ['OUTPUT_PATH'], 'w') n = int(input()) s = input() result = countingValleys(n, s) fptr.write(str(result) + '\n') fptr.close()

[ "noreply@github.com" ]

ankitniranjan.noreply@github.com

ab55393ddc0e46a0f229ce84b50466d0ac1cb266

65701888f7e09716b83ddbb965a50b7c62b0f287

/blocks/google/common_block.py

fb2ba923f68bc8aedfef5cc46a894ff664e758b9

[]

no_license

ColinKennedy/auto_docstring

6a4a27c16434cb6d94db435226758a09627d9252

dbca838630faf410a277069aedbecb82cfeedae9

refs/heads/master

2021-04-12T12:36:31.825008

2018-11-05T01:49:49

89,107,892

1

0

null

UTF-8

Python

false

7,343

py

#!/usr/bin/env python # -*- coding: utf-8 -*- # TODO : Just had an idea. Why not change the gross "if X.is_valid(obj): return X(obj) # into a single classmethod? That'd look way better and potentially be # easier to loop over # '''The classes and functions needed to parse the types of all astroid nodes. This module does most of the heavy-lifting for args return-types. It can parse functions within functions, infer an object's type, and even recursively traverse imported modules to get an object's type. ''' # IMPORT STANDARD LIBRARIES import abc import os # IMPORT THIRD-PARTY LIBRARIES import six # IMPORT LOCAL LIBRARIES from ...config import environment from ...parsing import visit from ...core import check from . import common_type @six.add_metaclass(abc.ABCMeta) class CommonBlock(object): '''An abstract class used to implement a Google-style block. Attributes: label (str): The block display text. ''' label = 'Header label' @staticmethod @abc.abstractmethod def draw(info): '''Create the docstring lines to represent the given `info`. Args: info (dict[str]): The parsed AST node whose type needs to be found and then converted into a string. Returns: list[str]: The lines to create. ''' return [] @abc.abstractproperty def name(self): '''str: A unique name to use to identify this block-type.''' return '_unique_id' @classmethod def get_starting_lines(cls): '''list[str]: Get the label used for the top of this block.''' return ['{}:'.format(cls.label)] @staticmethod def get_spacing(): return @staticmethod def get_spacing(): '''int: Get the number of newlines to separate each docstring block.''' try: return int(os.getenv('AUTO_DOCSTRING_BLOCK_SPACING', '1')) except TypeError: return 1 @staticmethod def _expand_types(obj, include_type=False): r'''Wrap the given `obj` with a specific docstring-class wrapper. Args: obj (`astroid.NodeNG`): Some node to wrap. include_type (bool, optional): If True and `obj` is a container of some kind, for example a list of strs, then `obj` will be printed like "list[str]". If False, `obj` would be printed as just "str". This parameter is used primarily mainly for keeping return-types from accidentally printing its container-type twice when the container is nested. Default is False. Returns: `SpecialType` or `ComprehensionContainerType` or `ContainerType` \ or `IterableType` or `Type`: . The wrapped type. ''' if common_type.SpecialType.is_valid(obj): return common_type.SpecialType(obj) obj = visit.get_value(obj) if common_type.DictComprehensionContainerType.is_valid(obj): return common_type.DictComprehensionContainerType(obj) if common_type.ComprehensionContainerType.is_valid(obj): return common_type.ComprehensionContainerType(obj) if common_type.ContainerType.is_valid(obj): return common_type.ContainerType(obj) if check.is_itertype(obj): return common_type.IterableType(obj, include_type=include_type) return common_type.Type(obj) @staticmethod def _change_type_to_str(*objs): '''Create the full string of all return-types for the given `objs`. Args: *objs (list[:class:`auto_docstring.blocks.google.common_block.Type`]): The types to change into strings. Returns: str: The final set of return types for the given objects. This string will be added to the auto-generated docstrings, directly. ''' items = [] for item in [obj.as_str() for obj in objs]: if item not in items: items.append(item) return common_type.make_items_text(items) @six.add_metaclass(abc.ABCMeta) class MultiTypeBlock(CommonBlock): '''The base-class used to create "Returns" and "Yields" blocks.''' _info_key = '_some_key' name = 'multitype_base_block' @classmethod def _process_args(cls, info): expected_object = info.get(cls._info_key) if not expected_object: return [] indent = '' # Check if I need this if-statement if info.get('lines'): indent = environment.get_default_indent() info['indent'] = indent obj_types = cls._expand_types(expected_object) type_info_as_str = cls._change_type_to_str(*obj_types) return [type_info_as_str] @classmethod def _build_indented_docstring_lines(cls, lines, indent='', multiline=False): return [cls._make_line(line, indent=indent, multiline=multiline) for line in lines] @classmethod def draw(cls, info): # '''Create the docstring lines to represent the given `info`. # Note: # If no data is found for cls._info_key, this method will return # an empty list. # Args: # info (dict[str, list[`astroid.NodeNG`]]): # The parsed AST node whose type needs to be found and then # converted into a string. # Returns: # list[str]: The lines to create. # ''' lines = cls._process_args(info) if not lines: return [] starting_lines = [] all_lines = info.get('lines', []) if all_lines: starting_lines = cls.get_starting_lines() multiline = is_multiline(all_lines) docstring_lines = cls._build_indented_docstring_lines( lines, info.get('indent', ''), multiline=is_multiline(all_lines), ) return starting_lines + docstring_lines @staticmethod def _make_line(obj_type, indent, multiline=False): '''Create the docstring line for the given input. Args: indent (str): The amount of space to add to the docstring block. obj_type (str): The type of the object. Example: "tuple[str]", "bool". multiline (`obj`, optional): If True, get the user's preferred separator and place it between the return type and the return description. If False, force the separator to just be " " so that the return statement will stay on a single line. Default is False. Returns: str: The created docstring line. ''' if obj_type: # This ":" is needed for parsing by auto_docstring obj_type = ':' + obj_type if not multiline: sep = ' ' else: sep = environment.get_description_separator() return '{indent}{{{obj_type}!f}}:{sep}{{!f}}.'.format( indent=indent, obj_type=obj_type, sep=sep, ) def is_multiline(lines): return len(lines) > 1

[ "colinvfx@gmail.com" ]

colinvfx@gmail.com

9b5f678ee01f74948e3abe78205622ca733d1def

f6d96e9505103428402ea9772fdd0b48c4dff7e9

/tests/test_models/test_place.py

4bd8e6e2e665353886e8de7c111a98acd68c7add

[]

no_license

KarenCampo777/AirBnB_clone

8271a2a7f75c01ea875b9232a939f1f58f484705

95051e3c7c05837b89966caae55bb54eef81c95f

refs/heads/master

2023-03-14T03:41:18.367359

2021-02-24T22:32:17

276,201,869

1

null

UTF-8

Python

false

2,527

py

#!/usr/bin/python3 """ Test module for place module """ from models.place import Place import models import unittest import os import datetime class TestPlace(unittest.TestCase): """ Testing an Place instance """ def setUp(self): """ Setting up the test instance """ self.my_base1 = Place() self.my_base2 = Place() def Tearown(self): """ Closing the test instance """ del self.my_base1 del self.my_base2 def test_create(self): """ Testing creation of an Place instance """ self.assertIsInstance(self.my_base1, Place) def test_permissions(self): """ Testing file permissions to be executable """ self.assertTrue(os.access("models/place.py", os.X_OK)) def test_id(self): """ Testing if attribute id is as unique as a string type """ self.assertIsInstance(self.my_base1.id, str) self.assertNotEqual(self.my_base1.id, self.my_base2.id) def test_dates(self): """ Testing created_at and updated_at of instances """ self.assertIsInstance(self.my_base1.created_at, datetime.datetime) self.assertIsInstance(self.my_base1.updated_at, datetime.datetime) prev_date = self.my_base1.updated_at self.my_base1.save() self.assertNotEqual(prev_date, self.my_base1.updated_at) def test_str_format(self): """ Testing the function __str__ to have the correct format [<class name>] (<self.id>) <self.__dict__> """ o = self.my_base1 msg1 = o.__str__() msg2 = "[{}] ({}) {}".format(o.__class__.__name__, o.id, o.__dict__) self.assertEqual(msg1, msg2) def test_to_dict(self): """ Testing to_dict function to return correct format """ ins = self.my_base1 obj = ins.to_dict() self.assertIsInstance(obj, dict) self.assertTrue('__class__' in obj) self.assertEqual(obj['__class__'], 'Place') self.assertIsInstance(obj['created_at'], str) self.assertIsInstance(obj['updated_at'], str) self.assertEqual(obj['created_at'], ins.created_at.isoformat()) self.assertEqual(obj['updated_at'], ins.updated_at.isoformat()) def test_docstring(self): """ Testing documentation on place """ self.assertIsNotNone(models.place.__doc__) self.assertIsNotNone(Place.__doc__)

[ "andresbaymon@gmail.com" ]

andresbaymon@gmail.com

ada10adc0bef6aee3f66cc6505c04af63ade6437

ca2818572d17285210792694ba1f07c99e11d9ad

/setup.py

209a4bd93203208084c183cf32cece8f76ddf3bd

[ "Apache-2.0" ]

permissive

tomzhang/codesnap

cc335e8a63af70ed0121b222eb4fc2e35841b0b0

04e11176888243052c46a6a04a1ba63a8f80d684

refs/heads/master

2022-11-29T16:23:05.625385

2020-08-09T07:11:58

null

0

null

UTF-8

Python

false

1,134

py

import setuptools from distutils.core import Extension with open("README.md") as f: long_description = f.read() setuptools.setup( name="codesnap", version="0.0.4", author="Tian Gao", author_email="gaogaotiantian@hotmail.com", description="A profiling tool that can visualize python code in flame graph", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/gaogaotiantian/codesnap", packages=setuptools.find_packages("src"), package_dir={"":"src"}, package_data={ "codesnap": [ "html/*.js", "html/*.css", "html/*.html" ] }, ext_modules=[ Extension( "codesnap.snaptrace", sources = [ "src/codesnap/modules/snaptrace.c", ] ) ], classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Topic :: Software Development :: Quality Assurance", ], python_requires=">=3.5", )

[ "gaogaotiantian@hotmail.com" ]

gaogaotiantian@hotmail.com

33bada0a6ebc9c86ad48aa12cb5fff42acd3588a

3b43cf4cfc666798ebe85ed1db8858034b13d45c

/tests/universal_functions_tests/power_tests/normal.py

dab58c185239f89bab51ee55c80dbe61e5d4326a

[ "Apache-2.0" ]

permissive

Pandinosaurus/legate.numpy

5428b80a0a53ab882cd74b5dbf5fd86c7ee82199

896f4fd9b32db445da6cdabf7b78d523fca96936

refs/heads/master

2023-06-27T04:33:52.982601

2021-07-01T21:39:52

358,820,941

0

Apache-2.0

2021-08-01T02:57:57

2021-04-17T08:06:05

C++

UTF-8

Python

false

1,000

py

# Copyright 2021 NVIDIA Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import numpy as np import legate.numpy as lg def test(): bases_np = np.random.randn(4, 5) # avoid fractional exponents exponents_np = np.random.randint(10, size=(4, 5)).astype(np.float64) bases = lg.array(bases_np) exponents = lg.array(exponents_np) assert lg.allclose( lg.power(bases, exponents), np.power(bases_np, exponents_np) ) if __name__ == "__main__": test()

[ "wonchanl@nvidia.com" ]

wonchanl@nvidia.com

8a4209560e01a9bb2625b02445afa69dcf3b28fc

e7ff2f9e21a94f2956b8c79f268dc6d45b41237b

/Frontend/node_modules/watchpack-chokidar2/node_modules/fsevents/build/config.gypi

b5962c025c83982c05fecf7c1819e71e4893c18a

[ "MIT" ]

permissive

vipul-07/MERN-Project

fcb4af686557b99b802404e8622905781e89bbc3

c0bdd3b5dfc73b2657b8563d069360e11466714a

refs/heads/master

2023-02-14T15:42:38.653627

2021-01-10T05:35:02

317,460,195

0

null

UTF-8

Python

false

5,709

gypi

# Do not edit. File was generated by node-gyp's "configure" step { "target_defaults": { "cflags": [], "default_configuration": "Release", "defines": [], "include_dirs": [], "libraries": [] }, "variables": { "asan": 0, "build_v8_with_gn": "false", "coverage": "false", "dcheck_always_on": 0, "debug_nghttp2": "false", "debug_node": "false", "enable_lto": "false", "enable_pgo_generate": "false", "enable_pgo_use": "false", "error_on_warn": "false", "force_dynamic_crt": 0, "host_arch": "x64", "icu_data_in": "../../deps/icu-tmp/icudt67l.dat", "icu_endianness": "l", "icu_gyp_path": "tools/icu/icu-generic.gyp", "icu_path": "deps/icu-small", "icu_small": "false", "icu_ver_major": "67", "is_debug": 0, "llvm_version": "11.0", "napi_build_version": "6", "node_byteorder": "little", "node_debug_lib": "false", "node_enable_d8": "false", "node_install_npm": "true", "node_module_version": 83, "node_no_browser_globals": "false", "node_prefix": "/usr/local", "node_release_urlbase": "https://nodejs.org/download/release/", "node_shared": "false", "node_shared_brotli": "false", "node_shared_cares": "false", "node_shared_http_parser": "false", "node_shared_libuv": "false", "node_shared_nghttp2": "false", "node_shared_openssl": "false", "node_shared_zlib": "false", "node_tag": "", "node_target_type": "executable", "node_use_bundled_v8": "true", "node_use_dtrace": "true", "node_use_etw": "false", "node_use_node_code_cache": "true", "node_use_node_snapshot": "true", "node_use_openssl": "true", "node_use_v8_platform": "true", "node_with_ltcg": "false", "node_without_node_options": "false", "openssl_fips": "", "openssl_is_fips": "false", "ossfuzz": "false", "shlib_suffix": "83.dylib", "target_arch": "x64", "v8_enable_31bit_smis_on_64bit_arch": 0, "v8_enable_gdbjit": 0, "v8_enable_i18n_support": 1, "v8_enable_inspector": 1, "v8_enable_lite_mode": 0, "v8_enable_object_print": 1, "v8_enable_pointer_compression": 0, "v8_no_strict_aliasing": 1, "v8_optimized_debug": 1, "v8_promise_internal_field_count": 1, "v8_random_seed": 0, "v8_trace_maps": 0, "v8_use_siphash": 1, "want_separate_host_toolset": 0, "xcode_version": "11.0", "nodedir": "/Users/apple/Library/Caches/node-gyp/14.10.1", "standalone_static_library": 1, "dry_run": "", "legacy_bundling": "", "save_dev": "", "browser": "", "commit_hooks": "true", "only": "", "viewer": "man", "also": "", "rollback": "true", "sign_git_commit": "", "audit": "true", "usage": "", "globalignorefile": "/usr/local/etc/npmignore", "init_author_url": "", "maxsockets": "50", "shell": "/bin/zsh", "metrics_registry": "https://registry.npmjs.org/", "parseable": "", "shrinkwrap": "true", "init_license": "ISC", "timing": "", "if_present": "", "cache_max": "Infinity", "init_author_email": "", "sign_git_tag": "", "cert": "", "git_tag_version": "true", "local_address": "", "long": "", "preid": "", "fetch_retries": "2", "registry": "https://registry.npmjs.org/", "key": "", "message": "%s", "versions": "", "globalconfig": "/usr/local/etc/npmrc", "always_auth": "", "logs_max": "10", "prefer_online": "", "cache_lock_retries": "10", "global_style": "", "update_notifier": "true", "audit_level": "low", "heading": "npm", "fetch_retry_mintimeout": "10000", "offline": "", "read_only": "", "searchlimit": "20", "access": "", "json": "", "allow_same_version": "", "description": "true", "engine_strict": "", "https_proxy": "", "init_module": "/Users/apple/.npm-init.js", "userconfig": "/Users/apple/.npmrc", "cidr": "", "node_version": "14.10.1", "user": "", "auth_type": "legacy", "editor": "vi", "ignore_prepublish": "", "save": "true", "script_shell": "", "tag": "latest", "before": "", "global": "", "progress": "true", "ham_it_up": "", "optional": "true", "searchstaleness": "900", "bin_links": "true", "force": "", "save_prod": "", "searchopts": "", "depth": "Infinity", "node_gyp": "/usr/local/lib/node_modules/npm/node_modules/node-gyp/bin/node-gyp.js", "rebuild_bundle": "true", "sso_poll_frequency": "500", "unicode": "true", "fetch_retry_maxtimeout": "60000", "ca": "", "save_prefix": "^", "scripts_prepend_node_path": "warn-only", "sso_type": "oauth", "strict_ssl": "true", "tag_version_prefix": "v", "dev": "", "fetch_retry_factor": "10", "group": "20", "save_exact": "", "cache_lock_stale": "60000", "prefer_offline": "", "version": "", "cache_min": "10", "otp": "", "cache": "/Users/apple/.npm", "searchexclude": "", "color": "true", "package_lock": "true", "fund": "true", "package_lock_only": "", "save_optional": "", "user_agent": "npm/6.14.8 node/v14.10.1 darwin x64", "ignore_scripts": "", "cache_lock_wait": "10000", "production": "", "save_bundle": "", "send_metrics": "", "init_version": "1.0.0", "node_options": "", "umask": "0022", "scope": "", "git": "git", "init_author_name": "", "onload_script": "", "tmp": "/var/folders/0w/px0kn_6s561dhjplhgbypnj80000gn/T", "unsafe_perm": "true", "format_package_lock": "true", "link": "", "prefix": "/usr/local" } }

[ "apple@Apples-MacBook-Pro.local" ]

apple@Apples-MacBook-Pro.local

f32cc09e9b5e4191dae2fb825a128f8ca6aa38c6

2e2a02ec8323982975ace3d249b22a42d8b97a1f

/skipper.py

11171dc6ca97629b3d735b09f2921f679e80ed68

[]

no_license

datagovua/os-budget-ukraine

4e8c6d0373aead42890349befbd69bf8e8fef0a1

3a45f89c3872c9b9b45fb1206da445989b37b335

refs/heads/master

2021-01-13T02:49:03.608617

2016-12-22T20:59:14

2016-12-23T01:14:22

77,156,721

0

null

UTF-8

Python

false

385

py

import logging from datapackage_pipelines.wrapper import ingest, spew _, datapackage, resource_iterator = ingest() def intTryParse(value): try: int(value) return True except ValueError: return False def process(res): for row in res: if intTryParse(row['1.0']): yield row spew(datapackage, (process(res) for res in resource_iterator))

[ "vanuan@gmail.com" ]

vanuan@gmail.com

b0258289543572c3d2fd2b3d83991eb4e2d9f4dc

24fe1f54fee3a3df952ca26cce839cc18124357a

/servicegraph/lib/python2.7/site-packages/acimodel-4.0_3d-py2.7.egg/cobra/modelimpl/orchs/svcsencap.py

83b1a538fc7c72069845b02465a56b59e320b8da

[]

no_license

aperiyed/servicegraph-cloudcenter

4b8dc9e776f6814cf07fe966fbd4a3481d0f45ff

9eb7975f2f6835e1c0528563a771526896306392

refs/heads/master

2023-05-10T17:27:18.022381

2020-01-20T09:18:28

235,065,676

0

null

2023-05-01T21:19:14

2020-01-20T09:36:37

Python

UTF-8

Python

false

8,108

py

# coding=UTF-8 # ********************************************************************** # Copyright (c) 2013-2019 Cisco Systems, Inc. All rights reserved # written by zen warriors, do not modify! # ********************************************************************** from cobra.mit.meta import ClassMeta from cobra.mit.meta import StatsClassMeta from cobra.mit.meta import CounterMeta from cobra.mit.meta import PropMeta from cobra.mit.meta import Category from cobra.mit.meta import SourceRelationMeta from cobra.mit.meta import NamedSourceRelationMeta from cobra.mit.meta import TargetRelationMeta from cobra.mit.meta import DeploymentPathMeta, DeploymentCategory from cobra.model.category import MoCategory, PropCategory, CounterCategory from cobra.mit.mo import Mo # ################################################## class SvcsEncap(Mo): """ Mo doc not defined in techpub!!! """ meta = ClassMeta("cobra.model.orchs.SvcsEncap") meta.moClassName = "orchsSvcsEncap" meta.rnFormat = "encap-%(name)s" meta.category = MoCategory.REGULAR meta.label = "IP Pool Resource Instance" meta.writeAccessMask = 0x2001 meta.readAccessMask = 0x2001 meta.isDomainable = False meta.isReadOnly = False meta.isConfigurable = True meta.isDeletable = True meta.isContextRoot = False meta.childClasses.add("cobra.model.tag.Tag") meta.childClasses.add("cobra.model.fault.Counts") meta.childClasses.add("cobra.model.health.Inst") meta.childClasses.add("cobra.model.aaa.RbacAnnotation") meta.childClasses.add("cobra.model.orchs.RsSvcsEncapToSvcAlloc") meta.childClasses.add("cobra.model.tag.Annotation") meta.childNamesAndRnPrefix.append(("cobra.model.orchs.RsSvcsEncapToSvcAlloc", "rssvcsEncapToSvcAlloc-")) meta.childNamesAndRnPrefix.append(("cobra.model.tag.Annotation", "annotationKey-")) meta.childNamesAndRnPrefix.append(("cobra.model.aaa.RbacAnnotation", "rbacDom-")) meta.childNamesAndRnPrefix.append(("cobra.model.tag.Tag", "tagKey-")) meta.childNamesAndRnPrefix.append(("cobra.model.fault.Counts", "fltCnts")) meta.childNamesAndRnPrefix.append(("cobra.model.health.Inst", "health")) meta.parentClasses.add("cobra.model.orchs.Config") meta.superClasses.add("cobra.model.orchs.Entity") meta.superClasses.add("cobra.model.orchs.Element") meta.superClasses.add("cobra.model.naming.NamedObject") meta.superClasses.add("cobra.model.naming.NamedIdentifiedObject") meta.rnPrefixes = [ ('encap-', True), ] prop = PropMeta("str", "annotation", "annotation", 38579, PropCategory.REGULAR) prop.label = "Annotation. Suggested format orchestrator:value" prop.isConfig = True prop.isAdmin = True prop.range = [(0, 128)] prop.regex = ['[a-zA-Z0-9_.:-]+'] meta.props.add("annotation", prop) prop = PropMeta("str", "childAction", "childAction", 4, PropCategory.CHILD_ACTION) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop._addConstant("deleteAll", "deleteall", 16384) prop._addConstant("deleteNonPresent", "deletenonpresent", 8192) prop._addConstant("ignore", "ignore", 4096) meta.props.add("childAction", prop) prop = PropMeta("str", "descr", "descr", 28290, PropCategory.REGULAR) prop.label = "Description" prop.isConfig = True prop.isAdmin = True prop.range = [(0, 128)] prop.regex = ['[a-zA-Z0-9\\!#$%()*,-./:;@ _{|}~?&+]+'] meta.props.add("descr", prop) prop = PropMeta("str", "dn", "dn", 1, PropCategory.DN) prop.label = "None" prop.isDn = True prop.isImplicit = True prop.isAdmin = True prop.isCreateOnly = True meta.props.add("dn", prop) prop = PropMeta("str", "encap", "encap", 28246, PropCategory.REGULAR) prop.label = "None" prop.isImplicit = True prop.isAdmin = True meta.props.add("encap", prop) prop = PropMeta("str", "encapNsName", "encapNsName", 28248, PropCategory.REGULAR) prop.label = "None" prop.isConfig = True prop.isAdmin = True prop.range = [(0, 512)] meta.props.add("encapNsName", prop) prop = PropMeta("str", "extMngdBy", "extMngdBy", 40718, PropCategory.REGULAR) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop.defaultValue = 0 prop.defaultValueStr = "undefined" prop._addConstant("msc", "msc", 1) prop._addConstant("undefined", "undefined", 0) meta.props.add("extMngdBy", prop) prop = PropMeta("str", "guid", "guid", 28255, PropCategory.REGULAR) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop.regex = ['[[a-z0-9]{8}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{12}]{0,1}'] meta.props.add("guid", prop) prop = PropMeta("str", "id", "id", 28253, PropCategory.REGULAR) prop.label = "Id" prop.isImplicit = True prop.isAdmin = True meta.props.add("id", prop) prop = PropMeta("str", "lcOwn", "lcOwn", 9, PropCategory.REGULAR) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop.defaultValue = 0 prop.defaultValueStr = "local" prop._addConstant("implicit", "implicit", 4) prop._addConstant("local", "local", 0) prop._addConstant("policy", "policy", 1) prop._addConstant("replica", "replica", 2) prop._addConstant("resolveOnBehalf", "resolvedonbehalf", 3) meta.props.add("lcOwn", prop) prop = PropMeta("str", "legLoc", "legLoc", 28245, PropCategory.REGULAR) prop.label = "None" prop.isConfig = True prop.isAdmin = True prop.range = [(0, 512)] meta.props.add("legLoc", prop) prop = PropMeta("str", "modTs", "modTs", 7, PropCategory.REGULAR) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop.defaultValue = 0 prop.defaultValueStr = "never" prop._addConstant("never", "never", 0) meta.props.add("modTs", prop) prop = PropMeta("str", "monPolDn", "monPolDn", 28679, PropCategory.REGULAR) prop.label = "Monitoring policy attached to this observable object" prop.isImplicit = True prop.isAdmin = True meta.props.add("monPolDn", prop) prop = PropMeta("str", "name", "name", 28294, PropCategory.REGULAR) prop.label = "Name" prop.isConfig = True prop.isAdmin = True prop.isCreateOnly = True prop.isNaming = True prop.range = [(1, 128)] meta.props.add("name", prop) prop = PropMeta("str", "nameAlias", "nameAlias", 28417, PropCategory.REGULAR) prop.label = "Name alias" prop.isConfig = True prop.isAdmin = True prop.range = [(0, 63)] prop.regex = ['[a-zA-Z0-9_.-]+'] meta.props.add("nameAlias", prop) prop = PropMeta("str", "rn", "rn", 2, PropCategory.RN) prop.label = "None" prop.isRn = True prop.isImplicit = True prop.isAdmin = True prop.isCreateOnly = True meta.props.add("rn", prop) prop = PropMeta("str", "rtrId", "rtrId", 28247, PropCategory.REGULAR) prop.label = "None" prop.isConfig = True prop.isAdmin = True meta.props.add("rtrId", prop) prop = PropMeta("str", "status", "status", 3, PropCategory.STATUS) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop._addConstant("created", "created", 2) prop._addConstant("deleted", "deleted", 8) prop._addConstant("modified", "modified", 4) meta.props.add("status", prop) prop = PropMeta("str", "uid", "uid", 8, PropCategory.REGULAR) prop.label = "None" prop.isImplicit = True prop.isAdmin = True meta.props.add("uid", prop) meta.namingProps.append(getattr(meta.props, "name")) # Deployment Meta meta.deploymentQuery = True meta.deploymentType = "Ancestor" meta.deploymentQueryPaths.append(DeploymentPathMeta("CtxToNwIf", "Private Network to Interface", "cobra.model.nw.If")) def __init__(self, parentMoOrDn, name, markDirty=True, **creationProps): namingVals = [name] Mo.__init__(self, parentMoOrDn, markDirty, *namingVals, **creationProps) # End of package file # ##################################################

[ "rrishike@cisco.com" ]

rrishike@cisco.com

80a7585e86a4e8633b65ccb5495c63da103934b7

8bbeb7b5721a9dbf40caa47a96e6961ceabb0128

/python3/216.Combination Sum III(组合总和 III).py

4c2a30d0cd8dad2c5c465ba3a4dfdb989f691e11

[ "MIT" ]

permissive

lishulongVI/leetcode

bb5b75642f69dfaec0c2ee3e06369c715125b1ba

6731e128be0fd3c0bdfe885c1a409ac54b929597

refs/heads/master

2020-03-23T22:17:40.335970

2018-07-23T14:46:06

null

0

null

UTF-8

Python

false

2,388

py

""" <div> Find all possible combinations of k numbers that add up to a number n, given that only numbers from 1 to 9 can be used and each combination should be a unique set of numbers. Note: <ul> <li>All numbers will be positive integers.</li> <li>The solution set must not contain duplicate combinations.</li> </ul> Example 1: <pre> Input: k = 3, n = 7 Output: [[1,2,4]] </pre> Example 2: <pre> Input: k = 3, n = 9 Output: [[1,2,6], [1,3,5], [2,3,4]] </pre> </div> 找出所有相加之和为 n 的 k 个数的组合。组合中只允许含有 1 - 9 的正整数，并且每种组合中不存在重复的数字。 说明： <ul> <li>所有数字都是正整数。</li> <li>解集不能包含重复的组合。 </li> </ul> 示例 1: <pre>输入: k = 3, n = 7 输出: [[1,2,4]] </pre> 示例 2: <pre>输入: k = 3, n = 9 输出: [[1,2,6], [1,3,5], [2,3,4]] </pre> 找出所有相加之和为 n 的 k 个数的组合。组合中只允许含有 1 - 9 的正整数，并且每种组合中不存在重复的数字。 说明： <ul> <li>所有数字都是正整数。</li> <li>解集不能包含重复的组合。 </li> </ul> 示例 1: <pre>输入: k = 3, n = 7 输出: [[1,2,4]] </pre> 示例 2: <pre>输入: k = 3, n = 9 输出: [[1,2,6], [1,3,5], [2,3,4]] </pre> """ class Solution: def combinationSum3(self, k, n): """ :type k: int :type n: int :rtype: List[List[int]] """

[ "lishulong@wecash.net" ]

lishulong@wecash.net

c5daf96e1ec9ac90dc1db252619f073fb6d4df6d

179a0f995f5a3eb7a6005f8e96498ef21b2bf166

/docs/conf.py

45ccdf6d81b9baf63f859bf4fc96836c47707904

[ "MIT" ]

permissive

VB6Hobbyst7/pycatia

845052a4584318bf0cf0861512203ddd337a7bca

cff309fe2b4802ff2b2c5c984f8064747f81065d

refs/heads/master

2023-04-14T20:28:51.427101

2021-04-27T11:03:42

null

0

null

UTF-8

Python

false

5,936

py

# -*- coding: utf-8 -*- # # Configuration file for the Sphinx documentation builder. # # This file does only contain a selection of the most common options. For a # full list see the documentation: # http://www.sphinx-doc.org/en/master/config # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys from unittest.mock import MagicMock sys.path.insert(0, os.path.abspath('..')) class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() MOCK_MODULES = ['pywin32', 'win32com.client', 'pywintypes'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # -- Project information ----------------------------------------------------- project = 'pycatia' copyright = '2020, Paul Bourne' author = 'Paul Bourne' # The short X.Y version version = '0.5.0' # The full version, including alpha/beta/rc tags release = version # -- General configuration --------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.intersphinx', 'sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.todo', 'sphinx_togglebutton', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The master toctree document. master_doc = 'index' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = None # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'alabaster' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { 'github_user': 'evereux', 'github_repo': 'pycatia', } # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] html_theme_path = [] html_css_files = [ 'css/pycatia.css', ] # Custom sidebar templates, must be a dictionary that maps document names # to template names. # # The default sidebars (for documents that don't match any pattern) are # defined by theme itself. Builtin themes are using these templates by # default: ``['localtoc.html', 'relations.html', 'sourcelink.html', # 'searchbox.html']``. # # html_sidebars = {} # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = 'pycatiadoc' # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'pycatia.tex', 'pycatia Documentation', 'Paul Bourne', 'manual'), ] # -- Options for manual page output ------------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'pycatia', 'pycatia Documentation', [author], 1) ] # -- Options for Texinfo output ---------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'pycatia', 'pycatia Documentation', author, 'pycatia', 'One line description of project.', 'Miscellaneous'), ] # -- Options for Epub output ------------------------------------------------- # Bibliographic Dublin Core info. epub_title = project # The unique identifier of the text. This can be a ISBN number # or the project homepage. # # epub_identifier = '' # A unique identification for the text. # # epub_uid = '' # A list of files that should not be packed into the epub file. epub_exclude_files = ['search.html'] # -- Extension configuration ------------------------------------------------- # -- Options for todo extension ---------------------------------------------- # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True

[ "evereux@gmail.com" ]

evereux@gmail.com

8c1605776199c122465a2aa10d3dade49beec409

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p02748/s229745856.py

07e6ff9b4f694f31ed7e0dfb26750d3f2b624a60

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

232

py

_, _, M = map(int, input().split()) A = list(map(int, input().split())) B = list(map(int, input().split())) xyc = [tuple(map(int, input().split())) for i in range(M)] print(min([min(A)+min(B)]+[A[x-1]+B[y-1]-c for x, y, c in xyc]))

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

572938151b792f0f6e8e2bb10d5c6bd6a452af48

e5504d8c4880993b82d5583a11c5cc4623e0eac2

/Arrays/loopInCircularArray__IMP.py

768b8bdc06fdc4ba7a36f6287179bf3b4b92d756

[]

no_license

noorulameenkm/DataStructuresAlgorithms

e5f87f426fc444d18f830e48569d2a7a50f5d7e0

7c3bb89326d2898f9e98590ceb8ee5fd7b3196f0

refs/heads/master

2023-06-08T19:29:42.507761

2023-05-28T16:20:19

219,270,731

2

0

null

UTF-8

Python

false

2,536

py

def circular_array_loop_exists(arr): # TODO: Write your code here for i in range(len(arr)): slow = fast = i is_forward = arr[i] >= 0 # if slow or fast becomes '-1' this means we can't find cycle for this number while True: # move one step for slow pointer slow = get_next_index(arr, is_forward, slow) # move one step for fast pointer fast = get_next_index(arr, is_forward, fast) if fast != -1: # move another step for fast pointer fast = get_next_index(arr, is_forward, fast) if slow == -1 or fast == -1 or slow == fast: break if slow != -1 and slow == fast: return True return False def get_next_index(arr, is_forward, current_index): direction = arr[current_index] >= 0 if is_forward != direction: return -1 # change in direction, return -1 next_index = (arr[current_index] + current_index) % len(arr) # one element cycle, return -1 if next_index == current_index: next_index = -1 return next_index def main(): print(circular_array_loop_exists([1, 2, -1, 2, 2])) print(circular_array_loop_exists([2, 2, -1, 2])) print(circular_array_loop_exists([2, 1, -1, -2])) main() """" We are given an array containing positive and negative numbers. Suppose the array contains a number ‘M’ at a particular index. Now, if ‘M’ is positive we will move forward ‘M’ indices and if ‘M’ is negative move backwards ‘M’ indices. You should assume that the array is circular which means two things: If, while moving forward, we reach the end of the array, we will jump to the first element to continue the movement. If, while moving backward, we reach the beginning of the array, we will jump to the last element to continue the movement. Write a method to determine if the array has a cycle. The cycle should have more than one element and should follow one direction which means the cycle should not contain both forward and backward movements. """ """" Alternate Method In our algorithm, we don’t keep a record of all the numbers that have been evaluated for cycles. We know that all such numbers will not produce a cycle for any other instance as well. If we can remember all the numbers that have been visited, our algorithm will improve to O(N) as, then, each number will be evaluated for cycles only once. We can keep track of this by creating a separate array however the space complexity of our algorithm will increase to O(N). """

[ "noorul.km@people10.com" ]

noorul.km@people10.com

3aefb338c74473c31e9b8b9f5b57d93c9d53d0e5

5f957add3e3f7a1885d4f1b106de72e93c8fcb1a

/ExerciciosPython/ex072.py

a2215342cdcfa208efd442734fd5f94405993530

[ "MIT" ]

permissive

mpatrickaires/curso-python

6e32cf785a3bc0076bb3ea24cd6d896604f4e774

aba023648527d53bfe18833b91210a7e528a84d7

refs/heads/main

2022-12-27T00:57:07.467940

2020-10-14T00:48:09

302,203,176

1

0

null

UTF-8

Python

false

698

py

extenso = ('zero', 'um', 'dois', 'três', 'quatro', 'cinco', 'seis', 'sete', 'oito', 'nove', 'dez', 'onze', 'doze', 'treze', 'catorze', 'quinze', 'dezesseis', 'dezessete', 'dezoito', 'dezenove', 'vinte') while True: numero = int(input('Digite um número entre 0 e 20: ')) while numero < 0 or numero > 20: numero = int(input('Tente novamente. Digite um número entre 0 e 20: ')) print(f'Você digitou o número {extenso[numero]}') continuar = str(input('Deseja continuar? [S/N] ')).strip().upper() while continuar != 'S' and continuar != 'N': continuar = str(input('Deseja continuar? [S/N] ')).strip().upper() if continuar == 'N': break

[ "mpatrickaires@gmail.com" ]

mpatrickaires@gmail.com

1114a68d8b2e5c4fd05992b6c8ee4ca498cc92af

755e4e6e966433fe887f0f28f14916696b1588d7

/code/exceptions/exceptions.py

7e62995995ecbc57b12ce62c9ad0de8d73a94b9e

[]

no_license

phildue/FingerspellingRecognition

f18518a6e2e29b769d131e5b54846f00213f3ff1

1b5236142734d7b50f0f4161ecc533b7d10347b8

refs/heads/master

2021-03-24T10:40:24.507766

2017-07-03T09:33:09

null

0

null

UTF-8

Python

false

167

py

class NotTrained(Exception): pass class NoRoiFound(Exception): pass class NoContoursFound(Exception): pass class DescriptorFailed(Exception): pass

[ "phild@protonmail.com" ]

phild@protonmail.com

d1684f57fb28491ecde85c741f45fcd4e4659cf8

ed9e4027cbd76fbac19598163b9673628cb07eea

/anjia/asgi.py

372aac7872de8b88fd3e438e294b71fb8dafce32

[ "BSD-2-Clause" ]

permissive

ankiwoong/python_kindergarten

3a1f9a486a32866b5f37ba4673dfc2135a85eec0

43b1e15969f0d35073e2f7fb1286d8c094fd80a8

refs/heads/master

2022-09-01T08:11:27.374802

2020-05-27T08:45:14

258,760,014

0

null

UTF-8

Python

false

387

py

""" ASGI config for anjia project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'anjia.settings') application = get_asgi_application()

[ "ankiwoong@gmail.com" ]

ankiwoong@gmail.com

6a50f6dc840ad5ee463050db663639df9a8ea7dd

e8b12e314782bf68347838599c8168e4a8019373

/CompareAlternatives.py

0d80231eb7ed1c3ac5094ee2f446c2fa5eed2155

[]

no_license

HPM573/Lab_ParallelProcessing

0ce7e4b615afe9e2e2a281f79684e9067003aa1b

f2e6401f4a5dc057a150914653079c0284c92b4b

refs/heads/main

2023-05-12T06:03:15.275404

2023-05-02T13:58:18

180,822,018

0

null

UTF-8

Python

false

1,908

py

import EconEvalInputData as D import ProbabilisticSupport as Support import ProbilisticParamClasses as P from ParallelClasses import ParallelMultiCohort N_COHORTS = 200 # number of cohorts if __name__ == '__main__': # this line is needed to avoid errors that occur on Windows computers # create a multi-cohort to simulate under mono therapy multiCohortMono = ParallelMultiCohort( ids=range(N_COHORTS), pop_size=D.POP_SIZE, therapy=P.Therapies.MONO ) multiCohortMono.simulate(sim_length=D.SIM_LENGTH) # create a multi-cohort to simulate under combi therapy multiCohortCombo = ParallelMultiCohort( ids=range(N_COHORTS), pop_size=D.POP_SIZE, therapy=P.Therapies.COMBO ) multiCohortCombo.simulate(sim_length=D.SIM_LENGTH) # print the estimates for the mean survival time and mean time to AIDS Support.print_outcomes(multi_cohort_outcomes=multiCohortMono.multiCohortOutcomes, therapy_name=P.Therapies.MONO) Support.print_outcomes(multi_cohort_outcomes=multiCohortCombo.multiCohortOutcomes, therapy_name=P.Therapies.COMBO) # draw survival curves and histograms Support.plot_survival_curves_and_histograms(multi_cohort_outcomes_mono=multiCohortMono.multiCohortOutcomes, multi_cohort_outcomes_combo=multiCohortCombo.multiCohortOutcomes) # print comparative outcomes Support.print_comparative_outcomes(multi_cohort_outcomes_mono=multiCohortMono.multiCohortOutcomes, multi_cohort_outcomes_combo=multiCohortCombo.multiCohortOutcomes) # report the CEA results Support.report_CEA_CBA(multi_cohort_outcomes_mono=multiCohortMono.multiCohortOutcomes, multi_cohort_outcomes_combo=multiCohortCombo.multiCohortOutcomes)

[ "reza.yaesoubi@yale.edu" ]

reza.yaesoubi@yale.edu

48b211d3ffc2fe351f125460bfa2de347c5ad89c

6fcfb638fa725b6d21083ec54e3609fc1b287d9e

/python/frenetic-lang_pyretic/pyretic-master/pyretic/tests/test_mac_learner.py

19c0e4482c157a77029346d963681440c374e52d

[]

no_license

LiuFang816/SALSTM_py_data

6db258e51858aeff14af38898fef715b46980ac1

d494b3041069d377d6a7a9c296a14334f2fa5acc

refs/heads/master

2022-12-25T06:39:52.222097

2019-12-12T08:49:07

227,546,525

10

7

null

2022-12-19T02:53:01

2019-12-12T07:29:39

Python

UTF-8

Python

false

2,387

py

#!/usr/bin/python from mininet.net import Mininet from mininet.node import RemoteController import os, shlex, subprocess, utils, time from utils import init ### Module Parameters def get_controller(): return 'pyretic.modules.mac_learner' def run_mininet(): # mn = Mininet() # s1 = mn.addSwitch('s1') # s2 = mn.addSwitch('s2') # s3 = mn.addSwitch('s3') # h1 = mn.addHost('h1') # h2 = mn.addHost('h2') # h3 = mn.addHost('h3') # mn.addLink(s1, s2) # mn.addLink(s1, s3) # mn.addLink(s2, s3) # mn.addLink(h1, s1) # mn.addLink(h2, s2) # mn.addLink(h3, s3) # mn.addController('c0', RemoteController) # time.sleep(1) # mn.run(mn.pingAll) # Alternately, run mininet via the command line. Note that we need to use # absolute path names because sudo mucks with the env. mn = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../mininet.sh')) cmd = '%s --topo cycle,3,4 --mac --test=pingall' % mn subprocess.call(shlex.split(cmd)) def process_controller_output(oldf, newf): lines = oldf.readlines() lines.sort() keywords = ['TEST', 'ERROR', 'error'] ## filter out lines that do not contain one of the keywords for line in lines: for kw in keywords: if line.find(kw) >= 0: newf.write(line) def process_mininet_output(oldf, newf): lines = oldf.readlines() lines.sort() keywords = ['TEST', 'ERROR', 'error', 'received'] ## filter out lines that do not contain one of the keywords for line in lines: for kw in keywords: if line.find(kw) >= 0: newf.write(line) ### Tests test_mac_learner = utils.TestModule( __name__, __file__, get_controller, run_mininet, process_controller_output, process_mininet_output) def test_mac_learner_i(init): utils.run_test(test_mac_learner, init.test_dir, init.benchmark_dir, '-m i') def test_mac_learner_r0(init): utils.run_test(test_mac_learner, init.test_dir, init.benchmark_dir, '-m r0') def test_mac_learner_p0(init): utils.run_test(test_mac_learner, init.test_dir, init.benchmark_dir, '-m p0') # def test_mac_learner_p0_nx(init): # utils.run_test(test_mac_learner, init.test_dir, init.benchmark_dir, '-m p0 --nx') ### Executing this file starts the mininet instance for this test. if __name__ == "__main__": run_mininet()

[ "659338505@qq.com" ]

659338505@qq.com

dc4ee8e84412fbe9e26fa41aea2ba61f0a80d687

3b11dc40c7d772fffeb4d8683e5c9791c41f6454

/custom/clients/ecobank/ecobank_inventory/models/inventory_account.py

54cf03c640a891acbae5ed78bf433efd0cd027f2

[]

no_license

Jacky-odoo/Ecobank

b986352abac9416ab00008a4abaec2b1f1a1f262

5c501bd03a22421f47c76380004bf3d62292f79d

refs/heads/main

2023-03-09T18:10:45.058530

2021-02-25T14:11:12

null

0

null

UTF-8

Python

false

1,139

py

from odoo import api, fields, models from odoo.exceptions import ValidationError class InventoryUser(models.Model): _name = 'inventory.account' _rec_name = 'name_and_code' name = fields.Char(string='Name', required=True) code = fields.Char(string='Code', required=True) name_and_code = fields.Char(compute='compute_name_code', store=True) @api.multi @api.depends('name', 'code') def compute_name_code(self): for rec in self: if rec.code and rec.name: rec.name_and_code = str(rec.name + " (" + rec.code + ")") @api.multi def copy(self, default=None): raise ValidationError("Sorry you are not allowed to perform this operation. Error Code BYT001") @api.constrains('name') def check_name(self): all_accounts = self.search([]) for account in all_accounts: if self.name.lower() == account.name.lower() and self.id != account.id: raise ValidationError("Error! Account Name already exist. BYT005") _sql_constraints = [ ('unique_code', 'unique (code)', "Account Code Already Exist !"), ]

[ "francisbnagura@gmail.com" ]

francisbnagura@gmail.com

101e6d98e6ea5327b9632183ef8eb52de0c552e9

ff5eea95bb0827cb086c32f4ec1c174b28e5b82d

/gammapy/background/tests/test_ring.py

047cad9a887193d1551fbd48446204c72bfc2e9e

[]

no_license

pflaumenmus/gammapy

4830cc5506a4052658f30077fa4e11d8c685ede0

7b5caf832c9950c886528ca107203ce9b83c7ebf

refs/heads/master

2021-01-15T23:27:46.521337

2013-09-25T14:23:35

null

0

null

UTF-8

Python

false

1,176

py

# Licensed under a 3-clause BSD style license - see LICENSE.rst from __future__ import division import unittest import pytest import numpy as np from numpy.testing import assert_almost_equal from astropy.io import fits from ..maps import Maps from ..ring import RingBgMaker, outer_ring_radius try: import scipy HAS_SCIPY = True except ImportError: HAS_SCIPY = False @pytest.mark.skipif('not HAS_SCIPY') class TestRingBgMaker(unittest.TestCase): def test_construction(self): r = RingBgMaker(0.3, 0.5) r.info() def test_correlate(self): image = np.zeros((10, 10)) image[5, 5] = 1 r = RingBgMaker(3, 6, 1) image = r.correlate(image) def test_correlate_maps(self): n_on = np.ones((200, 200)) hdu = fits.ImageHDU(n_on, name='n_on') maps = Maps([hdu]) maps['exclusion'].data[100:110, 100:110] = 0 r = RingBgMaker(10, 13, 1) r.correlate_maps(maps) class TestHelperFuntions(unittest.TestCase): def test_compute_r_o(self): actual = outer_ring_radius(1, 0, 1) assert_almost_equal(actual, 1) if __name__ == '__main__': unittest.main()

[ "Deil.Christoph@gmail.com" ]

Deil.Christoph@gmail.com

ff31f03d357f8dd02d1fef1e8193bb092e608bea

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p02708/s465285057.py

b9e8c08a651992f50a225309892a6784c1a1572f

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

257

py

from itertools import accumulate N,K = map(int,input().split()) acc = list(accumulate(range(N+1), lambda x,y:x+y)) ans = 0 mod = 10**9+7 for i in range(K, N+1): r = acc[N] - acc[N-i] l = acc[i-1] ans = (ans+r-l+1) % mod ans += 1 print(ans % mod)

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

38eaeac29ebaa70dc88d888b36fe8d2e3156dd76

083b3f5b0d23c269c6a9ff1ea413e70fb799a497

/Leetcode Challenge/09_September_2020/Python/Week 5/2_First Missing Positive.py

5daf39e470ef89206f9440b17c1cc1717578a4f7

[]

no_license

HectorIGH/Competitive-Programming

b2e02dff140d9ebb06c646f7be0b53ea0afe90c9

467058c63e8a7e76805feebe3020bac4d20516a6

refs/heads/master

2022-12-31T18:32:46.824626

2020-10-16T20:38:33

279,733,136

0

null

UTF-8

Python

false

1,454

py

#Given an unsorted integer array, find the smallest missing positive integer. # #Example 1: # #Input: [1,2,0] #Output: 3 #Example 2: # #Input: [3,4,-1,1] #Output: 2 #Example 3: # #Input: [7,8,9,11,12] #Output: 1 #Follow up: # #Your algorithm should run in O(n) time and uses constant extra space. # # Hide Hint #1 #Think about how you would solve the problem in non-constant space. Can you apply that logic to the existing space? # Hide Hint #2 #We don't care about duplicates or non-positive integers # Hide Hint #3 #Remember that O(2n) = O(n) class Solution: def firstMissingPositive(self, nums: List[int]) -> int: ''' nums = set((n for n in nums if n > 0)) i = 1 while True: if i not in nums: return i i += 1 ''' if len(nums) == 0: return 1 n = len(nums); containsone = False; for i in range(n): if nums[i] == 1: containsone = True break if not containsone: return 1 for i in range(n): if nums[i] <= 0 or nums[i] > n: nums[i] = 1 for i in range(n): val = nums[i] pos = abs(val) - 1 if nums[pos] > 0: nums[pos] = -1 * nums[pos]; for i in range(n): if nums[i] > 0: return i + 1 return n + 1

[ "HectorIGH@users.noreply.github.com" ]

HectorIGH@users.noreply.github.com

21cc1ba23778a7ba76d8b97034ae2a2236266abf

864acf7235e330123c3d68ed14cdd8bf8eed800b

/crm/accounts/models.py

be98a7f2e0926b1e0b0ec5e7fd8a599dfe9597b2

[]

no_license

wahid999/djangostuff

83f0ae53df5c53d192603d7aaf7ee72f8665c240

c102edfb13b8ba39930e44069122c5e545ef00ee

refs/heads/main

2023-07-04T20:02:32.550831

2021-08-13T16:07:20

399,344,439

0

null

UTF-8

Python

false

1,705

py

from django.db import models from django.contrib.auth.models import User # Create your models here. class Customer(models.Model): user = models.OneToOneField(User, null=True, blank=True, on_delete=models.CASCADE) name = models.CharField(max_length=200, null=True) phone = models.CharField(max_length=200, null=True) email = models.CharField(max_length=200, null=True) profile_pic = models.ImageField(default="IMG_3940.JPG", null=True, blank=True) date_created = models.DateTimeField(auto_now_add=True, null=True) def __str__(self): return self.name class Tag(models.Model): name = models.CharField(max_length=200, null=True) def __str__(self): return self.name class Product(models.Model): CATEGORY = ( ('Indoor', 'Indoor'), ('Out Door', 'Out Door'), ) name = models.CharField(max_length=200, null=True) price = models.FloatField(null=True) category = models.CharField(max_length=200, null=True, choices=CATEGORY) description = models.CharField(max_length=200, null=True, blank=True) date_created = models.DateTimeField(auto_now_add=True, null=True) tags = models.ManyToManyField(Tag) def __str__(self): return self.name class Order(models.Model): STATUS = ( ('Pending', 'Pending'), ('Out for delivery', 'Out for delivery'), ('Delivered', 'Delivered'), ) customer = models.ForeignKey(Customer, null=True, on_delete= models.SET_NULL) product = models.ForeignKey(Product, null=True, on_delete= models.SET_NULL) date_created = models.DateTimeField(auto_now_add=True, null=True) status = models.CharField(max_length=200, null=True, choices=STATUS) note = models.CharField(max_length=1000, null=True) def __str__(self): return self.product.name

[ "wahidhussainturi@gmail.com" ]

wahidhussainturi@gmail.com

7e06dd17c6c8f3382921b07d5a29bfd3f67c4817

846e642fd9b01d3b500d3efba4790761039eec24

/code/smtp.py

978b10738307ac891f4680f1e0a033f0d1ac1892

[]

no_license

sachinyadav3496/Machine_Learning_Workshop

ffea23799c0f8477d9b5cc19b98e7d33a6364390

37f433631d1ae4e4db37c4baae6cdc3a7619423e

refs/heads/master

2020-11-24T11:49:45.936367

2020-05-01T08:38:10

228,130,385

5

2

null

UTF-8

Python

false

2,080

py

import smtplib import getpass def Main(): print("\n\n*************************welcome************************\n") print("\nWelcom to Email Service \n") print("Enter your login details - \n") gmail_user = input("\n\nUserName : ") gmail_password = getpass.getpass("Password : ") try: server = smtplib.SMTP_SSL('smtp.gmail.com', 465) print("\n\nConnection established ") server.ehlo() server.login(gmail_user, gmail_password) print("\n\nYou have Successfully logged in your account ",gmail_user) except Exception as e: print("\n\nError!!! in Connection ") print(e) exit(0) sent_from = gmail_user i = int(input("\n\nEnter no. of recipients - ")) print("\n\nEnter Recipients Email Addressess - \n") to = [] for k in range(i): to.append(input()) print() subject = input("\n\nPlease Type in Subject of The Mail - ") print("\n\nType in Your Message (Type in EOF to FINISH)\n\n") message=[] while True: msg = input() if msg.upper() == 'EOF' : break else : message.append(msg) print("\n\nMessege is Ready for Delivery\n\n ") body = '\n'.join(message) email_text = """From:%s To:%s Subject:%s %s """%(sent_from, ", ".join(to), subject, body) try: print("\n\nEmail sending is in process - \n ") server.sendmail(sent_from, to, email_text) server.close() except Exception as e: print('\nSomething went wrong...',e) else: print("\nMessage Delivered to - \n") for i in to: print(i) print() print("\n\n**********************Exiting********************\n\n") print("\n\nThanks For using Mail Service \n\n") if __name__ == "__main__": Main()

[ "sachinyadav3496@gmail.com" ]

sachinyadav3496@gmail.com

ce3be2e0574e1ed136c469dfa1ef2ac357ed40cc

dfb8d3c365bd2ea27cef9af5cb00b7be1dae978d

/train.py

b23821e543d1b2a73205ead7e410f2b5b7bac887

[ "MIT" ]

permissive

Windstudent/IRM-based-Speech-Enhancement-using-DNN

dd0cedfd4150fed69c55d33a744d0a6520fdf2d5

27a6f73b5b7fa91a4796e093e6ea3e30508a5c15

refs/heads/master

2020-07-05T15:34:52.712226

2019-05-07T14:36:40

null

0

null

UTF-8

Python

false

2,823

py

import argparse import json import os import numpy as np import torch from torch.utils.data import DataLoader from data.test_dataset import TestDataset from data.train_dataset import TrainDataset from trainer.trainer import Trainer from utils.utils import initialize_config def main(config, resume): """ 训练脚本的入口函数 Notes: 1. 加载数据集 2. 初始化模型 3. 设置优化器 4. 选择损失函数 5. 训练脚本 run Args: config (dict): 配置项 resume (bool): 是否加载最近一次存储的模型断点 """ torch.manual_seed(config["seed"]) np.random.seed(config["seed"]) train_dataset = TrainDataset( mixture_dataset=config["train_dataset"]["mixture"], mask_dataset=config["train_dataset"]["clean"], limit=config["train_dataset"]["limit"], offset=config["train_dataset"]["offset"], ) train_data_loader = DataLoader( dataset=train_dataset, batch_size=config["train_dataset"]["batch_size"], num_workers=config["train_dataset"]["num_workers"], shuffle=config["train_dataset"]["shuffle"] ) valid_dataset = TestDataset( mixture_dataset=config["valid_dataset"]["mixture"], clean_dataset=config["valid_dataset"]["clean"], limit=config["valid_dataset"]["limit"], offset=config["valid_dataset"]["offset"], ) valid_data_loader = DataLoader( dataset=valid_dataset ) model = initialize_config(config["model"]) optimizer = torch.optim.Adam( params=model.parameters(), lr=config["optimizer"]["lr"] ) loss_function = initialize_config(config["loss_function"]) trainer = Trainer( config=config, resume=resume, model=model, loss_function=loss_function, optim=optimizer, train_dl=train_data_loader, validation_dl=valid_data_loader, ) trainer.train() if __name__ == '__main__': parser = argparse.ArgumentParser(description='IRM Estimation using DNN in Speech Enhancement') parser.add_argument("-C", "--config", required=True, type=str, help="训练配置文件（*.json）") parser.add_argument('-D', '--device', default=None, type=str, help="本次实验使用的 GPU 索引，e.g. '1,2,3'") parser.add_argument("-R", "--resume", action="store_true", help="是否从最近的一个断点处继续训练") args = parser.parse_args() if args.device: os.environ["CUDA_VISIBLE_DEVICES"] = args.device # load config file config = json.load(open(args.config)) config["train_config_path"] = args.config main(config, resume=args.resume)

[ "haoxiangsnr@gmail.com" ]

haoxiangsnr@gmail.com

93e0d1af53bc2b9efd06b47d2a1c4276bdb0b0bd

5390d79dad71ad0d9ff9d0777435dcaf4aad16b3

/chapter_06/favorite_number.py

124b9763eeac8593df0e93e0c0e845aa9bc3e5dd

[]

no_license

JasperMi/python_learning

19770d79cce900d968cec76dac11e45a3df9c34c

8111d0d12e4608484864dddb597522c6c60b54e8

refs/heads/master

2020-11-26T08:57:02.983869

2020-03-11T10:14:55

218,935,548

0

null

UTF-8

Python

false

497

py

favorite_numbers = { 'bob': 2, 'sarah': 6, 'martin': 8, 'katy': 9, 'tom': 10 } print('bob' + "'s favorite number is " + str(favorite_numbers['bob']) + ".") print('sarah' + "'s favorite number is " + str(favorite_numbers['sarah']) + ".") print('martin' + "'s favorite number is " + str(favorite_numbers['martin']) + ".") print('katy' + "'s favorite number is " + str(favorite_numbers['katy']) + ".") print('tom' + "'s favorite number is " + str(favorite_numbers['tom']) + ".")

[ "darmi19@163.com" ]

darmi19@163.com

b1f2bc27194e8f706625493989d95c5335783f9f

fc58366ed416de97380df7040453c9990deb7faa

/daoliagent/services/arp.py

7d9cf1622fdcd08505553150ef2cdef052d75232

[ "Apache-2.0" ]

permissive

foruy/openflow-multiopenstack

eb51e37b2892074234ebdd5b501b24aa1f72fb86

74140b041ac25ed83898ff3998e8dcbed35572bb

refs/heads/master

2016-09-13T08:24:09.713883

2016-05-19T01:16:58

58,977,485

1

0

null

UTF-8

Python

false

4,005

py

from oslo.config import cfg from ryu.lib.packet import arp from ryu.lib.packet import ethernet from ryu.lib.packet import packet from ryu.lib import addrconv from ryu.ofproto import ether from daoliagent.services.base import PacketBase from daoliagent.lib import SMAX from daoliagent.openstack.common import log as logging CONF = cfg.CONF LOG = logging.getLogger(__name__) class PacketARP(PacketBase): priority = 5 def _arp(self, msg, dp, in_port, pkt_ether, pkt_arp, address): ofp, ofp_parser, ofp_set, ofp_out = self.ofp_get(dp) actions = [ofp_parser.OFPActionSetField(eth_src=address), ofp_parser.OFPActionOutput(ofp.OFPP_IN_PORT)] match = ofp_parser.OFPMatch( in_port=in_port, eth_type=ether.ETH_TYPE_ARP, arp_spa=pkt_arp.src_ip, arp_tpa=pkt_arp.dst_ip) LOG.debug("arp response %(src_mac)s-%(src_ip)s -> %(dst_mac)s-%(dst_ip)s", {'src_mac': address, 'src_ip': pkt_arp.dst_ip, 'dst_mac': pkt_arp.src_mac, 'dst_ip': pkt_arp.src_ip}) self.add_flow(dp, match, actions) self.packet_out(msg, dp, actions) def _redirect(self, msg, dp, in_port, pkt_ether, pkt_arp, output): ofp, ofp_parser, ofp_set, ofp_out = self.ofp_get(dp) actions = [ofp_parser.OFPActionOutput(output)] match = ofp_parser.OFPMatch( in_port=in_port, eth_type=ether.ETH_TYPE_ARP, arp_spa=pkt_arp.src_ip, arp_tpa=pkt_arp.dst_ip) self.add_flow(dp, match, actions) self.packet_out(msg, dp, actions) def run(self, msg, pkt_ether, pkt_arp, gateway, **kwargs): dp = msg.datapath in_port = msg.match['in_port'] ofp, ofp_parser, ofp_set, ofp_out = self.ofp_get(dp) src_mac = pkt_arp.src_mac dst_ip = pkt_arp.dst_ip LOG.debug("arp request %(src_mac)s-%(src_ip)s -> %(dst_mac)s-%(dst_ip)s", {'src_mac': src_mac, 'src_ip': pkt_arp.src_ip, 'dst_mac': pkt_arp.dst_mac, 'dst_ip': dst_ip}) if gateway.int_dev != gateway.ext_dev: int_port = self.port_get(dp, devname=gateway.int_dev) tap_port = self.port_get(dp, devname=gateway.vint_dev) if not int_port or not tap_port: return True if in_port == int_port.port_no: if pkt_arp.dst_ip == gateway['int_ip']: self._redirect(msg, dp, in_port, pkt_ether, pkt_arp, tap_port.port_no) return True if in_port == tap_port.port_no: if pkt_arp.src_ip == gateway['int_ip']: self._redirect(msg, dp, in_port, pkt_ether, pkt_arp, int_port.port_no) return True port = self.port_get(dp, devname=gateway['ext_dev']) if not port: return True if in_port == port.port_no: if pkt_arp.dst_ip == gateway['ext_ip']: self._redirect(msg, dp, in_port, pkt_ether, pkt_arp, ofp.OFPP_LOCAL) return True if in_port == ofp.OFPP_LOCAL: if pkt_arp.src_ip == gateway['ext_ip']: self._redirect(msg, dp, in_port, pkt_ether, pkt_arp, port.port_no) return True num_ip = addrconv.ipv4._addr(dst_ip).value if pkt_arp.opcode != arp.ARP_REQUEST: LOG.debug("unknown arp op %s", pkt_arp.opcode) elif (num_ip & 0x0000FFFF == SMAX - 1): #br_port = self.port_get(dp, devname=gateway['vext_dev']) #self._arp(dp, in_port, pkt_ether, pkt_arp, br_port.hw_addr) self._arp(msg, dp, in_port, pkt_ether, pkt_arp, gateway['vint_mac']) else: servers = self.db.server_get_by_mac(src_mac, dst_ip, False) if servers['src'] and servers['dst']: self._arp(msg, dp, in_port, pkt_ether, pkt_arp, servers['dst'].mac_address) else: self._arp(msg, dp, in_port, pkt_ether, pkt_arp, gateway['vint_mac'])

[ "wenxiang.wang1204@gmail.com" ]

wenxiang.wang1204@gmail.com

0b2c7b6c78f2f20e685b99106e28b2dcfabe7a03

9d852841463c64f75da8a8579c32cea856d2073d

/leetcode/validate_binary_search_tree.py

4931cdb9a7da506dea78bd8a759a89b592284296

[]

no_license

LarsIndus/algorithms-DS

2d94a5ba3e17de7c8d9e7ac4ace8eb70bb2a7331

32a64a4522f8474ab63421b06e945f6e44a441e1

refs/heads/master

2023-04-26T00:13:06.026785

2021-05-20T18:55:12

243,239,680

0

null

UTF-8

Python

false

2,476

py

""" Leetcode Problem 98: Validate Binary Search Tree (Medium) Given the root of a binary tree, determine if it is a valid binary search tree (BST). A valid BST is defined as follows: - The left subtree of a node contains only nodes with keys less than the node's key. - The right subtree of a node contains only nodes with keys greater than the node's key. - Both the left and right subtrees must also be binary search trees. Complexity for this solution: O(n) time and space Source: https://www.youtube.com/watch?v=ofuXorE-JKE """ # Node implementation -------------------------------------------------------- class newNode: # Construct to create a newNode def __init__(self, data): self.data = data self.left = None self.right = None # Solution ------------------------------------------------------------------- def is_valid_BST(root): return helper(root, float("-inf"), float("inf")) def helper(root, min_value, max_value): if root is None: return True if root.data < min_value or root.data > max_value: return False valid_left = helper(root.left, min_value, root.data) valid_right = helper(root.right, root.data, max_value) return valid_left and valid_right # Testing -------------------------------------------------------------------- def main(): # Test 1: Empty tree tree = None if is_valid_BST(tree): print("Passed test 1 (emtpy tree).") else: print("Test 1 (empty tree) failed!") # Test 2: Only root node tree = newNode(1) if is_valid_BST(tree): print("Passed test 2 (only root node).") else: print("Test 2 (only root node) failed!") # Test 3: Valid BST tree = newNode(2) tree.left = newNode(1) tree.right = newNode(3) tree.left.left = newNode(0) tree.right.left = newNode(2) tree.right.right = newNode(9) if is_valid_BST(tree): print("Passed test 3 (valid tree).") else: print("Test 3 (valid tree) failed!") # Test 4: Non-valid BST tree = newNode(2) tree.left = newNode(1) tree.right = newNode(3) tree.left.left = newNode(0) tree.right.left = newNode(1) tree.right.right = newNode(9) if not is_valid_BST(tree): print("Passed test 4 (non-valid tree).") else: print("Test 4 (non-valid tree) failed!") if __name__ == '__main__': main()

[ "test@test.com" ]

test@test.com

c394f35d81a2eb6ac4c455dd44b7add384a8b18b

de24f83a5e3768a2638ebcf13cbe717e75740168

/moodledata/vpl_data/84/usersdata/203/57032/submittedfiles/lista1.py

4fe9d85ddd31f1891fa346e0bba2e39623b993ce

[]

no_license

rafaelperazzo/programacao-web

95643423a35c44613b0f64bed05bd34780fe2436

170dd5440afb9ee68a973f3de13a99aa4c735d79

refs/heads/master

2021-01-12T14:06:25.773146

2017-12-22T16:05:45

69,566,344

0

null

UTF-8

Python

false

360

py

# -*- coding: utf-8 -*- n=int(input('tamanho da lista: ')) l=[] somai=0 qi=0 somap=0 qp=0 for i in range (1,n+1,1): a=int(input('elemento da lista: ')) l.append(a) for i in range (1,len(lista),1): if l(i)%2==0: somap=somap+l(i) qp=qp+1 else: somai=somai+l(i) qi=qi+1 print(somai) print(somap) print(qi) print(qp)

[ "rafael.mota@ufca.edu.br" ]

rafael.mota@ufca.edu.br

9166a5025b83503317fc99cf5620f56acadc063c

35fb652b0b20e7352cacdc078e23464fad40ccf3

/web/controllers/food/food.py

ed79027f6fa2230bee2cb9150725d18254385a43

[]

no_license

xiaoheng14/flask_wx_order

52f8fe01a473855c22a43c2651b102c291dbde04

be3314fdb0266eecf4ca7f5a55b2ea24078857c9

refs/heads/master

2020-08-23T03:59:19.006943

2018-11-19T12:21:25

null

0

null

UTF-8

Python

false

615

py

# _*_ coding: utf-8 _*_ """ __author__ = 'lawtech' __date__ = '2018/10/27 3:14 PM' """ from flask import Blueprint from common.libs.helper import ops_render route_food = Blueprint('food_page', __name__) @route_food.route("/index") def index(): return ops_render("food/index.html") @route_food.route("/info") def info(): return ops_render("food/info.html") @route_food.route("/set") def set(): return ops_render("food/set.html") @route_food.route("/cat") def cat(): return ops_render("food/cat.html") @route_food.route("/cat-set") def catSet(): return ops_render("food/cat_set.html")

[ "584563542@qq.com" ]

584563542@qq.com

52fa7f6ab35d271fd30dbc1f96ddcee4a2df32b5

e74c2e5b85b9af58a6f9b4b6eea160fb66f6bb08

/aula11.py

a14bb8b989d099d4f7350a32cb0c4b75eb76c49b

[]

no_license

Nokutomi/AulaPython

670cc27986aa3a12e528f5d1602929a524b632fc

1e97e4821b12a0ad0a4438d682c1e4d61a10f61d

refs/heads/master

2022-11-15T08:38:47.401055

2020-07-08T02:49:54

275,640,239

0

null

UTF-8

Python

false

657

py

lista = [1,10] arquivo = open('teste.txt', 'r') try: texto = arquivo.read() divisao = 10 / 0 # numero = lista[3] # x = a # print('Fechando arquivo') # arquivo.close() except ZeroDivisionError: print('Nao e possivel realizar uma divisao por zero') except ArithmeticError: print('Houve um erro ao realizar uma operacao aritmetica') except IndexError: print('Erro ao acessar um indice invalido da lista') except Exception as ex: print('Erro desconhecido. Erro: {}'.format(ex)) else: print('Executa quando nao ocorre excecao') finally: print('Sempre executa') print('Fechando arquivo') arquivo.close()

[ "you@example.com" ]

you@example.com

a02a2341ab021509e596e6ab801c9b00af24f937

988385035443e5d46d29d96b15179509fd1c782e

/addToArrayForm.py

ea09a01733d9a2d3d3b61c25a1837f7b7368545e

[]

no_license

mwoitek/leetcode-python3

c120ee1b1eb8e17f3a301026f25c643be9852953

eb9989d3768eba82275a57243c99796e74ccdd48

refs/heads/master

2022-12-28T21:19:51.215210

2020-10-18T06:17:27

301,295,054

0

null

UTF-8

Python

false

257

py

class Solution: def addToArrayForm(self, A: List[int], K: int) -> List[int]: A_str = "".join([str(num) for num in A]) A_int = int(A_str) ans = A_int + K ans_list = [int(char) for char in str(ans)] return ans_list

[ "woitek@usp.br" ]

woitek@usp.br

7091c8bb4d092cb28c4a6f0d1fe1a329abcb2805

40b20d7e5f4381a64bd264a562c4ae6d6721b01c

/14-it-generator/sentence_gen.py

a17c48f6811da8c5180ec412bacbf4618080cabf

[ "MIT" ]

permissive

KyrieCham/example-code

7d2f0d5901bf80b49dd6b1e9ae1c37c9cb6df7f5

3dd11744d1c0b1f00860e985ee2a0761e73ef7e7

refs/heads/master

2020-04-18T00:56:06.384756

2019-01-22T19:27:43

167,098,245

1

0

MIT

2019-01-23T01:52:48

2019-01-23T01:52:47

null

UTF-8

Python

false

446

py

""" Sentence: iterate over words using a generator function """ import re import reprlib RE_WORD = re.compile('\w+') class Sentence: def __init__(self, text): self.text = text self.words = RE_WORD.findall(text) def __repr__(self): return 'Sentence(%s)' % reprlib.repr(self.text) def __iter__(self): for word in self.words: # <1> yield word # <2> return # <3> # done! <4>

[ "luciano@ramalho.org" ]

luciano@ramalho.org

c14cca36fd70f17c4adf7cf1050a549b485a5112

dd44e145ac547209f5f209bc9b1f09189bb8b5c7

/Python-OOP-July2021/04.Classes_and_objects-E/05.To-do-list/project/section.py

391f64c88e1e7f0db3acc9df9b8d20c2de06a156

[]

no_license

todorovventsi/Software-Engineering

e3c1be8f0f72c85619518bb914d2a4dbaac270f8

64ffa6c80b190e7c6f340aaf219986f769f175ab

refs/heads/master

2023-07-09T05:35:14.522958

2021-08-15T14:35:55

336,056,643

2

1

null

UTF-8

Python

false

1,049

py

class Section: def __init__(self, name): self.name = name self.tasks = [] def add_task(self, new_task): if new_task not in self.tasks: self.tasks.append(new_task) return f"Task {new_task.details()} is added to the section" return f"Task is already in the section {self.name}" def complete_task(self, task_name): for task in self.tasks: if task.name == task_name: task.completed = True return f"Completed task {task.name}" return f"Could not find task with the name {task_name}" def clean_section(self): completed_tasks = 0 for task in self.tasks: if task.completed: completed_tasks += 1 self.tasks.remove(task) return f"Cleared {completed_tasks} tasks." def view_section(self): first_row = f"Section {self.name}:\n" next_rows = [f"{task.details()}\n" for task in self.tasks] return f"{first_row}{''.join(next_rows)}"

[ "todorov.ventsi@gmail.com" ]

todorov.ventsi@gmail.com

9999bf5d93fa20451f61973a2e0ae14307aded8d

4b1cf07275a8f2abf30943b975d443485ef897ff

/data_generator.py

3805e30c71100e78de5cec92ba0c561a77bb426d

[ "MIT" ]

permissive

gipsyblues/edge_ml_emotion_recognition

a0e1e0acc98d11f710542218b2603f72a8a93a4b

028e9a9264e7df5c48a047677b48f0c15e059e6c

refs/heads/master

2023-06-27T02:53:18.707806

2021-07-28T06:48:30

null

0

null

UTF-8

Python

false

4,108

py

import numpy as np import cv2 import os import imgaug as ia import logging from imgaug import augmenters as iaa from imgaug.augmentables.segmaps import SegmentationMapsOnImage from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage def _create_augment_pipeline(): sometimes = lambda aug: iaa.Sometimes(0.1, aug) aug_pipe = iaa.Sequential( [ iaa.Fliplr(0.5), #iaa.Flipud(0.2), iaa.Affine(translate_percent={"x": (-0.1, 0.1), "y": (-0.1, 0.1)}), iaa.OneOf([iaa.Affine(scale=(0.8, 1.2)), iaa.Affine(rotate=(-10, 10)), iaa.Affine(shear=(-10, 10))]), sometimes(iaa.OneOf([ iaa.GaussianBlur((0, 3.0)), iaa.AverageBlur(k=(2, 7)), iaa.MedianBlur(k=(3, 11)), ])), sometimes(iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5))), sometimes(iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5)), sometimes(iaa.OneOf([ iaa.Dropout((0.01, 0.1), per_channel=0.5), iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2), ])), sometimes(iaa.Add((-10, 10), per_channel=0.5)), sometimes(iaa.Multiply((0.5, 1.5), per_channel=0.5)), sometimes(iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5)) ], random_order=True ) return aug_pipe def process_image_classification(image, desired_w = None, desired_h = None, aug_pipe = None): # resize the image to standard size if (desired_w and desired_h) or aug_pipe: if (desired_w and desired_h): # Rescale image image = ia.imresize_single_image(image, (desired_w, desired_h)) if aug_pipe: image = aug_pipe(image=image) return image class DataGenerator(): def __init__(self, X_train, y_train, batch_size=32, img_size = 48, prefix='appa-real/imgs/', shuffle=True, augment=None): self.X_train = X_train self.y_train = y_train self.batch_size = batch_size self.img_size = img_size self.prefix = prefix self.class_num = y_train.shape[1] self.shuffle = shuffle self.sample_num = len(X_train) self.augment = augment if self.augment: logging.info("Using augmentation for {self.prefix}") self.aug_pipe = _create_augment_pipeline() def __call__(self): while True: indexes = self.__get_exploration_order() itr_num = int(len(indexes) // (self.batch_size * 2)) for i in range(itr_num): batch_ids = indexes[i * self.batch_size * 2:(i + 1) * self.batch_size * 2] X, y = self.__data_generation(batch_ids) yield X, y def __get_exploration_order(self): indexes = np.arange(self.sample_num) if self.shuffle: np.random.shuffle(indexes) return indexes def __data_generation(self, batch_ids): X = np.zeros(shape=(self.batch_size, self.img_size, self.img_size, 3)) y = np.zeros(shape=(self.batch_size, self.class_num)) for i in range(self.batch_size): img = cv2.imread(self.prefix + self.X_train[batch_ids[i]], 1) try: if self.augment: img = process_image_classification(img, self.img_size, self.img_size, self.aug_pipe) except Exception as e: print(self.prefix + self.X_train[batch_ids[i]], e) img = img.astype(np.float32) img /= 255. img -= 0.5 img *= 2. img = img[:, :, ::-1] X[i, ::] = img y[i, :] = self.y_train[batch_ids[i]] return np.array(X), y

[ "dmitrywat@gmail.com" ]

dmitrywat@gmail.com

ca9547928ab7a957dabd169f16fc201dc6d06efe

b83ff584bfcd9fce7a337ba1253287fc9afd03c7

/cmdline_fluency_countdown.py

c6564c0a2aa5dcf88e15805c147edba2570aebac

[]

no_license

houstonhunt/fluencycountdown

6166eaf625f6e348213dcd5be8045ee218159900

d555b83972e05d09e1caafca61ea465c4ca3770c

refs/heads/master

2021-01-23T23:12:17.392090

2015-05-23T18:48:24

null

0

null

UTF-8

Python

false

1,720

py

#!/usr/bin/python # cmdline_fluency_countdown.py import pickle # used to save user progress (currently supporting 1 primary user) import ConfigParser, os # used to parse language file def init(): state = 0 try: pickle.load(open("save.p", "rb")) print "SUCCESS: loaded save file!" state = 1 except: config = ConfigParser.ConfigParser() config.read('lang.cfg') print "WELCOME: no save file found!" print "Type a [language] you want to learn (example: English)," print " or [list] then press [ENTER]" selected_lang = raw_input() # joke if selected_lang == "English": print "You already know English!" quit() elif selected_lang == "list": list(selected_lang, config) elif selected_language == def list(what, cp): if what == "list": print "list what? [all] [easy] [medium] [hard] [other] [about]" selected_lang = raw_input() if selected_lang == "all": list1(cp) list2(cp) list3(cp) listo(cp) elif selected_lang == "easy": list1(cp) elif selected_lang == "medium": list2(cp) elif selected_lang == "hard": list3(cp) elif selected_lang == "other": listo(cp) elif selected_lang == "about": print "Coded by Houston Hunt" print "Times to mastering a language for English speakers" print "is given by " + str(cp.get('Reference', 'reference')) def list1(cp): print cp.get('Languages', 'desc1') print str(cp.get('Languages', 'cat1')) def list2(cp): print str(cp.get('Languages', 'desc2')) print str(cp.get('Languages', 'cat2')) def list3(cp): print str(cp.get('Languages', 'desc3')) print str(cp.get('Languages', 'cat3')) def listo(cp): print str(cp.get('Languages', 'desco')) print str(cp.get('Languages', 'other')) init()

[ "root@localhost.localdomain" ]

root@localhost.localdomain

09db4be45d5d63793dcd85353daabc9d84d3ac5d

08ca7028e0488c420fff8c831e9d4fd3e32ee292

/models/wideresnet.py

59ba6496518eab9bc92f85bceb9a2459910e4762

[]

no_license

yogeshbalaji/Adversarial-training

0ee53fdbef2742788cbbc73ca592738347076fe2

3593c836f39c1313545fcc71e5ba8afa6f427326

refs/heads/master

2020-07-15T03:00:26.425582

2019-09-04T19:59:51

205,464,494

12

4

null

UTF-8

Python

false

3,517

py

from collections import OrderedDict import torch from torch import nn import torch.nn.functional as F from utils import data_normalize def init_weight(*args): return nn.Parameter(nn.init.kaiming_normal_(torch.zeros(*args), mode='fan_out', nonlinearity='relu')) class Block(nn.Module): """ Pre-activated ResNet block. """ def __init__(self, width): super().__init__() self.bn0 = nn.BatchNorm2d(width, affine=False) self.register_parameter('conv0', init_weight(width, width, 3, 3)) self.bn1 = nn.BatchNorm2d(width, affine=False) self.register_parameter('conv1', init_weight(width, width, 3, 3)) def forward(self, x): h = F.conv2d(F.relu(self.bn0(x)), self.conv0, padding=1) h = F.conv2d(F.relu(self.bn1(h)), self.conv1, padding=1) return x + h class DownsampleBlock(nn.Module): """ Downsample block. Does F.avg_pool2d + torch.cat instead of strided conv. """ def __init__(self, width): super().__init__() self.bn0 = nn.BatchNorm2d(width // 2, affine=False) self.register_parameter('conv0', init_weight(width, width // 2, 3, 3)) self.bn1 = nn.BatchNorm2d(width, affine=False) self.register_parameter('conv1', init_weight(width, width, 3, 3)) def forward(self, x): h = F.conv2d(F.relu(self.bn0(x)), self.conv0, padding=1, stride=2) h = F.conv2d(F.relu(self.bn1(h)), self.conv1, padding=1) x_d = F.avg_pool2d(x, kernel_size=3, padding=1, stride=2) x_d = torch.cat([x_d, torch.zeros_like(x_d)], dim=1) return x_d + h class WRN(nn.Module): """ Implementation of modified Wide Residual Network. Differences with pre-activated ResNet and Wide ResNet: * BatchNorm has no affine weight and bias parameters * First layer has 16 * width channels * Last fc layer is removed in favor of 1x1 conv + F.avg_pool2d * Downsample is done by F.avg_pool2d + torch.cat instead of strided conv First and last convolutional layers are kept in float32. """ def __init__(self, depth, width, num_classes): super().__init__() widths = [int(v * width) for v in (16, 32, 64)] n = (depth - 2) // 6 self.register_parameter('conv0', init_weight(widths[0], 3, 3, 3)) self.group0 = self._make_block(widths[0], n) self.group1 = self._make_block(widths[1], n, downsample=True) self.group2 = self._make_block(widths[2], n, downsample=True) self.bn = nn.BatchNorm2d(widths[2], affine=False) self.register_parameter('conv_last', init_weight(num_classes, widths[2], 1, 1)) self.bn_last = nn.BatchNorm2d(num_classes) self.mean = [125.3 / 255.0, 123.0 / 255.0, 113.9 / 255.0] self.std = [63.0 / 255.0, 62.1 / 255.0, 66.7 / 255.0] def _make_block(self, width, n, downsample=False): def select_block(j): if downsample and j == 0: return DownsampleBlock(width) return Block(width) return nn.Sequential(OrderedDict(('block%d' % i, select_block(i)) for i in range(n))) def forward(self, x): x = data_normalize(x, self.mean, self.std) h = F.conv2d(x, self.conv0, padding=1) h = self.group0(h) h = self.group1(h) h = self.group2(h) h = F.relu(self.bn(h)) h = F.conv2d(h, self.conv_last) h = self.bn_last(h) return F.avg_pool2d(h, kernel_size=h.shape[-2:]).view(h.shape[0], -1)

[ "yogesh22@ramawks95.umiacs.umd.edu" ]

yogesh22@ramawks95.umiacs.umd.edu

e34145873aede1b65f5e55265e1505cc6bde3391

387cf5f72ed6679a4d9e04bddd16998a190c4caf

/problems/programmers/lv3/pgs-67258-sweep-slow.py

6a3ef6ae150570c9680bfdc5e53635a2e6635517

[]

no_license

CodyBuilder-dev/Algorithm-Coding-Test

db4ee1e7565fbcef3140192225167eff42ad5c02

cca5c4ba8bc31679ab00aceccfd8d9d39c232f72

refs/heads/master

2021-07-24T00:34:41.888289

2021-07-21T14:29:00

219,123,221

1

0

null

UTF-8

Python

false

1,347

py

""" 제목 :보석 쇼핑 아이디어 : """ def solution(gems): s = set(gems) hash = {} #interval_list = [] # 리스트에 넣고 저장 best_answer = [123456,456789] for i,gem in enumerate(gems): if gem not in hash: hash[gem] = 0 hash[gem] = i if len(hash) ==len(s): temp_answer = [min(hash.values()) + 1, max(hash.values()) + 1] if temp_answer[1] - temp_answer[0] < best_answer[1] - best_answer[0]: best_answer = temp_answer elif temp_answer[1] - temp_answer[0] == best_answer[1] - best_answer[0] \ and temp_answer[0] < best_answer[0]: best_answer = temp_answer return best_answer print(solution(["DIA", "RUBY", "RUBY", "DIA", "DIA", "EMERALD", "SAPPHIRE", "DIA"])) print(solution(["AA", "AB", "AC", "AA", "AC"])) print(solution(["XYZ", "XYZ", "XYZ"])) print(solution(["ZZZ", "YYY", "NNNN", "YYY", "BBB"])) print(solution(["DIA", "EM", "EM", "RUB", "DIA"])) print(solution(["A", "A", "B"])) #5 #10 print(solution(["AD","AA", "AB", "AC", "AA", "AC", "AD", "AB"])) print(solution(["AD","AA", "AB", "AC", "AA", "AC", "AD", "AB", "AZ","AB","AC","AA"])) print(solution(["AD","AA", "AB", "AC", "AA", "AC", "AC", "AD", "AB","AZ","AB","AD","AC","AA","AB","AZ","AA"]))

[ "imspecial1@u.sogang.ac.kr" ]

imspecial1@u.sogang.ac.kr

c64f6276a76c1f9c5a452595cbcd25de501fd7f6

e65a448da4f82d6e7c95cfadc5e8dfd06ed05c62

/cinder/cinder/api/middleware/auth.py

cf898c9b07d780e57e877272a930772dd33360d5

[ "Apache-2.0" ]

permissive

bopopescu/devstack

7a9d11bcc37884f3686e7178ebc25c178a6da283

6b73b164af7e5895501f1ca5dafebbba90510846

refs/heads/master

2022-11-19T19:58:43.536574

2015-01-29T09:00:59

282,101,378

0

null

2020-07-24T02:17:48

2020-07-24T02:17:47

null

UTF-8

Python

false

6,014

py

# Copyright 2010 OpenStack Foundation # 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. """ Common Auth Middleware. """ import os from oslo.config import cfg from oslo.serialization import jsonutils import webob.dec import webob.exc from cinder.api.openstack import wsgi from cinder import context from cinder.i18n import _ from cinder.openstack.common import log as logging from cinder.openstack.common.middleware import request_id from cinder import wsgi as base_wsgi use_forwarded_for_opt = cfg.BoolOpt( 'use_forwarded_for', default=False, help='Treat X-Forwarded-For as the canonical remote address. ' 'Only enable this if you have a sanitizing proxy.') CONF = cfg.CONF CONF.register_opt(use_forwarded_for_opt) LOG = logging.getLogger(__name__) def pipeline_factory(loader, global_conf, **local_conf): """A paste pipeline replica that keys off of auth_strategy.""" pipeline = local_conf[CONF.auth_strategy] if not CONF.api_rate_limit: limit_name = CONF.auth_strategy + '_nolimit' pipeline = local_conf.get(limit_name, pipeline) pipeline = pipeline.split() filters = [loader.get_filter(n) for n in pipeline[:-1]] app = loader.get_app(pipeline[-1]) filters.reverse() for filter in filters: app = filter(app) return app class InjectContext(base_wsgi.Middleware): """Add a 'cinder.context' to WSGI environ.""" def __init__(self, context, *args, **kwargs): self.context = context super(InjectContext, self).__init__(*args, **kwargs) @webob.dec.wsgify(RequestClass=base_wsgi.Request) def __call__(self, req): req.environ['cinder.context'] = self.context return self.application class CinderKeystoneContext(base_wsgi.Middleware): """Make a request context from keystone headers.""" @webob.dec.wsgify(RequestClass=base_wsgi.Request) def __call__(self, req): user_id = req.headers.get('X_USER') user_id = req.headers.get('X_USER_ID', user_id) if user_id is None: LOG.debug("Neither X_USER_ID nor X_USER found in request") return webob.exc.HTTPUnauthorized() # get the roles roles = [r.strip() for r in req.headers.get('X_ROLE', '').split(',')] if 'X_TENANT_ID' in req.headers: # This is the new header since Keystone went to ID/Name project_id = req.headers['X_TENANT_ID'] else: # This is for legacy compatibility project_id = req.headers['X_TENANT'] project_name = req.headers.get('X_TENANT_NAME') req_id = req.environ.get(request_id.ENV_REQUEST_ID) # Get the auth token auth_token = req.headers.get('X_AUTH_TOKEN', req.headers.get('X_STORAGE_TOKEN')) # Build a context, including the auth_token... remote_address = req.remote_addr service_catalog = None if req.headers.get('X_SERVICE_CATALOG') is not None: try: catalog_header = req.headers.get('X_SERVICE_CATALOG') service_catalog = jsonutils.loads(catalog_header) except ValueError: raise webob.exc.HTTPInternalServerError( explanation=_('Invalid service catalog json.')) if CONF.use_forwarded_for: remote_address = req.headers.get('X-Forwarded-For', remote_address) ctx = context.RequestContext(user_id, project_id, project_name=project_name, roles=roles, auth_token=auth_token, remote_address=remote_address, service_catalog=service_catalog, request_id=req_id) req.environ['cinder.context'] = ctx return self.application class NoAuthMiddleware(base_wsgi.Middleware): """Return a fake token if one isn't specified.""" @webob.dec.wsgify(RequestClass=wsgi.Request) def __call__(self, req): if 'X-Auth-Token' not in req.headers: user_id = req.headers.get('X-Auth-User', 'admin') project_id = req.headers.get('X-Auth-Project-Id', 'admin') os_url = os.path.join(req.url, project_id) res = webob.Response() # NOTE(vish): This is expecting and returning Auth(1.1), whereas # keystone uses 2.0 auth. We should probably allow # 2.0 auth here as well. res.headers['X-Auth-Token'] = '%s:%s' % (user_id, project_id) res.headers['X-Server-Management-Url'] = os_url res.content_type = 'text/plain' res.status = '204' return res token = req.headers['X-Auth-Token'] user_id, _sep, project_id = token.partition(':') project_id = project_id or user_id remote_address = getattr(req, 'remote_address', '127.0.0.1') if CONF.use_forwarded_for: remote_address = req.headers.get('X-Forwarded-For', remote_address) ctx = context.RequestContext(user_id, project_id, is_admin=True, remote_address=remote_address) req.environ['cinder.context'] = ctx return self.application

[ "swethapts@gmail.com" ]

swethapts@gmail.com

f6e400373186312a9fcf3e60bc466491e7ced87f

780b6cca690a213ac908b1cd5faef5366a18dc4e

/276_newbie_bite/save1_passed.py

7568b69b77be52f1d12ae46c2c3d5cec4cd7fba1

[]

no_license

katkaypettitt/pybites-all

899180a588e460b343c00529c6a742527e4ea1bc

391c07ecac0d92d5dc7c537bcf92eb6c1fdda896

refs/heads/main

2023-08-22T16:33:11.171732

2021-10-24T17:29:44

null

0

null

UTF-8

Python

false

244

py

# Hint: Don't forget the 4 leading spaces to # indicate your code is within the function. a = 10 b = 5 def multiply_numbers(a, b): return a * b def enter_name(): username = input("What is your name?") return username

[ "70788275+katrinaalaimo@users.noreply.github.com" ]

70788275+katrinaalaimo@users.noreply.github.com

9de698aabcd24e0d8e7b125ea53adbb5167b3d8b

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p02394/s366374910.py

34d6b2cc6782e5002623f9419f9f8a358a2dd94e

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

142

py

w, h, x, y, r = map(int, input().split()) if 0 <= (x-r) and (x+r) <= w and 0 <= (y-r) and (y+r) <= h: print("Yes") else: print("No")

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

e2f80ae63c842ab915e70054164ea7ef16f417b2

15fb62305a2fa0146cc84b289642cc01a8407aab

/Python/119-pascalTriangle2.py

ca82b9b5ce299755fd88d42d79285542b566e463

[]

no_license

geniousisme/leetCode

ec9bc91864cbe7520b085bdab0db67539d3627bd

6e12d67e4ab2d197d588b65c1ddb1f9c52a7e047

refs/heads/master

2016-09-09T23:34:03.522079

2015-09-23T16:15:05

32,052,408

1

0

null

UTF-8

Python

false

413

py

from math import factorial class Solution: # @param {integer} rowIndex # @return {integer[]} def getRow(self, rowIndex): res = [] f = factorial n = rowIndex for k in xrange(rowIndex + 1): res.append(f(n) / f(k) / f(n - k)) return res if __name__ == '__main__': s = Solution() for i in xrange(10): print s.getRow(i)

[ "chia-hao.hsu@aiesec.net" ]

chia-hao.hsu@aiesec.net

790f7806b7f537150ccb4a127bd799627afad0e4

1f8344813458f669bdf77059220290a3b2a3cdd0

/tutorials-docs/thinking-in-coroutines/8_run_in_default_executor.py

81a53d28f3690104d9512aac1b837e073a2f0b81

[]

no_license

gridl/asyncio-study-group

7c03e8640070ebe8d1103f27bc3c3da37a5a661f

1ba9cf90e21b5174518032d467e89526da219576

refs/heads/master

2020-07-02T07:03:12.364097

2017-12-26T20:55:09

null

0

null

UTF-8

Python

false

687

py

import time import datetime import asyncio def blocking_call(seconds): print(seconds, datetime.datetime.now()) time.sleep(seconds) if __name__ == '__main__': loop = asyncio.get_event_loop() loop.call_later(5, loop.stop) for i in range(1,4): #по умолчанию используется concurrent.futures.ThreadPoolExecutor # для этого надо передать executor = None #количество потоков по умолчанию: #number of processors on the machine, multiplied by 5 loop.run_in_executor(None, blocking_call, i) try: loop.run_forever() finally: loop.close()

[ "nataliya.samoylenko@gmail.com" ]

nataliya.samoylenko@gmail.com

8ba3ca416a5d385c1158274f46e71ad3750148eb

e7af30370e277b459e1c49edcc0562d5b5c32abc

/Learning_ScikitLearn/Model/Linear_Classification/LogisticRegression_Classification.py

68bb53cef0d25d1f7959af186211991c7beda251

[]

no_license

justgolikeme/My_MachineLearning

208ab766478662cf36ffa7f9202fed0ad6f0ad28

948a84684a2a6f1c9e613948ed246062468016bd

refs/heads/master

2022-05-13T05:02:48.488269

2020-01-03T07:27:50

null

0

null

UTF-8

Python

false

3,023

py

# -*- coding: utf-8 -*- # @Time : 2019/12/16 15:55 # @Author : Mr.Lin ''' 用于分类的线性模型线性模型也广泛应用于分类问题。我们首先来看二分类。这时可以利用下面的公式进行预测： ŷ = w[0] * x[0] + w[1] * x[1] + …+ w[p] * x[p] + b > 0 这个公式看起来与线性回归的公式非常相似，但我们没有返回特征的加权求和，而是为预测设置了阈值（0）。如果函数值小于 0，我们就预测类别 -1；如果函数值大于 0，我们就预测类别 +1。对于所有用于分类的线性模型，这个预测规则都是通用的。同样，有很多种不同的方法来找出系数（w）和截距（b）。对于用于回归的线性模型，输出 ŷ 是特征的线性函数，是直线、平面或超平面（对于更高维的数据集）。对于用于分类的线性模型，决策边界是输入的线性函数。换句话说，（二元）线性分类器是利用直线、平面或超平面来分开两个类别的分类器。本节我们将看到这方面的例子。学习线性模型有很多种算法。这些算法的区别在于以下两点： • 系数和截距的特定组合对训练数据拟合好坏的度量方法； • 是否使用正则化，以及使用哪种正则化方法。不同的算法使用不同的方法来度量“对训练集拟合好坏”。由于数学上的技术原因，不可能调节 w 和 b 使得算法产生的误分类数量最少。对于我们的目的，以及对于许多应用而言，上面第一点（称为损失函数）的选择并不重要。最常见的两种线性分类算法是 Logistic 回归（logistic regression）和线性支持向量机（linear support vector machine，线性 SVM），前者在 linear_model.LogisticRegression 中实现，后者在 svm.LinearSVC （SVC 代表支持向量分类器）中实现。虽然 LogisticRegression 的名字中含有回归（regression），但它是一种分类算法，并不是回归算法，不应与 LinearRegression 混淆。 ''' from sklearn.cross_validation import cross_val_predict, cross_val_score from sklearn.linear_model import LogisticRegression from Learning_ScikitLearn.Model.Linear_Classification.Data_Source import X_test,X_train,y_train,y_test,data_y,data_X # logreg = LogisticRegression().fit(X_train, y_train) # print("Training set score: {:.3f}".format(logreg.score(X_train, y_train))) # print("Test set score: {:.3f}".format(logreg.score(X_test, y_test))) # Training set score: 0.955 # Test set score: 0.958 # # [0.94827586 0.9137931 0.92982456 0.94736842 0.96491228 0.98245614 # 0.94736842 0.94642857 0.96428571 0.96428571] # print("") # print(cross_val_score(logreg, data_X, data_y, cv=10)) def test_C_Parameter(): C = [0.1,1,10] for c in C: logreg = LogisticRegression(C=c) logreg.fit(X_train,y_train) print("C为：{}下的分数：{}\n".format(c,cross_val_score(logreg, data_X, data_y, cv=10))) test_C_Parameter()

[ "2669093302@qq.com" ]

2669093302@qq.com

7ad47d35b8b6d618120876ea81cee10cd4498f0f

329b48089c64ebefe78d52f1c71c73bdadadd4b4

/ML/m02_3_xor.py

f054586220c6b7e9e2f5ec6da088dfde56b25a5d

[]

no_license

variablejun/keras__R

7f854570952ed97c48715047015786d873e512cb

9faf4814b46cda1ac0ddbf2a2f8236fa0394f144

refs/heads/main

2023-07-13T19:32:25.950500

2021-08-22T18:26:52

398,870,548

1

0

null

UTF-8

Python

false

617

py

from sklearn.svm import LinearSVC import numpy as np from sklearn.metrics import accuracy_score #1 data x_data = [[0,0],[0,1],[1,0],[1,1]] y_data = [0,1,1,0] #2 model model = LinearSVC() #3 fit model.fit(x_data,y_data) #4 평가 y_predict = model.predict(x_data) print(x_data,' 의 예측값 : ',y_predict) results= model.score(x_data, y_data) print('score : ',results) acc = accuracy_score(y_data,y_predict) print('acc : ',acc) ''' [[0, 0], [0, 1], [1, 0], [1, 1]] 의 예측값 : [0 0 0 0] score : 0.5 acc : 0.5 [[0, 0], [0, 1], [1, 0], [1, 1]] 의 예측값 : [1 1 1 1] score : 0.5 acc : 0.5 '''

[ "crescendo0217@gmail.com" ]

crescendo0217@gmail.com

9d79f133ae46df0a2a814949bc56bb9b67709332

92754bb891a128687f3fbc48a312aded752b6bcd

/Algorithms/Python3.x/836-Rectangle_Overlap.py

109710852b3db1879f46f641e56714e64efbeca6

[]

no_license

daidai21/Leetcode

ddecaf0ffbc66604a464c3c9751f35f3abe5e7e5

eb726b3411ed11e2bd00fee02dc41b77f35f2632

refs/heads/master

2023-03-24T21:13:31.128127

2023-03-08T16:11:43

167,968,602

8

3

null

UTF-8

Python

false

611

py

# Runtime: 32 ms, faster than 89.53% of Python3 online submissions for Rectangle Overlap. # Memory Usage: 13.9 MB, less than 8.33% of Python3 online submissions for Rectangle Overlap. class Solution: def isRectangleOverlap(self, rec1: List[int], rec2: List[int]) -> bool: return rec1[0] < rec2[2] and rec2[0] < rec1[2] and rec1[1] < rec2[3] and rec2[1] < rec1[3] """ (left1, right1), (left2, right2) Meet the requirements of the topic Equivalent to : left1 < x < right1 && left2 < x < right2 left1 < x < right2 && left2 < x < right1 left1 < right2 && left2 < right1 """

[ "daidai4269@aliyun.com" ]

daidai4269@aliyun.com

bfb211f64cb26ced576000456975b8ac4e62ba43

dab869acd10a3dc76e2a924e24b6a4dffe0a875f

/Laban/build/bdist.win32/winexe/temp/numpy.core.operand_flag_tests.py

abe53bfc427cda30a4fdef6d870c6ffe58b6c013

[]

no_license

ranBernstein/Laban

d82aff9b0483dd007e03a06e51f7d635f62ed05d

54c88afa9493deacbdd182904cc5d180ecb208b4

refs/heads/master

2021-01-23T13:17:51.777880

2017-02-14T09:02:54

25,508,010

3

1

null

2017-02-14T09:02:55

2014-10-21T07:16:01

Tcl

UTF-8

Python

false

379

py

def __load(): import imp, os, sys try: dirname = os.path.dirname(__loader__.archive) except NameError: dirname = sys.prefix path = os.path.join(dirname, 'numpy.core.operand_flag_tests.pyd') #print "py2exe extension module", __name__, "->", path mod = imp.load_dynamic(__name__, path) ## mod.frozen = 1 __load() del __load

[ "bernstein.ran@gmail.com" ]

bernstein.ran@gmail.com

cde96ba8bed0f8a27d9a27fc09c79f90b37b0093

4781d9293b59a5072647bb179195b143c60621bd

/백준/3190_뱀/3190_뱀.py

466985fd6c7408c5d7d548c56da8c4c1f93da5da

[]

no_license

chriskwon96/Algorithm_codes

bf98131f66ca9c091fe63db68b220527800069c9

edb7b803370e87493dad4a38ee858bb7bb3fd31d

refs/heads/master

2023-08-15T18:48:26.809864

2021-10-12T13:43:21

387,803,476

0

null

UTF-8

Python

false

1,488

py

di = [0, -1, 0, +1] dj = [+1, 0, -1, 0] N = int(input()) matrix = [[0]*N for _ in range(N)] K = int(input()) for _ in range(K): #사과위치 1로 지정 i, j = map(int, input().split()) matrix[i-1][j-1] = 1 L = int(input()) q = [(0,0)] #뱀 몸 X1, k, cnt = 0, 0, 0 flag = 1 for _ in range(L): X, C = input().split() for _ in range(int(X)-X1): head = q[0] cnt += 1 n_x, n_y = head[0]+di[k], head[1]+dj[k] if 0<=n_x<N and 0<=n_y<N and ((n_x, n_y) not in q): #다음칸이 판 안에 있고 내 몸이 아니라면 q.insert(0, (n_x, n_y)) #머리좌표 q에 삽입 if matrix[n_x][n_y]: #사과라면 matrix[n_x][n_y] = 0 #사과 지워주기 else: q.pop() #사과가 아니면 꼬리 줄여주기 else: # 게임이 끝나면 print(cnt) flag = 0 break if not flag: break X1 = int(X) if C == 'L': k = (k+1)%4 else: k = (k-1)%4 if flag: #인풋을 다 받아도 끝나지 않았다면 head = q[0] n_x, n_y = head[0]+di[k], head[1]+dj[k] while 0<=n_x<N and 0<=n_y<N and ((n_x, n_y) not in q): cnt += 1 q.insert(0, (n_x, n_y)) if matrix[n_x][n_y]: #사과라면 matrix[n_x][n_y] = 0 #사과 지워주기 else: q.pop() #사과가 아니면 꼬리 줄여주기 n_x, n_y = n_x + di[k], n_y + dj[k] print(cnt+1)

[ "chriskwon96@naver.com" ]

chriskwon96@naver.com

a7556063e49aff2dda7e2b3cc964e43037048d34

6cb1d8f1416af7b7c5c83ab35cb6928ea9955aff

/ch07/rnnlm_gen.py

a30f1107227f403e0e15f919e3f9b09e39193409

[]

no_license

lee-saint/practice-nlp

f68ccc3140f725f3edcd7048c324b847583b7f20

19003fcd5f55f4f110417a3950a32bb5fba1850c

refs/heads/master

2020-12-01T20:05:15.014495

2020-01-21T09:22:18

230,750,152

0

null

UTF-8

Python

false

2,799

py

import numpy as np from common.functions import softmax from ch06.RNNLM import Rnnlm from ch06.better_rnnlm import BetterRnnlm from dataset import ptb class RnnlmGen(Rnnlm): def generate(self, start_id, skip_ids=None, sample_size=100): word_ids = [start_id] x = start_id while len(word_ids) < sample_size: x = np.array(x).reshape(1, 1) score = self.predict(x) p = softmax(score.flatten()) sampled = np.random.choice(len(p), size=1, p=p) if (skip_ids is None) or (sampled not in skip_ids): x = sampled word_ids.append(int(x)) return word_ids class BetterRnnlmGen(BetterRnnlm): def generate(self, start_id, skip_ids=None, sample_size=100): word_ids = [start_id] x = start_id while len(word_ids) < sample_size: x = np.array(x).reshape(1, 1) score = self.predict(x).flatten() p = softmax(score).flatten() sampled = np.random.choice(len(p), size=1, p=p) if (skip_ids is None) or (sampled not in skip_ids): x = sampled word_ids.append(int(x)) return word_ids if __name__ == '__main__': corpus, word_to_id, id_to_word = ptb.load_data('train') vocab_size = len(word_to_id) corpus_size = len(corpus) model = RnnlmGen() model.load_params('../ch06/Rnnlm.pkl') # 시작(start) 문자와 건너뜀(skip) 문자 설정 start_word = 'you' start_id = word_to_id[start_word] skip_words = ['N', '<unk>', '$'] skip_ids = [word_to_id[w] for w in skip_words] # 문장 생성 word_ids = model.generate(start_id, skip_ids) txt = ' '.join([id_to_word[i] for i in word_ids]) txt = txt.replace(' <eos>', '.\n') print(txt) better_model = BetterRnnlmGen() better_model.load_params('../ch06/BetterRnnlm.pkl') # 시작(start) 문자와 건너뜀(skip) 문자 설정 start_word = 'you' start_id = word_to_id[start_word] skip_words = ['N', '<unk>', '$'] skip_ids = [word_to_id[w] for w in skip_words] # 문장 생성 word_ids = better_model.generate(start_id, skip_ids) txt = ' '.join([id_to_word[i] for i in word_ids]) txt = txt.replace(' <eos>', '.\n') print(txt) better_model.reset_state() model.reset_state() start_words = 'the meaning of life is' start_ids = [word_to_id[w] for w in start_words.split(' ')] for x in start_ids[:-1]: x = np.array(x).reshape(1, 1) model.predict(x) word_ids = model.generate(start_ids[-1], skip_ids) word_ids = start_ids[:-1] + word_ids txt = ' '.join([id_to_word[i] for i in word_ids]) txt = txt.replace(' <eos>', '.\n') print('-' * 50) print(txt)

[ "plutorian131@gmail.com" ]

plutorian131@gmail.com

f45d517a51288fdf1af81238bef427c053fc9fbe

f47863b3a595cbe7ec1c02040e7214481e4f078a

/plugins/scan/libsys/1530.py

7d4393ead3d8ab208722872e6653f54514040048

[]

no_license

gobiggo/0bscan

fe020b8f6f325292bda2b1fec25e3c49a431f373

281cf7c5c2181907e6863adde27bd3977b4a3474

refs/heads/master

2020-04-10T20:33:55.008835

2018-11-17T10:05:41

null

0

null

UTF-8

Python

false

617

py

#!/usr/bin/python #-*- encoding:utf-8 -*- # title:汇文libsys图书管理系统敏感信息泄露 #http://www.wooyun.org/bugs/wooyun-2010-0125785 def assign(service, arg): if service == "libsys": return True, arg def audit(arg): payload = 'include/config.properties' url = arg + payload code, head,res, errcode, _ = curl.curl2(url) if code == 200 and 'host' and 'port' and 'user' and 'password' in res: security_warning(url) if __name__ == '__main__': audit(assign('libsys', 'http://www.njjnlib.cn:8080/')[1]) audit(assign('libsys', 'http://202.201.163.2:8080/')[1])

[ "zer0i3@aliyun.com" ]

zer0i3@aliyun.com

3a0aa4f6f46d50f9055d2000d1b39488f5c19f87

b341a8d120737297aa8fd394a23633dac9b5ccda

/accounts/migrations/0007_auto_20210122_1129.py

c7476e71ff6f0746f30db617c468bd59bbe23d1c

[]

no_license

Minari766/disney_side_stories

16d97cb02bf00aa5439d59f753abb9a4706a30aa

aa2d88b1b0fdd87a27f41318bd3ec7352229b6ff

refs/heads/main

2023-08-15T07:03:16.922579

2021-10-03T07:47:22

306,496,250

0

null

UTF-8

Python

false

445

py

# Generated by Django 2.2 on 2021-01-22 02:29 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('accounts', '0006_auto_20210122_0127'), ] operations = [ migrations.AlterField( model_name='customuser', name='icon', field=models.ImageField(blank=True, null=True, upload_to='images', verbose_name='アイコン'), ), ]

[ "mina3.ryu0728@gmail.com" ]

mina3.ryu0728@gmail.com

436645c364f840999119d1e57184125dbceeca14

1f006f0c7871fcde10986c4f5cec916f545afc9f

/apps/ice/plugins/oxml/oxml_wordNumbering_test.py

9d73299a89601ac0dd3e3d023fcdc93ea3e7a208

[]

no_license

ptsefton/integrated-content-environment

248b8cd29b29e8989ec1a154dd373814742a38c1

c1d6b5a1bea3df4dde10cb582fb0da361dd747bc

refs/heads/master

2021-01-10T04:46:09.319989

2011-05-05T01:42:52

36,273,470

0

null

UTF-8

Python

false

3,070

py

#!/usr/bin/env python # # Copyright (C) 2010 Distance and e-Learning Centre, # University of Southern Queensland # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA # from unittest import TestCase import sys from oxml_wordNumbering import WordNumbering testFile = "testData/numbering.xml" class WordNumberingTest(TestCase): def setUp(self): f = open(testFile, "rb") self.wordNumberingXmlStr = f.read() f.close() def tearDown(self): pass def testGetNumLevelInfo(self): #word/numbering.xml wordNum = WordNumbering(self.wordNumberingXmlStr) numId = "1" level = "0" info = wordNum.getNumLevelInfo(numId, level) expected = {'leftIndent': u'720', 'start': u'1', 'jc': u'left', 'text': u'%1.', 'format': u'decimal'} self.assertEquals(info, expected) def runUnitTests(locals): print "\n\n\n\n" if sys.platform=="cli": import clr import System.Console System.Console.Clear() print "---- Testing under IronPython ----" else: print "---- Testing ----" # Run only the selected tests args = list(sys.argv) sys.argv = sys.argv[:1] args.pop(0) runTests = args runTests = [ i.lower().strip(", ") for i in runTests] runTests = ["test"+i for i in runTests if not i.startswith("test")] + \ [i for i in runTests if i.startswith("test")] if runTests!=[]: testClasses = [i for i in locals.values() \ if hasattr(i, "__bases__") and \ (TestCase in i.__bases__)] testing = [] for x in testClasses: l = dir(x) l = [ i for i in l if i.startswith("test") and callable(getattr(x, i))] for i in l: if i.lower() not in runTests: delattr(x, i) else: testing.append(i) x = None num = len(testing) if num<1: print "No selected tests found! - %s" % str(args)[1:-1] elif num==1: print "Running selected test - %s" % (str(testing)[1:-1]) else: print "Running %s selected tests - %s" % (num, str(testing)[1:-1]) from unittest import main main() if __name__=="__main__": runUnitTests(locals()) sys.exit(0)

[ "raward@gmail.com@110e3293-9ef9-cb8f-f479-66bdb1942d05" ]

raward@gmail.com@110e3293-9ef9-cb8f-f479-66bdb1942d05

9330cd3f6095c574c0fa566a8d69be0fec19b834

a62a87ad976e3d35ea7879671190faf950ebaf3b

/scrapys/t.py

47ae7f7a675a471d9db25b8bb6a431b20fa33406

[]

no_license

YangXiaoo/Django-web

144c8c1800d2a67bf8d1d203210aa351d31e8fb3

97903f309234fd1421a19a52a083f214172d6c79

refs/heads/master

2020-03-24T11:29:20.296017

2019-01-20T14:54:16

142,687,045

0

null

UTF-8

Python

false

3,264

py

# -*- coding: utf-8 -*- import re import urllib2 import pandas as pd #获取原码 def get_content(page): headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36', 'Accept-Language': 'zh-CN,zh;q=0.8'} url ='http://search.51job.com/list/000000,000000,0000,00,9,99,python,2,'+ str(page)+'.html' req = urllib2.Request(url,headers=headers) res = urllib2.urlopen(req) html = res.read() re= unicode(html, "gbk").encode("utf8") return re def get(html): reg = re.compile(r'class="t1 ">.*? href="(.*?)".*? <a target="_blank" title="(.*?)".*? <a target="_blank" title="(.*?)" href="(.*?)".*?(.*?).*?(.*?).*? (.*?)',re.S) items=re.findall(reg,html) return items def info_get(url): headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36', 'Accept-Language': 'zh-CN,zh;q=0.8'} req = urllib2.Request(url,headers=headers) res = urllib2.urlopen(req) html = res.read() html = unicode(html, "gbk").encode("utf8") reg = re.compile(r'(.*?)',re.S) based_info = re.findall(reg,html) reg_p = re.compile(r'(.*?)',re.S) kind = re.findall(reg_p,html) return based_info,kind def address(url): headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36', 'Accept-Language': 'zh-CN,zh;q=0.8'} req = urllib2.Request(url,headers=headers) res = urllib2.urlopen(req) html = res.read() html = unicode(html, "gbk").encode("utf8") reg_a = re.compile(r'<div class="tBorderTop_box bmsg">.*?(.*?)',re.S) address = re.findall(reg_a,html) return address final = [] for j in range(1,2): print("正在爬取第"+str(j)+"页数据...") try: html=get_content(j) for i in get(html): result = {} with open ('51job.txt','a') as f: f.write(i[0]+'\t'+i[1]+'\t'+i[2]+'\t'+i[3]+'\t'+i[4]+'\t'+i[5]+'\t'+i[6]+'\n') f.close() result['info_link'] = i[0] info,kind = info_get(i[0]) count = 1 for n in info: if count == 1: result['experience'] = n[1] count += 1 elif count == 2: result['educational'] = n[1] count += 1 else: break result['work_type'] = kind[0] result['address'] = address result['name'] = i[1] result['company'] = i[2] result['company_link'] = i[3] result['work_place'] = i[4] result['salary'] = i[5] ad = address(i[3]) result['address'] = ad result['publish_time'] = i[6] final.append(result) except: pass df = pd.DataFrame(final) df.to_csv('51job-data_analysis.csv', mode = 'a',encoding = 'utf8')

[ "33798487+YangXiaoo@users.noreply.github.com" ]

33798487+YangXiaoo@users.noreply.github.com

91cf1bbafb30679fda22289ccab052d7605c72e6

503d2f8f5f5f547acb82f7299d86886691966ca5

/typical90/typical90_cf.py

f610d0f1035ed452bc7992ce2b7ed0d6160b139f

[]

no_license

Hironobu-Kawaguchi/atcoder

3fcb649cb920dd837a1ced6713bbb939ecc090a9

df4b55cc7d557bf61607ffde8bda8655cf129017

refs/heads/master

2023-08-21T14:13:13.856604

2023-08-12T14:53:03

197,216,790

0

null

UTF-8

Python

false

937

py

# https://atcoder.jp/contests/typical90/tasks/typical90_cf # # def input(): return sys.stdin.readline().rstrip() # # input = sys.stdin.readline # from numba import njit # from functools import lru_cache # import sys # input = sys.stdin.buffer.readline # sys.setrecursionlimit(10 ** 7) N = int(input()) S = input() ans = 0 last = [-1]*2 for i in range(N): if S[i]=='o': last[0] = i else: last[1] = i if last[0]==-1 or last[1]==-1: continue ans += min(last[0], last[1]) + 1 # print(ans, last) print(ans) # S = input() # n = int(input()) # N, K = map(int, input().split()) # l = list(map(int, (input().split()))) # A = [[int(i) for i in input().split()] for _ in range(N)] # import sys # it = map(int, sys.stdin.buffer.read().split()) # N = next(it) # @njit('(i8,i8[::1],i4[::1])', cache=True) # def main(): # @lru_cache(None) # def dfs(): # return # return # main()

[ "hironobukawaguchi3@gmail.com" ]

hironobukawaguchi3@gmail.com

b9edcccc00c10227f91be8740e4d744c0cea4347

2b8047e9e73a2f6fd43897cff19cb7e7c7c464d4

/docssrc/source/conf.py

5d48fbeb3fa4a5a1f8afc2bbac54d3f8fcfb3638

[ "MIT" ]

permissive

Peilonrayz/envee

548fe08330a3b43bee5da1d64a0e406c781b990e

66f5b6b1ff7f5966be794e1e3878418c560c1f65

refs/heads/master

2021-01-09T13:35:40.946529

2020-02-21T20:58:27

242,321,085

0

null

UTF-8

Python

false

984

py

import datetime import pathlib import sys try: import ConfigParser as configparser except ImportError: import configparser FILE_PATH = pathlib.Path(__file__).absolute() # Add documentation for tests TLD = FILE_PATH.parent.parent.parent sys.path.insert(0, str(TLD)) config = configparser.ConfigParser() config.read(TLD / "setup.cfg") project = "envee" author = "Peilonrayz" copyright = f"{datetime.datetime.now().year}, {author}" release = config.get("src", "version") master_doc = "index" templates_path = ["_templates"] exclude_patterns = [] doctest_global_setup = f""" import {project} """ extensions = [ "sphinx.ext.autodoc", "sphinx.ext.doctest", "sphinx.ext.graphviz", "sphinx.ext.githubpages", "sphinx.ext.intersphinx", "sphinx_autodoc_typehints", "sphinx_rtd_theme", ] intersphinx_mapping = {"python": ("https://docs.python.org/3", None)} html_theme = "sphinx_rtd_theme" html_static_path = ["_static"] set_type_checking_flag = True

[ "peilonrayz@gmail.com" ]

peilonrayz@gmail.com

fbdea07de6f18420b99a57b116c79adf1f0463a1

eac52a8ae7c539acedaedf8744bd8e20172f0af6

/general/decode_ways.py

33c70cc775b271c21d0bb448684acae24e9ffa65

[]

no_license

mshekhar/random-algs

3a0a0f6e6b21f6a59ed5e1970b7a2bc2044e191f

7c9a8455f49027a754038b23aaa2df61fe5397ca

refs/heads/master

2020-03-26T16:29:42.694785

2019-07-18T20:57:55

145,105,593

4

0

null

UTF-8

Python

false

2,895

py

# A message containing letters from A-Z is being encoded to numbers using the following mapping: # # 'A' -> 1 # 'B' -> 2 # ... # 'Z' -> 26 # Given a non-empty string containing only digits, determine the total number of ways to decode it. # # Example 1: # # Input: "12" # Output: 2 # Explanation: It could be decoded as "AB" (1 2) or "L" (12). # Example 2: # # Input: "226" # Output: 3 # Explanation: It could be decoded as "BZ" (2 26), "VF" (22 6), or "BBF" (2 2 6). # if not single_digit: # all_single_possible[c] = False # else: # all_single_possible[c] = all_single_possible[c - 1] and all_single_possible[c] # if c - 1 >= 0 and num_decodings[c - 1] > 0: # num_decodings[c] = num_decodings[c - 1] # # if c - 1 >= 0: # double_digit = self.get_decoding_count(s[c - 1] + i) # if double_digit: # print s[c - 1] + i, double_digit, num_decodings[c - 2] + int(all_single_possible[c - 2]) # if c - 2 >= 0 and num_decodings[c - 2] + int(all_single_possible[c - 2]) > 0: # num_decodings[c] += num_decodings[c - 2] + 1 # elif c == 1: # num_decodings[c] += 1 class Solution(object): def get_decoding_count(self, s): if not s.startswith('0') and 1 <= int(s) <= 26: return 1 return 0 def numDecodings(self, s): """ :type s: str :rtype: int """ num_decodings = [0] * len(s) all_single_possible = True for c, i in enumerate(s): single_digit = self.get_decoding_count(i) double_digit = 0 if c - 1 >= 0: double_digit = self.get_decoding_count(s[c - 1] + i) if not single_digit: all_single_possible = False if single_digit + double_digit > 0: if single_digit: num_decodings[c] = num_decodings[c - 1] if all_single_possible and not num_decodings[c]: num_decodings[c] = 1 if double_digit: if c - 2 >= 0 and num_decodings[c - 2] > 0: num_decodings[c] += num_decodings[c - 2] elif c == 1: num_decodings[c] += 1 # add one for all single decodings # print num_decodings, all_single_possible return num_decodings[-1] print Solution().numDecodings("12"), 2 print Solution().numDecodings("226"), 3 print Solution().numDecodings("10"), 1 print Solution().numDecodings("103"), 1 print Solution().numDecodings("1032"), 1 print Solution().numDecodings("10323"), 1 print Solution().numDecodings("012"), 0 print Solution().numDecodings("110"), 1 print Solution().numDecodings("1212"), 5 # 1 2 1 # 12 1 # 1 21 # # 1 2 1 2 # # 12 1 2 # 12 12 # # 1 21 2 # 1 2 12 # for i in ["0", "10", "10", "103", "1032", "10323"]: # print(Solution().numDecodings(i))

[ "mayank@moengage.com" ]

mayank@moengage.com

07a345dba33878564304037a609dba06de767c0c

36c00fe2afff4818c937e312ce0c6a79f35e2a77

/7-kyu/happy-birthday,-darling!/python/solution.py

ab407ea9bcebd79b2d18c37ed24e86ac2368a137

[]

no_license

p-lots/codewars

0a67b6ee4c91180ff78c648421b9d2d64463ddc3

535faeee475c6b398124d6f5002b0e111406e8bb

refs/heads/master

2023-08-23T22:14:33.635011

2023-08-23T13:30:37

195,320,309

0

null

2023-05-09T19:25:50

2019-07-05T01:40:15

Python

UTF-8

Python

false

164

py

def womens_age(n): base = n // 2 if n % 2 == 0 else (n - 1) // 2 new_n = 20 if n % 2 == 0 else 21 return f"{n}? That's just {new_n}, in base {base}!"

[ "paul.calotta@gmail.com" ]

paul.calotta@gmail.com

d0c7805015d0990484841901a310a10805e00cf6

39be02fe4f8e8362a7acc005f3e30dd6fe47990e

/newdata/oylereader.py

5ebdae4fcc852f8c821d74ed40ee95c9b06e915b

[]

no_license

seferlab/geneexpress

e2f6fdaa49e40cd48d0572cd9ddb5d2f45566adb

ac35bde5ba52d24981ece74e532f46bbfff9019e

refs/heads/master

2022-12-19T08:33:16.925160

2020-09-29T13:51:30

299,619,752

0

null

UTF-8

Python

false

826

py

import os import sys import math fname1 = "127 LCM time course Data Not normalized.txt" fname2 = "127 LCM time course Quantile Normalized logbased 2 transformed.txt" with open(fname1,"r") as infile: for line in infile: line = line.rstrip() vals = line.split("\r") splitted = vals[1].split("\t") items1 = [float(splitted[tind]) for tind in xrange(1,len(splitted))] with open(fname2,"r") as infile: for line in infile: line = line.rstrip() vals = line.split("\r") splitted = vals[1].split("\t") items2 = [float(splitted[tind]) for tind in xrange(1,len(splitted))] print items1[0:20] print [math.log(titem,2.0) for titem in items1[0:10]] print [math.log(titem+1.0,2.0) for titem in items1[0:10]] print items2[0:20] print items1[8:20]

[ "70752445+seferlab@users.noreply.github.com" ]

70752445+seferlab@users.noreply.github.com

f0b3e6949b78c44d35bdedc65dcdd7d848eae7f3

54f352a242a8ad6ff5516703e91da61e08d9a9e6

/Source Codes/CodeJamData/10/33/17.py

b549582d467c3879831e6f099d36ecf18d3abe31

[]

no_license

Kawser-nerd/CLCDSA

5cbd8a4c3f65173e4e8e0d7ed845574c4770c3eb

aee32551795763b54acb26856ab239370cac4e75

refs/heads/master

2022-02-09T11:08:56.588303

2022-01-26T18:53:40

211,783,197

23

9

null

UTF-8

Python

false

1,617

py

from itertools import count def board(rows): n = len(rows[0]) * 4 return [map(int, '{0:0{1}b}'.format(int(row, 16), n)) for row in rows] def squares(board): m, n = len(board), len(board[0]) #sq = {} for r in xrange(m): for c in xrange(n): if board[r][c] == 2: continue ns = findsquare(board, r, c) yield ns, -r, -c #fill(board, r, c, ns) #sq[ns] = sq.get(ns, 0) + 1 #return sq def solve(board): result = {} m, n = len(board), len(board[0]) while 1: try: n, r, c = max(squares(board)) except ValueError: return result result[n] = result.get(n, 0) + 1 fill(board, -r, -c, n) def fill(board, r, c, n): for i in xrange(r, r+n): for j in xrange(c, c+n): board[i][j] = 2 def findsquare(board, r, c): x = board[r][c] try: for s in count(1): for j in range(c, c+s+1): x = 1 - x if board[r+s][j] != x: return s for i in range(r+s-1, r-1, -1): x = 1 - x if board[i][c+s] != x: return s except IndexError: return s if __name__ == '__main__': import sys rl = iter(sys.stdin).next for case in range(1, int(rl())+1): M,N = map(int, rl().split()) lines = [rl().strip() for _ in range(M)] b = board(lines) sq = solve(b) print 'Case #%d: %s' % (case, len(sq)) for k, v in sorted(sq.items(), reverse=True): print k,v

[ "kwnafi@yahoo.com" ]

kwnafi@yahoo.com

238eb7c3a48a487377b765829fcb5eee86416ff5

24cf311c53c29e4e332cea01ee4de8196253a7b7

/accounts/urls.py

ca8992d712669175ee1ef3193b0ea2d6ab348261

[]

no_license

apengok/vsicravdoa

d017fe0c6a8606ef7bb74739354de1a2767b2a8a

e424b94007731189c2f14513798f2a9e9a45ba4c

refs/heads/master

2020-03-10T23:07:48.145583

2018-06-01T09:18:25

129,634,250

1

0

null

UTF-8

Python

false

777

py

from django.conf.urls import url from .views import ( AccountHomeView, # AccountEmailActivateView, UserDetailUpdateView ) app_name = 'account' urlpatterns = [ url(r'^$', AccountHomeView.as_view(), name='home'), url(r'^details/$', UserDetailUpdateView.as_view(), name='user-update'), # url(r'history/products/$', UserProductHistoryView.as_view(), name='user-product-history'), # url(r'^email/confirm/(?P<key>[0-9A-Za-z]+)/$', # AccountEmailActivateView.as_view(), # name='email-activate'), # url(r'^email/resend-activation/$', # AccountEmailActivateView.as_view(), # name='resend-activation'), ] # account/email/confirm/asdfads/ -> activation view

[ "apengok@163.com" ]

apengok@163.com

a20abcac99856f482d5e3f7ec4d5c5c93878dacd

98f505e8275ed888818d8d6f77d27a9c275b55d8

/face.py

a6d86359d258eda63f01fe71ba8a00892e28e706

[]

no_license

EHwooKim/telegram

13ac0afbd4ee5f91aa81b557183e9d8143fb1315

034ae64fa6283720fd55362b1b763cb3497ce4fc

refs/heads/master

2022-12-11T19:53:23.942523

2019-07-12T07:41:29

196,533,974

0

null

2022-12-08T05:52:25

2019-07-12T07:48:30

Python

UTF-8

Python

false

959

py

import pprint import requests from decouple import config # 0. 이미지 파일 file_url = 'https://api.telegram.org/file/bot823224197:AAFwM03Ie4P8dBH45aKI75sMO0okZpcIqic/photos/file_2.jpg' response = requests.get(file_url, stream=True) image = response.raw.read() # 1. 네이버 API 설정 naver_client_id = config('NAVER_CLIENT_ID') naver_client_secret = config('NAVER_CLIENT_SECRET') # 2. URL 설정 naver_url = 'https://openapi.naver.com/v1/vision/celebrity' # 3. 요청 보내기! POST headers = { 'X-Naver-Client-Id': naver_client_id, 'X-Naver-Client-Secret': naver_client_secret } response = requests.post(naver_url, headers=headers, files={'image':image}).json() best = response.get('faces')[0].get('celebrity') if best.get('confidence') > 0.2: text = f"{best.get('confidence')*100}%만큼 {best.get('value')}를 닮으셨네요~" else : text = '사람이 아닙니다' print(text)

[ "ehwoo0707@naver.com" ]

ehwoo0707@naver.com

52860b1da6917fcd830a4b178bd3d28e8c60bf70

99dfd25f07b748e0b9b04ac300e135dc20570e1c

/cart/urls.py

1731b6a31f6bbea06b4fcbb367549265a3127dd2

[]

no_license

suipingooi/tgc10-django-deploy-checkpointPAUL

1ec45e7135263703ff3472216f8fdcfdb379d7f3

46b62cdce8396c2b0cc57ec1fca4e77c0eee1e1a

refs/heads/master

2023-04-16T05:11:20.535480

2021-04-14T12:02:43

null

0

null

UTF-8

Python

false

398

py

from django.urls import path import cart.views urlpatterns = [ path('add/<book_id>', cart.views.add_to_cart, name="add_to_cart"), path('', cart.views.view_cart, name='view_cart'), path('remove/<book_id>', cart.views.remove_from_cart, name="remove_from_cart"), path('update_quantity/<book_id>', cart.views.update_quantity, name="update_cart_quantity") ]

[ "chorkunxin@yahoo.com" ]

chorkunxin@yahoo.com

2ca6a41f705f6ef795834db9d2bcbec1c4e7da99

9d0195aa83cc594a8c61f334b90375961e62d4fe

/JTTest/SL7/CMSSW_10_2_15/src/miniAODJobs600toInf/nano4.py

1366df4f33cc6ad0c152e7cd8e25ea82efda4cf6

[]

no_license

rsk146/CMS

4e49592fc64f6438051544c5de18598db36ed985

5f8dab8c59ae556598b9747b52b88205fffc4dbe

refs/heads/master

2022-12-01T03:57:12.126113

2020-08-04T03:29:27

284,863,383

0

null

UTF-8

Python

false

4,363

py

# Auto generated configuration file # using: # Revision: 1.19 # Source: /local/reps/CMSSW/CMSSW/Configuration/Applications/python/ConfigBuilder.py,v # with command line options: nanoAOD_jetToolbox_cff -s NANO --mc --eventcontent NANOAODSIM --datatier NANOAODSIM --no_exec --conditions 102X_upgrade2018_realistic_v19 --era Run2_2018,run2_nanoAOD_102Xv1 --customise_commands=process.add_(cms.Service('InitRootHandlers', EnableIMT = cms.untracked.bool(False))) --customise JMEAnalysis/JetToolbox/nanoAOD_jetToolbox_cff.nanoJTB_customizeMC --filein file:cms/xaastorage/MINIAOD/Xaa_photons/diPhoton/x500_a100/XaaNLOttQED0_BBAR_M-x500_a100_MINIAODSIM_1.root --fileout file:jetToolbox_nano_mc.root import FWCore.ParameterSet.Config as cms import files50 from files50 import * from Configuration.StandardSequences.Eras import eras process = cms.Process('NANO',eras.Run2_2018,eras.run2_nanoAOD_102Xv1) # import of standard configurations process.load('Configuration.StandardSequences.Services_cff') process.load('SimGeneral.HepPDTESSource.pythiapdt_cfi') process.load('FWCore.MessageService.MessageLogger_cfi') process.load('Configuration.EventContent.EventContent_cff') process.load('SimGeneral.MixingModule.mixNoPU_cfi') process.load('Configuration.StandardSequences.GeometryRecoDB_cff') process.load('Configuration.StandardSequences.MagneticField_cff') process.load('PhysicsTools.NanoAOD.nano_cff') process.load('Configuration.StandardSequences.EndOfProcess_cff') process.load('Configuration.StandardSequences.FrontierConditions_GlobalTag_cff') process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) ) # Input source process.source = cms.Source("PoolSource", # fileNames = cms.untracked.vstring('file:/cms/xaastorage/MINIAOD/2016/GJets/HT_100to200/GJet_100to200_1.root '), fileNames = cms.untracked.vstring(A), secondaryFileNames = cms.untracked.vstring() ) process.options = cms.untracked.PSet( ) # Production Info process.configurationMetadata = cms.untracked.PSet( annotation = cms.untracked.string('nanoAOD_jetToolbox_cff nevts:1'), name = cms.untracked.string('Applications'), version = cms.untracked.string('$Revision: 1.19 $') ) # Output definition process.NANOAODSIMoutput = cms.OutputModule("NanoAODOutputModule", compressionAlgorithm = cms.untracked.string('LZMA'), compressionLevel = cms.untracked.int32(9), dataset = cms.untracked.PSet( dataTier = cms.untracked.string('NANOAODSIM'), filterName = cms.untracked.string('') ), fileName = cms.untracked.string('file:jetToolbox_nano_mc_2018GJetsHT600toInf_50.root'), outputCommands = process.NANOAODSIMEventContent.outputCommands ) # Additional output definition # Other statements from Configuration.AlCa.GlobalTag import GlobalTag process.GlobalTag = GlobalTag(process.GlobalTag, '102X_upgrade2018_realistic_v19', '') # Path and EndPath definitions process.nanoAOD_step = cms.Path(process.nanoSequenceMC) process.endjob_step = cms.EndPath(process.endOfProcess) process.NANOAODSIMoutput_step = cms.EndPath(process.NANOAODSIMoutput) # Schedule definition process.schedule = cms.Schedule(process.nanoAOD_step,process.endjob_step,process.NANOAODSIMoutput_step) from PhysicsTools.PatAlgos.tools.helpers import associatePatAlgosToolsTask associatePatAlgosToolsTask(process) # customisation of the process. # Automatic addition of the customisation function from PhysicsTools.NanoAOD.nano_cff from PhysicsTools.NanoAOD.nano_cff import nanoAOD_customizeMC #call to customisation function nanoAOD_customizeMC imported from PhysicsTools.NanoAOD.nano_cff process = nanoAOD_customizeMC(process) # Automatic addition of the customisation function from JMEAnalysis.JetToolbox.nanoAOD_jetToolbox_cff from JMEAnalysis.JetToolbox.nanoAOD_jetToolbox_cff import nanoJTB_customizeMC #call to customisation function nanoJTB_customizeMC imported from JMEAnalysis.JetToolbox.nanoAOD_jetToolbox_cff process = nanoJTB_customizeMC(process) # End of customisation functions # Customisation from command line process.add_(cms.Service('InitRootHandlers', EnableIMT = cms.untracked.bool(False))) # Add early deletion of temporary data products to reduce peak memory need from Configuration.StandardSequences.earlyDeleteSettings_cff import customiseEarlyDelete process = customiseEarlyDelete(process) # End adding early deletion

[ "rsk146@scarletmail.rutgers.edu" ]

rsk146@scarletmail.rutgers.edu

fc521136d37bde83bf4b77d4966c06e6653d750b

3f6c16ea158a8fb4318b8f069156f1c8d5cff576

/.PyCharm2019.1/system/python_stubs/-1317042838/pandas/_libs/ops.py

a84d13f1d42b972f9af8b614b27048d38673cdb9

[]

no_license

sarthak-patidar/dotfiles

08494170d2c0fedc0bbe719cc7c60263ce6fd095

b62cd46f3491fd3f50c704f0255730af682d1f80

refs/heads/master

2020-06-28T23:42:17.236273

2019-10-01T13:56:27

200,369,900

0

null

2019-08-03T12:56:33

2019-08-03T11:53:29

Shell

UTF-8

Python

false

2,829

py

# encoding: utf-8 # module pandas._libs.ops # from /var/www/newsbytes/CPP/venv/lib/python3.6/site-packages/pandas/_libs/ops.cpython-36m-x86_64-linux-gnu.so # by generator 1.147 # no doc # imports import builtins as __builtins__ # <module 'builtins' (built-in)> import operator as operator # /usr/lib/python3.6/operator.py import numpy as np # /var/www/newsbytes/CPP/venv/lib/python3.6/site-packages/numpy/__init__.py # functions def maybe_convert_bool(*args, **kwargs): # real signature unknown pass def scalar_binop(*args, **kwargs): # real signature unknown """ Apply the given binary operator `op` between each element of the array `values` and the scalar `val`. Parameters ---------- values : ndarray[object] val : object op : binary operator Returns ------- result : ndarray[object] """ pass def scalar_compare(*args, **kwargs): # real signature unknown """ Compare each element of `values` array with the scalar `val`, with the comparison operation described by `op`. Parameters ---------- values : ndarray[object] val : object op : {operator.eq, operator.ne, operator.le, operator.lt, operator.ge, operator.gt} Returns ------- result : ndarray[bool] """ pass def vec_binop(*args, **kwargs): # real signature unknown """ Apply the given binary operator `op` pointwise to the elements of arrays `left` and `right`. Parameters ---------- left : ndarray[object] right : ndarray[object] op : binary operator Returns ------- result : ndarray[object] """ pass def vec_compare(*args, **kwargs): # real signature unknown """ Compare the elements of `left` with the elements of `right` pointwise, with the comparison operation described by `op`. Parameters ---------- left : ndarray[object] right : ndarray[object] op : {operator.eq, operator.ne, operator.le, operator.lt, operator.ge, operator.gt} Returns ------- result : ndarray[bool] """ pass def __pyx_unpickle_Enum(*args, **kwargs): # real signature unknown pass # no classes # variables with complex values __loader__ = None # (!) real value is '<_frozen_importlib_external.ExtensionFileLoader object at 0x7f43e7e6f8d0>' __spec__ = None # (!) real value is "ModuleSpec(name='pandas._libs.ops', loader=<_frozen_importlib_external.ExtensionFileLoader object at 0x7f43e7e6f8d0>, origin='/var/www/newsbytes/CPP/venv/lib/python3.6/site-packages/pandas/_libs/ops.cpython-36m-x86_64-linux-gnu.so')" __test__ = {}

[ "sarthakpatidar15@gmail.com" ]

sarthakpatidar15@gmail.com

ce3333447ac28a3d89c0757d6ada515e638e5bd2

8410bb5a2e8849bb3a554b95ddc713d88f3440c4

/aws-dev/awsdev9/venv/Lib/site-packages/dns/rdtypes/ANY/SOA.py

aec81cad8ac916e9bc71052ecbc4983cdabbd126

[ "MIT" ]

permissive

PacktPublishing/-AWS-Certified-Developer---Associate-Certification

ae99b6c1efb30e8fab5b76e3d8c821823a4cd852

b9838b4e038b42ad1813a296379cbbc40cab6286

refs/heads/master

2022-11-03T04:37:49.014335

2022-10-31T05:42:19

219,964,717

13

11

MIT

2021-06-02T00:57:45

2019-11-06T09:54:09

Python

UTF-8

Python

false

4,597

py

# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license # Copyright (C) 2003-2007, 2009-2011 Nominum, Inc. # # Permission to use, copy, modify, and distribute this software and its # documentation 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 NOMINUM DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM 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. import struct import dns.exception import dns.rdata import dns.name class SOA(dns.rdata.Rdata): """SOA record @ivar mname: the SOA MNAME (master name) field @type mname: dns.name.Name object @ivar rname: the SOA RNAME (responsible name) field @type rname: dns.name.Name object @ivar serial: The zone's serial number @type serial: int @ivar refresh: The zone's refresh value (in seconds) @type refresh: int @ivar retry: The zone's retry value (in seconds) @type retry: int @ivar expire: The zone's expiration value (in seconds) @type expire: int @ivar minimum: The zone's negative caching time (in seconds, called "minimum" for historical reasons) @type minimum: int @see: RFC 1035""" __slots__ = ['mname', 'rname', 'serial', 'refresh', 'retry', 'expire', 'minimum'] def __init__(self, rdclass, rdtype, mname, rname, serial, refresh, retry, expire, minimum): super(SOA, self).__init__(rdclass, rdtype) self.mname = mname self.rname = rname self.serial = serial self.refresh = refresh self.retry = retry self.expire = expire self.minimum = minimum def to_text(self, origin=None, relativize=True, **kw): mname = self.mname.choose_relativity(origin, relativize) rname = self.rname.choose_relativity(origin, relativize) return '%s %s %d %d %d %d %d' % ( mname, rname, self.serial, self.refresh, self.retry, self.expire, self.minimum) @classmethod def from_text(cls, rdclass, rdtype, tok, origin=None, relativize=True): mname = tok.get_name() rname = tok.get_name() mname = mname.choose_relativity(origin, relativize) rname = rname.choose_relativity(origin, relativize) serial = tok.get_uint32() refresh = tok.get_ttl() retry = tok.get_ttl() expire = tok.get_ttl() minimum = tok.get_ttl() tok.get_eol() return cls(rdclass, rdtype, mname, rname, serial, refresh, retry, expire, minimum) def to_wire(self, file, compress=None, origin=None): self.mname.to_wire(file, compress, origin) self.rname.to_wire(file, compress, origin) five_ints = struct.pack('!IIIII', self.serial, self.refresh, self.retry, self.expire, self.minimum) file.write(five_ints) def to_digestable(self, origin=None): return self.mname.to_digestable(origin) + \ self.rname.to_digestable(origin) + \ struct.pack('!IIIII', self.serial, self.refresh, self.retry, self.expire, self.minimum) @classmethod def from_wire(cls, rdclass, rdtype, wire, current, rdlen, origin=None): (mname, cused) = dns.name.from_wire(wire[: current + rdlen], current) current += cused rdlen -= cused (rname, cused) = dns.name.from_wire(wire[: current + rdlen], current) current += cused rdlen -= cused if rdlen != 20: raise dns.exception.FormError five_ints = struct.unpack('!IIIII', wire[current: current + rdlen]) if origin is not None: mname = mname.relativize(origin) rname = rname.relativize(origin) return cls(rdclass, rdtype, mname, rname, five_ints[0], five_ints[1], five_ints[2], five_ints[3], five_ints[4]) def choose_relativity(self, origin=None, relativize=True): self.mname = self.mname.choose_relativity(origin, relativize) self.rname = self.rname.choose_relativity(origin, relativize)

[ "sonalis@packtpub.com" ]

sonalis@packtpub.com

d8adcfa0328f753994b60200ace6ca4d145e0f23

3d5bcd57b893c95bbcbfafe77bbc33c65432c9ed

/Algorithms/LeetCode/L0079exist.py

c6486ef5bca5b6781c64631e90da4eed40b18976

[]

no_license

arunachalamev/PythonProgramming

c160f34c7cb90e82cd0d4762ff9dcb4abadf9c1c

ea188aaa1b72511aeb769a2829055d0aae55e73e

refs/heads/master

2021-06-04T03:50:37.976293

2020-11-12T19:52:28

97,364,002

3

0

null

UTF-8

Python

false

702

py

def exist(board, word): m,n = len(board), len(board[0]) def search(i,j,word): nonlocal m,n if len(word) == 0: return True if i<0 or i==m or j <0 or j==n or board[i][j] !=word[0]: return False board[i][j] = '#' for di,dj in [(0,1),(0,-1),(1,0),(-1,0)]: if search (i+di, j+dj , word[1:]): return True board[i][j] = word[0] return False for i,row in enumerate(board): for j,_ in enumerate(row): if search(i,j,word): return True return False print (exist([ ['A','B','C','E'], ['S','F','C','S'], ['A','D','E','E'] ], 'ABCCEDX'))

[ "arunachalamev@gmail.com" ]

arunachalamev@gmail.com

0bf7dd56ef9f8d3dc81a166b9e292152ff8911ac

2aba3c043ce4ef934adce0f65bd589268ec443c5

/AOJ/courses/ITP1/3_C.py

221621fc02dd16be341b7f831191bed733e02394

[]

no_license

kambehmw/algorithm_python

4f66593b77039d90515d1fcbecacdab8c811b92f

17222399dcc92fd8f908e5774a9883e2e89c486e

refs/heads/master

2020-06-02T12:44:11.322356

2020-05-18T13:22:05

191,157,113

0

null

UTF-8

Python

false

179

py

while True: x, y = map(int, input().split()) if x == 0 and y == 0: exit() else: if x < y: print(x, y) else: print(y, x)

[ "kanbe.hmw@gmail.com" ]

kanbe.hmw@gmail.com

8af8b1154126237b12e676c20db0981a5f9e3d8e

8a14a7724d00f1eb7791e53f8446e99ecc975605

/scripts/extract_features.py

95649f83351e38ae3501cff705bf80339edd1315

[ "Apache-2.0" ]

permissive

aschn/picolo

3fa7b26d079fc9687de9c3e1e34cae774bcf8416

1f8f50e0709fdaef31bc38045ef9fd0c46aae2b5

refs/heads/master

2020-04-30T01:37:36.587287

2013-07-19T00:32:05

9,307,233

1

0

null

UTF-8

Python

false

2,229

py

#!/usr/bin/env python # -*- coding: utf-8 -*- """ @author Anna Schneider @version 0.1 @brief Typical script using picolo to extract features from point particles """ import picolo from shapes import shape_factory_from_values import argparse import os.path as path import time import csv # start timer start = time.time() # parse command-line arguments brief = 'Typical script using picolo to extract features from point particles.' parser = argparse.ArgumentParser(description=brief) parser.add_argument('filename', type=str, help='path to xy coord file') parser.add_argument('shape', type=str, help='type of features to extract', choices=['UnitCell', 'Fourier', 'Zernike']) parser.add_argument('dist', type=float, help='distance cutoff to neighbors') parser.add_argument('--train', action='store_true', help='include flag to only get features for prespecified training rows') args = parser.parse_args() # set up file paths rootname, ext = path.splitext(args.filename) dirname = path.dirname(args.filename) # set up matcher matcher = picolo.Matcher(args.filename, delim=' ', name=rootname, trainingcol=2) # create and add default shape of correct type shape = shape_factory_from_values(args.shape, optdata={'neighbor_dist': args.dist, 'max_dist': args.dist}) matcher.shapes.add('test', shape) # get ndarray of features and particle ids by comparing to 'test' shape features = matcher.feature_matrix('test') # open csv writer outfile = '%s_%s_features.dat' % (rootname, args.shape) writer = csv.writer(open(outfile, 'w'), delimiter=' ') # write header writer.writerow(['id'] + shape.get_components()) # loop over particle ids if args.train: inds = matcher.training_ids else: inds = range(matcher.config.N) for ip in inds: # only write features for particles with valid shapes if matcher.get_features('test', ip).get('is_valid'): # write row of features writer.writerow([ip] + ['%0.4f' % x for x in features[ip]]) # end timer end = time.time() print 'Done with %s ... took %d seconds.' % (rootname, end-start)

[ "annarschneider@gmail.com" ]

annarschneider@gmail.com

1eadf13b44ed3ecced195ac1f6974c5866be1f8b

37efda4646f478b66674e384e1bc139e7874d972

/practice/RaodtoMillionaire.py

7677b54444573abaec9ffa4c8c2fa22f69a24b2b

[]

no_license

siberian122/kyoupuro

02c1c40f7c09ff0c07a1d50b727f860ad269d8b1

8bf5e5b354d82f44f54c80f1fc014c9519de3ca4

refs/heads/master

2023-04-04T02:45:29.445107

2021-04-20T07:37:47

299,248,378

1

0

null

UTF-8

Python

false

380

py

n = int(input()) a = list(map(int, input().split())) b = [] for i in range(n-1): num = a[i]-a[i+1] b.append(num) now = 1000 stock = 0 for i in range(n-1): if b[i] > 0: # 売る now += stock*a[i] stock = 0 elif now > 0 and b[i] < 0: # 買う stock += now//a[i] now = now % a[i] #print(now, stock) now += a[-1]*stock print(now)

[ "siberian1000@gmail.com" ]

siberian1000@gmail.com

c96667e76a4d649fc180fffd2ee6abb688e027cb

d4fdbd68c42d6b9babe347cb3b65535e4d782172

/tensorflow_datasets/image/voc_test.py

1bbb9140e84808b1f66441b6ba103c2e8483ec03

[ "Apache-2.0" ]

permissive

thanhkaist/datasets

2809260c5e95e96d136059bea042d1ed969a6fcf

02da35c558ec8ea704e744a2008c5cecb2e7a0a1

refs/heads/master

2020-06-04T16:13:14.603449

2019-06-14T22:01:33

2019-06-14T22:02:54

192,097,735

2

0

Apache-2.0

2019-06-15T16:02:18

null

UTF-8

Python

false

1,060

py

# coding=utf-8 # Copyright 2019 The TensorFlow Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for PASCAL VOC image data loading.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_datasets import testing from tensorflow_datasets.image import voc class Voc2007Test(testing.DatasetBuilderTestCase): DATASET_CLASS = voc.Voc2007 SPLITS = { 'train': 1, 'validation': 2, 'test': 3, } if __name__ == '__main__': testing.test_main()

[ "copybara-worker@google.com" ]

copybara-worker@google.com

2fc48de98fbc2450366953e3be1285d20c36401a

ac8ffabf4d7339c5466e53dafc3f7e87697f08eb

/python_solutions/1080.insufficient-nodes-in-root-to-leaf-paths.py

4ba1ede95bb6688d9b4c3e860ddfe8e1d3dd646d

[]

no_license

h4hany/leetcode

4cbf23ea7c5b5ecfd26aef61bfc109741f881591

9e4f6f1a2830bd9aab1bba374c98f0464825d435

refs/heads/master

2023-01-09T17:39:06.212421

2020-11-12T07:26:39

null

0

null

UTF-8

Python

false

2,173

py

from collections import Counter, defaultdict, OrderedDict, deque from bisect import bisect_left, bisect_right from functools import reduce, lru_cache from typing import List import itertools import math import heapq import string true = True false = False MIN, MAX, MOD = -0x3f3f3f3f, 0x3f3f3f3f, 1000000007 # # @lc app=leetcode id=1080 lang=python3 # # [1080] Insufficient Nodes in Root to Leaf Paths # # https://leetcode.com/problems/insufficient-nodes-in-root-to-leaf-paths/description/ # # algorithms # Medium (49.43%) # Total Accepted: 14.4K # Total Submissions: 29K # Testcase Example: '[1,2,3,4,-99,-99,7,8,9,-99,-99,12,13,-99,14]\n1' # # Given the root of a binary tree, consider all root to leaf paths: paths from # the root to any leaf. (A leaf is a node with no children.) # # A node is insufficient if every such root to leaf path intersecting this node # has sum strictly less than limit. # # Delete all insufficient nodes simultaneously, and return the root of the # resulting binary tree. # # # # Example 1: # # # # Input: root = [1,2,3,4,-99,-99,7,8,9,-99,-99,12,13,-99,14], limit = 1 # # Output: [1,2,3,4,null,null,7,8,9,null,14] # # # # Example 2: # # # # Input: root = [5,4,8,11,null,17,4,7,1,null,null,5,3], limit = 22 # # Output: [5,4,8,11,null,17,4,7,null,null,null,5] # # # # Example 3: # # # # Input: root = [1,2,-3,-5,null,4,null], limit = -1 # # Output: [1,null,-3,4] # # # # # Note: # # # The given tree will have between 1 and 5000 nodes. # -10^5 <= node.val <= 10^5 # -10^9 <= limit <= 10^9 # # # # # # # # Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right class Solution: def sufficientSubset(self, root: TreeNode, limit: int) -> TreeNode: if not root: return None if not root.left and not root.right: return root if root.val >= limit else None root.left = self.sufficientSubset(root.left, limit - root.val) root.right = self.sufficientSubset(root.right, limit - root.val) return None if not root.left and not root.right else root

[ "ssruoz@gmail.com" ]

ssruoz@gmail.com

75a63b080058ba26e1aa2ae9b422c95c519a403c

3e93c3bbe35c24bf7f1a75c612ab300f37063621

/C1/L1_18_mappingnameseq_namedtuple.py

f393d21fd2cf887c699056da4973e6a7725476db

[]

no_license

rengokantai/orpycok3ed

5ac0195a48f02dcc5bbc720e812f637464215e8f

50ce744265dc6af0d1a4724ea52348faeb47764d

refs/heads/master

2021-01-10T05:05:53.477092

2016-03-12T20:04:45

53,352,163

0

1

null

UTF-8

Python

false

693

py

__author__ = 'Hernan Y.Ke' from collections import namedtuple # memorize this syntax Me = namedtuple('Me',['first','last']) me = Me(1,2) print(me.first,me.last) she=[Me(3,4),Me(5,6)] #me = Me(first=1,last=2) # illegal! me = me._replace(first=3) print(me.first) # get namedtuple def get_num(tuplearr): res=0 for param in tuplearr: s = Me(*param) # iterate a array with namedtuple instance. param->All params of a instance res+=s.first+s.last return res print(get_num(she)) #replace all params def replace_params(tupleparams): return me._replace(**tupleparams) # two stars. kwargs newparams={'first':7,'last':8} print(replace_params(newparams))

[ "yuriqiao@gmail.com" ]

yuriqiao@gmail.com

ca2e60ef61a63bcc4473f3bb4ca159430fb5c13a

eb9f655206c43c12b497c667ba56a0d358b6bc3a

/python/testData/intentions/PyAnnotateTypesIntentionTest/methodAfterConstructorCall.py

0cdc87e27827504a3baf5a3c8d4524a6604e3e8c

[ "Apache-2.0" ]

permissive

JetBrains/intellij-community

2ed226e200ecc17c037dcddd4a006de56cd43941

05dbd4575d01a213f3f4d69aa4968473f2536142

refs/heads/master

2023-09-03T17:06:37.560889

2023-09-03T11:51:00

2023-09-03T12:12:27

2,489,216

16,288

6,635

Apache-2.0

2023-09-12T07:41:58

2011-09-30T13:33:05

null

UTF-8

Python

false

133

py

class MyClass: def __init__(self): pass def method(self, x): pass x = MyClass() foo = x.met<caret>hod(42)

[ "mikhail.golubev@jetbrains.com" ]

mikhail.golubev@jetbrains.com

55c2841b5ae6ddfc0e8c0cb6f34e33306f5fca3a

09e57dd1374713f06b70d7b37a580130d9bbab0d

/data/cirq_new/cirq_program/startCirq_pragma58.py

8e84b65dd9e10c0774f2965011964ccb0cbd933f

[ "BSD-3-Clause" ]

permissive

UCLA-SEAL/QDiff

ad53650034897abb5941e74539e3aee8edb600ab

d968cbc47fe926b7f88b4adf10490f1edd6f8819

refs/heads/main

2023-08-05T04:52:24.961998

2021-09-19T02:56:16

405,159,939

2

0

null

UTF-8

Python

false

2,364

py

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 5/15/20 4:49 PM # @File : grover.py # qubit number=4 # total number=11 import cirq import cirq.google as cg from typing import Optional import sys from math import log2 import numpy as np class Opty(cirq.PointOptimizer): def optimization_at( self, circuit: 'cirq.Circuit', index: int, op: 'cirq.Operation' ) -> Optional[cirq.PointOptimizationSummary]: if (isinstance(op, cirq.ops.GateOperation) and isinstance(op.gate, cirq.CZPowGate)): return cirq.PointOptimizationSummary( clear_span=1, clear_qubits=op.qubits, new_operations=[ cirq.CZ(*op.qubits), cirq.X.on_each(*op.qubits), cirq.X.on_each(*op.qubits), ] ) #thatsNoCode def make_circuit(n: int, input_qubit): c = cirq.Circuit() # circuit begin c.append(cirq.H.on(input_qubit[0])) # number=1 c.append(cirq.H.on(input_qubit[1])) # number=2 c.append(cirq.rx(1.6147786239451536).on(input_qubit[3])) # number=5 c.append(cirq.H.on(input_qubit[2])) # number=3 c.append(cirq.H.on(input_qubit[3])) # number=4 c.append(cirq.H.on(input_qubit[0])) # number=8 c.append(cirq.CZ.on(input_qubit[1],input_qubit[0])) # number=9 c.append(cirq.H.on(input_qubit[0])) # number=10 c.append(cirq.CNOT.on(input_qubit[1],input_qubit[0])) # number=7 # circuit end c.append(cirq.measure(*input_qubit, key='result')) return c def bitstring(bits): return ''.join(str(int(b)) for b in bits) if __name__ == '__main__': qubit_count = 4 input_qubits = [cirq.GridQubit(i, 0) for i in range(qubit_count)] circuit = make_circuit(qubit_count,input_qubits) circuit = cg.optimized_for_sycamore(circuit, optimizer_type='sqrt_iswap') circuit_sample_count =2000 simulator = cirq.Simulator() result = simulator.run(circuit, repetitions=circuit_sample_count) frequencies = result.histogram(key='result', fold_func=bitstring) writefile = open("../data/startCirq_pragma58.csv","w+") print(format(frequencies),file=writefile) print("results end", file=writefile) print(circuit.__len__(), file=writefile) print(circuit,file=writefile) writefile.close()

[ "wangjiyuan123@yeah.net" ]

wangjiyuan123@yeah.net

8dd7a8369a2f7b352443bc0d36d23dd32bcc554e

bf576b059cbecb0cbb8a6c885dcfded5bd685399

/4.Python course/3.Expand course/1.Small windmill/Small windmill.py

18c56da878b1cb6a7ef0d38234ce809b1bea040f

[]

no_license

YahboomTechnology/Superbit-expansion-board

0d3c2fd06c5df9280d230af429931af2c48dc6d5

4df7e03426d486d2b2f8f649359eee2d62851083

refs/heads/master

2023-04-07T03:16:15.786669

2023-03-29T01:12:57

206,778,307

13

8

null

UTF-8

Python

false

426

py

from microbit import * import superbit a = 135 display.show(Image.HEART) superbit.servo270(superbit.S1, 135) superbit.motor_control(superbit.M1, 255, 0) while True: if button_a.is_pressed(): a = a - 1 if a < 0: a = 0 superbit.servo270(superbit.S1, a) elif button_b.is_pressed(): a = a + 1 if a > 270: a = 270 superbit.servo270(superbit.S1, a)

[ "2448532184@qq.com" ]

2448532184@qq.com

0ab091f1bac3f6b3782abb3cf2f34ba686b858fc

6dcd5f4bb4c39e2d887e5d557e188ba4c8a75081

/src/UsersDB.py

3d3f2264fceef218c5169ec87a6f6ca4b65d695f

[]

no_license

Pella86/HappyRateBot

815653033593aedc22c779025d00bddec4614f46

f23f786a3c9dc19f2378958470d82974d018bd64

refs/heads/master

2020-03-22T00:16:38.670215

2018-07-22T11:50:53

139,234,809

1

null

2018-07-22T06:41:21

2018-06-30T09:01:21

Python

UTF-8

Python

false

4,288

py

# -*- coding: utf-8 -*- """ Created on Wed Jun 20 12:10:14 2018 @author: Mauro """ #============================================================================== # Imports #============================================================================== # py imports import os import hashlib import string # my imports import Databases import UserProfile import random import Logging #============================================================================== # logging #============================================================================== # create logger log = Logging.get_logger(__name__, "WARNING") #============================================================================== # Helpers #============================================================================== def get_hash_id(personid): pid = hashlib.sha256() pid.update(bytes(personid)) return pid.digest() #============================================================================== # User database #============================================================================== class UsersDB: def __init__(self): self.folder = "./databases/user_db" if not os.path.isdir(self.folder): os.mkdir(self.folder) self.database = Databases.Database(self.folder, "user_") self.database.loadDB() self.database.update_uid() log.info("loaded users database") folder = "./databases/banned_user_db" if not os.path.isdir(folder): os.mkdir(folder) self.banned_database = Databases.Database(folder, "banned_user_") def getUsersList(self): return self.database.getValues() def check_nickname(self, user, text): error_message = None alphanumeric = string.ascii_letters + string.digits if len(text) < 3: error_message = "too short" elif len(text) >= 15: error_message = "too long" elif not all(c in alphanumeric for c in text): error_message = "invalid character" elif text in [u.display_id for u in self.database.getValues()]: error_message = "already present" if error_message is None: user.display_id = text self.database[user.hash_id].setData(user) return True else: return error_message def banUser(self, user): duser = self.database[user.hash_id] self.deleteUser(user) def addUser(self, person, chatid): # hash the id hash_id = get_hash_id(person.id) if self.database.isNew(hash_id): log.info("added new user to database: {}".format(self.database.short_uid)) # create a unique display id start_number = 0x10000000 stop_number = 0xFFFFFFFF display_id = random.randint(start_number,stop_number) log.debug("display id {}".format(display_id)) # check for uniqueness display_id_list = [user.display_id for user in self.database.getValues()] while display_id in display_id_list: display_id = random.randint(start_number,stop_number) log.debug("new display id {}".format(display_id)) # language lang_tag = person.language_code if person.language_code else "en" # user instance user = UserProfile.UserProfile(hash_id, display_id, chatid, lang_tag) data = Databases.Data(hash_id, user) self.database.addData(data) def deleteUser(self, user): data = self.database[user.hash_id] self.database.deleteItem(data) def hGetUser(self, hash_id): return self.database[hash_id].getData() def getUser(self, person): log.debug("User already in database, got user") hash_id = get_hash_id(person.id) return self.database[hash_id].getData() def setUser(self, user): self.database[user.hash_id].setData(user) def update(self): log.info("updating database...") self.database.updateDB()

[ "pigmeo127@gmail.com" ]

pigmeo127@gmail.com

9980f2825f02826d27018b266928c8e25ef4e7d6

978248bf0f275ae688f194593aa32c267832b2b6

/xlsxwriter/test/comparison/test_autofilter06.py

354f84b2a2f1d14959c2854587b7e266fc15c235

[ "BSD-2-Clause-Views" ]

permissive

satish1337/XlsxWriter

b0c216b91be1b74d6cac017a152023aa1d581de2

0ab9bdded4f750246c41a439f6a6cecaf9179030

refs/heads/master

2021-01-22T02:35:13.158752

2015-03-31T20:32:28

33,300,989

1

0

null

UTF-8

Python

false

2,859

py

############################################################################### # # Tests for XlsxWriter. # # Copyright (c), 2013-2015, John McNamara, jmcnamara@cpan.org # from ..excel_comparsion_test import ExcelComparisonTest from ...workbook import Workbook class TestCompareXLSXFiles(ExcelComparisonTest): """ Test file created by XlsxWriter against a file created by Excel. """ def setUp(self): self.maxDiff = None filename = 'autofilter06.xlsx' test_dir = 'xlsxwriter/test/comparison/' self.got_filename = test_dir + '_test_' + filename self.exp_filename = test_dir + 'xlsx_files/' + filename self.txt_filename = test_dir + 'xlsx_files/' + 'autofilter_data.txt' self.ignore_files = [] self.ignore_elements = {} def test_create_file(self): """ Test the creation of a simple XlsxWriter file with an autofilter. This test corresponds to the following examples/autofilter.pl example: Example 6. Autofilter with filter for non-blanks. """ workbook = Workbook(self.got_filename) worksheet = workbook.add_worksheet() # Set the autofilter. worksheet.autofilter('A1:D51') # Add filter criteria. worksheet.filter_column(0, 'x == NonBlanks') # Open a text file with autofilter example data. textfile = open(self.txt_filename) # Read the headers from the first line of the input file. headers = textfile.readline().strip("\n").split() # Write out the headers. worksheet.write_row('A1', headers) # Start writing data after the headers. row = 1 # Read the rest of the text file and write it to the worksheet. for line in textfile: # Split the input data based on whitespace. data = line.strip("\n").split() # Convert the number data from the text file. for i, item in enumerate(data): try: data[i] = float(item) except ValueError: pass # Simulate a blank cell in the data. if row == 6: data[0] = '' # Get some of the field data. region = data[0] # Check for rows that match the filter. if region != '': # Row matches the filter, no further action required. pass else: # We need to hide rows that don't match the filter. worksheet.set_row(row, options={'hidden': True}) # Write out the row data. worksheet.write_row(row, 0, data) # Move on to the next worksheet row. row += 1 textfile.close() workbook.close() self.assertExcelEqual()

[ "jmcnamara@cpan.org" ]

jmcnamara@cpan.org

2f8c03f052351b799bfba46a92f2566cc993aedd

5181d3b3ef8fe301ea2d6b095260e9d327c2fd79

/scripts/dl/download_hrrr.py

dad9ed84e463252c8a1b7b4fff6d35e96c53d1d1

[]

no_license

danhreitz/iem

88113ef9c9c4a2918c9c2abdfd0510d5ca4ec819

ed490dcd6c2a8359f88cb805ccee8f6707566f57

refs/heads/master

2021-01-18T15:27:28.607250

2015-08-10T21:33:54

null

0

null

UTF-8

Python

false

2,340

py

""" Since the NOAAPort feed of HRRR data does not have radiation, we should download this manually from NCEP Run at 40 AFTER for the previous hour """ import urllib2 import sys import datetime import os def fetch(valid): """ Fetch the radiation data for this timestamp 80:54371554:d=2014101002:ULWRF:top of atmosphere:anl: 81:56146124:d=2014101002:DSWRF:surface:anl: """ uri = valid.strftime(("http://www.ftp.ncep.noaa.gov/data/nccf/" "nonoperational/com/hrrr/prod/hrrr.%Y%m%d/hrrr.t%Hz." "wrfprsf00.grib2.idx")) data = urllib2.urlopen(uri, timeout=30) offsets = [] neednext = False for line in data: tokens = line.split(":") if neednext: offsets[-1].append(int(tokens[1])) neednext = False if tokens[3] in ['ULWRF', 'DSWRF']: offsets.append([int(tokens[1]), ]) neednext = True # Save soil temp and water at surface, 10cm and 40cm if tokens[3] in ['TSOIL', 'SOILW']: if tokens[4] in ['0-0 m below ground', '0.01-0.01 m below ground', '0.04-0.04 m below ground']: offsets.append([int(tokens[1]), ]) neednext = True outfn = valid.strftime(("/mesonet/ARCHIVE/data/%Y/%m/%d/model/hrrr/" "%H/hrrr.t%Hz.3kmf00.grib2")) outdir = os.path.dirname(outfn) if not os.path.isdir(outdir): os.makedirs(outdir, mode=0775) # make sure LDM can then write to dir output = open(outfn, 'ab', 0664) req = urllib2.Request(uri[:-4]) if len(offsets) != 8: print("download_hrrr_rad warning, found %s gribs for %s" % ( len(offsets), valid)) for pr in offsets: req.headers['Range'] = 'bytes=%s-%s' % (pr[0], pr[1]) f = urllib2.urlopen(req, timeout=30) output.write(f.read()) output.close() def main(): """ Go Main Go""" ts = datetime.datetime.utcnow() - datetime.timedelta(hours=1) if len(sys.argv) == 5: ts = datetime.datetime(int(sys.argv[1]), int(sys.argv[2]), int(sys.argv[3]), int(sys.argv[4])) fetch(ts) if __name__ == '__main__': os.umask(0002) main()

[ "akrherz@iastate.edu" ]

akrherz@iastate.edu

3b79ecee53bab652825699f9a829541d12808883

53d22468fb1c9e0f4b4710a31fb7ac638549b8a7

/src/episode_stats.py

8c746bbebadc8b8367d5a5f0ae15a6bda7162cea

[ "MIT" ]

permissive

binderwang/drivebot

768bcfe224d94b931c45c41ced2a1b0067c6417d

a8fb86731c52b7594dd135e8759622c29172b557

refs/heads/master

2020-12-14T09:48:59.857490

2016-05-03T03:17:58

58,269,730

1

0

null

2016-05-07T14:33:18

2016-05-07T14:33:17

null

UTF-8

Python

false

261

py

#!/usr/bin/env python import json import sys episode_id = 0 for line in sys.stdin: episode = json.loads(line) rewards = [event['reward'] for event in episode] print "\t".join(map(str, [episode_id, len(episode), sum(rewards)])) episode_id += 1

[ "matthew.kelcey@gmail.com" ]

matthew.kelcey@gmail.com

f6b0c0ebfcfea1688b03ec725be8faebb3cbbbee

2598f255696842f043372dd68fe4d5fd48d1a41c

/Ofelia/expedient/src/python/expedient/clearinghouse/users/views.py

5bd342d561ba8106b5c71655fbdfedc0cbb0a6c3

[ "BSD-3-Clause" ]

permissive

zanetworker/C-BAS

8e5442df83626e95d9562497278869ee3c4fad51

695c6f72490a02bbb308d44526631dbf426ab900

refs/heads/master

2021-01-01T06:55:39.085086

2014-08-11T09:37:42

22,351,372

1

0

null

2014-08-08T16:15:54

2014-07-28T17:28:44

Python

UTF-8

Python

false

7,668

py

''' Created on Dec 3, 2009 @author: jnaous ''' from django.shortcuts import get_object_or_404 from django.http import HttpResponseRedirect, HttpResponseNotAllowed from django.core.urlresolvers import reverse from expedient.clearinghouse import users from django.views.generic import create_update, simple from django.contrib import auth from expedient.common.permissions.shortcuts import must_have_permission,\ give_permission_to from registration import views as registration_views from expedient.clearinghouse.users.forms import FullRegistrationForm from django.conf import settings from django.contrib.auth.models import User from django.contrib.auth.views import password_reset from expedient.clearinghouse.users.forms import LDAPPasswordResetForm def home(request): '''show list of users and form for adding users''' must_have_permission(request.user, User, "can_manage_users") user_list = auth.models.User.objects.all().order_by('username') if request.method == "GET": pwd_form = auth.forms.UserCreationForm() user_form = users.forms.UserForm() userprofile_form = users.forms.UserProfileForm() elif request.method == "POST": pwd_form = auth.forms.UserCreationForm(request.POST) user_form = users.forms.UserForm(request.POST) userprofile_form = users.forms.UserProfileForm(request.POST) # check that all data is valid if pwd_form.is_valid() and user_form.is_valid() and userprofile_form.is_valid(): # create the user first user = pwd_form.save() # use the user to save the user info user_form = users.forms.UserForm(request.POST, instance=user) user = user_form.save() # now store the user profile up = users.models.UserProfile(user=user) userprofile_form = users.forms.UserProfileForm(request.POST, instance=up) userprofile_form.save() return HttpResponseRedirect(reverse("users_saved", args=(user.id,))) else: return HttpResponseNotAllowed("GET", "POST") return simple.direct_to_template( request, template='expedient/clearinghouse/users/home.html', extra_context={ 'user_list': user_list, 'pwd_form': pwd_form, 'user_form': user_form, 'userprofile_form': userprofile_form, 'breadcrumbs': ( ("Home", reverse("home")), ("Manage users", request.path), ) }, ) def detail(request, user_id=None): if user_id == None: user = request.user else: user = get_object_or_404(auth.models.User, pk=user_id) must_have_permission(request.user, user, "can_edit_user") profile = users.models.UserProfile.get_or_create_profile(user) if request.method == "GET": if user_id == None: pwd_form = users.forms.PasswordChangeFormDisabled(user) else: pwd_form = users.forms.AdminPasswordChangeFormDisabled(user) user_form = users.forms.UserForm(instance=user) userprofile_form = users.forms.UserProfileForm(instance=profile) elif request.method == "POST": if request.POST.get("change_pwd", False): data = request.POST else: data = None if user_id == None: pwd_form = users.forms.PasswordChangeFormDisabled(user, data) else: pwd_form = users.forms.AdminPasswordChangeFormDisabled(user, data) user_form = users.forms.UserForm(request.POST, instance=user) userprofile_form = users.forms.UserProfileForm(request.POST, instance=profile) if user_form.is_valid() and userprofile_form.is_valid(): user = user_form.save() userprofile_form = users.forms.UserProfileForm(request.POST, instance=profile) userprofile_form.save() if request.POST.get("change_pwd", False) and pwd_form.is_valid(): pwd_form.save() return HttpResponseRedirect(reverse("users_saved", args=(user.id,))) elif "change_pwd" not in request.POST: return HttpResponseRedirect(reverse("users_saved", args=(user.id,))) else: return HttpResponseNotAllowed("GET", "POST") try: slice_set = user.slice_set.all() except AttributeError: slice_set = () return simple.direct_to_template( request, template='expedient/clearinghouse/users/detail.html', extra_context={ 'curr_user': user, 'slices': slice_set, 'pwd_form': pwd_form, 'user_form': user_form, 'show_owner': True, 'userprofile_form': userprofile_form, 'breadcrumbs': ( ("Home", reverse("home")), ("Account for %s" % user.username, reverse("users_detail", args=[user.id])), ) }, ) def saved(request, user_id): user = get_object_or_404(auth.models.User, pk=user_id) print user.id return simple.direct_to_template( request, template='expedient/clearinghouse/users/saved.html', extra_context={ 'curr_user': user, }, ) def delete(request, user_id): user = get_object_or_404(auth.models.User, pk=user_id) must_have_permission(request.user, user, "can_edit_user") return create_update.delete_object( request, auth.models.User, reverse("users_home"), user_id, template_name="expedient/clearinghouse/users/confirm_delete.html", ) def register(request): try: return registration_views.register( request, form_class=FullRegistrationForm) except Exception as e: print "[ERROR] Exception at 'expedient.clearinghouse.users.views': user '%s' (%s) could not fully register. RegistrationForm module returned: %s" % (request.POST['username'], request.POST['email'], str(e)) return simple.direct_to_template( request, template='registration/registration_incomplete.html', extra_context={ 'exception': e, 'root_email': settings.ROOT_EMAIL, 'failed_username': request.POST['username'], 'failed_email': request.POST['email'], }, ) def activate(request, activation_key): template_name = 'registration/activate.html' activation_key = activation_key.lower() # Normalize before trying anything with it. # Import only here to avoid every time warning 'DeprecationWarning: # the sha module is deprecated; use the hashlib module instead' from registration.models import RegistrationProfile account = RegistrationProfile.objects.activate_user(activation_key) if account: give_permission_to( "can_edit_user", account, account, can_delegate=True) return simple.direct_to_template( request, template=template_name, extra_context={ 'account': account, 'expiration_days': settings.ACCOUNT_ACTIVATION_DAYS, }, ) def my_password_reset(request): if request.method == 'GET': return password_reset(request) else: email = request.POST['email'] users = User.objects.filter(email = email) if len(users) == 1 and users[0].password == '!': return HttpResponseRedirect(settings.OFREG_URL+settings.OFREG_RESET_PATH) else: return password_reset(request, password_reset_form=LDAPPasswordResetForm)

[ "umar.toseef@eict.de" ]

umar.toseef@eict.de

8d637f9712aa8cd0fa725ea3c7b3285cb522f1da

be5a758c99f05c8ae8c224bf43335154114ee5f6

/kombu/compat.py

224f2e33e5d44865c3202047427a7e1c535ba30d

[ "BSD-3-Clause" ]

permissive

bradjasper/kombu

160ed1b5651f91a87752df40791d01c91ca1fe16

4c9ac1436eb0468508f8b2cf1bda997535e1326d

refs/heads/master

2021-01-16T00:23:17.928400

2010-07-28T17:25:32

null

0

null

UTF-8

Python

false

7,431

py

from itertools import count from kombu import entity from kombu import messaging def iterconsume(connection, consumer, no_ack=False, limit=None): consumer.consume(no_ack=no_ack) for iteration in count(0): if limit and iteration >= limit: raise StopIteration yield connection.drain_events() def entry_to_binding(queue, **options): binding_key = options.get("binding_key") or options.get("routing_key") e_durable = options.get("exchange_durable") or options.get("durable") e_auto_delete = options.get("exchange_auto_delete") or \ options.get("auto_delete") q_durable = options.get("queue_durable") or options.get("durable") q_auto_delete = options.get("queue_auto_delete") or \ options.get("auto_delete") e_arguments = options.get("exchange_arguments") q_arguments = options.get("queue_arguments") b_arguments = options.get("binding_arguments") exchange = entity.Exchange(options.get("exchange"), type=options.get("exchange_type"), delivery_mode=options.get("delivery_mode"), routing_key=options.get("routing_key"), durable=e_durable, auto_delete=e_auto_delete, arguments=e_arguments) return entity.Binding(queue, exchange=exchange, routing_key=binding_key, durable=q_durable, exclusive=options.get("exclusive"), auto_delete=q_auto_delete, queue_arguments=q_arguments, binding_arguments=b_arguments) class Publisher(messaging.Producer): exchange = "" exchange_type = "direct" routing_key = "" durable = True auto_delete = False _closed = False def __init__(self, connection, exchange=None, routing_key=None, exchange_type=None, durable=None, auto_delete=None, **kwargs): self.connection = connection self.backend = connection.channel() self.exchange = exchange or self.exchange self.exchange_type = exchange_type or self.exchange_type self.routing_key = routing_key or self.routing_key if auto_delete is not None: self.auto_delete = auto_delete if durable is not None: self.durable = durable if not isinstance(self.exchange, entity.Exchange): self.exchange = entity.Exchange(name=self.exchange, type=self.exchange_type, routing_key=self.routing_key, auto_delete=self.auto_delete, durable=self.durable) super(Publisher, self).__init__(self.backend, self.exchange, **kwargs) def send(self, *args, **kwargs): return self.publish(*args, **kwargs) def close(self): self.backend.close() self._closed = True def __enter__(self): return self def __exit__(self, *exc_info): self.close() class Consumer(messaging.Consumer): queue = "" exchange = "" routing_key = "" exchange_type = "direct" durable = True exclusive = False auto_delete = False exchange_type = "direct" _closed = False def __init__(self, connection, queue=None, exchange=None, routing_key=None, exchange_type=None, durable=None, exclusive=None, auto_delete=None, **kwargs): self.connection = connection self.backend = connection.channel() if durable is not None: self.durable = durable if exclusive is not None: self.exclusive = exclusive if auto_delete is not None: self.auto_delete = auto_delete self.queue = queue or self.queue self.exchange = exchange or self.exchange self.exchange_type = exchange_type or self.exchange_type self.routing_key = routing_key or self.routing_key exchange = entity.Exchange(self.exchange, type=self.exchange_type, routing_key=self.routing_key, auto_delete=self.auto_delete, durable=self.durable) binding = entity.Binding(self.queue, exchange=exchange, routing_key=self.routing_key, durable=self.durable, exclusive=self.exclusive, auto_delete=self.auto_delete) super(Consumer, self).__init__(self.backend, binding, **kwargs) def close(self): self.cancel() self.backend.close() self._closed = True def __enter__(self): return self def __exit__(self, *exc_info): self.close() def __iter__(self): return self.iterqueue(infinite=True) def fetch(self, no_ack=None, enable_callbacks=False): if no_ack is None: no_ack = self.no_ack message = self.bindings[0].get(no_ack) if message: if enable_callbacks: self.receive(message.payload, message) return message def process_next(self): raise NotImplementedError("Use fetch(enable_callbacks=True)") def discard_all(self, filterfunc=None): if filterfunc is not None: raise NotImplementedError( "discard_all does not implement filters") return self.purge() def iterconsume(self, limit=None, no_ack=None): return iterconsume(self.connection, self, no_ack, limit) def wait(self, limit=None): it = self.iterconsume(limit) return list(it) def iterqueue(self, limit=None, infinite=False): for items_since_start in count(): item = self.fetch() if (not infinite and item is None) or \ (limit and items_since_start >= limit): raise StopIteration yield item class _CSet(messaging.Consumer): def __init__(self, connection, *args, **kwargs): self.connection = connection self.backend = connection.channel() super(_CSet, self).__init__(self.backend, *args, **kwargs) def iterconsume(self, limit=None, no_ack=False): return iterconsume(self.connection, self, no_ack, limit) def discard_all(self): return self.purge() def add_consumer_from_dict(self, queue, **options): self.bindings.append(entry_to_binding(queue, **options)) def add_consumer(self, consumer): self.bindings.extend(consumer.bindings) def close(self): self.cancel() self.channel.close() def ConsumerSet(connection, from_dict=None, consumers=None, callbacks=None, **kwargs): bindings = [] if consumers: for consumer in consumers: map(bindings.extend, consumer.bindings) if from_dict: for queue_name, queue_options in from_dict.items(): bindings.append(entry_to_binding(queue_name, **queue_options)) return _CSet(connection, bindings, **kwargs)

[ "askh@opera.com" ]

askh@opera.com

c3af127904d957a29958033e8898da66cbee1238

70ed82598c7ae19dc3de4a3a8400e9767b8a74b0

/Net/BaseNet/ResNet/fine_tuning_2.py

a5c28f115867e33b9eb23304dfaf71d8d7a0216b

[]

no_license

UpCoder/MedicalImage

f255922b988392cd4c3a90715fb945ee20edb3b4

34c11562658e6f362ee7eb53740ba96209a22d45

refs/heads/master

2021-01-19T16:59:13.251726

2017-12-04T14:55:32

101,031,357

0

1

null

UTF-8

Python

false

6,832

py

# -*- coding: utf-8 -*- # 使用ｐａｔｃｈ训练好的模型，来对ＲＯＩ进行微调 from resnet import inference_small, loss import tensorflow as tf from Config import Config as sub_Config from Slice.MaxSlice.MaxSlice_Resize import MaxSlice_Resize from tensorflow.examples.tutorials.mnist import input_data from Tools import changed_shape, calculate_acc_error, acc_binary_acc import numpy as np from Patch.ValData import ValDataSet from Patch.Config import Config as patch_config def train(train_data_set, val_data_set, load_model_path, save_model_path): x = tf.placeholder( tf.float32, shape=[ None, sub_Config.IMAGE_W, sub_Config.IMAGE_H, sub_Config.IMAGE_CHANNEL ], name='input_x' ) y_ = tf.placeholder( tf.float32, shape=[ None, ] ) tf.summary.histogram( 'label', y_ ) global_step = tf.Variable(0, trainable=False) # variable_average = tf.train.ExponentialMovingAverage( # sub_Config.MOVING_AVERAGE_DECAY, # global_step # ) # vaeriable_average_op = variable_average.apply(tf.trainable_variables()) # regularizer = tf.contrib.layers.l2_regularizer(sub_Config.REGULARIZTION_RATE) is_training = tf.placeholder('bool', [], name='is_training') FLAGS = tf.app.flags.FLAGS tf.app.flags.DEFINE_string('data_dir', '/tmp/cifar-data', 'where to store the dataset') tf.app.flags.DEFINE_boolean('use_bn', True, 'use batch normalization. otherwise use biases') y = inference_small(x, is_training=is_training, num_classes=sub_Config.OUTPUT_NODE, use_bias=FLAGS.use_bn, num_blocks=3) tf.summary.histogram( 'logits', tf.argmax(y, 1) ) loss_ = loss( logits=y, labels=tf.cast(y_, np.int32) ) tf.summary.scalar( 'loss', loss_ ) train_op = tf.train.GradientDescentOptimizer( learning_rate=sub_Config.LEARNING_RATE ).minimize( loss=loss_, global_step=global_step ) # with tf.control_dependencies([train_step, vaeriable_average_op]): # train_op = tf.no_op(name='train') with tf.variable_scope('accuracy'): accuracy_tensor = tf.reduce_mean( tf.cast( tf.equal(x=tf.argmax(y, 1), y=tf.cast(y_, tf.int64)), tf.float32 ) ) tf.summary.scalar( 'accuracy', accuracy_tensor ) saver = tf.train.Saver() merge_op = tf.summary.merge_all() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) if load_model_path: saver.restore(sess, load_model_path) writer = tf.summary.FileWriter('./log/fine_tuning/train', tf.get_default_graph()) val_writer = tf.summary.FileWriter('./log/fine_tuning/val', tf.get_default_graph()) for i in range(sub_Config.ITERATOE_NUMBER): images, labels = train_data_set.get_next_batch(sub_Config.BATCH_SIZE, sub_Config.BATCH_DISTRIBUTION) images = changed_shape(images, [ len(images), sub_Config.IMAGE_W, sub_Config.IMAGE_W, sub_Config.IMAGE_CHANNEL ]) _, loss_value, accuracy_value, summary, global_step_value = sess.run( [train_op, loss_, accuracy_tensor, merge_op, global_step], feed_dict={ x: images, y_: labels } ) writer.add_summary( summary=summary, global_step=global_step_value ) if i % 100 == 0 and i != 0 and save_model_path is not None: # 保存模型　五分类每５００步保存一下模型 import os save_path = os.path.join(save_model_path, str(global_step_value)) if not os.path.exists(save_path): os.mkdir(save_path) save_path += '/model.ckpt' print 'mode saved path is ', save_path saver.save(sess, save_path) if i % 100 == 0: validation_images, validation_labels = val_data_set.get_next_batch() validation_images = changed_shape( validation_images, [ len(validation_images), sub_Config.IMAGE_W, sub_Config.IMAGE_W, 1 ] ) validation_accuracy, validation_loss, summary, logits = sess.run( [accuracy_tensor, loss_, merge_op, y], feed_dict={ x: validation_images, y_: validation_labels } ) calculate_acc_error( logits=np.argmax(logits, 1), label=validation_labels, show=True ) binary_acc = acc_binary_acc( logits=np.argmax(logits, 1), label=validation_labels, ) val_writer.add_summary(summary, global_step_value) print 'step is %d,training loss value is %g, accuracy is %g ' \ 'validation loss value is %g, accuracy is %g, binary_acc is %g' % \ (global_step_value, loss_value, accuracy_value, validation_loss, validation_accuracy, binary_acc) writer.close() val_writer.close() if __name__ == '__main__': phase_name = 'ART' state = '' traindatapath = '/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI/train' valdatapath = '/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI/val' val_dataset = ValDataSet(new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H], phase=phase_name, category_number=2, shuffle=True, data_path=valdatapath ) train_dataset = ValDataSet(new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H], phase=phase_name, category_number=2, data_path=traindatapath, shuffle=True, ) train( train_dataset, val_dataset, load_model_path=None, save_model_path='/home/give/PycharmProjects/MedicalImage/Net/BaseNet/ResNet/models/fine_tuning/2-128/' )

[ "546043882@qq.com" ]

546043882@qq.com

efc54871703ecce3f1cb626bd1351abbdff392ef

34ef83114e02b173bd2d55eb53ad399e738a8e3c

/django/test_bootstrap/blog/models.py

b12d7ddd4b0a24411e62b4e99bf00bcafa60e565

[]

no_license

vavilon/Python3

e976a18eb301e4953696d1e3f4730ed890da015a

8c79729747ce51d60ad685e6a2e58292954ed7eb

refs/heads/master

2023-01-09T13:44:37.408601

2018-01-25T22:41:14

100,892,055

0

1

null

2022-12-26T20:29:27

2017-08-20T22:23:06

Python

UTF-8

Python

false

506

py

from django.db import models # Create your models here. from django.utils import timezone class Post(models.Model): author = models.ForeignKey('auth.User') title = models.CharField(max_length=200) text = models.TextField() created_date = models.DateTimeField( default=timezone.now) published_date = models.DateTimeField( blank=True, null=True ) def publish(self): self.published_date = timezone.now() def __str__(self): return self.title

[ "overon4ek@gmail.com" ]

overon4ek@gmail.com

3fac458c8f38d04e4724c1f19199c6c517b324b6

675b72eae65f8e258794decf9627e5fdf8b04559

/plugin_tests/examples_test.py

aa8ae4a0e021a3a57aefdf2dd02021e68f45841a

[ "Apache-2.0" ]

permissive

jbeezley/large_image

368f730ea6fe2c4b75a9c3412c08ce8f41be545a

ac4cbaff4ae2fbbde425d3cd1aee2ff03e6235c8

refs/heads/master

2021-01-11T06:15:48.687563

2016-10-24T17:09:08

71,806,470

0

null

2016-10-24T16:04:04

2016-10-24T16:04:03

null

UTF-8

Python

false

1,695

py

#!/usr/bin/env python # -*- coding: utf-8 -*- ############################################################################## # Copyright Kitware Inc. # # Licensed under the Apache License, Version 2.0 ( the "License" ); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################## import os import subprocess import unittest class LargeImageExamplesTest(unittest.TestCase): def testAverageColor(self): # Test running the program testDir = os.path.dirname(os.path.realpath(__file__)) examplesDir = os.path.join(testDir, '../examples') prog = 'average_color.py' imagePath = os.path.join(os.environ['LARGE_IMAGE_DATA'], 'sample_image.ptif') process = subprocess.Popen( ['python', prog, imagePath, '-m', '1.25'], shell=False, stdout=subprocess.PIPE, cwd=examplesDir) results = process.stdout.readlines() self.assertEqual(len(results), 19) finalColor = [float(val) for val in results[-1].split()[-3:]] self.assertEqual(round(finalColor[0]), 245) self.assertEqual(round(finalColor[1]), 247) self.assertEqual(round(finalColor[2]), 247)

[ "david.manthey@kitware.com" ]

david.manthey@kitware.com

434153e344fd51bbd477726190b6bffce6f42c4d

3de3dae722829727edfdd6cc3b67443a69043475

/edexOsgi/com.raytheon.uf.common.dataplugin.text/pythonPackages/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/subscription/request/SubscriptionRequest.py

237472774c674b7b8fb879656ce996c5d08db82a

[ "LicenseRef-scancode-public-domain", "Apache-2.0" ]

permissive

Unidata/awips2

9aee5b7ec42c2c0a2fa4d877cb7e0b399db74acb

d76c9f96e6bb06f7239c563203f226e6a6fffeef

refs/heads/unidata_18.2.1

2023-08-18T13:00:15.110785

2023-08-09T06:06:06

19,332,079

161

75

NOASSERTION

2023-09-13T19:06:40

2014-05-01T00:59:04

Java

UTF-8

Python

false

1,268

py

## # This software was developed and / or modified by Raytheon Company, # pursuant to Contract DG133W-05-CQ-1067 with the US Government. # # U.S. EXPORT CONTROLLED TECHNICAL DATA # This software product contains export-restricted data whose # export/transfer/disclosure is restricted by U.S. law. Dissemination # to non-U.S. persons whether in the United States or abroad requires # an export license or other authorization. # # Contractor Name: Raytheon Company # Contractor Address: 6825 Pine Street, Suite 340 # Mail Stop B8 # Omaha, NE 68106 # 402.291.0100 # # See the AWIPS II Master Rights File ("Master Rights File.pdf") for # further licensing information. ## # File auto-generated against equivalent DynamicSerialize Java class # # SOFTWARE HISTORY # # Date Ticket# Engineer Description # ------------ ---------- ----------- -------------------------- # Sep 05, 2014 bclement Generated class SubscriptionRequest(object): def __init__(self): self.message = None def getMessage(self): return self.message def setMessage(self, message): self.message = message

[ "mjames@unidata.ucar.edu" ]

mjames@unidata.ucar.edu

d485028798e1c737f0af507daf3b21f679ec03ae

b55c368efdfe360123be1a2e7677cee53706d1f9

/VectorTrans/Main.py

7f33d97819742d7ae327669e60bb979628d2c4fb

[ "MIT" ]

permissive

ZzhiWang/DRImplicitVecXform

207cd6ef6edf5bc90b2abb1242e2d7bb3b322f95

2ec0c64fb098e29ce74929f5e19bce90b2f5791c

refs/heads/master

2023-03-17T14:51:34.755756

2020-08-01T09:26:35

null

0

null

UTF-8

Python

false

1,904

py

import numpy as np from Tools import Preprocess from VectorTrans.DRTrans import DRTrans from VectorTrans.MDSTrans import MDSTrans from VectorTrans.TSNETrans import TSNETrans from VectorTrans.PCATrans import PCATrans from VectorTrans.MDSTransPlus import MDSTransPlus from VectorTrans.TSNETransPlus import TSNETransPlus from VectorTrans.CreateJson import JsonFile def load_data(): X = np.loadtxt("..\\Data\\data.csv", dtype=np.float, delimiter=",") label = np.loadtxt("..\\Data\\label.csv", dtype=np.int, delimiter=",") return X, label def run_example(): dr_method = 'MDS' # 'MDS' 't-SNE' 'PCA' 'MDSPlus' 't-SNEPlus' X, label = load_data() repeat = Preprocess.has_repeat(X) if repeat: print("Please recheck the input data for duplicate points") return X = Preprocess.normalize(X) # Optional (n, d) = X.shape trans = DRTrans() if dr_method == 'MDS': trans = MDSTrans(X, label=label, y_init=None, y_precomputed=False) elif dr_method == 't-SNE': trans = TSNETrans(X, label=label, y_init=None, perplexity=30.0) elif dr_method == 'PCA': trans = PCATrans(X, label=label) elif dr_method == "MDSPlus": trans = MDSTransPlus(X, label=label, y_init=None, y_precomputed=False) elif dr_method == "t-SNEPlus": trans = TSNETransPlus(X, label=label, y_init=None, perplexity=30.0) else: print("This method is not supported at this time: ", dr_method) return trans.transform(nbrs_k=20, MAX_EIGEN_COUNT=4, yita=0.1) np.savetxt("..\\Data\\"+str(dr_method)+"_Y.csv", trans.Y, fmt='%.18e', delimiter=",") if n*d < 1024 ** 3 / 2: np.savetxt("..\\Data\\"+str(dr_method)+"_derivative.csv", trans.derivative, fmt='%.18e', delimiter=",") json_file = JsonFile(path="..\\Data\\") json_file.create_file(trans) if __name__ == '__main__': run_example()

[ "sdu2014@126.com" ]

sdu2014@126.com

b9fc0ded63c3c6f0ff7857c261a68f18076d6d8e

9dc8c299ee7d4a225002127cc03b4253c8a721fd

/libs/unittest/live_related_condition.py

5604fdc9fc852993f3b40a2a692f9a1c3da1f49b

[]

no_license

namesuqi/strategy_corgi

5df5d8c89bdf7a7c465c438048be20ef16120f4f

557b8f8eabf034c2a57c25e6bc581858dd4f1b6e

refs/heads/master

2020-03-07T04:00:18.313901

2018-03-29T07:50:50

127,253,453

0

null

UTF-8

Python

false

4,149

py

# !/usr/bin/python # coding=utf-8 # author: JinYiFan from config import * from libs.module.live_seeds import * import time def wait_for_second(wait_time): time.sleep(wait_time) def change_config(live_file_count, live_peer_count, rate_of_peer_and_file): """ 修改config文件的参数配置 :param live_file_count: 文件总数 :param live_peer_count: 播放的节点总数 :param rate_of_peer_and_file: 单个文件对应的播放节点数 """ orm.session.query(Configs).filter(Configs.role == "live_file_count").update( {"content": live_file_count}) orm.session.query(Configs).filter(Configs.role == "live_peer_count").update( {"content": live_peer_count}) orm.session.query(Configs).filter(Configs.role == "rate_of_peer_and_file").update( {"content": rate_of_peer_and_file}) orm.session.commit() orm.session.close() def change_peer_flow_to_0(): """ 将peer的CDN和P2P流量设为0 """ orm.session.query(Live_Peer).update({"cdn": 0, "p2p": 0}) orm.session.commit() orm.session.close() def change_LF_flow_to_0(): """ 将LF的CDN和P2P流量设为0 """ orm.session.query(Live_Seed).update({"upload": 0, "download": 0}) orm.session.commit() orm.session.close() def add_player(play_num): """ 增加播放节点 :param play_num: 增加的播放节点数 """ peer_num_infos = orm.session.query(Live_Online).offset(200).limit(play_num).all() file_id = orm.session.query(Live_Peer).first().file_id num = 0 for num in range(play_num): live_peer_sdk = Live_Peer(peer_id=peer_num_infos[num].peer_id, version=peer_num_infos[num].sdk_version, country=peer_num_infos[num].country, province_id=peer_num_infos[num].province_id, city_id=peer_num_infos[num].city_id, isp_id=peer_num_infos[num].isp_id, file_id=file_id, chunk_id=get_random_chunk_id(), operation=OPERATION, cdn=CDN, p2p=P2P, ssid=peer_num_infos[num].ssid, p2penable=P2PENABLE) orm.session.add(live_peer_sdk) num += 1 orm.session.commit() orm.session.close() def one_peer_multi_channel(channel_num): """ 一个播放节点播放多个频道 :param channel_num: 一个播放节点播放的频道数 """ peer_info = orm.session.query(Live_Peer).first() file_info = orm.session.query(Live_File).offset(5).limit(channel_num).all() for num in range(channel_num - 1): live_peer_sdk = Live_Peer(peer_id=peer_info.peer_id, version=peer_info.version, country=peer_info.country, province_id=peer_info.province_id, city_id=peer_info.city_id, isp_id=peer_info.isp_id, file_id=file_info[num].file_id, chunk_id=get_random_chunk_id(), operation=OPERATION, cdn=CDN, p2p=P2P, ssid=peer_info.ssid, p2penable=P2PENABLE) orm.session.add(live_peer_sdk) num += 1 orm.session.commit() orm.session.close() def del_player(del_num): """ 删除播放节点 :param del_num: 删除的播放节点数 """ peer_infos = orm.session.query(Live_Peer).all() session_ids = list() for peer_info in peer_infos: session_ids.append(peer_info.ssid) num = 0 for num in range(del_num): orm.session.query(Live_Peer).filter_by(ssid=session_ids[num]).delete() num += 1 orm.session.commit() orm.session.close() def del_seed(del_num): """ 删除雷锋节点 :param del_num: 删除的雷锋节点数 """ seed_infos = orm.session.query(Live_Seed).all() session_ids = list() for seed_info in seed_infos: session_ids.append(seed_info.ssid) num = 0 for num in range(del_num): orm.session.query(Live_Seed).filter_by(ssid=session_ids[num]).delete() num += 1 orm.session.commit() orm.session.close() if __name__ == "__main__": del_seed(20) # add_player(3) # one_peer_multi_channel(3) # del_player(2)

[ "suqi_name@163.com" ]

suqi_name@163.com

b8acc579b13a7bb35130f20698e3489073b14792

773deb7825ff84eec3e0cf6ae8266d07251df392

/CHAPTER05/bw41.py

c7231b7bd8d7d2ba190f52df2a0fa74e6f62a961

[]

no_license

kji0205/py

3ca9c2a351af05ce62d7c7c3c261ed98a7e8290d

b45ffb3424b7c0da8192d431cb7ad7933c60ef81

refs/heads/master

2021-01-20T18:57:51.603386

2016-06-23T14:24:57

61,639,536

0

null

UTF-8

Python

false

905

py

# 진정한 병렬성을 실현하려면 concurrent.futures를 고려하자 import logging from pprint import pprint from sys import stdout as STDOUT from time import time def gcd(pair): a, b = pair low = min(a, b) for i in range(low, 0, -1): if a % i == 0 and b % i == 0: return i numbers = [(1963309, 2265973), (2030677, 3814172), (1551645, 2229620), (2039045, 2020802)] start = time() results = list(map(gcd, numbers)) end = time() print('Took %.3f seconds' % (end - start)) # from concurrent.futures import ThreadPoolExecutor start = time() pool = ThreadPoolExecutor(max_workers=2) results = list(pool.map(gcd, numbers)) end = time() print('Took %.3f seconds' % (end - start)) # from concurrent.futures import ProcessPoolExecutor start = time() pool = ProcessPoolExecutor(max_workers=2) results = list(pool.map(gcd, numbers)) end = time() print('Took %.3f seconds' % (end - start))

[ "kji0205@gmail.com" ]

kji0205@gmail.com

ab12cc2538c903dfca478ff16c8508153a7312c9

994ea22f35c635fdf139af9282b0d3a3d86ea34a

/ud120-projects-intro_to_machine_learning/decision_tree/dt_author_id.py

667e184f992ddbc3679ee4787f6ce8ba6bcc894a

[]

no_license

zjyx147/Udacity

ac371fbc5b5b456e88b411657ef5a28c3b071c6c

d86fadd537dbacc6f8142b043e71527b0448bae3

refs/heads/master

2022-06-23T14:25:41.242353

2019-06-20T20:12:13

191,207,247

0

null

2022-06-21T22:07:35

2019-06-10T16:42:18

DIGITAL Command Language

UTF-8

Python

false

1,128

py

#!/usr/bin/python """ This is the code to accompany the Lesson 3 (decision tree) mini-project. Use a Decision Tree to identify emails from the Enron corpus by author: Sara has label 0 Chris has label 1 """ import sys from time import time sys.path.append("../tools/") from email_preprocess import preprocess ### features_train and features_test are the features for the training ### and testing datasets, respectively ### labels_train and labels_test are the corresponding item labels features_train, features_test, labels_train, labels_test = preprocess() ######################################################### ### your code goes here ### from sklearn import tree from sklearn.metrics import accuracy_score #features_train = features_train[:len(features_train)/100] #labels_train = labels_train[:len(labels_train)/100] clf = tree.DecisionTreeClassifier(min_samples_split=40) clf.fit(features_train, labels_train) pred = clf.predict(features_test) print len(features_train[0]) print "accuracy: ", accuracy_score(pred, labels_test) #########################################################

[ "zjyx147@gmail.com" ]

zjyx147@gmail.com