anton-l/python_sample_10k · Datasets at Hugging Face

nikaven/drake

17,712,445,144,014

6ed6df63a6dfcb060da4c77640f42819e89bc176

e6793e7eb54d2105c373a8af7ebc653b7ad94575

/tools/workspace/drake_visualizer/repository.bzl

e43653f11b269aa4c1ef9e6a887078c04514266f

[ "BSD-3-Clause" ]

permissive

https://github.com/nikaven/drake

5c59e88f79b530ddf62496452959abeaf8fff1e3

34bab4ecaa34ac09ade6dcb11cf7bc0d13c5bd4e

refs/heads/master

2020-03-31T01:37:25.441270

2018-10-05T19:42:22

151,788,663

7

0

null

true

2018-10-05T23:37:07

2018-10-05T23:30:44

2018-10-05T23:27:34

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null

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# -*- mode: python -*- # vi: set ft=python : """ Downloads and unpacks a precompiled version of drake-visualizer (a subset of Director, https://git.io/vNKjq) and makes it available to be used as a dependency of shell scripts. Archive naming convention: dv-<version>-g<commit>-python-<python version>-qt-<qt version> -vtk-<vtk version>-<platform>-<arch>[-<rebuild>] Build configuration: BUILD_SHARED_LIBS=OFF CMAKE_BUILD_TYPE=Release DD_QT_VERSION=5 USE_EXTERNAL_INSTALL=ON USE_LCM=ON USE_LCMGL=ON USE_SYSTEM_EIGEN=ON USE_SYSTEM_LCM=ON USE_SYSTEM_LIBBOT=ON USE_SYSTEM_VTK=ON Example: WORKSPACE: load( "@drake//tools/workspace/drake_visualizer:repository.bzl", "drake_visualizer_repository", ) drake_visualizer_repository(name = "foo") BUILD: sh_binary( name = "foobar", srcs = ["bar.sh"], data = ["@foo//:drake_visualizer"], ) Argument: name: A unique name for this rule. """ load("@drake//tools/workspace:os.bzl", "determine_os") # TODO(jamiesnape): Publish scripts used to create binaries. There will be a CI # job for developers to build new binaries on demand. def _impl(repository_ctx): os_result = determine_os(repository_ctx) if os_result.error != None: fail(os_result.error) if os_result.is_macos: archive = "dv-0.1.0-314-ga5a6f6f-python-2.7.15-qt-5.11.1-vtk-8.1.1-mac-x86_64.tar.gz" # noqa sha256 = "02f321cf6068068f1aa9747b6b7834c41cd5ccf53aef90ad58229f2c1bfa963c" # noqa elif os_result.ubuntu_release == "16.04": archive = "dv-0.1.0-314-ga5a6f6f-python-2.7.12-qt-5.5.1-vtk-8.1.1-xenial-x86_64.tar.gz" # noqa sha256 = "4bd36e80295006ce4bab57fa57b95b69511623abba80094fb2fdf1eaa18607f9" # noqa elif os_result.ubuntu_release == "18.04": archive = "dv-0.1.0-314-ga5a6f6f-python-2.7.15-qt-5.9.5-vtk-8.1.1-bionic-x86_64.tar.gz" # noqa sha256 = "49d4fe29285ebbc420d19bf91511e36e8b1eb03d23bc7912d982ae12c4b2b36c" # noqa else: fail("Operating system is NOT supported", attr = os_result) urls = [ x.format(archive = archive) for x in repository_ctx.attr.mirrors.get("director") ] root_path = repository_ctx.path("") repository_ctx.download_and_extract(urls, root_path, sha256 = sha256) file_content = """# -*- python -*- # DO NOT EDIT: generated by drake_visualizer_repository() licenses([ "notice", # Apache-2.0 AND BSD-3-Clause AND Python-2.0 "reciprocal", # MPL-2.0 "restricted", # LGPL-2.1-only AND LGPL-2.1-or-later AND LGPL-3.0-or-later "unencumbered", # Public-Domain ]) # drake-visualizer has the following non-system dependencies in addition to # those declared in deps: # bot2-lcmgl: LGPL-3.0-or-later # ctkPythonConsole: Apache-2.0 # Eigen: BSD-3-Clause AND MPL-2.0 AND Public-Domain # LCM: BSD-3-Clause AND LGPL-2.1-only AND LGPL-2.1-or-later # Python: Python-2.0 # PythonQt: LGPL-2.1-only # QtPropertyBrowser: LGPL-2.1-only # TODO(jamiesnape): Enumerate system dependencies. py_library( name = "drake_visualizer_python_deps", deps = [ "@lcm//:lcm-python", "@lcmtypes_bot2_core//:lcmtypes_bot2_core_py", # TODO(eric.cousineau): Expose VTK Python libraries here for Linux. "@lcmtypes_robotlocomotion//:lcmtypes_robotlocomotion_py", ], visibility = ["//visibility:public"], ) filegroup( name = "drake_visualizer", srcs = glob([ "lib/libPythonQt.*", "lib/libddApp.*", "lib/python2.7/site-packages/bot_lcmgl/**/*.py", "lib/python2.7/site-packages/director/**/*.py", "lib/python2.7/site-packages/director/**/*.so", "lib/python2.7/site-packages/urdf_parser_py/**/*.py", ]) + [ "bin/drake-visualizer", "share/doc/director/LICENSE.txt", ], data = [ ":drake_visualizer_python_deps", "@lcm//:libdrake_lcm.so", "@vtk", ], visibility = ["//visibility:public"], ) load("@drake//tools/install:install.bzl", "install_files") install_files( name = "install", dest = ".", files = [":drake_visualizer"], visibility = ["//visibility:public"], ) """ repository_ctx.file( "BUILD.bazel", content = file_content, executable = False, ) drake_visualizer_repository = repository_rule( attrs = { "mirrors": attr.string_list_dict(), }, implementation = _impl, )

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repository.bzl

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keshav-c17/python

755,914,284,463

ea636cdefecef6b50ec8ba3ba777ac196f2f1784

130ac79a00e2ae323452a3229f1bd6961d6cf1c3

/Hacker-rank/17_set_add().py

c7c1a33c1af3aec3fa805e36da65d21e048f9195

[]

no_license

https://github.com/keshav-c17/python

7c4bd1e11b359e131cc9ce2bed91e058b0a3ba54

067d4355912a27e512118aed6715113557c8b7e6

refs/heads/master

2023-05-01T15:15:41.605578

2021-05-27T00:16:11

322,216,929

0

null

n = int(input("Enter no. of countries: ")) list_of_countries = set() for i in range(n): list_of_countries.add(str(input("Enter names of the countries: "))) print(len(list_of_countries))

UTF-8

Python

false

191

py

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17_set_add().py

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0.680628

0

5

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42Paris/remote-challenges

12,463,995,113,879

d8d99efea4576155ac18120260c0174ad8c692a6

cec0e621218352b3685358c697ecbe9c725e6131

/chall02/hben-yah.py

f3ece231332d21de9edc99e8be282dce4cd57881

[]

no_license

https://github.com/42Paris/remote-challenges

ecf76c101f5f070455e2f54128faebef18a9b000

f9270e7981178243fccebeee2574e3dde91891fd

refs/heads/master

2021-05-21T19:36:36.177086

2020-06-12T12:22:35

252,772,141

74

209

null

false

2020-06-12T12:22:37

2020-04-03T15:33:34

2020-06-05T12:58:47

2020-06-12T12:22:36

1,920

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#!/usr/bin/python3 import sys import re morseDic = { 'a':'.-', 'b':'-...', 'c':'-.-.', 'd':'-..', 'e':'.', 'f':'..-.', 'g':'--.', 'h':'....', 'i':'..', 'j':'.---', 'k':'-.-', 'l':'.-..', 'm':'--', 'n':'-.', 'o':'---', 'p':'.--.', 'q':'--.-', 'r':'.-.', 's':'...', 't':'-', 'u':'..-', 'v':'...-', 'w':'.--', 'x':'-..-', 'y':'-.--', 'z':'--..' } def usage(): print("usage: ./hben-yah.py <a-zA-Z string>") exit() def str_replace(text, dic): rc = re.compile('|'.join(map(re.escape, dic))) def trans(match): return dic[match.group(0)] return rc.sub(trans, text) if len(sys.argv) != 2: usage() word = sys.argv[1] if not re.match("^[A-Za-z0-9 ]*$", word) or len(word) == 0: usage() morse = str_replace(word.lower(), morseDic) print(morse)

UTF-8

Python

false

764

py

713

hben-yah.py

55

0.413613

0.40445

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55

12.909091

59

stellakaniaru/hacker-rank

9,577,777,085,166

cc57b81aeff66ac4f143219c31cbca3298507570

c2843304f68aaa18f6a3d55221061fb19422b3a6

/anagram.py

6a0a0053be79509679ff2ac7f16108842e7166b3

[]

no_license

https://github.com/stellakaniaru/hacker-rank

974ced2514d1ae3ed5afb027b392e2545614e648

8dd886c00e4c2522a728b3456d64c8d4712ca2c0

refs/heads/master

2021-06-06T21:52:54.630431

2018-11-15T07:10:21

59,646,209

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'''Given a a string which is a concatation of two substrings, determine if the two are anagrams of each other''' def anagram(word): '''Should find out if a given word is an anagram or not''' #check if the word can be divided into equal substrings #return -1 if the word cant be divided into two equal strings if len(word) % 2 == 0: a,b = word[:len(word)/2], word[len(word)/2:] #sort the substrings to get them in list format a1 = sorted(a) b1 = sorted(b) c = [] #loop through to check if elements in both substrings are equal #return the elements not in both lists in a new list and output the length of the list for i in a1: if i not in b: c.append(i) return len(c) return '-1' # Tests = input('') # for i in range(Tests): # print anagram(str(input()))

UTF-8

Python

false

791

py

15

anagram.py

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0.677623

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BlueMoon3000/newsapp

11,630,771,466,199

e2b568a56b7e8a4714e624ef59b4fc2dcb7288a5

c42bf7ba6dc853ae6a4802a48cfaccdb8419ea8b

/core/migrations/0001_initial.py

db7c7052d61da06c122dc533c0f9dd495b65b20a

[]

no_license

https://github.com/BlueMoon3000/newsapp

6c2b41f4ef9ac85128a3edf680842c04604496dd

767f2265cff5896b364a9afd6945181602351f46

refs/heads/master

2021-01-15T11:49:20.804564

2015-12-20T01:34:30

15,790,768

0

null

# -*- coding: utf-8 -*- from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'AppUser' db.create_table(u'core_appuser', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('password', self.gf('django.db.models.fields.CharField')(max_length=128)), ('last_login', self.gf('django.db.models.fields.DateTimeField')(default=datetime.datetime.now)), ('email', self.gf('django.db.models.fields.CharField')(unique=True, max_length=255)), ('first_name', self.gf('django.db.models.fields.CharField')(max_length=50)), ('last_name', self.gf('django.db.models.fields.CharField')(max_length=60)), ('full_name', self.gf('django.db.models.fields.CharField')(max_length=140)), ('fb_user_id', self.gf('django.db.models.fields.CharField')(max_length=20)), ('fb_access_token', self.gf('django.db.models.fields.CharField')(max_length=255, null=True)), ('is_active', self.gf('django.db.models.fields.BooleanField')(default=True)), ('is_admin', self.gf('django.db.models.fields.BooleanField')(default=False)), ('last_updated_time', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, blank=True)), ('created_time', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), )) db.send_create_signal(u'core', ['AppUser']) # Adding model 'Topic' db.create_table(u'core_topic', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('title', self.gf('django.db.models.fields.CharField')(unique=True, max_length=100)), ('last_updated_time', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, blank=True)), ('created_time', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), )) db.send_create_signal(u'core', ['Topic']) # Adding model 'SearchTopic' db.create_table(u'core_searchtopic', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('title', self.gf('django.db.models.fields.CharField')(unique=True, max_length=100)), ('topic', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['core.Topic'])), ('is_master', self.gf('django.db.models.fields.BooleanField')(default=False)), ('last_updated_time', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, blank=True)), ('created_time', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), )) db.send_create_signal(u'core', ['SearchTopic']) # Adding model 'Article' db.create_table(u'core_article', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('url', self.gf('django.db.models.fields.CharField')(unique=True, max_length=500)), ('title', self.gf('django.db.models.fields.CharField')(max_length=250)), ('description', self.gf('django.db.models.fields.CharField')(max_length=1000)), ('pub_date', self.gf('django.db.models.fields.DateTimeField')()), ('last_updated_time', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, blank=True)), ('created_time', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), )) db.send_create_signal(u'core', ['Article']) def backwards(self, orm): # Deleting model 'AppUser' db.delete_table(u'core_appuser') # Deleting model 'Topic' db.delete_table(u'core_topic') # Deleting model 'SearchTopic' db.delete_table(u'core_searchtopic') # Deleting model 'Article' db.delete_table(u'core_article') models = { u'core.appuser': { 'Meta': {'object_name': 'AppUser'}, 'created_time': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'email': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '255'}), 'fb_access_token': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True'}), 'fb_user_id': ('django.db.models.fields.CharField', [], {'max_length': '20'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'full_name': ('django.db.models.fields.CharField', [], {'max_length': '140'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_admin': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '60'}), 'last_updated_time': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}) }, u'core.article': { 'Meta': {'object_name': 'Article'}, 'created_time': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.CharField', [], {'max_length': '1000'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_updated_time': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'pub_date': ('django.db.models.fields.DateTimeField', [], {}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '250'}), 'url': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '500'}) }, u'core.searchtopic': { 'Meta': {'object_name': 'SearchTopic'}, 'created_time': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_master': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_updated_time': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}), 'topic': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['core.Topic']"}) }, u'core.topic': { 'Meta': {'object_name': 'Topic'}, 'created_time': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_updated_time': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}) } } complete_apps = ['core']

UTF-8

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false

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py

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0001_initial.py

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evaseemefly/gsconfig

4,269,197,513,147

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/src/geoserver/customer_base.py

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[ "MIT" ]

permissive

https://github.com/evaseemefly/gsconfig

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a10900bd256018edf00cd1b508a0d1eb6c3ecba6

refs/heads/master

2021-02-11T18:47:06.512853

2020-03-31T09:36:09

2020-03-31T09:36:20

244,520,389

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MIT

true

2020-03-29T13:48:46

2020-03-03T02:12:25

2020-03-29T13:35:40

2020-03-29T13:48:45

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Python

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from catalog import Catalog # TODO:[-] 20-03-31 注意此处存在一个问题，由于修改了 gsconfig的源码，所以此处引用时是引用的系统环境中的 gsconfig(geoserver) from geoserver.workspace import Workspace # from workspace import Workspace class BaseCatalog: ''' 所有 cusotmer 子类需要继承的父类 ''' def __init__(self, cat: Catalog, work_space: str): ''' @param cat: cat 实例 @param work_space: 工作区名称 ''' self.cat = cat self.work_space = Workspace(cat,work_space)

UTF-8

Python

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customer_base.py

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xuelei-code/test

10,264,971,874,950

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a4ff9458cbf1b69c2cc85e522c2f23fea34252f9

/dataless.py

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[]

no_license

https://github.com/xuelei-code/test

06ace35f19375e728aeba857c5767019e80e78f4

0796565ce0a93ed600ca4346e628800e0d4623ac

refs/heads/master

2020-11-26T23:40:09.485007

2019-12-20T09:22:37

229,232,123

0

null

# -*- coding: utf-8 -*- """ Created on Mon Apr 23 15:48:47 2018 @author: lei """ import obspy from obspy import read, read_inventory from os import listdir from obspy.core import UTCDateTime # 读入时间 t0 = UTCDateTime("2018-04-22T16:30:00.000") # 读入仪器响应文件 inv = read_inventory('F:/work/mycode/test/FJ.dataless.xml') pre_filt = [0.001, 0.1, 10, 20] # 读入波形数据 filepath='F:/work/mycode/test/mseed' filename_list=listdir(filepath) for filename in filename_list: st = read(filepath + '/' + filename,starttime = t0 - 60, endtime = t0 + 300) # the corresponding response is included in ObsPy as a StationXML file # the routine automatically picks the correct response for each trace # define a filter band to prevent amplifying noise during the deconvolution re_filt = (0.005, 0.1, 10, 11) Vst = st.remove_response(inventory=inv, output='VEL', pre_filt=pre_filt) Ast = st.remove_response(inventory=inv, output='ACC', pre_filt=pre_filt) # Vst.plot() # Ast.plot() tr = Vst[0] print(tr.stats)

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Python

false

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dataless.py

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kishansharma3012/HistoNet

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c4f2bf91df7e9ff0f59a7956427e802bb7263a46

cf377c54bd6ea649703923cce924694e3747168b

/code/train.py

3428603e403fa63b6725b3742356ae9c3b0de035

[]

no_license

https://github.com/kishansharma3012/HistoNet

c2424028de90101f80b1e269f0e54855d355b4bc

62bb9120027dfe13b0968af59ad49b0bf57ed836

refs/heads/master

2020-08-12T16:05:45.130289

2020-05-02T20:29:26

214,796,600

2

0

null

import lasagne import pickle import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt plt.set_cmap('jet') import sys,os,time,random import numpy as np import theano import theano.tensor as T from data_utils import import_data, import_data_dsn seed = 0 random.seed(seed) np.random.seed(seed) lasagne.random.set_rng(np.random.RandomState(seed)) ef = 1024.0 def evaluate_histonet(dataset_image, dataset_countmap, dataset_count, dataset_hist, Hist_wt, test_op, data_mean, loss_list,\ visualize = False, path = 'Results/', loss_name = 'w_L1', Loss_wt= [0.5, 0.5,], num_bins = 8): """ Function: Evaluate HistoNet network performance on a dataset (For validation and Testing) Input: dataset_image : Input images dataset_countmap: Gt Redundant countmaps dataset_count: Gt object counts dataset_hist: Gt object size distribution histograms Hist_wt: Weights for L1 loss between predicted and target size histogram test_op: Operation for extracting output from network for a given input during test and validation(deterministic) data_mean: Mean image of training dataset loss_list: list of different losses (only for initializing the test loss history list) visualize: Boolean (to visualize the test results) path : path of the result folder loss_name: Name of the L1 loss for histogram (Weighted L1 loss - w_L1 or L1 loss - L1) Loss_wt: Weights for KL divergence loss and L1 loss for histogram num_bins: number of the bins of histogram Output: test_loss_history: list of lists of different losses [loss_total, loss_count, loss_pix, loss_kl, loss_l1, loss_l1_temp, loss_reg (0 for the test)] gt_count: list of object count in the test samples pred_count: list of predicted count in the test samples """ test_loss_history = [[] for _ in range(len(loss_list))] gt_count_list = [] pred_count_list = [] batchsize = 1 for i in range(0,len(dataset_image), batchsize): pred_countmap, pred_hist = test_op(dataset_image, range(i,i+batchsize)) if visualize: visualize_HistoNet_result(path, i, dataset_image[i], dataset_countmap[i], dataset_count[i], dataset_hist[i], \ pred_countmap, pred_hist, Hist_wt, data_mean, num_bins) err_pix= np.abs(pred_countmap - dataset_countmap[i:i+batchsize]).mean(axis=(2,3))[0][0] pred_count = (pred_countmap/(ef)).sum(axis=(1,2,3)) err_count = np.abs((dataset_count[i:i+batchsize]/ef).sum()-pred_count)[0] y_shape = pred_hist.shape[0] gt_hist = dataset_hist[i:i+batchsize] p_prob = pred_hist/pred_hist.sum(axis = 1, keepdims=True) + (1e-6) p_prob1 = p_prob/p_prob.sum(axis =1, keepdims = True) t_prob = gt_hist/gt_hist.sum(axis = 1, keepdims=True) + (1e-6) t_prob1 = t_prob/t_prob.sum(axis = 1, keepdims =True) kl = (t_prob1*np.log((t_prob1)/(p_prob1))) err_kl = kl.sum()/y_shape if loss_name == 'w_L1': err_l1 = (Hist_wt*np.abs(pred_hist - gt_hist)).sum()/y_shape err_l1_temp = np.abs(pred_hist - gt_hist).sum()/y_shape elif loss_name == 'L1': err_l1 = np.abs(pred_hist - gt_hist).sum()/y_shape err_l1_temp = (Hist_wt*np.abs(pred_hist - gt_hist)).sum()/y_shape err_total = Loss_wt[0]*err_kl + Loss_wt[1]*err_l1 + err_pix test_loss_history = update_loss_history(test_loss_history, [err_total, err_count, err_pix, err_kl, err_l1, err_l1_temp, 0.0]) gt_count_list.append((dataset_countmap[i:i+batchsize]/ef).sum()) pred_count_list.append(pred_count[0]) return test_loss_history, gt_count_list, pred_count_list def evaluate_histonet_dsn(dataset_image, dataset_countmap, dataset_count, dataset_hist, dataset_hist_dsn1, dataset_hist_dsn2, Hist_wt, Hist_wt_dsn1, Hist_wt_dsn2, test_op, data_mean, loss_list,\ visualize = False, path = 'Results/', loss_name = 'w_L1', Loss_wt= [0.5, 0.5,], num_bins = [2,4,8]): """ Function: Evaluate HistoNet network performance on a dataset (For validation and Testing) Input: dataset_image : Input images dataset_countmap: Gt Redundant countmaps dataset_count: Gt object counts dataset_hist: Gt object size distribution histograms dataset_hist_dsn1: Gt object size distribution histograms dsn1 dataset_hist_dsn2: Gt object size distribution histograms dsn2 Hist_wt: Weights for L1 loss between predicted and target size histogram Hist_wt_dsn1: Weights for L1 loss between predicted and target size histogram dsn1 Hist_wt_dsn2: Weights for L1 loss between predicted and target size histogram dsn2 test_op: Operation for extracting output from network for a given input during test and validation(deterministic) data_mean: Mean image of training dataset loss_list: list of different losses (only for initializing the test loss history list) visualize: Boolean (to visualize the test results) path : path of the result folder loss_name: Name of the L1 loss for histogram (Weighted L1 loss - w_L1 or L1 loss - L1) Loss_wt: Weights for KL divergence loss and L1 loss for histogram num_bins: list of number of bins of histogram [early layer output hist size, middle layer output hist size, final output hist size] Output: test_loss_history: list of lists of different losses [loss_total, loss_count, loss_pix, loss_kl, loss_l1, loss_l1_temp, loss_reg (0 for the test)] gt_count: list of object count in the test samples pred_count: list of predicted count in the test samples """ test_loss_history = [[] for _ in range(len(loss_list))] gt_count_list = [] pred_count_list = [] batchsize = 1 for i in range(0,len(dataset_image), batchsize): pred_countmap, pred_hist, pred_hist_dsn1, pred_hist_dsn2 = test_op(dataset_image, range(i,i+batchsize)) if visualize: visualize_HistoNet_DSN_result(path, i, dataset_image[i], dataset_countmap[i], dataset_count[i], dataset_hist[i], dataset_hist_dsn1[i], dataset_hist_dsn2[i], \ pred_countmap, pred_hist, pred_hist_dsn1, pred_hist_dsn2, Hist_wt, Hist_wt_dsn1, Hist_wt_dsn2, data_mean, num_bins) err_pix= np.abs(pred_countmap - dataset_countmap[i:i+batchsize]).mean(axis=(2,3))[0][0] pred_count = (pred_countmap/(ef)).sum(axis=(1,2,3)) err_count = np.abs((dataset_count[i:i+batchsize]/ef).sum()-pred_count)[0] y_shape = pred_hist.shape[0] gt_hist = dataset_hist[i:i+batchsize] p_prob = pred_hist/pred_hist.sum(axis = 1, keepdims=True) + (1e-6) p_prob1 = p_prob/p_prob.sum(axis =1, keepdims = True) t_prob = gt_hist/gt_hist.sum(axis = 1, keepdims=True) + (1e-6) t_prob1 = t_prob/t_prob.sum(axis = 1, keepdims =True) kl = (t_prob1*np.log((t_prob1)/(p_prob1))) err_kl = kl.sum()/y_shape # KL Div loss - DSN1 y_shape_dsn1 = pred_hist_dsn1.shape[0] gt_hist_dsn1 = dataset_hist_dsn1[i:i+batchsize] p_prob_dsn1 = pred_hist_dsn1/(pred_hist_dsn1.sum(axis = 1, keepdims=True) + 1e-6) + (1e-6) p_prob1_dsn1 = p_prob_dsn1/p_prob_dsn1.sum(axis =1, keepdims = True) t_prob_dsn1 = gt_hist_dsn1/gt_hist_dsn1.sum(axis = 1, keepdims=True) + (1e-6) t_prob1_dsn1 = t_prob_dsn1/t_prob_dsn1.sum(axis = 1, keepdims =True) kl_dsn1 = (t_prob1_dsn1*np.log((t_prob1_dsn1)/(p_prob1_dsn1))) err_kl_dsn1 = kl_dsn1.sum()/y_shape_dsn1 # KL Div loss - DSN2 y_shape_dsn2 = pred_hist_dsn2.shape[0] gt_hist_dsn2 = dataset_hist_dsn2[i:i+batchsize] p_prob_dsn2 = pred_hist_dsn2/(pred_hist_dsn2.sum(axis = 1, keepdims=True) + 1e-6) + (1e-6) p_prob1_dsn2 = p_prob_dsn2/p_prob_dsn2.sum(axis =1, keepdims = True) t_prob_dsn2 = gt_hist_dsn2/gt_hist_dsn2.sum(axis = 1, keepdims=True) + (1e-6) t_prob1_dsn2 = t_prob_dsn2/t_prob_dsn2.sum(axis = 1, keepdims =True) kl_dsn2 = (t_prob1_dsn2*np.log((t_prob1_dsn2)/(p_prob1_dsn2))) err_kl_dsn2 = kl_dsn2.sum()/y_shape_dsn2 if loss_name == 'w_L1': err_l1 = (Hist_wt*np.abs(pred_hist - gt_hist)).sum()/y_shape err_l1_temp = np.abs(pred_hist - gt_hist).sum()/y_shape err_l1_dsn1 = (Hist_wt_dsn1*np.abs(pred_hist_dsn1 - gt_hist_dsn1)).sum()/y_shape_dsn1 err_l1_temp_dsn1 = np.abs(pred_hist_dsn1 - gt_hist_dsn1).sum()/y_shape_dsn1 err_l1_dsn2 = (Hist_wt_dsn2*np.abs(pred_hist_dsn2 - gt_hist_dsn2)).sum()/y_shape_dsn2 err_l1_temp_dsn2 = np.abs(pred_hist_dsn2 - gt_hist_dsn2).sum()/y_shape_dsn2 elif loss_name == 'L1': err_l1 = np.abs(pred_hist - gt_hist).sum()/y_shape err_l1_temp = (Hist_wt*np.abs(pred_hist - gt_hist)).sum()/y_shape err_l1_dsn1 = np.abs(pred_hist_dsn1 - gt_hist_dsn1).sum()/y_shape_dsn1 err_l1_temp_dsn1 = (Hist_wt_dsn1*np.abs(pred_hist_dsn1 - gt_hist_dsn1)).sum()/y_shape_dsn1 err_l1_dsn2 = np.abs(pred_hist_dsn2 - gt_hist_dsn2).sum()/y_shape_dsn2 err_l1_temp_dsn2 = (Hist_wt_dsn2*np.abs(pred_hist_dsn2 - gt_hist_dsn2)).sum()/y_shape_dsn2 err_total = Loss_wt[0]*(err_kl + err_kl_dsn1 + err_kl_dsn2) + Loss_wt[1]*(err_l1 + err_l1_dsn1 + err_l1_dsn2) + err_pix test_loss_history = update_loss_history(test_loss_history, [err_total, err_count, err_pix, err_kl, err_kl_dsn1, err_kl_dsn2,\ err_l1, err_l1_dsn1, err_l1_dsn2, err_l1_temp, err_l1_temp_dsn1, err_l1_temp_dsn2, 0.0]) gt_count_list.append((dataset_countmap[i:i+batchsize]/ef).sum()) pred_count_list.append(pred_count[0]) return test_loss_history, gt_count_list, pred_count_list def save_network(net, file_name, directory): """ Function: save network weights Input: net : network file_name: name of the model file directory: Logging directory for saving model weights """ np.savez( directory +'/' + file_name, *lasagne.layers.get_all_param_values(net)) def load_network(net, model_path): """ Function: load network weights Input: net : network model_path: path of the model file """ with np.load(model_path) as f: param_values = [f['arr_%d' % i] for i in range(len(f.files))] lasagne.layers.set_all_param_values(net, param_values) def update_loss_history(losses_lists, new_values): for i in range(len(losses_lists)): losses_lists[i].append(float(new_values[i])) return losses_lists def trainer_histonet(net_count, net_hist, dataset_path, loss_list, placeholder_list, epochs, lr_value , \ lr_decay, batch_size, weight_decay_value, root_result, Experiment_name, \ train_op, test_op, loss_name = 'w_L1', Loss_wt = [0.5, 0.5], num_bins = 8, print_every=50): """ Function: Training and evaluating HistoNet network Input: net_count : count map output from network net_hist: histogram vector output from network dataset_path: path to the directory containing train, val and test dataset pickle files loss_list: list of different losses for count and size histogram prediction placeholder_list: list of placeholders for input and target epochs: number of epochs lr_value: Learning rate lr_decay: decay rate of Learning rate (0 - 1.0) batch_size: batch_size for training weight_decay_value: L2 regularization strength root_result: directory to training plots, test results Experiment_name: Name of the experiment train_op: Operation for extracting output from network for a given input during training(non deterministic) test_op: Operation for extracting output from network for a given input during test and validation(deterministic) loss_name: Name of the L1 loss for histogram (Weighted L1 loss - w_L1 or L1 loss - L1) Loss_wt: Weights for KL divergence loss and L1 loss for histogram num_bins: number of bins of histogram print_every: number of iteration after which to show the progress of training losses """ # Import Dataset train_set, val_set, test_set, data_mean = import_data(dataset_path, num_bins) np_train_dataset_x, np_train_dataset_y, np_train_dataset_c, np_train_dataset_s = train_set[0], train_set[1], train_set[2], train_set[3] np_val_dataset_x, np_val_dataset_y, np_val_dataset_c, np_val_dataset_s = val_set[0], val_set[1], val_set[2], val_set[3] np_test_dataset_x, np_test_dataset_y, np_test_dataset_c, np_test_dataset_s = test_set[0], test_set[1], test_set[2], test_set[3] print("Imported Data !!....") # Unpack losses and placeholders lr = theano.shared(np.array(0.0, dtype=theano.config.floatX)) loss_total, loss_count, loss_pix, loss_kl, loss_l1, loss_l1_temp, loss_reg = loss_list input_var, input_var_ex, target_var, target_var_hist, weight_var_hist = placeholder_list # Calculate weights for weighted L1 histogram loss Bins_var = np.linspace(0,200, num_bins + 1) center_bin_hist = (Bins_var[:-1] + Bins_var[1:])/2 Hist_wt = center_bin_hist/center_bin_hist.sum() Hist_wt = np.tile(Hist_wt, (batch_size,1)).astype(dtype = np.float64) # Preparing optimizer params = lasagne.layers.get_all_params([net_count, net_hist], trainable=True) updates = lasagne.updates.adam(loss_total, params, learning_rate=lr) train_fn = theano.function([input_var_ex], [loss_total, loss_count, loss_pix, loss_kl, loss_l1, loss_l1_temp, loss_reg], updates=updates, givens={input_var:np_train_dataset_x, target_var: np_train_dataset_y, target_var_hist: np_train_dataset_s, weight_var_hist: Hist_wt}) print("Training Function Compiled !!.....") lr.set_value(lr_value) best_valid_err = np.inf datasetlength = len(np_train_dataset_x) batch_size = 2 datasetlength = 4 print("batch_size", batch_size) print("lr", lr.eval()) print("datasetlength",datasetlength) training_plot_path = os.path.join(root_result, 'Training_plots') model_dir = os.path.join(root_result, Experiment_name) if not os.path.exists(root_result): os.mkdir(root_result) os.mkdir(training_plot_path) os.mkdir(model_dir) # Resetting Training and Validation loss per epoch history train_loss_epoch_history = [[] for _ in range(len(loss_list))] val_loss_epoch_history = [[] for _ in range(len(loss_list))] for epoch in range(epochs): train_loss_history = [[] for _ in range(len(loss_list))] todo = range(datasetlength) for i in range(0,datasetlength, batch_size): ex = todo[i:i+batch_size] err_total, err_count, err_pix, err_kl, err_l1, err_l1_temp, err_reg = train_fn(ex) if i%print_every == 0 : print("Epoch :", epoch," | Iteration : ", i ,"| Total_Loss :",np.around(err_total,2), \ "| Pix Loss :",np.around(err_pix,2), "| Count_Loss : ",np.around(err_count.mean(),2), \ "| kl_Loss:", np.around(err_kl,2), "| l1_Loss:", np.around(err_l1,2), "| l1_Loss_temp:", np.around(err_l1_temp,2), \ "| reg_Loss:", np.around(err_reg,2), "| Learning_rate:", np.around(lr.get_value(),5)) # Updating Loss history train_loss_history = update_loss_history(train_loss_history, [err_total, err_count, err_pix, err_kl, err_l1, err_l1_temp, err_reg]) # Learning rate decay lr.set_value(lasagne.utils.floatX(lr.get_value() * lr_decay)) val_loss_history, _, _ = evaluate_histonet(np_val_dataset_x[:4], np_val_dataset_y[:4], np_val_dataset_c[:4], np_val_dataset_s[:4], Hist_wt[0,:], test_op, \ data_mean, loss_list, loss_name = loss_name, Loss_wt = Loss_wt, num_bins= num_bins) # Updating Loss Epoch history train_loss_epoch_history = update_loss_history(train_loss_epoch_history, [np.mean(train_loss_history[i]) for i in range(len(train_loss_history))]) val_loss_epoch_history = update_loss_history(val_loss_epoch_history, [np.mean(val_loss_history[i]) for i in range(len(val_loss_history))]) # plot results plot_results(train_loss_epoch_history[0], val_loss_epoch_history[0], training_plot_path,'Total_loss',1) plot_results(train_loss_epoch_history[1], val_loss_epoch_history[1], training_plot_path,'Count_loss',2) plot_results(train_loss_epoch_history[2], val_loss_epoch_history[2], training_plot_path,'Pix_loss',3) plot_results(train_loss_epoch_history[3], val_loss_epoch_history[3], training_plot_path,'kl_loss',4) plot_results(train_loss_epoch_history[4], val_loss_epoch_history[4], training_plot_path,'l1_loss',5) plot_results(train_loss_epoch_history[5], val_loss_epoch_history[5], training_plot_path,'l1_loss_temp',6) plot_results(train_loss_epoch_history[6], val_loss_epoch_history[6], training_plot_path,'reg_loss',7) print("Epoch : ",epoch, "| Train_Total_Loss :", np.around(train_loss_epoch_history[0][-1],2), \ "| Val_Total_Loss :", np.around(val_loss_epoch_history[0][-1],2), \ "| Train_Count Loss:",np.around(train_loss_epoch_history[1][-1],2),\ "| Val_Count Loss:",np.around(val_loss_epoch_history[1][-1],2),\ "| Train_Pix_loss:",np.around(train_loss_epoch_history[2][-1],2),\ "| Val_Pix_loss:",np.around(val_loss_epoch_history[2][-1],2),\ "| Train KL_Loss:",np.around(train_loss_epoch_history[3][-1],2), \ "| Val KL_Loss:",np.around(val_loss_epoch_history[3][-1],2), \ "| Train L1_Loss:",np.around(train_loss_epoch_history[4][-1],2), \ "| Val L1_Loss:",np.around(val_loss_epoch_history[4][-1],2)) save_network([net_count, net_hist], 'model_' +str(epoch) + '.npz', model_dir) # saving best model if (val_loss_epoch_history[0][-1] < best_valid_err): best_valid_err = val_loss_epoch_history[0][-1] save_network([net_count, net_hist], 'model_best.npz', model_dir) Test_directory = root_result + '/Test_results/' if not os.path.exists(Test_directory): os.mkdir(Test_directory) # Loading best model load_network([net_count, net_hist], model_dir + '/model_best.npz') test_loss_history, _, _ = evaluate_histonet(np_test_dataset_x, np_test_dataset_y, np_test_dataset_c, np_test_dataset_s, Hist_wt[0,:], \ test_op, data_mean, loss_list, visualize = True, path = Test_directory, loss_name = loss_name, Loss_wt = Loss_wt, num_bins= num_bins) # saving results for test dataset Error_file = open(model_dir + '/Test_result_Summary.txt', "w") Error_file.write('\n Total_count_Mean abs Error :' + str(np.mean(test_loss_history[1]))) Error_file.write('\n Total_kl_Mean abs Error :' + str(np.mean(test_loss_history[3]))) Error_file.write('\n Total_l1_Mean abs Error :' + str(np.mean(test_loss_history[4]))) Error_file.write('\n Total_l1_ temp Mean abs Error :' + str(np.mean(test_loss_history[5]))) Error_file.close() def trainer_histonet_dsn(net_count, net_hist, net_hist_dsn1, net_hist_dsn2, dataset_path, loss_list, placeholder_list, epochs, lr_value , \ lr_decay, batch_size, weight_decay_value, root_result, Experiment_name, \ train_op, test_op, loss_name = 'w_L1', Loss_wt = [0.5, 0.5], num_bins = [2,4,8], print_every=50): """ Function: Training and evaluating HistoNet DSN network Input: net_count : count map output from network net_hist: histogram vector output of size num_bins[2]from network net_hist_dsn1: histogram vector output of size num_bins[0] from early part of the network net_hist_dsn1: histogram vector output of size num_bins[1] from middle part of the network dataset_path: path to the directory containing train, val and test dataset pickle files loss_list: list of different losses for count and size histogram prediction placeholder_list: list of placeholders for input and target epochs: number of epochs lr_value: Learning rate lr_decay: decay rate of Learning rate (0 - 1.0) batch_size: batch_size for training weight_decay_value: L2 regularization strength root_result: directory to training plots, test results Experiment_name: Name of the experiment train_op: Operation for extracting output from network for a given input during training(non deterministic) test_op: Operation for extracting output from network for a given input during test and validation(deterministic) loss_name: Name of the L1 loss for histogram (Weighted L1 loss - w_L1 or L1 loss - L1) Loss_wt: Weights for KL divergence loss and L1 loss for histogram num_bins: list of number of bins of histogram [early layer output hist size, middle layer output hist size, final output hist size] print_every: number of iteration after which to show the progress of training losses """ # Import Dataset train_set, val_set, test_set, data_mean = import_data_dsn(dataset_path, num_bins) np_train_dataset_x, np_train_dataset_y, np_train_dataset_c, np_train_dataset_s_dsn1, np_train_dataset_s_dsn2, np_train_dataset_s = train_set[0], train_set[1], train_set[2], train_set[3], train_set[4], train_set[5] np_val_dataset_x, np_val_dataset_y, np_val_dataset_c, np_val_dataset_s_dsn1, np_val_dataset_s_dsn2, np_val_dataset_s = val_set[0], val_set[1], val_set[2], val_set[3], val_set[4], val_set[5] np_test_dataset_x, np_test_dataset_y, np_test_dataset_c, np_test_dataset_s_dsn1, np_test_dataset_s_dsn2, np_test_dataset_s = test_set[0], test_set[1], test_set[2], test_set[3], test_set[4], test_set[5] print("Imported Data !!....") # Unpack losses and placeholders lr = theano.shared(np.array(0.0, dtype=theano.config.floatX)) loss_total, loss_count, loss_pix, loss_kl, loss_kl_dsn1, loss_kl_dsn2, loss_l1, loss_l1_dsn1, loss_l1_dsn2,\ loss_l1_temp, loss_l1_temp_dsn1, loss_l1_temp_dsn2, loss_reg = loss_list input_var, input_var_ex, target_var, target_var_hist, target_var_hist_dsn1, target_var_hist_dsn2, \ weight_var_hist, weight_var_hist_dsn1, weight_var_hist_dsn2 = placeholder_list # Calculate weights for weighted L1 histogram loss num_bins[0] Bins_var = np.linspace(0,200, num_bins[0] + 1) center_bin_hist = (Bins_var[:-1] + Bins_var[1:])/2 Hist_wt = center_bin_hist/center_bin_hist.sum() Hist_wt_dsn1 = np.tile(Hist_wt, (batch_size,1)).astype(dtype = np.float64) # Calculate weights for weighted L1 histogram loss num_bins[1] Bins_var = np.linspace(0,200, num_bins[1] + 1) center_bin_hist = (Bins_var[:-1] + Bins_var[1:])/2 Hist_wt = center_bin_hist/center_bin_hist.sum() Hist_wt_dsn2 = np.tile(Hist_wt, (batch_size,1)).astype(dtype = np.float64) # Calculate weights for weighted L1 histogram loss num_bins[2] Bins_var = np.linspace(0,200, num_bins[2] + 1) center_bin_hist = (Bins_var[:-1] + Bins_var[1:])/2 Hist_wt = center_bin_hist/center_bin_hist.sum() Hist_wt = np.tile(Hist_wt, (batch_size,1)).astype(dtype = np.float64) # Preparing optimizer params = lasagne.layers.get_all_params([net_count, net_hist, net_hist_dsn1, net_hist_dsn2], trainable=True) updates = lasagne.updates.adam(loss_total, params, learning_rate=lr) train_fn = theano.function([input_var_ex], [loss_total, loss_count, loss_pix, loss_kl, loss_kl_dsn1, loss_kl_dsn2, \ loss_l1, loss_l1_dsn1, loss_l1_dsn2, loss_l1_temp, loss_l1_temp_dsn1, loss_l1_temp_dsn2, loss_reg], updates=updates, givens={input_var:np_train_dataset_x, target_var: np_train_dataset_y, target_var_hist: np_train_dataset_s, weight_var_hist: Hist_wt, \ target_var_hist_dsn1: np_train_dataset_s_dsn1, weight_var_hist_dsn1: Hist_wt_dsn1,\ target_var_hist_dsn2: np_train_dataset_s_dsn2, weight_var_hist_dsn2: Hist_wt_dsn2 }) print("Training Function Compiled !!.....") lr.set_value(lr_value) best_valid_err = np.inf #dataset_length = len(np_train_dataset_x) dataset_length = 4 print("batch_size", batch_size) print("lr", lr.eval()) print("datasetlength",dataset_length) training_plot_path = os.path.join(root_result, 'Training_plots') model_dir = os.path.join(root_result, Experiment_name) if not os.path.exists(root_result): os.mkdir(root_result) os.mkdir(training_plot_path) os.mkdir(model_dir) # Resetting Training and Validation loss per epoch history train_loss_epoch_history =[[] for _ in range(len(loss_list))] val_loss_epoch_history = [[] for _ in range(len(loss_list))] for epoch in range(epochs): train_loss_history = [[] for _ in range(len(loss_list))] todo = range(dataset_length) for i in range(0,dataset_length, batch_size): ex = todo[i:i+batch_size] err_total, err_count, err_pix, err_kl, err_kl_dsn1, err_kl_dsn2, err_l1, err_l1_dsn1, err_l1_dsn2,\ err_l1_temp, err_l1_temp_dsn1, err_l1_temp_dsn2, err_reg = train_fn(ex) if i%print_every == 0 : print("Epoch :", epoch," | Iteration : ", i ,"| Total_Loss :",np.around(err_total,2), \ "| Pix Loss :",np.around(err_pix,2), "| Count_Loss : ",np.around(err_count.mean(),2), \ "| kl_Loss:", np.around(err_kl,2), "| l1_Loss:", np.around(err_l1,2), "| l1_Loss_temp:", np.around(err_l1_temp,2), \ "| kl_Loss dsn1:", np.around(err_kl_dsn1,2), "| l1_Loss dsn1:", np.around(err_l1_dsn1,2), "| l1_Loss_temp dsn1:", np.around(err_l1_temp_dsn1,2), \ "| kl_Loss dsn2:", np.around(err_kl_dsn2,2), "| l1_Loss dsn2:", np.around(err_l1_dsn2,2), "| l1_Loss_temp dsn2:", np.around(err_l1_temp_dsn2,2), \ "| reg_Loss:", np.around(err_reg,2), "| Learning_rate:", np.around(lr.get_value(),5)) # Updating Loss history train_loss_history = update_loss_history(train_loss_history, [err_total, err_count, err_pix, err_kl, err_kl_dsn1, err_kl_dsn2,\ err_l1, err_l1_dsn1, err_l1_dsn2, err_l1_temp, err_l1_temp_dsn1, err_l1_temp_dsn2, err_reg]) # Learning rate decay lr.set_value(lasagne.utils.floatX(lr.get_value() * lr_decay)) val_loss_history, _, _ = evaluate_histonet_dsn(np_val_dataset_x[:4], np_val_dataset_y[:4], np_val_dataset_c[:4], np_val_dataset_s[:4], np_val_dataset_s_dsn1[:4], np_val_dataset_s_dsn2[:4], Hist_wt[0,:], Hist_wt_dsn1[0,:], Hist_wt_dsn2[0,:], test_op, \ data_mean, loss_list, loss_name = loss_name, Loss_wt = Loss_wt, num_bins= num_bins) # Updating Loss Epoch history train_loss_epoch_history = update_loss_history(train_loss_epoch_history, [np.mean(train_loss_history[i]) for i in range(len(train_loss_history))]) val_loss_epoch_history = update_loss_history(val_loss_epoch_history, [np.mean(val_loss_history[i]) for i in range(len(val_loss_history))]) # plot results plot_results(train_loss_epoch_history[0], val_loss_epoch_history[0], training_plot_path,'Total_loss',1) plot_results(train_loss_epoch_history[1], val_loss_epoch_history[1], training_plot_path,'Count_loss',2) plot_results(train_loss_epoch_history[2], val_loss_epoch_history[2], training_plot_path,'Pix_loss',3) plot_results(train_loss_epoch_history[3], val_loss_epoch_history[3], training_plot_path,'kl_loss',4) plot_results(train_loss_epoch_history[4], val_loss_epoch_history[4], training_plot_path,'kl_loss dsn1',5) plot_results(train_loss_epoch_history[5], val_loss_epoch_history[5], training_plot_path,'kl_loss dsn2',6) plot_results(train_loss_epoch_history[6], val_loss_epoch_history[6], training_plot_path,'l1_loss',7) plot_results(train_loss_epoch_history[7], val_loss_epoch_history[7], training_plot_path,'l1_loss dsn1',8) plot_results(train_loss_epoch_history[8], val_loss_epoch_history[8], training_plot_path,'l1_loss dsn2',9) plot_results(train_loss_epoch_history[9], val_loss_epoch_history[9], training_plot_path,'l1_loss_temp',10) plot_results(train_loss_epoch_history[10], val_loss_epoch_history[10], training_plot_path,'l1_loss_temp dsn1',11) plot_results(train_loss_epoch_history[11], val_loss_epoch_history[11], training_plot_path,'l1_loss_temp dsn2',12) plot_results(train_loss_epoch_history[12], val_loss_epoch_history[12], training_plot_path,'reg_loss',13) print("Epoch : ",epoch, "| Train_Total_Loss :", np.around(train_loss_epoch_history[0][-1],2),\ "| Val_Total_Loss :", np.around(val_loss_epoch_history[0][-1],2),\ "| Train_Count Loss:",np.around(train_loss_epoch_history[1][-1],2),\ "| Val_Count Loss:",np.around(val_loss_epoch_history[1][-1],2),\ "| Train_Pix_loss:",np.around(train_loss_epoch_history[2][-1],2),\ "| Val_Pix_loss:",np.around(val_loss_epoch_history[2][-1],2),\ "| Train KL_Loss:",np.around(train_loss_epoch_history[3][-1],2),\ "| Val KL_Loss:",np.around(val_loss_epoch_history[3][-1],2),\ "| Train L1_Loss:",np.around(train_loss_epoch_history[6][-1],2),\ "| Val L1_Loss:",np.around(val_loss_epoch_history[6][-1],2),\ "| Train KL dsn1_Loss:",np.around(train_loss_epoch_history[4][-1],2),\ "| Val KL_dsn1 Loss:",np.around(val_loss_epoch_history[4][-1],2),\ "| Train L1 dsn1_Loss:",np.around(train_loss_epoch_history[7][-1],2),\ "| Val L1_dsn1 Loss:",np.around(val_loss_epoch_history[7][-1],2),\ "| Train KL_dsn2_Loss:",np.around(train_loss_epoch_history[5][-1],2),\ "| Val KL_dsn2_Loss:",np.around(val_loss_epoch_history[5][-1],2),\ "| Train L1_dsn2_Loss:",np.around(train_loss_epoch_history[8][-1],2),\ "| Val L1_dsn2_Loss:",np.around(val_loss_epoch_history[8][-1],2)) save_network([net_count, net_hist, net_hist_dsn1, net_hist_dsn2], 'model_' +str(epoch) + '.npz', model_dir) # saving best model if (val_loss_epoch_history[0][-1] < best_valid_err): best_valid_err = val_loss_epoch_history[0][-1] save_network([net_count, net_hist, net_hist_dsn1, net_hist_dsn2], 'model_best.npz', model_dir) Test_directory = root_result + '/Test_results/' if not os.path.exists(Test_directory): os.mkdir(Test_directory) # Loading best model load_network([net_count, net_hist, net_hist_dsn1, net_hist_dsn2], model_dir + '/model_best.npz') test_loss_history, _, _ = evaluate_histonet_dsn(np_test_dataset_x, np_test_dataset_y, np_test_dataset_c, np_test_dataset_s, np_test_dataset_s_dsn1, np_test_dataset_s_dsn2,\ Hist_wt[0,:], Hist_wt_dsn1[0,:], Hist_wt_dsn2[0,:], test_op, data_mean, loss_list, visualize = True, path = Test_directory, loss_name = loss_name, Loss_wt = Loss_wt, num_bins= num_bins) # saving results for test dataset Error_file = open(model_dir + '/Test_result_Summary.txt', "w") Error_file.write('\n Total_count_Mean abs Error :' + str(np.mean(test_loss_history[1]))) Error_file.write('\n Total_kl_Mean abs Error :' + str(np.mean(test_loss_history[3]))) Error_file.write('\n Total_l1_Mean abs Error :' + str(np.mean(test_loss_history[6]))) Error_file.write('\n Total_l1_ temp Mean abs Error :' + str(np.mean(test_loss_history[9]))) Error_file.close() def loss_func_histonet(net_count, net_hist, input_var, input_var_ex, reg, loss_name = 'w_L1', Loss_wt = [0.5, 0.5]): """ Function: Defining Loss functions for training HistoNet network Input: net_count : count map output from network net_hist: histogram vector output from network input_var: Place holder for input to the network input_var_ex: Place holder for index for data (input, target) reg: Regularization strength loss_name: Name of the L1 loss for histogram (Weighted L1 loss - w_L1 or L1 loss - L1) Loss_wt: Weights for KL divergence loss and L1 loss for histogram Output: loss_list: list of different losses for count and size histogram prediction placeholder_list: list of placeholders for input and target train_op: Operation for extracting output from network for a given input during training(non deterministic) test_op: Operation for extracting output from network for a given input during test and validation(deterministic) """ # Training forward pass prediction_count_map, prediction_hist = lasagne.layers.get_output([net_count, net_hist], deterministic=False) prediction_count = (prediction_count_map/ef).sum(axis=(2,3)) train_op = theano.function([input_var, input_var_ex], [prediction_count_map, prediction_hist]) # Val/Test forward pass prediction_count_map_t, prediction_hist_t = lasagne.layers.get_output([net_count, net_hist], deterministic=True) prediction_count_t = (prediction_count_map_t/ef).sum(axis=(2,3)) test_op = theano.function([input_var, input_var_ex], [prediction_count_map_t, prediction_hist_t]) # Placeholders for target and weights for histogram weighted L1 loss target_var = T.tensor4('target') target_var_hist = T.matrix('target_hist') weight_var_hist = T.matrix('weight_hist') #Mean Absolute Error is computed between each count of the count map, pixel wise l1_loss = T.abs_(prediction_count_map - target_var[input_var_ex]) loss_pix = l1_loss.mean() #Mean Absolute Error is computed for the overall count loss loss_count = T.abs_(prediction_count - (target_var[input_var_ex]/ef).sum(axis=(2,3))).mean() #KL DIV LOSS between probability distribution of target and predicted histogram to capture shape of histogram y_shape = prediction_hist.shape[0] target_hist = target_var_hist[input_var_ex] p_prob_1 = prediction_hist/prediction_hist.sum(axis = 1, keepdims=True) + (1e-6) p_prob = p_prob_1/p_prob_1.sum(axis=1, keepdims = True) t_prob_1 = target_hist/target_hist.sum(axis = 1, keepdims=True) + (1e-6) t_prob = t_prob_1/t_prob_1.sum(axis = 1, keepdims = True) kl= (t_prob*T.log((t_prob)/(p_prob))) loss_kl = kl.sum()/y_shape # weighted L1 or L1 loss between predicted and target histogram to capture scale of size histogram if loss_name == 'L1': loss_l1 = (T.abs_(prediction_hist - target_hist)).sum()/y_shape loss_l1_temp = (weight_var_hist*T.abs_(prediction_hist - target_hist)).sum()/y_shape elif loss_name == 'w_L1': loss_l1 = (weight_var_hist*T.abs_(prediction_hist - target_hist)).sum()/y_shape loss_l1_temp = (T.abs_(prediction_hist - target_hist)).sum()/y_shape # Regularization loss loss_reg = 0.5*reg*lasagne.regularization.regularize_network_params([net_count, net_hist], lasagne.regularization.l2) # Total Loss loss_total = loss_pix + Loss_wt[0]*loss_kl + Loss_wt[1]*loss_l1 + loss_reg loss_list = [loss_total, loss_count, loss_pix, loss_kl, loss_l1, loss_l1_temp, loss_reg] placeholder_list = [input_var, input_var_ex, target_var, target_var_hist, weight_var_hist] return loss_list, placeholder_list, train_op, test_op def loss_func_histonet_dsn(net_count, net_hist, net_hist_dsn1, net_hist_dsn2, input_var, input_var_ex, reg, loss_name = 'w_L1', Loss_wt = [0.5, 0.5]): """ Function: Defining Loss functions for training HistoNet network Input: net_count : count map output from network net_hist: histogram vector output from network net_hist_dsn1: histogram vector output from early part of the network net_hist_dsn2: histogram vector output from middle part of the network input_var: Place holder for input to the network input_var_ex: Place holder for index for data (input, target) reg: Regularization strength loss_name: Name of the L1 loss for histogram (Weighted L1 loss - w_L1 or L1 loss - L1) Loss_wt: Weights for KL divergence loss and L1 loss for histogram Output: loss_list: list of different losses for count and size histogram prediction placeholder_list: list of placeholders for input and target train_op: Operation for extracting output from network for a given input during training(non deterministic) test_op: Operation for extracting output from network for a given input during test and validation(deterministic) """ # Training forward pass prediction_count_map, prediction_hist, prediction_hist_dsn1, prediction_hist_dsn2 = lasagne.layers.get_output([net_count, net_hist, net_hist_dsn1, net_hist_dsn2], deterministic=False) prediction_count = (prediction_count_map/ef).sum(axis=(2,3)) train_op = theano.function([input_var, input_var_ex], [prediction_count_map, prediction_hist, prediction_hist_dsn1, prediction_hist_dsn2]) # Val/Test forward pass prediction_count_map_t, prediction_hist_t, prediction_hist_dsn1_t, prediction_hist_dsn2_t = lasagne.layers.get_output([net_count, net_hist, net_hist_dsn1, net_hist_dsn2], deterministic=True) prediction_count_t = (prediction_count_map_t/ef).sum(axis=(2,3)) test_op = theano.function([input_var, input_var_ex], [prediction_count_map_t, prediction_hist_t, prediction_hist_dsn1_t, prediction_hist_dsn2_t]) # Placeholders for target and weights for histogram weighted L1 loss target_var = T.tensor4('target') target_var_hist = T.matrix('target_hist') weight_var_hist = T.matrix('weight_hist') target_var_hist_dsn1 = T.matrix('target_hist_dsn1') weight_var_hist_dsn1 = T.matrix('weight_hist_dsn1') target_var_hist_dsn2 = T.matrix('target_hist_dsn2') weight_var_hist_dsn2 = T.matrix('weight_hist_dsn2') #Mean Absolute Error is computed between each count of the count map, pixel wise l1_loss = T.abs_(prediction_count_map - target_var[input_var_ex]) loss_pix = l1_loss.mean() #Mean Absolute Error is computed for the overall count loss loss_count = T.abs_(prediction_count - (target_var[input_var_ex]/ef).sum(axis=(2,3))).mean() #KL DIV LOSS between probability distribution of target and predicted histogram (main objective histogram) to capture shape of histogram y_shape = prediction_hist.shape[0] target_hist = target_var_hist[input_var_ex] p_prob_1 = prediction_hist/(prediction_hist.sum(axis = 1, keepdims=True) + 1e-6) + (1e-6) p_prob = p_prob_1/p_prob_1.sum(axis=1, keepdims = True) t_prob_1 = target_hist/target_hist.sum(axis = 1, keepdims=True) + (1e-6) t_prob = t_prob_1/t_prob_1.sum(axis = 1, keepdims = True) kl= (t_prob*T.log((t_prob)/(p_prob))) loss_kl = kl.sum()/y_shape #KL DIV LOSS - DSN1 between probability distribution of target and predicted histogram DSN1 to capture shape of histogram target_hist_dsn1 = target_var_hist_dsn1[input_var_ex] p_prob_1_dsn1 = prediction_hist_dsn1/(prediction_hist_dsn1.sum(axis = 1, keepdims=True) + 1e-6) + (1e-6) p_prob_dsn1 = p_prob_1_dsn1/p_prob_1_dsn1.sum(axis=1, keepdims = True) t_prob_1_dsn1 = target_hist_dsn1/target_hist_dsn1.sum(axis = 1, keepdims=True) + (1e-6) t_prob_dsn1 = t_prob_1_dsn1/t_prob_1_dsn1.sum(axis = 1, keepdims = True) kl_dsn1= (t_prob_dsn1*T.log((t_prob_dsn1)/(p_prob_dsn1))) loss_kl_dsn1 = kl_dsn1.sum()/y_shape #KL DIV LOSS - DSN2between probability distribution of target and predicted histogram DSN2 to capture shape of histogram target_hist_dsn2 = target_var_hist_dsn2[input_var_ex] p_prob_1_dsn2 = prediction_hist_dsn2/(prediction_hist_dsn2.sum(axis = 1, keepdims=True) + 1e-6) + (1e-6) p_prob_dsn2 = p_prob_1_dsn2/p_prob_1_dsn2.sum(axis=1, keepdims = True) t_prob_1_dsn2 = target_hist_dsn2/target_hist_dsn2.sum(axis = 1, keepdims=True) + (1e-6) t_prob_dsn2 = t_prob_1_dsn2/t_prob_1_dsn2.sum(axis = 1, keepdims = True) kl_dsn2= (t_prob_dsn2*T.log((t_prob_dsn2)/(p_prob_dsn2))) loss_kl_dsn2 = kl_dsn2.sum()/y_shape # weighted L1 or L1 loss between predicted and target histogram to capture scale of size histogram if loss_name == 'L1': loss_l1 = (T.abs_(prediction_hist - target_hist)).sum()/y_shape loss_l1_temp = (weight_var_hist*T.abs_(prediction_hist - target_hist)).sum()/y_shape loss_l1_dsn1 = (T.abs_(prediction_hist_dsn1 - target_hist_dsn1)).sum()/y_shape loss_l1_temp_dsn1 = (weight_var_hist_dsn1*T.abs_(prediction_hist_dsn1 - target_hist_dsn1)).sum()/y_shape loss_l1_dsn2 = (T.abs_(prediction_hist_dsn2 - target_hist_dsn2)).sum()/y_shape loss_l1_temp_dsn2 = (weight_var_hist_dsn2*T.abs_(prediction_hist_dsn2 - target_hist_dsn2)).sum()/y_shape elif loss_name == 'w_L1': loss_l1 = (weight_var_hist*T.abs_(prediction_hist - target_hist)).sum()/y_shape loss_l1_temp = (T.abs_(prediction_hist - target_hist)).sum()/y_shape loss_l1_dsn1 = (weight_var_hist_dsn1*T.abs_(prediction_hist_dsn1 - target_hist_dsn1)).sum()/y_shape loss_l1_temp_dsn1 = (T.abs_(prediction_hist_dsn1 - target_hist_dsn1)).sum()/y_shape loss_l1_dsn2 = (weight_var_hist_dsn2*T.abs_(prediction_hist_dsn2 - target_hist_dsn2)).sum()/y_shape loss_l1_temp_dsn2 = (T.abs_(prediction_hist_dsn2 - target_hist_dsn2)).sum()/y_shape # Regularization loss loss_reg = 0.5*reg*lasagne.regularization.regularize_network_params([net_count, net_hist, net_hist_dsn1, net_hist_dsn2], lasagne.regularization.l2) # Total Loss loss_total = loss_pix + Loss_wt[0]*(loss_kl + loss_kl_dsn1 + loss_kl_dsn2) + Loss_wt[1]*(loss_l1 + loss_l1_dsn1 + loss_l1_dsn2) + loss_reg loss_list = [loss_total, loss_count, loss_pix, loss_kl, loss_kl_dsn1, loss_kl_dsn2, loss_l1, loss_l1_dsn1, loss_l1_dsn2,\ loss_l1_temp, loss_l1_temp_dsn1, loss_l1_temp_dsn2, loss_reg] placeholder_list = [input_var, input_var_ex, target_var, target_var_hist, target_var_hist_dsn1, target_var_hist_dsn2, \ weight_var_hist, weight_var_hist_dsn1, weight_var_hist_dsn2] return loss_list, placeholder_list, train_op, test_op def plot_results( train_loss, val_loss, Log_dir, plotname, i ): """ Function: Plot results training and validation Input: train_loss : List of train loss per epoch val_loss: List of val loss per epoch Log_dir: Logging directory for saving training plots plotname: name of the loss plot i: random integer for different plot """ if not os.path.exists(Log_dir): os.mkdir(Log_dir) # Saving the train and val loss as .txt file Error_file = open(Log_dir + '/' + plotname + '_Summary.txt', "w") Error_file.write('Train_loss: ' + str(train_loss)) Error_file.write('\n val_loss: '+ str(val_loss)) Error_file.close() # Plotting training curves plt.figure(i+10, figsize=(15, 10)) plt.plot(range(len(train_loss)), train_loss, 'r', label = 'train') plt.plot(range(len(val_loss)), val_loss, 'g', label = 'val') plt.xlabel('epochs') plt.ylabel('loss') plt.legend() plt.title('Train & Validation ' + plotname) plt.savefig(Log_dir + '/' + plotname + '.png') plt.close() def visualize_HistoNet_result(path, i, image, gt_countmap, gt_count, gt_hist, pred_countmap, pred_hist, Hist_wt, data_mean, num_bins): """ Function: Visualizing results of HistoNet network Input: path : path of the result folder i : image number gt_countmap: Ground truth redundant count map gt_count: Ground truth object count gt_hist: Ground truth object size distribution histogram pred_countmap: predicted redundant count map pred_hist: predicted object size distribution histogram Hist_wt: Weights for L1 loss between predicted and target size histogram data_mean: Mean image of training dataset num_bins: number of the bins of histogram """ fig = plt.Figure(figsize=(18, 9), dpi=160) gcf = plt.gcf() gcf.set_size_inches(18, 15) fig.set_canvas(gcf.canvas) pred_count = [(l.sum()/(ef)).astype(np.int) for l in pred_countmap] img = image.transpose((1,2,0)) #De-Normalize img = img + data_mean ax2 = plt.subplot2grid((4,6), (0, 0), colspan=2) ax3 = plt.subplot2grid((4,6), (0, 2), colspan=5) ax4 = plt.subplot2grid((4,6), (1, 2), colspan=5) ax5 = plt.subplot2grid((4,6), (1, 0), rowspan=1) ax6 = plt.subplot2grid((4,6), (1, 1), rowspan=1) ax7 = plt.subplot2grid((4,6), (2, 0), colspan=5) ax8 = plt.subplot2grid((4,6), (3, 0), colspan=5) ax2.set_title("Input Image") ax2.imshow(img, interpolation='none', cmap='Greys_r') ax3.set_title("Target Countmap") ax3.imshow(np.concatenate((gt_countmap),axis=1), interpolation='none') ax4.set_title("Predicted Countmap") ax4.imshow(pred_countmap.squeeze(), interpolation='none') ax5.set_title("Target " + str(gt_count)) ax5.set_ylim((0, np.max(gt_count)*2)) ax5.set_xticks(np.arange(0, 1, 1.0)) ax5.bar(range(1),gt_count, align='center') ax6.set_title("Pred " + str(pred_count)) ax6.set_ylim((0, np.max(gt_count)*2)) ax6.set_xticks(np.arange(0, 1, 1.0)) ax6.bar(range(1),pred_count, align='center') Bins = np.linspace(0,200, num_bins + 1) ax7.set_title("Gt Histogram") ax7.hist(Bins[:-1], weights= gt_hist.T , bins = Bins) ax8.set_title("Pred Histogram") ax8.hist(Bins[:-1], weights= pred_hist.T , bins = Bins) if not os.path.exists(path + '/HistoNet_eval_result'): os.mkdir(path + '/HistoNet_eval_result') fig.savefig(path + '/HistoNet_eval_result/image-' + str(i) + '.png', bbox_inches='tight', pad_inches=0) def visualize_HistoNet_DSN_result(path, i, image, gt_countmap, gt_count, gt_hist, gt_hist_dsn1, gt_hist_dsn2, pred_countmap, pred_hist, pred_hist_dsn1, pred_hist_dsn2,\ Hist_wt, Hist_wt_dsn1, Hist_wt_dsn2, data_mean, num_bins): """ Function: Visualizing results of HistoNet network Input: path : path of the result folder i : image number gt_countmap: Ground truth redundant count map gt_count: Ground truth object count gt_hist: Ground truth object size distribution histogram dsn2 gt_hist_dsn1: Ground truth object size distribution histogram gt_hist_dsn2: Ground truth object size distribution histogram dsn1 pred_countmap: predicted redundant count map pred_hist: predicted object size distribution histogram pred_hist_dsn1: predicted object size distribution histogram dsn1 pred_hist_dsn2: predicted object size distribution histogram dsn2 Hist_wt: Weights for L1 loss between predicted and target size histogram Hist_wt_dsn1: Weights for L1 loss between predicted and target size histogram dsn1 Hist_wt_dsn2: Weights for L1 loss between predicted and target size histogram dsn2 data_mean: Mean image of training dataset num_bins: list of number of bins of histogram [early layer output hist size, middle layer output hist size, final output hist size] """ fig = plt.Figure(figsize=(18, 9), dpi=160) gcf = plt.gcf() gcf.set_size_inches(18, 15) fig.set_canvas(gcf.canvas) pred_count = [(l.sum()/(ef)).astype(np.int) for l in pred_countmap] img = image.transpose((1,2,0)) #De-Normalize img = img + data_mean ax2 = plt.subplot2grid((6,6), (0, 0), colspan=2) ax3 = plt.subplot2grid((6,6), (0, 2), colspan=5) ax4 = plt.subplot2grid((6,6), (1, 2), colspan=5) ax5 = plt.subplot2grid((6,6), (1, 0), rowspan=1) ax6 = plt.subplot2grid((6,6), (1, 1), rowspan=1) ax7 = plt.subplot2grid((6,6), (2, 0), colspan=5) ax8 = plt.subplot2grid((6,6), (3, 0), colspan=5) ax9 = plt.subplot2grid((6,6), (4, 0), colspan=3) ax10 = plt.subplot2grid((6,6), (4, 3), colspan=5) ax11 = plt.subplot2grid((6,6), (5, 0), colspan=3) ax12 = plt.subplot2grid((6,6), (5, 3), colspan=5) ax2.set_title("Input Image") ax2.imshow(img, interpolation='none', cmap='Greys_r') ax3.set_title("Target Countmap") ax3.imshow(np.concatenate((gt_countmap),axis=1), interpolation='none') ax4.set_title("Predicted Countmap") ax4.imshow(pred_countmap.squeeze(), interpolation='none') ax5.set_title("Target " + str(gt_count)) ax5.set_ylim((0, np.max(gt_count)*2)) ax5.set_xticks(np.arange(0, 1, 1.0)) ax5.bar(range(1),gt_count, align='center') ax6.set_title("Pred " + str(pred_count)) ax6.set_ylim((0, np.max(gt_count)*2)) ax6.set_xticks(np.arange(0, 1, 1.0)) ax6.bar(range(1),pred_count, align='center') Bins = np.linspace(0,200, num_bins[2] + 1) ax7.set_title("Gt Histogram") ax7.hist(Bins[:-1], weights= gt_hist.T , bins = Bins) ax8.set_title("Pred Histogram") ax8.hist(Bins[:-1], weights= pred_hist.T , bins = Bins) Bins2 = np.linspace(0,200, num_bins[0] + 1) ax9.set_title("Gt Histogram dsn1") ax9.hist(Bins2[:-1], weights= gt_hist_dsn1.T , bins = Bins2) ax11.set_title("Pred Histogram dsn1") ax11.hist(Bins2[:-1], weights= pred_hist_dsn1.T , bins = Bins2) Bins4 = np.linspace(0,200,num_bins[1] + 1) ax10.set_title("Gt Histogram dsn2") ax10.hist(Bins4[:-1], weights= gt_hist_dsn2.T , bins = Bins4) ax12.set_title("Pred Histogram dsn2") ax12.hist(Bins4[:-1], weights= pred_hist_dsn2.T , bins = Bins4) if not os.path.exists(path + '/HistoNet_DSN_eval_result'): os.mkdir(path + '/HistoNet_DSN_eval_result') fig.savefig(path + '/HistoNet_DSN_eval_result/image-' + str(i) + '.png', bbox_inches='tight', pad_inches=0)

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felipeumanzor/chilean_parliament_data

18,004,502,937,562

3504c97d3d645fefe12a4423ef41eab5d0ee8442

36ab20069d9da6168df6e703c8fa35d19bcb6292

/votoparlamentario/urls.py

a98f87c8d4387af91c3e17fba87cdb263e0268fe

[]

no_license

https://github.com/felipeumanzor/chilean_parliament_data

86570f3bbedaa9968dc7c7e83b801c8c5fb69d68

4670db93f03eee9b71e3378b09ce9a200e9a4364

refs/heads/master

2020-08-27T12:02:08.428061

2019-11-12T05:53:08

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from api.views import ( get_diputados, get_proyectos, get_proyectos_by_author, get_votos_by_diputado, get_gastos_by_diputado, get_diputado_by_id, get_proyecto_by_id, ) """votoparlamentario URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path urlpatterns = [ path("admin/", admin.site.urls), path("diputados/", get_diputados), path("diputados/<diputado_id>/", get_diputado_by_id), path("diputados/<diputado_id>/votos/", get_votos_by_diputado), path("diputados/<diputado_id>/gastos/", get_gastos_by_diputado), path("proyectos/", get_proyectos), path("proyectos/by_author/<author_id>/", get_proyectos_by_author), path("proyectos/<proyecto_id>/", get_proyecto_by_id), ]

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urls.py

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Steven-Ireland/ProfessorWho-Scraper

3,599,182,619,290

3e81adf3653ab347045460be984c85f818a42a6c

8425fb50a7f22d458875dd7154f4e7f8af8d2106

/parse.py

fb3e8568e33c7c489d5844849db1e8c912bee2ca

[]

no_license

https://github.com/Steven-Ireland/ProfessorWho-Scraper

2814cde9ab9a21326cea253ac6daa307aaf37afb

39299f495cce0ea50bbea2349a9109c7ff1b4f48

refs/heads/master

2016-08-07T20:23:44.085160

2015-02-04T02:09:21

29,502,390

0

null

import re class Parser: # works for 1-level html structures like table rows, but nesting breaks def __init__(self, dom, attr="html"): self.str = dom self.attr= attr def find(self, q): tag = "<"+q+".*?>.*?</"+q+">" # <q.*?>.*?</q> ret = [] for found in re.findall(tag, self.str, re.DOTALL|re.IGNORECASE): ret.append(Parser(found, q)) return ret def text(self): locator = "<"+self.attr+".*?>(.*?)</"+self.attr+">" innerHTML = re.search(locator, self.str, re.DOTALL|re.IGNORECASE).group(0) # <me>(innerHTML)</me> #Go through and find all plaintext inside element, i.e #<1> <2 class="BORDER">Hello World</2> !</1> #should return "Hello World!" plaintext = "" unclosedABs = 0 #Angle Brackets for c in innerHTML: if (c == '<'): unclosedABs+=1 continue #Move on elif (c == '>'): unclosedABs-=1 continue #Move on if (unclosedABs == 0): plaintext+=c return plaintext def prop(self, attribute): myStartTag = re.search("<"+self.attr+".*?>", self.str, re.IGNORECASE).group(0) locator = attribute+"=\"(.*?)\"" # <me attribute="(/a/b/c)"> return re.search(locator, myStartTag, re.IGNORECASE).group(1) def html(self): return self.str

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parse.py

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faridrashidi/trisicell

15,814,069,613,706

58bf0bff9b86d4fac731ba03505c9a1e3b529fb7

5e84c48158341afa34eae19a2e0db26b11d8a267

/trisicell/pp/__init__.py

5009d08d80e6a62d04e3c24c033248bc973eb7b2

[ "BSD-3-Clause" ]

permissive

https://github.com/faridrashidi/trisicell

53fbd97efd4121f717babd218661edbce48cb10e

eff50c7885053250656a60040612e8097c5032a1

refs/heads/master

2023-07-24T03:54:25.472928

2023-07-21T06:31:18

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BSD-3-Clause

false

2023-07-21T14:42:58

2021-02-26T20:54:06

2023-02-23T20:10:13

2023-07-21T14:42:57

162,366

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"""Pre/Post-Processing Module.""" from trisicell.pp._bifiltering import bifiltering from trisicell.pp._binary import ( binarym_filter_clonal_mutations, binarym_filter_nonsense_mutations, binarym_filter_private_mutations, binarym_statistics, consensus_combine, ) from trisicell.pp._readcount import ( build_scmatrix, filter_mut_mutant_must_present_in_at_least, filter_mut_reference_must_present_in_at_least, filter_mut_vaf_greater_than_coverage_mutant_greater_than, filter_snpeff, group_obs_apply_func, keep_cell_by_list, keep_mut_by_list, local_cluster_cells_then_merge_muts_pseudo_bulk, remove_cell_by_list, remove_mut_by_list, statistics, ) from trisicell.pp._tree import collapse __all__ = ( bifiltering, binarym_filter_clonal_mutations, binarym_filter_nonsense_mutations, binarym_filter_private_mutations, binarym_statistics, consensus_combine, build_scmatrix, filter_mut_mutant_must_present_in_at_least, filter_mut_reference_must_present_in_at_least, filter_mut_vaf_greater_than_coverage_mutant_greater_than, filter_snpeff, keep_cell_by_list, keep_mut_by_list, remove_cell_by_list, remove_mut_by_list, statistics, collapse, group_obs_apply_func, local_cluster_cells_then_merge_muts_pseudo_bulk, )

UTF-8

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py

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__init__.py

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leeil525/Python

3,341,484,604,058

5fe01d930661e24d8f856082989ba1534e0c6586

3bfc8c92a9aae68a7bd0aaf9fef7e208d0312cb1

/selenium.py

2cde1f255e67891265eec05ed7cf9fa0c75b396e

[]

no_license

https://github.com/leeil525/Python

7abcfecdc86a55939affc0754c59f747bf3a8973

cee85158428cc4922d2a4b9f14d56fada2e726a0

refs/heads/master

2021-01-20T09:06:51.671598

2021-01-20T06:30:31

null

0

null

from selenium import webdriver from selenium.webdriver.common.keys import Keys #输入的键盘位键操作 from selenium.webdriver.support.ui import Select #选项卡操作 from selenium.webdriver import ActionChains #拖拽元素 from selenium.webdriver.chrome.options import Options #设置Chrome手机模式 from selenium.webdriver.common.desired_capabilities import DesiredCapabilities #设置PhantomJS手机模式 #browser = webdriver.PhantomJS(executable_path='D:\\phantomjs-2.1.1-windows\\bin\\phantomjs.exe') PhantomJS 用法 #browser=webdriver.Chrome() '''PhantomJs手机模式并截图''' # dcap = dict(DesiredCapabilities.PHANTOMJS) # dcap["phantomjs.page.settings.userAgent"] = ( # "Mozilla/5.0 (Linux; Android 5.1.1; Nexus 6 Build/LYZ28E) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.23 Mobile Safari/537.36" # ) # driver = webdriver.PhantomJS(desired_capabilities=dcap) # driver.get("https://bilibili.com") # driver.get_screenshot_as_file('02.png') # driver.quit() '''Chrome手机模式''' # mobile_emulation = {"deviceName":"Apple iPhone 6"} # chrome_options = Options() # chrome_options.add_experimental_option("mobileEmulation", mobile_emulation) # browser = webdriver.Chrome(chrome_options=chrome_options) # 手机模式浏览 # browser.get("http://www.bilibili.com") #打开bilibili '''查找元素''' #element = browser.find_element_by_id("q") #根据ID查找 #element = browser.find_element_by_css_selector("input") #根据css选择器查找 #element = browser.find_element_by_name("passwd") #根据name属性查找 name='passwd' #element = browser.find_elements_by_tag_name("input") #根据标签查找<input></input> #element = browser.find_element_by_xpath('//*[@id="banner_link"]/div/div/form/input') #根据XPATH语法查找（搜索输入框） #上面是单个在element后加s就OK '''向表单填写,按键，点击''' #element.send_keys("test") #写入test #element.clear() #清除之前的文本 #element.send_keys("test2") #写入test #element.send_keys(Keys.RETURN) #模拟按enter #element.click() #鼠标点击 '''选项卡操作''' #select = Select(driver.find_element_by_name('name')) #select.select_by_index(index) #根据索引来选择 #select.select_by_visible_text("text") #根据值来选择 #select.select_by_value(value) #根据文字来选择 #select.deselect_all() #取消全部的选择 '''拖拽元素''' #element2 = browser.find_element_by_xpath('//*[@id="banner_link"]/div/div/form/input') #element1 = browser.find_element_by_xpath('//*[@id="banner_link"]/div/div/a') #action_chains = ActionChains(browser) #action_chains.drag_and_drop(element2, element1).perform() #2拖到1 #ActionChains(browser).drag_and_drop_by_offset(element2,10,10).perform() #把element2拖动（10，10）的距离，即向右下方拖动 #browser.close() #关闭浏览器

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py

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selenium.py

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0.69555

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sirius9/Project

6,124,623,389,661

1fa5fb75701bcae39f54eb46e8754039bfac04dc

574e4f93ebc4b557f82599c5ce9558c0391ea5ba

/03_Premierleague/function.py

738f34776f181b9fde6eeabceeee52f2f7caef4d

[]

no_license

https://github.com/sirius9/Project

950696144748cba58bfc69bc8febbf0a0defcaff

7f4de79ee722284396b7950b4d1e956c998bb7d9

refs/heads/master

2020-04-09T04:33:37.163893

2018-08-27T01:20:57

null

0

null

# 0. basic import numpy as np import pandas as pd import warnings warnings.filterwarnings('ignore') # 1. EDA import seaborn as sns import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib.pyplot import * # Statistic import scipy.stats as stats import urllib from statsmodels.formula.api import ols from statsmodels.stats.anova import anova_lm import matplotlib.pyplot as plt # 2. Feature Select from sklearn.preprocessing import LabelEncoder def category_to_ohe(train_col, test_col): ''' Train / Test 데이터에서 카테고리 데이터에 라벨링을 하기 위한 함수입니다. ''' le = LabelEncoder() le.fit(train_col) labeled_train_col = le.transform(train_col) labeled_train_col = labeled_train_col.reshape(len(labeled_train_col),1) labeled_test_col = le.transform(test_col) labeled_test_col = labeled_test_col.reshape(len(labeled_test_col),1) return labeled_train_col, labeled_test_col # 3. Modeling from statsmodels.stats.outliers_influence import variance_inflation_factor # 3.1 조건부 확률기반 생성모형 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis # LDA from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis # QDA from sklearn.naive_bayes import * # Naive basesian # 3.2 조건부 확률기반 판별모형 from sklearn.tree import DecisionTreeClassifier # 3.3 모형결합 (Ensenble) from sklearn.ensemble import VotingClassifier # voting from sklearn.ensemble import BaggingClassifier # bagging from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier # random Forest from sklearn.ensemble import AdaBoostClassifier # AdaBoost from sklearn.ensemble import GradientBoostingClassifier # GradientBoost import xgboost # xgboost # 3.4 판별함수 모형 from sklearn.linear_model import Perceptron # perceptron from sklearn.linear_model import SGDClassifier # SGD from sklearn.svm import SVC # support vector machine # 4. Optimizer from sklearn.model_selection import validation_curve # validation curve from sklearn.model_selection import GridSearchCV # gridseach from sklearn.model_selection import ParameterGrid # ParameterGrid # 5. Evaluation from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.metrics import * # make confusion matrix from sklearn.preprocessing import label_binarize # ROC curve from sklearn.metrics import auc # AUC from sklearn.metrics import roc_curve

UTF-8

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py

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function.py

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0.794893

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geedubey/soware

13,297,218,788,514

678ddc56c064f14b6bbbefc1f985f9f9cc6eee63

ece9ae460f93b877313c930af7d2e36219cd0942

/link.py

e0e02b78adefa0aabb8aa5e4886bffd168e5823a

[]

no_license

https://github.com/geedubey/soware

c49ae6c740e8c0da3879856236eeb23d91c997d4

138a204f10cfc6f0926a6773a5540c7943ff9adf

refs/heads/master

2020-03-23T09:29:40.880732

2018-09-09T17:57:44

141,391,014

0

null

# Credentials you get from registering a new application client_id = '77xdxq5i655wrv' client_secret = 'Ww37UQ9kcZ9Q9RVD' # OAuth endpoints given in the LinkedIn API documentation authorization_base_url = 'https://www.linkedin.com/uas/oauth2/authorization' token_url = 'https://www.linkedin.com/uas/oauth2/accessToken' from requests_oauthlib import OAuth2Session from requests_oauthlib.compliance_fixes import linkedin_compliance_fix import json linkedin = OAuth2Session(client_id, redirect_uri='https://localhost:8000/') linkedin = linkedin_compliance_fix(linkedin) # Redirect user to LinkedIn for authorization authorization_url, state = linkedin.authorization_url(authorization_base_url) print ('Please go here and authorize,', authorization_url) # Get the authorization verifier code from the callback url redirect_response = input('Paste the full redirect URL here:') # Fetch the access token linkedin.fetch_token(token_url, client_secret=client_secret, authorization_response=redirect_response) # Fetch a protected resource, i.e. user profile r = linkedin.get('https://api.linkedin.com/v1/people/~') #r = linkedin.get("https://api.linkedin.com/v2/me") print (r.content) #sharepost = { "comment": "Check out developer.linkedin.com! http://linkd.in/1FC2PyG", "visibility": {"code": "anyone"}} #p = linkedin.post("https://api.linkedin.com/v1/people/~/shares?format=json", json=sharepost) #print (p.content) comment = { "actor": "urn:li:person:t3gGTvjaJQ", "message": { "attributes": [], "text": "This is a comment on POST developer.linkedin.com" } } cp = linkedin.post("https://api.linkedin.com/v2/socialActions/urn:li:share:6443893670432243712/comments", json=comment) print (cp.content) #urn:li:share:6443893670432243712

UTF-8

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py

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link.py

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0.755253

0.71891

0

42

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122

shantanu609/PreCourse_1

3,839,700,768,747

98919ef1f3ef37bfe8811dce5734a53a0ddf0783

ce3418afa332e88827068bef0b996b820b144cba

/Exercise_3.py

44ecfdaec6c167fdaedc2313104372c23ce34966

[]

no_license

https://github.com/shantanu609/PreCourse_1

e7d32108b594d6ff0ff09d4cb9e6b5fb690e7af7

68a0929bc183aea6f4063ebb5faaf67cbbe24794

refs/heads/master

2022-07-17T09:32:53.746587

2020-05-20T01:36:46

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class ListNode: """ A node in a singly-linked list. """ def __init__(self, data=None, next=None): self.data = data self.next = None class SinglyLinkedList: def __init__(self): """ Create a new singly-linked list. Takes O(1) time. """ self.head = ListNode(0) # O(N) time | O(1) Space def append(self, data): """ Insert a new element at the end of the list. Takes O(n) time. """ node = self.head while node.next is not None: node = node.next node.next = ListNode(data) def find(self, key): """ Search for the first element with `data` matching `key`. Return the element or `None` if not found. Takes O(n) time. """ node = self.head.next while node: if node.data == key: return True node = node.next return False # O(N) time | O(1) Space def remove(self, key): """ Remove the first occurrence of `key` in the list. Takes O(n) time. """ node = self.head.next prev = self.head while node: if node.data == key: prev.next = node.next break node = node.next prev = prev.next if __name__ == '__main__': sl = SinglyLinkedList() sl.append(1) sl.append(2) sl.append(3) res = sl.find(2) print(res) sl.remove(2) print(sl.find(2))

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Mepond/bitcoin-abc

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#!/usr/bin/env python3 # Copyright (c) 2020-2022 The Bitcoin developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test the quorum detection of avalanche.""" from time import time from test_framework.avatools import ( create_coinbase_stakes, get_ava_p2p_interface, ) from test_framework.key import ECKey, ECPubKey from test_framework.messages import AvalancheVote, AvalancheVoteError from test_framework.test_framework import BitcoinTestFramework from test_framework.util import assert_equal from test_framework.wallet_util import bytes_to_wif class AvalancheQuorumTest(BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 self.extra_args = [ ['-enableavalanche=1', '-avacooldown=0', '-avatimeout=0', '-avaminquorumstake=100000000', '-avaminquorumconnectedstakeratio=0.8'] ] def mock_forward(self, delta): self.mock_time += delta self.nodes[0].setmocktime(self.mock_time) def run_test(self): self.mock_time = int(time()) self.mock_forward(0) # Create a local node to poll from and a helper to send polls from it # and assert on the response node = self.nodes[0] poll_node = get_ava_p2p_interface(node) poll_node_pubkey = ECPubKey() poll_node_pubkey.set(bytes.fromhex(node.getavalanchekey())) def poll_and_assert_response(expected): # Send poll for best block block = int(node.getbestblockhash(), 16) poll_node.send_poll([block]) # Get response and check that the vote is what we expect response = poll_node.wait_for_avaresponse() r = response.response assert poll_node_pubkey.verify_schnorr(response.sig, r.get_hash()) assert_equal(len(r.votes), 1) actual = repr(r.votes[0]) expected = repr(AvalancheVote(expected, block)) assert_equal(actual, expected) # Create peers to poll num_quorum_peers = 2 coinbase_key = node.get_deterministic_priv_key().key blocks = node.generate(num_quorum_peers) peers = [] for i in range(0, num_quorum_peers): keyHex = "12b004fff7f4b69ef8650e767f18f11ede158148b425660723b9f9a66e61f75" + \ str(i) k = ECKey() k.set(bytes.fromhex(keyHex), True) stakes = create_coinbase_stakes( node, [blocks[i]], coinbase_key) proof = node.buildavalancheproof(1, 1, bytes_to_wif(k.get_bytes()), stakes) peers.append({'key': k, 'proof': proof, 'stake': stakes}) def addavalanchenode(peer): pubkey = peer['key'].get_pubkey().get_bytes().hex() assert node.addavalanchenode( peer['node'].nodeid, pubkey, peer['proof']) is True # Start polling. The response should be UNKNOWN because there's no # score poll_and_assert_response(AvalancheVoteError.UNKNOWN) # Create one peer with half the score and add one node peers[0]['node'] = get_ava_p2p_interface(node) addavalanchenode(peers[0]) poll_and_assert_response(AvalancheVoteError.UNKNOWN) # Create a second peer with the other half and add one node peers[1]['node'] = get_ava_p2p_interface(node) addavalanchenode(peers[1]) poll_and_assert_response(AvalancheVoteError.ACCEPTED) # Disconnect peer 1's node which drops us below the threshold, but we've # latched that the quorum is established self.mock_forward(1) peers[1]['node'].peer_disconnect() peers[1]['node'].wait_for_disconnect() poll_and_assert_response(AvalancheVoteError.ACCEPTED) # Reconnect node and re-establish quorum peers[1]['node'] = get_ava_p2p_interface(node) addavalanchenode(peers[1]) poll_and_assert_response(AvalancheVoteError.ACCEPTED) if __name__ == '__main__': AvalancheQuorumTest().main()

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kitoku-magic/user_registration_form

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/python/src/controller/user_registration/user_registration_common_controller.py

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[]

no_license

https://github.com/kitoku-magic/user_registration_form

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refs/heads/master

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from python_library.src.custom_flask.controller import controller from src import abc from src import os from src.model.entity.birth_days_entity import birth_days_entity from src.model.entity.contact_methods_entity import contact_methods_entity from src.model.entity.jobs_entity import jobs_entity from src.model.entity.knew_triggers_entity import knew_triggers_entity from src.model.entity.prefectures_entity import prefectures_entity from src.model.entity.sexes_entity import sexes_entity from src.model.entity.users_entity import users_entity from src.model.repository.birth_days_repository import birth_days_repository from src.model.repository.contact_methods_repository import contact_methods_repository from src.model.repository.jobs_repository import jobs_repository from src.model.repository.knew_triggers_repository import knew_triggers_repository from src.model.repository.prefectures_repository import prefectures_repository from src.model.repository.sexes_repository import sexes_repository class user_registration_common_controller(controller): """ ユーザー登録の、共通処理を定義するクラス """ __abstract__ = True def __init__(self): super().__init__() request = self.get_request() # 使いやすさを考え、本処理を開始する前に、全てのフォームデータを設定しておく if 'GET' == request.environ['REQUEST_METHOD']: request_data = request.args elif 'POST' == request.environ['REQUEST_METHOD']: request_data = request.form self.__users_entity = users_entity() self.__users_entity.set_request_to_entity(request_data) @abc.abstractmethod def execute(self): pass def get_users_entity(self): return self.__users_entity def assign_all_form_data(self): """ 全てのフォーム項目に値を設定する """ properties = self.__users_entity.get_all_properties() for field, value in properties.items(): if value is None: value = '' # DBから郵便番号を取得して表示する時は、ハイフンを付ける if 'zip_code' == field and '' != value and '-' not in value and self.__users_entity.zip_code_error is None: value = value[0:3] + '-' + value[3:] self.add_response_data(field, value) def set_value_item(self): """ 選択項目の表示内容を取得して設定 """ value_items = [ # 性別 { 'repository': sexes_repository(sexes_entity()), 'method': 'find_all', 'select': (sexes_entity.sex_id, sexes_entity.sex_name), 'where': '', 'params': {}, 'order_by': 'sexes_sex_id ASC', 'template_param_name': 'sexes', }, # 都道府県 { 'repository': prefectures_repository(prefectures_entity()), 'method': 'find_all', 'select': (prefectures_entity.prefecture_id, prefectures_entity.prefecture_name), 'where': '', 'params': {}, 'order_by': 'prefectures_prefecture_id ASC', 'template_param_name': 'prefectures', }, # 連絡方法 { 'repository': contact_methods_repository(contact_methods_entity()), 'method': 'find_all', 'select': (contact_methods_entity.contact_method_id, contact_methods_entity.contact_method_name), 'where': '', 'params': {}, 'order_by': 'contact_methods_contact_method_id ASC', 'template_param_name': 'contact_methods', }, # 知ったきっかけ { 'repository': knew_triggers_repository(knew_triggers_entity()), 'method': 'find_all', 'select': (knew_triggers_entity.knew_trigger_id, knew_triggers_entity.knew_trigger_name), 'where': '', 'params': {}, 'order_by': 'knew_triggers_knew_trigger_id ASC', 'template_param_name': 'knew_triggers', }, ] for value_item in value_items: result_data = getattr(value_item['repository'], value_item['method'])( value_item['select'], value_item['where'], value_item['params'], value_item['order_by'] ) response_data = [] for row in result_data: response_dict = {'id': row[0], 'name': row[1]} response_data.append(response_dict) self.add_response_data(value_item['template_param_name'], response_data) # 以下は、特殊なケースの項目 # 誕生日 birth_days_repository_obj = birth_days_repository(birth_days_entity()) birth_days_all_data = birth_days_repository_obj.find_all( (birth_days_entity.birth_day_id, birth_days_entity.birth_day), '', {}, 'birth_days_birth_day_id ASC' ) birth_years = [] birth_months = [] birth_days = [] birth_day = None for row in birth_days_all_data: birth_day = row.birth_day if birth_day.year not in birth_years: birth_years.append(birth_day.year) if birth_day.month not in birth_months: birth_months.append(birth_day.month) if birth_day.day not in birth_days: birth_days.append(birth_day.day) self.add_response_data('birth_years', birth_years) self.add_response_data('birth_months', birth_months) self.add_response_data('birth_days', birth_days) # 職業（その他を末尾に表示させる） jobs_repository_obj = jobs_repository(jobs_entity()) jobs = jobs_repository_obj.find_all_order_by_job_other_last( (jobs_entity.job_id, jobs_entity.job_name), '', {}, 'jobs_job_id ASC' ) self.add_response_data('jobs', jobs) def select_value_item(self): """ 選択項目の選択状態を設定 """ # 単一選択項目 single_items = [ { 'name': 'birth_year', 'default_value': '', }, { 'name': 'birth_month', 'default_value': '', }, { 'name': 'birth_day', 'default_value': '', }, { 'name': 'prefecture_id', 'default_value': '', }, { 'name': 'sex_id', 'default_value': 1, }, { 'name': 'job_id', 'default_value': 2, }, { 'name': 'is_latest_news_hoped', 'default_value': 1, }, { 'name': 'is_personal_information_provide_agreed', 'default_value': '', }, ] for single_item in single_items: form_value = getattr(self.__users_entity, single_item['name'], None) if form_value is None: value = single_item['default_value'] else: value = form_value self.add_response_data(single_item['name'], value) # 複数選択項目 multiple_items = [ { 'name': 'user_contact_methods_collection', 'field': 'contact_method_id', 'default_value': [], }, { 'name': 'user_knew_triggers_collection', 'field': 'knew_trigger_id', 'default_value': [], }, ] for multiple_item in multiple_items: form_value = getattr(self.__users_entity, multiple_item['name'], None) if form_value is None: value = multiple_item['default_value'] else: ids = [] for entity in form_value: ids.append(int(getattr(entity, multiple_item['field'], None))) value = ids self.add_response_data(multiple_item['name'], value) def remove_upload_file(self, users_entity_obj): """ アップロードされたファイルを削除する """ if True == isinstance(users_entity_obj.file_name, str) and os.path.isfile(users_entity_obj.file_path): os.remove(users_entity_obj.file_path)

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import inspect print("1......") class UpperMetaClass(type): def __new__(upperattr_metaclass, future_class_name, future_class_parents, future_class_attr, **kwargs): print("upperattr_metaclass : ", upperattr_metaclass) print("future_class_name : ", future_class_name) print("future_class_parents : ", future_class_parents) print("future_class_attr : ", future_class_attr) upper_attr = {} for name,value in future_class_attr.items(): if not name.startswith('__'): upper_attr[name.upper()] = value else: upper_attr[name] = value new_class = super(UpperMetaClass, upperattr_metaclass).__new__(upperattr_metaclass, future_class_name, future_class_parents, upper_attr, **kwargs) new_class._prepare() return new_class def add_to_class(cls, name, value): # We should call the contribute_to_class method only if it's bound if not inspect.isclass(value) and hasattr(value, 'contribute_to_class'): value.contribute_to_class(cls, name) else: setattr(cls, name, value) def _prepare(cls): print("metaclass _pepare") cls.add_to_class("m", "123") print("2......") class ParentTest(metaclass=UpperMetaClass): parent_name = "hello parent metaclass" def __init__(self, *args, **kwargs): self.parent_m = 456 print("3......") class Test(ParentTest): name = "hello metaclass" def __init__(self, *args, **kwargs): self.m = 123 super().__init__() print("4......") test = Test() """ print("test is created...") print("test.parent_m : ", test.parent_m) print("test.PARENT_NAME : ", test.PARENT_NAME) """ """ test = Test() test.n = 123 print(test.__dict__) print(Test.__class__) print(hasattr(test, "NAME")) print(hasattr(test, "name")) """

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Zapix/gym-backend

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/gym_backend/exercises/migrations/0001_initial.py

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[]

no_license

https://github.com/Zapix/gym-backend

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refs/heads/master

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# -*- coding: utf-8 -*- from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'MuscleGroup' db.create_table(u'exercises_musclegroup', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=255)), )) db.send_create_signal(u'exercises', ['MuscleGroup']) # Adding model 'Exercise' db.create_table(u'exercises_exercise', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('muscle_group', self.gf('django.db.models.fields.related.ForeignKey')(related_name='exercise_set', to=orm['exercises.MuscleGroup'])), ('name', self.gf('django.db.models.fields.CharField')(max_length=255)), ('description', self.gf('django.db.models.fields.TextField')()), ('youtube_link', self.gf('django.db.models.fields.URLField')(max_length=200)), )) db.send_create_signal(u'exercises', ['Exercise']) def backwards(self, orm): # Deleting model 'MuscleGroup' db.delete_table(u'exercises_musclegroup') # Deleting model 'Exercise' db.delete_table(u'exercises_exercise') models = { u'exercises.exercise': { 'Meta': {'object_name': 'Exercise'}, 'description': ('django.db.models.fields.TextField', [], {}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'muscle_group': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'exercise_set'", 'to': u"orm['exercises.MuscleGroup']"}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'youtube_link': ('django.db.models.fields.URLField', [], {'max_length': '200'}) }, u'exercises.musclegroup': { 'Meta': {'ordering': "['id']", 'object_name': 'MuscleGroup'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}) } } complete_apps = ['exercises']

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TheEnbyperor/racing_sim

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refs/heads/master

2020-04-25T18:19:11.442235

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from inputs import devices stick = devices.gamepads[0] while True: events = stick.read() for event in events: print(event.ev_type, event.code, event.state)

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nub8p/2020-Summer-Jookgorithm

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/최민우/[20.07.12] 10825.py

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[]

no_license

https://github.com/nub8p/2020-Summer-Jookgorithm

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refs/heads/master

2023-05-05T15:27:31.009599

2021-05-03T18:59:20

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people = [] for i in range(int(input())): line = input().split() name = line[0] kor, eng, math = map(int, line[1:]) people.append({'name': name, 'kor': kor, 'eng': eng, 'math': math}) people.sort(key=lambda x: (-x['kor'], x['eng'], -x['math'], x['name'])) print('\n'.join(i['name'] for i in people))

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ceos-seo/data_cube_utilities

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refs/heads/master

2021-11-05T20:22:41.632698

2021-10-25T23:19:56

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2021-02-05T19:51:59

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import numpy as np import xarray import xarray.core.ops as ops import xarray as xr from itertools import islice def _window(seq, n=2): """Returns a sliding _window (of width n) over data from the iterable" " s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ... """ it = iter(seq) result = tuple(islice(it, n)) if len(result) == n: yield result for elem in it: result = result[1:] + (elem,) yield result def _composite_of_first(arrays, reverse=False, name_suffix="_composite"): #In memory of Rube Goldberg narrays = arrays.copy(deep=True) narrays.values = narrays.values[::-1] if reverse else narrays.values notnulls = [ops.notnull(array) for array in narrays] first_notnull = ops.argmax(ops.stack(notnulls), axis=0) composite = np.choose(first_notnull, narrays) return xr.DataArray( composite, coords=[narrays.latitude, narrays.longitude], dims=['latitude', 'longitude'], name="{band}{suffix}".format(band=narrays.name, suffix=name_suffix)) def _mosaic(dataset, most_recent_first=False, custom_label="_composite"): return xr.merge([ _composite_of_first(dataset[variable], reverse=most_recent_first, name_suffix=custom_label) for variable in dataset.data_vars ]) def _composite_by_average(dataset, custom_label="_composite"): composite = dataset.mean('time') return composite ## This should be the the only method called from dc baseline def generate_baseline(dataset, composite_size=5, mode="average", custom_label=""): ranges = _window(range(len(dataset.time)), n=composite_size + 1) reffs = (dataset.isel(time=list(frame)[:-1]) for frame in ranges) baselines = None if mode == "average": baselines = (_composite_by_average(ref, custom_label=custom_label) for ref in reffs) elif mode == "composite": baselines = (_mosaic(ref, most_recent_first=True, custom_label=custom_label) for ref in reffs) baseline = xr.concat(baselines, dim='time') baseline['time'] = dataset.time[composite_size:] return baseline

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CodeLogist/RAdam-PLSTM-FX-Trader

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/data.py

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https://github.com/CodeLogist/RAdam-PLSTM-FX-Trader

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a38c25a0e49d50a35bd586c15c2b3a10b293b648

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2021-05-18T19:03:47.494160

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import numpy as np import pandas as pd import os from datetime import datetime from sklearn.preprocessing import MinMaxScaler from sklearn.externals import joblib output_scale = 100 def get_day(x): try: y = datetime.strptime(x,'%Y/%m/%d') except: try: y = datetime.strptime(x,'%m/%d/%Y') except: y = datetime.strptime(x,'%d-%m-%y') y = str(y).split(' ')[0] return y def split(X, Y, ratio): """ Split in train and test test set """ train_len = int(len(X) * ratio) trainX = X[:train_len] trainY = Y[:train_len] testX = X[train_len:] testY = Y[train_len:] return trainX, trainY, testX, testY def create_dataset(dataframe, win_size, delta_t, scaler_path, save_scaler = True): dataframe.reset_index(drop=True,inplace=True) data = dataframe[['toHigh','toLow','toClose','Ticks','Spread',\ 'Date','Seconds']] # Disable SettingWithCopyWarning pd.options.mode.chained_assignment = None data['Hour'] = dataframe['Seconds'].apply(lambda x: int(str(x).split(':')[0])/23.) data['Date'] = dataframe['Date'].apply(lambda x: get_day(x)) data['Day'] = data['Date'].apply(lambda x: int(datetime.strptime(x, '%Y-%m-%d').weekday())/6) data['Month'] = data['Date'].apply(lambda x: (int(x.split('-')[1])-1)/11) data = data.drop(['Date','Seconds'],axis=1).values pd.options.mode.chained_assignment = 'warn' if save_scaler: # Create, set-up and save scaler scaler = MinMaxScaler(feature_range=(0, 1)) scaler.fit(data) joblib.dump(scaler, scaler_path) else: # load scaler scaler = joblib.load(scaler_path) # scale data data = scaler.transform(data) #outputs hilo = dataframe[['toHigh','toLow']].values*output_scale dataX, dataY = [], [] for i in range(0, len(data) - win_size, delta_t): a = data[i:(i+win_size), :] dataX.append(a) dataY.append(hilo[i + win_size]) return np.array(dataX), np.array(dataY) def get_win(df,win_size,scaler): df = df.tail(win_size) # Disable SettingWithCopyWarning pd.options.mode.chained_assignment = None data = df[['toHigh','toLow','toClose','Ticks','Spread']] # Convert datetime data.loc[:,'Hour'] = df.loc[:,'Datetime'].apply(lambda x: x.hour/23) data.loc[:,'Weekday'] = df.loc[:,'Datetime'].apply(lambda x: int(x.weekday())/6) data.loc[:,'Month'] = df.loc[:,'Datetime'].apply(lambda x: (x.month-1)/11) pd.options.mode.chained_assignment = 'warn' data = scaler.transform(data) return np.array(data)

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FrankSauve/390AntennaPod

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df4f4079423d660944750cc3295ae579baaca1e4

bfdf7c033eec65e7d7393145c809d643cf0824e2

/contributers.template.py

3127baa0c063dfccb2dcd064bd91e2609b1e0bec

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permissive

https://github.com/FrankSauve/390AntennaPod

5c790aa58ccd7e06fe5772ca10de00504a13b44a

dbed5b9841fbfb32ab51b61ab6b83a1950c83796

refs/heads/develop

2021-09-12T18:13:27.484257

2018-04-19T14:47:21

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2019-03-17T20:50:07

2018-01-09T16:28:34

2018-11-20T12:37:32

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#!/usr/bin/env python3 import requests import subprocess TRANSIFEX_USER = "" TRANSIFEX_PW = "" print('DEVELOPERS\n==========\n') p = subprocess.Popen("git log --format='%aN' | sort -fu", shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) for line in p.stdout.readlines(): output = line.decode() print(output, end='') language_codes = { "af_NA": "Afrikaans (Namibia)", "af_ZA": "Afrikaans (South Africa)", "af": "Afrikaans", "ak_GH": "Akan (Ghana)", "ak": "Akan", "sq_AL": "Albanian (Albania)", "sq": "Albanian", "am_ET": "Amharic (Ethiopia)", "am": "Amharic", "ar_DZ": "Arabic (Algeria)", "ar_BH": "Arabic (Bahrain)", "ar_EG": "Arabic (Egypt)", "ar_IQ": "Arabic (Iraq)", "ar_JO": "Arabic (Jordan)", "ar_KW": "Arabic (Kuwait)", "ar_LB": "Arabic (Lebanon)", "ar_LY": "Arabic (Libya)", "ar_MA": "Arabic (Morocco)", "ar_OM": "Arabic (Oman)", "ar_QA": "Arabic (Qatar)", "ar_SA": "Arabic (Saudi Arabia)", "ar_SD": "Arabic (Sudan)", "ar_SY": "Arabic (Syria)", "ar_TN": "Arabic (Tunisia)", "ar_AE": "Arabic (United Arab Emirates)", "ar_YE": "Arabic (Yemen)", "ar": "Arabic", "hy_AM": "Armenian (Armenia)", "hy": "Armenian", "as_IN": "Assamese (India)", "as": "Assamese", "asa_TZ": "Asu (Tanzania)", "asa": "Asu", "az_Cyrl": "Azerbaijani (Cyrillic)", "az_Cyrl_AZ": "Azerbaijani (Cyrillic, Azerbaijan)", "az_Latn": "Azerbaijani (Latin)", "az_Latn_AZ": "Azerbaijani (Latin, Azerbaijan)", "az": "Azerbaijani", "bm_ML": "Bambara (Mali)", "bm": "Bambara", "eu_ES": "Basque (Spain)", "eu": "Basque", "be_BY": "Belarusian (Belarus)", "be": "Belarusian", "bem_ZM": "Bemba (Zambia)", "bem": "Bemba", "bez_TZ": "Bena (Tanzania)", "bez": "Bena", "bn_BD": "Bengali (Bangladesh)", "bn_IN": "Bengali (India)", "bn": "Bengali", "bs_BA": "Bosnian (Bosnia and Herzegovina)", "bs": "Bosnian", "bg_BG": "Bulgarian (Bulgaria)", "bg": "Bulgarian", "my_MM": "Burmese (Myanmar [Burma])", "my": "Burmese", "ca_ES": "Catalan (Spain)", "ca": "Catalan", "tzm_Latn": "Central Morocco Tamazight (Latin)", "tzm_Latn_MA": "Central Morocco Tamazight (Latin, Morocco)", "tzm": "Central Morocco Tamazight", "chr_US": "Cherokee (United States)", "chr": "Cherokee", "cgg_UG": "Chiga (Uganda)", "cgg": "Chiga", "zh": "Chinese (Simplified)", "zh_CN": "Chinese (China)", "zh_HK": "Chinese (Hong Kong SAR China)", "zh_MO": "Chinese (Macau SAR China)", "zh_MO_SG": "Chinese (Singapore)", "zh_TW": "Chinese (Taiwan)", "zh": "Chinese", "kw_GB": "Cornish (United Kingdom)", "kw": "Cornish", "hr_HR": "Croatian (Croatia)", "hr": "Croatian", "cs_CZ": "Czech (Czech Republic)", "cs": "Czech", "da_DK": "Danish (Denmark)", "da": "Danish", "nl_BE": "Dutch (Belgium)", "nl_NL": "Dutch (Netherlands)", "nl": "Dutch", "ebu_KE": "Embu (Kenya)", "ebu": "Embu", "en_AS": "English (American Samoa)", "en_AU": "English (Australia)", "en_BE": "English (Belgium)", "en_BZ": "English (Belize)", "en_BW": "English (Botswana)", "en_CA": "English (Canada)", "en_GU": "English (Guam)", "en_HK": "English (Hong Kong SAR China)", "en_IN": "English (India)", "en_IE": "English (Ireland)", "en_JM": "English (Jamaica)", "en_MT": "English (Malta)", "en_MH": "English (Marshall Islands)", "en_MU": "English (Mauritius)", "en_NA": "English (Namibia)", "en_NZ": "English (New Zealand)", "en_MP": "English (Northern Mariana Islands)", "en_PK": "English (Pakistan)", "en_PH": "English (Philippines)", "en_SG": "English (Singapore)", "en_ZA": "English (South Africa)", "en_TT": "English (Trinidad and Tobago)", "en_UM": "English (U.S. Minor Outlying Islands)", "en_VI": "English (U.S. Virgin Islands)", "en_GB": "English (United Kingdom)", "en_US": "English (United States)", "en_ZW": "English (Zimbabwe)", "en": "English", "eo": "Esperanto", "et_EE": "Estonian (Estonia)", "et": "Estonian", "ee_GH": "Ewe (Ghana)", "ee_TG": "Ewe (Togo)", "ee": "Ewe", "fo_FO": "Faroese (Faroe Islands)", "fo": "Faroese", "fil_PH": "Filipino (Philippines)", "fil": "Filipino", "fi_FI": "Finnish (Finland)", "fi": "Finnish", "fr_BE": "French (Belgium)", "fr_BJ": "French (Benin)", "fr_BF": "French (Burkina Faso)", "fr_BI": "French (Burundi)", "fr_CM": "French (Cameroon)", "fr_CA": "French (Canada)", "fr_CF": "French (Central African Republic)", "fr_TD": "French (Chad)", "fr_KM": "French (Comoros)", "fr_CG": "French (Congo - Brazzaville)", "fr_CD": "French (Congo - Kinshasa)", "fr_CI": "French (Côte d’Ivoire)", "fr_DJ": "French (Djibouti)", "fr_GQ": "French (Equatorial Guinea)", "fr_FR": "French (France)", "fr_GA": "French (Gabon)", "fr_GP": "French (Guadeloupe)", "fr_GN": "French (Guinea)", "fr_LU": "French (Luxembourg)", "fr_MG": "French (Madagascar)", "fr_ML": "French (Mali)", "fr_MQ": "French (Martinique)", "fr_MC": "French (Monaco)", "fr_NE": "French (Niger)", "fr_RW": "French (Rwanda)", "fr_RE": "French (Réunion)", "fr_BL": "French (Saint Barthélemy)", "fr_MF": "French (Saint Martin)", "fr_SN": "French (Senegal)", "fr_CH": "French (Switzerland)", "fr_TG": "French (Togo)", "fr": "French", "ff_SN": "Fulah (Senegal)", "ff": "Fulah", "gl_ES": "Galician (Spain)", "gl": "Galician", "lg_UG": "Ganda (Uganda)", "lg": "Ganda", "ka_GE": "Georgian (Georgia)", "ka": "Georgian", "de_AT": "German (Austria)", "de_BE": "German (Belgium)", "de_DE": "German (Germany)", "de_LI": "German (Liechtenstein)", "de_LU": "German (Luxembourg)", "de_CH": "German (Switzerland)", "de": "German", "el_CY": "Greek (Cyprus)", "el_GR": "Greek (Greece)", "el": "Greek", "gu_IN": "Gujarati (India)", "gu": "Gujarati", "guz_KE": "Gusii (Kenya)", "guz": "Gusii", "ha_Latn": "Hausa (Latin)", "ha_Latn_GH": "Hausa (Latin, Ghana)", "ha_Latn_NE": "Hausa (Latin, Niger)", "ha_Latn_NG": "Hausa (Latin, Nigeria)", "ha": "Hausa", "haw_US": "Hawaiian (United States)", "haw": "Hawaiian", "he_IL": "Hebrew (Israel)", "he": "Hebrew", "hi_IN": "Hindi (India)", "hi": "Hindi", "hu_HU": "Hungarian (Hungary)", "hu": "Hungarian", "is_IS": "Icelandic (Iceland)", "is": "Icelandic", "ig_NG": "Igbo (Nigeria)", "ig": "Igbo", "id_ID": "Indonesian (Indonesia)", "id": "Indonesian", "ga_IE": "Irish (Ireland)", "ga": "Irish", "it_IT": "Italian (Italy)", "it_CH": "Italian (Switzerland)", "it": "Italian", "ja_JP": "Japanese (Japan)", "ja": "Japanese", "kea_CV": "Kabuverdianu (Cape Verde)", "kea": "Kabuverdianu", "kab_DZ": "Kabyle (Algeria)", "kab": "Kabyle", "kl_GL": "Kalaallisut (Greenland)", "kl": "Kalaallisut", "kln_KE": "Kalenjin (Kenya)", "kln": "Kalenjin", "kam_KE": "Kamba (Kenya)", "kam": "Kamba", "kn_IN": "Kannada (India)", "kn": "Kannada", "kk_Cyrl": "Kazakh (Cyrillic)", "kk_Cyrl_KZ": "Kazakh (Cyrillic, Kazakhstan)", "kk": "Kazakh", "km_KH": "Khmer (Cambodia)", "km": "Khmer", "ki_KE": "Kikuyu (Kenya)", "ki": "Kikuyu", "rw_RW": "Kinyarwanda (Rwanda)", "rw": "Kinyarwanda", "kok_IN": "Konkani (India)", "kok": "Konkani", "ko_KR": "Korean (South Korea)", "ko": "Korean", "khq_ML": "Koyra Chiini (Mali)", "khq": "Koyra Chiini", "ses_ML": "Koyraboro Senni (Mali)", "ses": "Koyraboro Senni", "lag_TZ": "Langi (Tanzania)", "lag": "Langi", "lv_LV": "Latvian (Latvia)", "lv": "Latvian", "lt_LT": "Lithuanian (Lithuania)", "lt": "Lithuanian", "luo_KE": "Luo (Kenya)", "luo": "Luo", "luy_KE": "Luyia (Kenya)", "luy": "Luyia", "mk_MK": "Macedonian (Macedonia)", "mk": "Macedonian", "jmc_TZ": "Machame (Tanzania)", "jmc": "Machame", "kde_TZ": "Makonde (Tanzania)", "kde": "Makonde", "mg_MG": "Malagasy (Madagascar)", "mg": "Malagasy", "ms_BN": "Malay (Brunei)", "ms_MY": "Malay (Malaysia)", "ms": "Malay", "ml_IN": "Malayalam (India)", "ml": "Malayalam", "mt_MT": "Maltese (Malta)", "mt": "Maltese", "gv_GB": "Manx (United Kingdom)", "gv": "Manx", "mr_IN": "Marathi (India)", "mr": "Marathi", "mas_KE": "Masai (Kenya)", "mas_TZ": "Masai (Tanzania)", "mas": "Masai", "mer_KE": "Meru (Kenya)", "mer": "Meru", "mfe_MU": "Morisyen (Mauritius)", "mfe": "Morisyen", "naq_NA": "Nama (Namibia)", "naq": "Nama", "ne_IN": "Nepali (India)", "ne_NP": "Nepali (Nepal)", "ne": "Nepali", "nd_ZW": "North Ndebele (Zimbabwe)", "nd": "North Ndebele", "no": "Norwegian", "nb_NO": "Norwegian Bokmål (Norway)", "nb": "Norwegian Bokmål", "nn_NO": "Norwegian Nynorsk (Norway)", "nn": "Norwegian Nynorsk", "nyn_UG": "Nyankole (Uganda)", "nyn": "Nyankole", "or_IN": "Oriya (India)", "or": "Oriya", "om_ET": "Oromo (Ethiopia)", "m_KE": "Oromo (Kenya)", "om": "Oromo", "ps_AF": "Pashto (Afghanistan)", "ps": "Pashto", "fa_AF": "Persian (Afghanistan)", "fa_IR": "Persian (Iran)", "fa": "Persian", "pl_PL": "Polish (Poland)", "pl": "Polish", "pt_BR": "Portuguese (Brazil)", "pt_GW": "Portuguese (Guinea-Bissau)", "pt_MZ": "Portuguese (Mozambique)", "pt_PT": "Portuguese (Portugal)", "pt": "Portuguese", "pa_Arab": "Punjabi (Arabic)", "pa_Arab_PK": "Punjabi (Arabic, Pakistan)", "pa_Guru": "Punjabi (Gurmukhi)", "pa_Guru_IN": "Punjabi (Gurmukhi, India)", "pa": "Punjabi", "ro_MD": "Romanian (Moldova)", "ro_RO": "Romanian (Romania)", "ro": "Romanian", "rm_CH": "Romansh (Switzerland)", "rm": "Romansh", "rof_TZ": "Rombo (Tanzania)", "rof": "Rombo", "ru_MD": "Russian (Moldova)", "ru_RU": "Russian (Russia)", "ru_UA": "Russian (Ukraine)", "ru": "Russian", "rwk_TZ": "Rwa (Tanzania)", "rwk": "Rwa", "saq_KE": "Samburu (Kenya)", "saq": "Samburu", "sg_CF": "Sango (Central African Republic)", "sg": "Sango", "seh_MZ": "Sena (Mozambique)", "seh": "Sena", "sr_Cyrl": "Serbian (Cyrillic)", "sr_Cyrl_BA": "Serbian (Cyrillic, Bosnia and Herzegovina)", "sr_Cyrl_ME": "Serbian (Cyrillic, Montenegro)", "sr_Cyrl_RS": "Serbian (Cyrillic, Serbia)", "sr_Latn": "Serbian (Latin)", "sr_Latn_BA": "Serbian (Latin, Bosnia and Herzegovina)", "sr_Latn_ME": "Serbian (Latin, Montenegro)", "sr_Latn_RS": "Serbian (Latin, Serbia)", "sr": "Serbian", "sn_ZW": "Shona (Zimbabwe)", "sn": "Shona", "ii_CN": "Sichuan Yi (China)", "ii": "Sichuan Yi", "si_LK": "Sinhala (Sri Lanka)", "si": "Sinhala", "sk_SK": "Slovak (Slovakia)", "sk": "Slovak", "sl_SI": "Slovenian (Slovenia)", "sl": "Slovenian", "xog_UG": "Soga (Uganda)", "xog": "Soga", "so_DJ": "Somali (Djibouti)", "so_ET": "Somali (Ethiopia)", "so_KE": "Somali (Kenya)", "so_SO": "Somali (Somalia)", "so": "Somali", "es_AR": "Spanish (Argentina)", "es_BO": "Spanish (Bolivia)", "es_CL": "Spanish (Chile)", "es_CO": "Spanish (Colombia)", "es_CR": "Spanish (Costa Rica)", "es_DO": "Spanish (Dominican Republic)", "es_EC": "Spanish (Ecuador)", "es_SV": "Spanish (El Salvador)", "es_GQ": "Spanish (Equatorial Guinea)", "es_GT": "Spanish (Guatemala)", "es_HN": "Spanish (Honduras)", "es_419": "Spanish (Latin America)", "es_MX": "Spanish (Mexico)", "es_NI": "Spanish (Nicaragua)", "es_PA": "Spanish (Panama)", "es_PY": "Spanish (Paraguay)", "es_PE": "Spanish (Peru)", "es_PR": "Spanish (Puerto Rico)", "es_ES": "Spanish (Spain)", "es_US": "Spanish (United States)", "es_UY": "Spanish (Uruguay)", "es_VE": "Spanish (Venezuela)", "es": "Spanish", "sw_KE": "Swahili (Kenya)", "sw_TZ": "Swahili (Tanzania)", "sw": "Swahili", "sv_FI": "Swedish (Finland)", "sv_SE": "Swedish (Sweden)", "sv": "Swedish", "gsw_CH": "Swiss German (Switzerland)", "gsw": "Swiss German", "shi_Latn": "Tachelhit (Latin)", "shi_Latn_MA": "Tachelhit (Latin, Morocco)", "shi_Tfng": "Tachelhit (Tifinagh)", "shi_Tfng_MA": "Tachelhit (Tifinagh, Morocco)", "shi": "Tachelhit", "dav_KE": "Taita (Kenya)", "dav": "Taita", "ta_IN": "Tamil (India)", "ta_LK": "Tamil (Sri Lanka)", "ta": "Tamil", "te_IN": "Telugu (India)", "te": "Telugu", "teo_KE": "Teso (Kenya)", "teo_UG": "Teso (Uganda)", "teo": "Teso", "th_TH": "Thai (Thailand)", "th": "Thai", "bo_CN": "Tibetan (China)", "bo_IN": "Tibetan (India)", "bo": "Tibetan", "ti_ER": "Tigrinya (Eritrea)", "ti_ET": "Tigrinya (Ethiopia)", "ti": "Tigrinya", "to_TO": "Tonga (Tonga)", "to": "Tonga", "tr_TR": "Turkish (Turkey)", "tr": "Turkish", "uk_UA": "Ukrainian (Ukraine)", "uk": "Ukrainian", "ur_IN": "Urdu (India)", "ur_PK": "Urdu (Pakistan)", "ur": "Urdu", "uz_Arab": "Uzbek (Arabic)", "uz_Arab_AF": "Uzbek (Arabic, Afghanistan)", "uz_Cyrl": "Uzbek (Cyrillic)", "uz_Cyrl_UZ": "Uzbek (Cyrillic, Uzbekistan)", "uz_Latn": "Uzbek (Latin)", "uz_Latn_UZ": "Uzbek (Latin, Uzbekistan)", "uz": "Uzbek", "vi_VN": "Vietnamese (Vietnam)", "vi": "Vietnamese", "vun_TZ": "Vunjo (Tanzania)", "vun": "Vunjo", "cy_GB": "Welsh (United Kingdom)", "cy": "Welsh", "yo_NG": "Yoruba (Nigeria)", "yo": "Yoruba", "zu_ZA": "Zulu (South Africa)", "zu": "Zulu" } print('\n\nTRANSLATORS\n===========\n') r = requests.get('http://www.transifex.com/api/2/project/antennapod/languages/', auth=(TRANSIFEX_USER, TRANSIFEX_PW)) for lang in r.json(): lang_contributers = lang['coordinators'] + lang['reviewers'] + lang['translators'] lang_contributers = sorted(lang_contributers, key=str.lower) print(language_codes[lang['language_code']], ": ", ', '.join(lang_contributers), sep="")

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false

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juancordoba00/carsapp

6,665,789,292,244

ed5c3975a1538dab371199856800e84145d82928

64204193d70c5ab855dc2d591ac656d5557f4b91

/carsapp/migrations/0023_auto_20210705_1940.py

0e01364c12993427b06d6fc917b680f245024541

[]

no_license

https://github.com/juancordoba00/carsapp

563150e5c381a10fd4cbac23bf3b58d69de4516c

38d404a34cdaf5676c83ed6dcb0442e32a12a865

refs/heads/master

2023-06-16T22:49:43.558107

2021-07-07T15:20:37

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# Generated by Django 3.1.4 on 2021-07-06 00:40 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('carsapp', '0022_auto_20210705_1437'), ] operations = [ migrations.AlterField( model_name='detallesfacturas', name='precio', field=models.IntegerField(default=0, null=True), ), migrations.AlterField( model_name='servicios', name='valor_Servicio', field=models.IntegerField(default=0), ), ]

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sanshitangmalaban/GraduationProject

19,224,273,655,630

d83965fb1fcb877c2f587d42b3725535fbd5a37d

6c75f452e81c9b8106d4aafe02f16b76cfac0bd2

/MyTest/tests.py

e4c241a501f00d169a6dcae330585a4716150d47

[]

no_license

https://github.com/sanshitangmalaban/GraduationProject

4aebe6226c1251a7ca4b2deddcf69a13f799e9be

b47ce4dc4a490017072c785261ccf0421500a4b0

refs/heads/master

2020-03-24T18:20:52.697395

2018-07-30T15:03:22

142,889,763

1

0

null

from django.test import TestCase import sys,os # Create your tests here. # import time # print(int(time.time())) # url = '/images/ysu_logo.png' # url_list = url.split('/') # print(url_list) import requests,json # print(os.path.dirname(os.path.abspath(__file__))) # print(sys.path.append(os.path.dirname(os.path.abspath(__file__)))) def get_goods_history(goods_url): url = "http://tool.manmanbuy.com/history.aspx?DA=1&action=gethistory&url=https%3A//item.jd.hk/3800679.html&bjid=&spbh=&cxid=&zkid=&w=951&token="+token print(url) headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.167 Safari/537.36", "referer": "www.manmanbuy.com", } response = requests.request('get', url=url, headers=headers) response_json = json.loads(response.text) print(response_json) if __name__ =="__main__": get_goods_history("http://product.dangdang.com/1005161885.html")

UTF-8

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false

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ryanmuller/quill-lightside

1,511,828,508,187

d0c28c04bab60b0e4fdf6d4c53085b6c6af95853

f88750fd480330bf332b3567c54a4761899a957a

/lightbox.py

5be44d5895224f8393b8f2240b6310e38427e58e

[]

no_license

https://github.com/ryanmuller/quill-lightside

ca76c37242c475fd4e37cbc467185960bfac3163

56f958933017eba1a9b06ed9a6ef916a33297488

refs/heads/master

2020-05-17T11:06:07.596998

2014-12-28T21:13:55

null

0

null

import requests import urlparse import json import random import operator token = "" with open('config/token', 'r') as f: token = f.read().strip() prompts = [ "Why did the United States declare independence?", "How did the moon form?", "In Macbeth, whose ambition is the driving force of the play?", "How does Tom Sawyer change over the course of the story?", "What is Gestalt psychology?" ] class LightboxResource(object): base_url = "https://api.getlightbox.com/api/" endpoint = None HEADERS = { "Authorization": "Token {token}".format(token=token), "Content-Type": "application/json", } @staticmethod def endpoint_for(resource): return urlparse.urljoin(LightboxResource.base_url, resource+"/") @staticmethod def id_from_url(url): return url.split('/')[-1] @classmethod def url_for(cls, _id): return urlparse.urljoin(cls.endpoint, str(_id)) # TODO fail elegantly @classmethod def all(cls): r = requests.get(cls.endpoint, headers=cls.HEADERS) response = r.json() objs = [ cls(cls.id_from_url(obj['url']), obj) for obj in response['results'] ] return objs @classmethod def find(cls, _id): r = requests.get(cls.url_for(_id), headers=cls.HEADERS) obj = cls(_id, r.json()) return obj @classmethod def create(cls, data): r = requests.post(cls.endpoint, data=json.dumps(data), headers=cls.HEADERS) response = r.json() obj = cls(cls.id_from_url(response['url']), response) return obj # TODO test me def reload(self): self = type(self).find(self._id) return self # TODO inherit only from Tasks # TODO better info from status def process(self): if 'process' in self.response: r = requests.post(self.response['process'], headers=type(self).HEADERS) return r.status_code == 200 or r.status_code == 202 else: return False def url(self): return type(self).url_for(self._id) def destroy(self): r = requests.delete(self.url(), headers=type(self).HEADERS) return self def __init__(self, _id, response={}): self._id = _id self.response = response class Prompt(LightboxResource): endpoint = LightboxResource.endpoint_for("prompts") @classmethod def create(cls, title="", text="", description=""): return super(Prompt, cls).create({ 'title': title, 'text': text, 'description': description }) def text(self): return self.response['text'] class Grader(LightboxResource): endpoint = LightboxResource.endpoint_for("graders") @classmethod def create(cls, prompt_id, name="Grader"): return super(Grader, cls).create({ 'prompt': Prompt.url_for(prompt_id), 'name': name }) def prompt(self): p = self.response['prompt'] return Prompt(LightboxResource.id_from_url(p['url']), p) class Lightbox(LightboxResource): endpoint = LightboxResource.endpoint_for("lightboxes") @classmethod def create(cls, grader_id, name="Lightbox"): return super(Lightbox, cls).create({ 'grader': Grader.url_for(grader_id), 'name': name }) # TODO set in __init__? def name(self): return self.response['name'] def grader(self): return Grader.find(LightboxResource.id_from_url(self.response['grader'])) def answer_set(self): aset = self.response['answer_set'] return AnswerSet(LightboxResource.id_from_url(aset['url']), aset) class Corpus(LightboxResource): endpoint = LightboxResource.endpoint_for("corpora") params_endpoint = LightboxResource.endpoint_for("corpus-upload-parameters") s3_params = {} # note: description is required @classmethod def create(cls, prompt_id=None, description="primary corpus"): if not prompt_id: return None return super(Corpus, cls).create({ 'prompt': Prompt.url_for(prompt_id), 'description': description }) @classmethod def get_s3_params(cls): r = requests.get(cls.params_endpoint, headers=cls.HEADERS) cls.s3_params = r.json() @classmethod def send_file(cls, filename): cls.get_s3_params() data = { 'AWSAccessKeyId': cls.s3_params['access_key_id'], 'key': cls.s3_params['key'], 'acl': 'public-read', 'Policy': cls.s3_params['policy'], 'Signature': cls.s3_params['signature'], 'success_action_status': '201', } files = { 'file': open(filename, 'rb') } r = requests.post(cls.s3_params['s3_endpoint'], data=data, files=files) return r.status_code == 201 class CorpusUploadTask(LightboxResource): endpoint = LightboxResource.endpoint_for("corpus-upload-tasks") @classmethod def create(cls, corpus_id, s3_key, content_type="text/csv"): if not s3_key and 'key' in Corpus.s3_params: s3_key = Corpus.s3_params['key'] if not corpus_id or not s3_key: return None return super(CorpusUploadTask, cls).create({ 'corpus': Corpus.url_for(corpus_id), 's3_key': s3_key, 'content_type': content_type }) class Author(LightboxResource): endpoint = LightboxResource.endpoint_for("authors") @classmethod def create(cls, designator=("%016x" % random.getrandbits(64)), email=""): return super(Author, cls).create({ 'designator': designator, 'email': email }) class TrainingAnswer(LightboxResource): endpoint = LightboxResource.endpoint_for("training-answers") @classmethod def create(cls, corpus_id, text): if not corpus_id or not text: return None return super(TrainingAnswer, cls).create({ 'corpus': Corpus.url_for(corpus_id), 'text': text }) class ResolvedScore(LightboxResource): endpoint = LightboxResource.endpoint_for("resolved-scores") @classmethod def create(cls, training_answer_id, label): if not training_answer_id or not label: return None return super(ResolvedScore, cls).create({ 'training_answer': TrainingAnswer.url_for(training_answer_id), 'label': label }) class HumanScore(LightboxResource): endpoint = LightboxResource.endpoint_for("human-scores") @classmethod def create(cls, training_answer_id, label): if not training_answer_id or not label: return None return super(HumanScore, cls).create({ 'training_answer': TrainingAnswer.url_for(training_answer_id), 'label': label }) class TrainingTask(LightboxResource): endpoint = LightboxResource.endpoint_for("training-tasks") @classmethod def create(cls, corpus_id, grader_id): return super(TrainingTask, cls).create({ 'corpus': Corpus.url_for(corpus_id), 'grader': Grader.url_for(grader_id) }) class TrainedModel(LightboxResource): endpoint = LightboxResource.endpoint_for("trained-models") class AnswerSet(LightboxResource): endpoint = LightboxResource.endpoint_for("answer-sets") # FIXME @classmethod def create(cls, lightbox_id): return super(AnswerSet, cls).create({ 'lightbox': Lightbox.url_for(lightbox_id) }) class Answer(LightboxResource): endpoint = LightboxResource.endpoint_for("answers") @classmethod def create(cls, author_id, answer_set_id, text): if not author_id or not answer_set_id or not text: return None return super(Answer, cls).create({ 'author': Author.url_for(author_id), 'answer_set': AnswerSet.url_for(answer_set_id), 'text': text }) def prediction_result(self): if 'prediction_results' in self.response and len(self.response['prediction_results']) > 0: return PredictionResult.find(LightboxResource.id_from_url(self.response['prediction_results'][0])) else: return None def label(self): pr = self.prediction_result() if pr: return pr.label() else: return None class PredictionTask(LightboxResource): endpoint = LightboxResource.endpoint_for("prediction-tasks") @classmethod def create(cls, answer_set_id): return super(PredictionTask, cls).create({ 'answer_set': AnswerSet.url_for(answer_set_id) }) # GET /prediction-results/ # GET /prediction-results/(int: prediction_result_id) class PredictionResult(LightboxResource): endpoint = LightboxResource.endpoint_for("prediction-results") # TODO cleanup training data and figure out value=0 issue def label(self): distribution = {k:v for (k,v) in self.response['distribution'].iteritems() if k != 'D' and k != '4'} return max(distribution.iteritems(), key=operator.itemgetter(1))[0] if __name__ == "__main__": # get s3 params #Corpus.get_s3_params() #print Corpus.s3_params # create a new prompt #p = Prompt("test", "This is a test. Don't answer me!", "only a test") #print p._id # 35, 36 -> test prompts #Prompt.find(35) #p = Prompt.create("test2", "Your answer means nothing, NOTHING!", "yet another test") Prompt.all() # A Proclamation About Rosa Parks # Prompt: 20 (?) # Grader: 34 # Lightbox: 30 # AnswerSet: 30 # Author: 1 # Answer: 1580 # test # prompt: 35 # grader: 44 # lightbox: 31 # answerset: 31 # answer: 1581 # # Water Cycle # prompt: 37 # grader: 45 # lightbox: 33 # answer-set: 33 # corpus: 164 # training answer: 41503 # resolved score: 41503 # human score: 1 # s3 key: corpus-uploads/2014/10/28/56d59c80102d4e7db5bc5707fce24759.csv # training task: 96 # trained model: 164 # prediction task: 1584

UTF-8

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false

10,327

py

8

lightbox.py

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0.603079

0.589716

0

350

28.502857

110

Dingrongdeer/X-Village-2018-Exercise

3,607,772,539,212

3d650702ee34f48dd1a42bd3d2a652bb41c40f69

b69ae92ed6bf0d7fd73e73aeb6cdd04e4a024931

/Lesson02-Python_basic/ex3.py

f90abfada8458201ae6120044931aabba465fa36

[]

no_license

https://github.com/Dingrongdeer/X-Village-2018-Exercise

2c68706b8e6cdabf967f8d73d35f09c77f8545c8

856209c28f82798a2ffeede96669ab703f9950e8

refs/heads/master

2020-03-23T07:44:45.888142

2018-07-17T14:06:23

141,288,101

0

null

def guess_luckynumber(): import random lucky_number=random.randint(1,100) number=int(input("清輸入終極密碼:")) while number!= lucky_number: if number<lucky_number: print ("再大一點") number=int(input("Please enter an luncky number:")) else: print ("再小一點") number=int(input("Please enter an luncky number:")) print ("猜對了！") guess_luckynumber()

UTF-8

Python

false

467

py

1

ex3.py

1

0.571096

0.561772

0

15

27.666667

63

JoshuaGhost/e2expred

5,394,478,939,405

0a36d847d1859685dc370564028951dc94cb1229

a9a7c00fd287f4c33d58719f8ec53c17f8560486

/latent_rationale/common/regularizers.py

b5fb2d89b25316e57577023b84988b84084edbf5

[ "MIT" ]

permissive

https://github.com/JoshuaGhost/e2expred

f0ac2bf66c172aad9b47b6d4ebd9ad1ad7ed489b

f4dee47c41748a64509b68daee83d97919b6c978

refs/heads/main

2023-05-14T17:55:13.365088

2021-01-13T17:11:14

329,074,920

0

null

import torch def l0_for_hardkuma(dists, mask): batch_size = mask.size(0) lengths = mask.sum(1).float() # pre-compute for regularizers: pdf(0.) if len(dists) == 1: pdf0 = dists[0].pdf(0.) else: pdf0 = [] for t in range(len(dists)): pdf_t = dists[t].pdf(0.) pdf0.append(pdf_t) pdf0 = torch.stack(pdf0, dim=1) # [B, T, 1] pdf0 = pdf0.squeeze(-1) pdf0 = torch.where(mask, pdf0, pdf0.new_zeros([1])) # [B, T] # L0 regularizer pdf_nonzero = 1. - pdf0 # [B, T] pdf_nonzero = torch.where(mask, pdf_nonzero, pdf_nonzero.new_zeros([1])) l0 = pdf_nonzero.sum(1) / (lengths + 1e-9) # [B] l0 = l0.sum() / batch_size return l0

UTF-8

Python

false

729

py

28

regularizers.py

21

0.541838

0.496571

0

26

27.076923

76

Eiriksak/Network_Security

3,865,470,594,134

9ae42a74ff1b62f22bee91bcec4f82e72ccbb2c4

191353ac721faf66d17ae0a281ab9e0436a51e63

/Assignment3/test_dsa.py

2144bbbe21aca78d71a05291e9bf914c7362bbb3

[]

no_license

https://github.com/Eiriksak/Network_Security

b9ff2f5356a2c70ceb63ceaefd9a75b6819217a6

ada377298cf0f0d550c410f7f511e061cbef0a9b

refs/heads/master

2023-01-07T00:20:21.748482

2020-11-03T19:53:23

300,072,354

0

null

import dsa import argparse if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--mode', choices=['normal', 'attacker'], required=True, help='Test DSA normally or as an attacker') args = parser.parse_args() if args.mode == 'normal': p, q, g = dsa.gen_pubkeys(L=512) # User 1 generates shared global public key values x, y = dsa.gen_userkeys(p, q ,g) # User 1 generates private and public keys message = input("(User1) Message: ") r, s, message = dsa.sign_message(message, p, q, g, x) # User 1 signs message print(f'(User1) have now signed the message and generated these numbers:\nr: {r}\ns: {s}') # User 2 will now verify the message it receives from User 1 print(f'\n\n********** (User2) has reveived the message and will now verify it **********') verified = dsa.verify_message(message, p, q, g, r, s, y) if verified: print("The message was verified!") else: print("The message was not verifed..") elif args.mode == 'attacker': p, q, g = dsa.gen_pubkeys(L=512) # User 1 generates shared global public key values x, y = dsa.gen_userkeys(p, q ,g) # User 1 generates private and public keys message = input("(User1) Message: ") r, s, message = dsa.sign_message(message, p, q, g, x) # User 1 signs message print(f'(User1) have now signed the message and generated these numbers:\nr: {r}\ns: {s}') delim = '#'*20 print(f'{delim } ATTACKER INTERUPTS {delim}\nEdit the original message') message = input('(Attacker) Edit message: ') # User 2 will now verify the faked message it receives from the attacker print(f'\n\n********** (User2) has reveived the message and will now verify it **********') verified = dsa.verify_message(message, p, q, g, r, s, y) if verified: print("The message was verified!") else: print("The message was not verifed..")

UTF-8

Python

false

2,042

py

21

test_dsa.py

16

0.598433

0.587169

0

41

48.658537

124

morawi/TextGAN

3,100,966,416,773

16f509a5afb52b503601e0694e395162ce25281d

64a9b85e24f8c038384473df988a5a480d8db20b

/B_model.py

c8fc5834237c3d5d1d9cff4df6c5bb3c5506f926

[ "LicenseRef-scancode-other-permissive", "MIT" ]

permissive

https://github.com/morawi/TextGAN

4a8af74e499b89a7ddded8637c53b1611df11b42

1633c7cf6ee60c624da6aa2248c41c264225fa8e

refs/heads/master

2020-05-14T16:44:44.136345

2019-08-28T00:46:37

181,877,666

2

0

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri May 17 11:41:03 2019 @author: Mohammed Al-Rawi """ from misc_functions import binarize_tensor def get_loss_of_B_classify(real_A, real_B): # item is a string 'A', 'A_neg' of the sample optimizer_classify.zero_grad() B_ = binarize_tensor(G_AB(real_A).detach()) output = B_classify(B_) target = criterion_classify_labeling(B_, binarize_tensor(real_B)) loss_B = criterion_classify(output, target) return loss_B def train_B_classify(no_epochs): print('Training B-Classifier') B_classify.train() loss_of_model = [] # time.sleep(1); # pbar = tqdm(total=no_epochs); # time.sleep(1) loss = None for epoch_ in range(1, no_epochs): scheduler_B.step() total_loss = 0 # pbar.update(1) for i, batch in enumerate(dataloader): real_B_pos = batch['B'].type(Tensor).to(device) # +ve Ground Truth real_B_neg = batch['B_neg'].type(Tensor).to(device) # -ve Ground Truth loss_B_pos = get_loss_of_B_classify(batch['A'].type(Tensor).to(device), real_B_pos) # ''' +ve phase ''' loss_B_neg = get_loss_of_B_classify(batch['A_neg'].type(Tensor).to(device), real_B_neg) # ''' -ve phase pass ''' loss = torch.min(loss_B_pos, loss_B_neg) / ( torch.max(loss_B_pos, loss_B_neg)+ 1) loss.backward() optimizer_classify.step() total_loss += loss.cpu().data.detach().numpy().tolist() print(', ', total_loss/len(dataloader.dataset), end='') loss_of_model.append(total_loss/len(dataloader.dataset)) return loss_of_model def test_B_classify(test_loss, test_dataloader): loss = 0 B_classify.eval() with torch.no_grad(): for batch_idx, batch in enumerate(test_dataloader): real_B = batch['B'].type(Tensor) # since we are thresholding, there is no difference between B and B_neg real_A_pos = batch['A'].type(Tensor) real_A_neg = batch['A_neg'].type(Tensor) GAN_B_pos = G_AB(real_A_pos).detach() GAN_B_neg = G_AB(real_A_neg).detach() out_B_pos = B_classify(binarize_tensor(GAN_B_pos)).detach() # if we do thresholding in training, we should do it here out_B_neg = B_classify(binarize_tensor(GAN_B_neg)).detach() if out_B_neg<out_B_pos: B_good = GAN_B_neg else: B_good = GAN_B_pos loss += test_loss(real_B, B_good) x = G_AB( G_AB(GAN_B_pos) + G_AB(GAN_B_neg ) ) img_sample = torch.cat( (real_A_pos, real_B, GAN_B_pos, GAN_B_neg, G_AB(GAN_B_pos), G_AB(GAN_B_neg), x ), 0) save_image(img_sample, 'images/%s/%s.png' % (opt.dataset_name, batch_idx), nrow=7, normalize=True) model_id = 1 torch.save(B_classify.state_dict(), 'saved_models/%s/model_classify_%d.pth' % (opt.dataset_name, model_id)) return loss/len(test_dataloader.dataset) #lr_classify = 0.01 #B_classify = torchvis_models.resnet18(pretrained=True) #B_classify.fc = torch.nn.Linear(2048, 1) #2048 for 256x256 image #B_classify.aux_logits = False #B_classify = B_classify.to(device) #optimizer_classify = torch.optim.Adam(B_classify.parameters(), lr=lr_classify) #criterion_classify_labeling = torch.nn.L1Loss() #criterion_classify = torch.nn.L1Loss() #scheduler_B = torch.optim.lr_scheduler.MultiStepLR(optimizer_classify, # milestones=[50, 150, 300] , gamma= 0.1) # my_loss = train_B_classify(100) test_performance = test_B_classify(criterion_classify, val_dataloader) print(test_performance.item())

UTF-8

Python

false

4,336

py

9

B_model.py

7

0.515913

0.502306

0

104

40.701923

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danieltimko/aoc2020

9,431,748,189,525

0d9b0f26d355be4808c1b6ca3fd7cf0be277ad7f

5a6aa88acebefcb79d7e33ed0898b9f841816119

/2021/21.py

afd295bfcd248abb0a722ee107670e6be3e980ee

[]

no_license

https://github.com/danieltimko/aoc2020

396cc7ce1380f30f18dbb3523ad9a7b5025dc34f

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refs/heads/master

2023-02-07T05:23:01.748630

2022-12-25T06:14:02

318,085,942

0

null

from itertools import product def task1(positions): def roll(): n = 1 while True: yield n n = 1 if n == 100 else n+1 scores = [0, 0] rolls = 0 gen = roll() while True: for i in range(2): rollsum = next(gen) + next(gen) + next(gen) rolls += 3 positions[i] = (positions[i] + rollsum) % 10 scores[i] += positions[i] + 1 if scores[i] >= 1000: return rolls * min(scores) def task2(positions): def get_subresult(pos1, pos2, score1, score2): if score1 >= 21: return 1, 0 if score2 >= 21: return 0, 1 if (pos1, pos2, score1, score2) in cache: return cache[(pos1, pos2, score1, score2)] n1 = 0 n2 = 0 for rolls in list(product([1, 2, 3], repeat=3)): new_pos1 = (pos1 + sum(rolls)) % 10 # switch players every round subresult2, subresult1 = get_subresult(pos2, new_pos1, score2, score1+new_pos1+1) n1 += subresult1 n2 += subresult2 cache[(pos1, pos2, score1, score2)] = (n1, n2) return n1, n2 cache = {} return max(get_subresult(positions[0], positions[1], 0, 0)) def run(): print(task1([1, 0])) print(task2([1, 0])) run()

UTF-8

Python

false

1,347

py

75

21.py

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0.499629

0.437268

0

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24.903846

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aajenkins/LTSPM

14,826,227,125,581

f4fe4554070b44c0b2e700d016e90bfa26f29804

4964e8c02053a9616c3fe4800cacd6d74acb2f34

/cofeb_analysis/irmn/image_erosion_stripe_width.py

64c6522110b9e9c7c92a291422ced4e22915e88e

[]

no_license

https://github.com/aajenkins/LTSPM

cbb714c68701fc47f196d29f383fa9fcd6a51f5c

75f640e1b2a7173b083690ec4c84337199bd44ab

refs/heads/master

2020-07-03T15:35:10.578245

2018-03-19T21:45:37

74,162,494

0

null

# @Author: Jenkins Alec <alec> # @Date: 2017-02-15T11:26:35-08:00 # @Project: LTSPM analysis # @Last modified by: alec # @Last modified time: 2017-02-15T12:01:11-08:00 import matplotlib.pyplot as plt import matplotlib.pylab as pylab import numpy as np from scipy.ndimage.morphology import binary_erosion from scipy.misc import imread scannum = 1836 impath = '/Users/alec/UCSB/scan_images/irmn/domains'+str(scannum)+'contour.png' domains = imread(impath, flatten=True) bdomains = 1*domains bdomains = np.multiply(1/255,domains).astype(int) number_erosions = 15 image_sum = np.zeros((number_erosions)) for i in range(number_erosions): image_sum[i] = np.sum(bdomains) bdomains = binary_erosion(bdomains).astype(np.int) plt.close('all') fig1, ax1 = plt.subplots() plt.imshow(bdomains) fig1, ax1 = plt.subplots() plt.plot(image_sum) plt.show()

UTF-8

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859

py

154

image_erosion_stripe_width.py

142

0.733411

0.67404

0

34

24.264706

79

styler00dollar/Colab-BasicSR

16,741,782,554,234

7649bf53ad2b2c5763ff30b3460145cc11fa1683

af94f63c4ba68c1e06e2550449569a11ee53bee6

/code/scripts/fix_state_dict.py

adc8e1eb5f274a918680b2b43764a34d24d5f3cf

[]

no_license

https://github.com/styler00dollar/Colab-BasicSR

4b79da57a7afaff32bf2d671442a5171e0cc1e87

626d93bf8d3d3db5447ae6946d4ba8bb3d00a482

refs/heads/master

2023-06-13T04:15:05.808917

2023-05-27T01:17:56

321,114,389

4

1

null

import torch # from this model model1 = torch.load("team04_rlfn.pth") # into this model model2 = torch.load("Checkpoint_0_0_G.pth") for k in model1.keys(): try: if "upsampler.0" in k: continue print(f"setting {k}") model2[k] = model1[k] except Exception as e: print(e) pass torch.save(model2, "fixed.pth") print("done")

UTF-8

Python

false

385

py

106

fix_state_dict.py

95

0.587013

0.558442

0

21

17.333333

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stereoliza/learning

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7918b5e9997744e2a641c231c80dd257ee742460

/main.py

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[]

no_license

https://github.com/stereoliza/learning

cc72cd5d907ae06e01f73bbc30be3499fcd20e6c

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refs/heads/main

2023-09-06T02:24:00.169886

2021-11-11T23:41:24

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0

null

false

2021-11-11T23:41:25

2021-08-09T14:07:20

2021-10-30T20:22:19

2021-11-11T23:41:24

33

0

Python

false

from location import find_location import sys ip = sys.argv[1] location = find_location(str(ip)) print(location)

UTF-8

Python

false

119

py

11

main.py

10

0.731092

0.722689

0

7

15.428571

34

BahruDa/alx-higher_level_programming-2

15,367,393,024,180

cd7d96d55cb8b434fca5647acb17ec73ede09aa8

b7f25449c2baae8823f1307e134a775d6ab64272

/0x0F-python-object_relational_mapping/model_state.py

ff208ecad1588628c4dad4a3a23774bab485effb

[]

no_license

https://github.com/BahruDa/alx-higher_level_programming-2

85f5155328750430949d49e8d2d27761c71360fe

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refs/heads/main

2023-08-15T03:07:14.699713

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#!/usr/bin/python3 """ a python module that contains the class definition of a State and an instance Base = declarative_base(): """ from sqlalchemy import Column, Integer, String from sqlalchemy.ext.declarative import declarative_base Base = declarative_base() class State(Base): ''' defines a state Attr: id (int): auto-generated, unique integer, not null, & is a primary key name (): a column of a string with 128 chars and can’t be null ''' __tablename__ = 'states' id = Column(Integer, primary_key=True, nullable=False, unique=True) name = Column(String(128), nullable=False) def __init__(self, name): self.name = name

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py

123

model_state.py

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paulhodge/graveyard

2,791,728,745,102

a17ec3de4d11d39dde5b6e8c6e885ae0bcf9c060

16294f14ad0e8d2e29201fd1c3d132b7793481ca

/euler_11.py

aba69eb057fd15cdc072c27a6d66fbd35bf2e0a0

[]

no_license

https://github.com/paulhodge/graveyard

05fc3519a9c26be88947867d55980c8a11a573e7

9862a8ddc69bebb8f8df2a57647360d07da34efc

refs/heads/master

2015-08-06T09:34:28.833025

2012-02-02T06:31:00

2,037,445

0

null

def quadratic(a, b, c=None): import math, cmath if c: # (ax^2 + bx + c = 0) a, b = b / float(a), c / float(a) t = a / 2.0 r = t**2 - b if r >= 0: # real roots y1 = math.sqrt(r) else: # complex roots y1 = cmath.sqrt(r) y2 = -y1 return y1 - t, y2 - t def triangle_numbers(): i = 1 result = 1 yield result while True: i += 1 result += i yield result def triangle(i): out = int(i*i / 2 + i / 2) if i % 2 == 1: out = out + 1 return out print map(triangle, range(1,20)) def find_divisors(n): result = set() for d in range(2, n): if (n / d) * d == n: result.add(d) result.add(1) result.add(n) return result def product(l): out = 1 for i in l: out *= i return out smallest_with_500_divisors = product(range(1,501)) print smallest_with_500_divisors print quadratic(.5,.5,smallest_with_500_divisors) def find(): for n in triangle_numbers(): if n < smallest_with_500_divisors: continue print n if len(find_divisors(n)) > 500: print find_divisors(n) print n exit(0)

UTF-8

Python

false

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py

18

euler_11.py

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0.527397

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kimlin20011/data_mining_assignment

5,308,579,620,768

74e16a6b4a4df6ae972c01703c9a495263bca960

c4cbbbeded13f7cfefbd9a40932bc083c6ae0e8d

/q2_6/test.py

972f2d5b73acab962ad5f0037e51d457ff7d58ff

[]

no_license

https://github.com/kimlin20011/data_mining_assignment

e0e51d94b5822aa91b5d1c2d2317ba829ad1ef26

527463b1005877def75e5100ee9088b423016fb8

refs/heads/main

2023-06-24T04:54:33.078896

2021-07-28T07:08:58

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null

from sklearn import tree import pandas as pd import graphviz df = pd.read_csv("data.csv", encoding="utf-8") print(df) features = list(df.columns[1:21]) X = df[features] y = df["class"] classifier = tree.DecisionTreeClassifier() classifier = classifier.fit(X, y) tree.plot_tree(classifier) dot_data = tree.export_graphviz(classifier, out_file=None, feature_names=features, class_names=["不能打羽球", "可以打球"], filled=True, rounded=True, leaves_parallel=True) graph = graphviz.Source(dot_data)

UTF-8

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603

py

22

test.py

12

0.622222

0.615385

0

19

29.789474

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kowoolim/youtube

15,736,760,181,537

c1fb57c8b8b67246e66583bc669d2c50b115faf0

8e8d193d504ce8b136efe8d8c451eb00629b9b7c

/address/admin.py

99e45cb95ba198bb59a88bfd0effea5dfaff6069

[]

no_license

https://github.com/kowoolim/youtube

b8a84e57f57b6ca5430a3f9b3186efa37a7aa06a

fd1bfe3870bb5770e9590dd767405c3e7e272567

refs/heads/master

2023-03-12T11:44:46.495748

2021-03-01T09:18:09

343,347,541

0

null

from django.contrib import admin from address.models import Address class AddressAdmin(admin.ModelAdmin): #화면에 출력할 필드 목록을 튜플로 지정 list_display = ("name","tel","email","address") admin.site.register(Address, AddressAdmin)

UTF-8

Python

false

262

py

28

admin.py

18

0.747826

0

8

27.75

51

csmith49/bach

19,310,172,996,698

72ceac3369c60850c964cf69ec3b34904b7bdd6d

1dcb9060349ac3136b39dc3bb552b9e8b39d66be

/tools/epee/epee.py

2df0eec0854761b4bced94850e78dfaf5fe41ea9

[]

no_license

https://github.com/csmith49/bach

8b0b2c04a68f3a0155083d4f7997a279f5e3e4a5

80b0c1269de3786ab76d627c4fd85205d640ca12

refs/heads/master

2021-05-07T23:15:29.353469

2017-10-18T14:18:48

107,418,191

1

0

null

from argparse import ArgumentParser from inspect import signature from importlib import machinery from csv import writer import os # store generators in a map, use decorator to tag them # label them with add_generator anywhere after this section _GENERATORS = {} def add_generator(key): def deco(f): _GENERATORS[key] = f() return f return deco def generate(*args): return [next(_GENERATORS[arg]) for arg in args] # machinery for loading annotated functions from a file class Function(object): def __init__(self, f): self._function = f params = signature(f).parameters self.inputs = [params[p].annotation for p in list(params)] self.name = self._function.__name__ def __call__(self, *args): return self._function(*args) def load_functions(path): module = machinery.SourceFileLoader('sig', path).load_module() functions = [] for name in filter(lambda s: "__" not in s, dir(module)): functions.append(Function(getattr(module, name))) return functions # entry point - args is the structure provided by argparse def main(): parser = ArgumentParser(prog="epee", description="generating CSV files for Bach") parser.add_argument('functions') parser.add_argument('-v', '--verbose', action='store_true') parser.add_argument('-d', '--delimiter', type=str, default="\t") parser.add_argument('-c', '--count', type=int, default=1000) parser.add_argument('-o', '--output', default="output") parser.add_argument('-e', '--error', default="") args = parser.parse_args() functions = load_functions(os.path.join(os.getcwd(), args.functions)) for f in functions: out = "{}/{}.facts".format(args.output, f.name) filename = os.path.join(os.getcwd(), out) os.makedirs(os.path.dirname(filename), exist_ok=True) with open(filename, 'w', newline='') as csvfile: csvwriter = writer(csvfile, delimiter=args.delimiter) for i in range(args.count): inputs = generate(*f.inputs) try: output = f(*inputs) except Exception as e: if args.verbose: print(e) output = args.error csvwriter.writerow(inputs + [output]) # GENERATORS # for dict @add_generator('dict') def dict_gen(): from random import randint while True: size = randint(0, 3) d = {} for i in range(1, size + 1): key = randint(0, 2) value = randint(0, 1) d[key] = value yield d # for finitefield (actually does fp_199) @add_generator('fp17') def fp_gen(): from random import randint while True: yield randint(0, 198) # for geometry @add_generator('point') def point_gen(): from random import randint while True: x = randint(-1, 1) y = x yield (x, y) @add_generator('rect') def rect_gen(): from random import randint while True: x = randint(-1, 1) y = x size = randint(1, 6) yield (x, y, size) @add_generator('int') def int_gen(): from numpy import random while True: a = random.normal(0, 2, 1) yield int(a[0]) @add_generator('rad') def rad_gen(): from numpy import random while True: a = random.normal(0, 2, 1) yield int(a[0]) @add_generator('posint') def posint_gen(): from random import randint while True: a = randint(1, 3) yield a # for list @add_generator('cons_int') def consint_gen(): from random import randint while True: yield randint(0, 2) @add_generator('list') def list_gen(): from random import randint while True: size = randint(0, 3) yield [randint(0, 2) for r in range(size)] # for matrix @add_generator('tensor') def tensor_gen(): from random import randint while True: x1 = randint(-1, 2) y1 = randint(-1, 1) x2 = randint(-1, 1) y2 = randint(-1, 1) yield (x1, x2, y1, y2) # for queue @add_generator('queue') def queue_gen(): from random import randint while True: size = randint(0, 3) s = [] for i in range(1, size + 1): value = randint(0, 1) s.append(value) yield s # for sets @add_generator('set') def set_gen(): from random import randint from numpy import random while True: size = abs(int(random.normal(0, 3, 1)[0])) s = set([]) for i in range(1, size + 1): value = randint(0, 2) s.add(value) yield s # for strings @add_generator('string') def string_gen(): from random import randint while True: size = randint(0,4) s = "" for i in range(1, size + 1): x = randint(0, 2) if x == 0: s += "1" if x == 1: s += "a" if x == 2: s += "A" if s == "": s = "@" yield s # for trig @add_generator('arc') def arc_gen(): from random import randint while True: yield randint(-10, 10) @add_generator('radian') def radian_gen(): from random import randint while True: yield randint(-10, 10) # if called as a standalone, execute as expected if __name__ == "__main__": main()

UTF-8

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py

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epee.py

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0.567548

0.55073

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211

24.64455

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pokuk76/HandsOnScikitLearnAndTensorflow

12,275,016,560,309

a30ea85709208cac42a6bc1e0f57b5137f71de4f

e1d133db78cac139e6fa9530f9431b6e81085a45

/Classification/fold.py

bd6afd95fcaa89f2a6522b7662a0db313ba9007a

[]

no_license

https://github.com/pokuk76/HandsOnScikitLearnAndTensorflow

0113dc5439ce91ee5426ac91e51b842a70f2d14e

17b87108da2be980ec913fa1b344f9a5cab7c100

refs/heads/master

2020-12-15T08:07:00.039241

2019-12-05T13:40:04

null

0

null

import numpy as np import pullMnist as pm import split_train_test as stt def foldMnistData(X_train, y_train): shuffle_index = np.random.permutation(60000) return X_train[shuffle_index], y_train[shuffle_index] mnist = pm.pull_mnist() X_train, X_test, y_train, y_test = stt.split_train_test(mnist) X_train, y_train = foldMnistData(X_train, y_train)

UTF-8

Python

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py

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fold.py

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0

11

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int-brain-lab/iblscripts

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c426e4479613459ce98a25df744efc651919ee9c

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/deploy/serverpc/utils/scan_fix_passive_files.py

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[ "MIT" ]

permissive

https://github.com/int-brain-lab/iblscripts

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refs/heads/master

2023-08-31T23:59:04.889864

2023-08-04T14:42:56

187,838,029

3

9

MIT

false

2023-08-18T18:14:14

2019-05-21T13:00:12

2023-06-27T20:36:36

2023-08-18T18:14:14

11,950

3

8

7

Jupyter Notebook

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from pathlib import Path import argparse import logging from ibllib.pipes.scan_fix_passive_files import execute log = logging.getLogger('ibllib') if __name__ == "__main__": parser = argparse.ArgumentParser(description='Scan and fix badly transfered passive sessions') parser.add_argument('root_data_folder', default='/mnt/s0/Data', help='Root data folder [/mnt/s0/Data]') parser.add_argument('--dry', required=False, default=False, action='store_true', help='Dry run? default: False') args = parser.parse_args() # returns data from the options specified (echo) root_path = Path(args.root_data_folder) if not root_path.exists(): log.error(f"{root_path} does not exist") from_to_pairs, moved_ok = execute(root_path, dry=args.dry) if args.dry: log.info(from_to_pairs)

UTF-8

Python

false

842

py

162

scan_fix_passive_files.py

110

0.67696

0.674584

0

21

39.095238

107

MrJVM/Python-Practice

609,885,356,838

7a91052052d19dabd406fc8d5153002de1afe39b

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/basics/prime_practice.py

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permissive

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2f8716b149afa18f6979329cc12036e63c515823

refs/heads/master

2021-04-24T00:26:52.442841

2020-04-08T17:26:32

250,043,590

0

null

from math import sqrt value = 10 print(f'The value is {value}') d = dict(tom='22',sam='50') for k,v in d.items(): print("Key={} Value={}",k,v) #Collection comprehension def is_prime(x): if x < 2: return False for i in range(2, int(sqrt(x))+1): if x%i == 0: return False return True l = [x for x in range(101) if is_prime(x)] print(l)

UTF-8

Python

false

384

py

7

prime_practice.py

6

0.565104

0.53125

0

21

17.333333

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vindafadilla/ta_backend

11,252,814,315,618

222dae8bef272455ca93060e6e890e58f9c7cac9

dea2639a56122d36624d7ab2687bff4a32c8cd90

/datacleaning/dataCleaning.py

f875fd4e8e3ecbd24d497566d4218f6a8e038daf

[]

no_license

https://github.com/vindafadilla/ta_backend

139bb8e4026f07ca4a6658683e466f0ce3fe486e

db4b819bda668c1531d12c49ca61454dec2b57bb

refs/heads/master

2021-01-20T04:12:27.883453

2017-06-21T01:38:53

89,660,284

0

null

from ta_backend.datapreparation.dataTwitter import DataTwitter from ta_backend.datapreparation.appropriateDataTweetFilter import AppropriateDataTweetFilter from ta_backend.datacleaning.normalization import Normalization class DataCleaning: def dataCleaningProcess(self, listTweet): try: normalization = Normalization() print(listTweet["tweetList"][1]["text"]) listDataTweetNormalized = [] for index in range(len(listTweet["tweetList"])): dataTweetWithoutHTMLChar = normalization.escapingHTMLChar(listTweet["tweetList"][index]["text_processed"]) listTweet["tweetList"][index]["text_processed"] = dataTweetWithoutHTMLChar dataTwitterURLRemoved = normalization.urlRemoval(listTweet["tweetList"][index]["text_processed"]) listTweet["tweetList"][index]["text_processed"] = dataTwitterURLRemoved dataTweetWhitespacesNormalized = normalization.whitespace(listTweet["tweetList"][index]["text_processed"]) listTweet["tweetList"][index]["text_processed"] = dataTweetWhitespacesNormalized dataTweetAbbreviationNormalized = normalization.abbreviations(listTweet["tweetList"][index]["text_processed"]) listTweet["tweetList"][index]["text_processed"] = dataTweetAbbreviationNormalized dataTweetContractionNormalized = normalization.contractions(listTweet["tweetList"][index]["text_processed"]) listTweet["tweetList"][index]["text_processed"] = dataTweetContractionNormalized dataTweetSpellfixNormalized = normalization.spellfix(listTweet["tweetList"][index]["text_processed"]) listTweet["tweetList"][index]["text_processed"] = dataTweetSpellfixNormalized dataTweetAttachedWordsSplitted = normalization.splitAttachedWords(listTweet["tweetList"][index]["text_processed"]) listTweet["tweetList"][index]["text_processed"] = dataTweetAttachedWordsSplitted listDataTweetNormalized.append(listTweet["tweetList"][index]) tweetperDay = { 'datePeriod': listTweet["datePeriod"], 'tweetList': listDataTweetNormalized } return tweetperDay except Exception as e: return e # def insertToDatabase(self, listTweet):

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py

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dataCleaning.py

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0.686134

0.685714

0

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wani-hackase/wani-writeup

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16f266b31fda360387ad509d4036acc0ab56e6f4

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/2020/03-b01lers/cry-safety/solve.py

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permissive

https://github.com/wani-hackase/wani-writeup

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refs/heads/master

2021-12-27T16:48:21.017138

2021-12-16T10:30:14

170,531,761

26

1

MIT

false

2020-06-25T07:29:11

2019-02-13T15:32:43

2020-05-28T03:48:01

2020-06-25T07:27:17

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HTML

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import gmpy2 e = 0x10001 with open('flag.enc', 'rb') as f: flag = f.read() flag = int.from_bytes(flag, byteorder='little') m, exact = gmpy2.iroot(flag, e) m = int(m) if exact: flag = m.to_bytes(m.bit_length()//8+1, byteorder='little') print(flag) else: print('Failed')

UTF-8

Python

false

289

py

235

solve.py

86

0.619377

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0

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HonglinChu/SiamTrackers

9,603,546,880,003

99b4f1a19786ba49db6b026b9f543f6849c04de9

abbc2d332bdfa036ac12438983e6d74cf4107e64

/SiamFCpp/SiamFCpp-video_analyst/siamfcpp/evaluation/davis_benchmark/davis2017/results.py

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[]

permissive

https://github.com/HonglinChu/SiamTrackers

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refs/heads/master

2023-08-29T06:50:59.532271

2023-03-06T09:13:53

253,718,080

1,166

243

Apache-2.0

false

2023-08-03T16:39:53

2020-04-07T07:24:00

2023-08-03T02:47:52

2023-08-03T16:39:53

67,076

1,054

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Python

false

import os import numpy as np from PIL import Image import sys class Results(object): def __init__(self, root_dir): self.root_dir = root_dir def _read_mask(self, sequence, frame_id): try: mask_path = os.path.join(self.root_dir, sequence, '{}.png'.format(frame_id)) return np.array(Image.open(mask_path)) except IOError as err: sys.stdout.write(sequence + " frame %s not found!\n" % frame_id) sys.stdout.write( "The frames have to be indexed PNG files placed inside the corespondent sequence " "folder.\nThe indexes have to match with the initial frame.\n") sys.stderr.write("IOError: " + err.strerror + "\n") sys.exit() def read_masks(self, sequence, masks_id): mask_0 = self._read_mask(sequence, masks_id[0]) masks = np.zeros((len(masks_id), *mask_0.shape)) for ii, m in enumerate(masks_id): masks[ii, ...] = self._read_mask(sequence, m) num_objects = int(np.max(masks)) tmp = np.ones((num_objects, *masks.shape)) tmp = tmp * np.arange(1, num_objects + 1)[:, None, None, None] masks = (tmp == masks[None, ...]) > 0 return masks

UTF-8

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false

1,285

py

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results.py

318

0.558755

0.554086

0

33

37.939394

98

Rapator-Tech-Academy/rapator-final-project

335,007,452,715

6c79149ac877d2d8fc101ba38d8dd241d769e539

06d1a334cbf7c02db43ba3890a9ef8f1d862123e

/app/core/admin.py

d7d55b2b3cb3a3f3aedede5907f9255d711cf992

[]

no_license

https://github.com/Rapator-Tech-Academy/rapator-final-project

42a85b7229e7b68a806c48fb69e9a65f21f2fd62

63e6df63f7a16d5402a7b437da3939d93549ca8e

refs/heads/main

2023-03-19T04:21:30.527519

2021-03-04T09:09:28

322,076,812

0

2

null

false

2021-03-04T09:09:29

2020-12-16T19:06:10

2021-02-24T00:48:14

2021-03-04T09:09:28

31,204

0

1

0

Python

false

from django.contrib import admin from django.contrib.flatpages.admin import FlatPageAdmin from django.contrib.flatpages.models import FlatPage from django.db import models from mptt.admin import MPTTModelAdmin from ckeditor.widgets import CKEditorWidget from mptt.admin import MPTTModelAdmin from core.models import Category, City, ProductImage, Product class FlatPageCustom(FlatPageAdmin): formfield_overrides = { models.TextField: {'widget': CKEditorWidget} } admin.site.unregister(FlatPage) admin.site.register(FlatPage, FlatPageCustom) admin.site.register(City) admin.site.register(Category, MPTTModelAdmin) admin.site.register(ProductImage) admin.site.register(Product)

UTF-8

Python

false

701

py

83

admin.py

37

0.810271

0

27

24.925926

61

hugovk/EveryElection

1,778,116,494,175

f1e817b0dae2360b08971327f9c86d274decdb0c

d8c209163273688c9eb460674b89a10a951e4cb4

/every_election/apps/organisations/constants.py

bb4ffe68603cb5280acb1724b7483e2bfd8bf24c

[ "MIT" ]

permissive

https://github.com/hugovk/EveryElection

9fa51edee42de06ab8caff225c64dfed4c61211b

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refs/heads/master

2018-04-07T15:37:19.278115

2017-04-25T13:08:19

89,626,719

0

null

true

2017-04-27T18:18:44

2017-04-27T18:10:16

2017-04-27T18:10:19

2017-04-27T18:12:30

32,698

0

1

Python

null

PARENT_TO_CHILD_AREAS = { 'DIS': ['DIW',], 'MTD': ['MTW',], 'CTY': ['CED',], 'LBO': ['LBW',], 'CED': ['CPC',], 'UTA': ['UTW', 'UTE'], 'NIA': ['NIE',], 'COI': ['COP',], } CHILD_TO_PARENT_AREAS = { 'DIW': 'DIS', 'MTW': 'MTD', 'UTW': 'UTA', 'UTE': 'UTA', 'CED': 'CTY', 'LBW': 'LBO', 'CPC': 'CED', 'COP': 'COI', } AREAS_WITHOUT_PCCS = [ "metropolitan", "city-of-london", "northern-ireland", ] AREAS_IN_WALES = [ 'south-wales', 'north-wales', 'gwent', 'dyfed-powys', ] POLICE_AREA_NAME_TO_GSS = { 'avon-and-somerset': [ 'E10000027', 'E06000022', 'E06000023', 'E06000024', 'E06000025'], 'bedfordshire': ['E06000055', 'E06000056', 'E06000032'], 'cambridgeshire': ['E10000003', 'E06000031'], 'cheshire': ['E06000049', 'E06000050', 'E06000006', 'E06000007'], 'cleveland': ['E06000001', 'E06000002', 'E06000003', 'E06000004'], 'cumbria': ['E10000006'], 'derbyshire': ['E10000007', 'E06000015'], 'devon-and-cornwall': [ 'E10000008', 'E06000052', 'E06000026', 'E06000027', 'E06000053'], 'dorset': ['E10000009', 'E06000028', 'E06000029'], 'durham': ['E06000005', 'E06000047'], 'dyfed-powys': ['W06000008', 'W06000010', 'W06000009', 'W06000023'], 'essex': ['E10000012', 'E06000033', 'E06000034'], 'gloucestershire': ['E10000013'], 'warwickshire': ['E10000031'], 'greater-manchester': [ 'E08000001', 'E08000002', 'E08000003', 'E08000004', 'E08000005', 'E08000006', 'E08000007', 'E08000008', 'E08000009', 'E08000010'], 'gwent': ['W06000021', 'W06000019', 'W06000018', 'W06000022', 'W06000020'], 'hampshire': ['E10000014', 'E06000046', 'E06000044', 'E06000045'], 'hertfordshire': ['E10000015'], 'humberside': ['E06000011', 'E06000012', 'E06000013', 'E06000010'], 'kent': ['E10000016', 'E06000035'], 'lancashire': ['E10000017', 'E06000008', 'E06000009'], 'leicestershire': ['E10000018', 'E06000016', 'E06000017'], 'lincolnshire': ['E10000019'], 'merseyside': [ 'E08000011', 'E08000012', 'E08000014', 'E08000015', 'E08000013'], 'norfolk': ['E10000020'], 'north-wales': ['W06000001', 'W06000002', 'W06000004', 'W06000005', 'W06000003', 'W06000006'], 'north-yorkshire': ['E10000023', 'E06000014'], 'northamptonshire': ['E10000021'], 'northumbria': ['E06000057', 'E08000037', 'E08000021', 'E08000022', 'E08000023', 'E08000024'], 'nottinghamshire': ['E10000024', 'E06000018'], 'south-wales': ['W06000015', 'W06000011', 'W06000013', 'W06000024', 'W06000012', 'W06000016', 'W06000014'], 'south-yorkshire': ['E08000016', 'E08000017', 'E08000018', 'E08000019'], 'staffordshire': ['E10000028', 'E06000021'], 'suffolk': ['E10000029'], 'surrey': ['E10000030'], 'sussex': ['E10000011', 'E10000032', 'E06000043'], 'thames-valley': ['E06000036', 'E06000038', 'E06000039', 'E06000037', 'E06000040', 'E06000041', 'E10000002', 'E10000025', 'E06000042'], 'west-mercia': ['E06000051', 'E10000034', 'E06000019', 'E06000020'], 'west-midlands': ['E08000025', 'E08000026', 'E08000027', 'E08000028', 'E08000029', 'E08000030', 'E08000031'], 'west-yorkshire': [ 'E08000032', 'E08000033', 'E08000034', 'E08000035', 'E08000036'], 'wiltshire': ['E06000054', 'E06000030'], } COMBINED_AUTHORITY_SLUG_TO_GSS = { 'cambridgeshire-and-peterborough': ['E06000031', 'E10000003', 'E07000008', 'E07000011', 'E07000009', 'E07000012', 'E07000010'], 'greater-manchester-ca': ['E08000001', 'E08000002', 'E08000003', 'E08000004', 'E08000005', 'E08000006', 'E08000007', 'E08000008', 'E08000009', 'E08000010'], 'liverpool-city-ca': ['E06000006', 'E08000011', 'E08000012', 'E08000013', 'E08000014', 'E08000015'], 'tees-valley': ['E06000005', 'E06000001', 'E06000002', 'E06000003', 'E06000004'], 'west-midlands': ['E08000025', 'E08000026', 'E08000027', 'E08000028', 'E08000029', 'E08000030', 'E08000031'], 'west-of-england': ['E06000022', 'E06000023', 'E06000025'], }

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peernode/offline_platform

7,490,422,977,018

86b60e420087a6b9c9e82ffdd77831c6cb059dbc

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/flashVoD/rate/connect_ratio_views.py

0a7671df5d1fabbdbd7972fba15156d83f9755d2

[ "Apache-2.0" ]

permissive

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refs/heads/master

2020-05-26T21:45:44.997335

2015-01-27T02:04:58

29,892,543

0

null

# -*- coding: utf-8 -*- import json from django.http import HttpResponse from rate.models import * def update_connect_ratio(request): result="ok" if request.method=='POST': decodes=json.loads(request.body) try: ver=version_info.get_version(decodes['ver']) create_date='%s-%s-%s'%(decodes['create_date'][0:4], decodes['create_date'][4:6], decodes['create_date'][6:8]) # total suc info total_obj=connect_ratio(Date=create_date, Version=ver, ConnectType=0, Hour=decodes['hour'], PostTotal=decodes['post_total'], Fenzi=decodes['suc_total']) total_obj.save() # total offline info offline_obj=connect_ratio(Date=create_date, Version=ver, ConnectType=2, Hour=decodes['hour'], PostTotal=decodes['post_total'], Fenzi=decodes['fail_total']) offline_obj.save() # first suc info first_obj=connect_ratio(Date=create_date, Version=ver, ConnectType=1, Hour=decodes['hour'], PostTotal=decodes['first_post_total'], Fenzi=decodes['first_suc_total']) first_obj.save() # first offline info first_offline_obj=connect_ratio(Date=create_date, Version=ver, ConnectType=3, Hour=decodes['hour'], PostTotal=decodes['first_post_total'], Fenzi=decodes['first_fail_total']) first_offline_obj.save() except ValueError, e: result="error: %s"%e print e except Exception, e: result="error: %s"%e print e else: result="error" respStr=json.dumps({"result":result}) return HttpResponse(respStr, content_type="application/json")

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122

Gregorh98/PlanterCostCalculator

6,597,069,783,886

0bc508fc1d5b5309cedead5f4aaf1026e189863f

3210361298a54c2415ac9b70dc4fcd59051a1de9

/planters.py

caa45fa9f84702a50fd273bc1beff0ce35a9f5ab

[]

no_license

https://github.com/Gregorh98/PlanterCostCalculator

dc6f421157fbd59c55e59791e3d9d245bdcc6012

24907112b8247f27d20819e34814c64c0aa3e696

refs/heads/main

2023-03-02T19:30:30.749010

2021-02-13T23:35:29

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null

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2021-02-07T01:09:50

2021-02-04T19:02:03

2021-02-07T01:09:11

2021-02-07T01:09:09

49

0

Python

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import planks, linerRoll import json, math class Planter(): def __init__(self, dimensions, settings): self.height, self.width, self.length = dimensions self.settings = settings self.markup = self.settings["general"]["markup"] self.measurementType = self.settings["general"]["defaultUnitOfMeasurement"] self.plank = planks.Plank(self.settings) if self.length >= 90: self.numberOfSupportPlanks = 6 else: self.numberOfSupportPlanks = 4 def getAreaInside(self): areaOfAirFloor = (self.width-(self.plank.height*2))*(self.length-(self.plank.height*2)) innerArea = areaOfAirFloor * self.height return innerArea def getAreaOutside(self): innerArea = self.getAreaInside() totalArea = (self.width * self.length) * self.height outsideArea = totalArea - innerArea return outsideArea def calcScrewsNeeded(self): return 50 def calcScrewCost(self): screwsPerPlanter = self.calcScrewsNeeded() screwsPerBox = self.settings["screws"]["numberPerBox"] costPerBox = self.settings["screws"]["cost"] costPerPlanter = ((costPerBox/screwsPerBox)*screwsPerPlanter) return (costPerPlanter) def calcWoodCostArea(self): totalArea = self.getAreaOutside() costPerArea = totalArea*self.plank.getCostPerCm3() return costPerArea def calcWoodCostPlanks(self): woodCost = self.calcWoodNeeded() * self.plank.cost return woodCost def calcLinerCost(self): #calculate areas of all box faces and ground #Multiply the sum of these by liner cost per cm2 self.liner = linerRoll.Liner(self.settings) longSideAreas = 2*((self.length*self.height) - (2*self.plank.height)) shortSideAreas = 2*((self.width*self.height) - (2*self.plank.height)) baseArea = ((self.length*self.width)-(4*self.plank.width)) totalAreaToCover = longSideAreas+shortSideAreas+baseArea totalLinerCost = self.liner.getCostPerCm2()*totalAreaToCover return totalLinerCost def calcTotalCost(self): total = self.calcScrewCost() + self.calcWoodCostPlanks() + self.calcLinerCost() return total def calcWoodNeeded(self): planterArea = self.getAreaOutside() plankArea = self.plank.getArea() supportPlanks = ((self.plank.height*self.plank.width*self.height)*self.numberOfSupportPlanks) woodNeeded = math.ceil((planterArea+supportPlanks)/plankArea) return woodNeeded def calcPlanksNeeded(self): planksNeededForSections = int((2*(self.height/self.plank.width))) lengthPlankDimensions = (str(self.length)+ "x" + str(self.plank.width)) widthPlankDimensions = (str(self.width) + "x" + str(self.plank.width)) supportPlankDimensions = (str(self.height) + "x" + str(self.plank.width)) planksNeeded = [["Length", lengthPlankDimensions, planksNeededForSections], ["Width", widthPlankDimensions, planksNeededForSections], ["Supports", supportPlankDimensions, self.numberOfSupportPlanks]] return planksNeeded def calcSoilNeeded(self): innerVolume = self.getAreaInside() soilLitresNeeded = math.ceil(innerVolume/1000) return soilLitresNeeded

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planters.py

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pgmac/aws_helpers

9,818,295,246,684

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ca769b501ae3b5a9cc62c203aca749579eea5abf

/route_53.py

a29d69cffb0df7c123fb288b01a9332d082c1c3a

[]

no_license

https://github.com/pgmac/aws_helpers

3fb8c08cf9f9acfbb53916c193eea0532cf15777

ce0b7c22d2c1f78400dde758330cb2394751f19c

refs/heads/master

2022-10-19T09:31:07.212200

2022-09-19T07:33:50

123,996,034

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null

#!/usr/bin/python3 """List the Route 53 DNS Entries""" from pprint import pprint import boto3 from botocore.exceptions import ClientError def rr_display(client, rrset, level=0): #print(rrset) print("{}{}\t{}".format("\t"*level, rrset['Name'], rrset['Type'])) print("{}{}".format("\t"*(level+1), rrset['ResourceRecords'])) def zone_display(client, zone_det, level=0): """Display the zone details""" print("{}{}".format("\t"*level, zone_det['Name'])) #print(zone_det) response = client.list_resource_record_sets(HostedZoneId=zone_det['Id']) zone = [rr_display(client, rrset, (level+1)) for rrset in response['ResourceRecordSets']] return zone def main(): """Get the details""" r53 = boto3.client('route53') try: response = r53.list_hosted_zones() zones = [zone_display(r53, item) for item in response['HostedZones']] #pprint(out_put) #_ = (print(out_item) for out_item in out_put) except ClientError as c_e: print(c_e) return zones if __name__ == '__main__': main()

UTF-8

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py

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route_53.py

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SEA-group/wowp_scripts

12,506,944,802,968

fae8ae7215f5149ae378762fbc38b38ad1fe1590

3b504a983f1807ae7c5af51078bfab8c187fc82d

/client/fm/FMAvatarMethods.py

eae6f704f9f35b890ede8902abad6704e7ef4eda

[]

no_license

https://github.com/SEA-group/wowp_scripts

7d35fd213db95ea6b3dbd1ec6d3e0f13de86ba58

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refs/heads/master

2021-09-07T23:10:13.706605

2018-03-02T17:23:48

117,280,141

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null

# Embedded file name: scripts/client/fm/FMAvatarMethods.py def fmEmpty(func): return func def fmAvatarMethods(decorator): if decorator is None: decorator = fmEmpty def addFMAvatarMethods(objClass): class decorated(objClass): @decorator def fmData(self, data): pass @decorator def fmInit(self, ticksPerUpdate, position, rotation): pass @decorator def fmSync(self, frames): pass @decorator def fmDiff(self, data): pass @decorator def fmBinDataBegin(self): pass @decorator def fmBinData(self, data): pass @decorator def fmBinDataComplete(self): pass decorated.__name__ = objClass.__name__ return decorated return addFMAvatarMethods

UTF-8

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971

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FMAvatarMethods.py

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lxtxl/aws_cli

18,056,042,525,306

f319c11571d96425064427abda317d1a87ac3181

91d1a6968b90d9d461e9a2ece12b465486e3ccc2

/ec2_write_1/route-table_delete.py

bfb1a78ebfb2bfbaf6ac1d06ffec84059302e827

[]

no_license

https://github.com/lxtxl/aws_cli

c31fc994c9a4296d6bac851e680d5adbf7e93481

aaf35df1b7509abf5601d3f09ff1fece482facda

refs/heads/master

2023-02-06T09:00:33.088379

2020-12-27T13:38:45

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#!/usr/bin/python # -*- codding: utf-8 -*- import os import sys sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__)))) from common.execute_command import write_one_parameter # url : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/delete-route-table.html if __name__ == '__main__': """ associate-route-table : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/associate-route-table.html create-route-table : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/create-route-table.html describe-route-tables : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/describe-route-tables.html disassociate-route-table : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/disassociate-route-table.html """ parameter_display_string = """ # route-table-id : The ID of the route table. """ add_option_dict = {} ####################################################################### # parameter display string add_option_dict["parameter_display_string"] = parameter_display_string # ex: add_option_dict["no_value_parameter_list"] = "--single-parameter" write_one_parameter("ec2", "delete-route-table", "route-table-id", add_option_dict)

UTF-8

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route-table_delete.py

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J-Pai/408DaisyJetson

1,735,166,815,570

89ef5193ee265e22e2c4386e82a93a56dda47c42

12af2534582974d8f07ccde9deda54e4d8967a73

/tests/pure_face_tracking.py

e89753a08f5c6f06d3fad38c5c20bb8818c9b58d

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permissive

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refs/heads/master

2021-01-24T00:04:30.947233

2019-08-29T21:50:15

122,752,585

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#!/usr/bin/env python3 import numpy as np import cv2 import face_recognition import sys from multiprocessing import Queue from multiprocessing.managers import SyncManager from queue import Queue as ImageQueue from pylibfreenect2 import Freenect2, SyncMultiFrameListener from pylibfreenect2 import FrameType, Registration, Frame from pylibfreenect2 import setGlobalLogger setGlobalLogger(None) print("OpenGL Pipeline") from pylibfreenect2 import OpenGLPacketPipeline print("Starting Tracking") def __draw_bbox(valid, frame, bbox, color, text): if not valid: return cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2, 1) cv2.putText(frame, text, (bbox[0], bbox[1] - 4), \ cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1) def __scale_frame(frame, scale_factor = 1): if scale_factor == 1: return frame return cv2.resize(frame, (0,0), fx=scale_factor, fy=scale_factor) def face_locations(image): pass class NeuronManager(SyncManager): pass NeuronManager.register('get_web_neuron') NeuronManager.register('get_alexa_neuron') manager = NeuronManager(address=('', 4081), authkey=b'daisy') manager.connect() web_neuron = manager.get_web_neuron() alexa_neuron = manager.get_alexa_neuron() faces = { "JessePai": "../faces/JPai-1.jpg", # "VladMok": "./faces/Vlad.jpg", # "TeddyMen": "./faces/TMen-1.jpg" } known_faces = {} for person in faces: image = face_recognition.load_image_file(faces[person]) print(person) face_encoding_list = face_recognition.face_encodings(image) if len(face_encoding_list) > 0: known_faces[person] = face_encoding_list[0] else: print("\tCould not find face for person...") pipeline = OpenGLPacketPipeline() target = "JessePai" fn = Freenect2() num_devices = fn.enumerateDevices() if num_devices == 0: print("No device connected!") serial = fn.getDeviceSerialNumber(0) device = fn.openDevice(serial, pipeline = pipeline) listener = SyncMultiFrameListener(FrameType.Color | FrameType.Depth) device.setColorFrameListener(listener) device.setIrAndDepthFrameListener(listener) device.start() registration = Registration(device.getIrCameraParams(), device.getColorCameraParams()) undistorted = Frame(512, 424, 4) registered = Frame(512, 424, 4) bigdepth = Frame(1920, 1082, 4) trackerObj = None face_process_frame = True bbox = None track_bbox = None while True: timer = cv2.getTickCount() frames = listener.waitForNewFrame() color = frames["color"] depth = frames["depth"] registration.apply(color, depth, undistorted, registered, bigdepth=bigdepth) bd = np.resize(bigdepth.asarray(np.float32), (1080, 1920)) c = cv2.cvtColor(color.asarray(), cv2.COLOR_RGB2BGR) face_bbox = None new_track_bbox = None face_locations = face_recognition.face_locations(c, number_of_times_to_upsample=0, model="cnn") face_encodings = face_recognition.face_encodings(c, face_locations) for face_encoding in face_encodings: matches = face_recognition.compare_faces( [known_faces[target]], face_encoding, 0.6) if len(matches) > 0 and matches[0]: (top, right, bottom, left) = face_locations[0] face_bbox = (left, top, right, bottom) mid_w = int((left + right) / 2) mid_h = int((top + bottom) / 2) break __draw_bbox(face_bbox is not None, c, face_bbox, (0, 0, 255), target) c = __scale_frame(c, scale_factor = 0.5) fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer) cv2.putText(c, "FPS : " + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,255), 1) image = cv2.imencode('.jpg', c)[1].tostring() web_neuron.update([('image', image)]) listener.release(frames) self.so.close() cv2.destroyAllWindows() device.stop() device.close()

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hglasser/phonebook-app

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/set_an_entry.py

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[]

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https://github.com/hglasser/phonebook-app

e7bab09b34e7cdffcd39d7abc13d847f84feaf8d

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refs/heads/master

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2018-07-09T13:24:30

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def set_an_entry(phonebook): name = raw_input("Name: ") phone_number = raw_input("Enter phone number formatted like this ###-###-####: ") phonebook[name] = phone_number return "Entry stored for %s" % name

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ProkopHapala/SimpleSimulationEngine

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/python/pySimE/space/exp/pykep/lambert_Prokop.py

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permissive

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refs/heads/master

2022-09-05T01:02:42.820199

2022-08-28T10:22:41

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4

null

import numpy as np import matplotlib as mpl from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from PyKEP import epoch, DAY2SEC, planet_ss, AU, MU_SUN, lambert_problem from PyKEP.orbit_plots import plot_planet, plot_lambert mpl.rcParams['legend.fontsize'] = 10 fig = plt.figure() ax = fig.gca(projection='3d') t1 = epoch(0) t2 = epoch(740) dt = (t2.mjd2000 - t1.mjd2000) * DAY2SEC ax.scatter(0,0,0, color='y') pl = planet_ss('earth') plot_planet(ax,pl, t0=t1, color=(0.8,0.8,1), legend=True, units = AU) rE,vE = pl.eph(t1) pl = planet_ss('mars') plot_planet(ax,pl, t0=t2, color=(0.8,0.8,1), legend=True, units = AU) rM, vM = pl.eph(t2) l = lambert_problem(rE,rM,dt,MU_SUN) nmax = l.get_Nmax() print "max number of revolutions",nmax plot_lambert(ax,l , color=(1,0,0), legend=True, units = AU) for i in range(1,nmax*2+1): print i plot_lambert(ax,l,sol=i, color=(1,0,i/float(nmax*2)), legend=True, units = AU) def axisEqual3D(ax): extents = np.array([getattr(ax, 'get_{}lim'.format(dim))() for dim in 'xyz']) sz = extents[:,1] - extents[:,0] centers = np.mean(extents, axis=1) maxsize = max(abs(sz)) r = maxsize/2 for ctr, dim in zip(centers, 'xyz'): getattr(ax, 'set_{}lim'.format(dim))(ctr - r, ctr + r) axisEqual3D(ax) plt.show()

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Muele93/Ndivhu

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def sum_array(array): """funtion returns the sum of all items in array""" x= len(array) if x == 1: return array[0] else: return array[0] + sum_array(array[1:],) def fibonacci(n): """"Returns the nth term in a fibonacci sequence when n==0 and n==1""" if n==0 or n==1: return n else: #When n>1, the nth term of a fibonacci sequence is returned by adding the 2 previous terms return fibonacci(n-1) + fibonacci(n-2) def factorial(n): """Returns the factorial of n when it is greater than 0""" if n==0: return 1 #The factorial of 0 is always 1 else: return n* factorial(n-1) def reverse(word): """The function returns a string in reverse""" reversed_string = '' index = len(word) while index: index =index- 1 reversed_string = reversed_string + word[index] return reversed_string

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from unittest import TestCase import numpy as np from tsp.tspsolver import NaiveTSPSolver from tsp.tspsolver import NearestNeighbor from tsp.tspsolver import Christofides class TestTSPSolver(TestCase): def __init__(self, *args): super().__init__(*args) # From: https://people.sc.fsu.edu/~jburkardt/datasets/tsp/tsp.html self.d = np.array([[0.0, 3.0, 4.0, 2.0, 7.0], [3.0, 0.0, 4.0, 6.0, 3.0], [4.0, 4.0, 0.0, 5.0, 8.0], [2.0, 6.0, 5.0, 0.0, 6.0], [7.0, 3.0, 8.0, 6.0, 0.0]]) def test_naive(self): solver = NaiveTSPSolver(self.d) solver.solve() self.assertEqual(solver.dist, 19) self.assertEqual(solver.solution, [0, 2, 1, 4, 3]) def test_nearest_neighbor(self): solver = NearestNeighbor(self.d) solver.solve() self.assertEqual(solver.dist, 21) self.assertEqual(solver.solution, [0, 3, 2, 1, 4]) def test_christofides(self): solver = Christofides(self.d) solver.solve() self.assertEqual(solver.dist, 21) self.assertEqual(solver.solution, [0, 2, 3, 4, 1])

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2023-02-11T18:20:10

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"""Python switch-statement pseudo-implementation Mimics C-style switch statements The following code blocks should be equivalent ----------------------------------------------- switch(arg): case 1: // Handle case case 2: // Handle case default: // Handle default case ----------------------------------------------- class Case1(SwitchCall): def __init__(self, arg1, arg2): self.__arg1 = arg1 self.__arg2 = arg2 def do_call(self, *args, **kwargs) # Handle call return self.__arg1 - self.__arg2 class Case2(SwitchCall): def __init__(self, arg1, arg2): self.__arg1 = arg1 self.__arg2 = arg2 def do_call(self, *args, **kwargs) # Handle call return self.__arg1 * self.__arg2 class CaseDefault(SwitchCall): def __init__(self, arg1, arg2): self.__arg1 = arg1 self.__arg2 = arg2 def do_call(self, *args, **kwargs) return self.__arg1 + self.__arg2 switch(arg, { case_1 : Case1(arg1, arg2), case_2 : Case2(arg1, arg2) }, CaseDefault(arg1, arg2)) """ __author__ = 'Thomas Li Fredriksen' __license__ = 'MIT' class SwitchCall(object): """Switch-call master class All switch-call objects must inherit from this class """ def do_call(self, *args, **kwargs): """Overload this function to simulate function call""" pass def __call__(self, *args, **kwargs): """Call do_call-method""" self.do_call(args, kwargs) class switch(object): def __init__(self, key, cases, default=None): """Switch-statement implementation :param key: Switch parameter :param cases: Dictionary of callbacks :param default: Default callback if key is not in cases """ ret = None try: ret = cases[key]() except KeyError: if default: ret = default() finally: return ret

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/app/api/api_v1/endpoints/example.py

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from fastapi import APIRouter from app.schemas import echo, hello router = APIRouter() @router.get('/hello', response_model=hello.Hello) def hello_world(): return {'msg': 'Hello World'} @router.post('/echo', response_model=echo.Echo) def echo(msg_in: echo.Echo): return {'msg': echo}

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dragos-vacariu/Python-Exercises

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/Basic Practice/program18 distance between circle point.py

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#clasa PUNCT cu 2 parametri x, y #clasa CERC mosteneste PUNCT, si defineste o raza #CERC are o metoda de calculul a ariei class PUNCT: #constructor def __init__(self, xCoord, yCoord): self.x=xCoord #non-static field self.y=yCoord #non-static field #public method def calculateDistance(self, Punct): distanceX = Punct.x - self.x distanceY = Punct.y - self.y distance = (distanceX**2) + (distanceY**2) return distance ** 0.5 #operator overloading def __add__(self, other): #overloading the "+" operator for addition if(isinstance(other, POLYLINIE)): points = [self] for x in other.Points: points.append(x) return POLYLINIE(*points) else: polilinie = POLYLINIE(self, other) return polilinie #built-in function overloading def __str__(self): return "Punct(" +str(self.x) + ", " +str(self.y) +")" class CERC(PUNCT): #CERC inherits from PUNCT #constructor def __init__(self, Punct, Rad): super().__init__(Punct.x, Punct.y) #super() returns the parent class self.Radius = float(Rad) #public methods def getArea(self): return 3.14 * self.Radius**2 def calculateDistanceToObject(self, Data): if isinstance(Data, CERC): return super().calculateDistance(Data) - self.Radius - Data.Radius elif isinstance(Data, PUNCT): return super().calculateDistance(Data) - self.Radius class SEGMENT(PUNCT): def calculateDistance(self, Punct1, Punct2): distanceX = Punct2.x - Punct1.x distanceY = Punct2.y - Punct1.y distance = (distanceX**2) + (distanceY**2) return distance ** 0.5 #constructor def __init__(self, Punct1, Punct2): super().__init__(Punct1.x, Punct1.y) super().__init__(Punct2.x, Punct2.y) self.length = self.calculateDistance(Punct1,Punct2) #build-in function overloading def __str__(self): #redefining the function __str__ so it can be printed directly print(obj) return "Length of segment: " + str(self.length) #Operator overloading def __gt__(self, other): #overloading > operators (gt comes from greater) return self.length < other.length #also work for <, because of negations def __ge__(self, other): #overloading >= operators (ge comes from greater than or equal to) return self.length < other.length #also work for <= because of negations def __eq__(self, other): #overloading == operator return self.length == other.length class POLYLINIE: #constructor def __init__(self, *Points): self.Points=list(Points) #built-in function overloading def __str__(self): value="Polilinie(" for p in self.Points: value+=str(p)+", " value+=")" return value #operator overloading def __add__(self, other): #overloading the "+" operator for addition puncte = [] for x in self.Points: puncte.append(x) puncte.append(other) return POLYLINIE(*puncte) def __iter__(self): #making class iterable self.counter=0 def __next__(self): #making clas iterable if self.counter < len(self.Points): self.counter+=1 else: raise Exception ("Object reached boundaries") x = 3; y = 4 #x = float(input("p1.x: ")) #y = float(input("p1.y: ")) punctUnu = PUNCT(x,y) cerc = CERC(punctUnu, Rad = 2.0) x = 5; y = 6 #x = float(input("p2.x: ")) #y = float(input("p2.y: ")) punctDoi = PUNCT(x,y) cerc2 = CERC(punctDoi, Rad = 3.0) print("\nArea of circle = " + str(cerc.getArea())) print("Distance PointToPoint = " + str(punctUnu.calculateDistance(punctDoi))) print("Distance CircleToPoint = " + str(cerc.calculateDistanceToObject(punctDoi))) print("Distance CircleToCircle = " + str(cerc.calculateDistanceToObject(cerc2))) segment = SEGMENT(punctUnu, punctDoi) print(segment) segmentSec = SEGMENT(punctDoi, PUNCT(10,7)) print() #put a new line print(segmentSec) print(segment < segmentSec) print(segment >= segmentSec) print(segment == segmentSec) print() #put a new line polyline = POLYLINIE(punctUnu, punctDoi) #print(polyline) print("Result: ") polylineDoi = punctUnu + (punctDoi + punctUnu) # building a polyline from 3 points print(polylineDoi)

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inputNumber = int(input("Input the pyramid level : ")) blankText = " " text = input("Input the character for pyramid monomer : ") for i in range(inputNumber): print(blankText*(inputNumber-(i+1)),end=text*((2*i)+1)) print(blankText*(inputNumber-(i+1))) print("-"*12,"The End","-"*12) ''' # Pattern print(" "*2,end="*") print(" "*2) print(" "*1,end="***") print(" "*1) print(" "*0,end="*****") print(" "*0) '''

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LillyParker/Python_ExercisesNMNH

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/Week2Hmwk_calculator.py

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no_license

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2021-01-19T03:35:04.862394

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# Homework for Week2 of the Python course at NMNH # Assignment: Make a program that will perform addition, subtraction, division or multiplication of two numbers. Use appropriate error messages. print("Welcome to the calculator!","\nPlease enter two numbers, followed by the operator to use.") try: response1 = input("Enter the first number:") num1=float(response1) response2 = input("Enter the second number:") num2=float(response2) except: "Please enter a valid number" print("Addition=1","\nSubtraction=2","\nDivision=3","\nMultiplication=4") while True: response3 = input("Please enter the number for the operator you want, when finished, enter done:") operator=str(response3) if operator=="1": print("The sum of ",num1,"and ",num2,"equals: ",num1+num2) elif operator=="2": print("The difference between ",num1,"and ",num2,"equals: ",num1-num2) elif operator=="3": print("The quotient of ",num1,"and ",num2,"equals: ",num1/num2) elif operator=="4": print("The product of ",num1,"and ",num2,"equal: ",num1*num2) elif operator=="done": break else: print("Please enter a valid operator") print("Thanks for using the calculator!")

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aegiacometti/netconf-backup

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2020-10-22T13:21:20

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import sys import time import glob import os device_hostname = sys.argv[1] historic_files_to_keep = sys.argv[2] def remove_old_files(): list_files = glob.glob('./backups/{}*'.format(device_hostname)) if len(list_files) >= int(historic_files_to_keep): list_files.sort() for index in range(int(historic_files_to_keep)-1, len(list_files)-1): os.remove(list_files[index]) def rename_current_file(): get_date = time.localtime() current_date = time.strftime('%Y-%m-%d-%H-%M-%S', get_date) filename = './backups/{}-{}.ucs'.format(device_hostname, current_date) os.rename('./backups/backup.ucs', filename) if __name__ == '__main__': rename_current_file() remove_old_files()

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BrenoBaiardi/GSC

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/test_GSC.py

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no_license

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2020-07-01T23:03:35.391405

2019-08-08T21:05:55

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# O sistema dos correios de Gotham City tiveram um problema e perderam seu validador de CEPs. Hoje, sua missão é criar um validador de CEPs baseados em algumas pequenas regras listadas abaixo: # # 1. O CEP é um número maior que 100.000 e menor que 999999 # 2. O CEP não pode conter nenhum nenhum dígito repetitivo alternado em pares # # # 121426 # Aqui, 1 é um dígito repetitivo alternado em par. # 523563 # Aqui nenhum digito é alternado. # 552523 # Aqui os números 2 e 5 são dígitos alternados repetitivos em par. # 112233 # Aqui nenhum dígito é repetitivo alternado em par. from unittest import TestCase from GSC import GSC class TestGSC(TestCase): def test_verificar_cep_dentro_de_range_aceito(self): CEP = str(112233) self.assertEqual(True, GSC.validar_cep(CEP)) def test_verificar_cep_abaixo_de_range_aceito(self): CEP = "002233" with self.assertRaises(ValueError): GSC.validar_cep(CEP) def test_verificar_cep_acima_de_range_aceito(self): CEP = str(1999999) with self.assertRaises(ValueError): GSC.validar_cep(CEP) def test_verificar_cep_nao_repetido_alternado(self): CEP = str(112233) self.assertEqual(True, GSC.validar_cep(CEP)) def test_verificar_cep_nao_alternado(self): CEP = str(523563) self.assertEqual(True, GSC.validar_cep(CEP)) def test_verificar_todos_numeros_repetidos(self): CEP = str(999999) self.assertEqual(False, GSC.validar_cep(CEP)) def test_verificar_1_digito_repetido_alternado_par_no_comeco(self): CEP = str(121426) self.assertEqual(False, GSC.validar_cep(CEP)) def test_verificar_1_digito_repetido_alternado_par_no_meio(self): CEP = str(123256) self.assertEqual(False, GSC.validar_cep(CEP)) def test_verificar_1_digito_repetido_alternado_par_no_fim(self): CEP = str(163252) self.assertEqual(False, GSC.validar_cep(CEP)) def test_verificar_2_digitos_repetidos_alternados_pares(self): CEP = str(552523) self.assertEqual(False, GSC.validar_cep(CEP)) def test_verificar_entrada_de_CEP_por_texto(self): CEP = str(112233) self.assertEqual(True, GSC.validar_cep(CEP)) def test_verificar_entrada_de_CEP_por_numero(self): CEP = 112233 self.assertEqual(True, GSC.validar_cep(CEP)) def test_verificar_entrada_de_CEP_texto_formatado(self): try: CEP = '1-1-2-2-3-3' GSC.validar_cep(CEP) CEP = '1.1.2.2.3.3' GSC.validar_cep(CEP) CEP = '1 1 2 2 3 3' GSC.validar_cep(CEP) CEP = '11 22 33' GSC.validar_cep(CEP) except: self.fail()

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vaibhavkollipara/URLShortener

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/shortener/models.py

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no_license

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refs/heads/master

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from django.db import models from .utils import create_shortcode from django.conf import settings SHORTCODE_MAX = getattr(settings, "SHORTCODE_MAX",15) class ShortURLManager(models.Manager): def all(self,*args,**kwargs): qs_main = super(ShortURLManager,self).all(*args,**kwargs) qs = qs_main.filter(active=True) return qs def refresh_shorturls(self): qs = ShortURL.objects.filter(id__gte=1) new_codes = 0 for q in qs: q.shorturl = None q.save() new_codes += 1 return "refreshed {} urls".format(new_codes) class ShortURL(models.Model): url = models.CharField(max_length=250) shorturl = models.CharField(max_length=SHORTCODE_MAX, unique=True, blank=True ) updated = models.DateTimeField(auto_now=True) timestamp = models.DateTimeField(auto_now_add=True) active = models.BooleanField(default=True) objects = ShortURLManager() def save(self,*args,**kwargs): if self.shorturl is None or self.shorturl=='': self.shorturl = create_shortcode(self) super(ShortURL,self).save(*args,**kwargs) def __str__(self): return str(self.url) def __unicode__(self): return str(self.url) def get_shorturl(self): return

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wenshixiaoying/chapter7

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#!/usr/bin/python3 #-*- coding: utf-8 -*- import os #返回木马所在主机的环境变量 def run(**kwargs): print('This is environment module') return str(os.environ)

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luispedro/mahotas

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refs/heads/master

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2023-06-09T12:57:00

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NOASSERTION

false

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# -*- coding: utf-8 -*- # Copyright (C) 2008-2014, Luis Pedro Coelho <[email protected]> # vim: set ts=4 sts=4 sw=4 expandtab smartindent: # # License: MIT (see COPYING file) from __future__ import division import numpy as np import mahotas as mh from . import convolve, gaussian_filter _hsobel_filter = np.array([ [-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])/8. _vsobel_filter = np.array([ [-1, -2, -1], [ 0, 0, 0], [ 1, 2, 1]])/8. __all__ = [ 'sobel', 'dog', ] def sobel(img, just_filter=False): ''' edges = sobel(img, just_filter=False) Compute edges using Sobel's algorithm `edges` is a binary image of edges computed according to Sobel's algorithm. This implementation is tuned to match MATLAB's implementation. Parameters ---------- img : Any 2D-ndarray just_filter : boolean, optional If true, then return the result of filtering the image with the sobel filters, but do not threashold (default is False). Returns ------- edges : ndarray Binary image of edges, unless `just_filter`, in which case it will be an array of floating point values. ''' # This is based on Octave's implementation, # but with some reverse engineering to match Matlab exactly img = np.array(img, dtype=float) if img.ndim != 2: raise ValueError('mahotas.sobel: Only available for 2-dimensional images') img -= img.min() ptp = img.ptp() if ptp == 0: return img img /= ptp # Using 'nearest' seems to be MATLAB's implementation vfiltered = convolve(img, _vsobel_filter, mode='nearest') hfiltered = convolve(img, _hsobel_filter, mode='nearest') vfiltered **= 2 hfiltered **= 2 filtered = vfiltered filtered += hfiltered if just_filter: return filtered thresh = 2*np.sqrt(filtered.mean()) return mh.regmax(filtered) * (np.sqrt(filtered) > thresh) def dog(img, sigma1 = 2, multiplier = 1.001, just_filter = False): ''' edges = dog(img, sigma1 = 2, thresh= None, just_filter = False) Compute edges using the Difference of Gaussian (DoG) operator. `edges` is a binary image of edges. Parameters ---------- img : Any 2D-ndarray sigma1 : the sigma value of the first Gaussian filter. The second filter will have sigma value 1.001*sigma1 multiplier : the multiplier to get sigma2. sigma2 = sigma1 * multiplier just_filter : boolean, optional If true, then return the result of filtering the image with the DoG filters, no zero-crossing is detected (default is False). Returns ------- edges : ndarray Binary image of edges, unless `just_filter`, in which case it will be an array of floating point values. ''' img = np.array(img, dtype=float) if img.ndim != 2: raise ValueError('mahotas.dog: Only available for 2-dimensional images') sigma2 = sigma1 * multiplier G1 = gaussian_filter(img, sigma1, mode = 'nearest') G2 = gaussian_filter(img, sigma2, mode = 'nearest') DoG = G2 - G1 (m, n) = img.shape if not just_filter: e = np.zeros((m, n), dtype=bool) else: return DoG thresh = .75 * np.mean(abs(DoG)) # Look for the zero crossings: +-, -+ and their transposes # Choose the edge to be the negative point rr = np.arange(1, m-2) cc = np.arange(1, n-2) (rx,cx) = np.nonzero( np.logical_and(np.logical_and(DoG[np.ix_(rr,cc)] < 0, DoG[np.ix_(rr,cc+1)] > 0), abs( DoG[np.ix_(rr,cc)] - DoG[np.ix_(rr,cc+1)]) > thresh) ) # [- +] e[(rx,cx)] = 1 (rx,cx) = np.nonzero( np.logical_and(np.logical_and(DoG[np.ix_(rr,cc-1)] > 0, DoG[np.ix_(rr,cc+1)] < 0), abs( DoG[np.ix_(rr,cc-1)] - DoG[np.ix_(rr,cc)]) > thresh) ) # [+ -] e[(rx,cx)] = 1 (rx,cx) = np.nonzero( np.logical_and(np.logical_and(DoG[np.ix_(rr,cc)] < 0, DoG[np.ix_(rr+1,cc)] > 0), abs( DoG[np.ix_(rr,cc)] - DoG[np.ix_(rr+1,cc)]) > thresh) ) # [- +]' e[(rx,cx)] = 1 (rx,cx) = np.nonzero( np.logical_and(np.logical_and(DoG[np.ix_(rr-1,cc)] > 0, DoG[np.ix_(rr,cc)] < 0), abs( DoG[np.ix_(rr-1,cc)] - DoG[np.ix_(rr,cc)]) > thresh) ) # [+ -]' e[(rx,cx)] = 1 # Another case: DoG can be precisely zero (rz,cz) = np.nonzero(DoG[np.ix_(rr,cc)] == 0) if rz.size != 0: # Look for the zero crossings: +0-, -0+ and their transposes # The edge lies on the Zero point (rx,cx) = np.nonzero( np.logical_and(np.logical_and(DoG[np.ix_(rz,cz-1)] < 0, DoG[np.ix_(rz,cz+1)] > 0), abs( DoG[np.ix_(rz,cz+1)] - DoG[np.ix_(rz,cz-1)]) > thresh) ) # [- 0 +] e[(rx,cx)] = 1 (rx,cx) = np.nonzero( np.logical_and(np.logical_and(DoG[np.ix_(rz,cz-1)] > 0, DoG[np.ix_(rz,cz+1)] < 0), abs( DoG[np.ix_(rz,cz-1)] - DoG[np.ix_(rz,cz+1)]) > thresh) ) # [+ 0 -] e[(rx,cx)] = 1 (rx,cx) = np.nonzero( np.logical_and(np.logical_and(DoG[np.ix_(rz-1,cz)] < 0, DoG[np.ix_(rz+1,cz)] > 0), abs( DoG[np.ix_(rz+1,cz)] - DoG[np.ix_(rz-1,cz)]) > thresh) ) # [- 0 +]' e[(rx,cx)] = 1 (rx,cx) = np.nonzero( np.logical_and(np.logical_and(DoG[np.ix_(rz-1,cz)] > 0, DoG[np.ix_(rz+1,cz)] < 0), abs( DoG[np.ix_(rz-1,cz)] - DoG[np.ix_(rz+1,cz)]) > thresh) ) # [+ 0 -]' e[(rx,cx)] = 1 return e

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s-kyum/Python

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/ch02/calcu.py

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#산술 연산자 print("7+4=",7+4) print("7-4=",7-4) print("7*4=",7*4) print("7/4=",7/4) print("7%4=",7%4) print("7//4=",7//4) #몫 print("7**4=",7**4) #제곱

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Silentsoul04/FTSP_2020

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/Forsk Pandas/baltimore.py

9cc6cda3afb6fa44620494517d06203ed8625c01

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2022-12-21T20:44:32.031640

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# -*- coding: utf-8 -*- """ Created on Mon Mar 2 18:01:43 2020 @author: Rajesh """ """ Code Challenge Name: Baltimore City Analysis Filename: baltimore.py Problem Statement: Read the Baltimore_City_Employee_Salaries_FY2014.csv file and perform the following task : 0. remove the dollar signs in the AnnualSalary field and assign it as a int. 0. Group the data on JobTitle and AnnualSalary, and aggregate with sum, mean, etc. Sort the data and display to show who get the highest salary 0. Try to group on JobTitle only and sort the data and display 0. How many employess are there for each JobRoles and Graph it 0. Graph and show which Job Title spends the most 0. List All the Agency ID and Agency Name 0. Find all the missing Gross data in the dataset """ import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv('E:\Forsk Pandas\CSV files\Baltimore_City_Employee_Salaries_FY2014.csv') print(df) df.columns # remove the dollar signs in the AnnualSalary field and assign it as a int. df['AnnualSalary'] #Annual salary is in float and we need to convert into int64 format. """ 0 11310.0 1 53428.0 2 68300.0 3 62000.0 4 43999.0 18976 11310.0 18977 11310.0 18978 43999.0 18979 44104.0 18980 53568.0 Name: AnnualSalary, Length: 18981, dtype: float64 """ df['AnnualSalary'].astype('int64') """ 0 11310 1 53428 2 68300 3 62000 4 43999 18976 11310 18977 11310 18978 43999 18979 44104 18980 53568 Name: AnnualSalary, Length: 18981, dtype: int64 """ # Group the data on JobTitle and AnnualSalary, and aggregate with sum, mean, etc. #Sort the data and display to show who get the highest salary df[['JobTitle','AnnualSalary']] df['AnnualSalary'].agg(['sum','mean','std']) sum 7.546237e+08 mean 3.975679e+04 std 2.517305e+04 Name: AnnualSalary, dtype: float64 # Try to group on JobTitle only and sort the data and display. df['JobTitle'] df['JobTitle'].unique() df['JobTitle'].count() # 18981 sort=df['JobTitle'] sorted(sort) # How many employess are there for each JobRoles and Graph it. df.columns df['JobTitle'].value_counts() df['JobTitle'].unique() plt.pie(df['JobTitle'].value_counts(),labels=df['JobTitle'].unique(),radius =3 , autopct='%1.2f%%') plt.savefig('E:\Forsk Pandas\Baltmore.jpg') plt.show() df.columns # Graph and show which Job Title spends the most. df['HireDate'].value_counts() df['HireDate'].unique() plt.pie(df['HireDate'].value_counts(dropna=False),labels=df['HireDate'].unique(),radius =3 , autopct='%1.2f%%') plt.savefig('E:\Forsk Pandas\Baltmore1.jpg') plt.show() # List All the Agency ID and Agency Name. df[['AgencyID','Agency']] AgencyID Agency 0 W02200 Youth Summer 1 A03031 OED-Employment Dev 2 A29005 States Attorneys Office 3 A65026 HLTH-Health Department 4 A99416 Police Department ... ... 18976 W02235 Youth Summer 18977 W02629 Youth Summer 18978 A99416 Police Department 18979 A99262 Police Department 18980 A50206 DPW-Water & Waste Water [18981 rows x 2 columns] # Find all the missing Gross data in the dataset . df[df[df.columns].isnull()] Name JobTitle AgencyID Agency HireDate AnnualSalary GrossPay 0 NaN NaN NaN NaN NaN NaN NaN 1 NaN NaN NaN NaN NaN NaN NaN 2 NaN NaN NaN NaN NaN NaN NaN 3 NaN NaN NaN NaN NaN NaN NaN 4 NaN NaN NaN NaN NaN NaN NaN ... ... ... ... ... ... ... 18976 NaN NaN NaN NaN NaN NaN NaN 18977 NaN NaN NaN NaN NaN NaN NaN 18978 NaN NaN NaN NaN NaN NaN NaN 18979 NaN NaN NaN NaN NaN NaN NaN 18980 NaN NaN NaN NaN NaN NaN NaN [18981 rows x 7 columns] --------------------------------------------------------------------------------------- plt.figure(figsize= (50,10)) x = df['JobTitle'].unique() y = df['JobTitle'].value_counts() plt.bar(x,y) # simple line plot plt.plot(x,y) plt.savefig('E:\Forsk Pandas\Baltmore_Bar.jpg') plt.show()

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ronrest/deepQ_bananas

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import torch import torch.nn as nn import torch.nn.functional as F class QNetwork(nn.Module): """ A neural network that represents the Q-action values for a reinforcement learning agent. Two fully connected hidden layers, with ReLu activations, followed by a fully connected output layer. """ def __init__(self, state_size, action_size, seed=333, n_hidden=32): """ Initialize the weights and build the components of the model. Args: state_size (int): Shape of the state (as the input layer size) action_size (int): Number of actions (as the output layer size) seed (int): Random seed, for reproducibility n_hidden (int): Number of nodes in the hidden layers. """ super(QNetwork, self).__init__() self.seed = torch.manual_seed(seed) self.fc1 = nn.Linear(in_features=state_size, out_features=n_hidden, bias=True) self.fc2 = nn.Linear(in_features=n_hidden, out_features=n_hidden, bias=True) self.fc_out = nn.Linear(in_features=n_hidden, out_features=action_size, bias=False) def forward(self, state): """ Builds the forward pass structure of the network, which maps from state to action values """ x = F.relu(self.fc1(state)) x = F.relu(self.fc2(x)) x = self.fc_out(x) return x

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AllenInstitute/bmtk

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# Copyright 2017. Allen Institute. All rights reserved # # Redistribution and use in source and binary forms, with or without modification, are permitted provided that the # following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following # disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote # products derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, # INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # import numpy as np import pandas as pd from .column_property import ColumnProperty from .node import Node, NodeSet from .edge import Edge, EdgeSet class Group(object): """A container containig a node/edge population groups. A node or edge population will have one or more groups, each having a unique identifier. Each group shared the same columns and datatypes, thus each group is essentially a different model. """ def __init__(self, group_id, h5_group, parent): self._group_id = int(group_id) self._parent = parent self._types_table = parent.types_table self._h5_group = h5_group self._types_index_col = self._types_table.index_column_name self._group_columns = ColumnProperty.from_h5(h5_group) # TODO: combine group_columns, group_column_names and group_columns_map, doesn't need to be 3 structures self._group_column_map = {col.name: col for col in self._group_columns} self._group_column_names = set(col.name for col in self._group_columns) self._group_table = {prop: h5_group[prop.name] for prop in self._group_columns} self._ncolumns = len(self._group_columns) self._all_columns = self._group_columns + self._types_table.columns self._all_column_names = set(col.name for col in self._all_columns) self._nrows = 0 # number of group members # For storing dynamics_params subgroup (if it exists) self._has_dynamics_params = 'dynamics_params' in self._h5_group and len(self._h5_group['dynamics_params']) > 0 self._dynamics_params_columns = [] # An index of all the rows in parent population that map onto a member of this group self._parent_indicies = None # A list of parent rows indicies self._parent_indicies_built = False self.check_format() @property def group_id(self): return self._group_id @property def has_dynamics_params(self): return False @property def columns(self): return self._group_columns @property def group_columns(self): return self._group_columns @property def all_columns(self): return self._all_columns @property def has_gids(self): return self._parent.has_gids @property def parent(self): return self._parent def get_dataset(self, column_name): return self._group_table[column_name] def column(self, column_name, group_only=False): if column_name in self._group_column_map: return self._group_column_map[column_name] elif not group_only and column_name in self._types_table.columns: return self._types_table.column(column_name) else: return KeyError def check_format(self): # Check that all the properties have the same number of rows col_counts = [col.nrows for col in self._group_columns + self._dynamics_params_columns] if len(set(col_counts)) > 1: # TODO: Would be nice to warn user which dataset have different size raise Exception('properties in {}/{} have different ranks'.format(self._parent.name, self._group_id)) elif len(set(col_counts)) == 1: self._nrows = col_counts[0] def build_indicies(self, force=False): raise NotImplementedError def to_dataframe(self): raise NotImplementedError def get_values(self, property_name, all_rows=False): """Returns all values for a group property. Note that a row within a group may not have a corresponding node/edge, or they may have a different order or multiple node/edges may share the same group row. Setting all_rows=False will return all the values as you see if you iterated through all the population's items. Setting all_rows=True just returns the data as a list as they appear in the dataset (will be faster). :param property_name: Name of dataset property/column to fetch. :param all_rows: Set false to return order in which they appear in population, false to return entire dataset :return: A list of values for the given column name. """ raise NotImplementedError def __len__(self): return self._nrows def __getitem__(self, group_index): group_props = {} for cname, h5_obj in self._group_table.items(): group_props[cname] = h5_obj[group_index] return group_props def __contains__(self, prop_name): """Search that a column name exists in this group""" return prop_name in self._group_column_names class NodeGroup(Group): def __init__(self, group_id, h5_group, parent): super(NodeGroup, self).__init__(group_id, h5_group, parent) # Note: Don't call build_indicies right away so uses can call __getitem__ without having to load all the # node_ids @property def node_ids(self): self.build_indicies() # print self._parent_indicies return self._parent.inode_ids(self._parent_indicies) @property def node_type_ids(self): self.build_indicies() return self._parent.inode_type_ids(self._parent_indicies) @property def gids(self): self.build_indicies() return self._parent.igids(self._parent_indicies) def build_indicies(self, force=False): if self._parent_indicies_built and not force: return # TODO: Check for the special case where there is only one group # TODO: If memory becomes an issue on very larget nodes (10's of millions) consider using a generator # I've pushed the actual building of the population->group indicies onto the parent population self._parent_indicies = self._parent.group_indicies(self.group_id, build_cache=True) self._parent_indicies_built = True def get_values(self, property_name, filtered_indicies=True): self.build_indicies() # TODO: Check if property_name is node_id, node_type, or gid if property_name in self._group_columns: if not filtered_indicies: # Just return all values in dataset return np.array(self._group_table[property_name]) else: # Return only those values for group indicies with associated nodes grp_indicies = self._parent.igroup_indicies(self._parent_indicies) # It is possible that the group_index is unorderd or contains duplicates which will cause h5py slicing # to fail. Thus convert to a numpy array # TODO: loading the entire table is not good if the filtered nodes is small, consider building. tmp_array = np.array(self._group_table[property_name]) return tmp_array[grp_indicies] elif property_name in self._parent.node_types_table.columns: # For properties that come from node-types table we need to build the results from scratch # TODO: Need to performance test, I think this code could be optimized. node_types_table = self._parent.node_types_table nt_col = node_types_table.column(property_name) tmp_array = np.empty(shape=len(self._parent_indicies), dtype=nt_col.dtype) for i, ntid in enumerate(self.node_type_ids): tmp_array[i] = node_types_table[ntid][property_name] return tmp_array def to_dataframe(self): self.build_indicies() # Build a dataframe of group properties # TODO: Include dynamics_params? properties_df = pd.DataFrame() for col in self._group_columns: if col.dimension > 1: for i in range(col.dimension): # TODO: see if column name exists in the attributes col_name = '{}.{}'.format(col.name, i) properties_df[col_name] = pd.Series(self._h5_group[col.name][:, i]) else: properties_df[col.name] = pd.Series(self._h5_group[col.name]) # Build a dataframe of parent node (node_id, gid, node_types, etc) root_df = pd.DataFrame() root_df['node_type_id'] = pd.Series(self.node_type_ids) root_df['node_id'] = pd.Series(self.node_ids) root_df['node_group_index'] = pd.Series(self._parent.igroup_indicies(self._parent_indicies)) # used as pivot if self._parent.has_gids: root_df['gid'] = self.gids # merge group props df with parent df results_df = root_df.merge(properties_df, how='left', left_on='node_group_index', right_index=True) results_df = results_df.drop('node_group_index', axis=1) # Build node_types dataframe and merge node_types_df = self._parent.node_types_table.to_dataframe() # remove properties that exist in the group node_types_cols = [c.name for c in self._parent.node_types_table.columns if c not in self._group_columns] node_types_df = node_types_df[node_types_cols] # TODO: consider caching these results return results_df.merge(node_types_df, how='left', left_on='node_type_id', right_index=True) def filter(self, **filter_props): """Filter all nodes in the group by key=value pairs. The filter specifications may apply to either node_type or group column properties. Currently at the moment it only supports equivlency. An intersection (and operator) is done for every different filter pair. This will produce a generator of all nodes matching the the filters. for node in filter(pop_name='VIp', depth=10.0): assert(node['pop_name'] == 'VIp' and node['depth'] == 10.0) :param filter_props: keys and their values to filter nodes on. :return: A generator that produces all valid nodes within the group with matching key==value pairs. """ # TODO: Integrate this with NodeSet. self.build_indicies() node_types_table = self._parent.node_types_table node_type_filter = set(node_types_table.node_type_ids) # list of valid node_type_ids type_filter = False group_prop_filter = {} # list of 'prop_name'==prov_val for group datasets group_filter = False node_id_filter = [] # Build key==value lists for filter_key, filter_val in filter_props.items(): # TODO: Check if node_type_id is an input if filter_key in self._group_columns: # keep of list of group_popertiess to filter group_prop_filter[filter_key] = filter_val group_filter = True elif filter_key in node_types_table.columns: # for node_types we just keep a list of all node_type_ids with matching key==value pairs node_type_filter &= set(node_types_table.find(filter_key, filter_val)) type_filter = True elif filter_key in ['node_id', 'node_ids']: node_id_filter += filter_val else: # TODO: should we raise an exception? # TODO: User logger print('Could not find property {} in either group or types table. Ignoring.'.format(filter_key)) # iterate through all nodes, skipping ones that don't have matching key==value pairs for indx in self._parent_indicies: # TODO: Don't build the node until you filter out node_type_id node = self._parent.get_row(indx) if type_filter and node.node_type_id not in node_type_filter: # confirm node_type_id is a correct one continue if node_id_filter and node.node_id not in node_id_filter: continue if group_filter: # Filter by group property values # TODO: Allow group properties to handle lists src_failed = True for k, v in group_prop_filter.items(): if node[k] != v: break else: src_failed = False if src_failed: continue yield node def __iter__(self): self.build_indicies() # Pass a list of indicies into the NodeSet, the NodeSet will take care of the iteration return NodeSet(self._parent_indicies, self._parent).__iter__() class EdgeGroup(Group): def __init__(self, group_id, h5_group, parent): super(EdgeGroup, self).__init__(group_id, h5_group, parent) self._indicies_count = 0 # Used to keep track of number of indicies (since it contains multple ranges) self.__itr_index = 0 self.__itr_range = [] self.__itr_range_idx = 0 self.__itr_range_max = 0 def build_indicies(self, force=False): if self._parent_indicies_built and not force: return # Saves indicies as a (potentially empty) list of ranges # TODO: Turn index into generator, allows for cheaper iteration over the group self._indicies_count, self._parent_indicies = self._parent.group_indicies(self.group_id, build_cache=False) self._parent_indicies_built = True def to_dataframe(self): self.build_indicies() # Build a dataframe of group properties # TODO: Include dynamics_params? properties_df = pd.DataFrame() for col in self._group_columns: if col.dimension > 1: for i in range(col.dimension): # TODO: see if column name exists in the attributes col_name = '{}.{}'.format(col.name, i) properties_df[col_name] = pd.Series(self._h5_group[col.name][:, i]) else: properties_df[col.name] = pd.Series(self._h5_group[col.name]) # Build a dataframe of parent node root_df = pd.DataFrame() root_df['edge_type_id'] = pd.Series(self.edge_type_ids) root_df['source_node_id'] = pd.Series(self.src_node_ids) root_df['target_node_id'] = pd.Series(self.trg_node_ids) root_df['edge_group_index'] = pd.Series(self._parent.group_indicies(self.group_id, as_list=True)) # pivot col # merge group props df with parent df results_df = root_df.merge(properties_df, how='left', left_on='edge_group_index', right_index=True) results_df = results_df.drop('edge_group_index', axis=1) # Build node_types dataframe and merge edge_types_df = self._parent.edge_types_table.to_dataframe() # remove properties that exist in the group edge_types_cols = [c.name for c in self._parent.edge_types_table.columns if c not in self._group_columns] edge_types_df = edge_types_df[edge_types_cols] # TODO: consider caching these results return results_df.merge(edge_types_df, how='left', left_on='edge_type_id', right_index=True) def _get_parent_ds(self, parent_ds): self.build_indicies() ds_vals = np.zeros(self._indicies_count, dtype=parent_ds.dtype) c_indx = 0 for indx_range in self._parent_indicies: indx_beg, indx_end = indx_range[0], indx_range[1] n_indx = c_indx + (indx_end - indx_beg) ds_vals[c_indx:n_indx] = parent_ds[indx_beg:indx_end] c_indx = n_indx return ds_vals @property def src_node_ids(self): return self._get_parent_ds(self.parent._source_node_id_ds) @property def trg_node_ids(self): return self._get_parent_ds(self.parent._target_node_id_ds) @property def edge_type_ids(self): return self._get_parent_ds(self.parent._type_id_ds) def get_values(self, property_name, all_rows=False): # TODO: Need to take into account if property_name is in the edge-types if property_name not in self.columns: raise KeyError if all_rows: return np.array(self._h5_group[property_name]) else: self.build_indicies() # Go through all ranges and build the return list dataset = self._h5_group[property_name] return_list = np.empty(self._indicies_count, self._h5_group[property_name].dtype) i = 0 for r_beg, r_end in self._parent_indicies: r_len = r_end - r_beg return_list[i:(i+r_len)] = dataset[r_beg:r_end] i += r_len return return_list def filter(self, **filter_props): # TODO: I'm not sure If I want to do this? Need to check on a larger dataset than I currently have. raise NotImplementedError def __iter__(self): self.build_indicies() # TODO: Implement using an EdgeSet if len(self._parent_indicies) == 0: self.__itr_max_range = 0 self.__itr_range = [] self.__itr_index = 0 else: # Stop at the largest range end (I'm not sure if the indicies are ordered, if we can make it ordered then # in the future just use self_parent_indicies[-1][1] self.__itr_range_max = len(self._parent_indicies) self.__itr_range_idx = 0 self.__itr_range = self._parent_indicies[0] self.__itr_index = self.__itr_range[0] return self def next(self): return self.__next__() def __next__(self): if self.__itr_range_idx >= self.__itr_range_max: raise StopIteration nxt_edge = self._parent.get_row(self.__itr_index) self.__itr_index += 1 if self.__itr_index >= self.__itr_range[1]: # iterator has moved past the current range self.__itr_range_idx += 1 if self.__itr_range_idx < self.__itr_range_max: # move the iterator onto next range self.__itr_range = self._parent_indicies[self.__itr_range_idx] # update range self.__itr_index = self.__itr_range[0] # update iterator to start and the beginning of new range else: self.__itr_range = [] return nxt_edge

UTF-8

Python

false

19,742

py

625

group.py

329

0.625975

0.622581

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42.199125

120

itdream-dev/python

13,374,528,198,069

b703be32bf63e0b44cc06559ad5e182fae9754cb

079c07c5d97eb60d36269e27309e84b25ea0aaeb

/guidehero-backend/lib/models/answer.py

8a8bf5de12fd00b9b80792ab04d4528620647756

[]

no_license

https://github.com/itdream-dev/python

3aa44329673f05e2a86e1cba56cb88101c777233

eda81b802b99f45933bdf0d22b508837cfa538f0

refs/heads/master

2023-03-05T12:27:42.776870

2020-05-11T15:54:45

null

0

null

# -*- coding: utf-8 -*- from tag import Tag # NOQA from lib.registry import get_registry db = get_registry()['DB'] relationship_table = db.Table( 'answer_association', db.Column('tag_id', db.Integer, db.ForeignKey('tag.id')), db.Column('answer_id', db.Integer, db.ForeignKey('answer.id')) ) class Answer(db.Model): id = db.Column(db.Integer, primary_key=True) title = db.Column(db.String(255), nullable=False) tags = db.relationship( 'Tag', secondary=relationship_table, backref='answer' )

UTF-8

Python

false

532

py

221

answer.py

194

0.657895

0.650376

0

20

25.6

66

MBMunzi/Math_Algorithm

4,750,233,871,073

6b923be428214cc757a10072a284b8662aa4c3a1

5668fc337f2365f3b5cf1efb98ba7d975ed5da20

/Test.Polynomial.py

279ae69a70405b794248fd7aa03a3ed26cdf101e

[]

no_license

https://github.com/MBMunzi/Math_Algorithm

c65a2d0698820301c8b3d6e06cf982227762a2f6

8f46c3c91535504fded01c13ce5f2d059ef8e878

refs/heads/master

2020-09-25T09:38:35.439611

2020-03-17T20:02:36

225,976,873

0

null

import unittest from .Polynomio import Polynomial class TestPolynomial(unittest.TestCase): def test_evaluate_roots_about_polynomial(self): roots = [-3.0, -2.0, 1.0] self.assertEqual(roots, Polynomial([1, 4, 1, -6])) def test_Evaluate_Polynomial(self): self.assertEqual(0, Polynomial.evaluate_polynomial(1)) if __name__ == "__main__": unittest.main()

UTF-8

Python

false

394

py

2

Test.Polynomial.py

2

0.654822

0.624365

0

16

23.5

62

victor-nerd/finansez-python

17,875,653,900,137

bb962265c165fec97c34d6ab7c983d0a8f47a1d4

b6b4a0f011f9fadcde3a800e73e0afbf2377ad79

/finansez/finansez/api/serializers/category_view.py

6ebf682e49a9649a0e10f3fa10ecb2c1d810b919

[]

no_license

https://github.com/victor-nerd/finansez-python

c65a3a3369665176c8bd56ad993c9f0a9af78b41

48f83fc34d5ef534551f31c70c79e96e50ae2a74

refs/heads/master

2019-04-07T11:32:55.586359

2017-04-01T08:15:17

86,661,342

0

null

from rest_framework import serializers from rest_framework.exceptions import ValidationError from rest_framework.fields import empty from finansez.api.responses.category_info import CategoryInfoResponse from finansez.api.serializers.base import BaseApiSerializer from finansez.core.models import Category class CategoryViewSerializer(BaseApiSerializer): id = serializers.UUIDField(required=True) def __init__(self, instance=None, data=empty, **kwargs): self.category = None super().__init__(instance, data, **kwargs) def validate(self, data): try: self.category = Category.objects.get(id=data['id'], user=self.user, deleted=False) except Category.DoesNotExist: raise ValidationError({'id': 'Категория не существует, удалена или принадлежит другому пользователю.'}) return data def process_internal(self): return CategoryInfoResponse(self.category).get_response()

UTF-8

Python

false

1,020

py

90

category_view.py

77

0.729927

0

26

35.846154

115

dr-dos-ok/Code_Jam_Webscraper

7,997,229,131,612

9b6826e35989d84b628504b9d0e073131df53c2a

15f321878face2af9317363c5f6de1e5ddd9b749

/solutions_python/Problem_43/14.py

96aa39bf1ca67c879035913bde0bf7096e818b9f

[]

no_license

https://github.com/dr-dos-ok/Code_Jam_Webscraper

c06fd59870842664cd79c41eb460a09553e1c80a

26a35bf114a3aa30fc4c677ef069d95f41665cc0

refs/heads/master

2020-04-06T08:17:40.938460

2018-10-14T10:12:47

null

0

null

execfile('utils.py') def process(fn): cases = [x.strip() for x in read1(fn)[1:]] res = [processCase(x) for x in cases] write2(fn+".out",res) def processCase(case): scase = set(case) base = len(scase) lencase = len(case) res = 0 if base == 1: base = 2 digitsOrder = [1,0]+range(2,base) digitsIndex = 0 T = {} for i in range(lencase): if case[i] not in T: T[case[i]] = digitsOrder[digitsIndex] digitsIndex += 1 res *= base res += T[case[i]] return str(res)

UTF-8

Python

false

597

py

60,747

14.py

60,742

0.500838

0.482412

0

28

19.321429

49

Bikashbhaila/mycode

16,844,861,745,760

6b4315768d7faa0acbfa75a4142351aaa32ac660

cc4b04c90f9546f734efb938c5a4d6694b2f4b84

/FizzBuzz/fizz_buzz01.py

0db8d99df5e07c9bfd4ea868cf41df79b0536962

[]

no_license

https://github.com/Bikashbhaila/mycode

f87befdca556c06815e8815c466c5f667a7f780b

cd82fb965f1c26480a07921ab391415919b27013

refs/heads/main

2023-07-10T16:15:11.907952

2021-08-13T21:01:54

392,015,300

0

null

#!/usr/bin/env python3 def readFile(): with open("numfile.txt", "r") as input_file: numlist = [] for num in input_file: numlist.append(int(num)) return numlist def playGame(numlist): fizz, buzz, fizzbuzz = 0, 0, 0 for num in numlist: if num % 3 == 0 and num % 5 == 0: print("FizzBuzz") fizzbuzz +=1 elif num % 3 == 0: print("Fizz") fizz +=1 elif num % 5 == 0: print("Buzz") fizzbuzz+=1 else: print(num) return "Fizzes: {fizz} Buzzes: {buzz} FizzBuzzes: {fizzbuzz}" def main(): playGame(readFile()) if __name__ == "__main__": main()

UTF-8

Python

false

714

py

25

fizz_buzz01.py

22

0.483193

0.462185

0

31

22.032258

66

sala0320/Algorithm

13,615,046,328,710

b4e074fb1b50cc816971e7daff5823637235a6d8

75730862a05fe5f53655f784f299c542d9c55c37

/BFS+DFS/BackJoon/1520.py

32926ad64d601fc318798bfd321a5ca8c09a852b

[]

no_license

https://github.com/sala0320/Algorithm

630404c0f053b0e87c0a16804245fa836a24e29b

c4abc8070b7e9c4cbee7e691b4e80c07efce0f3d

refs/heads/main

2023-04-21T08:04:55.565437

2022-02-25T05:27:31

365,438,662

0

null

#DFS + DP import sys sys.setrecursionlimit(10**5) input = sys.stdin.readline M, N = map(int, input().split()) board = [list(map(int, input().split())) for _ in range(M)] dir = [[1,0], [0,1], [-1,0], [0,-1]] dp = [[-1 for _ in range(N)] for _ in range(M)] def dfs(x, y): #끝점에 도달하면 1 반환(끝점에서 끝점까지 갈 수 있는 길은 하나니까) if x == M-1 and y == N-1: return 1 #한번도 방문하지 않은 곳이라면 if dp[x][y] == -1: dp[x][y] = 0 for d in dir: nx = x + d[0] ny = y + d[1] if nx < 0 or nx >= M or ny < 0 or ny >= N: continue #다음으로 가는 곳이 지금보다 작으면 if board[nx][ny] < board[x][y]: #dp 현재 위치에서 끝점까지 갈 수 있는 경로의 수 = 다음 위치에서 끝점까지 갈 수 있는 경로의 수들의 합 dp[x][y] += dfs(nx, ny) return dp[x][y] print(dfs(0,0)) print(dp)

UTF-8

Python

false

1,014

py

136

1520.py

122

0.467312

0.438257

0

30

26.566667

77

tedrepo/go_bot

16,338,055,601,612

830de61f6f095c8483fe03bca2f0e3675a33e5aa

f4406f32e727707cc787212520dd952f73afdcc7

/slotfill.py

0987323ede680629fe1554f39bb75b9e854c2434

[]

no_license

https://github.com/tedrepo/go_bot

3256b8c53e7f962bad801a9797587e10341d2a8a

3f02f189346c0a8011c64b2a6ce25c65baf1e574

refs/heads/master

2020-03-28T03:47:27.421768

2018-09-09T12:18:44

147,671,447

0

1

null

# -*- coding: utf-8 -*- import json from fuzzywuzzy import process from overrides import overrieds from math import exp from collections import defaultdict class DstcSlotFillingNetwork(): """ """ def __init__(self, threshold=0.8, **kwargs): self.threshold = threshold self.load() def __call__(self, tokens_batch, tags_batch, *args, **kwargs): slots = [{}] * len(tokens_batch) m = [i for i, v in enumerate(tokens_batch) if v] if m: tags_batch = [tags_batch[i] for i in m] tokens_batch = [tokens_batch[i] for i in m] for i, tokens, tags in zip(m, tokens_batch, tags_batch): slots[i] = self.predict_slots(tokens, tags) return slots def predict_slots(slot, tokens, tags): entities, slots = self._chunk_finder(tokens, tags) slot_values = {} for entity, slot in zip(entities, slots): match, score = self.ner2slot(entity, slot) if score >= self.threshold * 100: slot_values[slot] = match return slot_values def ner2slot(self, input_entity, slot): if isinstance(input_entity, list): input_entity = " ".join(input_entity) entities = [] normalized_slot_vals = [] for entity_name in self._slot_vals[slot]: for entity in self._slot_vals[slot][entity_name]: entites.append(entity) normalized_slot_vals.append(entity_name) best_match, score = process.extract(input_entity, entites, limit=2**20)[0] def _chunk_finder(tokens, tags): # For BIO labeled sequence of tags extract all named entities form tokens prev_tag = '' chunk_tokens = [] entities = [] slots = [] for token, tag in zip(tokens, tags): curent_tag = tag.split('-')[-1].strip() current_prefix = tag.split('-')[0] if tag.startswith('B-'): if len(chunk_tokens) > 0: entities.append(' '.join(chunk_tokens)) slots.append(prev_tag) chunk_tokens = [] chunk_tokens.append(token) if current_prefix == 'I': if curent_tag != prev_tag: if len(chunk_tokens) > 0: entities.append(' '.join(chunk_tokens)) slots.append(prev_tag) chunk_tokens = [] else: chunk_tokens.append(token) if current_prefix == 'O': if len(chunk_tokens) > 0: entities.append(' '.join(chunk_tokens)) slots.append(prev_tag) chunk_tokens = [] prev_tag = curent_tag if len(chunk_tokens) > 0: entities.append(' '.join(chunk_tokens)) slots.append(prev_tag) return entities, slots def save(self,*args, **kwargs): with open(self.save_path, 'w', encoding='utf8') as f: json.dump(self._slot_vals, f) def load(self, *args, **kwargs): if open(self.load_path, encoding='utf8') as f: self._slot_vals = json.laod(f) class SlotFillingComponent(): def __init__(self, threshold=0.7, return_all=False, **kwargs): self.threshold = threshold self.return_all = return_all self._slot_vals = None self.load() def __call__(self, batch, *args, **kwargs): if isinstance(batch[0], str): batch = [tokenize_reg(instance.strip()) for instance in batch] slots = [{}] * len(batch)

UTF-8

Python

false

3,650

py

20

slotfill.py

19

0.532877

0.526575

0

107

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zhanglei1949/AttackSpeakerVerificationSystem

11,175,504,908,020

3c2f3cc8b9708fd532a90c06d1019c6f43ea5728

56aba02e07581eb3de286fe361d3e590af5aee03

/utils/copy_vad_clip.py

4207858e078eff899d351e19d4ca4f2247af551a

[]

no_license

https://github.com/zhanglei1949/AttackSpeakerVerificationSystem

51daf281a7fe9b919fd6ef85703a3ef0a2e664c9

e698dec73132b420b16beb3ae544fe3eaffdddb8

refs/heads/master

2020-06-13T01:02:34.452105

2019-08-01T06:46:42

194,480,818

5

0

null

import glob import numpy as np import os import soundfile import sys sys.path.append('../speakerVerificationSystem/') from pre_process import read_audio import constants as c def VAD_and_save(source_path, target_path, target_len = 25840): # do vad audio = read_audio(source_path) # vad doneinside if (len(audio) > target_len): a = int( (len(audio) - target_len)/2 ) audio = audio[a : a + target_len] elif (len(audio < target_len)): return soundfile.write(target_path, audio, c.SAMPLE_RATE, subtype='PCM_16') def get_wavs(dir, pattern): res = glob.glob(os.path.join(dir, pattern)) return res def do_vad(source_dir, target_dir): wavs = get_wavs(source_dir, "*/*/*.wav") print("obtain %d wavs files" % len(wavs)) for i in range(len(wavs)): wav = wavs[i] basename = wav.split('/')[-1] new_basename = basename[:-4] + '-clip.wav' target_wav_path = target_dir + new_basename VAD_and_save(wav, target_wav_path) if (i%100 == 0): print(wav, target_wav_path) def clipped_audio(x, num_frames=c.NUM_FRAMES): if x.shape[0] > num_frames + 20: bias = np.random.randint(20, x.shape[0] - num_frames) clipped_x = x[bias: num_frames + bias] elif x.shape[0] > num_frames: bias = np.random.randint(0, x.shape[0] - num_frames) clipped_x = x[bias: num_frames + bias] else: clipped_x = x return clipped_x if __name__ == '__main__': source_dir = '/home/lei/d/LibriSpeech/dev-clean/' target_dir = '/home/lei/2019/dataset/LibriSpeech/dev-clean-clip/' do_vad(source_dir, target_dir)

UTF-8

Python

false

1,653

py

66

copy_vad_clip.py

30

0.61222

0.595886

0

52

30.788462

72

nilankh/LeetCodeProblems

12,446,815,238,888

9d136cd73bb4c51c917d7f8f8cb86f7d1f51132d

46dacdcbaca4f1a3eeb6cc69aba7f37460673200

/Queue/QueueUsingTwoStacks#232.py

42aee906279af3445f109e99d7d4fd078a2f23b5

[]

no_license

https://github.com/nilankh/LeetCodeProblems

668bcdc9736d89898fc39082f5271391d5e27395

3594ea53ccc9de69edfbc67e6eab6e3b20959f4d

refs/heads/master

2023-06-24T13:09:45.809535

2021-07-22T16:36:12

266,919,021

0

null

#232 class QueueUsingTwoStacks: def __init__(self): self.__s1=[] self.__s2=[] def enqueue(self,data): #(O(n)) while(len(self.__s1)!=0): self.__s2.append(self.__s1.pop()) self.__s1.append(data) while(len(self.__s2)!=0): self.__s1.append(self.__s2.pop()) return def dequeue(self): #(O(1)) if(len(self.__s1)==0): return -1 return self.__s1.pop() def front(self): if(len(self.__s1)==0): return -1 return self.__s1[-1] def size(self): return len(self.__s1) def isEmpty(self): return self.size()==0 q=QueueUsingTwoStacks() q.enqueue(1) q.enqueue(2) q.enqueue(3) q.enqueue(4) while(q.isEmpty() is False): print(q.front()) q.dequeue()

UTF-8

Python

false

841

py

134

QueueUsingTwoStacks#232.py

134

0.485137

0.449465

0

47

16.702128

45

mdrohmann/txtemplates

16,904,991,315,209

2c4e48653d564ab41273c9abe2a60f113c5ccf79

b4687bd0817c6d00d8afde2721102416462cd73f

/tests/server_templates/test_server_templates.py

fd392113ed8a233092ec762e2013eaf4c2b6b26c

[]

no_license

https://github.com/mdrohmann/txtemplates

a8818b5213c38f1d27d497e3578129d07f764c06

b346b15d3eb465ec828a31fea0a00df3ae582942

refs/heads/master

2020-06-02T21:37:31.690441

2015-04-21T16:00:41

40,129,367

0

null

# encoding: utf-8 import os import argparse import sys import re import pytest from txtemplates import server_templates import txtemplates def test_get_parser_error(capsys): parser = server_templates.get_parser() with pytest.raises(SystemExit): parser.parse_args([]) _, err = capsys.readouterr() assert re.search('error: too few arguments', err) @pytest.mark.parametrize('argstr, expected', [ ('module', {'name': 'module', 'module': 'txtemplates'}), ('module package', {'name': 'module', 'module': 'package'}), ('module package -C directory -f', {'directory': 'directory', 'force_overwrite': True}) ]) def test_get_parser(argstr, expected): parser = server_templates.get_parser() args = parser.parse_args(argstr.split(' ')) for (k, v) in expected.items(): assert hasattr(args, k) assert getattr(args, k) == v def test_get_target_module(): directory = os.path.dirname(os.path.dirname(txtemplates.__file__)) args = argparse.Namespace(module='txtemplates', directory=directory) module = server_templates.get_target_module(args) assert module == txtemplates args = argparse.Namespace(module='txtemplates', directory='/tmp') module = server_templates.get_target_module(args) assert module == txtemplates @pytest.fixture(scope="function") def testpackage(tmpdir): p = tmpdir.mkdir("testpackage").join("__init__.py") p.write("") args = argparse.Namespace(module='testpackage', directory=str(tmpdir)) package = server_templates.get_target_module(args) return tmpdir, package def test_dirs(testpackage): tempdir, package = testpackage basedir = str(tempdir) parser = server_templates.get_parser() args = parser.parse_args('module testpackage'.split(' ')) dirs = server_templates.Dirs(args, package) assert dirs.module == os.path.join(basedir, 'testpackage', 'module') assert dirs.twistedplugin == os.path.join( basedir, 'testpackage', 'twisted', 'plugins') assert dirs.testbase == os.path.join(basedir, 'tests') assert dirs.test == os.path.join(basedir, 'tests', 'module') def test_run(testpackage, monkeypatch, capsys): tempdir, package = testpackage monkeypatch.setattr( sys, "argv", "main.py testmodule testpackage -C {}" .format(str(tempdir)).split(" ")) server_templates.main() files = [str(f)[len(str(tempdir)):] for f in tempdir.visit() if not str(f).endswith('.pyc') and not '__pycache__' in str(f)] assert len(files) == 21 assert '/testpackage/testmodule/backend/__init__.py' in files assert '/tests/testmodule/test_testmodule_backend.py' in files # second run should skip all files p = tempdir.join('testpackage').join('__init__.py') text = "# This should not be overwritten" p.write(text) server_templates.main() out, _ = capsys.readouterr() assert re.search(text, p.read()) assert re.search('exists: Skipped', out) # another run with overwrite flag turned on, should overwrite the existing # files. monkeypatch.setattr( sys, "argv", "main.py testmodule testpackage -C {} -f" .format(str(tempdir)).split(" ")) server_templates.main() p = tempdir.join('testpackage').join('__init__.py') out, _ = capsys.readouterr() assert re.search(text, p.read()) assert not re.search('exists: Skipped', out) # vim:set ft=python sw=4 et spell spelllang=en:

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test_server_templates.py

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0.658159

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haka913/programmers

6,313,601,944,888

ba5056e8cb541938e5f4463209523bc333afff21

0c95ba47207f85f1d0d8a500f174f459226695f3

/p_3_단속카메라.py

ddb950a9dddb6be1497ea9c8afecae97646a185a

[]

no_license

https://github.com/haka913/programmers

31cf37117ce220e3992ff2f3cb5a4c59f280c457

2cee9ef1a77fb7fe8a1b6aa738485720efafc254

refs/heads/master

2022-12-04T14:40:00.993494

2020-09-02T12:48:30

255,633,491

0

null

def solution(routes): routes.sort() cam = 30000 answer = 0 for route in reversed(routes): if cam > route[1]: answer += 1 cam = route[0] return answer print(solution(routes=[[-20, 15], [-14, -5], [-18, -13], [-5, -3]]))

UTF-8

Python

false

275

py

82

p_3_단속카메라.py

82

0.498182

0.418182

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14

18.642857

68

vivekshah1801/Python-RPC-Demo

9,904,194,634,430

dcf9ffd7491cd6dcd51b3ddcf3e64d46bde58296

08cb94509796aae2a8b0ed4fd28bd51801ef157a

/client.py

491d1c6d24268d5817a330f58dd44c52030d8f3a

[]

no_license

https://github.com/vivekshah1801/Python-RPC-Demo

3f1e85fd18220aa24f11bb7485bddec91761d7a2

4dcd569c6c0cee634b43922f2a141a573fbfdb59

refs/heads/master

2022-12-11T05:24:50.142356

2020-08-31T05:04:08

291,620,497

1

0

null

import rpyc host = "localhost" port = 9876 r = rpyc.connect(host, port).root print(r) print(r.helloworld()) print("Done")

UTF-8

Python

false

125

py

3

client.py

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0.688

0.656

0

11

10.454545

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thohajati/scard-login

4,209,067,982,147

10ebeb1e39f6a5659547b4a162ca14705268be89

2d8b7f7f5d59005966b7802089a5f6f6ba8fee03

/SCardLogin_test.py

3f71d8099f0a52c1709977be4f30c9e767910cb0

[]

no_license

https://github.com/thohajati/scard-login

b1f9d37f80e94d0f4c50b3cc9e923e4d4e262149

04afa1e6bc981bd5e6faf74459005949ba768c38

refs/heads/master

2021-01-19T16:42:50.552786

2017-08-21T07:43:44

101,021,172

0

null

import SCardLogin if __name__ == '__main__': dict = {} print("Insert your card!") dict = SCardLogin.get_scard_login() print(str(dict))

UTF-8

Python

false

227

py

5

SCardLogin_test.py

3

0.387665

0

8

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devjaynemorais/tcc_gen_figures

5,729,486,418,889

3dad758e817bc40522f5e92a650c2d9e47733873

287df5ff9d3f3384af2c9bb33126caf1df558858

/bin/countstrategies.py

e9e1ec316fbb84703bd6186996456718ec2beaa6

[]

no_license

https://github.com/devjaynemorais/tcc_gen_figures

eba58a6453c1f2a277ae0f79684c94c834f12cf5

07a1f7d0a060e797749926446546608a3bb14788

refs/heads/master

2022-07-07T19:22:02.479523

2020-05-12T00:27:55

263,218,044

0

null

# -*- coding: utf-8 -*- import sys import pandas as pd from importlib import reload reload(sys) # Classe que contem todas as estrategias # de contagem de acertos class EstrategiasDeContagem: def __init__(self): self.pond_df = pd.read_csv( 'data/medidas_ponderadas.csv', encoding='utf-8', sep=',') self.pond_df = self.pond_df.set_index('PA') self.pts_articulacao = {} self.pts_articulacao['1-Quadril-EN'] = 'Espaco neutro do Quadril' self.pts_articulacao['2-Quadril'] = 'Quadril' self.pts_articulacao['3-Quadril-E'] = 'E Quadril' self.pts_articulacao['4-Quadril-D'] = 'D Quadril' self.pts_articulacao['5-Estomago-EN'] = 'Espaco neutro do Estomago' self.pts_articulacao['6-Estomago'] = 'Estomago' self.pts_articulacao['7-Estomago-E'] = 'E Estomago' self.pts_articulacao['8-Estomago-D'] = 'D Estomago' self.pts_articulacao['9-Peito-EN'] = 'Espaco neutro do Peito' self.pts_articulacao['10-Peito'] = 'Peito' self.pts_articulacao['11-Peito-E'] = 'E Peito' self.pts_articulacao['12-Peito-D'] = 'D Peito' self.pts_articulacao['13-Pescoco-EN'] = 'Espaco neutro do Pescoco' self.pts_articulacao['14-Pescoco'] = 'Pescoco' self.pts_articulacao['15-Ombro-E'] = 'E Ombro' self.pts_articulacao['16-Ombro-D'] = 'D Ombro' self.pts_articulacao['17-Cabeca-EN'] = 'Espaco neutro da Cabeca' self.pts_articulacao['18-Cabeca'] = 'Cabeca' self.pts_articulacao['19-Cabeca-E'] = 'E Cabeca' self.pts_articulacao['20-Cabeca-D'] = 'D da Cabeca' self.pts_articulacao['21-Acima-Cabeca'] = 'Acima da Cabeca' self.pts_articulacao_inverse = {} self.pts_articulacao_inverse['1-Quadril-EN'] = 'Espaco neutro do Quadril' self.pts_articulacao_inverse['2-Quadril'] = 'Quadril' self.pts_articulacao_inverse['3-Quadril-E'] = 'D Quadril' self.pts_articulacao_inverse['4-Quadril-D'] = 'E Quadril' self.pts_articulacao_inverse['5-Estomago-EN'] = 'Espaco neutro do Estomago' self.pts_articulacao_inverse['6-Estomago'] = 'Estomago' self.pts_articulacao_inverse['7-Estomago-E'] = 'D Estomago' self.pts_articulacao_inverse['8-Estomago-D'] = 'E Estomago' self.pts_articulacao_inverse['9-Peito-EN'] = 'Espaco neutro do Peito' self.pts_articulacao_inverse['10-Peito'] = 'Peito' self.pts_articulacao_inverse['11-Peito-E'] = 'D Peito' self.pts_articulacao_inverse['12-Peito-D'] = 'E Peito' self.pts_articulacao_inverse['13-Pescoco-EN'] = 'Espaco neutro do Pescoco' self.pts_articulacao_inverse['14-Pescoco'] = 'Pescoco' self.pts_articulacao_inverse['15-Ombro-E'] = 'D Ombro' self.pts_articulacao_inverse['16-Ombro-D'] = 'E Ombro' self.pts_articulacao_inverse['17-Cabeca-EN'] = 'Espaco neutro da Cabeca' self.pts_articulacao_inverse['18-Cabeca'] = 'Cabeca' self.pts_articulacao_inverse['19-Cabeca-E'] = 'D Cabeca' self.pts_articulacao_inverse['20-Cabeca-D'] = 'E da Cabeca' self.pts_articulacao_inverse['21-Acima-Cabeca'] = 'Acima da Cabeca' # Estrategia baseada em condicionais para contagem de acertos def todos(self, dataframe, args): m_direita = str(args[0]) m_esquerda = str(args[1]) ponto_articulacao = str(args[2]) movimento = str(args[3]) nome = str(args[4]) if(movimento == 'Ambas'): if((m_direita == self.pts_articulacao[ponto_articulacao]) and (m_esquerda == self.pts_articulacao[ponto_articulacao])): return 1 elif ((m_direita == self.pts_articulacao_inverse[ponto_articulacao]) and (m_esquerda == self.pts_articulacao_inverse[ponto_articulacao])): return 1 elif(m_direita == 'nan' or m_direita == 'FALHOU') or (m_esquerda == 'nan' or m_esquerda == 'FALHOU'): return 3 else: return 0 elif(movimento == 'Mao Esquerda'): if (m_esquerda == self.pts_articulacao[ponto_articulacao]): return 1 elif (m_esquerda == self.pts_articulacao_inverse[ponto_articulacao]): return 1 elif(m_esquerda == 'nan' or m_esquerda == 'FALHOU'): return 3 else: return 0 elif(movimento == 'Mao Direita'): if(m_direita == self.pts_articulacao[ponto_articulacao]): return 1 elif (m_direita == self.pts_articulacao_inverse[ponto_articulacao]): return 1 elif(m_direita == 'nan' or m_direita == 'FALHOU'): return 3 else: return 0 else: return 4 def ponderado(self, dataframe, args): m_direita = str(args[0]) m_esquerda = str(args[1]) ponto_articulacao = str(args[2]) movimento = str(args[3]) nome = str(args[4]) if (movimento == 'Mao Esquerda'): if (m_esquerda != 'FALHOU'): return self.pond_df.loc[ponto_articulacao][m_esquerda] if (movimento == 'Mao Direita'): if (m_direita != 'FALHOU'): return self.pond_df.loc[ponto_articulacao][m_direita] if (movimento == 'Ambas'): if (m_direita != 'FALHOU' and m_esquerda != 'FALHOU'): resultado_soma = (self.pond_df.loc[ponto_articulacao][m_esquerda] + self.pond_df.loc[ponto_articulacao][m_direita]) if (resultado_soma != 0): return resultado_soma / 2 return 0

UTF-8

Python

false

5,815

py

10

countstrategies.py

5

0.573001

0.556836

0

134

42.298507

154

doublevcodes/pyformatter

16,853,451,672,749

bc67a2474975ea4518c4cc1e4bb20392cf9f3b74

9738536bad6e3c7644ea99c0bae2ce8b41535ed1

/formatter/pep8/statement/comparisons.py

0df1d4ce8dbe777c8efb0ff47c6b71c1b91456b5

[]

no_license

https://github.com/doublevcodes/pyformatter

7788518dfda49d5d974b851ba9682ac5c8850df4

1a061c071e66b90cc4e7f1fd2ce11b3754729ad5

refs/heads/master

2023-06-23T07:29:15.691694

2021-07-21T16:05:01

387,006,646

0

null

false

2021-07-18T23:45:35

2021-07-17T17:59:57

2021-07-18T21:17:21

2021-07-18T23:45:35

11

0

1

Python

false

import ast import itertools from formatter.pep8.helpers import _reduce_module, _replace_tokens class ComparisonParser: def __init__(self, source: str) -> None: self.source = source return None def get_comparisons(self): for comp in self.get_none_comparisons(): yield comp def get_none_comparisons(self): comparisons = [comparison for comparison in self._parse()] none_comparisons = [ comparison for comparison in comparisons if (isinstance(comparison.left, ast.Constant) and (comparison.left.value) is None) or (any((isinstance(comparator, ast.Constant) and (comparator.value is None)) for comparator in comparison.comparators)) ] for comparison in none_comparisons: yield ( ast.unparse( ComparisonParser.fix_none_comparisons(comparison) ), comparison.lineno, comparison.end_lineno, comparison.col_offset, comparison.end_col_offset ) def _parse(self): tree: ast.Module = ast.parse(self.source) comparisons = list(set([node for node in _reduce_module(tree) if isinstance(node, ast.Compare)])) return comparisons @staticmethod def fix_none_comparisons(comparison: ast.Compare) -> ast.Compare: FIXED_COMP_OPS = { ast.Eq: ast.Is(), ast.NotEq: ast.IsNot(), } if comparison.ops[0] not in (ast.Is, ast.IsNot): return ast.Compare( left=comparison.left, ops=[FIXED_COMP_OPS.get(type(comparison.ops[0]), comparison.ops[0])], comparators=comparison.comparators ) else: return comparison class ComparisonFormatter: def __init__(self, filenames) -> None: self.filenames = filenames def format_comparisons(self) -> None: for filename in self.filenames: with open(filename, "r+") as file: comparisonparser = ComparisonParser(file.read()) new_comparisons = [comp for comp in comparisonparser.get_comparisons()] _replace_tokens(filename, new_comparisons)

UTF-8

Python

false

2,271

py

12

comparisons.py

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0.590489

0.588727

0

68

32.411765

132

Diego07101985/challenge-bravo

15,762,529,996,549

9db29cfb82b4addee668d25baa3d1e729785e57d

4dd53b57cb310428e07ad85e96f4ef5463e6f366

/desafio/currency/repository.py

24fa0cf6e1d583d1ac243ab382c9bf0e049d8fde

[]

no_license

https://github.com/Diego07101985/challenge-bravo

91177ab13899dc492b59c0c7b713dfd7a07cd0a5

f8df7e6663ce1ce13d1f7a670acbc85e205c788b

refs/heads/master

2022-12-01T21:10:04.241867

2020-08-17T20:28:12

285,547,846

0

null

true

2020-08-17T20:24:36

2020-08-06T11:01:34

2020-08-16T23:30:13

2020-08-17T20:24:35

74

0

Python

false

from desafio.currency.model import Currency from desafio.extensions import session_scope class CurrencyRepository(): def get_all_currency(self): with session_scope() as session: return session.query(Currency).all() def get_currency_by_simbol_currency(self, currency): with session_scope() as session: currency = session.query(Currency).filter( Currency.simbol_currency == currency.simbol_currency).first() return currency def get_currency_by_id(self, currency): with session_scope() as session: currency = session.query(Currency).filter( Currency.id == currency.id).first() return currency def insert(self, currency): with session_scope() as session: session.add(currency) currency = session.query(Currency).filter( Currency.simbol_currency == currency.simbol_currency).first() print('Insert', currency) return currency.id def update(self, currency): with session_scope() as session: session.query(Currency).filter( Currency.id == currency.id).update( {"simbol_currency": currency.simbol_currency, "name_description": currency.name_description} ) update_currency = session.query(Currency).filter( Currency.simbol_currency == currency.simbol_currency).first() return update_currency def delete(self, currency): with session_scope() as session: currency = session.query(Currency).filter( currency.simbol_currency == currency.simbol_currency).first() session.delete(currency) currency = session.query(Currency).filter( currency.simbol_currency == currency.simbol_currency).first() return currency is None

UTF-8

Python

false

1,923

py

29

repository.py

18

0.615185

0

49

38.244898

77

nelli369/Data_Scraping

14,018,773,298,266

d511a695f575d9c7b1ac1ac8d137af4f5ed6ae69

1c789a24715863c405e194e6e4447cbb652d0737

/BooksToScrape3.py

c4c28ff8d2c15cbe5c630029ceb74ce008a0370e

[]

no_license

https://github.com/nelli369/Data_Scraping

b14522a9089d7d52f575ed761c368b5f7d546692

60f27562660a54ac23a25e65a253c38b404c7f2e

refs/heads/master

2022-11-14T10:08:18.497811

2020-07-01T14:05:13

274,640,854

0

null

# -*- coding: utf-8 -*- """DS hmk2 pr3.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/16q0T7UI1N82myYQ1iyOyCiLJKZ5j-pM_ """ !pip install scrapy import time import requests import numpy as np import pandas as pd from scrapy.http import TextResponse import re def books_scraper(url,base_url="http://books.toscrape.com/"): page = requests.get(url) response = TextResponse(body=page.text,url=url,encoding="utf-8") title = response.css("h3 > a::attr(title)").extract() price_l = response.css("p.price_color::text").extract() price = [i.replace("A", "") for i in price_l] book_url = response.css("h3 >a::attr(href)").extract() picture_url = response.css("img::attr(src)").extract() star = response.css("p[class^='star-rating']::attr(class)").extract() star_rating = [] for i in star: star_rating.append(i.replace("star-rating", "")) stock =response.css("p.price_color ~ p[class^='instock']::attr(class)").extract() instock = [i.replace("availability", " ") for i in stock] base_url = "http://books.toscrape.com/catalogue/" bookurl = [base_url + i for i in book_url] picurl = [base_url + i for i in picture_url] return pd.DataFrame({"Title":title, "Price":price, "Book_urls":bookurl, "Image_urls":picurl,"Star_Rating":star_rating,"Instock":instock}) books = [] for i in range(1,1000): pages =books_scraper(url = f"http://books.toscrape.com/catalogue/page-{i}.html") if pages.shape[0] == 0: break else: books.append(pages) books = pd.concat(books) books

UTF-8

Python

false

1,635

py

9

BooksToScrape3.py

4

0.658104

0.644648

0

48

33.041667

142

wpovell/twitter-kov

1,941,325,217,831

0776c314498d9efea6a0a49f365091f7382fe028

16d8ed403af2184c9db4026cc84b9fd040beeeea

/TwitterKov/util/recall.py

67cb907bc1d69b0bbb67d387750abde4a84a5326

[]

no_license

https://github.com/wpovell/twitter-kov

a51267e026aae983b0579d2f193d8c2e98f2f61b

371a7894783d33e68d64d67feca9379ba166017a

refs/heads/master

2016-09-06T15:13:55.346790

2014-10-11T04:04:57

11,493,473

0

null

import json import os import datetime import inspect import importlib class recall: def __init__(self, fname=None): self.callingFile = inspect.stack()[1][1] if fname is None: self.fname = ''.join(self.callingFile.split('.')[:-1]) + '.log' else: self.fname = '/'.join(self.callingFile.split('/')[:-1]) + '/' + fname def __call__(self, func): class ret: def __init__(self, fname, callingFile): self.original_func = func self.fname = fname self.callingFile = callingFile def original(self, *args, **kwargs): self.original_func(*args, **kwargs) def __call__(self, *args, **kwargs): if os.path.isfile(self.fname): with open(self.fname) as f: history = json.load(f) else: history = [] history.insert(0, { 'cwd': os.getcwd(), 'file': self.callingFile, 'funcname': func.__name__, 'time':str(datetime.datetime.now()), 'args':args, 'kwargs':kwargs }) with open(self.fname, 'w') as f: json.dump(history, f) return func(*args, **kwargs) return ret(self.fname, self.callingFile) if __name__ == '__main__': from sys import argv history = 0 if len(argv) > 1: fileName = argv[1] if not os.path.isfile(fileName): print("Log file does not exist.") exit(1) if len(argv) > 2 and argv[2].isdigit(): history = int(argv[2]) else: print("No log file provided.") exit(1) direct = os.path.dirname(fileName) with open(fileName) as f: data = json.load(f) data = data[history] rpath = ''.join(os.path.relpath(data['file'], data['cwd']).split('.')[:-1]) func = getattr(importlib.import_module(rpath.replace('/', '.')), data['funcname']) func.original(*data['args'], **data['kwargs'])

UTF-8

Python

false

2,152

py

31

recall.py

30

0.486524

0.480019

0

61

34.295082

86

AlanShea/Comsci_12700-Assignments

5,686,536,727,560

4cc32122f62e64c88de7460ef3afa4d0c227dde3

ae6f66a3d1e667ae94e282a039bd1cb140310dbb

/32. Air Pollution.py

6a385332b1cc886a1698b2ee26a2bdad0b975ebe

[]

no_license

https://github.com/AlanShea/Comsci_12700-Assignments

f76dfd8a0af82b8d0a789bb01d8e882935237679

1bb67f2397f114919a1f36fd6826b64d1bcdb07e

refs/heads/master

2023-02-06T01:10:25.500465

2020-12-19T01:02:07

322,739,064

0

null

#Name: Alan Q. Shea #Email: [email protected] #Date: October 21, 2020 import pandas as pd import matplotlib.pyplot as plt fileIn = input("Enter name of input file: ") fileOut = input("Enter name of output file: ") data = pd.read_csv(fileIn) avgAsthma = data.groupby("geo_entity_name").mean()["data_valuemessage"] avgAsthma.plot.bar() plt.gcf().subplots_adjust(bottom = 0.5) dataGraph = plt.gcf() dataGraph.savefig(fileOut)

UTF-8

Python

false

445

py

66

32. Air Pollution.py

51

0.721348

0.698876

0

18

23.722222

71

DarthThanatos/QuantumSimulator

11,647,951,338,360

2ed27490a2dab502938bf45becc51ea009a02d49

11e43f083afad6e84c947332bb60327a1539b836

/view/ProbabilitiesTable.py

59089e844d407cf43318d949b87be75248df3a44

[]

no_license

https://github.com/DarthThanatos/QuantumSimulator

e33a226efc06e08ccda44a5ccb5727ae31dbfbab

9f05113b9b0c980536afb9bddca6a9596afdb459

refs/heads/master

2020-04-02T15:23:45.380210

2019-06-02T08:26:23

154,566,220

0

null

import wx import wx.grid class ButtonRenderer(wx.grid.GridCellRenderer): def __init__(self, state_details_fun): wx.grid.GridCellRenderer.__init__(self) self.down = False self.click_handled = False self.__state_details_fun = state_details_fun def Draw(self, grid, attr, dc, rect, row, col, isSelected): if self.down: state = wx.CONTROL_PRESSED | wx.CONTROL_SELECTED else: state = 0 x,y = rect.GetTopLeft() tw, th = dc.GetTextExtent("argand") w, h = rect.GetWidth(), rect.GetHeight() wx.RendererNative.Get().DrawPushButton(grid, dc, rect, state) dc.DrawText("argand", x + w/2 - tw/2, y + h/2 - th/2) if self.down and not self.click_handled: self.click_handled = True self.HandleClick() def HandleClick(self): self.__state_details_fun() def GetBestSize(self, grid, attr, dc, row, col): text = grid.GetCellValue(row, col) dc.SetFont(attr.GetFont()) w, h = dc.GetTextExtent(text) return wx.Size(w, h) def Clone(self): return ButtonRenderer(self.__state_details_fun) class StateDetailsFun: def __init__(self, amplitude, probabilities_mediator): self.__amplitude = amplitude self.__probabilities_mediator = probabilities_mediator def __call__(self): self.__probabilities_mediator.visualise_amplitude(self.__amplitude) COLUMNS = 4 class ProbabilitiesTable(wx.grid.Grid): def __init__(self, parent, register_representation, probabilities_mediator): wx.grid.Grid.__init__(self, parent) self.__probabilities_mediator = probabilities_mediator self.CreateGrid(len(register_representation), COLUMNS) self.__renderer = ButtonRenderer(StateDetailsFun(0, self.__probabilities_mediator)) for i, row_representation in enumerate(register_representation): self.__update_row(row_representation, i) self.__setup_titles() self.SetRowLabelSize(0) self.SetGridLineColour(wx.WHITE) self.GetGridWindow().Bind(wx.EVT_LEFT_DOWN, self.__on_left_down) self.GetGridWindow().Bind(wx.EVT_LEFT_UP, self.__on_left_up) self.Bind(wx.grid.EVT_GRID_ROW_SIZE, self.__on_resize) self.Bind(wx.grid.EVT_GRID_COL_SIZE, self.__on_resize) def __on_resize(self, _): self.__probabilities_mediator.probs_table_resized() def __update_row(self, row_representation, i): for j in range(COLUMNS): self.SetReadOnly(i, j, True) if j == 4: pass # self.SetCellRenderer(i, 4, ButtonRenderer(StateDetailsFun(complex(row_representation[3]), self.__probabilities_mediator))) else: self.SetCellAlignment(i, j, wx.ALIGN_CENTRE, wx.ALIGN_CENTRE) self.SetCellValue(i, j, row_representation[j]) def __setup_titles(self): titles = ["val", "qubits", "prob", "ampl", ""] for i in range(4): self.SetColLabelValue(i, titles[i]) def __on_left_down(self, evt): col, row = self.__hit_test_cell(evt.GetPosition().x, evt.GetPosition().y) if isinstance(self.GetCellRenderer(row, col), ButtonRenderer): self.GetCellRenderer(row, col).down = True self.Refresh() evt.Skip() def __on_left_up(self, evt): col, row = self.__hit_test_cell(evt.GetPosition().x, evt.GetPosition().y) if isinstance(self.GetCellRenderer(row, col), ButtonRenderer): self.GetCellRenderer(row, col).down = False self.GetCellRenderer(row, col).click_handled = False self.Refresh() evt.Skip() def __hit_test_cell(self, x, y): x, y = self.CalcUnscrolledPosition(x, y) return self.XToCol(x), self.YToRow(y) if __name__ == '__main__': app = wx.App(0) frame = ProbabilitiesTable(None, [], None) app.MainLoop()

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3,968

py

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ProbabilitiesTable.py

93

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vishnun/python-programs

8,744,553,434,865

f85392f60b2534a32bfcdfcb32c28157917fe061

c7ffca464ef7bef4f64df5b471c9b6eca2c80e29

/string_to_int.py

953d298b3ab4a492cbea341cf52caabb5c24b119

[ "MIT" ]

permissive

https://github.com/vishnun/python-programs

2f3f2d6fc4a8ec3268a111d134dd57bb8d32df77

8e27f88a2a7174a0ca10596a328c4031bedd602c

refs/heads/master

2021-01-11T18:47:25.801435

2017-02-05T07:10:08

79,625,310

0

null

def is_valid_digit(digit): return 0 <= digit <= 9 def str_to_int(num_str): num_str, sign = get_sign(num_str) value = 0 for digit in num_str: digit = ord(digit) - ord('0') if is_valid_digit(digit): value = value * 10 + digit else: return "Not a number" if sign == '-': value = -value return value def get_sign(num_str): sign = '+' if num_str[0] == '-' or num_str[0] == '+': sign = num_str[0] num_str = num_str[1:] return num_str, sign

UTF-8

Python

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465

py

18

string_to_int.py

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0.587097

0.565591

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43

jordan-simonovski/seefood-slackbot

17,265,768,555,272

69ff4ddd51adb54ba6453f0628f39fe6527bc071

ce62f2bf8e6495d62223831611e6903f149f394e

/modules/slackMessage.py

5e9ae0f544fab3c547e1aea5958d1c30ac2cf30b

[]

no_license

https://github.com/jordan-simonovski/seefood-slackbot

c3e57239fdbb206502509d9541d3c8a7867b1688

be639afc91995cd088751544547a1eb29aff36e2

refs/heads/master

2021-01-21T11:37:39.572209

2017-05-23T12:45:31

91,748,549

6

0

null

import requests import json def buildSlackMessage(imageUrl, hotdogAnswer): slackMessage = { "response_type": "in_channel", "attachments": [{ "title": hotdogAnswer, "fallback": "This is going to rollback your deployment", "color": "#3AA3E3", "attachment_type": "default", "image_url": imageUrl }] } return slackMessage def notifySlackChannel(requestForm, hotdogAnswer): myMessage = buildSlackMessage(requestForm['text'], hotdogAnswer) return myMessage # webHookUrl = requestForm['response_url'] # response = requests.post(webHookUrl, data=json.dumps(myMessage)) # if response.status_code == 200: # return "ok" # return "there was an error getting a response from slack"

UTF-8

Python

false

799

py

8

slackMessage.py

5

0.634543

0.627034

0

25

31

70

princeofdatamining/django_celery_management

16,621,523,441,809

b5b40b1b1f6ffacf4b82a767b5af37927d636039

86e7f45989e4081fdc7d1676043839560e7dec72

/django_celery_management/__init__.py

80e48593a02c51b0b61679e79c600b2c387b6c26

[]

no_license

https://github.com/princeofdatamining/django_celery_management

d861f537c91202a35cc2f7bed8a14ca4473552d8

9ef99a4ecb30d16f5bfe3d24141be617308e4308

refs/heads/master

2020-09-20T05:34:13.911441

2020-06-29T07:20:08

94,501,014

0

null

__version__ = "1.0.0" default_app_config = 'django_celery_management.apps.django_celery_managementAppConfig'

UTF-8

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110

py

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__init__.py

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0.754545

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peteranny/research

18,554,258,728,692

f3a0706a68857bc36d682b4b4ed2de90792c0f63

327ce897521087bf9a3d44dd0bc2377e85bb8425

/lib/factory.py

29606d6cf508e6094a2adea43cf88f1105275799

[]

no_license

https://github.com/peteranny/research

75f1fdefe322049c3344ececfd6405fbb562cd95

f2674f536d0f758682a1dc195cadcd577205390a

refs/heads/master

2021-01-23T08:39:58.492377

2015-01-18T17:07:07

27,134,312

0

null

''' @ Ting-Yi Shih #Usage Example from factory import Factory Factory(source=[1,2,3], filename="factory_out", func=lambda x:str(x), num_workers=1).run() source: a list/an iterator/a generator of items filename: output filename func: function that returns a string as the result of the item can take either 1 argument: item, or take 2 arguments: item and fparam item: 1 out of source fparam(optional): parameters fparam: parameters to this function shared by all items (None by default) if func takes exactly 1 argument, fparam must be None if func takes exactly 2 arguments, fparam must not be None num_worker: number of process to be executed ''' from __future__ import division class Factory: def __init__(this, source=[], filename="factory_out", func=lambda x:str(x), fparam=None, num_workers=1): this.source = source this.fout_name = filename this.func = func this.num_workers = num_workers this.fparam = fparam @staticmethod def worker(worker_id, in_queue, func, fparam, fout): # make args a tuple while True: item = in_queue.get() result = func(item, fparam) if fparam else func(item) # input=item,args output=string fout.write("%s\n"%result) fout.flush() in_queue.task_done() @staticmethod def progressor(in_queue,total_num): if total_num==0: return import time,sys while True: Factory.pg("%5.1f%%..."%(100-in_queue.qsize()/total_num*100)) time.sleep(1) @staticmethod def pg(msg, br=False): import sys sys.stderr.write("\rFactory: "+msg+"\033[K"+("\n" if br else "")) sys.stderr.flush() def test(this): try: this.source = iter(this.source) except: raise Exception("source should be a iterable") import inspect if this.fparam is None: if len(inspect.getargspec(this.func).args)!=1: raise Exception("function should take exactly 1 argument: item") if this.fparam is not None: if len(inspect.getargspec(this.func).args)!=2: raise Exception("function should take exactly 2 arguments:item and parameters") if this.fout_name=="": raise Exception("filename cannot be an empty string") if type(this.num_workers)!=type(1) or this.num_workers<=0: raise Exception("invalid value of num_workers") try: item = next(this.source) except: raise Exception("source is empty") result = this.func(item, this.fparam) if this.fparam else this.func(item) if type(result)!=type(""): raise Exception("function should return a string") with open("%s_part"%this.fout_name, "w") as fout: fout.write("%s\n"%result) fout.flush() def run(this): Factory.pg("configuration test...") this.test() # check configuration source = this.source fout_name = this.fout_name func = this.func fparam = this.fparam num_workers = this.num_workers worker = this.worker progressor = this.progressor # queue settings Factory.pg("arranging source elements...") from multiprocessing import JoinableQueue,Process in_queue = JoinableQueue() for item in source: in_queue.put(item) # worker progressing progressor = Process(target=progressor, args=(in_queue, in_queue.qsize())) import time start_time = time.time() progressor.start() # worker settings fouts, workers = [], [] for w_id in xrange(num_workers): fouts.append(open("%s_part%d"%(fout_name,w_id),"w")) workers.append(Process(target=worker, args=(w_id, in_queue, func, fparam, fouts[w_id]))) workers[w_id].start() # post processing in_queue.join() for w_id in xrange(num_workers): workers[w_id].terminate() progressor.terminate() end_time = time.time() Factory.pg("working done (%.1fs lapsed)"%(end_time - start_time), br=True) import os os.system("cat %s_part* > %s"%(fout_name,fout_name)) os.system("rm -f %s_part*"%(fout_name)) # useful when the output is not str (not recommended) def obj2line(obj): return str(obj) def line2obj(line): import ast return ast.literal_eval(line) # useful when batch processing (1 result for all instead of for each) (not recommended) def src2chunk(source, nChunks): import math source = list(source) chunk_size = int(math.ceil(len(source)/nChunks)) return [source[i:i+chunk_size] for i in xrange(0,len(source),chunk_size)] def mergeLines(filename, res, update): with open(filename, "r") as fin: for line in fin: res_prt = line2obj(line) res = update(res,res_prt) with open(filename, "w") as fout: fout.write(obj2line(res)) if __name__ == "__main__": def simple(x,fparam): import time time.sleep(fparam) return str(x) Factory(source=range(10), filename="factory_out", func=simple, fparam=3, num_workers=5).run()

UTF-8

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factory.py

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ruyfreire/python-introducao

8,504,035,253,532

5168794c689ad3c0d48c2b75fa24d232a8267be6

e2adbbc7512933027100747db1db79653b45a716

/sessao4/02_metodos/get_set.py

2589c60e6f9d39bb760a25e32024f1142d3103e3

[]

no_license

https://github.com/ruyfreire/python-introducao

128dfeb24f7981a3b16891bf8c421e7a2767fee9

9088d216de7fe940f523b2a3c817fcc340aba730

refs/heads/master

2023-05-29T10:13:58.801677

2021-04-18T18:29:15

277,381,147

0

null

false

2021-06-09T00:41:30

2020-07-05T20:49:51

2021-06-09T00:41:07

2021-06-08T23:21:50

11,942

0

1

Python

false

""" Métodos Getter e Setter - Getter Métodos getter obtém o valor de um atributo da instancia, e possuem o decorador '@property' ex: class MinhaClasse: def __init__(self, nome): self.nome = nome @property def nome(self): return self._nome - Setter Métodos setter inserem o valor de um atributo da instancia, e possuem o decorador '@[nome_do_atributo].setter' ex: class MinhaClasse: def __init__(self, nome): self.nome = nome @nome.setter def nome(self, valor): self._nome = valor """ from random import randint class Produto: def __init__(self, nome): self._nome = nome @property def nome(self): id = randint(100, 999) return f'{id}_{self._nome}' @nome.setter def nome(self, valor): self._nome = valor print('\n##### Inserindo valor com setter #####') nome = 'Caneca' print(nome) p1 = Produto(nome) print('\n##### Obtendo valor SEM o getter #####') print(p1._nome) print('\n##### Obtendo valor COM o getter #####') print(p1.nome)

UTF-8

Python

false

1,054

py

160

get_set.py

128

0.614286

0.605714

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18.444444

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FrauElster/ocr_test

5,712,306,548,340

3d7035159fe5fc03521eb4d9a4dda4cbb640b702

fe0d709d75885c2e0250c1fb1dd18b1945d7b7c9

/ocr-test/create_pdf.py

89e7a1824ace649682ae5750724622226c5f280e

[]

no_license

https://github.com/FrauElster/ocr_test

2845ab0c0d73d274961f2d46c28dfe242d6a15b7

8d0cd2df411fd45f18dbee1d8ab1c02ce352d364

refs/heads/master

2022-02-13T20:44:14.378611

2019-07-31T10:50:43

196,885,809

0

null

import os from fpdf import FPDF import glob from typing import List from .FileHandler import FileHandler FONTS = ['Courier', 'Arial', 'Times'] def main(min_font_size: int, max_font_size: int): assert min_font_size <= max_font_size lorem: str = FileHandler.load_file(FileHandler.get_path('../lorem.txt')) create_pdfs(min_font_size, max_font_size, lorem) FileHandler.delete_with_ending('../fonts', 'pkl') def create_pdfs(min_font_size: int, max_font_size: int, text: str): special_fonts = get_special_fonts() for font_size in range(min_font_size, max_font_size + 1): for font in FONTS: pdf = FPDF('P', 'mm', 'A4') pdf.set_font(font) make_pdf(pdf, font, font_size, text) for special_font in special_fonts: pdf = FPDF('P', 'mm', 'A4') pdf.add_font(family=special_font, fname=special_font, uni=True) print(f'Special Font: {special_font}') pdf.set_font(special_font) font_name = special_font.split('/')[-1] font_name = font_name.split('.')[0] make_pdf(pdf, font_name, font_size, text) def get_special_fonts(): os.environ['FPDF_FONTPATH'] = FileHandler.get_path('../fonts') os.environ['FPDF_CACHE_MODE'] = FileHandler.get_path('1') special_fonts: List[str] = [] # for filename in glob.glob(os.path.join(FileHandler.get_path('../fonts'), '*.ttf')): for filename in glob.glob(os.path.join(FileHandler.get_path('../fonts'), '*.ttf')): print(f'DEBUG: {filename}') special_fonts.append(filename) return special_fonts def make_pdf(pdf, font_name: str, font_size: int, lorem: str): dir_path = FileHandler.get_path('../out_create') if not os.path.isdir(dir_path): os.mkdir(dir_path) title = f'{font_name}_{font_size}.pdf' pdf.add_page() pdf.set_font_size(font_size) pdf.multi_cell(150, 10, txt=lorem) pdf.output(f'{dir_path}/{title}') print(f'Created {title}')

UTF-8

Python

false

2,003

py

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create_pdf.py

5

0.614578

0.609086

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smautner/pig

18,837,726,560,357

55452bd17fc5f1fac5f55683f85acdbf577409bd

9419966d7debad3ed14738c90ad5020fe59713cf

/yoda/nanaman/milad_data.py

f70b7d8b556f4de4791357c364d6239e57fb1526

[]

no_license

https://github.com/smautner/pig

378ef98a8d90e60a8e304536495e5d9fc4664a2f

784877db54d078b4ccca839ef9b3d77c3b032508

refs/heads/master

2023-08-30T23:44:05.986116

2023-08-16T12:10:21

209,278,646

1

2

null

false

2020-11-09T13:26:13

2019-09-18T10:13:27

2020-11-04T14:41:59

2020-11-09T13:26:12

4,316

1

0

Jupyter Notebook

false

from lmz import Map,Zip,Filter,Grouper,Range,Transpose import yoda.filein from yoda import filein, alignment, ali2graph , simpleMl from ubergauss import tools as ut import eden.graph as eg def rfamOme_labeldistribution(): '''just looks at the distribution of labels in the rfamome dataset''' import glob from collections import Counter f = glob.glob(f'../../rfamome/*.stk') m = map(alignment.grepfamily, f) # the name is in the filename c = Counter(m) print(sum([v for v in c.values() if v > 2])) print(c) def runbaseline(): alis = filein.loadrfamome(path = f'../../rfamome') alis = filein.addstructure(alis) # a.struct alis = filein.addcov(alis) # a.rscape [(start,stop,e-valus)] alis = yoda.filein.process_cov(alis, debug = False) graphs = Map(ali2graph.scclust,alis) # not done! X = eg.vectorize(graphs) y = [a.label for a in alis] # 1nn check print(f"{simpleMl.knn_accuracy(X,y,n_neighbors = 1)=}") print(f"{simpleMl.knn_f1(X,y,n_neighbors = 1,cv_strati_splits = 3)=}") # cv kmeans clustering print(f"{simpleMl.kmeans_ari(X,y,k=28)=}") # loguru! # so.grpint pos RNA # https://github.com/fabriziocosta/EDeN/blob/master/eden/display/__init__.py # vectorizer should have many options... why are the fake nodes decreasing performance? # especially removing the fakenodes is improving performance, with the so.rna stuff # i should be able to debug and see if i implemented it correctly # experiment a bit with this dataset... # then build the rfam tree or cluster the clan ones!

UTF-8

Python

false

1,609

py

38

milad_data.py

31

0.675575

0.66936

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48

32.520833

91

payerhans/patienten

5,987,184,412,324

f79cb14a6318a44e33f4ada2c9ca0c241b264a1d

66576843eff9f518131c27cd9c92504a0b6dbd5b

/vvt/apps.py

7d47cbdee7d8951e8ecfeb0afdebf24ec27234ac

[]

no_license

https://github.com/payerhans/patienten

431049d99e295e9d59f9516440fcdad46ae9bb9b

3c490762a9dd666a35ba6619713780dc6515990f

refs/heads/master

2020-03-11T03:14:12.796823

2018-05-28T14:41:23

129,741,162

0

null

from django.apps import AppConfig class VvtConfig(AppConfig): name = 'vvt'

UTF-8

Python

false

81

py

36

apps.py

15

0.728395

0

5

15.2

33