fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

7907b5defff558818aeaf6006e2b2b5c51929273

3,086

py

Python

lib/markov_usernames.py

jabbalaci/Bash-Utils

c6fb115834a221c4aaba8eaa37f650beea45ef29

[ "MIT" ]

73

2015-03-31T01:12:26.000Z

2021-07-10T19:45:04.000Z

lib/markov_usernames.py

doc22940/Bash-Utils

c6fb115834a221c4aaba8eaa37f650beea45ef29

[ "MIT" ]

2

2017-01-06T17:17:42.000Z

2017-08-23T18:35:55.000Z

lib/markov_usernames.py

doc22940/Bash-Utils

c6fb115834a221c4aaba8eaa37f650beea45ef29

[ "MIT" ]

27

2015-01-03T18:51:23.000Z

2020-11-15T11:49:51.000Z

#!/usr/bin/env python3 import random import sys """ Markov chains name generator in Python From http://roguebasin.roguelikedevelopment.org/index.php?title=Markov_chains_name_generator_in_Python . """ # from http://www.geocities.com/anvrill/names/cc_goth.html PLACES = ['Adara', 'Adena', 'Adrianne', 'Alarice', 'Alvita', 'Amara', 'Ambika', 'Antonia', 'Araceli', 'Balandria', 'Basha', 'Beryl', 'Bryn', 'Callia', 'Caryssa', 'Cassandra', 'Casondrah', 'Chatha', 'Ciara', 'Cynara', 'Cytheria', 'Dabria', 'Darcei', 'Deandra', 'Deirdre', 'Delores', 'Desdomna', 'Devi', 'Dominique', 'Drucilla', 'Duvessa', 'Ebony', 'Fantine', 'Fuscienne', 'Gabi', 'Gallia', 'Hanna', 'Hedda', 'Jerica', 'Jetta', 'Joby', 'Kacila', 'Kagami', 'Kala', 'Kallie', 'Keelia', 'Kerry', 'Kerry-Ann', 'Kimberly', 'Killian', 'Kory', 'Lilith', 'Lucretia', 'Lysha', 'Mercedes', 'Mia', 'Maura', 'Perdita', 'Quella', 'Riona', 'Safiya', 'Salina', 'Severin', 'Sidonia', 'Sirena', 'Solita', 'Tempest', 'Thea', 'Treva', 'Trista', 'Vala', 'Winta'] ############################################################################### # Markov Name model # A random name generator, by Peter Corbett # http://www.pick.ucam.org/~ptc24/mchain.html # This script is hereby entered into the public domain ############################################################################### class Mdict: def __init__(self): self.d = {} def __getitem__(self, key): if key in self.d: return self.d[key] else: raise KeyError(key) def add_key(self, prefix, suffix): if prefix in self.d: self.d[prefix].append(suffix) else: self.d[prefix] = [suffix] def get_suffix(self,prefix): l = self[prefix] return random.choice(l) class MName: """ A name from a Markov chain """ def __init__(self, chainlen = 2): """ Building the dictionary """ if chainlen > 10 or chainlen < 1: print("Chain length must be between 1 and 10, inclusive") sys.exit(0) self.mcd = Mdict() oldnames = [] self.chainlen = chainlen for l in PLACES: l = l.strip() oldnames.append(l) s = " " * chainlen + l for n in range(0,len(l)): self.mcd.add_key(s[n:n+chainlen], s[n+chainlen]) self.mcd.add_key(s[len(l):len(l)+chainlen], "\n") def New(self): """ New name from the Markov chain """ prefix = " " * self.chainlen name = "" suffix = "" while True: suffix = self.mcd.get_suffix(prefix) if suffix == "\n" or len(name) > 9: break else: name = name + suffix prefix = prefix[1:] + suffix return name.capitalize() ############################################################################# if __name__ == "__main__": li = [] for i in range(10): li.append(MName().New()) for e in sorted(li): print(e.lower())

34.288889

125

0.515554

import random import sys PLACES = ['Adara', 'Adena', 'Adrianne', 'Alarice', 'Alvita', 'Amara', 'Ambika', 'Antonia', 'Araceli', 'Balandria', 'Basha', 'Beryl', 'Bryn', 'Callia', 'Caryssa', 'Cassandra', 'Casondrah', 'Chatha', 'Ciara', 'Cynara', 'Cytheria', 'Dabria', 'Darcei', 'Deandra', 'Deirdre', 'Delores', 'Desdomna', 'Devi', 'Dominique', 'Drucilla', 'Duvessa', 'Ebony', 'Fantine', 'Fuscienne', 'Gabi', 'Gallia', 'Hanna', 'Hedda', 'Jerica', 'Jetta', 'Joby', 'Kacila', 'Kagami', 'Kala', 'Kallie', 'Keelia', 'Kerry', 'Kerry-Ann', 'Kimberly', 'Killian', 'Kory', 'Lilith', 'Lucretia', 'Lysha', 'Mercedes', 'Mia', 'Maura', 'Perdita', 'Quella', 'Riona', 'Safiya', 'Salina', 'Severin', 'Sidonia', 'Sirena', 'Solita', 'Tempest', 'Thea', 'Treva', 'Trista', 'Vala', 'Winta']

true

7907b60fa02e035476ed63d1da9f37e07224dad8

92

py

Python

server/ascii_art_server/api/ascii_art/apps.py

jmacera95/ascii-art

ad37b3e8f15f6ca87e4ac8237385c5aa18d6176a

[ "MIT" ]

29

2020-10-01T12:23:46.000Z

2022-01-30T10:46:39.000Z

server/ascii_art_server/api/ascii_art/apps.py

jmacera95/ascii-art

ad37b3e8f15f6ca87e4ac8237385c5aa18d6176a

[ "MIT" ]

101

2020-10-01T05:31:33.000Z

2021-10-05T11:39:15.000Z

server/ascii_art_server/api/ascii_art/apps.py

jmacera95/ascii-art

ad37b3e8f15f6ca87e4ac8237385c5aa18d6176a

[ "MIT" ]

163

2020-10-01T07:15:05.000Z

2022-03-07T17:57:27.000Z

from django.apps import AppConfig class AsciiArtConfig(AppConfig): name = 'ascii_art'

15.333333

33

0.76087

from django.apps import AppConfig class AsciiArtConfig(AppConfig): name = 'ascii_art'

true

7907b70c20a6aaa8d7acbddba4f082ba383a82db

812

py

Python

model/contact.py

Valeryiar/python_training

c4e92ee783134bb22a38b6cb38f912cf99bef2d8

[ "Apache-2.0" ]

null

model/contact.py

Valeryiar/python_training

c4e92ee783134bb22a38b6cb38f912cf99bef2d8

[ "Apache-2.0" ]

null

model/contact.py

Valeryiar/python_training

c4e92ee783134bb22a38b6cb38f912cf99bef2d8

[ "Apache-2.0" ]

null

from sys import maxsize class Contact: def __init__(self, firstname=None, lastname=None, homephone=None, mobilephone=None,workphone=None, secondaryphone=None, id=None): self.firstname=firstname self.lastname=lastname self.homephone=homephone self.workphone = workphone self.mobilephone=mobilephone self.secondaryphone=secondaryphone self.id=id def __repr__(self): return "%s:%s %s" % (self.id, self.firstname, self.lastname) def __eq__(self, other): return (self.id is None or other.id is None or self.id==other.id)\ and self.firstname==other.firstname and self.lastname==other.lastname def id_or_max (self): if self.id: return int (self.id) else: return maxsize

31.230769

134

0.64532

from sys import maxsize class Contact: def __init__(self, firstname=None, lastname=None, homephone=None, mobilephone=None,workphone=None, secondaryphone=None, id=None): self.firstname=firstname self.lastname=lastname self.homephone=homephone self.workphone = workphone self.mobilephone=mobilephone self.secondaryphone=secondaryphone self.id=id def __repr__(self): return "%s:%s %s" % (self.id, self.firstname, self.lastname) def __eq__(self, other): return (self.id is None or other.id is None or self.id==other.id)\ and self.firstname==other.firstname and self.lastname==other.lastname def id_or_max (self): if self.id: return int (self.id) else: return maxsize

true

7907b7928e68a7671c81899ce83e679795100c4c

5,048

py

Python

network/layers/convolution_im2col.py

metataro/DirectFeedbackAlignment

7e2cbc3f001ac2290a15440628bb2b97d4ec52ab

[ "MIT" ]

5

2020-04-30T11:36:46.000Z

2021-09-09T06:08:34.000Z

network/layers/convolution_im2col.py

metataro/DirectFeedbackAlignment

7e2cbc3f001ac2290a15440628bb2b97d4ec52ab

[ "MIT" ]

null

network/layers/convolution_im2col.py

metataro/DirectFeedbackAlignment

7e2cbc3f001ac2290a15440628bb2b97d4ec52ab

[ "MIT" ]

1

2021-01-07T03:10:32.000Z

import numpy as np from network.activation import Activation from network.layer import Layer from network.utils.im2col_cython import im2col_cython, col2im_cython class Convolution(Layer): def __init__(self, filter_shape, stride, padding, dropout_rate: float = 0, activation: Activation = None, last_layer=False, weight_initializer=None, fb_weight_initializer=None) -> None: assert len(filter_shape) == 4, \ "invalid filter shape: 4-tuple required, {}-tuple given".format(len(filter_shape)) super().__init__() self.filter_shape = filter_shape self.stride = stride self.padding = padding self.dropout_rate = dropout_rate self.activation = activation self.last_layer = last_layer self.weight_initializer = weight_initializer self.fb_weight_initializer = fb_weight_initializer def initialize(self, input_size, num_classes, train_method) -> tuple: assert np.size(input_size) == 3, \ "invalid input size: 3-tuple required for convolution layer" c_in, h_in, w_in = input_size f, c_f, h_f, w_f = self.filter_shape assert c_in == c_f, \ "input channel dimension ({}) not compatible with filter channel dimension ({})".format(c_in, c_f) assert (h_in - h_f + 2 * self.padding) % self.stride == 0, \ "filter width ({}) not compatible with input width ({})".format(h_f, h_in) assert (w_in - w_f + 2 * self.padding) % self.stride == 0, \ "filter height ({}) not compatible with input height ({})".format(h_f, h_in) self.h_out = ((h_in - h_f + 2 * self.padding) // self.stride) + 1 self.w_out = ((w_in - w_f + 2 * self.padding) // self.stride) + 1 # initialize weights if self.weight_initializer is None: sqrt_fan_in = np.sqrt(c_in * h_in * w_in) self.W = np.random.uniform(low=-1 / sqrt_fan_in, high=1 / sqrt_fan_in, size=self.filter_shape) else: self.W = self.weight_initializer.init(dim=(f, c_f, h_f, w_f)) # initialize feedback weights if self.fb_weight_initializer is None: sqrt_fan_out = np.sqrt(f * self.h_out * self.w_out) # self.B = np.random.uniform(low=-1 / sqrt_fan_out, high=1 / sqrt_fan_out, size=(num_classes, f, self.h_out, self.w_out)) self.B = np.random.uniform(low=-1 / sqrt_fan_out, high=1 / sqrt_fan_out, size=(num_classes, f * self.h_out * self.w_out)) else: # self.B = self.fb_weight_initializer.init(dim=(num_classes, f, self.h_out, self.w_out)) self.B = self.fb_weight_initializer.init(dim=(num_classes, f * self.h_out * self.w_out)) # initialize bias units self.b = np.zeros(f) return f, self.h_out, self.w_out def forward(self, X, mode='predict') -> np.ndarray: n_in, c, h_in, w_in = X.shape n_f, c, h_f, w_f = self.W.shape self.x_cols = im2col_cython(X, h_f, w_f, self.padding, self.stride) # <-> z = self.W.reshape((n_f, -1)).dot(self.x_cols) z += self.b.reshape(-1, 1) # + z = z.reshape(n_f, self.h_out, self.w_out, n_in).transpose(3, 0, 1, 2) self.a_in = X if self.activation is None: self.a_out = z else: self.a_out = self.activation.forward(z) if mode == 'train' and self.dropout_rate > 0: # self.dropout_mask = np.random.binomial(size=self.a_out.shape, n=1, p=1 - self.dropout_rate) self.dropout_mask = (np.random.rand(*self.a_out.shape) > self.dropout_rate).astype(int) self.a_out *= self.dropout_mask return self.a_out def dfa(self, E: np.ndarray) -> tuple: # E = np.einsum('ij,jklm->iklm', E, self.B) n_f, c_f, h_f, w_f = self.W.shape E = np.dot(E, self.B).reshape((-1, n_f, self.h_out, self.w_out)) if self.dropout_rate > 0: E *= self.dropout_mask if self.activation is None: E *= self.a_out else: E *= self.activation.gradient(self.a_out) dW = E.transpose((1, 2, 3, 0)).reshape(n_f, -1).dot(self.x_cols.T).reshape(self.W.shape) db = np.sum(E, axis=(0, 2, 3)) return dW, db def back_prob(self, E: np.ndarray) -> tuple: if self.dropout_rate > 0: E *= self.dropout_mask n_in, c_in, h_in, w_in = self.a_in.shape n_f, c_f, h_f, w_f = self.W.shape if self.activation is None: E *= self.a_out else: E *= self.activation.gradient(self.a_out) delta_reshaped = E.transpose((1, 2, 3, 0)).reshape(n_f, -1) dX_cols = self.W.reshape(n_f, -1).T.dot(delta_reshaped) dX = col2im_cython(dX_cols, n_in, c_in, h_in, w_in, h_f, w_f, self.padding, self.stride) dW = delta_reshaped.dot(self.x_cols.T).reshape(self.W.shape) db = np.sum(E, axis=(0, 2, 3)) return dX, dW, db def has_weights(self) -> bool: return True

40.384

133

0.602813

import numpy as np from network.activation import Activation from network.layer import Layer from network.utils.im2col_cython import im2col_cython, col2im_cython class Convolution(Layer): def __init__(self, filter_shape, stride, padding, dropout_rate: float = 0, activation: Activation = None, last_layer=False, weight_initializer=None, fb_weight_initializer=None) -> None: assert len(filter_shape) == 4, \ "invalid filter shape: 4-tuple required, {}-tuple given".format(len(filter_shape)) super().__init__() self.filter_shape = filter_shape self.stride = stride self.padding = padding self.dropout_rate = dropout_rate self.activation = activation self.last_layer = last_layer self.weight_initializer = weight_initializer self.fb_weight_initializer = fb_weight_initializer def initialize(self, input_size, num_classes, train_method) -> tuple: assert np.size(input_size) == 3, \ "invalid input size: 3-tuple required for convolution layer" c_in, h_in, w_in = input_size f, c_f, h_f, w_f = self.filter_shape assert c_in == c_f, \ "input channel dimension ({}) not compatible with filter channel dimension ({})".format(c_in, c_f) assert (h_in - h_f + 2 * self.padding) % self.stride == 0, \ "filter width ({}) not compatible with input width ({})".format(h_f, h_in) assert (w_in - w_f + 2 * self.padding) % self.stride == 0, \ "filter height ({}) not compatible with input height ({})".format(h_f, h_in) self.h_out = ((h_in - h_f + 2 * self.padding) // self.stride) + 1 self.w_out = ((w_in - w_f + 2 * self.padding) // self.stride) + 1 if self.weight_initializer is None: sqrt_fan_in = np.sqrt(c_in * h_in * w_in) self.W = np.random.uniform(low=-1 / sqrt_fan_in, high=1 / sqrt_fan_in, size=self.filter_shape) else: self.W = self.weight_initializer.init(dim=(f, c_f, h_f, w_f)) if self.fb_weight_initializer is None: sqrt_fan_out = np.sqrt(f * self.h_out * self.w_out) self.B = np.random.uniform(low=-1 / sqrt_fan_out, high=1 / sqrt_fan_out, size=(num_classes, f * self.h_out * self.w_out)) else: self.B = self.fb_weight_initializer.init(dim=(num_classes, f * self.h_out * self.w_out)) self.b = np.zeros(f) return f, self.h_out, self.w_out def forward(self, X, mode='predict') -> np.ndarray: n_in, c, h_in, w_in = X.shape n_f, c, h_f, w_f = self.W.shape self.x_cols = im2col_cython(X, h_f, w_f, self.padding, self.stride) z = self.W.reshape((n_f, -1)).dot(self.x_cols) z += self.b.reshape(-1, 1) z = z.reshape(n_f, self.h_out, self.w_out, n_in).transpose(3, 0, 1, 2) self.a_in = X if self.activation is None: self.a_out = z else: self.a_out = self.activation.forward(z) if mode == 'train' and self.dropout_rate > 0: self.dropout_mask = (np.random.rand(*self.a_out.shape) > self.dropout_rate).astype(int) self.a_out *= self.dropout_mask return self.a_out def dfa(self, E: np.ndarray) -> tuple: n_f, c_f, h_f, w_f = self.W.shape E = np.dot(E, self.B).reshape((-1, n_f, self.h_out, self.w_out)) if self.dropout_rate > 0: E *= self.dropout_mask if self.activation is None: E *= self.a_out else: E *= self.activation.gradient(self.a_out) dW = E.transpose((1, 2, 3, 0)).reshape(n_f, -1).dot(self.x_cols.T).reshape(self.W.shape) db = np.sum(E, axis=(0, 2, 3)) return dW, db def back_prob(self, E: np.ndarray) -> tuple: if self.dropout_rate > 0: E *= self.dropout_mask n_in, c_in, h_in, w_in = self.a_in.shape n_f, c_f, h_f, w_f = self.W.shape if self.activation is None: E *= self.a_out else: E *= self.activation.gradient(self.a_out) delta_reshaped = E.transpose((1, 2, 3, 0)).reshape(n_f, -1) dX_cols = self.W.reshape(n_f, -1).T.dot(delta_reshaped) dX = col2im_cython(dX_cols, n_in, c_in, h_in, w_in, h_f, w_f, self.padding, self.stride) dW = delta_reshaped.dot(self.x_cols.T).reshape(self.W.shape) db = np.sum(E, axis=(0, 2, 3)) return dX, dW, db def has_weights(self) -> bool: return True

true

7907b8df1fef2c3216b7a8ddf3f4e55cf27d281e

580

py

Python

var/spack/repos/builtin/packages/perl-file-listing/package.py

HaochengLIU/spack

26e51ff1705a4d6234e2a0cf734f93f7f95df5cb

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

2

2018-11-27T03:39:44.000Z

2021-09-06T15:50:35.000Z

var/spack/repos/builtin/packages/perl-file-listing/package.py

HaochengLIU/spack

26e51ff1705a4d6234e2a0cf734f93f7f95df5cb

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

1

2019-01-11T20:11:52.000Z

var/spack/repos/builtin/packages/perl-file-listing/package.py

HaochengLIU/spack

26e51ff1705a4d6234e2a0cf734f93f7f95df5cb

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

1

2020-10-14T14:20:17.000Z

# Copyright 2013-2018 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class PerlFileListing(PerlPackage): """Parse directory listing""" homepage = "http://search.cpan.org/~gaas/File-Listing-6.04/lib/File/Listing.pm" url = "http://search.cpan.org/CPAN/authors/id/G/GA/GAAS/File-Listing-6.04.tar.gz" version('6.04', '83f636b477741f3a014585bb9cc079a6') depends_on('perl-http-date', type=('build', 'run'))

32.222222

90

0.715517

from spack import * class PerlFileListing(PerlPackage): homepage = "http://search.cpan.org/~gaas/File-Listing-6.04/lib/File/Listing.pm" url = "http://search.cpan.org/CPAN/authors/id/G/GA/GAAS/File-Listing-6.04.tar.gz" version('6.04', '83f636b477741f3a014585bb9cc079a6') depends_on('perl-http-date', type=('build', 'run'))

true

7907b99c86f63ebda9f0e3eb4ef5f5e50c0aacaa

204

py

Python

maingui/urls.py

edgarceron/agent_console

a75501957722a349c7276e4d199425897f351bc0

[ "BSD-3-Clause" ]

null

maingui/urls.py

edgarceron/agent_console

a75501957722a349c7276e4d199425897f351bc0

[ "BSD-3-Clause" ]

3

2021-03-30T13:46:24.000Z

2021-09-22T19:18:18.000Z

maingui/urls.py

edgarceron/agent_console

a75501957722a349c7276e4d199425897f351bc0

[ "BSD-3-Clause" ]

null

""" Contains the urls for the maingui module""" from django.urls import path from . import views urlpatterns = [ path('', views.index, name='index'), path('login', views.login, name='login'), ]

20.4

47

0.661765

from django.urls import path from . import views urlpatterns = [ path('', views.index, name='index'), path('login', views.login, name='login'), ]

true

7907b9d4eae1ce641ff64a556aea8dba136a09bb

5,022

py

Python

tests/test_headerpage.py

BradleyPelton/NetflixSelenium

ec4cb51266538b5ed4679f8c265723751b906a7c

[ "MIT" ]

1

2020-04-29T01:54:28.000Z

tests/test_headerpage.py

Souleymane03/NetflixSelenium

ec4cb51266538b5ed4679f8c265723751b906a7c

[ "MIT" ]

null

tests/test_headerpage.py

Souleymane03/NetflixSelenium

ec4cb51266538b5ed4679f8c265723751b906a7c

[ "MIT" ]

2

2021-09-13T12:45:57.000Z

2022-01-14T23:36:26.000Z

import unittest import xmlrunner # from selenium import webdriver import pagemodels.headerpage import tests.pickledlogin import browserconfig # VIDEO OF EXECUTION # https://gyazo.com/b20fd223076bf34c1f2c9b94a4f1fe0a # 2020-04-20 All tests passing, refactor complete # All tests passed 5 executions in a row. v1 ready to ship. # BUG- First execution will murder the cookies and break the following tests. # interestingly, every subsequent test will pass once cookies are hard reset. class HeaderPageTests(unittest.TestCase): """Test cases for the use of the header features atop most netflix pages.""" @classmethod def setUpClass(cls): """Launch the webdriver of choice with selected options(see browserconfig.py). Then login using pickled cookies(see tests/pickledlogin.py).""" if browserconfig.current_browser in ['chrome', 'firefox']: cls.driver = browserconfig.driver_runner( executable_path=browserconfig.driver_path, desired_capabilities=browserconfig.capabilities ) elif browserconfig.current_browser == 'edge': cls.driver = browserconfig.driver_runner( executable_path=browserconfig.driver_path, capabilities=browserconfig.capabilities ) tests.pickledlogin.pickled_login(cls.driver) @classmethod def tearDownClass(cls): """Closes the browser and shuts down the driver executable.""" cls.driver.quit() def setUp(self): """Return to the home page, netflix.com/browse, the staging place for header tests.""" self.driver.get("https://netflix.com/browse") def test_logout_from_header(self): """Logout from the header.""" header_page = pagemodels.headerpage.HeaderPage(self.driver) header_page.logout() # user is redirected to https://www.netflix.com/logout after loging out self.assertIn('logout', self.driver.current_url) # CLEANUP # log back in using the pickled cookies tests.pickledlogin.pickled_login(self.driver) def test_navigate_home_from_my_list(self): """Using the giant Netflix logo in the top left, navigate to the home page /browse/ from the my-list page.""" self.driver.get("https://www.netflix.com/browse/my-list") header_page = pagemodels.headerpage.HeaderPage(self.driver) header_page.navigate_to_home() self.assertEqual("https://www.netflix.com/browse", self.driver.current_url) def test_navigate_to_manage_profile(self): """Using the header account dropdown, navigate to the manage profile page.""" header_page = pagemodels.headerpage.HeaderPage(self.driver) header_page.navigate_to_manage_profile() # user is redirected to https://www.netflix.com/profiles/manage self.assertIn('profiles/manage', self.driver.current_url) def test_search_for_shawshank(self): """Using the search field, search for 'shawshank' and assert that shawshank was found.""" header_page = pagemodels.headerpage.HeaderPage(self.driver) header_page.search("shawshank") self.assertIn("The Shawshank Redemption", self.driver.page_source) # I kind of like this assert now that I think about it. Its testing both the search # function and Netflix's search algorithm. # NOTE- test will not fail if "The Shawkshank Redemeption" is removed. Netflix displays # "similar to {title_name}" for titles its search algorithm recognizes def test_click_top_notification(self): """Click the top notification and assert that the page has changed.""" header_page = pagemodels.headerpage.HeaderPage(self.driver) header_page.click_top_notification() # Assert that we navigated to a notification page or a title page(only 2 options) self.assertTrue( 'title' in self.driver.current_url or 'notification' in self.driver.current_url ) # DIDNT MAKE THE FIRST CUT OF TESTS # I could have 5 more test here for each one of the header buttons. # Those are about as elementary of tests as possible. Skipping them but TODO- OKAY TO HAVE # def test_clear_all_notifications(self): # """ this is easy to do, but impossible to perfect. Netflix doesnt allow any sort of # 'mark notification as unread' so I have no way of generating notifications. Since I have # no way of managing the state, THIS TEST CAN NEVER BE RAN MORE THAN ONCE A DAY. Thus I am # forced to leave it out in order to avoid inconsistent test results""" # header_page = pagemodels.headerpage.HeaderPage(self.driver) # header_page.clear_notifications() if __name__ == '__main__': with open(r'xmltestresults\pretestresults.xml', 'wb') as output: unittest.main( testRunner=xmlrunner.XMLTestRunner(output=output), failfast=False, buffer=False, catchbreak=False)

41.504132

98

0.696734

import unittest import xmlrunner import pagemodels.headerpage import tests.pickledlogin import browserconfig class HeaderPageTests(unittest.TestCase): @classmethod def setUpClass(cls): if browserconfig.current_browser in ['chrome', 'firefox']: cls.driver = browserconfig.driver_runner( executable_path=browserconfig.driver_path, desired_capabilities=browserconfig.capabilities ) elif browserconfig.current_browser == 'edge': cls.driver = browserconfig.driver_runner( executable_path=browserconfig.driver_path, capabilities=browserconfig.capabilities ) tests.pickledlogin.pickled_login(cls.driver) @classmethod def tearDownClass(cls): cls.driver.quit() def setUp(self): self.driver.get("https://netflix.com/browse") def test_logout_from_header(self): header_page = pagemodels.headerpage.HeaderPage(self.driver) header_page.logout() self.assertIn('logout', self.driver.current_url) tests.pickledlogin.pickled_login(self.driver) def test_navigate_home_from_my_list(self): self.driver.get("https://www.netflix.com/browse/my-list") header_page = pagemodels.headerpage.HeaderPage(self.driver) header_page.navigate_to_home() self.assertEqual("https://www.netflix.com/browse", self.driver.current_url) def test_navigate_to_manage_profile(self): header_page = pagemodels.headerpage.HeaderPage(self.driver) header_page.navigate_to_manage_profile() self.assertIn('profiles/manage', self.driver.current_url) def test_search_for_shawshank(self): header_page = pagemodels.headerpage.HeaderPage(self.driver) header_page.search("shawshank") self.assertIn("The Shawshank Redemption", self.driver.page_source) # NOTE- test will not fail if "The Shawkshank Redemeption" is removed. Netflix displays # "similar to {title_name}" for titles its search algorithm recognizes def test_click_top_notification(self): header_page = pagemodels.headerpage.HeaderPage(self.driver) header_page.click_top_notification() # Assert that we navigated to a notification page or a title page(only 2 options) self.assertTrue( 'title' in self.driver.current_url or 'notification' in self.driver.current_url ) # DIDNT MAKE THE FIRST CUT OF TESTS # I could have 5 more test here for each one of the header buttons. # Those are about as elementary of tests as possible. Skipping them but TODO- OKAY TO HAVE # def test_clear_all_notifications(self): # """ this is easy to do, but impossible to perfect. Netflix doesnt allow any sort of # 'mark notification as unread' so I have no way of generating notifications. Since I have # no way of managing the state, THIS TEST CAN NEVER BE RAN MORE THAN ONCE A DAY. Thus I am # forced to leave it out in order to avoid inconsistent test results""" # header_page = pagemodels.headerpage.HeaderPage(self.driver) # header_page.clear_notifications() if __name__ == '__main__': with open(r'xmltestresults\pretestresults.xml', 'wb') as output: unittest.main( testRunner=xmlrunner.XMLTestRunner(output=output), failfast=False, buffer=False, catchbreak=False)

true

7907ba669ecb1cd7087e5929d798def7f2883838

22,279

py

Python

setup.py

312day/airflow

3ecf919f01a1d96a4dc6b1c8a0a9494539ed5a65

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

1

2020-09-15T02:32:55.000Z

setup.py

312day/airflow

3ecf919f01a1d96a4dc6b1c8a0a9494539ed5a65

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

null

setup.py

312day/airflow

3ecf919f01a1d96a4dc6b1c8a0a9494539ed5a65

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

null

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Setup.py for the Airflow project.""" import io import logging import os import subprocess import sys import unittest from importlib import util from os.path import dirname from textwrap import wrap from typing import Dict, Iterable, List from setuptools import Command, find_packages, setup logger = logging.getLogger(__name__) # Kept manually in sync with airflow.__version__ spec = util.spec_from_file_location("airflow.version", os.path.join('airflow', 'version.py')) # noqa mod = util.module_from_spec(spec) spec.loader.exec_module(mod) # type: ignore version = mod.version # type: ignore PY3 = sys.version_info[0] == 3 PY38 = PY3 and sys.version_info[1] >= 8 my_dir = dirname(__file__) try: with io.open(os.path.join(my_dir, 'README.md'), encoding='utf-8') as f: long_description = f.read() except FileNotFoundError: long_description = '' def airflow_test_suite(): """Test suite for Airflow tests""" test_loader = unittest.TestLoader() test_suite = test_loader.discover(os.path.join(my_dir, 'tests'), pattern='test_*.py') return test_suite class CleanCommand(Command): """ Command to tidy up the project root. Registered as cmdclass in setup() so it can be called with ``python setup.py extra_clean``. """ description = "Tidy up the project root" user_options = [] # type: List[str] def initialize_options(self): """Set default values for options.""" def finalize_options(self): """Set final values for options.""" def run(self): # noqa """Run command to remove temporary files and directories.""" os.chdir(my_dir) os.system('rm -vrf ./build ./dist ./*.pyc ./*.tgz ./*.egg-info') class CompileAssets(Command): """ Compile and build the frontend assets using yarn and webpack. Registered as cmdclass in setup() so it can be called with ``python setup.py compile_assets``. """ description = "Compile and build the frontend assets" user_options = [] # type: List[str] def initialize_options(self): """Set default values for options.""" def finalize_options(self): """Set final values for options.""" def run(self): # noqa """Run a command to compile and build assets.""" subprocess.check_call('./airflow/www/compile_assets.sh') class ListExtras(Command): """ List all available extras Registered as cmdclass in setup() so it can be called with ``python setup.py list_extras``. """ description = "List available extras" user_options = [] # type: List[str] def initialize_options(self): """Set default values for options.""" def finalize_options(self): """Set final values for options.""" def run(self): # noqa """List extras.""" print("\n".join(wrap(", ".join(EXTRAS_REQUIREMENTS.keys()), 100))) def git_version(version_: str) -> str: """ Return a version to identify the state of the underlying git repo. The version will indicate whether the head of the current git-backed working directory is tied to a release tag or not : it will indicate the former with a 'release:{version}' prefix and the latter with a 'dev0' prefix. Following the prefix will be a sha of the current branch head. Finally, a "dirty" suffix is appended to indicate that uncommitted changes are present. :param str version_: Semver version :return: Found Airflow version in Git repo :rtype: str """ try: import git try: repo = git.Repo(os.path.join(*[my_dir, '.git'])) except git.NoSuchPathError: logger.warning('.git directory not found: Cannot compute the git version') return '' except git.InvalidGitRepositoryError: logger.warning('Invalid .git directory not found: Cannot compute the git version') return '' except ImportError: logger.warning('gitpython not found: Cannot compute the git version.') return '' if repo: sha = repo.head.commit.hexsha if repo.is_dirty(): return '.dev0+{sha}.dirty'.format(sha=sha) # commit is clean return '.release:{version}+{sha}'.format(version=version_, sha=sha) else: return 'no_git_version' def write_version(filename: str = os.path.join(*[my_dir, "airflow", "git_version"])): """ Write the Semver version + git hash to file, e.g. ".dev0+2f635dc265e78db6708f59f68e8009abb92c1e65". :param str filename: Destination file to write """ text = "{}".format(git_version(version)) with open(filename, 'w') as file: file.write(text) # 'Start dependencies group' and 'Start dependencies group' are mark for ./scripts/ci/check_order_setup.py # If you change this mark you should also change ./scripts/ci/check_order_setup.py # Start dependencies group amazon = [ 'boto3>=1.12.0,<2.0.0', 'watchtower~=0.7.3', ] apache_beam = [ 'apache-beam[gcp]', ] async_packages = [ 'eventlet>= 0.9.7', 'gevent>=0.13', 'greenlet>=0.4.9', ] atlas = [ 'atlasclient>=0.1.2', ] azure = [ 'azure-batch>=8.0.0', 'azure-cosmos>=3.0.1,<4', 'azure-datalake-store>=0.0.45', 'azure-identity>=1.3.1', 'azure-keyvault>=4.1.0', 'azure-kusto-data>=0.0.43,<0.1', 'azure-mgmt-containerinstance>=1.5.0,<2.0', 'azure-mgmt-datalake-store>=0.5.0', 'azure-mgmt-resource>=2.2.0', 'azure-storage>=0.34.0, <0.37.0', 'azure-storage-blob<12.0', ] cassandra = [ 'cassandra-driver>=3.13.0,<3.21.0', ] celery = [ 'celery~=4.4.2', 'flower>=0.7.3, <1.0', 'tornado>=4.2.0, <6.0', # Dep of flower. Pin to a version that works on Py3.5.2 'vine~=1.3', # https://stackoverflow.com/questions/32757259/celery-no-module-named-five ] cgroups = [ 'cgroupspy>=0.1.4', ] cloudant = [ 'cloudant>=2.0', ] dask = [ 'cloudpickle>=1.4.1, <1.5.0', 'distributed>=2.11.1, <2.20' ] databricks = [ 'requests>=2.20.0, <3', ] datadog = [ 'datadog>=0.14.0', ] doc = [ 'sphinx>=2.1.2', 'sphinx-argparse>=0.1.13', 'sphinx-autoapi==1.0.0', 'sphinx-copybutton', 'sphinx-jinja~=1.1', 'sphinx-rtd-theme>=0.1.6', 'sphinxcontrib-httpdomain>=1.7.0', "sphinxcontrib-redoc>=1.6.0", "sphinxcontrib-spelling==5.2.1" ] docker = [ 'docker~=3.0', ] druid = [ 'pydruid>=0.4.1', ] elasticsearch = [ 'elasticsearch>7, <7.6.0', 'elasticsearch-dbapi==0.1.0', 'elasticsearch-dsl>=5.0.0', ] exasol = [ 'pyexasol>=0.5.1,<1.0.0', ] facebook = [ 'facebook-business>=6.0.2', ] flask_oauth = [ 'Flask-OAuthlib>=0.9.1,<0.9.6', # Flask OAuthLib 0.9.6 requires Flask-Login 0.5.0 - breaks FAB 'oauthlib!=2.0.3,!=2.0.4,!=2.0.5,<3.0.0,>=1.1.2', 'requests-oauthlib==1.1.0', ] google = [ 'PyOpenSSL', 'google-ads>=4.0.0', 'google-api-python-client>=1.6.0,<2.0.0', 'google-auth>=1.0.0,<2.0.0', 'google-auth-httplib2>=0.0.1', 'google-cloud-automl>=0.4.0,<2.0.0', 'google-cloud-bigquery-datatransfer>=0.4.0,<2.0.0', 'google-cloud-bigtable>=1.0.0,<2.0.0', 'google-cloud-container>=0.1.1,<2.0.0', 'google-cloud-datacatalog>=0.5.0, <0.8', # TODO: we should migrate to 1.0 likely and add <2.0.0 then 'google-cloud-dataproc>=1.0.1,<2.0.0', 'google-cloud-dlp>=0.11.0,<2.0.0', 'google-cloud-kms>=1.2.1,<2.0.0', 'google-cloud-language>=1.1.1,<2.0.0', 'google-cloud-logging>=1.14.0,<2.0.0', 'google-cloud-monitoring>=0.34.0,<2.0.0', 'google-cloud-pubsub>=1.0.0,<2.0.0', 'google-cloud-redis>=0.3.0,<2.0.0', 'google-cloud-secret-manager>=0.2.0,<2.0.0', 'google-cloud-spanner>=1.10.0,<2.0.0', 'google-cloud-speech>=0.36.3,<2.0.0', 'google-cloud-storage>=1.16,<2.0.0', 'google-cloud-tasks>=1.2.1,<2.0.0', 'google-cloud-texttospeech>=0.4.0,<2.0.0', 'google-cloud-translate>=1.5.0,<2.0.0', 'google-cloud-videointelligence>=1.7.0,<2.0.0', 'google-cloud-vision>=0.35.2,<2.0.0', 'grpcio-gcp>=0.2.2', 'pandas-gbq', ] grpc = [ 'google-auth>=1.0.0, <2.0.0dev', 'google-auth-httplib2>=0.0.1', 'grpcio>=1.15.0', ] hashicorp = [ 'hvac~=0.10', ] hdfs = [ 'snakebite-py3', ] hive = [ 'hmsclient>=0.1.0', 'pyhive[hive]>=0.6.0', ] jdbc = [ 'jaydebeapi>=1.1.1', ] jenkins = [ 'python-jenkins>=1.0.0', ] jira = [ 'JIRA>1.0.7', ] kerberos = [ 'pykerberos>=1.1.13', 'requests_kerberos>=0.10.0', 'thrift_sasl>=0.2.0', ] kubernetes = [ 'cryptography>=2.0.0', 'kubernetes>=3.0.0', ] kylin = [ 'kylinpy>=2.6' ] ldap = [ 'ldap3>=2.5.1', ] mongo = [ 'dnspython>=1.13.0,<2.0.0', 'pymongo>=3.6.0', ] mssql = [ 'pymssql~=2.1.1', ] mysql = [ 'mysql-connector-python>=8.0.11, <=8.0.18', 'mysqlclient>=1.3.6,<1.4', ] odbc = [ 'pyodbc', ] oracle = [ 'cx_Oracle>=5.1.2', ] pagerduty = [ 'pypd>=1.1.0', ] papermill = [ 'papermill[all]>=1.2.1', 'nteract-scrapbook[all]>=0.3.1', ] password = [ 'bcrypt>=2.0.0', 'flask-bcrypt>=0.7.1', ] pinot = [ 'pinotdb==0.1.1', ] plexus = [ 'arrow>=0.16.0', ] postgres = [ 'psycopg2-binary>=2.7.4', ] presto = [ 'presto-python-client>=0.7.0,<0.8' ] qds = [ 'qds-sdk>=1.10.4', ] rabbitmq = [ 'amqp', ] redis = [ 'redis~=3.2', ] salesforce = [ 'simple-salesforce>=1.0.0', ] samba = [ 'pysmbclient>=0.1.3', ] segment = [ 'analytics-python>=1.2.9', ] sendgrid = [ 'sendgrid>=6.0.0,<7', ] sentry = [ 'blinker>=1.1', 'sentry-sdk>=0.8.0', ] singularity = ['spython>=0.0.56'] slack = [ 'slackclient>=2.0.0,<3.0.0', ] snowflake = [ 'snowflake-connector-python>=1.5.2', 'snowflake-sqlalchemy>=1.1.0', ] spark = [ 'pyspark', ] ssh = [ 'paramiko>=2.6.0', 'pysftp>=0.2.9', 'sshtunnel>=0.1.4,<0.2', ] statsd = [ 'statsd>=3.3.0, <4.0', ] tableau = [ 'tableauserverclient~=0.12', ] vertica = [ 'vertica-python>=0.5.1', ] virtualenv = [ 'virtualenv', ] webhdfs = [ 'hdfs[avro,dataframe,kerberos]>=2.0.4', ] winrm = [ 'pywinrm~=0.4', ] yandexcloud = [ 'yandexcloud>=0.22.0', ] zendesk = [ 'zdesk', ] # End dependencies group all_dbs = (cassandra + cloudant + druid + exasol + hdfs + hive + mongo + mssql + mysql + pinot + postgres + presto + vertica) ############################################################################################################ # IMPORTANT NOTE!!!!!!!!!!!!!!! # IF you are removing dependencies from this list, please make sure that you also increase # DEPENDENCIES_EPOCH_NUMBER in the Dockerfile.ci ############################################################################################################ devel = [ 'beautifulsoup4~=4.7.1', 'blinker', 'bowler', 'click==6.7', 'contextdecorator;python_version<"3.4"', 'coverage', 'docutils', 'flake8>=3.6.0', 'flake8-colors', 'flaky', 'freezegun', 'github3.py', 'gitpython', 'ipdb', 'jira', 'mongomock', 'moto>=1.3.14,<2.0.0', 'parameterized', 'paramiko', 'pipdeptree', 'pre-commit', 'pylint==2.5.3', 'pysftp', 'pytest', 'pytest-cov', 'pytest-instafail', 'pytest-rerunfailures', 'pytest-timeouts', 'pytest-xdist', 'pywinrm', 'qds-sdk>=1.9.6', 'requests_mock', 'setuptools', 'wheel', 'yamllint', ] ############################################################################################################ # IMPORTANT NOTE!!!!!!!!!!!!!!! # IF you are removing dependencies from the above list, please make sure that you also increase # DEPENDENCIES_EPOCH_NUMBER in the Dockerfile.ci ############################################################################################################ if PY3: devel += ['mypy==0.770'] else: devel += ['unittest2'] devel_minreq = cgroups + devel + doc + kubernetes + mysql + password devel_hadoop = devel_minreq + hdfs + hive + kerberos + presto + webhdfs PROVIDERS_REQUIREMENTS: Dict[str, Iterable[str]] = { "amazon": amazon, "apache.cassandra": cassandra, "apache.druid": druid, "apache.hdfs": hdfs, "apache.hive": hive, "apache.kylin": kylin, "apache.livy": [], "apache.pig": [], "apache.pinot": pinot, "apache.spark": spark, "apache.sqoop": [], "celery": celery, "cloudant": cloudant, "cncf.kubernetes": kubernetes, "databricks": databricks, "datadog": datadog, "dingding": [], "discord": [], "docker": docker, "elasticsearch": [], "exasol": exasol, "facebook": facebook, "ftp": [], "google": google, "grpc": grpc, "hashicorp": hashicorp, "http": [], "imap": [], "jdbc": jdbc, "jenkins": jenkins, "jira": jira, "microsoft.azure": azure, "microsoft.mssql": mssql, "microsoft.winrm": winrm, "mongo": mongo, "mysql": mysql, "odbc": odbc, "openfaas": [], "opsgenie": [], "oracle": oracle, "pagerduty": pagerduty, "papermill": papermill, "plexus": plexus, "postgres": postgres, "presto": presto, "qubole": qds, "redis": redis, "salesforce": salesforce, "samba": samba, "segment": segment, "sftp": ssh, "singularity": singularity, "slack": slack, "snowflake": snowflake, "sqlite": [], "ssh": ssh, "vertica": vertica, "yandex": yandexcloud, "zendesk": zendesk, } EXTRAS_REQUIREMENTS: Dict[str, Iterable[str]] = { 'all_dbs': all_dbs, 'amazon': amazon, 'apache.atlas': atlas, 'apache.beam': apache_beam, "apache.cassandra": cassandra, "apache.druid": druid, "apache.hdfs": hdfs, "apache.hive": hive, "apache.kylin": kylin, "apache.pinot": pinot, "apache.webhdfs": webhdfs, 'async': async_packages, 'atlas': atlas, # TODO: remove this in Airflow 2.1 'aws': amazon, # TODO: remove this in Airflow 2.1 'azure': azure, # TODO: remove this in Airflow 2.1 'cassandra': cassandra, # TODO: remove this in Airflow 2.1 'celery': celery, 'cgroups': cgroups, 'cloudant': cloudant, 'cncf.kubernetes': kubernetes, 'dask': dask, 'databricks': databricks, 'datadog': datadog, 'devel': devel_minreq, 'devel_hadoop': devel_hadoop, 'doc': doc, 'docker': docker, 'druid': druid, # TODO: remove this in Airflow 2.1 'elasticsearch': elasticsearch, 'exasol': exasol, 'facebook': facebook, 'gcp': google, # TODO: remove this in Airflow 2.1 'gcp_api': google, # TODO: remove this in Airflow 2.1 'github_enterprise': flask_oauth, 'google': google, 'google_auth': flask_oauth, 'grpc': grpc, 'hashicorp': hashicorp, 'hdfs': hdfs, # TODO: remove this in Airflow 2.1 'hive': hive, # TODO: remove this in Airflow 2.1 'jdbc': jdbc, 'jira': jira, 'kerberos': kerberos, 'kubernetes': kubernetes, # TODO: remove this in Airflow 2.1 'ldap': ldap, "microsoft.azure": azure, "microsoft.mssql": mssql, "microsoft.winrm": winrm, 'mongo': mongo, 'mssql': mssql, # TODO: remove this in Airflow 2.1 'mysql': mysql, 'odbc': odbc, 'oracle': oracle, 'pagerduty': pagerduty, 'papermill': papermill, 'password': password, 'pinot': pinot, # TODO: remove this in Airflow 2.1 'plexus': plexus, 'postgres': postgres, 'presto': presto, 'qds': qds, 'rabbitmq': rabbitmq, 'redis': redis, 'salesforce': salesforce, 'samba': samba, 'segment': segment, 'sendgrid': sendgrid, 'sentry': sentry, 'singularity': singularity, 'slack': slack, 'snowflake': snowflake, 'spark': spark, 'ssh': ssh, 'statsd': statsd, 'tableau': tableau, 'vertica': vertica, 'virtualenv': virtualenv, 'webhdfs': webhdfs, # TODO: remove this in Airflow 2.1 'winrm': winrm, # TODO: remove this in Airflow 2.1 'yandexcloud': yandexcloud, } # Make devel_all contain all providers + extras + unique devel_all = list(set(devel + [req for req_list in EXTRAS_REQUIREMENTS.values() for req in req_list] + [req for req_list in PROVIDERS_REQUIREMENTS.values() for req in req_list])) PACKAGES_EXCLUDED_FOR_ALL = [ ] if PY3: PACKAGES_EXCLUDED_FOR_ALL.extend([ 'snakebite', ]) if PY38: PACKAGES_EXCLUDED_FOR_ALL.extend([ 'pymssql', ]) # Those packages are excluded because they break tests (downgrading mock) and they are # not needed to run our test suite. PACKAGES_EXCLUDED_FOR_CI = [ 'apache-beam', ] def is_package_excluded(package: str, exclusion_list: List[str]): """ Checks if package should be excluded. :param package: package name (beginning of it) :param exclusion_list: list of excluded packages :return: true if package should be excluded """ return any([package.startswith(excluded_package) for excluded_package in exclusion_list]) devel_all = [package for package in devel_all if not is_package_excluded( package=package, exclusion_list=PACKAGES_EXCLUDED_FOR_ALL) ] devel_ci = [package for package in devel_all if not is_package_excluded( package=package, exclusion_list=PACKAGES_EXCLUDED_FOR_CI + PACKAGES_EXCLUDED_FOR_ALL) ] EXTRAS_REQUIREMENTS.update( { 'all': devel_all, 'devel_ci': devel_ci, } ) ##################################################################################################### # IMPORTANT NOTE!!!!!!!!!!!!!!! # IF you are removing dependencies from this list, please make sure that you also increase # DEPENDENCIES_EPOCH_NUMBER in the Dockerfile.ci ##################################################################################################### INSTALL_REQUIREMENTS = [ 'alembic>=1.2, <2.0', 'argcomplete~=1.10', 'attrs~=19.3', 'cached_property~=1.5', 'cattrs~=1.0', 'colorlog==4.0.2', 'connexion[swagger-ui,flask]>=2.6.0,<3', 'croniter>=0.3.17, <0.4', 'cryptography>=0.9.3', 'dill>=0.2.2, <0.4', 'flask>=1.1.0, <2.0', 'flask-appbuilder>2.3.4,~=3.0', 'flask-caching>=1.3.3, <2.0.0', 'flask-login>=0.3, <0.5', 'flask-swagger==0.2.13', 'flask-wtf>=0.14.2, <0.15', 'funcsigs>=1.0.0, <2.0.0', 'graphviz>=0.12', 'gunicorn>=19.5.0, <20.0', 'iso8601>=0.1.12', 'jinja2>=2.10.1, <2.12.0', 'json-merge-patch==0.2', 'jsonschema~=3.0', 'lazy_object_proxy~=1.3', 'lockfile>=0.12.2', 'markdown>=2.5.2, <3.0', 'markupsafe>=1.1.1, <2.0', 'marshmallow-oneofschema>=2.0.1', 'pandas>=0.17.1, <2.0', 'pendulum~=2.0', 'pep562~=1.0;python_version<"3.7"', 'psutil>=4.2.0, <6.0.0', 'pygments>=2.0.1, <3.0', 'python-daemon>=2.1.1', 'python-dateutil>=2.3, <3', 'python-nvd3~=0.15.0', 'python-slugify>=3.0.0,<5.0', 'requests>=2.20.0, <3', 'setproctitle>=1.1.8, <2', 'sqlalchemy~=1.3', 'sqlalchemy_jsonfield~=0.9', 'tabulate>=0.7.5, <0.9', 'tenacity>=4.12.0, <5.2', 'termcolor>=1.1.0', 'thrift>=0.9.2', 'typing;python_version<"3.6"', 'typing-extensions>=3.7.4;python_version<"3.8"', 'tzlocal>=1.4,<2.0.0', 'unicodecsv>=0.14.1', 'werkzeug<1.0.0', ] def do_setup(): """Perform the Airflow package setup.""" write_version() setup( name='apache-airflow', description='Programmatically author, schedule and monitor data pipelines', long_description=long_description, long_description_content_type='text/markdown', license='Apache License 2.0', version=version, packages=find_packages(include=['airflow', 'airflow.*']), package_data={ 'airflow': ['py.typed'], '': ['airflow/alembic.ini', "airflow/git_version", "*.ipynb", "airflow/providers/cncf/kubernetes/example_dags/*.yaml"], 'airflow.api_connexion.openapi': ['*.yaml'], 'airflow.serialization': ["*.json"], }, include_package_data=True, zip_safe=False, entry_points={ "console_scripts": [ "airflow = airflow.__main__:main", ], }, install_requires=INSTALL_REQUIREMENTS, setup_requires=[ 'bowler', 'docutils', 'gitpython', 'setuptools', 'wheel', ], extras_require=EXTRAS_REQUIREMENTS, classifiers=[ 'Development Status :: 5 - Production/Stable', 'Environment :: Console', 'Environment :: Web Environment', 'Intended Audience :: Developers', 'Intended Audience :: System Administrators', 'License :: OSI Approved :: Apache Software License', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: System :: Monitoring', ], author='Apache Software Foundation', author_email='dev@airflow.apache.org', url='http://airflow.apache.org/', download_url=( 'https://dist.apache.org/repos/dist/release/airflow/' + version), cmdclass={ 'extra_clean': CleanCommand, 'compile_assets': CompileAssets, 'list_extras': ListExtras, }, test_suite='setup.airflow_test_suite', python_requires='~=3.6', ) if __name__ == "__main__": do_setup()

27.471023

108

0.586786

import io import logging import os import subprocess import sys import unittest from importlib import util from os.path import dirname from textwrap import wrap from typing import Dict, Iterable, List from setuptools import Command, find_packages, setup logger = logging.getLogger(__name__) spec = util.spec_from_file_location("airflow.version", os.path.join('airflow', 'version.py')) mod = util.module_from_spec(spec) spec.loader.exec_module(mod) version = mod.version PY3 = sys.version_info[0] == 3 PY38 = PY3 and sys.version_info[1] >= 8 my_dir = dirname(__file__) try: with io.open(os.path.join(my_dir, 'README.md'), encoding='utf-8') as f: long_description = f.read() except FileNotFoundError: long_description = '' def airflow_test_suite(): test_loader = unittest.TestLoader() test_suite = test_loader.discover(os.path.join(my_dir, 'tests'), pattern='test_*.py') return test_suite class CleanCommand(Command): description = "Tidy up the project root" user_options = [] def initialize_options(self): def finalize_options(self): def run(self): os.chdir(my_dir) os.system('rm -vrf ./build ./dist ./*.pyc ./*.tgz ./*.egg-info') class CompileAssets(Command): description = "Compile and build the frontend assets" user_options = [] def initialize_options(self): def finalize_options(self): def run(self): subprocess.check_call('./airflow/www/compile_assets.sh') class ListExtras(Command): description = "List available extras" user_options = [] def initialize_options(self): def finalize_options(self): def run(self): print("\n".join(wrap(", ".join(EXTRAS_REQUIREMENTS.keys()), 100))) def git_version(version_: str) -> str: try: import git try: repo = git.Repo(os.path.join(*[my_dir, '.git'])) except git.NoSuchPathError: logger.warning('.git directory not found: Cannot compute the git version') return '' except git.InvalidGitRepositoryError: logger.warning('Invalid .git directory not found: Cannot compute the git version') return '' except ImportError: logger.warning('gitpython not found: Cannot compute the git version.') return '' if repo: sha = repo.head.commit.hexsha if repo.is_dirty(): return '.dev0+{sha}.dirty'.format(sha=sha) return '.release:{version}+{sha}'.format(version=version_, sha=sha) else: return 'no_git_version' def write_version(filename: str = os.path.join(*[my_dir, "airflow", "git_version"])): text = "{}".format(git_version(version)) with open(filename, 'w') as file: file.write(text) amazon = [ 'boto3>=1.12.0,<2.0.0', 'watchtower~=0.7.3', ] apache_beam = [ 'apache-beam[gcp]', ] async_packages = [ 'eventlet>= 0.9.7', 'gevent>=0.13', 'greenlet>=0.4.9', ] atlas = [ 'atlasclient>=0.1.2', ] azure = [ 'azure-batch>=8.0.0', 'azure-cosmos>=3.0.1,<4', 'azure-datalake-store>=0.0.45', 'azure-identity>=1.3.1', 'azure-keyvault>=4.1.0', 'azure-kusto-data>=0.0.43,<0.1', 'azure-mgmt-containerinstance>=1.5.0,<2.0', 'azure-mgmt-datalake-store>=0.5.0', 'azure-mgmt-resource>=2.2.0', 'azure-storage>=0.34.0, <0.37.0', 'azure-storage-blob<12.0', ] cassandra = [ 'cassandra-driver>=3.13.0,<3.21.0', ] celery = [ 'celery~=4.4.2', 'flower>=0.7.3, <1.0', 'tornado>=4.2.0, <6.0', 'vine~=1.3', ] cgroups = [ 'cgroupspy>=0.1.4', ] cloudant = [ 'cloudant>=2.0', ] dask = [ 'cloudpickle>=1.4.1, <1.5.0', 'distributed>=2.11.1, <2.20' ] databricks = [ 'requests>=2.20.0, <3', ] datadog = [ 'datadog>=0.14.0', ] doc = [ 'sphinx>=2.1.2', 'sphinx-argparse>=0.1.13', 'sphinx-autoapi==1.0.0', 'sphinx-copybutton', 'sphinx-jinja~=1.1', 'sphinx-rtd-theme>=0.1.6', 'sphinxcontrib-httpdomain>=1.7.0', "sphinxcontrib-redoc>=1.6.0", "sphinxcontrib-spelling==5.2.1" ] docker = [ 'docker~=3.0', ] druid = [ 'pydruid>=0.4.1', ] elasticsearch = [ 'elasticsearch>7, <7.6.0', 'elasticsearch-dbapi==0.1.0', 'elasticsearch-dsl>=5.0.0', ] exasol = [ 'pyexasol>=0.5.1,<1.0.0', ] facebook = [ 'facebook-business>=6.0.2', ] flask_oauth = [ 'Flask-OAuthlib>=0.9.1,<0.9.6', 'oauthlib!=2.0.3,!=2.0.4,!=2.0.5,<3.0.0,>=1.1.2', 'requests-oauthlib==1.1.0', ] google = [ 'PyOpenSSL', 'google-ads>=4.0.0', 'google-api-python-client>=1.6.0,<2.0.0', 'google-auth>=1.0.0,<2.0.0', 'google-auth-httplib2>=0.0.1', 'google-cloud-automl>=0.4.0,<2.0.0', 'google-cloud-bigquery-datatransfer>=0.4.0,<2.0.0', 'google-cloud-bigtable>=1.0.0,<2.0.0', 'google-cloud-container>=0.1.1,<2.0.0', 'google-cloud-datacatalog>=0.5.0, <0.8', 'google-cloud-dataproc>=1.0.1,<2.0.0', 'google-cloud-dlp>=0.11.0,<2.0.0', 'google-cloud-kms>=1.2.1,<2.0.0', 'google-cloud-language>=1.1.1,<2.0.0', 'google-cloud-logging>=1.14.0,<2.0.0', 'google-cloud-monitoring>=0.34.0,<2.0.0', 'google-cloud-pubsub>=1.0.0,<2.0.0', 'google-cloud-redis>=0.3.0,<2.0.0', 'google-cloud-secret-manager>=0.2.0,<2.0.0', 'google-cloud-spanner>=1.10.0,<2.0.0', 'google-cloud-speech>=0.36.3,<2.0.0', 'google-cloud-storage>=1.16,<2.0.0', 'google-cloud-tasks>=1.2.1,<2.0.0', 'google-cloud-texttospeech>=0.4.0,<2.0.0', 'google-cloud-translate>=1.5.0,<2.0.0', 'google-cloud-videointelligence>=1.7.0,<2.0.0', 'google-cloud-vision>=0.35.2,<2.0.0', 'grpcio-gcp>=0.2.2', 'pandas-gbq', ] grpc = [ 'google-auth>=1.0.0, <2.0.0dev', 'google-auth-httplib2>=0.0.1', 'grpcio>=1.15.0', ] hashicorp = [ 'hvac~=0.10', ] hdfs = [ 'snakebite-py3', ] hive = [ 'hmsclient>=0.1.0', 'pyhive[hive]>=0.6.0', ] jdbc = [ 'jaydebeapi>=1.1.1', ] jenkins = [ 'python-jenkins>=1.0.0', ] jira = [ 'JIRA>1.0.7', ] kerberos = [ 'pykerberos>=1.1.13', 'requests_kerberos>=0.10.0', 'thrift_sasl>=0.2.0', ] kubernetes = [ 'cryptography>=2.0.0', 'kubernetes>=3.0.0', ] kylin = [ 'kylinpy>=2.6' ] ldap = [ 'ldap3>=2.5.1', ] mongo = [ 'dnspython>=1.13.0,<2.0.0', 'pymongo>=3.6.0', ] mssql = [ 'pymssql~=2.1.1', ] mysql = [ 'mysql-connector-python>=8.0.11, <=8.0.18', 'mysqlclient>=1.3.6,<1.4', ] odbc = [ 'pyodbc', ] oracle = [ 'cx_Oracle>=5.1.2', ] pagerduty = [ 'pypd>=1.1.0', ] papermill = [ 'papermill[all]>=1.2.1', 'nteract-scrapbook[all]>=0.3.1', ] password = [ 'bcrypt>=2.0.0', 'flask-bcrypt>=0.7.1', ] pinot = [ 'pinotdb==0.1.1', ] plexus = [ 'arrow>=0.16.0', ] postgres = [ 'psycopg2-binary>=2.7.4', ] presto = [ 'presto-python-client>=0.7.0,<0.8' ] qds = [ 'qds-sdk>=1.10.4', ] rabbitmq = [ 'amqp', ] redis = [ 'redis~=3.2', ] salesforce = [ 'simple-salesforce>=1.0.0', ] samba = [ 'pysmbclient>=0.1.3', ] segment = [ 'analytics-python>=1.2.9', ] sendgrid = [ 'sendgrid>=6.0.0,<7', ] sentry = [ 'blinker>=1.1', 'sentry-sdk>=0.8.0', ] singularity = ['spython>=0.0.56'] slack = [ 'slackclient>=2.0.0,<3.0.0', ] snowflake = [ 'snowflake-connector-python>=1.5.2', 'snowflake-sqlalchemy>=1.1.0', ] spark = [ 'pyspark', ] ssh = [ 'paramiko>=2.6.0', 'pysftp>=0.2.9', 'sshtunnel>=0.1.4,<0.2', ] statsd = [ 'statsd>=3.3.0, <4.0', ] tableau = [ 'tableauserverclient~=0.12', ] vertica = [ 'vertica-python>=0.5.1', ] virtualenv = [ 'virtualenv', ] webhdfs = [ 'hdfs[avro,dataframe,kerberos]>=2.0.4', ] winrm = [ 'pywinrm~=0.4', ] yandexcloud = [ 'yandexcloud>=0.22.0', ] zendesk = [ 'zdesk', ] all_dbs = (cassandra + cloudant + druid + exasol + hdfs + hive + mongo + mssql + mysql + pinot + postgres + presto + vertica)

true

7907bbde602b8418e4c329c85b5cbbce7741029a

14,197

py

Python

lib/pb_io.py

NingAnMe/snow_cover_of_remote_sensing

aabd0f4754eb5200203fc8a90f06b603dcd260a8

[ "Apache-2.0" ]

1

2020-08-19T08:34:53.000Z

lib/pb_io.py

NingAnMe/snow_cover_of_remote_sensing

aabd0f4754eb5200203fc8a90f06b603dcd260a8

[ "Apache-2.0" ]

null

lib/pb_io.py

NingAnMe/snow_cover_of_remote_sensing

aabd0f4754eb5200203fc8a90f06b603dcd260a8

[ "Apache-2.0" ]

1

2020-07-01T16:32:15.000Z

# coding: utf-8 import errno import os import random import re from contextlib import contextmanager import h5py import numpy as np import time import yaml from datetime import datetime def write_yaml_file(yaml_dict, file_yaml): path_yaml = os.path.dirname(file_yaml) if not os.path.isdir(path_yaml): os.makedirs(path_yaml) with open(file_yaml, 'w') as stream: yaml.dump(yaml_dict, stream, default_flow_style=False) def make_sure_path_exists(path): try: os.makedirs(path) except OSError as exception: if exception.errno != errno.EEXIST: raise def find_file(path, reg): """ path: 要遍历的目录 reg: 符合条件的文件 """ FileLst = [] try: lst = os.walk(path) for root, dirs, files in lst: for name in files: try: m = re.match(reg, name) except Exception as e: continue if m: FileLst.append(os.path.join(root, name)) except Exception as e: print(str(e)) return sorted(FileLst) def path_replace_ymd(path, ymd): """ path:替换路径中的日期 ,path中%YYYY%MM%DD%JJJ 等关键字会被ymd日期实例 ymd: yyyymmdd (20180101) """ # 转成datetime类型 ymd = datetime.strptime(ymd, '%Y%m%d') yy = ymd.strftime('%Y') mm = ymd.strftime('%m') dd = ymd.strftime('%d') jj = ymd.strftime('%j') path = path.replace('%YYYY', yy) path = path.replace('%MM', mm) path = path.replace('%DD', dd) path = path.replace('%JJJ', jj) return path def is_none(*args): """ 判断传入的变量中是否有 None :param args: :return: """ has_none = False for arg in args: if arg is None: has_none = True return has_none def copy_attrs_h5py(pre_object, out_object): """ 复制 file、dataset 或者 group 的属性 :param pre_object: 被复制属性的 dataset 或者 group :param out_object: 复制属性的 dataset 或者 group :return: """ for akey in list(pre_object.attrs.keys()): out_object.attrs[akey] = pre_object.attrs[akey] def read_dataset_hdf5(file_path, set_name): """ 读取 hdf5 文件，返回一个 numpy 多维数组 :param file_path: (unicode)文件路径 :param set_name: (str or list)表的名字 :return: 如果传入的表名字是一个字符串，返回 numpy.ndarray 如果传入的表名字是一个列表，返回一个字典，key 是表名字， value 是 numpy.ndarry """ if isinstance(set_name, str): if os.path.isfile(file_path): file_h5py = h5py.File(file_path, 'r') data = file_h5py.get(set_name)[:] dataset = np.array(data) file_h5py.close() return dataset else: raise ValueError('value error: file_path') elif isinstance(set_name, list): datasets = {} if os.path.isfile(file_path): file_h5py = h5py.File(file_path, 'r') for name in set_name: data = file_h5py.get(name)[:] dataset = np.array(data) datasets[name] = dataset file_h5py.close() return datasets else: raise ValueError('value error: file_path') else: raise ValueError('value error: set_name') def attrs2dict(attrs): """ 将一个 HDF5 attr 类转为 Dict 类 :return: """ d = {} for k, v in list(attrs.items()): d[k] = v return d @contextmanager def progress_lock(max_wait_time=5): try: sleep_time = 0 lock = "progress.lock" while True: if os.path.isfile(lock): if sleep_time > max_wait_time: try: os.remove(lock) break except: continue else: random_number = random.random() * 0.1 sleep_time += random_number time.sleep(random_number) else: break with open(lock, "w"): pass yield finally: try: os.remove(lock) except: pass def write_txt(in_file, head, bodys, keylens=8): """ description: wangpeng add 20180615 (写入或更新txt) :in_file 写入文件位置 :head 文件头信息 :bodys 文件体 :keylens 更新文件使用的第一列关键字长度 """ allLines = [] DICT_D = {} FilePath = os.path.dirname(in_file) if not os.path.exists(FilePath): os.makedirs(FilePath) if os.path.isfile(in_file) and os.path.getsize(in_file) != 0: fp = open(in_file, 'r') fp.readline() Lines = fp.readlines() fp.close() # 使用字典特性，保证时间唯一，读取数据 for Line in Lines: DICT_D[Line[:keylens]] = Line[keylens:] # 添加或更改数据 for Line in bodys: DICT_D[Line[:keylens]] = Line[keylens:] # 按照时间排序 newLines = sorted( iter(DICT_D.items()), key=lambda d: d[0], reverse=False) for i in range(len(newLines)): allLines.append(str(newLines[i][0]) + str(newLines[i][1])) fp = open(in_file, 'w') fp.write(head) fp.writelines(allLines) fp.close() else: fp = open(in_file, 'w') fp.write(head) fp.writelines(bodys) fp.close() def str_format(string, values): """ 格式化字符串 :param string:(str) "DCC: %sat_sensor_Projection_%ymd（分辨率 %resolution 度）" :param values:(dict) {"sat_sensor": sat_sensor, "resolution": str(resolution), "ymd": ymd} :return: DCC: FY3D+MERSI_Projection_201712（分辨率 1 度） """ if not isinstance(string, str): return for k, v in values.items(): string = string.replace("%" + str(k), str(v)) return string def get_files_by_ymd(dir_path, time_start, time_end, ext=None, pattern_ymd=None): """ :param dir_path: 文件夹 :param time_start: 开始时间 :param time_end: 结束时间 :param ext: 后缀名, '.hdf5' :param pattern_ymd: 匹配时间的模式, 可以是 r".*(\d{8})_(\d{4})_" :return: list """ files_found = [] if pattern_ymd is not None: pattern = pattern_ymd else: pattern = r".*(\d{8})" for root, dirs, files in os.walk(dir_path): for file_name in files: if ext is not None: if '.' not in ext: ext = '.' + ext if os.path.splitext(file_name)[1].lower() != ext.lower(): continue re_result = re.match(pattern, file_name) if re_result is not None: time_file = ''.join(re_result.groups()) else: continue if int(time_start) <= int(time_file) <= int(time_end): files_found.append(os.path.join(root, file_name)) files_found.sort() return files_found class ReadOrbitCrossFile(object): """ test """ @staticmethod def read_cross_file(in_file, file_type): """ :param in_file: :param file_type: :return: """ data = { 'ymdhms1': None, 'ymdhms2': None, 'lon1': None, 'lat1': None, 'lon2': None, 'lat2': None, 'fix_name': None # 只有固定点才有 } if not os.path.isfile(in_file): print('***WARNING***File is not exist: {}'.format(in_file)) return data # with open(in_file, 'r') as fp: # lines_10 = fp.readlines()[0: 10] # # count = 0 # for line in lines_10: # print count, line.split() # count += 1 if file_type == 'leo_area': data_raw = np.loadtxt(in_file, skiprows=10, dtype={ 'names': ('d1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7'), 'formats': ('S8', 'S8', 'S8', 'f4', 'f4', 'f4', 'f4')}) if data_raw.size != 0: ymd = data_raw['d1'] hms1 = data_raw['d2'] hms2 = data_raw['d3'] ymdhms1 = list(map(ymdhms2date, ymd, hms1)) ymdhms2 = list(map(ymdhms2date, ymd, hms2)) data['ymdhms1'] = ymdhms1 data['ymdhms2'] = ymdhms2 data['lat1'] = data_raw['d4'] data['lon1'] = data_raw['d5'] data['lat2'] = data_raw['d6'] data['lon2'] = data_raw['d7'] elif file_type == 'leo_leo': data_raw = np.loadtxt(in_file, skiprows=10, dtype={ 'names': ('d1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8', 'd9'), 'formats': ('S8', 'S8', 'f4', 'f4', 'S8', 'f4', 'f4', 'f4', 'f4')}) if data_raw.size != 0: ymd = data_raw['d1'] hms1 = data_raw['d2'] hms2 = data_raw['d5'] ymdhms1 = list(map(ymdhms2date, ymd, hms1)) ymdhms2 = list(map(ymdhms2date, ymd, hms2)) data['ymdhms1'] = ymdhms1 data['ymdhms2'] = ymdhms2 data['lat1'] = data_raw['d3'] data['lon1'] = data_raw['d4'] data['lat2'] = data_raw['d6'] data['lon2'] = data_raw['d7'] elif file_type == 'leo_fix': # 数据 data_raw = np.loadtxt(in_file, skiprows=10, dtype={ 'names': ('d1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8',), 'formats': ('S8', 'S8', 'S8', 'f4', 'f4', 'f4', 'f4', 'f4')}) if data_raw.size != 0: ymd = data_raw['d1'] hms1 = data_raw['d2'] hms2 = data_raw['d2'] ymdhms1 = list(map(ymdhms2date, ymd, hms1)) ymdhms2 = list(map(ymdhms2date, ymd, hms2)) data['ymdhms1'] = ymdhms1 data['ymdhms2'] = ymdhms2 data['lat1'] = data_raw['d6'] data['lon1'] = data_raw['d7'] data['lat2'] = data_raw['d4'] data['lon2'] = data_raw['d5'] data['fix_name'] = data_raw['d3'] elif file_type == 'geo_leo': # 信息 data_raw = np.loadtxt(in_file, skiprows=10, dtype={ 'names': ('d1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7'), 'formats': ('S8', 'S8', 'S8', 'f4', 'f4', 'f4', 'f4')}) if data_raw.size != 0: ymd = data_raw['d1'] hms1 = data_raw['d2'] hms2 = data_raw['d3'] ymdhms1 = list(map(ymdhms2date, ymd, hms1)) ymdhms2 = list(map(ymdhms2date, ymd, hms2)) data['ymdhms1'] = ymdhms1 data['ymdhms2'] = ymdhms2 data['lat1'] = data_raw['d4'] data['lon1'] = data_raw['d5'] data['lat2'] = data_raw['d6'] data['lon2'] = data_raw['d7'] else: raise KeyError('Cant handle this file type： {}'.format(file_type)) return data def ymdhms2date(ymd, hms): """ ymd = 20180101 hms = 04:04:04 """ ymdhms = ymd + hms return datetime.strptime(ymdhms, '%Y%m%d%H:%M:%S') def CombineTimeList(TimeList): # 将时间段list中有重叠的时间段进行融合为新的时间段 newTimeList = [] # 默认排序,升序 TimeList.sort() # 标记有时间融合的时间 stime = TimeList[0][0] etime = TimeList[0][1] for i in range(1, len(TimeList), 1): if TimeList[i][1] <= etime: continue elif TimeList[i][0] <= etime <= TimeList[i][1]: etime = TimeList[i][1] elif TimeList[i][0] > etime: newTimeList.append([stime, etime]) stime = TimeList[i][0] etime = TimeList[i][1] newTimeList.append([stime, etime]) return newTimeList def get_files_by_date(dir_path, time_start=None, time_end=None, ext=None, pattern=None): """ :param dir_path: 文件夹 :param time_start: 开始时间 :param time_end: 结束时间 :param ext: 后缀名, '.hdf5' :param pattern: 匹配时间的模式 :return: list """ files_found = [] for root, dirs, files in os.walk(dir_path): for file_name in files: if ext is not None: if '.' not in ext: ext = '.' + ext if os.path.splitext(file_name)[1].lower() != ext.lower(): continue if pattern is not None: re_result = re.match(pattern, file_name) if re_result is None: continue if time_start is not None: time_file = ''.join(re_result.groups()) if not int(time_start) <= int(time_file) <= int(time_end): continue files_found.append(os.path.join(root, file_name)) files_found.sort() return files_found if __name__ == '__main__': pass path_out_map = str_format('/abc/%YYYY%MM%DD', { 'YYYY': '20180101', 'MM': '01', 'DD': '01', }) print(path_out_map) # path1 = "E:/projects/ocrs/cfg/global.cfg" # path2 = "E:/projects/ocrs/cfg/FY3B+MERSI.yaml" # c = Config(path1) # c = Config(path2) # print c.error # l = c.__dict__.keys() # l = sorted(l) # for k in l: # print k, ":", c.__dict__[k] # print k # ################# test ReadOrbitCrossFile ################ # LEO_AREA # leo_area_name = r'C:\Users\wangpeng\Desktop\tmp\cross\AQUA_australia_LEO_AREA_20171221.txt' # read_data = ReadOrbitCrossFile.read_cross_file(leo_area_name, 'leo_area') # LEO_LEO # leo_leo_name = r'C:\Users\wangpeng\Desktop\tmp\cross\FENGYUN-3D_NPP_LEO_LEO_20180901.txt' # read_data = ReadOrbitCrossFile.read_cross_file(leo_leo_name, 'leo_leo') # LEO_FIX # leo_fix_name = r'C:\Users\wangpeng\Desktop\tmp\cross\AQUA_FIX_LEO_FIX_20181101.txt' # read_data = ReadOrbitCrossFile.read_cross_file(leo_fix_name, 'leo_fix') # GEO_LEO # geo_leo_name = r'C:\Users\wangpeng\Desktop\tmp\cross\FENGYUN-2F_METOP-A_GEO_LEO20181101.txt' # read_data = ReadOrbitCrossFile.read_cross_file(geo_leo_name, 'geo_leo') # keys = read_data.keys() # keys.sort() # for data_name in keys: # print data_name, type(read_data[data_name]), read_data[data_name]

29.332645

98

0.520885

import errno import os import random import re from contextlib import contextmanager import h5py import numpy as np import time import yaml from datetime import datetime def write_yaml_file(yaml_dict, file_yaml): path_yaml = os.path.dirname(file_yaml) if not os.path.isdir(path_yaml): os.makedirs(path_yaml) with open(file_yaml, 'w') as stream: yaml.dump(yaml_dict, stream, default_flow_style=False) def make_sure_path_exists(path): try: os.makedirs(path) except OSError as exception: if exception.errno != errno.EEXIST: raise def find_file(path, reg): FileLst = [] try: lst = os.walk(path) for root, dirs, files in lst: for name in files: try: m = re.match(reg, name) except Exception as e: continue if m: FileLst.append(os.path.join(root, name)) except Exception as e: print(str(e)) return sorted(FileLst) def path_replace_ymd(path, ymd): ymd = datetime.strptime(ymd, '%Y%m%d') yy = ymd.strftime('%Y') mm = ymd.strftime('%m') dd = ymd.strftime('%d') jj = ymd.strftime('%j') path = path.replace('%YYYY', yy) path = path.replace('%MM', mm) path = path.replace('%DD', dd) path = path.replace('%JJJ', jj) return path def is_none(*args): has_none = False for arg in args: if arg is None: has_none = True return has_none def copy_attrs_h5py(pre_object, out_object): for akey in list(pre_object.attrs.keys()): out_object.attrs[akey] = pre_object.attrs[akey] def read_dataset_hdf5(file_path, set_name): if isinstance(set_name, str): if os.path.isfile(file_path): file_h5py = h5py.File(file_path, 'r') data = file_h5py.get(set_name)[:] dataset = np.array(data) file_h5py.close() return dataset else: raise ValueError('value error: file_path') elif isinstance(set_name, list): datasets = {} if os.path.isfile(file_path): file_h5py = h5py.File(file_path, 'r') for name in set_name: data = file_h5py.get(name)[:] dataset = np.array(data) datasets[name] = dataset file_h5py.close() return datasets else: raise ValueError('value error: file_path') else: raise ValueError('value error: set_name') def attrs2dict(attrs): d = {} for k, v in list(attrs.items()): d[k] = v return d @contextmanager def progress_lock(max_wait_time=5): try: sleep_time = 0 lock = "progress.lock" while True: if os.path.isfile(lock): if sleep_time > max_wait_time: try: os.remove(lock) break except: continue else: random_number = random.random() * 0.1 sleep_time += random_number time.sleep(random_number) else: break with open(lock, "w"): pass yield finally: try: os.remove(lock) except: pass def write_txt(in_file, head, bodys, keylens=8): allLines = [] DICT_D = {} FilePath = os.path.dirname(in_file) if not os.path.exists(FilePath): os.makedirs(FilePath) if os.path.isfile(in_file) and os.path.getsize(in_file) != 0: fp = open(in_file, 'r') fp.readline() Lines = fp.readlines() fp.close() for Line in Lines: DICT_D[Line[:keylens]] = Line[keylens:] for Line in bodys: DICT_D[Line[:keylens]] = Line[keylens:] newLines = sorted( iter(DICT_D.items()), key=lambda d: d[0], reverse=False) for i in range(len(newLines)): allLines.append(str(newLines[i][0]) + str(newLines[i][1])) fp = open(in_file, 'w') fp.write(head) fp.writelines(allLines) fp.close() else: fp = open(in_file, 'w') fp.write(head) fp.writelines(bodys) fp.close() def str_format(string, values): if not isinstance(string, str): return for k, v in values.items(): string = string.replace("%" + str(k), str(v)) return string def get_files_by_ymd(dir_path, time_start, time_end, ext=None, pattern_ymd=None): files_found = [] if pattern_ymd is not None: pattern = pattern_ymd else: pattern = r".*(\d{8})" for root, dirs, files in os.walk(dir_path): for file_name in files: if ext is not None: if '.' not in ext: ext = '.' + ext if os.path.splitext(file_name)[1].lower() != ext.lower(): continue re_result = re.match(pattern, file_name) if re_result is not None: time_file = ''.join(re_result.groups()) else: continue if int(time_start) <= int(time_file) <= int(time_end): files_found.append(os.path.join(root, file_name)) files_found.sort() return files_found class ReadOrbitCrossFile(object): @staticmethod def read_cross_file(in_file, file_type): data = { 'ymdhms1': None, 'ymdhms2': None, 'lon1': None, 'lat1': None, 'lon2': None, 'lat2': None, 'fix_name': None } if not os.path.isfile(in_file): print('***WARNING***File is not exist: {}'.format(in_file)) return data if file_type == 'leo_area': data_raw = np.loadtxt(in_file, skiprows=10, dtype={ 'names': ('d1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7'), 'formats': ('S8', 'S8', 'S8', 'f4', 'f4', 'f4', 'f4')}) if data_raw.size != 0: ymd = data_raw['d1'] hms1 = data_raw['d2'] hms2 = data_raw['d3'] ymdhms1 = list(map(ymdhms2date, ymd, hms1)) ymdhms2 = list(map(ymdhms2date, ymd, hms2)) data['ymdhms1'] = ymdhms1 data['ymdhms2'] = ymdhms2 data['lat1'] = data_raw['d4'] data['lon1'] = data_raw['d5'] data['lat2'] = data_raw['d6'] data['lon2'] = data_raw['d7'] elif file_type == 'leo_leo': data_raw = np.loadtxt(in_file, skiprows=10, dtype={ 'names': ('d1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8', 'd9'), 'formats': ('S8', 'S8', 'f4', 'f4', 'S8', 'f4', 'f4', 'f4', 'f4')}) if data_raw.size != 0: ymd = data_raw['d1'] hms1 = data_raw['d2'] hms2 = data_raw['d5'] ymdhms1 = list(map(ymdhms2date, ymd, hms1)) ymdhms2 = list(map(ymdhms2date, ymd, hms2)) data['ymdhms1'] = ymdhms1 data['ymdhms2'] = ymdhms2 data['lat1'] = data_raw['d3'] data['lon1'] = data_raw['d4'] data['lat2'] = data_raw['d6'] data['lon2'] = data_raw['d7'] elif file_type == 'leo_fix': data_raw = np.loadtxt(in_file, skiprows=10, dtype={ 'names': ('d1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8',), 'formats': ('S8', 'S8', 'S8', 'f4', 'f4', 'f4', 'f4', 'f4')}) if data_raw.size != 0: ymd = data_raw['d1'] hms1 = data_raw['d2'] hms2 = data_raw['d2'] ymdhms1 = list(map(ymdhms2date, ymd, hms1)) ymdhms2 = list(map(ymdhms2date, ymd, hms2)) data['ymdhms1'] = ymdhms1 data['ymdhms2'] = ymdhms2 data['lat1'] = data_raw['d6'] data['lon1'] = data_raw['d7'] data['lat2'] = data_raw['d4'] data['lon2'] = data_raw['d5'] data['fix_name'] = data_raw['d3'] elif file_type == 'geo_leo': data_raw = np.loadtxt(in_file, skiprows=10, dtype={ 'names': ('d1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7'), 'formats': ('S8', 'S8', 'S8', 'f4', 'f4', 'f4', 'f4')}) if data_raw.size != 0: ymd = data_raw['d1'] hms1 = data_raw['d2'] hms2 = data_raw['d3'] ymdhms1 = list(map(ymdhms2date, ymd, hms1)) ymdhms2 = list(map(ymdhms2date, ymd, hms2)) data['ymdhms1'] = ymdhms1 data['ymdhms2'] = ymdhms2 data['lat1'] = data_raw['d4'] data['lon1'] = data_raw['d5'] data['lat2'] = data_raw['d6'] data['lon2'] = data_raw['d7'] else: raise KeyError('Cant handle this file type： {}'.format(file_type)) return data def ymdhms2date(ymd, hms): ymdhms = ymd + hms return datetime.strptime(ymdhms, '%Y%m%d%H:%M:%S') def CombineTimeList(TimeList): newTimeList = [] TimeList.sort() stime = TimeList[0][0] etime = TimeList[0][1] for i in range(1, len(TimeList), 1): if TimeList[i][1] <= etime: continue elif TimeList[i][0] <= etime <= TimeList[i][1]: etime = TimeList[i][1] elif TimeList[i][0] > etime: newTimeList.append([stime, etime]) stime = TimeList[i][0] etime = TimeList[i][1] newTimeList.append([stime, etime]) return newTimeList def get_files_by_date(dir_path, time_start=None, time_end=None, ext=None, pattern=None): files_found = [] for root, dirs, files in os.walk(dir_path): for file_name in files: if ext is not None: if '.' not in ext: ext = '.' + ext if os.path.splitext(file_name)[1].lower() != ext.lower(): continue if pattern is not None: re_result = re.match(pattern, file_name) if re_result is None: continue if time_start is not None: time_file = ''.join(re_result.groups()) if not int(time_start) <= int(time_file) <= int(time_end): continue files_found.append(os.path.join(root, file_name)) files_found.sort() return files_found if __name__ == '__main__': pass path_out_map = str_format('/abc/%YYYY%MM%DD', { 'YYYY': '20180101', 'MM': '01', 'DD': '01', }) print(path_out_map)

true

7907bcae44e61c9e2873b378dd845bff6c95d2e0

5,898

py

Python

src/cryptography/x509/ocsp.py

g-goessel/cryptography

a07de31096767abd3b4529ae29c0487c8f21310b

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause" ]

null

src/cryptography/x509/ocsp.py

g-goessel/cryptography

a07de31096767abd3b4529ae29c0487c8f21310b

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause" ]

null

src/cryptography/x509/ocsp.py

g-goessel/cryptography

a07de31096767abd3b4529ae29c0487c8f21310b

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause" ]

null

# This file is dual licensed under the terms of the Apache License, Version # 2.0, and the BSD License. See the LICENSE file in the root of this repository # for complete details. from __future__ import absolute_import, division, print_function import abc from enum import Enum import six from cryptography.hazmat.primitives import hashes from cryptography.x509 import Certificate _OIDS_TO_HASH = { "1.3.14.3.2.26": hashes.SHA1(), "2.16.840.1.101.3.4.2.4": hashes.SHA224(), "2.16.840.1.101.3.4.2.1": hashes.SHA256(), "2.16.840.1.101.3.4.2.2": hashes.SHA384(), "2.16.840.1.101.3.4.2.3": hashes.SHA512(), } class OCSPResponseStatus(Enum): SUCCESSFUL = 0 MALFORMED_REQUEST = 1 INTERNAL_ERROR = 2 TRY_LATER = 3 SIG_REQUIRED = 5 UNAUTHORIZED = 6 _RESPONSE_STATUS_TO_ENUM = dict((x.value, x) for x in OCSPResponseStatus) class OCSPCertStatus(Enum): GOOD = 0 REVOKED = 1 UNKNOWN = 2 _CERT_STATUS_TO_ENUM = dict((x.value, x) for x in OCSPCertStatus) def load_der_ocsp_request(data): from cryptography.hazmat.backends.openssl.backend import backend return backend.load_der_ocsp_request(data) def load_der_ocsp_response(data): from cryptography.hazmat.backends.openssl.backend import backend return backend.load_der_ocsp_response(data) class OCSPRequestBuilder(object): def __init__(self, request=None): self._request = request def add_certificate(self, cert, issuer, algorithm): if self._request is not None: raise ValueError("Only one certificate can be added to a request") allowed_hashes = ( hashes.SHA1, hashes.SHA224, hashes.SHA256, hashes.SHA384, hashes.SHA512 ) if not isinstance(algorithm, allowed_hashes): raise ValueError( "Algorithm must be SHA1, SHA224, SHA256, SHA384, or SHA512" ) if ( not isinstance(cert, Certificate) or not isinstance(issuer, Certificate) ): raise TypeError("cert and issuer must be a Certificate") return OCSPRequestBuilder((cert, issuer, algorithm)) def build(self): from cryptography.hazmat.backends.openssl.backend import backend if self._request is None: raise ValueError("You must add a certificate before building") return backend.create_ocsp_request(self) @six.add_metaclass(abc.ABCMeta) class OCSPRequest(object): @abc.abstractproperty def issuer_key_hash(self): """ The hash of the issuer public key """ @abc.abstractproperty def issuer_name_hash(self): """ The hash of the issuer name """ @abc.abstractproperty def hash_algorithm(self): """ The hash algorithm used in the issuer name and key hashes """ @abc.abstractproperty def serial_number(self): """ The serial number of the cert whose status is being checked """ @abc.abstractmethod def public_bytes(self, encoding): """ Serializes the request to DER """ @abc.abstractproperty def extensions(self): """ The list of request extensions. Not single request extensions. """ @six.add_metaclass(abc.ABCMeta) class OCSPResponse(object): @abc.abstractproperty def response_status(self): """ The status of the response. This is a value from the OCSPResponseStatus enumeration """ @abc.abstractproperty def signature_algorithm_oid(self): """ The ObjectIdentifier of the signature algorithm """ @abc.abstractproperty def signature(self): """ The signature bytes """ @abc.abstractproperty def tbs_response_bytes(self): """ The tbsResponseData bytes """ @abc.abstractproperty def certificates(self): """ A list of certificates used to help build a chain to verify the OCSP response. This situation occurs when the OCSP responder uses a delegate certificate. """ @abc.abstractproperty def responder_key_hash(self): """ The responder's key hash or None """ @abc.abstractproperty def responder_name(self): """ The responder's Name or None """ @abc.abstractproperty def produced_at(self): """ The time the response was produced """ @abc.abstractproperty def certificate_status(self): """ The status of the certificate (an element from the OCSPCertStatus enum) """ @abc.abstractproperty def revocation_time(self): """ The date of when the certificate was revoked or None if not revoked. """ @abc.abstractproperty def revocation_reason(self): """ The reason the certificate was revoked or None if not specified or not revoked. """ @abc.abstractproperty def this_update(self): """ The most recent time at which the status being indicated is known by the responder to have been correct """ @abc.abstractproperty def next_update(self): """ The time when newer information will be available """ @abc.abstractproperty def issuer_key_hash(self): """ The hash of the issuer public key """ @abc.abstractproperty def issuer_name_hash(self): """ The hash of the issuer name """ @abc.abstractproperty def hash_algorithm(self): """ The hash algorithm used in the issuer name and key hashes """ @abc.abstractproperty def serial_number(self): """ The serial number of the cert whose status is being checked """

25.097872

79

0.62784

from __future__ import absolute_import, division, print_function import abc from enum import Enum import six from cryptography.hazmat.primitives import hashes from cryptography.x509 import Certificate _OIDS_TO_HASH = { "1.3.14.3.2.26": hashes.SHA1(), "2.16.840.1.101.3.4.2.4": hashes.SHA224(), "2.16.840.1.101.3.4.2.1": hashes.SHA256(), "2.16.840.1.101.3.4.2.2": hashes.SHA384(), "2.16.840.1.101.3.4.2.3": hashes.SHA512(), } class OCSPResponseStatus(Enum): SUCCESSFUL = 0 MALFORMED_REQUEST = 1 INTERNAL_ERROR = 2 TRY_LATER = 3 SIG_REQUIRED = 5 UNAUTHORIZED = 6 _RESPONSE_STATUS_TO_ENUM = dict((x.value, x) for x in OCSPResponseStatus) class OCSPCertStatus(Enum): GOOD = 0 REVOKED = 1 UNKNOWN = 2 _CERT_STATUS_TO_ENUM = dict((x.value, x) for x in OCSPCertStatus) def load_der_ocsp_request(data): from cryptography.hazmat.backends.openssl.backend import backend return backend.load_der_ocsp_request(data) def load_der_ocsp_response(data): from cryptography.hazmat.backends.openssl.backend import backend return backend.load_der_ocsp_response(data) class OCSPRequestBuilder(object): def __init__(self, request=None): self._request = request def add_certificate(self, cert, issuer, algorithm): if self._request is not None: raise ValueError("Only one certificate can be added to a request") allowed_hashes = ( hashes.SHA1, hashes.SHA224, hashes.SHA256, hashes.SHA384, hashes.SHA512 ) if not isinstance(algorithm, allowed_hashes): raise ValueError( "Algorithm must be SHA1, SHA224, SHA256, SHA384, or SHA512" ) if ( not isinstance(cert, Certificate) or not isinstance(issuer, Certificate) ): raise TypeError("cert and issuer must be a Certificate") return OCSPRequestBuilder((cert, issuer, algorithm)) def build(self): from cryptography.hazmat.backends.openssl.backend import backend if self._request is None: raise ValueError("You must add a certificate before building") return backend.create_ocsp_request(self) @six.add_metaclass(abc.ABCMeta) class OCSPRequest(object): @abc.abstractproperty def issuer_key_hash(self): @abc.abstractproperty def issuer_name_hash(self): @abc.abstractproperty def hash_algorithm(self): @abc.abstractproperty def serial_number(self): @abc.abstractmethod def public_bytes(self, encoding): @abc.abstractproperty def extensions(self): @six.add_metaclass(abc.ABCMeta) class OCSPResponse(object): @abc.abstractproperty def response_status(self): @abc.abstractproperty def signature_algorithm_oid(self): @abc.abstractproperty def signature(self): @abc.abstractproperty def tbs_response_bytes(self): @abc.abstractproperty def certificates(self): @abc.abstractproperty def responder_key_hash(self): @abc.abstractproperty def responder_name(self): @abc.abstractproperty def produced_at(self): @abc.abstractproperty def certificate_status(self): @abc.abstractproperty def revocation_time(self): @abc.abstractproperty def revocation_reason(self): @abc.abstractproperty def this_update(self): @abc.abstractproperty def next_update(self): @abc.abstractproperty def issuer_key_hash(self): @abc.abstractproperty def issuer_name_hash(self): @abc.abstractproperty def hash_algorithm(self): @abc.abstractproperty def serial_number(self):

true

7907bd0d9cc99ca9d621701fa22b6c735cb6601d

5,290

py

Python

configs/bottom_up/hrnet/coco/hrnet_w32_coco_512x512.py

RuisongZhou/mmpose

a79c649ba07e8a9db24f1467826b9432c09134c6

[ "Apache-2.0" ]

null

configs/bottom_up/hrnet/coco/hrnet_w32_coco_512x512.py

RuisongZhou/mmpose

a79c649ba07e8a9db24f1467826b9432c09134c6

[ "Apache-2.0" ]

null

configs/bottom_up/hrnet/coco/hrnet_w32_coco_512x512.py

RuisongZhou/mmpose

a79c649ba07e8a9db24f1467826b9432c09134c6

[ "Apache-2.0" ]

1

2021-12-29T08:21:50.000Z

log_level = 'INFO' load_from = None resume_from = None dist_params = dict(backend='nccl') workflow = [('train', 1)] checkpoint_config = dict(interval=10) evaluation = dict(interval=100, metric='mAP') optimizer = dict( type='Adam', lr=0.0015, ) optimizer_config = dict(grad_clip=None) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[200, 260]) total_epochs = 300 log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook'), # dict(type='TensorboardLoggerHook') ]) channel_cfg = dict( dataset_joints=17, dataset_channel=[ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], ], inference_channel=[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ]) data_cfg = dict( image_size=512, base_size=256, base_sigma=2, heatmap_size=[128], num_joints=channel_cfg['dataset_joints'], dataset_channel=channel_cfg['dataset_channel'], inference_channel=channel_cfg['inference_channel'], num_scales=1, scale_aware_sigma=False, ) # model settings model = dict( type='BottomUp', pretrained='models/pytorch/imagenet/hrnet_w32-36af842e.pth', backbone=dict( type='HRNet', in_channels=3, extra=dict( stage1=dict( num_modules=1, num_branches=1, block='BOTTLENECK', num_blocks=(4, ), num_channels=(64, )), stage2=dict( num_modules=1, num_branches=2, block='BASIC', num_blocks=(4, 4), num_channels=(32, 64)), stage3=dict( num_modules=4, num_branches=3, block='BASIC', num_blocks=(4, 4, 4), num_channels=(32, 64, 128)), stage4=dict( num_modules=3, num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), num_channels=(32, 64, 128, 256))), ), keypoint_head=dict( type='BottomUpSimpleHead', in_channels=32, num_joints=17, num_deconv_layers=0, tag_per_joint=True, with_ae_loss=[True], extra=dict(final_conv_kernel=1, )), train_cfg=dict( num_joints=channel_cfg['dataset_joints'], img_size=data_cfg['image_size']), test_cfg=dict( num_joints=channel_cfg['dataset_joints'], max_num_people=30, scale_factor=[1], with_heatmaps=[True], with_ae=[True], project2image=True, nms_kernel=5, nms_padding=2, tag_per_joint=True, detection_threshold=0.1, tag_threshold=1, use_detection_val=True, ignore_too_much=False, adjust=True, refine=True, flip_test=True), loss_pose=dict( type='MultiLossFactory', num_joints=17, num_stages=1, ae_loss_type='exp', with_ae_loss=[True], push_loss_factor=[0.001], pull_loss_factor=[0.001], with_heatmaps_loss=[True], heatmaps_loss_factor=[1.0], ), ) train_pipeline = [ dict(type='LoadImageFromFile'), dict( type='BottomUpRandomAffine', rot_factor=30, scale_factor=[0.75, 1.5], scale_type='short', trans_factor=40), dict(type='BottomUpRandomFlip', flip_prob=0.5), dict(type='ToTensor'), dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), dict( type='BottomUpGenerateTarget', sigma=2, max_num_people=30, ), dict( type='Collect', keys=['img', 'joints', 'targets', 'masks'], meta_keys=[]), ] val_pipeline = [ dict(type='LoadImageFromFile'), dict(type='BottomUpGetImgSize', test_scale_factor=[1]), dict( type='BottomUpResizeAlign', transforms=[ dict(type='ToTensor'), dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]), dict( type='Collect', keys=[ 'img', ], meta_keys=[ 'image_file', 'aug_data', 'test_scale_factor', 'base_size', 'center', 'scale', 'flip_index' ]), ] test_pipeline = val_pipeline data_root = 'data/coco' data = dict( samples_per_gpu=24, workers_per_gpu=1, train=dict( type='BottomUpCocoDataset', ann_file=f'{data_root}/annotations/person_keypoints_train2017.json', img_prefix=f'{data_root}/train2017/', data_cfg=data_cfg, pipeline=train_pipeline), val=dict( type='BottomUpCocoDataset', ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', img_prefix=f'{data_root}/val2017/', data_cfg=data_cfg, pipeline=val_pipeline), test=dict( type='BottomUpCocoDataset', ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', img_prefix=f'{data_root}/val2017/', data_cfg=data_cfg, pipeline=val_pipeline), )

26.852792

76

0.561248

log_level = 'INFO' load_from = None resume_from = None dist_params = dict(backend='nccl') workflow = [('train', 1)] checkpoint_config = dict(interval=10) evaluation = dict(interval=100, metric='mAP') optimizer = dict( type='Adam', lr=0.0015, ) optimizer_config = dict(grad_clip=None) lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[200, 260]) total_epochs = 300 log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook'), ]) channel_cfg = dict( dataset_joints=17, dataset_channel=[ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], ], inference_channel=[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ]) data_cfg = dict( image_size=512, base_size=256, base_sigma=2, heatmap_size=[128], num_joints=channel_cfg['dataset_joints'], dataset_channel=channel_cfg['dataset_channel'], inference_channel=channel_cfg['inference_channel'], num_scales=1, scale_aware_sigma=False, ) model = dict( type='BottomUp', pretrained='models/pytorch/imagenet/hrnet_w32-36af842e.pth', backbone=dict( type='HRNet', in_channels=3, extra=dict( stage1=dict( num_modules=1, num_branches=1, block='BOTTLENECK', num_blocks=(4, ), num_channels=(64, )), stage2=dict( num_modules=1, num_branches=2, block='BASIC', num_blocks=(4, 4), num_channels=(32, 64)), stage3=dict( num_modules=4, num_branches=3, block='BASIC', num_blocks=(4, 4, 4), num_channels=(32, 64, 128)), stage4=dict( num_modules=3, num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), num_channels=(32, 64, 128, 256))), ), keypoint_head=dict( type='BottomUpSimpleHead', in_channels=32, num_joints=17, num_deconv_layers=0, tag_per_joint=True, with_ae_loss=[True], extra=dict(final_conv_kernel=1, )), train_cfg=dict( num_joints=channel_cfg['dataset_joints'], img_size=data_cfg['image_size']), test_cfg=dict( num_joints=channel_cfg['dataset_joints'], max_num_people=30, scale_factor=[1], with_heatmaps=[True], with_ae=[True], project2image=True, nms_kernel=5, nms_padding=2, tag_per_joint=True, detection_threshold=0.1, tag_threshold=1, use_detection_val=True, ignore_too_much=False, adjust=True, refine=True, flip_test=True), loss_pose=dict( type='MultiLossFactory', num_joints=17, num_stages=1, ae_loss_type='exp', with_ae_loss=[True], push_loss_factor=[0.001], pull_loss_factor=[0.001], with_heatmaps_loss=[True], heatmaps_loss_factor=[1.0], ), ) train_pipeline = [ dict(type='LoadImageFromFile'), dict( type='BottomUpRandomAffine', rot_factor=30, scale_factor=[0.75, 1.5], scale_type='short', trans_factor=40), dict(type='BottomUpRandomFlip', flip_prob=0.5), dict(type='ToTensor'), dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), dict( type='BottomUpGenerateTarget', sigma=2, max_num_people=30, ), dict( type='Collect', keys=['img', 'joints', 'targets', 'masks'], meta_keys=[]), ] val_pipeline = [ dict(type='LoadImageFromFile'), dict(type='BottomUpGetImgSize', test_scale_factor=[1]), dict( type='BottomUpResizeAlign', transforms=[ dict(type='ToTensor'), dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]), dict( type='Collect', keys=[ 'img', ], meta_keys=[ 'image_file', 'aug_data', 'test_scale_factor', 'base_size', 'center', 'scale', 'flip_index' ]), ] test_pipeline = val_pipeline data_root = 'data/coco' data = dict( samples_per_gpu=24, workers_per_gpu=1, train=dict( type='BottomUpCocoDataset', ann_file=f'{data_root}/annotations/person_keypoints_train2017.json', img_prefix=f'{data_root}/train2017/', data_cfg=data_cfg, pipeline=train_pipeline), val=dict( type='BottomUpCocoDataset', ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', img_prefix=f'{data_root}/val2017/', data_cfg=data_cfg, pipeline=val_pipeline), test=dict( type='BottomUpCocoDataset', ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', img_prefix=f'{data_root}/val2017/', data_cfg=data_cfg, pipeline=val_pipeline), )

true

7907bd73b4e8282649e6b7e28481dfe006bdaf9e

819

py

Python

personal_gallery/urls.py

mikengugy/The-Gallery

5943fdd8d2e8f9c58f14712ebb83f61c38064fcf

[ "MIT" ]

null

personal_gallery/urls.py

mikengugy/The-Gallery

5943fdd8d2e8f9c58f14712ebb83f61c38064fcf

[ "MIT" ]

null

personal_gallery/urls.py

mikengugy/The-Gallery

5943fdd8d2e8f9c58f14712ebb83f61c38064fcf

[ "MIT" ]

null

"""personal_gallery URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.11/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url, include from django.contrib import admin urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'',include('gallery.urls')) ]

35.608696

79

0.703297

from django.conf.urls import url, include from django.contrib import admin urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'',include('gallery.urls')) ]

true

7907bf0fe2c31c6210afad6b7212a1eed48833f9

21,296

py

Python

sdk/network/azure-mgmt-network/azure/mgmt/network/v2020_08_01/operations/_routes_operations.py

beltr0n/azure-sdk-for-python

2f7fb8bee881b0fc0386a0ad5385755ceedd0453

[ "MIT" ]

2

2021-03-24T06:26:11.000Z

2021-04-18T15:55:59.000Z

sdk/network/azure-mgmt-network/azure/mgmt/network/v2020_08_01/operations/_routes_operations.py

beltr0n/azure-sdk-for-python

2f7fb8bee881b0fc0386a0ad5385755ceedd0453

[ "MIT" ]

4

2019-04-17T17:57:49.000Z

2020-04-24T21:11:22.000Z

sdk/network/azure-mgmt-network/azure/mgmt/network/v2020_08_01/operations/_routes_operations.py

beltr0n/azure-sdk-for-python

2f7fb8bee881b0fc0386a0ad5385755ceedd0453

[ "MIT" ]

2

2021-05-23T16:46:31.000Z

2021-05-26T23:51:09.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import TYPE_CHECKING import warnings from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.paging import ItemPaged from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpRequest, HttpResponse from azure.core.polling import LROPoller, NoPolling, PollingMethod from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.arm_polling import ARMPolling from .. import models as _models if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar, Union T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] class RoutesOperations(object): """RoutesOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.network.v2020_08_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def _delete_initial( self, resource_group_name, # type: str route_table_name, # type: str route_name, # type: str **kwargs # type: Any ): # type: (...) -> None cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" # Construct URL url = self._delete_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'routeName': self._serialize.url("route_name", route_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes/{routeName}'} # type: ignore def begin_delete( self, resource_group_name, # type: str route_table_name, # type: str route_name, # type: str **kwargs # type: Any ): # type: (...) -> LROPoller[None] """Deletes the specified route from a route table. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param route_table_name: The name of the route table. :type route_table_name: str :param route_name: The name of the route. :type route_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: True for ARMPolling, False for no polling, or a polling object for personal polling strategy :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either None or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[None] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType[None] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._delete_initial( resource_group_name=resource_group_name, route_table_name=route_table_name, route_name=route_name, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'routeName': self._serialize.url("route_name", route_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes/{routeName}'} # type: ignore def get( self, resource_group_name, # type: str route_table_name, # type: str route_name, # type: str **kwargs # type: Any ): # type: (...) -> "_models.Route" """Gets the specified route from a route table. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param route_table_name: The name of the route table. :type route_table_name: str :param route_name: The name of the route. :type route_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: Route, or the result of cls(response) :rtype: ~azure.mgmt.network.v2020_08_01.models.Route :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.Route"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" # Construct URL url = self.get.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'routeName': self._serialize.url("route_name", route_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('Route', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes/{routeName}'} # type: ignore def _create_or_update_initial( self, resource_group_name, # type: str route_table_name, # type: str route_name, # type: str route_parameters, # type: "_models.Route" **kwargs # type: Any ): # type: (...) -> "_models.Route" cls = kwargs.pop('cls', None) # type: ClsType["_models.Route"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._create_or_update_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'routeName': self._serialize.url("route_name", route_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(route_parameters, 'Route') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('Route', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('Route', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes/{routeName}'} # type: ignore def begin_create_or_update( self, resource_group_name, # type: str route_table_name, # type: str route_name, # type: str route_parameters, # type: "_models.Route" **kwargs # type: Any ): # type: (...) -> LROPoller["_models.Route"] """Creates or updates a route in the specified route table. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param route_table_name: The name of the route table. :type route_table_name: str :param route_name: The name of the route. :type route_name: str :param route_parameters: Parameters supplied to the create or update route operation. :type route_parameters: ~azure.mgmt.network.v2020_08_01.models.Route :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: True for ARMPolling, False for no polling, or a polling object for personal polling strategy :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either Route or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.network.v2020_08_01.models.Route] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.Route"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._create_or_update_initial( resource_group_name=resource_group_name, route_table_name=route_table_name, route_name=route_name, route_parameters=route_parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('Route', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'routeName': self._serialize.url("route_name", route_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes/{routeName}'} # type: ignore def list( self, resource_group_name, # type: str route_table_name, # type: str **kwargs # type: Any ): # type: (...) -> Iterable["_models.RouteListResult"] """Gets all routes in a route table. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param route_table_name: The name of the route table. :type route_table_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either RouteListResult or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2020_08_01.models.RouteListResult] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.RouteListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('RouteListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes'} # type: ignore

48.290249

210

0.656884

from typing import TYPE_CHECKING import warnings from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.paging import ItemPaged from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpRequest, HttpResponse from azure.core.polling import LROPoller, NoPolling, PollingMethod from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.arm_polling import ARMPolling from .. import models as _models if TYPE_CHECKING: from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar, Union T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] class RoutesOperations(object): models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def _delete_initial( self, resource_group_name, route_table_name, route_name, **kwargs ): cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" url = self._delete_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'routeName': self._serialize.url("route_name", route_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes/{routeName}'} def begin_delete( self, resource_group_name, route_table_name, route_name, **kwargs ): polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = self._delete_initial( resource_group_name=resource_group_name, route_table_name=route_table_name, route_name=route_name, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'routeName': self._serialize.url("route_name", route_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes/{routeName}'} def get( self, resource_group_name, route_table_name, route_name, **kwargs ): cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" url = self.get.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'routeName': self._serialize.url("route_name", route_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('Route', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes/{routeName}'} def _create_or_update_initial( self, resource_group_name, route_table_name, route_name, route_parameters, **kwargs ): cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._create_or_update_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'routeName': self._serialize.url("route_name", route_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(route_parameters, 'Route') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('Route', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('Route', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes/{routeName}'} def begin_create_or_update( self, resource_group_name, route_table_name, route_name, route_parameters, **kwargs ): polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = self._create_or_update_initial( resource_group_name=resource_group_name, route_table_name=route_table_name, route_name=route_name, route_parameters=route_parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('Route', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'routeName': self._serialize.url("route_name", route_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes/{routeName}'} def list( self, resource_group_name, route_table_name, **kwargs ): cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" def prepare_request(next_link=None): header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: url = self.list.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'routeTableName': self._serialize.url("route_table_name", route_table_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('RouteListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/routeTables/{routeTableName}/routes'}

true

7907bf7de4d5ddf50b6b47dc45d9746c62fd5862

385

py

Python

exercises/chapter01/exc_01_07.py

deep-diver/fastai-course

1a0a39311fba0e1b3f4720a612a17dc7c708d9bb

[ "MIT" ]

null

exercises/chapter01/exc_01_07.py

deep-diver/fastai-course

1a0a39311fba0e1b3f4720a612a17dc7c708d9bb

[ "MIT" ]

null

exercises/chapter01/exc_01_07.py

deep-diver/fastai-course

1a0a39311fba0e1b3f4720a612a17dc7c708d9bb

[ "MIT" ]

null

from fastcore.foundation import L # 0~11 숫자를 포함한 L을 생성합니다 (range 사용) t = ____________ print(t) # L의 내용을 두 배 불립니다 t __ 2 print(t) # 0이 담긴 위치 (0, 12) 를 튜플 방식으로 찾아서 반환합니다 t_1 = t[_, __] print(t_1) # 0이 담긴 위치 (0, 12) 를 마스킹 방식으로 찾아서 반환합니다 # - 마스크를 만듭니다 0과 12번째 위치에만 True를 넣습니다 mask = L([True]) mask += L([False] * 11) mask += L([True]) mask += L([False] * 11) t_2 = t______ print(t_2)

16.73913

39

0.631169

from fastcore.foundation import L t = ____________ print(t) t __ 2 print(t) t_1 = t[_, __] print(t_1) mask = L([True]) mask += L([False] * 11) mask += L([True]) mask += L([False] * 11) t_2 = t______ print(t_2)

false

true

7907c004db5251908f4e2a3c9cfc31f44a4e2609

5,237

py

Python

trim_segments.py

aerospike-examples/modeling-user-segmentation

17298905c2be913cf096be54bf4e3c0cfd014701

[ "MIT" ]

4

2020-07-28T21:56:43.000Z

2020-10-24T21:58:07.000Z

trim_segments.py

aerospike-examples/modeling-user-segmentation

17298905c2be913cf096be54bf4e3c0cfd014701

[ "MIT" ]

null

trim_segments.py

aerospike-examples/modeling-user-segmentation

17298905c2be913cf096be54bf4e3c0cfd014701

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import print_function import argparse import aerospike from aerospike import exception as e try: from aerospike_helpers.operations import map_operations as mh except: pass # Needs Aerospike client >= 3.4.0 import datetime import pprint import random import sys import time argparser = argparse.ArgumentParser(add_help=False) argparser.add_argument( "--help", dest="help", action="store_true", help="Displays this message." ) argparser.add_argument( "-U", "--username", dest="username", metavar="<USERNAME>", help="Username to connect to database.", ) argparser.add_argument( "-P", "--password", dest="password", metavar="<PASSWORD>", help="Password to connect to database.", ) argparser.add_argument( "-h", "--host", dest="host", default="127.0.0.1", metavar="<ADDRESS>", help="Address of Aerospike server.", ) argparser.add_argument( "-p", "--port", dest="port", type=int, default=3000, metavar="<PORT>", help="Port of the Aerospike server.", ) argparser.add_argument( "-n", "--namespace", dest="namespace", default="test", metavar="<NS>", help="Port of the Aerospike server.", ) argparser.add_argument( "-s", "--set", dest="set", default="profiles", metavar="<SET>", help="Port of the Aerospike server.", ) argparser.add_argument( "-i", "--interactive", dest="interactive", action="store_true", help="Interactive Mode", ) options = argparser.parse_args() if options.help: argparser.print_help() print() sys.exit(1) def version_tuple(version): return tuple(int(i) for i in version.split(".")) def pause(): input("Hit return to continue") if options.namespace and options.namespace != "None": namespace = options.namespace else: namespace = None set = options.set if options.set and options.set != "None" else None config = {"hosts": [(options.host, options.port)]} try: client = aerospike.client(config).connect(options.username, options.password) policy = {"key": aerospike.POLICY_KEY_SEND} except e.ClientError as e: if not options.quiet: print("Error: {0} [{1}]".format(e.msg, e.code)) sys.exit(2) version = client.info_all("version") release = list(version.values())[0][1].split(" ")[-1] if version_tuple(aerospike.__version__) < version_tuple("3.4.0") or version_tuple( release ) < version_tuple("4.6"): print( "\nPlease use Python client >= 3.4.0, ", "Aerospike database >= 4.6 for this example.", ) sys.exit(3) pp = pprint.PrettyPrinter(indent=2) spacer = "=" * 30 epoch = datetime.datetime(2019, 1, 1) now = datetime.datetime.now() try: # Find all segments whose TTL is before this hour key = (namespace, set, "u3") current_hour = int((now - epoch).total_seconds() / 3600) print("\nCurrent hour is {} hours since epoch".format(current_hour)) if options.interactive: pause() ops = [ mh.map_get_by_value_range( "u", [0, aerospike.null()], [current_hour - 1, aerospike.null()], aerospike.MAP_RETURN_KEY, False, ), mh.map_size("u"), ] _, _, b = client.operate_ordered(key, ops) stale_segments, total_segments = b print("This user has a total of {} segments".format(total_segments[1])) print( "Of those, a total of {} segments should be cleaned".format( len(stale_segments[1]) ) ) print("Show all segments with a segment TTL before the current hour:") print(stale_segments) print(spacer) # Clean up the stale segments using a background scan with a transaction # attached to it print("Clean the stale segments from the entire namespace") if options.interactive: pause() ops = [ mh.map_remove_by_value_range( "u", [0, aerospike.null()], [current_hour - 1, aerospike.null()], aerospike.MAP_RETURN_NONE, False, ) ] # _, _, _ = client.operate_ordered(key, ops) scan = client.scan(namespace, set) scan.add_ops(ops) job_id = scan.execute_background() # wait for job to finish while True: response = client.job_info(job_id, aerospike.JOB_SCAN) if response["status"] != aerospike.JOB_STATUS_INPROGRESS: break time.sleep(0.25) ops = [ mh.map_get_by_value_range( "u", [0, aerospike.null()], [current_hour - 1, aerospike.null()], aerospike.MAP_RETURN_KEY, False, ), mh.map_size("u"), ] _, _, b = client.operate_ordered(key, ops) stale_segments, total_segments = b print("This user now has a total of {} segments".format(total_segments[1])) print( "Of those, a total of {} segments should be cleaned".format( len(stale_segments[1]) ) ) print("Show all segments with a segment TTL before the current hour:") print(stale_segments) print(spacer) except Exception as e: print("Error: {0} [{1}]".format(e.msg, e.code)) client.close()

26.185

82

0.618675

from __future__ import print_function import argparse import aerospike from aerospike import exception as e try: from aerospike_helpers.operations import map_operations as mh except: pass import datetime import pprint import random import sys import time argparser = argparse.ArgumentParser(add_help=False) argparser.add_argument( "--help", dest="help", action="store_true", help="Displays this message." ) argparser.add_argument( "-U", "--username", dest="username", metavar="<USERNAME>", help="Username to connect to database.", ) argparser.add_argument( "-P", "--password", dest="password", metavar="<PASSWORD>", help="Password to connect to database.", ) argparser.add_argument( "-h", "--host", dest="host", default="127.0.0.1", metavar="<ADDRESS>", help="Address of Aerospike server.", ) argparser.add_argument( "-p", "--port", dest="port", type=int, default=3000, metavar="<PORT>", help="Port of the Aerospike server.", ) argparser.add_argument( "-n", "--namespace", dest="namespace", default="test", metavar="<NS>", help="Port of the Aerospike server.", ) argparser.add_argument( "-s", "--set", dest="set", default="profiles", metavar="<SET>", help="Port of the Aerospike server.", ) argparser.add_argument( "-i", "--interactive", dest="interactive", action="store_true", help="Interactive Mode", ) options = argparser.parse_args() if options.help: argparser.print_help() print() sys.exit(1) def version_tuple(version): return tuple(int(i) for i in version.split(".")) def pause(): input("Hit return to continue") if options.namespace and options.namespace != "None": namespace = options.namespace else: namespace = None set = options.set if options.set and options.set != "None" else None config = {"hosts": [(options.host, options.port)]} try: client = aerospike.client(config).connect(options.username, options.password) policy = {"key": aerospike.POLICY_KEY_SEND} except e.ClientError as e: if not options.quiet: print("Error: {0} [{1}]".format(e.msg, e.code)) sys.exit(2) version = client.info_all("version") release = list(version.values())[0][1].split(" ")[-1] if version_tuple(aerospike.__version__) < version_tuple("3.4.0") or version_tuple( release ) < version_tuple("4.6"): print( "\nPlease use Python client >= 3.4.0, ", "Aerospike database >= 4.6 for this example.", ) sys.exit(3) pp = pprint.PrettyPrinter(indent=2) spacer = "=" * 30 epoch = datetime.datetime(2019, 1, 1) now = datetime.datetime.now() try: key = (namespace, set, "u3") current_hour = int((now - epoch).total_seconds() / 3600) print("\nCurrent hour is {} hours since epoch".format(current_hour)) if options.interactive: pause() ops = [ mh.map_get_by_value_range( "u", [0, aerospike.null()], [current_hour - 1, aerospike.null()], aerospike.MAP_RETURN_KEY, False, ), mh.map_size("u"), ] _, _, b = client.operate_ordered(key, ops) stale_segments, total_segments = b print("This user has a total of {} segments".format(total_segments[1])) print( "Of those, a total of {} segments should be cleaned".format( len(stale_segments[1]) ) ) print("Show all segments with a segment TTL before the current hour:") print(stale_segments) print(spacer) print("Clean the stale segments from the entire namespace") if options.interactive: pause() ops = [ mh.map_remove_by_value_range( "u", [0, aerospike.null()], [current_hour - 1, aerospike.null()], aerospike.MAP_RETURN_NONE, False, ) ] scan = client.scan(namespace, set) scan.add_ops(ops) job_id = scan.execute_background() while True: response = client.job_info(job_id, aerospike.JOB_SCAN) if response["status"] != aerospike.JOB_STATUS_INPROGRESS: break time.sleep(0.25) ops = [ mh.map_get_by_value_range( "u", [0, aerospike.null()], [current_hour - 1, aerospike.null()], aerospike.MAP_RETURN_KEY, False, ), mh.map_size("u"), ] _, _, b = client.operate_ordered(key, ops) stale_segments, total_segments = b print("This user now has a total of {} segments".format(total_segments[1])) print( "Of those, a total of {} segments should be cleaned".format( len(stale_segments[1]) ) ) print("Show all segments with a segment TTL before the current hour:") print(stale_segments) print(spacer) except Exception as e: print("Error: {0} [{1}]".format(e.msg, e.code)) client.close()

true

7907c0368df1b812547c44bf06beb92abe4a726f

1,245

py

Python

handlers/brainstorming/chat.py

tech-sketch/Brain_Hacker

4bbd7cc6f680d8d94b7ffd63612c6374a7cd5b8c

[ "MIT" ]

7

2015-09-10T06:36:36.000Z

2021-02-04T08:41:33.000Z

handlers/brainstorming/chat.py

tech-sketch/Brain_Hacker

4bbd7cc6f680d8d94b7ffd63612c6374a7cd5b8c

[ "MIT" ]

40

2015-07-07T02:09:29.000Z

2015-08-10T01:28:35.000Z

handlers/brainstorming/chat.py

Hironsan/Brain_Hacker

4bbd7cc6f680d8d94b7ffd63612c6374a7cd5b8c

[ "MIT" ]

3

2015-07-10T01:57:58.000Z

2016-07-11T06:09:45.000Z

# -*- coding: utf-8 -*- import random import itertools from collections import defaultdict class Chat(object): cache_size = 200 # user_num = list(range(1, 100)) # random.shuffle(user_num) colors = ['赤', '青', '黄', '緑', '紫', '黒', '茶', '灰色', '金', '銀'] fruits = ['りんご', 'みかん', 'メロン', 'パイナップル', 'ぶどう', '梨', 'いちご', 'もも', 'さくらんぼ', 'バナナ'] fruits_with_color = itertools.product(colors, fruits) user_name = list(map(lambda n: n[0]+n[1], fruits_with_color)) random.shuffle(user_name) def __init__(self): self.cache = defaultdict(list) self.nickname_dic = defaultdict(dict) def set_nickname(self, room_id, client_name): self.nickname_dic[room_id].update({client_name:str(self.__get_random_name())}) def get_nickname(self, room_id, client_name): return self.nickname_dic[room_id][client_name] def update_cache(self, chat, room_id): self.cache[room_id].append(chat) if len(self.cache[room_id]) > self.cache_size: self.cache[room_id] = self.cache[-self.cache_size:] def __get_random_name(self): return self.user_name.pop() def clear_caches(self, room_id): del self.cache[room_id] del self.nickname_dic[room_id]

31.923077

86

0.64257

import random import itertools from collections import defaultdict class Chat(object): cache_size = 200 colors = ['赤', '青', '黄', '緑', '紫', '黒', '茶', '灰色', '金', '銀'] fruits = ['りんご', 'みかん', 'メロン', 'パイナップル', 'ぶどう', '梨', 'いちご', 'もも', 'さくらんぼ', 'バナナ'] fruits_with_color = itertools.product(colors, fruits) user_name = list(map(lambda n: n[0]+n[1], fruits_with_color)) random.shuffle(user_name) def __init__(self): self.cache = defaultdict(list) self.nickname_dic = defaultdict(dict) def set_nickname(self, room_id, client_name): self.nickname_dic[room_id].update({client_name:str(self.__get_random_name())}) def get_nickname(self, room_id, client_name): return self.nickname_dic[room_id][client_name] def update_cache(self, chat, room_id): self.cache[room_id].append(chat) if len(self.cache[room_id]) > self.cache_size: self.cache[room_id] = self.cache[-self.cache_size:] def __get_random_name(self): return self.user_name.pop() def clear_caches(self, room_id): del self.cache[room_id] del self.nickname_dic[room_id]

true

7907c07549e9309d5fdbd156fb259124e7e693f6

9,869

py

Python

tests/api/test_all_apis.py

brighthive/authserver

848201324761269bc96b75ad9cb5242e2a6ee5a5

[ "MIT" ]

3

2019-07-31T16:10:26.000Z

2021-05-14T20:06:07.000Z

tests/api/test_all_apis.py

brighthive/authserver

848201324761269bc96b75ad9cb5242e2a6ee5a5

[ "MIT" ]

25

2019-08-20T20:19:59.000Z

2021-05-14T19:06:41.000Z

tests/api/test_all_apis.py

brighthive/authserver

848201324761269bc96b75ad9cb5242e2a6ee5a5

[ "MIT" ]

1

2020-04-29T18:18:21.000Z

"""Test all API endpoints. This test class exercises all client facing APIs. It is also usesful as a tool for demonstrating how to interact with the various APIs. """ import json import pytest from expects import (be, be_above, be_above_or_equal, contain, equal, expect, raise_error) from flask import Response from tests.utils import post_users from authserver.db import User, db ROLES = [ { 'role': 'get:programs', 'description': 'Get from programs data resource', 'rules': { 'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'key4': 'value4' } }, { 'role': 'administer:programs', 'description': 'All access on programs data resource', 'rules': { 'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'key4': 'value4' } }, { 'role': 'edit:providers', 'description': 'Edit providers only' }, { 'role': 'view:providers', 'description': 'View providers only', 'rules': { 'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'key4': 'value4' } } ] USERS = [ { 'username': 'user1', 'password': 'password', 'person_id': 'c0ffee-c0ffee-1' }, { 'username': 'user2', 'password': 'password', 'person_id': 'c0ffee-c0ffee-2' }, { 'username': 'user3', 'password': 'password', 'person_id': 'c0ffee-c0ffee-3' }, { 'username': 'user4', 'password': 'password', 'person_id': 'c0ffee-c0ffee-4' }, { 'username': 'user5', 'password': 'password', 'person_id': 'c0ffee-c0ffee-5' }, { 'username': 'user6', 'password': 'password', 'person_id': 'c0ffee-c0ffee-6' }, { 'username': 'user7', 'password': 'password', 'person_id': 'c0ffee-c0ffee-7' }, { 'username': 'user8', 'password': 'password', 'person_id': 'c0ffee-c0ffee-8' } ] CLIENTS = [ { 'client_name': 'test client 1', 'user_id': '' }, { 'client_name': 'test client 2', 'user_id': '' }, { 'client_name': 'test client 3', 'user_id': '' }, { 'client_name': 'test client 4', 'user_id': '' }, { 'client_name': 'test client 5', 'user_id': '' }, { 'client_name': 'test client6', 'user_id': '' }, { 'client_name': 'test client 7', 'user_id': '' }, { 'client_name': 'test client 8', 'user_id': '' } ] class TestAllAPIs(object): def test_all_apis(self, client, token_generator): # Common headers go in this dict headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} # Create users, and clients user_ids = post_users(USERS, client, token_generator.get_token(client)) client_ids = self._post_clients(client, user_ids, token_generator) # Create roles role_ids = [] for role in ROLES: response = client.post( '/roles', data=json.dumps(role), headers=headers) expect(response.status_code).to(equal(201)) role_ids.append(response.json['response'][0]['id']) # Assign clients to users and roles to client for i, client_id in enumerate(client_ids): request_body = { 'user_id': user_ids[i], 'roles': role_ids } response = client.patch( '/clients/{}'.format(client_id), data=json.dumps(request_body), headers=headers) expect(response.status_code).to(equal(200)) # Ensure that clients actually have roles, users, and other crucial fields for client_id in client_ids: response = client.get( '/clients/{}'.format(client_id), headers=headers) result = response.json['response'] expect(result['id']).to(equal(client_id)) expect(result['client_id_issued_at']).to(be_above(0)) expect(user_ids).to(contain(result['user_id'])) expect(len(result['roles'])).to(equal(len(role_ids))) self._cleanup(client, token_generator, user_ids=user_ids, role_ids=role_ids) def test_client_secret_delete_rotate(self, client, token_generator): headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} user_ids = post_users(USERS, client, token_generator.get_token(client)) client_ids = self._post_clients(client, user_ids, token_generator) client_to_patch = client_ids[0] response = client.post('/clients?action=delete_secret', data=json.dumps({"id": client_to_patch}), headers=headers) expect(response.status_code).to(equal(200)) response = client.get('/clients/{}'.format(client_to_patch), headers=headers) expect(response.json['response']['client_secret']).to(equal(None)) response = client.post('/clients?action=rotate_secret', data=json.dumps({"id": client_to_patch}), headers=headers) expect(response.status_code).to(equal(200)) response = client.get('/clients/{}'.format(client_to_patch), headers=headers) expect(len(response.json['response']['client_secret'])).to(equal(48)) self._cleanup(client, token_generator, user_ids=user_ids) def test_client_post_invalid_action(self, client, token_generator): headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} user_ids = post_users(USERS, client, token_generator.get_token(client)) client_ids = self._post_clients(client, user_ids, token_generator) client_to_patch = client_ids[0] response = client.post('/clients?action=some_invalid_action', data=json.dumps({"id": client_to_patch}), headers=headers) expect(response.status_code).to(equal(422)) expect(response.json['messages']).to(contain("Invalid query param!")) self._cleanup(client, token_generator, user_ids=user_ids) def _post_clients(self, client, user_ids, token_generator): ''' Helper function that creates (and tests creating) a collection of Clients. ''' headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} client_ids = [] for i, api_client in enumerate(CLIENTS): api_client['user_id'] = user_ids[i] response = client.post('/clients', data=json.dumps(api_client), headers=headers) expect(response.status_code).to(equal(201)) client_ids.append(response.json['response'][0]['id']) expect(len(client_ids)).to(equal(8)) return client_ids def _cleanup(self, client, token_generator, role_ids=[], user_ids=[]): headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} for role_id in role_ids: response = client.delete( '/roles/{}'.format(role_id), headers=headers) expect(response.status_code).to(equal(200)) for user_id in user_ids: response = client.delete( '/users/{}'.format(user_id), headers=headers) expect(response.status_code).to(equal(200)) def test_assign_scope_to_user(self, client, token_generator): CLIENT = { } USER = { 'username': 'test_user_scope', 'password': 'secret', 'person_id': 'c0ffee-c0ffee-c0ffee-99', 'role_id': '' } ROLE = { 'role': 'Administrator', 'description': 'An administrative user role.' } SCOPE = { 'scope': 'action:do-all-the-things', 'description': 'A scope that grants the holder superpowers' } headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} # Create a role response = client.post('/roles', data=json.dumps(ROLE), headers=headers) expect(response.status_code).to(be(201)) role_id = response.json['response'][0]['id'] # Create a scope response = client.post('/scopes', data=json.dumps(SCOPE), headers=headers) expect(response.status_code).to(be(201)) scope_id = response.json['response'][0]['id'] # Bind the scope to the role response = client.post(f'/roles/{role_id}/scopes', data=json.dumps({'scope_id': scope_id}), headers=headers) expect(response.status_code).to(be(201)) # Create a user and make the user an administrator USER['role_id'] = role_id response = client.post('/users', data=json.dumps(USER), headers=headers) expect(response.status_code).to(be(201)) user_id = response.json['response'][0]['id'] # Cleanup response = client.delete(f'/users/{user_id}', headers=headers) expect(response.status_code).to(be(200)) response = client.delete(f'/roles/{role_id}/scopes/{scope_id}', headers=headers) expect(response.status_code).to(be(200)) response = client.delete(f'/roles/{role_id}', headers=headers) expect(response.status_code).to(be(200)) response = client.delete(f'/scopes/{scope_id}', headers=headers) expect(response.status_code).to(be(200))

34.031034

118

0.580201

import json import pytest from expects import (be, be_above, be_above_or_equal, contain, equal, expect, raise_error) from flask import Response from tests.utils import post_users from authserver.db import User, db ROLES = [ { 'role': 'get:programs', 'description': 'Get from programs data resource', 'rules': { 'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'key4': 'value4' } }, { 'role': 'administer:programs', 'description': 'All access on programs data resource', 'rules': { 'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'key4': 'value4' } }, { 'role': 'edit:providers', 'description': 'Edit providers only' }, { 'role': 'view:providers', 'description': 'View providers only', 'rules': { 'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'key4': 'value4' } } ] USERS = [ { 'username': 'user1', 'password': 'password', 'person_id': 'c0ffee-c0ffee-1' }, { 'username': 'user2', 'password': 'password', 'person_id': 'c0ffee-c0ffee-2' }, { 'username': 'user3', 'password': 'password', 'person_id': 'c0ffee-c0ffee-3' }, { 'username': 'user4', 'password': 'password', 'person_id': 'c0ffee-c0ffee-4' }, { 'username': 'user5', 'password': 'password', 'person_id': 'c0ffee-c0ffee-5' }, { 'username': 'user6', 'password': 'password', 'person_id': 'c0ffee-c0ffee-6' }, { 'username': 'user7', 'password': 'password', 'person_id': 'c0ffee-c0ffee-7' }, { 'username': 'user8', 'password': 'password', 'person_id': 'c0ffee-c0ffee-8' } ] CLIENTS = [ { 'client_name': 'test client 1', 'user_id': '' }, { 'client_name': 'test client 2', 'user_id': '' }, { 'client_name': 'test client 3', 'user_id': '' }, { 'client_name': 'test client 4', 'user_id': '' }, { 'client_name': 'test client 5', 'user_id': '' }, { 'client_name': 'test client6', 'user_id': '' }, { 'client_name': 'test client 7', 'user_id': '' }, { 'client_name': 'test client 8', 'user_id': '' } ] class TestAllAPIs(object): def test_all_apis(self, client, token_generator): headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} user_ids = post_users(USERS, client, token_generator.get_token(client)) client_ids = self._post_clients(client, user_ids, token_generator) role_ids = [] for role in ROLES: response = client.post( '/roles', data=json.dumps(role), headers=headers) expect(response.status_code).to(equal(201)) role_ids.append(response.json['response'][0]['id']) for i, client_id in enumerate(client_ids): request_body = { 'user_id': user_ids[i], 'roles': role_ids } response = client.patch( '/clients/{}'.format(client_id), data=json.dumps(request_body), headers=headers) expect(response.status_code).to(equal(200)) for client_id in client_ids: response = client.get( '/clients/{}'.format(client_id), headers=headers) result = response.json['response'] expect(result['id']).to(equal(client_id)) expect(result['client_id_issued_at']).to(be_above(0)) expect(user_ids).to(contain(result['user_id'])) expect(len(result['roles'])).to(equal(len(role_ids))) self._cleanup(client, token_generator, user_ids=user_ids, role_ids=role_ids) def test_client_secret_delete_rotate(self, client, token_generator): headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} user_ids = post_users(USERS, client, token_generator.get_token(client)) client_ids = self._post_clients(client, user_ids, token_generator) client_to_patch = client_ids[0] response = client.post('/clients?action=delete_secret', data=json.dumps({"id": client_to_patch}), headers=headers) expect(response.status_code).to(equal(200)) response = client.get('/clients/{}'.format(client_to_patch), headers=headers) expect(response.json['response']['client_secret']).to(equal(None)) response = client.post('/clients?action=rotate_secret', data=json.dumps({"id": client_to_patch}), headers=headers) expect(response.status_code).to(equal(200)) response = client.get('/clients/{}'.format(client_to_patch), headers=headers) expect(len(response.json['response']['client_secret'])).to(equal(48)) self._cleanup(client, token_generator, user_ids=user_ids) def test_client_post_invalid_action(self, client, token_generator): headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} user_ids = post_users(USERS, client, token_generator.get_token(client)) client_ids = self._post_clients(client, user_ids, token_generator) client_to_patch = client_ids[0] response = client.post('/clients?action=some_invalid_action', data=json.dumps({"id": client_to_patch}), headers=headers) expect(response.status_code).to(equal(422)) expect(response.json['messages']).to(contain("Invalid query param!")) self._cleanup(client, token_generator, user_ids=user_ids) def _post_clients(self, client, user_ids, token_generator): headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} client_ids = [] for i, api_client in enumerate(CLIENTS): api_client['user_id'] = user_ids[i] response = client.post('/clients', data=json.dumps(api_client), headers=headers) expect(response.status_code).to(equal(201)) client_ids.append(response.json['response'][0]['id']) expect(len(client_ids)).to(equal(8)) return client_ids def _cleanup(self, client, token_generator, role_ids=[], user_ids=[]): headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} for role_id in role_ids: response = client.delete( '/roles/{}'.format(role_id), headers=headers) expect(response.status_code).to(equal(200)) for user_id in user_ids: response = client.delete( '/users/{}'.format(user_id), headers=headers) expect(response.status_code).to(equal(200)) def test_assign_scope_to_user(self, client, token_generator): CLIENT = { } USER = { 'username': 'test_user_scope', 'password': 'secret', 'person_id': 'c0ffee-c0ffee-c0ffee-99', 'role_id': '' } ROLE = { 'role': 'Administrator', 'description': 'An administrative user role.' } SCOPE = { 'scope': 'action:do-all-the-things', 'description': 'A scope that grants the holder superpowers' } headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'} response = client.post('/roles', data=json.dumps(ROLE), headers=headers) expect(response.status_code).to(be(201)) role_id = response.json['response'][0]['id'] response = client.post('/scopes', data=json.dumps(SCOPE), headers=headers) expect(response.status_code).to(be(201)) scope_id = response.json['response'][0]['id'] response = client.post(f'/roles/{role_id}/scopes', data=json.dumps({'scope_id': scope_id}), headers=headers) expect(response.status_code).to(be(201)) USER['role_id'] = role_id response = client.post('/users', data=json.dumps(USER), headers=headers) expect(response.status_code).to(be(201)) user_id = response.json['response'][0]['id'] response = client.delete(f'/users/{user_id}', headers=headers) expect(response.status_code).to(be(200)) response = client.delete(f'/roles/{role_id}/scopes/{scope_id}', headers=headers) expect(response.status_code).to(be(200)) response = client.delete(f'/roles/{role_id}', headers=headers) expect(response.status_code).to(be(200)) response = client.delete(f'/scopes/{scope_id}', headers=headers) expect(response.status_code).to(be(200))

true

7907c133d53c8bdfe722aa209a8f1b9d0cef570c

7,130

py

Python

scripts/schema-context.py

bedroesb/ro-crate

1d6a1423308c65549eeac17bcd785733e9078622

[ "Apache-2.0" ]

null

scripts/schema-context.py

bedroesb/ro-crate

1d6a1423308c65549eeac17bcd785733e9078622

[ "Apache-2.0" ]

null

scripts/schema-context.py

bedroesb/ro-crate

1d6a1423308c65549eeac17bcd785733e9078622

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # Copyright 2019 The University of Manchester UK # Copyright 2019 RO-Crate contributors <https://github.com/ResearchObject/ro-crate/graphs/contributors> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This script retrieves the schema.org properties to generate the corresponding simplified @context for RO-Crate adding our additional properties. Run as: ./schema-context.py 0.3-DRAFT > ../docs/0.3-DRAFT/context.jsonld """ import sys import json import requests from collections import OrderedDict import urllib.request # Our own version ROCRATE_VERSION="1.1-DRAFT" # Update version from http://schema.org/docs/releases.html # NOTE: Breaks due to https://github.com/schemaorg/schemaorg/issues/2805 SCHEMA_VERSION="10.0" # Update from https://bioschemas.org/profiles/Workflow/ BIOSCHEMA_WORKFLOW_PROFILE = "https://bioschemas.org/profiles/ComputationalWorkflow/0.5-DRAFT-2020_07_21" BIOSCHEMA_WORKFLOW_NS = "https://bioschemas.org/ComputationalWorkflow" BIOSCHEMA_FORMAL_PARAMETER_NS = "https://bioschemas.org/FormalParameter" BIOSCHEMA_FORMAL_PARAMETER_PROFILE = "https://bioschemas.org/profiles/FormalParameter/0.1-DRAFT-2020_07_21" def main(): #url="http://schema.org/version/%s/schemaorgcontext.jsonld" % SCHEMA_VERSION # Workaround for https://github.com/schemaorg/schemaorg/issues/2805 url="https://raw.githubusercontent.com/schemaorg/schemaorg/V%s-release/data/releases/%s/schemaorgcontext.jsonld" % (SCHEMA_VERSION, SCHEMA_VERSION) with urllib.request.urlopen(url) as f: schema = json.load(f) if len(sys.argv) > 2: version = sys.argv[1] tag = sys.argv[2] elif len(sys.argv) > 1: tag = version = sys.argv[1] else: tag = version = ROCRATE_VERSION schemakeys = list(schema["@context"].keys()) schemakeys.sort() # they are usually sorted anyway j = OrderedDict() j["@id"] = "https://w3id.org/ro/crate/%s/context" % version j["name"] = "RO-Crate JSON-LD Context", j["version"] = tag j["url"] = {"@id": "https://w3id.org/ro/crate/%s" % version} j["schemaVersion"] = {"@id": "http://schema.org/version/%s/" % SCHEMA_VERSION} j["isBasedOn"] = [ {"@id": "http://schema.org/version/%s/" % SCHEMA_VERSION}, {"@id": "https://pcdm.org/2016/04/18/models"}, {"@id": BIOSCHEMA_WORKFLOW_PROFILE }, {"@id": BIOSCHEMA_FORMAL_PARAMETER_PROFILE } ] j["license"] = {"@id": "https://creativecommons.org/publicdomain/zero/1.0/"} context = OrderedDict() j["@context"] = context for k in schemakeys: if ":" in k: # URL like https://www.w3.org/wiki/WebSchemas/SchemaDotOrgSources#TP continue if "@" in k: # @vocab? continue definition = schema["@context"][k] if not "@id" in definition or isinstance(definition, str): continue # bibo etc. context[k] = schema["@context"][k]["@id"].replace("schema:", "http://schema.org/") context.update(ADDITIONAL) json.dump(j, sys.stdout, ensure_ascii=False, indent=5) # indent4 to match existing! print() ## newline # Ordered so we keep a somewhat ordered presentation in the JSON ADDITIONAL = OrderedDict([ # This list should correspond to listing in # https://researchobject.github.io/ro-crate/0.3-DRAFT/#additional-metadata-standards ("File", "http://schema.org/MediaObject"), ("path", "http://schema.org/contentUrl"), ("Journal", "http://schema.org/Periodical"), ("cite-as", "https://www.w3.org/ns/iana/link-relations/relation#cite-as"), ("hasFile", "http://pcdm.org/models#hasFile"), ("hasMember", "http://pcdm.org/models#hasMember"), ("RepositoryCollection", "http://pcdm.org/models#Collection"), ("RepositoryObject", "http://pcdm.org/models#object"), # Temporary namespace for properties/types # proposed https://bioschemas.org/profiles/Workflow/ draft 0.5 # Remove if/when added to schema.org release! ## BEGIN ("ComputationalWorkflow", BIOSCHEMA_WORKFLOW_NS), ("input", BIOSCHEMA_WORKFLOW_NS + "#input"), ("output", BIOSCHEMA_WORKFLOW_NS + "#output"), ("FormalParameter", BIOSCHEMA_FORMAL_PARAMETER_NS), # https://github.com/schemaorg/schemaorg/issues/383#issuecomment-651040576 ("funding", "http://schema.org/funding"), ## END ("wasDerivedFrom", "http://www.w3.org/ns/prov#wasDerivedFrom"), ("importedFrom", "http://purl.org/pav/importedFrom"), ("importedOn", "http://purl.org/pav/importedOn"), ("importedBy", "http://purl.org/pav/importedBy"), ("retrievedFrom", "http://purl.org/pav/retrievedFrom"), ("retrievedOn", "http://purl.org/pav/retrievedOn"), ("retrievedBy", "http://purl.org/pav/retrievedBy"), ("conformsTo", "http://purl.org/dc/terms/conformsTo"), ("@label", "http://www.w3.org/2000/01/rdf-schema#label"), ("pcdm", "http://pcdm.org/models#"), ("bibo", "http://purl.org/ontology/bibo/"), ("cc", "http://creativecommons.org/ns#"), ("dct", "http://purl.org/dc/terms/"), ("foaf", "http://xmlns.com/foaf/0.1/"), ("rdf", "http://www.w3.org/1999/02/22-rdf-syntax-ns#"), ("rdfa", "http://www.w3.org/ns/rdfa#"), ("rdfs", "http://www.w3.org/2000/01/rdf-schema#"), ("schema", "http://schema.org/"), ("frapo", "http://purl.org/cerif/frapo/"), ("rel", "https://www.w3.org/ns/iana/link-relations/relation#"), ("pav", "http://purl.org/pav/"), ("prov", "http://www.w3.org/ns/prov#"), ("wfdesc", "http://purl.org/ro/wfdesc#"), ("wfprov", "http://purl.org/ro/wfprov#"), ("roterms", "http://purl.org/ro/roterms#"), ("wf4ever", "http://purl.org/ro/wf4ever#"), # Disabled, see https://github.com/ResearchObject/ro-crate/pull/73 # ("@base", None) ]) if __name__=="__main__": if "-v" in sys.argv or "--version" in sys.argv: print("schema-context.py %s" % ROCRATE_VERSION) print("schema.org %s" % SCHEMA_VERSION) sys.exit(0) elif "-h" in sys.argv or "--help" in sys.argv: print("schema-context.py [VERSION] [TAG]") print("") print("Generates context.jsonld from schema.org and additional terms") print(" VERSION is RO-Crate Specification version (default: %s)" % ROCRATE_VERSION) print(" TAG is RO-Crate Semantic Versioning tag (default same as VERSION)") sys.exit(0) else: main()

40.511364

151

0.638289

import sys import json import requests from collections import OrderedDict import urllib.request ROCRATE_VERSION="1.1-DRAFT" SCHEMA_VERSION="10.0" BIOSCHEMA_WORKFLOW_PROFILE = "https://bioschemas.org/profiles/ComputationalWorkflow/0.5-DRAFT-2020_07_21" BIOSCHEMA_WORKFLOW_NS = "https://bioschemas.org/ComputationalWorkflow" BIOSCHEMA_FORMAL_PARAMETER_NS = "https://bioschemas.org/FormalParameter" BIOSCHEMA_FORMAL_PARAMETER_PROFILE = "https://bioschemas.org/profiles/FormalParameter/0.1-DRAFT-2020_07_21" def main(): url="https://raw.githubusercontent.com/schemaorg/schemaorg/V%s-release/data/releases/%s/schemaorgcontext.jsonld" % (SCHEMA_VERSION, SCHEMA_VERSION) with urllib.request.urlopen(url) as f: schema = json.load(f) if len(sys.argv) > 2: version = sys.argv[1] tag = sys.argv[2] elif len(sys.argv) > 1: tag = version = sys.argv[1] else: tag = version = ROCRATE_VERSION schemakeys = list(schema["@context"].keys()) schemakeys.sort() j = OrderedDict() j["@id"] = "https://w3id.org/ro/crate/%s/context" % version j["name"] = "RO-Crate JSON-LD Context", j["version"] = tag j["url"] = {"@id": "https://w3id.org/ro/crate/%s" % version} j["schemaVersion"] = {"@id": "http://schema.org/version/%s/" % SCHEMA_VERSION} j["isBasedOn"] = [ {"@id": "http://schema.org/version/%s/" % SCHEMA_VERSION}, {"@id": "https://pcdm.org/2016/04/18/models"}, {"@id": BIOSCHEMA_WORKFLOW_PROFILE }, {"@id": BIOSCHEMA_FORMAL_PARAMETER_PROFILE } ] j["license"] = {"@id": "https://creativecommons.org/publicdomain/zero/1.0/"} context = OrderedDict() j["@context"] = context for k in schemakeys: if ":" in k: continue if "@" in k: continue definition = schema["@context"][k] if not "@id" in definition or isinstance(definition, str): continue context[k] = schema["@context"][k]["@id"].replace("schema:", "http://schema.org/") context.update(ADDITIONAL) json.dump(j, sys.stdout, ensure_ascii=False, indent=5) print() IONAL = OrderedDict([ schema.org/MediaObject"), ("path", "http://schema.org/contentUrl"), ("Journal", "http://schema.org/Periodical"), ("cite-as", "https://www.w3.org/ns/iana/link-relations/relation#cite-as"), ("hasFile", "http://pcdm.org/models#hasFile"), ("hasMember", "http://pcdm.org/models#hasMember"), ("RepositoryCollection", "http://pcdm.org/models#Collection"), ("RepositoryObject", "http://pcdm.org/models#object"), ("ComputationalWorkflow", BIOSCHEMA_WORKFLOW_NS), ("input", BIOSCHEMA_WORKFLOW_NS + "#input"), ("output", BIOSCHEMA_WORKFLOW_NS + "#output"), ("FormalParameter", BIOSCHEMA_FORMAL_PARAMETER_NS), "http://schema.org/funding"), ("wasDerivedFrom", "http://www.w3.org/ns/prov#wasDerivedFrom"), ("importedFrom", "http://purl.org/pav/importedFrom"), ("importedOn", "http://purl.org/pav/importedOn"), ("importedBy", "http://purl.org/pav/importedBy"), ("retrievedFrom", "http://purl.org/pav/retrievedFrom"), ("retrievedOn", "http://purl.org/pav/retrievedOn"), ("retrievedBy", "http://purl.org/pav/retrievedBy"), ("conformsTo", "http://purl.org/dc/terms/conformsTo"), ("@label", "http://www.w3.org/2000/01/rdf-schema#label"), ("pcdm", "http://pcdm.org/models#"), ("bibo", "http://purl.org/ontology/bibo/"), ("cc", "http://creativecommons.org/ns#"), ("dct", "http://purl.org/dc/terms/"), ("foaf", "http://xmlns.com/foaf/0.1/"), ("rdf", "http://www.w3.org/1999/02/22-rdf-syntax-ns#"), ("rdfa", "http://www.w3.org/ns/rdfa#"), ("rdfs", "http://www.w3.org/2000/01/rdf-schema#"), ("schema", "http://schema.org/"), ("frapo", "http://purl.org/cerif/frapo/"), ("rel", "https://www.w3.org/ns/iana/link-relations/relation#"), ("pav", "http://purl.org/pav/"), ("prov", "http://www.w3.org/ns/prov#"), ("wfdesc", "http://purl.org/ro/wfdesc#"), ("wfprov", "http://purl.org/ro/wfprov#"), ("roterms", "http://purl.org/ro/roterms#"), ("wf4ever", "http://purl.org/ro/wf4ever#"), ]) if __name__=="__main__": if "-v" in sys.argv or "--version" in sys.argv: print("schema-context.py %s" % ROCRATE_VERSION) print("schema.org %s" % SCHEMA_VERSION) sys.exit(0) elif "-h" in sys.argv or "--help" in sys.argv: print("schema-context.py [VERSION] [TAG]") print("") print("Generates context.jsonld from schema.org and additional terms") print(" VERSION is RO-Crate Specification version (default: %s)" % ROCRATE_VERSION) print(" TAG is RO-Crate Semantic Versioning tag (default same as VERSION)") sys.exit(0) else: main()

true

7907c198e790378a78592d3e658f9fdde576711c

361

py

Python

sps/sps/doctype/district/district.py

tushar7724/SPS

4a32b740830117327b3597d4e16127ac0a90a3ef

[ "MIT" ]

null

sps/sps/doctype/district/district.py

tushar7724/SPS

4a32b740830117327b3597d4e16127ac0a90a3ef

[ "MIT" ]

null

sps/sps/doctype/district/district.py

tushar7724/SPS

4a32b740830117327b3597d4e16127ac0a90a3ef

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2019, TUSHAR TAJNE and contributors # For license information, please see license.txt from __future__ import unicode_literals import frappe from frappe.model.document import Document from frappe import _ class District(Document): def validate(self): name = str(self.district.capitalize()) self.name = _(name) pass

25.785714

51

0.759003

from __future__ import unicode_literals import frappe from frappe.model.document import Document from frappe import _ class District(Document): def validate(self): name = str(self.district.capitalize()) self.name = _(name) pass

true

7907c1db45762c8f70eb23a2c7709179bbcd65e5

39,387

py

Python

scons/scons-local-1.2.0.d20090919/SCons/SConf.py

peterlama/pivy

ad7b50f9a3ce0b69d05184c059fd6de12b90839b

[ "0BSD" ]

null

scons/scons-local-1.2.0.d20090919/SCons/SConf.py

peterlama/pivy

ad7b50f9a3ce0b69d05184c059fd6de12b90839b

[ "0BSD" ]

null

scons/scons-local-1.2.0.d20090919/SCons/SConf.py

peterlama/pivy

ad7b50f9a3ce0b69d05184c059fd6de12b90839b

[ "0BSD" ]

null

"""SCons.SConf Autoconf-like configuration support. """ # # Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009 The SCons Foundation # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY # KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # __revision__ = "src/engine/SCons/SConf.py 4369 2009/09/19 15:58:29 scons" import os import re import string import StringIO import sys import traceback import types import SCons.Action import SCons.Builder import SCons.Errors import SCons.Job import SCons.Node.FS import SCons.Taskmaster import SCons.Util import SCons.Warnings import SCons.Conftest from SCons.Debug import Trace # Turn off the Conftest error logging SCons.Conftest.LogInputFiles = 0 SCons.Conftest.LogErrorMessages = 0 # Set build_type = None build_types = ['clean', 'help'] def SetBuildType(type): global build_type build_type = type # to be set, if we are in dry-run mode dryrun = 0 AUTO=0 # use SCons dependency scanning for up-to-date checks FORCE=1 # force all tests to be rebuilt CACHE=2 # force all tests to be taken from cache (raise an error, if necessary) cache_mode = AUTO def SetCacheMode(mode): """Set the Configure cache mode. mode must be one of "auto", "force", or "cache".""" global cache_mode if mode == "auto": cache_mode = AUTO elif mode == "force": cache_mode = FORCE elif mode == "cache": cache_mode = CACHE else: raise ValueError, "SCons.SConf.SetCacheMode: Unknown mode " + mode progress_display = SCons.Util.display # will be overwritten by SCons.Script def SetProgressDisplay(display): """Set the progress display to use (called from SCons.Script)""" global progress_display progress_display = display SConfFS = None _ac_build_counter = 0 # incremented, whenever TryBuild is called _ac_config_logs = {} # all config.log files created in this build _ac_config_hs = {} # all config.h files created in this build sconf_global = None # current sconf object def _createConfigH(target, source, env): t = open(str(target[0]), "w") defname = re.sub('[^A-Za-z0-9_]', '_', string.upper(str(target[0]))) t.write("""#ifndef %(DEFNAME)s_SEEN #define %(DEFNAME)s_SEEN """ % {'DEFNAME' : defname}) t.write(source[0].get_contents()) t.write(""" #endif /* %(DEFNAME)s_SEEN */ """ % {'DEFNAME' : defname}) t.close() def _stringConfigH(target, source, env): return "scons: Configure: creating " + str(target[0]) def CreateConfigHBuilder(env): """Called just before the building targets phase begins.""" if len(_ac_config_hs) == 0: return action = SCons.Action.Action(_createConfigH, _stringConfigH) sconfigHBld = SCons.Builder.Builder(action=action) env.Append( BUILDERS={'SConfigHBuilder':sconfigHBld} ) for k in _ac_config_hs.keys(): env.SConfigHBuilder(k, env.Value(_ac_config_hs[k])) class SConfWarning(SCons.Warnings.Warning): pass SCons.Warnings.enableWarningClass(SConfWarning) # some error definitions class SConfError(SCons.Errors.UserError): def __init__(self,msg): SCons.Errors.UserError.__init__(self,msg) class ConfigureDryRunError(SConfError): """Raised when a file or directory needs to be updated during a Configure process, but the user requested a dry-run""" def __init__(self,target): if not isinstance(target, SCons.Node.FS.File): msg = 'Cannot create configure directory "%s" within a dry-run.' % str(target) else: msg = 'Cannot update configure test "%s" within a dry-run.' % str(target) SConfError.__init__(self,msg) class ConfigureCacheError(SConfError): """Raised when a use explicitely requested the cache feature, but the test is run the first time.""" def __init__(self,target): SConfError.__init__(self, '"%s" is not yet built and cache is forced.' % str(target)) # define actions for building text files def _createSource( target, source, env ): fd = open(str(target[0]), "w") fd.write(source[0].get_contents()) fd.close() def _stringSource( target, source, env ): return (str(target[0]) + ' <-\n |' + string.replace( source[0].get_contents(), '\n', "\n |" ) ) # python 2.2 introduces types.BooleanType BooleanTypes = [types.IntType] if hasattr(types, 'BooleanType'): BooleanTypes.append(types.BooleanType) class SConfBuildInfo(SCons.Node.FS.FileBuildInfo): """ Special build info for targets of configure tests. Additional members are result (did the builder succeed last time?) and string, which contains messages of the original build phase. """ result = None # -> 0/None -> no error, != 0 error string = None # the stdout / stderr output when building the target def set_build_result(self, result, string): self.result = result self.string = string class Streamer: """ 'Sniffer' for a file-like writable object. Similar to the unix tool tee. """ def __init__(self, orig): self.orig = orig self.s = StringIO.StringIO() def write(self, str): if self.orig: self.orig.write(str) self.s.write(str) def writelines(self, lines): for l in lines: self.write(l + '\n') def getvalue(self): """ Return everything written to orig since the Streamer was created. """ return self.s.getvalue() def flush(self): if self.orig: self.orig.flush() self.s.flush() class SConfBuildTask(SCons.Taskmaster.AlwaysTask): """ This is almost the same as SCons.Script.BuildTask. Handles SConfErrors correctly and knows about the current cache_mode. """ def display(self, message): if sconf_global.logstream: sconf_global.logstream.write("scons: Configure: " + message + "\n") def display_cached_string(self, bi): """ Logs the original builder messages, given the SConfBuildInfo instance bi. """ if not isinstance(bi, SConfBuildInfo): SCons.Warnings.warn(SConfWarning, "The stored build information has an unexpected class: %s" % bi.__class__) else: self.display("The original builder output was:\n" + string.replace(" |" + str(bi.string), "\n", "\n |")) def failed(self): # check, if the reason was a ConfigureDryRunError or a # ConfigureCacheError and if yes, reraise the exception exc_type = self.exc_info()[0] if issubclass(exc_type, SConfError): raise elif issubclass(exc_type, SCons.Errors.BuildError): # we ignore Build Errors (occurs, when a test doesn't pass) # Clear the exception to prevent the contained traceback # to build a reference cycle. self.exc_clear() else: self.display('Caught exception while building "%s":\n' % self.targets[0]) try: excepthook = sys.excepthook except AttributeError: # Earlier versions of Python don't have sys.excepthook... def excepthook(type, value, tb): traceback.print_tb(tb) print type, value apply(excepthook, self.exc_info()) return SCons.Taskmaster.Task.failed(self) def collect_node_states(self): # returns (is_up_to_date, cached_error, cachable) # where is_up_to_date is 1, if the node(s) are up_to_date # cached_error is 1, if the node(s) are up_to_date, but the # build will fail # cachable is 0, if some nodes are not in our cache T = 0 changed = False cached_error = False cachable = True for t in self.targets: if T: Trace('%s' % (t)) bi = t.get_stored_info().binfo if isinstance(bi, SConfBuildInfo): if T: Trace(': SConfBuildInfo') if cache_mode == CACHE: t.set_state(SCons.Node.up_to_date) if T: Trace(': set_state(up_to-date)') else: if T: Trace(': get_state() %s' % t.get_state()) if T: Trace(': changed() %s' % t.changed()) if (t.get_state() != SCons.Node.up_to_date and t.changed()): changed = True if T: Trace(': changed %s' % changed) cached_error = cached_error or bi.result else: if T: Trace(': else') # the node hasn't been built in a SConf context or doesn't # exist cachable = False changed = ( t.get_state() != SCons.Node.up_to_date ) if T: Trace(': changed %s' % changed) if T: Trace('\n') return (not changed, cached_error, cachable) def execute(self): if not self.targets[0].has_builder(): return sconf = sconf_global is_up_to_date, cached_error, cachable = self.collect_node_states() if cache_mode == CACHE and not cachable: raise ConfigureCacheError(self.targets[0]) elif cache_mode == FORCE: is_up_to_date = 0 if cached_error and is_up_to_date: self.display("Building \"%s\" failed in a previous run and all " "its sources are up to date." % str(self.targets[0])) binfo = self.targets[0].get_stored_info().binfo self.display_cached_string(binfo) raise SCons.Errors.BuildError # will be 'caught' in self.failed elif is_up_to_date: self.display("\"%s\" is up to date." % str(self.targets[0])) binfo = self.targets[0].get_stored_info().binfo self.display_cached_string(binfo) elif dryrun: raise ConfigureDryRunError(self.targets[0]) else: # note stdout and stderr are the same here s = sys.stdout = sys.stderr = Streamer(sys.stdout) try: env = self.targets[0].get_build_env() if cache_mode == FORCE: # Set up the Decider() to force rebuilds by saying # that every source has changed. Note that we still # call the environment's underlying source decider so # that the correct .sconsign info will get calculated # and keep the build state consistent. def force_build(dependency, target, prev_ni, env_decider=env.decide_source): env_decider(dependency, target, prev_ni) return True env.Decider(force_build) env['PSTDOUT'] = env['PSTDERR'] = s try: sconf.cached = 0 self.targets[0].build() finally: sys.stdout = sys.stderr = env['PSTDOUT'] = \ env['PSTDERR'] = sconf.logstream except KeyboardInterrupt: raise except SystemExit: exc_value = sys.exc_info()[1] raise SCons.Errors.ExplicitExit(self.targets[0],exc_value.code) except Exception, e: for t in self.targets: binfo = t.get_binfo() binfo.__class__ = SConfBuildInfo binfo.set_build_result(1, s.getvalue()) sconsign_entry = SCons.SConsign.SConsignEntry() sconsign_entry.binfo = binfo #sconsign_entry.ninfo = self.get_ninfo() # We'd like to do this as follows: # t.store_info(binfo) # However, we need to store it as an SConfBuildInfo # object, and store_info() will turn it into a # regular FileNodeInfo if the target is itself a # regular File. sconsign = t.dir.sconsign() sconsign.set_entry(t.name, sconsign_entry) sconsign.merge() raise e else: for t in self.targets: binfo = t.get_binfo() binfo.__class__ = SConfBuildInfo binfo.set_build_result(0, s.getvalue()) sconsign_entry = SCons.SConsign.SConsignEntry() sconsign_entry.binfo = binfo #sconsign_entry.ninfo = self.get_ninfo() # We'd like to do this as follows: # t.store_info(binfo) # However, we need to store it as an SConfBuildInfo # object, and store_info() will turn it into a # regular FileNodeInfo if the target is itself a # regular File. sconsign = t.dir.sconsign() sconsign.set_entry(t.name, sconsign_entry) sconsign.merge() class SConfBase: """This is simply a class to represent a configure context. After creating a SConf object, you can call any tests. After finished with your tests, be sure to call the Finish() method, which returns the modified environment. Some words about caching: In most cases, it is not necessary to cache Test results explicitely. Instead, we use the scons dependency checking mechanism. For example, if one wants to compile a test program (SConf.TryLink), the compiler is only called, if the program dependencies have changed. However, if the program could not be compiled in a former SConf run, we need to explicitely cache this error. """ def __init__(self, env, custom_tests = {}, conf_dir='$CONFIGUREDIR', log_file='$CONFIGURELOG', config_h = None, _depth = 0): """Constructor. Pass additional tests in the custom_tests-dictinary, e.g. custom_tests={'CheckPrivate':MyPrivateTest}, where MyPrivateTest defines a custom test. Note also the conf_dir and log_file arguments (you may want to build tests in the VariantDir, not in the SourceDir) """ global SConfFS if not SConfFS: SConfFS = SCons.Node.FS.default_fs or \ SCons.Node.FS.FS(env.fs.pathTop) if sconf_global is not None: raise (SCons.Errors.UserError, "Only one SConf object may be active at one time") self.env = env if log_file is not None: log_file = SConfFS.File(env.subst(log_file)) self.logfile = log_file self.logstream = None self.lastTarget = None self.depth = _depth self.cached = 0 # will be set, if all test results are cached # add default tests default_tests = { 'CheckCC' : CheckCC, 'CheckCXX' : CheckCXX, 'CheckSHCC' : CheckSHCC, 'CheckSHCXX' : CheckSHCXX, 'CheckFunc' : CheckFunc, 'CheckType' : CheckType, 'CheckTypeSize' : CheckTypeSize, 'CheckDeclaration' : CheckDeclaration, 'CheckHeader' : CheckHeader, 'CheckCHeader' : CheckCHeader, 'CheckCXXHeader' : CheckCXXHeader, 'CheckLib' : CheckLib, 'CheckLibWithHeader' : CheckLibWithHeader, } self.AddTests(default_tests) self.AddTests(custom_tests) self.confdir = SConfFS.Dir(env.subst(conf_dir)) if config_h is not None: config_h = SConfFS.File(config_h) self.config_h = config_h self._startup() def Finish(self): """Call this method after finished with your tests: env = sconf.Finish() """ self._shutdown() return self.env def Define(self, name, value = None, comment = None): """ Define a pre processor symbol name, with the optional given value in the current config header. If value is None (default), then #define name is written. If value is not none, then #define name value is written. comment is a string which will be put as a C comment in the header, to explain the meaning of the value (appropriate C comments /* and */ will be put automatically.""" lines = [] if comment: comment_str = "/* %s */" % comment lines.append(comment_str) if value is not None: define_str = "#define %s %s" % (name, value) else: define_str = "#define %s" % name lines.append(define_str) lines.append('') self.config_h_text = self.config_h_text + string.join(lines, '\n') def BuildNodes(self, nodes): """ Tries to build the given nodes immediately. Returns 1 on success, 0 on error. """ if self.logstream is not None: # override stdout / stderr to write in log file oldStdout = sys.stdout sys.stdout = self.logstream oldStderr = sys.stderr sys.stderr = self.logstream # the engine assumes the current path is the SConstruct directory ... old_fs_dir = SConfFS.getcwd() old_os_dir = os.getcwd() SConfFS.chdir(SConfFS.Top, change_os_dir=1) # Because we take responsibility here for writing out our # own .sconsign info (see SConfBuildTask.execute(), above), # we override the store_info() method with a null place-holder # so we really control how it gets written. for n in nodes: n.store_info = n.do_not_store_info ret = 1 try: # ToDo: use user options for calc save_max_drift = SConfFS.get_max_drift() SConfFS.set_max_drift(0) tm = SCons.Taskmaster.Taskmaster(nodes, SConfBuildTask) # we don't want to build tests in parallel jobs = SCons.Job.Jobs(1, tm ) jobs.run() for n in nodes: state = n.get_state() if (state != SCons.Node.executed and state != SCons.Node.up_to_date): # the node could not be built. we return 0 in this case ret = 0 finally: SConfFS.set_max_drift(save_max_drift) os.chdir(old_os_dir) SConfFS.chdir(old_fs_dir, change_os_dir=0) if self.logstream is not None: # restore stdout / stderr sys.stdout = oldStdout sys.stderr = oldStderr return ret def pspawn_wrapper(self, sh, escape, cmd, args, env): """Wrapper function for handling piped spawns. This looks to the calling interface (in Action.py) like a "normal" spawn, but associates the call with the PSPAWN variable from the construction environment and with the streams to which we want the output logged. This gets slid into the construction environment as the SPAWN variable so Action.py doesn't have to know or care whether it's spawning a piped command or not. """ return self.pspawn(sh, escape, cmd, args, env, self.logstream, self.logstream) def TryBuild(self, builder, text = None, extension = ""): """Low level TryBuild implementation. Normally you don't need to call that - you can use TryCompile / TryLink / TryRun instead """ global _ac_build_counter # Make sure we have a PSPAWN value, and save the current # SPAWN value. try: self.pspawn = self.env['PSPAWN'] except KeyError: raise SCons.Errors.UserError('Missing PSPAWN construction variable.') try: save_spawn = self.env['SPAWN'] except KeyError: raise SCons.Errors.UserError('Missing SPAWN construction variable.') nodesToBeBuilt = [] f = "conftest_" + str(_ac_build_counter) pref = self.env.subst( builder.builder.prefix ) suff = self.env.subst( builder.builder.suffix ) target = self.confdir.File(pref + f + suff) try: # Slide our wrapper into the construction environment as # the SPAWN function. self.env['SPAWN'] = self.pspawn_wrapper sourcetext = self.env.Value(text) if text is not None: textFile = self.confdir.File(f + extension) textFileNode = self.env.SConfSourceBuilder(target=textFile, source=sourcetext) nodesToBeBuilt.extend(textFileNode) source = textFileNode else: source = None nodes = builder(target = target, source = source) if not SCons.Util.is_List(nodes): nodes = [nodes] nodesToBeBuilt.extend(nodes) result = self.BuildNodes(nodesToBeBuilt) finally: self.env['SPAWN'] = save_spawn _ac_build_counter = _ac_build_counter + 1 if result: self.lastTarget = nodes[0] else: self.lastTarget = None return result def TryAction(self, action, text = None, extension = ""): """Tries to execute the given action with optional source file contents <text> and optional source file extension <extension>, Returns the status (0 : failed, 1 : ok) and the contents of the output file. """ builder = SCons.Builder.Builder(action=action) self.env.Append( BUILDERS = {'SConfActionBuilder' : builder} ) ok = self.TryBuild(self.env.SConfActionBuilder, text, extension) del self.env['BUILDERS']['SConfActionBuilder'] if ok: outputStr = self.lastTarget.get_contents() return (1, outputStr) return (0, "") def TryCompile( self, text, extension): """Compiles the program given in text to an env.Object, using extension as file extension (e.g. '.c'). Returns 1, if compilation was successful, 0 otherwise. The target is saved in self.lastTarget (for further processing). """ return self.TryBuild(self.env.Object, text, extension) def TryLink( self, text, extension ): """Compiles the program given in text to an executable env.Program, using extension as file extension (e.g. '.c'). Returns 1, if compilation was successful, 0 otherwise. The target is saved in self.lastTarget (for further processing). """ return self.TryBuild(self.env.Program, text, extension ) def TryRun(self, text, extension ): """Compiles and runs the program given in text, using extension as file extension (e.g. '.c'). Returns (1, outputStr) on success, (0, '') otherwise. The target (a file containing the program's stdout) is saved in self.lastTarget (for further processing). """ ok = self.TryLink(text, extension) if( ok ): prog = self.lastTarget pname = prog.path output = self.confdir.File(os.path.basename(pname)+'.out') node = self.env.Command(output, prog, [ [ pname, ">", "${TARGET}"] ]) ok = self.BuildNodes(node) if ok: outputStr = output.get_contents() return( 1, outputStr) return (0, "") class TestWrapper: """A wrapper around Tests (to ensure sanity)""" def __init__(self, test, sconf): self.test = test self.sconf = sconf def __call__(self, *args, **kw): if not self.sconf.active: raise (SCons.Errors.UserError, "Test called after sconf.Finish()") context = CheckContext(self.sconf) ret = apply(self.test, (context,) + args, kw) if self.sconf.config_h is not None: self.sconf.config_h_text = self.sconf.config_h_text + context.config_h context.Result("error: no result") return ret def AddTest(self, test_name, test_instance): """Adds test_class to this SConf instance. It can be called with self.test_name(...)""" setattr(self, test_name, SConfBase.TestWrapper(test_instance, self)) def AddTests(self, tests): """Adds all the tests given in the tests dictionary to this SConf instance """ for name in tests.keys(): self.AddTest(name, tests[name]) def _createDir( self, node ): dirName = str(node) if dryrun: if not os.path.isdir( dirName ): raise ConfigureDryRunError(dirName) else: if not os.path.isdir( dirName ): os.makedirs( dirName ) node._exists = 1 def _startup(self): """Private method. Set up logstream, and set the environment variables necessary for a piped build """ global _ac_config_logs global sconf_global global SConfFS self.lastEnvFs = self.env.fs self.env.fs = SConfFS self._createDir(self.confdir) self.confdir.up().add_ignore( [self.confdir] ) if self.logfile is not None and not dryrun: # truncate logfile, if SConf.Configure is called for the first time # in a build if _ac_config_logs.has_key(self.logfile): log_mode = "a" else: _ac_config_logs[self.logfile] = None log_mode = "w" fp = open(str(self.logfile), log_mode) self.logstream = SCons.Util.Unbuffered(fp) # logfile may stay in a build directory, so we tell # the build system not to override it with a eventually # existing file with the same name in the source directory self.logfile.dir.add_ignore( [self.logfile] ) tb = traceback.extract_stack()[-3-self.depth] old_fs_dir = SConfFS.getcwd() SConfFS.chdir(SConfFS.Top, change_os_dir=0) self.logstream.write('file %s,line %d:\n\tConfigure(confdir = %s)\n' % (tb[0], tb[1], str(self.confdir)) ) SConfFS.chdir(old_fs_dir) else: self.logstream = None # we use a special builder to create source files from TEXT action = SCons.Action.Action(_createSource, _stringSource) sconfSrcBld = SCons.Builder.Builder(action=action) self.env.Append( BUILDERS={'SConfSourceBuilder':sconfSrcBld} ) self.config_h_text = _ac_config_hs.get(self.config_h, "") self.active = 1 # only one SConf instance should be active at a time ... sconf_global = self def _shutdown(self): """Private method. Reset to non-piped spawn""" global sconf_global, _ac_config_hs if not self.active: raise SCons.Errors.UserError, "Finish may be called only once!" if self.logstream is not None and not dryrun: self.logstream.write("\n") self.logstream.close() self.logstream = None # remove the SConfSourceBuilder from the environment blds = self.env['BUILDERS'] del blds['SConfSourceBuilder'] self.env.Replace( BUILDERS=blds ) self.active = 0 sconf_global = None if not self.config_h is None: _ac_config_hs[self.config_h] = self.config_h_text self.env.fs = self.lastEnvFs class CheckContext: """Provides a context for configure tests. Defines how a test writes to the screen and log file. A typical test is just a callable with an instance of CheckContext as first argument: def CheckCustom(context, ...) context.Message('Checking my weird test ... ') ret = myWeirdTestFunction(...) context.Result(ret) Often, myWeirdTestFunction will be one of context.TryCompile/context.TryLink/context.TryRun. The results of those are cached, for they are only rebuild, if the dependencies have changed. """ def __init__(self, sconf): """Constructor. Pass the corresponding SConf instance.""" self.sconf = sconf self.did_show_result = 0 # for Conftest.py: self.vardict = {} self.havedict = {} self.headerfilename = None self.config_h = "" # config_h text will be stored here # we don't regenerate the config.h file after each test. That means, # that tests won't be able to include the config.h file, and so # they can't do an #ifdef HAVE_XXX_H. This shouldn't be a major # issue, though. If it turns out, that we need to include config.h # in tests, we must ensure, that the dependencies are worked out # correctly. Note that we can't use Conftest.py's support for config.h, # cause we will need to specify a builder for the config.h file ... def Message(self, text): """Inform about what we are doing right now, e.g. 'Checking for SOMETHING ... ' """ self.Display(text) self.sconf.cached = 1 self.did_show_result = 0 def Result(self, res): """Inform about the result of the test. res may be an integer or a string. In case of an integer, the written text will be 'yes' or 'no'. The result is only displayed when self.did_show_result is not set. """ if type(res) in BooleanTypes: if res: text = "yes" else: text = "no" elif type(res) == types.StringType: text = res else: raise TypeError, "Expected string, int or bool, got " + str(type(res)) if self.did_show_result == 0: # Didn't show result yet, do it now. self.Display(text + "\n") self.did_show_result = 1 def TryBuild(self, *args, **kw): return apply(self.sconf.TryBuild, args, kw) def TryAction(self, *args, **kw): return apply(self.sconf.TryAction, args, kw) def TryCompile(self, *args, **kw): return apply(self.sconf.TryCompile, args, kw) def TryLink(self, *args, **kw): return apply(self.sconf.TryLink, args, kw) def TryRun(self, *args, **kw): return apply(self.sconf.TryRun, args, kw) def __getattr__( self, attr ): if( attr == 'env' ): return self.sconf.env elif( attr == 'lastTarget' ): return self.sconf.lastTarget else: raise AttributeError, "CheckContext instance has no attribute '%s'" % attr #### Stuff used by Conftest.py (look there for explanations). def BuildProg(self, text, ext): self.sconf.cached = 1 # TODO: should use self.vardict for $CC, $CPPFLAGS, etc. return not self.TryBuild(self.env.Program, text, ext) def CompileProg(self, text, ext): self.sconf.cached = 1 # TODO: should use self.vardict for $CC, $CPPFLAGS, etc. return not self.TryBuild(self.env.Object, text, ext) def CompileSharedObject(self, text, ext): self.sconf.cached = 1 # TODO: should use self.vardict for $SHCC, $CPPFLAGS, etc. return not self.TryBuild(self.env.SharedObject, text, ext) def RunProg(self, text, ext): self.sconf.cached = 1 # TODO: should use self.vardict for $CC, $CPPFLAGS, etc. st, out = self.TryRun(text, ext) return not st, out def AppendLIBS(self, lib_name_list): oldLIBS = self.env.get( 'LIBS', [] ) self.env.Append(LIBS = lib_name_list) return oldLIBS def PrependLIBS(self, lib_name_list): oldLIBS = self.env.get( 'LIBS', [] ) self.env.Prepend(LIBS = lib_name_list) return oldLIBS def SetLIBS(self, val): oldLIBS = self.env.get( 'LIBS', [] ) self.env.Replace(LIBS = val) return oldLIBS def Display(self, msg): if self.sconf.cached: # We assume that Display is called twice for each test here # once for the Checking for ... message and once for the result. # The self.sconf.cached flag can only be set between those calls msg = "(cached) " + msg self.sconf.cached = 0 progress_display(msg, append_newline=0) self.Log("scons: Configure: " + msg + "\n") def Log(self, msg): if self.sconf.logstream is not None: self.sconf.logstream.write(msg) #### End of stuff used by Conftest.py. def SConf(*args, **kw): if kw.get(build_type, True): kw['_depth'] = kw.get('_depth', 0) + 1 for bt in build_types: try: del kw[bt] except KeyError: pass return apply(SConfBase, args, kw) else: return SCons.Util.Null() def CheckFunc(context, function_name, header = None, language = None): res = SCons.Conftest.CheckFunc(context, function_name, header = header, language = language) context.did_show_result = 1 return not res def CheckType(context, type_name, includes = "", language = None): res = SCons.Conftest.CheckType(context, type_name, header = includes, language = language) context.did_show_result = 1 return not res def CheckTypeSize(context, type_name, includes = "", language = None, expect = None): res = SCons.Conftest.CheckTypeSize(context, type_name, header = includes, language = language, expect = expect) context.did_show_result = 1 return res def CheckDeclaration(context, declaration, includes = "", language = None): res = SCons.Conftest.CheckDeclaration(context, declaration, includes = includes, language = language) context.did_show_result = 1 return not res def createIncludesFromHeaders(headers, leaveLast, include_quotes = '""'): # used by CheckHeader and CheckLibWithHeader to produce C - #include # statements from the specified header (list) if not SCons.Util.is_List(headers): headers = [headers] l = [] if leaveLast: lastHeader = headers[-1] headers = headers[:-1] else: lastHeader = None for s in headers: l.append("#include %s%s%s\n" % (include_quotes[0], s, include_quotes[1])) return string.join(l, ''), lastHeader def CheckHeader(context, header, include_quotes = '<>', language = None): """ A test for a C or C++ header file. """ prog_prefix, hdr_to_check = \ createIncludesFromHeaders(header, 1, include_quotes) res = SCons.Conftest.CheckHeader(context, hdr_to_check, prog_prefix, language = language, include_quotes = include_quotes) context.did_show_result = 1 return not res def CheckCC(context): res = SCons.Conftest.CheckCC(context) context.did_show_result = 1 return not res def CheckCXX(context): res = SCons.Conftest.CheckCXX(context) context.did_show_result = 1 return not res def CheckSHCC(context): res = SCons.Conftest.CheckSHCC(context) context.did_show_result = 1 return not res def CheckSHCXX(context): res = SCons.Conftest.CheckSHCXX(context) context.did_show_result = 1 return not res # Bram: Make this function obsolete? CheckHeader() is more generic. def CheckCHeader(context, header, include_quotes = '""'): """ A test for a C header file. """ return CheckHeader(context, header, include_quotes, language = "C") # Bram: Make this function obsolete? CheckHeader() is more generic. def CheckCXXHeader(context, header, include_quotes = '""'): """ A test for a C++ header file. """ return CheckHeader(context, header, include_quotes, language = "C++") def CheckLib(context, library = None, symbol = "main", header = None, language = None, autoadd = 1): """ A test for a library. See also CheckLibWithHeader. Note that library may also be None to test whether the given symbol compiles without flags. """ if library == []: library = [None] if not SCons.Util.is_List(library): library = [library] # ToDo: accept path for the library res = SCons.Conftest.CheckLib(context, library, symbol, header = header, language = language, autoadd = autoadd) context.did_show_result = 1 return not res # XXX # Bram: Can only include one header and can't use #ifdef HAVE_HEADER_H. def CheckLibWithHeader(context, libs, header, language, call = None, autoadd = 1): # ToDo: accept path for library. Support system header files. """ Another (more sophisticated) test for a library. Checks, if library and header is available for language (may be 'C' or 'CXX'). Call maybe be a valid expression _with_ a trailing ';'. As in CheckLib, we support library=None, to test if the call compiles without extra link flags. """ prog_prefix, dummy = \ createIncludesFromHeaders(header, 0) if libs == []: libs = [None] if not SCons.Util.is_List(libs): libs = [libs] res = SCons.Conftest.CheckLib(context, libs, None, prog_prefix, call = call, language = language, autoadd = autoadd) context.did_show_result = 1 return not res # Local Variables: # tab-width:4 # indent-tabs-mode:nil # End: # vim: set expandtab tabstop=4 shiftwidth=4:

37.945087

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0.595146

"""SCons.SConf Autoconf-like configuration support. """ __revision__ = "src/engine/SCons/SConf.py 4369 2009/09/19 15:58:29 scons" import os import re import string import StringIO import sys import traceback import types import SCons.Action import SCons.Builder import SCons.Errors import SCons.Job import SCons.Node.FS import SCons.Taskmaster import SCons.Util import SCons.Warnings import SCons.Conftest from SCons.Debug import Trace SCons.Conftest.LogInputFiles = 0 SCons.Conftest.LogErrorMessages = 0 build_type = None build_types = ['clean', 'help'] def SetBuildType(type): global build_type build_type = type dryrun = 0 AUTO=0 FORCE=1 CACHE=2 cache_mode = AUTO def SetCacheMode(mode): """Set the Configure cache mode. mode must be one of "auto", "force", or "cache".""" global cache_mode if mode == "auto": cache_mode = AUTO elif mode == "force": cache_mode = FORCE elif mode == "cache": cache_mode = CACHE else: raise ValueError, "SCons.SConf.SetCacheMode: Unknown mode " + mode progress_display = SCons.Util.display def SetProgressDisplay(display): """Set the progress display to use (called from SCons.Script)""" global progress_display progress_display = display SConfFS = None _ac_build_counter = 0 _ac_config_logs = {} _ac_config_hs = {} sconf_global = None def _createConfigH(target, source, env): t = open(str(target[0]), "w") defname = re.sub('[^A-Za-z0-9_]', '_', string.upper(str(target[0]))) t.write("""#ifndef %(DEFNAME)s_SEEN #define %(DEFNAME)s_SEEN """ % {'DEFNAME' : defname}) t.write(source[0].get_contents()) t.write(""" #endif /* %(DEFNAME)s_SEEN */ """ % {'DEFNAME' : defname}) t.close() def _stringConfigH(target, source, env): return "scons: Configure: creating " + str(target[0]) def CreateConfigHBuilder(env): """Called just before the building targets phase begins.""" if len(_ac_config_hs) == 0: return action = SCons.Action.Action(_createConfigH, _stringConfigH) sconfigHBld = SCons.Builder.Builder(action=action) env.Append( BUILDERS={'SConfigHBuilder':sconfigHBld} ) for k in _ac_config_hs.keys(): env.SConfigHBuilder(k, env.Value(_ac_config_hs[k])) class SConfWarning(SCons.Warnings.Warning): pass SCons.Warnings.enableWarningClass(SConfWarning) class SConfError(SCons.Errors.UserError): def __init__(self,msg): SCons.Errors.UserError.__init__(self,msg) class ConfigureDryRunError(SConfError): """Raised when a file or directory needs to be updated during a Configure process, but the user requested a dry-run""" def __init__(self,target): if not isinstance(target, SCons.Node.FS.File): msg = 'Cannot create configure directory "%s" within a dry-run.' % str(target) else: msg = 'Cannot update configure test "%s" within a dry-run.' % str(target) SConfError.__init__(self,msg) class ConfigureCacheError(SConfError): """Raised when a use explicitely requested the cache feature, but the test is run the first time.""" def __init__(self,target): SConfError.__init__(self, '"%s" is not yet built and cache is forced.' % str(target)) def _createSource( target, source, env ): fd = open(str(target[0]), "w") fd.write(source[0].get_contents()) fd.close() def _stringSource( target, source, env ): return (str(target[0]) + ' <-\n |' + string.replace( source[0].get_contents(), '\n', "\n |" ) ) BooleanTypes = [types.IntType] if hasattr(types, 'BooleanType'): BooleanTypes.append(types.BooleanType) class SConfBuildInfo(SCons.Node.FS.FileBuildInfo): """ Special build info for targets of configure tests. Additional members are result (did the builder succeed last time?) and string, which contains messages of the original build phase. """ result = None string = None def set_build_result(self, result, string): self.result = result self.string = string class Streamer: """ 'Sniffer' for a file-like writable object. Similar to the unix tool tee. """ def __init__(self, orig): self.orig = orig self.s = StringIO.StringIO() def write(self, str): if self.orig: self.orig.write(str) self.s.write(str) def writelines(self, lines): for l in lines: self.write(l + '\n') def getvalue(self): """ Return everything written to orig since the Streamer was created. """ return self.s.getvalue() def flush(self): if self.orig: self.orig.flush() self.s.flush() class SConfBuildTask(SCons.Taskmaster.AlwaysTask): """ This is almost the same as SCons.Script.BuildTask. Handles SConfErrors correctly and knows about the current cache_mode. """ def display(self, message): if sconf_global.logstream: sconf_global.logstream.write("scons: Configure: " + message + "\n") def display_cached_string(self, bi): """ Logs the original builder messages, given the SConfBuildInfo instance bi. """ if not isinstance(bi, SConfBuildInfo): SCons.Warnings.warn(SConfWarning, "The stored build information has an unexpected class: %s" % bi.__class__) else: self.display("The original builder output was:\n" + string.replace(" |" + str(bi.string), "\n", "\n |")) def failed(self): exc_type = self.exc_info()[0] if issubclass(exc_type, SConfError): raise elif issubclass(exc_type, SCons.Errors.BuildError): # Clear the exception to prevent the contained traceback # to build a reference cycle. self.exc_clear() else: self.display('Caught exception while building "%s":\n' % self.targets[0]) try: excepthook = sys.excepthook except AttributeError: # Earlier versions of Python don't have sys.excepthook... def excepthook(type, value, tb): traceback.print_tb(tb) print type, value apply(excepthook, self.exc_info()) return SCons.Taskmaster.Task.failed(self) def collect_node_states(self): T = 0 changed = False cached_error = False cachable = True for t in self.targets: if T: Trace('%s' % (t)) bi = t.get_stored_info().binfo if isinstance(bi, SConfBuildInfo): if T: Trace(': SConfBuildInfo') if cache_mode == CACHE: t.set_state(SCons.Node.up_to_date) if T: Trace(': set_state(up_to-date)') else: if T: Trace(': get_state() %s' % t.get_state()) if T: Trace(': changed() %s' % t.changed()) if (t.get_state() != SCons.Node.up_to_date and t.changed()): changed = True if T: Trace(': changed %s' % changed) cached_error = cached_error or bi.result else: if T: Trace(': else') cachable = False changed = ( t.get_state() != SCons.Node.up_to_date ) if T: Trace(': changed %s' % changed) if T: Trace('\n') return (not changed, cached_error, cachable) def execute(self): if not self.targets[0].has_builder(): return sconf = sconf_global is_up_to_date, cached_error, cachable = self.collect_node_states() if cache_mode == CACHE and not cachable: raise ConfigureCacheError(self.targets[0]) elif cache_mode == FORCE: is_up_to_date = 0 if cached_error and is_up_to_date: self.display("Building \"%s\" failed in a previous run and all " "its sources are up to date." % str(self.targets[0])) binfo = self.targets[0].get_stored_info().binfo self.display_cached_string(binfo) raise SCons.Errors.BuildError elif is_up_to_date: self.display("\"%s\" is up to date." % str(self.targets[0])) binfo = self.targets[0].get_stored_info().binfo self.display_cached_string(binfo) elif dryrun: raise ConfigureDryRunError(self.targets[0]) else: s = sys.stdout = sys.stderr = Streamer(sys.stdout) try: env = self.targets[0].get_build_env() if cache_mode == FORCE: # that the correct .sconsign info will get calculated # and keep the build state consistent. def force_build(dependency, target, prev_ni, env_decider=env.decide_source): env_decider(dependency, target, prev_ni) return True env.Decider(force_build) env['PSTDOUT'] = env['PSTDERR'] = s try: sconf.cached = 0 self.targets[0].build() finally: sys.stdout = sys.stderr = env['PSTDOUT'] = \ env['PSTDERR'] = sconf.logstream except KeyboardInterrupt: raise except SystemExit: exc_value = sys.exc_info()[1] raise SCons.Errors.ExplicitExit(self.targets[0],exc_value.code) except Exception, e: for t in self.targets: binfo = t.get_binfo() binfo.__class__ = SConfBuildInfo binfo.set_build_result(1, s.getvalue()) sconsign_entry = SCons.SConsign.SConsignEntry() sconsign_entry.binfo = binfo #sconsign_entry.ninfo = self.get_ninfo() # We'd like to do this as follows: sconsign = t.dir.sconsign() sconsign.set_entry(t.name, sconsign_entry) sconsign.merge() raise e else: for t in self.targets: binfo = t.get_binfo() binfo.__class__ = SConfBuildInfo binfo.set_build_result(0, s.getvalue()) sconsign_entry = SCons.SConsign.SConsignEntry() sconsign_entry.binfo = binfo # t.store_info(binfo) # However, we need to store it as an SConfBuildInfo # object, and store_info() will turn it into a # regular FileNodeInfo if the target is itself a # regular File. sconsign = t.dir.sconsign() sconsign.set_entry(t.name, sconsign_entry) sconsign.merge() class SConfBase: """This is simply a class to represent a configure context. After creating a SConf object, you can call any tests. After finished with your tests, be sure to call the Finish() method, which returns the modified environment. Some words about caching: In most cases, it is not necessary to cache Test results explicitely. Instead, we use the scons dependency checking mechanism. For example, if one wants to compile a test program (SConf.TryLink), the compiler is only called, if the program dependencies have changed. However, if the program could not be compiled in a former SConf run, we need to explicitely cache this error. """ def __init__(self, env, custom_tests = {}, conf_dir='$CONFIGUREDIR', log_file='$CONFIGURELOG', config_h = None, _depth = 0): """Constructor. Pass additional tests in the custom_tests-dictinary, e.g. custom_tests={'CheckPrivate':MyPrivateTest}, where MyPrivateTest defines a custom test. Note also the conf_dir and log_file arguments (you may want to build tests in the VariantDir, not in the SourceDir) """ global SConfFS if not SConfFS: SConfFS = SCons.Node.FS.default_fs or \ SCons.Node.FS.FS(env.fs.pathTop) if sconf_global is not None: raise (SCons.Errors.UserError, "Only one SConf object may be active at one time") self.env = env if log_file is not None: log_file = SConfFS.File(env.subst(log_file)) self.logfile = log_file self.logstream = None self.lastTarget = None self.depth = _depth self.cached = 0 # will be set, if all test results are cached # add default tests default_tests = { 'CheckCC' : CheckCC, 'CheckCXX' : CheckCXX, 'CheckSHCC' : CheckSHCC, 'CheckSHCXX' : CheckSHCXX, 'CheckFunc' : CheckFunc, 'CheckType' : CheckType, 'CheckTypeSize' : CheckTypeSize, 'CheckDeclaration' : CheckDeclaration, 'CheckHeader' : CheckHeader, 'CheckCHeader' : CheckCHeader, 'CheckCXXHeader' : CheckCXXHeader, 'CheckLib' : CheckLib, 'CheckLibWithHeader' : CheckLibWithHeader, } self.AddTests(default_tests) self.AddTests(custom_tests) self.confdir = SConfFS.Dir(env.subst(conf_dir)) if config_h is not None: config_h = SConfFS.File(config_h) self.config_h = config_h self._startup() def Finish(self): """Call this method after finished with your tests: env = sconf.Finish() """ self._shutdown() return self.env def Define(self, name, value = None, comment = None): """ Define a pre processor symbol name, with the optional given value in the current config header. If value is None (default), then #define name is written. If value is not none, then #define name value is written. comment is a string which will be put as a C comment in the header, to explain the meaning of the value (appropriate C comments /* and */ will be put automatically.""" lines = [] if comment: comment_str = "/* %s */" % comment lines.append(comment_str) if value is not None: define_str = "#define %s %s" % (name, value) else: define_str = "#define %s" % name lines.append(define_str) lines.append('') self.config_h_text = self.config_h_text + string.join(lines, '\n') def BuildNodes(self, nodes): """ Tries to build the given nodes immediately. Returns 1 on success, 0 on error. """ if self.logstream is not None: # override stdout / stderr to write in log file oldStdout = sys.stdout sys.stdout = self.logstream oldStderr = sys.stderr sys.stderr = self.logstream # the engine assumes the current path is the SConstruct directory ... old_fs_dir = SConfFS.getcwd() old_os_dir = os.getcwd() SConfFS.chdir(SConfFS.Top, change_os_dir=1) # Because we take responsibility here for writing out our # own .sconsign info (see SConfBuildTask.execute(), above), # we override the store_info() method with a null place-holder # so we really control how it gets written. for n in nodes: n.store_info = n.do_not_store_info ret = 1 try: # ToDo: use user options for calc save_max_drift = SConfFS.get_max_drift() SConfFS.set_max_drift(0) tm = SCons.Taskmaster.Taskmaster(nodes, SConfBuildTask) # we don't want to build tests in parallel jobs = SCons.Job.Jobs(1, tm ) jobs.run() for n in nodes: state = n.get_state() if (state != SCons.Node.executed and state != SCons.Node.up_to_date): ret = 0 finally: SConfFS.set_max_drift(save_max_drift) os.chdir(old_os_dir) SConfFS.chdir(old_fs_dir, change_os_dir=0) if self.logstream is not None: sys.stdout = oldStdout sys.stderr = oldStderr return ret def pspawn_wrapper(self, sh, escape, cmd, args, env): """Wrapper function for handling piped spawns. This looks to the calling interface (in Action.py) like a "normal" spawn, but associates the call with the PSPAWN variable from the construction environment and with the streams to which we want the output logged. This gets slid into the construction environment as the SPAWN variable so Action.py doesn't have to know or care whether it's spawning a piped command or not. """ return self.pspawn(sh, escape, cmd, args, env, self.logstream, self.logstream) def TryBuild(self, builder, text = None, extension = ""): """Low level TryBuild implementation. Normally you don't need to call that - you can use TryCompile / TryLink / TryRun instead """ global _ac_build_counter # Make sure we have a PSPAWN value, and save the current # SPAWN value. try: self.pspawn = self.env['PSPAWN'] except KeyError: raise SCons.Errors.UserError('Missing PSPAWN construction variable.') try: save_spawn = self.env['SPAWN'] except KeyError: raise SCons.Errors.UserError('Missing SPAWN construction variable.') nodesToBeBuilt = [] f = "conftest_" + str(_ac_build_counter) pref = self.env.subst( builder.builder.prefix ) suff = self.env.subst( builder.builder.suffix ) target = self.confdir.File(pref + f + suff) try: # Slide our wrapper into the construction environment as # the SPAWN function. self.env['SPAWN'] = self.pspawn_wrapper sourcetext = self.env.Value(text) if text is not None: textFile = self.confdir.File(f + extension) textFileNode = self.env.SConfSourceBuilder(target=textFile, source=sourcetext) nodesToBeBuilt.extend(textFileNode) source = textFileNode else: source = None nodes = builder(target = target, source = source) if not SCons.Util.is_List(nodes): nodes = [nodes] nodesToBeBuilt.extend(nodes) result = self.BuildNodes(nodesToBeBuilt) finally: self.env['SPAWN'] = save_spawn _ac_build_counter = _ac_build_counter + 1 if result: self.lastTarget = nodes[0] else: self.lastTarget = None return result def TryAction(self, action, text = None, extension = ""): """Tries to execute the given action with optional source file contents <text> and optional source file extension <extension>, Returns the status (0 : failed, 1 : ok) and the contents of the output file. """ builder = SCons.Builder.Builder(action=action) self.env.Append( BUILDERS = {'SConfActionBuilder' : builder} ) ok = self.TryBuild(self.env.SConfActionBuilder, text, extension) del self.env['BUILDERS']['SConfActionBuilder'] if ok: outputStr = self.lastTarget.get_contents() return (1, outputStr) return (0, "") def TryCompile( self, text, extension): """Compiles the program given in text to an env.Object, using extension as file extension (e.g. '.c'). Returns 1, if compilation was successful, 0 otherwise. The target is saved in self.lastTarget (for further processing). """ return self.TryBuild(self.env.Object, text, extension) def TryLink( self, text, extension ): """Compiles the program given in text to an executable env.Program, using extension as file extension (e.g. '.c'). Returns 1, if compilation was successful, 0 otherwise. The target is saved in self.lastTarget (for further processing). """ return self.TryBuild(self.env.Program, text, extension ) def TryRun(self, text, extension ): """Compiles and runs the program given in text, using extension as file extension (e.g. '.c'). Returns (1, outputStr) on success, (0, '') otherwise. The target (a file containing the program's stdout) is saved in self.lastTarget (for further processing). """ ok = self.TryLink(text, extension) if( ok ): prog = self.lastTarget pname = prog.path output = self.confdir.File(os.path.basename(pname)+'.out') node = self.env.Command(output, prog, [ [ pname, ">", "${TARGET}"] ]) ok = self.BuildNodes(node) if ok: outputStr = output.get_contents() return( 1, outputStr) return (0, "") class TestWrapper: """A wrapper around Tests (to ensure sanity)""" def __init__(self, test, sconf): self.test = test self.sconf = sconf def __call__(self, *args, **kw): if not self.sconf.active: raise (SCons.Errors.UserError, "Test called after sconf.Finish()") context = CheckContext(self.sconf) ret = apply(self.test, (context,) + args, kw) if self.sconf.config_h is not None: self.sconf.config_h_text = self.sconf.config_h_text + context.config_h context.Result("error: no result") return ret def AddTest(self, test_name, test_instance): """Adds test_class to this SConf instance. It can be called with self.test_name(...)""" setattr(self, test_name, SConfBase.TestWrapper(test_instance, self)) def AddTests(self, tests): """Adds all the tests given in the tests dictionary to this SConf instance """ for name in tests.keys(): self.AddTest(name, tests[name]) def _createDir( self, node ): dirName = str(node) if dryrun: if not os.path.isdir( dirName ): raise ConfigureDryRunError(dirName) else: if not os.path.isdir( dirName ): os.makedirs( dirName ) node._exists = 1 def _startup(self): """Private method. Set up logstream, and set the environment variables necessary for a piped build """ global _ac_config_logs global sconf_global global SConfFS self.lastEnvFs = self.env.fs self.env.fs = SConfFS self._createDir(self.confdir) self.confdir.up().add_ignore( [self.confdir] ) if self.logfile is not None and not dryrun: if _ac_config_logs.has_key(self.logfile): log_mode = "a" else: _ac_config_logs[self.logfile] = None log_mode = "w" fp = open(str(self.logfile), log_mode) self.logstream = SCons.Util.Unbuffered(fp) self.logfile.dir.add_ignore( [self.logfile] ) tb = traceback.extract_stack()[-3-self.depth] old_fs_dir = SConfFS.getcwd() SConfFS.chdir(SConfFS.Top, change_os_dir=0) self.logstream.write('file %s,line %d:\n\tConfigure(confdir = %s)\n' % (tb[0], tb[1], str(self.confdir)) ) SConfFS.chdir(old_fs_dir) else: self.logstream = None action = SCons.Action.Action(_createSource, _stringSource) sconfSrcBld = SCons.Builder.Builder(action=action) self.env.Append( BUILDERS={'SConfSourceBuilder':sconfSrcBld} ) self.config_h_text = _ac_config_hs.get(self.config_h, "") self.active = 1 sconf_global = self def _shutdown(self): """Private method. Reset to non-piped spawn""" global sconf_global, _ac_config_hs if not self.active: raise SCons.Errors.UserError, "Finish may be called only once!" if self.logstream is not None and not dryrun: self.logstream.write("\n") self.logstream.close() self.logstream = None blds = self.env['BUILDERS'] del blds['SConfSourceBuilder'] self.env.Replace( BUILDERS=blds ) self.active = 0 sconf_global = None if not self.config_h is None: _ac_config_hs[self.config_h] = self.config_h_text self.env.fs = self.lastEnvFs class CheckContext: """Provides a context for configure tests. Defines how a test writes to the screen and log file. A typical test is just a callable with an instance of CheckContext as first argument: def CheckCustom(context, ...) context.Message('Checking my weird test ... ') ret = myWeirdTestFunction(...) context.Result(ret) Often, myWeirdTestFunction will be one of context.TryCompile/context.TryLink/context.TryRun. The results of those are cached, for they are only rebuild, if the dependencies have changed. """ def __init__(self, sconf): """Constructor. Pass the corresponding SConf instance.""" self.sconf = sconf self.did_show_result = 0 self.vardict = {} self.havedict = {} self.headerfilename = None self.config_h = "" # that tests won't be able to include the config.h file, and so def Message(self, text): """Inform about what we are doing right now, e.g. 'Checking for SOMETHING ... ' """ self.Display(text) self.sconf.cached = 1 self.did_show_result = 0 def Result(self, res): """Inform about the result of the test. res may be an integer or a string. In case of an integer, the written text will be 'yes' or 'no'. The result is only displayed when self.did_show_result is not set. """ if type(res) in BooleanTypes: if res: text = "yes" else: text = "no" elif type(res) == types.StringType: text = res else: raise TypeError, "Expected string, int or bool, got " + str(type(res)) if self.did_show_result == 0: self.Display(text + "\n") self.did_show_result = 1 def TryBuild(self, *args, **kw): return apply(self.sconf.TryBuild, args, kw) def TryAction(self, *args, **kw): return apply(self.sconf.TryAction, args, kw) def TryCompile(self, *args, **kw): return apply(self.sconf.TryCompile, args, kw) def TryLink(self, *args, **kw): return apply(self.sconf.TryLink, args, kw) def TryRun(self, *args, **kw): return apply(self.sconf.TryRun, args, kw) def __getattr__( self, attr ): if( attr == 'env' ): return self.sconf.env elif( attr == 'lastTarget' ): return self.sconf.lastTarget else: raise AttributeError, "CheckContext instance has no attribute '%s'" % attr #### Stuff used by Conftest.py (look there for explanations). def BuildProg(self, text, ext): self.sconf.cached = 1 # TODO: should use self.vardict for $CC, $CPPFLAGS, etc. return not self.TryBuild(self.env.Program, text, ext) def CompileProg(self, text, ext): self.sconf.cached = 1 # TODO: should use self.vardict for $CC, $CPPFLAGS, etc. return not self.TryBuild(self.env.Object, text, ext) def CompileSharedObject(self, text, ext): self.sconf.cached = 1 # TODO: should use self.vardict for $SHCC, $CPPFLAGS, etc. return not self.TryBuild(self.env.SharedObject, text, ext) def RunProg(self, text, ext): self.sconf.cached = 1 # TODO: should use self.vardict for $CC, $CPPFLAGS, etc. st, out = self.TryRun(text, ext) return not st, out def AppendLIBS(self, lib_name_list): oldLIBS = self.env.get( 'LIBS', [] ) self.env.Append(LIBS = lib_name_list) return oldLIBS def PrependLIBS(self, lib_name_list): oldLIBS = self.env.get( 'LIBS', [] ) self.env.Prepend(LIBS = lib_name_list) return oldLIBS def SetLIBS(self, val): oldLIBS = self.env.get( 'LIBS', [] ) self.env.Replace(LIBS = val) return oldLIBS def Display(self, msg): if self.sconf.cached: # We assume that Display is called twice for each test here # once for the Checking for ... message and once for the result. # The self.sconf.cached flag can only be set between those calls msg = "(cached) " + msg self.sconf.cached = 0 progress_display(msg, append_newline=0) self.Log("scons: Configure: " + msg + "\n") def Log(self, msg): if self.sconf.logstream is not None: self.sconf.logstream.write(msg) #### End of stuff used by Conftest.py. def SConf(*args, **kw): if kw.get(build_type, True): kw['_depth'] = kw.get('_depth', 0) + 1 for bt in build_types: try: del kw[bt] except KeyError: pass return apply(SConfBase, args, kw) else: return SCons.Util.Null() def CheckFunc(context, function_name, header = None, language = None): res = SCons.Conftest.CheckFunc(context, function_name, header = header, language = language) context.did_show_result = 1 return not res def CheckType(context, type_name, includes = "", language = None): res = SCons.Conftest.CheckType(context, type_name, header = includes, language = language) context.did_show_result = 1 return not res def CheckTypeSize(context, type_name, includes = "", language = None, expect = None): res = SCons.Conftest.CheckTypeSize(context, type_name, header = includes, language = language, expect = expect) context.did_show_result = 1 return res def CheckDeclaration(context, declaration, includes = "", language = None): res = SCons.Conftest.CheckDeclaration(context, declaration, includes = includes, language = language) context.did_show_result = 1 return not res def createIncludesFromHeaders(headers, leaveLast, include_quotes = '""'): # used by CheckHeader and CheckLibWithHeader to produce C - #include # statements from the specified header (list) if not SCons.Util.is_List(headers): headers = [headers] l = [] if leaveLast: lastHeader = headers[-1] headers = headers[:-1] else: lastHeader = None for s in headers: l.append("#include %s%s%s\n" % (include_quotes[0], s, include_quotes[1])) return string.join(l, ''), lastHeader def CheckHeader(context, header, include_quotes = '<>', language = None): """ A test for a C or C++ header file. """ prog_prefix, hdr_to_check = \ createIncludesFromHeaders(header, 1, include_quotes) res = SCons.Conftest.CheckHeader(context, hdr_to_check, prog_prefix, language = language, include_quotes = include_quotes) context.did_show_result = 1 return not res def CheckCC(context): res = SCons.Conftest.CheckCC(context) context.did_show_result = 1 return not res def CheckCXX(context): res = SCons.Conftest.CheckCXX(context) context.did_show_result = 1 return not res def CheckSHCC(context): res = SCons.Conftest.CheckSHCC(context) context.did_show_result = 1 return not res def CheckSHCXX(context): res = SCons.Conftest.CheckSHCXX(context) context.did_show_result = 1 return not res # Bram: Make this function obsolete? CheckHeader() is more generic. def CheckCHeader(context, header, include_quotes = '""'): """ A test for a C header file. """ return CheckHeader(context, header, include_quotes, language = "C") # Bram: Make this function obsolete? CheckHeader() is more generic. def CheckCXXHeader(context, header, include_quotes = '""'): """ A test for a C++ header file. """ return CheckHeader(context, header, include_quotes, language = "C++") def CheckLib(context, library = None, symbol = "main", header = None, language = None, autoadd = 1): """ A test for a library. See also CheckLibWithHeader. Note that library may also be None to test whether the given symbol compiles without flags. """ if library == []: library = [None] if not SCons.Util.is_List(library): library = [library] # ToDo: accept path for the library res = SCons.Conftest.CheckLib(context, library, symbol, header = header, language = language, autoadd = autoadd) context.did_show_result = 1 return not res # XXX # Bram: Can only include one header and can't use def CheckLibWithHeader(context, libs, header, language, call = None, autoadd = 1): """ Another (more sophisticated) test for a library. Checks, if library and header is available for language (may be 'C' or 'CXX'). Call maybe be a valid expression _with_ a trailing ';'. As in CheckLib, we support library=None, to test if the call compiles without extra link flags. """ prog_prefix, dummy = \ createIncludesFromHeaders(header, 0) if libs == []: libs = [None] if not SCons.Util.is_List(libs): libs = [libs] res = SCons.Conftest.CheckLib(context, libs, None, prog_prefix, call = call, language = language, autoadd = autoadd) context.did_show_result = 1 return not res

false

true

7907c232c1a56b6ea80e5d08bb7cefa7387b189c

1,433

py

Python

rasters/project_to_behrmann.py

samuelbosch/phd

97348f3b9795dc0529b02060df576455b4d184a9

[ "Unlicense" ]

null

rasters/project_to_behrmann.py

samuelbosch/phd

97348f3b9795dc0529b02060df576455b4d184a9

[ "Unlicense" ]

null

rasters/project_to_behrmann.py

samuelbosch/phd

97348f3b9795dc0529b02060df576455b4d184a9

[ "Unlicense" ]

null

""" Small script to generate gdal_warp commands for projecting rasters to the Behrmann projection to be able to run the generated bat file you should have gdalwarp in your path or run it from an OSGeo4W Shell """ import os root = r"D:\a\data\BioOracle_scenarios_30s_min250" output = root + r"_equal_area" #os.path.abspath(os.path.join(root, r'..\ascii_equalarea')) nodata = "-9999" def create_bat(): proj = "+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +ellps=WGS84 +units=m +no_defs" with open('project_to_behrmann.bat', 'w') as bat: for r, dirs, files in os.walk(root): for f in files: n, ext = os.path.splitext(f) if ext == '.asc': ## output of ascii files from gdalwarp is not supported temptiff = os.path.join(output, n + '.tiff') bat.write('gdalwarp -of GTiff -multi -srcnodata %s -dstnodata %s -t_srs "%s" "%s" "%s"\n' % (nodata, proj, os.path.join(r, f), temptiff)) ## convert output tiff to ascii outdir = r.replace(root, output) if not os.path.exists(outdir): os.makedirs(outdir) bat.write('gdal_translate -of AAIGrid "%s" "%s"\n' % (temptiff, os.path.join(outdir,f))) ## delete temp file bat.write('del "%s"\n'%temptiff) if __name__ == '__main__': create_bat()

43.424242

157

0.586183

import os root = r"D:\a\data\BioOracle_scenarios_30s_min250" output = root + r"_equal_area" nodata = "-9999" def create_bat(): proj = "+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +ellps=WGS84 +units=m +no_defs" with open('project_to_behrmann.bat', 'w') as bat: for r, dirs, files in os.walk(root): for f in files: n, ext = os.path.splitext(f) if ext == '.asc': + '.tiff') bat.write('gdalwarp -of GTiff -multi -srcnodata %s -dstnodata %s -t_srs "%s" "%s" "%s"\n' % (nodata, proj, os.path.join(r, f), temptiff)) r.replace(root, output) if not os.path.exists(outdir): os.makedirs(outdir) bat.write('gdal_translate -of AAIGrid "%s" "%s"\n' % (temptiff, os.path.join(outdir,f))) bat.write('del "%s"\n'%temptiff) if __name__ == '__main__': create_bat()

true

7907c3382bc7560cb9f38ba5bb20d7e6252a6fba

3,097

py

Python

ivanti_security_controls/icon_ivanti_security_controls/actions/start_patch_scan/action.py

killstrelok/insightconnect-plugins

911358925f4233ab273dbd8172e8b7b9188ebc01

[ "MIT" ]

null

ivanti_security_controls/icon_ivanti_security_controls/actions/start_patch_scan/action.py

killstrelok/insightconnect-plugins

911358925f4233ab273dbd8172e8b7b9188ebc01

[ "MIT" ]

1

2021-02-23T23:57:37.000Z

ivanti_security_controls/icon_ivanti_security_controls/actions/start_patch_scan/action.py

killstrelok/insightconnect-plugins

911358925f4233ab273dbd8172e8b7b9188ebc01

[ "MIT" ]

null

import insightconnect_plugin_runtime from .schema import StartPatchScanInput, StartPatchScanOutput, Input, Output, Component # Custom imports below from insightconnect_plugin_runtime.exceptions import PluginException import polling2 class StartPatchScan(insightconnect_plugin_runtime.Action): def __init__(self): super(self.__class__, self).__init__( name='start_patch_scan', description=Component.DESCRIPTION, input=StartPatchScanInput(), output=StartPatchScanOutput()) def run(self, params={}): endpoint_names = params.get(Input.HOSTNAMES, []) machine_group_ids = params.get(Input.MACHINE_GROUP_IDS, []) use_machine_credential = params.get(Input.USE_MACHINE_CREDENTIAL, False) max_poll_time = params.get(Input.MAX_POLL_TIME) if not endpoint_names and not machine_group_ids: raise PluginException(cause='No hostnames or machine group IDs specified.', assistance='Either hostnames or machine group IDs must be specified.' ) if use_machine_credential: if not endpoint_names: raise PluginException(cause='Machine credentials can only be set to true if hostname is specified.', assistance='Either provide a valid hostname or set machine credentials to False.') payload = { "credentialId": params.get(Input.CREDENTIAL_ID), "diagnosticTraceEnabled": params.get(Input.DIAGNOSTIC_TRACE_ENABLED), "endpointNames": endpoint_names, "machinegroupIds": machine_group_ids, "name": params.get(Input.NAME), "runAsCredentialId": params.get(Input.RUN_AS_CREDENTIAL_ID), "templateId": params.get(Input.TEMPLATE_ID), "useMachineCredential": use_machine_credential } self.connection.ivanti_api.create_session_credential() scan = self.connection.ivanti_api.start_patch_scan(payload) try: operation_location_url = scan.headers.get("Operation-Location") polling2.poll(lambda: self.connection.ivanti_api.get_operation_location(operation_location_url) .get("percentComplete") == 100, step=10, timeout=max_poll_time) except KeyError as e: raise PluginException( cause=f'{e} not found within the header.', assistance=f'If the issue persists please contact support.') except polling2.TimeoutException as e: raise PluginException( cause='Action timeout.', assistance=f'This scan has exceeded the maximum poll time of {max_poll_time}.') operation_location = self.connection.ivanti_api.get_operation_location(operation_location_url) scan_details = scan.json() scan_details['isComplete'] = True scan_details['updatedOn'] = operation_location['lastAction'] return { Output.SCAN_DETAILS: scan_details }

46.223881

120

0.656765

import insightconnect_plugin_runtime from .schema import StartPatchScanInput, StartPatchScanOutput, Input, Output, Component from insightconnect_plugin_runtime.exceptions import PluginException import polling2 class StartPatchScan(insightconnect_plugin_runtime.Action): def __init__(self): super(self.__class__, self).__init__( name='start_patch_scan', description=Component.DESCRIPTION, input=StartPatchScanInput(), output=StartPatchScanOutput()) def run(self, params={}): endpoint_names = params.get(Input.HOSTNAMES, []) machine_group_ids = params.get(Input.MACHINE_GROUP_IDS, []) use_machine_credential = params.get(Input.USE_MACHINE_CREDENTIAL, False) max_poll_time = params.get(Input.MAX_POLL_TIME) if not endpoint_names and not machine_group_ids: raise PluginException(cause='No hostnames or machine group IDs specified.', assistance='Either hostnames or machine group IDs must be specified.' ) if use_machine_credential: if not endpoint_names: raise PluginException(cause='Machine credentials can only be set to true if hostname is specified.', assistance='Either provide a valid hostname or set machine credentials to False.') payload = { "credentialId": params.get(Input.CREDENTIAL_ID), "diagnosticTraceEnabled": params.get(Input.DIAGNOSTIC_TRACE_ENABLED), "endpointNames": endpoint_names, "machinegroupIds": machine_group_ids, "name": params.get(Input.NAME), "runAsCredentialId": params.get(Input.RUN_AS_CREDENTIAL_ID), "templateId": params.get(Input.TEMPLATE_ID), "useMachineCredential": use_machine_credential } self.connection.ivanti_api.create_session_credential() scan = self.connection.ivanti_api.start_patch_scan(payload) try: operation_location_url = scan.headers.get("Operation-Location") polling2.poll(lambda: self.connection.ivanti_api.get_operation_location(operation_location_url) .get("percentComplete") == 100, step=10, timeout=max_poll_time) except KeyError as e: raise PluginException( cause=f'{e} not found within the header.', assistance=f'If the issue persists please contact support.') except polling2.TimeoutException as e: raise PluginException( cause='Action timeout.', assistance=f'This scan has exceeded the maximum poll time of {max_poll_time}.') operation_location = self.connection.ivanti_api.get_operation_location(operation_location_url) scan_details = scan.json() scan_details['isComplete'] = True scan_details['updatedOn'] = operation_location['lastAction'] return { Output.SCAN_DETAILS: scan_details }

true

7907c353fe7228172d12dfe2ebf9d989531d716e

583

py

Python

ecs/meetings/migrations/0010_meeting_documents_zip.py

programmierfabrik/ecs

2389a19453e21b2ea4e40b272552bcbd42b926a9

[ "Apache-2.0" ]

9

2017-02-13T18:17:13.000Z

2020-11-21T20:15:54.000Z

ecs/meetings/migrations/0010_meeting_documents_zip.py

programmierfabrik/ecs

2389a19453e21b2ea4e40b272552bcbd42b926a9

[ "Apache-2.0" ]

2

2021-05-20T14:26:47.000Z

2021-05-20T14:26:48.000Z

ecs/meetings/migrations/0010_meeting_documents_zip.py

programmierfabrik/ecs

2389a19453e21b2ea4e40b272552bcbd42b926a9

[ "Apache-2.0" ]

4

2017-04-02T18:48:59.000Z

2021-11-23T15:40:35.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('documents', '0004_uuidfield'), ('meetings', '0009_auto_20170106_1414'), ] operations = [ migrations.AddField( model_name='meeting', name='documents_zip', field=models.ForeignKey(to='documents.Document', related_name='zip_for_meeting', null=True, on_delete=django.db.models.deletion.SET_NULL), ), ]

26.5

150

0.656947

from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('documents', '0004_uuidfield'), ('meetings', '0009_auto_20170106_1414'), ] operations = [ migrations.AddField( model_name='meeting', name='documents_zip', field=models.ForeignKey(to='documents.Document', related_name='zip_for_meeting', null=True, on_delete=django.db.models.deletion.SET_NULL), ), ]

true

7907c41ce2e0c1f14bb1bc6af581366dc6383f46

176

py

Python

stringdb/__init__.py

gpp-rnd/stringdb

8e3b6e0ccbf14c866049f70d85b7e3e7a3f1c210

[ "MIT" ]

4

2020-06-28T17:53:37.000Z

2022-01-25T20:12:36.000Z

stringdb/__init__.py

gpp-rnd/stringdb

8e3b6e0ccbf14c866049f70d85b7e3e7a3f1c210

[ "MIT" ]

null

stringdb/__init__.py

gpp-rnd/stringdb

8e3b6e0ccbf14c866049f70d85b7e3e7a3f1c210

[ "MIT" ]

1

2021-08-12T20:11:26.000Z

"""Top-level package for stringdb. Imports the api module""" from .api import * __author__ = """Peter C DeWeirdt""" __email__ = 'petedeweirdt@gmail.com' __version__ = '0.1.5'

25.142857

60

0.710227

from .api import * __author__ = """Peter C DeWeirdt""" __email__ = 'petedeweirdt@gmail.com' __version__ = '0.1.5'

true

7907c47eba7b3aeac3adac5b4cdd9651ee1d73c2

5,433

py

Python

tensorflow/contrib/batching/python/ops/batch_ops.py

ekyuho/tensorflow

e0b721190502346e5485010c8db78339e08c5951

[ "Apache-2.0" ]

null

tensorflow/contrib/batching/python/ops/batch_ops.py

ekyuho/tensorflow

e0b721190502346e5485010c8db78339e08c5951

[ "Apache-2.0" ]

null

tensorflow/contrib/batching/python/ops/batch_ops.py

ekyuho/tensorflow

e0b721190502346e5485010c8db78339e08c5951

[ "Apache-2.0" ]

3

2018-03-09T05:23:57.000Z

2021-08-11T02:38:31.000Z

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Operations for automatic batching and unbatching.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.batching.ops import gen_batch_ops # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.contrib.batching.ops.gen_batch_ops import * # pylint: enable=wildcard-import from tensorflow.contrib.util import loader from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader _batch_ops = loader.load_op_library( resource_loader.get_path_to_datafile("_batch_ops.so")) @ops.RegisterGradient("Batch") def _BatchGrad(op, *out_grads): # pylint: disable=invalid-name """Gradient for batch op.""" gradients = [] for i in range(len(op.inputs)): gradients.append( gen_batch_ops.unbatch( out_grads[i], op.outputs[-2], op.outputs[-1], timeout_micros=op.get_attr("grad_timeout_micros"), shared_name="batch_gradient_{}_{}".format(op.name, i))) return gradients @ops.RegisterGradient("Unbatch") def _UnbatchGrad(op, grad): # pylint: disable=invalid-name return [ gen_batch_ops.unbatch_grad( op.inputs[0], op.inputs[1], grad, op.inputs[2], shared_name="unbatch_gradient_{}".format(op.name)), None, None ] def batch_function(num_batch_threads, max_batch_size, batch_timeout_micros, allowed_batch_sizes=None, grad_timeout_micros=60 * 1000 * 1000, unbatch_timeout_micros=60 * 1000 * 1000): """Batches the computation done by the decorated function. So, for example, in the following code ```python @batch_function(1, 2, 3) def layer(a): return tf.matmul(a, a) b = layer(w) ``` if more than one session.run call is simultaneously trying to compute `b` the values of `w` will be gathered, non-deterministically concatenated along the first axis, and only one thread will run the computation. See the documentation of the `Batch` op for more details. Assumes that all arguments of the decorated function are Tensors which will be batched along their first dimension. SparseTensor is not supported. The return value of the decorated function must be a Tensor or a list/tuple of Tensors. Args: num_batch_threads: Number of scheduling threads for processing batches of work. Determines the number of batches processed in parallel. max_batch_size: Batch sizes will never be bigger than this. batch_timeout_micros: Maximum number of microseconds to wait before outputting an incomplete batch. allowed_batch_sizes: Optional list of allowed batch sizes. If left empty, does nothing. Otherwise, supplies a list of batch sizes, causing the op to pad batches up to one of those sizes. The entries must increase monotonically, and the final entry must equal max_batch_size. grad_timeout_micros: The timeout to use for the gradient. See the documentation of the unbatch op for more details. Defaults to 60s. unbatch_timeout_micros: The timeout to use for unbatching. See the documentation of the unbatch op for more details. Defaults to 60s. Returns: The decorated function will return the unbatched computation output Tensors. """ def decorator(f): # pylint: disable=missing-docstring def decorated(*args): with ops.name_scope("batch") as name: for a in args: if not isinstance(a, ops.Tensor): raise ValueError("All arguments to functions decorated with " "`batch_function` are supposed to be Tensors; " "found %s" % repr(a)) batched_tensors, batch_index, id_t = gen_batch_ops.batch( args, num_batch_threads=num_batch_threads, max_batch_size=max_batch_size, batch_timeout_micros=batch_timeout_micros, allowed_batch_sizes=allowed_batch_sizes, grad_timeout_micros=grad_timeout_micros, shared_name=name) outputs = f(*batched_tensors) if isinstance(outputs, ops.Tensor): outputs_list = [outputs] else: outputs_list = outputs with ops.name_scope("unbatch") as unbatch_name: unbatched = [ gen_batch_ops.unbatch(t, batch_index, id_t, timeout_micros=unbatch_timeout_micros, shared_name=unbatch_name) for t in outputs_list] if isinstance(outputs, ops.Tensor): return unbatched[0] return unbatched return decorated return decorator

39.086331

80

0.683416

from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.batching.ops import gen_batch_ops from tensorflow.contrib.batching.ops.gen_batch_ops import * from tensorflow.contrib.util import loader from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader _batch_ops = loader.load_op_library( resource_loader.get_path_to_datafile("_batch_ops.so")) @ops.RegisterGradient("Batch") def _BatchGrad(op, *out_grads): gradients = [] for i in range(len(op.inputs)): gradients.append( gen_batch_ops.unbatch( out_grads[i], op.outputs[-2], op.outputs[-1], timeout_micros=op.get_attr("grad_timeout_micros"), shared_name="batch_gradient_{}_{}".format(op.name, i))) return gradients @ops.RegisterGradient("Unbatch") def _UnbatchGrad(op, grad): return [ gen_batch_ops.unbatch_grad( op.inputs[0], op.inputs[1], grad, op.inputs[2], shared_name="unbatch_gradient_{}".format(op.name)), None, None ] def batch_function(num_batch_threads, max_batch_size, batch_timeout_micros, allowed_batch_sizes=None, grad_timeout_micros=60 * 1000 * 1000, unbatch_timeout_micros=60 * 1000 * 1000): def decorator(f): def decorated(*args): with ops.name_scope("batch") as name: for a in args: if not isinstance(a, ops.Tensor): raise ValueError("All arguments to functions decorated with " "`batch_function` are supposed to be Tensors; " "found %s" % repr(a)) batched_tensors, batch_index, id_t = gen_batch_ops.batch( args, num_batch_threads=num_batch_threads, max_batch_size=max_batch_size, batch_timeout_micros=batch_timeout_micros, allowed_batch_sizes=allowed_batch_sizes, grad_timeout_micros=grad_timeout_micros, shared_name=name) outputs = f(*batched_tensors) if isinstance(outputs, ops.Tensor): outputs_list = [outputs] else: outputs_list = outputs with ops.name_scope("unbatch") as unbatch_name: unbatched = [ gen_batch_ops.unbatch(t, batch_index, id_t, timeout_micros=unbatch_timeout_micros, shared_name=unbatch_name) for t in outputs_list] if isinstance(outputs, ops.Tensor): return unbatched[0] return unbatched return decorated return decorator

true

7907c5b0b2ac0f60620b0c4b94b611b56eee5608

921

py

Python

apps/utils/migrations/0012_auto_20160824_0543.py

itsMagondu/MaMaSe

0287e092121155314e76124425ef26bb4154847f

[ "Apache-2.0" ]

3

2016-03-08T15:15:00.000Z

2020-03-05T05:32:19.000Z

apps/utils/migrations/0012_auto_20160824_0543.py

itsMagondu/MaMaSe

0287e092121155314e76124425ef26bb4154847f

[ "Apache-2.0" ]

65

2015-09-25T13:32:12.000Z

2022-03-11T23:22:12.000Z

apps/utils/migrations/0012_auto_20160824_0543.py

itsMagondu/MaMaSe

0287e092121155314e76124425ef26bb4154847f

[ "Apache-2.0" ]

2

2017-05-16T07:56:10.000Z

2020-06-06T06:01:31.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.9.7 on 2016-08-24 05:43 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('utils', '0011_auto_20160822_1127'), ] operations = [ migrations.CreateModel( name='River', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.TextField(unique=True)), ('added', models.DateTimeField(auto_now_add=True)), ], ), migrations.AlterField( model_name='channel', name='river', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='rivers', to='utils.River'), ), ]

30.7

145

0.605863

from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('utils', '0011_auto_20160822_1127'), ] operations = [ migrations.CreateModel( name='River', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.TextField(unique=True)), ('added', models.DateTimeField(auto_now_add=True)), ], ), migrations.AlterField( model_name='channel', name='river', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='rivers', to='utils.River'), ), ]

true

7907c5c116d79823d0d30543920cab7e5bb67543

455

py

Python

data/scripts/templates/object/creature/npc/base/shared_dantari_base_male.py

obi-two/GameServer

7d37024e2291a97d49522610cd8f1dbe5666afc2

[ "MIT" ]

20

2015-02-23T15:11:56.000Z

2022-03-18T20:56:48.000Z

data/scripts/templates/object/creature/npc/base/shared_dantari_base_male.py

apathyboy/swganh

665128efe9154611dec4cb5efc61d246dd095984

[ "MIT" ]

null

data/scripts/templates/object/creature/npc/base/shared_dantari_base_male.py

apathyboy/swganh

665128efe9154611dec4cb5efc61d246dd095984

[ "MIT" ]

20

2015-04-04T16:35:59.000Z

2022-03-24T14:54:37.000Z

#### NOTICE: THIS FILE IS AUTOGENERATED #### MODIFICATIONS MAY BE LOST IF DONE IMPROPERLY #### PLEASE SEE THE ONLINE DOCUMENTATION FOR EXAMPLES from swgpy.object import * def create(kernel): result = Creature() result.template = "object/creature/npc/base/shared_dantari_base_male.iff" result.attribute_template_id = 9 result.stfName("npc_name","dantari_base_male") #### BEGIN MODIFICATIONS #### #### END MODIFICATIONS #### return result

26.764706

74

0.731868

true

7907c846856a644330fc1027d3a5e81c1ee1afa6

19,128

py

Python

src/schemathesis/runner/__init__.py

hlobit/schemathesis

55cea2ca907fdec12c963721a22a3372d0b24abe

[ "MIT" ]

null

src/schemathesis/runner/__init__.py

hlobit/schemathesis

55cea2ca907fdec12c963721a22a3372d0b24abe

[ "MIT" ]

null

src/schemathesis/runner/__init__.py

hlobit/schemathesis

55cea2ca907fdec12c963721a22a3372d0b24abe

[ "MIT" ]

null

import ctypes import logging import threading import time from contextlib import contextmanager from queue import Queue from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Tuple, Union, cast import attr import hypothesis import hypothesis.errors import requests from _pytest.logging import LogCaptureHandler, catching_logs from requests.auth import HTTPDigestAuth, _basic_auth_str from .._hypothesis import make_test_or_exception from ..checks import DEFAULT_CHECKS from ..constants import USER_AGENT from ..exceptions import InvalidSchema from ..loaders import from_uri from ..models import Case, Endpoint, Status, TestResult, TestResultSet from ..schemas import BaseSchema from ..utils import WSGIResponse, capture_hypothesis_output, get_base_url from . import events DEFAULT_DEADLINE = 500 # pragma: no mutate RawAuth = Tuple[str, str] # pragma: no mutate def get_hypothesis_settings(hypothesis_options: Optional[Dict[str, Any]] = None) -> hypothesis.settings: # Default settings, used as a parent settings object below settings = hypothesis.settings(deadline=DEFAULT_DEADLINE) if hypothesis_options is not None: settings = hypothesis.settings(settings, **hypothesis_options) return settings # pylint: disable=too-many-instance-attributes @attr.s class BaseRunner: schema: BaseSchema = attr.ib() checks: Iterable[Callable] = attr.ib() hypothesis_settings: hypothesis.settings = attr.ib(converter=get_hypothesis_settings) auth: Optional[RawAuth] = attr.ib(default=None) auth_type: Optional[str] = attr.ib(default=None) headers: Optional[Dict[str, Any]] = attr.ib(default=None) request_timeout: Optional[int] = attr.ib(default=None) seed: Optional[int] = attr.ib(default=None) def execute(self,) -> Generator[events.ExecutionEvent, None, None]: """Common logic for all runners.""" results = TestResultSet() initialized = events.Initialized( results=results, schema=self.schema, checks=self.checks, hypothesis_settings=self.hypothesis_settings ) yield initialized yield from self._execute(results) yield events.Finished(results=results, schema=self.schema, running_time=time.time() - initialized.start_time) def _execute(self, results: TestResultSet) -> Generator[events.ExecutionEvent, None, None]: raise NotImplementedError @attr.s(slots=True) class SingleThreadRunner(BaseRunner): """Fast runner that runs tests sequentially in the main thread.""" def _execute(self, results: TestResultSet) -> Generator[events.ExecutionEvent, None, None]: auth = get_requests_auth(self.auth, self.auth_type) with get_session(auth, self.headers) as session: for endpoint, test in self.schema.get_all_tests(network_test, self.hypothesis_settings, self.seed): for event in run_test( self.schema, endpoint, test, self.checks, results, session=session, request_timeout=self.request_timeout, ): yield event if isinstance(event, events.Interrupted): return @attr.s(slots=True) class SingleThreadWSGIRunner(SingleThreadRunner): def _execute(self, results: TestResultSet) -> Generator[events.ExecutionEvent, None, None]: for endpoint, test in self.schema.get_all_tests(wsgi_test, self.hypothesis_settings, self.seed): for event in run_test( self.schema, endpoint, test, self.checks, results, auth=self.auth, auth_type=self.auth_type, headers=self.headers, ): yield event if isinstance(event, events.Interrupted): return def _run_task( test_template: Callable, tasks_queue: Queue, events_queue: Queue, schema: BaseSchema, checks: Iterable[Callable], settings: hypothesis.settings, seed: Optional[int], results: TestResultSet, **kwargs: Any, ) -> None: # pylint: disable=too-many-arguments with capture_hypothesis_output(): while not tasks_queue.empty(): endpoint = tasks_queue.get() test = make_test_or_exception(endpoint, test_template, settings, seed) for event in run_test(schema, endpoint, test, checks, results, **kwargs): events_queue.put(event) def thread_task( tasks_queue: Queue, events_queue: Queue, schema: BaseSchema, checks: Iterable[Callable], settings: hypothesis.settings, auth: Optional[RawAuth], auth_type: Optional[str], headers: Optional[Dict[str, Any]], seed: Optional[int], results: TestResultSet, kwargs: Any, ) -> None: """A single task, that threads do. Pretty similar to the default one-thread flow, but includes communication with the main thread via the events queue. """ # pylint: disable=too-many-arguments prepared_auth = get_requests_auth(auth, auth_type) with get_session(prepared_auth, headers) as session: _run_task( network_test, tasks_queue, events_queue, schema, checks, settings, seed, results, session=session, **kwargs ) def wsgi_thread_task( tasks_queue: Queue, events_queue: Queue, schema: BaseSchema, checks: Iterable[Callable], settings: hypothesis.settings, seed: Optional[int], results: TestResultSet, kwargs: Any, ) -> None: # pylint: disable=too-many-arguments _run_task(wsgi_test, tasks_queue, events_queue, schema, checks, settings, seed, results, **kwargs) def stop_worker(thread_id: int) -> None: """Raise an error in a thread so it is possible to asynchronously stop thread execution.""" ctypes.pythonapi.PyThreadState_SetAsyncExc(ctypes.c_long(thread_id), ctypes.py_object(SystemExit)) class ThreadInterrupted(Exception): """Special exception when worker thread received SIGINT.""" @attr.s(slots=True) class ThreadPoolRunner(BaseRunner): """Spread different tests among multiple worker threads.""" workers_num: int = attr.ib(default=2) def _execute(self, results: TestResultSet) -> Generator[events.ExecutionEvent, None, None]: """All events come from a queue where different workers push their events.""" tasks_queue = self._get_tasks_queue() # Events are pushed by workers via a separate queue events_queue: Queue = Queue() workers = self._init_workers(tasks_queue, events_queue, results) def stop_workers() -> None: for worker in workers: # workers are initialized at this point and `worker.ident` is set with an integer value ident = cast(int, worker.ident) stop_worker(ident) worker.join() is_finished = False try: while not is_finished: # Sleep is needed for performance reasons # each call to `is_alive` of an alive worker waits for a lock # iterations without waiting are too frequent and a lot of time will be spent on waiting for this locks time.sleep(0.001) is_finished = all(not worker.is_alive() for worker in workers) while not events_queue.empty(): event = events_queue.get() yield event if isinstance(event, events.Interrupted): # Thread received SIGINT # We could still have events in the queue, but ignore them to keep the logic simple # for now, could be improved in the future to show more info in such corner cases raise ThreadInterrupted except ThreadInterrupted: stop_workers() except KeyboardInterrupt: stop_workers() yield events.Interrupted(results=results, schema=self.schema) def _get_tasks_queue(self) -> Queue: """All endpoints are distributed among all workers via a queue.""" tasks_queue: Queue = Queue() tasks_queue.queue.extend(self.schema.get_all_endpoints()) return tasks_queue def _init_workers(self, tasks_queue: Queue, events_queue: Queue, results: TestResultSet) -> List[threading.Thread]: """Initialize & start workers that will execute tests.""" workers = [ threading.Thread( target=self._get_task(), kwargs=self._get_worker_kwargs(tasks_queue, events_queue, results) ) for _ in range(self.workers_num) ] for worker in workers: worker.start() return workers def _get_task(self) -> Callable: return thread_task def _get_worker_kwargs(self, tasks_queue: Queue, events_queue: Queue, results: TestResultSet) -> Dict[str, Any]: return { "tasks_queue": tasks_queue, "events_queue": events_queue, "schema": self.schema, "checks": self.checks, "settings": self.hypothesis_settings, "auth": self.auth, "auth_type": self.auth_type, "headers": self.headers, "seed": self.seed, "results": results, "kwargs": {"request_timeout": self.request_timeout}, } class ThreadPoolWSGIRunner(ThreadPoolRunner): def _get_task(self) -> Callable: return wsgi_thread_task def _get_worker_kwargs(self, tasks_queue: Queue, events_queue: Queue, results: TestResultSet) -> Dict[str, Any]: return { "tasks_queue": tasks_queue, "events_queue": events_queue, "schema": self.schema, "checks": self.checks, "settings": self.hypothesis_settings, "seed": self.seed, "results": results, "kwargs": {"auth": self.auth, "auth_type": self.auth_type, "headers": self.headers}, } def execute_from_schema( schema: BaseSchema, checks: Iterable[Callable], *, workers_num: int = 1, hypothesis_options: Optional[Dict[str, Any]] = None, auth: Optional[RawAuth] = None, auth_type: Optional[str] = None, headers: Optional[Dict[str, Any]] = None, request_timeout: Optional[int] = None, seed: Optional[int] = None, ) -> Generator[events.ExecutionEvent, None, None]: """Execute tests for the given schema. Provides the main testing loop and preparation step. """ runner: BaseRunner if workers_num > 1: if schema.app: runner = ThreadPoolWSGIRunner( schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, workers_num=workers_num, ) else: runner = ThreadPoolRunner( schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, request_timeout=request_timeout, ) else: if schema.app: runner = SingleThreadWSGIRunner( schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, ) else: runner = SingleThreadRunner( schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, request_timeout=request_timeout, ) yield from runner.execute() def run_test( schema: BaseSchema, endpoint: Endpoint, test: Union[Callable, InvalidSchema], checks: Iterable[Callable], results: TestResultSet, **kwargs: Any, ) -> Generator[events.ExecutionEvent, None, None]: """A single test run with all error handling needed.""" # pylint: disable=too-many-arguments result = TestResult(endpoint=endpoint) yield events.BeforeExecution(results=results, schema=schema, endpoint=endpoint) hypothesis_output: List[str] = [] try: if isinstance(test, InvalidSchema): status = Status.error result.add_error(test) else: with capture_hypothesis_output() as hypothesis_output: test(checks, result, **kwargs) status = Status.success except AssertionError: status = Status.failure except hypothesis.errors.Flaky: status = Status.error result.mark_errored() # Sometimes Hypothesis detects inconsistent test results and checks are not available if result.checks: flaky_example = result.checks[-1].example else: flaky_example = None result.add_error( hypothesis.errors.Flaky( "Tests on this endpoint produce unreliable results: \n" "Falsified on the first call but did not on a subsequent one" ), flaky_example, ) except hypothesis.errors.Unsatisfiable: # We need more clear error message here status = Status.error result.add_error(hypothesis.errors.Unsatisfiable("Unable to satisfy schema parameters for this endpoint")) except KeyboardInterrupt: yield events.Interrupted(results=results, schema=schema) return except Exception as error: status = Status.error result.add_error(error) # Fetch seed value, hypothesis generates it during test execution result.seed = getattr(test, "_hypothesis_internal_use_seed", None) or getattr( test, "_hypothesis_internal_use_generated_seed", None ) results.append(result) yield events.AfterExecution( results=results, schema=schema, endpoint=endpoint, status=status, hypothesis_output=hypothesis_output ) def execute( # pylint: disable=too-many-arguments schema_uri: str, checks: Iterable[Callable] = DEFAULT_CHECKS, api_options: Optional[Dict[str, Any]] = None, loader_options: Optional[Dict[str, Any]] = None, hypothesis_options: Optional[Dict[str, Any]] = None, loader: Callable = from_uri, ) -> TestResultSet: generator = prepare( schema_uri=schema_uri, checks=checks, api_options=api_options, loader_options=loader_options, hypothesis_options=hypothesis_options, loader=loader, ) all_events = list(generator) finished = all_events[-1] return finished.results def prepare( # pylint: disable=too-many-arguments schema_uri: str, checks: Iterable[Callable] = DEFAULT_CHECKS, workers_num: int = 1, api_options: Optional[Dict[str, Any]] = None, loader_options: Optional[Dict[str, Any]] = None, hypothesis_options: Optional[Dict[str, Any]] = None, loader: Callable = from_uri, seed: Optional[int] = None, ) -> Generator[events.ExecutionEvent, None, None]: """Prepare a generator that will run test cases against the given API definition.""" api_options = api_options or {} loader_options = loader_options or {} if "base_url" not in loader_options: loader_options["base_url"] = get_base_url(schema_uri) schema = loader(schema_uri, **loader_options) return execute_from_schema( schema, checks, hypothesis_options=hypothesis_options, seed=seed, workers_num=workers_num, **api_options ) def network_test( case: Case, checks: Iterable[Callable], result: TestResult, session: requests.Session, request_timeout: Optional[int], ) -> None: """A single test body that will be executed against the target.""" # pylint: disable=too-many-arguments timeout = prepare_timeout(request_timeout) response = case.call(session=session, timeout=timeout) _run_checks(case, checks, result, response) def wsgi_test( case: Case, checks: Iterable[Callable], result: TestResult, auth: Optional[RawAuth], auth_type: Optional[str], headers: Optional[Dict[str, Any]], ) -> None: # pylint: disable=too-many-arguments headers = _prepare_wsgi_headers(headers, auth, auth_type) with catching_logs(LogCaptureHandler(), level=logging.DEBUG) as recorded: response = case.call_wsgi(headers=headers) result.logs.extend(recorded.records) _run_checks(case, checks, result, response) def _prepare_wsgi_headers( headers: Optional[Dict[str, Any]], auth: Optional[RawAuth], auth_type: Optional[str] ) -> Dict[str, Any]: headers = headers or {} headers.setdefault("User-agent", USER_AGENT) wsgi_auth = get_wsgi_auth(auth, auth_type) if wsgi_auth: headers["Authorization"] = wsgi_auth return headers def _run_checks( case: Case, checks: Iterable[Callable], result: TestResult, response: Union[requests.Response, WSGIResponse] ) -> None: errors = None for check in checks: check_name = check.__name__ try: check(response, result) result.add_success(check_name, case) except AssertionError as exc: errors = True # pragma: no mutate result.add_failure(check_name, case, str(exc)) if errors is not None: # An exception needed to trigger Hypothesis shrinking & flaky tests detection logic # The message doesn't matter raise AssertionError def prepare_timeout(timeout: Optional[int]) -> Optional[float]: """Request timeout is in milliseconds, but `requests` uses seconds""" output: Optional[Union[int, float]] = timeout if timeout is not None: output = timeout / 1000 return output @contextmanager def get_session( auth: Optional[Union[HTTPDigestAuth, RawAuth]] = None, headers: Optional[Dict[str, Any]] = None ) -> Generator[requests.Session, None, None]: with requests.Session() as session: if auth is not None: session.auth = auth session.headers["User-agent"] = USER_AGENT if headers is not None: session.headers.update(**headers) yield session def get_requests_auth(auth: Optional[RawAuth], auth_type: Optional[str]) -> Optional[Union[HTTPDigestAuth, RawAuth]]: if auth and auth_type == "digest": return HTTPDigestAuth(*auth) return auth def get_wsgi_auth(auth: Optional[RawAuth], auth_type: Optional[str]) -> Optional[str]: if auth: if auth_type == "digest": raise ValueError("Digest auth is not supported for WSGI apps") return _basic_auth_str(*auth) return None

35.88743

120

0.644657

import ctypes import logging import threading import time from contextlib import contextmanager from queue import Queue from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Tuple, Union, cast import attr import hypothesis import hypothesis.errors import requests from _pytest.logging import LogCaptureHandler, catching_logs from requests.auth import HTTPDigestAuth, _basic_auth_str from .._hypothesis import make_test_or_exception from ..checks import DEFAULT_CHECKS from ..constants import USER_AGENT from ..exceptions import InvalidSchema from ..loaders import from_uri from ..models import Case, Endpoint, Status, TestResult, TestResultSet from ..schemas import BaseSchema from ..utils import WSGIResponse, capture_hypothesis_output, get_base_url from . import events DEFAULT_DEADLINE = 500 RawAuth = Tuple[str, str] def get_hypothesis_settings(hypothesis_options: Optional[Dict[str, Any]] = None) -> hypothesis.settings: settings = hypothesis.settings(deadline=DEFAULT_DEADLINE) if hypothesis_options is not None: settings = hypothesis.settings(settings, **hypothesis_options) return settings @attr.s class BaseRunner: schema: BaseSchema = attr.ib() checks: Iterable[Callable] = attr.ib() hypothesis_settings: hypothesis.settings = attr.ib(converter=get_hypothesis_settings) auth: Optional[RawAuth] = attr.ib(default=None) auth_type: Optional[str] = attr.ib(default=None) headers: Optional[Dict[str, Any]] = attr.ib(default=None) request_timeout: Optional[int] = attr.ib(default=None) seed: Optional[int] = attr.ib(default=None) def execute(self,) -> Generator[events.ExecutionEvent, None, None]: results = TestResultSet() initialized = events.Initialized( results=results, schema=self.schema, checks=self.checks, hypothesis_settings=self.hypothesis_settings ) yield initialized yield from self._execute(results) yield events.Finished(results=results, schema=self.schema, running_time=time.time() - initialized.start_time) def _execute(self, results: TestResultSet) -> Generator[events.ExecutionEvent, None, None]: raise NotImplementedError @attr.s(slots=True) class SingleThreadRunner(BaseRunner): def _execute(self, results: TestResultSet) -> Generator[events.ExecutionEvent, None, None]: auth = get_requests_auth(self.auth, self.auth_type) with get_session(auth, self.headers) as session: for endpoint, test in self.schema.get_all_tests(network_test, self.hypothesis_settings, self.seed): for event in run_test( self.schema, endpoint, test, self.checks, results, session=session, request_timeout=self.request_timeout, ): yield event if isinstance(event, events.Interrupted): return @attr.s(slots=True) class SingleThreadWSGIRunner(SingleThreadRunner): def _execute(self, results: TestResultSet) -> Generator[events.ExecutionEvent, None, None]: for endpoint, test in self.schema.get_all_tests(wsgi_test, self.hypothesis_settings, self.seed): for event in run_test( self.schema, endpoint, test, self.checks, results, auth=self.auth, auth_type=self.auth_type, headers=self.headers, ): yield event if isinstance(event, events.Interrupted): return def _run_task( test_template: Callable, tasks_queue: Queue, events_queue: Queue, schema: BaseSchema, checks: Iterable[Callable], settings: hypothesis.settings, seed: Optional[int], results: TestResultSet, **kwargs: Any, ) -> None: with capture_hypothesis_output(): while not tasks_queue.empty(): endpoint = tasks_queue.get() test = make_test_or_exception(endpoint, test_template, settings, seed) for event in run_test(schema, endpoint, test, checks, results, **kwargs): events_queue.put(event) def thread_task( tasks_queue: Queue, events_queue: Queue, schema: BaseSchema, checks: Iterable[Callable], settings: hypothesis.settings, auth: Optional[RawAuth], auth_type: Optional[str], headers: Optional[Dict[str, Any]], seed: Optional[int], results: TestResultSet, kwargs: Any, ) -> None: prepared_auth = get_requests_auth(auth, auth_type) with get_session(prepared_auth, headers) as session: _run_task( network_test, tasks_queue, events_queue, schema, checks, settings, seed, results, session=session, **kwargs ) def wsgi_thread_task( tasks_queue: Queue, events_queue: Queue, schema: BaseSchema, checks: Iterable[Callable], settings: hypothesis.settings, seed: Optional[int], results: TestResultSet, kwargs: Any, ) -> None: _run_task(wsgi_test, tasks_queue, events_queue, schema, checks, settings, seed, results, **kwargs) def stop_worker(thread_id: int) -> None: ctypes.pythonapi.PyThreadState_SetAsyncExc(ctypes.c_long(thread_id), ctypes.py_object(SystemExit)) class ThreadInterrupted(Exception): @attr.s(slots=True) class ThreadPoolRunner(BaseRunner): workers_num: int = attr.ib(default=2) def _execute(self, results: TestResultSet) -> Generator[events.ExecutionEvent, None, None]: tasks_queue = self._get_tasks_queue() events_queue: Queue = Queue() workers = self._init_workers(tasks_queue, events_queue, results) def stop_workers() -> None: for worker in workers: ident = cast(int, worker.ident) stop_worker(ident) worker.join() is_finished = False try: while not is_finished: time.sleep(0.001) is_finished = all(not worker.is_alive() for worker in workers) while not events_queue.empty(): event = events_queue.get() yield event if isinstance(event, events.Interrupted): raise ThreadInterrupted except ThreadInterrupted: stop_workers() except KeyboardInterrupt: stop_workers() yield events.Interrupted(results=results, schema=self.schema) def _get_tasks_queue(self) -> Queue: tasks_queue: Queue = Queue() tasks_queue.queue.extend(self.schema.get_all_endpoints()) return tasks_queue def _init_workers(self, tasks_queue: Queue, events_queue: Queue, results: TestResultSet) -> List[threading.Thread]: workers = [ threading.Thread( target=self._get_task(), kwargs=self._get_worker_kwargs(tasks_queue, events_queue, results) ) for _ in range(self.workers_num) ] for worker in workers: worker.start() return workers def _get_task(self) -> Callable: return thread_task def _get_worker_kwargs(self, tasks_queue: Queue, events_queue: Queue, results: TestResultSet) -> Dict[str, Any]: return { "tasks_queue": tasks_queue, "events_queue": events_queue, "schema": self.schema, "checks": self.checks, "settings": self.hypothesis_settings, "auth": self.auth, "auth_type": self.auth_type, "headers": self.headers, "seed": self.seed, "results": results, "kwargs": {"request_timeout": self.request_timeout}, } class ThreadPoolWSGIRunner(ThreadPoolRunner): def _get_task(self) -> Callable: return wsgi_thread_task def _get_worker_kwargs(self, tasks_queue: Queue, events_queue: Queue, results: TestResultSet) -> Dict[str, Any]: return { "tasks_queue": tasks_queue, "events_queue": events_queue, "schema": self.schema, "checks": self.checks, "settings": self.hypothesis_settings, "seed": self.seed, "results": results, "kwargs": {"auth": self.auth, "auth_type": self.auth_type, "headers": self.headers}, } def execute_from_schema( schema: BaseSchema, checks: Iterable[Callable], *, workers_num: int = 1, hypothesis_options: Optional[Dict[str, Any]] = None, auth: Optional[RawAuth] = None, auth_type: Optional[str] = None, headers: Optional[Dict[str, Any]] = None, request_timeout: Optional[int] = None, seed: Optional[int] = None, ) -> Generator[events.ExecutionEvent, None, None]: runner: BaseRunner if workers_num > 1: if schema.app: runner = ThreadPoolWSGIRunner( schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, workers_num=workers_num, ) else: runner = ThreadPoolRunner( schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, request_timeout=request_timeout, ) else: if schema.app: runner = SingleThreadWSGIRunner( schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, ) else: runner = SingleThreadRunner( schema=schema, checks=checks, hypothesis_settings=hypothesis_options, auth=auth, auth_type=auth_type, headers=headers, seed=seed, request_timeout=request_timeout, ) yield from runner.execute() def run_test( schema: BaseSchema, endpoint: Endpoint, test: Union[Callable, InvalidSchema], checks: Iterable[Callable], results: TestResultSet, **kwargs: Any, ) -> Generator[events.ExecutionEvent, None, None]: result = TestResult(endpoint=endpoint) yield events.BeforeExecution(results=results, schema=schema, endpoint=endpoint) hypothesis_output: List[str] = [] try: if isinstance(test, InvalidSchema): status = Status.error result.add_error(test) else: with capture_hypothesis_output() as hypothesis_output: test(checks, result, **kwargs) status = Status.success except AssertionError: status = Status.failure except hypothesis.errors.Flaky: status = Status.error result.mark_errored() if result.checks: flaky_example = result.checks[-1].example else: flaky_example = None result.add_error( hypothesis.errors.Flaky( "Tests on this endpoint produce unreliable results: \n" "Falsified on the first call but did not on a subsequent one" ), flaky_example, ) except hypothesis.errors.Unsatisfiable: status = Status.error result.add_error(hypothesis.errors.Unsatisfiable("Unable to satisfy schema parameters for this endpoint")) except KeyboardInterrupt: yield events.Interrupted(results=results, schema=schema) return except Exception as error: status = Status.error result.add_error(error) result.seed = getattr(test, "_hypothesis_internal_use_seed", None) or getattr( test, "_hypothesis_internal_use_generated_seed", None ) results.append(result) yield events.AfterExecution( results=results, schema=schema, endpoint=endpoint, status=status, hypothesis_output=hypothesis_output ) def execute( schema_uri: str, checks: Iterable[Callable] = DEFAULT_CHECKS, api_options: Optional[Dict[str, Any]] = None, loader_options: Optional[Dict[str, Any]] = None, hypothesis_options: Optional[Dict[str, Any]] = None, loader: Callable = from_uri, ) -> TestResultSet: generator = prepare( schema_uri=schema_uri, checks=checks, api_options=api_options, loader_options=loader_options, hypothesis_options=hypothesis_options, loader=loader, ) all_events = list(generator) finished = all_events[-1] return finished.results def prepare( schema_uri: str, checks: Iterable[Callable] = DEFAULT_CHECKS, workers_num: int = 1, api_options: Optional[Dict[str, Any]] = None, loader_options: Optional[Dict[str, Any]] = None, hypothesis_options: Optional[Dict[str, Any]] = None, loader: Callable = from_uri, seed: Optional[int] = None, ) -> Generator[events.ExecutionEvent, None, None]: api_options = api_options or {} loader_options = loader_options or {} if "base_url" not in loader_options: loader_options["base_url"] = get_base_url(schema_uri) schema = loader(schema_uri, **loader_options) return execute_from_schema( schema, checks, hypothesis_options=hypothesis_options, seed=seed, workers_num=workers_num, **api_options ) def network_test( case: Case, checks: Iterable[Callable], result: TestResult, session: requests.Session, request_timeout: Optional[int], ) -> None: timeout = prepare_timeout(request_timeout) response = case.call(session=session, timeout=timeout) _run_checks(case, checks, result, response) def wsgi_test( case: Case, checks: Iterable[Callable], result: TestResult, auth: Optional[RawAuth], auth_type: Optional[str], headers: Optional[Dict[str, Any]], ) -> None: headers = _prepare_wsgi_headers(headers, auth, auth_type) with catching_logs(LogCaptureHandler(), level=logging.DEBUG) as recorded: response = case.call_wsgi(headers=headers) result.logs.extend(recorded.records) _run_checks(case, checks, result, response) def _prepare_wsgi_headers( headers: Optional[Dict[str, Any]], auth: Optional[RawAuth], auth_type: Optional[str] ) -> Dict[str, Any]: headers = headers or {} headers.setdefault("User-agent", USER_AGENT) wsgi_auth = get_wsgi_auth(auth, auth_type) if wsgi_auth: headers["Authorization"] = wsgi_auth return headers def _run_checks( case: Case, checks: Iterable[Callable], result: TestResult, response: Union[requests.Response, WSGIResponse] ) -> None: errors = None for check in checks: check_name = check.__name__ try: check(response, result) result.add_success(check_name, case) except AssertionError as exc: errors = True result.add_failure(check_name, case, str(exc)) if errors is not None: raise AssertionError def prepare_timeout(timeout: Optional[int]) -> Optional[float]: output: Optional[Union[int, float]] = timeout if timeout is not None: output = timeout / 1000 return output @contextmanager def get_session( auth: Optional[Union[HTTPDigestAuth, RawAuth]] = None, headers: Optional[Dict[str, Any]] = None ) -> Generator[requests.Session, None, None]: with requests.Session() as session: if auth is not None: session.auth = auth session.headers["User-agent"] = USER_AGENT if headers is not None: session.headers.update(**headers) yield session def get_requests_auth(auth: Optional[RawAuth], auth_type: Optional[str]) -> Optional[Union[HTTPDigestAuth, RawAuth]]: if auth and auth_type == "digest": return HTTPDigestAuth(*auth) return auth def get_wsgi_auth(auth: Optional[RawAuth], auth_type: Optional[str]) -> Optional[str]: if auth: if auth_type == "digest": raise ValueError("Digest auth is not supported for WSGI apps") return _basic_auth_str(*auth) return None

true

7907ca2e64ffc8d1c2dc911818d33d542a896f45

1,184

py

Python

atomate/lammps/database.py

Zhuoying/atomate

067023f0f740d3abac47b7ae7743c1c31eff8a06

[ "BSD-3-Clause-LBNL" ]

167

2017-01-26T00:14:19.000Z

2022-03-18T20:47:58.000Z

atomate/lammps/database.py

Zhuoying/atomate

067023f0f740d3abac47b7ae7743c1c31eff8a06

[ "BSD-3-Clause-LBNL" ]

422

2016-12-16T18:21:15.000Z

2022-03-23T22:13:19.000Z

atomate/lammps/database.py

Zhuoying/atomate

067023f0f740d3abac47b7ae7743c1c31eff8a06

[ "BSD-3-Clause-LBNL" ]

158

2016-12-16T18:28:00.000Z

2022-03-28T11:40:03.000Z

""" This module defines the database classes. """ import pymongo from atomate.utils.database import CalcDb from atomate.utils.utils import get_logger __author__ = "Kiran Mathew" __credits__ = "Anubhav Jain" __email__ = "kmathew@lbl.gov" logger = get_logger(__name__) class LammpsCalcDb(CalcDb): def __init__( self, host="localhost", port=27017, database="lammps", collection="tasks", user=None, password=None, **kwargs ): super().__init__(host, port, database, collection, user, password, **kwargs) def build_indexes(self, indexes=None, background=True): indexes = indexes or [] self.collection.create_index("task_id", unique=True, background=background) self.collection.create_index( [("completed_at", pymongo.DESCENDING)], background=background ) for i in indexes: self.collection.create_index(i, background=background) def reset(self): self.collection.delete_many({}) self.db.counter.delete_one({"_id": "taskid"}) self.db.counter.insert_one({"_id": "taskid", "c": 0}) self.build_indexes()

26.909091

84

0.642736

import pymongo from atomate.utils.database import CalcDb from atomate.utils.utils import get_logger __author__ = "Kiran Mathew" __credits__ = "Anubhav Jain" __email__ = "kmathew@lbl.gov" logger = get_logger(__name__) class LammpsCalcDb(CalcDb): def __init__( self, host="localhost", port=27017, database="lammps", collection="tasks", user=None, password=None, **kwargs ): super().__init__(host, port, database, collection, user, password, **kwargs) def build_indexes(self, indexes=None, background=True): indexes = indexes or [] self.collection.create_index("task_id", unique=True, background=background) self.collection.create_index( [("completed_at", pymongo.DESCENDING)], background=background ) for i in indexes: self.collection.create_index(i, background=background) def reset(self): self.collection.delete_many({}) self.db.counter.delete_one({"_id": "taskid"}) self.db.counter.insert_one({"_id": "taskid", "c": 0}) self.build_indexes()

true

7907cbe7ab34e79a8c97739c577f7313d2fcda1c

21,110

py

Python

recbole/model/knowledge_aware_recommender/kgnnls.py

xingkongxiaxia/xx

a75e3894adfd05f5167ca76c48d1bf8626ee8588

[ "MIT" ]

4

2021-04-23T07:47:53.000Z

2022-02-01T13:48:33.000Z

recbole/model/knowledge_aware_recommender/kgnnls.py

xingkongxiaxia/RecBole

ce51d75406592d6bc25bb803f773f0788496fd97

[ "MIT" ]

null

recbole/model/knowledge_aware_recommender/kgnnls.py

xingkongxiaxia/RecBole

ce51d75406592d6bc25bb803f773f0788496fd97

[ "MIT" ]

null

# -*- coding: utf-8 -*- # @Time : 2020/10/3 # @Author : Changxin Tian # @Email : cx.tian@outlook.com r""" KGNNLS ################################################ Reference: Hongwei Wang et al. "Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems." in KDD 2019. Reference code: https://github.com/hwwang55/KGNN-LS """ import torch import torch.nn as nn import numpy as np import random from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization class KGNNLS(KnowledgeRecommender): r"""KGNN-LS is a knowledge-based recommendation model. KGNN-LS transforms the knowledge graph into a user-specific weighted graph and then apply a graph neural network to compute personalized item embeddings. To provide better inductive bias, KGNN-LS relies on label smoothness assumption, which posits that adjacent items in the knowledge graph are likely to have similar user relevance labels/scores. Label smoothness provides regularization over the edge weights and it is equivalent to a label propagation scheme on a graph. """ input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(KGNNLS, self).__init__(config, dataset) # load parameters info self.embedding_size = config['embedding_size'] self.neighbor_sample_size = config['neighbor_sample_size'] self.aggregator_class = config['aggregator'] # which aggregator to use # number of iterations when computing entity representation self.n_iter = config['n_iter'] self.reg_weight = config['reg_weight'] # weight of l2 regularization # weight of label Smoothness regularization self.ls_weight = config['ls_weight'] # define embedding self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) self.entity_embedding = nn.Embedding( self.n_entities, self.embedding_size) self.relation_embedding = nn.Embedding( self.n_relations + 1, self.embedding_size) # sample neighbors and construct interaction table kg_graph = dataset.kg_graph(form='coo', value_field='relation_id') adj_entity, adj_relation = self.construct_adj(kg_graph) self.adj_entity, self.adj_relation = adj_entity.to( self.device), adj_relation.to(self.device) inter_feat = dataset.dataset.inter_feat.values pos_users = torch.from_numpy(inter_feat[:, 0]) pos_items = torch.from_numpy(inter_feat[:, 1]) pos_label = torch.ones(pos_items.shape) pos_interaction_table, self.offset = self.get_interaction_table( pos_users, pos_items, pos_label) self.interaction_table = self.sample_neg_interaction( pos_interaction_table, self.offset) # define function self.softmax = nn.Softmax(dim=-1) self.linear_layers = torch.nn.ModuleList() for i in range(self.n_iter): self.linear_layers.append(nn.Linear( self.embedding_size if not self.aggregator_class == 'concat' else self.embedding_size * 2, self.embedding_size)) self.ReLU = nn.ReLU() self.Tanh = nn.Tanh() self.bce_loss = nn.BCEWithLogitsLoss() self.l2_loss = EmbLoss() # parameters initialization self.apply(xavier_normal_initialization) def get_interaction_table(self, user_id, item_id, y): r"""Get interaction_table that is used for fetching user-item interaction label in LS regularization. Args: user_id(torch.Tensor): the user id in user-item interactions, shape: [n_interactions, 1] item_id(torch.Tensor): the item id in user-item interactions, shape: [n_interactions, 1] y(torch.Tensor): the label in user-item interactions, shape: [n_interactions, 1] Returns: tuple: - interaction_table(dict): key: user_id * 10^offset + item_id; value: y_{user_id, item_id} - offset(int): The offset that is used for calculating the key(index) in interaction_table """ offset = len(str(self.n_entities)) offset = 10 ** offset keys = user_id * offset + item_id keys = keys.int().cpu().numpy().tolist() values = y.float().cpu().numpy().tolist() interaction_table = dict(zip(keys, values)) return interaction_table, offset def sample_neg_interaction(self, pos_interaction_table, offset): r"""Sample neg_interaction to construct train data. Args: pos_interaction_table(dict): the interaction_table that only contains pos_interaction. offset(int): The offset that is used for calculating the key(index) in interaction_table Returns: interaction_table(dict): key: user_id * 10^offset + item_id; value: y_{user_id, item_id} """ pos_num = len(pos_interaction_table) neg_num = 0 neg_interaction_table = {} while neg_num < pos_num: user_id = random.randint(0, self.n_users) item_id = random.randint(0, self.n_items) keys = user_id * offset + item_id if keys not in pos_interaction_table: neg_interaction_table[keys] = 0. neg_num += 1 interaction_table = {**pos_interaction_table, **neg_interaction_table} return interaction_table def construct_adj(self, kg_graph): r"""Get neighbors and corresponding relations for each entity in the KG. Args: kg_graph(scipy.sparse.coo_matrix): an undirected graph Returns: tuple: - adj_entity (torch.LongTensor): each line stores the sampled neighbor entities for a given entity, shape: [n_entities, neighbor_sample_size] - adj_relation (torch.LongTensor): each line stores the corresponding sampled neighbor relations, shape: [n_entities, neighbor_sample_size] """ # self.logger.info('constructing knowledge graph ...') # treat the KG as an undirected graph kg_dict = dict() for triple in zip(kg_graph.row, kg_graph.data, kg_graph.col): head = triple[0] relation = triple[1] tail = triple[2] if head not in kg_dict: kg_dict[head] = [] kg_dict[head].append((tail, relation)) if tail not in kg_dict: kg_dict[tail] = [] kg_dict[tail].append((head, relation)) # self.logger.info('constructing adjacency matrix ...') # each line of adj_entity stores the sampled neighbor entities for a given entity # each line of adj_relation stores the corresponding sampled neighbor relations entity_num = kg_graph.shape[0] adj_entity = np.zeros( [entity_num, self.neighbor_sample_size], dtype=np.int64) adj_relation = np.zeros( [entity_num, self.neighbor_sample_size], dtype=np.int64) for entity in range(entity_num): if entity not in kg_dict.keys(): adj_entity[entity] = np.array( [entity] * self.neighbor_sample_size) adj_relation[entity] = np.array( [0] * self.neighbor_sample_size) continue neighbors = kg_dict[entity] n_neighbors = len(neighbors) if n_neighbors >= self.neighbor_sample_size: sampled_indices = np.random.choice(list(range(n_neighbors)), size=self.neighbor_sample_size, replace=False) else: sampled_indices = np.random.choice(list(range(n_neighbors)), size=self.neighbor_sample_size, replace=True) adj_entity[entity] = np.array( [neighbors[i][0] for i in sampled_indices]) adj_relation[entity] = np.array( [neighbors[i][1] for i in sampled_indices]) return torch.from_numpy(adj_entity), torch.from_numpy(adj_relation) def get_neighbors(self, items): r"""Get neighbors and corresponding relations for each entity in items from adj_entity and adj_relation. Args: items(torch.LongTensor): The input tensor that contains item's id, shape: [batch_size, ] Returns: tuple: - entities(list): Entities is a list of i-iter (i = 0, 1, ..., n_iter) neighbors for the batch of items. dimensions of entities: {[batch_size, 1], [batch_size, n_neighbor], [batch_size, n_neighbor^2], ..., [batch_size, n_neighbor^n_iter]} - relations(list): Relations is a list of i-iter (i = 0, 1, ..., n_iter) corresponding relations for entities. Relations have the same shape as entities. """ items = torch.unsqueeze(items, dim=1) entities = [items] relations = [] for i in range(self.n_iter): index = torch.flatten(entities[i]) neighbor_entities = torch.reshape(torch.index_select( self.adj_entity, 0, index), (self.batch_size, -1)) neighbor_relations = torch.reshape(torch.index_select( self.adj_relation, 0, index), (self.batch_size, -1)) entities.append(neighbor_entities) relations.append(neighbor_relations) return entities, relations def aggregate(self, user_embeddings, entities, relations): r"""For each item, aggregate the entity representation and its neighborhood representation into a single vector. Args: user_embeddings(torch.FloatTensor): The embeddings of users, shape: [batch_size, embedding_size] entities(list): entities is a list of i-iter (i = 0, 1, ..., n_iter) neighbors for the batch of items. dimensions of entities: {[batch_size, 1], [batch_size, n_neighbor], [batch_size, n_neighbor^2], ..., [batch_size, n_neighbor^n_iter]} relations(list): relations is a list of i-iter (i = 0, 1, ..., n_iter) corresponding relations for entities. relations have the same shape as entities. Returns: item_embeddings(torch.FloatTensor): The embeddings of items, shape: [batch_size, embedding_size] """ entity_vectors = [self.entity_embedding(i) for i in entities] relation_vectors = [self.relation_embedding(i) for i in relations] for i in range(self.n_iter): entity_vectors_next_iter = [] for hop in range(self.n_iter - i): shape = (self.batch_size, -1, self.neighbor_sample_size, self.embedding_size) self_vectors = entity_vectors[hop] neighbor_vectors = torch.reshape( entity_vectors[hop + 1], shape) neighbor_relations = torch.reshape( relation_vectors[hop], shape) # mix_neighbor_vectors user_embeddings = torch.reshape(user_embeddings, (self.batch_size, 1, 1, self.embedding_size)) # [batch_size, 1, 1, dim] user_relation_scores = torch.mean(user_embeddings * neighbor_relations, dim=-1) # [batch_size, -1, n_neighbor] user_relation_scores_normalized = torch.unsqueeze(self.softmax(user_relation_scores), dim=-1) # [batch_size, -1, n_neighbor, 1] neighbors_agg = torch.mean(user_relation_scores_normalized * neighbor_vectors, dim=2) # [batch_size, -1, dim] if self.aggregator_class == 'sum': output = torch.reshape( self_vectors + neighbors_agg, (-1, self.embedding_size)) # [-1, dim] elif self.aggregator_class == 'neighbor': output = torch.reshape( neighbors_agg, (-1, self.embedding_size)) # [-1, dim] elif self.aggregator_class == 'concat': # [batch_size, -1, dim * 2] output = torch.cat([self_vectors, neighbors_agg], dim=-1) output = torch.reshape( output, (-1, self.embedding_size * 2)) # [-1, dim * 2] else: raise Exception("Unknown aggregator: " + self.aggregator_class) output = self.linear_layers[i](output) # [batch_size, -1, dim] output = torch.reshape( output, [self.batch_size, -1, self.embedding_size]) if i == self.n_iter - 1: vector = self.Tanh(output) else: vector = self.ReLU(output) entity_vectors_next_iter.append(vector) entity_vectors = entity_vectors_next_iter res = torch.reshape( entity_vectors[0], (self.batch_size, self.embedding_size)) return res def label_smoothness_predict(self, user_embeddings, user, entities, relations): r"""Predict the label of items by label smoothness. Args: user_embeddings(torch.FloatTensor): The embeddings of users, shape: [batch_size*2, embedding_size], user(torch.FloatTensor): the index of users, shape: [batch_size*2] entities(list): entities is a list of i-iter (i = 0, 1, ..., n_iter) neighbors for the batch of items. dimensions of entities: {[batch_size*2, 1], [batch_size*2, n_neighbor], [batch_size*2, n_neighbor^2], ..., [batch_size*2, n_neighbor^n_iter]} relations(list): relations is a list of i-iter (i = 0, 1, ..., n_iter) corresponding relations for entities. relations have the same shape as entities. Returns: predicted_labels(torch.FloatTensor): The predicted label of items, shape: [batch_size*2] """ # calculate initial labels; calculate updating masks for label propagation entity_labels = [] # True means the label of this item is reset to initial value during label propagation reset_masks = [] holdout_item_for_user = None for entities_per_iter in entities: users = torch.unsqueeze(user, dim=1) # [batch_size, 1] user_entity_concat = users * self.offset + \ entities_per_iter # [batch_size, n_neighbor^i] # the first one in entities is the items to be held out if holdout_item_for_user is None: holdout_item_for_user = user_entity_concat def lookup_interaction_table(x, _): x = int(x) label = self.interaction_table.setdefault(x, 0.5) return label initial_label = user_entity_concat.clone().cpu().double() initial_label.map_(initial_label, lookup_interaction_table) initial_label = initial_label.float().to(self.device) # False if the item is held out holdout_mask = (holdout_item_for_user - user_entity_concat).bool() # True if the entity is a labeled item reset_mask = (initial_label - 0.5).bool() reset_mask = torch.logical_and( reset_mask, holdout_mask) # remove held-out items initial_label = holdout_mask.float() * initial_label + torch.logical_not( holdout_mask).float() * 0.5 # label initialization reset_masks.append(reset_mask) entity_labels.append(initial_label) # we do not need the reset_mask for the last iteration reset_masks = reset_masks[:-1] # label propagation relation_vectors = [self.relation_embedding(i) for i in relations] for i in range(self.n_iter): entity_labels_next_iter = [] for hop in range(self.n_iter - i): masks = reset_masks[hop] self_labels = entity_labels[hop] neighbor_labels = torch.reshape(entity_labels[hop + 1], [self.batch_size, -1, self.neighbor_sample_size]) neighbor_relations = torch.reshape(relation_vectors[hop], [self.batch_size, -1, self.neighbor_sample_size, self.embedding_size]) # mix_neighbor_labels user_embeddings = torch.reshape(user_embeddings, [self.batch_size, 1, 1, self.embedding_size]) # [batch_size, 1, 1, dim] user_relation_scores = torch.mean(user_embeddings * neighbor_relations, dim=-1) # [batch_size, -1, n_neighbor] user_relation_scores_normalized = self.softmax( user_relation_scores) # [batch_size, -1, n_neighbor] neighbors_aggregated_label = torch.mean(user_relation_scores_normalized * neighbor_labels, dim=2) # [batch_size, -1, dim] # [batch_size, -1] output = masks.float() * self_labels + torch.logical_not(masks).float() * \ neighbors_aggregated_label entity_labels_next_iter.append(output) entity_labels = entity_labels_next_iter predicted_labels = entity_labels[0].squeeze(-1) return predicted_labels def forward(self, user, item): self.batch_size = item.shape[0] # [batch_size, dim] user_e = self.user_embedding(user) # entities is a list of i-iter (i = 0, 1, ..., n_iter) neighbors for the batch of items. dimensions of entities: # {[batch_size, 1], [batch_size, n_neighbor], [batch_size, n_neighbor^2], ..., [batch_size, n_neighbor^n_iter]} entities, relations = self.get_neighbors(item) # [batch_size, dim] item_e = self.aggregate(user_e, entities, relations) return user_e, item_e def calculate_ls_loss(self, user, item, target): r"""Calculate label smoothness loss. Args: user(torch.FloatTensor): the index of users, shape: [batch_size*2], item(torch.FloatTensor): the index of items, shape: [batch_size*2], target(torch.FloatTensor): the label of user-item, shape: [batch_size*2], Returns: ls_loss: label smoothness loss """ user_e = self.user_embedding(user) entities, relations = self.get_neighbors(item) predicted_labels = self.label_smoothness_predict( user_e, user, entities, relations) ls_loss = self.bce_loss(predicted_labels, target) return ls_loss def calculate_loss(self, interaction): user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] target = torch.zeros( len(user) * 2, dtype=torch.float32).to(self.device) target[:len(user)] = 1 users = torch.cat((user, user)) items = torch.cat((pos_item, neg_item)) user_e, item_e = self.forward(users, items) predict = torch.mul(user_e, item_e).sum(dim=1) rec_loss = self.bce_loss(predict, target) ls_loss = self.calculate_ls_loss(users, items, target) l2_loss = self.l2_loss(user_e, item_e) loss = rec_loss + self.ls_weight * ls_loss + self.reg_weight * l2_loss return loss def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_e, item_e = self.forward(user, item) return torch.mul(user_e, item_e).sum(dim=1) def full_sort_predict(self, interaction): user_index = interaction[self.USER_ID] item_index = torch.tensor(range(self.n_items)).to(self.device) user = torch.unsqueeze(user_index, dim=1).repeat( 1, item_index.shape[0]) user = torch.flatten(user) item = torch.unsqueeze(item_index, dim=0).repeat( user_index.shape[0], 1) item = torch.flatten(item) user_e, item_e = self.forward(user, item) score = torch.mul(user_e, item_e).sum(dim=1) return score.view(-1)

46.091703

120

0.597726

import torch import torch.nn as nn import numpy as np import random from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization class KGNNLS(KnowledgeRecommender): input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(KGNNLS, self).__init__(config, dataset) self.embedding_size = config['embedding_size'] self.neighbor_sample_size = config['neighbor_sample_size'] self.aggregator_class = config['aggregator'] self.n_iter = config['n_iter'] self.reg_weight = config['reg_weight'] self.ls_weight = config['ls_weight'] self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) self.entity_embedding = nn.Embedding( self.n_entities, self.embedding_size) self.relation_embedding = nn.Embedding( self.n_relations + 1, self.embedding_size) kg_graph = dataset.kg_graph(form='coo', value_field='relation_id') adj_entity, adj_relation = self.construct_adj(kg_graph) self.adj_entity, self.adj_relation = adj_entity.to( self.device), adj_relation.to(self.device) inter_feat = dataset.dataset.inter_feat.values pos_users = torch.from_numpy(inter_feat[:, 0]) pos_items = torch.from_numpy(inter_feat[:, 1]) pos_label = torch.ones(pos_items.shape) pos_interaction_table, self.offset = self.get_interaction_table( pos_users, pos_items, pos_label) self.interaction_table = self.sample_neg_interaction( pos_interaction_table, self.offset) self.softmax = nn.Softmax(dim=-1) self.linear_layers = torch.nn.ModuleList() for i in range(self.n_iter): self.linear_layers.append(nn.Linear( self.embedding_size if not self.aggregator_class == 'concat' else self.embedding_size * 2, self.embedding_size)) self.ReLU = nn.ReLU() self.Tanh = nn.Tanh() self.bce_loss = nn.BCEWithLogitsLoss() self.l2_loss = EmbLoss() self.apply(xavier_normal_initialization) def get_interaction_table(self, user_id, item_id, y): offset = len(str(self.n_entities)) offset = 10 ** offset keys = user_id * offset + item_id keys = keys.int().cpu().numpy().tolist() values = y.float().cpu().numpy().tolist() interaction_table = dict(zip(keys, values)) return interaction_table, offset def sample_neg_interaction(self, pos_interaction_table, offset): pos_num = len(pos_interaction_table) neg_num = 0 neg_interaction_table = {} while neg_num < pos_num: user_id = random.randint(0, self.n_users) item_id = random.randint(0, self.n_items) keys = user_id * offset + item_id if keys not in pos_interaction_table: neg_interaction_table[keys] = 0. neg_num += 1 interaction_table = {**pos_interaction_table, **neg_interaction_table} return interaction_table def construct_adj(self, kg_graph): kg_dict = dict() for triple in zip(kg_graph.row, kg_graph.data, kg_graph.col): head = triple[0] relation = triple[1] tail = triple[2] if head not in kg_dict: kg_dict[head] = [] kg_dict[head].append((tail, relation)) if tail not in kg_dict: kg_dict[tail] = [] kg_dict[tail].append((head, relation)) entity_num = kg_graph.shape[0] adj_entity = np.zeros( [entity_num, self.neighbor_sample_size], dtype=np.int64) adj_relation = np.zeros( [entity_num, self.neighbor_sample_size], dtype=np.int64) for entity in range(entity_num): if entity not in kg_dict.keys(): adj_entity[entity] = np.array( [entity] * self.neighbor_sample_size) adj_relation[entity] = np.array( [0] * self.neighbor_sample_size) continue neighbors = kg_dict[entity] n_neighbors = len(neighbors) if n_neighbors >= self.neighbor_sample_size: sampled_indices = np.random.choice(list(range(n_neighbors)), size=self.neighbor_sample_size, replace=False) else: sampled_indices = np.random.choice(list(range(n_neighbors)), size=self.neighbor_sample_size, replace=True) adj_entity[entity] = np.array( [neighbors[i][0] for i in sampled_indices]) adj_relation[entity] = np.array( [neighbors[i][1] for i in sampled_indices]) return torch.from_numpy(adj_entity), torch.from_numpy(adj_relation) def get_neighbors(self, items): items = torch.unsqueeze(items, dim=1) entities = [items] relations = [] for i in range(self.n_iter): index = torch.flatten(entities[i]) neighbor_entities = torch.reshape(torch.index_select( self.adj_entity, 0, index), (self.batch_size, -1)) neighbor_relations = torch.reshape(torch.index_select( self.adj_relation, 0, index), (self.batch_size, -1)) entities.append(neighbor_entities) relations.append(neighbor_relations) return entities, relations def aggregate(self, user_embeddings, entities, relations): entity_vectors = [self.entity_embedding(i) for i in entities] relation_vectors = [self.relation_embedding(i) for i in relations] for i in range(self.n_iter): entity_vectors_next_iter = [] for hop in range(self.n_iter - i): shape = (self.batch_size, -1, self.neighbor_sample_size, self.embedding_size) self_vectors = entity_vectors[hop] neighbor_vectors = torch.reshape( entity_vectors[hop + 1], shape) neighbor_relations = torch.reshape( relation_vectors[hop], shape) user_embeddings = torch.reshape(user_embeddings, (self.batch_size, 1, 1, self.embedding_size)) user_relation_scores = torch.mean(user_embeddings * neighbor_relations, dim=-1) user_relation_scores_normalized = torch.unsqueeze(self.softmax(user_relation_scores), dim=-1) neighbors_agg = torch.mean(user_relation_scores_normalized * neighbor_vectors, dim=2) if self.aggregator_class == 'sum': output = torch.reshape( self_vectors + neighbors_agg, (-1, self.embedding_size)) elif self.aggregator_class == 'neighbor': output = torch.reshape( neighbors_agg, (-1, self.embedding_size)) elif self.aggregator_class == 'concat': output = torch.cat([self_vectors, neighbors_agg], dim=-1) output = torch.reshape( output, (-1, self.embedding_size * 2)) else: raise Exception("Unknown aggregator: " + self.aggregator_class) output = self.linear_layers[i](output) output = torch.reshape( output, [self.batch_size, -1, self.embedding_size]) if i == self.n_iter - 1: vector = self.Tanh(output) else: vector = self.ReLU(output) entity_vectors_next_iter.append(vector) entity_vectors = entity_vectors_next_iter res = torch.reshape( entity_vectors[0], (self.batch_size, self.embedding_size)) return res def label_smoothness_predict(self, user_embeddings, user, entities, relations): entity_labels = [] reset_masks = [] holdout_item_for_user = None for entities_per_iter in entities: users = torch.unsqueeze(user, dim=1) user_entity_concat = users * self.offset + \ entities_per_iter if holdout_item_for_user is None: holdout_item_for_user = user_entity_concat def lookup_interaction_table(x, _): x = int(x) label = self.interaction_table.setdefault(x, 0.5) return label initial_label = user_entity_concat.clone().cpu().double() initial_label.map_(initial_label, lookup_interaction_table) initial_label = initial_label.float().to(self.device) holdout_mask = (holdout_item_for_user - user_entity_concat).bool() reset_mask = (initial_label - 0.5).bool() reset_mask = torch.logical_and( reset_mask, holdout_mask) initial_label = holdout_mask.float() * initial_label + torch.logical_not( holdout_mask).float() * 0.5 reset_masks.append(reset_mask) entity_labels.append(initial_label) reset_masks = reset_masks[:-1] relation_vectors = [self.relation_embedding(i) for i in relations] for i in range(self.n_iter): entity_labels_next_iter = [] for hop in range(self.n_iter - i): masks = reset_masks[hop] self_labels = entity_labels[hop] neighbor_labels = torch.reshape(entity_labels[hop + 1], [self.batch_size, -1, self.neighbor_sample_size]) neighbor_relations = torch.reshape(relation_vectors[hop], [self.batch_size, -1, self.neighbor_sample_size, self.embedding_size]) user_embeddings = torch.reshape(user_embeddings, [self.batch_size, 1, 1, self.embedding_size]) user_relation_scores = torch.mean(user_embeddings * neighbor_relations, dim=-1) user_relation_scores_normalized = self.softmax( user_relation_scores) neighbors_aggregated_label = torch.mean(user_relation_scores_normalized * neighbor_labels, dim=2) utput = masks.float() * self_labels + torch.logical_not(masks).float() * \ neighbors_aggregated_label entity_labels_next_iter.append(output) entity_labels = entity_labels_next_iter predicted_labels = entity_labels[0].squeeze(-1) return predicted_labels def forward(self, user, item): self.batch_size = item.shape[0] user_e = self.user_embedding(user) entities, relations = self.get_neighbors(item) item_e = self.aggregate(user_e, entities, relations) return user_e, item_e def calculate_ls_loss(self, user, item, target): user_e = self.user_embedding(user) entities, relations = self.get_neighbors(item) predicted_labels = self.label_smoothness_predict( user_e, user, entities, relations) ls_loss = self.bce_loss(predicted_labels, target) return ls_loss def calculate_loss(self, interaction): user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] target = torch.zeros( len(user) * 2, dtype=torch.float32).to(self.device) target[:len(user)] = 1 users = torch.cat((user, user)) items = torch.cat((pos_item, neg_item)) user_e, item_e = self.forward(users, items) predict = torch.mul(user_e, item_e).sum(dim=1) rec_loss = self.bce_loss(predict, target) ls_loss = self.calculate_ls_loss(users, items, target) l2_loss = self.l2_loss(user_e, item_e) loss = rec_loss + self.ls_weight * ls_loss + self.reg_weight * l2_loss return loss def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_e, item_e = self.forward(user, item) return torch.mul(user_e, item_e).sum(dim=1) def full_sort_predict(self, interaction): user_index = interaction[self.USER_ID] item_index = torch.tensor(range(self.n_items)).to(self.device) user = torch.unsqueeze(user_index, dim=1).repeat( 1, item_index.shape[0]) user = torch.flatten(user) item = torch.unsqueeze(item_index, dim=0).repeat( user_index.shape[0], 1) item = torch.flatten(item) user_e, item_e = self.forward(user, item) score = torch.mul(user_e, item_e).sum(dim=1) return score.view(-1)

true

7907cc9bf16ce33e7051101f1275c55a5b458738

2,292

py

Python

monitoring/google/cloud/monitoring_v3/gapic/notification_channel_service_client_config.py

jo2y/google-cloud-python

1b76727be16bc4335276f793340bb72d32be7166

[ "Apache-2.0" ]

1

2018-06-29T17:53:28.000Z

monitoring/google/cloud/monitoring_v3/gapic/notification_channel_service_client_config.py

jo2y/google-cloud-python

1b76727be16bc4335276f793340bb72d32be7166

[ "Apache-2.0" ]

1

2021-06-25T15:16:57.000Z

monitoring/google/cloud/monitoring_v3/gapic/notification_channel_service_client_config.py

jo2y/google-cloud-python

1b76727be16bc4335276f793340bb72d32be7166

[ "Apache-2.0" ]

1

2021-06-30T11:44:03.000Z

config = { "interfaces": { "google.monitoring.v3.NotificationChannelService": { "retry_codes": { "idempotent": ["DEADLINE_EXCEEDED", "UNAVAILABLE"], "non_idempotent": [] }, "retry_params": { "default": { "initial_retry_delay_millis": 100, "retry_delay_multiplier": 1.3, "max_retry_delay_millis": 60000, "initial_rpc_timeout_millis": 20000, "rpc_timeout_multiplier": 1.0, "max_rpc_timeout_millis": 20000, "total_timeout_millis": 600000 } }, "methods": { "ListNotificationChannelDescriptors": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default" }, "GetNotificationChannelDescriptor": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default" }, "ListNotificationChannels": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default" }, "GetNotificationChannel": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default" }, "CreateNotificationChannel": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default" }, "UpdateNotificationChannel": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default" }, "DeleteNotificationChannel": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default" } } } } }

38.847458

67

0.442845

config = { "interfaces": { "google.monitoring.v3.NotificationChannelService": { "retry_codes": { "idempotent": ["DEADLINE_EXCEEDED", "UNAVAILABLE"], "non_idempotent": [] }, "retry_params": { "default": { "initial_retry_delay_millis": 100, "retry_delay_multiplier": 1.3, "max_retry_delay_millis": 60000, "initial_rpc_timeout_millis": 20000, "rpc_timeout_multiplier": 1.0, "max_rpc_timeout_millis": 20000, "total_timeout_millis": 600000 } }, "methods": { "ListNotificationChannelDescriptors": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default" }, "GetNotificationChannelDescriptor": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default" }, "ListNotificationChannels": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default" }, "GetNotificationChannel": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default" }, "CreateNotificationChannel": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default" }, "UpdateNotificationChannel": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default" }, "DeleteNotificationChannel": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default" } } } } }

true

7907cd972452380f69e7a4e249088458c43baec0

5,533

py

Python

data/cnews_loader_bert.py

a414351664/Bert-THUCNews

4dad6900eb9ace8b4e4b3c33e97df9851796a442

[ "MIT" ]

24

2019-01-22T11:03:57.000Z

2021-09-15T03:06:11.000Z

data/cnews_loader_bert.py

pengwei-iie/Bert-THUCNews

a20749225091533b530f0e539bfaacbd3524fe99

[ "MIT" ]

2

2019-05-15T11:03:36.000Z

2019-06-29T14:36:33.000Z

data/cnews_loader_bert.py

pengwei-iie/Bert-THUCNews

a20749225091533b530f0e539bfaacbd3524fe99

[ "MIT" ]

16

2019-01-22T11:03:57.000Z

2021-04-18T15:29:30.000Z

# coding: utf-8 import sys from collections import Counter import numpy as np import tensorflow.contrib.keras as kr import tensorflow as tf if sys.version_info[0] > 2: is_py3 = True else: # reload(sys) sys.setdefaultencoding("utf-8") is_py3 = False def native_word(word, encoding='utf-8'): """如果在python2下面使用python3训练的模型，可考虑调用此函数转化一下字符编码""" if not is_py3: return word.encode(encoding) else: return word def native_content(content): if not is_py3: return content.decode('utf-8') else: return content def open_file(filename, mode='r'): """ 常用文件操作，可在python2和python3间切换. mode: 'r' or 'w' for read or write """ if is_py3: return open(filename, mode, encoding='utf-8', errors='ignore') else: return open(filename, mode) def read_file(filename): """读取文件数据""" contents, labels = [], [] with open_file(filename) as f: for line in f: # while True: # line = f.readline() try: label, content = line.strip().split('\t') contents.append(content) if content: # contents.append(list(native_content(content))) labels.append(native_content(label)) except: pass # if not line: # break return contents, labels def build_vocab(train_dir, vocab_dir, vocab_size=5000): """根据训练集构建词汇表，存储, x， y""" data_train, _ = read_file(train_dir) all_data = [] for content in data_train: all_data.extend(content) counter = Counter(all_data) count_pairs = counter.most_common(vocab_size - 1) words, _ = list(zip(*count_pairs)) # 添加一个 <PAD> 来将所有文本pad为同一长度 words = ['<PAD>'] + list(words) open_file(vocab_dir, mode='w').write('\n'.join(words) + '\n') def read_vocab(vocab_dir): """读取词汇表""" # words = open_file(vocab_dir).read().strip().split('\n') with open_file(vocab_dir) as fp: # 如果是py2 则每个值都转化为unicode words = [native_content(_.strip()) for _ in fp.readlines()] word_to_id = dict(zip(words, range(len(words)))) return words, word_to_id def read_category(): """读取分类目录，固定""" categories = ['体育', '财经', '房产', '家居', '教育', '科技', '时尚', '时政', '游戏', '娱乐'] categories = [native_content(x) for x in categories] cat_to_id = dict(zip(categories, range(len(categories)))) return categories, cat_to_id def to_words(content, words): """将id表示的内容转换为文字""" return ''.join(words[x] for x in content) def process_file(filename, word_to_id, cat_to_id, max_length=600): """将文件转换为id表示""" contents, labels = read_file(filename) # np.save('./train_x.npy', contents) # np.savetxt('./train_x.txt', contents, fmt='%s') data_id, label_id = [], [] for i in range(len(contents)): # data_id.append([word_to_id[x] for x in contents[i] if x in word_to_id]) label_id.append(cat_to_id[labels[i]]) # 使用keras提供的pad_sequences来将文本pad为固定长度 # x_pad = kr.preprocessing.sequence.pad_sequences(data_id, max_length) y_pad = kr.utils.to_categorical(label_id, num_classes=len(cat_to_id)) # 将标签转换为one-hot表示 return contents, y_pad def batch_iter(x, y, batch_size=64): """生成批次数据""" data_len = len(x) num_batch = int((data_len - 1) / batch_size) + 1 # 区别在于shuffle直接在原来的数组上进行操作，改变原来数组的顺序，无返回值。 # 而permutation不直接在原来的数组上进行操作，而是返回一个新的打乱顺序的数组，并不改变原来的数组。 indices = np.random.permutation(np.arange(data_len)) x_shuffle = np.array(x)[indices] y_shuffle = y[indices] for i in range(num_batch): start_id = i * batch_size end_id = min((i + 1) * batch_size, data_len) # yield x[start_id:end_id], y[start_id:end_id] yield x_shuffle[start_id:end_id], y_shuffle[start_id:end_id] def attention(inputs, attention_size, l2_reg_lambda): """ Attention mechanism layer. :param inputs: outputs of RNN/Bi-RNN layer (not final state) :param attention_size: linear size of attention weights :return: outputs of the passed RNN/Bi-RNN reduced with attention vector """ # In case of Bi-RNN input we need to concatenate outputs of its forward and backward parts if isinstance(inputs, tuple): inputs = tf.concat(2, inputs) sequence_length = inputs.get_shape()[1].value # the length of sequences processed in the antecedent RNN layer hidden_size = inputs.get_shape()[2].value # hidden size of the RNN layer # Attention mechanism W,b 相当于对RNN的输出做一个非线性变化，得到的结果在和u做内积 W_omega = tf.get_variable("W_omega", initializer=tf.random_normal([hidden_size, attention_size], stddev=0.1)) b_omega = tf.get_variable("b_omega", initializer=tf.random_normal([attention_size], stddev=0.1)) u_omega = tf.get_variable("u_omega", initializer=tf.random_normal([attention_size], stddev=0.1)) v = tf.tanh(tf.matmul(tf.reshape(inputs, [-1, hidden_size]), W_omega) + tf.reshape(b_omega, [1, -1])) vu = tf.matmul(v, tf.reshape(u_omega, [-1, 1])) exps = tf.reshape(tf.exp(vu), [-1, sequence_length]) alphas = exps / tf.reshape(tf.reduce_sum(exps, 1), [-1, 1]) # Output of Bi-RNN is reduced with attention vector output = tf.reduce_sum(inputs * tf.reshape(alphas, [-1, sequence_length, 1]), 1) #if l2_reg_lambda > 0: # l2_loss += tf.nn.l2_loss(W_omega) # l2_loss += tf.nn.l2_loss(b_omega) # l2_loss += tf.nn.l2_loss(u_omega) # tf.add_to_collection('losses', l2_loss) return output

32.739645

114

0.646665

import sys from collections import Counter import numpy as np import tensorflow.contrib.keras as kr import tensorflow as tf if sys.version_info[0] > 2: is_py3 = True else: sys.setdefaultencoding("utf-8") is_py3 = False def native_word(word, encoding='utf-8'): if not is_py3: return word.encode(encoding) else: return word def native_content(content): if not is_py3: return content.decode('utf-8') else: return content def open_file(filename, mode='r'): if is_py3: return open(filename, mode, encoding='utf-8', errors='ignore') else: return open(filename, mode) def read_file(filename): contents, labels = [], [] with open_file(filename) as f: for line in f: try: label, content = line.strip().split('\t') contents.append(content) if content: labels.append(native_content(label)) except: pass return contents, labels def build_vocab(train_dir, vocab_dir, vocab_size=5000): data_train, _ = read_file(train_dir) all_data = [] for content in data_train: all_data.extend(content) counter = Counter(all_data) count_pairs = counter.most_common(vocab_size - 1) words, _ = list(zip(*count_pairs)) words = ['<PAD>'] + list(words) open_file(vocab_dir, mode='w').write('\n'.join(words) + '\n') def read_vocab(vocab_dir): with open_file(vocab_dir) as fp: words = [native_content(_.strip()) for _ in fp.readlines()] word_to_id = dict(zip(words, range(len(words)))) return words, word_to_id def read_category(): categories = ['体育', '财经', '房产', '家居', '教育', '科技', '时尚', '时政', '游戏', '娱乐'] categories = [native_content(x) for x in categories] cat_to_id = dict(zip(categories, range(len(categories)))) return categories, cat_to_id def to_words(content, words): return ''.join(words[x] for x in content) def process_file(filename, word_to_id, cat_to_id, max_length=600): contents, labels = read_file(filename) data_id, label_id = [], [] for i in range(len(contents)): label_id.append(cat_to_id[labels[i]]) y_pad = kr.utils.to_categorical(label_id, num_classes=len(cat_to_id)) return contents, y_pad def batch_iter(x, y, batch_size=64): data_len = len(x) num_batch = int((data_len - 1) / batch_size) + 1 indices = np.random.permutation(np.arange(data_len)) x_shuffle = np.array(x)[indices] y_shuffle = y[indices] for i in range(num_batch): start_id = i * batch_size end_id = min((i + 1) * batch_size, data_len) yield x_shuffle[start_id:end_id], y_shuffle[start_id:end_id] def attention(inputs, attention_size, l2_reg_lambda): if isinstance(inputs, tuple): inputs = tf.concat(2, inputs) sequence_length = inputs.get_shape()[1].value hidden_size = inputs.get_shape()[2].value W_omega = tf.get_variable("W_omega", initializer=tf.random_normal([hidden_size, attention_size], stddev=0.1)) b_omega = tf.get_variable("b_omega", initializer=tf.random_normal([attention_size], stddev=0.1)) u_omega = tf.get_variable("u_omega", initializer=tf.random_normal([attention_size], stddev=0.1)) v = tf.tanh(tf.matmul(tf.reshape(inputs, [-1, hidden_size]), W_omega) + tf.reshape(b_omega, [1, -1])) vu = tf.matmul(v, tf.reshape(u_omega, [-1, 1])) exps = tf.reshape(tf.exp(vu), [-1, sequence_length]) alphas = exps / tf.reshape(tf.reduce_sum(exps, 1), [-1, 1]) output = tf.reduce_sum(inputs * tf.reshape(alphas, [-1, sequence_length, 1]), 1) return output

true

7907cdad81d346362d6a01b224b8cd2d83f39bf2

677

py

Python

setup.py

pharmbio/robot-imager

2256cea4cf7b28d0f575769d3675c97299ede10d

[ "MIT" ]

null

setup.py

pharmbio/robot-imager

2256cea4cf7b28d0f575769d3675c97299ede10d

[ "MIT" ]

null

setup.py

pharmbio/robot-imager

2256cea4cf7b28d0f575769d3675c97299ede10d

[ "MIT" ]

null

from setuptools import setup requirements = ''' flask ''' name='imager' console_scripts = f''' pf-moves={name}.moves_gui:main pf-flash={name}.flash:main imager={name}.cli:main ''' packages=f''' {name} {name}.utils ''' setup( name=name, packages=packages.split(), version='0.1', description='IMX imaging using the PreciseFlex robot arm and LiCONiC fridge', url='https://github.com/pharmbio/robot-imager', author='Dan Rosén', author_email='dan.rosen@farmbio.uu.se', python_requires='>=3.10', license='MIT', install_requires=requirements.split(), entry_points={'console_scripts': console_scripts.split()} )

20.515152

81

0.660266

from setuptools import setup requirements = ''' flask ''' name='imager' console_scripts = f''' pf-moves={name}.moves_gui:main pf-flash={name}.flash:main imager={name}.cli:main ''' packages=f''' {name} {name}.utils ''' setup( name=name, packages=packages.split(), version='0.1', description='IMX imaging using the PreciseFlex robot arm and LiCONiC fridge', url='https://github.com/pharmbio/robot-imager', author='Dan Rosén', author_email='dan.rosen@farmbio.uu.se', python_requires='>=3.10', license='MIT', install_requires=requirements.split(), entry_points={'console_scripts': console_scripts.split()} )

true

7907ce85299f0f04d1bb20affad859f9baaa78bc

35,432

py

Python

train.py

alexeypechorin/tibetan-transductive

e2356d5c0a7cbc2f2359d9cf5b6b18729fecd8de

[ "MIT" ]

1

2019-12-08T05:26:20.000Z

train.py

alexeypechorin/tibetan-transductive

e2356d5c0a7cbc2f2359d9cf5b6b18729fecd8de

[ "MIT" ]

null

train.py

alexeypechorin/tibetan-transductive

e2356d5c0a7cbc2f2359d9cf5b6b18729fecd8de

[ "MIT" ]

1

2020-09-03T14:51:53.000Z

import os import click import numpy as np from tqdm import tqdm from models.model_loader import load_model from torchvision.transforms import Compose from dataset.data_transform import Resize, Rotation, ElasticAndSine, ColorGradGausNoise, AddWidth, Normalize, ToGray, OnlyElastic, OnlySine, ColorGrad, ColorGausNoise from dataset.text_data import TextDataset, TextDatasetRandomFont from dataset.collate_fn import text_collate from utils.data_visualization import TbSummary from lr_policy import StepLR, DannLR import pickle as pkl import glob import torch from torch import nn from torch import optim from torch.autograd import Variable from torch.utils.data import DataLoader from warpctc_pytorch import CTCLoss from test import test from models.new_vat import VATLoss, VATLossSign, LabeledATLoss, LabeledAtAndUnlabeledTestVatLoss, VATonRnnSign, VATonRnnCnnSign, VATonCnnSign from dataset.dataset_metadata import SynthDataInfo @click.command() @click.option('--base-data-dir', type=str, default=os.path.expandvars ('../Data/'), help='Path to base data directory (all other data paths are relative to this one).') @click.option('--train-data-path', type=str, default=os.path.expandvars ('Synthetic/Prepared/data_train.txt'), help='Path to training dataset (image path to line text) text file (relative to base-data-dir)') @click.option('--train-base-dir', type=str, default=os.path.expandvars( 'Synthetic/Prepared/Images'), help='Path to directory containing training images (relative to base-data-dir)') @click.option('--orig-eval-data-path', type=str, default=os.path.expandvars( 'Test/Prepared/im2line.txt'), help='Path to original test dataset (image path to line text) text file (relative to base-data-dir)') @click.option('--orig-eval-base-dir', type=str, default=os.path.expandvars( 'Test/Prepared/LineImages'), help='Path to directory containing original test images (relative to base-data-dir)') @click.option('--synth-eval-data-path', type=str, default=os.path.expandvars ('Synthetic/Prepared/data_val.txt'), help='Path to synthetic evaluation dataset (image path to line text) text file (relative to base-data-dir)') @click.option('--synth-eval-base-dir', type=str, default=os.path.expandvars( 'Synthetic/Prepared/Images'), help='Path to directory containing synthetic evaluation images (relative to base-data-dir)') @click.option('--lexicon-path', type=str, default=os.path.expandvars('char_to_class.pkl'), help='Path to alphabet lexicon (letter to id), relative to base-data-dir.') @click.option('--seq-proj', type=str, default="10x20", help='Projection of sequence') @click.option('--backend', type=str, default="resnet18", help='Backend network to use (default is resnet18)') @click.option('--snapshot', type=str, default=None, help='Path to pre-trained weights') @click.option('--input-height', type=int, default=64, help='Height of input images to network') @click.option('--base-lr', type=float, default=1e-4, help='Base learning rate.') # was e-3 #@click.option('--lr-decay', type=float, default=1e-4, help='Base learning rate') # was 0.0001 @click.option('--elastic-alpha', type=float, default=34, help='Elastic augmentation parameter alpha.') @click.option('--elastic-sigma', type=float, default=3, help='Elastic augmentation parameter sigma.') @click.option('--step-size', type=int, default=500, help='Step size for step lr change.') @click.option('--max-iter', type=int, default=6000, help='Max iterations for taining') @click.option('--batch-size', type=int, default=8, help='Batch size for training') @click.option('--output-dir', type=str, default='../Output/exp1', help='Path to save output snapshot') @click.option('--test-iter', type=int, default=1000, help='Number of iterations between test evaluation.') @click.option('--show-iter', type=int, default=1000, help='Number of iterations between showing images in tensorboard.') @click.option('--test-init', type=bool, default=False, help='Wether to test after network initialization initialization') @click.option('--use-gpu', type=bool, default=True, help='Whether to use the gpu') @click.option('--use-no-font-repeat-data', type=bool, default=True, help='Parameter to remove (always true) - whether to use random training data.') @click.option('--do-vat', type=bool, default=False, help='Whether to do VAT on synthetic trainig data') @click.option('--do-at', type=bool, default=False, help='Whether to do AT on synthetic trainig data') @click.option('--vat-ratio', type=float, default=1, help='Ratio of vat on train data loss vs base loss') @click.option('--test-vat-ratio', type=float, default=1, help='Ratio on vat on test data loss vs base loss') @click.option('--vat-epsilon', type=float, default=2.5, help='VAT on train hyperparameter - epsilon') @click.option('--vat-ip', type=int, default=1, help='VAT on train hyperparameter - number of power iterations') @click.option('--vat-xi', type=float, default=10., help='VAT on train hyperparameter - xi') @click.option('--vat-sign', type=bool, default=False, help='VAT on train hyperparameter - whether to do sign on vat loss') @click.option('--do-remove-augs', type=bool, default=False, help='Whether to remove some of the augmentations (for ablation study)') @click.option('--aug-to-remove', type=str, default='', help="with augmentation to remover out of ['elastic', 'sine', 'sine_rotate', 'rotation', 'color_aug', 'color_gaus', 'color_sine']") @click.option('--do-beam-search', type=bool, default=False, help='whether to do beam search inference in evaluation') @click.option('--dropout-conv', type=bool, default=False, help='Whether to do dropout between convolution and rnn.') @click.option('--dropout-rnn', type=bool, default=False, help='Whether to do dropout in rnn.') @click.option('--dropout-output', type=bool, default=False, help='Whether to do dropout after rnn.') @click.option('--do-ema', type=bool, default=False, help='Whether to do exponential moving average on weights') @click.option('--do-gray', type=bool, default=False, help='whether to use grayscale instread of rgb') @click.option('--do-test-vat', type=bool, default=False, help='Whether to do VAT loss on original test data') @click.option('--do-test-entropy', type=bool, default=False, help='Whether to do entropy loss on original test data') @click.option('--do-test-vat-cnn', type=bool, default=False, help='Whether to do VAT loss on original test data only for cnn part') @click.option('--do-test-vat-rnn', type=bool, default=False, help='Whether to do VAT loss on original test data only for rnn part') @click.option('--ada-after-rnn', type=bool, default=False, help='Whether to do adversarial domain adaptaion on rnn part') @click.option('--ada-before-rnn', type=bool, default=False, help='Whether to do adversarial domain adaptaion on cnn part') @click.option('--do-ada-lr', type=bool, default=False, help='Whether to do lr rule suitable of adversarial domain adaptaion (from article)') @click.option('--ada-ratio', type=float, default=1, help='Ratio of ADA loss vs base loss') @click.option('--rnn-hidden-size', type=int, default=128, help='Size of rnn hidden layer') @click.option('--do-lr-step', type=bool, default=False, help='Visualize output') @click.option('--dataset-name', type=str, default='tibetan', help='Dataset name, currently wiener or tibetan') def main(base_data_dir, train_data_path, train_base_dir, orig_eval_data_path, orig_eval_base_dir, synth_eval_data_path, synth_eval_base_dir, lexicon_path, seq_proj, backend, snapshot, input_height, base_lr, elastic_alpha, elastic_sigma, step_size, max_iter, batch_size, output_dir, test_iter, show_iter, test_init, use_gpu, use_no_font_repeat_data, do_vat, do_at, vat_ratio, test_vat_ratio, vat_epsilon, vat_ip, vat_xi, vat_sign, do_remove_augs, aug_to_remove, do_beam_search, dropout_conv, dropout_rnn, dropout_output, do_ema, do_gray, do_test_vat, do_test_entropy, do_test_vat_cnn, do_test_vat_rnn, ada_after_rnn, ada_before_rnn, do_ada_lr, ada_ratio, rnn_hidden_size, do_lr_step, dataset_name ): if not do_lr_step and not do_ada_lr: raise NotImplementedError('learning rate should be either step or ada.') train_data_path = os.path.join(base_data_dir, train_data_path) train_base_dir = os.path.join(base_data_dir, train_base_dir) synth_eval_data_path = os.path.join(base_data_dir, synth_eval_data_path) synth_eval_base_dir = os.path.join(base_data_dir, synth_eval_base_dir) orig_eval_data_path = os.path.join(base_data_dir, orig_eval_data_path) orig_eval_base_dir = os.path.join(base_data_dir, orig_eval_base_dir) lexicon_path = os.path.join(base_data_dir, lexicon_path) all_parameters = locals() cuda = use_gpu #print(train_base_dir) if output_dir is not None: os.makedirs(output_dir, exist_ok=True) tb_writer = TbSummary(output_dir) output_dir = os.path.join(output_dir, 'model') os.makedirs(output_dir, exist_ok=True) with open(lexicon_path, 'rb') as f: lexicon = pkl.load(f) #print(sorted(lexicon.items(), key=operator.itemgetter(1))) with open(os.path.join(output_dir, 'params.txt'),'w') as f: f.writelines(str(all_parameters)) print(all_parameters) print('new vat') sin_magnitude = 4 rotate_max_angle = 2 dataset_info = SynthDataInfo(None, None, None, dataset_name.lower()) train_fonts = dataset_info.font_names all_args = locals() allowed_removals = ['elastic', 'sine', 'sine_rotate', 'rotation', 'color_aug', 'color_gaus', 'color_sine'] if do_remove_augs and aug_to_remove not in allowed_removals: raise Exception('augmentation removal value is not allowed.') if do_remove_augs: rand_trans = [] if aug_to_remove == 'elastic': print('doing sine transform :)') rand_trans.append(OnlySine(sin_magnitude=sin_magnitude)) elif aug_to_remove in ['sine', 'sine_rotate']: print('doing elastic transform :)') rand_trans.append(OnlyElastic(elastic_alpha=elastic_alpha, elastic_sigma=elastic_sigma)) if aug_to_remove not in ['elastic', 'sine', 'sine_rotate']: print('doing elastic transform :)') print('doing sine transform :)') rand_trans.append(ElasticAndSine(elastic_alpha=elastic_alpha, elastic_sigma=elastic_sigma, sin_magnitude=sin_magnitude)) if aug_to_remove not in ['rotation', 'sine_rotate']: print('doing rotation transform :)') rand_trans.append(Rotation(angle=rotate_max_angle, fill_value=255)) if aug_to_remove not in ['color_aug', 'color_gaus', 'color_sine']: print('doing color_aug transform :)') rand_trans.append(ColorGradGausNoise()) elif aug_to_remove == 'color_gaus': print('doing color_sine transform :)') rand_trans.append(ColorGrad()) elif aug_to_remove == 'color_sine': print('doing color_gaus transform :)') rand_trans.append(ColorGausNoise()) else: print('doing all transforms :)') rand_trans = [ ElasticAndSine(elastic_alpha=elastic_alpha, elastic_sigma=elastic_sigma, sin_magnitude=sin_magnitude), Rotation(angle=rotate_max_angle, fill_value=255), ColorGradGausNoise()] if do_gray: rand_trans = rand_trans + [Resize(hight=input_height), AddWidth(), ToGray(), Normalize()] else: rand_trans = rand_trans + [Resize(hight=input_height), AddWidth(), Normalize()] transform_random = Compose(rand_trans) if do_gray: transform_simple = Compose([ Resize(hight=input_height), AddWidth(), ToGray(), Normalize() ]) else: transform_simple = Compose([ Resize(hight=input_height), AddWidth(), Normalize() ]) if use_no_font_repeat_data: print('creating dataset') train_data = TextDatasetRandomFont(data_path=train_data_path, lexicon=lexicon, base_path=train_base_dir, transform=transform_random, fonts=train_fonts) print('finished creating dataset') else: print('train data path:\n{}'.format(train_data_path)) print('train_base_dir:\n{}'.format(train_base_dir)) train_data = TextDataset(data_path=train_data_path, lexicon=lexicon, base_path=train_base_dir, transform=transform_random, fonts=train_fonts) synth_eval_data = TextDataset(data_path=synth_eval_data_path, lexicon=lexicon, base_path=synth_eval_base_dir, transform=transform_random, fonts=train_fonts) orig_eval_data = TextDataset(data_path=orig_eval_data_path, lexicon=lexicon, base_path=orig_eval_base_dir, transform=transform_simple, fonts=None) if do_test_vat or do_test_vat_rnn or do_test_vat_cnn: orig_vat_data = TextDataset(data_path=orig_eval_data_path, lexicon=lexicon, base_path=orig_eval_base_dir, transform=transform_simple, fonts=None) if ada_after_rnn or ada_before_rnn: orig_ada_data = TextDataset(data_path=orig_eval_data_path, lexicon=lexicon, base_path=orig_eval_base_dir, transform=transform_simple, fonts=None) #else: # train_data = TestDataset(transform=transform, abc=abc).set_mode("train") # synth_eval_data = TestDataset(transform=transform, abc=abc).set_mode("test") # orig_eval_data = TestDataset(transform=transform, abc=abc).set_mode("test") seq_proj = [int(x) for x in seq_proj.split('x')] net = load_model(lexicon=train_data.get_lexicon(), seq_proj=seq_proj, backend=backend, snapshot=snapshot, cuda=cuda, do_beam_search=do_beam_search, dropout_conv=dropout_conv, dropout_rnn=dropout_rnn, dropout_output=dropout_output, do_ema=do_ema, ada_after_rnn=ada_after_rnn, ada_before_rnn=ada_before_rnn, rnn_hidden_size=rnn_hidden_size ) optimizer = optim.Adam(net.parameters(), lr = base_lr, weight_decay=0.0001) if do_ada_lr: print('using ada lr') lr_scheduler = DannLR(optimizer, max_iter=max_iter) elif do_lr_step: print('using step lr') lr_scheduler = StepLR(optimizer, step_size=step_size, max_iter=max_iter) loss_function = CTCLoss() synth_avg_ed_best = float("inf") orig_avg_ed_best = float("inf") epoch_count = 0 if do_test_vat or do_test_vat_rnn or do_test_vat_cnn: collate_vat = lambda x: text_collate(x, do_mask=True) vat_load = DataLoader(orig_vat_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate_vat) vat_len = len(vat_load) cur_vat = 0 vat_iter = iter(vat_load) if ada_after_rnn or ada_before_rnn: collate_ada = lambda x: text_collate(x, do_mask=True) ada_load = DataLoader(orig_ada_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate_ada) ada_len = len(ada_load) cur_ada = 0 ada_iter = iter(ada_load) loss_domain = torch.nn.NLLLoss() while True: collate = lambda x: text_collate(x, do_mask=(do_vat or ada_before_rnn or ada_after_rnn)) data_loader = DataLoader(train_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate) loss_mean_ctc = [] loss_mean_vat = [] loss_mean_at = [] loss_mean_comp = [] loss_mean_total = [] loss_mean_test_vat = [] loss_mean_test_pseudo = [] loss_mean_test_rand = [] loss_mean_ada_rnn_s = [] loss_mean_ada_rnn_t = [] loss_mean_ada_cnn_s = [] loss_mean_ada_cnn_t = [] iterator = tqdm(data_loader) iter_count = 0 for iter_num, sample in enumerate(iterator): total_iter = (epoch_count * len(data_loader)) + iter_num if ((total_iter > 1) and total_iter % test_iter == 0) or (test_init and total_iter == 0): # epoch_count != 0 and print("Test phase") net = net.eval() if do_ema: net.start_test() synth_acc, synth_avg_ed, synth_avg_no_stop_ed, synth_avg_loss = test(net, synth_eval_data, synth_eval_data.get_lexicon(), cuda, visualize=False, dataset_info=dataset_info, batch_size=batch_size, tb_writer=tb_writer, n_iter=total_iter, initial_title='val_synth', loss_function=loss_function, output_path=os.path.join( output_dir, 'results'), do_beam_search=False) orig_acc, orig_avg_ed, orig_avg_no_stop_ed, orig_avg_loss = test(net, orig_eval_data, orig_eval_data.get_lexicon(), cuda, visualize=False, dataset_info=dataset_info, batch_size=batch_size, tb_writer=tb_writer, n_iter=total_iter, initial_title='test_orig', loss_function=loss_function, output_path=os.path.join(output_dir, 'results'), do_beam_search=do_beam_search) net = net.train() #save periodic if output_dir is not None and total_iter // 30000: periodic_save = os.path.join(output_dir, 'periodic_save') os.makedirs(periodic_save, exist_ok=True) old_save = glob.glob(os.path.join(periodic_save,'*')) torch.save(net.state_dict(), os.path.join(output_dir, "crnn_" + backend + "_" + str(total_iter))) if orig_avg_no_stop_ed < orig_avg_ed_best: orig_avg_ed_best = orig_avg_no_stop_ed if output_dir is not None: torch.save(net.state_dict(), os.path.join(output_dir, "crnn_" + backend + "_best")) if synth_avg_no_stop_ed < synth_avg_ed_best: synth_avg_ed_best = synth_avg_no_stop_ed if do_ema: net.end_test() print("synth: avg_ed_best: {}\t avg_ed: {}; avg_nostop_ed: {}; acc: {}".format(synth_avg_ed_best, synth_avg_ed, synth_avg_no_stop_ed, synth_acc)) print("orig: avg_ed_best: {}\t avg_ed: {}; avg_nostop_ed: {}; acc: {}".format(orig_avg_ed_best, orig_avg_ed, orig_avg_no_stop_ed, orig_acc)) tb_writer.get_writer().add_scalars('data/test', {'synth_ed_total': synth_avg_ed, 'synth_ed_no_stop': synth_avg_no_stop_ed, 'synth_avg_loss': synth_avg_loss, 'orig_ed_total': orig_avg_ed, 'orig_ed_no_stop': orig_avg_no_stop_ed, 'orig_avg_loss': orig_avg_loss }, total_iter) if len(loss_mean_ctc) > 0: train_dict = {'mean_ctc_loss': np.mean(loss_mean_ctc)} if do_vat: train_dict = {**train_dict, **{'mean_vat_loss':np.mean(loss_mean_vat)}} if do_at: train_dict = {**train_dict, **{'mean_at_loss':np.mean(loss_mean_at)}} if do_test_vat: train_dict = {**train_dict, **{'mean_test_vat_loss': np.mean(loss_mean_test_vat)}} if do_test_vat_rnn and do_test_vat_cnn: train_dict = {**train_dict, **{'mean_test_vat_crnn_loss': np.mean(loss_mean_test_vat)}} elif do_test_vat_rnn: train_dict = {**train_dict, **{'mean_test_vat_rnn_loss': np.mean(loss_mean_test_vat)}} elif do_test_vat_cnn: train_dict = {**train_dict, **{'mean_test_vat_cnn_loss': np.mean(loss_mean_test_vat)}} if ada_after_rnn: train_dict = {**train_dict, **{'mean_ada_rnn_s_loss': np.mean(loss_mean_ada_rnn_s), 'mean_ada_rnn_t_loss': np.mean(loss_mean_ada_rnn_t)}} if ada_before_rnn: train_dict = {**train_dict, **{'mean_ada_cnn_s_loss': np.mean(loss_mean_ada_cnn_s), 'mean_ada_cnn_t_loss': np.mean(loss_mean_ada_cnn_t)}} print(train_dict) tb_writer.get_writer().add_scalars('data/train', train_dict, total_iter) ''' # for multi-gpu support if sample["img"].size(0) % len(gpu.split(',')) != 0: continue ''' optimizer.zero_grad() imgs = Variable(sample["img"]) #print("images sizes are:") #print(sample["img"].shape) if do_vat or ada_after_rnn or ada_before_rnn: mask = sample['mask'] labels_flatten = Variable(sample["seq"]).view(-1) label_lens = Variable(sample["seq_len"].int()) #print("image sequence length is:") #print(sample["im_seq_len"]) #print("label sequence length is:") #print(sample["seq_len"].view(1,-1)) img_seq_lens = sample["im_seq_len"] if cuda: imgs = imgs.cuda() if do_vat or ada_after_rnn or ada_before_rnn: mask = mask.cuda() if do_ada_lr: ada_p = float(iter_count) / max_iter lr_scheduler.update(ada_p) if ada_before_rnn or ada_after_rnn: if not do_ada_lr: ada_p = float(iter_count) / max_iter ada_alpha = 2. / (1. + np.exp(-10. * ada_p)) - 1 if cur_ada >= ada_len: ada_load = DataLoader(orig_ada_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate_ada) ada_len = len(ada_load) cur_ada = 0 ada_iter = iter(ada_load) ada_batch = next(ada_iter) cur_ada += 1 ada_imgs = Variable(ada_batch["img"]) ada_img_seq_lens = ada_batch["im_seq_len"] ada_mask = ada_batch['mask'].byte() if cuda: ada_imgs = ada_imgs.cuda() _, ada_cnn, ada_rnn = net(ada_imgs, ada_img_seq_lens, ada_alpha=ada_alpha, mask=ada_mask) if ada_before_rnn: ada_num_features = ada_cnn.size(0) else: ada_num_features = ada_rnn.size(0) domain_label = torch.zeros(ada_num_features) domain_label = domain_label.long() if cuda: domain_label = domain_label.cuda() domain_label = Variable(domain_label) if ada_before_rnn: err_ada_cnn_t = loss_domain(ada_cnn, domain_label) if ada_after_rnn: err_ada_rnn_t = loss_domain(ada_rnn, domain_label) if do_test_vat and do_at: # test part! if cur_vat >= vat_len: vat_load = DataLoader(orig_vat_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate_vat) vat_len = len(vat_load) cur_vat = 0 vat_iter = iter(vat_load) test_vat_batch = next(vat_iter) cur_vat += 1 test_vat_mask = test_vat_batch['mask'] test_vat_imgs = Variable(test_vat_batch["img"]) test_vat_img_seq_lens = test_vat_batch["im_seq_len"] if cuda: test_vat_imgs = test_vat_imgs.cuda() test_vat_mask = test_vat_mask.cuda() # train part at_test_vat_loss = LabeledAtAndUnlabeledTestVatLoss(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) at_loss, test_vat_loss = at_test_vat_loss(model=net, train_x=imgs, train_labels_flatten=labels_flatten, train_img_seq_lens=img_seq_lens, train_label_lens=label_lens, batch_size=batch_size, test_x=test_vat_imgs, test_seq_len=test_vat_img_seq_lens, test_mask=test_vat_mask) elif do_test_vat or do_test_vat_rnn or do_test_vat_cnn: if cur_vat >= vat_len: vat_load = DataLoader(orig_vat_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate_vat) vat_len = len(vat_load) cur_vat = 0 vat_iter = iter(vat_load) vat_batch = next(vat_iter) cur_vat += 1 vat_mask = vat_batch['mask'] vat_imgs = Variable(vat_batch["img"]) vat_img_seq_lens = vat_batch["im_seq_len"] if cuda: vat_imgs = vat_imgs.cuda() vat_mask = vat_mask.cuda() if do_test_vat: if do_test_vat_rnn or do_test_vat_cnn: raise "can only do one of do_test_vat | (do_test_vat_rnn, do_test_vat_cnn)" if vat_sign == True: test_vat_loss = VATLossSign(do_test_entropy=do_test_entropy, xi=vat_xi, eps=vat_epsilon, ip=vat_ip) else: test_vat_loss = VATLoss(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) elif do_test_vat_rnn and do_test_vat_cnn: test_vat_loss = VATonRnnCnnSign(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) elif do_test_vat_rnn: test_vat_loss = VATonRnnSign(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) elif do_test_vat_cnn: test_vat_loss = VATonCnnSign(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) if do_test_vat_cnn and do_test_vat_rnn: test_vat_loss, cnn_lds, rnn_lds = test_vat_loss(net, vat_imgs, vat_img_seq_lens, vat_mask) elif do_test_vat: test_vat_loss = test_vat_loss(net, vat_imgs, vat_img_seq_lens, vat_mask) elif do_vat: vat_loss = VATLoss(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) vat_loss = vat_loss(net, imgs, img_seq_lens, mask) elif do_at: at_loss = LabeledATLoss(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) at_loss = at_loss(net, imgs, labels_flatten, img_seq_lens, label_lens, batch_size) if ada_after_rnn or ada_before_rnn: preds, ada_cnn, ada_rnn = net(imgs, img_seq_lens, ada_alpha=ada_alpha, mask=mask) if ada_before_rnn: ada_num_features = ada_cnn.size(0) else: ada_num_features = ada_rnn.size(0) domain_label = torch.ones(ada_num_features) domain_label = domain_label.long() if cuda: domain_label = domain_label.cuda() domain_label = Variable(domain_label) if ada_before_rnn: err_ada_cnn_s = loss_domain(ada_cnn, domain_label) if ada_after_rnn: err_ada_rnn_s = loss_domain(ada_rnn, domain_label) else: preds = net(imgs, img_seq_lens) ''' if output_dir is not None: if (show_iter is not None and iter_num != 0 and iter_num % show_iter == 0): print_data_visuals(net, tb_writer, train_data.get_lexicon(), sample["img"], labels_flatten, label_lens, preds, ((epoch_count * len(data_loader)) + iter_num)) ''' loss_ctc = loss_function(preds, labels_flatten, Variable(torch.IntTensor(np.array(img_seq_lens))), label_lens) / batch_size if loss_ctc.data[0] in [float("inf"), -float("inf")]: print("warnning: loss should not be inf.") continue total_loss = loss_ctc if do_vat: #mask = sample['mask'] #if cuda: # mask = mask.cuda() #vat_loss = virtual_adversarial_loss(net, imgs, img_seq_lens, mask, is_training=True, do_entropy=False, epsilon=vat_epsilon, num_power_iterations=1, # xi=1e-6, average_loss=True) total_loss = total_loss + vat_ratio * vat_loss.cpu() if do_test_vat or do_test_vat_rnn or do_test_vat_cnn: total_loss = total_loss + test_vat_ratio * test_vat_loss.cpu() if ada_before_rnn: total_loss = total_loss + ada_ratio * err_ada_cnn_s.cpu() + ada_ratio * err_ada_cnn_t.cpu() if ada_after_rnn: total_loss = total_loss + ada_ratio * err_ada_rnn_s.cpu() + ada_ratio * err_ada_rnn_t.cpu() total_loss.backward() nn.utils.clip_grad_norm(net.parameters(), 10.0) if -400 < loss_ctc.data[0] < 400: loss_mean_ctc.append(loss_ctc.data[0]) if -1000 < total_loss.data[0] < 1000: loss_mean_total.append(total_loss.data[0]) if len(loss_mean_total) > 100: loss_mean_total = loss_mean_total[-100:] status = "epoch: {0:5d}; iter_num: {1:5d}; lr: {2:.2E}; loss_mean: {3:.3f}; loss: {4:.3f}".format(epoch_count, lr_scheduler.last_iter, lr_scheduler.get_lr(), np.mean(loss_mean_total), loss_ctc.data[0]) if ada_after_rnn: loss_mean_ada_rnn_s.append(err_ada_rnn_s.data[0]) loss_mean_ada_rnn_t.append(err_ada_rnn_t.data[0]) status += "; ladatrnns: {0:.3f}; ladatrnnt: {1:.3f}".format( err_ada_rnn_s.data[0], err_ada_rnn_t.data[0] ) if ada_before_rnn: loss_mean_ada_cnn_s.append(err_ada_cnn_s.data[0]) loss_mean_ada_cnn_t.append(err_ada_cnn_t.data[0]) status += "; ladatcnns: {0:.3f}; ladatcnnt: {1:.3f}".format( err_ada_cnn_s.data[0], err_ada_cnn_t.data[0] ) if do_vat: loss_mean_vat.append(vat_loss.data[0]) status += "; lvat: {0:.3f}".format( vat_loss.data[0] ) if do_at: loss_mean_at.append(at_loss.data[0]) status += "; lat: {0:.3f}".format( at_loss.data[0] ) if do_test_vat: loss_mean_test_vat.append(test_vat_loss.data[0]) status += "; l_tvat: {0:.3f}".format( test_vat_loss.data[0] ) if do_test_vat_rnn or do_test_vat_cnn: loss_mean_test_vat.append(test_vat_loss.data[0]) if do_test_vat_rnn and do_test_vat_cnn: status += "; l_tvatc: {}".format( cnn_lds.data[0] ) status += "; l_tvatr: {}".format( rnn_lds.data[0] ) else: status += "; l_tvat: {}".format( test_vat_loss.data[0] ) iterator.set_description(status) optimizer.step() if do_lr_step: lr_scheduler.step() if do_ema: net.udate_ema() iter_count += 1 if output_dir is not None: torch.save(net.state_dict(), os.path.join(output_dir, "crnn_" + backend + "_last")) epoch_count += 1 return if __name__ == '__main__': main()

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import os import click import numpy as np from tqdm import tqdm from models.model_loader import load_model from torchvision.transforms import Compose from dataset.data_transform import Resize, Rotation, ElasticAndSine, ColorGradGausNoise, AddWidth, Normalize, ToGray, OnlyElastic, OnlySine, ColorGrad, ColorGausNoise from dataset.text_data import TextDataset, TextDatasetRandomFont from dataset.collate_fn import text_collate from utils.data_visualization import TbSummary from lr_policy import StepLR, DannLR import pickle as pkl import glob import torch from torch import nn from torch import optim from torch.autograd import Variable from torch.utils.data import DataLoader from warpctc_pytorch import CTCLoss from test import test from models.new_vat import VATLoss, VATLossSign, LabeledATLoss, LabeledAtAndUnlabeledTestVatLoss, VATonRnnSign, VATonRnnCnnSign, VATonCnnSign from dataset.dataset_metadata import SynthDataInfo @click.command() @click.option('--base-data-dir', type=str, default=os.path.expandvars ('../Data/'), help='Path to base data directory (all other data paths are relative to this one).') @click.option('--train-data-path', type=str, default=os.path.expandvars ('Synthetic/Prepared/data_train.txt'), help='Path to training dataset (image path to line text) text file (relative to base-data-dir)') @click.option('--train-base-dir', type=str, default=os.path.expandvars( 'Synthetic/Prepared/Images'), help='Path to directory containing training images (relative to base-data-dir)') @click.option('--orig-eval-data-path', type=str, default=os.path.expandvars( 'Test/Prepared/im2line.txt'), help='Path to original test dataset (image path to line text) text file (relative to base-data-dir)') @click.option('--orig-eval-base-dir', type=str, default=os.path.expandvars( 'Test/Prepared/LineImages'), help='Path to directory containing original test images (relative to base-data-dir)') @click.option('--synth-eval-data-path', type=str, default=os.path.expandvars ('Synthetic/Prepared/data_val.txt'), help='Path to synthetic evaluation dataset (image path to line text) text file (relative to base-data-dir)') @click.option('--synth-eval-base-dir', type=str, default=os.path.expandvars( 'Synthetic/Prepared/Images'), help='Path to directory containing synthetic evaluation images (relative to base-data-dir)') @click.option('--lexicon-path', type=str, default=os.path.expandvars('char_to_class.pkl'), help='Path to alphabet lexicon (letter to id), relative to base-data-dir.') @click.option('--seq-proj', type=str, default="10x20", help='Projection of sequence') @click.option('--backend', type=str, default="resnet18", help='Backend network to use (default is resnet18)') @click.option('--snapshot', type=str, default=None, help='Path to pre-trained weights') @click.option('--input-height', type=int, default=64, help='Height of input images to network') @click.option('--base-lr', type=float, default=1e-4, help='Base learning rate.') on('--elastic-alpha', type=float, default=34, help='Elastic augmentation parameter alpha.') @click.option('--elastic-sigma', type=float, default=3, help='Elastic augmentation parameter sigma.') @click.option('--step-size', type=int, default=500, help='Step size for step lr change.') @click.option('--max-iter', type=int, default=6000, help='Max iterations for taining') @click.option('--batch-size', type=int, default=8, help='Batch size for training') @click.option('--output-dir', type=str, default='../Output/exp1', help='Path to save output snapshot') @click.option('--test-iter', type=int, default=1000, help='Number of iterations between test evaluation.') @click.option('--show-iter', type=int, default=1000, help='Number of iterations between showing images in tensorboard.') @click.option('--test-init', type=bool, default=False, help='Wether to test after network initialization initialization') @click.option('--use-gpu', type=bool, default=True, help='Whether to use the gpu') @click.option('--use-no-font-repeat-data', type=bool, default=True, help='Parameter to remove (always true) - whether to use random training data.') @click.option('--do-vat', type=bool, default=False, help='Whether to do VAT on synthetic trainig data') @click.option('--do-at', type=bool, default=False, help='Whether to do AT on synthetic trainig data') @click.option('--vat-ratio', type=float, default=1, help='Ratio of vat on train data loss vs base loss') @click.option('--test-vat-ratio', type=float, default=1, help='Ratio on vat on test data loss vs base loss') @click.option('--vat-epsilon', type=float, default=2.5, help='VAT on train hyperparameter - epsilon') @click.option('--vat-ip', type=int, default=1, help='VAT on train hyperparameter - number of power iterations') @click.option('--vat-xi', type=float, default=10., help='VAT on train hyperparameter - xi') @click.option('--vat-sign', type=bool, default=False, help='VAT on train hyperparameter - whether to do sign on vat loss') @click.option('--do-remove-augs', type=bool, default=False, help='Whether to remove some of the augmentations (for ablation study)') @click.option('--aug-to-remove', type=str, default='', help="with augmentation to remover out of ['elastic', 'sine', 'sine_rotate', 'rotation', 'color_aug', 'color_gaus', 'color_sine']") @click.option('--do-beam-search', type=bool, default=False, help='whether to do beam search inference in evaluation') @click.option('--dropout-conv', type=bool, default=False, help='Whether to do dropout between convolution and rnn.') @click.option('--dropout-rnn', type=bool, default=False, help='Whether to do dropout in rnn.') @click.option('--dropout-output', type=bool, default=False, help='Whether to do dropout after rnn.') @click.option('--do-ema', type=bool, default=False, help='Whether to do exponential moving average on weights') @click.option('--do-gray', type=bool, default=False, help='whether to use grayscale instread of rgb') @click.option('--do-test-vat', type=bool, default=False, help='Whether to do VAT loss on original test data') @click.option('--do-test-entropy', type=bool, default=False, help='Whether to do entropy loss on original test data') @click.option('--do-test-vat-cnn', type=bool, default=False, help='Whether to do VAT loss on original test data only for cnn part') @click.option('--do-test-vat-rnn', type=bool, default=False, help='Whether to do VAT loss on original test data only for rnn part') @click.option('--ada-after-rnn', type=bool, default=False, help='Whether to do adversarial domain adaptaion on rnn part') @click.option('--ada-before-rnn', type=bool, default=False, help='Whether to do adversarial domain adaptaion on cnn part') @click.option('--do-ada-lr', type=bool, default=False, help='Whether to do lr rule suitable of adversarial domain adaptaion (from article)') @click.option('--ada-ratio', type=float, default=1, help='Ratio of ADA loss vs base loss') @click.option('--rnn-hidden-size', type=int, default=128, help='Size of rnn hidden layer') @click.option('--do-lr-step', type=bool, default=False, help='Visualize output') @click.option('--dataset-name', type=str, default='tibetan', help='Dataset name, currently wiener or tibetan') def main(base_data_dir, train_data_path, train_base_dir, orig_eval_data_path, orig_eval_base_dir, synth_eval_data_path, synth_eval_base_dir, lexicon_path, seq_proj, backend, snapshot, input_height, base_lr, elastic_alpha, elastic_sigma, step_size, max_iter, batch_size, output_dir, test_iter, show_iter, test_init, use_gpu, use_no_font_repeat_data, do_vat, do_at, vat_ratio, test_vat_ratio, vat_epsilon, vat_ip, vat_xi, vat_sign, do_remove_augs, aug_to_remove, do_beam_search, dropout_conv, dropout_rnn, dropout_output, do_ema, do_gray, do_test_vat, do_test_entropy, do_test_vat_cnn, do_test_vat_rnn, ada_after_rnn, ada_before_rnn, do_ada_lr, ada_ratio, rnn_hidden_size, do_lr_step, dataset_name ): if not do_lr_step and not do_ada_lr: raise NotImplementedError('learning rate should be either step or ada.') train_data_path = os.path.join(base_data_dir, train_data_path) train_base_dir = os.path.join(base_data_dir, train_base_dir) synth_eval_data_path = os.path.join(base_data_dir, synth_eval_data_path) synth_eval_base_dir = os.path.join(base_data_dir, synth_eval_base_dir) orig_eval_data_path = os.path.join(base_data_dir, orig_eval_data_path) orig_eval_base_dir = os.path.join(base_data_dir, orig_eval_base_dir) lexicon_path = os.path.join(base_data_dir, lexicon_path) all_parameters = locals() cuda = use_gpu if output_dir is not None: os.makedirs(output_dir, exist_ok=True) tb_writer = TbSummary(output_dir) output_dir = os.path.join(output_dir, 'model') os.makedirs(output_dir, exist_ok=True) with open(lexicon_path, 'rb') as f: lexicon = pkl.load(f) with open(os.path.join(output_dir, 'params.txt'),'w') as f: f.writelines(str(all_parameters)) print(all_parameters) print('new vat') sin_magnitude = 4 rotate_max_angle = 2 dataset_info = SynthDataInfo(None, None, None, dataset_name.lower()) train_fonts = dataset_info.font_names all_args = locals() allowed_removals = ['elastic', 'sine', 'sine_rotate', 'rotation', 'color_aug', 'color_gaus', 'color_sine'] if do_remove_augs and aug_to_remove not in allowed_removals: raise Exception('augmentation removal value is not allowed.') if do_remove_augs: rand_trans = [] if aug_to_remove == 'elastic': print('doing sine transform :)') rand_trans.append(OnlySine(sin_magnitude=sin_magnitude)) elif aug_to_remove in ['sine', 'sine_rotate']: print('doing elastic transform :)') rand_trans.append(OnlyElastic(elastic_alpha=elastic_alpha, elastic_sigma=elastic_sigma)) if aug_to_remove not in ['elastic', 'sine', 'sine_rotate']: print('doing elastic transform :)') print('doing sine transform :)') rand_trans.append(ElasticAndSine(elastic_alpha=elastic_alpha, elastic_sigma=elastic_sigma, sin_magnitude=sin_magnitude)) if aug_to_remove not in ['rotation', 'sine_rotate']: print('doing rotation transform :)') rand_trans.append(Rotation(angle=rotate_max_angle, fill_value=255)) if aug_to_remove not in ['color_aug', 'color_gaus', 'color_sine']: print('doing color_aug transform :)') rand_trans.append(ColorGradGausNoise()) elif aug_to_remove == 'color_gaus': print('doing color_sine transform :)') rand_trans.append(ColorGrad()) elif aug_to_remove == 'color_sine': print('doing color_gaus transform :)') rand_trans.append(ColorGausNoise()) else: print('doing all transforms :)') rand_trans = [ ElasticAndSine(elastic_alpha=elastic_alpha, elastic_sigma=elastic_sigma, sin_magnitude=sin_magnitude), Rotation(angle=rotate_max_angle, fill_value=255), ColorGradGausNoise()] if do_gray: rand_trans = rand_trans + [Resize(hight=input_height), AddWidth(), ToGray(), Normalize()] else: rand_trans = rand_trans + [Resize(hight=input_height), AddWidth(), Normalize()] transform_random = Compose(rand_trans) if do_gray: transform_simple = Compose([ Resize(hight=input_height), AddWidth(), ToGray(), Normalize() ]) else: transform_simple = Compose([ Resize(hight=input_height), AddWidth(), Normalize() ]) if use_no_font_repeat_data: print('creating dataset') train_data = TextDatasetRandomFont(data_path=train_data_path, lexicon=lexicon, base_path=train_base_dir, transform=transform_random, fonts=train_fonts) print('finished creating dataset') else: print('train data path:\n{}'.format(train_data_path)) print('train_base_dir:\n{}'.format(train_base_dir)) train_data = TextDataset(data_path=train_data_path, lexicon=lexicon, base_path=train_base_dir, transform=transform_random, fonts=train_fonts) synth_eval_data = TextDataset(data_path=synth_eval_data_path, lexicon=lexicon, base_path=synth_eval_base_dir, transform=transform_random, fonts=train_fonts) orig_eval_data = TextDataset(data_path=orig_eval_data_path, lexicon=lexicon, base_path=orig_eval_base_dir, transform=transform_simple, fonts=None) if do_test_vat or do_test_vat_rnn or do_test_vat_cnn: orig_vat_data = TextDataset(data_path=orig_eval_data_path, lexicon=lexicon, base_path=orig_eval_base_dir, transform=transform_simple, fonts=None) if ada_after_rnn or ada_before_rnn: orig_ada_data = TextDataset(data_path=orig_eval_data_path, lexicon=lexicon, base_path=orig_eval_base_dir, transform=transform_simple, fonts=None) seq_proj = [int(x) for x in seq_proj.split('x')] net = load_model(lexicon=train_data.get_lexicon(), seq_proj=seq_proj, backend=backend, snapshot=snapshot, cuda=cuda, do_beam_search=do_beam_search, dropout_conv=dropout_conv, dropout_rnn=dropout_rnn, dropout_output=dropout_output, do_ema=do_ema, ada_after_rnn=ada_after_rnn, ada_before_rnn=ada_before_rnn, rnn_hidden_size=rnn_hidden_size ) optimizer = optim.Adam(net.parameters(), lr = base_lr, weight_decay=0.0001) if do_ada_lr: print('using ada lr') lr_scheduler = DannLR(optimizer, max_iter=max_iter) elif do_lr_step: print('using step lr') lr_scheduler = StepLR(optimizer, step_size=step_size, max_iter=max_iter) loss_function = CTCLoss() synth_avg_ed_best = float("inf") orig_avg_ed_best = float("inf") epoch_count = 0 if do_test_vat or do_test_vat_rnn or do_test_vat_cnn: collate_vat = lambda x: text_collate(x, do_mask=True) vat_load = DataLoader(orig_vat_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate_vat) vat_len = len(vat_load) cur_vat = 0 vat_iter = iter(vat_load) if ada_after_rnn or ada_before_rnn: collate_ada = lambda x: text_collate(x, do_mask=True) ada_load = DataLoader(orig_ada_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate_ada) ada_len = len(ada_load) cur_ada = 0 ada_iter = iter(ada_load) loss_domain = torch.nn.NLLLoss() while True: collate = lambda x: text_collate(x, do_mask=(do_vat or ada_before_rnn or ada_after_rnn)) data_loader = DataLoader(train_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate) loss_mean_ctc = [] loss_mean_vat = [] loss_mean_at = [] loss_mean_comp = [] loss_mean_total = [] loss_mean_test_vat = [] loss_mean_test_pseudo = [] loss_mean_test_rand = [] loss_mean_ada_rnn_s = [] loss_mean_ada_rnn_t = [] loss_mean_ada_cnn_s = [] loss_mean_ada_cnn_t = [] iterator = tqdm(data_loader) iter_count = 0 for iter_num, sample in enumerate(iterator): total_iter = (epoch_count * len(data_loader)) + iter_num if ((total_iter > 1) and total_iter % test_iter == 0) or (test_init and total_iter == 0): print("Test phase") net = net.eval() if do_ema: net.start_test() synth_acc, synth_avg_ed, synth_avg_no_stop_ed, synth_avg_loss = test(net, synth_eval_data, synth_eval_data.get_lexicon(), cuda, visualize=False, dataset_info=dataset_info, batch_size=batch_size, tb_writer=tb_writer, n_iter=total_iter, initial_title='val_synth', loss_function=loss_function, output_path=os.path.join( output_dir, 'results'), do_beam_search=False) orig_acc, orig_avg_ed, orig_avg_no_stop_ed, orig_avg_loss = test(net, orig_eval_data, orig_eval_data.get_lexicon(), cuda, visualize=False, dataset_info=dataset_info, batch_size=batch_size, tb_writer=tb_writer, n_iter=total_iter, initial_title='test_orig', loss_function=loss_function, output_path=os.path.join(output_dir, 'results'), do_beam_search=do_beam_search) net = net.train() if output_dir is not None and total_iter // 30000: periodic_save = os.path.join(output_dir, 'periodic_save') os.makedirs(periodic_save, exist_ok=True) old_save = glob.glob(os.path.join(periodic_save,'*')) torch.save(net.state_dict(), os.path.join(output_dir, "crnn_" + backend + "_" + str(total_iter))) if orig_avg_no_stop_ed < orig_avg_ed_best: orig_avg_ed_best = orig_avg_no_stop_ed if output_dir is not None: torch.save(net.state_dict(), os.path.join(output_dir, "crnn_" + backend + "_best")) if synth_avg_no_stop_ed < synth_avg_ed_best: synth_avg_ed_best = synth_avg_no_stop_ed if do_ema: net.end_test() print("synth: avg_ed_best: {}\t avg_ed: {}; avg_nostop_ed: {}; acc: {}".format(synth_avg_ed_best, synth_avg_ed, synth_avg_no_stop_ed, synth_acc)) print("orig: avg_ed_best: {}\t avg_ed: {}; avg_nostop_ed: {}; acc: {}".format(orig_avg_ed_best, orig_avg_ed, orig_avg_no_stop_ed, orig_acc)) tb_writer.get_writer().add_scalars('data/test', {'synth_ed_total': synth_avg_ed, 'synth_ed_no_stop': synth_avg_no_stop_ed, 'synth_avg_loss': synth_avg_loss, 'orig_ed_total': orig_avg_ed, 'orig_ed_no_stop': orig_avg_no_stop_ed, 'orig_avg_loss': orig_avg_loss }, total_iter) if len(loss_mean_ctc) > 0: train_dict = {'mean_ctc_loss': np.mean(loss_mean_ctc)} if do_vat: train_dict = {**train_dict, **{'mean_vat_loss':np.mean(loss_mean_vat)}} if do_at: train_dict = {**train_dict, **{'mean_at_loss':np.mean(loss_mean_at)}} if do_test_vat: train_dict = {**train_dict, **{'mean_test_vat_loss': np.mean(loss_mean_test_vat)}} if do_test_vat_rnn and do_test_vat_cnn: train_dict = {**train_dict, **{'mean_test_vat_crnn_loss': np.mean(loss_mean_test_vat)}} elif do_test_vat_rnn: train_dict = {**train_dict, **{'mean_test_vat_rnn_loss': np.mean(loss_mean_test_vat)}} elif do_test_vat_cnn: train_dict = {**train_dict, **{'mean_test_vat_cnn_loss': np.mean(loss_mean_test_vat)}} if ada_after_rnn: train_dict = {**train_dict, **{'mean_ada_rnn_s_loss': np.mean(loss_mean_ada_rnn_s), 'mean_ada_rnn_t_loss': np.mean(loss_mean_ada_rnn_t)}} if ada_before_rnn: train_dict = {**train_dict, **{'mean_ada_cnn_s_loss': np.mean(loss_mean_ada_cnn_s), 'mean_ada_cnn_t_loss': np.mean(loss_mean_ada_cnn_t)}} print(train_dict) tb_writer.get_writer().add_scalars('data/train', train_dict, total_iter) optimizer.zero_grad() imgs = Variable(sample["img"]) if do_vat or ada_after_rnn or ada_before_rnn: mask = sample['mask'] labels_flatten = Variable(sample["seq"]).view(-1) label_lens = Variable(sample["seq_len"].int()) img_seq_lens = sample["im_seq_len"] if cuda: imgs = imgs.cuda() if do_vat or ada_after_rnn or ada_before_rnn: mask = mask.cuda() if do_ada_lr: ada_p = float(iter_count) / max_iter lr_scheduler.update(ada_p) if ada_before_rnn or ada_after_rnn: if not do_ada_lr: ada_p = float(iter_count) / max_iter ada_alpha = 2. / (1. + np.exp(-10. * ada_p)) - 1 if cur_ada >= ada_len: ada_load = DataLoader(orig_ada_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate_ada) ada_len = len(ada_load) cur_ada = 0 ada_iter = iter(ada_load) ada_batch = next(ada_iter) cur_ada += 1 ada_imgs = Variable(ada_batch["img"]) ada_img_seq_lens = ada_batch["im_seq_len"] ada_mask = ada_batch['mask'].byte() if cuda: ada_imgs = ada_imgs.cuda() _, ada_cnn, ada_rnn = net(ada_imgs, ada_img_seq_lens, ada_alpha=ada_alpha, mask=ada_mask) if ada_before_rnn: ada_num_features = ada_cnn.size(0) else: ada_num_features = ada_rnn.size(0) domain_label = torch.zeros(ada_num_features) domain_label = domain_label.long() if cuda: domain_label = domain_label.cuda() domain_label = Variable(domain_label) if ada_before_rnn: err_ada_cnn_t = loss_domain(ada_cnn, domain_label) if ada_after_rnn: err_ada_rnn_t = loss_domain(ada_rnn, domain_label) if do_test_vat and do_at: if cur_vat >= vat_len: vat_load = DataLoader(orig_vat_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate_vat) vat_len = len(vat_load) cur_vat = 0 vat_iter = iter(vat_load) test_vat_batch = next(vat_iter) cur_vat += 1 test_vat_mask = test_vat_batch['mask'] test_vat_imgs = Variable(test_vat_batch["img"]) test_vat_img_seq_lens = test_vat_batch["im_seq_len"] if cuda: test_vat_imgs = test_vat_imgs.cuda() test_vat_mask = test_vat_mask.cuda() at_test_vat_loss = LabeledAtAndUnlabeledTestVatLoss(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) at_loss, test_vat_loss = at_test_vat_loss(model=net, train_x=imgs, train_labels_flatten=labels_flatten, train_img_seq_lens=img_seq_lens, train_label_lens=label_lens, batch_size=batch_size, test_x=test_vat_imgs, test_seq_len=test_vat_img_seq_lens, test_mask=test_vat_mask) elif do_test_vat or do_test_vat_rnn or do_test_vat_cnn: if cur_vat >= vat_len: vat_load = DataLoader(orig_vat_data, batch_size=batch_size, num_workers=4, shuffle=True, collate_fn=collate_vat) vat_len = len(vat_load) cur_vat = 0 vat_iter = iter(vat_load) vat_batch = next(vat_iter) cur_vat += 1 vat_mask = vat_batch['mask'] vat_imgs = Variable(vat_batch["img"]) vat_img_seq_lens = vat_batch["im_seq_len"] if cuda: vat_imgs = vat_imgs.cuda() vat_mask = vat_mask.cuda() if do_test_vat: if do_test_vat_rnn or do_test_vat_cnn: raise "can only do one of do_test_vat | (do_test_vat_rnn, do_test_vat_cnn)" if vat_sign == True: test_vat_loss = VATLossSign(do_test_entropy=do_test_entropy, xi=vat_xi, eps=vat_epsilon, ip=vat_ip) else: test_vat_loss = VATLoss(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) elif do_test_vat_rnn and do_test_vat_cnn: test_vat_loss = VATonRnnCnnSign(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) elif do_test_vat_rnn: test_vat_loss = VATonRnnSign(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) elif do_test_vat_cnn: test_vat_loss = VATonCnnSign(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) if do_test_vat_cnn and do_test_vat_rnn: test_vat_loss, cnn_lds, rnn_lds = test_vat_loss(net, vat_imgs, vat_img_seq_lens, vat_mask) elif do_test_vat: test_vat_loss = test_vat_loss(net, vat_imgs, vat_img_seq_lens, vat_mask) elif do_vat: vat_loss = VATLoss(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) vat_loss = vat_loss(net, imgs, img_seq_lens, mask) elif do_at: at_loss = LabeledATLoss(xi=vat_xi, eps=vat_epsilon, ip=vat_ip) at_loss = at_loss(net, imgs, labels_flatten, img_seq_lens, label_lens, batch_size) if ada_after_rnn or ada_before_rnn: preds, ada_cnn, ada_rnn = net(imgs, img_seq_lens, ada_alpha=ada_alpha, mask=mask) if ada_before_rnn: ada_num_features = ada_cnn.size(0) else: ada_num_features = ada_rnn.size(0) domain_label = torch.ones(ada_num_features) domain_label = domain_label.long() if cuda: domain_label = domain_label.cuda() domain_label = Variable(domain_label) if ada_before_rnn: err_ada_cnn_s = loss_domain(ada_cnn, domain_label) if ada_after_rnn: err_ada_rnn_s = loss_domain(ada_rnn, domain_label) else: preds = net(imgs, img_seq_lens) loss_ctc = loss_function(preds, labels_flatten, Variable(torch.IntTensor(np.array(img_seq_lens))), label_lens) / batch_size if loss_ctc.data[0] in [float("inf"), -float("inf")]: print("warnning: loss should not be inf.") continue total_loss = loss_ctc if do_vat: total_loss = total_loss + vat_ratio * vat_loss.cpu() if do_test_vat or do_test_vat_rnn or do_test_vat_cnn: total_loss = total_loss + test_vat_ratio * test_vat_loss.cpu() if ada_before_rnn: total_loss = total_loss + ada_ratio * err_ada_cnn_s.cpu() + ada_ratio * err_ada_cnn_t.cpu() if ada_after_rnn: total_loss = total_loss + ada_ratio * err_ada_rnn_s.cpu() + ada_ratio * err_ada_rnn_t.cpu() total_loss.backward() nn.utils.clip_grad_norm(net.parameters(), 10.0) if -400 < loss_ctc.data[0] < 400: loss_mean_ctc.append(loss_ctc.data[0]) if -1000 < total_loss.data[0] < 1000: loss_mean_total.append(total_loss.data[0]) if len(loss_mean_total) > 100: loss_mean_total = loss_mean_total[-100:] status = "epoch: {0:5d}; iter_num: {1:5d}; lr: {2:.2E}; loss_mean: {3:.3f}; loss: {4:.3f}".format(epoch_count, lr_scheduler.last_iter, lr_scheduler.get_lr(), np.mean(loss_mean_total), loss_ctc.data[0]) if ada_after_rnn: loss_mean_ada_rnn_s.append(err_ada_rnn_s.data[0]) loss_mean_ada_rnn_t.append(err_ada_rnn_t.data[0]) status += "; ladatrnns: {0:.3f}; ladatrnnt: {1:.3f}".format( err_ada_rnn_s.data[0], err_ada_rnn_t.data[0] ) if ada_before_rnn: loss_mean_ada_cnn_s.append(err_ada_cnn_s.data[0]) loss_mean_ada_cnn_t.append(err_ada_cnn_t.data[0]) status += "; ladatcnns: {0:.3f}; ladatcnnt: {1:.3f}".format( err_ada_cnn_s.data[0], err_ada_cnn_t.data[0] ) if do_vat: loss_mean_vat.append(vat_loss.data[0]) status += "; lvat: {0:.3f}".format( vat_loss.data[0] ) if do_at: loss_mean_at.append(at_loss.data[0]) status += "; lat: {0:.3f}".format( at_loss.data[0] ) if do_test_vat: loss_mean_test_vat.append(test_vat_loss.data[0]) status += "; l_tvat: {0:.3f}".format( test_vat_loss.data[0] ) if do_test_vat_rnn or do_test_vat_cnn: loss_mean_test_vat.append(test_vat_loss.data[0]) if do_test_vat_rnn and do_test_vat_cnn: status += "; l_tvatc: {}".format( cnn_lds.data[0] ) status += "; l_tvatr: {}".format( rnn_lds.data[0] ) else: status += "; l_tvat: {}".format( test_vat_loss.data[0] ) iterator.set_description(status) optimizer.step() if do_lr_step: lr_scheduler.step() if do_ema: net.udate_ema() iter_count += 1 if output_dir is not None: torch.save(net.state_dict(), os.path.join(output_dir, "crnn_" + backend + "_last")) epoch_count += 1 return if __name__ == '__main__': main()

true

7907cff8bda5b4bf85fd1cfe56569ea160b3bb19

1,565

py

Python

python/friesian/example/wnd/csv_to_parquet.py

DirkFi/BigDL

7493209165c046116470b9a1e1c8f527915d6f1e

[ "Apache-2.0" ]

3

2021-07-14T01:28:47.000Z

2022-03-02T01:16:32.000Z

python/friesian/example/wnd/csv_to_parquet.py

DirkFi/BigDL

7493209165c046116470b9a1e1c8f527915d6f1e

[ "Apache-2.0" ]

null

python/friesian/example/wnd/csv_to_parquet.py

DirkFi/BigDL

7493209165c046116470b9a1e1c8f527915d6f1e

[ "Apache-2.0" ]

null

# # Copyright 2016 The BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from argparse import ArgumentParser from pyspark.sql import SparkSession from pyspark.sql.types import * LABEL_COL = 0 INT_COLS = list(range(1, 14)) CAT_COLS = list(range(14, 40)) if __name__ == '__main__': parser = ArgumentParser() parser.add_argument('--input', type=str, required=True, help="The path to the csv file to be processed.") parser.add_argument('--output', type=str, default=".", help="The path to the folder to save the parquet data.") args = parser.parse_args() spark = SparkSession.builder.getOrCreate() input = args.input output = args.output label_fields = [StructField('_c%d' % LABEL_COL, IntegerType())] int_fields = [StructField('_c%d' % i, IntegerType()) for i in INT_COLS] str_fields = [StructField('_c%d' % i, StringType()) for i in CAT_COLS] schema = StructType(label_fields + int_fields + str_fields) df = spark.read.schema(schema).option('sep', '\t').csv(input) df.write.parquet(output, mode="overwrite")

36.395349

114

0.723323

from argparse import ArgumentParser from pyspark.sql import SparkSession from pyspark.sql.types import * LABEL_COL = 0 INT_COLS = list(range(1, 14)) CAT_COLS = list(range(14, 40)) if __name__ == '__main__': parser = ArgumentParser() parser.add_argument('--input', type=str, required=True, help="The path to the csv file to be processed.") parser.add_argument('--output', type=str, default=".", help="The path to the folder to save the parquet data.") args = parser.parse_args() spark = SparkSession.builder.getOrCreate() input = args.input output = args.output label_fields = [StructField('_c%d' % LABEL_COL, IntegerType())] int_fields = [StructField('_c%d' % i, IntegerType()) for i in INT_COLS] str_fields = [StructField('_c%d' % i, StringType()) for i in CAT_COLS] schema = StructType(label_fields + int_fields + str_fields) df = spark.read.schema(schema).option('sep', '\t').csv(input) df.write.parquet(output, mode="overwrite")

true

7907d0c90f7aa7e834df6c7dc70100df4279026e

2,986

py

Python

NBABet/Telegram.py

davideganna/NBA_Bet

dba00542b8ed63a5a7290f25209270b32d18fb86

[ "MIT" ]

4

2021-08-02T07:49:51.000Z

2021-12-14T18:49:27.000Z

NBABet/Telegram.py

davideganna/NBA_Bet

dba00542b8ed63a5a7290f25209270b32d18fb86

[ "MIT" ]

1

2021-08-03T14:55:13.000Z

NBABet/Telegram.py

davideganna/NBA_Bet

dba00542b8ed63a5a7290f25209270b32d18fb86

[ "MIT" ]

null

# --------------------- Telegram.py --------------------------------- # # Allows the integration with Telegram Bot. # ------------------------------------------------------------------- # from numpy.core.fromnumeric import around, std import requests import Elo from Models import Models import Helper import pandas as pd import numpy as np class TelegramBot(): """ Allows integration with the Telegram Bot. """ def __init__(self): self.url = 'https://api.telegram.org/' with open('secrets/telegram_secrets') as f: lines = f.readlines() self.bot_token = lines[0].strip() self.chat_id = lines[1].strip() def send_message(self, d:dict): df = pd.read_csv('past_data/2021_2022/split_stats_per_game.csv') df = Helper.add_features_to_df(df) n = 3 train_df = pd.read_csv('past_data/average_seasons/average_NSeasons_prod.csv') # Standardize the DataFrame std_df, scaler = Helper.standardize_DataFrame(train_df) clf = Models.build_RF_classifier(std_df) text = "🏀 Tonight's Games: Home vs. Away 🏀\n\n" for home, away in d.items(): last_N_games_away = df.loc[df['Team_away'] == away].tail(n) last_N_games_home = df.loc[df['Team_home'] == home].tail(n) to_predict = pd.concat( [ last_N_games_away[Models.away_features].mean(), last_N_games_home[Models.home_features].mean() ], axis=0)[Models.features] prob_home_rf, prob_away_rf = clf.predict_proba(scaler.transform(to_predict.values.reshape(1,-1)))[0] prob_away_elo, prob_home_elo = Elo.get_probas(away, home) if ((prob_home_rf > 0.5) and (prob_home_elo > 0.5)): prob_home = str(around((prob_home_rf + prob_home_elo)/2, decimals=3)) odds_home = str(around(1/float(prob_home), decimals=2)) if float(prob_home) >= 0.6: text = text + home + '(' + prob_home + ' --> ' + odds_home + ') vs. ' + away + '\n\ RF Prob.: ' + str(around(prob_home_rf, decimals=3)) + '\n\ Elo Prob.: ' + str(around(prob_home_elo, decimals=3)) + '\n\n' if ((prob_away_rf > 0.5) and (prob_away_elo > 0.5)): prob_away = str(around((prob_away_rf + prob_away_elo)/2, decimals=3)) odds_away = str(around(1/float(prob_away), decimals=2)) if float(prob_away) >= 0.6: text = text + home + ' vs. ' + away + '(' + prob_away + ' --> ' + odds_away + ')' + '\n\ RF Prob.: ' + str(around(prob_away_rf, decimals=3)) + '\n\ Elo Prob.: ' + str(around(prob_away_elo, decimals=3)) + '\n\n' query = self.url + self.bot_token + '/sendMessage?' + self.chat_id + '&text=' + text requests.request("POST", query)

43.275362

112

0.540522

from numpy.core.fromnumeric import around, std import requests import Elo from Models import Models import Helper import pandas as pd import numpy as np class TelegramBot(): def __init__(self): self.url = 'https://api.telegram.org/' with open('secrets/telegram_secrets') as f: lines = f.readlines() self.bot_token = lines[0].strip() self.chat_id = lines[1].strip() def send_message(self, d:dict): df = pd.read_csv('past_data/2021_2022/split_stats_per_game.csv') df = Helper.add_features_to_df(df) n = 3 train_df = pd.read_csv('past_data/average_seasons/average_NSeasons_prod.csv') std_df, scaler = Helper.standardize_DataFrame(train_df) clf = Models.build_RF_classifier(std_df) text = "🏀 Tonight's Games: Home vs. Away 🏀\n\n" for home, away in d.items(): last_N_games_away = df.loc[df['Team_away'] == away].tail(n) last_N_games_home = df.loc[df['Team_home'] == home].tail(n) to_predict = pd.concat( [ last_N_games_away[Models.away_features].mean(), last_N_games_home[Models.home_features].mean() ], axis=0)[Models.features] prob_home_rf, prob_away_rf = clf.predict_proba(scaler.transform(to_predict.values.reshape(1,-1)))[0] prob_away_elo, prob_home_elo = Elo.get_probas(away, home) if ((prob_home_rf > 0.5) and (prob_home_elo > 0.5)): prob_home = str(around((prob_home_rf + prob_home_elo)/2, decimals=3)) odds_home = str(around(1/float(prob_home), decimals=2)) if float(prob_home) >= 0.6: text = text + home + '(' + prob_home + ' --> ' + odds_home + ') vs. ' + away + '\n\ RF Prob.: ' + str(around(prob_home_rf, decimals=3)) + '\n\ Elo Prob.: ' + str(around(prob_home_elo, decimals=3)) + '\n\n' if ((prob_away_rf > 0.5) and (prob_away_elo > 0.5)): prob_away = str(around((prob_away_rf + prob_away_elo)/2, decimals=3)) odds_away = str(around(1/float(prob_away), decimals=2)) if float(prob_away) >= 0.6: text = text + home + ' vs. ' + away + '(' + prob_away + ' --> ' + odds_away + ')' + '\n\ RF Prob.: ' + str(around(prob_away_rf, decimals=3)) + '\n\ Elo Prob.: ' + str(around(prob_away_elo, decimals=3)) + '\n\n' query = self.url + self.bot_token + '/sendMessage?' + self.chat_id + '&text=' + text requests.request("POST", query)

true

7907d0cb7b597721da3ae026d431521a2fa0335b

3,946

py

Python

td/oauth.py

southpaw27/td-ameritrade-python-api

ddb2a48b7cc2ffe00c31b4a4cef55dce39c7a442

[ "MIT" ]

610

2019-11-08T04:56:28.000Z

2022-03-29T18:17:01.000Z

td/oauth.py

southpaw27/td-ameritrade-python-api

ddb2a48b7cc2ffe00c31b4a4cef55dce39c7a442

[ "MIT" ]

177

2019-12-22T18:03:48.000Z

2022-03-12T20:37:40.000Z

td/oauth.py

southpaw27/td-ameritrade-python-api

ddb2a48b7cc2ffe00c31b4a4cef55dce39c7a442

[ "MIT" ]

248

2019-11-08T04:56:38.000Z

2022-03-29T20:09:22.000Z

import os import pathlib from flask import Flask from flask import request from flask import redirect from flask import url_for from flask import session from flask import render_template from flask.json import jsonify from td.app.auth import FlaskTDAuth from configparser import ConfigParser # Define the templates folder. template_folder_path: pathlib.Path = pathlib.Path(__file__).parents[0] template_folder_path: pathlib.Path = template_folder_path.joinpath('templates') # Create the App. app = Flask('TD_oAuth_App', template_folder=template_folder_path.resolve()) @app.route("/") def home(): """Step 1: User Authorization. Redirect the user/resource owner to the OAuth provider (i.e. Github) using an URL with a few key OAuth parameters. """ return render_template("index.html") @app.route("/login") def demo(): """Step 1: User Authorization. Redirect the user/resource owner to the OAuth provider (i.e. Github) using an URL with a few key OAuth parameters. """ # Build the authorization URL. auth_tuple = app.config['auth_client'].authorization_url() # State is used to prevent CSRF, keep this for later. session['oauth_state'] = auth_tuple[1] return redirect(auth_tuple[0]) @app.route("/login/callback", methods=["GET"]) def callback(): """ Step 3: Retrieving an access token. The user has been redirected back from the provider to your registered callback URL. With this redirection comes an authorization code included in the redirect URL. We will use that to obtain an access token. """ # Grab the Refresh and Access Token. token_dict = app.config['auth_client'].grab_access_token_and_refresh_token(url=request.url) # Store it in the Session. session['oauth_token'] = token_dict if app.config['call_close']: return redirect(url_for('shutdown')) return jsonify(token_dict) @app.route("/login/refresh", methods=["GET"]) def refresh(): # Grab the Refresh Token. refresh_token_dict = app.config['auth_client'].grab_refresh_token() return jsonify(refresh_token_dict) def shutdown_server(): func = request.environ.get('werkzeug.server.shutdown') if func is None: raise RuntimeError('Not running with the Werkzeug Server') func() @app.route('/shutdown', methods=['POST']) def shutdown(): shutdown_server() return 'Server shutting down...' def run(flask_client: FlaskTDAuth, close_after: bool = False): certs_pem = pathlib.Path(__file__).parents[0].joinpath('certs/cert.pem') certs_key = pathlib.Path(__file__).parents[0].joinpath('certs/key.pem') app.secret_key = os.environ.get("SECRET_KEY") or os.urandom(24) app.config['auth_client'] = flask_client app.config['call_close'] = close_after app.run( ssl_context=(certs_pem, certs_key), host='localhost', port=5000, debug=True ) if __name__ == "__main__": # Grab configuration values. config = ConfigParser() config.read('config/config.ini') client_id = config.get('main', 'client_id') redirect_uri = config.get('main', 'redirect_uri') credentials = config.get('main','json_path') # Define the Secret Key. app.secret_key = os.environ.get("SECRET_KEY") or os.urandom(24) # Define the App Configurations. app.config['auth_client'] = FlaskTDAuth( client_id=client_id, redirect_uri=redirect_uri, credentials_file=pathlib.Path(credentials) ) # Run the App. app.run( ssl_context=('td/certs/cert.pem', 'td/certs/key.pem'), host='localhost', port=5000, debug=True ) # flask_td_app = FlaskAppTD(client_id=client_id, redirect_uri=redirect_uri, credentials_file=credentials) # flask_td_app.run() # This allows us to use a plain HTTP callback # os.environ['OAUTHLIB_INSECURE_TRANSPORT'] = "1" # # app.run(ssl_context="adhoc")

28.594203

109

0.696655

import os import pathlib from flask import Flask from flask import request from flask import redirect from flask import url_for from flask import session from flask import render_template from flask.json import jsonify from td.app.auth import FlaskTDAuth from configparser import ConfigParser template_folder_path: pathlib.Path = pathlib.Path(__file__).parents[0] template_folder_path: pathlib.Path = template_folder_path.joinpath('templates') app = Flask('TD_oAuth_App', template_folder=template_folder_path.resolve()) @app.route("/") def home(): return render_template("index.html") @app.route("/login") def demo(): auth_tuple = app.config['auth_client'].authorization_url() session['oauth_state'] = auth_tuple[1] return redirect(auth_tuple[0]) @app.route("/login/callback", methods=["GET"]) def callback(): token_dict = app.config['auth_client'].grab_access_token_and_refresh_token(url=request.url) session['oauth_token'] = token_dict if app.config['call_close']: return redirect(url_for('shutdown')) return jsonify(token_dict) @app.route("/login/refresh", methods=["GET"]) def refresh(): refresh_token_dict = app.config['auth_client'].grab_refresh_token() return jsonify(refresh_token_dict) def shutdown_server(): func = request.environ.get('werkzeug.server.shutdown') if func is None: raise RuntimeError('Not running with the Werkzeug Server') func() @app.route('/shutdown', methods=['POST']) def shutdown(): shutdown_server() return 'Server shutting down...' def run(flask_client: FlaskTDAuth, close_after: bool = False): certs_pem = pathlib.Path(__file__).parents[0].joinpath('certs/cert.pem') certs_key = pathlib.Path(__file__).parents[0].joinpath('certs/key.pem') app.secret_key = os.environ.get("SECRET_KEY") or os.urandom(24) app.config['auth_client'] = flask_client app.config['call_close'] = close_after app.run( ssl_context=(certs_pem, certs_key), host='localhost', port=5000, debug=True ) if __name__ == "__main__": config = ConfigParser() config.read('config/config.ini') client_id = config.get('main', 'client_id') redirect_uri = config.get('main', 'redirect_uri') credentials = config.get('main','json_path') app.secret_key = os.environ.get("SECRET_KEY") or os.urandom(24) app.config['auth_client'] = FlaskTDAuth( client_id=client_id, redirect_uri=redirect_uri, credentials_file=pathlib.Path(credentials) ) app.run( ssl_context=('td/certs/cert.pem', 'td/certs/key.pem'), host='localhost', port=5000, debug=True )

true

7907d0f4268a7e165b3ca17d49d01c36511c2cad

2,207

py

Python

web_console_v2/api/fedlearner_webconsole/workflow_template/slots_formatter.py

duanbing/fedlearner

5cce3c1fe09abe66879274a0ad3dc8e2f25a322d

[ "Apache-2.0" ]

null

web_console_v2/api/fedlearner_webconsole/workflow_template/slots_formatter.py

duanbing/fedlearner

5cce3c1fe09abe66879274a0ad3dc8e2f25a322d

[ "Apache-2.0" ]

null

web_console_v2/api/fedlearner_webconsole/workflow_template/slots_formatter.py

duanbing/fedlearner

5cce3c1fe09abe66879274a0ad3dc8e2f25a322d

[ "Apache-2.0" ]

null

# Copyright 2020 The FedLearner Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # coding: utf-8 from flatten_dict import flatten from fedlearner_webconsole.proto.workflow_definition_pb2 import Slot from fedlearner_webconsole.workflow_template.template_validaor \ import YamlTemplate class _YamlTemplate(YamlTemplate): # Which placeholders in the template should be interpreted idpattern = r'Slot_[a-z0-9_]*' def substitute(self, mapping): return super()._substitute(mapping, fixed_placeholder=None, ignore_invalid=True) def format_yaml(yaml, **kwargs): """Formats a yaml template. Example usage: format_yaml('{"abc": ${x.y}}', x={'y': 123}) output should be '{"abc": 123}' """ template = _YamlTemplate(yaml) try: return template.substitute(flatten(kwargs or {}, reducer='dot')) except KeyError as e: raise RuntimeError( 'Unknown placeholder: {}'.format(e.args[0])) from e def generate_yaml_template(base_yaml, slots_proto): """ Args: base_yaml: A string representation of one type job's base yaml. slots_proto: A proto map object representation of modification template's operable smallest units. Returns: string: A yaml_template """ slots = {} for key in slots_proto: if slots_proto[key].reference_type == Slot.ReferenceType.DEFAULT: slots[key] = slots_proto[key].default else: slots[key] = f'${{{slots_proto[key].reference}}}' return format_yaml(base_yaml, **slots)

34.484375

74

0.663344

from flatten_dict import flatten from fedlearner_webconsole.proto.workflow_definition_pb2 import Slot from fedlearner_webconsole.workflow_template.template_validaor \ import YamlTemplate class _YamlTemplate(YamlTemplate): idpattern = r'Slot_[a-z0-9_]*' def substitute(self, mapping): return super()._substitute(mapping, fixed_placeholder=None, ignore_invalid=True) def format_yaml(yaml, **kwargs): template = _YamlTemplate(yaml) try: return template.substitute(flatten(kwargs or {}, reducer='dot')) except KeyError as e: raise RuntimeError( 'Unknown placeholder: {}'.format(e.args[0])) from e def generate_yaml_template(base_yaml, slots_proto): slots = {} for key in slots_proto: if slots_proto[key].reference_type == Slot.ReferenceType.DEFAULT: slots[key] = slots_proto[key].default else: slots[key] = f'${{{slots_proto[key].reference}}}' return format_yaml(base_yaml, **slots)

true

7907d127cf12169a5fef2281a97299f28b322d70

13,055

py

Python

code/python/QuotesAPIforDigitalPortals/v3/fds/sdk/QuotesAPIforDigitalPortals/model/inline_response20013.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

6

2022-02-07T16:34:18.000Z

2022-03-30T08:04:57.000Z

code/python/QuotesAPIforDigitalPortals/v3/fds/sdk/QuotesAPIforDigitalPortals/model/inline_response20013.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

2

2022-02-07T05:25:57.000Z

2022-03-07T14:18:04.000Z

code/python/QuotesAPIforDigitalPortals/v3/fds/sdk/QuotesAPIforDigitalPortals/model/inline_response20013.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

null

""" Quotes API For Digital Portals The quotes API combines endpoints for retrieving security end-of-day, delayed, and realtime prices with performance key figures and basic reference data on the security and market level. The API supports over 20 different price types for each quote and comes with basic search endpoints based on security identifiers and instrument names. Market coverage is included in the *Sample Use Cases* section below. The Digital Portal use case is focused on high-performance applications that are * serving millions of end-users, * accessible by client browsers via the internet, * supporting subscriptions for streamed updates out-of-the-box, * typically combining a wide variety of *for Digital Portals*-APIs into a highly use-case specific solution for customers, * integrated into complex infrastructures such as existing frontend frameworks, authentication services. All APIs labelled *for Digital Portals* have been designed for direct use by client web applications and feature extreme low latency: The average response time across all endpoints is 30 ms whereas 99% of all requests are answered in close to under 300ms. See the Time Series API for Digital Portals for direct access to price histories, and the News API for Digital Portals for searching and fetching related news. # noqa: E501 The version of the OpenAPI document: 2 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from fds.sdk.QuotesAPIforDigitalPortals.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from fds.sdk.QuotesAPIforDigitalPortals.exceptions import ApiAttributeError def lazy_import(): from fds.sdk.QuotesAPIforDigitalPortals.model.inline_response20013_data import InlineResponse20013Data from fds.sdk.QuotesAPIforDigitalPortals.model.inline_response200_meta import InlineResponse200Meta globals()['InlineResponse20013Data'] = InlineResponse20013Data globals()['InlineResponse200Meta'] = InlineResponse200Meta class InlineResponse20013(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ lazy_import() return { 'data': ([InlineResponse20013Data],), # noqa: E501 'meta': (InlineResponse200Meta,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'data': 'data', # noqa: E501 'meta': 'meta', # noqa: E501 } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 """InlineResponse20013 - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) data ([InlineResponse20013Data]): List of Internet media types.. [optional] # noqa: E501 meta (InlineResponse200Meta): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """InlineResponse20013 - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) data ([InlineResponse20013Data]): List of Internet media types.. [optional] # noqa: E501 meta (InlineResponse200Meta): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

48.712687

1,302

0.603294

import re import sys from fds.sdk.QuotesAPIforDigitalPortals.model_utils import ( ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from fds.sdk.QuotesAPIforDigitalPortals.exceptions import ApiAttributeError def lazy_import(): from fds.sdk.QuotesAPIforDigitalPortals.model.inline_response20013_data import InlineResponse20013Data from fds.sdk.QuotesAPIforDigitalPortals.model.inline_response200_meta import InlineResponse200Meta globals()['InlineResponse20013Data'] = InlineResponse20013Data globals()['InlineResponse200Meta'] = InlineResponse200Meta class InlineResponse20013(ModelNormal): allowed_values = { } validations = { } @cached_property def additional_properties_type(): lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) _nullable = False @cached_property def openapi_types(): lazy_import() return { 'data': ([InlineResponse20013Data],), 'meta': (InlineResponse200Meta,), } @cached_property def discriminator(): return None attribute_map = { 'data': 'data', 'meta': 'meta', } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

true

7907d2520b97c6db4ca765d799011e4657476b9e

62

py

Python

medical_prescription/chat/validators/__init__.py

ristovao/2017.2-Receituario-Medico

5387eb80dfb354e948abe64f7d8bbe087fc4f136

[ "MIT" ]

11

2017-09-19T00:29:40.000Z

2018-04-05T23:52:39.000Z

medical_prescription/chat/validators/__init__.py

ristovao/2017.2-Receituario-Medico

5387eb80dfb354e948abe64f7d8bbe087fc4f136

[ "MIT" ]

271

2017-09-09T00:07:28.000Z

2017-12-07T05:00:45.000Z

medical_prescription/chat/validators/__init__.py

ristovao/2017.2-Receituario-Medico

5387eb80dfb354e948abe64f7d8bbe087fc4f136

[ "MIT" ]

26

2017-08-31T20:48:49.000Z

2018-03-21T15:11:27.000Z

# Local Django from .messagevalidator import MessageValidator

20.666667

46

0.854839

from .messagevalidator import MessageValidator

true

7907d27ba6ed261852f88877a4e315e999f4610d

1,191

py

Python

twitter_app/iris_classifier.py

Struth-Rourke/twitter_flask_app

f73ad147f216ad77f8010ef6c02da4784dbfa9c8

[ "MIT" ]

null

twitter_app/iris_classifier.py

Struth-Rourke/twitter_flask_app

f73ad147f216ad77f8010ef6c02da4784dbfa9c8

[ "MIT" ]

3

2021-09-08T02:05:54.000Z

2022-03-12T00:36:59.000Z

twitter_app/iris_classifier.py

Struth-Rourke/twitter_flask_app

f73ad147f216ad77f8010ef6c02da4784dbfa9c8

[ "MIT" ]

null

# twitter_app/iris_classifier.py import os import pickle from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression MODEL_FILEPATH = os.path.join(os.path.dirname(__file__), "..", "models", "latest_model.pkl") def train_and_save_model(): print("TRAINING THE MODEL...") X, y = load_iris(return_X_y=True) #print(type(X), X.shape) #> <class 'numpy.ndarray'> (150, 4) #print(type(y), y.shape) #> <class 'numpy.ndarray'> (150,) classifier = LogisticRegression() # for example classifier.fit(X, y) print("SAVING THE MODEL...") with open(MODEL_FILEPATH, "wb") as model_file: pickle.dump(classifier, model_file) return classifier def load_model(): print("LOADING THE MODEL...") with open(MODEL_FILEPATH, "rb") as model_file: saved_model = pickle.load(model_file) return saved_model if __name__ == "__main__": #train_and_save_model() clf = load_model() print("CLASSIFIER:", clf) X, y = load_iris(return_X_y=True) # just to have some data to use when predicting inputs = X[:2, :] print(type(inputs), inputs) result = clf.predict(inputs) print("RESULT:", result)

27.697674

92

0.677582

import os import pickle from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression MODEL_FILEPATH = os.path.join(os.path.dirname(__file__), "..", "models", "latest_model.pkl") def train_and_save_model(): print("TRAINING THE MODEL...") X, y = load_iris(return_X_y=True) ) print("SAVING THE MODEL...") with open(MODEL_FILEPATH, "wb") as model_file: pickle.dump(classifier, model_file) return classifier def load_model(): print("LOADING THE MODEL...") with open(MODEL_FILEPATH, "rb") as model_file: saved_model = pickle.load(model_file) return saved_model if __name__ == "__main__": clf = load_model() print("CLASSIFIER:", clf) X, y = load_iris(return_X_y=True) inputs = X[:2, :] print(type(inputs), inputs) result = clf.predict(inputs) print("RESULT:", result)

true

7907d4cc1d7a5cfd49346651d20eeb4152d5b9ce

195

py

Python

convertextract/parsers/tsv_parser.py

roedoejet/convertextract

bf194a7d81d847d68690ea0d58dc47a70259cd78

[ "MIT" ]

12

2016-10-20T16:17:04.000Z

2022-03-10T06:36:59.000Z

convertextract/parsers/tsv_parser.py

roedoejet/convertextract

bf194a7d81d847d68690ea0d58dc47a70259cd78

[ "MIT" ]

3

2018-01-12T00:41:26.000Z

2020-08-12T05:04:45.000Z

convertextract/parsers/tsv_parser.py

roedoejet/convertextract

bf194a7d81d847d68690ea0d58dc47a70259cd78

[ "MIT" ]

3

2020-08-18T21:47:03.000Z

2022-02-03T06:32:46.000Z

import csv from convertextract.parsers.csv_parser import Parser as BaseParser class Parser(BaseParser): """Extract text from tab separated values files (.tsv). """ delimiter = '\t'

21.666667

66

0.717949

import csv from convertextract.parsers.csv_parser import Parser as BaseParser class Parser(BaseParser): delimiter = '\t'

true

7907d57b99b40944d3cd9e2c239492ab9355ef78

1,161

py

Python

crawler/test_code/test_selenium.py

Coslate/NBA_Win_Predictor

c8f4fb5a12fdd36bd43e573510bfb2307f37ec1f

[ "MIT" ]

null

crawler/test_code/test_selenium.py

Coslate/NBA_Win_Predictor

c8f4fb5a12fdd36bd43e573510bfb2307f37ec1f

[ "MIT" ]

null

crawler/test_code/test_selenium.py

Coslate/NBA_Win_Predictor

c8f4fb5a12fdd36bd43e573510bfb2307f37ec1f

[ "MIT" ]

null

#! /usr/bin/env python3.6 from selenium import webdriver import time browser = webdriver.Chrome(executable_path='/home/coslate/anaconda3/bin/chromedriver') #url = 'https://stats.nba.com/leaders' url = 'http://stats.nba.com/teams/traditional/#!?sort=W_PCT&dir=-1' browser.get(url) time.sleep(5) #browser.find_element_by_xpath('/html/body/main/div[2]/div/div[2]/div/div/div[1]/div[1]/div/div/label/select/option[3]').click() #browser.find_element_by_xpath('/html/body/main/div[2]/div/div[2]/div/div/div[1]/div[2]/div/div/label/select/option[2]').click() #browser.find_element_by_xpath('/html/body/main/div[2]/div/div[2]/div/div/nba-stat-table/div[3]/div/div/select/option[1]').click() #table = browser.find_element_by_class_name('nba-stat-table__overflow') table = browser.find_elements_by_xpath('/html/body/main/div[2]/div/div[2]/div/div/nba-stat-table/div[2]/div[1]/table/tbody') line1 = browser.find_element_by_xpath('//tr[@index="0"]') print(line1.text) print("All the window handles : ") print(browser.window_handles) # 查看所有window handles print("The current window handle : ") print(browser.current_window_handle) # 查看所有window handles browser.close()

44.653846

130

0.750215

from selenium import webdriver import time browser = webdriver.Chrome(executable_path='/home/coslate/anaconda3/bin/chromedriver') url = 'http://stats.nba.com/teams/traditional/#!?sort=W_PCT&dir=-1' browser.get(url) time.sleep(5) table = browser.find_elements_by_xpath('/html/body/main/div[2]/div/div[2]/div/div/nba-stat-table/div[2]/div[1]/table/tbody') line1 = browser.find_element_by_xpath('//tr[@index="0"]') print(line1.text) print("All the window handles : ") print(browser.window_handles) print("The current window handle : ") print(browser.current_window_handle) browser.close()

true

7907d5d8da7a9d75d151f7561ae03dee7c281322

10,916

py

Python

algorithm/python/topological_sort.py

yennanliu/Python_basics

6a597442d39468295946cefbfb11d08f61424dc3

[ "Unlicense" ]

null

algorithm/python/topological_sort.py

yennanliu/Python_basics

6a597442d39468295946cefbfb11d08f61424dc3

[ "Unlicense" ]

null

algorithm/python/topological_sort.py

yennanliu/Python_basics

6a597442d39468295946cefbfb11d08f61424dc3

[ "Unlicense" ]

null

#--------------------------------------------------------------- # ALGORITHM DEMO : TOPLOGICAL SORT #--------------------------------------------------------------- # Topological Sort is a algorithm can find "ordering" on an "order dependency" graph # Concept # https://blog.techbridge.cc/2020/05/10/leetcode-topological-sort/ # https://alrightchiu.github.io/SecondRound/graph-li-yong-dfsxun-zhao-dagde-topological-sorttuo-pu-pai-xu.html # V0 # IDEA : implement topologicalSortUtil, topologicalSort, and addEdge methods # step 1) maintain a stack, save "ordering" nodes in it (and return in final step) # step 2) init visited as [False]*self.V (all nodes are NOT visited yet) # step 3) iterate over all vertices in graph, if not visited, then run topologicalSortUtil # step 4) return result (stack) from collections import defaultdict class Graph: def __init__(self, vertices): self.graph = defaultdict(list) self.V = vertices # for build graph def addEdge(self, u, v): self.graph[u].append(v) def topologicalSortUtil(self, v, visited, stack): visited[v] = True ### NOTE this !!! (self.graph[v]) for k in self.graph[v]: if visited[k] == False: self.topologicalSortUtil(k, visited, stack) # stack.insert(0,v) # instead of insert v to idx = 0, we can still append v to stack and reverse it and return (e.g. return stack[::-1]) """ ### NOTE !! stack.append(v) is wrong, we SHOULD use stack.insert(0,v) """ stack.insert(0,v) def topologicalSort(self): visited = [False] * self.V stack = [] ### NOTE this !!! (range(self.V)) for v in range(self.V): # call tologicalSortUtil only if visited[v] == False (the vertice is not visited yet) if visited[v] == False: self.topologicalSortUtil(v, visited, stack) # return the result in inverse order return stack[::-1] ### TEST {"A": 0, "B":1, "C":2, "D": 3} v = 4 g = Graph(v) g.addEdge(0, 1) g.addEdge(0, 2) g.addEdge(2, 3) g.addEdge(3, 1) print (g.graph) # ans should be TableB, TableD, TableC, TableA. r = g.topologicalSort() print (r) # V0' from collections import defaultdict class Graph: def __init__(self, v): self.graph = defaultdict(list) self.v = v def addEdge(self, a, b): self.graph[a].append(b) def topologicalSortUtil(self, x, visited, stack): # V1 if visited[x]: return for k in self.graph[x]: self.topologicalSortUtil(k, visited, stack) visited[x] = True stack.insert(0, x) # V2 # visited[v] = True # ### NOTE this !!! (self.graph[v]) # for k in self.graph[v]: # if visited[k] == False: # self.topologicalSortUtil(k, visited, stack) # # stack.insert(0,v) # instead of insert v to idx = 0, we can still append v to stack and reverse it and return (e.g. return stack[::-1]) # """ # ### NOTE !! stack.append(v) is wrong, we SHOULD use stack.insert(0,v) # """ # stack.insert(0,v) def topologicalSort(self): visited = [False] * self.v stack = [] for x in range(self.v): if not visited[x]: self.topologicalSortUtil(x, visited, stack) print ("stack = " + str(stack)) return stack[::-1] # V0'' # IDEA : implement topologicalSortUtil, topologicalSort, and addEdge methods from collections import defaultdict class Graph: def __init__(self,vertices): self.graph = defaultdict(list) self.V = vertices # for testing (build graph) def addEdge(self,u,v): self.graph[u].append(v) def topologicalSortUtil(self,v,visited,stack): visited[v] = True for i in self.graph[v]: if visited[i] == False: self.topologicalSortUtil(i,visited,stack) stack.insert(0,v) def topologicalSort(self): visited = [False]*self.V stack =[] for i in range(self.V): if visited[i] == False: self.topologicalSortUtil(i,visited,stack) print (stack) # V1 # https://www.geeksforgeeks.org/topological-sorting/ # Python program to print topological sorting of a DAG from collections import defaultdict class Graph: def __init__(self, vertices): self.graph = defaultdict(list) # dictionary containing adjacency List self.V = vertices # No. of vertices # function to add an edge to graph def addEdge(self, u, v): self.graph[u].append(v) # A recursive function used by topologicalSort def topologicalSortUtil(self, v, visited, stack): # Mark the current node as visited. visited[v] = True # Recur for all the vertices adjacent to this vertex for i in self.graph[v]: if visited[i] == False: self.topologicalSortUtil(i, visited, stack) # Push current vertex to stack which stores result #stack.append(v) stack.insert(0,v) # The function to do Topological Sort. It uses recursive # topologicalSortUtil() def topologicalSort(self): # Mark all the vertices as not visited visited = [False]*self.V stack = [] # Call the recursive helper function to store Topological # Sort starting from all vertices one by one for i in range(self.V): if visited[i] == False: self.topologicalSortUtil(i, visited, stack) # Print contents of the stack print(stack[::-1]) # return list in reverse order # TEST # Driver Code # g = Graph(6) # g.addEdge(5, 2) # g.addEdge(5, 0) # g.addEdge(4, 0) # g.addEdge(4, 1) # g.addEdge(2, 3) # g.addEdge(3, 1) # # print ("Following is a Topological Sort of the given graph") # # # Function Call # g.topologicalSort() # V1 # https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py """Topological Sort.""" # a # / \ # b c # / \ # d e # edges = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []} # vertices = ["a", "b", "c", "d", "e"] class Graph: def topological_sort(self, start, visited, sort): """Perform topological sort on a directed acyclic graph.""" current = start # add current to visited visited.append(current) neighbors = edges[current] for neighbor in neighbors: # if neighbor not in visited, visit if neighbor not in visited: sort = topological_sort(neighbor, visited, sort) # if all neighbors visited add current to sort sort.append(current) # if all vertices haven't been visited select a new one to visit if len(visited) != len(vertices): for vertice in vertices: if vertice not in visited: sort = topological_sort(vertice, visited, sort) # return sort return sort # TEST # edges = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []} # vertices = ["a", "b", "c", "d", "e"] # sort = topological_sort("a", [], []) # print(sort) # V1' # http://www.runoob.com/python3/python-topological-sorting.html class Graph: from collections import defaultdict def __init__(self,vertices): self.graph = defaultdict(list) self.V = vertices def addEdge(self,u,v): self.graph[u].append(v) def topologicalSortUtil(self,v,visited,stack): visited[v] = True for i in self.graph[v]: if visited[i] == False: self.topologicalSortUtil(i,visited,stack) stack.insert(0,v) def topologicalSort(self): visited = [False]*self.V stack =[] for i in range(self.V): if visited[i] == False: self.topologicalSortUtil(i,visited,stack) print (stack) # TEST # g= Graph(6) # g.addEdge(5, 2); # g.addEdge(5, 0); # g.addEdge(4, 0); # g.addEdge(4, 1); # g.addEdge(2, 3); # g.addEdge(3, 1); # print ("output of Topological Sort ") # g.topologicalSort() # [5, 4, 2, 3, 1, 0] # V2 # https://zhuanlan.zhihu.com/p/69858335 def topoSort(graph): in_degrees = dict((u,0) for u in graph) # init (value with 0) num = len(in_degrees) for u in graph: for v in graph[u]: in_degrees[v] += 1 Q = [u for u in in_degrees if in_degrees[u] == 0] Seq = [] while Q: u = Q.pop() Seq.append(u) for v in graph[u]: in_degrees[v] -= 1 if in_degrees[v] == 0: Q.append(v) if len(Seq) == num: return Seq else: return None # TEST # G = { # 'a':'bf', # 'b':'cdf', # 'c':'d', # 'd':'ef', # 'e':'f', # 'f':'' # } # print(topoSort(G)) # ['a', 'b', 'c', 'd', 'e', 'f'] # V3 # https://www.educative.io/courses/grokking-the-coding-interview/m25rBmwLV00 from collections import deque def topological_sort(vertices, edges): sortedOrder = [] if vertices <= 0: return sortedOrder # a. Initialize the graph inDegree = {i: 0 for i in range(vertices)} # count of incoming edges graph = {i: [] for i in range(vertices)} # adjacency list graph # b. Build the graph for edge in edges: parent, child = edge[0], edge[1] graph[parent].append(child) # put the child into it's parent's list inDegree[child] += 1 # increment child's inDegree # c. Find all sources i.e., all vertices with 0 in-degrees sources = deque() for key in inDegree: if inDegree[key] == 0: sources.append(key) # d. For each source, add it to the sortedOrder and subtract one from all of its children's in-degrees # if a child's in-degree becomes zero, add it to the sources queue while sources: vertex = sources.popleft() sortedOrder.append(vertex) for child in graph[vertex]: # get the node's children to decrement their in-degrees inDegree[child] -= 1 if inDegree[child] == 0: sources.append(child) # topological sort is not possible as the graph has a cycle if len(sortedOrder) != vertices: return [] return sortedOrder # TEST # def main(): # print("Topological sort: " + # str(topological_sort(4, [[3, 2], [3, 0], [2, 0], [2, 1]]))) # print("Topological sort: " + # str(topological_sort(5, [[4, 2], [4, 3], [2, 0], [2, 1], [3, 1]]))) # print("Topological sort: " + # str(topological_sort(7, [[6, 4], [6, 2], [5, 3], [5, 4], [3, 0], [3, 1], [3, 2], [4, 1]]))) #main()

30.322222

146

0.563576

from collections import defaultdict class Graph: def __init__(self, vertices): self.graph = defaultdict(list) self.V = vertices def addEdge(self, u, v): self.graph[u].append(v) def topologicalSortUtil(self, v, visited, stack): visited[v] = True alse: self.topologicalSortUtil(k, visited, stack) sited[v] == False: self.topologicalSortUtil(v, visited, stack) return stack[::-1] :1, "C":2, "D": 3} v = 4 g = Graph(v) g.addEdge(0, 1) g.addEdge(0, 2) g.addEdge(2, 3) g.addEdge(3, 1) print (g.graph) r = g.topologicalSort() print (r) from collections import defaultdict class Graph: def __init__(self, v): self.graph = defaultdict(list) self.v = v def addEdge(self, a, b): self.graph[a].append(b) def topologicalSortUtil(self, x, visited, stack): # V1 if visited[x]: return for k in self.graph[x]: self.topologicalSortUtil(k, visited, stack) visited[x] = True stack.insert(0, x) # V2 # visited[v] = True # ### NOTE this !!! (self.graph[v]) # for k in self.graph[v]: # if visited[k] == False: # self.topologicalSortUtil(k, visited, stack) # # stack.insert(0,v) # instead of insert v to idx = 0, we can still append v to stack and reverse it and return (e.g. return stack[::-1]) # """ # ### NOTE !! stack.append(v) is wrong, we SHOULD use stack.insert(0,v) # """ # stack.insert(0,v) def topologicalSort(self): visited = [False] * self.v stack = [] for x in range(self.v): if not visited[x]: self.topologicalSortUtil(x, visited, stack) print ("stack = " + str(stack)) return stack[::-1] # V0'' # IDEA : implement topologicalSortUtil, topologicalSort, and addEdge methods from collections import defaultdict class Graph: def __init__(self,vertices): self.graph = defaultdict(list) self.V = vertices # for testing (build graph) def addEdge(self,u,v): self.graph[u].append(v) def topologicalSortUtil(self,v,visited,stack): visited[v] = True for i in self.graph[v]: if visited[i] == False: self.topologicalSortUtil(i,visited,stack) stack.insert(0,v) def topologicalSort(self): visited = [False]*self.V stack =[] for i in range(self.V): if visited[i] == False: self.topologicalSortUtil(i,visited,stack) print (stack) # V1 # https://www.geeksforgeeks.org/topological-sorting/ # Python program to print topological sorting of a DAG from collections import defaultdict class Graph: def __init__(self, vertices): self.graph = defaultdict(list) # dictionary containing adjacency List self.V = vertices # No. of vertices # function to add an edge to graph def addEdge(self, u, v): self.graph[u].append(v) # A recursive function used by topologicalSort def topologicalSortUtil(self, v, visited, stack): # Mark the current node as visited. visited[v] = True # Recur for all the vertices adjacent to this vertex for i in self.graph[v]: if visited[i] == False: self.topologicalSortUtil(i, visited, stack) # Push current vertex to stack which stores result #stack.append(v) stack.insert(0,v) # The function to do Topological Sort. It uses recursive # topologicalSortUtil() def topologicalSort(self): # Mark all the vertices as not visited visited = [False]*self.V stack = [] # Call the recursive helper function to store Topological # Sort starting from all vertices one by one for i in range(self.V): if visited[i] == False: self.topologicalSortUtil(i, visited, stack) # Print contents of the stack print(stack[::-1]) # return list in reverse order # TEST # Driver Code # g = Graph(6) # g.addEdge(5, 2) # g.addEdge(5, 0) # g.addEdge(4, 0) # g.addEdge(4, 1) # g.addEdge(2, 3) # g.addEdge(3, 1) # # print ("Following is a Topological Sort of the given graph") # # # Function Call # g.topologicalSort() # V1 # https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py # a # / \ # b c # / \ # d e # edges = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []} # vertices = ["a", "b", "c", "d", "e"] class Graph: def topological_sort(self, start, visited, sort): current = start # add current to visited visited.append(current) neighbors = edges[current] for neighbor in neighbors: # if neighbor not in visited, visit if neighbor not in visited: sort = topological_sort(neighbor, visited, sort) # if all neighbors visited add current to sort sort.append(current) # if all vertices haven't been visited select a new one to visit if len(visited) != len(vertices): for vertice in vertices: if vertice not in visited: sort = topological_sort(vertice, visited, sort) return sort # http://www.runoob.com/python3/python-topological-sorting.html class Graph: from collections import defaultdict def __init__(self,vertices): self.graph = defaultdict(list) self.V = vertices def addEdge(self,u,v): self.graph[u].append(v) def topologicalSortUtil(self,v,visited,stack): visited[v] = True for i in self.graph[v]: if visited[i] == False: self.topologicalSortUtil(i,visited,stack) stack.insert(0,v) def topologicalSort(self): visited = [False]*self.V stack =[] for i in range(self.V): if visited[i] == False: self.topologicalSortUtil(i,visited,stack) print (stack) # TEST # g= Graph(6) # g.addEdge(5, 2); # g.addEdge(5, 0); # g.addEdge(4, 0); # g.addEdge(4, 1); # g.addEdge(2, 3); # g.addEdge(3, 1); # print ("output of Topological Sort ") # g.topologicalSort() # [5, 4, 2, 3, 1, 0] # V2 # https://zhuanlan.zhihu.com/p/69858335 def topoSort(graph): in_degrees = dict((u,0) for u in graph) # init (value with 0) num = len(in_degrees) for u in graph: for v in graph[u]: in_degrees[v] += 1 Q = [u for u in in_degrees if in_degrees[u] == 0] Seq = [] while Q: u = Q.pop() Seq.append(u) for v in graph[u]: in_degrees[v] -= 1 if in_degrees[v] == 0: Q.append(v) if len(Seq) == num: return Seq else: return None # TEST # G = { # 'a':'bf', # 'b':'cdf', # 'c':'d', # 'd':'ef', # 'e':'f', # 'f':'' # } # print(topoSort(G)) # ['a', 'b', 'c', 'd', 'e', 'f'] # V3 # https://www.educative.io/courses/grokking-the-coding-interview/m25rBmwLV00 from collections import deque def topological_sort(vertices, edges): sortedOrder = [] if vertices <= 0: return sortedOrder # a. Initialize the graph inDegree = {i: 0 for i in range(vertices)} # count of incoming edges graph = {i: [] for i in range(vertices)} # adjacency list graph # b. Build the graph for edge in edges: parent, child = edge[0], edge[1] graph[parent].append(child) # put the child into it's parent's list inDegree[child] += 1 # increment child's inDegree sources = deque() for key in inDegree: if inDegree[key] == 0: sources.append(key) # if a child's in-degree becomes zero, add it to the sources queue while sources: vertex = sources.popleft() sortedOrder.append(vertex) for child in graph[vertex]: inDegree[child] -= 1 if inDegree[child] == 0: sources.append(child) # topological sort is not possible as the graph has a cycle if len(sortedOrder) != vertices: return [] return sortedOrder # TEST # def main(): # print("Topological sort: " + # str(topological_sort(4, [[3, 2], [3, 0], [2, 0], [2, 1]]))) # print("Topological sort: " + # str(topological_sort(5, [[4, 2], [4, 3], [2, 0], [2, 1], [3, 1]]))) # print("Topological sort: " + # str(topological_sort(7, [[6, 4], [6, 2], [5, 3], [5, 4], [3, 0], [3, 1], [3, 2], [4, 1]]))) #main()

true

7907d6df00104e5dbb8f2efc2f845186d93d01d2

912

py

Python

examples/data.py

zkx741481546/keract

6f25711e54f7f8b5387fff8f79ad35a0a1113d33

[ "MIT" ]

null

examples/data.py

zkx741481546/keract

6f25711e54f7f8b5387fff8f79ad35a0a1113d33

[ "MIT" ]

null

examples/data.py

zkx741481546/keract

6f25711e54f7f8b5387fff8f79ad35a0a1113d33

[ "MIT" ]

1

2019-03-22T17:10:38.000Z

import keras from keras.datasets import mnist # input image dimensions img_rows, img_cols = 28, 28 input_shape = (img_rows, img_cols, 1) num_classes = 10 def get_mnist_data(): # the data, shuffled and split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) return x_train, y_train, x_test, y_test

31.448276

70

0.710526

import keras from keras.datasets import mnist img_rows, img_cols = 28, 28 input_shape = (img_rows, img_cols, 1) num_classes = 10 def get_mnist_data(): (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) return x_train, y_train, x_test, y_test

true

7907d6e31c23a931fa12193e8b0d2f539025e7e2

1,356

py

Python

google/cloud/aiplatform/v1/schema/predict/instance_v1/types/text_sentiment.py

TheMichaelHu/python-aiplatform

e03f373a7e44c354eda88875a41c771f6d7e3ce1

[ "Apache-2.0" ]

null

google/cloud/aiplatform/v1/schema/predict/instance_v1/types/text_sentiment.py

TheMichaelHu/python-aiplatform

e03f373a7e44c354eda88875a41c771f6d7e3ce1

[ "Apache-2.0" ]

null

google/cloud/aiplatform/v1/schema/predict/instance_v1/types/text_sentiment.py

TheMichaelHu/python-aiplatform

e03f373a7e44c354eda88875a41c771f6d7e3ce1

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import proto # type: ignore __protobuf__ = proto.module( package="google.cloud.aiplatform.v1.schema.predict.instance", manifest={ "TextSentimentPredictionInstance", }, ) class TextSentimentPredictionInstance(proto.Message): r"""Prediction input format for Text Sentiment. Attributes: content (str): The text snippet to make the predictions on. mime_type (str): The MIME type of the text snippet. The supported MIME types are listed below. - text/plain """ content = proto.Field( proto.STRING, number=1, ) mime_type = proto.Field( proto.STRING, number=2, ) __all__ = tuple(sorted(__protobuf__.manifest))

27.12

74

0.676254

import proto __protobuf__ = proto.module( package="google.cloud.aiplatform.v1.schema.predict.instance", manifest={ "TextSentimentPredictionInstance", }, ) class TextSentimentPredictionInstance(proto.Message): content = proto.Field( proto.STRING, number=1, ) mime_type = proto.Field( proto.STRING, number=2, ) __all__ = tuple(sorted(__protobuf__.manifest))

true

7907d6ec77fd61d93ef1f5aa9370235babd38542

2,998

py

Python

checktheplug/data/ServerDao.py

maximx1/checktheplug

585068666a93cee0c6e8dd80c92511d6cee5ca04

[ "MIT" ]

null

checktheplug/data/ServerDao.py

maximx1/checktheplug

585068666a93cee0c6e8dd80c92511d6cee5ca04

[ "MIT" ]

26

2015-02-04T15:09:54.000Z

2015-03-22T02:44:14.000Z

checktheplug/data/ServerDao.py

maximx1/checktheplug

585068666a93cee0c6e8dd80c92511d6cee5ca04

[ "MIT" ]

null

import sqlite3 from checktheplug.models.Server import Server """ Operations to manage accessing the server database. """ class ServerDao: """ Sets up the object with the sql connection. """ def __init__(self, settings): self.conn = sqlite3.connect(settings.database) """ Add Server to the database. """ def add(self, new_server): if new_server: try: with self.conn: cur = self.conn.cursor() cur.execute("INSERT INTO servers(host, url) values(?, ?)", (new_server.host, new_server.url)) return(Server(cur.lastrowid, new_server.host, new_server.url), None) except sqlite3.IntegrityError as er: return (None, "There was a db issue: " + str(er)) else: return (None, "No server passed in") """ Find all the servers for a particular app. """ def find_by_app_id(self, app_id): try: with self.conn: cur = self.conn.cursor() cur.execute("SELECT id, host, url from servers where app_id = ?", (app_id,)) server_rows = cur.fetchall() return (list(map(lambda x: Server(x[0], x[1], x[2], app_id), server_rows)), None) except Exception as er: return (None, "There was a db issue: " + str(er)) """ Find x number of available servers or all that are available. """ def find_available_servers(self, quantity): try: with self.conn: cur = self.conn.cursor() cur.execute("SELECT id, host, url from servers where app_id = null limit = ?", (quantity,)) server_rows = cur.fetchall() return (list(map(lambda x: Server(x[0], x[1], x[2], None), server_rows)), None) except Exception as er: return (None, "There was a db issue: " + str(er)) """ Retrieve all servers. """ def retrieve_all_servers(self): try: with self.conn: cur = self.conn.cursor() cur.execute("SELECT id, host, url from servers") server_rows = cur.fetchall() return (list(map(lambda x: Server(x[0], x[1], x[2], None), server_rows)), None) except Exception as er: return (None, "There was a db issue: " + str(er)) """ Tie an app to a number of servers. """ def tie_app_to_servers(self, app_id, available_servers): try: with self.conn: cur = self.conn.cursor() server_id_string = ', '.join("?" * available_servers) cur.execute("update servers set app_id = ? where id in ({0})".format(server_id_string), tuple([app_id] + available_servers)) return (None, "ok") except Exception as er: return (None, "There was a db issue: " + str(er))

37.012346

140

0.53936

import sqlite3 from checktheplug.models.Server import Server class ServerDao: def __init__(self, settings): self.conn = sqlite3.connect(settings.database) def add(self, new_server): if new_server: try: with self.conn: cur = self.conn.cursor() cur.execute("INSERT INTO servers(host, url) values(?, ?)", (new_server.host, new_server.url)) return(Server(cur.lastrowid, new_server.host, new_server.url), None) except sqlite3.IntegrityError as er: return (None, "There was a db issue: " + str(er)) else: return (None, "No server passed in") def find_by_app_id(self, app_id): try: with self.conn: cur = self.conn.cursor() cur.execute("SELECT id, host, url from servers where app_id = ?", (app_id,)) server_rows = cur.fetchall() return (list(map(lambda x: Server(x[0], x[1], x[2], app_id), server_rows)), None) except Exception as er: return (None, "There was a db issue: " + str(er)) def find_available_servers(self, quantity): try: with self.conn: cur = self.conn.cursor() cur.execute("SELECT id, host, url from servers where app_id = null limit = ?", (quantity,)) server_rows = cur.fetchall() return (list(map(lambda x: Server(x[0], x[1], x[2], None), server_rows)), None) except Exception as er: return (None, "There was a db issue: " + str(er)) def retrieve_all_servers(self): try: with self.conn: cur = self.conn.cursor() cur.execute("SELECT id, host, url from servers") server_rows = cur.fetchall() return (list(map(lambda x: Server(x[0], x[1], x[2], None), server_rows)), None) except Exception as er: return (None, "There was a db issue: " + str(er)) def tie_app_to_servers(self, app_id, available_servers): try: with self.conn: cur = self.conn.cursor() server_id_string = ', '.join("?" * available_servers) cur.execute("update servers set app_id = ? where id in ({0})".format(server_id_string), tuple([app_id] + available_servers)) return (None, "ok") except Exception as er: return (None, "There was a db issue: " + str(er))

true

7907d718e907edc5a762d32039926501bb9d4317

14,299

py

Python

airbyte-integrations/connectors/source-slack/source_slack/source.py

rclmenezes/airbyte

84ba3e79b3d223954fc2d997df02ff35c9d39840

[ "MIT" ]

1

2021-08-06T10:21:40.000Z

airbyte-integrations/connectors/source-slack/source_slack/source.py

rclmenezes/airbyte

84ba3e79b3d223954fc2d997df02ff35c9d39840

[ "MIT" ]

null

airbyte-integrations/connectors/source-slack/source_slack/source.py

rclmenezes/airbyte

84ba3e79b3d223954fc2d997df02ff35c9d39840

[ "MIT" ]

1

2021-05-31T00:08:34.000Z

# # MIT License # # Copyright (c) 2020 Airbyte # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # from abc import ABC, abstractmethod from typing import Any, Iterable, List, Mapping, MutableMapping, Optional, Tuple import pendulum import requests from airbyte_cdk import AirbyteLogger from airbyte_cdk.models import SyncMode from airbyte_cdk.sources import AbstractSource from airbyte_cdk.sources.streams import Stream from airbyte_cdk.sources.streams.http import HttpStream from airbyte_cdk.sources.streams.http.auth import TokenAuthenticator from pendulum import DateTime, Period from slack_sdk import WebClient class SlackStream(HttpStream, ABC): url_base = "https://slack.com/api/" primary_key = "id" page_size = 100 def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]: # Slack uses a cursor-based pagination strategy. # Extract the cursor from the response if it exists and return it in a format that can be used to update request parameters json_response = response.json() next_cursor = json_response.get("response_metadata", {}).get("next_cursor") if next_cursor: return {"cursor": next_cursor} def request_params( self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, next_page_token: Mapping[str, Any] = None, ) -> MutableMapping[str, Any]: params = {"limit": self.page_size} if next_page_token: params.update(**next_page_token) return params def parse_response( self, response: requests.Response, stream_state: Mapping[str, Any] = None, stream_slice: Mapping[str, Any] = None, next_page_token: Mapping[str, Any] = None, ) -> Iterable[MutableMapping]: json_response = response.json() yield from json_response.get(self.data_field, []) def backoff_time(self, response: requests.Response) -> Optional[float]: # This method is called if we run into the rate limit. Slack puts the retry time in the `Retry-After` response header so we # we return that value. If the response is anything other than a 429 (e.g: 5XX) fall back on default retry behavior. # https://api.slack.com/docs/rate-limits#web if response.status_code == 429: return int(response.headers.get("Retry-After", 0)) @property @abstractmethod def data_field(self) -> str: """The name of the field in the response which contains the data""" class Channels(SlackStream): data_field = "channels" def path(self, **kwargs) -> str: return "conversations.list" def request_params(self, **kwargs) -> MutableMapping[str, Any]: params = super().request_params(**kwargs) params["types"] = "public_channel" return params class ChannelMembers(SlackStream): data_field = "members" def path(self, **kwargs) -> str: return "conversations.members" def request_params(self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, **kwargs) -> MutableMapping[str, Any]: params = super().request_params(stream_state=stream_state, stream_slice=stream_slice, **kwargs) params["channel"] = stream_slice["channel_id"] return params def parse_response(self, response: requests.Response, stream_slice: Mapping[str, Any] = None, **kwargs) -> Iterable[Mapping]: for member_id in super().parse_response(response, **kwargs): # Slack just returns raw IDs as a string, so we want to put them in a "join table" format yield {"member_id": member_id, "channel_id": stream_slice["channel_id"]} def stream_slices(self, **kwargs) -> Iterable[Optional[Mapping[str, any]]]: channels_stream = Channels(authenticator=self.authenticator) for channel_record in channels_stream.read_records(sync_mode=SyncMode.full_refresh): yield {"channel_id": channel_record["id"]} class Users(SlackStream): data_field = "members" def path(self, **kwargs) -> str: return "users.list" # Incremental Streams def chunk_date_range(start_date: DateTime, interval=pendulum.duration(days=1)) -> Iterable[Period]: """ Yields a list of the beginning and ending timestamps of each day between the start date and now. The return value is a pendulum.period """ now = pendulum.now() # Each stream_slice contains the beginning and ending timestamp for a 24 hour period while start_date <= now: end_date = start_date + interval yield pendulum.period(start_date, end_date) start_date = end_date class IncrementalMessageStream(SlackStream, ABC): data_field = "messages" cursor_field = "float_ts" primary_key = ["channel_id", "ts"] def __init__(self, default_start_date: DateTime, **kwargs): self._start_ts = default_start_date.timestamp() super().__init__(**kwargs) def request_params(self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, **kwargs) -> MutableMapping[str, Any]: params = super().request_params(stream_state=stream_state, stream_slice=stream_slice, **kwargs) params.update(**stream_slice) return params def parse_response(self, response: requests.Response, stream_slice: Mapping[str, Any] = None, **kwargs) -> Iterable[Mapping]: for record in super().parse_response(response, **kwargs): record[self.primary_key[0]] = stream_slice.get("channel", "") record[self.cursor_field] = float(record[self.primary_key[1]]) yield record def get_updated_state(self, current_stream_state: MutableMapping[str, Any], latest_record: Mapping[str, Any]) -> Mapping[str, Any]: current_stream_state = current_stream_state or {} current_stream_state[self.cursor_field] = max( latest_record[self.cursor_field], current_stream_state.get(self.cursor_field, self._start_ts) ) return current_stream_state class ChannelMessages(IncrementalMessageStream): def path(self, **kwargs) -> str: return "conversations.history" def stream_slices(self, stream_state: Mapping[str, Any] = None, **kwargs) -> Iterable[Optional[Mapping[str, any]]]: stream_state = stream_state or {} start_date = pendulum.from_timestamp(stream_state.get(self.cursor_field, self._start_ts)) for period in chunk_date_range(start_date): yield {"oldest": period.start.timestamp(), "latest": period.end.timestamp()} def read_records(self, stream_slice: Optional[Mapping[str, Any]] = None, **kwargs) -> Iterable[Mapping[str, Any]]: # Channel is provided when reading threads if "channel" in stream_slice: yield from super().read_records(stream_slice=stream_slice, **kwargs) else: # if channel is not provided, then get channels and read accordingly channels = Channels(authenticator=self.authenticator) for channel_record in channels.read_records(sync_mode=SyncMode.full_refresh): stream_slice["channel"] = channel_record["id"] yield from super().read_records(stream_slice=stream_slice, **kwargs) class Threads(IncrementalMessageStream): def __init__(self, lookback_window: Mapping[str, int], **kwargs): self.messages_lookback_window = lookback_window super().__init__(**kwargs) def path(self, **kwargs) -> str: return "conversations.replies" def stream_slices(self, stream_state: Mapping[str, Any] = None, **kwargs) -> Iterable[Optional[Mapping[str, any]]]: """ The logic for incrementally syncing threads is not very obvious, so buckle up. To get all messages in a thread, one must specify the channel and timestamp of the parent (first) message of that thread, basically its ID. One complication is that threads can be updated at any time in the future. Therefore, if we wanted to comprehensively sync data i.e: get every single response in a thread, we'd have to read every message in the slack instance every time we ran a sync, because otherwise there is no way to guarantee that a thread deep in the past didn't receive a new message. A pragmatic workaround is to say we want threads to be at least N days fresh i.e: look back N days into the past, get every message since, and read all of the thread responses. This is essentially the approach we're taking here via slicing: create slices from N days into the past and read all messages in threads since then. We could optionally filter out records we have already read, but that's omitted to keep the logic simple to reason about. Good luck. """ stream_state = stream_state or {} channels_stream = Channels(authenticator=self.authenticator) if self.cursor_field in stream_state: # Since new messages can be posted to threads continuously after the parent message has been posted, we get messages from the latest date # found in the state minus 7 days to pick up any new messages in threads. # If there is state always use lookback messages_start_date = pendulum.from_timestamp(stream_state[self.cursor_field]) - self.messages_lookback_window else: # If there is no state i.e: this is the first sync then there is no use for lookback, just get messages from the default start date messages_start_date = pendulum.from_timestamp(self._start_ts) messages_stream = ChannelMessages(authenticator=self.authenticator, default_start_date=messages_start_date) for message_chunk in messages_stream.stream_slices(stream_state={self.cursor_field: messages_start_date.timestamp()}): self.logger.info(f"Syncing replies {message_chunk}") for channel in channels_stream.read_records(sync_mode=SyncMode.full_refresh): message_chunk["channel"] = channel["id"] for message in messages_stream.read_records(sync_mode=SyncMode.full_refresh, stream_slice=message_chunk): yield {"channel": channel["id"], self.cursor_field: message[self.primary_key]} class JoinChannelsStream(HttpStream): """ This class is a special stream which joins channels because the Slack API only returns messages from channels this bot is in. Its responses should only be logged for debugging reasons, not read as records. """ url_base = "https://slack.com/api/" http_method = "POST" primary_key = "id" def parse_response(self, response: requests.Response, stream_slice: Mapping[str, Any] = None, **kwargs) -> Iterable[Mapping]: return [{"message": f"Successfully joined channel: {stream_slice['channel_name']}"}] def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]: return None # No pagination def path(self, **kwargs) -> str: return "conversations.join" def stream_slices(self, **kwargs) -> Iterable[Optional[Mapping[str, any]]]: channels_stream = Channels(authenticator=self.authenticator) for channel in channels_stream.read_records(sync_mode=SyncMode.full_refresh): yield {"channel": channel["id"], "channel_name": channel["name"]} def request_body_json(self, stream_slice: Mapping = None, **kwargs) -> Optional[Mapping]: return {"channel": stream_slice["channel"]} class SourceSlack(AbstractSource): def check_connection(self, logger: AirbyteLogger, config: Mapping[str, Any]) -> Tuple[bool, Optional[Any]]: slack_client = WebClient(token=config["api_token"]) users = slack_client.users_list(limit=1).get("members", []) if len(users) > 0: return True, None else: return False, "There are no users in the given Slack instance" def streams(self, config: Mapping[str, Any]) -> List[Stream]: authenticator = TokenAuthenticator(config["api_token"]) default_start_date = pendulum.parse(config["start_date"]) threads_lookback_window = pendulum.Duration(days=config["lookback_window"]) streams = [ Channels(authenticator=authenticator), ChannelMembers(authenticator=authenticator), ChannelMessages(authenticator=authenticator, default_start_date=default_start_date), Threads(authenticator=authenticator, default_start_date=default_start_date, lookback_window=threads_lookback_window), Users(authenticator=authenticator), ] # To sync data from channels, the bot backed by this token needs to join all those channels. This operation is idempotent. if config["join_channels"]: logger = AirbyteLogger() logger.info("joining Slack channels") join_channels_stream = JoinChannelsStream(authenticator=authenticator) for stream_slice in join_channels_stream.stream_slices(): for message in join_channels_stream.read_records(sync_mode=SyncMode.full_refresh, stream_slice=stream_slice): logger.info(message["message"]) return streams

47.822742

150

0.699979

from abc import ABC, abstractmethod from typing import Any, Iterable, List, Mapping, MutableMapping, Optional, Tuple import pendulum import requests from airbyte_cdk import AirbyteLogger from airbyte_cdk.models import SyncMode from airbyte_cdk.sources import AbstractSource from airbyte_cdk.sources.streams import Stream from airbyte_cdk.sources.streams.http import HttpStream from airbyte_cdk.sources.streams.http.auth import TokenAuthenticator from pendulum import DateTime, Period from slack_sdk import WebClient class SlackStream(HttpStream, ABC): url_base = "https://slack.com/api/" primary_key = "id" page_size = 100 def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]: json_response = response.json() next_cursor = json_response.get("response_metadata", {}).get("next_cursor") if next_cursor: return {"cursor": next_cursor} def request_params( self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, next_page_token: Mapping[str, Any] = None, ) -> MutableMapping[str, Any]: params = {"limit": self.page_size} if next_page_token: params.update(**next_page_token) return params def parse_response( self, response: requests.Response, stream_state: Mapping[str, Any] = None, stream_slice: Mapping[str, Any] = None, next_page_token: Mapping[str, Any] = None, ) -> Iterable[MutableMapping]: json_response = response.json() yield from json_response.get(self.data_field, []) def backoff_time(self, response: requests.Response) -> Optional[float]: if response.status_code == 429: return int(response.headers.get("Retry-After", 0)) @property @abstractmethod def data_field(self) -> str: class Channels(SlackStream): data_field = "channels" def path(self, **kwargs) -> str: return "conversations.list" def request_params(self, **kwargs) -> MutableMapping[str, Any]: params = super().request_params(**kwargs) params["types"] = "public_channel" return params class ChannelMembers(SlackStream): data_field = "members" def path(self, **kwargs) -> str: return "conversations.members" def request_params(self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, **kwargs) -> MutableMapping[str, Any]: params = super().request_params(stream_state=stream_state, stream_slice=stream_slice, **kwargs) params["channel"] = stream_slice["channel_id"] return params def parse_response(self, response: requests.Response, stream_slice: Mapping[str, Any] = None, **kwargs) -> Iterable[Mapping]: for member_id in super().parse_response(response, **kwargs): yield {"member_id": member_id, "channel_id": stream_slice["channel_id"]} def stream_slices(self, **kwargs) -> Iterable[Optional[Mapping[str, any]]]: channels_stream = Channels(authenticator=self.authenticator) for channel_record in channels_stream.read_records(sync_mode=SyncMode.full_refresh): yield {"channel_id": channel_record["id"]} class Users(SlackStream): data_field = "members" def path(self, **kwargs) -> str: return "users.list" def chunk_date_range(start_date: DateTime, interval=pendulum.duration(days=1)) -> Iterable[Period]: now = pendulum.now() while start_date <= now: end_date = start_date + interval yield pendulum.period(start_date, end_date) start_date = end_date class IncrementalMessageStream(SlackStream, ABC): data_field = "messages" cursor_field = "float_ts" primary_key = ["channel_id", "ts"] def __init__(self, default_start_date: DateTime, **kwargs): self._start_ts = default_start_date.timestamp() super().__init__(**kwargs) def request_params(self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, **kwargs) -> MutableMapping[str, Any]: params = super().request_params(stream_state=stream_state, stream_slice=stream_slice, **kwargs) params.update(**stream_slice) return params def parse_response(self, response: requests.Response, stream_slice: Mapping[str, Any] = None, **kwargs) -> Iterable[Mapping]: for record in super().parse_response(response, **kwargs): record[self.primary_key[0]] = stream_slice.get("channel", "") record[self.cursor_field] = float(record[self.primary_key[1]]) yield record def get_updated_state(self, current_stream_state: MutableMapping[str, Any], latest_record: Mapping[str, Any]) -> Mapping[str, Any]: current_stream_state = current_stream_state or {} current_stream_state[self.cursor_field] = max( latest_record[self.cursor_field], current_stream_state.get(self.cursor_field, self._start_ts) ) return current_stream_state class ChannelMessages(IncrementalMessageStream): def path(self, **kwargs) -> str: return "conversations.history" def stream_slices(self, stream_state: Mapping[str, Any] = None, **kwargs) -> Iterable[Optional[Mapping[str, any]]]: stream_state = stream_state or {} start_date = pendulum.from_timestamp(stream_state.get(self.cursor_field, self._start_ts)) for period in chunk_date_range(start_date): yield {"oldest": period.start.timestamp(), "latest": period.end.timestamp()} def read_records(self, stream_slice: Optional[Mapping[str, Any]] = None, **kwargs) -> Iterable[Mapping[str, Any]]: if "channel" in stream_slice: yield from super().read_records(stream_slice=stream_slice, **kwargs) else: channels = Channels(authenticator=self.authenticator) for channel_record in channels.read_records(sync_mode=SyncMode.full_refresh): stream_slice["channel"] = channel_record["id"] yield from super().read_records(stream_slice=stream_slice, **kwargs) class Threads(IncrementalMessageStream): def __init__(self, lookback_window: Mapping[str, int], **kwargs): self.messages_lookback_window = lookback_window super().__init__(**kwargs) def path(self, **kwargs) -> str: return "conversations.replies" def stream_slices(self, stream_state: Mapping[str, Any] = None, **kwargs) -> Iterable[Optional[Mapping[str, any]]]: stream_state = stream_state or {} channels_stream = Channels(authenticator=self.authenticator) if self.cursor_field in stream_state: messages_start_date = pendulum.from_timestamp(stream_state[self.cursor_field]) - self.messages_lookback_window else: messages_start_date = pendulum.from_timestamp(self._start_ts) messages_stream = ChannelMessages(authenticator=self.authenticator, default_start_date=messages_start_date) for message_chunk in messages_stream.stream_slices(stream_state={self.cursor_field: messages_start_date.timestamp()}): self.logger.info(f"Syncing replies {message_chunk}") for channel in channels_stream.read_records(sync_mode=SyncMode.full_refresh): message_chunk["channel"] = channel["id"] for message in messages_stream.read_records(sync_mode=SyncMode.full_refresh, stream_slice=message_chunk): yield {"channel": channel["id"], self.cursor_field: message[self.primary_key]} class JoinChannelsStream(HttpStream): url_base = "https://slack.com/api/" http_method = "POST" primary_key = "id" def parse_response(self, response: requests.Response, stream_slice: Mapping[str, Any] = None, **kwargs) -> Iterable[Mapping]: return [{"message": f"Successfully joined channel: {stream_slice['channel_name']}"}] def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]: return None def path(self, **kwargs) -> str: return "conversations.join" def stream_slices(self, **kwargs) -> Iterable[Optional[Mapping[str, any]]]: channels_stream = Channels(authenticator=self.authenticator) for channel in channels_stream.read_records(sync_mode=SyncMode.full_refresh): yield {"channel": channel["id"], "channel_name": channel["name"]} def request_body_json(self, stream_slice: Mapping = None, **kwargs) -> Optional[Mapping]: return {"channel": stream_slice["channel"]} class SourceSlack(AbstractSource): def check_connection(self, logger: AirbyteLogger, config: Mapping[str, Any]) -> Tuple[bool, Optional[Any]]: slack_client = WebClient(token=config["api_token"]) users = slack_client.users_list(limit=1).get("members", []) if len(users) > 0: return True, None else: return False, "There are no users in the given Slack instance" def streams(self, config: Mapping[str, Any]) -> List[Stream]: authenticator = TokenAuthenticator(config["api_token"]) default_start_date = pendulum.parse(config["start_date"]) threads_lookback_window = pendulum.Duration(days=config["lookback_window"]) streams = [ Channels(authenticator=authenticator), ChannelMembers(authenticator=authenticator), ChannelMessages(authenticator=authenticator, default_start_date=default_start_date), Threads(authenticator=authenticator, default_start_date=default_start_date, lookback_window=threads_lookback_window), Users(authenticator=authenticator), ] if config["join_channels"]: logger = AirbyteLogger() logger.info("joining Slack channels") join_channels_stream = JoinChannelsStream(authenticator=authenticator) for stream_slice in join_channels_stream.stream_slices(): for message in join_channels_stream.read_records(sync_mode=SyncMode.full_refresh, stream_slice=stream_slice): logger.info(message["message"]) return streams

true

7907d7bda5f235bc65679ea4efce65cf68e3b788

30,892

py

Python

examples/applications/plot_cyclical_feature_engineering.py

patrickctrf/scikit-learn

d6735f4851d828984a0517de954b9b88c74919fe

[ "BSD-3-Clause" ]

1

2021-02-09T18:15:01.000Z

examples/applications/plot_cyclical_feature_engineering.py

patrickctrf/scikit-learn

d6735f4851d828984a0517de954b9b88c74919fe

[ "BSD-3-Clause" ]

null

examples/applications/plot_cyclical_feature_engineering.py

patrickctrf/scikit-learn

d6735f4851d828984a0517de954b9b88c74919fe

[ "BSD-3-Clause" ]

null

""" ================================ Time-related feature engineering ================================ This notebook introduces different strategies to leverage time-related features for a bike sharing demand regression task that is highly dependent on business cycles (days, weeks, months) and yearly season cycles. In the process, we introduce how to perform periodic feature engineering using the :class:`sklearn.preprocessing.SplineTransformer` class and its `extrapolation="periodic"` option. """ # %% # Data exploration on the Bike Sharing Demand dataset # --------------------------------------------------- # # We start by loading the data from the OpenML repository. from sklearn.datasets import fetch_openml bike_sharing = fetch_openml("Bike_Sharing_Demand", version=2, as_frame=True) df = bike_sharing.frame # %% # To get a quick understanding of the periodic patterns of the data, let us # have a look at the average demand per hour during a week. # # Note that the week starts on a Sunday, during the weekend. We can clearly # distinguish the commute patterns in the morning and evenings of the work days # and the leisure use of the bikes on the weekends with a more spread peak # demand around the middle of the days: import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(12, 4)) average_week_demand = df.groupby(["weekday", "hour"]).mean()["count"] average_week_demand.plot(ax=ax) _ = ax.set( title="Average hourly bike demand during the week", xticks=[i * 24 for i in range(7)], xticklabels=["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"], xlabel="Time of the week", ylabel="Number of bike rentals", ) # %% # # The target of the prediction problem is the absolute count of bike rentals on # a hourly basis: df["count"].max() # %% [markdown] # # Let us rescale the target variable (number of hourly bike rentals) to predict # a relative demand so that the mean absolute error is more easily interpreted # as a fraction of the maximum demand. # # .. note:: # # The fit method of the models used in this notebook all minimize the # mean squared error to estimate the conditional mean instead of the mean # absolute error that would fit an estimator of the conditional median. # # When reporting performance measure on the test set in the discussion, we # instead choose to focus on the mean absolute error that is more # intuitive than the (root) mean squared error. Note however that the best # models for one metric are also the best for the other in this study. y = df["count"] / 1000 # %% fig, ax = plt.subplots(figsize=(12, 4)) y.hist(bins=30, ax=ax) _ = ax.set( xlabel="Fraction of rented fleet demand", ylabel="Number of hours", ) # %% # The input feature data frame is a time annotated hourly log of variables # describing the weather conditions. It includes both numerical and categorical # variables. Note that the time information has already been expanded into # several complementary columns. # X = df.drop("count", axis="columns") X # %% # .. note:: # # If the time information was only present as a date or datetime column, we # could have expanded it into hour-in-the-day, day-in-the-week, # day-in-the-month, month-in-the-year using pandas: # https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-date-components # # We now introspect the distribution of the categorical variables, starting # with `"weather"`: # X["weather"].value_counts() # %% # Since there are only 3 `"heavy_rain"` events, we cannot use this category to # train machine learning models with cross validation. Instead, we simplify the # representation by collapsing those into the `"rain"` category. # X["weather"].replace(to_replace="heavy_rain", value="rain", inplace=True) # %% X["weather"].value_counts() # %% # As expected, the `"season"` variable is well balanced: # X["season"].value_counts() # %% # Time-based cross-validation # --------------------------- # # Since the dataset is a time-ordered event log (hourly demand), we will use a # time-sensitive cross-validation splitter to evaluate our demand forecasting # model as realistically as possible. We use a gap of 2 days between the train # and test side of the splits. We also limit the training set size to make the # performance of the CV folds more stable. # # 1000 test datapoints should be enough to quantify the performance of the # model. This represents a bit less than a month and a half of contiguous test # data: from sklearn.model_selection import TimeSeriesSplit ts_cv = TimeSeriesSplit( n_splits=5, gap=48, max_train_size=10000, test_size=1000, ) # %% # Let us manually inspect the various splits to check that the # `TimeSeriesSplit` works as we expect, starting with the first split: all_splits = list(ts_cv.split(X, y)) train_0, test_0 = all_splits[0] # %% X.iloc[test_0] # %% X.iloc[train_0] # %% # We now inspect the last split: train_4, test_4 = all_splits[4] # %% X.iloc[test_4] # %% X.iloc[train_4] # %% # All is well. We are now ready to do some predictive modeling! # # Gradient Boosting # ----------------- # # Gradient Boosting Regression with decision trees is often flexible enough to # efficiently handle heteorogenous tabular data with a mix of categorical and # numerical features as long as the number of samples is large enough. # # Here, we do minimal ordinal encoding for the categorical variables and then # let the model know that it should treat those as categorical variables by # using a dedicated tree splitting rule. Since we use an ordinal encoder, we # pass the list of categorical values explicitly to use a logical order when # encoding the categories as integer instead of the lexicographical order. This # also has the added benefit of preventing any issue with unknown categories # when using cross-validation. # # The numerical variable need no preprocessing and, for the sake of simplicity, # we only try the default hyper-parameters for this model: from sklearn.pipeline import make_pipeline from sklearn.preprocessing import OrdinalEncoder from sklearn.compose import ColumnTransformer from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.model_selection import cross_validate categorical_columns = [ "weather", "season", "holiday", "workingday", ] categories = [ ["clear", "misty", "rain"], ["spring", "summer", "fall", "winter"], ["False", "True"], ["False", "True"], ] ordinal_encoder = OrdinalEncoder(categories=categories) gbrt_pipeline = make_pipeline( ColumnTransformer( transformers=[ ("categorical", ordinal_encoder, categorical_columns), ], remainder="passthrough", ), HistGradientBoostingRegressor( categorical_features=range(4), ), ) # %% # # Lets evaluate our gradient boosting model with the mean absolute error of the # relative demand averaged accross our 5 time-based cross-validation splits: def evaluate(model, X, y, cv): cv_results = cross_validate( model, X, y, cv=ts_cv, scoring=["neg_mean_absolute_error", "neg_root_mean_squared_error"], ) mae = -cv_results["test_neg_mean_absolute_error"] rmse = -cv_results["test_neg_root_mean_squared_error"] print( f"Mean Absolute Error: {mae.mean():.3f} +/- {mae.std():.3f}\n" f"Root Mean Squared Error: {rmse.mean():.3f} +/- {rmse.std():.3f}" ) evaluate(gbrt_pipeline, X, y, cv=ts_cv) # %% # This model has an average error around 4 to 5% of the maximum demand. This is # quite good for a first trial without any hyper-parameter tuning! We just had # to make the categorical variables explicit. Note that the time related # features are passed as is, i.e. without processing them. But this is not much # of a problem for tree-based models as they can learn a non-monotonic # relationship between ordinal input features and the target. # # This is not the case for linear regression model as we will see in the # following. # # Naive linear regression # ----------------------- # # As usual for linear models, categorical variables need to be one-hot encoded. # For consistency, we scale the numerical features to the same 0-1 range using # class:`sklearn.preprocessing.MinMaxScaler`, although in this case it does not # impact the results much because they are already on comparable scales: from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import RidgeCV import numpy as np one_hot_encoder = OneHotEncoder(handle_unknown="ignore", sparse=False) alphas = np.logspace(-6, 6, 25) naive_linear_pipeline = make_pipeline( ColumnTransformer( transformers=[ ("categorical", one_hot_encoder, categorical_columns), ], remainder=MinMaxScaler(), ), RidgeCV(alphas=alphas), ) evaluate(naive_linear_pipeline, X, y, cv=ts_cv) # %% # # The performance is not good: the average error is around 14% of the maximum # demand. This is more than three times higher than the average error of the # gradient boosting model. We can suspect that the naive original encoding of # the periodic time-related features might prevent the linear regression model # to properly leverage the time information: linear regression does not model # non-monotonic relationships between the input features and the target. # Non-linear terms have to be engineered in the input. # # For example, the raw numerical encoding of the `"hour"` feature prevents the # linear model from recognizing that an increase of hour in the morning from 6 # to 8 should have a strong positive impact on the number of bike rentals while # a increase of similar magnitude in the evening from 18 to 20 should have a # strong negative impact on the predicted number of bike rentals. # # Time-steps as categories # ------------------------ # # Since the time features are encoded in a discrete manner using integers (24 # unique values in the "hours" feature), we could decide to treat those as # categorical variables and ignore any assumption implied by the ordering of # the hour values using a one-hot encoding. # # Using one-hot encoding for the time features gives the linear model a lot # more flexibility as we introduce one additional feature per discrete time # level. one_hot_linear_pipeline = make_pipeline( ColumnTransformer( transformers=[ ("categorical", one_hot_encoder, categorical_columns), ("one_hot_time", one_hot_encoder, ["hour", "weekday", "month"]), ], remainder=MinMaxScaler(), ), RidgeCV(alphas=alphas), ) evaluate(one_hot_linear_pipeline, X, y, cv=ts_cv) # %% # The average error rate of this model is 10% which is much better than using # the original ordinal encoding of the time feature, confirming our intuition # that the linear regression model benefit from the added flexibility to not # treat time progression in a monotonic manner. # # However, this introduces a very large number of new features. If the time of # the day was represented in minutes since the start of the day instead of # hours, one-hot encoding would have introduced 1440 features instead of 24. # This could cause some significant overfitting. To avoid this we could use # :func:`sklearn.preprocessing.KBinsDiscretizer` instead to re-bin the number # of levels of fine-grained ordinal or numerical variables while still # benefitting from the non-monotonic expressivity advantages of one-hot # encoding. # # Finally, we also observe than one-hot encoding completely ignores the # ordering of the hour levels while this could be an interesting inductive bias # to preserve to some level. In the following we try to explore smooth, # non-monotonic encoding that locally preserves the relative ordering of time # features. # # Trigonometric features # ---------------------- # # As a first attempt, we can try to encode each of those periodic features # using a sine and cosine transform with the matching period. # # Each ordinal time feature is transformed into 2 features that together encode # equivalent information in a non-monotonic way, and more importantly without # any jump between the first and the last value of the periodic range. from sklearn.preprocessing import FunctionTransformer def sin_transformer(period): return FunctionTransformer(lambda x: np.sin(x / period * 2 * np.pi)) def cos_transformer(period): return FunctionTransformer(lambda x: np.cos(x / period * 2 * np.pi)) # %% # # Let us visualize the effect of this feature expansion on some synthetic hour # data with a bit of extrapolation beyond hour=23: import pandas as pd hour_df = pd.DataFrame( np.arange(26).reshape(-1, 1), columns=["hour"], ) hour_df["hour_sin"] = sin_transformer(24).fit_transform(hour_df)["hour"] hour_df["hour_cos"] = cos_transformer(24).fit_transform(hour_df)["hour"] hour_df.plot(x="hour") _ = plt.title("Trigonometric encoding for the 'hour' feature") # %% # # Let's use a 2D scatter plot with the hours encoded as colors to better see # how this representation maps the 24 hours of the day to a 2D space, akin to # some sort of 24 hour version of an analog clock. Note that the "25th" hour is # mapped back to the 1st hour because of the periodic nature of the sine/cosine # representation. fig, ax = plt.subplots(figsize=(7, 5)) sp = ax.scatter(hour_df["hour_sin"], hour_df["hour_cos"], c=hour_df["hour"]) ax.set( xlabel="sin(hour)", ylabel="cos(hour)", ) _ = fig.colorbar(sp) # %% # # We can now build a feature extraction pipeline using this strategy: cyclic_cossin_transformer = ColumnTransformer( transformers=[ ("categorical", one_hot_encoder, categorical_columns), ("month_sin", sin_transformer(12), ["month"]), ("month_cos", cos_transformer(12), ["month"]), ("weekday_sin", sin_transformer(7), ["weekday"]), ("weekday_cos", cos_transformer(7), ["weekday"]), ("hour_sin", sin_transformer(24), ["hour"]), ("hour_cos", cos_transformer(24), ["hour"]), ], remainder=MinMaxScaler(), ) cyclic_cossin_linear_pipeline = make_pipeline( cyclic_cossin_transformer, RidgeCV(alphas=alphas), ) evaluate(cyclic_cossin_linear_pipeline, X, y, cv=ts_cv) # %% # # The performance of our linear regression model with this simple feature # engineering is a bit better than using the original ordinal time features but # worse than using the one-hot encoded time features. We will further analyze # possible reasons for this disappointing outcome at the end of this notebook. # # Periodic spline features # ------------------------ # # We can try an alternative encoding of the periodic time-related features # using spline transformations with a large enough number of splines, and as a # result a larger number of expanded features: from sklearn.preprocessing import SplineTransformer def periodic_spline_transformer(period, n_splines=None, degree=3): if n_splines is None: n_splines = period n_knots = n_splines + 1 # periodic and include_bias is True return SplineTransformer( degree=degree, n_knots=n_knots, knots=np.linspace(0, period, n_knots).reshape(n_knots, 1), extrapolation="periodic", include_bias=True, ) # %% # # Again, let us visualize the effect of this feature expansion on some # synthetic hour data with a bit of extrapolation beyond hour=23: hour_df = pd.DataFrame( np.linspace(0, 26, 1000).reshape(-1, 1), columns=["hour"], ) splines = periodic_spline_transformer(24, n_splines=12).fit_transform(hour_df) splines_df = pd.DataFrame( splines, columns=[f"spline_{i}" for i in range(splines.shape[1])], ) pd.concat([hour_df, splines_df], axis="columns").plot(x="hour", cmap=plt.cm.tab20b) _ = plt.title("Periodic spline-based encoding for the 'hour' feature") # %% # Thanks to the use of the `extrapolation="periodic"` parameter, we observe # that the feature encoding stays smooth when extrapolating beyond midnight. # # We can now build a predictive pipeline using this alternative periodic # feature engineering strategy. # # It is possible to use fewer splines than discrete levels for those ordinal # values. This makes spline-based encoding more efficient than one-hot encoding # while preserving most of the expressivity: cyclic_spline_transformer = ColumnTransformer( transformers=[ ("categorical", one_hot_encoder, categorical_columns), ("cyclic_month", periodic_spline_transformer(12, n_splines=6), ["month"]), ("cyclic_weekday", periodic_spline_transformer(7, n_splines=3), ["weekday"]), ("cyclic_hour", periodic_spline_transformer(24, n_splines=12), ["hour"]), ], remainder=MinMaxScaler(), ) cyclic_spline_linear_pipeline = make_pipeline( cyclic_spline_transformer, RidgeCV(alphas=alphas), ) evaluate(cyclic_spline_linear_pipeline, X, y, cv=ts_cv) # %% # Spline features make it possible for the linear model to successfully # leverage the periodic time-related features and reduce the error from ~14% to # ~10% of the maximum demand, which is similar to what we observed with the # one-hot encoded features. # # Qualitative analysis of the impact of features on linear models predictions # --------------------------------------------------------------------------- # # Here, we want to visualize the impact of the feature engineering choices on # the time related shape of the predictions. # # To do so we consider an arbitrary time-based split to compare the predictions # on a range of held out data points. naive_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) naive_linear_predictions = naive_linear_pipeline.predict(X.iloc[test_0]) one_hot_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) one_hot_linear_predictions = one_hot_linear_pipeline.predict(X.iloc[test_0]) cyclic_cossin_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) cyclic_cossin_linear_predictions = cyclic_cossin_linear_pipeline.predict(X.iloc[test_0]) cyclic_spline_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) cyclic_spline_linear_predictions = cyclic_spline_linear_pipeline.predict(X.iloc[test_0]) # %% # We visualize those predictions by zooming on the last 96 hours (4 days) of # the test set to get some qualitative insights: last_hours = slice(-96, None) fig, ax = plt.subplots(figsize=(12, 4)) fig.suptitle("Predictions by linear models") ax.plot( y.iloc[test_0].values[last_hours], "x-", alpha=0.2, label="Actual demand", color="black", ) ax.plot(naive_linear_predictions[last_hours], "x-", label="Ordinal time features") ax.plot( cyclic_cossin_linear_predictions[last_hours], "x-", label="Trigonometric time features", ) ax.plot( cyclic_spline_linear_predictions[last_hours], "x-", label="Spline-based time features", ) ax.plot( one_hot_linear_predictions[last_hours], "x-", label="One-hot time features", ) _ = ax.legend() # %% # We can draw the following conclusions from the above plot: # # - the **raw ordinal time-related features** are problematic because they do # not capture the natural periodicity: we observe a big jump in the # predictions at the end of each day when the hour features goes from 23 back # to 0. We can expect similar artifacts at the end of each week or each year. # # - as expected, the **trigonometric features** (sine and cosine) do not have # these discontinuities at midnight but the linear regression model fails to # leverage those features to properly model intra-day variations. # Using trigonometric features for higher harmonics or additional # trigonometric features for the natural period with different phases could # potentially fix this problem. # # - the **periodic spline-based features** fix those two problems at once: they # give more expressivity to the linear model by making it possible to focus # on specific hours thanks to the use of 12 splines. Furthermore the # `extrapolation="periodic"` option enforces a smooth representation between # `hour=23` and `hour=0`. # # - the **one-hot encoded features** behave similarly to the periodic # spline-based features but are more spiky: for instance they can better # model the morning peak during the week days since this peak lasts shorter # than an hour. However, we will see in the following that what can be an # advantage for linear models is not necessarily one for more expressive # models. # %% # We can also compare the number of features extracted by each feature # engineering pipeline: naive_linear_pipeline[:-1].transform(X).shape # %% one_hot_linear_pipeline[:-1].transform(X).shape # %% cyclic_cossin_linear_pipeline[:-1].transform(X).shape # %% cyclic_spline_linear_pipeline[:-1].transform(X).shape # %% # This confirms that the one-hot encoding and the spline encoding strategies # create a lot more features for the time representation than the alternatives, # which in turn gives the downstream linear model more flexibility (degrees of # freedom) to avoid underfitting. # # Finally, we observe that none of the linear models can approximate the true # bike rentals demand, especially for the peaks that can be very sharp at rush # hours during the working days but much flatter during the week-ends: the most # accurate linear models based on splines or one-hot encoding tend to forecast # peaks of commuting-related bike rentals even on the week-ends and # under-estimate the commuting-related events during the working days. # # These systematic prediction errors reveal a form of under-fitting and can be # explained by the lack of non-additive modeling of the interactions between # features (in this case "workingday" and features derived from "hours"). This # issue will be addressed in the following section. # %% # Modeling pairwise interactions with splines and polynomial features # ------------------------------------------------------------------- # # Linear models alone cannot model interaction effects between input features. # It does not help that some features are marginally non-linear as is the case # with features constructed by `SplineTransformer` (or one-hot encoding or # binning). # # However, it is possible to use the `PolynomialFeatures` class on coarse # grained splined encoded hours to model the "workingday"/"hours" interaction # explicitly without introducing too many new variables: from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import FeatureUnion hour_workday_interaction = make_pipeline( ColumnTransformer( [ ("cyclic_hour", periodic_spline_transformer(24, n_splines=8), ["hour"]), ("workingday", FunctionTransformer(lambda x: x == "True"), ["workingday"]), ] ), PolynomialFeatures(degree=2, interaction_only=True, include_bias=False), ) # %% # Those features are then combined with the ones already computed in the # previous spline-base pipeline. We can observe a nice performance improvemnt # by modeling this pairwise interaction explicitly: cyclic_spline_interactions_pipeline = make_pipeline( FeatureUnion( [ ("marginal", cyclic_spline_transformer), ("interactions", hour_workday_interaction), ] ), RidgeCV(alphas=alphas), ) evaluate(cyclic_spline_interactions_pipeline, X, y, cv=ts_cv) # %% # Modeling non-linear feature interactions with kernels # ----------------------------------------------------- # # The previous analysis highlighted the need to model the interactions between # `"workingday"` and `"hours"`. Another example of a such a non-linear # interactions that we would like to model could be the impact of the rain that # might not be the same during the working days and the week-ends and holidays # for instance. # # To model all such interactions, we could either use a polynomial expansion on # all marginal features at once, after their spline-based expansion. However # this would create a quadratic number of features which can cause overfitting # and computational tractability issues. # # Alternatively we can use the Nyström method to compute an approximate # polynomial kernel expansion. Let us try the latter: from sklearn.kernel_approximation import Nystroem cyclic_spline_poly_pipeline = make_pipeline( cyclic_spline_transformer, Nystroem(kernel="poly", degree=2, n_components=300, random_state=0), RidgeCV(alphas=alphas), ) evaluate(cyclic_spline_poly_pipeline, X, y, cv=ts_cv) # %% # # We observe that this model can almost rival the performance of the gradient # boosted trees with an average error around 6% of the maximum demand. # # Note that while the final step of this pipeline is a linear regression model, # the intermediate steps such as the spline feature extraction and the Nyström # kernel approximation are highly non-linear. As a result the compound pipeline # is much more expressive than a simple linear regression model with raw features. # # For the sake of completeness, we also evaluate the combination of one-hot # encoding and kernel approximation: one_hot_poly_pipeline = make_pipeline( ColumnTransformer( transformers=[ ("categorical", one_hot_encoder, categorical_columns), ("one_hot_time", one_hot_encoder, ["hour", "weekday", "month"]), ], remainder="passthrough", ), Nystroem(kernel="poly", degree=2, n_components=300, random_state=0), RidgeCV(alphas=alphas), ) evaluate(one_hot_poly_pipeline, X, y, cv=ts_cv) # %% # While one-hot features were competitive with spline-based features when using # linear models, this is no longer the case when using a low-rank approximation # of a non-linear kernel: this can be explained by the fact that spline # features are smoother and allow the kernel approximation to find a more # expressive decision function. # # Let us now have a qualitative look at the predictions of the kernel models # and of the gradient boosted trees that should be able to better model # non-linear interactions between features: gbrt_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) gbrt_predictions = gbrt_pipeline.predict(X.iloc[test_0]) one_hot_poly_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) one_hot_poly_predictions = one_hot_poly_pipeline.predict(X.iloc[test_0]) cyclic_spline_poly_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) cyclic_spline_poly_predictions = cyclic_spline_poly_pipeline.predict(X.iloc[test_0]) # %% # Again we zoom on the last 4 days of the test set: last_hours = slice(-96, None) fig, ax = plt.subplots(figsize=(12, 4)) fig.suptitle("Predictions by non-linear regression models") ax.plot( y.iloc[test_0].values[last_hours], "x-", alpha=0.2, label="Actual demand", color="black", ) ax.plot( gbrt_predictions[last_hours], "x-", label="Gradient Boosted Trees", ) ax.plot( one_hot_poly_predictions[last_hours], "x-", label="One-hot + polynomial kernel", ) ax.plot( cyclic_spline_poly_predictions[last_hours], "x-", label="Splines + polynomial kernel", ) _ = ax.legend() # %% # First, note that trees can naturally model non-linear feature interactions # since, by default, decision trees are allowed to grow beyond a depth of 2 # levels. # # Here we can observe that the combinations of spline features and non-linear # kernels works quite well and can almost rival the accuracy of the gradient # boosting regression trees. # # On the contrary, one-hot time features do not perform that well with the low # rank kernel model. In particular they significantly over-estimate the low # demand hours more than the competing models. # # We also observe that none of the models can successfully predict some of the # peak rentals at the rush hours during the working days. It is possible that # access to additional features would be required to further improve the # accuracy of the predictions. For instance, it could be useful to have access # to the geographical repartition of the fleet at any point in time or the # fraction of bikes that are immobilized because they need servicing. # # Let us finally get a more quantative look at the prediction errors of those # three models using the true vs predicted demand scatter plots: fig, axes = plt.subplots(ncols=3, figsize=(12, 4), sharey=True) fig.suptitle("Non-linear regression models") predictions = [ one_hot_poly_predictions, cyclic_spline_poly_predictions, gbrt_predictions, ] labels = [ "One hot + polynomial kernel", "Splines + polynomial kernel", "Gradient Boosted Trees", ] for ax, pred, label in zip(axes, predictions, labels): ax.scatter(y.iloc[test_0].values, pred, alpha=0.3, label=label) ax.plot([0, 1], [0, 1], "--", label="Perfect model") ax.set( xlim=(0, 1), ylim=(0, 1), xlabel="True demand", ylabel="Predicted demand", ) ax.legend() # %% # This visualization confirms the conclusions we draw on the previous plot. # # All models under-estimate the high demand events (working days rush hours), # but gradient boosting a bit less so. The low demand events are well predicted # on average by gradient boosting while the one-hot polynomial regression # pipeline seems to systematically over-estimate demand in that regime. Overall # the predictions of the gradient boosted trees are closer to the diagonal than # for the kernel models. # # Concluding remarks # ------------------ # # We note that we could have obtained slightly better results for kernel models # by using more components (higher rank kernel approximation) at the cost of # longer fit and prediction durations. For large values of `n_components`, the # performance of the one-hot features would even match the spline features. # # The `Nystroem` + `RidgeCV` classifier could also have been replaced by # :class:`~sklearn.neural_network.MLPRegressor` with one or two hidden layers # and we would have obtained quite similar results. # # The dataset we used in this case study is sampled on a hourly basis. However # cyclic spline-based features could model time-within-day or time-within-week # very efficiently with finer-grained time resolutions (for instance with # measurements taken every minute instead of every hours) without introducing # more features. One-hot encoding time representations would not offer this # flexibility. # # Finally, in this notebook we used `RidgeCV` because it is very efficient from # a computational point of view. However it models the target variable as a # Gaussian random variable with constant variance. For positive regression # problems, it is likely that using a Poisson or Gamma distribution would make # more sense. This could be achieved by using # `GridSearchCV(TweedieRegressor(power=2), param_grid({"alpha": alphas}))` # instead of `RidgeCV`.

37.354293

97

0.733685

from sklearn.datasets import fetch_openml bike_sharing = fetch_openml("Bike_Sharing_Demand", version=2, as_frame=True) df = bike_sharing.frame import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(12, 4)) average_week_demand = df.groupby(["weekday", "hour"]).mean()["count"] average_week_demand.plot(ax=ax) _ = ax.set( title="Average hourly bike demand during the week", xticks=[i * 24 for i in range(7)], xticklabels=["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"], xlabel="Time of the week", ylabel="Number of bike rentals", ) df["count"].max() y = df["count"] / 1000 fig, ax = plt.subplots(figsize=(12, 4)) y.hist(bins=30, ax=ax) _ = ax.set( xlabel="Fraction of rented fleet demand", ylabel="Number of hours", ) X = df.drop("count", axis="columns") X ue_counts() X["weather"].replace(to_replace="heavy_rain", value="rain", inplace=True) X["weather"].value_counts() X["season"].value_counts() from sklearn.model_selection import TimeSeriesSplit ts_cv = TimeSeriesSplit( n_splits=5, gap=48, max_train_size=10000, test_size=1000, ) all_splits = list(ts_cv.split(X, y)) train_0, test_0 = all_splits[0] X.iloc[test_0] X.iloc[train_0] train_4, test_4 = all_splits[4] X.iloc[test_4] X.iloc[train_4] from sklearn.pipeline import make_pipeline from sklearn.preprocessing import OrdinalEncoder from sklearn.compose import ColumnTransformer from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.model_selection import cross_validate categorical_columns = [ "weather", "season", "holiday", "workingday", ] categories = [ ["clear", "misty", "rain"], ["spring", "summer", "fall", "winter"], ["False", "True"], ["False", "True"], ] ordinal_encoder = OrdinalEncoder(categories=categories) gbrt_pipeline = make_pipeline( ColumnTransformer( transformers=[ ("categorical", ordinal_encoder, categorical_columns), ], remainder="passthrough", ), HistGradientBoostingRegressor( categorical_features=range(4), ), ) def evaluate(model, X, y, cv): cv_results = cross_validate( model, X, y, cv=ts_cv, scoring=["neg_mean_absolute_error", "neg_root_mean_squared_error"], ) mae = -cv_results["test_neg_mean_absolute_error"] rmse = -cv_results["test_neg_root_mean_squared_error"] print( f"Mean Absolute Error: {mae.mean():.3f} +/- {mae.std():.3f}\n" f"Root Mean Squared Error: {rmse.mean():.3f} +/- {rmse.std():.3f}" ) evaluate(gbrt_pipeline, X, y, cv=ts_cv) from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import RidgeCV import numpy as np one_hot_encoder = OneHotEncoder(handle_unknown="ignore", sparse=False) alphas = np.logspace(-6, 6, 25) naive_linear_pipeline = make_pipeline( ColumnTransformer( transformers=[ ("categorical", one_hot_encoder, categorical_columns), ], remainder=MinMaxScaler(), ), RidgeCV(alphas=alphas), ) evaluate(naive_linear_pipeline, X, y, cv=ts_cv) one_hot_linear_pipeline = make_pipeline( ColumnTransformer( transformers=[ ("categorical", one_hot_encoder, categorical_columns), ("one_hot_time", one_hot_encoder, ["hour", "weekday", "month"]), ], remainder=MinMaxScaler(), ), RidgeCV(alphas=alphas), ) evaluate(one_hot_linear_pipeline, X, y, cv=ts_cv) from sklearn.preprocessing import FunctionTransformer def sin_transformer(period): return FunctionTransformer(lambda x: np.sin(x / period * 2 * np.pi)) def cos_transformer(period): return FunctionTransformer(lambda x: np.cos(x / period * 2 * np.pi)) import pandas as pd hour_df = pd.DataFrame( np.arange(26).reshape(-1, 1), columns=["hour"], ) hour_df["hour_sin"] = sin_transformer(24).fit_transform(hour_df)["hour"] hour_df["hour_cos"] = cos_transformer(24).fit_transform(hour_df)["hour"] hour_df.plot(x="hour") _ = plt.title("Trigonometric encoding for the 'hour' feature") # how this representation maps the 24 hours of the day to a 2D space, akin to # some sort of 24 hour version of an analog clock. Note that the "25th" hour is # mapped back to the 1st hour because of the periodic nature of the sine/cosine # representation. fig, ax = plt.subplots(figsize=(7, 5)) sp = ax.scatter(hour_df["hour_sin"], hour_df["hour_cos"], c=hour_df["hour"]) ax.set( xlabel="sin(hour)", ylabel="cos(hour)", ) _ = fig.colorbar(sp) # %% # # We can now build a feature extraction pipeline using this strategy: cyclic_cossin_transformer = ColumnTransformer( transformers=[ ("categorical", one_hot_encoder, categorical_columns), ("month_sin", sin_transformer(12), ["month"]), ("month_cos", cos_transformer(12), ["month"]), ("weekday_sin", sin_transformer(7), ["weekday"]), ("weekday_cos", cos_transformer(7), ["weekday"]), ("hour_sin", sin_transformer(24), ["hour"]), ("hour_cos", cos_transformer(24), ["hour"]), ], remainder=MinMaxScaler(), ) cyclic_cossin_linear_pipeline = make_pipeline( cyclic_cossin_transformer, RidgeCV(alphas=alphas), ) evaluate(cyclic_cossin_linear_pipeline, X, y, cv=ts_cv) # %% # # The performance of our linear regression model with this simple feature # engineering is a bit better than using the original ordinal time features but # worse than using the one-hot encoded time features. We will further analyze # possible reasons for this disappointing outcome at the end of this notebook. # # Periodic spline features # ------------------------ # # We can try an alternative encoding of the periodic time-related features # using spline transformations with a large enough number of splines, and as a # result a larger number of expanded features: from sklearn.preprocessing import SplineTransformer def periodic_spline_transformer(period, n_splines=None, degree=3): if n_splines is None: n_splines = period n_knots = n_splines + 1 # periodic and include_bias is True return SplineTransformer( degree=degree, n_knots=n_knots, knots=np.linspace(0, period, n_knots).reshape(n_knots, 1), extrapolation="periodic", include_bias=True, ) # %% # # Again, let us visualize the effect of this feature expansion on some # synthetic hour data with a bit of extrapolation beyond hour=23: hour_df = pd.DataFrame( np.linspace(0, 26, 1000).reshape(-1, 1), columns=["hour"], ) splines = periodic_spline_transformer(24, n_splines=12).fit_transform(hour_df) splines_df = pd.DataFrame( splines, columns=[f"spline_{i}" for i in range(splines.shape[1])], ) pd.concat([hour_df, splines_df], axis="columns").plot(x="hour", cmap=plt.cm.tab20b) _ = plt.title("Periodic spline-based encoding for the 'hour' feature") # %% # Thanks to the use of the `extrapolation="periodic"` parameter, we observe # that the feature encoding stays smooth when extrapolating beyond midnight. # # We can now build a predictive pipeline using this alternative periodic # feature engineering strategy. # # It is possible to use fewer splines than discrete levels for those ordinal # values. This makes spline-based encoding more efficient than one-hot encoding # while preserving most of the expressivity: cyclic_spline_transformer = ColumnTransformer( transformers=[ ("categorical", one_hot_encoder, categorical_columns), ("cyclic_month", periodic_spline_transformer(12, n_splines=6), ["month"]), ("cyclic_weekday", periodic_spline_transformer(7, n_splines=3), ["weekday"]), ("cyclic_hour", periodic_spline_transformer(24, n_splines=12), ["hour"]), ], remainder=MinMaxScaler(), ) cyclic_spline_linear_pipeline = make_pipeline( cyclic_spline_transformer, RidgeCV(alphas=alphas), ) evaluate(cyclic_spline_linear_pipeline, X, y, cv=ts_cv) # %% # Spline features make it possible for the linear model to successfully # leverage the periodic time-related features and reduce the error from ~14% to # ~10% of the maximum demand, which is similar to what we observed with the # one-hot encoded features. # # Qualitative analysis of the impact of features on linear models predictions # --------------------------------------------------------------------------- # # Here, we want to visualize the impact of the feature engineering choices on # the time related shape of the predictions. # # To do so we consider an arbitrary time-based split to compare the predictions # on a range of held out data points. naive_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) naive_linear_predictions = naive_linear_pipeline.predict(X.iloc[test_0]) one_hot_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) one_hot_linear_predictions = one_hot_linear_pipeline.predict(X.iloc[test_0]) cyclic_cossin_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) cyclic_cossin_linear_predictions = cyclic_cossin_linear_pipeline.predict(X.iloc[test_0]) cyclic_spline_linear_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) cyclic_spline_linear_predictions = cyclic_spline_linear_pipeline.predict(X.iloc[test_0]) # %% # We visualize those predictions by zooming on the last 96 hours (4 days) of # the test set to get some qualitative insights: last_hours = slice(-96, None) fig, ax = plt.subplots(figsize=(12, 4)) fig.suptitle("Predictions by linear models") ax.plot( y.iloc[test_0].values[last_hours], "x-", alpha=0.2, label="Actual demand", color="black", ) ax.plot(naive_linear_predictions[last_hours], "x-", label="Ordinal time features") ax.plot( cyclic_cossin_linear_predictions[last_hours], "x-", label="Trigonometric time features", ) ax.plot( cyclic_spline_linear_predictions[last_hours], "x-", label="Spline-based time features", ) ax.plot( one_hot_linear_predictions[last_hours], "x-", label="One-hot time features", ) _ = ax.legend() # %% # We can draw the following conclusions from the above plot: # # - the **raw ordinal time-related features** are problematic because they do # not capture the natural periodicity: we observe a big jump in the # predictions at the end of each day when the hour features goes from 23 back # to 0. We can expect similar artifacts at the end of each week or each year. # # - as expected, the **trigonometric features** (sine and cosine) do not have # these discontinuities at midnight but the linear regression model fails to # leverage those features to properly model intra-day variations. # Using trigonometric features for higher harmonics or additional # trigonometric features for the natural period with different phases could # potentially fix this problem. # # - the **periodic spline-based features** fix those two problems at once: they # give more expressivity to the linear model by making it possible to focus # on specific hours thanks to the use of 12 splines. Furthermore the # `extrapolation="periodic"` option enforces a smooth representation between # `hour=23` and `hour=0`. # # - the **one-hot encoded features** behave similarly to the periodic # spline-based features but are more spiky: for instance they can better # model the morning peak during the week days since this peak lasts shorter # than an hour. However, we will see in the following that what can be an # advantage for linear models is not necessarily one for more expressive # models. # %% # We can also compare the number of features extracted by each feature # engineering pipeline: naive_linear_pipeline[:-1].transform(X).shape # %% one_hot_linear_pipeline[:-1].transform(X).shape # %% cyclic_cossin_linear_pipeline[:-1].transform(X).shape # %% cyclic_spline_linear_pipeline[:-1].transform(X).shape # %% # This confirms that the one-hot encoding and the spline encoding strategies # create a lot more features for the time representation than the alternatives, # which in turn gives the downstream linear model more flexibility (degrees of # freedom) to avoid underfitting. # # Finally, we observe that none of the linear models can approximate the true # bike rentals demand, especially for the peaks that can be very sharp at rush # hours during the working days but much flatter during the week-ends: the most # accurate linear models based on splines or one-hot encoding tend to forecast # peaks of commuting-related bike rentals even on the week-ends and # under-estimate the commuting-related events during the working days. # # These systematic prediction errors reveal a form of under-fitting and can be # explained by the lack of non-additive modeling of the interactions between # features (in this case "workingday" and features derived from "hours"). This # issue will be addressed in the following section. # %% # Modeling pairwise interactions with splines and polynomial features # ------------------------------------------------------------------- # # Linear models alone cannot model interaction effects between input features. # It does not help that some features are marginally non-linear as is the case # with features constructed by `SplineTransformer` (or one-hot encoding or # binning). # # However, it is possible to use the `PolynomialFeatures` class on coarse # grained splined encoded hours to model the "workingday"/"hours" interaction # explicitly without introducing too many new variables: from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import FeatureUnion hour_workday_interaction = make_pipeline( ColumnTransformer( [ ("cyclic_hour", periodic_spline_transformer(24, n_splines=8), ["hour"]), ("workingday", FunctionTransformer(lambda x: x == "True"), ["workingday"]), ] ), PolynomialFeatures(degree=2, interaction_only=True, include_bias=False), ) # %% # Those features are then combined with the ones already computed in the # previous spline-base pipeline. We can observe a nice performance improvemnt # by modeling this pairwise interaction explicitly: cyclic_spline_interactions_pipeline = make_pipeline( FeatureUnion( [ ("marginal", cyclic_spline_transformer), ("interactions", hour_workday_interaction), ] ), RidgeCV(alphas=alphas), ) evaluate(cyclic_spline_interactions_pipeline, X, y, cv=ts_cv) # %% # Modeling non-linear feature interactions with kernels # ----------------------------------------------------- # # The previous analysis highlighted the need to model the interactions between # `"workingday"` and `"hours"`. Another example of a such a non-linear # interactions that we would like to model could be the impact of the rain that # might not be the same during the working days and the week-ends and holidays # for instance. # # To model all such interactions, we could either use a polynomial expansion on # all marginal features at once, after their spline-based expansion. However # this would create a quadratic number of features which can cause overfitting # and computational tractability issues. # # Alternatively we can use the Nyström method to compute an approximate # polynomial kernel expansion. Let us try the latter: from sklearn.kernel_approximation import Nystroem cyclic_spline_poly_pipeline = make_pipeline( cyclic_spline_transformer, Nystroem(kernel="poly", degree=2, n_components=300, random_state=0), RidgeCV(alphas=alphas), ) evaluate(cyclic_spline_poly_pipeline, X, y, cv=ts_cv) # %% # # We observe that this model can almost rival the performance of the gradient # boosted trees with an average error around 6% of the maximum demand. # # Note that while the final step of this pipeline is a linear regression model, # the intermediate steps such as the spline feature extraction and the Nyström # kernel approximation are highly non-linear. As a result the compound pipeline # is much more expressive than a simple linear regression model with raw features. # # For the sake of completeness, we also evaluate the combination of one-hot # encoding and kernel approximation: one_hot_poly_pipeline = make_pipeline( ColumnTransformer( transformers=[ ("categorical", one_hot_encoder, categorical_columns), ("one_hot_time", one_hot_encoder, ["hour", "weekday", "month"]), ], remainder="passthrough", ), Nystroem(kernel="poly", degree=2, n_components=300, random_state=0), RidgeCV(alphas=alphas), ) evaluate(one_hot_poly_pipeline, X, y, cv=ts_cv) # %% # While one-hot features were competitive with spline-based features when using # linear models, this is no longer the case when using a low-rank approximation # of a non-linear kernel: this can be explained by the fact that spline # features are smoother and allow the kernel approximation to find a more # expressive decision function. # # Let us now have a qualitative look at the predictions of the kernel models # and of the gradient boosted trees that should be able to better model # non-linear interactions between features: gbrt_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) gbrt_predictions = gbrt_pipeline.predict(X.iloc[test_0]) one_hot_poly_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) one_hot_poly_predictions = one_hot_poly_pipeline.predict(X.iloc[test_0]) cyclic_spline_poly_pipeline.fit(X.iloc[train_0], y.iloc[train_0]) cyclic_spline_poly_predictions = cyclic_spline_poly_pipeline.predict(X.iloc[test_0]) # %% # Again we zoom on the last 4 days of the test set: last_hours = slice(-96, None) fig, ax = plt.subplots(figsize=(12, 4)) fig.suptitle("Predictions by non-linear regression models") ax.plot( y.iloc[test_0].values[last_hours], "x-", alpha=0.2, label="Actual demand", color="black", ) ax.plot( gbrt_predictions[last_hours], "x-", label="Gradient Boosted Trees", ) ax.plot( one_hot_poly_predictions[last_hours], "x-", label="One-hot + polynomial kernel", ) ax.plot( cyclic_spline_poly_predictions[last_hours], "x-", label="Splines + polynomial kernel", ) _ = ax.legend() # %% # First, note that trees can naturally model non-linear feature interactions # since, by default, decision trees are allowed to grow beyond a depth of 2 # levels. # # Here we can observe that the combinations of spline features and non-linear # kernels works quite well and can almost rival the accuracy of the gradient # boosting regression trees. # # On the contrary, one-hot time features do not perform that well with the low # rank kernel model. In particular they significantly over-estimate the low # demand hours more than the competing models. # # We also observe that none of the models can successfully predict some of the # peak rentals at the rush hours during the working days. It is possible that # access to additional features would be required to further improve the # accuracy of the predictions. For instance, it could be useful to have access # to the geographical repartition of the fleet at any point in time or the # fraction of bikes that are immobilized because they need servicing. # # Let us finally get a more quantative look at the prediction errors of those # three models using the true vs predicted demand scatter plots: fig, axes = plt.subplots(ncols=3, figsize=(12, 4), sharey=True) fig.suptitle("Non-linear regression models") predictions = [ one_hot_poly_predictions, cyclic_spline_poly_predictions, gbrt_predictions, ] labels = [ "One hot + polynomial kernel", "Splines + polynomial kernel", "Gradient Boosted Trees", ] for ax, pred, label in zip(axes, predictions, labels): ax.scatter(y.iloc[test_0].values, pred, alpha=0.3, label=label) ax.plot([0, 1], [0, 1], "--", label="Perfect model") ax.set( xlim=(0, 1), ylim=(0, 1), xlabel="True demand", ylabel="Predicted demand", ) ax.legend() # %% # This visualization confirms the conclusions we draw on the previous plot. # # All models under-estimate the high demand events (working days rush hours), # but gradient boosting a bit less so. The low demand events are well predicted # on average by gradient boosting while the one-hot polynomial regression # pipeline seems to systematically over-estimate demand in that regime. Overall # the predictions of the gradient boosted trees are closer to the diagonal than # for the kernel models. # # Concluding remarks # ------------------ # # We note that we could have obtained slightly better results for kernel models # by using more components (higher rank kernel approximation) at the cost of # longer fit and prediction durations. For large values of `n_components`, the # performance of the one-hot features would even match the spline features. # # The `Nystroem` + `RidgeCV` classifier could also have been replaced by # :class:`~sklearn.neural_network.MLPRegressor` with one or two hidden layers # and we would have obtained quite similar results. # # The dataset we used in this case study is sampled on a hourly basis. However # cyclic spline-based features could model time-within-day or time-within-week # very efficiently with finer-grained time resolutions (for instance with # measurements taken every minute instead of every hours) without introducing # more features. One-hot encoding time representations would not offer this # flexibility. # # Finally, in this notebook we used `RidgeCV` because it is very efficient from # a computational point of view. However it models the target variable as a # Gaussian random variable with constant variance. For positive regression # problems, it is likely that using a Poisson or Gamma distribution would make # more sense. This could be achieved by using # `GridSearchCV(TweedieRegressor(power=2), param_grid({"alpha": alphas}))` # instead of `RidgeCV`.

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2,532

py

Python

mars/tensor/arithmetic/hypot.py

hxri/mars

f7864f00911883b94800b63856f0e57648d3d9b4

[ "Apache-2.0" ]

2,413

2018-12-06T09:37:11.000Z

2022-03-30T15:47:39.000Z

mars/tensor/arithmetic/hypot.py

hxri/mars

f7864f00911883b94800b63856f0e57648d3d9b4

[ "Apache-2.0" ]

1,335

2018-12-07T03:06:18.000Z

2022-03-31T11:45:57.000Z

mars/tensor/arithmetic/hypot.py

hxri/mars

f7864f00911883b94800b63856f0e57648d3d9b4

[ "Apache-2.0" ]

329

2018-12-07T03:12:41.000Z

2022-03-29T21:49:57.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2021 Alibaba Group Holding Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from ... import opcodes as OperandDef from ..utils import infer_dtype from .core import TensorBinOp from .utils import arithmetic_operand @arithmetic_operand(sparse_mode='binary_and') class TensorHypot(TensorBinOp): _op_type_ = OperandDef.HYPOT _func_name = 'hypot' @infer_dtype(np.hypot) def hypot(x1, x2, out=None, where=None, **kwargs): """ Given the "legs" of a right triangle, return its hypotenuse. Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise. If `x1` or `x2` is scalar_like (i.e., unambiguously cast-able to a scalar type), it is broadcast for use with each element of the other argument. (See Examples) Parameters ---------- x1, x2 : array_like Leg of the triangle(s). out : Tensor, None, or tuple of Tensor and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or `None`, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs Returns ------- z : Tensor The hypotenuse of the triangle(s). Examples -------- >>> import mars.tensor as mt >>> mt.hypot(3*mt.ones((3, 3)), 4*mt.ones((3, 3))).execute() array([[ 5., 5., 5.], [ 5., 5., 5.], [ 5., 5., 5.]]) Example showing broadcast of scalar_like argument: >>> mt.hypot(3*mt.ones((3, 3)), [4]).execute() array([[ 5., 5., 5.], [ 5., 5., 5.], [ 5., 5., 5.]]) """ op = TensorHypot(**kwargs) return op(x1, x2, out=out, where=where)

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79

0.646919

import numpy as np from ... import opcodes as OperandDef from ..utils import infer_dtype from .core import TensorBinOp from .utils import arithmetic_operand @arithmetic_operand(sparse_mode='binary_and') class TensorHypot(TensorBinOp): _op_type_ = OperandDef.HYPOT _func_name = 'hypot' @infer_dtype(np.hypot) def hypot(x1, x2, out=None, where=None, **kwargs): op = TensorHypot(**kwargs) return op(x1, x2, out=out, where=where)

true

7907d84162afccf8d4510a0f463af3a1ee4581ef

9,083

py

Python

boldui/__init__.py

Wazzaps/boldui

447a392946b9e8e78e0f7a358d11247a6a55ea4e

[ "MIT" ]

3

2022-01-04T15:22:50.000Z

2022-01-08T18:18:20.000Z

boldui/__init__.py

Wazzaps/boldui

447a392946b9e8e78e0f7a358d11247a6a55ea4e

[ "MIT" ]

null

boldui/__init__.py

Wazzaps/boldui

447a392946b9e8e78e0f7a358d11247a6a55ea4e

[ "MIT" ]

1

2022-01-28T00:27:05.000Z

#!/usr/bin/env python3 from __future__ import annotations import contextlib import json import os import socket import struct import boldui.hotrefresh from simplexp import Expr, var, Oplist from typing import List class Actions: UPDATE_SCENE = 0 HANDLER_REPLY = 1 SET_VAR = 2 WATCH_ACK = 3 def stringify_op(obj, indent=0): result = '' if isinstance(obj, list): result += '[' if len(obj) != 0: result += '\n' for op in obj: result += ' ' * (indent + 2) + stringify_op(op, indent + 2) + ',\n' if len(obj) != 0: result += ' ' * indent result += ']' return result elif isinstance(obj, dict) and 'type' in obj: if obj['type'] in ('clear', 'rect', 'rrect', 'reply', 'setVar', 'evtHnd', 'watch', 'ackWatch', 'if', 'text', 'save', 'restore', 'clipRect', 'image'): result += 'Ops.' + obj['type'] + '(' if len(obj.keys()) != 1: result += '\n' for key in obj.keys(): if key == 'type': continue result += ' ' * (indent + 2) + f'{key}={stringify_op(obj[key], indent + 2)},\n' result += ' ' * indent result += ')' return result return repr(obj) class Ops: @staticmethod def clear(color): return {'type': 'clear', 'color': color} @staticmethod def rect(rect, color): return {'type': 'rect', 'rect': rect, 'color': color} @staticmethod def rrect(rect, color, radius): return {'type': 'rrect', 'rect': rect, 'color': color, 'radius': radius} @staticmethod def reply(ident: int, data: List[Expr | int | float | None]): return {'type': 'reply', 'id': ident, 'data': data} @staticmethod def set_var(name: str, value: Expr): return {'type': 'setVar', 'name': name, 'value': value} @staticmethod def event_handler(rect, events, handler, oplist): return { 'type': 'evtHnd', 'rect': rect, 'events': events, 'handler': handler, 'oplist': oplist, } @staticmethod def watch_var(id, cond, wait_for_roundtrip, handler): return { 'type': 'watch', 'id': id, 'cond': cond, 'waitForRoundtrip': wait_for_roundtrip, 'handler': handler } @staticmethod def ack_watch(id): return { 'type': 'ackWatch', 'id': id, } @staticmethod def text(text, x, y, font_size, color): return { 'type': 'text', 'text': text, 'x': x, 'y': y, 'fontSize': font_size, 'color': color, } @staticmethod def if_(cond, t, f): return {'type': 'if', 'cond': cond, 'then': t, 'else': f} @staticmethod def save(): return {'type': 'save'} @staticmethod def restore(): return {'type': 'restore'} @staticmethod def clip_rect(rect): return {'type': 'clipRect', 'rect': rect} @staticmethod def image(uri, rect): return {'type': 'image', 'uri': uri, 'rect': rect} class ProtocolServer: def __init__(self, address, reply_handler=None): self.pending_vars = {} self.address = address self._scene = None self._cached_scene = None self.reply_handler = reply_handler if os.path.exists(address): os.remove(address) SYSTEMD_SOCK_FD = 3 self.server = socket.fromfd(SYSTEMD_SOCK_FD, socket.AF_UNIX, socket.SOCK_STREAM) self.socket = None self._is_batch = False self._batch_scene_updated = False self._batch_vars = None hotrefresh.init(self) @property def scene(self): if self._cached_scene is None: if callable(self._scene): self._cached_scene = self._scene() else: self._cached_scene = self._scene return self._cached_scene @scene.setter def scene(self, value): self._scene = value self._cached_scene = None if self._is_batch: self._batch_scene_updated = True else: self._send_scene() def refresh_scene(self): self._cached_scene = None if self._is_batch: self._batch_scene_updated = True else: self._send_scene() @contextlib.contextmanager def batch_update(self): assert not self._is_batch self._is_batch = True self._batch_scene_updated = False self._batch_vars = {} yield if self._batch_scene_updated: self._send_scene() elif self._batch_vars: self._send_remote_var([(name, val) for name, val in self._batch_vars.items()]) self._is_batch = False self._batch_scene_updated = False self._batch_vars = None def serve(self): while True: print('Waiting for connection...') self.server.listen(1) self.socket, addr = self.server.accept() print('Client connected', addr) self.socket.send(b"BoldUI\x00\x01") # Read header header = self.socket.recv(8) if header != b"BoldUI\x00\x01": print("Invalid header, disconnecting") break print("Handshake complete, sending initial scene") if self.scene: self._send_scene() for var in self.pending_vars: self.set_remote_var(var, self.pending_vars[var][0], self.pending_vars[var][1]) print(f'Server PID is {os.getpid()}') while True: packet = b'' packet_length = self.socket.recv(4) if not packet_length: break packet_length = int.from_bytes(packet_length, 'big') while len(packet) < packet_length: packet += self.socket.recv(packet_length - len(packet)) if not packet: break self._handle_packet(packet) print('Client disconnected') break def _send_packet(self, packet): # print('Sending packet:', packet) self.socket.send(len(packet).to_bytes(4, 'big') + packet) def _handle_packet(self, packet): action = int.from_bytes(packet[:4], 'big') data = packet[4:] if action == Actions.HANDLER_REPLY: reply_count = int.from_bytes(data[:2], 'big') data = data[2:] with self.batch_update(): for i in range(reply_count): reply_len = int.from_bytes(data[:2], 'big') reply_id = int.from_bytes(data[2:6], 'big') reply_data = data[6:6+reply_len] data_array = [] while reply_data: item_type = reply_data[0] if item_type == 0: data_array.append(int.from_bytes(reply_data[1:9], 'big', signed=True)) reply_data = reply_data[9:] elif item_type == 1: data_array.append(struct.unpack('>d', reply_data[1:9])[0]) reply_data = reply_data[9:] else: raise ValueError(f"Unknown item type {item_type}") if self.reply_handler: # print(f'Reply: {hex(reply_id)} : {data_array}') self.reply_handler(reply_id, data_array) else: print('[app] Unknown packet type:', packet) def _send_scene(self): if self.socket: combined_scene = self.scene if self._batch_vars is not None: for key, value in self._batch_vars.items(): combined_scene['vars'][key]['value'] = json.dumps(Oplist(Expr.to_dict(value)).to_list()) self._send_packet(Actions.UPDATE_SCENE.to_bytes(4, 'big') + json.dumps(self.scene).encode()) def set_remote_var(self, name, val_type, value): self.pending_vars[name] = (val_type, value) if self._is_batch: self._batch_vars[name] = value else: self._send_remote_var([(name, value)]) def _send_remote_var(self, set_vars): if self.socket: parts = [] for name, value in set_vars: value = Oplist(Expr.to_dict(value)).to_list() parts.append(name.encode() + b'\x00' + json.dumps(value).encode()) self._send_packet(Actions.SET_VAR.to_bytes(4, 'big') + b'\x00'.join(parts)) def send_watch_ack(self, ack_id: int): if self.socket: self._send_packet(Actions.WATCH_ACK.to_bytes(4, 'big') + ack_id.to_bytes(8, 'big'))

31.106164

124

0.526148

from __future__ import annotations import contextlib import json import os import socket import struct import boldui.hotrefresh from simplexp import Expr, var, Oplist from typing import List class Actions: UPDATE_SCENE = 0 HANDLER_REPLY = 1 SET_VAR = 2 WATCH_ACK = 3 def stringify_op(obj, indent=0): result = '' if isinstance(obj, list): result += '[' if len(obj) != 0: result += '\n' for op in obj: result += ' ' * (indent + 2) + stringify_op(op, indent + 2) + ',\n' if len(obj) != 0: result += ' ' * indent result += ']' return result elif isinstance(obj, dict) and 'type' in obj: if obj['type'] in ('clear', 'rect', 'rrect', 'reply', 'setVar', 'evtHnd', 'watch', 'ackWatch', 'if', 'text', 'save', 'restore', 'clipRect', 'image'): result += 'Ops.' + obj['type'] + '(' if len(obj.keys()) != 1: result += '\n' for key in obj.keys(): if key == 'type': continue result += ' ' * (indent + 2) + f'{key}={stringify_op(obj[key], indent + 2)},\n' result += ' ' * indent result += ')' return result return repr(obj) class Ops: @staticmethod def clear(color): return {'type': 'clear', 'color': color} @staticmethod def rect(rect, color): return {'type': 'rect', 'rect': rect, 'color': color} @staticmethod def rrect(rect, color, radius): return {'type': 'rrect', 'rect': rect, 'color': color, 'radius': radius} @staticmethod def reply(ident: int, data: List[Expr | int | float | None]): return {'type': 'reply', 'id': ident, 'data': data} @staticmethod def set_var(name: str, value: Expr): return {'type': 'setVar', 'name': name, 'value': value} @staticmethod def event_handler(rect, events, handler, oplist): return { 'type': 'evtHnd', 'rect': rect, 'events': events, 'handler': handler, 'oplist': oplist, } @staticmethod def watch_var(id, cond, wait_for_roundtrip, handler): return { 'type': 'watch', 'id': id, 'cond': cond, 'waitForRoundtrip': wait_for_roundtrip, 'handler': handler } @staticmethod def ack_watch(id): return { 'type': 'ackWatch', 'id': id, } @staticmethod def text(text, x, y, font_size, color): return { 'type': 'text', 'text': text, 'x': x, 'y': y, 'fontSize': font_size, 'color': color, } @staticmethod def if_(cond, t, f): return {'type': 'if', 'cond': cond, 'then': t, 'else': f} @staticmethod def save(): return {'type': 'save'} @staticmethod def restore(): return {'type': 'restore'} @staticmethod def clip_rect(rect): return {'type': 'clipRect', 'rect': rect} @staticmethod def image(uri, rect): return {'type': 'image', 'uri': uri, 'rect': rect} class ProtocolServer: def __init__(self, address, reply_handler=None): self.pending_vars = {} self.address = address self._scene = None self._cached_scene = None self.reply_handler = reply_handler if os.path.exists(address): os.remove(address) SYSTEMD_SOCK_FD = 3 self.server = socket.fromfd(SYSTEMD_SOCK_FD, socket.AF_UNIX, socket.SOCK_STREAM) self.socket = None self._is_batch = False self._batch_scene_updated = False self._batch_vars = None hotrefresh.init(self) @property def scene(self): if self._cached_scene is None: if callable(self._scene): self._cached_scene = self._scene() else: self._cached_scene = self._scene return self._cached_scene @scene.setter def scene(self, value): self._scene = value self._cached_scene = None if self._is_batch: self._batch_scene_updated = True else: self._send_scene() def refresh_scene(self): self._cached_scene = None if self._is_batch: self._batch_scene_updated = True else: self._send_scene() @contextlib.contextmanager def batch_update(self): assert not self._is_batch self._is_batch = True self._batch_scene_updated = False self._batch_vars = {} yield if self._batch_scene_updated: self._send_scene() elif self._batch_vars: self._send_remote_var([(name, val) for name, val in self._batch_vars.items()]) self._is_batch = False self._batch_scene_updated = False self._batch_vars = None def serve(self): while True: print('Waiting for connection...') self.server.listen(1) self.socket, addr = self.server.accept() print('Client connected', addr) self.socket.send(b"BoldUI\x00\x01") header = self.socket.recv(8) if header != b"BoldUI\x00\x01": print("Invalid header, disconnecting") break print("Handshake complete, sending initial scene") if self.scene: self._send_scene() for var in self.pending_vars: self.set_remote_var(var, self.pending_vars[var][0], self.pending_vars[var][1]) print(f'Server PID is {os.getpid()}') while True: packet = b'' packet_length = self.socket.recv(4) if not packet_length: break packet_length = int.from_bytes(packet_length, 'big') while len(packet) < packet_length: packet += self.socket.recv(packet_length - len(packet)) if not packet: break self._handle_packet(packet) print('Client disconnected') break def _send_packet(self, packet): self.socket.send(len(packet).to_bytes(4, 'big') + packet) def _handle_packet(self, packet): action = int.from_bytes(packet[:4], 'big') data = packet[4:] if action == Actions.HANDLER_REPLY: reply_count = int.from_bytes(data[:2], 'big') data = data[2:] with self.batch_update(): for i in range(reply_count): reply_len = int.from_bytes(data[:2], 'big') reply_id = int.from_bytes(data[2:6], 'big') reply_data = data[6:6+reply_len] data_array = [] while reply_data: item_type = reply_data[0] if item_type == 0: data_array.append(int.from_bytes(reply_data[1:9], 'big', signed=True)) reply_data = reply_data[9:] elif item_type == 1: data_array.append(struct.unpack('>d', reply_data[1:9])[0]) reply_data = reply_data[9:] else: raise ValueError(f"Unknown item type {item_type}") if self.reply_handler: self.reply_handler(reply_id, data_array) else: print('[app] Unknown packet type:', packet) def _send_scene(self): if self.socket: combined_scene = self.scene if self._batch_vars is not None: for key, value in self._batch_vars.items(): combined_scene['vars'][key]['value'] = json.dumps(Oplist(Expr.to_dict(value)).to_list()) self._send_packet(Actions.UPDATE_SCENE.to_bytes(4, 'big') + json.dumps(self.scene).encode()) def set_remote_var(self, name, val_type, value): self.pending_vars[name] = (val_type, value) if self._is_batch: self._batch_vars[name] = value else: self._send_remote_var([(name, value)]) def _send_remote_var(self, set_vars): if self.socket: parts = [] for name, value in set_vars: value = Oplist(Expr.to_dict(value)).to_list() parts.append(name.encode() + b'\x00' + json.dumps(value).encode()) self._send_packet(Actions.SET_VAR.to_bytes(4, 'big') + b'\x00'.join(parts)) def send_watch_ack(self, ack_id: int): if self.socket: self._send_packet(Actions.WATCH_ACK.to_bytes(4, 'big') + ack_id.to_bytes(8, 'big'))

true

7907d909b3eb1f58c7d792226124e4131a05139f

4,219

py

Python

Graphing/MeanActivityHorizontalBarChart.py

actuatech/fuel-tourism

60e6953cdcccf164e5cd03916a1c3b3c2b071a85

[ "MIT" ]

null

Graphing/MeanActivityHorizontalBarChart.py

actuatech/fuel-tourism

60e6953cdcccf164e5cd03916a1c3b3c2b071a85

[ "MIT" ]

null

Graphing/MeanActivityHorizontalBarChart.py

actuatech/fuel-tourism

60e6953cdcccf164e5cd03916a1c3b3c2b071a85

[ "MIT" ]

null

import plotly.graph_objects as go import pandas as pd from .Colors import COLOR_DISCRETE_MAP from Classification import CATEGORIES def all_categories_grouping(row: pd.Series) -> str: """ Merge Category, Fuel and segment to a single string for unique categorization """ if row['Fuel'] == 'Battery Electric': return row['Category'] + ' / ' + row['Fuel'] else: try: result = row['Fuel'] + ' / ' + row['Segment'] + ' / ' + row['Euro Standard'] except: # For Off Road type with no Segment nor Euro Standard result = row['Fuel'] return result def activity_horizontal_bar_chart(stock_and_mileage_df: pd.DataFrame.groupby, output_folder): """ Horizontal bar chart representing mean activity and other activities per unique categorization :param stock_and_mileage_df: Dataframe of the vehicles registration list :param output_folder: output folder name where to store resulting chart :return: an html file containing the horizontal bar chart of the mean activity """ data = stock_and_mileage_df.copy() # Delete off road data data = data[data['Category'] != 'Off Road'] # Create single column classification data['segmentation'] = data.apply(lambda row: all_categories_grouping(row), axis=1) horizontal_plot = go.Figure() # Add Activity statistics and stock traces horizontal_plot.add_trace(go.Scatter(y=data['segmentation'], x=data['Max_Activity'], mode='markers', name='Activitat màxima', marker_color='rgb(288, 26, 28)' )) horizontal_plot.add_trace(go.Scatter(y=data['segmentation'], x=data['Min_Activity'], mode='markers', name='Activitat mínima', marker_color='rgb(229, 196, 148)' )) horizontal_plot.add_trace(go.Scatter(y=data['segmentation'], x=data['Std_Activity'], mode='markers', name="Desviació standard de l'activitat", marker=dict( color='rgb(800, 800, 800)', opacity=0) )) horizontal_plot.add_trace(go.Scatter(y=data['segmentation'], x=data['Stock'], mode='markers', name="Estoc", marker=dict( color='rgb(800, 800, 800)', opacity=0) )) horizontal_plot.add_trace(go.Scatter(y=data['segmentation'], x=data['Mean_Lifetime_Activity'], mode='markers', name="Lifetime cumulative activity mitja", marker=dict( color='rgb(800, 800, 800)', opacity=0) )) # For each category add the mean activity bar chart (to diferenciate by same colors as Stock distribution Pie Chart) for category in CATEGORIES: horizontal_plot.add_trace(go.Bar( y=data[data['Category'] == category]['segmentation'], x=data[data['Category'] == category]['Mean_Activity'], orientation='h', marker_color=COLOR_DISCRETE_MAP[category], name=f'Activitat mitjana {category}' )) # Update plot information horizontal_plot.update_layout( title="Activitat mitjana anual segons classificació del parc de vehicles d'Andorra", title_x=0.5, height=4000, width=1500, template='plotly_white', xaxis_title='Activitat mitja (km/any)', yaxis_title='Tipologia de vehicle', hovermode="y unified", hoverlabel=dict(namelength=100), xaxis_range=[0, stock_and_mileage_df['Max_Activity'].max()*1.05], xaxis=dict( tickmode='array', tickvals=[0, 5000, 15000, 25000, 50000, 100000, 150000, 200000], ticktext=['0', '5k', '15k', '25k', '50k', '100k', '150k', '200k']) ) horizontal_plot.update_xaxes(showgrid=True, zeroline=True) horizontal_plot.show() # Save plot to html file filename = output_folder + "Activitat mitjana anual segons classificació del parc de vehicles d'Andorra.html" horizontal_plot.write_html(filename)

44.882979

120

0.605357

import plotly.graph_objects as go import pandas as pd from .Colors import COLOR_DISCRETE_MAP from Classification import CATEGORIES def all_categories_grouping(row: pd.Series) -> str: if row['Fuel'] == 'Battery Electric': return row['Category'] + ' / ' + row['Fuel'] else: try: result = row['Fuel'] + ' / ' + row['Segment'] + ' / ' + row['Euro Standard'] except: result = row['Fuel'] return result def activity_horizontal_bar_chart(stock_and_mileage_df: pd.DataFrame.groupby, output_folder): data = stock_and_mileage_df.copy() data = data[data['Category'] != 'Off Road'] data['segmentation'] = data.apply(lambda row: all_categories_grouping(row), axis=1) horizontal_plot = go.Figure() horizontal_plot.add_trace(go.Scatter(y=data['segmentation'], x=data['Max_Activity'], mode='markers', name='Activitat màxima', marker_color='rgb(288, 26, 28)' )) horizontal_plot.add_trace(go.Scatter(y=data['segmentation'], x=data['Min_Activity'], mode='markers', name='Activitat mínima', marker_color='rgb(229, 196, 148)' )) horizontal_plot.add_trace(go.Scatter(y=data['segmentation'], x=data['Std_Activity'], mode='markers', name="Desviació standard de l'activitat", marker=dict( color='rgb(800, 800, 800)', opacity=0) )) horizontal_plot.add_trace(go.Scatter(y=data['segmentation'], x=data['Stock'], mode='markers', name="Estoc", marker=dict( color='rgb(800, 800, 800)', opacity=0) )) horizontal_plot.add_trace(go.Scatter(y=data['segmentation'], x=data['Mean_Lifetime_Activity'], mode='markers', name="Lifetime cumulative activity mitja", marker=dict( color='rgb(800, 800, 800)', opacity=0) )) # For each category add the mean activity bar chart (to diferenciate by same colors as Stock distribution Pie Chart) for category in CATEGORIES: horizontal_plot.add_trace(go.Bar( y=data[data['Category'] == category]['segmentation'], x=data[data['Category'] == category]['Mean_Activity'], orientation='h', marker_color=COLOR_DISCRETE_MAP[category], name=f'Activitat mitjana {category}' )) # Update plot information horizontal_plot.update_layout( title="Activitat mitjana anual segons classificació del parc de vehicles d'Andorra", title_x=0.5, height=4000, width=1500, template='plotly_white', xaxis_title='Activitat mitja (km/any)', yaxis_title='Tipologia de vehicle', hovermode="y unified", hoverlabel=dict(namelength=100), xaxis_range=[0, stock_and_mileage_df['Max_Activity'].max()*1.05], xaxis=dict( tickmode='array', tickvals=[0, 5000, 15000, 25000, 50000, 100000, 150000, 200000], ticktext=['0', '5k', '15k', '25k', '50k', '100k', '150k', '200k']) ) horizontal_plot.update_xaxes(showgrid=True, zeroline=True) horizontal_plot.show() filename = output_folder + "Activitat mitjana anual segons classificació del parc de vehicles d'Andorra.html" horizontal_plot.write_html(filename)

true

7907daba23b38f50e978b56a229f23898be066ee

645

py

Python

context.py

kumailkermalli16/rcwa

a946c3819e5e52ad9c92a8a73c48360749b06196

[ "MIT" ]

11

2020-03-11T08:46:55.000Z

2021-04-14T04:43:43.000Z

context.py

FelixSCT/rcwa

82571bd35e6b01994ccbd0c58080f3c80dc65024

[ "MIT" ]

20

2020-10-02T00:25:19.000Z

2021-04-15T03:08:16.000Z

context.py

FelixSCT/rcwa

82571bd35e6b01994ccbd0c58080f3c80dc65024

[ "MIT" ]

5

2021-09-20T08:07:51.000Z

2022-03-30T08:34:30.000Z

""" Adds the source files to the path for files in any subdirectory TODO: check that we have not alredy added to our path. """ import os import sys fileLocation = os.path.dirname(os.path.abspath(__file__)) sourceLocation = os.path.abspath(os.path.join(fileLocation, 'RCWA/source/')) nkLocation = os.path.abspath(os.path.join(fileLocation, 'RCWA/nkData/')) netlistLocation = os.path.abspath(os.path.join(fileLocation, 'RCWA/netlist/')) testLocation = os.path.abspath(os.path.join(fileLocation, 'RCWA/test/')) sys.path.insert(0, sourceLocation) sys.path.insert(0, nkLocation) sys.path.insert(0, netlistLocation) sys.path.insert(0, testLocation)

35.833333

78

0.765891

import os import sys fileLocation = os.path.dirname(os.path.abspath(__file__)) sourceLocation = os.path.abspath(os.path.join(fileLocation, 'RCWA/source/')) nkLocation = os.path.abspath(os.path.join(fileLocation, 'RCWA/nkData/')) netlistLocation = os.path.abspath(os.path.join(fileLocation, 'RCWA/netlist/')) testLocation = os.path.abspath(os.path.join(fileLocation, 'RCWA/test/')) sys.path.insert(0, sourceLocation) sys.path.insert(0, nkLocation) sys.path.insert(0, netlistLocation) sys.path.insert(0, testLocation)

true

7907dac376e1994a1443e54cf3d587bc2aeb6ada

3,458

py

Python

mlprimitives/adapters/pandas.py

albact7/MLPrimitives

9dbcbe219315a9b79aae825a34f5108802d8a19d

[ "MIT" ]

42

2018-07-31T07:33:45.000Z

2020-10-26T05:51:35.000Z

mlprimitives/adapters/pandas.py

albact7/MLPrimitives

9dbcbe219315a9b79aae825a34f5108802d8a19d

[ "MIT" ]

177

2018-08-28T18:06:20.000Z

2020-11-17T18:41:22.000Z

mlprimitives/adapters/pandas.py

albact7/MLPrimitives

9dbcbe219315a9b79aae825a34f5108802d8a19d

[ "MIT" ]

28

2018-07-18T13:47:59.000Z

2020-10-21T18:53:15.000Z

import warnings from mlprimitives.utils import import_object _RESAMPLE_AGGS = [ 'mean', 'median', 'prod', 'quantile', 'std', 'sum', 'var', ] def resample(df, rule, on=None, groupby=(), aggregation='mean', reset_index=True, time_index=None): """pd.DataFrame.resample adapter. Call the `df.resample` method on the given time_index and afterwards call the indicated aggregation. Optionally group the dataframe by the indicated columns before performing the resampling. If groupby option is used, the result is a multi-index datagrame. Args: df (pandas.DataFrame): DataFrame to resample. rule (str or int): The offset string or object representing target conversion or an integer value that will be interpreted as the number of seconds. on (str or None): Name of the column to use as the time index. If ``None`` is given, the DataFrame index is used. groupby (list): Optional list of columns to group by. aggregation (callable or str): Function or name of the function to use for the aggregation. If a name is given, it can either be one of the standard pandas aggregation functions or the fully qualified name of a python function that will be imported and used. reset_index (bool): Whether to reset the index after aggregating time_index (str or None): Deprecated: This has been renamed to `on`. Name of the column to use as the time index. If ``None`` is given, the DataFrame is index is used. Returns: pandas.Dataframe: resampled dataframe """ if on is None and time_index is not None: message = ( 'resample `time_series` argument deprecated and will be removed' ' in future versions of MLPrimitives. Please use `on` instead.' ) warnings.warn(message, DeprecationWarning, stacklevel=2) on = time_index if groupby: df = df.groupby(groupby) if isinstance(rule, int): rule = '{}s'.format(rule) dtir = df.resample(rule, on=on) if not callable(aggregation) and aggregation not in _RESAMPLE_AGGS: try: aggregation = import_object(aggregation) except (AttributeError, ImportError, ValueError): pass df = dtir.aggregate(aggregation) for name in df.index.names: if name in df: del df[name] if reset_index: df.reset_index(inplace=True) return df def _join_names(names): """Join the names of a multi-level index with an underscore.""" levels = (str(name) for name in names if name != '') return '_'.join(levels) def unstack(df, level=-1, reset_index=True): """pd.DataFrame.unstack adapter. Call the `df.unstack` method using the indicated level and afterwards join the column names using an underscore. Args: df (pandas.DataFrame): DataFrame to unstack. level (str, int or list): Level(s) of index to unstack, can pass level name reset_index (bool): Whether to reset the index after unstacking Returns: pandas.Dataframe: unstacked dataframe """ df = df.unstack(level=level) if reset_index: df = df.reset_index() df.columns = df.columns.map(_join_names) return df

30.333333

97

0.633314

import warnings from mlprimitives.utils import import_object _RESAMPLE_AGGS = [ 'mean', 'median', 'prod', 'quantile', 'std', 'sum', 'var', ] def resample(df, rule, on=None, groupby=(), aggregation='mean', reset_index=True, time_index=None): if on is None and time_index is not None: message = ( 'resample `time_series` argument deprecated and will be removed' ' in future versions of MLPrimitives. Please use `on` instead.' ) warnings.warn(message, DeprecationWarning, stacklevel=2) on = time_index if groupby: df = df.groupby(groupby) if isinstance(rule, int): rule = '{}s'.format(rule) dtir = df.resample(rule, on=on) if not callable(aggregation) and aggregation not in _RESAMPLE_AGGS: try: aggregation = import_object(aggregation) except (AttributeError, ImportError, ValueError): pass df = dtir.aggregate(aggregation) for name in df.index.names: if name in df: del df[name] if reset_index: df.reset_index(inplace=True) return df def _join_names(names): levels = (str(name) for name in names if name != '') return '_'.join(levels) def unstack(df, level=-1, reset_index=True): df = df.unstack(level=level) if reset_index: df = df.reset_index() df.columns = df.columns.map(_join_names) return df

true

7907db87a8072c9923356373293a2eb7c7a3e234

2,178

py

Python

pulsar/scripts/_configure_slurm.py

usegalaxy-eu/pulsar

4dcaf61cceded8f0a83801cf1e9847e62656809f

[ "Apache-2.0" ]

1

2021-05-18T02:27:00.000Z

pulsar/scripts/_configure_slurm.py

usegalaxy-eu/pulsar

4dcaf61cceded8f0a83801cf1e9847e62656809f

[ "Apache-2.0" ]

null

pulsar/scripts/_configure_slurm.py

usegalaxy-eu/pulsar

4dcaf61cceded8f0a83801cf1e9847e62656809f

[ "Apache-2.0" ]

null

""" This file is also being used by the GalaxyCloudRunner (gcr) Docker image. """ from getpass import getuser from multiprocessing import cpu_count from socket import gethostname from string import Template SLURM_CONFIG_TEMPLATE = ''' # slurm.conf file generated by configurator.html. # Put this file on all nodes of your cluster. # See the slurm.conf man page for more information. # ControlMachine=$hostname #ControlAddr= #BackupController= #BackupAddr= # AuthType=auth/munge CacheGroups=0 #CheckpointType=checkpoint/none CryptoType=crypto/munge MpiDefault=none #PluginDir= #PlugStackConfig= #PrivateData=jobs ProctrackType=proctrack/pgid #Prolog= #PrologSlurmctld= #PropagatePrioProcess=0 #PropagateResourceLimits= #PropagateResourceLimitsExcept= ReturnToService=1 #SallocDefaultCommand= SlurmctldPidFile=/var/run/slurmctld.pid SlurmctldPort=6817 SlurmdPidFile=/var/run/slurmd.pid SlurmdPort=6818 SlurmdSpoolDir=/tmp/slurmd SlurmUser=$user #SlurmdUser=root #SrunEpilog= #SrunProlog= StateSaveLocation=/tmp SwitchType=switch/none #TaskEpilog= TaskPlugin=task/none #TaskPluginParam= #TaskProlog= InactiveLimit=0 KillWait=30 MinJobAge=300 #OverTimeLimit=0 SlurmctldTimeout=120 SlurmdTimeout=300 #UnkillableStepTimeout=60 #VSizeFactor=0 Waittime=0 FastSchedule=1 SchedulerType=sched/backfill SchedulerPort=7321 SelectType=select/linear #SelectTypeParameters= AccountingStorageType=accounting_storage/none #AccountingStorageUser= AccountingStoreJobComment=YES ClusterName=cluster #DebugFlags= #JobCompHost= #JobCompLoc= #JobCompPass= #JobCompPort= JobCompType=jobcomp/none #JobCompUser= JobAcctGatherFrequency=30 JobAcctGatherType=jobacct_gather/none SlurmctldDebug=3 #SlurmctldLogFile= SlurmdDebug=3 #SlurmdLogFile= NodeName=$hostname CPUs=$cpus State=UNKNOWN PartitionName=debug Nodes=$hostname Default=YES MaxTime=INFINITE State=UP ''' def main(): template_params = {"hostname": gethostname(), "user": getuser(), "cpus": cpu_count()} config_contents = Template(SLURM_CONFIG_TEMPLATE).substitute(template_params) open("/etc/slurm-llnl/slurm.conf", "w").write(config_contents) if __name__ == "__main__": main()

22.453608

81

0.800735

from getpass import getuser from multiprocessing import cpu_count from socket import gethostname from string import Template SLURM_CONFIG_TEMPLATE = ''' # slurm.conf file generated by configurator.html. # Put this file on all nodes of your cluster. # See the slurm.conf man page for more information. # ControlMachine=$hostname #ControlAddr= #BackupController= #BackupAddr= # AuthType=auth/munge CacheGroups=0 #CheckpointType=checkpoint/none CryptoType=crypto/munge MpiDefault=none #PluginDir= #PlugStackConfig= #PrivateData=jobs ProctrackType=proctrack/pgid #Prolog= #PrologSlurmctld= #PropagatePrioProcess=0 #PropagateResourceLimits= #PropagateResourceLimitsExcept= ReturnToService=1 #SallocDefaultCommand= SlurmctldPidFile=/var/run/slurmctld.pid SlurmctldPort=6817 SlurmdPidFile=/var/run/slurmd.pid SlurmdPort=6818 SlurmdSpoolDir=/tmp/slurmd SlurmUser=$user #SlurmdUser=root #SrunEpilog= #SrunProlog= StateSaveLocation=/tmp SwitchType=switch/none #TaskEpilog= TaskPlugin=task/none #TaskPluginParam= #TaskProlog= InactiveLimit=0 KillWait=30 MinJobAge=300 #OverTimeLimit=0 SlurmctldTimeout=120 SlurmdTimeout=300 #UnkillableStepTimeout=60 #VSizeFactor=0 Waittime=0 FastSchedule=1 SchedulerType=sched/backfill SchedulerPort=7321 SelectType=select/linear #SelectTypeParameters= AccountingStorageType=accounting_storage/none #AccountingStorageUser= AccountingStoreJobComment=YES ClusterName=cluster #DebugFlags= #JobCompHost= #JobCompLoc= #JobCompPass= #JobCompPort= JobCompType=jobcomp/none #JobCompUser= JobAcctGatherFrequency=30 JobAcctGatherType=jobacct_gather/none SlurmctldDebug=3 #SlurmctldLogFile= SlurmdDebug=3 #SlurmdLogFile= NodeName=$hostname CPUs=$cpus State=UNKNOWN PartitionName=debug Nodes=$hostname Default=YES MaxTime=INFINITE State=UP ''' def main(): template_params = {"hostname": gethostname(), "user": getuser(), "cpus": cpu_count()} config_contents = Template(SLURM_CONFIG_TEMPLATE).substitute(template_params) open("/etc/slurm-llnl/slurm.conf", "w").write(config_contents) if __name__ == "__main__": main()

true

7907dbe959db150ecdba03325fe09e0918098f76

1,551

py

Python

src/transmittals/tests/test_templates.py

PhaseDMS/phase

4f776d0b1b5e7916a3e26aee890b3c2b9454ef0e

[ "MIT" ]

2

2021-09-10T19:40:30.000Z

2022-01-31T07:15:51.000Z

src/transmittals/tests/test_templates.py

PhaseDMS/phase

4f776d0b1b5e7916a3e26aee890b3c2b9454ef0e

[ "MIT" ]

null

src/transmittals/tests/test_templates.py

PhaseDMS/phase

4f776d0b1b5e7916a3e26aee890b3c2b9454ef0e

[ "MIT" ]

1

2021-09-10T19:40:42.000Z

from django.test import TestCase from django.urls import reverse from accounts.factories import UserFactory from transmittals.factories import create_transmittal ack_button = '<a id="action-ack-transmittal"' class TransmittalActionTests(TestCase): def setUp(self): self.trs = create_transmittal() self.doc = self.trs.document self.category = self.doc.category self.url = reverse( "document_detail", args=[ self.category.organisation.slug, self.category.slug, self.doc.document_key, ], ) self.user = UserFactory( name="User", password="pass", is_superuser=True, category=self.category ) self.client.login(username=self.user.email, password="pass") def test_internal_user_cannot_ack_transmittal(self): self.assertIsNone(self.trs.ack_of_receipt_date) self.assertFalse(self.user.is_external) res = self.client.get(self.url) self.assertNotContains(res, ack_button) def test_external_user_can_ack_transmittal(self): self.user.is_external = True self.user.save() res = self.client.get(self.url) self.assertContains(res, ack_button) def test_transmittal_cannot_be_acked_twice(self): self.user.is_external = True self.trs.ack_receipt(self.user) self.assertIsNotNone(self.trs.ack_of_receipt_date) res = self.client.get(self.url) self.assertNotContains(res, ack_button)

30.411765

83

0.661509

from django.test import TestCase from django.urls import reverse from accounts.factories import UserFactory from transmittals.factories import create_transmittal ack_button = '<a id="action-ack-transmittal"' class TransmittalActionTests(TestCase): def setUp(self): self.trs = create_transmittal() self.doc = self.trs.document self.category = self.doc.category self.url = reverse( "document_detail", args=[ self.category.organisation.slug, self.category.slug, self.doc.document_key, ], ) self.user = UserFactory( name="User", password="pass", is_superuser=True, category=self.category ) self.client.login(username=self.user.email, password="pass") def test_internal_user_cannot_ack_transmittal(self): self.assertIsNone(self.trs.ack_of_receipt_date) self.assertFalse(self.user.is_external) res = self.client.get(self.url) self.assertNotContains(res, ack_button) def test_external_user_can_ack_transmittal(self): self.user.is_external = True self.user.save() res = self.client.get(self.url) self.assertContains(res, ack_button) def test_transmittal_cannot_be_acked_twice(self): self.user.is_external = True self.trs.ack_receipt(self.user) self.assertIsNotNone(self.trs.ack_of_receipt_date) res = self.client.get(self.url) self.assertNotContains(res, ack_button)

true

7907dcbcc0a65cc92db33cb1ee33822cd81fa136

2,223

py

Python

Server/Model/ModelUser.py

CorneliusTantius/TCON-API-V2

e9628df57291af10a824148e6a8edbb48e13c4e5

[ "MIT" ]

1

2021-10-05T17:46:46.000Z

Server/Model/ModelUser.py

CorneliusTantius/TCON-API-V2

e9628df57291af10a824148e6a8edbb48e13c4e5

[ "MIT" ]

null

Server/Model/ModelUser.py

CorneliusTantius/TCON-API-V2

e9628df57291af10a824148e6a8edbb48e13c4e5

[ "MIT" ]

null

### Package Import ### from bson import ObjectId from pydantic import BaseModel from pydantic import fields from pydantic.fields import Field from typing import Optional ### AppCode Import ### from Server.Model.POID import PyObjectId ############################################################################### class User(BaseModel): Id: PyObjectId = Field(default_factory=PyObjectId, alias='_id') FirstName: str = Field(alias='FirstName') LastName: str = Field(alias='LastName') Email: str = Field(alias='Email') PhoneNumber: str = Field(alias='PhoneNumber') Password: str = Field(alias='Password') About: Optional[str] = Field(alias = 'About') ProfileUrl: Optional[str] = Field(alias='ProfileUrl') class Config: allow_population_by_field_name = True arbitrary_types_allowed = True json_encoders = {ObjectId: str} schema_extra = { "example": { "FirstName": "Jane", "LastName": "Doe", "Email": "jdoe@example.com", "PhoneNumber": "6285588974456", "Password": "jdoee" } } ############################################################################### class UserUpdateModel(BaseModel): FirstName: Optional[str] = Field(alias ='FirstName') LastName: Optional[str] = Field(alias='LastName') Email: Optional[str] = Field(alias='Email') PhoneNumber: Optional[str] = Field(alias='PhoneNumber') Password: Optional[str] = Field(alias='Password') About: Optional[str] = Field(alias = 'About') ProfileUrl: Optional[str] = Field(alias='ProfileUrl') class Config: arbitrary_types_allowed = True json_encoders = {ObjectId: str} schema_extra = { "example": { "FirstName": "Jane", "LastName": "Doe", "Email": "jdoe@example.com", "PhoneNumber": "6285588974456", "Password": "jdoee", "About": "About jane doe", "ProfileUrl": "https://profileurlembed.com/file/janedoe" } } ###############################################################################

35.854839

79

0.530364

BaseModel from pydantic import fields from pydantic.fields import Field from typing import Optional

true

7907dcd4b357df9d1515ca7dd479f62ed609eb66

688

py

Python

hcap/settings/general/middleware.py

fabiommendes/capacidade_hospitalar

4f675b574573eb3f51e6be8a927ea230bf2712c7

[ "MIT" ]

null

hcap/settings/general/middleware.py

fabiommendes/capacidade_hospitalar

4f675b574573eb3f51e6be8a927ea230bf2712c7

[ "MIT" ]

31

2020-04-11T13:38:17.000Z

2021-09-22T18:51:11.000Z

hcap/settings/general/middleware.py

fabiommendes/capacidade_hospitalar

4f675b574573eb3f51e6be8a927ea230bf2712c7

[ "MIT" ]

1

2020-04-08T17:04:39.000Z

""" django: https://docs.djangoproject.com/en/3.0/topics/http/middleware/ https://docs.djangoproject.com/en/3.0/ref/settings/#middleware """ MIDDLEWARE = ( "django_prometheus.middleware.PrometheusBeforeMiddleware", "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", "django_prometheus.middleware.PrometheusAfterMiddleware", )

38.222222

66

0.77907

MIDDLEWARE = ( "django_prometheus.middleware.PrometheusBeforeMiddleware", "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", "django_prometheus.middleware.PrometheusAfterMiddleware", )

true

7907ddb022b5f6355e69c096183a25f632e01923

22,793

py

Python

armin_analysis/model_tests.py

arminbahl/mutant_zebrafish_behavior

17bee04b35c23b0f93fcecac9758e6ba19872be1

[ "MIT" ]

null

armin_analysis/model_tests.py

arminbahl/mutant_zebrafish_behavior

17bee04b35c23b0f93fcecac9758e6ba19872be1

[ "MIT" ]

null

armin_analysis/model_tests.py

arminbahl/mutant_zebrafish_behavior

17bee04b35c23b0f93fcecac9758e6ba19872be1

[ "MIT" ]

null

import pylab as pl from get_fish_info import get_fish_info from fit_integrator_model import get_model_result, get_target_result import numpy as np from pathlib import Path import gmm_model_fit import pandas as pd from pymoo.factory import get_problem, get_visualization, get_decomposition # import random # # for dt in [0.001, 0.002, 0.005, 0.01, 0.1]: # # tau = 4 # Is = np.arange(0, 30, dt) # xs = np.empty_like(Is) # xs[0] # # for i in range(1, len(Is)): # dx = random.gauss(0.2, 5) - xs[i - 1] # xs[i] = xs[i - 1] + dx * dt / tau # pl.plot(Is, xs) # pl.show() # sdf root_path = Path("/Users/arminbahl/Desktop/mutant_behavior_data/surrogate_fish1") #root_path = Path("/Users/arminbahl/Desktop/mutant_behavior_data/scn1lab_NIBR") #root_path = Path("/Users/arminbahl/Desktop/mutant_behavior_data/disc1_hetinx") df = pd.read_hdf(root_path / "all_data.h5", key="all_bouts") # # df_extracted_features, df_extracted_binned_features, \ # df_extracted_binned_features_same_direction, \ # df_extracted_binned_features_heading_angle_change_histograms, \ # df_extracted_binned_features_inter_bout_interval_histograms = get_mean_fish_info(df) # # print(df_extracted_features) # pl.plot(df_extracted_features.loc["wt", :]["correctness"]) # pl.plot(df_extracted_features.loc["het", :]["correctness"]) # pl.plot(df_extracted_features.loc["hom", :]["correctness"]) # # pl.figure() # pl.plot(df_extracted_features.loc["wt", :]["inter_bout_interval"]) # pl.plot(df_extracted_features.loc["het", :]["inter_bout_interval"]) # pl.plot(df_extracted_features.loc["hom", :]["inter_bout_interval"]) # # pl.figure() # pl.plot(df_extracted_binned_features.loc["wt", 0]) # pl.plot(df_extracted_binned_features.loc["wt", 1]) # pl.plot(df_extracted_binned_features.loc["wt", 2]) # pl.plot(df_extracted_binned_features.loc["wt", 3]) # # pl.figure() # pl.plot(df_extracted_binned_features_same_direction.loc["wt"]) # pl.plot(df_extracted_binned_features_same_direction.loc["het"]) # pl.plot(df_extracted_binned_features_same_direction.loc["hom"]) # # # pl.figure() # pl.plot(df_extracted_binned_features_heading_angle_change_histograms.loc["wt", 0]) # pl.plot(df_extracted_binned_features_heading_angle_change_histograms.loc["wt", 1]) # pl.plot(df_extracted_binned_features_heading_angle_change_histograms.loc["wt", 2]) # pl.plot(df_extracted_binned_features_heading_angle_change_histograms.loc["wt", 3]) # # pl.show() # # # pl.show() # # # print(df_extracted_features) # gg # sdf genotype = "hom" target_df_correctness_as_function_of_coherence, \ target_df_inter_bout_interval_as_function_of_coherence, \ target_df_binned_correctness, \ target_df_binned_same_direction, \ target_df_binned_features_heading_angle_change_histograms, \ target_df_binned_features_inter_bout_interval_histograms, \ target_df_gmm_fitting_results = get_target_result(root_path, genotype) # colors = ["#000000", "#330000", "#990000", "#CC3333"] # # for i in range(4): # pl.plot(target_df_binned_features_heading_angle_change_histograms.loc[i, :].droplevel("stim"), label=f"Coherence {i*25}%", color=colors[i], linewidth=2) # # pl.xlabel("Heading angle change (deg)") # pl.ylabel("Probability") # pl.legend() # # fig = pl.figure() # fig.suptitle("Target functions") # pl.subplot(211) # pl.plot(target_df_correctness_as_function_of_coherence, 'o-', color='black') # pl.xlabel("Coherence (%)") # pl.ylabel("Correctness (%)") # pl.subplot(212) # pl.plot(target_df_inter_bout_interval_as_function_of_coherence, 'o-', color='black') # pl.xlabel("Coherence (%)") # pl.ylabel("Inter-bout interval (s)") # medianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick') errornames = ["Error: 'Correctness as function of coherence'", "Error: 'Inter-bout interval as function of coherence'", "Error: 'Binned correctness at 25, 50, 100 %'", "Error: 'Binned same direction'", "Error: 'Histogram weights'"] #errornames = ["Mixed"] repeat = 1 X = np.load(root_path / f"leaky_integrator_model2_X_{genotype}_{repeat}.npy") F = np.load(root_path / f"leaky_integrator_model2_F_{genotype}_{repeat}.npy") # # # for i in range(7): # F[-1, :, i] = F[-1, :, i] / np.max(F[-1, :, i]) # print(F.shape) # # i6 = np.argmin(F[-1, :, 0] + F[-1, :, 1] + F[-1, :, 2] + F[-1, :, 3] + F[-1, :, 4] + F[-1, :, 5] + F[-1, :, 6]) # print(F[-1, i6, 0]) # dd #get_decomposition("asf").do(F[-1], [1, 1, 1, 1, 1, 1, 1]).argmin() #print(I) #sdfsdf #X = np.load(root_path / f"leaky_integrator_model2_X_{genotype}_{repeat}_single_error.npy") #F = np.load(root_path / f"leaky_integrator_model2_F_{genotype}_{repeat}_single_error.npy") # from pymoo.factory import get_decision_making, get_reference_directions # # ref_dirs = get_reference_directions("das-dennis", 4, n_partitions=12) # F = get_problem("dtlz1").pareto_front(ref_dirs) # # weights = np.array([10.25, 10.25, 0.25, 0.25]) # a, pseudo_weights = get_decision_making("pseudo-weights", weights).do(F, return_pseudo_weights=True) # pl.plot(F[:, 0], F[:,1], 'o') # pl.plot(F[a, 0], F[a,1], 'o') # pl.show() # # print(a, pseudo_weights, F.shape) # ghj from pymoo.factory import get_decision_making, get_reference_directions #weights = [1000, 1000, 1000, 0, 0, 0, 0] #a, pseudo_weights = get_decision_making("pseudo-weights", weights).do(F[-1], return_pseudo_weights=True) #print(pseudo_weights[0]) #print(a, pseudo_weights) #dfg for i in range(5): #pl.hist(F[-1, :, i]) #pl.show() #print(np.percentile(F[-1, :, i], 75)) #print(np.max(F[-1, :, i]) - np.min(F[-1, :, i])) F[-1, :, i] = F[-1, :, i] / np.percentile(F[-1, :, i], 75) # print(F.shape) # #i6 = a #i1 = np.argmin(F[-1, :, 0]) # i2 = np.argmin(F[-1, :, 1]) # i3 = np.argmin(F[-1, :, 0] + F[-1, :, 1]*500) # i4 = np.argmin(F[-1, :, 0] + F[-1, :, 1]*500 + F[-1, :, 3]) # i5 = np.argmin(F[-1, :, 0] + F[-1, :, 1]*500 + F[-1, :, 3] + F[-1, :, 5]*0.25) # #i6 = np.argmin(F[-1, :, 0] + F[-1, :, 1]*500 + F[-1, :, 3] + F[-1, :, 5]*0.25 + F[-1, :, 6]*5800) # #i6 = np.argmin(F[-1, :, 0] + F[-1, :, 1] * 2500 + F[-1, :, 3] * 5 + F[-1, :, 5] * 0.5 + F[-1, :, 6] * 6800) # i6 = np.argmin(F[-1, :, 0]*500 + F[-1, :, 1]*2500 + F[-1, :, 3]*50 + F[-1, :, 5]*0.5 + F[-1, :, 6]*4500) i6 = np.argmin(F[-1, :, 0] + 3*F[-1, :, 1] + F[-1, :, 2] + F[-1, :, 3] + F[-1, :, 4]) #from pymoo.factory import get_decision_making #dm = get_decision_making("high-tradeoff") #I = dm.do(pf) # print(F.shape) # np.set_printoptions(precision=4, suppress=True) # print((X[-1, i])) # #gdfgh # for error_i in range(len(errornames)): # pl.figure() # pl.title(errornames[error_i]) # bp = pl.boxplot(F[:, :, error_i].T, whis=[5, 95], showfliers=False, medianprops=medianprops) # for gen in range(50): # sc = pl.scatter([gen+1], [F[gen, :, error_i].min()], s=5, marker='.', c='firebrick') # pl.yscale("log") # pl.xlabel("Generation") # pl.ylabel("Log Error") # pl.show() # dd # # pl.figure() # pl.title("Compromise between all error functions") # #error = F[:, :, 0] + F[:, :, 1]*500 + F[:, :, 3] + F[:, :, 5]*0.25 + F[:, :, 6]*500 # error = F[:, :, 0] + F[:, :, 1]*2500 + F[:, :, 3]*5 + F[:, :, 5]*0.5 + F[:, :, 6]*1500 # # bp = pl.boxplot(error.T, whis=[5, 95], showfliers=False, medianprops=medianprops) # for gen in range(50): # sc = pl.scatter([gen + 1], [error[gen].min()], s=10, marker='.', c='firebrick') # pl.yscale("log") # pl.xlabel("Generation") # pl.ylabel("Log Error") # pl.show() # pl.figure() # pl.scatter(F[-1, :, 0], F[-1, :, 1], s=10, marker='.', c='C0', label='Individual') # pl.scatter(F[-1, i1, 0], F[-1, i1, 1], s=15, marker='o', c='C1', label="Best for 'Correctness as function of coherence'") # pl.scatter(F[-1, i2, 0], F[-1, i2, 1], s=15, marker='o', c='C2', label="Best for 'Inter-bout interval as function of coherence'") # pl.scatter(F[-1, i3, 0], F[-1, i3, 1], s=15, marker='o', c='C3', label="Compromise") # pl.legend() # pl.xlabel(errornames[0]) # pl.ylabel(errornames[1]) # # # pl.figure() # pl.scatter(F[-1, :, 0] + F[-1, :, 1]*500, F[-1, :, 3], s=10, marker='.', c='C0', label='Individual') # pl.scatter(F[-1, i1, 0] + F[-1, i1, 1]*500, F[-1, i1, 3], s=15, marker='o', c='C1', label="Best for 'Correctness as function of coherence'") # pl.scatter(F[-1, i2, 0] + F[-1, i2, 1]*500, F[-1, i2, 3], s=15, marker='o', c='C2', label="Best for 'Inter-bout interval as function of coherence'") # pl.scatter(F[-1, i3, 0] + F[-1, i3, 1]*500, F[-1, i3, 3], s=15, marker='o', c='C3', label="Compromise between 1 and 2") # pl.scatter(F[-1, i4, 0] + F[-1, i4, 1]*500, F[-1, i4, 3], s=15, marker='o', c='C4', label="Compromise between all") # pl.legend() # pl.xlabel("Compromise between 1 and 2") # pl.ylabel(errornames[3]) # # pl.figure() # pl.scatter(F[-1, :, 0] + F[-1, :, 1]*500 + F[-1, :, 3], F[-1, :, 5], s=10, marker='.', c='C0', label='Individual') # pl.scatter(F[-1, i1, 0] + F[-1, i1, 1]*500 + F[-1, i1, 3], F[-1, i1, 5], s=15, marker='o', c='C1', label="Best for 'Correctness as function of coherence'") # pl.scatter(F[-1, i2, 0] + F[-1, i2, 1]*500 + F[-1, i2, 3], F[-1, i2, 5], s=15, marker='o', c='C2', label="Best for 'Inter-bout interval as function of coherence'") # pl.scatter(F[-1, i3, 0] + F[-1, i3, 1]*500 + F[-1, i3, 3], F[-1, i3, 5], s=15, marker='o', c='C3', label="Compromise between 1 and 2") # pl.scatter(F[-1, i4, 0] + F[-1, i4, 1]*500 + F[-1, i4, 3], F[-1, i4, 5], s=15, marker='o', c='C4', label="Compromise between 1, 2, and 3") # pl.scatter(F[-1, i5, 0] + F[-1, i5, 1]*500 + F[-1, i5, 3], F[-1, i5, 5], s=15, marker='o', c='C5', label="Compromise between all") # pl.legend() # pl.xlabel("Compromise between 1, 2, and 3") # pl.ylabel(errornames[5]) # # # pl.figure() # pl.scatter(F[-1, :, 0] + F[-1, :, 1]*500 + F[-1, :, 3] + F[-1, :, 5]*0.25, F[-1, :, 6], s=10, marker='.', c='C0', label='Individual') # pl.scatter(F[-1, i1, 0] + F[-1, i1, 1]*500 + F[-1, i1, 3] + F[-1, i1, 5]*0.25, F[-1, i1, 6], s=15, marker='o', c='C1', label="Best for 'Correctness as function of coherence'") # pl.scatter(F[-1, i2, 0] + F[-1, i2, 1]*500 + F[-1, i2, 3] + F[-1, i2, 5]*0.25, F[-1, i2, 6], s=15, marker='o', c='C2', label="Best for 'Inter-bout interval as function of coherence'") # pl.scatter(F[-1, i3, 0] + F[-1, i3, 1]*500 + F[-1, i3, 3] + F[-1, i3, 5]*0.25, F[-1, i3, 6], s=15, marker='o', c='C3', label="Compromise between 1 and 2") # pl.scatter(F[-1, i4, 0] + F[-1, i4, 1]*500 + F[-1, i4, 3] + F[-1, i4, 5]*0.25, F[-1, i4, 6], s=15, marker='o', c='C4', label="Compromise between 1, 2, and 3") # pl.scatter(F[-1, i5, 0] + F[-1, i5, 1]*500 + F[-1, i5, 3] + F[-1, i5, 5]*0.25, F[-1, i5, 6], s=15, marker='o', c='C5', label="Compromise between 1, 2, 3, and 4") # pl.scatter(F[-1, i6, 0] + F[-1, i6, 1]*500 + F[-1, i6, 3] + F[-1, i6, 5]*0.25, F[-1, i6, 6], s=15, marker='o', c='C6', label="Compromise between all") # pl.legend() # pl.xlabel("Compromise between 1, 2, 3, and 4") # pl.ylabel(errornames[6]) # # fig = pl.figure() # model_df_correctness_as_function_of_coherence, \ # model_df_inter_bout_interval_as_function_of_coherence, \ # model_df_binned_correctness, \ # model_df_binned_same_direction, \ # model_df_binned_features_heading_angle_change_histograms, \ # model_df_binned_features_inter_bout_interval_histograms, \ # model_df_gmm_fitting_results = get_model_result(X[-1, i1]) # fig.suptitle("Best for 'Correctness as function of coherence'") # pl.subplot(211) # pl.plot([0, 25, 50, 100], target_df_correctness_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_correctness_as_function_of_coherence.values, 'o--', color='C1') # pl.xlabel("Coherence (%)") # pl.ylabel("Correctness (%)") # pl.subplot(212) # pl.plot([0, 25, 50, 100], target_df_inter_bout_interval_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_inter_bout_interval_as_function_of_coherence.values, 'o--', color='C1') # pl.xlabel("Coherence (%)") # pl.ylabel("Inter-bout interval (s)") # # fig = pl.figure() # model_df_correctness_as_function_of_coherence, \ # model_df_inter_bout_interval_as_function_of_coherence, \ # model_df_binned_correctness, \ # model_df_binned_same_direction, \ # model_df_binned_features_heading_angle_change_histograms, \ # model_df_binned_features_inter_bout_interval_histograms, \ # model_df_gmm_fitting_results = get_model_result(X[-1, i2]) # fig.suptitle("Best for 'Inter-bout interval as function of coherence'") # pl.subplot(211) # pl.plot([0, 25, 50, 100], target_df_correctness_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_correctness_as_function_of_coherence.values, 'o--', color='C2') # pl.xlabel("Coherence (%)") # pl.ylabel("Correctness (%)") # pl.subplot(212) # pl.plot([0, 25, 50, 100], target_df_inter_bout_interval_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_inter_bout_interval_as_function_of_coherence.values, 'o--', color='C2') # pl.xlabel("Coherence (%)") # pl.ylabel("Inter-bout interval (s)") # # fig = pl.figure() # model_df_correctness_as_function_of_coherence, \ # model_df_inter_bout_interval_as_function_of_coherence, \ # model_df_binned_correctness, \ # model_df_binned_same_direction, \ # model_df_binned_features_heading_angle_change_histograms, \ # model_df_binned_features_inter_bout_interval_histograms, \ # model_df_gmm_fitting_results = get_model_result(X[-1, i3]) # fig.suptitle("Compromise between 'Correctness and inter-bout interval as function of coherence'") # pl.subplot(211) # pl.plot([0, 25, 50, 100], target_df_correctness_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_correctness_as_function_of_coherence.values, 'o--', color='C3') # pl.xlabel("Coherence (%)") # pl.ylabel("Correctness (%)") # pl.subplot(212) # pl.plot([0, 25, 50, 100], target_df_inter_bout_interval_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_inter_bout_interval_as_function_of_coherence.values, 'o--', color='C3') # pl.xlabel("Coherence (%)") # pl.ylabel("Inter-bout interval (s)") # # fig = pl.figure() # model_df_correctness_as_function_of_coherence, \ # model_df_inter_bout_interval_as_function_of_coherence, \ # model_df_binned_correctness, \ # model_df_binned_same_direction, \ # model_df_binned_features_heading_angle_change_histograms, \ # model_df_binned_features_inter_bout_interval_histograms, \ # model_df_gmm_fitting_results = get_model_result(X[-1, i3]) # fig.suptitle("Compromise between 'Correctness and inter-bout interval as function of coherence'") # pl.subplot(221) # pl.plot([0, 25, 50, 100], target_df_correctness_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_correctness_as_function_of_coherence.values, 'o--', color='C3') # pl.xlabel("Coherence (%)") # pl.ylabel("Correctness (%)") # pl.subplot(222) # pl.plot([0, 25, 50, 100], target_df_inter_bout_interval_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_inter_bout_interval_as_function_of_coherence.values, 'o--', color='C3') # pl.xlabel("Coherence (%)") # pl.ylabel("Inter-bout interval (s)") # pl.subplot(223) # for i in range(4): # pl.plot(target_df_binned_correctness.loc[i, :].droplevel("stim"), 'o-', color='black') # pl.plot(model_df_binned_correctness.loc[i, :].droplevel("stim"), 'o--', color='C3') # pl.xlabel("Correctness (%)") # pl.ylabel("Time (s)") # # # fig = pl.figure() # model_df_correctness_as_function_of_coherence, \ # model_df_inter_bout_interval_as_function_of_coherence, \ # model_df_binned_correctness, \ # model_df_binned_same_direction, \ # model_df_binned_features_heading_angle_change_histograms, \ # model_df_binned_features_inter_bout_interval_histograms, \ # model_df_gmm_fitting_results = get_model_result(X[-1, i4]) # fig.suptitle("Compromise between all three error functions") # pl.subplot(221) # pl.plot([0, 25, 50, 100], target_df_correctness_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_correctness_as_function_of_coherence.values, 'o--', color='C4') # pl.xlabel("Coherence (%)") # pl.ylabel("Correctness (%)") # pl.subplot(222) # pl.plot([0, 25, 50, 100], target_df_inter_bout_interval_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_inter_bout_interval_as_function_of_coherence.values, 'o--', color='C4') # pl.xlabel("Coherence (%)") # pl.ylabel("Inter-bout interval (s)") # pl.subplot(223) # for i in range(4): # pl.plot(target_df_binned_correctness.loc[i, :].droplevel("stim"), 'o-', color='black') # pl.plot(model_df_binned_correctness.loc[i, :].droplevel("stim"), 'o--', color='C4') # pl.xlabel("Correctness (%)") # pl.ylabel("Time (s)") # # # fig = pl.figure() # model_df_correctness_as_function_of_coherence, \ # model_df_inter_bout_interval_as_function_of_coherence, \ # model_df_binned_correctness, \ # model_df_binned_same_direction, \ # model_df_binned_features_heading_angle_change_histograms, \ # model_df_binned_features_inter_bout_interval_histograms, \ # model_df_gmm_fitting_results = get_model_result(X[-1, i5]) # fig.suptitle("Compromise between all four error functions") # pl.subplot(221) # pl.plot([0, 25, 50, 100], target_df_correctness_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_correctness_as_function_of_coherence.values, 'o--', color='C5') # pl.xlabel("Coherence (%)") # pl.ylabel("Correctness (%)") # pl.subplot(222) # pl.plot([0, 25, 50, 100], target_df_inter_bout_interval_as_function_of_coherence.values, 'o-', color='black') # pl.plot([0, 25, 50, 100], model_df_inter_bout_interval_as_function_of_coherence.values, 'o--', color='C5') # pl.xlabel("Coherence (%)") # pl.ylabel("Inter-bout interval (s)") # pl.subplot(223) # for i in range(4): # pl.plot(target_df_binned_correctness.loc[i, :].droplevel("stim"), 'o-', color='black') # pl.plot(model_df_binned_correctness.loc[i, :].droplevel("stim"), 'o--', color='C5') # pl.xlabel("Correctness (%)") # pl.ylabel("Time (s)") # pl.subplot(224) # pl.plot(target_df_binned_same_direction, 'o-', color='black') # pl.plot(model_df_binned_same_direction, 'o--', color='C5') # pl.xlabel("Time since last bout (s)") # pl.ylabel("Correctness (%)") fig = pl.figure() model_df_correctness_as_function_of_coherence, \ model_df_inter_bout_interval_as_function_of_coherence, \ model_df_binned_correctness, \ model_df_binned_same_direction, \ model_df_binned_features_heading_angle_change_histograms, \ model_df_binned_features_inter_bout_interval_histograms, \ model_df_gmm_fitting_results = get_model_result(X[-1, i6]) fig.suptitle("Compromise between all five error functions") pl.subplot(231) pl.plot([0, 25, 50, 100], target_df_correctness_as_function_of_coherence.values, 'o-', color='black') pl.plot([0, 25, 50, 100], model_df_correctness_as_function_of_coherence.values, 'o--', color='C6') pl.xlabel("Coherence (%)") pl.ylabel("Correctness (%)") pl.subplot(232) pl.plot([0, 25, 50, 100], target_df_inter_bout_interval_as_function_of_coherence.values, 'o-', color='black') pl.plot([0, 25, 50, 100], model_df_inter_bout_interval_as_function_of_coherence.values, 'o--', color='C6') pl.xlabel("Coherence (%)") pl.ylabel("Inter-bout interval (s)") pl.subplot(233) for i in range(4): pl.plot(target_df_binned_correctness.loc[i, :].droplevel("stim"), 'o-', color='black') pl.plot(model_df_binned_correctness.loc[i, :].droplevel("stim"), 'o--', color='C6') pl.xlabel("Time (s)") pl.ylabel("Correctness (%)") pl.subplot(234) pl.plot(target_df_binned_same_direction, 'o-', color='black') pl.plot(model_df_binned_same_direction, 'o--', color='C6') pl.xlabel("Time since last bout (s)") pl.ylabel("Correctness (%)") # pl.subplot(235) # pl.plot(target_df_gmm_fitting_results.index*25, target_df_gmm_fitting_results["w_left"].values, '-o', color='black', label='s_left') # pl.plot(target_df_gmm_fitting_results.index*25, target_df_gmm_fitting_results["w_center"].values, '-o', color='black', label='s_center') # pl.plot(target_df_gmm_fitting_results.index*25, target_df_gmm_fitting_results["w_right"].values, '-o', color='black', label='s_right') # # pl.plot(model_df_gmm_fitting_results.index*25, model_df_gmm_fitting_results["w_left"].values, '--o', color='C6', label='s_left') # pl.plot(model_df_gmm_fitting_results.index*25, model_df_gmm_fitting_results["w_center"].values, '--o', color='C6', label='s_center') # pl.plot(model_df_gmm_fitting_results.index*25, model_df_gmm_fitting_results["w_right"].values, '--o', color='C6', label='s_right') # pl.xlabel("Coherence (%)") # pl.ylabel("Weight") # pl.legend() pl.subplot(235) for i in range(4): pl.plot(target_df_binned_features_heading_angle_change_histograms.loc[i, :].droplevel("stim"), color=f"black") pl.plot(model_df_binned_features_heading_angle_change_histograms.loc[i, :].droplevel("stim"), color=f"C6", linestyle='--') pl.xlabel("Heading angle change") pl.ylabel("Probability") pl.show() found_parameters = [] for repeat in range(12): for genotype in ["wt", "het", "hom"]: X = np.load(root_path / f"leaky_integrator_model2_X_{genotype}_{repeat}.npy") F = np.load(root_path / f"leaky_integrator_model2_F_{genotype}_{repeat}.npy") for i in range(5): #F[-1, :, i] = F[-1, :, i] / np.median(F[-1, :, i]) F[-1, :, i] = F[-1, :, i] / np.percentile(F[-1, :, i], 75) #i6 = np.argmin(F[-1, :, 0] + F[-1, :, 1] + 5 * F[-1, :, 3] + F[-1, :, 5] + 5 * F[-1, :, 6]) #i6 = np.argmin(F[-1, :, 0] + 5 * F[-1, :, 1] + 20 * F[-1, :, 4] + F[-1, :, 5] + 5 * F[-1, :, 6]) i6 = np.argmin(F[-1, :, 0] + 3 * F[-1, :, 1] + F[-1, :, 2] + F[-1, :, 3] + F[-1, :, 4]) #i6 = np.argmin(F[-1, :, 0] + 2 * F[-1, :, 1] + F[-1, :, 2] + 3 * F[-1, :, 3] + F[-1, :, 5] + F[-1, :, 6]) #i6 = np.argmin(F[-1, :, 0] + F[-1, :, 1] * 500 + F[-1, :, 3] + F[-1, :, 5] * 0.25 + F[-1, :, 6] * 500) #i6 = np.argmin(F[-1, :, 0] + F[-1, :, 1] * 2500 + F[-1, :, 3] * 5 + F[-1, :, 5] * 0.5 + F[-1, :, 6] * 1500) #i6 = np.argmin(F[-1, :, 0]*500 + F[-1, :, 1]*2500 + F[-1, :, 3]*50 + F[-1, :, 5]*0.5 + F[-1, :, 6]*4500) found_parameters.append([genotype, repeat, 49] + list(X[-1, i6, :])) df = pd.DataFrame(found_parameters, columns=["genotype", "repeat", "gen", "tau", "sigma", "T", "p_below", "p_above"]).astype(dtype={"repeat": "int64", "gen": "int64"}, copy=False) df.set_index(["genotype", 'repeat', 'gen'], inplace=True) df.sort_index(inplace=True) df.to_hdf(root_path / "found_parameters.h5", key="parameters", complevel=9)

47.092975

185

0.660641

import pylab as pl from get_fish_info import get_fish_info from fit_integrator_model import get_model_result, get_target_result import numpy as np from pathlib import Path import gmm_model_fit import pandas as pd from pymoo.factory import get_problem, get_visualization, get_decomposition root_path = Path("/Users/arminbahl/Desktop/mutant_behavior_data/surrogate_fish1") df = pd.read_hdf(root_path / "all_data.h5", key="all_bouts") genotype = "hom" target_df_correctness_as_function_of_coherence, \ target_df_inter_bout_interval_as_function_of_coherence, \ target_df_binned_correctness, \ target_df_binned_same_direction, \ target_df_binned_features_heading_angle_change_histograms, \ target_df_binned_features_inter_bout_interval_histograms, \ target_df_gmm_fitting_results = get_target_result(root_path, genotype) medianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick') errornames = ["Error: 'Correctness as function of coherence'", "Error: 'Inter-bout interval as function of coherence'", "Error: 'Binned correctness at 25, 50, 100 %'", "Error: 'Binned same direction'", "Error: 'Histogram weights'"] repeat = 1 X = np.load(root_path / f"leaky_integrator_model2_X_{genotype}_{repeat}.npy") F = np.load(root_path / f"leaky_integrator_model2_F_{genotype}_{repeat}.npy") from pymoo.factory import get_decision_making, get_reference_directions for i in range(5): F[-1, :, i] = F[-1, :, i] / np.percentile(F[-1, :, i], 75) fig = pl.figure() model_df_correctness_as_function_of_coherence, \ model_df_inter_bout_interval_as_function_of_coherence, \ model_df_binned_correctness, \ model_df_binned_same_direction, \ model_df_binned_features_heading_angle_change_histograms, \ model_df_binned_features_inter_bout_interval_histograms, \ model_df_gmm_fitting_results = get_model_result(X[-1, i6]) fig.suptitle("Compromise between all five error functions") pl.subplot(231) pl.plot([0, 25, 50, 100], target_df_correctness_as_function_of_coherence.values, 'o-', color='black') pl.plot([0, 25, 50, 100], model_df_correctness_as_function_of_coherence.values, 'o--', color='C6') pl.xlabel("Coherence (%)") pl.ylabel("Correctness (%)") pl.subplot(232) pl.plot([0, 25, 50, 100], target_df_inter_bout_interval_as_function_of_coherence.values, 'o-', color='black') pl.plot([0, 25, 50, 100], model_df_inter_bout_interval_as_function_of_coherence.values, 'o--', color='C6') pl.xlabel("Coherence (%)") pl.ylabel("Inter-bout interval (s)") pl.subplot(233) for i in range(4): pl.plot(target_df_binned_correctness.loc[i, :].droplevel("stim"), 'o-', color='black') pl.plot(model_df_binned_correctness.loc[i, :].droplevel("stim"), 'o--', color='C6') pl.xlabel("Time (s)") pl.ylabel("Correctness (%)") pl.subplot(234) pl.plot(target_df_binned_same_direction, 'o-', color='black') pl.plot(model_df_binned_same_direction, 'o--', color='C6') pl.xlabel("Time since last bout (s)") pl.ylabel("Correctness (%)") pl.subplot(235) for i in range(4): pl.plot(target_df_binned_features_heading_angle_change_histograms.loc[i, :].droplevel("stim"), color=f"black") pl.plot(model_df_binned_features_heading_angle_change_histograms.loc[i, :].droplevel("stim"), color=f"C6", linestyle='--') pl.xlabel("Heading angle change") pl.ylabel("Probability") pl.show() found_parameters = [] for repeat in range(12): for genotype in ["wt", "het", "hom"]: X = np.load(root_path / f"leaky_integrator_model2_X_{genotype}_{repeat}.npy") F = np.load(root_path / f"leaky_integrator_model2_F_{genotype}_{repeat}.npy") for i in range(5): F[-1, :, i] = F[-1, :, i] / np.percentile(F[-1, :, i], 75) i6 = np.argmin(F[-1, :, 0] + 3 * F[-1, :, 1] + F[-1, :, 2] + F[-1, :, 3] + F[-1, :, 4]) found_parameters.append([genotype, repeat, 49] + list(X[-1, i6, :])) df = pd.DataFrame(found_parameters, columns=["genotype", "repeat", "gen", "tau", "sigma", "T", "p_below", "p_above"]).astype(dtype={"repeat": "int64", "gen": "int64"}, copy=False) df.set_index(["genotype", 'repeat', 'gen'], inplace=True) df.sort_index(inplace=True) df.to_hdf(root_path / "found_parameters.h5", key="parameters", complevel=9)

true

7907de8a2a7c5b8051e6416660fe0ab2b6c12acc

12,375

py

Python

src/analysis/TrainMood.py

pjshu/QQZoneMood

b637e4f26fa34aed415c326a50c708a91d79ec19

[ "MIT" ]

487

2018-12-12T10:53:34.000Z

2022-03-27T08:38:42.000Z

src/analysis/TrainMood.py

DanielisLearning/QQZoneMood

bc949855271a4d9944e1501599755cfdfdb8cfd6

[ "MIT" ]

18

2019-04-07T11:32:13.000Z

2021-04-26T13:07:12.000Z

src/analysis/TrainMood.py

DanielisLearning/QQZoneMood

bc949855271a4d9944e1501599755cfdfdb8cfd6

[ "MIT" ]

126

2018-12-12T10:54:24.000Z

2022-03-13T16:32:36.000Z

from src.analysis.QQZoneAnalysis import QQZoneAnalysis import json from src.util.constant import BASE_DIR from src.util.util import get_mktime2 import pandas as pd import re from src.analysis.SentimentClassify import SentimentClassify class TrainMood(QQZoneAnalysis): """ 生成各种训练需要的数据集 """ def __init__(self, use_redis=False, debug=True, file_name_head=''): QQZoneAnalysis.__init__(self, use_redis=use_redis, debug=debug, username=file_name_head, analysis_friend=False) TRAIN_BASE_DIR = BASE_DIR + file_name_head + '/data/train/' self.MOOD_DATA_SCORE_FILE_NAME = TRAIN_BASE_DIR + 'score_mood_data.csv' self.RE_DO_SENTIMENT_FILE_NAME = TRAIN_BASE_DIR + 're_do_mood_data.csv' self.TEXT_LABEL_TRAIN_DATA = TRAIN_BASE_DIR + 'mood_text.csv' self.TRAIN_DATA_AFTER_CLASSIFIC = TRAIN_BASE_DIR + 'mood_classific.csv' self.TEXT_LABEL_RESULT_TRAIN_DATA = '../data/train3/text_' + file_name_head + '_label.csv' self.TEXT_CLASSIFICATION_DATA_SET = '../data/train/' self.FINAL_RESULT_TRAIN_DATA = '../data/train/' + file_name_head + '_final_train.csv' self.mood_data_df = pd.read_csv(self.MOOD_DATA_FILE_NAME) self.IMAGE_OBJECT_FILE_NAME = '../data/train3/' + file_name_head + '_image_object.csv' self.MOOD_DATA_AFTER_OBJECT = '../data/train/' + file_name_head + '_after_object.csv' self.sc = SentimentClassify() self.mood_data_df['score'] = '-1' self.label_dict = {'1': '旅游与运动', '2': '爱情与家庭', '3': '学习与工作', '4': '广告', '5': '生活日常', '6': '其他', '7': '人生感悟'} self.label_dict_reverse = {v: k for k, v in self.label_dict.items()} def calculate_score_for_each_mood(self): """ 利用谷歌nima模型对图片进行评分 paper: https://arxiv.org/abs/1709.05424 pytorch model: https://github.com/truskovskiyk/nima.pytorch.git 计算每条说说中图片的平均分对于没有图片的按均值进行填充 :return: """ # nima模型预测结果文件 self.IMAGE_SCORE_FILE_PATH = '/Users/maicius/code/nima.pytorch/nima/result_dict.json' with open(self.IMAGE_SCORE_FILE_PATH, 'r', encoding='utf-8') as r: self.image_score_dict = json.load(r) self.image_score_df = pd.DataFrame(self.image_score_dict) mean_score = self.image_score_df[self.image_score_df['score'] != -1].mean()[0] self.image_score_df.loc[self.image_score_df.score == -1, 'score'] = mean_score tid_list = self.mood_data_df['tid'].values for tid in tid_list: scores = self.image_score_df[self.image_score_df.image.str.contains(tid)].score if len(scores) > 0: self.mood_data_df.loc[self.mood_data_df.tid == tid, 'score'] = round(scores.mean(), 2) self.mood_data_df.fillna(mean_score) print("score shape:", self.mood_data_df.shape) self.mood_data_df.to_csv(self.MOOD_DATA_SCORE_FILE_NAME) def calculate_send_time(self): """ 计算每条说说的发送时间分为以下五种类型： 0.午夜：0点-4点 1.凌晨：4点-8点 2.上午：8点-12点 3.下午：12点-16点 4.傍晚：16点-20点 5.晚上：20点-24点 :return: """ day_begin_time = self.mood_data_df['time'].apply(lambda x: get_mktime2(x)) day_time_stamp = self.mood_data_df['time_stamp'] time_diff = day_time_stamp - day_begin_time # 四个小时的时间差 time_step = 60 * 60 * 4 time_state = time_diff.apply(lambda x: x // time_step) self.mood_data_df['time_state'] = time_state print('send time:', self.mood_data_df.shape) def export_df_after_clean(self): try: self.mood_data_df.drop(['Unnamed: 0'], axis=1, inplace=True) except BaseException as e: print(e) self.mood_data_df.to_csv(self.MOOD_DATA_SCORE_FILE_NAME) def export_train_text(self): train_text = pd.read_csv(self.label_path + 'result/' + 'final.csv') train_text = train_text[['type', 'content']] train_text.columns = ['Y', 'content'] train_text.fillna('空', inplace=True) train_text.Y = train_text.Y.apply(lambda x: self.label_dict[str(int(x))]) train_text.content = train_text.content.apply(lambda x: str(x).replace('\n', '')) train_text.content = train_text.content.apply(lambda x: str(x).replace(' ', '')) train_text.content = train_text.content.apply(lambda x: remove_waste_emoji(x)) train_text.fillna('空', inplace=True) train_dataset = train_text.sample(frac=0.8) val_dataset = train_text.sample(frac=0.3) test_dataset = train_text.sample(frac=0.3) self.print_label_dict(train_text) self.print_label_dict(train_dataset) self.print_label_dict(val_dataset) self.print_label_dict(test_dataset) train_dataset.to_csv(self.TEXT_CLASSIFICATION_DATA_SET + 'text_train.csv', sep='\t', index=None, header=None) val_dataset.to_csv(self.TEXT_CLASSIFICATION_DATA_SET + 'text_val.csv', sep='\t', index=None, header=None) test_dataset.to_csv(self.TEXT_CLASSIFICATION_DATA_SET + 'text_test.csv', sep='\t', index=None, header=None) self.calculate_avg_length(train_text) # train_text.to_csv(self.TEXT_LABEL_TRAIN_DATA, sep=' ', index=None, header=None) def calculate_avg_length(self, data_df): num = data_df.shape[0] content_list = data_df.content.sum() print(len(content_list) / num) def calculate_sentiment(self): print("Begin to calculate sentiment...") self.mood_data_df.content = self.mood_data_df.content.apply(lambda x: str(x).replace('\n', '')) self.mood_data_df.content = self.mood_data_df.content.apply(lambda x: str(x).replace(' ', '')) self.mood_data_df.content = self.mood_data_df.content.apply(lambda x: remove_waste_emoji(str(x))) # 使用apply会导致超过qps限额 # sentiments = self.mood_data_df['content'].apply(lambda x: self.sc.get_sentiment_for_text(x)) # self.mood_data_df['sentiment'] = sentiments self.mood_data_df['sentiments'] = -1 for i in range(self.mood_data_df.shape[0]): content = self.mood_data_df.loc[i, 'content'] sentiment = self.sc.get_sentiment_for_text(content) print('content:', content, 'senti:', sentiment) self.mood_data_df.loc[i, 'sentiments'] = sentiment self.mood_data_df = self.re_do_sentiment(self.mood_data_df) try: self.mood_data_df.drop(['Unnamed: 0'], axis=1, inplace=True) except BaseException as e: print(e) self.mood_data_df.to_csv('after_sentiment.csv') print("text sentiment:", self.mood_data_df.shape) def print_label_dict(self, data_df): for item in self.label_dict.values(): print(item, data_df.loc[data_df.Y == item, :].shape[0]) print('==========') def re_do_sentiment(self, data_df): # data_df = pd.read_csv(self.MOOD_DATA_SCORE_FILE_NAME) for i in range(data_df.shape[0]): sentiment = data_df.loc[i, 'sentiments'] content = data_df.loc[i, 'content'] if sentiment == -1: content = content.replace('\u2207', '') content = content.replace('\ue40c', '') content = content.replace('\ue412', '') content = content.replace('\ue056', '') sentiment = self.sc.get_sentiment_for_text(str(content)) data_df.loc[i, 'sentiments'] = sentiment data_df.to_csv(self.RE_DO_SENTIMENT_FILE_NAME) return data_df def export_classification_data(self): """ 导出待分类待的数据 :return: """ data = pd.read_csv(self.RE_DO_SENTIMENT_FILE_NAME) data_df = data[['content']] data_df['Y'] = '旅游与运动' data_df.fillna('空', inplace=True) columns = ['Y', 'content'] data_df = data_df.ix[:, columns] print(data_df.shape) data_df.to_csv(self.TEXT_CLASSIFICATION_DATA_SET + 'text_maicius.csv', sep='\t') def combine_text_type_data(self): data = pd.read_csv(self.MOOD_DATA_SCORE_FILE_NAME) print('mood_after_object_data:', data.shape) label = pd.read_csv(self.TEXT_LABEL_RESULT_TRAIN_DATA) print('label data:', label.shape) label_y = label['Y'] data['type'] = label_y data.to_csv(self.TRAIN_DATA_AFTER_CLASSIFIC) def attach_image_object_for_each_mood(self): with open('qq_big_image.json', 'r', encoding='utf-8') as r: data = json.load(r) with open('category.json', 'r', encoding='utf-8') as r: category = json.load(r) category_df = pd.DataFrame(category) image_object_df = pd.DataFrame( columns=['tid', 'person', 'vehicle', 'outdoor', 'animal', 'accessory', 'sports', 'kitchen', 'food', 'furniture', 'electronic', 'appliance', 'indoor']) i = 0 for key, value in data.items(): tid = key.split('--')[0].split('/')[-1] if image_object_df.loc[image_object_df.tid == tid].shape[0] == 0: image_object_df.loc[i, 'tid'] = tid i +=1 for item in value: item = item.split(' ')[0] super_cate = category_df.loc[category_df.name.str.contains(item), 'supercategory'] if len(super_cate) > 0: print(super_cate) image_object_df.loc[image_object_df.tid == tid, super_cate.values[0]] = 1 image_object_df.fillna(0, inplace=True) image_object_df['vector'] = 0 image_object_df['vector'] = image_object_df['tid'].apply(lambda x: image_object_df.loc[image_object_df.tid == x,'person':].values[0]) image_object_df.to_csv(self.IMAGE_OBJECT_FILE_NAME) def combine_image_object(self): image_object_df = pd.read_csv(self.IMAGE_OBJECT_FILE_NAME) mood_data_df = pd.read_csv(self.TRAIN_DATA_AFTER_CLASSIFIC) try: mood_data_df.drop(['vector'], axis=1, inplace=True) except BaseException as e: print(e) image_object = image_object_df[['tid', 'vector']] print(image_object_df.shape, mood_data_df.shape) result = pd.merge(mood_data_df, image_object, on='tid', how='left') print(result.shape) result.to_csv(self.MOOD_DATA_AFTER_OBJECT) def export_final_train_data(self): data = pd.read_csv(self.MOOD_DATA_AFTER_OBJECT) train = data[['n_E', 'score', 'time_state', 'sentiments', 'type', 'vector']] train = train.loc[6:, :] self.mean_score = self.image_score_df[self.image_score_df['score'] != -1].mean()[0] train.score = train['score'].apply(lambda x: self.change_neg_image_score(x)) train.type = train['type'].map(self.label_dict_reverse) train.vector.fillna('[0 0 0 0 0 0 0 0 0 0 0 0 0]', inplace=True) train.vector = train.vector.apply(lambda x: self.change_vector_to_int(x)) train.sort_values(by='n_E', inplace=True, ascending=False) train.to_csv(self.FINAL_RESULT_TRAIN_DATA) def change_neg_image_score(self, score): if score == -1: return self.mean_score else: return score def change_vector_to_int(self, vector): vector = re.findall(re.compile('[0-9]'), vector) str_vector = "".join(vector) sum = 0 length = len(str_vector) for i in range(length): sum += int(str_vector[i]) **(length - 1) return sum def remove_waste_emoji(text): text = re.subn(re.compile('\[em\].*?\[\/em\]'), '', text)[0] text = re.subn(re.compile('@\{.*?\}'), '', text)[0] return text if __name__ == '__main__': train = TrainMood(use_redis=True, debug=True, file_name_head='maicius') # train.calculate_score_for_each_mood() # train.calculate_send_time() # train.calculate_sentiment() # train.export_df_after_clean() train.export_train_text() # train.export_classification_data() # train.attach_image_object_for_each_mood() # train.combine_text_type_data() # train.combine_image_object() # train.export_final_train_data()

43.269231

141

0.627232

from src.analysis.QQZoneAnalysis import QQZoneAnalysis import json from src.util.constant import BASE_DIR from src.util.util import get_mktime2 import pandas as pd import re from src.analysis.SentimentClassify import SentimentClassify class TrainMood(QQZoneAnalysis): def __init__(self, use_redis=False, debug=True, file_name_head=''): QQZoneAnalysis.__init__(self, use_redis=use_redis, debug=debug, username=file_name_head, analysis_friend=False) TRAIN_BASE_DIR = BASE_DIR + file_name_head + '/data/train/' self.MOOD_DATA_SCORE_FILE_NAME = TRAIN_BASE_DIR + 'score_mood_data.csv' self.RE_DO_SENTIMENT_FILE_NAME = TRAIN_BASE_DIR + 're_do_mood_data.csv' self.TEXT_LABEL_TRAIN_DATA = TRAIN_BASE_DIR + 'mood_text.csv' self.TRAIN_DATA_AFTER_CLASSIFIC = TRAIN_BASE_DIR + 'mood_classific.csv' self.TEXT_LABEL_RESULT_TRAIN_DATA = '../data/train3/text_' + file_name_head + '_label.csv' self.TEXT_CLASSIFICATION_DATA_SET = '../data/train/' self.FINAL_RESULT_TRAIN_DATA = '../data/train/' + file_name_head + '_final_train.csv' self.mood_data_df = pd.read_csv(self.MOOD_DATA_FILE_NAME) self.IMAGE_OBJECT_FILE_NAME = '../data/train3/' + file_name_head + '_image_object.csv' self.MOOD_DATA_AFTER_OBJECT = '../data/train/' + file_name_head + '_after_object.csv' self.sc = SentimentClassify() self.mood_data_df['score'] = '-1' self.label_dict = {'1': '旅游与运动', '2': '爱情与家庭', '3': '学习与工作', '4': '广告', '5': '生活日常', '6': '其他', '7': '人生感悟'} self.label_dict_reverse = {v: k for k, v in self.label_dict.items()} def calculate_score_for_each_mood(self): self.IMAGE_SCORE_FILE_PATH = '/Users/maicius/code/nima.pytorch/nima/result_dict.json' with open(self.IMAGE_SCORE_FILE_PATH, 'r', encoding='utf-8') as r: self.image_score_dict = json.load(r) self.image_score_df = pd.DataFrame(self.image_score_dict) mean_score = self.image_score_df[self.image_score_df['score'] != -1].mean()[0] self.image_score_df.loc[self.image_score_df.score == -1, 'score'] = mean_score tid_list = self.mood_data_df['tid'].values for tid in tid_list: scores = self.image_score_df[self.image_score_df.image.str.contains(tid)].score if len(scores) > 0: self.mood_data_df.loc[self.mood_data_df.tid == tid, 'score'] = round(scores.mean(), 2) self.mood_data_df.fillna(mean_score) print("score shape:", self.mood_data_df.shape) self.mood_data_df.to_csv(self.MOOD_DATA_SCORE_FILE_NAME) def calculate_send_time(self): day_begin_time = self.mood_data_df['time'].apply(lambda x: get_mktime2(x)) day_time_stamp = self.mood_data_df['time_stamp'] time_diff = day_time_stamp - day_begin_time time_step = 60 * 60 * 4 time_state = time_diff.apply(lambda x: x // time_step) self.mood_data_df['time_state'] = time_state print('send time:', self.mood_data_df.shape) def export_df_after_clean(self): try: self.mood_data_df.drop(['Unnamed: 0'], axis=1, inplace=True) except BaseException as e: print(e) self.mood_data_df.to_csv(self.MOOD_DATA_SCORE_FILE_NAME) def export_train_text(self): train_text = pd.read_csv(self.label_path + 'result/' + 'final.csv') train_text = train_text[['type', 'content']] train_text.columns = ['Y', 'content'] train_text.fillna('空', inplace=True) train_text.Y = train_text.Y.apply(lambda x: self.label_dict[str(int(x))]) train_text.content = train_text.content.apply(lambda x: str(x).replace('\n', '')) train_text.content = train_text.content.apply(lambda x: str(x).replace(' ', '')) train_text.content = train_text.content.apply(lambda x: remove_waste_emoji(x)) train_text.fillna('空', inplace=True) train_dataset = train_text.sample(frac=0.8) val_dataset = train_text.sample(frac=0.3) test_dataset = train_text.sample(frac=0.3) self.print_label_dict(train_text) self.print_label_dict(train_dataset) self.print_label_dict(val_dataset) self.print_label_dict(test_dataset) train_dataset.to_csv(self.TEXT_CLASSIFICATION_DATA_SET + 'text_train.csv', sep='\t', index=None, header=None) val_dataset.to_csv(self.TEXT_CLASSIFICATION_DATA_SET + 'text_val.csv', sep='\t', index=None, header=None) test_dataset.to_csv(self.TEXT_CLASSIFICATION_DATA_SET + 'text_test.csv', sep='\t', index=None, header=None) self.calculate_avg_length(train_text) def calculate_avg_length(self, data_df): num = data_df.shape[0] content_list = data_df.content.sum() print(len(content_list) / num) def calculate_sentiment(self): print("Begin to calculate sentiment...") self.mood_data_df.content = self.mood_data_df.content.apply(lambda x: str(x).replace('\n', '')) self.mood_data_df.content = self.mood_data_df.content.apply(lambda x: str(x).replace(' ', '')) self.mood_data_df.content = self.mood_data_df.content.apply(lambda x: remove_waste_emoji(str(x))) self.mood_data_df['sentiments'] = -1 for i in range(self.mood_data_df.shape[0]): content = self.mood_data_df.loc[i, 'content'] sentiment = self.sc.get_sentiment_for_text(content) print('content:', content, 'senti:', sentiment) self.mood_data_df.loc[i, 'sentiments'] = sentiment self.mood_data_df = self.re_do_sentiment(self.mood_data_df) try: self.mood_data_df.drop(['Unnamed: 0'], axis=1, inplace=True) except BaseException as e: print(e) self.mood_data_df.to_csv('after_sentiment.csv') print("text sentiment:", self.mood_data_df.shape) def print_label_dict(self, data_df): for item in self.label_dict.values(): print(item, data_df.loc[data_df.Y == item, :].shape[0]) print('==========') def re_do_sentiment(self, data_df): for i in range(data_df.shape[0]): sentiment = data_df.loc[i, 'sentiments'] content = data_df.loc[i, 'content'] if sentiment == -1: content = content.replace('\u2207', '') content = content.replace('\ue40c', '') content = content.replace('\ue412', '') content = content.replace('\ue056', '') sentiment = self.sc.get_sentiment_for_text(str(content)) data_df.loc[i, 'sentiments'] = sentiment data_df.to_csv(self.RE_DO_SENTIMENT_FILE_NAME) return data_df def export_classification_data(self): data = pd.read_csv(self.RE_DO_SENTIMENT_FILE_NAME) data_df = data[['content']] data_df['Y'] = '旅游与运动' data_df.fillna('空', inplace=True) columns = ['Y', 'content'] data_df = data_df.ix[:, columns] print(data_df.shape) data_df.to_csv(self.TEXT_CLASSIFICATION_DATA_SET + 'text_maicius.csv', sep='\t') def combine_text_type_data(self): data = pd.read_csv(self.MOOD_DATA_SCORE_FILE_NAME) print('mood_after_object_data:', data.shape) label = pd.read_csv(self.TEXT_LABEL_RESULT_TRAIN_DATA) print('label data:', label.shape) label_y = label['Y'] data['type'] = label_y data.to_csv(self.TRAIN_DATA_AFTER_CLASSIFIC) def attach_image_object_for_each_mood(self): with open('qq_big_image.json', 'r', encoding='utf-8') as r: data = json.load(r) with open('category.json', 'r', encoding='utf-8') as r: category = json.load(r) category_df = pd.DataFrame(category) image_object_df = pd.DataFrame( columns=['tid', 'person', 'vehicle', 'outdoor', 'animal', 'accessory', 'sports', 'kitchen', 'food', 'furniture', 'electronic', 'appliance', 'indoor']) i = 0 for key, value in data.items(): tid = key.split('--')[0].split('/')[-1] if image_object_df.loc[image_object_df.tid == tid].shape[0] == 0: image_object_df.loc[i, 'tid'] = tid i +=1 for item in value: item = item.split(' ')[0] super_cate = category_df.loc[category_df.name.str.contains(item), 'supercategory'] if len(super_cate) > 0: print(super_cate) image_object_df.loc[image_object_df.tid == tid, super_cate.values[0]] = 1 image_object_df.fillna(0, inplace=True) image_object_df['vector'] = 0 image_object_df['vector'] = image_object_df['tid'].apply(lambda x: image_object_df.loc[image_object_df.tid == x,'person':].values[0]) image_object_df.to_csv(self.IMAGE_OBJECT_FILE_NAME) def combine_image_object(self): image_object_df = pd.read_csv(self.IMAGE_OBJECT_FILE_NAME) mood_data_df = pd.read_csv(self.TRAIN_DATA_AFTER_CLASSIFIC) try: mood_data_df.drop(['vector'], axis=1, inplace=True) except BaseException as e: print(e) image_object = image_object_df[['tid', 'vector']] print(image_object_df.shape, mood_data_df.shape) result = pd.merge(mood_data_df, image_object, on='tid', how='left') print(result.shape) result.to_csv(self.MOOD_DATA_AFTER_OBJECT) def export_final_train_data(self): data = pd.read_csv(self.MOOD_DATA_AFTER_OBJECT) train = data[['n_E', 'score', 'time_state', 'sentiments', 'type', 'vector']] train = train.loc[6:, :] self.mean_score = self.image_score_df[self.image_score_df['score'] != -1].mean()[0] train.score = train['score'].apply(lambda x: self.change_neg_image_score(x)) train.type = train['type'].map(self.label_dict_reverse) train.vector.fillna('[0 0 0 0 0 0 0 0 0 0 0 0 0]', inplace=True) train.vector = train.vector.apply(lambda x: self.change_vector_to_int(x)) train.sort_values(by='n_E', inplace=True, ascending=False) train.to_csv(self.FINAL_RESULT_TRAIN_DATA) def change_neg_image_score(self, score): if score == -1: return self.mean_score else: return score def change_vector_to_int(self, vector): vector = re.findall(re.compile('[0-9]'), vector) str_vector = "".join(vector) sum = 0 length = len(str_vector) for i in range(length): sum += int(str_vector[i]) **(length - 1) return sum def remove_waste_emoji(text): text = re.subn(re.compile('\[em\].*?\[\/em\]'), '', text)[0] text = re.subn(re.compile('@\{.*?\}'), '', text)[0] return text if __name__ == '__main__': train = TrainMood(use_redis=True, debug=True, file_name_head='maicius') train.export_train_text()

true

7907deac94beb95385049e8d105c2829b468beae

1,944

py

Python

AnomalyDetection/DB.py

Py-Contributors/Hands-on-Machine-learning-with-Scikit-learn-Tensorflow-and-Keras

cbb392b85e82d135adcd9591c43bfb4adaa73972

[ "MIT" ]

4

2020-09-29T11:04:08.000Z

2020-10-31T19:35:24.000Z

AnomalyDetection/DB.py

codePerfectPlus/Hands-on-Machine-learning-with-Scikit-learn-Tensorflow-and-Keras

cbb392b85e82d135adcd9591c43bfb4adaa73972

[ "MIT" ]

4

2020-10-11T03:50:01.000Z

2020-11-04T08:24:23.000Z

AnomalyDetection/DB.py

Py-Contributors/Hands-on-Machine-learning-with-Scikit-learn-Tensorflow-and-Keras

cbb392b85e82d135adcd9591c43bfb4adaa73972

[ "MIT" ]

3

2020-09-27T07:43:12.000Z

2020-11-02T08:11:40.000Z

import numpy as np import pandas as pd %matplotlib auto import matplotlib.pyplot as plt import seaborn as sns from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.svm import SVC from matplotlib.colors import ListedColormap from sklearn.covariance import EllipticEnvelope from sklearn.cluster import KMeans from sklearn. plt.style.use("fivethirtyeight") fig=plt.figure(figsize=(12,15)) data=pd.read_csv("Social_Network_Ads.csv") data=data.iloc[:,2:] treeclass=RandomForestClassifier(n_estimators=100,max_depth=10) X,y=data.iloc[:,:-1].values,data.iloc[:,-1].values def plotting_decision_(X,Y,CL): X=StandardScaler().fit_transform(X) X_train,x_test,Y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0) xx_min,xx_max=X[:,0].min()-0.5,X[:,0].max()+0.6 xx,yy=np.meshgrid(np.arange(xx_min,xx_max,0.2),np.arange(xx_min,xx_max,0.2)) cmap_bright=ListedColormap(["red","azure"]) cl=CL() cl.fit(X_train,Y_train) score=cl.predict(x_test) Z=cl.decision_function(np.c_[xx.ravel(),yy.ravel()]) Z=Z.reshape(xx.shape) plt.contour(xx,yy,Z,cmap=plt.cm.jet) plt.scatter(X_train[:,0],X_train[:,1],c=Y_train,cmap=cmap_bright) plt.text(xx.max()-.3,xx.min()+.3,(np.mean(score)),size=15,horizontalalignment="right") #sns.relplot(x="Age",y="EstimatedSalary",data=data,hue="Purchased") #sns.boxplot(x=data["Purchased"],y=data["EstimatedSalary"],whis=2,saturation=0.6) #from sklearn.ensemble import IsolationForest #IF=IsolationForest(n_estimators=100,bootstrap=False) #IF.fit(X[:,0].reshape(-1,1)) #xx=np.linspace(X[:,0].min()-5,X[:,0].max()+5,len(data)).reshape(-1,1) #outlier=IF.predict(xx) #anomaly_score=IF.decision_function(xx) #plt.plot(xx,anomaly_score,label="automated")

30.375

90

0.739712

import numpy as np import pandas as pd %matplotlib auto import matplotlib.pyplot as plt import seaborn as sns from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.svm import SVC from matplotlib.colors import ListedColormap from sklearn.covariance import EllipticEnvelope from sklearn.cluster import KMeans from sklearn. plt.style.use("fivethirtyeight") fig=plt.figure(figsize=(12,15)) data=pd.read_csv("Social_Network_Ads.csv") data=data.iloc[:,2:] treeclass=RandomForestClassifier(n_estimators=100,max_depth=10) X,y=data.iloc[:,:-1].values,data.iloc[:,-1].values def plotting_decision_(X,Y,CL): X=StandardScaler().fit_transform(X) X_train,x_test,Y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0) xx_min,xx_max=X[:,0].min()-0.5,X[:,0].max()+0.6 xx,yy=np.meshgrid(np.arange(xx_min,xx_max,0.2),np.arange(xx_min,xx_max,0.2)) cmap_bright=ListedColormap(["red","azure"]) cl=CL() cl.fit(X_train,Y_train) score=cl.predict(x_test) Z=cl.decision_function(np.c_[xx.ravel(),yy.ravel()]) Z=Z.reshape(xx.shape) plt.contour(xx,yy,Z,cmap=plt.cm.jet) plt.scatter(X_train[:,0],X_train[:,1],c=Y_train,cmap=cmap_bright) plt.text(xx.max()-.3,xx.min()+.3,(np.mean(score)),size=15,horizontalalignment="right")

false

true

7907deeedee248487711662c9648165afa8d28f6

4,606

py

Python

tech_project/lib/python2.7/site-packages/cms/test_utils/util/context_managers.py

priyamshah112/Project-Descripton-Blog

8e01016c6be79776c4f5ca75563fa3daa839e39e

[ "MIT" ]

4

2019-05-09T02:09:54.000Z

2021-11-09T11:27:19.000Z

cms/test_utils/util/context_managers.py

thisisalamin/django-cms

eeb1e4712b3866e243daf800c142e2199e4be9df

[ "BSD-3-Clause" ]

5

2018-08-29T04:17:41.000Z

2018-09-04T05:15:38.000Z

cms/test_utils/util/context_managers.py

thisisalamin/django-cms

eeb1e4712b3866e243daf800c142e2199e4be9df

[ "BSD-3-Clause" ]

4

2019-01-26T09:58:37.000Z

2019-06-24T08:12:43.000Z

# -*- coding: utf-8 -*- import sys from contextlib import contextmanager from shutil import rmtree as _rmtree from tempfile import template, mkdtemp, _exists from cms.apphook_pool import apphook_pool from django.contrib.auth import get_user_model from django.utils.six.moves import StringIO from django.utils.translation import get_language, activate class NULL: pass class StdOverride(object): def __init__(self, std='out', buffer=None): self.std = std self.buffer = buffer or StringIO() def __enter__(self): setattr(sys, 'std%s' % self.std, self.buffer) return self.buffer def __exit__(self, type, value, traceback): setattr(sys, 'std%s' % self.std, getattr(sys, '__std%s__' % self.std)) class StdoutOverride(StdOverride): """ This overrides Python's the standard output and redirects it to a StringIO object, so that on can test the output of the program. example: lines = None with StdoutOverride() as buffer: # print stuff lines = buffer.getvalue() """ def __init__(self, buffer=None): super(StdoutOverride, self).__init__('out', buffer) class LanguageOverride(object): def __init__(self, language): self.newlang = language def __enter__(self): self.oldlang = get_language() activate(self.newlang) def __exit__(self, type, value, traceback): activate(self.oldlang) class TemporaryDirectory: """Create and return a temporary directory. This has the same behavior as mkdtemp but can be used as a context manager. For example: with TemporaryDirectory() as tmpdir: ... Upon exiting the context, the directory and everthing contained in it are removed. """ def __init__(self, suffix="", prefix=template, dir=None): self.name = mkdtemp(suffix, prefix, dir) def __enter__(self): return self.name def cleanup(self): if _exists(self.name): _rmtree(self.name) def __exit__(self, exc, value, tb): self.cleanup() class UserLoginContext(object): def __init__(self, testcase, user): self.testcase = testcase self.user = user def __enter__(self): loginok = self.testcase.client.login(username=getattr(self.user, get_user_model().USERNAME_FIELD), password=getattr(self.user, get_user_model().USERNAME_FIELD)) self.old_user = getattr(self.testcase, 'user', None) self.testcase.user = self.user self.testcase.assertTrue(loginok) def __exit__(self, exc, value, tb): self.testcase.user = self.old_user if not self.testcase.user: delattr(self.testcase, 'user') self.testcase.client.logout() class ChangeModel(object): """ Changes attributes on a model while within the context. These changes *ARE* saved to the database for the context! """ def __init__(self, instance, **overrides): self.instance = instance self.overrides = overrides def __enter__(self): self.old = {} for key, value in self.overrides.items(): self.old[key] = getattr(self.instance, key, NULL) setattr(self.instance, key, value) self.instance.save() def __exit__(self, exc, value, tb): for key in self.overrides.keys(): old_value = self.old[key] if old_value is NULL: delattr(self.instance, key) else: setattr(self.instance, key, old_value) self.instance.save() @contextmanager def disable_logger(logger): old = logger.disabled logger.disabled = True yield logger.disabled = old @contextmanager def apphooks(*hooks): _apphooks = apphook_pool.apphooks _apps = apphook_pool.apps _discovered = apphook_pool.discovered apphook_pool.clear() for hook in hooks: apphook_pool.register(hook) try: yield finally: apphook_pool.apphooks = _apphooks apphook_pool.apps = _apps apphook_pool.discovered = _discovered @contextmanager def signal_tester(*signals): env = SignalTester() for signal in signals: signal.connect(env) try: yield env finally: for signal in signals: signal.disconnect(env) class SignalTester(object): def __init__(self): self.call_count = 0 self.calls = [] def __call__(self, *args, **kwargs): self.call_count += 1 self.calls.append((args, kwargs))

26.32

106

0.640035

import sys from contextlib import contextmanager from shutil import rmtree as _rmtree from tempfile import template, mkdtemp, _exists from cms.apphook_pool import apphook_pool from django.contrib.auth import get_user_model from django.utils.six.moves import StringIO from django.utils.translation import get_language, activate class NULL: pass class StdOverride(object): def __init__(self, std='out', buffer=None): self.std = std self.buffer = buffer or StringIO() def __enter__(self): setattr(sys, 'std%s' % self.std, self.buffer) return self.buffer def __exit__(self, type, value, traceback): setattr(sys, 'std%s' % self.std, getattr(sys, '__std%s__' % self.std)) class StdoutOverride(StdOverride): def __init__(self, buffer=None): super(StdoutOverride, self).__init__('out', buffer) class LanguageOverride(object): def __init__(self, language): self.newlang = language def __enter__(self): self.oldlang = get_language() activate(self.newlang) def __exit__(self, type, value, traceback): activate(self.oldlang) class TemporaryDirectory: def __init__(self, suffix="", prefix=template, dir=None): self.name = mkdtemp(suffix, prefix, dir) def __enter__(self): return self.name def cleanup(self): if _exists(self.name): _rmtree(self.name) def __exit__(self, exc, value, tb): self.cleanup() class UserLoginContext(object): def __init__(self, testcase, user): self.testcase = testcase self.user = user def __enter__(self): loginok = self.testcase.client.login(username=getattr(self.user, get_user_model().USERNAME_FIELD), password=getattr(self.user, get_user_model().USERNAME_FIELD)) self.old_user = getattr(self.testcase, 'user', None) self.testcase.user = self.user self.testcase.assertTrue(loginok) def __exit__(self, exc, value, tb): self.testcase.user = self.old_user if not self.testcase.user: delattr(self.testcase, 'user') self.testcase.client.logout() class ChangeModel(object): def __init__(self, instance, **overrides): self.instance = instance self.overrides = overrides def __enter__(self): self.old = {} for key, value in self.overrides.items(): self.old[key] = getattr(self.instance, key, NULL) setattr(self.instance, key, value) self.instance.save() def __exit__(self, exc, value, tb): for key in self.overrides.keys(): old_value = self.old[key] if old_value is NULL: delattr(self.instance, key) else: setattr(self.instance, key, old_value) self.instance.save() @contextmanager def disable_logger(logger): old = logger.disabled logger.disabled = True yield logger.disabled = old @contextmanager def apphooks(*hooks): _apphooks = apphook_pool.apphooks _apps = apphook_pool.apps _discovered = apphook_pool.discovered apphook_pool.clear() for hook in hooks: apphook_pool.register(hook) try: yield finally: apphook_pool.apphooks = _apphooks apphook_pool.apps = _apps apphook_pool.discovered = _discovered @contextmanager def signal_tester(*signals): env = SignalTester() for signal in signals: signal.connect(env) try: yield env finally: for signal in signals: signal.disconnect(env) class SignalTester(object): def __init__(self): self.call_count = 0 self.calls = [] def __call__(self, *args, **kwargs): self.call_count += 1 self.calls.append((args, kwargs))

true

7907dfe24f2f594e8548feacc56082d64042413b

297

py

Python

signing/processorimpl/sayhiimplementation.py

nkrowlan/signing-server

53f9b8ffef493526c467d59b93fc71a6644a7b6a

[ "Apache-2.0" ]

null

signing/processorimpl/sayhiimplementation.py

nkrowlan/signing-server

53f9b8ffef493526c467d59b93fc71a6644a7b6a

[ "Apache-2.0" ]

null

signing/processorimpl/sayhiimplementation.py

nkrowlan/signing-server

53f9b8ffef493526c467d59b93fc71a6644a7b6a

[ "Apache-2.0" ]

null

from twisted.internet import defer from signing.processor import expose class SayHiImplementation(object): """ Responds with 'hello, %s' % arg """ @expose def say_hi(self, identifier): d = defer.Deferred() d.callback('hello, %s' % identifier) return d

22.846154

44

0.632997

from twisted.internet import defer from signing.processor import expose class SayHiImplementation(object): @expose def say_hi(self, identifier): d = defer.Deferred() d.callback('hello, %s' % identifier) return d

true

7907e202d68831469e2b292777fc5eca272ffa62

1,093

py

Python

yolo/config.py

banayoyo/yolo

c12a2f2097d0b892f1268bc51b44d3905c3ab75a

[ "MIT" ]

null

yolo/config.py

banayoyo/yolo

c12a2f2097d0b892f1268bc51b44d3905c3ab75a

[ "MIT" ]

null

yolo/config.py

banayoyo/yolo

c12a2f2097d0b892f1268bc51b44d3905c3ab75a

[ "MIT" ]

null

import os # # path and dataset parameter # #该cfg文件，是通过import的方式进行配置的。并不是main的arg配置 DATA_PATH = 'data' PASCAL_PATH = os.path.join(DATA_PATH, 'pascal_voc') CACHE_PATH = os.path.join(PASCAL_PATH, 'cache') OUTPUT_DIR = os.path.join(PASCAL_PATH, 'output') WEIGHTS_DIR = os.path.join(PASCAL_PATH, 'weights') WEIGHTS_FILE = None # WEIGHTS_FILE = os.path.join(DATA_PATH, 'weights', 'YOLO_small.ckpt') CLASSES = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] FLIPPED = True # # model parameter # IMAGE_SIZE = 448 CELL_SIZE = 7 BOXES_PER_CELL = 2 ALPHA = 0.1 DISP_CONSOLE = False OBJECT_SCALE = 1.0 NOOBJECT_SCALE = 1.0 CLASS_SCALE = 2.0 COORD_SCALE = 5.0 # # solver parameter # GPU = '' LEARNING_RATE = 0.0001 DECAY_STEPS = 30000 DECAY_RATE = 0.1 STAIRCASE = True BATCH_SIZE = 45 MAX_ITER = 15000 SUMMARY_ITER = 10 SAVE_ITER = 1000 # # test parameter # THRESHOLD = 0.2 IOU_THRESHOLD = 0.5

14.012821

71

0.670631

import os DATA_PATH = 'data' PASCAL_PATH = os.path.join(DATA_PATH, 'pascal_voc') CACHE_PATH = os.path.join(PASCAL_PATH, 'cache') OUTPUT_DIR = os.path.join(PASCAL_PATH, 'output') WEIGHTS_DIR = os.path.join(PASCAL_PATH, 'weights') WEIGHTS_FILE = None CLASSES = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] FLIPPED = True IMAGE_SIZE = 448 CELL_SIZE = 7 BOXES_PER_CELL = 2 ALPHA = 0.1 DISP_CONSOLE = False OBJECT_SCALE = 1.0 NOOBJECT_SCALE = 1.0 CLASS_SCALE = 2.0 COORD_SCALE = 5.0 GPU = '' LEARNING_RATE = 0.0001 DECAY_STEPS = 30000 DECAY_RATE = 0.1 STAIRCASE = True BATCH_SIZE = 45 MAX_ITER = 15000 SUMMARY_ITER = 10 SAVE_ITER = 1000 THRESHOLD = 0.2 IOU_THRESHOLD = 0.5

true

7907e21d7d422ea320284f04fa9d9aa2b281c061

1,896

py

Python

leetcode-CP/Problem solving/496. Next Greater Element I.py

vijay2020pc/100-days-of-code

b59e54471015b294bad408289e6d9101d7494b01

[ "MIT" ]

null

leetcode-CP/Problem solving/496. Next Greater Element I.py

vijay2020pc/100-days-of-code

b59e54471015b294bad408289e6d9101d7494b01

[ "MIT" ]

null

leetcode-CP/Problem solving/496. Next Greater Element I.py

vijay2020pc/100-days-of-code

b59e54471015b294bad408289e6d9101d7494b01

[ "MIT" ]

null

The next greater element of some element x in an array is the first greater element that is to the right of x in the same array. You are given two distinct 0-indexed integer arrays nums1 and nums2, where nums1 is a subset of nums2. For each 0 <= i < nums1.length, find the index j such that nums1[i] == nums2[j] and determine the next greater element of nums2[j] in nums2. If there is no next greater element, then the answer for this query is -1. Return an array ans of length nums1.length such that ans[i] is the next greater element as described above. Example 1: Input: nums1 = [4,1,2], nums2 = [1,3,4,2] Output: [-1,3,-1] Explanation: The next greater element for each value of nums1 is as follows: - 4 is underlined in nums2 = [1,3,4,2]. There is no next greater element, so the answer is -1. - 1 is underlined in nums2 = [1,3,4,2]. The next greater element is 3. - 2 is underlined in nums2 = [1,3,4,2]. There is no next greater element, so the answer is -1. Example 2: Input: nums1 = [2,4], nums2 = [1,2,3,4] Output: [3,-1] Explanation: The next greater element for each value of nums1 is as follows: - 2 is underlined in nums2 = [1,2,3,4]. The next greater element is 3. - 4 is underlined in nums2 = [1,2,3,4]. There is no next greater element, so the answer is -1. Constraints: 1 <= nums1.length <= nums2.length <= 1000 0 <= nums1[i], nums2[i] <= 104 All integers in nums1 and nums2 are unique. All the integers of nums1 also appear in nums2. Solution: class Solution: def nextGreaterElement(self, nums1: List[int], nums2: List[int]) -> List[int]: ans = defaultdict(lambda: -1) stack = [] for i in range(len(nums2)): while stack and stack[-1] < nums2[i]: ans[stack.pop()] = nums2[i] stack.append(nums2[i]) for i in range(len(nums1)): nums1[i] = ans[nums1[i]] return nums1

39.5

215

0.670359

The next greater element of some element x in an array is the first greater element that is to the right of x in the same array. You are given two distinct 0-indexed integer arrays nums1 and nums2, where nums1 is a subset of nums2. For each 0 <= i < nums1.length, find the index j such that nums1[i] == nums2[j] and determine the next greater element of nums2[j] in nums2. If there is no next greater element, then the answer for this query is -1. Return an array ans of length nums1.length such that ans[i] is the next greater element as described above. Example 1: Input: nums1 = [4,1,2], nums2 = [1,3,4,2] Output: [-1,3,-1] Explanation: The next greater element for each value of nums1 is as follows: - 4 is underlined in nums2 = [1,3,4,2]. There is no next greater element, so the answer is -1. - 1 is underlined in nums2 = [1,3,4,2]. The next greater element is 3. - 2 is underlined in nums2 = [1,3,4,2]. There is no next greater element, so the answer is -1. Example 2: Input: nums1 = [2,4], nums2 = [1,2,3,4] Output: [3,-1] Explanation: The next greater element for each value of nums1 is as follows: - 2 is underlined in nums2 = [1,2,3,4]. The next greater element is 3. - 4 is underlined in nums2 = [1,2,3,4]. There is no next greater element, so the answer is -1. Constraints: 1 <= nums1.length <= nums2.length <= 1000 0 <= nums1[i], nums2[i] <= 104 All integers in nums1 and nums2 are unique. All the integers of nums1 also appear in nums2. Solution: class Solution: def nextGreaterElement(self, nums1: List[int], nums2: List[int]) -> List[int]: ans = defaultdict(lambda: -1) stack = [] for i in range(len(nums2)): while stack and stack[-1] < nums2[i]: ans[stack.pop()] = nums2[i] stack.append(nums2[i]) for i in range(len(nums1)): nums1[i] = ans[nums1[i]] return nums1

false

true

7907e2624a3fa7f31efa4869977ca38688805362

3,045

py

Python

huaweicloud-sdk-bss/huaweicloudsdkbss/v2/model/update_indirect_partner_account_response.py

githubmilesma/huaweicloud-sdk-python-v3

9d9449ed68a609ca65f0aa50b5b2a1c28445bf03

[ "Apache-2.0" ]

1

2021-04-16T07:59:28.000Z

huaweicloud-sdk-bss/huaweicloudsdkbss/v2/model/update_indirect_partner_account_response.py

Lencof/huaweicloud-sdk-python-v3

d13dc4e2830a83e295be6e4de021999b3376e34e

[ "Apache-2.0" ]

null

huaweicloud-sdk-bss/huaweicloudsdkbss/v2/model/update_indirect_partner_account_response.py

Lencof/huaweicloud-sdk-python-v3

d13dc4e2830a83e295be6e4de021999b3376e34e

[ "Apache-2.0" ]

1

2022-01-17T02:24:18.000Z

# coding: utf-8 import pprint import re import six from huaweicloudsdkcore.sdk_response import SdkResponse class UpdateIndirectPartnerAccountResponse(SdkResponse): """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ sensitive_list = [] openapi_types = { 'transfer_id': 'str' } attribute_map = { 'transfer_id': 'transfer_id' } def __init__(self, transfer_id=None): """UpdateIndirectPartnerAccountResponse - a model defined in huaweicloud sdk""" super().__init__() self._transfer_id = None self.discriminator = None if transfer_id is not None: self.transfer_id = transfer_id @property def transfer_id(self): """Gets the transfer_id of this UpdateIndirectPartnerAccountResponse. 事务流水ID，只有成功响应才会返回。 :return: The transfer_id of this UpdateIndirectPartnerAccountResponse. :rtype: str """ return self._transfer_id @transfer_id.setter def transfer_id(self, transfer_id): """Sets the transfer_id of this UpdateIndirectPartnerAccountResponse. 事务流水ID，只有成功响应才会返回。 :param transfer_id: The transfer_id of this UpdateIndirectPartnerAccountResponse. :type: str """ self._transfer_id = transfer_id def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: if attr in self.sensitive_list: result[attr] = "****" else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, UpdateIndirectPartnerAccountResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

27.432432

89

0.572742

import pprint import re import six from huaweicloudsdkcore.sdk_response import SdkResponse class UpdateIndirectPartnerAccountResponse(SdkResponse): sensitive_list = [] openapi_types = { 'transfer_id': 'str' } attribute_map = { 'transfer_id': 'transfer_id' } def __init__(self, transfer_id=None): super().__init__() self._transfer_id = None self.discriminator = None if transfer_id is not None: self.transfer_id = transfer_id @property def transfer_id(self): return self._transfer_id @transfer_id.setter def transfer_id(self, transfer_id): self._transfer_id = transfer_id def to_dict(self): result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: if attr in self.sensitive_list: result[attr] = "****" else: result[attr] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, UpdateIndirectPartnerAccountResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

7907e2ef9017c5ca381a123f82ee9a08309d578c

3,199

py

Python

server/app/scrapers/maarten.py

damienallen/makelaardij-notify

ea8e37e1b0f867487b90590c5273e7fb25d868cf

[ "MIT" ]

null

server/app/scrapers/maarten.py

damienallen/makelaardij-notify

ea8e37e1b0f867487b90590c5273e7fb25d868cf

[ "MIT" ]

15

2021-02-13T23:46:28.000Z

2021-02-25T15:36:08.000Z

server/app/scrapers/maarten.py

damienallen/makelaardij-notify

ea8e37e1b0f867487b90590c5273e7fb25d868cf

[ "MIT" ]

null

import asyncio from typing import List from app.common import SkipListing from app.scrapers.base import BaseScraper class MaartenScraper(BaseScraper): MAKELAARDIJ: str = "maarten" BASE_URL: str = "https://www.maartenmakelaardij.nl" # Specific functions async def extract_object_urls(self, soup) -> List[str]: """ Extract apartment object urls """ items = soup.find_all("a") urls: List[str] = [] for item in items: if "woning/rotterdam-" in item["href"]: urls.append(item["href"]) return list(set(urls)) async def get_page_url(self, page_num: int) -> str: """ Format page url """ return f"{self.BASE_URL}/aanbod/rotterdam/" async def get_apartment_urls(self) -> List[str]: """ Fetch list of apartment urls from inventory """ urls = await self.scrape_page(0) return urls def extract_features(self, soup): """ Extract feature metadata from listing """ meta_data = { "makelaardij": self.MAKELAARDIJ, "building": {}, "unit": {"energy": {}, "tags": []}, } dt = soup.find_all("dt") dd = soup.find_all("dd") # Features for ind, key in enumerate(dt): if "Bouwjaar" in key.string: meta_data["building"]["year_constructed"] = self.find_int( dd[ind].string ) elif "Woonoppervlakte" in key.string: meta_data["unit"]["area"] = self.find_float(dd[ind].text.split(" ")[0]) elif "Aantal kamers" in key.string: meta_data["unit"]["num_rooms"] = self.find_int(dd[ind].text) elif "verdiepingen" in key.string: meta_data["unit"]["num_floors"] = self.find_int(dd[ind].text) elif "Status" in key.string: meta_data["available"] = "Beschikbaar" in dd[ind].text elif "Buitenruimte" in key.string and "TUIN" in dd[ind].text: meta_data["unit"]["tags"].append("garden") # Other fields meta_data["address"] = soup.find("span", {"class": "adres"}).string meta_data["asking_price"] = self.find_int( soup.find("span", {"class": "price"}).string.replace(".", "") ) description = soup.find("div", {"id": "read-more-content"}).children for p in description: p_text = str(p.text) if "Eigen grond" in p_text: meta_data["unit"]["own_land"] = True elif "erfpacht" in p_text: meta_data["unit"]["own_land"] = False if "Energielabel" in p_text: label = p_text.split("Energielabel: ")[1][0] meta_data["unit"]["energy"]["label"] = label break # Bounce broken listings if not meta_data["unit"].get("area"): raise SkipListing("Unable to find area") return meta_data if __name__ == "__main__": scraper = MaartenScraper() loop = asyncio.get_event_loop() loop.run_until_complete(scraper.start())

29.897196

87

0.546733

import asyncio from typing import List from app.common import SkipListing from app.scrapers.base import BaseScraper class MaartenScraper(BaseScraper): MAKELAARDIJ: str = "maarten" BASE_URL: str = "https://www.maartenmakelaardij.nl" async def extract_object_urls(self, soup) -> List[str]: items = soup.find_all("a") urls: List[str] = [] for item in items: if "woning/rotterdam-" in item["href"]: urls.append(item["href"]) return list(set(urls)) async def get_page_url(self, page_num: int) -> str: return f"{self.BASE_URL}/aanbod/rotterdam/" async def get_apartment_urls(self) -> List[str]: urls = await self.scrape_page(0) return urls def extract_features(self, soup): meta_data = { "makelaardij": self.MAKELAARDIJ, "building": {}, "unit": {"energy": {}, "tags": []}, } dt = soup.find_all("dt") dd = soup.find_all("dd") for ind, key in enumerate(dt): if "Bouwjaar" in key.string: meta_data["building"]["year_constructed"] = self.find_int( dd[ind].string ) elif "Woonoppervlakte" in key.string: meta_data["unit"]["area"] = self.find_float(dd[ind].text.split(" ")[0]) elif "Aantal kamers" in key.string: meta_data["unit"]["num_rooms"] = self.find_int(dd[ind].text) elif "verdiepingen" in key.string: meta_data["unit"]["num_floors"] = self.find_int(dd[ind].text) elif "Status" in key.string: meta_data["available"] = "Beschikbaar" in dd[ind].text elif "Buitenruimte" in key.string and "TUIN" in dd[ind].text: meta_data["unit"]["tags"].append("garden") meta_data["address"] = soup.find("span", {"class": "adres"}).string meta_data["asking_price"] = self.find_int( soup.find("span", {"class": "price"}).string.replace(".", "") ) description = soup.find("div", {"id": "read-more-content"}).children for p in description: p_text = str(p.text) if "Eigen grond" in p_text: meta_data["unit"]["own_land"] = True elif "erfpacht" in p_text: meta_data["unit"]["own_land"] = False if "Energielabel" in p_text: label = p_text.split("Energielabel: ")[1][0] meta_data["unit"]["energy"]["label"] = label break if not meta_data["unit"].get("area"): raise SkipListing("Unable to find area") return meta_data if __name__ == "__main__": scraper = MaartenScraper() loop = asyncio.get_event_loop() loop.run_until_complete(scraper.start())

true

7907e346838018cc0f6c31d867e0acc883026424

131

py

Python

app/forms.py

Ronlin1/To-Do-App

e641c0bd125643bfe050df6268a1f0224cdbbe5b

[ "MIT" ]

null

app/forms.py

Ronlin1/To-Do-App

e641c0bd125643bfe050df6268a1f0224cdbbe5b

[ "MIT" ]

null

app/forms.py

Ronlin1/To-Do-App

e641c0bd125643bfe050df6268a1f0224cdbbe5b

[ "MIT" ]

null

from django import forms from .models import Todo class TodoForm(forms.ModelForm): class Meta: model = Todo fields="__all__"

16.375

32

0.755725

from django import forms from .models import Todo class TodoForm(forms.ModelForm): class Meta: model = Todo fields="__all__"

true

7907e3bfe411dcc29a4789cb39d72e3f363b0c16

2,230

py

Python

tests/diff.py

kdeyev/mongomock

c321eea5e00086dd6db1552477a3a474a9f4438e

[ "BSD-3-Clause" ]

null

tests/diff.py

kdeyev/mongomock

c321eea5e00086dd6db1552477a3a474a9f4438e

[ "BSD-3-Clause" ]

null

tests/diff.py

kdeyev/mongomock

c321eea5e00086dd6db1552477a3a474a9f4438e

[ "BSD-3-Clause" ]

null

import datetime from platform import python_version from six import integer_types, string_types, text_type class _NO_VALUE(object): pass # we don't use NOTHING because it might be returned from various APIs NO_VALUE = _NO_VALUE() _SUPPORTED_TYPES = (float, bool, str, datetime.datetime, type(None)) + \ string_types + integer_types + (text_type, bytes) + (type,) if python_version() < '3.0': dict_type = dict else: from collections import abc dict_type = abc.Mapping def diff(a, b, path=None): path = _make_path(path) if isinstance(a, (list, tuple)): return _diff_sequences(a, b, path) if type(a).__name__ == 'SON': a = dict(a) if type(b).__name__ == 'SON': b = dict(b) if isinstance(a, dict_type): return _diff_dicts(a, b, path) if type(a).__name__ == 'ObjectId': a = str(a) if type(b).__name__ == 'ObjectId': b = str(b) if type(a).__name__ == 'Int64': a = int(a) if type(b).__name__ == 'Int64': b = int(b) if not isinstance(a, _SUPPORTED_TYPES): raise NotImplementedError( 'Unsupported diff type: {0}'.format(type(a))) # pragma: no cover if not isinstance(b, _SUPPORTED_TYPES): raise NotImplementedError( 'Unsupported diff type: {0}'.format(type(b))) # pragma: no cover if a != b: return [(path[:], a, b)] return [] def _diff_dicts(a, b, path): if not isinstance(a, type(b)): return [(path[:], type(a), type(b))] returned = [] for key in set(a) | set(b): a_value = a.get(key, NO_VALUE) b_value = b.get(key, NO_VALUE) path.append(key) if a_value is NO_VALUE or b_value is NO_VALUE: returned.append((path[:], a_value, b_value)) else: returned.extend(diff(a_value, b_value, path)) path.pop() return returned def _diff_sequences(a, b, path): if len(a) != len(b): return [(path[:], a, b)] returned = [] for i, a_i in enumerate(a): path.append(i) returned.extend(diff(a_i, b[i], path)) path.pop() return returned def _make_path(path): if path is None: return [] return path

26.86747

77

0.590583

import datetime from platform import python_version from six import integer_types, string_types, text_type class _NO_VALUE(object): pass NO_VALUE = _NO_VALUE() _SUPPORTED_TYPES = (float, bool, str, datetime.datetime, type(None)) + \ string_types + integer_types + (text_type, bytes) + (type,) if python_version() < '3.0': dict_type = dict else: from collections import abc dict_type = abc.Mapping def diff(a, b, path=None): path = _make_path(path) if isinstance(a, (list, tuple)): return _diff_sequences(a, b, path) if type(a).__name__ == 'SON': a = dict(a) if type(b).__name__ == 'SON': b = dict(b) if isinstance(a, dict_type): return _diff_dicts(a, b, path) if type(a).__name__ == 'ObjectId': a = str(a) if type(b).__name__ == 'ObjectId': b = str(b) if type(a).__name__ == 'Int64': a = int(a) if type(b).__name__ == 'Int64': b = int(b) if not isinstance(a, _SUPPORTED_TYPES): raise NotImplementedError( 'Unsupported diff type: {0}'.format(type(a))) # pragma: no cover if not isinstance(b, _SUPPORTED_TYPES): raise NotImplementedError( 'Unsupported diff type: {0}'.format(type(b))) # pragma: no cover if a != b: return [(path[:], a, b)] return [] def _diff_dicts(a, b, path): if not isinstance(a, type(b)): return [(path[:], type(a), type(b))] returned = [] for key in set(a) | set(b): a_value = a.get(key, NO_VALUE) b_value = b.get(key, NO_VALUE) path.append(key) if a_value is NO_VALUE or b_value is NO_VALUE: returned.append((path[:], a_value, b_value)) else: returned.extend(diff(a_value, b_value, path)) path.pop() return returned def _diff_sequences(a, b, path): if len(a) != len(b): return [(path[:], a, b)] returned = [] for i, a_i in enumerate(a): path.append(i) returned.extend(diff(a_i, b[i], path)) path.pop() return returned def _make_path(path): if path is None: return [] return path

true

7907e427b919f20227e8b4dd1ddb1ff67822781a

262

py

Python

2018_3_Cooper_Type/RoboFont/simple_interpolation.py

benkiel/python_workshops

9483c1fd5f7dd87e595289efb7376e1b81ff5ede

[ "MIT" ]

6

2018-03-24T17:31:51.000Z

2021-11-18T06:02:09.000Z

2018_3_Cooper_Type/RoboFont/simple_interpolation.py

benkiel/python_workshops

9483c1fd5f7dd87e595289efb7376e1b81ff5ede

[ "MIT" ]

null

2018_3_Cooper_Type/RoboFont/simple_interpolation.py

benkiel/python_workshops

9483c1fd5f7dd87e595289efb7376e1b81ff5ede

[ "MIT" ]

null

font = CurrentFont() one = font['A'] two = font['A.2'] steps = 4 if one.isCompatible(two): for x in range(steps): n = "A.interp" + str(x+1) g = font.newGlyph(n) f = (x+1)/(steps+1) print f g.interpolate(f, one, two)

18.714286

34

0.515267

font = CurrentFont() one = font['A'] two = font['A.2'] steps = 4 if one.isCompatible(two): for x in range(steps): n = "A.interp" + str(x+1) g = font.newGlyph(n) f = (x+1)/(steps+1) print f g.interpolate(f, one, two)

false

true

7907e4b3624c79f06dda602a207ad90a216917bc

11,498

py

Python

stor/plotters/plotters.py

Stor-Network/stor-blockchain

3c3cd1a3b99592e88160107ca5b81afc0937b992

[ "Apache-2.0" ]

19

2021-06-29T20:06:09.000Z

2022-02-09T04:33:00.000Z

stor/plotters/plotters.py

Stor-Network/stor-blockchain

3c3cd1a3b99592e88160107ca5b81afc0937b992

[ "Apache-2.0" ]

8

2021-07-04T03:21:51.000Z

2021-12-27T07:56:09.000Z

stor/plotters/plotters.py

Stor-Network/stor-blockchain

3c3cd1a3b99592e88160107ca5b81afc0937b992

[ "Apache-2.0" ]

6

2021-10-04T17:15:30.000Z

2022-03-15T08:40:01.000Z

import argparse import binascii import os from enum import Enum from stor.plotters.bladebit import get_bladebit_install_info, plot_bladebit from stor.plotters.chiapos import get_chiapos_install_info, plot_stor from stor.plotters.madmax import get_madmax_install_info, plot_madmax from stor.plotters.install_plotter import install_plotter from pathlib import Path from typing import Any, Dict, Optional class Options(Enum): TMP_DIR = 1 TMP_DIR2 = 2 FINAL_DIR = 3 K = 4 MEMO = 5 ID = 6 BUFF = 7 NUM_BUCKETS = 8 STRIPE_SIZE = 9 NUM_THREADS = 10 NOBITFIELD = 11 PLOT_COUNT = 12 MADMAX_NUM_BUCKETS_PHRASE3 = 13 MADMAX_WAITFORCOPY = 14 POOLKEY = 15 FARMERKEY = 16 MADMAX_TMPTOGGLE = 17 POOLCONTRACT = 18 MADMAX_RMULTI2 = 19 BLADEBIT_WARMSTART = 20 BLADEBIT_NONUMA = 21 VERBOSE = 22 OVERRIDE_K = 23 ALT_FINGERPRINT = 24 EXCLUDE_FINAL_DIR = 25 CONNECT_TO_DAEMON = 26 stor_plotter = [ Options.TMP_DIR, Options.TMP_DIR2, Options.FINAL_DIR, Options.K, Options.MEMO, Options.ID, Options.BUFF, Options.NUM_BUCKETS, Options.STRIPE_SIZE, Options.NUM_THREADS, Options.NOBITFIELD, Options.OVERRIDE_K, Options.ALT_FINGERPRINT, Options.POOLCONTRACT, Options.FARMERKEY, Options.POOLKEY, Options.PLOT_COUNT, Options.EXCLUDE_FINAL_DIR, Options.CONNECT_TO_DAEMON, ] madmax_plotter = [ Options.K, Options.PLOT_COUNT, Options.NUM_THREADS, Options.NUM_BUCKETS, Options.MADMAX_NUM_BUCKETS_PHRASE3, Options.TMP_DIR, Options.TMP_DIR2, Options.FINAL_DIR, Options.MADMAX_WAITFORCOPY, Options.POOLKEY, Options.FARMERKEY, Options.POOLCONTRACT, Options.MADMAX_TMPTOGGLE, Options.MADMAX_RMULTI2, Options.CONNECT_TO_DAEMON, ] bladebit_plotter = [ Options.NUM_THREADS, Options.PLOT_COUNT, Options.FARMERKEY, Options.POOLKEY, Options.POOLCONTRACT, Options.ID, Options.BLADEBIT_WARMSTART, Options.BLADEBIT_NONUMA, Options.FINAL_DIR, Options.VERBOSE, Options.CONNECT_TO_DAEMON, ] def get_plotters_root_path(root_path: Path) -> Path: return root_path / "plotters" def build_parser(subparsers, root_path, option_list, name, plotter_desc): parser = subparsers.add_parser(name, description=plotter_desc) for option in option_list: if option is Options.K: parser.add_argument( "-k", "--size", type=int, help="K value.", default=32, ) u_default = 0 if name == "chiapos" else 256 if option is Options.NUM_BUCKETS: parser.add_argument( "-u", "--buckets", type=int, help="Number of buckets.", default=u_default, ) if option is Options.STRIPE_SIZE: parser.add_argument( "-s", "--stripes", type=int, help="Stripe size.", default=0, ) if option is Options.TMP_DIR: parser.add_argument( "-t", "--tmp_dir", type=str, dest="tmpdir", help="Temporary directory 1.", default=str(root_path) + "/", ) if option is Options.TMP_DIR2: parser.add_argument( "-2", "--tmp_dir2", type=str, dest="tmpdir2", help="Temporary directory 2.", default=str(root_path) + "/", ) if option is Options.FINAL_DIR: parser.add_argument( "-d", "--final_dir", type=str, dest="finaldir", help="Final directory.", default=str(root_path) + "/", ) if option is Options.BUFF: parser.add_argument( "-b", "--buffer", type=int, help="Size of the buffer, in MB.", default=0, ) r_default = 4 if name == "madmax" else 0 if option is Options.NUM_THREADS: parser.add_argument( "-r", "--threads", type=int, help="Num threads.", default=r_default, ) if option is Options.NOBITFIELD: parser.add_argument( "-e", "--nobitfield", action="store_true", help="Disable bitfield.", default=False, ) if option is Options.MEMO: parser.add_argument( "-m", "--memo", type=binascii.unhexlify, help="Memo variable.", ) if option is Options.ID: parser.add_argument( "-i", "--id", type=binascii.unhexlify, help="Plot id", ) if option is Options.PLOT_COUNT: parser.add_argument( "-n", "--count", type=int, help="Number of plots to create (default = 1)", default=1, ) if option is Options.MADMAX_NUM_BUCKETS_PHRASE3: parser.add_argument( "-v", "--buckets3", type=int, help="Number of buckets for phase 3+4 (default = 256)", default=256, ) if option is Options.MADMAX_WAITFORCOPY: parser.add_argument( "-w", "--waitforcopy", action="store_true", help="Wait for copy to start next plot", default=False, ) if option is Options.MADMAX_TMPTOGGLE: parser.add_argument( "-G", "--tmptoggle", action="store_true", help="Alternate tmpdir/tmpdir2 (default = false)", default=False, ) if option is Options.POOLCONTRACT: parser.add_argument( "-c", "--contract", type=str, help="Pool Contract Address (64 chars)", default="", ) if option is Options.MADMAX_RMULTI2: parser.add_argument( "-K", "--rmulti2", type=int, help="Thread multiplier for P2 (default = 1)", default=1, ) if option is Options.POOLKEY: parser.add_argument( "-p", "--pool-key", type=binascii.unhexlify, help="Pool Public Key (48 bytes)", default="", ) if option is Options.FARMERKEY: parser.add_argument( "-f", "--farmerkey", type=binascii.unhexlify, help="Farmer Public Key (48 bytes)", default="", ) if option is Options.BLADEBIT_WARMSTART: parser.add_argument( "-w", "--warmstart", action="store_true", help="Warm start", default=False, ) if option is Options.BLADEBIT_NONUMA: parser.add_argument( "-m", "--nonuma", action="store_true", help="Disable numa", default=False, ) if option is Options.VERBOSE: parser.add_argument( "-v", "--verbose", action="store_true", help="Set verbose", default=False, ) if option is Options.OVERRIDE_K: parser.add_argument( "--override-k", dest="override", action="store_true", help="Force size smaller than 32", default=False, ) if option is Options.ALT_FINGERPRINT: parser.add_argument( "-a", "--alt_fingerprint", type=int, default=None, help="Enter the alternative fingerprint of the key you want to use", ) if option is Options.EXCLUDE_FINAL_DIR: parser.add_argument( "-x", "--exclude_final_dir", action="store_true", help="Skips adding [final dir] to harvester for farming", default=False, ) if option is Options.CONNECT_TO_DAEMON: parser.add_argument( "-D", "--connect-to-daemon", action="store_true", help=argparse.SUPPRESS, default=False, ) def call_plotters(root_path: Path, args): # Add `plotters` section in STOR_ROOT. stor_root_path = root_path root_path = get_plotters_root_path(root_path) if not root_path.is_dir(): if os.path.exists(root_path): try: os.remove(root_path) except Exception as e: print(f"Exception deleting old root path: {type(e)} {e}.") if not os.path.exists(root_path): print(f"Creating plotters folder within STOR_ROOT: {root_path}") try: os.mkdir(root_path) except Exception as e: print(f"Cannot create plotters root path {root_path} {type(e)} {e}.") plotters = argparse.ArgumentParser(description="Available options.") subparsers = plotters.add_subparsers(help="Available options", dest="plotter") build_parser(subparsers, root_path, stor_plotter, "chiapos", "Storpos Plotter") build_parser(subparsers, root_path, madmax_plotter, "madmax", "Madmax Plotter") build_parser(subparsers, root_path, bladebit_plotter, "bladebit", "Bladebit Plotter") install_parser = subparsers.add_parser("install", description="Install custom plotters.") install_parser.add_argument( "install_plotter", type=str, help="The plotters available for installing. Choose from madmax or bladebit." ) args = plotters.parse_args(args) if args.plotter == "chiapos": plot_stor(args, stor_root_path) if args.plotter == "madmax": plot_madmax(args, stor_root_path, root_path) if args.plotter == "bladebit": plot_bladebit(args, stor_root_path, root_path) if args.plotter == "install": install_plotter(args.install_plotter, root_path) def get_available_plotters(root_path) -> Dict[str, Any]: plotters_root_path: Path = get_plotters_root_path(root_path) plotters: Dict[str, Any] = {} chiapos: Optional[Dict[str, Any]] = get_chiapos_install_info() bladebit: Optional[Dict[str, Any]] = get_bladebit_install_info(plotters_root_path) madmax: Optional[Dict[str, Any]] = get_madmax_install_info(plotters_root_path) if chiapos is not None: plotters["chiapos"] = chiapos if bladebit is not None: plotters["bladebit"] = bladebit if madmax is not None: plotters["madmax"] = madmax return plotters

31.075676

114

0.529483

import argparse import binascii import os from enum import Enum from stor.plotters.bladebit import get_bladebit_install_info, plot_bladebit from stor.plotters.chiapos import get_chiapos_install_info, plot_stor from stor.plotters.madmax import get_madmax_install_info, plot_madmax from stor.plotters.install_plotter import install_plotter from pathlib import Path from typing import Any, Dict, Optional class Options(Enum): TMP_DIR = 1 TMP_DIR2 = 2 FINAL_DIR = 3 K = 4 MEMO = 5 ID = 6 BUFF = 7 NUM_BUCKETS = 8 STRIPE_SIZE = 9 NUM_THREADS = 10 NOBITFIELD = 11 PLOT_COUNT = 12 MADMAX_NUM_BUCKETS_PHRASE3 = 13 MADMAX_WAITFORCOPY = 14 POOLKEY = 15 FARMERKEY = 16 MADMAX_TMPTOGGLE = 17 POOLCONTRACT = 18 MADMAX_RMULTI2 = 19 BLADEBIT_WARMSTART = 20 BLADEBIT_NONUMA = 21 VERBOSE = 22 OVERRIDE_K = 23 ALT_FINGERPRINT = 24 EXCLUDE_FINAL_DIR = 25 CONNECT_TO_DAEMON = 26 stor_plotter = [ Options.TMP_DIR, Options.TMP_DIR2, Options.FINAL_DIR, Options.K, Options.MEMO, Options.ID, Options.BUFF, Options.NUM_BUCKETS, Options.STRIPE_SIZE, Options.NUM_THREADS, Options.NOBITFIELD, Options.OVERRIDE_K, Options.ALT_FINGERPRINT, Options.POOLCONTRACT, Options.FARMERKEY, Options.POOLKEY, Options.PLOT_COUNT, Options.EXCLUDE_FINAL_DIR, Options.CONNECT_TO_DAEMON, ] madmax_plotter = [ Options.K, Options.PLOT_COUNT, Options.NUM_THREADS, Options.NUM_BUCKETS, Options.MADMAX_NUM_BUCKETS_PHRASE3, Options.TMP_DIR, Options.TMP_DIR2, Options.FINAL_DIR, Options.MADMAX_WAITFORCOPY, Options.POOLKEY, Options.FARMERKEY, Options.POOLCONTRACT, Options.MADMAX_TMPTOGGLE, Options.MADMAX_RMULTI2, Options.CONNECT_TO_DAEMON, ] bladebit_plotter = [ Options.NUM_THREADS, Options.PLOT_COUNT, Options.FARMERKEY, Options.POOLKEY, Options.POOLCONTRACT, Options.ID, Options.BLADEBIT_WARMSTART, Options.BLADEBIT_NONUMA, Options.FINAL_DIR, Options.VERBOSE, Options.CONNECT_TO_DAEMON, ] def get_plotters_root_path(root_path: Path) -> Path: return root_path / "plotters" def build_parser(subparsers, root_path, option_list, name, plotter_desc): parser = subparsers.add_parser(name, description=plotter_desc) for option in option_list: if option is Options.K: parser.add_argument( "-k", "--size", type=int, help="K value.", default=32, ) u_default = 0 if name == "chiapos" else 256 if option is Options.NUM_BUCKETS: parser.add_argument( "-u", "--buckets", type=int, help="Number of buckets.", default=u_default, ) if option is Options.STRIPE_SIZE: parser.add_argument( "-s", "--stripes", type=int, help="Stripe size.", default=0, ) if option is Options.TMP_DIR: parser.add_argument( "-t", "--tmp_dir", type=str, dest="tmpdir", help="Temporary directory 1.", default=str(root_path) + "/", ) if option is Options.TMP_DIR2: parser.add_argument( "-2", "--tmp_dir2", type=str, dest="tmpdir2", help="Temporary directory 2.", default=str(root_path) + "/", ) if option is Options.FINAL_DIR: parser.add_argument( "-d", "--final_dir", type=str, dest="finaldir", help="Final directory.", default=str(root_path) + "/", ) if option is Options.BUFF: parser.add_argument( "-b", "--buffer", type=int, help="Size of the buffer, in MB.", default=0, ) r_default = 4 if name == "madmax" else 0 if option is Options.NUM_THREADS: parser.add_argument( "-r", "--threads", type=int, help="Num threads.", default=r_default, ) if option is Options.NOBITFIELD: parser.add_argument( "-e", "--nobitfield", action="store_true", help="Disable bitfield.", default=False, ) if option is Options.MEMO: parser.add_argument( "-m", "--memo", type=binascii.unhexlify, help="Memo variable.", ) if option is Options.ID: parser.add_argument( "-i", "--id", type=binascii.unhexlify, help="Plot id", ) if option is Options.PLOT_COUNT: parser.add_argument( "-n", "--count", type=int, help="Number of plots to create (default = 1)", default=1, ) if option is Options.MADMAX_NUM_BUCKETS_PHRASE3: parser.add_argument( "-v", "--buckets3", type=int, help="Number of buckets for phase 3+4 (default = 256)", default=256, ) if option is Options.MADMAX_WAITFORCOPY: parser.add_argument( "-w", "--waitforcopy", action="store_true", help="Wait for copy to start next plot", default=False, ) if option is Options.MADMAX_TMPTOGGLE: parser.add_argument( "-G", "--tmptoggle", action="store_true", help="Alternate tmpdir/tmpdir2 (default = false)", default=False, ) if option is Options.POOLCONTRACT: parser.add_argument( "-c", "--contract", type=str, help="Pool Contract Address (64 chars)", default="", ) if option is Options.MADMAX_RMULTI2: parser.add_argument( "-K", "--rmulti2", type=int, help="Thread multiplier for P2 (default = 1)", default=1, ) if option is Options.POOLKEY: parser.add_argument( "-p", "--pool-key", type=binascii.unhexlify, help="Pool Public Key (48 bytes)", default="", ) if option is Options.FARMERKEY: parser.add_argument( "-f", "--farmerkey", type=binascii.unhexlify, help="Farmer Public Key (48 bytes)", default="", ) if option is Options.BLADEBIT_WARMSTART: parser.add_argument( "-w", "--warmstart", action="store_true", help="Warm start", default=False, ) if option is Options.BLADEBIT_NONUMA: parser.add_argument( "-m", "--nonuma", action="store_true", help="Disable numa", default=False, ) if option is Options.VERBOSE: parser.add_argument( "-v", "--verbose", action="store_true", help="Set verbose", default=False, ) if option is Options.OVERRIDE_K: parser.add_argument( "--override-k", dest="override", action="store_true", help="Force size smaller than 32", default=False, ) if option is Options.ALT_FINGERPRINT: parser.add_argument( "-a", "--alt_fingerprint", type=int, default=None, help="Enter the alternative fingerprint of the key you want to use", ) if option is Options.EXCLUDE_FINAL_DIR: parser.add_argument( "-x", "--exclude_final_dir", action="store_true", help="Skips adding [final dir] to harvester for farming", default=False, ) if option is Options.CONNECT_TO_DAEMON: parser.add_argument( "-D", "--connect-to-daemon", action="store_true", help=argparse.SUPPRESS, default=False, ) def call_plotters(root_path: Path, args): stor_root_path = root_path root_path = get_plotters_root_path(root_path) if not root_path.is_dir(): if os.path.exists(root_path): try: os.remove(root_path) except Exception as e: print(f"Exception deleting old root path: {type(e)} {e}.") if not os.path.exists(root_path): print(f"Creating plotters folder within STOR_ROOT: {root_path}") try: os.mkdir(root_path) except Exception as e: print(f"Cannot create plotters root path {root_path} {type(e)} {e}.") plotters = argparse.ArgumentParser(description="Available options.") subparsers = plotters.add_subparsers(help="Available options", dest="plotter") build_parser(subparsers, root_path, stor_plotter, "chiapos", "Storpos Plotter") build_parser(subparsers, root_path, madmax_plotter, "madmax", "Madmax Plotter") build_parser(subparsers, root_path, bladebit_plotter, "bladebit", "Bladebit Plotter") install_parser = subparsers.add_parser("install", description="Install custom plotters.") install_parser.add_argument( "install_plotter", type=str, help="The plotters available for installing. Choose from madmax or bladebit." ) args = plotters.parse_args(args) if args.plotter == "chiapos": plot_stor(args, stor_root_path) if args.plotter == "madmax": plot_madmax(args, stor_root_path, root_path) if args.plotter == "bladebit": plot_bladebit(args, stor_root_path, root_path) if args.plotter == "install": install_plotter(args.install_plotter, root_path) def get_available_plotters(root_path) -> Dict[str, Any]: plotters_root_path: Path = get_plotters_root_path(root_path) plotters: Dict[str, Any] = {} chiapos: Optional[Dict[str, Any]] = get_chiapos_install_info() bladebit: Optional[Dict[str, Any]] = get_bladebit_install_info(plotters_root_path) madmax: Optional[Dict[str, Any]] = get_madmax_install_info(plotters_root_path) if chiapos is not None: plotters["chiapos"] = chiapos if bladebit is not None: plotters["bladebit"] = bladebit if madmax is not None: plotters["madmax"] = madmax return plotters

true

7907e50f30897314ff3f27a94e696e46fe598cc1

26,018

py

Python

meshdynamic/meshDynamic-Density.py

deepkashiwa/DeepUrbanEvent

3356ee3030893e2806d23541b2650ec73dab3075

[ "MIT" ]

17

2019-04-09T06:28:22.000Z

2022-03-13T09:31:55.000Z

meshdynamic/meshDynamic-Density.py

deepkashiwa/DeepUrbanEvent

3356ee3030893e2806d23541b2650ec73dab3075

[ "MIT" ]

2

2021-04-12T02:23:01.000Z

2021-06-01T02:21:10.000Z

meshdynamic/meshDynamic-Density.py

deepkashiwa/DeepUrbanEvent

3356ee3030893e2806d23541b2650ec73dab3075

[ "MIT" ]

1

2021-07-30T10:22:41.000Z

import csv import numpy as np import os import sys import time import jismesh.utils as ju import pandas as pd curPath = os.path.abspath(os.path.dirname(__file__)) rootPath = os.path.split(curPath)[0] sys.path.append(rootPath) from common.datastructure.Point import Point from common.datastructure.Mesh import Mesh # meshTokyo = Mesh('tokyo','500m') # GRIDNUMBER = meshTokyo.lonNum * meshTokyo.latNum # print(meshTokyo.size, GRIDNUMBER) # InterpolatedStep = 12 def getTimestamps(fileName): last_tid = '' D = [] with open(fileName, "r") as rf: reader = csv.reader(rf) for line in reader: tid = line[0] if last_tid != '' and last_tid != tid: break timestamp = line[1] D.append(timestamp) last_tid = tid return D def getMesh(mesh, readFileName, writeFileName): cnt = 0 wf = open(writeFileName, 'w') with open(readFileName, 'r') as rf: for line in csv.reader(rf): if cnt % 1000000 == 0: print(cnt) tid = line[0] timestamp = line[1] p = Point(float(line[2]), float(line[3])) meshid = mesh.inWhichGrid(p) wf.write(','.join([tid, timestamp, str(meshid)])+'\n') cnt += 1 wf.close() def genMeshDynamic(mesh, fileName, meshFileName): MD = {} with open(fileName, "r") as rf: reader = csv.reader(rf) for line in reader: tid = line[0] timestamp = line[1] meshid = line[2] key = (timestamp, meshid) if key in MD: MD[key].add(tid) else: MD[key] = set(tid) wf = open(meshFileName, 'w') Timestamps = getTimestamps(fileName) for ts in Timestamps: for meshid in range(mesh.lonNum * mesh.latNum): key = (ts, str(meshid)) if key in MD: value = len(MD[key]) else: value = 0 wf.write(','.join([key[0], key[1], str(value)]) + '\n') wf.close() def getGrids(fileName): last_tid = '' G = [] with open(fileName, "r") as rf: reader = csv.reader(rf) for line in reader: tid = line[0] if last_tid != '' and last_tid != tid: break grid = line[1] G.append(grid) last_tid = tid return G def getDynamicMesh_mobmap(trajFileName, dynamicFileName, meshcode_level): Timestamps = getTimestamps(trajFileName) TIMENUMBER = len(Timestamps) TS = {} for i in range(TIMENUMBER): TS[Timestamps[i]] = i print('getDynamicMesh Started : ', time.ctime()) R = [] for i in range(TIMENUMBER): R.append({}) with open(trajFileName, 'r') as rf: reader = csv.reader(rf) for line in reader: # tid = line[0] timestamp = line[1] lon = float(line[2]) lat = float(line[3]) meshcode = ju.to_meshcode(lat, lon, meshcode_level) if meshcode in R[TS[timestamp]]: R[TS[timestamp]][meshcode] += 1 else: R[TS[timestamp]][meshcode] = 1 print('getDynamicMesh Count Ended : ', time.ctime()) with open(dynamicFileName, 'w') as wf: wf.write("@dynamic-mesh\n") wf.write("@use-mesh-code," + str(meshcode_level)) for i in range(len(R)): timestamp = Timestamps[i] for key in R[i]: meshcode = key meshpop = R[i][meshcode] wf.write(','.join([timestamp, meshcode, str(meshpop)]) + '\n') print('getDynamicMesh Ended : ', time.ctime()) def getDynamicMeshMobmap(trajFileName, dynamicFileName, meshcode_level): Timestamps = getTimestamps(trajFileName) TIMENUMBER = len(Timestamps) TS = {} for i in range(TIMENUMBER): TS[Timestamps[i]] = i print('getDynamicMesh Started : ', time.ctime()) R = [] for i in range(TIMENUMBER): R.append({}) with open(trajFileName, 'r') as rf: reader = csv.reader(rf) for line in reader: # tid = line[0] timestamp = line[1] lon = float(line[2]) lat = float(line[3]) meshcode = ju.to_meshcode(lat, lon, meshcode_level) if meshcode in R[TS[timestamp]]: R[TS[timestamp]][meshcode] += 1 else: R[TS[timestamp]][meshcode] = 1 with open(dynamicFileName, 'w') as wf: wf.write("@dynamic-mesh\n") wf.write("@use-mesh-code," + str(meshcode_level)) for i in range(len(R)): timestamp = Timestamps[i] for key in R[i]: meshcode = key meshpop = R[i][meshcode] wf.write(','.join([timestamp, meshcode, str(meshpop)]) + '\n') print('getDynamicMesh Ended : ', time.ctime()) def getRfromDynamicMeshMobmap(meshcode_level, dynamicFileName, dynamicFileName1, dynamicFileName2): df1 = pd.read_csv(dynamicFileName, header=None, skiprows=2) df1.iloc[:,2] = np.log10(df1.iloc[:,2]+1) * 100 df2 = pd.read_csv(dynamicFileName, header=None, skiprows=2) df2.iloc[:, 2] = np.log(df2.iloc[:,2]+1) * 100 with open(dynamicFileName1, 'w') as wf: wf.write("@dynamic-mesh\n") wf.write("@use-mesh-code," + str(meshcode_level) + '\n') with open(dynamicFileName2, 'w') as wf: wf.write("@dynamic-mesh\n") wf.write("@use-mesh-code," + str(meshcode_level) + '\n') df1.to_csv(dynamicFileName1, header=False, index=False, mode='a') df2.to_csv(dynamicFileName2, header=False, index=False, mode='a') def getDynamicMeshMobmapR(R, trajFileName, dynamicFileName, meshcode_level): Timestamps = getTimestamps(trajFileName) print('getDynamicMesh Count Ended : ', time.ctime()) with open(dynamicFileName, 'w') as wf: wf.write("@dynamic-mesh\n") wf.write("@use-mesh-code," + str(meshcode_level)) for i in range(len(R)): timestamp = Timestamps[i] for key in R[i]: meshcode = key meshpop = R[i][meshcode] wf.write(','.join([timestamp, meshcode, str(meshpop)]) + '\n') print('getDynamicMesh Ended : ', time.ctime()) def genMeshDynamicTimeInterval(fileName, meshFileName, startTimestamp, endTimestamp): Timestamps = getTimestamps(fileName) startIndex = Timestamps.index(startTimestamp) endIndex = Timestamps.index(endTimestamp) Interval = [Timestamps[t] for t in range(startIndex, endIndex)] def strHH(timestamp): return timestamp[11:13] + timestamp[14:16] wf = open(meshFileName[:-4] + '_' + strHH(startTimestamp) + '_' + strHH(endTimestamp) + '.csv', 'w') with open(meshFileName, 'r') as rf: for line in csv.reader(rf): if line[0] in Interval: wf.write(','.join(line) + '\n') else: pass wf.close() def genMeshDynamicTimeInterval_Mobmap(fileName, meshFileName, startTimestamp, endTimestamp): Timestamps = getTimestamps(fileName) startIndex = Timestamps.index(startTimestamp) endIndex = Timestamps.index(endTimestamp) Interval = [Timestamps[t] for t in range(startIndex, endIndex)] def strHH(timestamp): return timestamp[11:13] + timestamp[14:16] wf = open(meshFileName[:-4] + '_' + strHH(startTimestamp) + '_' + strHH(endTimestamp) + '.csv', 'w') with open(meshFileName, 'r') as rf: for line in csv.reader(rf): if line[0] == '@dynamic-mesh' or '"@use-mesh-code': wf.write(line + '\n') if line[0] in Interval: wf.write(','.join(line) + '\n') else: pass wf.close() def genMeshDynamicMobmap(mesh, meshFileName, mobmapFile, timestamp): wf = open(mobmapFile, 'w') wf.write('@static-mesh' + '\n') wf.write(','.join([str(x) for x in [mesh.minLat, mesh.minLon, mesh.dLat, mesh.dLon]]) + '\n') with open(meshFileName, 'r') as rf: for line in csv.reader(rf): if timestamp != line[0]: continue else: meshid = line[1] number = line[2] xi, yi = mesh.Index[int(meshid)] wf.write(','.join([str(item) for item in [yi, xi, number]]) + '\n') wf.close() def loadGTrajectory(fileName): print('loadTrajectory Started : ', time.ctime()) TDB = {} with open(fileName, 'r') as rf: reader = csv.reader(rf) for line in reader: tid = line[0] # timestamp = line[1] meshid = line[2] if tid in TDB: TDB[tid].append(meshid) else: TDB[tid] = [meshid] print('loadTrajectory Ended : ', time.ctime()) return TDB def getINDEX(mesh, gTrajFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum print('getTrajectoryINDEX Started : ', time.ctime()) Timestamps = getTimestamps(gTrajFileName) print('timestamps...', len(Timestamps)) TDB = loadGTrajectory(gTrajFileName) INDEX = [] for i in range(len(Timestamps)): INDEX.append([]) for G in range(GRIDNUMBER): INDEX[i].append(set()) # set().add # print(np.array(INDEX).shape) for tid in TDB: traj = TDB[tid] for i in range(len(traj)): HH = i if traj[i] == 'None': pass else: gid = int(traj[i]) INDEX[HH][gid].add(tid) # set().add return INDEX def getGridImageIndex(mesh, window=15): GRIDNUMBER = mesh.lonNum * mesh.latNum IMG = [] for g in range(GRIDNUMBER): R = np.zeros((window, window), dtype='int32') current_x, current_y = mesh.Index[g] start = 0 - window // 2 end = window + start for i, dx in enumerate(list(range(start, end))): for j, dy in enumerate(list(range(start, end))): x = current_x + dx y = current_y + dy if mesh.inMesh(x, y): grid = mesh.ReverseIndex[(x, y)] R[j][i] = grid else: R[j][i] = -1 R = R[::-1, :] IMG.append(R) return IMG def genGridTransit(mesh, gTrajFileName, transitFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum print('genGridTransit Started : ', time.ctime()) transitWriteFile = open(transitFileName, 'w') INDEX = getINDEX(mesh, gTrajFileName) Timestamps = getTimestamps(gTrajFileName) GridImageIndex = getGridImageIndex(mesh) print('INDEX, Timestamps, GridImageIndex have been prepared.', time.ctime()) for i in range(len(Timestamps) - 1): for j in range(GRIDNUMBER): cur_time = i next_time = i + 1 cur_grid = j transitgrids = GridImageIndex[cur_grid] Transit = np.zeros(transitgrids.shape, dtype='int32') for ii in range(transitgrids.shape[0]): for jj in range(transitgrids.shape[1]): next_grid = transitgrids[ii][jj] if next_grid != -1: trajfirst = INDEX[cur_time][cur_grid] trajsecond = INDEX[next_time][next_grid] transit_num = len(trajfirst & trajsecond) Transit[ii][jj] = transit_num else: pass FlattedTransit = Transit.reshape(-1).tolist() lineitem = [str(i), str(j)] lineitem.extend([str(t) for t in FlattedTransit]) line = ','.join(lineitem) + '\n' transitWriteFile.write(line) print('genGridTransit timestamp: ', i) transitWriteFile.close() print('genGridTransit Ended: ', time.ctime()) # This grid transit version is for 1minutes trajectory, more accurate, not for 5minutes. # !!!!!!!!!!!!!!!!!!!! 1 minute trajectory data. # TT is supposed to be 288 not 289 because it is interval. def genGridTransit_5minutes_from_1minute(mesh, gTrajFileName, transitFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum print('genGridTransit Started : ', time.ctime()) transitWriteFile = open(transitFileName, 'w') INDEX = getINDEX(mesh, gTrajFileName) # Timestamps = getTimestamps(gTrajFileName) GridImageIndex = getGridImageIndex(mesh) print('INDEX, Timestamps, GridImageIndex have been prepared.', time.ctime()) TT, SPAN = 24 * 12, 5 for i in range(TT): for j in range(GRIDNUMBER): cur_time = i cur_grid = j transitgrids = GridImageIndex[cur_grid] Transit = np.zeros(transitgrids.shape, dtype='int32') for ii in range(transitgrids.shape[0]): for jj in range(transitgrids.shape[1]): next_grid = transitgrids[ii][jj] if next_grid != -1: cur_time_start = cur_time * SPAN cur_time_end = (cur_time + 1) * SPAN + 1 SS = set() for pp in range(cur_time_start, cur_time_end): trajfirst = INDEX[pp][cur_grid] for qq in range(pp, cur_time_end): trajsecond = INDEX[qq][next_grid] SS.update(trajfirst & trajsecond) transit_num = len(SS) Transit[ii][jj] = transit_num else: pass FlattedTransit = Transit.reshape(-1).tolist() lineitem = [str(i), str(j)] lineitem.extend([str(t) for t in FlattedTransit]) line = ','.join(lineitem) + '\n' transitWriteFile.write(line) print('genGridTransit timestamp: ', i) transitWriteFile.close() print('genGridTransit Ended: ', time.ctime()) def getGridTransit(mesh, gTrajFileName, transitFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum Timestamps = getTimestamps(gTrajFileName) TIMENUMBER = len(Timestamps) - 1 # -1 is because of transit print('getGridTransit Started : ', time.ctime()) R = [] for i in range(TIMENUMBER): R.append([]) for j in range(GRIDNUMBER): R[i].append([]) with open(transitFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = int(line[0]) grid = int(line[1]) R[timestamp][grid] = line[2:] R = np.array(R, dtype='int32') # 144, 6000, 225 R = R.reshape(R.shape[0], mesh.lonNum, mesh.latNum, R.shape[2]) R = np.swapaxes(R, 2, 1) R = R[:, ::-1, :, :] # 144, 75, 80, 225 return R def getGridPop(mesh, gTrajFileName, popFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum Timestamps = getTimestamps(gTrajFileName) TIMENUMBER = len(Timestamps) TS = {} for i in range(TIMENUMBER): TS[Timestamps[i]] = i print('getGridPop Started : ', time.ctime()) R = [] for i in range(TIMENUMBER): R.append([]) for j in range(GRIDNUMBER): R[i].append([]) with open(popFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = TS[line[0]] grid = int(line[1]) R[timestamp][grid] = int(line[2]) R = np.array(R, dtype='int32') # shape 145, 6000 R = R.reshape(R.shape[0], int(R.shape[1] ** 0.5), int(R.shape[1] ** 0.5), 1) R = np.swapaxes(R, 2, 1) R = R[:, ::-1, :, :] # shape 145, 80, 80, 1 return R def getGridPopPartition(R, M, K): # Original 8*8 matrix N = 8 = M*K # M = 4 # M*M sub matrix # K = 2 # each sub matrix has the size of K * K P = [] for i in range(M): for j in range(M): P.append(R[:, i*K:i*K+K, j*K:j*K+K, :]) return np.array(P) def getGridPop2DNumpy(mesh, gTrajFileName, popFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum Timestamps = getTimestamps(gTrajFileName) TIMENUMBER = len(Timestamps) TS = {} for i in range(TIMENUMBER): TS[Timestamps[i]] = i print('getGridPop Started : ', time.ctime()) R = [] for i in range(TIMENUMBER): R.append([]) for j in range(GRIDNUMBER): R[i].append([]) with open(popFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = TS[line[0]] grid = int(line[1]) R[timestamp][grid] = int(line[2]) R = np.array(R, dtype='int32') # shape 145, 6000 return R def getGridPopTimeInterval(mesh, popFileName): print('getGridPop', popFileName, time.ctime()) GRIDNUMBER = mesh.lonNum * mesh.latNum Timestamps = [] lastTimestamp = '' with open(popFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = line[0] if timestamp != lastTimestamp: Timestamps.append(timestamp) lastTimestamp = timestamp TIMENUMBER = len(Timestamps) TS = {} for i in range(TIMENUMBER): TS[Timestamps[i]] = i R = [] for i in range(TIMENUMBER): R.append([]) for j in range(GRIDNUMBER): R[i].append([]) with open(popFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = TS[line[0]] grid = int(line[1]) R[timestamp][grid] = int(line[2]) R = np.array(R, dtype='int32') # shape 145, 6000 R = R.reshape(R.shape[0], int(R.shape[1] ** 0.5), int(R.shape[1] ** 0.5), 1) R = np.swapaxes(R, 2, 1) R = R[:, ::-1, :, :] # shape 145, 75, 80, 1 return R def getGridTransitTimeInterval(mesh, transitFileName): print('getGridTransit Started : ', transitFileName, time.ctime()) GRIDNUMBER = mesh.lonNum * mesh.latNum # Timestamps = [] # lastTimestamp = '' # with open(transitFileName, 'r') as rf: # tansistReader = csv.reader(rf) # for line in tansistReader: # timestamp = line[0] # if timestamp != lastTimestamp: # Timestamps.append(timestamp) # lastTimestamp = timestamp # TIMENUMBER = len(Timestamps) TIMENUMBER = 24 * 12 R = [] for i in range(TIMENUMBER): R.append([]) for j in range(GRIDNUMBER): R[i].append([]) with open(transitFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = int(line[0]) grid = int(line[1]) R[timestamp][grid] = line[2:] R = np.array(R, dtype='int32') # 144, 6000, 225 R = R.reshape(R.shape[0], mesh.lonNum, mesh.latNum, R.shape[2]) R = np.swapaxes(R, 2, 1) R = R[:, ::-1, :, :] # 144, 75, 80, 225 return R def shuffleTrainValidateTest(InterpolatedStep, path, fileName, R, testRate=0.2): TIMESTEP = InterpolatedStep * 2 Sequence = [] for i in range(R.shape[0] - TIMESTEP): Sequence.append(R[i:i+TIMESTEP, :, :, :]) Sequence = np.array(Sequence, dtype='int32') INDEX = list(range(len(Sequence))) np.random.shuffle(INDEX) np.random.shuffle(INDEX) np.random.shuffle(INDEX) trainINDEX = INDEX[:int(len(INDEX) * (1 - testRate))] testINDEX = INDEX[int(len(INDEX) * (1 - testRate)):] train = Sequence[trainINDEX] test = Sequence[testINDEX] np.save(path + 'train_' + fileName, train) np.save(path + 'test_' + fileName, test) print(train.shape, test.shape) # trainINDEX = INDEX[:int(len(INDEX) * (1 - testRate - validateRate))] # validateINDEX = INDEX[int(len(INDEX) * (1 - testRate - validateRate)):int(len(INDEX) * (1 - testRate))] # testINDEX = INDEX[int(len(INDEX) * (1 - testRate)):] # train = Sequence[trainINDEX] # validate = Sequence[validateINDEX] # test = Sequence[testINDEX] # np.save(path + 'train_' + fileName, train) # np.save(path + 'validate_' + fileName, validate) # np.save(path + 'test_' + fileName, test) # print(train.shape, validate.shape, test.shape) # or directly return not save to file because just too big. # return train, validate, test def getShuffledTrainTest(path, fileName, TrainTest): return np.load(path + TrainTest + '_' + fileName + '.npy') def testcode(mesh): GRIDNUMBER = mesh.lonNum * mesh.latNum window = 5 R = np.zeros((window, window), dtype='int32') center = mesh.ReverseIndex[(2,2)] current_x, current_y = mesh.Index[center] start = 0 - window // 2 end = window + start for i, dx in enumerate(list(range(start, end))): for j, dy in enumerate(list(range(start, end))): x = current_x + dx y = current_y + dy if mesh.inMesh(x, y): grid = mesh.ReverseIndex[(x, y)] R[j][i] = grid else: R[j][i] = -1 R = R[::-1, :] print(R) for i in range(len(R)): print(R[i]) for i in range(len(R)): print(R[i][0], R[i][1], R[i][2], R[i][3], R[i][4]) T = R.reshape(-1) print(T.tolist()) P = T.reshape(window, window) print(P) print(R.shape) print(R[54][4178]) print(np.max(R) == 3369) print(mesh.Index[3369]) x, y = mesh.Index[3369] lon, lat = mesh.minLon + (x + 0.5) * mesh.dLon, \ mesh.minLat + (y + 0.5) * mesh.dLat print(lon, lat) print(mesh.lonNum, mesh.latNum) T = np.array(range(GRIDNUMBER)) T = T.reshape(mesh.lonNum, mesh.latNum) T = np.swapaxes(T, 1, 0) T = T[::-1, :] print(T) print(T.shape) def run5min201802(mesh, dataPATH, dates): print('Now is getting trainig XS and YS...', dates) # timestamp = '2011-10-20 09:00:00' # filenameTime = timestamp[0:4] + timestamp[5:7] + timestamp[8:10] \ # + timestamp[11:13] + timestamp[14:16] + timestamp[17:19] # print(filenameTime) for date in dates: # first step: from trajectory point to mesh getMesh(dataPATH + date + 'tokyo_interpo5min.csv', dataPATH + date + 'tokyo_' + mesh.size + '_5min.csv') # second step: calculate mesh population at each timestamp genMeshDynamic(dataPATH + date + 'tokyo_' + mesh.size + '_5min.csv', dataPATH + date + 'tokyo_' + mesh.size + '_5min_pop.csv') # fourth step: mesh transit between two consecutive timestamps genGridTransit(dataPATH + date + 'tokyo_' + mesh.size + '_5min.csv', dataPATH + date + 'tokyo_' + mesh.size + '_5min_transit.csv') def getHHTransit(HH): assert HH <= 22, 'Hour should not be over 22.' dataPATH = '../interpo_data/' date = '20111020' R = getGridTransit(dataPATH + date + 'tokyo_meshtransit10min_1min_15.csv') # (144, 72, 80, 225) R = R[HH*6:HH*6+6, :, :, :] # (6, 72, 80, 225) R = R.reshape(R.shape[0], -1, R.shape[-1]) # (6, 5760, 225) R = R.transpose(1, 0, 2) # (5760, 6, 225) R = R.reshape(R.shape[0], R.shape[1], int(R.shape[2]**0.5), int(R.shape[2]**0.5), 1) return R def runCrowdDensity(): dataPATH = '../interpo_data/' meshTokyo = Mesh('tokyo', '500m') #meshcode_level = 4 alldates = ["20110217","20110218","20110219","20110220", "20110221", "20110222","20110223", "20110224", "20110225", "20110226", "20110227"] for date in alldates: print('this is date', date) getMesh(meshTokyo, dataPATH + date + 'tokyo_interpo5min.csv', dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min.csv') genMeshDynamic(meshTokyo, dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min.csv', dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min_pop.csv') # def runCrowdFlow_from5min(): # from common.dataparam.Param import alldates # dataPATH = '../interpo_data/' # meshTokyo = Mesh('tokyo', '500m') # #meshcode_level = 4 # # for date in alldates: # print('this is date', date) # genGridTransit(meshTokyo, # dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min.csv', # dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min_transit_from5min.csv') # paper crowd flow is from 1min.!!!!!!!!!!!! def runCrowdFlow(): dataPATH = '../interpo_data/' meshTokyo = Mesh('tokyo', '500m') #meshcode_level = 4 alldates = ["20110217", "20110218", "20110219", "20110220", "20110221", "20110222", "20110223", "20110224", "20110225", "20110226", "20110227"] for date in alldates: print('this is date', date) getMesh(meshTokyo, dataPATH + date + 'tokyo_interpo1min.csv', dataPATH + date + 'tokyo_' + meshTokyo.size + '_1min.csv') genGridTransit_5minutes_from_1minute(meshTokyo, dataPATH + date + 'tokyo_' + meshTokyo.size + '_1min.csv', dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min_transit.csv') def main(): runCrowdDensity() if __name__ == '__main__': main()

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import csv import numpy as np import os import sys import time import jismesh.utils as ju import pandas as pd curPath = os.path.abspath(os.path.dirname(__file__)) rootPath = os.path.split(curPath)[0] sys.path.append(rootPath) from common.datastructure.Point import Point from common.datastructure.Mesh import Mesh def getTimestamps(fileName): last_tid = '' D = [] with open(fileName, "r") as rf: reader = csv.reader(rf) for line in reader: tid = line[0] if last_tid != '' and last_tid != tid: break timestamp = line[1] D.append(timestamp) last_tid = tid return D def getMesh(mesh, readFileName, writeFileName): cnt = 0 wf = open(writeFileName, 'w') with open(readFileName, 'r') as rf: for line in csv.reader(rf): if cnt % 1000000 == 0: print(cnt) tid = line[0] timestamp = line[1] p = Point(float(line[2]), float(line[3])) meshid = mesh.inWhichGrid(p) wf.write(','.join([tid, timestamp, str(meshid)])+'\n') cnt += 1 wf.close() def genMeshDynamic(mesh, fileName, meshFileName): MD = {} with open(fileName, "r") as rf: reader = csv.reader(rf) for line in reader: tid = line[0] timestamp = line[1] meshid = line[2] key = (timestamp, meshid) if key in MD: MD[key].add(tid) else: MD[key] = set(tid) wf = open(meshFileName, 'w') Timestamps = getTimestamps(fileName) for ts in Timestamps: for meshid in range(mesh.lonNum * mesh.latNum): key = (ts, str(meshid)) if key in MD: value = len(MD[key]) else: value = 0 wf.write(','.join([key[0], key[1], str(value)]) + '\n') wf.close() def getGrids(fileName): last_tid = '' G = [] with open(fileName, "r") as rf: reader = csv.reader(rf) for line in reader: tid = line[0] if last_tid != '' and last_tid != tid: break grid = line[1] G.append(grid) last_tid = tid return G def getDynamicMesh_mobmap(trajFileName, dynamicFileName, meshcode_level): Timestamps = getTimestamps(trajFileName) TIMENUMBER = len(Timestamps) TS = {} for i in range(TIMENUMBER): TS[Timestamps[i]] = i print('getDynamicMesh Started : ', time.ctime()) R = [] for i in range(TIMENUMBER): R.append({}) with open(trajFileName, 'r') as rf: reader = csv.reader(rf) for line in reader: timestamp = line[1] lon = float(line[2]) lat = float(line[3]) meshcode = ju.to_meshcode(lat, lon, meshcode_level) if meshcode in R[TS[timestamp]]: R[TS[timestamp]][meshcode] += 1 else: R[TS[timestamp]][meshcode] = 1 print('getDynamicMesh Count Ended : ', time.ctime()) with open(dynamicFileName, 'w') as wf: wf.write("@dynamic-mesh\n") wf.write("@use-mesh-code," + str(meshcode_level)) for i in range(len(R)): timestamp = Timestamps[i] for key in R[i]: meshcode = key meshpop = R[i][meshcode] wf.write(','.join([timestamp, meshcode, str(meshpop)]) + '\n') print('getDynamicMesh Ended : ', time.ctime()) def getDynamicMeshMobmap(trajFileName, dynamicFileName, meshcode_level): Timestamps = getTimestamps(trajFileName) TIMENUMBER = len(Timestamps) TS = {} for i in range(TIMENUMBER): TS[Timestamps[i]] = i print('getDynamicMesh Started : ', time.ctime()) R = [] for i in range(TIMENUMBER): R.append({}) with open(trajFileName, 'r') as rf: reader = csv.reader(rf) for line in reader: timestamp = line[1] lon = float(line[2]) lat = float(line[3]) meshcode = ju.to_meshcode(lat, lon, meshcode_level) if meshcode in R[TS[timestamp]]: R[TS[timestamp]][meshcode] += 1 else: R[TS[timestamp]][meshcode] = 1 with open(dynamicFileName, 'w') as wf: wf.write("@dynamic-mesh\n") wf.write("@use-mesh-code," + str(meshcode_level)) for i in range(len(R)): timestamp = Timestamps[i] for key in R[i]: meshcode = key meshpop = R[i][meshcode] wf.write(','.join([timestamp, meshcode, str(meshpop)]) + '\n') print('getDynamicMesh Ended : ', time.ctime()) def getRfromDynamicMeshMobmap(meshcode_level, dynamicFileName, dynamicFileName1, dynamicFileName2): df1 = pd.read_csv(dynamicFileName, header=None, skiprows=2) df1.iloc[:,2] = np.log10(df1.iloc[:,2]+1) * 100 df2 = pd.read_csv(dynamicFileName, header=None, skiprows=2) df2.iloc[:, 2] = np.log(df2.iloc[:,2]+1) * 100 with open(dynamicFileName1, 'w') as wf: wf.write("@dynamic-mesh\n") wf.write("@use-mesh-code," + str(meshcode_level) + '\n') with open(dynamicFileName2, 'w') as wf: wf.write("@dynamic-mesh\n") wf.write("@use-mesh-code," + str(meshcode_level) + '\n') df1.to_csv(dynamicFileName1, header=False, index=False, mode='a') df2.to_csv(dynamicFileName2, header=False, index=False, mode='a') def getDynamicMeshMobmapR(R, trajFileName, dynamicFileName, meshcode_level): Timestamps = getTimestamps(trajFileName) print('getDynamicMesh Count Ended : ', time.ctime()) with open(dynamicFileName, 'w') as wf: wf.write("@dynamic-mesh\n") wf.write("@use-mesh-code," + str(meshcode_level)) for i in range(len(R)): timestamp = Timestamps[i] for key in R[i]: meshcode = key meshpop = R[i][meshcode] wf.write(','.join([timestamp, meshcode, str(meshpop)]) + '\n') print('getDynamicMesh Ended : ', time.ctime()) def genMeshDynamicTimeInterval(fileName, meshFileName, startTimestamp, endTimestamp): Timestamps = getTimestamps(fileName) startIndex = Timestamps.index(startTimestamp) endIndex = Timestamps.index(endTimestamp) Interval = [Timestamps[t] for t in range(startIndex, endIndex)] def strHH(timestamp): return timestamp[11:13] + timestamp[14:16] wf = open(meshFileName[:-4] + '_' + strHH(startTimestamp) + '_' + strHH(endTimestamp) + '.csv', 'w') with open(meshFileName, 'r') as rf: for line in csv.reader(rf): if line[0] in Interval: wf.write(','.join(line) + '\n') else: pass wf.close() def genMeshDynamicTimeInterval_Mobmap(fileName, meshFileName, startTimestamp, endTimestamp): Timestamps = getTimestamps(fileName) startIndex = Timestamps.index(startTimestamp) endIndex = Timestamps.index(endTimestamp) Interval = [Timestamps[t] for t in range(startIndex, endIndex)] def strHH(timestamp): return timestamp[11:13] + timestamp[14:16] wf = open(meshFileName[:-4] + '_' + strHH(startTimestamp) + '_' + strHH(endTimestamp) + '.csv', 'w') with open(meshFileName, 'r') as rf: for line in csv.reader(rf): if line[0] == '@dynamic-mesh' or '"@use-mesh-code': wf.write(line + '\n') if line[0] in Interval: wf.write(','.join(line) + '\n') else: pass wf.close() def genMeshDynamicMobmap(mesh, meshFileName, mobmapFile, timestamp): wf = open(mobmapFile, 'w') wf.write('@static-mesh' + '\n') wf.write(','.join([str(x) for x in [mesh.minLat, mesh.minLon, mesh.dLat, mesh.dLon]]) + '\n') with open(meshFileName, 'r') as rf: for line in csv.reader(rf): if timestamp != line[0]: continue else: meshid = line[1] number = line[2] xi, yi = mesh.Index[int(meshid)] wf.write(','.join([str(item) for item in [yi, xi, number]]) + '\n') wf.close() def loadGTrajectory(fileName): print('loadTrajectory Started : ', time.ctime()) TDB = {} with open(fileName, 'r') as rf: reader = csv.reader(rf) for line in reader: tid = line[0] # timestamp = line[1] meshid = line[2] if tid in TDB: TDB[tid].append(meshid) else: TDB[tid] = [meshid] print('loadTrajectory Ended : ', time.ctime()) return TDB def getINDEX(mesh, gTrajFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum print('getTrajectoryINDEX Started : ', time.ctime()) Timestamps = getTimestamps(gTrajFileName) print('timestamps...', len(Timestamps)) TDB = loadGTrajectory(gTrajFileName) INDEX = [] for i in range(len(Timestamps)): INDEX.append([]) for G in range(GRIDNUMBER): INDEX[i].append(set()) # set().add # print(np.array(INDEX).shape) for tid in TDB: traj = TDB[tid] for i in range(len(traj)): HH = i if traj[i] == 'None': pass else: gid = int(traj[i]) INDEX[HH][gid].add(tid) # set().add return INDEX def getGridImageIndex(mesh, window=15): GRIDNUMBER = mesh.lonNum * mesh.latNum IMG = [] for g in range(GRIDNUMBER): R = np.zeros((window, window), dtype='int32') current_x, current_y = mesh.Index[g] start = 0 - window // 2 end = window + start for i, dx in enumerate(list(range(start, end))): for j, dy in enumerate(list(range(start, end))): x = current_x + dx y = current_y + dy if mesh.inMesh(x, y): grid = mesh.ReverseIndex[(x, y)] R[j][i] = grid else: R[j][i] = -1 R = R[::-1, :] IMG.append(R) return IMG def genGridTransit(mesh, gTrajFileName, transitFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum print('genGridTransit Started : ', time.ctime()) transitWriteFile = open(transitFileName, 'w') INDEX = getINDEX(mesh, gTrajFileName) Timestamps = getTimestamps(gTrajFileName) GridImageIndex = getGridImageIndex(mesh) print('INDEX, Timestamps, GridImageIndex have been prepared.', time.ctime()) for i in range(len(Timestamps) - 1): for j in range(GRIDNUMBER): cur_time = i next_time = i + 1 cur_grid = j transitgrids = GridImageIndex[cur_grid] Transit = np.zeros(transitgrids.shape, dtype='int32') for ii in range(transitgrids.shape[0]): for jj in range(transitgrids.shape[1]): next_grid = transitgrids[ii][jj] if next_grid != -1: trajfirst = INDEX[cur_time][cur_grid] trajsecond = INDEX[next_time][next_grid] transit_num = len(trajfirst & trajsecond) Transit[ii][jj] = transit_num else: pass FlattedTransit = Transit.reshape(-1).tolist() lineitem = [str(i), str(j)] lineitem.extend([str(t) for t in FlattedTransit]) line = ','.join(lineitem) + '\n' transitWriteFile.write(line) print('genGridTransit timestamp: ', i) transitWriteFile.close() print('genGridTransit Ended: ', time.ctime()) # This grid transit version is for 1minutes trajectory, more accurate, not for 5minutes. # !!!!!!!!!!!!!!!!!!!! 1 minute trajectory data. # TT is supposed to be 288 not 289 because it is interval. def genGridTransit_5minutes_from_1minute(mesh, gTrajFileName, transitFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum print('genGridTransit Started : ', time.ctime()) transitWriteFile = open(transitFileName, 'w') INDEX = getINDEX(mesh, gTrajFileName) # Timestamps = getTimestamps(gTrajFileName) GridImageIndex = getGridImageIndex(mesh) print('INDEX, Timestamps, GridImageIndex have been prepared.', time.ctime()) TT, SPAN = 24 * 12, 5 for i in range(TT): for j in range(GRIDNUMBER): cur_time = i cur_grid = j transitgrids = GridImageIndex[cur_grid] Transit = np.zeros(transitgrids.shape, dtype='int32') for ii in range(transitgrids.shape[0]): for jj in range(transitgrids.shape[1]): next_grid = transitgrids[ii][jj] if next_grid != -1: cur_time_start = cur_time * SPAN cur_time_end = (cur_time + 1) * SPAN + 1 SS = set() for pp in range(cur_time_start, cur_time_end): trajfirst = INDEX[pp][cur_grid] for qq in range(pp, cur_time_end): trajsecond = INDEX[qq][next_grid] SS.update(trajfirst & trajsecond) transit_num = len(SS) Transit[ii][jj] = transit_num else: pass FlattedTransit = Transit.reshape(-1).tolist() lineitem = [str(i), str(j)] lineitem.extend([str(t) for t in FlattedTransit]) line = ','.join(lineitem) + '\n' transitWriteFile.write(line) print('genGridTransit timestamp: ', i) transitWriteFile.close() print('genGridTransit Ended: ', time.ctime()) def getGridTransit(mesh, gTrajFileName, transitFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum Timestamps = getTimestamps(gTrajFileName) TIMENUMBER = len(Timestamps) - 1 # -1 is because of transit print('getGridTransit Started : ', time.ctime()) R = [] for i in range(TIMENUMBER): R.append([]) for j in range(GRIDNUMBER): R[i].append([]) with open(transitFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = int(line[0]) grid = int(line[1]) R[timestamp][grid] = line[2:] R = np.array(R, dtype='int32') # 144, 6000, 225 R = R.reshape(R.shape[0], mesh.lonNum, mesh.latNum, R.shape[2]) R = np.swapaxes(R, 2, 1) R = R[:, ::-1, :, :] # 144, 75, 80, 225 return R def getGridPop(mesh, gTrajFileName, popFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum Timestamps = getTimestamps(gTrajFileName) TIMENUMBER = len(Timestamps) TS = {} for i in range(TIMENUMBER): TS[Timestamps[i]] = i print('getGridPop Started : ', time.ctime()) R = [] for i in range(TIMENUMBER): R.append([]) for j in range(GRIDNUMBER): R[i].append([]) with open(popFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = TS[line[0]] grid = int(line[1]) R[timestamp][grid] = int(line[2]) R = np.array(R, dtype='int32') # shape 145, 6000 R = R.reshape(R.shape[0], int(R.shape[1] ** 0.5), int(R.shape[1] ** 0.5), 1) R = np.swapaxes(R, 2, 1) R = R[:, ::-1, :, :] # shape 145, 80, 80, 1 return R def getGridPopPartition(R, M, K): # Original 8*8 matrix N = 8 = M*K # M = 4 # M*M sub matrix # K = 2 # each sub matrix has the size of K * K P = [] for i in range(M): for j in range(M): P.append(R[:, i*K:i*K+K, j*K:j*K+K, :]) return np.array(P) def getGridPop2DNumpy(mesh, gTrajFileName, popFileName): GRIDNUMBER = mesh.lonNum * mesh.latNum Timestamps = getTimestamps(gTrajFileName) TIMENUMBER = len(Timestamps) TS = {} for i in range(TIMENUMBER): TS[Timestamps[i]] = i print('getGridPop Started : ', time.ctime()) R = [] for i in range(TIMENUMBER): R.append([]) for j in range(GRIDNUMBER): R[i].append([]) with open(popFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = TS[line[0]] grid = int(line[1]) R[timestamp][grid] = int(line[2]) R = np.array(R, dtype='int32') # shape 145, 6000 return R def getGridPopTimeInterval(mesh, popFileName): print('getGridPop', popFileName, time.ctime()) GRIDNUMBER = mesh.lonNum * mesh.latNum Timestamps = [] lastTimestamp = '' with open(popFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = line[0] if timestamp != lastTimestamp: Timestamps.append(timestamp) lastTimestamp = timestamp TIMENUMBER = len(Timestamps) TS = {} for i in range(TIMENUMBER): TS[Timestamps[i]] = i R = [] for i in range(TIMENUMBER): R.append([]) for j in range(GRIDNUMBER): R[i].append([]) with open(popFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = TS[line[0]] grid = int(line[1]) R[timestamp][grid] = int(line[2]) R = np.array(R, dtype='int32') # shape 145, 6000 R = R.reshape(R.shape[0], int(R.shape[1] ** 0.5), int(R.shape[1] ** 0.5), 1) R = np.swapaxes(R, 2, 1) R = R[:, ::-1, :, :] # shape 145, 75, 80, 1 return R def getGridTransitTimeInterval(mesh, transitFileName): print('getGridTransit Started : ', transitFileName, time.ctime()) GRIDNUMBER = mesh.lonNum * mesh.latNum # Timestamps = [] # lastTimestamp = '' # with open(transitFileName, 'r') as rf: # tansistReader = csv.reader(rf) # for line in tansistReader: # timestamp = line[0] # if timestamp != lastTimestamp: # Timestamps.append(timestamp) # lastTimestamp = timestamp # TIMENUMBER = len(Timestamps) TIMENUMBER = 24 * 12 R = [] for i in range(TIMENUMBER): R.append([]) for j in range(GRIDNUMBER): R[i].append([]) with open(transitFileName, 'r') as rf: tansistReader = csv.reader(rf) for line in tansistReader: timestamp = int(line[0]) grid = int(line[1]) R[timestamp][grid] = line[2:] R = np.array(R, dtype='int32') # 144, 6000, 225 R = R.reshape(R.shape[0], mesh.lonNum, mesh.latNum, R.shape[2]) R = np.swapaxes(R, 2, 1) R = R[:, ::-1, :, :] # 144, 75, 80, 225 return R def shuffleTrainValidateTest(InterpolatedStep, path, fileName, R, testRate=0.2): TIMESTEP = InterpolatedStep * 2 Sequence = [] for i in range(R.shape[0] - TIMESTEP): Sequence.append(R[i:i+TIMESTEP, :, :, :]) Sequence = np.array(Sequence, dtype='int32') INDEX = list(range(len(Sequence))) np.random.shuffle(INDEX) np.random.shuffle(INDEX) np.random.shuffle(INDEX) trainINDEX = INDEX[:int(len(INDEX) * (1 - testRate))] testINDEX = INDEX[int(len(INDEX) * (1 - testRate)):] train = Sequence[trainINDEX] test = Sequence[testINDEX] np.save(path + 'train_' + fileName, train) np.save(path + 'test_' + fileName, test) print(train.shape, test.shape) # trainINDEX = INDEX[:int(len(INDEX) * (1 - testRate - validateRate))] # validateINDEX = INDEX[int(len(INDEX) * (1 - testRate - validateRate)):int(len(INDEX) * (1 - testRate))] # testINDEX = INDEX[int(len(INDEX) * (1 - testRate)):] # train = Sequence[trainINDEX] # validate = Sequence[validateINDEX] # test = Sequence[testINDEX] # np.save(path + 'train_' + fileName, train) # np.save(path + 'validate_' + fileName, validate) # np.save(path + 'test_' + fileName, test) # print(train.shape, validate.shape, test.shape) # or directly return not save to file because just too big. # return train, validate, test def getShuffledTrainTest(path, fileName, TrainTest): return np.load(path + TrainTest + '_' + fileName + '.npy') def testcode(mesh): GRIDNUMBER = mesh.lonNum * mesh.latNum window = 5 R = np.zeros((window, window), dtype='int32') center = mesh.ReverseIndex[(2,2)] current_x, current_y = mesh.Index[center] start = 0 - window // 2 end = window + start for i, dx in enumerate(list(range(start, end))): for j, dy in enumerate(list(range(start, end))): x = current_x + dx y = current_y + dy if mesh.inMesh(x, y): grid = mesh.ReverseIndex[(x, y)] R[j][i] = grid else: R[j][i] = -1 R = R[::-1, :] print(R) for i in range(len(R)): print(R[i]) for i in range(len(R)): print(R[i][0], R[i][1], R[i][2], R[i][3], R[i][4]) T = R.reshape(-1) print(T.tolist()) P = T.reshape(window, window) print(P) print(R.shape) print(R[54][4178]) print(np.max(R) == 3369) print(mesh.Index[3369]) x, y = mesh.Index[3369] lon, lat = mesh.minLon + (x + 0.5) * mesh.dLon, \ mesh.minLat + (y + 0.5) * mesh.dLat print(lon, lat) print(mesh.lonNum, mesh.latNum) T = np.array(range(GRIDNUMBER)) T = T.reshape(mesh.lonNum, mesh.latNum) T = np.swapaxes(T, 1, 0) T = T[::-1, :] print(T) print(T.shape) def run5min201802(mesh, dataPATH, dates): print('Now is getting trainig XS and YS...', dates) # timestamp = '2011-10-20 09:00:00' # filenameTime = timestamp[0:4] + timestamp[5:7] + timestamp[8:10] \ # + timestamp[11:13] + timestamp[14:16] + timestamp[17:19] # print(filenameTime) for date in dates: # first step: from trajectory point to mesh getMesh(dataPATH + date + 'tokyo_interpo5min.csv', dataPATH + date + 'tokyo_' + mesh.size + '_5min.csv') # second step: calculate mesh population at each timestamp genMeshDynamic(dataPATH + date + 'tokyo_' + mesh.size + '_5min.csv', dataPATH + date + 'tokyo_' + mesh.size + '_5min_pop.csv') # fourth step: mesh transit between two consecutive timestamps genGridTransit(dataPATH + date + 'tokyo_' + mesh.size + '_5min.csv', dataPATH + date + 'tokyo_' + mesh.size + '_5min_transit.csv') def getHHTransit(HH): assert HH <= 22, 'Hour should not be over 22.' dataPATH = '../interpo_data/' date = '20111020' R = getGridTransit(dataPATH + date + 'tokyo_meshtransit10min_1min_15.csv') # (144, 72, 80, 225) R = R[HH*6:HH*6+6, :, :, :] # (6, 72, 80, 225) R = R.reshape(R.shape[0], -1, R.shape[-1]) # (6, 5760, 225) R = R.transpose(1, 0, 2) # (5760, 6, 225) R = R.reshape(R.shape[0], R.shape[1], int(R.shape[2]**0.5), int(R.shape[2]**0.5), 1) return R def runCrowdDensity(): dataPATH = '../interpo_data/' meshTokyo = Mesh('tokyo', '500m') #meshcode_level = 4 alldates = ["20110217","20110218","20110219","20110220", "20110221", "20110222","20110223", "20110224", "20110225", "20110226", "20110227"] for date in alldates: print('this is date', date) getMesh(meshTokyo, dataPATH + date + 'tokyo_interpo5min.csv', dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min.csv') genMeshDynamic(meshTokyo, dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min.csv', dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min_pop.csv') # def runCrowdFlow_from5min(): # from common.dataparam.Param import alldates # dataPATH = '../interpo_data/' # meshTokyo = Mesh('tokyo', '500m') # #meshcode_level = 4 # # for date in alldates: # print('this is date', date) # genGridTransit(meshTokyo, # dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min.csv', # dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min_transit_from5min.csv') # paper crowd flow is from 1min.!!!!!!!!!!!! def runCrowdFlow(): dataPATH = '../interpo_data/' meshTokyo = Mesh('tokyo', '500m') #meshcode_level = 4 alldates = ["20110217", "20110218", "20110219", "20110220", "20110221", "20110222", "20110223", "20110224", "20110225", "20110226", "20110227"] for date in alldates: print('this is date', date) getMesh(meshTokyo, dataPATH + date + 'tokyo_interpo1min.csv', dataPATH + date + 'tokyo_' + meshTokyo.size + '_1min.csv') genGridTransit_5minutes_from_1minute(meshTokyo, dataPATH + date + 'tokyo_' + meshTokyo.size + '_1min.csv', dataPATH + date + 'tokyo_' + meshTokyo.size + '_5min_transit.csv') def main(): runCrowdDensity() if __name__ == '__main__': main()

true

7907e70df2cf5af58c8335db36af71166ec3b539

54,315

py

Python

Lib/site-packages/plotly/graph_objs/_splom.py

tytanya/my-first-blog

2b40adb0816c3546e90ad6ca1e7fb50d924c1536

[ "bzip2-1.0.6" ]

4

2020-02-05T11:26:47.000Z

2021-05-26T07:48:46.000Z

Lib/site-packages/plotly/graph_objs/_splom.py

tytanya/my-first-blog

2b40adb0816c3546e90ad6ca1e7fb50d924c1536

[ "bzip2-1.0.6" ]

6

2021-03-18T22:27:08.000Z

2022-03-11T23:40:50.000Z

venv/lib/python3.7/site-packages/plotly/graph_objs/_splom.py

kylenahas/180LoginV1

8f64be6e6016d47dff8febfcfa3bbd56e9042f89

[ "MIT" ]

1

2020-02-02T21:17:12.000Z

from plotly.basedatatypes import BaseTraceType import copy class Splom(BaseTraceType): # customdata # ---------- @property def customdata(self): """ Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, "scatter" traces also appends customdata items in the markers DOM elements The 'customdata' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self['customdata'] @customdata.setter def customdata(self, val): self['customdata'] = val # customdatasrc # ------------- @property def customdatasrc(self): """ Sets the source reference on plot.ly for customdata . The 'customdatasrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self['customdatasrc'] @customdatasrc.setter def customdatasrc(self, val): self['customdatasrc'] = val # diagonal # -------- @property def diagonal(self): """ The 'diagonal' property is an instance of Diagonal that may be specified as: - An instance of plotly.graph_objs.splom.Diagonal - A dict of string/value properties that will be passed to the Diagonal constructor Supported dict properties: visible Determines whether or not subplots on the diagonal are displayed. Returns ------- plotly.graph_objs.splom.Diagonal """ return self['diagonal'] @diagonal.setter def diagonal(self, val): self['diagonal'] = val # dimensions # ---------- @property def dimensions(self): """ The 'dimensions' property is a tuple of instances of Dimension that may be specified as: - A list or tuple of instances of plotly.graph_objs.splom.Dimension - A list or tuple of dicts of string/value properties that will be passed to the Dimension constructor Supported dict properties: axis plotly.graph_objs.splom.dimension.Axis instance or dict with compatible properties label Sets the label corresponding to this splom dimension. name When used in a template, named items are created in the output figure in addition to any items the figure already has in this array. You can modify these items in the output figure by making your own item with `templateitemname` matching this `name` alongside your modifications (including `visible: false` or `enabled: false` to hide it). Has no effect outside of a template. templateitemname Used to refer to a named item in this array in the template. Named items from the template will be created even without a matching item in the input figure, but you can modify one by making an item with `templateitemname` matching its `name`, alongside your modifications (including `visible: false` or `enabled: false` to hide it). If there is no template or no matching item, this item will be hidden unless you explicitly show it with `visible: true`. values Sets the dimension values to be plotted. valuessrc Sets the source reference on plot.ly for values . visible Determines whether or not this dimension is shown on the graph. Note that even visible false dimension contribute to the default grid generate by this splom trace. Returns ------- tuple[plotly.graph_objs.splom.Dimension] """ return self['dimensions'] @dimensions.setter def dimensions(self, val): self['dimensions'] = val # dimensiondefaults # ----------------- @property def dimensiondefaults(self): """ When used in a template (as layout.template.data.splom.dimensiondefaults), sets the default property values to use for elements of splom.dimensions The 'dimensiondefaults' property is an instance of Dimension that may be specified as: - An instance of plotly.graph_objs.splom.Dimension - A dict of string/value properties that will be passed to the Dimension constructor Supported dict properties: Returns ------- plotly.graph_objs.splom.Dimension """ return self['dimensiondefaults'] @dimensiondefaults.setter def dimensiondefaults(self, val): self['dimensiondefaults'] = val # hoverinfo # --------- @property def hoverinfo(self): """ Determines which trace information appear on hover. If `none` or `skip` are set, no information is displayed upon hovering. But, if `none` is set, click and hover events are still fired. The 'hoverinfo' property is a flaglist and may be specified as a string containing: - Any combination of ['x', 'y', 'z', 'text', 'name'] joined with '+' characters (e.g. 'x+y') OR exactly one of ['all', 'none', 'skip'] (e.g. 'skip') - A list or array of the above Returns ------- Any|numpy.ndarray """ return self['hoverinfo'] @hoverinfo.setter def hoverinfo(self, val): self['hoverinfo'] = val # hoverinfosrc # ------------ @property def hoverinfosrc(self): """ Sets the source reference on plot.ly for hoverinfo . The 'hoverinfosrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self['hoverinfosrc'] @hoverinfosrc.setter def hoverinfosrc(self, val): self['hoverinfosrc'] = val # hoverlabel # ---------- @property def hoverlabel(self): """ The 'hoverlabel' property is an instance of Hoverlabel that may be specified as: - An instance of plotly.graph_objs.splom.Hoverlabel - A dict of string/value properties that will be passed to the Hoverlabel constructor Supported dict properties: bgcolor Sets the background color of the hover labels for this trace bgcolorsrc Sets the source reference on plot.ly for bgcolor . bordercolor Sets the border color of the hover labels for this trace. bordercolorsrc Sets the source reference on plot.ly for bordercolor . font Sets the font used in hover labels. namelength Sets the length (in number of characters) of the trace name in the hover labels for this trace. -1 shows the whole name regardless of length. 0-3 shows the first 0-3 characters, and an integer >3 will show the whole name if it is less than that many characters, but if it is longer, will truncate to `namelength - 3` characters and add an ellipsis. namelengthsrc Sets the source reference on plot.ly for namelength . Returns ------- plotly.graph_objs.splom.Hoverlabel """ return self['hoverlabel'] @hoverlabel.setter def hoverlabel(self, val): self['hoverlabel'] = val # hovertemplate # ------------- @property def hovertemplate(self): """ Template string used for rendering the information that appear on hover box. Note that this will override `hoverinfo`. Variables are inserted using %{variable}, for example "y: %{y}". Numbers are formatted using d3-format's syntax %{variable:d3-format}, for example "Price: %{y:$.2f}". See http s://github.com/d3/d3-format/blob/master/README.md#locale_format for details on the formatting syntax. The variables available in `hovertemplate` are the ones emitted as event data described at this link https://plot.ly/javascript/plotlyjs-events/#event- data. Additionally, every attributes that can be specified per- point (the ones that are `arrayOk: true`) are available. Anything contained in tag `<extra>` is displayed in the secondary box, for example "<extra>{fullData.name}</extra>". The 'hovertemplate' property is a string and must be specified as: - A string - A number that will be converted to a string - A tuple, list, or one-dimensional numpy array of the above Returns ------- str|numpy.ndarray """ return self['hovertemplate'] @hovertemplate.setter def hovertemplate(self, val): self['hovertemplate'] = val # hovertemplatesrc # ---------------- @property def hovertemplatesrc(self): """ Sets the source reference on plot.ly for hovertemplate . The 'hovertemplatesrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self['hovertemplatesrc'] @hovertemplatesrc.setter def hovertemplatesrc(self, val): self['hovertemplatesrc'] = val # hovertext # --------- @property def hovertext(self): """ Same as `text`. The 'hovertext' property is a string and must be specified as: - A string - A number that will be converted to a string - A tuple, list, or one-dimensional numpy array of the above Returns ------- str|numpy.ndarray """ return self['hovertext'] @hovertext.setter def hovertext(self, val): self['hovertext'] = val # hovertextsrc # ------------ @property def hovertextsrc(self): """ Sets the source reference on plot.ly for hovertext . The 'hovertextsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self['hovertextsrc'] @hovertextsrc.setter def hovertextsrc(self, val): self['hovertextsrc'] = val # ids # --- @property def ids(self): """ Assigns id labels to each datum. These ids for object constancy of data points during animation. Should be an array of strings, not numbers or any other type. The 'ids' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self['ids'] @ids.setter def ids(self, val): self['ids'] = val # idssrc # ------ @property def idssrc(self): """ Sets the source reference on plot.ly for ids . The 'idssrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self['idssrc'] @idssrc.setter def idssrc(self, val): self['idssrc'] = val # legendgroup # ----------- @property def legendgroup(self): """ Sets the legend group for this trace. Traces part of the same legend group hide/show at the same time when toggling legend items. The 'legendgroup' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self['legendgroup'] @legendgroup.setter def legendgroup(self, val): self['legendgroup'] = val # marker # ------ @property def marker(self): """ The 'marker' property is an instance of Marker that may be specified as: - An instance of plotly.graph_objs.splom.Marker - A dict of string/value properties that will be passed to the Marker constructor Supported dict properties: autocolorscale Determines whether the colorscale is a default palette (`autocolorscale: true`) or the palette determined by `marker.colorscale`. Has an effect only if in `marker.color`is set to a numerical array. In case `colorscale` is unspecified or `autocolorscale` is true, the default palette will be chosen according to whether numbers in the `color` array are all positive, all negative or mixed. cauto Determines whether or not the color domain is computed with respect to the input data (here in `marker.color`) or the bounds set in `marker.cmin` and `marker.cmax` Has an effect only if in `marker.color`is set to a numerical array. Defaults to `false` when `marker.cmin` and `marker.cmax` are set by the user. cmax Sets the upper bound of the color domain. Has an effect only if in `marker.color`is set to a numerical array. Value should have the same units as in `marker.color` and if set, `marker.cmin` must be set as well. cmid Sets the mid-point of the color domain by scaling `marker.cmin` and/or `marker.cmax` to be equidistant to this point. Has an effect only if in `marker.color`is set to a numerical array. Value should have the same units as in `marker.color`. Has no effect when `marker.cauto` is `false`. cmin Sets the lower bound of the color domain. Has an effect only if in `marker.color`is set to a numerical array. Value should have the same units as in `marker.color` and if set, `marker.cmax` must be set as well. color Sets themarkercolor. It accepts either a specific color or an array of numbers that are mapped to the colorscale relative to the max and min values of the array or relative to `marker.cmin` and `marker.cmax` if set. colorbar plotly.graph_objs.splom.marker.ColorBar instance or dict with compatible properties colorscale Sets the colorscale. Has an effect only if in `marker.color`is set to a numerical array. The colorscale must be an array containing arrays mapping a normalized value to an rgb, rgba, hex, hsl, hsv, or named color string. At minimum, a mapping for the lowest (0) and highest (1) values are required. For example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To control the bounds of the colorscale in color space, use`marker.cmin` and `marker.cmax`. Alternatively, `colorscale` may be a palette name string of the following list: Greys,YlGnBu ,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,R ainbow,Portland,Jet,Hot,Blackbody,Earth,Electri c,Viridis,Cividis. colorsrc Sets the source reference on plot.ly for color . line plotly.graph_objs.splom.marker.Line instance or dict with compatible properties opacity Sets the marker opacity. opacitysrc Sets the source reference on plot.ly for opacity . reversescale Reverses the color mapping if true. Has an effect only if in `marker.color`is set to a numerical array. If true, `marker.cmin` will correspond to the last color in the array and `marker.cmax` will correspond to the first color. showscale Determines whether or not a colorbar is displayed for this trace. Has an effect only if in `marker.color`is set to a numerical array. size Sets the marker size (in px). sizemin Has an effect only if `marker.size` is set to a numerical array. Sets the minimum size (in px) of the rendered marker points. sizemode Has an effect only if `marker.size` is set to a numerical array. Sets the rule for which the data in `size` is converted to pixels. sizeref Has an effect only if `marker.size` is set to a numerical array. Sets the scale factor used to determine the rendered size of marker points. Use with `sizemin` and `sizemode`. sizesrc Sets the source reference on plot.ly for size . symbol Sets the marker symbol type. Adding 100 is equivalent to appending "-open" to a symbol name. Adding 200 is equivalent to appending "-dot" to a symbol name. Adding 300 is equivalent to appending "-open-dot" or "dot- open" to a symbol name. symbolsrc Sets the source reference on plot.ly for symbol . Returns ------- plotly.graph_objs.splom.Marker """ return self['marker'] @marker.setter def marker(self, val): self['marker'] = val # name # ---- @property def name(self): """ Sets the trace name. The trace name appear as the legend item and on hover. The 'name' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self['name'] @name.setter def name(self, val): self['name'] = val # opacity # ------- @property def opacity(self): """ Sets the opacity of the trace. The 'opacity' property is a number and may be specified as: - An int or float in the interval [0, 1] Returns ------- int|float """ return self['opacity'] @opacity.setter def opacity(self, val): self['opacity'] = val # selected # -------- @property def selected(self): """ The 'selected' property is an instance of Selected that may be specified as: - An instance of plotly.graph_objs.splom.Selected - A dict of string/value properties that will be passed to the Selected constructor Supported dict properties: marker plotly.graph_objs.splom.selected.Marker instance or dict with compatible properties Returns ------- plotly.graph_objs.splom.Selected """ return self['selected'] @selected.setter def selected(self, val): self['selected'] = val # selectedpoints # -------------- @property def selectedpoints(self): """ Array containing integer indices of selected points. Has an effect only for traces that support selections. Note that an empty array means an empty selection where the `unselected` are turned on for all points, whereas, any other non-array values means no selection all where the `selected` and `unselected` styles have no effect. The 'selectedpoints' property accepts values of any type Returns ------- Any """ return self['selectedpoints'] @selectedpoints.setter def selectedpoints(self, val): self['selectedpoints'] = val # showlegend # ---------- @property def showlegend(self): """ Determines whether or not an item corresponding to this trace is shown in the legend. The 'showlegend' property must be specified as a bool (either True, or False) Returns ------- bool """ return self['showlegend'] @showlegend.setter def showlegend(self, val): self['showlegend'] = val # showlowerhalf # ------------- @property def showlowerhalf(self): """ Determines whether or not subplots on the lower half from the diagonal are displayed. The 'showlowerhalf' property must be specified as a bool (either True, or False) Returns ------- bool """ return self['showlowerhalf'] @showlowerhalf.setter def showlowerhalf(self, val): self['showlowerhalf'] = val # showupperhalf # ------------- @property def showupperhalf(self): """ Determines whether or not subplots on the upper half from the diagonal are displayed. The 'showupperhalf' property must be specified as a bool (either True, or False) Returns ------- bool """ return self['showupperhalf'] @showupperhalf.setter def showupperhalf(self, val): self['showupperhalf'] = val # stream # ------ @property def stream(self): """ The 'stream' property is an instance of Stream that may be specified as: - An instance of plotly.graph_objs.splom.Stream - A dict of string/value properties that will be passed to the Stream constructor Supported dict properties: maxpoints Sets the maximum number of points to keep on the plots from an incoming stream. If `maxpoints` is set to 50, only the newest 50 points will be displayed on the plot. token The stream id number links a data trace on a plot with a stream. See https://plot.ly/settings for more details. Returns ------- plotly.graph_objs.splom.Stream """ return self['stream'] @stream.setter def stream(self, val): self['stream'] = val # text # ---- @property def text(self): """ Sets text elements associated with each (x,y) pair to appear on hover. If a single string, the same string appears over all the data points. If an array of string, the items are mapped in order to the this trace's (x,y) coordinates. The 'text' property is a string and must be specified as: - A string - A number that will be converted to a string - A tuple, list, or one-dimensional numpy array of the above Returns ------- str|numpy.ndarray """ return self['text'] @text.setter def text(self, val): self['text'] = val # textsrc # ------- @property def textsrc(self): """ Sets the source reference on plot.ly for text . The 'textsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self['textsrc'] @textsrc.setter def textsrc(self, val): self['textsrc'] = val # uid # --- @property def uid(self): """ Assign an id to this trace, Use this to provide object constancy between traces during animations and transitions. The 'uid' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self['uid'] @uid.setter def uid(self, val): self['uid'] = val # uirevision # ---------- @property def uirevision(self): """ Controls persistence of some user-driven changes to the trace: `constraintrange` in `parcoords` traces, as well as some `editable: true` modifications such as `name` and `colorbar.title`. Defaults to `layout.uirevision`. Note that other user-driven trace attribute changes are controlled by `layout` attributes: `trace.visible` is controlled by `layout.legend.uirevision`, `selectedpoints` is controlled by `layout.selectionrevision`, and `colorbar.(x|y)` (accessible with `config: {editable: true}`) is controlled by `layout.editrevision`. Trace changes are tracked by `uid`, which only falls back on trace index if no `uid` is provided. So if your app can add/remove traces before the end of the `data` array, such that the same trace has a different index, you can still preserve user-driven changes if you give each trace a `uid` that stays with it as it moves. The 'uirevision' property accepts values of any type Returns ------- Any """ return self['uirevision'] @uirevision.setter def uirevision(self, val): self['uirevision'] = val # unselected # ---------- @property def unselected(self): """ The 'unselected' property is an instance of Unselected that may be specified as: - An instance of plotly.graph_objs.splom.Unselected - A dict of string/value properties that will be passed to the Unselected constructor Supported dict properties: marker plotly.graph_objs.splom.unselected.Marker instance or dict with compatible properties Returns ------- plotly.graph_objs.splom.Unselected """ return self['unselected'] @unselected.setter def unselected(self, val): self['unselected'] = val # visible # ------- @property def visible(self): """ Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible). The 'visible' property is an enumeration that may be specified as: - One of the following enumeration values: [True, False, 'legendonly'] Returns ------- Any """ return self['visible'] @visible.setter def visible(self, val): self['visible'] = val # xaxes # ----- @property def xaxes(self): """ Sets the list of x axes corresponding to dimensions of this splom trace. By default, a splom will match the first N xaxes where N is the number of input dimensions. Note that, in case where `diagonal.visible` is false and `showupperhalf` or `showlowerhalf` is false, this splom trace will generate one less x-axis and one less y-axis. The 'xaxes' property is an info array that may be specified as: * a list of elements where: The 'xaxes[i]' property is an identifier of a particular subplot, of type 'x', that may be specified as the string 'x' optionally followed by an integer >= 1 (e.g. 'x', 'x1', 'x2', 'x3', etc.) Returns ------- list """ return self['xaxes'] @xaxes.setter def xaxes(self, val): self['xaxes'] = val # yaxes # ----- @property def yaxes(self): """ Sets the list of y axes corresponding to dimensions of this splom trace. By default, a splom will match the first N yaxes where N is the number of input dimensions. Note that, in case where `diagonal.visible` is false and `showupperhalf` or `showlowerhalf` is false, this splom trace will generate one less x-axis and one less y-axis. The 'yaxes' property is an info array that may be specified as: * a list of elements where: The 'yaxes[i]' property is an identifier of a particular subplot, of type 'y', that may be specified as the string 'y' optionally followed by an integer >= 1 (e.g. 'y', 'y1', 'y2', 'y3', etc.) Returns ------- list """ return self['yaxes'] @yaxes.setter def yaxes(self, val): self['yaxes'] = val # type # ---- @property def type(self): return self._props['type'] # property parent name # -------------------- @property def _parent_path_str(self): return '' # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ customdata Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, "scatter" traces also appends customdata items in the markers DOM elements customdatasrc Sets the source reference on plot.ly for customdata . diagonal plotly.graph_objs.splom.Diagonal instance or dict with compatible properties dimensions plotly.graph_objs.splom.Dimension instance or dict with compatible properties dimensiondefaults When used in a template (as layout.template.data.splom.dimensiondefaults), sets the default property values to use for elements of splom.dimensions hoverinfo Determines which trace information appear on hover. If `none` or `skip` are set, no information is displayed upon hovering. But, if `none` is set, click and hover events are still fired. hoverinfosrc Sets the source reference on plot.ly for hoverinfo . hoverlabel plotly.graph_objs.splom.Hoverlabel instance or dict with compatible properties hovertemplate Template string used for rendering the information that appear on hover box. Note that this will override `hoverinfo`. Variables are inserted using %{variable}, for example "y: %{y}". Numbers are formatted using d3-format's syntax %{variable:d3-format}, for example "Price: %{y:$.2f}". See https://github.com/d3/d3-format /blob/master/README.md#locale_format for details on the formatting syntax. The variables available in `hovertemplate` are the ones emitted as event data described at this link https://plot.ly/javascript/plotlyjs-events/#event-data. Additionally, every attributes that can be specified per-point (the ones that are `arrayOk: true`) are available. Anything contained in tag `<extra>` is displayed in the secondary box, for example "<extra>{fullData.name}</extra>". hovertemplatesrc Sets the source reference on plot.ly for hovertemplate . hovertext Same as `text`. hovertextsrc Sets the source reference on plot.ly for hovertext . ids Assigns id labels to each datum. These ids for object constancy of data points during animation. Should be an array of strings, not numbers or any other type. idssrc Sets the source reference on plot.ly for ids . legendgroup Sets the legend group for this trace. Traces part of the same legend group hide/show at the same time when toggling legend items. marker plotly.graph_objs.splom.Marker instance or dict with compatible properties name Sets the trace name. The trace name appear as the legend item and on hover. opacity Sets the opacity of the trace. selected plotly.graph_objs.splom.Selected instance or dict with compatible properties selectedpoints Array containing integer indices of selected points. Has an effect only for traces that support selections. Note that an empty array means an empty selection where the `unselected` are turned on for all points, whereas, any other non-array values means no selection all where the `selected` and `unselected` styles have no effect. showlegend Determines whether or not an item corresponding to this trace is shown in the legend. showlowerhalf Determines whether or not subplots on the lower half from the diagonal are displayed. showupperhalf Determines whether or not subplots on the upper half from the diagonal are displayed. stream plotly.graph_objs.splom.Stream instance or dict with compatible properties text Sets text elements associated with each (x,y) pair to appear on hover. If a single string, the same string appears over all the data points. If an array of string, the items are mapped in order to the this trace's (x,y) coordinates. textsrc Sets the source reference on plot.ly for text . uid Assign an id to this trace, Use this to provide object constancy between traces during animations and transitions. uirevision Controls persistence of some user-driven changes to the trace: `constraintrange` in `parcoords` traces, as well as some `editable: true` modifications such as `name` and `colorbar.title`. Defaults to `layout.uirevision`. Note that other user-driven trace attribute changes are controlled by `layout` attributes: `trace.visible` is controlled by `layout.legend.uirevision`, `selectedpoints` is controlled by `layout.selectionrevision`, and `colorbar.(x|y)` (accessible with `config: {editable: true}`) is controlled by `layout.editrevision`. Trace changes are tracked by `uid`, which only falls back on trace index if no `uid` is provided. So if your app can add/remove traces before the end of the `data` array, such that the same trace has a different index, you can still preserve user-driven changes if you give each trace a `uid` that stays with it as it moves. unselected plotly.graph_objs.splom.Unselected instance or dict with compatible properties visible Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible). xaxes Sets the list of x axes corresponding to dimensions of this splom trace. By default, a splom will match the first N xaxes where N is the number of input dimensions. Note that, in case where `diagonal.visible` is false and `showupperhalf` or `showlowerhalf` is false, this splom trace will generate one less x-axis and one less y-axis. yaxes Sets the list of y axes corresponding to dimensions of this splom trace. By default, a splom will match the first N yaxes where N is the number of input dimensions. Note that, in case where `diagonal.visible` is false and `showupperhalf` or `showlowerhalf` is false, this splom trace will generate one less x-axis and one less y-axis. """ def __init__( self, arg=None, customdata=None, customdatasrc=None, diagonal=None, dimensions=None, dimensiondefaults=None, hoverinfo=None, hoverinfosrc=None, hoverlabel=None, hovertemplate=None, hovertemplatesrc=None, hovertext=None, hovertextsrc=None, ids=None, idssrc=None, legendgroup=None, marker=None, name=None, opacity=None, selected=None, selectedpoints=None, showlegend=None, showlowerhalf=None, showupperhalf=None, stream=None, text=None, textsrc=None, uid=None, uirevision=None, unselected=None, visible=None, xaxes=None, yaxes=None, **kwargs ): """ Construct a new Splom object Splom traces generate scatter plot matrix visualizations. Each splom `dimensions` items correspond to a generated axis. Values for each of those dimensions are set in `dimensions[i].values`. Splom traces support all `scattergl` marker style attributes. Specify `layout.grid` attributes and/or layout x-axis and y-axis attributes for more control over the axis positioning and style. Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.Splom customdata Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, "scatter" traces also appends customdata items in the markers DOM elements customdatasrc Sets the source reference on plot.ly for customdata . diagonal plotly.graph_objs.splom.Diagonal instance or dict with compatible properties dimensions plotly.graph_objs.splom.Dimension instance or dict with compatible properties dimensiondefaults When used in a template (as layout.template.data.splom.dimensiondefaults), sets the default property values to use for elements of splom.dimensions hoverinfo Determines which trace information appear on hover. If `none` or `skip` are set, no information is displayed upon hovering. But, if `none` is set, click and hover events are still fired. hoverinfosrc Sets the source reference on plot.ly for hoverinfo . hoverlabel plotly.graph_objs.splom.Hoverlabel instance or dict with compatible properties hovertemplate Template string used for rendering the information that appear on hover box. Note that this will override `hoverinfo`. Variables are inserted using %{variable}, for example "y: %{y}". Numbers are formatted using d3-format's syntax %{variable:d3-format}, for example "Price: %{y:$.2f}". See https://github.com/d3/d3-format /blob/master/README.md#locale_format for details on the formatting syntax. The variables available in `hovertemplate` are the ones emitted as event data described at this link https://plot.ly/javascript/plotlyjs-events/#event-data. Additionally, every attributes that can be specified per-point (the ones that are `arrayOk: true`) are available. Anything contained in tag `<extra>` is displayed in the secondary box, for example "<extra>{fullData.name}</extra>". hovertemplatesrc Sets the source reference on plot.ly for hovertemplate . hovertext Same as `text`. hovertextsrc Sets the source reference on plot.ly for hovertext . ids Assigns id labels to each datum. These ids for object constancy of data points during animation. Should be an array of strings, not numbers or any other type. idssrc Sets the source reference on plot.ly for ids . legendgroup Sets the legend group for this trace. Traces part of the same legend group hide/show at the same time when toggling legend items. marker plotly.graph_objs.splom.Marker instance or dict with compatible properties name Sets the trace name. The trace name appear as the legend item and on hover. opacity Sets the opacity of the trace. selected plotly.graph_objs.splom.Selected instance or dict with compatible properties selectedpoints Array containing integer indices of selected points. Has an effect only for traces that support selections. Note that an empty array means an empty selection where the `unselected` are turned on for all points, whereas, any other non-array values means no selection all where the `selected` and `unselected` styles have no effect. showlegend Determines whether or not an item corresponding to this trace is shown in the legend. showlowerhalf Determines whether or not subplots on the lower half from the diagonal are displayed. showupperhalf Determines whether or not subplots on the upper half from the diagonal are displayed. stream plotly.graph_objs.splom.Stream instance or dict with compatible properties text Sets text elements associated with each (x,y) pair to appear on hover. If a single string, the same string appears over all the data points. If an array of string, the items are mapped in order to the this trace's (x,y) coordinates. textsrc Sets the source reference on plot.ly for text . uid Assign an id to this trace, Use this to provide object constancy between traces during animations and transitions. uirevision Controls persistence of some user-driven changes to the trace: `constraintrange` in `parcoords` traces, as well as some `editable: true` modifications such as `name` and `colorbar.title`. Defaults to `layout.uirevision`. Note that other user-driven trace attribute changes are controlled by `layout` attributes: `trace.visible` is controlled by `layout.legend.uirevision`, `selectedpoints` is controlled by `layout.selectionrevision`, and `colorbar.(x|y)` (accessible with `config: {editable: true}`) is controlled by `layout.editrevision`. Trace changes are tracked by `uid`, which only falls back on trace index if no `uid` is provided. So if your app can add/remove traces before the end of the `data` array, such that the same trace has a different index, you can still preserve user-driven changes if you give each trace a `uid` that stays with it as it moves. unselected plotly.graph_objs.splom.Unselected instance or dict with compatible properties visible Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible). xaxes Sets the list of x axes corresponding to dimensions of this splom trace. By default, a splom will match the first N xaxes where N is the number of input dimensions. Note that, in case where `diagonal.visible` is false and `showupperhalf` or `showlowerhalf` is false, this splom trace will generate one less x-axis and one less y-axis. yaxes Sets the list of y axes corresponding to dimensions of this splom trace. By default, a splom will match the first N yaxes where N is the number of input dimensions. Note that, in case where `diagonal.visible` is false and `showupperhalf` or `showlowerhalf` is false, this splom trace will generate one less x-axis and one less y-axis. Returns ------- Splom """ super(Splom, self).__init__('splom') # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.Splom constructor must be a dict or an instance of plotly.graph_objs.Splom""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop('skip_invalid', False) # Import validators # ----------------- from plotly.validators import (splom as v_splom) # Initialize validators # --------------------- self._validators['customdata'] = v_splom.CustomdataValidator() self._validators['customdatasrc'] = v_splom.CustomdatasrcValidator() self._validators['diagonal'] = v_splom.DiagonalValidator() self._validators['dimensions'] = v_splom.DimensionsValidator() self._validators['dimensiondefaults'] = v_splom.DimensionValidator() self._validators['hoverinfo'] = v_splom.HoverinfoValidator() self._validators['hoverinfosrc'] = v_splom.HoverinfosrcValidator() self._validators['hoverlabel'] = v_splom.HoverlabelValidator() self._validators['hovertemplate'] = v_splom.HovertemplateValidator() self._validators['hovertemplatesrc' ] = v_splom.HovertemplatesrcValidator() self._validators['hovertext'] = v_splom.HovertextValidator() self._validators['hovertextsrc'] = v_splom.HovertextsrcValidator() self._validators['ids'] = v_splom.IdsValidator() self._validators['idssrc'] = v_splom.IdssrcValidator() self._validators['legendgroup'] = v_splom.LegendgroupValidator() self._validators['marker'] = v_splom.MarkerValidator() self._validators['name'] = v_splom.NameValidator() self._validators['opacity'] = v_splom.OpacityValidator() self._validators['selected'] = v_splom.SelectedValidator() self._validators['selectedpoints'] = v_splom.SelectedpointsValidator() self._validators['showlegend'] = v_splom.ShowlegendValidator() self._validators['showlowerhalf'] = v_splom.ShowlowerhalfValidator() self._validators['showupperhalf'] = v_splom.ShowupperhalfValidator() self._validators['stream'] = v_splom.StreamValidator() self._validators['text'] = v_splom.TextValidator() self._validators['textsrc'] = v_splom.TextsrcValidator() self._validators['uid'] = v_splom.UidValidator() self._validators['uirevision'] = v_splom.UirevisionValidator() self._validators['unselected'] = v_splom.UnselectedValidator() self._validators['visible'] = v_splom.VisibleValidator() self._validators['xaxes'] = v_splom.XaxesValidator() self._validators['yaxes'] = v_splom.YaxesValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop('customdata', None) self['customdata'] = customdata if customdata is not None else _v _v = arg.pop('customdatasrc', None) self['customdatasrc' ] = customdatasrc if customdatasrc is not None else _v _v = arg.pop('diagonal', None) self['diagonal'] = diagonal if diagonal is not None else _v _v = arg.pop('dimensions', None) self['dimensions'] = dimensions if dimensions is not None else _v _v = arg.pop('dimensiondefaults', None) self['dimensiondefaults' ] = dimensiondefaults if dimensiondefaults is not None else _v _v = arg.pop('hoverinfo', None) self['hoverinfo'] = hoverinfo if hoverinfo is not None else _v _v = arg.pop('hoverinfosrc', None) self['hoverinfosrc'] = hoverinfosrc if hoverinfosrc is not None else _v _v = arg.pop('hoverlabel', None) self['hoverlabel'] = hoverlabel if hoverlabel is not None else _v _v = arg.pop('hovertemplate', None) self['hovertemplate' ] = hovertemplate if hovertemplate is not None else _v _v = arg.pop('hovertemplatesrc', None) self['hovertemplatesrc' ] = hovertemplatesrc if hovertemplatesrc is not None else _v _v = arg.pop('hovertext', None) self['hovertext'] = hovertext if hovertext is not None else _v _v = arg.pop('hovertextsrc', None) self['hovertextsrc'] = hovertextsrc if hovertextsrc is not None else _v _v = arg.pop('ids', None) self['ids'] = ids if ids is not None else _v _v = arg.pop('idssrc', None) self['idssrc'] = idssrc if idssrc is not None else _v _v = arg.pop('legendgroup', None) self['legendgroup'] = legendgroup if legendgroup is not None else _v _v = arg.pop('marker', None) self['marker'] = marker if marker is not None else _v _v = arg.pop('name', None) self['name'] = name if name is not None else _v _v = arg.pop('opacity', None) self['opacity'] = opacity if opacity is not None else _v _v = arg.pop('selected', None) self['selected'] = selected if selected is not None else _v _v = arg.pop('selectedpoints', None) self['selectedpoints' ] = selectedpoints if selectedpoints is not None else _v _v = arg.pop('showlegend', None) self['showlegend'] = showlegend if showlegend is not None else _v _v = arg.pop('showlowerhalf', None) self['showlowerhalf' ] = showlowerhalf if showlowerhalf is not None else _v _v = arg.pop('showupperhalf', None) self['showupperhalf' ] = showupperhalf if showupperhalf is not None else _v _v = arg.pop('stream', None) self['stream'] = stream if stream is not None else _v _v = arg.pop('text', None) self['text'] = text if text is not None else _v _v = arg.pop('textsrc', None) self['textsrc'] = textsrc if textsrc is not None else _v _v = arg.pop('uid', None) self['uid'] = uid if uid is not None else _v _v = arg.pop('uirevision', None) self['uirevision'] = uirevision if uirevision is not None else _v _v = arg.pop('unselected', None) self['unselected'] = unselected if unselected is not None else _v _v = arg.pop('visible', None) self['visible'] = visible if visible is not None else _v _v = arg.pop('xaxes', None) self['xaxes'] = xaxes if xaxes is not None else _v _v = arg.pop('yaxes', None) self['yaxes'] = yaxes if yaxes is not None else _v # Read-only literals # ------------------ from _plotly_utils.basevalidators import LiteralValidator self._props['type'] = 'splom' self._validators['type'] = LiteralValidator( plotly_name='type', parent_name='splom', val='splom' ) arg.pop('type', None) # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False

36.773866

89

0.572273

from plotly.basedatatypes import BaseTraceType import copy class Splom(BaseTraceType): @property def customdata(self): return self['customdata'] @customdata.setter def customdata(self, val): self['customdata'] = val @property def customdatasrc(self): return self['customdatasrc'] @customdatasrc.setter def customdatasrc(self, val): self['customdatasrc'] = val @property def diagonal(self): return self['diagonal'] @diagonal.setter def diagonal(self, val): self['diagonal'] = val @property def dimensions(self): return self['dimensions'] @dimensions.setter def dimensions(self, val): self['dimensions'] = val @property def dimensiondefaults(self): return self['dimensiondefaults'] @dimensiondefaults.setter def dimensiondefaults(self, val): self['dimensiondefaults'] = val @property def hoverinfo(self): return self['hoverinfo'] @hoverinfo.setter def hoverinfo(self, val): self['hoverinfo'] = val @property def hoverinfosrc(self): return self['hoverinfosrc'] @hoverinfosrc.setter def hoverinfosrc(self, val): self['hoverinfosrc'] = val @property def hoverlabel(self): return self['hoverlabel'] @hoverlabel.setter def hoverlabel(self, val): self['hoverlabel'] = val @property def hovertemplate(self): return self['hovertemplate'] @hovertemplate.setter def hovertemplate(self, val): self['hovertemplate'] = val @property def hovertemplatesrc(self): return self['hovertemplatesrc'] @hovertemplatesrc.setter def hovertemplatesrc(self, val): self['hovertemplatesrc'] = val @property def hovertext(self): return self['hovertext'] @hovertext.setter def hovertext(self, val): self['hovertext'] = val @property def hovertextsrc(self): return self['hovertextsrc'] @hovertextsrc.setter def hovertextsrc(self, val): self['hovertextsrc'] = val @property def ids(self): return self['ids'] @ids.setter def ids(self, val): self['ids'] = val @property def idssrc(self): return self['idssrc'] @idssrc.setter def idssrc(self, val): self['idssrc'] = val @property def legendgroup(self): return self['legendgroup'] @legendgroup.setter def legendgroup(self, val): self['legendgroup'] = val @property def marker(self): return self['marker'] @marker.setter def marker(self, val): self['marker'] = val @property def name(self): return self['name'] @name.setter def name(self, val): self['name'] = val @property def opacity(self): return self['opacity'] @opacity.setter def opacity(self, val): self['opacity'] = val @property def selected(self): return self['selected'] @selected.setter def selected(self, val): self['selected'] = val @property def selectedpoints(self): return self['selectedpoints'] @selectedpoints.setter def selectedpoints(self, val): self['selectedpoints'] = val @property def showlegend(self): return self['showlegend'] @showlegend.setter def showlegend(self, val): self['showlegend'] = val @property def showlowerhalf(self): return self['showlowerhalf'] @showlowerhalf.setter def showlowerhalf(self, val): self['showlowerhalf'] = val @property def showupperhalf(self): return self['showupperhalf'] @showupperhalf.setter def showupperhalf(self, val): self['showupperhalf'] = val @property def stream(self): return self['stream'] @stream.setter def stream(self, val): self['stream'] = val @property def text(self): return self['text'] @text.setter def text(self, val): self['text'] = val @property def textsrc(self): return self['textsrc'] @textsrc.setter def textsrc(self, val): self['textsrc'] = val @property def uid(self): return self['uid'] @uid.setter def uid(self, val): self['uid'] = val @property def uirevision(self): return self['uirevision'] @uirevision.setter def uirevision(self, val): self['uirevision'] = val @property def unselected(self): return self['unselected'] @unselected.setter def unselected(self, val): self['unselected'] = val @property def visible(self): return self['visible'] @visible.setter def visible(self, val): self['visible'] = val @property def xaxes(self): return self['xaxes'] @xaxes.setter def xaxes(self, val): self['xaxes'] = val @property def yaxes(self): return self['yaxes'] @yaxes.setter def yaxes(self, val): self['yaxes'] = val @property def type(self): return self._props['type'] @property def _parent_path_str(self): return '' @property def _prop_descriptions(self): return """\ customdata Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, "scatter" traces also appends customdata items in the markers DOM elements customdatasrc Sets the source reference on plot.ly for customdata . diagonal plotly.graph_objs.splom.Diagonal instance or dict with compatible properties dimensions plotly.graph_objs.splom.Dimension instance or dict with compatible properties dimensiondefaults When used in a template (as layout.template.data.splom.dimensiondefaults), sets the default property values to use for elements of splom.dimensions hoverinfo Determines which trace information appear on hover. If `none` or `skip` are set, no information is displayed upon hovering. But, if `none` is set, click and hover events are still fired. hoverinfosrc Sets the source reference on plot.ly for hoverinfo . hoverlabel plotly.graph_objs.splom.Hoverlabel instance or dict with compatible properties hovertemplate Template string used for rendering the information that appear on hover box. Note that this will override `hoverinfo`. Variables are inserted using %{variable}, for example "y: %{y}". Numbers are formatted using d3-format's syntax %{variable:d3-format}, for example "Price: %{y:$.2f}". See https://github.com/d3/d3-format /blob/master/README.md#locale_format for details on the formatting syntax. The variables available in `hovertemplate` are the ones emitted as event data described at this link https://plot.ly/javascript/plotlyjs-events/#event-data. Additionally, every attributes that can be specified per-point (the ones that are `arrayOk: true`) are available. Anything contained in tag `<extra>` is displayed in the secondary box, for example "<extra>{fullData.name}</extra>". hovertemplatesrc Sets the source reference on plot.ly for hovertemplate . hovertext Same as `text`. hovertextsrc Sets the source reference on plot.ly for hovertext . ids Assigns id labels to each datum. These ids for object constancy of data points during animation. Should be an array of strings, not numbers or any other type. idssrc Sets the source reference on plot.ly for ids . legendgroup Sets the legend group for this trace. Traces part of the same legend group hide/show at the same time when toggling legend items. marker plotly.graph_objs.splom.Marker instance or dict with compatible properties name Sets the trace name. The trace name appear as the legend item and on hover. opacity Sets the opacity of the trace. selected plotly.graph_objs.splom.Selected instance or dict with compatible properties selectedpoints Array containing integer indices of selected points. Has an effect only for traces that support selections. Note that an empty array means an empty selection where the `unselected` are turned on for all points, whereas, any other non-array values means no selection all where the `selected` and `unselected` styles have no effect. showlegend Determines whether or not an item corresponding to this trace is shown in the legend. showlowerhalf Determines whether or not subplots on the lower half from the diagonal are displayed. showupperhalf Determines whether or not subplots on the upper half from the diagonal are displayed. stream plotly.graph_objs.splom.Stream instance or dict with compatible properties text Sets text elements associated with each (x,y) pair to appear on hover. If a single string, the same string appears over all the data points. If an array of string, the items are mapped in order to the this trace's (x,y) coordinates. textsrc Sets the source reference on plot.ly for text . uid Assign an id to this trace, Use this to provide object constancy between traces during animations and transitions. uirevision Controls persistence of some user-driven changes to the trace: `constraintrange` in `parcoords` traces, as well as some `editable: true` modifications such as `name` and `colorbar.title`. Defaults to `layout.uirevision`. Note that other user-driven trace attribute changes are controlled by `layout` attributes: `trace.visible` is controlled by `layout.legend.uirevision`, `selectedpoints` is controlled by `layout.selectionrevision`, and `colorbar.(x|y)` (accessible with `config: {editable: true}`) is controlled by `layout.editrevision`. Trace changes are tracked by `uid`, which only falls back on trace index if no `uid` is provided. So if your app can add/remove traces before the end of the `data` array, such that the same trace has a different index, you can still preserve user-driven changes if you give each trace a `uid` that stays with it as it moves. unselected plotly.graph_objs.splom.Unselected instance or dict with compatible properties visible Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible). xaxes Sets the list of x axes corresponding to dimensions of this splom trace. By default, a splom will match the first N xaxes where N is the number of input dimensions. Note that, in case where `diagonal.visible` is false and `showupperhalf` or `showlowerhalf` is false, this splom trace will generate one less x-axis and one less y-axis. yaxes Sets the list of y axes corresponding to dimensions of this splom trace. By default, a splom will match the first N yaxes where N is the number of input dimensions. Note that, in case where `diagonal.visible` is false and `showupperhalf` or `showlowerhalf` is false, this splom trace will generate one less x-axis and one less y-axis. """ def __init__( self, arg=None, customdata=None, customdatasrc=None, diagonal=None, dimensions=None, dimensiondefaults=None, hoverinfo=None, hoverinfosrc=None, hoverlabel=None, hovertemplate=None, hovertemplatesrc=None, hovertext=None, hovertextsrc=None, ids=None, idssrc=None, legendgroup=None, marker=None, name=None, opacity=None, selected=None, selectedpoints=None, showlegend=None, showlowerhalf=None, showupperhalf=None, stream=None, text=None, textsrc=None, uid=None, uirevision=None, unselected=None, visible=None, xaxes=None, yaxes=None, **kwargs ): super(Splom, self).__init__('splom') if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.Splom constructor must be a dict or an instance of plotly.graph_objs.Splom""" ) self._skip_invalid = kwargs.pop('skip_invalid', False) from plotly.validators import (splom as v_splom) self._validators['customdata'] = v_splom.CustomdataValidator() self._validators['customdatasrc'] = v_splom.CustomdatasrcValidator() self._validators['diagonal'] = v_splom.DiagonalValidator() self._validators['dimensions'] = v_splom.DimensionsValidator() self._validators['dimensiondefaults'] = v_splom.DimensionValidator() self._validators['hoverinfo'] = v_splom.HoverinfoValidator() self._validators['hoverinfosrc'] = v_splom.HoverinfosrcValidator() self._validators['hoverlabel'] = v_splom.HoverlabelValidator() self._validators['hovertemplate'] = v_splom.HovertemplateValidator() self._validators['hovertemplatesrc' ] = v_splom.HovertemplatesrcValidator() self._validators['hovertext'] = v_splom.HovertextValidator() self._validators['hovertextsrc'] = v_splom.HovertextsrcValidator() self._validators['ids'] = v_splom.IdsValidator() self._validators['idssrc'] = v_splom.IdssrcValidator() self._validators['legendgroup'] = v_splom.LegendgroupValidator() self._validators['marker'] = v_splom.MarkerValidator() self._validators['name'] = v_splom.NameValidator() self._validators['opacity'] = v_splom.OpacityValidator() self._validators['selected'] = v_splom.SelectedValidator() self._validators['selectedpoints'] = v_splom.SelectedpointsValidator() self._validators['showlegend'] = v_splom.ShowlegendValidator() self._validators['showlowerhalf'] = v_splom.ShowlowerhalfValidator() self._validators['showupperhalf'] = v_splom.ShowupperhalfValidator() self._validators['stream'] = v_splom.StreamValidator() self._validators['text'] = v_splom.TextValidator() self._validators['textsrc'] = v_splom.TextsrcValidator() self._validators['uid'] = v_splom.UidValidator() self._validators['uirevision'] = v_splom.UirevisionValidator() self._validators['unselected'] = v_splom.UnselectedValidator() self._validators['visible'] = v_splom.VisibleValidator() self._validators['xaxes'] = v_splom.XaxesValidator() self._validators['yaxes'] = v_splom.YaxesValidator() _v = arg.pop('customdata', None) self['customdata'] = customdata if customdata is not None else _v _v = arg.pop('customdatasrc', None) self['customdatasrc' ] = customdatasrc if customdatasrc is not None else _v _v = arg.pop('diagonal', None) self['diagonal'] = diagonal if diagonal is not None else _v _v = arg.pop('dimensions', None) self['dimensions'] = dimensions if dimensions is not None else _v _v = arg.pop('dimensiondefaults', None) self['dimensiondefaults' ] = dimensiondefaults if dimensiondefaults is not None else _v _v = arg.pop('hoverinfo', None) self['hoverinfo'] = hoverinfo if hoverinfo is not None else _v _v = arg.pop('hoverinfosrc', None) self['hoverinfosrc'] = hoverinfosrc if hoverinfosrc is not None else _v _v = arg.pop('hoverlabel', None) self['hoverlabel'] = hoverlabel if hoverlabel is not None else _v _v = arg.pop('hovertemplate', None) self['hovertemplate' ] = hovertemplate if hovertemplate is not None else _v _v = arg.pop('hovertemplatesrc', None) self['hovertemplatesrc' ] = hovertemplatesrc if hovertemplatesrc is not None else _v _v = arg.pop('hovertext', None) self['hovertext'] = hovertext if hovertext is not None else _v _v = arg.pop('hovertextsrc', None) self['hovertextsrc'] = hovertextsrc if hovertextsrc is not None else _v _v = arg.pop('ids', None) self['ids'] = ids if ids is not None else _v _v = arg.pop('idssrc', None) self['idssrc'] = idssrc if idssrc is not None else _v _v = arg.pop('legendgroup', None) self['legendgroup'] = legendgroup if legendgroup is not None else _v _v = arg.pop('marker', None) self['marker'] = marker if marker is not None else _v _v = arg.pop('name', None) self['name'] = name if name is not None else _v _v = arg.pop('opacity', None) self['opacity'] = opacity if opacity is not None else _v _v = arg.pop('selected', None) self['selected'] = selected if selected is not None else _v _v = arg.pop('selectedpoints', None) self['selectedpoints' ] = selectedpoints if selectedpoints is not None else _v _v = arg.pop('showlegend', None) self['showlegend'] = showlegend if showlegend is not None else _v _v = arg.pop('showlowerhalf', None) self['showlowerhalf' ] = showlowerhalf if showlowerhalf is not None else _v _v = arg.pop('showupperhalf', None) self['showupperhalf' ] = showupperhalf if showupperhalf is not None else _v _v = arg.pop('stream', None) self['stream'] = stream if stream is not None else _v _v = arg.pop('text', None) self['text'] = text if text is not None else _v _v = arg.pop('textsrc', None) self['textsrc'] = textsrc if textsrc is not None else _v _v = arg.pop('uid', None) self['uid'] = uid if uid is not None else _v _v = arg.pop('uirevision', None) self['uirevision'] = uirevision if uirevision is not None else _v _v = arg.pop('unselected', None) self['unselected'] = unselected if unselected is not None else _v _v = arg.pop('visible', None) self['visible'] = visible if visible is not None else _v _v = arg.pop('xaxes', None) self['xaxes'] = xaxes if xaxes is not None else _v _v = arg.pop('yaxes', None) self['yaxes'] = yaxes if yaxes is not None else _v from _plotly_utils.basevalidators import LiteralValidator self._props['type'] = 'splom' self._validators['type'] = LiteralValidator( plotly_name='type', parent_name='splom', val='splom' ) arg.pop('type', None) self._process_kwargs(**dict(arg, **kwargs)) self._skip_invalid = False

true

7907e7aa9cfdf06580aff6881f0cf146bb88eecb

628

py

Python

Part 1/Chapter 7/exercise_7.6.py

kg55555/pypractice

1867f001b3d2a7174ea00d7b9e2fa22e9f1877ef

[ "MIT" ]

null

Part 1/Chapter 7/exercise_7.6.py

kg55555/pypractice

1867f001b3d2a7174ea00d7b9e2fa22e9f1877ef

[ "MIT" ]

null

Part 1/Chapter 7/exercise_7.6.py

kg55555/pypractice

1867f001b3d2a7174ea00d7b9e2fa22e9f1877ef

[ "MIT" ]

null

repeat_age = 0 while repeat_age < 3: age = int( input(f"Check your movie ticket price by typing your age below. You may check {3 - repeat_age} more times\n")) if age < 3: print("Your ticket is free!") elif 3 <= age <= 12: print("Your ticket costs $10") elif age > 12: print("Your ticket costs $15") if repeat_age == 2: break check = input("Would you like to check another ticket price? Type 'quit' to exit this program\n") if check == 'quit': break repeat_age += 1 print(f"Thank you for using our service! You have checked {repeat_age + 1} times")

34.888889

118

0.61465

repeat_age = 0 while repeat_age < 3: age = int( input(f"Check your movie ticket price by typing your age below. You may check {3 - repeat_age} more times\n")) if age < 3: print("Your ticket is free!") elif 3 <= age <= 12: print("Your ticket costs $10") elif age > 12: print("Your ticket costs $15") if repeat_age == 2: break check = input("Would you like to check another ticket price? Type 'quit' to exit this program\n") if check == 'quit': break repeat_age += 1 print(f"Thank you for using our service! You have checked {repeat_age + 1} times")

true

7907e86fa266d45c6b06d853531bbb74f1ff95d1

1,735

py

Python

fastv8/doc/_extensions/backports.py

gantech/fastv8DriverProgram

565b0f8f6b019a112d7b35f9d841a6af04cb6cce

[ "Apache-2.0" ]

null

fastv8/doc/_extensions/backports.py

gantech/fastv8DriverProgram

565b0f8f6b019a112d7b35f9d841a6af04cb6cce

[ "Apache-2.0" ]

null

fastv8/doc/_extensions/backports.py

gantech/fastv8DriverProgram

565b0f8f6b019a112d7b35f9d841a6af04cb6cce

[ "Apache-2.0" ]

5

2018-09-20T08:27:07.000Z

2021-06-27T01:15:44.000Z

import collections Set = set KEY, PREV, NEXT = range(3) class OrderedSet(collections.MutableSet): """ From: http://code.activestate.com/recipes/576694/ """ def __init__(self, iterable=None): self.end = end = [] end += [None, end, end] # sentinel node for doubly linked list self.map = {} # key --> [key, prev, next] if iterable is not None: self |= iterable def __len__(self): return len(self.map) def __contains__(self, key): return key in self.map def add(self, key): if key not in self.map: end = self.end curr = end[PREV] curr[NEXT] = end[PREV] = self.map[key] = [key, curr, end] def discard(self, key): if key in self.map: key, prev, next = self.map.pop(key) prev[NEXT] = next next[PREV] = prev def __iter__(self): end = self.end curr = end[NEXT] while curr is not end: yield curr[KEY] curr = curr[NEXT] def __reversed__(self): end = self.end curr = end[PREV] while curr is not end: yield curr[KEY] curr = curr[PREV] def pop(self, last=True): if not self: raise KeyError('set is empty') key = next(reversed(self)) if last else next(iter(self)) self.discard(key) return key def __repr__(self): if not self: return '%s()' % (self.__class__.__name__,) return '%s(%r)' % (self.__class__.__name__, list(self)) def __eq__(self, other): if isinstance(other, OrderedSet): return len(self) == len(other) and list(self) == list(other) return set(self) == set(other) def __del__(self): self.clear() # remove circular references if __name__=="__main__": a = OrderedSet()

22.828947

74

0.591931

import collections Set = set KEY, PREV, NEXT = range(3) class OrderedSet(collections.MutableSet): def __init__(self, iterable=None): self.end = end = [] end += [None, end, end] self.map = {} if iterable is not None: self |= iterable def __len__(self): return len(self.map) def __contains__(self, key): return key in self.map def add(self, key): if key not in self.map: end = self.end curr = end[PREV] curr[NEXT] = end[PREV] = self.map[key] = [key, curr, end] def discard(self, key): if key in self.map: key, prev, next = self.map.pop(key) prev[NEXT] = next next[PREV] = prev def __iter__(self): end = self.end curr = end[NEXT] while curr is not end: yield curr[KEY] curr = curr[NEXT] def __reversed__(self): end = self.end curr = end[PREV] while curr is not end: yield curr[KEY] curr = curr[PREV] def pop(self, last=True): if not self: raise KeyError('set is empty') key = next(reversed(self)) if last else next(iter(self)) self.discard(key) return key def __repr__(self): if not self: return '%s()' % (self.__class__.__name__,) return '%s(%r)' % (self.__class__.__name__, list(self)) def __eq__(self, other): if isinstance(other, OrderedSet): return len(self) == len(other) and list(self) == list(other) return set(self) == set(other) def __del__(self): self.clear() if __name__=="__main__": a = OrderedSet()

true

7907e91bb87fb323f8cc6dcc83cbd1f8983ee6b2

695

py

Python

tests/test_user_commands.py

TakingItCasual/easymc

e0feb3c6e9ea172fc0128561789b965c29ed45ca

[ "MIT" ]

null

tests/test_user_commands.py

TakingItCasual/easymc

e0feb3c6e9ea172fc0128561789b965c29ed45ca

[ "MIT" ]

1

2021-01-01T11:12:14.000Z

tests/test_user_commands.py

TakingItCasual/easymc

e0feb3c6e9ea172fc0128561789b965c29ed45ca

[ "MIT" ]

null

from time import sleep from ec2mc import __main__ def test_user_commands(): """test all user commands.""" assert __main__.main([ "user", "create", "ec2mc_test_user", "setup_users", "--default" ]) is not False sleep(5) assert __main__.main([ "user", "list" ]) is not False assert __main__.main([ "user", "set_group", "EC2MC_TEST_USER", "basic_users" ]) is not False assert __main__.main([ "user", "be", "takingitcasual" ]) is not False assert __main__.main([ "user", "rotate_key", "Ec2Mc_TeSt_UsEr" ]) is not False assert __main__.main([ "user", "delete", "eC2mC_tEsT_uSeR" ]) is not False

26.730769

71

0.604317

from time import sleep from ec2mc import __main__ def test_user_commands(): assert __main__.main([ "user", "create", "ec2mc_test_user", "setup_users", "--default" ]) is not False sleep(5) assert __main__.main([ "user", "list" ]) is not False assert __main__.main([ "user", "set_group", "EC2MC_TEST_USER", "basic_users" ]) is not False assert __main__.main([ "user", "be", "takingitcasual" ]) is not False assert __main__.main([ "user", "rotate_key", "Ec2Mc_TeSt_UsEr" ]) is not False assert __main__.main([ "user", "delete", "eC2mC_tEsT_uSeR" ]) is not False

true

7907e9266410cff8eabd178fb2aa50dec1922640

693

py

Python

tests/test_logic/test_rhythm/test_Part.py

aParthemer/MidiCompose

1bed3d47b7b9b484b0ea02ba5e15bf8b51aaf11b

[ "MIT" ]

null

tests/test_logic/test_rhythm/test_Part.py

aParthemer/MidiCompose

1bed3d47b7b9b484b0ea02ba5e15bf8b51aaf11b

[ "MIT" ]

7

2022-02-01T23:48:46.000Z

2022-03-17T02:36:34.000Z

tests/test_logic/test_rhythm/test_Part.py

aParthemer/MidiCompose

1bed3d47b7b9b484b0ea02ba5e15bf8b51aaf11b

[ "MIT" ]

null

import pytest from MidiCompose.logic.rhythm.beat import Beat from MidiCompose.logic.rhythm.measure import Measure from MidiCompose.logic.rhythm.part import Part @pytest.fixture def part_1(): m1 = Measure([Beat([1,2,1,2]), Beat([1,0,0,1])]) m2 = Measure([Beat([2,2,1,1]), Beat([2,2,2,2])]) part = Part([m1,m2]) return part def test_empty_constructor(): p = Part() assert p.n_measures == 1 assert p.n_beats == 1 assert p.n_note_on == 0 def test_n_note_on(part_1): assert part_1.n_note_on == 6 def test_iterator(part_1): for m in part_1: assert type(m) == Measure

19.25

53

0.590188

import pytest from MidiCompose.logic.rhythm.beat import Beat from MidiCompose.logic.rhythm.measure import Measure from MidiCompose.logic.rhythm.part import Part @pytest.fixture def part_1(): m1 = Measure([Beat([1,2,1,2]), Beat([1,0,0,1])]) m2 = Measure([Beat([2,2,1,1]), Beat([2,2,2,2])]) part = Part([m1,m2]) return part def test_empty_constructor(): p = Part() assert p.n_measures == 1 assert p.n_beats == 1 assert p.n_note_on == 0 def test_n_note_on(part_1): assert part_1.n_note_on == 6 def test_iterator(part_1): for m in part_1: assert type(m) == Measure

true

7907ec371b511a265e56c2629766fb2c36188a38

3,156

py

Python

amzASINScrapper/amzASINScrapper/settings.py

sunil-dhaka/python-webScrappers

1f5bd923bd6d3ddce9e209f8d50e08d6b12648ac

[ "MIT" ]

null

amzASINScrapper/amzASINScrapper/settings.py

sunil-dhaka/python-webScrappers

1f5bd923bd6d3ddce9e209f8d50e08d6b12648ac

[ "MIT" ]

null

amzASINScrapper/amzASINScrapper/settings.py

sunil-dhaka/python-webScrappers

1f5bd923bd6d3ddce9e209f8d50e08d6b12648ac

[ "MIT" ]

null

# Scrapy settings for amzASINScrapper project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # https://docs.scrapy.org/en/latest/topics/settings.html # https://docs.scrapy.org/en/latest/topics/downloader-middleware.html # https://docs.scrapy.org/en/latest/topics/spider-middleware.html BOT_NAME = 'amzASINScrapper' SPIDER_MODULES = ['amzASINScrapper.spiders'] NEWSPIDER_MODULE = 'amzASINScrapper.spiders' # Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = 'amzASINScrapper (+http://www.yourdomain.com)' # Obey robots.txt rules ROBOTSTXT_OBEY = True # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs #DOWNLOAD_DELAY = 3 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) #COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: #DEFAULT_REQUEST_HEADERS = { # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', # 'Accept-Language': 'en', #} # Enable or disable spider middlewares # See https://docs.scrapy.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { # 'amzASINScrapper.middlewares.AmzasinscrapperSpiderMiddleware': 543, #} # Enable or disable downloader middlewares # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { # 'amzASINScrapper.middlewares.AmzasinscrapperDownloaderMiddleware': 543, #} # Enable or disable extensions # See https://docs.scrapy.org/en/latest/topics/extensions.html #EXTENSIONS = { # 'scrapy.extensions.telnet.TelnetConsole': None, #} # Configure item pipelines # See https://docs.scrapy.org/en/latest/topics/item-pipeline.html #ITEM_PIPELINES = { # 'amzASINScrapper.pipelines.AmzasinscrapperPipeline': 300, #} # Enable and configure the AutoThrottle extension (disabled by default) # See https://docs.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 5 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 60 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings #HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

35.460674

103

0.78327

BOT_NAME = 'amzASINScrapper' SPIDER_MODULES = ['amzASINScrapper.spiders'] NEWSPIDER_MODULE = 'amzASINScrapper.spiders' ROBOTSTXT_OBEY = True

true

7907ec6fdb92f0a64458d8e3aa6c8595c39b15a1

1,072

py

Python

tests/test_parse_import_time.py

Victor333Huesca/importtime-output-wrapper

15941ffe30a93a2d5ec1832e16df160caa1d51e4

[ "MIT" ]

1

2021-02-10T13:15:47.000Z

tests/test_parse_import_time.py

dominikwalk/importtime_output_wrapper

67c94371cd92ea66f4dbdd8840cf6120db4160c0

[ "MIT" ]

1

2021-09-01T19:25:33.000Z

tests/test_parse_import_time.py

dominikwalk/importtime_output_wrapper

67c94371cd92ea66f4dbdd8840cf6120db4160c0

[ "MIT" ]

null

import pytest from importtime_output_wrapper import Import from importtime_output_wrapper import parse_import_time from importtime_output_wrapper import InvalidInput imp_a0 = Import(name="a0", t_self=4, t_cumu=5, depth=2, childs=[]) imp_a1 = Import(name="a1", t_self=3, t_cumu=4, depth=2, childs=[]) imp_b0 = Import(name="b0", t_self=4, t_cumu=5, depth=2, childs=[]) imp_b1 = Import(name="b1", t_self=3, t_cumu=4, depth=2, childs=[]) imp_b = Import(name="b", t_self=2, t_cumu=3, depth=1, childs=[imp_b0, imp_b1]) imp_a = Import(name="a", t_self=1, t_cumu=2, depth=1, childs=[imp_a0, imp_a1]) root = Import(name="root", t_self=0, t_cumu=0, depth=0, childs=[imp_a, imp_b]) test_tree = [root] with open("tests/sample_importtime_output") as f: test_output_string = f.read() @pytest.mark.parametrize(("test_input", "expected"), ((test_output_string, test_tree),)) def test_parse_std_err(test_input, expected): assert parse_import_time(test_input) == expected def test_parse_empty_std_err(): with pytest.raises(InvalidInput): parse_import_time("")

34.580645

88

0.731343

import pytest from importtime_output_wrapper import Import from importtime_output_wrapper import parse_import_time from importtime_output_wrapper import InvalidInput imp_a0 = Import(name="a0", t_self=4, t_cumu=5, depth=2, childs=[]) imp_a1 = Import(name="a1", t_self=3, t_cumu=4, depth=2, childs=[]) imp_b0 = Import(name="b0", t_self=4, t_cumu=5, depth=2, childs=[]) imp_b1 = Import(name="b1", t_self=3, t_cumu=4, depth=2, childs=[]) imp_b = Import(name="b", t_self=2, t_cumu=3, depth=1, childs=[imp_b0, imp_b1]) imp_a = Import(name="a", t_self=1, t_cumu=2, depth=1, childs=[imp_a0, imp_a1]) root = Import(name="root", t_self=0, t_cumu=0, depth=0, childs=[imp_a, imp_b]) test_tree = [root] with open("tests/sample_importtime_output") as f: test_output_string = f.read() @pytest.mark.parametrize(("test_input", "expected"), ((test_output_string, test_tree),)) def test_parse_std_err(test_input, expected): assert parse_import_time(test_input) == expected def test_parse_empty_std_err(): with pytest.raises(InvalidInput): parse_import_time("")

true

7907ed3159ccaed9f860fc0385957aca72dd15fc

4,310

py

Python

contents/2_Q_Learning_maze/maze_env.py

zhao-jin/Reinforcement-learning-with-tensorflow

a4a816f1570be55016909f703fb1fd1ceae9c5a0

[ "MIT" ]

null

contents/2_Q_Learning_maze/maze_env.py

zhao-jin/Reinforcement-learning-with-tensorflow

a4a816f1570be55016909f703fb1fd1ceae9c5a0

[ "MIT" ]

null

contents/2_Q_Learning_maze/maze_env.py

zhao-jin/Reinforcement-learning-with-tensorflow

a4a816f1570be55016909f703fb1fd1ceae9c5a0

[ "MIT" ]

null

""" Reinforcement learning maze example. Red rectangle: explorer. Black rectangles: hells [reward = -1]. Yellow bin circle: paradise [reward = +1]. All other states: ground [reward = 0]. This script is the environment part of this example. The RL is in RL_brain.py. View more on my tutorial page: https://morvanzhou.github.io/tutorials/ """ import numpy as np import time import sys if sys.version_info.major == 2: import Tkinter as tk else: import tkinter as tk UNIT = 40 # pixels MAZE_H = 4 # grid height MAZE_W = 4 # grid width class Maze(tk.Tk, object): def __init__(self): super(Maze, self).__init__() self.action_space = ['u', 'd', 'l', 'r'] self.n_actions = len(self.action_space) self.title('maze') self.geometry('{0}x{1}'.format(MAZE_H * UNIT, MAZE_H * UNIT)) self._build_maze() def _build_maze(self): self.canvas = tk.Canvas(self, bg='white', height=MAZE_H * UNIT, width=MAZE_W * UNIT) # create grids for c in range(0, MAZE_W * UNIT, UNIT): x0, y0, x1, y1 = c, 0, c, MAZE_H * UNIT self.canvas.create_line(x0, y0, x1, y1) for r in range(0, MAZE_H * UNIT, UNIT): x0, y0, x1, y1 = 0, r, MAZE_W * UNIT, r self.canvas.create_line(x0, y0, x1, y1) # create origin origin = np.array([20, 20]) # hell hell1_center = origin + np.array([UNIT * 2, UNIT]) self.hell1 = self.canvas.create_rectangle( hell1_center[0] - 15, hell1_center[1] - 15, hell1_center[0] + 15, hell1_center[1] + 15, fill='black') # hell hell2_center = origin + np.array([UNIT, UNIT * 2]) self.hell2 = self.canvas.create_rectangle( hell2_center[0] - 15, hell2_center[1] - 15, hell2_center[0] + 15, hell2_center[1] + 15, fill='black') # create oval oval_center = origin + UNIT * 2 self.oval = self.canvas.create_oval( oval_center[0] - 15, oval_center[1] - 15, oval_center[0] + 15, oval_center[1] + 15, fill='yellow') # create red rect self.rect = self.canvas.create_rectangle( origin[0] - 15, origin[1] - 15, origin[0] + 15, origin[1] + 15, fill='red') # pack all self.canvas.pack() def reset(self): self.update() time.sleep(0.1) self.canvas.delete(self.rect) origin = np.array([20, 20]) self.rect = self.canvas.create_rectangle( origin[0] - 15, origin[1] - 15, origin[0] + 15, origin[1] + 15, fill='red') # return observation return self.canvas.coords(self.rect) def step(self, action): s = self.canvas.coords(self.rect) base_action = np.array([0, 0]) if action == 0: # up if s[1] > UNIT: base_action[1] -= UNIT elif action == 1: # down if s[1] < (MAZE_H - 1) * UNIT: base_action[1] += UNIT elif action == 2: # right if s[0] < (MAZE_W - 1) * UNIT: base_action[0] += UNIT elif action == 3: # left if s[0] > UNIT: base_action[0] -= UNIT self.canvas.move(self.rect, base_action[0], base_action[1]) # move agent s_ = self.canvas.coords(self.rect) # next state # reward function if s_ == self.canvas.coords(self.oval): reward = 1 done = True #s_ = 'terminal' elif s_ in [self.canvas.coords(self.hell1), self.canvas.coords(self.hell2)]: reward = -1 done = True #s_ = 'terminal' else: reward = 0 done = False return s_, reward, done def render(self): time.sleep(0.01) self.update() def update(): for t in range(10): s = env.reset() while True: env.render() a = 1 s, r, done = env.step(a) if done: break if __name__ == '__main__': env = Maze() env.after(100, update) env.mainloop()

29.121622

84

0.517633

import numpy as np import time import sys if sys.version_info.major == 2: import Tkinter as tk else: import tkinter as tk UNIT = 40 MAZE_H = 4 MAZE_W = 4 class Maze(tk.Tk, object): def __init__(self): super(Maze, self).__init__() self.action_space = ['u', 'd', 'l', 'r'] self.n_actions = len(self.action_space) self.title('maze') self.geometry('{0}x{1}'.format(MAZE_H * UNIT, MAZE_H * UNIT)) self._build_maze() def _build_maze(self): self.canvas = tk.Canvas(self, bg='white', height=MAZE_H * UNIT, width=MAZE_W * UNIT) for c in range(0, MAZE_W * UNIT, UNIT): x0, y0, x1, y1 = c, 0, c, MAZE_H * UNIT self.canvas.create_line(x0, y0, x1, y1) for r in range(0, MAZE_H * UNIT, UNIT): x0, y0, x1, y1 = 0, r, MAZE_W * UNIT, r self.canvas.create_line(x0, y0, x1, y1) origin = np.array([20, 20]) hell1_center = origin + np.array([UNIT * 2, UNIT]) self.hell1 = self.canvas.create_rectangle( hell1_center[0] - 15, hell1_center[1] - 15, hell1_center[0] + 15, hell1_center[1] + 15, fill='black') hell2_center = origin + np.array([UNIT, UNIT * 2]) self.hell2 = self.canvas.create_rectangle( hell2_center[0] - 15, hell2_center[1] - 15, hell2_center[0] + 15, hell2_center[1] + 15, fill='black') oval_center = origin + UNIT * 2 self.oval = self.canvas.create_oval( oval_center[0] - 15, oval_center[1] - 15, oval_center[0] + 15, oval_center[1] + 15, fill='yellow') self.rect = self.canvas.create_rectangle( origin[0] - 15, origin[1] - 15, origin[0] + 15, origin[1] + 15, fill='red') self.canvas.pack() def reset(self): self.update() time.sleep(0.1) self.canvas.delete(self.rect) origin = np.array([20, 20]) self.rect = self.canvas.create_rectangle( origin[0] - 15, origin[1] - 15, origin[0] + 15, origin[1] + 15, fill='red') return self.canvas.coords(self.rect) def step(self, action): s = self.canvas.coords(self.rect) base_action = np.array([0, 0]) if action == 0: if s[1] > UNIT: base_action[1] -= UNIT elif action == 1: if s[1] < (MAZE_H - 1) * UNIT: base_action[1] += UNIT elif action == 2: if s[0] < (MAZE_W - 1) * UNIT: base_action[0] += UNIT elif action == 3: if s[0] > UNIT: base_action[0] -= UNIT self.canvas.move(self.rect, base_action[0], base_action[1]) s_ = self.canvas.coords(self.rect) if s_ == self.canvas.coords(self.oval): reward = 1 done = True elif s_ in [self.canvas.coords(self.hell1), self.canvas.coords(self.hell2)]: reward = -1 done = True else: reward = 0 done = False return s_, reward, done def render(self): time.sleep(0.01) self.update() def update(): for t in range(10): s = env.reset() while True: env.render() a = 1 s, r, done = env.step(a) if done: break if __name__ == '__main__': env = Maze() env.after(100, update) env.mainloop()

true

7907eda9ab081c94bfbf0706c3bbf2a82b8b0777

52,414

py

Python

vendors/rez-2.23.1-py2.7/rez/vendor/memcache/memcache.py

ColinKennedy/tk-config-default2-respawn

855fb8033daa549b92615792442f19a7f9c4f55c

[ "Linux-OpenIB" ]

4

2019-01-11T03:41:28.000Z

2019-09-12T06:57:17.000Z

vendors/rez-2.23.1-py2.7/rez/vendor/memcache/memcache.py

ColinKennedy/tk-config-default2-respawn

855fb8033daa549b92615792442f19a7f9c4f55c

[ "Linux-OpenIB" ]

null

vendors/rez-2.23.1-py2.7/rez/vendor/memcache/memcache.py

ColinKennedy/tk-config-default2-respawn

855fb8033daa549b92615792442f19a7f9c4f55c

[ "Linux-OpenIB" ]

2

2019-01-10T05:00:18.000Z

2020-02-15T16:32:56.000Z

#!/usr/bin/env python """ client module for memcached (memory cache daemon) Overview ======== See U{the MemCached homepage<http://www.danga.com/memcached>} for more about memcached. Usage summary ============= This should give you a feel for how this module operates:: import memcache mc = memcache.Client(['127.0.0.1:11211'], debug=0) mc.set("some_key", "Some value") value = mc.get("some_key") mc.set("another_key", 3) mc.delete("another_key") mc.set("key", "1") # note that the key used for incr/decr must be a string. mc.incr("key") mc.decr("key") The standard way to use memcache with a database is like this:: key = derive_key(obj) obj = mc.get(key) if not obj: obj = backend_api.get(...) mc.set(key, obj) # we now have obj, and future passes through this code # will use the object from the cache. Detailed Documentation ====================== More detailed documentation is available in the L{Client} class. """ import sys import socket import time import os import re try: import cPickle as pickle except ImportError: import pickle from binascii import crc32 # zlib version is not cross-platform def cmemcache_hash(key): return((((crc32(key) & 0xffffffff) >> 16) & 0x7fff) or 1) serverHashFunction = cmemcache_hash def useOldServerHashFunction(): """Use the old python-memcache server hash function.""" global serverHashFunction serverHashFunction = crc32 try: from zlib import compress, decompress _supports_compress = True except ImportError: _supports_compress = False # quickly define a decompress just in case we recv compressed data. def decompress(val): raise _Error("received compressed data but I don't support compression (import error)") try: from cStringIO import StringIO except ImportError: from StringIO import StringIO valid_key_chars_re = re.compile('[\x21-\x7e\x80-\xff]+$') # Original author: Evan Martin of Danga Interactive __author__ = "Sean Reifschneider <jafo-memcached@tummy.com>" __version__ = "1.53" __copyright__ = "Copyright (C) 2003 Danga Interactive" # http://en.wikipedia.org/wiki/Python_Software_Foundation_License __license__ = "Python Software Foundation License" SERVER_MAX_KEY_LENGTH = 250 # Storing values larger than 1MB requires recompiling memcached. If you do, # this value can be changed by doing "memcache.SERVER_MAX_VALUE_LENGTH = N" # after importing this module. SERVER_MAX_VALUE_LENGTH = 1024*1024 class _Error(Exception): pass class _ConnectionDeadError(Exception): pass try: # Only exists in Python 2.4+ from threading import local except ImportError: # TODO: add the pure-python local implementation class local(object): pass _DEAD_RETRY = 30 # number of seconds before retrying a dead server. _SOCKET_TIMEOUT = 3 # number of seconds before sockets timeout. class Client(local): """ Object representing a pool of memcache servers. See L{memcache} for an overview. In all cases where a key is used, the key can be either: 1. A simple hashable type (string, integer, etc.). 2. A tuple of C{(hashvalue, key)}. This is useful if you want to avoid making this module calculate a hash value. You may prefer, for example, to keep all of a given user's objects on the same memcache server, so you could use the user's unique id as the hash value. @group Setup: __init__, set_servers, forget_dead_hosts, disconnect_all, debuglog @group Insertion: set, add, replace, set_multi @group Retrieval: get, get_multi @group Integers: incr, decr @group Removal: delete, delete_multi @sort: __init__, set_servers, forget_dead_hosts, disconnect_all, debuglog,\ set, set_multi, add, replace, get, get_multi, incr, decr, delete, delete_multi """ _FLAG_PICKLE = 1<<0 _FLAG_INTEGER = 1<<1 _FLAG_LONG = 1<<2 _FLAG_COMPRESSED = 1<<3 _SERVER_RETRIES = 10 # how many times to try finding a free server. # exceptions for Client class MemcachedKeyError(Exception): pass class MemcachedKeyLengthError(MemcachedKeyError): pass class MemcachedKeyCharacterError(MemcachedKeyError): pass class MemcachedKeyNoneError(MemcachedKeyError): pass class MemcachedKeyTypeError(MemcachedKeyError): pass class MemcachedStringEncodingError(Exception): pass def __init__(self, servers, debug=0, pickleProtocol=0, pickler=pickle.Pickler, unpickler=pickle.Unpickler, pload=None, pid=None, server_max_key_length=SERVER_MAX_KEY_LENGTH, server_max_value_length=SERVER_MAX_VALUE_LENGTH, dead_retry=_DEAD_RETRY, socket_timeout=_SOCKET_TIMEOUT, cache_cas = False, flush_on_reconnect=0, check_keys=True): """ Create a new Client object with the given list of servers. @param servers: C{servers} is passed to L{set_servers}. @param debug: whether to display error messages when a server can't be contacted. @param pickleProtocol: number to mandate protocol used by (c)Pickle. @param pickler: optional override of default Pickler to allow subclassing. @param unpickler: optional override of default Unpickler to allow subclassing. @param pload: optional persistent_load function to call on pickle loading. Useful for cPickle since subclassing isn't allowed. @param pid: optional persistent_id function to call on pickle storing. Useful for cPickle since subclassing isn't allowed. @param dead_retry: number of seconds before retrying a blacklisted server. Default to 30 s. @param socket_timeout: timeout in seconds for all calls to a server. Defaults to 3 seconds. @param cache_cas: (default False) If true, cas operations will be cached. WARNING: This cache is not expired internally, if you have a long-running process you will need to expire it manually via client.reset_cas(), or the cache can grow unlimited. @param server_max_key_length: (default SERVER_MAX_KEY_LENGTH) Data that is larger than this will not be sent to the server. @param server_max_value_length: (default SERVER_MAX_VALUE_LENGTH) Data that is larger than this will not be sent to the server. @param flush_on_reconnect: optional flag which prevents a scenario that can cause stale data to be read: If there's more than one memcached server and the connection to one is interrupted, keys that mapped to that server will get reassigned to another. If the first server comes back, those keys will map to it again. If it still has its data, get()s can read stale data that was overwritten on another server. This flag is off by default for backwards compatibility. @param check_keys: (default True) If True, the key is checked to ensure it is the correct length and composed of the right characters. """ local.__init__(self) self.debug = debug self.dead_retry = dead_retry self.socket_timeout = socket_timeout self.flush_on_reconnect = flush_on_reconnect self.set_servers(servers) self.stats = {} self.cache_cas = cache_cas self.reset_cas() self.do_check_key = check_keys # Allow users to modify pickling/unpickling behavior self.pickleProtocol = pickleProtocol self.pickler = pickler self.unpickler = unpickler self.persistent_load = pload self.persistent_id = pid self.server_max_key_length = server_max_key_length self.server_max_value_length = server_max_value_length # figure out the pickler style file = StringIO() try: pickler = self.pickler(file, protocol = self.pickleProtocol) self.picklerIsKeyword = True except TypeError: self.picklerIsKeyword = False def reset_cas(self): """ Reset the cas cache. This is only used if the Client() object was created with "cache_cas=True". If used, this cache does not expire internally, so it can grow unbounded if you do not clear it yourself. """ self.cas_ids = {} def set_servers(self, servers): """ Set the pool of servers used by this client. @param servers: an array of servers. Servers can be passed in two forms: 1. Strings of the form C{"host:port"}, which implies a default weight of 1. 2. Tuples of the form C{("host:port", weight)}, where C{weight} is an integer weight value. """ self.servers = [_Host(s, self.debug, dead_retry=self.dead_retry, socket_timeout=self.socket_timeout, flush_on_reconnect=self.flush_on_reconnect) for s in servers] self._init_buckets() def get_stats(self, stat_args = None): '''Get statistics from each of the servers. @param stat_args: Additional arguments to pass to the memcache "stats" command. @return: A list of tuples ( server_identifier, stats_dictionary ). The dictionary contains a number of name/value pairs specifying the name of the status field and the string value associated with it. The values are not converted from strings. ''' data = [] for s in self.servers: if not s.connect(): continue if s.family == socket.AF_INET: name = '%s:%s (%s)' % ( s.ip, s.port, s.weight ) elif s.family == socket.AF_INET6: name = '[%s]:%s (%s)' % ( s.ip, s.port, s.weight ) else: name = 'unix:%s (%s)' % ( s.address, s.weight ) if not stat_args: s.send_cmd('stats') else: s.send_cmd('stats ' + stat_args) serverData = {} data.append(( name, serverData )) readline = s.readline while 1: line = readline() if not line or line.strip() in ('END', 'RESET'): break stats = line.split(' ', 2) serverData[stats[1]] = stats[2] return(data) def get_slabs(self): data = [] for s in self.servers: if not s.connect(): continue if s.family == socket.AF_INET: name = '%s:%s (%s)' % ( s.ip, s.port, s.weight ) elif s.family == socket.AF_INET6: name = '[%s]:%s (%s)' % ( s.ip, s.port, s.weight ) else: name = 'unix:%s (%s)' % ( s.address, s.weight ) serverData = {} data.append(( name, serverData )) s.send_cmd('stats items') readline = s.readline while 1: line = readline() if not line or line.strip() == 'END': break item = line.split(' ', 2) #0 = STAT, 1 = ITEM, 2 = Value slab = item[1].split(':', 2) #0 = items, 1 = Slab #, 2 = Name if slab[1] not in serverData: serverData[slab[1]] = {} serverData[slab[1]][slab[2]] = item[2] return data def flush_all(self): """Expire all data in memcache servers that are reachable.""" for s in self.servers: if not s.connect(): continue s.flush() def debuglog(self, str): if self.debug: sys.stderr.write("MemCached: %s\n" % str) def _statlog(self, func): if func not in self.stats: self.stats[func] = 1 else: self.stats[func] += 1 def forget_dead_hosts(self): """ Reset every host in the pool to an "alive" state. """ for s in self.servers: s.deaduntil = 0 def _init_buckets(self): self.buckets = [] for server in self.servers: for i in range(server.weight): self.buckets.append(server) def _get_server(self, key): if isinstance(key, tuple): serverhash, key = key else: serverhash = serverHashFunction(key) for i in range(Client._SERVER_RETRIES): server = self.buckets[serverhash % len(self.buckets)] if server.connect(): #print "(using server %s)" % server, return server, key serverhash = serverHashFunction(str(serverhash) + str(i)) return None, None def disconnect_all(self): for s in self.servers: s.close_socket() def delete_multi(self, keys, time=0, key_prefix=''): ''' Delete multiple keys in the memcache doing just one query. >>> notset_keys = mc.set_multi({'key1' : 'val1', 'key2' : 'val2'}) >>> mc.get_multi(['key1', 'key2']) == {'key1' : 'val1', 'key2' : 'val2'} 1 >>> mc.delete_multi(['key1', 'key2']) 1 >>> mc.get_multi(['key1', 'key2']) == {} 1 This method is recommended over iterated regular L{delete}s as it reduces total latency, since your app doesn't have to wait for each round-trip of L{delete} before sending the next one. @param keys: An iterable of keys to clear @param time: number of seconds any subsequent set / update commands should fail. Defaults to 0 for no delay. @param key_prefix: Optional string to prepend to each key when sending to memcache. See docs for L{get_multi} and L{set_multi}. @return: 1 if no failure in communication with any memcacheds. @rtype: int ''' self._statlog('delete_multi') server_keys, prefixed_to_orig_key = self._map_and_prefix_keys(keys, key_prefix) # send out all requests on each server before reading anything dead_servers = [] rc = 1 for server in server_keys.iterkeys(): bigcmd = [] write = bigcmd.append if time != None: for key in server_keys[server]: # These are mangled keys write("delete %s %d\r\n" % (key, time)) else: for key in server_keys[server]: # These are mangled keys write("delete %s\r\n" % key) try: server.send_cmds(''.join(bigcmd)) except socket.error, msg: rc = 0 if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) dead_servers.append(server) # if any servers died on the way, don't expect them to respond. for server in dead_servers: del server_keys[server] for server, keys in server_keys.iteritems(): try: for key in keys: server.expect("DELETED") except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) rc = 0 return rc def delete(self, key, time=0): '''Deletes a key from the memcache. @return: Nonzero on success. @param time: number of seconds any subsequent set / update commands should fail. Defaults to None for no delay. @rtype: int ''' if self.do_check_key: self.check_key(key) server, key = self._get_server(key) if not server: return 0 self._statlog('delete') if time != None and time != 0: cmd = "delete %s %d" % (key, time) else: cmd = "delete %s" % key try: server.send_cmd(cmd) line = server.readline() if line and line.strip() in ['DELETED', 'NOT_FOUND']: return 1 self.debuglog('Delete expected DELETED or NOT_FOUND, got: %s' % repr(line)) except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return 0 def incr(self, key, delta=1): """ Sends a command to the server to atomically increment the value for C{key} by C{delta}, or by 1 if C{delta} is unspecified. Returns None if C{key} doesn't exist on server, otherwise it returns the new value after incrementing. Note that the value for C{key} must already exist in the memcache, and it must be the string representation of an integer. >>> mc.set("counter", "20") # returns 1, indicating success 1 >>> mc.incr("counter") 21 >>> mc.incr("counter") 22 Overflow on server is not checked. Be aware of values approaching 2**32. See L{decr}. @param delta: Integer amount to increment by (should be zero or greater). @return: New value after incrementing. @rtype: int """ return self._incrdecr("incr", key, delta) def decr(self, key, delta=1): """ Like L{incr}, but decrements. Unlike L{incr}, underflow is checked and new values are capped at 0. If server value is 1, a decrement of 2 returns 0, not -1. @param delta: Integer amount to decrement by (should be zero or greater). @return: New value after decrementing or None on error. @rtype: int """ return self._incrdecr("decr", key, delta) def _incrdecr(self, cmd, key, delta): if self.do_check_key: self.check_key(key) server, key = self._get_server(key) if not server: return None self._statlog(cmd) cmd = "%s %s %d" % (cmd, key, delta) try: server.send_cmd(cmd) line = server.readline() if line == None or line.strip() =='NOT_FOUND': return None return int(line) except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return None def add(self, key, val, time = 0, min_compress_len = 0): ''' Add new key with value. Like L{set}, but only stores in memcache if the key doesn't already exist. @return: Nonzero on success. @rtype: int ''' return self._set("add", key, val, time, min_compress_len) def append(self, key, val, time=0, min_compress_len=0): '''Append the value to the end of the existing key's value. Only stores in memcache if key already exists. Also see L{prepend}. @return: Nonzero on success. @rtype: int ''' return self._set("append", key, val, time, min_compress_len) def prepend(self, key, val, time=0, min_compress_len=0): '''Prepend the value to the beginning of the existing key's value. Only stores in memcache if key already exists. Also see L{append}. @return: Nonzero on success. @rtype: int ''' return self._set("prepend", key, val, time, min_compress_len) def replace(self, key, val, time=0, min_compress_len=0): '''Replace existing key with value. Like L{set}, but only stores in memcache if the key already exists. The opposite of L{add}. @return: Nonzero on success. @rtype: int ''' return self._set("replace", key, val, time, min_compress_len) def set(self, key, val, time=0, min_compress_len=0): '''Unconditionally sets a key to a given value in the memcache. The C{key} can optionally be an tuple, with the first element being the server hash value and the second being the key. If you want to avoid making this module calculate a hash value. You may prefer, for example, to keep all of a given user's objects on the same memcache server, so you could use the user's unique id as the hash value. @return: Nonzero on success. @rtype: int @param time: Tells memcached the time which this value should expire, either as a delta number of seconds, or an absolute unix time-since-the-epoch value. See the memcached protocol docs section "Storage Commands" for more info on <exptime>. We default to 0 == cache forever. @param min_compress_len: The threshold length to kick in auto-compression of the value using the zlib.compress() routine. If the value being cached is a string, then the length of the string is measured, else if the value is an object, then the length of the pickle result is measured. If the resulting attempt at compression yeilds a larger string than the input, then it is discarded. For backwards compatability, this parameter defaults to 0, indicating don't ever try to compress. ''' return self._set("set", key, val, time, min_compress_len) def cas(self, key, val, time=0, min_compress_len=0): '''Sets a key to a given value in the memcache if it hasn't been altered since last fetched. (See L{gets}). The C{key} can optionally be an tuple, with the first element being the server hash value and the second being the key. If you want to avoid making this module calculate a hash value. You may prefer, for example, to keep all of a given user's objects on the same memcache server, so you could use the user's unique id as the hash value. @return: Nonzero on success. @rtype: int @param time: Tells memcached the time which this value should expire, either as a delta number of seconds, or an absolute unix time-since-the-epoch value. See the memcached protocol docs section "Storage Commands" for more info on <exptime>. We default to 0 == cache forever. @param min_compress_len: The threshold length to kick in auto-compression of the value using the zlib.compress() routine. If the value being cached is a string, then the length of the string is measured, else if the value is an object, then the length of the pickle result is measured. If the resulting attempt at compression yeilds a larger string than the input, then it is discarded. For backwards compatability, this parameter defaults to 0, indicating don't ever try to compress. ''' return self._set("cas", key, val, time, min_compress_len) def _map_and_prefix_keys(self, key_iterable, key_prefix): """Compute the mapping of server (_Host instance) -> list of keys to stuff onto that server, as well as the mapping of prefixed key -> original key. """ # Check it just once ... key_extra_len=len(key_prefix) if key_prefix and self.do_check_key: self.check_key(key_prefix) # server (_Host) -> list of unprefixed server keys in mapping server_keys = {} prefixed_to_orig_key = {} # build up a list for each server of all the keys we want. for orig_key in key_iterable: if isinstance(orig_key, tuple): # Tuple of hashvalue, key ala _get_server(). Caller is essentially telling us what server to stuff this on. # Ensure call to _get_server gets a Tuple as well. str_orig_key = str(orig_key[1]) server, key = self._get_server((orig_key[0], key_prefix + str_orig_key)) # Gotta pre-mangle key before hashing to a server. Returns the mangled key. else: str_orig_key = str(orig_key) # set_multi supports int / long keys. server, key = self._get_server(key_prefix + str_orig_key) # Now check to make sure key length is proper ... if self.do_check_key: self.check_key(str_orig_key, key_extra_len=key_extra_len) if not server: continue if server not in server_keys: server_keys[server] = [] server_keys[server].append(key) prefixed_to_orig_key[key] = orig_key return (server_keys, prefixed_to_orig_key) def set_multi(self, mapping, time=0, key_prefix='', min_compress_len=0): ''' Sets multiple keys in the memcache doing just one query. >>> notset_keys = mc.set_multi({'key1' : 'val1', 'key2' : 'val2'}) >>> mc.get_multi(['key1', 'key2']) == {'key1' : 'val1', 'key2' : 'val2'} 1 This method is recommended over regular L{set} as it lowers the number of total packets flying around your network, reducing total latency, since your app doesn't have to wait for each round-trip of L{set} before sending the next one. @param mapping: A dict of key/value pairs to set. @param time: Tells memcached the time which this value should expire, either as a delta number of seconds, or an absolute unix time-since-the-epoch value. See the memcached protocol docs section "Storage Commands" for more info on <exptime>. We default to 0 == cache forever. @param key_prefix: Optional string to prepend to each key when sending to memcache. Allows you to efficiently stuff these keys into a pseudo-namespace in memcache: >>> notset_keys = mc.set_multi( ... {'key1' : 'val1', 'key2' : 'val2'}, key_prefix='subspace_') >>> len(notset_keys) == 0 True >>> mc.get_multi(['subspace_key1', 'subspace_key2']) == {'subspace_key1' : 'val1', 'subspace_key2' : 'val2'} True Causes key 'subspace_key1' and 'subspace_key2' to be set. Useful in conjunction with a higher-level layer which applies namespaces to data in memcache. In this case, the return result would be the list of notset original keys, prefix not applied. @param min_compress_len: The threshold length to kick in auto-compression of the value using the zlib.compress() routine. If the value being cached is a string, then the length of the string is measured, else if the value is an object, then the length of the pickle result is measured. If the resulting attempt at compression yeilds a larger string than the input, then it is discarded. For backwards compatability, this parameter defaults to 0, indicating don't ever try to compress. @return: List of keys which failed to be stored [ memcache out of memory, etc. ]. @rtype: list ''' self._statlog('set_multi') server_keys, prefixed_to_orig_key = self._map_and_prefix_keys( mapping.iterkeys(), key_prefix) # send out all requests on each server before reading anything dead_servers = [] notstored = [] # original keys. for server in server_keys.iterkeys(): bigcmd = [] write = bigcmd.append try: for key in server_keys[server]: # These are mangled keys store_info = self._val_to_store_info( mapping[prefixed_to_orig_key[key]], min_compress_len) if store_info: write("set %s %d %d %d\r\n%s\r\n" % (key, store_info[0], time, store_info[1], store_info[2])) else: notstored.append(prefixed_to_orig_key[key]) server.send_cmds(''.join(bigcmd)) except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) dead_servers.append(server) # if any servers died on the way, don't expect them to respond. for server in dead_servers: del server_keys[server] # short-circuit if there are no servers, just return all keys if not server_keys: return(mapping.keys()) for server, keys in server_keys.iteritems(): try: for key in keys: if server.readline() == 'STORED': continue else: notstored.append(prefixed_to_orig_key[key]) #un-mangle. except (_Error, socket.error), msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return notstored def _val_to_store_info(self, val, min_compress_len): """ Transform val to a storable representation, returning a tuple of the flags, the length of the new value, and the new value itself. """ flags = 0 if isinstance(val, str): pass elif isinstance(val, int): flags |= Client._FLAG_INTEGER val = "%d" % val # force no attempt to compress this silly string. min_compress_len = 0 elif isinstance(val, long): flags |= Client._FLAG_LONG val = "%d" % val # force no attempt to compress this silly string. min_compress_len = 0 else: flags |= Client._FLAG_PICKLE file = StringIO() if self.picklerIsKeyword: pickler = self.pickler(file, protocol = self.pickleProtocol) else: pickler = self.pickler(file, self.pickleProtocol) if self.persistent_id: pickler.persistent_id = self.persistent_id pickler.dump(val) val = file.getvalue() lv = len(val) # We should try to compress if min_compress_len > 0 and we could # import zlib and this string is longer than our min threshold. if min_compress_len and _supports_compress and lv > min_compress_len: comp_val = compress(val) # Only retain the result if the compression result is smaller # than the original. if len(comp_val) < lv: flags |= Client._FLAG_COMPRESSED val = comp_val # silently do not store if value length exceeds maximum if self.server_max_value_length != 0 and \ len(val) > self.server_max_value_length: return(0) return (flags, len(val), val) def _set(self, cmd, key, val, time, min_compress_len = 0): if self.do_check_key: self.check_key(key) server, key = self._get_server(key) if not server: return 0 def _unsafe_set(): self._statlog(cmd) store_info = self._val_to_store_info(val, min_compress_len) if not store_info: return(0) if cmd == 'cas': if key not in self.cas_ids: return self._set('set', key, val, time, min_compress_len) fullcmd = "%s %s %d %d %d %d\r\n%s" % ( cmd, key, store_info[0], time, store_info[1], self.cas_ids[key], store_info[2]) else: fullcmd = "%s %s %d %d %d\r\n%s" % ( cmd, key, store_info[0], time, store_info[1], store_info[2]) try: server.send_cmd(fullcmd) return(server.expect("STORED", raise_exception=True) == "STORED") except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return 0 try: return _unsafe_set() except _ConnectionDeadError: # retry once try: if server._get_socket(): return _unsafe_set() except (_ConnectionDeadError, socket.error), msg: server.mark_dead(msg) return 0 def _get(self, cmd, key): if self.do_check_key: self.check_key(key) server, key = self._get_server(key) if not server: return None def _unsafe_get(): self._statlog(cmd) try: server.send_cmd("%s %s" % (cmd, key)) rkey = flags = rlen = cas_id = None if cmd == 'gets': rkey, flags, rlen, cas_id, = self._expect_cas_value(server, raise_exception=True) if rkey and self.cache_cas: self.cas_ids[rkey] = cas_id else: rkey, flags, rlen, = self._expectvalue(server, raise_exception=True) if not rkey: return None try: value = self._recv_value(server, flags, rlen) finally: server.expect("END", raise_exception=True) except (_Error, socket.error), msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return None return value try: return _unsafe_get() except _ConnectionDeadError: # retry once try: if server.connect(): return _unsafe_get() return None except (_ConnectionDeadError, socket.error), msg: server.mark_dead(msg) return None def get(self, key): '''Retrieves a key from the memcache. @return: The value or None. ''' return self._get('get', key) def gets(self, key): '''Retrieves a key from the memcache. Used in conjunction with 'cas'. @return: The value or None. ''' return self._get('gets', key) def get_multi(self, keys, key_prefix=''): ''' Retrieves multiple keys from the memcache doing just one query. >>> success = mc.set("foo", "bar") >>> success = mc.set("baz", 42) >>> mc.get_multi(["foo", "baz", "foobar"]) == {"foo": "bar", "baz": 42} 1 >>> mc.set_multi({'k1' : 1, 'k2' : 2}, key_prefix='pfx_') == [] 1 This looks up keys 'pfx_k1', 'pfx_k2', ... . Returned dict will just have unprefixed keys 'k1', 'k2'. >>> mc.get_multi(['k1', 'k2', 'nonexist'], key_prefix='pfx_') == {'k1' : 1, 'k2' : 2} 1 get_mult [ and L{set_multi} ] can take str()-ables like ints / longs as keys too. Such as your db pri key fields. They're rotored through str() before being passed off to memcache, with or without the use of a key_prefix. In this mode, the key_prefix could be a table name, and the key itself a db primary key number. >>> mc.set_multi({42: 'douglass adams', 46 : 'and 2 just ahead of me'}, key_prefix='numkeys_') == [] 1 >>> mc.get_multi([46, 42], key_prefix='numkeys_') == {42: 'douglass adams', 46 : 'and 2 just ahead of me'} 1 This method is recommended over regular L{get} as it lowers the number of total packets flying around your network, reducing total latency, since your app doesn't have to wait for each round-trip of L{get} before sending the next one. See also L{set_multi}. @param keys: An array of keys. @param key_prefix: A string to prefix each key when we communicate with memcache. Facilitates pseudo-namespaces within memcache. Returned dictionary keys will not have this prefix. @return: A dictionary of key/value pairs that were available. If key_prefix was provided, the keys in the retured dictionary will not have it present. ''' self._statlog('get_multi') server_keys, prefixed_to_orig_key = self._map_and_prefix_keys(keys, key_prefix) # send out all requests on each server before reading anything dead_servers = [] for server in server_keys.iterkeys(): try: server.send_cmd("get %s" % " ".join(server_keys[server])) except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) dead_servers.append(server) # if any servers died on the way, don't expect them to respond. for server in dead_servers: del server_keys[server] retvals = {} for server in server_keys.iterkeys(): try: line = server.readline() while line and line != 'END': rkey, flags, rlen = self._expectvalue(server, line) # Bo Yang reports that this can sometimes be None if rkey is not None: val = self._recv_value(server, flags, rlen) retvals[prefixed_to_orig_key[rkey]] = val # un-prefix returned key. line = server.readline() except (_Error, socket.error), msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return retvals def _expect_cas_value(self, server, line=None, raise_exception=False): if not line: line = server.readline(raise_exception) if line and line[:5] == 'VALUE': resp, rkey, flags, len, cas_id = line.split() return (rkey, int(flags), int(len), int(cas_id)) else: return (None, None, None, None) def _expectvalue(self, server, line=None, raise_exception=False): if not line: line = server.readline(raise_exception) if line and line[:5] == 'VALUE': resp, rkey, flags, len = line.split() flags = int(flags) rlen = int(len) return (rkey, flags, rlen) else: return (None, None, None) def _recv_value(self, server, flags, rlen): rlen += 2 # include \r\n buf = server.recv(rlen) if len(buf) != rlen: raise _Error("received %d bytes when expecting %d" % (len(buf), rlen)) if len(buf) == rlen: buf = buf[:-2] # strip \r\n if flags & Client._FLAG_COMPRESSED: buf = decompress(buf) if flags == 0 or flags == Client._FLAG_COMPRESSED: # Either a bare string or a compressed string now decompressed... val = buf elif flags & Client._FLAG_INTEGER: val = int(buf) elif flags & Client._FLAG_LONG: val = long(buf) elif flags & Client._FLAG_PICKLE: try: file = StringIO(buf) unpickler = self.unpickler(file) if self.persistent_load: unpickler.persistent_load = self.persistent_load val = unpickler.load() except Exception, e: self.debuglog('Pickle error: %s\n' % e) return None else: self.debuglog("unknown flags on get: %x\n" % flags) return val def check_key(self, key, key_extra_len=0): """Checks sanity of key. Fails if: Key length is > SERVER_MAX_KEY_LENGTH (Raises MemcachedKeyLength). Contains control characters (Raises MemcachedKeyCharacterError). Is not a string (Raises MemcachedStringEncodingError) Is an unicode string (Raises MemcachedStringEncodingError) Is not a string (Raises MemcachedKeyError) Is None (Raises MemcachedKeyError) """ if isinstance(key, tuple): key = key[1] if not key: raise Client.MemcachedKeyNoneError("Key is None") if isinstance(key, unicode): raise Client.MemcachedStringEncodingError( "Keys must be str()'s, not unicode. Convert your unicode " "strings using mystring.encode(charset)!") if not isinstance(key, str): raise Client.MemcachedKeyTypeError("Key must be str()'s") if isinstance(key, basestring): if self.server_max_key_length != 0 and \ len(key) + key_extra_len > self.server_max_key_length: raise Client.MemcachedKeyLengthError("Key length is > %s" % self.server_max_key_length) if not valid_key_chars_re.match(key): raise Client.MemcachedKeyCharacterError( "Control characters not allowed") class _Host(object): def __init__(self, host, debug=0, dead_retry=_DEAD_RETRY, socket_timeout=_SOCKET_TIMEOUT, flush_on_reconnect=0): self.dead_retry = dead_retry self.socket_timeout = socket_timeout self.debug = debug self.flush_on_reconnect = flush_on_reconnect if isinstance(host, tuple): host, self.weight = host else: self.weight = 1 # parse the connection string m = re.match(r'^(?P<proto>unix):(?P<path>.*)$', host) if not m: m = re.match(r'^(?P<proto>inet6):' r'\[(?P<host>[^\[\]]+)\](:(?P<port>[0-9]+))?$', host) if not m: m = re.match(r'^(?P<proto>inet):' r'(?P<host>[^:]+)(:(?P<port>[0-9]+))?$', host) if not m: m = re.match(r'^(?P<host>[^:]+)(:(?P<port>[0-9]+))?$', host) if not m: raise ValueError('Unable to parse connection string: "%s"' % host) hostData = m.groupdict() if hostData.get('proto') == 'unix': self.family = socket.AF_UNIX self.address = hostData['path'] elif hostData.get('proto') == 'inet6': self.family = socket.AF_INET6 self.ip = hostData['host'] self.port = int(hostData.get('port') or 11211) self.address = ( self.ip, self.port ) else: self.family = socket.AF_INET self.ip = hostData['host'] self.port = int(hostData.get('port') or 11211) self.address = ( self.ip, self.port ) self.deaduntil = 0 self.socket = None self.flush_on_next_connect = 0 self.buffer = '' def debuglog(self, str): if self.debug: sys.stderr.write("MemCached: %s\n" % str) def _check_dead(self): if self.deaduntil and self.deaduntil > time.time(): return 1 self.deaduntil = 0 return 0 def connect(self): if self._get_socket(): return 1 return 0 def mark_dead(self, reason): self.debuglog("MemCache: %s: %s. Marking dead." % (self, reason)) self.deaduntil = time.time() + self.dead_retry if self.flush_on_reconnect: self.flush_on_next_connect = 1 self.close_socket() def _get_socket(self): if self._check_dead(): return None if self.socket: return self.socket s = socket.socket(self.family, socket.SOCK_STREAM) if hasattr(s, 'settimeout'): s.settimeout(self.socket_timeout) try: s.connect(self.address) except socket.timeout, msg: self.mark_dead("connect: %s" % msg) return None except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] self.mark_dead("connect: %s" % msg[1]) return None self.socket = s self.buffer = '' if self.flush_on_next_connect: self.flush() self.flush_on_next_connect = 0 return s def close_socket(self): if self.socket: self.socket.close() self.socket = None def send_cmd(self, cmd): self.socket.sendall(cmd + '\r\n') def send_cmds(self, cmds): """ cmds already has trailing \r\n's applied """ self.socket.sendall(cmds) def readline(self, raise_exception=False): """Read a line and return it. If "raise_exception" is set, raise _ConnectionDeadError if the read fails, otherwise return an empty string. """ buf = self.buffer if self.socket: recv = self.socket.recv else: recv = lambda bufsize: '' while True: index = buf.find('\r\n') if index >= 0: break data = recv(4096) if not data: # connection close, let's kill it and raise self.mark_dead('connection closed in readline()') if raise_exception: raise _ConnectionDeadError() else: return '' buf += data self.buffer = buf[index+2:] return buf[:index] def expect(self, text, raise_exception=False): line = self.readline(raise_exception) if line != text: self.debuglog("while expecting '%s', got unexpected response '%s'" % (text, line)) return line def recv(self, rlen): self_socket_recv = self.socket.recv buf = self.buffer while len(buf) < rlen: foo = self_socket_recv(max(rlen - len(buf), 4096)) buf += foo if not foo: raise _Error( 'Read %d bytes, expecting %d, ' 'read returned 0 length bytes' % ( len(buf), rlen )) self.buffer = buf[rlen:] return buf[:rlen] def flush(self): self.send_cmd('flush_all') self.expect('OK') def __str__(self): d = '' if self.deaduntil: d = " (dead until %d)" % self.deaduntil if self.family == socket.AF_INET: return "inet:%s:%d%s" % (self.address[0], self.address[1], d) elif self.family == socket.AF_INET6: return "inet6:[%s]:%d%s" % (self.address[0], self.address[1], d) else: return "unix:%s%s" % (self.address, d) def _doctest(): import doctest, memcache servers = ["127.0.0.1:11211"] mc = Client(servers, debug=1) globs = {"mc": mc} return doctest.testmod(memcache, globs=globs) if __name__ == "__main__": failures = 0 print "Testing docstrings..." _doctest() print "Running tests:" print serverList = [["127.0.0.1:11211"]] if '--do-unix' in sys.argv: serverList.append([os.path.join(os.getcwd(), 'memcached.socket')]) for servers in serverList: mc = Client(servers, debug=1) def to_s(val): if not isinstance(val, basestring): return "%s (%s)" % (val, type(val)) return "%s" % val def test_setget(key, val): global failures print "Testing set/get {'%s': %s} ..." % (to_s(key), to_s(val)), mc.set(key, val) newval = mc.get(key) if newval == val: print "OK" return 1 else: print "FAIL"; failures = failures + 1 return 0 class FooStruct(object): def __init__(self): self.bar = "baz" def __str__(self): return "A FooStruct" def __eq__(self, other): if isinstance(other, FooStruct): return self.bar == other.bar return 0 test_setget("a_string", "some random string") test_setget("an_integer", 42) if test_setget("long", long(1<<30)): print "Testing delete ...", if mc.delete("long"): print "OK" else: print "FAIL"; failures = failures + 1 print "Checking results of delete ..." if mc.get("long") == None: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing get_multi ...", print mc.get_multi(["a_string", "an_integer"]) # removed from the protocol #if test_setget("timed_delete", 'foo'): # print "Testing timed delete ...", # if mc.delete("timed_delete", 1): # print "OK" # else: # print "FAIL"; failures = failures + 1 # print "Checking results of timed delete ..." # if mc.get("timed_delete") == None: # print "OK" # else: # print "FAIL"; failures = failures + 1 print "Testing get(unknown value) ...", print to_s(mc.get("unknown_value")) f = FooStruct() test_setget("foostruct", f) print "Testing incr ...", x = mc.incr("an_integer", 1) if x == 43: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing decr ...", x = mc.decr("an_integer", 1) if x == 42: print "OK" else: print "FAIL"; failures = failures + 1 sys.stdout.flush() # sanity tests print "Testing sending spaces...", sys.stdout.flush() try: x = mc.set("this has spaces", 1) except Client.MemcachedKeyCharacterError, msg: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing sending control characters...", try: x = mc.set("this\x10has\x11control characters\x02", 1) except Client.MemcachedKeyCharacterError, msg: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing using insanely long key...", try: x = mc.set('a'*SERVER_MAX_KEY_LENGTH, 1) except Client.MemcachedKeyLengthError, msg: print "FAIL"; failures = failures + 1 else: print "OK" try: x = mc.set('a'*SERVER_MAX_KEY_LENGTH + 'a', 1) except Client.MemcachedKeyLengthError, msg: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing sending a unicode-string key...", try: x = mc.set(unicode('keyhere'), 1) except Client.MemcachedStringEncodingError, msg: print "OK", else: print "FAIL",; failures = failures + 1 try: x = mc.set((unicode('a')*SERVER_MAX_KEY_LENGTH).encode('utf-8'), 1) except: print "FAIL",; failures = failures + 1 else: print "OK", import pickle s = pickle.loads('V\\u4f1a\np0\n.') try: x = mc.set((s*SERVER_MAX_KEY_LENGTH).encode('utf-8'), 1) except Client.MemcachedKeyLengthError: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing using a value larger than the memcached value limit..." print 'NOTE: "MemCached: while expecting[...]" is normal...' x = mc.set('keyhere', 'a'*SERVER_MAX_VALUE_LENGTH) if mc.get('keyhere') == None: print "OK", else: print "FAIL",; failures = failures + 1 x = mc.set('keyhere', 'a'*SERVER_MAX_VALUE_LENGTH + 'aaa') if mc.get('keyhere') == None: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing set_multi() with no memcacheds running", mc.disconnect_all() errors = mc.set_multi({'keyhere' : 'a', 'keythere' : 'b'}) if errors != []: print "FAIL"; failures = failures + 1 else: print "OK" print "Testing delete_multi() with no memcacheds running", mc.disconnect_all() ret = mc.delete_multi({'keyhere' : 'a', 'keythere' : 'b'}) if ret != 1: print "FAIL"; failures = failures + 1 else: print "OK" if failures > 0: print '*** THERE WERE FAILED TESTS' sys.exit(1) sys.exit(0) # vim: ts=4 sw=4 et :

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""" client module for memcached (memory cache daemon) Overview ======== See U{the MemCached homepage<http://www.danga.com/memcached>} for more about memcached. Usage summary ============= This should give you a feel for how this module operates:: import memcache mc = memcache.Client(['127.0.0.1:11211'], debug=0) mc.set("some_key", "Some value") value = mc.get("some_key") mc.set("another_key", 3) mc.delete("another_key") mc.set("key", "1") # note that the key used for incr/decr must be a string. mc.incr("key") mc.decr("key") The standard way to use memcache with a database is like this:: key = derive_key(obj) obj = mc.get(key) if not obj: obj = backend_api.get(...) mc.set(key, obj) # we now have obj, and future passes through this code # will use the object from the cache. Detailed Documentation ====================== More detailed documentation is available in the L{Client} class. """ import sys import socket import time import os import re try: import cPickle as pickle except ImportError: import pickle from binascii import crc32 def cmemcache_hash(key): return((((crc32(key) & 0xffffffff) >> 16) & 0x7fff) or 1) serverHashFunction = cmemcache_hash def useOldServerHashFunction(): """Use the old python-memcache server hash function.""" global serverHashFunction serverHashFunction = crc32 try: from zlib import compress, decompress _supports_compress = True except ImportError: _supports_compress = False def decompress(val): raise _Error("received compressed data but I don't support compression (import error)") try: from cStringIO import StringIO except ImportError: from StringIO import StringIO valid_key_chars_re = re.compile('[\x21-\x7e\x80-\xff]+$') # Original author: Evan Martin of Danga Interactive __author__ = "Sean Reifschneider <jafo-memcached@tummy.com>" __version__ = "1.53" __copyright__ = "Copyright (C) 2003 Danga Interactive" # http://en.wikipedia.org/wiki/Python_Software_Foundation_License __license__ = "Python Software Foundation License" SERVER_MAX_KEY_LENGTH = 250 # Storing values larger than 1MB requires recompiling memcached. If you do, # this value can be changed by doing "memcache.SERVER_MAX_VALUE_LENGTH = N" # after importing this module. SERVER_MAX_VALUE_LENGTH = 1024*1024 class _Error(Exception): pass class _ConnectionDeadError(Exception): pass try: # Only exists in Python 2.4+ from threading import local except ImportError: # TODO: add the pure-python local implementation class local(object): pass _DEAD_RETRY = 30 # number of seconds before retrying a dead server. _SOCKET_TIMEOUT = 3 # number of seconds before sockets timeout. class Client(local): """ Object representing a pool of memcache servers. See L{memcache} for an overview. In all cases where a key is used, the key can be either: 1. A simple hashable type (string, integer, etc.). 2. A tuple of C{(hashvalue, key)}. This is useful if you want to avoid making this module calculate a hash value. You may prefer, for example, to keep all of a given user's objects on the same memcache server, so you could use the user's unique id as the hash value. @group Setup: __init__, set_servers, forget_dead_hosts, disconnect_all, debuglog @group Insertion: set, add, replace, set_multi @group Retrieval: get, get_multi @group Integers: incr, decr @group Removal: delete, delete_multi @sort: __init__, set_servers, forget_dead_hosts, disconnect_all, debuglog,\ set, set_multi, add, replace, get, get_multi, incr, decr, delete, delete_multi """ _FLAG_PICKLE = 1<<0 _FLAG_INTEGER = 1<<1 _FLAG_LONG = 1<<2 _FLAG_COMPRESSED = 1<<3 _SERVER_RETRIES = 10 # how many times to try finding a free server. # exceptions for Client class MemcachedKeyError(Exception): pass class MemcachedKeyLengthError(MemcachedKeyError): pass class MemcachedKeyCharacterError(MemcachedKeyError): pass class MemcachedKeyNoneError(MemcachedKeyError): pass class MemcachedKeyTypeError(MemcachedKeyError): pass class MemcachedStringEncodingError(Exception): pass def __init__(self, servers, debug=0, pickleProtocol=0, pickler=pickle.Pickler, unpickler=pickle.Unpickler, pload=None, pid=None, server_max_key_length=SERVER_MAX_KEY_LENGTH, server_max_value_length=SERVER_MAX_VALUE_LENGTH, dead_retry=_DEAD_RETRY, socket_timeout=_SOCKET_TIMEOUT, cache_cas = False, flush_on_reconnect=0, check_keys=True): """ Create a new Client object with the given list of servers. @param servers: C{servers} is passed to L{set_servers}. @param debug: whether to display error messages when a server can't be contacted. @param pickleProtocol: number to mandate protocol used by (c)Pickle. @param pickler: optional override of default Pickler to allow subclassing. @param unpickler: optional override of default Unpickler to allow subclassing. @param pload: optional persistent_load function to call on pickle loading. Useful for cPickle since subclassing isn't allowed. @param pid: optional persistent_id function to call on pickle storing. Useful for cPickle since subclassing isn't allowed. @param dead_retry: number of seconds before retrying a blacklisted server. Default to 30 s. @param socket_timeout: timeout in seconds for all calls to a server. Defaults to 3 seconds. @param cache_cas: (default False) If true, cas operations will be cached. WARNING: This cache is not expired internally, if you have a long-running process you will need to expire it manually via client.reset_cas(), or the cache can grow unlimited. @param server_max_key_length: (default SERVER_MAX_KEY_LENGTH) Data that is larger than this will not be sent to the server. @param server_max_value_length: (default SERVER_MAX_VALUE_LENGTH) Data that is larger than this will not be sent to the server. @param flush_on_reconnect: optional flag which prevents a scenario that can cause stale data to be read: If there's more than one memcached server and the connection to one is interrupted, keys that mapped to that server will get reassigned to another. If the first server comes back, those keys will map to it again. If it still has its data, get()s can read stale data that was overwritten on another server. This flag is off by default for backwards compatibility. @param check_keys: (default True) If True, the key is checked to ensure it is the correct length and composed of the right characters. """ local.__init__(self) self.debug = debug self.dead_retry = dead_retry self.socket_timeout = socket_timeout self.flush_on_reconnect = flush_on_reconnect self.set_servers(servers) self.stats = {} self.cache_cas = cache_cas self.reset_cas() self.do_check_key = check_keys # Allow users to modify pickling/unpickling behavior self.pickleProtocol = pickleProtocol self.pickler = pickler self.unpickler = unpickler self.persistent_load = pload self.persistent_id = pid self.server_max_key_length = server_max_key_length self.server_max_value_length = server_max_value_length # figure out the pickler style file = StringIO() try: pickler = self.pickler(file, protocol = self.pickleProtocol) self.picklerIsKeyword = True except TypeError: self.picklerIsKeyword = False def reset_cas(self): """ Reset the cas cache. This is only used if the Client() object was created with "cache_cas=True". If used, this cache does not expire internally, so it can grow unbounded if you do not clear it yourself. """ self.cas_ids = {} def set_servers(self, servers): """ Set the pool of servers used by this client. @param servers: an array of servers. Servers can be passed in two forms: 1. Strings of the form C{"host:port"}, which implies a default weight of 1. 2. Tuples of the form C{("host:port", weight)}, where C{weight} is an integer weight value. """ self.servers = [_Host(s, self.debug, dead_retry=self.dead_retry, socket_timeout=self.socket_timeout, flush_on_reconnect=self.flush_on_reconnect) for s in servers] self._init_buckets() def get_stats(self, stat_args = None): '''Get statistics from each of the servers. @param stat_args: Additional arguments to pass to the memcache "stats" command. @return: A list of tuples ( server_identifier, stats_dictionary ). The dictionary contains a number of name/value pairs specifying the name of the status field and the string value associated with it. The values are not converted from strings. ''' data = [] for s in self.servers: if not s.connect(): continue if s.family == socket.AF_INET: name = '%s:%s (%s)' % ( s.ip, s.port, s.weight ) elif s.family == socket.AF_INET6: name = '[%s]:%s (%s)' % ( s.ip, s.port, s.weight ) else: name = 'unix:%s (%s)' % ( s.address, s.weight ) if not stat_args: s.send_cmd('stats') else: s.send_cmd('stats ' + stat_args) serverData = {} data.append(( name, serverData )) readline = s.readline while 1: line = readline() if not line or line.strip() in ('END', 'RESET'): break stats = line.split(' ', 2) serverData[stats[1]] = stats[2] return(data) def get_slabs(self): data = [] for s in self.servers: if not s.connect(): continue if s.family == socket.AF_INET: name = '%s:%s (%s)' % ( s.ip, s.port, s.weight ) elif s.family == socket.AF_INET6: name = '[%s]:%s (%s)' % ( s.ip, s.port, s.weight ) else: name = 'unix:%s (%s)' % ( s.address, s.weight ) serverData = {} data.append(( name, serverData )) s.send_cmd('stats items') readline = s.readline while 1: line = readline() if not line or line.strip() == 'END': break item = line.split(' ', 2) #0 = STAT, 1 = ITEM, 2 = Value slab = item[1].split(':', 2) #0 = items, 1 = Slab #, 2 = Name if slab[1] not in serverData: serverData[slab[1]] = {} serverData[slab[1]][slab[2]] = item[2] return data def flush_all(self): """Expire all data in memcache servers that are reachable.""" for s in self.servers: if not s.connect(): continue s.flush() def debuglog(self, str): if self.debug: sys.stderr.write("MemCached: %s\n" % str) def _statlog(self, func): if func not in self.stats: self.stats[func] = 1 else: self.stats[func] += 1 def forget_dead_hosts(self): """ Reset every host in the pool to an "alive" state. """ for s in self.servers: s.deaduntil = 0 def _init_buckets(self): self.buckets = [] for server in self.servers: for i in range(server.weight): self.buckets.append(server) def _get_server(self, key): if isinstance(key, tuple): serverhash, key = key else: serverhash = serverHashFunction(key) for i in range(Client._SERVER_RETRIES): server = self.buckets[serverhash % len(self.buckets)] if server.connect(): #print "(using server %s)" % server, return server, key serverhash = serverHashFunction(str(serverhash) + str(i)) return None, None def disconnect_all(self): for s in self.servers: s.close_socket() def delete_multi(self, keys, time=0, key_prefix=''): ''' Delete multiple keys in the memcache doing just one query. >>> notset_keys = mc.set_multi({'key1' : 'val1', 'key2' : 'val2'}) >>> mc.get_multi(['key1', 'key2']) == {'key1' : 'val1', 'key2' : 'val2'} 1 >>> mc.delete_multi(['key1', 'key2']) 1 >>> mc.get_multi(['key1', 'key2']) == {} 1 This method is recommended over iterated regular L{delete}s as it reduces total latency, since your app doesn't have to wait for each round-trip of L{delete} before sending the next one. @param keys: An iterable of keys to clear @param time: number of seconds any subsequent set / update commands should fail. Defaults to 0 for no delay. @param key_prefix: Optional string to prepend to each key when sending to memcache. See docs for L{get_multi} and L{set_multi}. @return: 1 if no failure in communication with any memcacheds. @rtype: int ''' self._statlog('delete_multi') server_keys, prefixed_to_orig_key = self._map_and_prefix_keys(keys, key_prefix) dead_servers = [] rc = 1 for server in server_keys.iterkeys(): bigcmd = [] write = bigcmd.append if time != None: for key in server_keys[server]: write("delete %s %d\r\n" % (key, time)) else: for key in server_keys[server]: write("delete %s\r\n" % key) try: server.send_cmds(''.join(bigcmd)) except socket.error, msg: rc = 0 if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) dead_servers.append(server) for server in dead_servers: del server_keys[server] for server, keys in server_keys.iteritems(): try: for key in keys: server.expect("DELETED") except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) rc = 0 return rc def delete(self, key, time=0): '''Deletes a key from the memcache. @return: Nonzero on success. @param time: number of seconds any subsequent set / update commands should fail. Defaults to None for no delay. @rtype: int ''' if self.do_check_key: self.check_key(key) server, key = self._get_server(key) if not server: return 0 self._statlog('delete') if time != None and time != 0: cmd = "delete %s %d" % (key, time) else: cmd = "delete %s" % key try: server.send_cmd(cmd) line = server.readline() if line and line.strip() in ['DELETED', 'NOT_FOUND']: return 1 self.debuglog('Delete expected DELETED or NOT_FOUND, got: %s' % repr(line)) except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return 0 def incr(self, key, delta=1): """ Sends a command to the server to atomically increment the value for C{key} by C{delta}, or by 1 if C{delta} is unspecified. Returns None if C{key} doesn't exist on server, otherwise it returns the new value after incrementing. Note that the value for C{key} must already exist in the memcache, and it must be the string representation of an integer. >>> mc.set("counter", "20") # returns 1, indicating success 1 >>> mc.incr("counter") 21 >>> mc.incr("counter") 22 Overflow on server is not checked. Be aware of values approaching 2**32. See L{decr}. @param delta: Integer amount to increment by (should be zero or greater). @return: New value after incrementing. @rtype: int """ return self._incrdecr("incr", key, delta) def decr(self, key, delta=1): """ Like L{incr}, but decrements. Unlike L{incr}, underflow is checked and new values are capped at 0. If server value is 1, a decrement of 2 returns 0, not -1. @param delta: Integer amount to decrement by (should be zero or greater). @return: New value after decrementing or None on error. @rtype: int """ return self._incrdecr("decr", key, delta) def _incrdecr(self, cmd, key, delta): if self.do_check_key: self.check_key(key) server, key = self._get_server(key) if not server: return None self._statlog(cmd) cmd = "%s %s %d" % (cmd, key, delta) try: server.send_cmd(cmd) line = server.readline() if line == None or line.strip() =='NOT_FOUND': return None return int(line) except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return None def add(self, key, val, time = 0, min_compress_len = 0): ''' Add new key with value. Like L{set}, but only stores in memcache if the key doesn't already exist. @return: Nonzero on success. @rtype: int ''' return self._set("add", key, val, time, min_compress_len) def append(self, key, val, time=0, min_compress_len=0): '''Append the value to the end of the existing key's value. Only stores in memcache if key already exists. Also see L{prepend}. @return: Nonzero on success. @rtype: int ''' return self._set("append", key, val, time, min_compress_len) def prepend(self, key, val, time=0, min_compress_len=0): '''Prepend the value to the beginning of the existing key's value. Only stores in memcache if key already exists. Also see L{append}. @return: Nonzero on success. @rtype: int ''' return self._set("prepend", key, val, time, min_compress_len) def replace(self, key, val, time=0, min_compress_len=0): '''Replace existing key with value. Like L{set}, but only stores in memcache if the key already exists. The opposite of L{add}. @return: Nonzero on success. @rtype: int ''' return self._set("replace", key, val, time, min_compress_len) def set(self, key, val, time=0, min_compress_len=0): '''Unconditionally sets a key to a given value in the memcache. The C{key} can optionally be an tuple, with the first element being the server hash value and the second being the key. If you want to avoid making this module calculate a hash value. You may prefer, for example, to keep all of a given user's objects on the same memcache server, so you could use the user's unique id as the hash value. @return: Nonzero on success. @rtype: int @param time: Tells memcached the time which this value should expire, either as a delta number of seconds, or an absolute unix time-since-the-epoch value. See the memcached protocol docs section "Storage Commands" for more info on <exptime>. We default to 0 == cache forever. @param min_compress_len: The threshold length to kick in auto-compression of the value using the zlib.compress() routine. If the value being cached is a string, then the length of the string is measured, else if the value is an object, then the length of the pickle result is measured. If the resulting attempt at compression yeilds a larger string than the input, then it is discarded. For backwards compatability, this parameter defaults to 0, indicating don't ever try to compress. ''' return self._set("set", key, val, time, min_compress_len) def cas(self, key, val, time=0, min_compress_len=0): '''Sets a key to a given value in the memcache if it hasn't been altered since last fetched. (See L{gets}). The C{key} can optionally be an tuple, with the first element being the server hash value and the second being the key. If you want to avoid making this module calculate a hash value. You may prefer, for example, to keep all of a given user's objects on the same memcache server, so you could use the user's unique id as the hash value. @return: Nonzero on success. @rtype: int @param time: Tells memcached the time which this value should expire, either as a delta number of seconds, or an absolute unix time-since-the-epoch value. See the memcached protocol docs section "Storage Commands" for more info on <exptime>. We default to 0 == cache forever. @param min_compress_len: The threshold length to kick in auto-compression of the value using the zlib.compress() routine. If the value being cached is a string, then the length of the string is measured, else if the value is an object, then the length of the pickle result is measured. If the resulting attempt at compression yeilds a larger string than the input, then it is discarded. For backwards compatability, this parameter defaults to 0, indicating don't ever try to compress. ''' return self._set("cas", key, val, time, min_compress_len) def _map_and_prefix_keys(self, key_iterable, key_prefix): """Compute the mapping of server (_Host instance) -> list of keys to stuff onto that server, as well as the mapping of prefixed key -> original key. """ key_extra_len=len(key_prefix) if key_prefix and self.do_check_key: self.check_key(key_prefix) server_keys = {} prefixed_to_orig_key = {} for orig_key in key_iterable: if isinstance(orig_key, tuple): str_orig_key = str(orig_key[1]) server, key = self._get_server((orig_key[0], key_prefix + str_orig_key)) else: str_orig_key = str(orig_key) server, key = self._get_server(key_prefix + str_orig_key) if self.do_check_key: self.check_key(str_orig_key, key_extra_len=key_extra_len) if not server: continue if server not in server_keys: server_keys[server] = [] server_keys[server].append(key) prefixed_to_orig_key[key] = orig_key return (server_keys, prefixed_to_orig_key) def set_multi(self, mapping, time=0, key_prefix='', min_compress_len=0): ''' Sets multiple keys in the memcache doing just one query. >>> notset_keys = mc.set_multi({'key1' : 'val1', 'key2' : 'val2'}) >>> mc.get_multi(['key1', 'key2']) == {'key1' : 'val1', 'key2' : 'val2'} 1 This method is recommended over regular L{set} as it lowers the number of total packets flying around your network, reducing total latency, since your app doesn't have to wait for each round-trip of L{set} before sending the next one. @param mapping: A dict of key/value pairs to set. @param time: Tells memcached the time which this value should expire, either as a delta number of seconds, or an absolute unix time-since-the-epoch value. See the memcached protocol docs section "Storage Commands" for more info on <exptime>. We default to 0 == cache forever. @param key_prefix: Optional string to prepend to each key when sending to memcache. Allows you to efficiently stuff these keys into a pseudo-namespace in memcache: >>> notset_keys = mc.set_multi( ... {'key1' : 'val1', 'key2' : 'val2'}, key_prefix='subspace_') >>> len(notset_keys) == 0 True >>> mc.get_multi(['subspace_key1', 'subspace_key2']) == {'subspace_key1' : 'val1', 'subspace_key2' : 'val2'} True Causes key 'subspace_key1' and 'subspace_key2' to be set. Useful in conjunction with a higher-level layer which applies namespaces to data in memcache. In this case, the return result would be the list of notset original keys, prefix not applied. @param min_compress_len: The threshold length to kick in auto-compression of the value using the zlib.compress() routine. If the value being cached is a string, then the length of the string is measured, else if the value is an object, then the length of the pickle result is measured. If the resulting attempt at compression yeilds a larger string than the input, then it is discarded. For backwards compatability, this parameter defaults to 0, indicating don't ever try to compress. @return: List of keys which failed to be stored [ memcache out of memory, etc. ]. @rtype: list ''' self._statlog('set_multi') server_keys, prefixed_to_orig_key = self._map_and_prefix_keys( mapping.iterkeys(), key_prefix) dead_servers = [] notstored = [] for server in server_keys.iterkeys(): bigcmd = [] write = bigcmd.append try: for key in server_keys[server]: store_info = self._val_to_store_info( mapping[prefixed_to_orig_key[key]], min_compress_len) if store_info: write("set %s %d %d %d\r\n%s\r\n" % (key, store_info[0], time, store_info[1], store_info[2])) else: notstored.append(prefixed_to_orig_key[key]) server.send_cmds(''.join(bigcmd)) except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) dead_servers.append(server) for server in dead_servers: del server_keys[server] # short-circuit if there are no servers, just return all keys if not server_keys: return(mapping.keys()) for server, keys in server_keys.iteritems(): try: for key in keys: if server.readline() == 'STORED': continue else: notstored.append(prefixed_to_orig_key[key]) #un-mangle. except (_Error, socket.error), msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return notstored def _val_to_store_info(self, val, min_compress_len): """ Transform val to a storable representation, returning a tuple of the flags, the length of the new value, and the new value itself. """ flags = 0 if isinstance(val, str): pass elif isinstance(val, int): flags |= Client._FLAG_INTEGER val = "%d" % val # force no attempt to compress this silly string. min_compress_len = 0 elif isinstance(val, long): flags |= Client._FLAG_LONG val = "%d" % val # force no attempt to compress this silly string. min_compress_len = 0 else: flags |= Client._FLAG_PICKLE file = StringIO() if self.picklerIsKeyword: pickler = self.pickler(file, protocol = self.pickleProtocol) else: pickler = self.pickler(file, self.pickleProtocol) if self.persistent_id: pickler.persistent_id = self.persistent_id pickler.dump(val) val = file.getvalue() lv = len(val) # We should try to compress if min_compress_len > 0 and we could # import zlib and this string is longer than our min threshold. if min_compress_len and _supports_compress and lv > min_compress_len: comp_val = compress(val) # Only retain the result if the compression result is smaller # than the original. if len(comp_val) < lv: flags |= Client._FLAG_COMPRESSED val = comp_val # silently do not store if value length exceeds maximum if self.server_max_value_length != 0 and \ len(val) > self.server_max_value_length: return(0) return (flags, len(val), val) def _set(self, cmd, key, val, time, min_compress_len = 0): if self.do_check_key: self.check_key(key) server, key = self._get_server(key) if not server: return 0 def _unsafe_set(): self._statlog(cmd) store_info = self._val_to_store_info(val, min_compress_len) if not store_info: return(0) if cmd == 'cas': if key not in self.cas_ids: return self._set('set', key, val, time, min_compress_len) fullcmd = "%s %s %d %d %d %d\r\n%s" % ( cmd, key, store_info[0], time, store_info[1], self.cas_ids[key], store_info[2]) else: fullcmd = "%s %s %d %d %d\r\n%s" % ( cmd, key, store_info[0], time, store_info[1], store_info[2]) try: server.send_cmd(fullcmd) return(server.expect("STORED", raise_exception=True) == "STORED") except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return 0 try: return _unsafe_set() except _ConnectionDeadError: # retry once try: if server._get_socket(): return _unsafe_set() except (_ConnectionDeadError, socket.error), msg: server.mark_dead(msg) return 0 def _get(self, cmd, key): if self.do_check_key: self.check_key(key) server, key = self._get_server(key) if not server: return None def _unsafe_get(): self._statlog(cmd) try: server.send_cmd("%s %s" % (cmd, key)) rkey = flags = rlen = cas_id = None if cmd == 'gets': rkey, flags, rlen, cas_id, = self._expect_cas_value(server, raise_exception=True) if rkey and self.cache_cas: self.cas_ids[rkey] = cas_id else: rkey, flags, rlen, = self._expectvalue(server, raise_exception=True) if not rkey: return None try: value = self._recv_value(server, flags, rlen) finally: server.expect("END", raise_exception=True) except (_Error, socket.error), msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return None return value try: return _unsafe_get() except _ConnectionDeadError: # retry once try: if server.connect(): return _unsafe_get() return None except (_ConnectionDeadError, socket.error), msg: server.mark_dead(msg) return None def get(self, key): '''Retrieves a key from the memcache. @return: The value or None. ''' return self._get('get', key) def gets(self, key): '''Retrieves a key from the memcache. Used in conjunction with 'cas'. @return: The value or None. ''' return self._get('gets', key) def get_multi(self, keys, key_prefix=''): ''' Retrieves multiple keys from the memcache doing just one query. >>> success = mc.set("foo", "bar") >>> success = mc.set("baz", 42) >>> mc.get_multi(["foo", "baz", "foobar"]) == {"foo": "bar", "baz": 42} 1 >>> mc.set_multi({'k1' : 1, 'k2' : 2}, key_prefix='pfx_') == [] 1 This looks up keys 'pfx_k1', 'pfx_k2', ... . Returned dict will just have unprefixed keys 'k1', 'k2'. >>> mc.get_multi(['k1', 'k2', 'nonexist'], key_prefix='pfx_') == {'k1' : 1, 'k2' : 2} 1 get_mult [ and L{set_multi} ] can take str()-ables like ints / longs as keys too. Such as your db pri key fields. They're rotored through str() before being passed off to memcache, with or without the use of a key_prefix. In this mode, the key_prefix could be a table name, and the key itself a db primary key number. >>> mc.set_multi({42: 'douglass adams', 46 : 'and 2 just ahead of me'}, key_prefix='numkeys_') == [] 1 >>> mc.get_multi([46, 42], key_prefix='numkeys_') == {42: 'douglass adams', 46 : 'and 2 just ahead of me'} 1 This method is recommended over regular L{get} as it lowers the number of total packets flying around your network, reducing total latency, since your app doesn't have to wait for each round-trip of L{get} before sending the next one. See also L{set_multi}. @param keys: An array of keys. @param key_prefix: A string to prefix each key when we communicate with memcache. Facilitates pseudo-namespaces within memcache. Returned dictionary keys will not have this prefix. @return: A dictionary of key/value pairs that were available. If key_prefix was provided, the keys in the retured dictionary will not have it present. ''' self._statlog('get_multi') server_keys, prefixed_to_orig_key = self._map_and_prefix_keys(keys, key_prefix) # send out all requests on each server before reading anything dead_servers = [] for server in server_keys.iterkeys(): try: server.send_cmd("get %s" % " ".join(server_keys[server])) except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) dead_servers.append(server) # if any servers died on the way, don't expect them to respond. for server in dead_servers: del server_keys[server] retvals = {} for server in server_keys.iterkeys(): try: line = server.readline() while line and line != 'END': rkey, flags, rlen = self._expectvalue(server, line) if rkey is not None: val = self._recv_value(server, flags, rlen) retvals[prefixed_to_orig_key[rkey]] = val line = server.readline() except (_Error, socket.error), msg: if isinstance(msg, tuple): msg = msg[1] server.mark_dead(msg) return retvals def _expect_cas_value(self, server, line=None, raise_exception=False): if not line: line = server.readline(raise_exception) if line and line[:5] == 'VALUE': resp, rkey, flags, len, cas_id = line.split() return (rkey, int(flags), int(len), int(cas_id)) else: return (None, None, None, None) def _expectvalue(self, server, line=None, raise_exception=False): if not line: line = server.readline(raise_exception) if line and line[:5] == 'VALUE': resp, rkey, flags, len = line.split() flags = int(flags) rlen = int(len) return (rkey, flags, rlen) else: return (None, None, None) def _recv_value(self, server, flags, rlen): rlen += 2 buf = server.recv(rlen) if len(buf) != rlen: raise _Error("received %d bytes when expecting %d" % (len(buf), rlen)) if len(buf) == rlen: buf = buf[:-2] if flags & Client._FLAG_COMPRESSED: buf = decompress(buf) if flags == 0 or flags == Client._FLAG_COMPRESSED: val = buf elif flags & Client._FLAG_INTEGER: val = int(buf) elif flags & Client._FLAG_LONG: val = long(buf) elif flags & Client._FLAG_PICKLE: try: file = StringIO(buf) unpickler = self.unpickler(file) if self.persistent_load: unpickler.persistent_load = self.persistent_load val = unpickler.load() except Exception, e: self.debuglog('Pickle error: %s\n' % e) return None else: self.debuglog("unknown flags on get: %x\n" % flags) return val def check_key(self, key, key_extra_len=0): """Checks sanity of key. Fails if: Key length is > SERVER_MAX_KEY_LENGTH (Raises MemcachedKeyLength). Contains control characters (Raises MemcachedKeyCharacterError). Is not a string (Raises MemcachedStringEncodingError) Is an unicode string (Raises MemcachedStringEncodingError) Is not a string (Raises MemcachedKeyError) Is None (Raises MemcachedKeyError) """ if isinstance(key, tuple): key = key[1] if not key: raise Client.MemcachedKeyNoneError("Key is None") if isinstance(key, unicode): raise Client.MemcachedStringEncodingError( "Keys must be str()'s, not unicode. Convert your unicode " "strings using mystring.encode(charset)!") if not isinstance(key, str): raise Client.MemcachedKeyTypeError("Key must be str()'s") if isinstance(key, basestring): if self.server_max_key_length != 0 and \ len(key) + key_extra_len > self.server_max_key_length: raise Client.MemcachedKeyLengthError("Key length is > %s" % self.server_max_key_length) if not valid_key_chars_re.match(key): raise Client.MemcachedKeyCharacterError( "Control characters not allowed") class _Host(object): def __init__(self, host, debug=0, dead_retry=_DEAD_RETRY, socket_timeout=_SOCKET_TIMEOUT, flush_on_reconnect=0): self.dead_retry = dead_retry self.socket_timeout = socket_timeout self.debug = debug self.flush_on_reconnect = flush_on_reconnect if isinstance(host, tuple): host, self.weight = host else: self.weight = 1 m = re.match(r'^(?P<proto>unix):(?P<path>.*)$', host) if not m: m = re.match(r'^(?P<proto>inet6):' r'\[(?P<host>[^\[\]]+)\](:(?P<port>[0-9]+))?$', host) if not m: m = re.match(r'^(?P<proto>inet):' r'(?P<host>[^:]+)(:(?P<port>[0-9]+))?$', host) if not m: m = re.match(r'^(?P<host>[^:]+)(:(?P<port>[0-9]+))?$', host) if not m: raise ValueError('Unable to parse connection string: "%s"' % host) hostData = m.groupdict() if hostData.get('proto') == 'unix': self.family = socket.AF_UNIX self.address = hostData['path'] elif hostData.get('proto') == 'inet6': self.family = socket.AF_INET6 self.ip = hostData['host'] self.port = int(hostData.get('port') or 11211) self.address = ( self.ip, self.port ) else: self.family = socket.AF_INET self.ip = hostData['host'] self.port = int(hostData.get('port') or 11211) self.address = ( self.ip, self.port ) self.deaduntil = 0 self.socket = None self.flush_on_next_connect = 0 self.buffer = '' def debuglog(self, str): if self.debug: sys.stderr.write("MemCached: %s\n" % str) def _check_dead(self): if self.deaduntil and self.deaduntil > time.time(): return 1 self.deaduntil = 0 return 0 def connect(self): if self._get_socket(): return 1 return 0 def mark_dead(self, reason): self.debuglog("MemCache: %s: %s. Marking dead." % (self, reason)) self.deaduntil = time.time() + self.dead_retry if self.flush_on_reconnect: self.flush_on_next_connect = 1 self.close_socket() def _get_socket(self): if self._check_dead(): return None if self.socket: return self.socket s = socket.socket(self.family, socket.SOCK_STREAM) if hasattr(s, 'settimeout'): s.settimeout(self.socket_timeout) try: s.connect(self.address) except socket.timeout, msg: self.mark_dead("connect: %s" % msg) return None except socket.error, msg: if isinstance(msg, tuple): msg = msg[1] self.mark_dead("connect: %s" % msg[1]) return None self.socket = s self.buffer = '' if self.flush_on_next_connect: self.flush() self.flush_on_next_connect = 0 return s def close_socket(self): if self.socket: self.socket.close() self.socket = None def send_cmd(self, cmd): self.socket.sendall(cmd + '\r\n') def send_cmds(self, cmds): """ cmds already has trailing \r\n's applied """ self.socket.sendall(cmds) def readline(self, raise_exception=False): """Read a line and return it. If "raise_exception" is set, raise _ConnectionDeadError if the read fails, otherwise return an empty string. """ buf = self.buffer if self.socket: recv = self.socket.recv else: recv = lambda bufsize: '' while True: index = buf.find('\r\n') if index >= 0: break data = recv(4096) if not data: # connection close, let's kill it and raise self.mark_dead('connection closed in readline()') if raise_exception: raise _ConnectionDeadError() else: return '' buf += data self.buffer = buf[index+2:] return buf[:index] def expect(self, text, raise_exception=False): line = self.readline(raise_exception) if line != text: self.debuglog("while expecting '%s', got unexpected response '%s'" % (text, line)) return line def recv(self, rlen): self_socket_recv = self.socket.recv buf = self.buffer while len(buf) < rlen: foo = self_socket_recv(max(rlen - len(buf), 4096)) buf += foo if not foo: raise _Error( 'Read %d bytes, expecting %d, ' 'read returned 0 length bytes' % ( len(buf), rlen )) self.buffer = buf[rlen:] return buf[:rlen] def flush(self): self.send_cmd('flush_all') self.expect('OK') def __str__(self): d = '' if self.deaduntil: d = " (dead until %d)" % self.deaduntil if self.family == socket.AF_INET: return "inet:%s:%d%s" % (self.address[0], self.address[1], d) elif self.family == socket.AF_INET6: return "inet6:[%s]:%d%s" % (self.address[0], self.address[1], d) else: return "unix:%s%s" % (self.address, d) def _doctest(): import doctest, memcache servers = ["127.0.0.1:11211"] mc = Client(servers, debug=1) globs = {"mc": mc} return doctest.testmod(memcache, globs=globs) if __name__ == "__main__": failures = 0 print "Testing docstrings..." _doctest() print "Running tests:" print serverList = [["127.0.0.1:11211"]] if '--do-unix' in sys.argv: serverList.append([os.path.join(os.getcwd(), 'memcached.socket')]) for servers in serverList: mc = Client(servers, debug=1) def to_s(val): if not isinstance(val, basestring): return "%s (%s)" % (val, type(val)) return "%s" % val def test_setget(key, val): global failures print "Testing set/get {'%s': %s} ..." % (to_s(key), to_s(val)), mc.set(key, val) newval = mc.get(key) if newval == val: print "OK" return 1 else: print "FAIL"; failures = failures + 1 return 0 class FooStruct(object): def __init__(self): self.bar = "baz" def __str__(self): return "A FooStruct" def __eq__(self, other): if isinstance(other, FooStruct): return self.bar == other.bar return 0 test_setget("a_string", "some random string") test_setget("an_integer", 42) if test_setget("long", long(1<<30)): print "Testing delete ...", if mc.delete("long"): print "OK" else: print "FAIL"; failures = failures + 1 print "Checking results of delete ..." if mc.get("long") == None: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing get_multi ...", print mc.get_multi(["a_string", "an_integer"]) print "Testing get(unknown value) ...", print to_s(mc.get("unknown_value")) f = FooStruct() test_setget("foostruct", f) print "Testing incr ...", x = mc.incr("an_integer", 1) if x == 43: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing decr ...", x = mc.decr("an_integer", 1) if x == 42: print "OK" else: print "FAIL"; failures = failures + 1 sys.stdout.flush() print "Testing sending spaces...", sys.stdout.flush() try: x = mc.set("this has spaces", 1) except Client.MemcachedKeyCharacterError, msg: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing sending control characters...", try: x = mc.set("this\x10has\x11control characters\x02", 1) except Client.MemcachedKeyCharacterError, msg: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing using insanely long key...", try: x = mc.set('a'*SERVER_MAX_KEY_LENGTH, 1) except Client.MemcachedKeyLengthError, msg: print "FAIL"; failures = failures + 1 else: print "OK" try: x = mc.set('a'*SERVER_MAX_KEY_LENGTH + 'a', 1) except Client.MemcachedKeyLengthError, msg: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing sending a unicode-string key...", try: x = mc.set(unicode('keyhere'), 1) except Client.MemcachedStringEncodingError, msg: print "OK", else: print "FAIL",; failures = failures + 1 try: x = mc.set((unicode('a')*SERVER_MAX_KEY_LENGTH).encode('utf-8'), 1) except: print "FAIL",; failures = failures + 1 else: print "OK", import pickle s = pickle.loads('V\\u4f1a\np0\n.') try: x = mc.set((s*SERVER_MAX_KEY_LENGTH).encode('utf-8'), 1) except Client.MemcachedKeyLengthError: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing using a value larger than the memcached value limit..." print 'NOTE: "MemCached: while expecting[...]" is normal...' x = mc.set('keyhere', 'a'*SERVER_MAX_VALUE_LENGTH) if mc.get('keyhere') == None: print "OK", else: print "FAIL",; failures = failures + 1 x = mc.set('keyhere', 'a'*SERVER_MAX_VALUE_LENGTH + 'aaa') if mc.get('keyhere') == None: print "OK" else: print "FAIL"; failures = failures + 1 print "Testing set_multi() with no memcacheds running", mc.disconnect_all() errors = mc.set_multi({'keyhere' : 'a', 'keythere' : 'b'}) if errors != []: print "FAIL"; failures = failures + 1 else: print "OK" print "Testing delete_multi() with no memcacheds running", mc.disconnect_all() ret = mc.delete_multi({'keyhere' : 'a', 'keythere' : 'b'}) if ret != 1: print "FAIL"; failures = failures + 1 else: print "OK" if failures > 0: print '*** THERE WERE FAILED TESTS' sys.exit(1) sys.exit(0)

false

true

7907ee4e628b32129acbf4bf8f02deab8f4d8296

80

py

Python

qtpy/_version.py

hwansysgit/qtpy

e79af98a46a2fa029a625a44ed71ba96953e0d27

[ "MIT" ]

null

qtpy/_version.py

hwansysgit/qtpy

e79af98a46a2fa029a625a44ed71ba96953e0d27

[ "MIT" ]

1

2021-01-30T19:12:13.000Z

qtpy/_version.py

hwansysgit/qtpy

e79af98a46a2fa029a625a44ed71ba96953e0d27

[ "MIT" ]

null

version_info = (1, 5, 0, 'dev0') __version__ = '.'.join(map(str, version_info))

26.666667

46

0.65

version_info = (1, 5, 0, 'dev0') __version__ = '.'.join(map(str, version_info))

true

7907eeddd2f2d25f2b3bc404e33f8130b7b979d4

872

py

Python

src/fancontroller.py

olivierbenard/raspberrypi-fan-controller

f79439a0d1beee285b104917c721bee483ad0b4a

[ "MIT" ]

null

src/fancontroller.py

olivierbenard/raspberrypi-fan-controller

f79439a0d1beee285b104917c721bee483ad0b4a

[ "MIT" ]

null

src/fancontroller.py

olivierbenard/raspberrypi-fan-controller

f79439a0d1beee285b104917c721bee483ad0b4a

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- import time import vcgencmd from gpiozero import OutputDevice # IMPORTANT: maximum temperature is 85°C and cpu throttled at 80°C ON_THRESHOLD = 70 # (degrees Celsius) fan starts at this temperature OFF_THRESHOLD = 60 # (degrees Celsius) fan shuts down at this temperature SLEEP_INTERVAL = 5 # (seconds) how often the core temperature is checked GPIO_PIN = 18 # (number) which GPIO pin is used to control the fan def main(): vc = vcgencmd.Vcgencmd() fan = OutputDevice(GPIO_PIN) while True: temperature = int(vc.measure_temp()) # NOTE: fan.value = 1 if "on" else 0 if temperature > ON_THRESHOLD and not fan.value: fan.on() elif fan.value and temperature < OFF_THRESHOLD: fan.off() time.sleep(SLEEP_INTERVAL) if __name__ == '__main__': main()

26.424242

73

0.673165

import time import vcgencmd from gpiozero import OutputDevice ON_THRESHOLD = 70 OFF_THRESHOLD = 60 SLEEP_INTERVAL = 5 GPIO_PIN = 18 def main(): vc = vcgencmd.Vcgencmd() fan = OutputDevice(GPIO_PIN) while True: temperature = int(vc.measure_temp()) if temperature > ON_THRESHOLD and not fan.value: fan.on() elif fan.value and temperature < OFF_THRESHOLD: fan.off() time.sleep(SLEEP_INTERVAL) if __name__ == '__main__': main()

true

7907ef0d781da87b4aa04d715e0bd5be9db67085

2,295

py

Python

tests/returns/test_get_backupdir_path.py

tombaker/mklists_old

cf3ca814cf2cfc785a8cdbddd33162b9ee658570

[ "MIT" ]

1

2021-07-02T03:41:57.000Z

tests/returns/test_get_backupdir_path.py

tombaker/mklists_old

cf3ca814cf2cfc785a8cdbddd33162b9ee658570

[ "MIT" ]

null

tests/returns/test_get_backupdir_path.py

tombaker/mklists_old

cf3ca814cf2cfc785a8cdbddd33162b9ee658570

[ "MIT" ]

null

"""Returns full pathname of backup directory.""" import os import pytest from pathlib import Path from mklists.constants import CONFIGFILE_NAME from mklists.returns import get_backupdir_path def test_get_backupdir_path(tmp_path): """Returns backups Path named for default working directory.""" os.chdir(tmp_path) Path(CONFIGFILE_NAME).write_text("config stuff") backdir = "_backups" datestr = "2020-01-03_1646" workingdir = Path("agenda") workingdir.mkdir() os.chdir(workingdir) actual = get_backupdir_path(backdir=backdir, now=datestr) expected = Path(tmp_path) / backdir / str(workingdir) / datestr expected_explicit = Path(tmp_path) / "_backups" / "agenda" / "2020-01-03_1646" assert actual == expected assert actual == expected_explicit def test_get_backupdir_path_given_datadir(tmp_path): """Returns backups Path named for specified working directory.""" os.chdir(tmp_path) Path(CONFIGFILE_NAME).write_text("config stuff") workingdir = Path(tmp_path).joinpath("todolists/a") workingdir.mkdir(parents=True, exist_ok=True) workingdir_shortname_expected = "todolists_a" backdir = "_backups" datestr = "2020-01-03_1646_06488910" actual = get_backupdir_path(datadir=workingdir, backdir=backdir, now=datestr) expected = Path(tmp_path) / backdir / workingdir_shortname_expected / datestr assert actual == expected def test_get_backupdir_path_given_datadir_with_slash(tmp_path): """Returns backups Path named for specified working directory ending with slash.""" os.chdir(tmp_path) Path(CONFIGFILE_NAME).write_text("config stuff") workingdir = Path(tmp_path).joinpath("todolists/a/") workingdir.mkdir(parents=True, exist_ok=True) workingdir_shortname_expected = "todolists_a" backdir = "_backups" datestr = "2020-01-03_1646_06488910" actual = get_backupdir_path(datadir=workingdir, backdir=backdir, now=datestr) expected = Path(tmp_path) / backdir / workingdir_shortname_expected / datestr assert actual == expected def test_get_backupdir_path_raise_exception_if_rootdir_not_found(tmp_path): """Raises exception if no rootdir is found (rootdir is None).""" os.chdir(tmp_path) with pytest.raises(SystemExit): get_backupdir_path()

38.898305

87

0.747277

import os import pytest from pathlib import Path from mklists.constants import CONFIGFILE_NAME from mklists.returns import get_backupdir_path def test_get_backupdir_path(tmp_path): os.chdir(tmp_path) Path(CONFIGFILE_NAME).write_text("config stuff") backdir = "_backups" datestr = "2020-01-03_1646" workingdir = Path("agenda") workingdir.mkdir() os.chdir(workingdir) actual = get_backupdir_path(backdir=backdir, now=datestr) expected = Path(tmp_path) / backdir / str(workingdir) / datestr expected_explicit = Path(tmp_path) / "_backups" / "agenda" / "2020-01-03_1646" assert actual == expected assert actual == expected_explicit def test_get_backupdir_path_given_datadir(tmp_path): os.chdir(tmp_path) Path(CONFIGFILE_NAME).write_text("config stuff") workingdir = Path(tmp_path).joinpath("todolists/a") workingdir.mkdir(parents=True, exist_ok=True) workingdir_shortname_expected = "todolists_a" backdir = "_backups" datestr = "2020-01-03_1646_06488910" actual = get_backupdir_path(datadir=workingdir, backdir=backdir, now=datestr) expected = Path(tmp_path) / backdir / workingdir_shortname_expected / datestr assert actual == expected def test_get_backupdir_path_given_datadir_with_slash(tmp_path): os.chdir(tmp_path) Path(CONFIGFILE_NAME).write_text("config stuff") workingdir = Path(tmp_path).joinpath("todolists/a/") workingdir.mkdir(parents=True, exist_ok=True) workingdir_shortname_expected = "todolists_a" backdir = "_backups" datestr = "2020-01-03_1646_06488910" actual = get_backupdir_path(datadir=workingdir, backdir=backdir, now=datestr) expected = Path(tmp_path) / backdir / workingdir_shortname_expected / datestr assert actual == expected def test_get_backupdir_path_raise_exception_if_rootdir_not_found(tmp_path): os.chdir(tmp_path) with pytest.raises(SystemExit): get_backupdir_path()

true

7907ef1fdaae719e2541f77a2157c6cc097c2789

3,238

py

Python

libra_client/shell/account_commands.py

yuan-xy/libra-client

697058bfa7bc8e8a7a2598dae4bb289f44524dba

[ "MIT" ]

30

2019-09-16T12:50:33.000Z

2020-10-27T20:06:26.000Z

libra_client/shell/account_commands.py

yuan-xy/libra-client

697058bfa7bc8e8a7a2598dae4bb289f44524dba

[ "MIT" ]

7

2019-09-18T14:23:09.000Z

2020-03-31T10:10:04.000Z

libra_client/shell/account_commands.py

yuan-xy/libra-client

697058bfa7bc8e8a7a2598dae4bb289f44524dba

[ "MIT" ]

12

2019-09-22T15:43:56.000Z

2020-08-07T08:51:35.000Z

from libra_client.cli.command import Command, blocking_cmd class AccountCommand(Command): def get_aliases(self): return ["account", "a"] def get_description(self): return "Account operations" def execute(self, client, params, **kwargs): commands = [ AccountCommandCreate(), AccountCommandListAccounts(), AccountCommandRecoverWallet(), AccountCommandWriteRecovery(), AccountCommandMint() ] self.subcommand_execute(params[0], commands, client, params[1:], **kwargs) class AccountCommandCreate(Command): def get_aliases(self): return ["create", "c"] def get_description(self): return "Create an account. Returns reference ID to use in other operations" def execute(self, client, params, **kwargs): print(">> Creating/retrieving next account from wallet") index, account = client.create_next_account() print( "Created/retrieved account #{} address {}".format( index, account.address.hex() ) ) class AccountCommandListAccounts(Command): def get_aliases(self): return ["list", "la"] def get_description(self): return "Print all accounts that were created or loaded" def execute(self, client, params, **kwargs): client.print_all_accounts() class AccountCommandRecoverWallet(Command): def get_aliases(self): return ["recover", "r"] def get_params_help(self): return "<file_path>" def get_description(self): return "Recover Libra wallet from the file path" def execute(self, client, params, **kwargs): print(">> Recovering Wallet") accounts = client.recover_wallet_accounts(params[1]) print(f"Wallet recovered and the first {len(accounts)} child accounts were derived") for index, data in enumerate(accounts): print("#{} address {}".format(index, data.address.hex())) class AccountCommandWriteRecovery(Command): def get_aliases(self): return ["write", "w"] def get_params_help(self): return "<file_path>" def get_description(self): return "Save Libra wallet mnemonic recovery seed to disk" def execute(self, client, params, **kwargs): print(">> Saving Libra wallet mnemonic recovery seed to disk") client.write_recovery(params[1]) print("Saved mnemonic seed to disk") class AccountCommandMint(Command): def get_aliases(self): return ["mint", "mintb", "m", "mb"] def get_params_help(self): return "<receiver_account_ref_id>|<receiver_account_address> <number_of_coins>" def get_description(self): return "Mint coins to the account. Suffix 'b' is for blocking" def execute(self, client, params, **kwargs): print(">> Minting coins") is_blocking = blocking_cmd(params[0]) client.mint_coins(params[1], params[2], is_blocking) if is_blocking: print("Finished minting!") else: print("Mint request submitted")

31.436893

93

0.617356

from libra_client.cli.command import Command, blocking_cmd class AccountCommand(Command): def get_aliases(self): return ["account", "a"] def get_description(self): return "Account operations" def execute(self, client, params, **kwargs): commands = [ AccountCommandCreate(), AccountCommandListAccounts(), AccountCommandRecoverWallet(), AccountCommandWriteRecovery(), AccountCommandMint() ] self.subcommand_execute(params[0], commands, client, params[1:], **kwargs) class AccountCommandCreate(Command): def get_aliases(self): return ["create", "c"] def get_description(self): return "Create an account. Returns reference ID to use in other operations" def execute(self, client, params, **kwargs): print(">> Creating/retrieving next account from wallet") index, account = client.create_next_account() print( "Created/retrieved account #{} address {}".format( index, account.address.hex() ) ) class AccountCommandListAccounts(Command): def get_aliases(self): return ["list", "la"] def get_description(self): return "Print all accounts that were created or loaded" def execute(self, client, params, **kwargs): client.print_all_accounts() class AccountCommandRecoverWallet(Command): def get_aliases(self): return ["recover", "r"] def get_params_help(self): return "<file_path>" def get_description(self): return "Recover Libra wallet from the file path" def execute(self, client, params, **kwargs): print(">> Recovering Wallet") accounts = client.recover_wallet_accounts(params[1]) print(f"Wallet recovered and the first {len(accounts)} child accounts were derived") for index, data in enumerate(accounts): print("#{} address {}".format(index, data.address.hex())) class AccountCommandWriteRecovery(Command): def get_aliases(self): return ["write", "w"] def get_params_help(self): return "<file_path>" def get_description(self): return "Save Libra wallet mnemonic recovery seed to disk" def execute(self, client, params, **kwargs): print(">> Saving Libra wallet mnemonic recovery seed to disk") client.write_recovery(params[1]) print("Saved mnemonic seed to disk") class AccountCommandMint(Command): def get_aliases(self): return ["mint", "mintb", "m", "mb"] def get_params_help(self): return "<receiver_account_ref_id>|<receiver_account_address> <number_of_coins>" def get_description(self): return "Mint coins to the account. Suffix 'b' is for blocking" def execute(self, client, params, **kwargs): print(">> Minting coins") is_blocking = blocking_cmd(params[0]) client.mint_coins(params[1], params[2], is_blocking) if is_blocking: print("Finished minting!") else: print("Mint request submitted")

true

7907ef245208b5af256a9d929c6bca2cff8343b5

1,079

py

Python

setup.py

abduhbm/label-studio

9a5110d411073e951b84099fa29a5abfc7c0f41d

[ "Apache-2.0" ]

5

2021-04-09T07:54:38.000Z

2021-09-28T11:42:22.000Z

setup.py

abduhbm/label-studio

9a5110d411073e951b84099fa29a5abfc7c0f41d

[ "Apache-2.0" ]

10

2021-01-12T05:56:29.000Z

2021-05-11T21:37:59.000Z

setup.py

abduhbm/label-studio

9a5110d411073e951b84099fa29a5abfc7c0f41d

[ "Apache-2.0" ]

3

2020-09-28T21:34:47.000Z

2021-01-29T02:04:19.000Z

import setuptools import label_studio print('Label Studio', label_studio.__version__) # Readme with open('README.md', 'r') as f: long_description = f.read() # Module dependencies with open('requirements.txt') as f: requirements = f.read().splitlines() setuptools.setup( name='label-studio', version=label_studio.__version__, author='Heartex', author_email="hello@heartex.ai", description='Label Studio annotation tool', long_description=long_description, long_description_content_type='text/markdown', url='https://github.com/heartexlabs/label-studio', packages=setuptools.find_packages(), include_package_data=True, classifiers=[ 'Programming Language :: Python :: 3', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', ], install_requires=requirements, python_requires='>=3.5', entry_points={ 'console_scripts': [ 'label-studio=label_studio.server:main', 'label-studio-ml=label_studio.ml.server:main' ], } )

27.666667

57

0.674699

import setuptools import label_studio print('Label Studio', label_studio.__version__) with open('README.md', 'r') as f: long_description = f.read() with open('requirements.txt') as f: requirements = f.read().splitlines() setuptools.setup( name='label-studio', version=label_studio.__version__, author='Heartex', author_email="hello@heartex.ai", description='Label Studio annotation tool', long_description=long_description, long_description_content_type='text/markdown', url='https://github.com/heartexlabs/label-studio', packages=setuptools.find_packages(), include_package_data=True, classifiers=[ 'Programming Language :: Python :: 3', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', ], install_requires=requirements, python_requires='>=3.5', entry_points={ 'console_scripts': [ 'label-studio=label_studio.server:main', 'label-studio-ml=label_studio.ml.server:main' ], } )

true

7907ef80ff200a67a76e74217b1255fe2d30d7ab

14,958

py

Python

test/functional/interface_rest.py

100milliondollars/NeuQ

8670b9e50d4e2edfd2f35dc3058b3112ffb46986

[ "MIT" ]

1

2019-08-13T01:44:54.000Z

test/functional/interface_rest.py

100milliondollars/NeuQ

8670b9e50d4e2edfd2f35dc3058b3112ffb46986

[ "MIT" ]

null

test/functional/interface_rest.py

100milliondollars/NeuQ

8670b9e50d4e2edfd2f35dc3058b3112ffb46986

[ "MIT" ]

2

2019-08-11T22:01:50.000Z

2019-08-13T15:15:12.000Z

#!/usr/bin/env python3 # Copyright (c) 2014-2019 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test the REST API.""" import binascii from decimal import Decimal from enum import Enum from io import BytesIO import json from struct import pack, unpack import http.client import urllib.parse from test_framework.qtumconfig import COINBASE_MATURITY, INITIAL_BLOCK_REWARD from test_framework.qtum import convert_btc_address_to_qtum from test_framework.test_framework import BitcoinTestFramework from test_framework.util import ( assert_equal, assert_greater_than, assert_greater_than_or_equal, hex_str_to_bytes, ) from test_framework.messages import CBlockHeader BLOCK_HEADER_SIZE = len(CBlockHeader().serialize()) class ReqType(Enum): JSON = 1 BIN = 2 HEX = 3 class RetType(Enum): OBJ = 1 BYTES = 2 JSON = 3 def filter_output_indices_by_value(vouts, value): for vout in vouts: if vout['value'] == value: yield vout['n'] class RESTTest (BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 2 self.extra_args = [["-rest"], []] self.supports_cli = False def skip_test_if_missing_module(self): self.skip_if_no_wallet() def test_rest_request(self, uri, http_method='GET', req_type=ReqType.JSON, body='', status=200, ret_type=RetType.JSON): rest_uri = '/rest' + uri if req_type == ReqType.JSON: rest_uri += '.json' elif req_type == ReqType.BIN: rest_uri += '.bin' elif req_type == ReqType.HEX: rest_uri += '.hex' conn = http.client.HTTPConnection(self.url.hostname, self.url.port) self.log.debug('%s %s %s', http_method, rest_uri, body) if http_method == 'GET': conn.request('GET', rest_uri) elif http_method == 'POST': conn.request('POST', rest_uri, body) resp = conn.getresponse() assert_equal(resp.status, status) if ret_type == RetType.OBJ: return resp elif ret_type == RetType.BYTES: return resp.read() elif ret_type == RetType.JSON: return json.loads(resp.read().decode('utf-8'), parse_float=Decimal) def run_test(self): self.url = urllib.parse.urlparse(self.nodes[0].url) self.log.info("Mine blocks and send Bitcoin to node 1") # Random address so node1's balance doesn't increase not_related_address = convert_btc_address_to_qtum("2MxqoHEdNQTyYeX1mHcbrrpzgojbosTpCvJ") self.nodes[0].generate(1) self.sync_all() for i in range(0, COINBASE_MATURITY, 100): self.nodes[1].generatetoaddress(100, not_related_address) self.sync_all() assert_equal(self.nodes[0].getbalance(), INITIAL_BLOCK_REWARD) txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() self.log.info("Test the /tx URI") json_obj = self.test_rest_request("/tx/{}".format(txid)) assert_equal(json_obj['txid'], txid) # Check hex format response hex_response = self.test_rest_request("/tx/{}".format(txid), req_type=ReqType.HEX, ret_type=RetType.OBJ) assert_greater_than_or_equal(int(hex_response.getheader('content-length')), json_obj['size']*2) spent = (json_obj['vin'][0]['txid'], json_obj['vin'][0]['vout']) # get the vin to later check for utxo (should be spent by then) # get n of 0.1 outpoint n, = filter_output_indices_by_value(json_obj['vout'], Decimal('0.1')) spending = (txid, n) self.log.info("Query an unspent TXO using the /getutxos URI") self.nodes[1].generatetoaddress(1, not_related_address) self.sync_all() bb_hash = self.nodes[0].getbestblockhash() assert_equal(self.nodes[1].getbalance(), Decimal("0.1")) # Check chainTip response json_obj = self.test_rest_request("/getutxos/{}-{}".format(*spending)) assert_equal(json_obj['chaintipHash'], bb_hash) # Make sure there is one utxo assert_equal(len(json_obj['utxos']), 1) assert_equal(json_obj['utxos'][0]['value'], Decimal('0.1')) self.log.info("Query a spent TXO using the /getutxos URI") json_obj = self.test_rest_request("/getutxos/{}-{}".format(*spent)) # Check chainTip response assert_equal(json_obj['chaintipHash'], bb_hash) # Make sure there is no utxo in the response because this outpoint has been spent assert_equal(len(json_obj['utxos']), 0) # Check bitmap assert_equal(json_obj['bitmap'], "0") self.log.info("Query two TXOs using the /getutxos URI") json_obj = self.test_rest_request("/getutxos/{}-{}/{}-{}".format(*(spending + spent))) assert_equal(len(json_obj['utxos']), 1) assert_equal(json_obj['bitmap'], "10") self.log.info("Query the TXOs using the /getutxos URI with a binary response") bin_request = b'\x01\x02' for txid, n in [spending, spent]: bin_request += hex_str_to_bytes(txid) bin_request += pack("i", n) bin_response = self.test_rest_request("/getutxos", http_method='POST', req_type=ReqType.BIN, body=bin_request, ret_type=RetType.BYTES) output = BytesIO(bin_response) chain_height, = unpack("<i", output.read(4)) response_hash = output.read(32)[::-1].hex() assert_equal(bb_hash, response_hash) # check if getutxo's chaintip during calculation was fine assert_equal(chain_height, COINBASE_MATURITY+2) # chain height must be 102 self.log.info("Test the /getutxos URI with and without /checkmempool") # Create a transaction, check that it's found with /checkmempool, but # not found without. Then confirm the transaction and check that it's # found with or without /checkmempool. # do a tx and don't sync txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) json_obj = self.test_rest_request("/tx/{}".format(txid)) # get the spent output to later check for utxo (should be spent by then) spent = (json_obj['vin'][0]['txid'], json_obj['vin'][0]['vout']) # get n of 0.1 outpoint n, = filter_output_indices_by_value(json_obj['vout'], Decimal('0.1')) spending = (txid, n) json_obj = self.test_rest_request("/getutxos/{}-{}".format(*spending)) assert_equal(len(json_obj['utxos']), 0) json_obj = self.test_rest_request("/getutxos/checkmempool/{}-{}".format(*spending)) assert_equal(len(json_obj['utxos']), 1) json_obj = self.test_rest_request("/getutxos/{}-{}".format(*spent)) assert_equal(len(json_obj['utxos']), 1) json_obj = self.test_rest_request("/getutxos/checkmempool/{}-{}".format(*spent)) assert_equal(len(json_obj['utxos']), 0) self.nodes[0].generate(1) self.sync_all() json_obj = self.test_rest_request("/getutxos/{}-{}".format(*spending)) assert_equal(len(json_obj['utxos']), 1) json_obj = self.test_rest_request("/getutxos/checkmempool/{}-{}".format(*spending)) assert_equal(len(json_obj['utxos']), 1) # Do some invalid requests self.test_rest_request("/getutxos", http_method='POST', req_type=ReqType.JSON, body='{"checkmempool', status=400, ret_type=RetType.OBJ) self.test_rest_request("/getutxos", http_method='POST', req_type=ReqType.BIN, body='{"checkmempool', status=400, ret_type=RetType.OBJ) self.test_rest_request("/getutxos/checkmempool", http_method='POST', req_type=ReqType.JSON, status=400, ret_type=RetType.OBJ) # Test limits long_uri = '/'.join(["{}-{}".format(txid, n_) for n_ in range(20)]) self.test_rest_request("/getutxos/checkmempool/{}".format(long_uri), http_method='POST', status=400, ret_type=RetType.OBJ) long_uri = '/'.join(['{}-{}'.format(txid, n_) for n_ in range(15)]) self.test_rest_request("/getutxos/checkmempool/{}".format(long_uri), http_method='POST', status=200) self.nodes[0].generate(1) # generate block to not affect upcoming tests self.sync_all() self.log.info("Test the /block, /blockhashbyheight and /headers URIs") bb_hash = self.nodes[0].getbestblockhash() # Check result if block does not exists assert_equal(self.test_rest_request('/headers/1/0000000000000000000000000000000000000000000000000000000000000000'), []) self.test_rest_request('/block/0000000000000000000000000000000000000000000000000000000000000000', status=404, ret_type=RetType.OBJ) # Check result if block is not in the active chain self.nodes[0].invalidateblock(bb_hash) assert_equal(self.test_rest_request('/headers/1/{}'.format(bb_hash)), []) self.test_rest_request('/block/{}'.format(bb_hash)) self.nodes[0].reconsiderblock(bb_hash) # Check binary format response = self.test_rest_request("/block/{}".format(bb_hash), req_type=ReqType.BIN, ret_type=RetType.OBJ) assert_greater_than(int(response.getheader('content-length')), BLOCK_HEADER_SIZE) response_bytes = response.read() # Compare with block header response_header = self.test_rest_request("/headers/1/{}".format(bb_hash), req_type=ReqType.BIN, ret_type=RetType.OBJ) assert_equal(int(response_header.getheader('content-length')), 181) response_header_bytes = response_header.read() assert_equal(response_bytes[:181], response_header_bytes) # Check block hex format response_hex = self.test_rest_request("/block/{}".format(bb_hash), req_type=ReqType.HEX, ret_type=RetType.OBJ) assert_greater_than(int(response_hex.getheader('content-length')), BLOCK_HEADER_SIZE*2) response_hex_bytes = response_hex.read().strip(b'\n') assert_equal(binascii.hexlify(response_bytes), response_hex_bytes) # Compare with hex block header response_header_hex = self.test_rest_request("/headers/1/{}".format(bb_hash), req_type=ReqType.HEX, ret_type=RetType.OBJ) assert_greater_than(int(response_header_hex.getheader('content-length')), BLOCK_HEADER_SIZE*2) response_header_hex_bytes = response_header_hex.read(BLOCK_HEADER_SIZE*2) assert_equal(binascii.hexlify(response_bytes[:BLOCK_HEADER_SIZE]), response_header_hex_bytes) # Check json format block_json_obj = self.test_rest_request("/block/{}".format(bb_hash)) assert_equal(block_json_obj['hash'], bb_hash) assert_equal(self.test_rest_request("/blockhashbyheight/{}".format(block_json_obj['height']))['blockhash'], bb_hash) # Check hex/bin format resp_hex = self.test_rest_request("/blockhashbyheight/{}".format(block_json_obj['height']), req_type=ReqType.HEX, ret_type=RetType.OBJ) assert_equal(resp_hex.read().decode('utf-8').rstrip(), bb_hash) resp_bytes = self.test_rest_request("/blockhashbyheight/{}".format(block_json_obj['height']), req_type=ReqType.BIN, ret_type=RetType.BYTES) blockhash = resp_bytes[::-1].hex() assert_equal(blockhash, bb_hash) # Check invalid blockhashbyheight requests resp = self.test_rest_request("/blockhashbyheight/abc", ret_type=RetType.OBJ, status=400) assert_equal(resp.read().decode('utf-8').rstrip(), "Invalid height: abc") resp = self.test_rest_request("/blockhashbyheight/1000000", ret_type=RetType.OBJ, status=404) assert_equal(resp.read().decode('utf-8').rstrip(), "Block height out of range") resp = self.test_rest_request("/blockhashbyheight/-1", ret_type=RetType.OBJ, status=400) assert_equal(resp.read().decode('utf-8').rstrip(), "Invalid height: -1") self.test_rest_request("/blockhashbyheight/", ret_type=RetType.OBJ, status=400) # Compare with json block header json_obj = self.test_rest_request("/headers/1/{}".format(bb_hash)) assert_equal(len(json_obj), 1) # ensure that there is one header in the json response assert_equal(json_obj[0]['hash'], bb_hash) # request/response hash should be the same # Compare with normal RPC block response rpc_block_json = self.nodes[0].getblock(bb_hash) for key in ['hash', 'confirmations', 'height', 'version', 'merkleroot', 'time', 'nonce', 'bits', 'difficulty', 'chainwork', 'previousblockhash']: assert_equal(json_obj[0][key], rpc_block_json[key]) # See if we can get 5 headers in one response self.nodes[1].generate(5) self.sync_all() json_obj = self.test_rest_request("/headers/5/{}".format(bb_hash)) assert_equal(len(json_obj), 5) # now we should have 5 header objects self.log.info("Test tx inclusion in the /mempool and /block URIs") # Make 3 tx and mine them on node 1 txs = [] txs.append(self.nodes[0].sendtoaddress(not_related_address, 11)) txs.append(self.nodes[0].sendtoaddress(not_related_address, 11)) txs.append(self.nodes[0].sendtoaddress(not_related_address, 11)) self.sync_all() # Check that there are exactly 3 transactions in the TX memory pool before generating the block json_obj = self.test_rest_request("/mempool/info") assert_equal(json_obj['size'], 3) # the size of the memory pool should be greater than 3x ~100 bytes assert_greater_than(json_obj['bytes'], 300) # Check that there are our submitted transactions in the TX memory pool json_obj = self.test_rest_request("/mempool/contents") for i, tx in enumerate(txs): assert tx in json_obj assert_equal(json_obj[tx]['spentby'], txs[i + 1:i + 2]) assert_equal(json_obj[tx]['depends'], txs[i - 1:i]) # Now mine the transactions newblockhash = self.nodes[1].generate(1) self.sync_all() # Check if the 3 tx show up in the new block json_obj = self.test_rest_request("/block/{}".format(newblockhash[0])) non_coinbase_txs = {tx['txid'] for tx in json_obj['tx'] if 'coinbase' not in tx['vin'][0]} assert_equal(non_coinbase_txs, set(txs)) # Check the same but without tx details json_obj = self.test_rest_request("/block/notxdetails/{}".format(newblockhash[0])) for tx in txs: assert tx in json_obj['tx'] self.log.info("Test the /chaininfo URI") bb_hash = self.nodes[0].getbestblockhash() json_obj = self.test_rest_request("/chaininfo") assert_equal(json_obj['bestblockhash'], bb_hash) if __name__ == '__main__': RESTTest().main()

44.650746

153

0.663993

import binascii from decimal import Decimal from enum import Enum from io import BytesIO import json from struct import pack, unpack import http.client import urllib.parse from test_framework.qtumconfig import COINBASE_MATURITY, INITIAL_BLOCK_REWARD from test_framework.qtum import convert_btc_address_to_qtum from test_framework.test_framework import BitcoinTestFramework from test_framework.util import ( assert_equal, assert_greater_than, assert_greater_than_or_equal, hex_str_to_bytes, ) from test_framework.messages import CBlockHeader BLOCK_HEADER_SIZE = len(CBlockHeader().serialize()) class ReqType(Enum): JSON = 1 BIN = 2 HEX = 3 class RetType(Enum): OBJ = 1 BYTES = 2 JSON = 3 def filter_output_indices_by_value(vouts, value): for vout in vouts: if vout['value'] == value: yield vout['n'] class RESTTest (BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 2 self.extra_args = [["-rest"], []] self.supports_cli = False def skip_test_if_missing_module(self): self.skip_if_no_wallet() def test_rest_request(self, uri, http_method='GET', req_type=ReqType.JSON, body='', status=200, ret_type=RetType.JSON): rest_uri = '/rest' + uri if req_type == ReqType.JSON: rest_uri += '.json' elif req_type == ReqType.BIN: rest_uri += '.bin' elif req_type == ReqType.HEX: rest_uri += '.hex' conn = http.client.HTTPConnection(self.url.hostname, self.url.port) self.log.debug('%s %s %s', http_method, rest_uri, body) if http_method == 'GET': conn.request('GET', rest_uri) elif http_method == 'POST': conn.request('POST', rest_uri, body) resp = conn.getresponse() assert_equal(resp.status, status) if ret_type == RetType.OBJ: return resp elif ret_type == RetType.BYTES: return resp.read() elif ret_type == RetType.JSON: return json.loads(resp.read().decode('utf-8'), parse_float=Decimal) def run_test(self): self.url = urllib.parse.urlparse(self.nodes[0].url) self.log.info("Mine blocks and send Bitcoin to node 1") not_related_address = convert_btc_address_to_qtum("2MxqoHEdNQTyYeX1mHcbrrpzgojbosTpCvJ") self.nodes[0].generate(1) self.sync_all() for i in range(0, COINBASE_MATURITY, 100): self.nodes[1].generatetoaddress(100, not_related_address) self.sync_all() assert_equal(self.nodes[0].getbalance(), INITIAL_BLOCK_REWARD) txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() self.log.info("Test the /tx URI") json_obj = self.test_rest_request("/tx/{}".format(txid)) assert_equal(json_obj['txid'], txid) hex_response = self.test_rest_request("/tx/{}".format(txid), req_type=ReqType.HEX, ret_type=RetType.OBJ) assert_greater_than_or_equal(int(hex_response.getheader('content-length')), json_obj['size']*2) spent = (json_obj['vin'][0]['txid'], json_obj['vin'][0]['vout']) n, = filter_output_indices_by_value(json_obj['vout'], Decimal('0.1')) spending = (txid, n) self.log.info("Query an unspent TXO using the /getutxos URI") self.nodes[1].generatetoaddress(1, not_related_address) self.sync_all() bb_hash = self.nodes[0].getbestblockhash() assert_equal(self.nodes[1].getbalance(), Decimal("0.1")) json_obj = self.test_rest_request("/getutxos/{}-{}".format(*spending)) assert_equal(json_obj['chaintipHash'], bb_hash) assert_equal(len(json_obj['utxos']), 1) assert_equal(json_obj['utxos'][0]['value'], Decimal('0.1')) self.log.info("Query a spent TXO using the /getutxos URI") json_obj = self.test_rest_request("/getutxos/{}-{}".format(*spent)) assert_equal(json_obj['chaintipHash'], bb_hash) assert_equal(len(json_obj['utxos']), 0) assert_equal(json_obj['bitmap'], "0") self.log.info("Query two TXOs using the /getutxos URI") json_obj = self.test_rest_request("/getutxos/{}-{}/{}-{}".format(*(spending + spent))) assert_equal(len(json_obj['utxos']), 1) assert_equal(json_obj['bitmap'], "10") self.log.info("Query the TXOs using the /getutxos URI with a binary response") bin_request = b'\x01\x02' for txid, n in [spending, spent]: bin_request += hex_str_to_bytes(txid) bin_request += pack("i", n) bin_response = self.test_rest_request("/getutxos", http_method='POST', req_type=ReqType.BIN, body=bin_request, ret_type=RetType.BYTES) output = BytesIO(bin_response) chain_height, = unpack("<i", output.read(4)) response_hash = output.read(32)[::-1].hex() assert_equal(bb_hash, response_hash) assert_equal(chain_height, COINBASE_MATURITY+2) # chain height must be 102 self.log.info("Test the /getutxos URI with and without /checkmempool") # Create a transaction, check that it's found with /checkmempool, but # found with or without /checkmempool. # do a tx and don't sync txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) json_obj = self.test_rest_request("/tx/{}".format(txid)) spent = (json_obj['vin'][0]['txid'], json_obj['vin'][0]['vout']) n, = filter_output_indices_by_value(json_obj['vout'], Decimal('0.1')) spending = (txid, n) json_obj = self.test_rest_request("/getutxos/{}-{}".format(*spending)) assert_equal(len(json_obj['utxos']), 0) json_obj = self.test_rest_request("/getutxos/checkmempool/{}-{}".format(*spending)) assert_equal(len(json_obj['utxos']), 1) json_obj = self.test_rest_request("/getutxos/{}-{}".format(*spent)) assert_equal(len(json_obj['utxos']), 1) json_obj = self.test_rest_request("/getutxos/checkmempool/{}-{}".format(*spent)) assert_equal(len(json_obj['utxos']), 0) self.nodes[0].generate(1) self.sync_all() json_obj = self.test_rest_request("/getutxos/{}-{}".format(*spending)) assert_equal(len(json_obj['utxos']), 1) json_obj = self.test_rest_request("/getutxos/checkmempool/{}-{}".format(*spending)) assert_equal(len(json_obj['utxos']), 1) self.test_rest_request("/getutxos", http_method='POST', req_type=ReqType.JSON, body='{"checkmempool', status=400, ret_type=RetType.OBJ) self.test_rest_request("/getutxos", http_method='POST', req_type=ReqType.BIN, body='{"checkmempool', status=400, ret_type=RetType.OBJ) self.test_rest_request("/getutxos/checkmempool", http_method='POST', req_type=ReqType.JSON, status=400, ret_type=RetType.OBJ) long_uri = '/'.join(["{}-{}".format(txid, n_) for n_ in range(20)]) self.test_rest_request("/getutxos/checkmempool/{}".format(long_uri), http_method='POST', status=400, ret_type=RetType.OBJ) long_uri = '/'.join(['{}-{}'.format(txid, n_) for n_ in range(15)]) self.test_rest_request("/getutxos/checkmempool/{}".format(long_uri), http_method='POST', status=200) self.nodes[0].generate(1) self.sync_all() self.log.info("Test the /block, /blockhashbyheight and /headers URIs") bb_hash = self.nodes[0].getbestblockhash() assert_equal(self.test_rest_request('/headers/1/0000000000000000000000000000000000000000000000000000000000000000'), []) self.test_rest_request('/block/0000000000000000000000000000000000000000000000000000000000000000', status=404, ret_type=RetType.OBJ) self.nodes[0].invalidateblock(bb_hash) assert_equal(self.test_rest_request('/headers/1/{}'.format(bb_hash)), []) self.test_rest_request('/block/{}'.format(bb_hash)) self.nodes[0].reconsiderblock(bb_hash) response = self.test_rest_request("/block/{}".format(bb_hash), req_type=ReqType.BIN, ret_type=RetType.OBJ) assert_greater_than(int(response.getheader('content-length')), BLOCK_HEADER_SIZE) response_bytes = response.read() response_header = self.test_rest_request("/headers/1/{}".format(bb_hash), req_type=ReqType.BIN, ret_type=RetType.OBJ) assert_equal(int(response_header.getheader('content-length')), 181) response_header_bytes = response_header.read() assert_equal(response_bytes[:181], response_header_bytes) response_hex = self.test_rest_request("/block/{}".format(bb_hash), req_type=ReqType.HEX, ret_type=RetType.OBJ) assert_greater_than(int(response_hex.getheader('content-length')), BLOCK_HEADER_SIZE*2) response_hex_bytes = response_hex.read().strip(b'\n') assert_equal(binascii.hexlify(response_bytes), response_hex_bytes) response_header_hex = self.test_rest_request("/headers/1/{}".format(bb_hash), req_type=ReqType.HEX, ret_type=RetType.OBJ) assert_greater_than(int(response_header_hex.getheader('content-length')), BLOCK_HEADER_SIZE*2) response_header_hex_bytes = response_header_hex.read(BLOCK_HEADER_SIZE*2) assert_equal(binascii.hexlify(response_bytes[:BLOCK_HEADER_SIZE]), response_header_hex_bytes) block_json_obj = self.test_rest_request("/block/{}".format(bb_hash)) assert_equal(block_json_obj['hash'], bb_hash) assert_equal(self.test_rest_request("/blockhashbyheight/{}".format(block_json_obj['height']))['blockhash'], bb_hash) resp_hex = self.test_rest_request("/blockhashbyheight/{}".format(block_json_obj['height']), req_type=ReqType.HEX, ret_type=RetType.OBJ) assert_equal(resp_hex.read().decode('utf-8').rstrip(), bb_hash) resp_bytes = self.test_rest_request("/blockhashbyheight/{}".format(block_json_obj['height']), req_type=ReqType.BIN, ret_type=RetType.BYTES) blockhash = resp_bytes[::-1].hex() assert_equal(blockhash, bb_hash) resp = self.test_rest_request("/blockhashbyheight/abc", ret_type=RetType.OBJ, status=400) assert_equal(resp.read().decode('utf-8').rstrip(), "Invalid height: abc") resp = self.test_rest_request("/blockhashbyheight/1000000", ret_type=RetType.OBJ, status=404) assert_equal(resp.read().decode('utf-8').rstrip(), "Block height out of range") resp = self.test_rest_request("/blockhashbyheight/-1", ret_type=RetType.OBJ, status=400) assert_equal(resp.read().decode('utf-8').rstrip(), "Invalid height: -1") self.test_rest_request("/blockhashbyheight/", ret_type=RetType.OBJ, status=400) json_obj = self.test_rest_request("/headers/1/{}".format(bb_hash)) assert_equal(len(json_obj), 1) assert_equal(json_obj[0]['hash'], bb_hash) rpc_block_json = self.nodes[0].getblock(bb_hash) for key in ['hash', 'confirmations', 'height', 'version', 'merkleroot', 'time', 'nonce', 'bits', 'difficulty', 'chainwork', 'previousblockhash']: assert_equal(json_obj[0][key], rpc_block_json[key]) self.nodes[1].generate(5) self.sync_all() json_obj = self.test_rest_request("/headers/5/{}".format(bb_hash)) assert_equal(len(json_obj), 5) self.log.info("Test tx inclusion in the /mempool and /block URIs") txs = [] txs.append(self.nodes[0].sendtoaddress(not_related_address, 11)) txs.append(self.nodes[0].sendtoaddress(not_related_address, 11)) txs.append(self.nodes[0].sendtoaddress(not_related_address, 11)) self.sync_all() json_obj = self.test_rest_request("/mempool/info") assert_equal(json_obj['size'], 3) assert_greater_than(json_obj['bytes'], 300) json_obj = self.test_rest_request("/mempool/contents") for i, tx in enumerate(txs): assert tx in json_obj assert_equal(json_obj[tx]['spentby'], txs[i + 1:i + 2]) assert_equal(json_obj[tx]['depends'], txs[i - 1:i]) newblockhash = self.nodes[1].generate(1) self.sync_all() json_obj = self.test_rest_request("/block/{}".format(newblockhash[0])) non_coinbase_txs = {tx['txid'] for tx in json_obj['tx'] if 'coinbase' not in tx['vin'][0]} assert_equal(non_coinbase_txs, set(txs)) json_obj = self.test_rest_request("/block/notxdetails/{}".format(newblockhash[0])) for tx in txs: assert tx in json_obj['tx'] self.log.info("Test the /chaininfo URI") bb_hash = self.nodes[0].getbestblockhash() json_obj = self.test_rest_request("/chaininfo") assert_equal(json_obj['bestblockhash'], bb_hash) if __name__ == '__main__': RESTTest().main()

true

7907efe633bb6e70cd40c9d08e2ff6f97c40fd3d

768

py

Python

lipame/lipa/models.py

savioabuga/lipame

3f34d1679aa1e4981763a31f2ffd4767a19f6a1b

[ "MIT" ]

1

2018-06-18T08:56:56.000Z

lipame/lipa/models.py

savioabuga/lipame

3f34d1679aa1e4981763a31f2ffd4767a19f6a1b

[ "MIT" ]

null

lipame/lipa/models.py

savioabuga/lipame

3f34d1679aa1e4981763a31f2ffd4767a19f6a1b

[ "MIT" ]

null

from __future__ import unicode_literals from django.db import models from model_utils import Choices from model_utils.models import TimeStampedModel, StatusModel from django.conf import settings class Booking(TimeStampedModel, StatusModel): TRAVEL_CLASSES = Choices('economy', 'first_class') STATUS = Choices(('pending', 'Pending'), ('paid', 'Paid'), ('failed', 'Failed')) date_of_travel = models.DateTimeField() travel_class = models.CharField(choices=TRAVEL_CLASSES, default=TRAVEL_CLASSES.economy, max_length=30) status = models.CharField(choices=STATUS, default=STATUS.pending, max_length=20) user = models.ForeignKey(settings.AUTH_USER_MODEL, null=False, blank=False) payment_reference = models.CharField(max_length=100, blank=True)

48

106

0.778646

from __future__ import unicode_literals from django.db import models from model_utils import Choices from model_utils.models import TimeStampedModel, StatusModel from django.conf import settings class Booking(TimeStampedModel, StatusModel): TRAVEL_CLASSES = Choices('economy', 'first_class') STATUS = Choices(('pending', 'Pending'), ('paid', 'Paid'), ('failed', 'Failed')) date_of_travel = models.DateTimeField() travel_class = models.CharField(choices=TRAVEL_CLASSES, default=TRAVEL_CLASSES.economy, max_length=30) status = models.CharField(choices=STATUS, default=STATUS.pending, max_length=20) user = models.ForeignKey(settings.AUTH_USER_MODEL, null=False, blank=False) payment_reference = models.CharField(max_length=100, blank=True)

true

7907f01c80895bd5e3f5eace8e627fedf6a053da

439

py

Python

labinfo13/Candies.py

MatiwsxD/ayed-2019-1

a5fdbe3a055405150122cf3875cdb0c6afd9eff0

[ "MIT" ]

null

labinfo13/Candies.py

MatiwsxD/ayed-2019-1

a5fdbe3a055405150122cf3875cdb0c6afd9eff0

[ "MIT" ]

null

labinfo13/Candies.py

MatiwsxD/ayed-2019-1

a5fdbe3a055405150122cf3875cdb0c6afd9eff0

[ "MIT" ]

null

N = int(input()) line = [] for a in range(N): line.append(int(input())) total = 0 curIter = 1 while min(line) < 999999: valleys = [] for a in range(N): if line[a] < 999999: if (a == 0 or line[a] <= line[a - 1]) and (a == N - 1 or line[a] <= line[a + 1]): valleys.append(a) for a in valleys: line[a] = 999999 total += (curIter * len(valleys)) curIter += 1 print(total)

20.904762

93

0.503417

N = int(input()) line = [] for a in range(N): line.append(int(input())) total = 0 curIter = 1 while min(line) < 999999: valleys = [] for a in range(N): if line[a] < 999999: if (a == 0 or line[a] <= line[a - 1]) and (a == N - 1 or line[a] <= line[a + 1]): valleys.append(a) for a in valleys: line[a] = 999999 total += (curIter * len(valleys)) curIter += 1 print(total)

true

7907f0307739bc565385613ad4d4efd3ff531aa9

17,412

py

Python

src/transformers/trainer_tf.py

tilmanbeck/adapter-transformers

ed42ced6983891060bb160c5c4f2c5d64d2c205c

[ "Apache-2.0" ]

63

2020-12-09T18:58:16.000Z

2022-03-21T02:34:35.000Z

src/transformers/trainer_tf.py

tilmanbeck/adapter-transformers

ed42ced6983891060bb160c5c4f2c5d64d2c205c

[ "Apache-2.0" ]

5

2021-01-29T10:33:04.000Z

2021-08-25T14:15:27.000Z

src/transformers/trainer_tf.py

tilmanbeck/adapter-transformers

ed42ced6983891060bb160c5c4f2c5d64d2c205c

[ "Apache-2.0" ]

18

2020-12-11T20:36:04.000Z

2021-12-12T07:04:20.000Z

"""Tensorflow trainer class.""" import logging import math import os from typing import Callable, Dict, Optional import numpy as np import tensorflow as tf from .modeling_tf_utils import TFPreTrainedModel, shape_list from .optimization_tf import GradientAccumulator, create_optimizer from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput from .training_args_tf import TFTrainingArguments logger = logging.getLogger(__name__) class TFTrainer: model: TFPreTrainedModel args: TFTrainingArguments # something similar to a PT Dataset. # This is just temporary before to have # a framework-agnostic approach for datasets. train_dataset: Optional[tf.data.Dataset] eval_dataset: Optional[tf.data.Dataset] compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None prediction_loss_only: bool def __init__( self, model: TFPreTrainedModel, args: TFTrainingArguments, train_dataset: Optional[tf.data.Dataset] = None, eval_dataset: Optional[tf.data.Dataset] = None, compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None, prediction_loss_only=False, ): self.model = model self.args = args self.train_dataset = train_dataset self.eval_dataset = eval_dataset self.compute_metrics = compute_metrics self.prediction_loss_only = prediction_loss_only self.gradient_accumulator = GradientAccumulator() self._setup_training() def _setup_training(self) -> None: """ Setup the different steps to train a model: - check if all the data are given - create the proper strategy - create the features - prepare the model settings """ self._prepare_dataset() with self.args.strategy.scope(): self._create_optimizer() _ = self.optimizer.iterations self._set_loss_and_metric() self._create_checkpoint_manager() self._create_summary_writer() def _set_loss_and_metric(self) -> None: """ Create the training loss and metric with their name. Allowed names are those listed in the Tensorflow documentation and those contained in the transformers library. """ try: self.loss = tf.keras.losses.get( { "class_name": self.args.loss_name, "config": {"from_logits": True, "reduction": tf.keras.losses.Reduction.NONE}, } ) except TypeError: self.loss = tf.keras.losses.get( {"class_name": self.args.loss_name, "config": {"reduction": tf.keras.losses.Reduction.NONE}} ) def _create_summary_writer(self) -> None: """ Create a summary writer to be able to read the logs in Tensorboard. """ self.writer = tf.summary.create_file_writer(self.args.logging_dir) def _prepare_dataset(self) -> None: """ Prepare the training, validation and test data. """ if self.train_dataset is not None: self.num_train_examples = self.train_dataset.reduce(tf.constant(0), lambda x, _: x + 1).numpy() if self.args.max_steps > 0: self.train_steps = self.args.max_steps else: self.train_steps: int = math.ceil(self.num_train_examples / self.args.train_batch_size) self.train_dataset = ( self.train_dataset.cache() .shuffle(self.num_train_examples) .batch(self.args.train_batch_size) .prefetch(tf.data.experimental.AUTOTUNE) ) if self.args.max_steps > 0: self.train_dataset = self.train_dataset.repeat(-1) self.train_dataset = self.args.strategy.experimental_distribute_dataset(self.train_dataset) else: self.train_steps = 0 if self.eval_dataset is not None: self.eval_dataset = ( self.eval_dataset.batch(self.args.eval_batch_size).cache().prefetch(tf.data.experimental.AUTOTUNE) ) self.eval_dataset = self.args.strategy.experimental_distribute_dataset(self.eval_dataset) def _create_optimizer(self) -> None: """ Create the training optimizer with its name. Allowed names are those listed in the Tensorflow documentation and those contained in the transformers library. """ if self.args.optimizer_name == "adamw": self.optimizer = create_optimizer( self.args.learning_rate, self.train_steps, self.args.warmup_steps, self.args.end_lr ) else: try: self.optimizer = tf.keras.optimizers.get( { "class_name": self.args.optimizer_name, "config": {"learning_rate": self.args.learning_rate, "epsilon": self.args.adam_epsilon}, } ) except TypeError: # This is for the case where the optimizer is not Adam-like such as SGD self.optimizer = tf.keras.optimizers.get( {"class_name": self.args.optimizer_name, "config": {"learning_rate": self.args.learning_rate}} ) logger.info("Created an/a {} optimizer".format(self.args.optimizer_name)) def _create_checkpoint_manager(self, max_to_keep: int = 5, load_model: bool = True) -> None: """ Create a checkpoint manager in order to be able to make the training fault-tolerant. Args: max_to_keep: the maximum number of checkpoints to keep in the checkpoint path. load_model: if we want to start the training from the latest checkpoint. """ ckpt = tf.train.Checkpoint(optimizer=self.optimizer, model=self.model) self.model.ckpt_manager = tf.train.CheckpointManager(ckpt, PREFIX_CHECKPOINT_DIR, max_to_keep=max_to_keep) if load_model: ckpt.restore(self.model.ckpt_manager.latest_checkpoint).expect_partial() @tf.function def _evaluate_steps(self, per_replica_features, per_replica_labels): """ One step evaluation across replica. Args: per_replica_features: the batched features. per_replica_labels: the batched labels. Returns: The loss corresponding to the given batch. """ per_replica_loss, per_replica_logits = self.args.strategy.experimental_run_v2( self._run_model, args=(per_replica_features, per_replica_labels, False) ) try: reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, axis=0) except ValueError: reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, None) return reduced_loss, per_replica_logits def _prediction_loop( self, dataset: tf.data.Dataset, description: str, prediction_loss_only: Optional[bool] = None ) -> PredictionOutput: logger.info("***** Running %s *****", description) logger.info(" Batch size = %d", self.args.eval_batch_size) label_ids: np.ndarray = None preds: np.ndarray = None step: int = 1 for features, labels in dataset: step = tf.convert_to_tensor(step, dtype=tf.int64) loss, logits = self._evaluate_steps(features, labels) loss = tf.reduce_mean(loss) if not prediction_loss_only: if self.args.n_gpu > 1: for val in logits.values: if preds is None: preds = val.numpy() else: preds = np.append(preds, val.numpy(), axis=0) for val in labels.values: if label_ids is None: label_ids = val.numpy() else: label_ids = np.append(label_ids, val.numpy(), axis=0) else: if preds is None: preds = logits.numpy() else: preds = np.append(preds, logits.numpy(), axis=0) if label_ids is None: label_ids = labels.numpy() else: label_ids = np.append(label_ids, labels.numpy(), axis=0) step += 1 if self.compute_metrics is not None and preds is not None and label_ids is not None: metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids)) else: metrics = {} metrics["eval_loss"] = loss.numpy() for key in list(metrics.keys()): if not key.startswith("eval_"): metrics[f"eval_{key}"] = metrics.pop(key) return PredictionOutput(predictions=preds, label_ids=label_ids, metrics=metrics) def evaluate( self, eval_dataset: Optional[tf.data.Dataset] = None, prediction_loss_only: Optional[bool] = None ) -> Dict[str, float]: """ Prediction/evaluation loop, shared by `evaluate()` and `predict()`. """ if eval_dataset is None: eval_dataset = self.eval_dataset output = self._prediction_loop(eval_dataset, description="Evaluation") return output.metrics def train(self) -> None: """ Train method to train the model. """ if self.args.debug: tf.summary.trace_on(graph=True, profiler=True) self.gradient_accumulator.reset() iterations = self.optimizer.iterations if iterations.numpy() > 0: logger.info("Start the training from the last checkpoint") start_epoch = (iterations.numpy() // self.train_steps) + 1 else: start_epoch = 1 tf.summary.experimental.set_step(iterations) epochs = 1 if self.args.max_steps > 0 else self.args.num_train_epochs logger.info("***** Running training *****") logger.info(" Num examples = %d", self.num_train_examples) logger.info(" Num Epochs = %d", epochs) logger.info(" Total optimization steps = %d", self.train_steps) for epoch in range(start_epoch, int(epochs + 1)): for training_loss in self._training_steps(): step = iterations.numpy() if self.args.debug: with self.writer.as_default(): tf.summary.scalar("loss", training_loss, step=step) if step == 1 and self.args.debug: with self.writer.as_default(): tf.summary.trace_export(name="training", step=step, profiler_outdir=self.args.logging_dir) if self.args.evaluate_during_training and step % self.args.eval_steps == 0: logs = {} results = self.evaluate() for key, value in results.items(): eval_key = "eval_{}".format(key) logs[eval_key] = value if callable(self.optimizer.learning_rate): logs["learning_rate"] = self.optimizer.learning_rate(step).numpy() else: logs["learning_rate"] = self.optimizer.learning_rate.numpy() logger.info("Epoch {} Step {} Validation Metrics {}".format(epoch, step, logs)) with self.writer.as_default(): for k, v in logs.items(): tf.summary.scalar(k, v, step=step) if step % self.args.logging_steps == 0: logger.info("Epoch {} Step {} Train Loss {:.4f}".format(epoch, step, training_loss.numpy())) if step % self.args.save_steps == 0: ckpt_save_path = self.model.ckpt_manager.save() logger.info("Saving checkpoint for step {} at {}".format(step, ckpt_save_path)) if step % self.train_steps == 0: break def _training_steps(self): """ Returns a generator over training steps (i.e. parameters update). """ for i, loss in enumerate(self._accumulate_next_gradients()): if i % self.args.gradient_accumulation_steps == 0: self._apply_gradients() yield loss @tf.function def _apply_gradients(self): """Applies the gradients (cross-replica).""" self.args.strategy.experimental_run_v2(self._step) def _step(self): """Applies gradients and resets accumulation.""" gradient_scale = self.gradient_accumulator.step * self.args.strategy.num_replicas_in_sync gradients = [ gradient / tf.cast(gradient_scale, gradient.dtype) for gradient in self.gradient_accumulator.gradients ] gradients = [(tf.clip_by_value(grad, -self.args.max_grad_norm, self.args.max_grad_norm)) for grad in gradients] self.optimizer.apply_gradients(list(zip(gradients, self.model.trainable_variables))) self.gradient_accumulator.reset() def _accumulate_next_gradients(self): """Accumulates the gradients from the next element in dataset.""" iterator = iter(self.train_dataset) @tf.function def _accumulate_next(): per_replica_features, per_replica_labels = next(iterator) return self._accumulate_gradients(per_replica_features, per_replica_labels) while True: try: yield _accumulate_next() except tf.errors.OutOfRangeError: break def _accumulate_gradients(self, per_replica_features, per_replica_labels): """Accumulates the gradients across all the replica.""" per_replica_loss = self.args.strategy.experimental_run_v2( self._forward, args=(per_replica_features, per_replica_labels) ) try: reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, axis=0) except ValueError: reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, None) return reduced_loss def _forward(self, features, labels): """Forwards a training example and accumulates the gradients.""" per_example_loss, _ = self._run_model(features, labels, True) gradients = tf.gradients(per_example_loss, self.model.trainable_variables) gradients = [ g if g is not None else tf.zeros_like(v) for g, v in zip(gradients, self.model.trainable_variables) ] self.gradient_accumulator(gradients) return per_example_loss def _run_model(self, features, labels, training): """ Computes the loss of the given features and labels pair. Args: features: the batched features. labels: the batched labels. training: run the model in training mode or not """ if self.args.mode == "text-classification" or self.args.mode == "token-classification": logits = self.model(features, training=training)[0] else: logits = self.model(features, training=training) if self.args.mode == "token-classification": active_loss = tf.reshape(labels, (-1,)) != -1 reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss) labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss) loss = self.loss(labels, reduced_logits) elif self.args.mode == "question-answering": start_loss = self.loss(labels["start_position"], logits[0]) end_loss = self.loss(labels["end_position"], logits[1]) loss = (start_loss + end_loss) / 2.0 else: loss = self.loss(labels, logits) loss += sum(self.model.losses) * (1.0 / self.args.n_gpu) return loss, logits def predict(self, test_dataset: tf.data.Dataset) -> PredictionOutput: """ Run prediction and return predictions and potential metrics. Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method will also return metrics, like in evaluate(). Args: test_dataset: something similar to a PT Dataset. This is just temporary before to have a framework-agnostic approach for datasets. """ test_dataset = test_dataset.batch(self.args.eval_batch_size) test_dataset = self.args.strategy.experimental_distribute_dataset(test_dataset) return self._prediction_loop(test_dataset, description="Prediction") def save_model(self) -> None: """ Save the pretrained model and create a Tensorflow saved model. """ logger.info("Saving model in {}".format(self.args.output_dir)) path = os.path.join(self.args.output_dir, "saved_model") logger.info("Saving model in {}".format(path)) os.makedirs(path, exist_ok=True) self.model.save_pretrained(self.args.output_dir)

39.844394

119

0.608086

import logging import math import os from typing import Callable, Dict, Optional import numpy as np import tensorflow as tf from .modeling_tf_utils import TFPreTrainedModel, shape_list from .optimization_tf import GradientAccumulator, create_optimizer from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput from .training_args_tf import TFTrainingArguments logger = logging.getLogger(__name__) class TFTrainer: model: TFPreTrainedModel args: TFTrainingArguments train_dataset: Optional[tf.data.Dataset] eval_dataset: Optional[tf.data.Dataset] compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None prediction_loss_only: bool def __init__( self, model: TFPreTrainedModel, args: TFTrainingArguments, train_dataset: Optional[tf.data.Dataset] = None, eval_dataset: Optional[tf.data.Dataset] = None, compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None, prediction_loss_only=False, ): self.model = model self.args = args self.train_dataset = train_dataset self.eval_dataset = eval_dataset self.compute_metrics = compute_metrics self.prediction_loss_only = prediction_loss_only self.gradient_accumulator = GradientAccumulator() self._setup_training() def _setup_training(self) -> None: self._prepare_dataset() with self.args.strategy.scope(): self._create_optimizer() _ = self.optimizer.iterations self._set_loss_and_metric() self._create_checkpoint_manager() self._create_summary_writer() def _set_loss_and_metric(self) -> None: try: self.loss = tf.keras.losses.get( { "class_name": self.args.loss_name, "config": {"from_logits": True, "reduction": tf.keras.losses.Reduction.NONE}, } ) except TypeError: self.loss = tf.keras.losses.get( {"class_name": self.args.loss_name, "config": {"reduction": tf.keras.losses.Reduction.NONE}} ) def _create_summary_writer(self) -> None: self.writer = tf.summary.create_file_writer(self.args.logging_dir) def _prepare_dataset(self) -> None: if self.train_dataset is not None: self.num_train_examples = self.train_dataset.reduce(tf.constant(0), lambda x, _: x + 1).numpy() if self.args.max_steps > 0: self.train_steps = self.args.max_steps else: self.train_steps: int = math.ceil(self.num_train_examples / self.args.train_batch_size) self.train_dataset = ( self.train_dataset.cache() .shuffle(self.num_train_examples) .batch(self.args.train_batch_size) .prefetch(tf.data.experimental.AUTOTUNE) ) if self.args.max_steps > 0: self.train_dataset = self.train_dataset.repeat(-1) self.train_dataset = self.args.strategy.experimental_distribute_dataset(self.train_dataset) else: self.train_steps = 0 if self.eval_dataset is not None: self.eval_dataset = ( self.eval_dataset.batch(self.args.eval_batch_size).cache().prefetch(tf.data.experimental.AUTOTUNE) ) self.eval_dataset = self.args.strategy.experimental_distribute_dataset(self.eval_dataset) def _create_optimizer(self) -> None: if self.args.optimizer_name == "adamw": self.optimizer = create_optimizer( self.args.learning_rate, self.train_steps, self.args.warmup_steps, self.args.end_lr ) else: try: self.optimizer = tf.keras.optimizers.get( { "class_name": self.args.optimizer_name, "config": {"learning_rate": self.args.learning_rate, "epsilon": self.args.adam_epsilon}, } ) except TypeError: self.optimizer = tf.keras.optimizers.get( {"class_name": self.args.optimizer_name, "config": {"learning_rate": self.args.learning_rate}} ) logger.info("Created an/a {} optimizer".format(self.args.optimizer_name)) def _create_checkpoint_manager(self, max_to_keep: int = 5, load_model: bool = True) -> None: ckpt = tf.train.Checkpoint(optimizer=self.optimizer, model=self.model) self.model.ckpt_manager = tf.train.CheckpointManager(ckpt, PREFIX_CHECKPOINT_DIR, max_to_keep=max_to_keep) if load_model: ckpt.restore(self.model.ckpt_manager.latest_checkpoint).expect_partial() @tf.function def _evaluate_steps(self, per_replica_features, per_replica_labels): per_replica_loss, per_replica_logits = self.args.strategy.experimental_run_v2( self._run_model, args=(per_replica_features, per_replica_labels, False) ) try: reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, axis=0) except ValueError: reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, None) return reduced_loss, per_replica_logits def _prediction_loop( self, dataset: tf.data.Dataset, description: str, prediction_loss_only: Optional[bool] = None ) -> PredictionOutput: logger.info("***** Running %s *****", description) logger.info(" Batch size = %d", self.args.eval_batch_size) label_ids: np.ndarray = None preds: np.ndarray = None step: int = 1 for features, labels in dataset: step = tf.convert_to_tensor(step, dtype=tf.int64) loss, logits = self._evaluate_steps(features, labels) loss = tf.reduce_mean(loss) if not prediction_loss_only: if self.args.n_gpu > 1: for val in logits.values: if preds is None: preds = val.numpy() else: preds = np.append(preds, val.numpy(), axis=0) for val in labels.values: if label_ids is None: label_ids = val.numpy() else: label_ids = np.append(label_ids, val.numpy(), axis=0) else: if preds is None: preds = logits.numpy() else: preds = np.append(preds, logits.numpy(), axis=0) if label_ids is None: label_ids = labels.numpy() else: label_ids = np.append(label_ids, labels.numpy(), axis=0) step += 1 if self.compute_metrics is not None and preds is not None and label_ids is not None: metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids)) else: metrics = {} metrics["eval_loss"] = loss.numpy() for key in list(metrics.keys()): if not key.startswith("eval_"): metrics[f"eval_{key}"] = metrics.pop(key) return PredictionOutput(predictions=preds, label_ids=label_ids, metrics=metrics) def evaluate( self, eval_dataset: Optional[tf.data.Dataset] = None, prediction_loss_only: Optional[bool] = None ) -> Dict[str, float]: if eval_dataset is None: eval_dataset = self.eval_dataset output = self._prediction_loop(eval_dataset, description="Evaluation") return output.metrics def train(self) -> None: if self.args.debug: tf.summary.trace_on(graph=True, profiler=True) self.gradient_accumulator.reset() iterations = self.optimizer.iterations if iterations.numpy() > 0: logger.info("Start the training from the last checkpoint") start_epoch = (iterations.numpy() // self.train_steps) + 1 else: start_epoch = 1 tf.summary.experimental.set_step(iterations) epochs = 1 if self.args.max_steps > 0 else self.args.num_train_epochs logger.info("***** Running training *****") logger.info(" Num examples = %d", self.num_train_examples) logger.info(" Num Epochs = %d", epochs) logger.info(" Total optimization steps = %d", self.train_steps) for epoch in range(start_epoch, int(epochs + 1)): for training_loss in self._training_steps(): step = iterations.numpy() if self.args.debug: with self.writer.as_default(): tf.summary.scalar("loss", training_loss, step=step) if step == 1 and self.args.debug: with self.writer.as_default(): tf.summary.trace_export(name="training", step=step, profiler_outdir=self.args.logging_dir) if self.args.evaluate_during_training and step % self.args.eval_steps == 0: logs = {} results = self.evaluate() for key, value in results.items(): eval_key = "eval_{}".format(key) logs[eval_key] = value if callable(self.optimizer.learning_rate): logs["learning_rate"] = self.optimizer.learning_rate(step).numpy() else: logs["learning_rate"] = self.optimizer.learning_rate.numpy() logger.info("Epoch {} Step {} Validation Metrics {}".format(epoch, step, logs)) with self.writer.as_default(): for k, v in logs.items(): tf.summary.scalar(k, v, step=step) if step % self.args.logging_steps == 0: logger.info("Epoch {} Step {} Train Loss {:.4f}".format(epoch, step, training_loss.numpy())) if step % self.args.save_steps == 0: ckpt_save_path = self.model.ckpt_manager.save() logger.info("Saving checkpoint for step {} at {}".format(step, ckpt_save_path)) if step % self.train_steps == 0: break def _training_steps(self): for i, loss in enumerate(self._accumulate_next_gradients()): if i % self.args.gradient_accumulation_steps == 0: self._apply_gradients() yield loss @tf.function def _apply_gradients(self): self.args.strategy.experimental_run_v2(self._step) def _step(self): gradient_scale = self.gradient_accumulator.step * self.args.strategy.num_replicas_in_sync gradients = [ gradient / tf.cast(gradient_scale, gradient.dtype) for gradient in self.gradient_accumulator.gradients ] gradients = [(tf.clip_by_value(grad, -self.args.max_grad_norm, self.args.max_grad_norm)) for grad in gradients] self.optimizer.apply_gradients(list(zip(gradients, self.model.trainable_variables))) self.gradient_accumulator.reset() def _accumulate_next_gradients(self): iterator = iter(self.train_dataset) @tf.function def _accumulate_next(): per_replica_features, per_replica_labels = next(iterator) return self._accumulate_gradients(per_replica_features, per_replica_labels) while True: try: yield _accumulate_next() except tf.errors.OutOfRangeError: break def _accumulate_gradients(self, per_replica_features, per_replica_labels): per_replica_loss = self.args.strategy.experimental_run_v2( self._forward, args=(per_replica_features, per_replica_labels) ) try: reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, axis=0) except ValueError: reduced_loss = self.args.strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_loss, None) return reduced_loss def _forward(self, features, labels): per_example_loss, _ = self._run_model(features, labels, True) gradients = tf.gradients(per_example_loss, self.model.trainable_variables) gradients = [ g if g is not None else tf.zeros_like(v) for g, v in zip(gradients, self.model.trainable_variables) ] self.gradient_accumulator(gradients) return per_example_loss def _run_model(self, features, labels, training): if self.args.mode == "text-classification" or self.args.mode == "token-classification": logits = self.model(features, training=training)[0] else: logits = self.model(features, training=training) if self.args.mode == "token-classification": active_loss = tf.reshape(labels, (-1,)) != -1 reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss) labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss) loss = self.loss(labels, reduced_logits) elif self.args.mode == "question-answering": start_loss = self.loss(labels["start_position"], logits[0]) end_loss = self.loss(labels["end_position"], logits[1]) loss = (start_loss + end_loss) / 2.0 else: loss = self.loss(labels, logits) loss += sum(self.model.losses) * (1.0 / self.args.n_gpu) return loss, logits def predict(self, test_dataset: tf.data.Dataset) -> PredictionOutput: test_dataset = test_dataset.batch(self.args.eval_batch_size) test_dataset = self.args.strategy.experimental_distribute_dataset(test_dataset) return self._prediction_loop(test_dataset, description="Prediction") def save_model(self) -> None: logger.info("Saving model in {}".format(self.args.output_dir)) path = os.path.join(self.args.output_dir, "saved_model") logger.info("Saving model in {}".format(path)) os.makedirs(path, exist_ok=True) self.model.save_pretrained(self.args.output_dir)

true

7907f27f7a8b22d2515ba776646986e401ea3035

7,294

py

Python

methods/latent-pp-models-mem-rjmcmc.py

wdempsey/sense2stop-lvm

ea44d5f9199382d30e4c5a5ff4bd524313ceb5b2

[ "CECILL-B" ]

1

2020-04-18T11:16:02.000Z

methods/latent-pp-models-mem-rjmcmc.py

wdempsey/sense2stop-lvm

ea44d5f9199382d30e4c5a5ff4bd524313ceb5b2

[ "CECILL-B" ]

6

2020-04-13T18:38:04.000Z

2022-03-12T00:55:56.000Z

methods/latent-pp-models-mem-rjmcmc.py

wdempsey/sense2stop-lvm

ea44d5f9199382d30e4c5a5ff4bd524313ceb5b2

[ "CECILL-B" ]

1

2020-07-02T04:47:00.000Z

#%% import pymc3 as pm import arviz as az import pandas as pd import numpy as np from datetime import datetime from scipy import stats import os import pickle from scipy import special import theano.tensor as tt ## List down file paths exec(open('../env_vars.py').read()) dir_data = os.environ['dir_data'] dir_picklejar = os.environ['dir_picklejar'] #%% ############################################################################### # Read in preparation: data_dates data frame ############################################################################### filename = os.path.join(os.path.realpath(dir_picklejar), 'save_all_dict') infile = open(filename,'rb') clean_data = pickle.load(infile) infile.close() #%% ''' Delete all times > 1hr before start time. Extend day to handle all other times and remove duplicates Need to move this part of code to pre-processing at some point ''' for key in clean_data.keys(): temp = clean_data[key] for days in temp.keys(): day_temp = temp[days] if len(day_temp['hours_since_start_day']) > 0: ## Check if any times < or > 1hr day_temp['hours_since_start_day'] = day_temp['hours_since_start_day'].iloc[np.where(day_temp['hours_since_start_day'] > -1)] day_temp['hours_since_start_day'] = day_temp['hours_since_start_day'].iloc[np.where(day_temp['day_length'] - day_temp['hours_since_start_day'] > -1)] day_min = np.min(day_temp['hours_since_start_day']) day_min = np.min([day_min,0]) day_max = np.max(day_temp['hours_since_start_day']) day_max = np.max([day_max, day_temp['day_length']]) day_temp['hours_since_start_day'] = day_temp['hours_since_start_day'] - day_min day_temp['hours_since_start_day'] = np.unique(day_temp['hours_since_start_day']) day_temp['day_length'] = day_max - day_min #%% ############################################################################### # Estimation using pymc3 ############################################################################### def exponential_log_complementary_cdf(x, lam): ''' log complementary CDF of exponential distribution ''' return -lam*x def exponential_log_pdf(x, lam): ''' log complementary CDF of exponential distribution ''' return np.log(lam)-lam*x def selfreport_mem(observed, latent, dimon): ''' observed: Observed self report times latent: Vector of latent smoking events (length is max) dimon: Integer saying how many of the latent entries are currently included ''' total = 1.0 temp_latent = latent[tt.arange(dimon)] if not tt.all(tt.eq(observed,temp_latent)): total = -1000000 else: total = tt.prod(tt.eq(temp_latent,observed)*0.9 + (1-tt.eq(temp_latent,observed))*0.1) return total max_events = 0.0 # Defining max number of events for participants in clean_data.keys(): for days in clean_data[participants].keys(): max_events = np.max([max_events,len(clean_data[participants][days]['hours_since_start_day'])]) max_events = max_events + 10 # Just to be safe let's add a few more max_events = max_events.astype('int') #%% ############################################################################### ''' Estimation using pymc3. Model is a static graph so we handle this by having a maximum number of events within a day length max_events that tells us which events are "on" ''' ############################################################################### with pm.Model() as model: # ------------------------------------------------------------------------- # Priors # ------------------------------------------------------------------------- beta = pm.Normal('beta', mu=0, sd=10) loglamb_observed = beta lamb_observed = np.exp(loglamb_observed) # ------------------------------------------------------------------------- # Likelihood # ------------------------------------------------------------------------- for participants in clean_data.keys(): for days in clean_data[participants].keys(): if len(clean_data[participants][days]['hours_since_start_day']) > 0: pp_rate = lamb_observed*clean_data[participants][days]['day_length'] num_sr = len(clean_data[participants][days]['hours_since_start_day']) sr = clean_data[participants][days]['hours_since_start_day'] day_length = clean_data[participants][days]['day_length'] init = np.append(sr, np.repeat(0,max_events-num_sr)) smoke_length = pm.Poisson('num_smokes_%d_%d'%(participants, days), mu=pp_rate, testval = num_sr) # Number of Events in Day smoke_times = pm.Uniform('smoke_times_%d_%d'%(participants, days), lower = 0.0, upper = day_length, shape = max_events, testval = init) # Location of Events in Day sr_times = pm.Potential('sr_times_%d_%d'%(participants, days), selfreport_mem(observed=sr, latent=smoke_times, dimon = smoke_length)) #%% # Sample from posterior distribution with model: # posterior_samples = pm.sample(draws=5000, tune=5000, cores=1, target_accept=0.80) posterior_samples = pm.sample(draws = 2000, tune=2000, init='adapt_diag', cores = 1) #%% # Calculate 95% credible interval model_summary_logscale = az.summary(posterior_samples, credible_interval=.95) model_summary_logscale = model_summary_logscale[['mean','hpd_2.5%','hpd_97.5%']] # Produce trace plots pm.traceplot(posterior_samples) # Collect results collect_results = {'model':model, 'posterior_samples':posterior_samples, 'model_summary_logscale':model_summary_logscale} #%% # Remove variable from workspace del model, posterior_samples, model_summary_logscale #%% ############################################################################### # Print results from all models ############################################################################### import matplotlib.pyplot as plt # Model 0 pm.traceplot(collect_results['posterior_samples']) print(collect_results['model_summary_logscale']) plt.figure(figsize=(4,8)) pm.forestplot(collect_results['posterior_samples'], var_names=['beta'], credible_interval=0.95) pm.forestplot(collect_results['posterior_samples'], var_names=['beta_day'], credible_interval=0.95) #pm.forestplot(collect_results['0']['posterior_samples'], var_names=['alpha'], credible_interval=0.95) # %% filename = os.path.join(os.path.realpath(dir_picklejar), 'rjmcmc_models') outfile = open(filename, 'wb') pickle.dump(collect_results, outfile) outfile.close() # %% REsidual code for safekeeping # # Y_hat_latent = pm.Determinist(of Y_diff_latent) # # Y_observed = pm.Potential('Y_observed', selfreport_mem(Y_hat_latent)) ## Y_hat_observed is 'hours_since_start_day' ## Given hours_since_start_day, use smartdumbRJ.py to generate a new latent event times (Y_hat_latent) ## Given Y_hat_latent, take diff sequence and model as exponential holding times # loglamb_observed = beta # lamb_observed = np.exp(loglamb_observed) # # Define Y_hat_latent # # Take sequence of differences, Y_diff_latent # Y_diff_latent = pm.Exponential('Y_diff_latent', lam = lamb_observed)

40.522222

179

0.612832

import pymc3 as pm import arviz as az import pandas as pd import numpy as np from datetime import datetime from scipy import stats import os import pickle from scipy import special import theano.tensor as tt s.py').read()) dir_data = os.environ['dir_data'] dir_picklejar = os.environ['dir_picklejar']

true

7907f3583ea01d37420da656639526fb3fd56434

2,129

py

Python

src/probnum/filtsmooth/filtsmoothposterior.py

ralfrost/probnum

6b0988009a9dd7ecda87ba28c9d5c0b8019981b6

[ "MIT" ]

null

src/probnum/filtsmooth/filtsmoothposterior.py

ralfrost/probnum

6b0988009a9dd7ecda87ba28c9d5c0b8019981b6

[ "MIT" ]

2

2020-12-28T19:37:16.000Z

2020-12-28T19:37:31.000Z

src/probnum/filtsmooth/filtsmoothposterior.py

admdev8/probnum

792b6299bac247cf8b1b5056756f0f078855d83a

[ "MIT" ]

null

"""Abstract Base Class for posteriors over states after applying filtering/smoothing""" from abc import ABC, abstractmethod class FiltSmoothPosterior(ABC): """Posterior Distribution over States after Filtering/Smoothing""" @abstractmethod def __call__(self, location): """Evaluate the time-continuous posterior for a given location Parameters ---------- location : float Location, or time, at which to evaluate the posterior. Returns ------- rv : `RandomVariable` """ raise NotImplementedError @abstractmethod def __len__(self): """Length of the discrete-time solution Corresponds to the number of filtering/smoothing steps """ raise NotImplementedError @abstractmethod def __getitem__(self, idx): """Return the corresponding index/slice of the discrete-time solution""" raise NotImplementedError def sample(self, locations=None, size=()): """ Draw samples from the filtering/smoothing posterior. If nothing is specified, a single sample is drawn (supported on self.locations). If locations are specified, the samples are drawn on those locations. If size is specified, more than a single sample is drawn. Parameters ---------- locations : array_like, optional Locations on which the samples are wanted. Default is none, which implies that self.location is used. size : int or tuple of ints, optional Indicates how many samples are drawn. Default is an empty tuple, in which case a single sample is returned. Returns ------- numpy.ndarray Drawn samples. If size has shape (A1, ..., Z1), locations have shape (L,), and the state space model has shape (A2, ..., Z2), the output has shape (A1, ..., Z1, L, A2, ..., Z2). For example: size=4, len(locations)=4, dim=3 gives shape (4, 4, 3). """ raise NotImplementedError("Sampling not implemented.")

33.793651

90

0.620479

from abc import ABC, abstractmethod class FiltSmoothPosterior(ABC): @abstractmethod def __call__(self, location): raise NotImplementedError @abstractmethod def __len__(self): raise NotImplementedError @abstractmethod def __getitem__(self, idx): raise NotImplementedError def sample(self, locations=None, size=()): raise NotImplementedError("Sampling not implemented.")

true

7907f3922b8472ef13adfa2259c4e2a7cd6c0a0f

77

py

Python

src/main/python/exceptions/BluetoothException.py

jjoyce0510/autonomous-shipping-vessel

6757ecd77ad6ef422223413c57f60278b88b543b

[ "MIT" ]

1

2017-11-08T15:20:09.000Z

src/main/python/exceptions/BluetoothException.py

jjoyce0510/autonomous-shipping-vessel

6757ecd77ad6ef422223413c57f60278b88b543b

[ "MIT" ]

null

src/main/python/exceptions/BluetoothException.py

jjoyce0510/autonomous-shipping-vessel

6757ecd77ad6ef422223413c57f60278b88b543b

[ "MIT" ]

null

# Defines a bluetooth exception class BluetoothException(Exception): pass

25.666667

36

0.805195

class BluetoothException(Exception): pass

true

7907f44ecfc19c72ef0f9f60e37c7282e3451efc

8,091

py

Python

distributed/multi_lock.py

edyounis/distributed

bb091d5ec7d3ce4eb4a58e0957cba9cdf3da1d6a

[ "BSD-3-Clause" ]

null

distributed/multi_lock.py

edyounis/distributed

bb091d5ec7d3ce4eb4a58e0957cba9cdf3da1d6a

[ "BSD-3-Clause" ]

null

distributed/multi_lock.py

edyounis/distributed

bb091d5ec7d3ce4eb4a58e0957cba9cdf3da1d6a

[ "BSD-3-Clause" ]

null

from __future__ import annotations import asyncio import logging import uuid from collections import defaultdict from collections.abc import Hashable from dask.utils import parse_timedelta from distributed.client import Client from distributed.utils import TimeoutError, log_errors from distributed.worker import get_worker logger = logging.getLogger(__name__) class MultiLockExtension: """An extension for the scheduler to manage MultiLocks This adds the following routes to the scheduler * multi_lock_acquire * multi_lock_release The approach is to maintain `self.locks` that maps a lock (unique name given to `MultiLock(names=, ...)` at creation) to a list of users (instances of `MultiLock`) that "requests" the lock. Additionally, `self.requests` maps a user to its requested locks and `self.requests_left` maps a user to the number of locks still need. Every time a user `x` gets to the front in `self.locks[name] = [x, ...]` it means that `x` now holds the lock `name` and when it holds all the requested locks `acquire()` can return. Finally, `self.events` contains all the events users are waiting on to finish. """ def __init__(self, scheduler): self.scheduler = scheduler self.locks = defaultdict(list) # lock -> users self.requests = {} # user -> locks self.requests_left = {} # user -> locks still needed self.events = {} self.scheduler.handlers.update( {"multi_lock_acquire": self.acquire, "multi_lock_release": self.release} ) def _request_locks(self, locks: list[str], id: Hashable, num_locks: int) -> bool: """Request locks Parameters ---------- locks: List[str] Names of the locks to request. id: Hashable Identifier of the `MultiLock` instance requesting the locks. num_locks: int Number of locks in `locks` requesting Return ------ result: bool Whether `num_locks` requested locks are free immediately or not. """ assert id not in self.requests self.requests[id] = set(locks) assert len(locks) >= num_locks and num_locks > 0 self.requests_left[id] = num_locks locks = sorted(locks, key=lambda x: len(self.locks[x])) for i, lock in enumerate(locks): self.locks[lock].append(id) if len(self.locks[lock]) == 1: # The lock was free self.requests_left[id] -= 1 if self.requests_left[id] == 0: # Got all locks needed # Since we got all locks need, we can remove the rest of the requests self.requests[id] -= set(locks[i + 1 :]) return True return False def _refain_locks(self, locks, id): """Cancel/release previously requested/acquired locks Parameters ---------- locks: List[str] Names of the locks to refain. id: Hashable Identifier of the `MultiLock` instance refraining the locks. """ waiters_ready = set() for lock in locks: if self.locks[lock][0] == id: self.locks[lock].pop(0) if self.locks[lock]: new_first = self.locks[lock][0] self.requests_left[new_first] -= 1 if self.requests_left[new_first] <= 0: # Notice, `self.requests_left[new_first]` might go below zero # if more locks are freed than requested. self.requests_left[new_first] = 0 waiters_ready.add(new_first) else: self.locks[lock].remove(id) assert id not in self.locks[lock] del self.requests[id] del self.requests_left[id] for waiter in waiters_ready: self.scheduler.loop.add_callback(self.events[waiter].set) async def acquire(self, locks=None, id=None, timeout=None, num_locks=None): with log_errors(): if not self._request_locks(locks, id, num_locks): assert id not in self.events event = asyncio.Event() self.events[id] = event future = event.wait() if timeout is not None: future = asyncio.wait_for(future, timeout) try: await future except TimeoutError: self._refain_locks(locks, id) return False finally: del self.events[id] # At this point `id` acquired all `locks` assert self.requests_left[id] == 0 return True def release(self, id=None): with log_errors(): self._refain_locks(self.requests[id], id) class MultiLock: """Distributed Centralized Lock Parameters ---------- names: List[str] Names of the locks to acquire. Choosing the same name allows two disconnected processes to coordinate a lock. client: Client (optional) Client to use for communication with the scheduler. If not given, the default global client will be used. Examples -------- >>> lock = MultiLock(['x', 'y']) # doctest: +SKIP >>> lock.acquire(timeout=1) # doctest: +SKIP >>> # do things with protected resource 'x' and 'y' >>> lock.release() # doctest: +SKIP """ def __init__(self, names=[], client=None): try: self.client = client or Client.current() except ValueError: # Initialise new client self.client = get_worker().client self.names = names self.id = uuid.uuid4().hex self._locked = False def acquire(self, blocking=True, timeout=None, num_locks=None): """Acquire the lock Parameters ---------- blocking : bool, optional If false, don't wait on the lock in the scheduler at all. timeout : string or number or timedelta, optional Seconds to wait on the lock in the scheduler. This does not include local coroutine time, network transfer time, etc.. It is forbidden to specify a timeout when blocking is false. Instead of number of seconds, it is also possible to specify a timedelta in string format, e.g. "200ms". num_locks : int, optional Number of locks needed. If None, all locks are needed Examples -------- >>> lock = MultiLock(['x', 'y']) # doctest: +SKIP >>> lock.acquire(timeout="1s") # doctest: +SKIP Returns ------- True or False whether or not it successfully acquired the lock """ timeout = parse_timedelta(timeout) if not blocking: if timeout is not None: raise ValueError("can't specify a timeout for a non-blocking call") timeout = 0 result = self.client.sync( self.client.scheduler.multi_lock_acquire, locks=self.names, id=self.id, timeout=timeout, num_locks=num_locks or len(self.names), ) self._locked = True return result def release(self): """Release the lock if already acquired""" if not self.locked(): raise ValueError("Lock is not yet acquired") ret = self.client.sync(self.client.scheduler.multi_lock_release, id=self.id) self._locked = False return ret def locked(self): return self._locked def __enter__(self): self.acquire() return self def __exit__(self, *args, **kwargs): self.release() async def __aenter__(self): await self.acquire() return self async def __aexit__(self, *args, **kwargs): await self.release() def __reduce__(self): return (type(self), (self.names,))

33.995798

89

0.582252

from __future__ import annotations import asyncio import logging import uuid from collections import defaultdict from collections.abc import Hashable from dask.utils import parse_timedelta from distributed.client import Client from distributed.utils import TimeoutError, log_errors from distributed.worker import get_worker logger = logging.getLogger(__name__) class MultiLockExtension: def __init__(self, scheduler): self.scheduler = scheduler self.locks = defaultdict(list) self.requests = {} self.requests_left = {} self.events = {} self.scheduler.handlers.update( {"multi_lock_acquire": self.acquire, "multi_lock_release": self.release} ) def _request_locks(self, locks: list[str], id: Hashable, num_locks: int) -> bool: assert id not in self.requests self.requests[id] = set(locks) assert len(locks) >= num_locks and num_locks > 0 self.requests_left[id] = num_locks locks = sorted(locks, key=lambda x: len(self.locks[x])) for i, lock in enumerate(locks): self.locks[lock].append(id) if len(self.locks[lock]) == 1: self.requests_left[id] -= 1 if self.requests_left[id] == 0: self.requests[id] -= set(locks[i + 1 :]) return True return False def _refain_locks(self, locks, id): waiters_ready = set() for lock in locks: if self.locks[lock][0] == id: self.locks[lock].pop(0) if self.locks[lock]: new_first = self.locks[lock][0] self.requests_left[new_first] -= 1 if self.requests_left[new_first] <= 0: self.requests_left[new_first] = 0 waiters_ready.add(new_first) else: self.locks[lock].remove(id) assert id not in self.locks[lock] del self.requests[id] del self.requests_left[id] for waiter in waiters_ready: self.scheduler.loop.add_callback(self.events[waiter].set) async def acquire(self, locks=None, id=None, timeout=None, num_locks=None): with log_errors(): if not self._request_locks(locks, id, num_locks): assert id not in self.events event = asyncio.Event() self.events[id] = event future = event.wait() if timeout is not None: future = asyncio.wait_for(future, timeout) try: await future except TimeoutError: self._refain_locks(locks, id) return False finally: del self.events[id] assert self.requests_left[id] == 0 return True def release(self, id=None): with log_errors(): self._refain_locks(self.requests[id], id) class MultiLock: def __init__(self, names=[], client=None): try: self.client = client or Client.current() except ValueError: self.client = get_worker().client self.names = names self.id = uuid.uuid4().hex self._locked = False def acquire(self, blocking=True, timeout=None, num_locks=None): timeout = parse_timedelta(timeout) if not blocking: if timeout is not None: raise ValueError("can't specify a timeout for a non-blocking call") timeout = 0 result = self.client.sync( self.client.scheduler.multi_lock_acquire, locks=self.names, id=self.id, timeout=timeout, num_locks=num_locks or len(self.names), ) self._locked = True return result def release(self): if not self.locked(): raise ValueError("Lock is not yet acquired") ret = self.client.sync(self.client.scheduler.multi_lock_release, id=self.id) self._locked = False return ret def locked(self): return self._locked def __enter__(self): self.acquire() return self def __exit__(self, *args, **kwargs): self.release() async def __aenter__(self): await self.acquire() return self async def __aexit__(self, *args, **kwargs): await self.release() def __reduce__(self): return (type(self), (self.names,))

true

7907f4644ab1640bc7cc59c4b8f7b8282a59b140

755

py

Python

scripts/knn_voronoi_plot.py

Drishttii/pyprobml

30b120e7d4f81ade55c10250193d98398040574b

[ "MIT" ]

null

scripts/knn_voronoi_plot.py

Drishttii/pyprobml

30b120e7d4f81ade55c10250193d98398040574b

[ "MIT" ]

null

scripts/knn_voronoi_plot.py

Drishttii/pyprobml

30b120e7d4f81ade55c10250193d98398040574b

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt import pyprobml_utils as pml from scipy.spatial import KDTree, Voronoi, voronoi_plot_2d np.random.seed(42) data = np.random.rand(25, 2) vor = Voronoi(data) print('Using scipy.spatial.voronoi_plot_2d, wait...') voronoi_plot_2d(vor) xlim = plt.xlim() ylim = plt.ylim() pml.savefig('knnVoronoiMesh.pdf') plt.show() print('Using scipy.spatial.KDTree, wait a few seconds...') plt.figure() tree = KDTree(data) x = np.linspace(xlim[0], xlim[1], 200) y = np.linspace(ylim[0], ylim[1], 200) xx, yy = np.meshgrid(x, y) xy = np.c_[xx.ravel(), yy.ravel()] plt.plot(data[:, 0], data[:, 1], 'ko') plt.pcolormesh(x, y, tree.query(xy)[1].reshape(200, 200), cmap='jet') pml.savefig('knnVoronoiColor.pdf') plt.show()

25.166667

69

0.701987

import numpy as np import matplotlib.pyplot as plt import pyprobml_utils as pml from scipy.spatial import KDTree, Voronoi, voronoi_plot_2d np.random.seed(42) data = np.random.rand(25, 2) vor = Voronoi(data) print('Using scipy.spatial.voronoi_plot_2d, wait...') voronoi_plot_2d(vor) xlim = plt.xlim() ylim = plt.ylim() pml.savefig('knnVoronoiMesh.pdf') plt.show() print('Using scipy.spatial.KDTree, wait a few seconds...') plt.figure() tree = KDTree(data) x = np.linspace(xlim[0], xlim[1], 200) y = np.linspace(ylim[0], ylim[1], 200) xx, yy = np.meshgrid(x, y) xy = np.c_[xx.ravel(), yy.ravel()] plt.plot(data[:, 0], data[:, 1], 'ko') plt.pcolormesh(x, y, tree.query(xy)[1].reshape(200, 200), cmap='jet') pml.savefig('knnVoronoiColor.pdf') plt.show()

true

7907f7b28f08f68ba5e1078d40db835496770b86

21,749

py

Python

tcex/bin/validate.py

phuerta-tc/tcex

4a4e800e1a6114c1fde663f8c3ab7a1d58045c79

[ "Apache-2.0" ]

null

tcex/bin/validate.py

phuerta-tc/tcex

4a4e800e1a6114c1fde663f8c3ab7a1d58045c79

[ "Apache-2.0" ]

null

tcex/bin/validate.py

phuerta-tc/tcex

4a4e800e1a6114c1fde663f8c3ab7a1d58045c79

[ "Apache-2.0" ]

null

#!/usr/bin/env python """TcEx Framework Validate Module.""" # standard library import ast import importlib import json import os import sys import traceback from collections import deque from pathlib import Path from typing import Dict, Union # third-party import colorama as c # from jsonschema import SchemaError, ValidationError, validate from pydantic import ValidationError from stdlib_list import stdlib_list # first-party from tcex.app_config.install_json import InstallJson from tcex.app_config.job_json import JobJson from tcex.app_config.layout_json import LayoutJson from tcex.app_config.tcex_json import TcexJson from tcex.bin.bin_abc import BinABC try: # standard library import sqlite3 except ModuleNotFoundError: # this module is only required for certain CLI commands pass class Validate(BinABC): """Validate syntax, imports, and schemas. * Python and JSON file syntax * Python import modules * install.json schema * layout.json schema """ def __init__(self, ignore_validation: bool) -> None: """Initialize Class properties.""" super().__init__() self.ignore_validation = ignore_validation # class properties self._app_packages = [] self._install_json_schema = None self._layout_json_schema = None self.config = {} self.ij = InstallJson() self.invalid_json_files = [] self.lj = LayoutJson() self.tj = TcexJson() # initialize validation data self.validation_data = self._validation_data @property def _validation_data(self) -> Dict[str, list]: """Return structure for validation data.""" return { 'errors': [], 'fileSyntax': [], 'layouts': [], 'moduleImports': [], 'schema': [], 'feeds': [], } def _check_node_import(self, node: Union[ast.Import, ast.ImportFrom], filename: str) -> None: """.""" if isinstance(node, ast.Import): for n in node.names: m = n.name.split('.')[0] if not self.check_import_stdlib(m): m_status = self.check_imported(m) if not m_status: self.validation_data['errors'].append( f'Module validation failed for {filename} ' f'module "{m}" could not be imported).' ) self.validation_data['moduleImports'].append( {'filename': filename, 'module': m, 'status': m_status} ) elif isinstance(node, ast.ImportFrom): m = node.module.split('.')[0] if not self.check_import_stdlib(m): m_status = self.check_imported(m) if not m_status: self.validation_data['errors'].append( f'Module validation failed for {filename} ' f'module "{m}" could not be imported).' ) self.validation_data['moduleImports'].append( {'filename': filename, 'module': m, 'status': m_status} ) def check_imports(self) -> None: """Check the projects top level directory for missing imports. This method will check only files ending in **.py** and does not handle imports validation for sub-directories. """ for filename in sorted(os.listdir(self.app_path)): if not filename.endswith('.py'): continue fq_path = os.path.join(self.app_path, filename) with open(fq_path, 'rb') as f: # TODO: [low] is there a better way? code_lines = deque([(f.read(), 1)]) while code_lines: code, _ = code_lines.popleft() # pylint: disable=unused-variable try: parsed_code = ast.parse(code) for node in ast.walk(parsed_code): self._check_node_import(node, filename) except SyntaxError: pass @staticmethod def check_import_stdlib(module: str) -> bool: """Check if module is in Python stdlib. Args: module: The name of the module to check. Returns: bool: Returns True if the module is in the stdlib or template. """ if ( module in stdlib_list('3.6') or module in stdlib_list('3.7') or module in stdlib_list('3.8') or module in ['app', 'args', 'base_app_input', 'job_app', 'playbook_app', 'run', 'service_app'] ): return True return False @staticmethod def check_imported(module: str) -> bool: """Check whether the provide module can be imported (package installed). Args: module: The name of the module to check availability. Returns: bool: True if the module can be imported, False otherwise. """ try: del sys.modules[module] except (AttributeError, KeyError): pass # https://docs.python.org/3/library/importlib.html#checking-if-a-module-can-be-imported find_spec = importlib.util.find_spec(module) found = find_spec is not None if found is True: # if dist-packages|site-packages in module_path the import doesn't count try: if 'dist-packages' in find_spec.origin: found = False except TypeError: pass try: if 'site-packages' in find_spec.origin: found = False except TypeError: pass return found def check_install_json(self) -> None: """Check all install.json files for valid schema.""" if 'install.json' in self.invalid_json_files: return status = True try: self.ij.model except ValidationError as ex: self.invalid_json_files.append(self.ij.fqfn.name) status = False for error in json.loads(ex.json()): location = [str(location) for location in error.get('loc')] self.validation_data['errors'].append( '''Schema validation failed for install.json. ''' f'''{error.get('msg')}: {' -> '.join(location)}''' ) except ValueError: # any JSON decode error will be caught during syntax validation return self.validation_data['schema'].append({'filename': self.ij.fqfn.name, 'status': status}) def check_job_json(self) -> None: """Validate feed files for feed job apps.""" if 'install.json' in self.invalid_json_files: # can't proceed if install.json can't be read return # use developer defined app version (deprecated) or package_version from InstallJson model app_version = self.tj.model.package.app_version or self.ij.model.package_version program_name = (f'''{self.tj.model.package.app_name}_{app_version}''').replace('_', ' ') status = True for feed in self.ij.model.feeds: if feed.job_file in self.invalid_json_files: # no need to check if schema if json is invalid continue jj = JobJson(filename=feed.job_file) # validate the job file exists if not jj.fqfn.is_file(): self.validation_data['errors'].append( f'''Schema validation failed for {feed.job_file}. ''' f'''The job.json file could not be found.''' ) continue try: # validate the schema jj.model except ValidationError as ex: status = False for error in json.loads(ex.json()): location = [str(location) for location in error.get('loc')] self.validation_data['errors'].append( f'''Schema validation failed for {feed.job_file}. ''' f'''{error.get('msg')}: {' -> '.join(location)}''' ) # validate program name if status is True and jj.model.program_name != program_name: status = False self.validation_data['errors'].append( f'''Schema validation failed for {feed.job_file}. ''' f'''The job.json programName {jj.model.program_name} != {program_name}.''' ) # validate program version if status is True and jj.model.program_version != self.ij.model.program_version: status = False self.validation_data['errors'].append( f'''Schema validation failed for {feed.job_file}. The job.json program''' f'''Version {jj.model.program_version} != {self.ij.model.program_version}.''' ) self.validation_data['schema'].append({'filename': feed.job_file, 'status': status}) def check_layout_json(self) -> None: """Check all layout.json files for valid schema.""" if not self.lj.has_layout or 'layout.json' in self.invalid_json_files: return status = True try: self.lj.model except ValidationError as ex: self.invalid_json_files.append(self.ij.fqfn.name) status = False for error in json.loads(ex.json()): location = [str(location) for location in error.get('loc')] self.validation_data['errors'].append( f'''Schema validation failed for layout.json. ''' f'''{error.get('msg')}: {' -> '.join(location)}''' ) except ValueError: # any JSON decode error will be caught during syntax validation return self.validation_data['schema'].append({'filename': self.lj.fqfn.name, 'status': status}) if status is True: self.check_layout_params() def check_layout_params(self) -> None: """Check that the layout.json is consistent with install.json. The layout.json files references the params.name from the install.json file. The method will validate that no reference appear for inputs in install.json that don't exist. """ # do not track hidden or serviceConfig inputs as they should not be in layouts.json ij_input_names = list(self.ij.model.filter_params(service_config=False, hidden=False)) ij_output_names = [o.name for o in self.ij.model.playbook.output_variables] # Check for duplicate inputs for name in self.ij.validate.validate_duplicate_input(): self.validation_data['errors'].append( f'Duplicate input name found in install.json ({name})' ) status = False # Check for duplicate sequence numbers for sequence in self.ij.validate.validate_duplicate_sequence(): self.validation_data['errors'].append( f'Duplicate sequence number found in install.json ({sequence})' ) status = False # Check for duplicate outputs variables for output in self.ij.validate.validate_duplicate_output(): self.validation_data['errors'].append( f'Duplicate output variable name found in install.json ({output})' ) status = False if 'sqlite3' in sys.modules: # create temporary inputs tables self.permutations.db_create_table(self.permutations._input_table, ij_input_names) # inputs status = True for i in self.lj.model.inputs: for p in i.parameters: if p.name not in ij_input_names: # update validation data errors self.validation_data['errors'].append( 'Layouts input.parameters[].name validations failed ' f'''("{p.get('name')}" is defined in layout.json, ''' 'but hidden or not found in install.json).' ) status = False else: # any item in list afterwards is a problem ij_input_names.remove(p.name) if 'sqlite3' in sys.modules: if p.display: display_query = ( f'''SELECT * FROM {self.permutations._input_table}''' # nosec f''' WHERE {p.display}''' ) try: self.permutations.db_conn.execute(display_query.replace('"', '')) except sqlite3.Error: self.validation_data['errors'].append( '''Layouts input.parameters[].display validations failed ''' f'''("{p.display}" query is an invalid statement).''' ) status = False # update validation data for module self.validation_data['layouts'].append({'params': 'inputs', 'status': status}) if ij_input_names: input_names = ','.join(ij_input_names) # update validation data errors self.validation_data['errors'].append( f'Layouts input.parameters[].name validations failed ("{input_names}" ' 'values from install.json were not included in layout.json.' ) status = False # outputs status = True for o in self.lj.model.outputs: if o.name not in ij_output_names: # update validation data errors self.validation_data['errors'].append( f'''Layouts output validations failed ({o.name} is defined ''' '''in layout.json, but not found in install.json).''' ) status = False if 'sqlite3' in sys.modules: if o.display: display_query = ( f'''SELECT * FROM {self.permutations._input_table} ''' # nosec f'''WHERE {o.display}''' ) try: self.permutations.db_conn.execute(display_query.replace('"', '')) except sqlite3.Error: self.validation_data['errors'].append( f"""Layouts outputs.display validations failed ("{o.display}" """ f"""query is an invalid statement).""" ) status = False # update validation data for module self.validation_data['layouts'].append({'params': 'outputs', 'status': status}) def check_syntax(self, app_path=None) -> None: """Run syntax on each ".py" and ".json" file. Args: app_path (str, optional): The path of Python files. """ fqpn = Path(app_path or os.getcwd()) for fqfn in sorted(fqpn.iterdir()): error = None status = True if fqfn.name.endswith('.py'): try: with fqfn.open(mode='rb') as fh: ast.parse(fh.read(), filename=fqfn.name) except SyntaxError: status = False # cleanup output e = [] for line in traceback.format_exc().split('\n')[-5:-2]: e.append(line.strip()) error = ' '.join(e) elif fqfn.name.endswith('.json'): try: with fqfn.open() as fh: json.load(fh) except ValueError as e: # update tracker for common files self.invalid_json_files.append(fqfn.name) status = False error = e else: # skip unsupported file types continue if error: # update validation data errors self.validation_data['errors'].append( f'Syntax validation failed for {fqfn.name} ({error}).' ) # store status for this file self.validation_data['fileSyntax'].append({'filename': fqfn.name, 'status': status}) def interactive(self) -> None: """[App Builder] Run in interactive mode.""" while True: line = sys.stdin.readline().strip() if line == 'quit': sys.exit() elif line == 'validate': self.check_syntax() self.check_imports() self.check_install_json() self.check_layout_json() self.check_job_json() self.print_json() # reset validation_data self.validation_data = self._validation_data def print_json(self) -> None: """[App Builder] Print JSON output.""" print(json.dumps({'validation_data': self.validation_data})) # TODO: [low] switch to typer echo? def _print_file_syntax_results(self) -> None: """Print file syntax results.""" if self.validation_data.get('fileSyntax'): print(f'\n{c.Style.BRIGHT}{c.Fore.BLUE}Validated File Syntax:') print(f'''{c.Style.BRIGHT}{'File:'!s:<60}{'Status:'!s:<25}''') for f in self.validation_data.get('fileSyntax'): status_color = self.status_color(f.get('status')) status_value = self.status_value(f.get('status')) print(f"{f.get('filename')!s:<60}{status_color}{status_value!s:<25}") def _print_imports_results(self) -> None: """Print import results.""" if self.validation_data.get('moduleImports'): print(f'\n{c.Style.BRIGHT}{c.Fore.BLUE}Validated Imports:') print(f'''{c.Style.BRIGHT}{'File:'!s:<30}{'Module:'!s:<30}{'Status:'!s:<25}''') for f in self.validation_data.get('moduleImports'): status_color = self.status_color(f.get('status')) status_value = self.status_value(f.get('status')) print( f'''{f.get('filename')!s:<30}{c.Fore.WHITE}''' f'''{f.get('module')!s:<30}{status_color}{status_value!s:<25}''' ) def _print_schema_results(self) -> None: """Print schema results.""" if self.validation_data.get('schema'): print(f'\n{c.Style.BRIGHT}{c.Fore.BLUE}Validated Schema:') print(f'''{c.Style.BRIGHT}{'File:'!s:<60}{'Status:'!s:<25}''') for f in self.validation_data.get('schema'): status_color = self.status_color(f.get('status')) status_value = self.status_value(f.get('status')) print(f'''{f.get('filename')!s:<60}{status_color}{status_value!s:<25}''') def _print_layouts_results(self) -> None: """Print layout results.""" if self.validation_data.get('layouts'): print(f'\n{c.Style.BRIGHT}{c.Fore.BLUE}Validated Layouts:') print(f'''{c.Style.BRIGHT}{'Params:'!s:<60}{'Status:'!s:<25}''') for f in self.validation_data.get('layouts'): status_color = self.status_color(f.get('status')) status_value = self.status_value(f.get('status')) print(f"{f.get('params')!s:<60}{status_color}{status_value!s:<25}") def _print_feed_results(self) -> None: """Print feed results.""" if self.validation_data.get('feeds'): print(f'\n{c.Style.BRIGHT}{c.Fore.BLUE}Validated Feed Jobs:') print(f'''{c.Style.BRIGHT}{'Feeds:'!s:<60}{'Status:'!s:<25}''') for f in self.validation_data.get('feeds'): status_color = self.status_color(f.get('status')) status_value = self.status_value(f.get('status')) print(f"{f.get('name')!s:<60}{status_color}{status_value!s:<25}") def _print_errors(self) -> None: """Print errors results.""" if self.validation_data.get('errors'): print('\n') # separate errors from normal output for error in self.validation_data.get('errors'): # print all errors print(f'* {c.Fore.RED}{error}') # ignore exit code if not self.ignore_validation: self.exit_code = 1 def print_results(self) -> None: """Print results.""" # Validating Syntax self._print_file_syntax_results() # Validating Imports self._print_imports_results() # Validating Schema self._print_schema_results() # Validating Layouts self._print_layouts_results() # Validating Feed Job Definition Files self._print_feed_results() self._print_errors() @staticmethod def status_color(status) -> str: """Return the appropriate status color.""" return c.Fore.GREEN if status else c.Fore.RED @staticmethod def status_value(status) -> str: """Return the appropriate status color.""" return 'passed' if status else 'failed'

39.400362

98

0.542186

import ast import importlib import json import os import sys import traceback from collections import deque from pathlib import Path from typing import Dict, Union import colorama as c from pydantic import ValidationError from stdlib_list import stdlib_list from tcex.app_config.install_json import InstallJson from tcex.app_config.job_json import JobJson from tcex.app_config.layout_json import LayoutJson from tcex.app_config.tcex_json import TcexJson from tcex.bin.bin_abc import BinABC try: import sqlite3 except ModuleNotFoundError: pass class Validate(BinABC): def __init__(self, ignore_validation: bool) -> None: super().__init__() self.ignore_validation = ignore_validation self._app_packages = [] self._install_json_schema = None self._layout_json_schema = None self.config = {} self.ij = InstallJson() self.invalid_json_files = [] self.lj = LayoutJson() self.tj = TcexJson() self.validation_data = self._validation_data @property def _validation_data(self) -> Dict[str, list]: return { 'errors': [], 'fileSyntax': [], 'layouts': [], 'moduleImports': [], 'schema': [], 'feeds': [], } def _check_node_import(self, node: Union[ast.Import, ast.ImportFrom], filename: str) -> None: if isinstance(node, ast.Import): for n in node.names: m = n.name.split('.')[0] if not self.check_import_stdlib(m): m_status = self.check_imported(m) if not m_status: self.validation_data['errors'].append( f'Module validation failed for {filename} ' f'module "{m}" could not be imported).' ) self.validation_data['moduleImports'].append( {'filename': filename, 'module': m, 'status': m_status} ) elif isinstance(node, ast.ImportFrom): m = node.module.split('.')[0] if not self.check_import_stdlib(m): m_status = self.check_imported(m) if not m_status: self.validation_data['errors'].append( f'Module validation failed for {filename} ' f'module "{m}" could not be imported).' ) self.validation_data['moduleImports'].append( {'filename': filename, 'module': m, 'status': m_status} ) def check_imports(self) -> None: for filename in sorted(os.listdir(self.app_path)): if not filename.endswith('.py'): continue fq_path = os.path.join(self.app_path, filename) with open(fq_path, 'rb') as f: code_lines = deque([(f.read(), 1)]) while code_lines: code, _ = code_lines.popleft() try: parsed_code = ast.parse(code) for node in ast.walk(parsed_code): self._check_node_import(node, filename) except SyntaxError: pass @staticmethod def check_import_stdlib(module: str) -> bool: if ( module in stdlib_list('3.6') or module in stdlib_list('3.7') or module in stdlib_list('3.8') or module in ['app', 'args', 'base_app_input', 'job_app', 'playbook_app', 'run', 'service_app'] ): return True return False @staticmethod def check_imported(module: str) -> bool: try: del sys.modules[module] except (AttributeError, KeyError): pass ind_spec(module) found = find_spec is not None if found is True: try: if 'dist-packages' in find_spec.origin: found = False except TypeError: pass try: if 'site-packages' in find_spec.origin: found = False except TypeError: pass return found def check_install_json(self) -> None: if 'install.json' in self.invalid_json_files: return status = True try: self.ij.model except ValidationError as ex: self.invalid_json_files.append(self.ij.fqfn.name) status = False for error in json.loads(ex.json()): location = [str(location) for location in error.get('loc')] self.validation_data['errors'].append( '''Schema validation failed for install.json. ''' f'''{error.get('msg')}: {' -> '.join(location)}''' ) except ValueError: # any JSON decode error will be caught during syntax validation return self.validation_data['schema'].append({'filename': self.ij.fqfn.name, 'status': status}) def check_job_json(self) -> None: if 'install.json' in self.invalid_json_files: # can't proceed if install.json can't be read return # use developer defined app version (deprecated) or package_version from InstallJson model app_version = self.tj.model.package.app_version or self.ij.model.package_version program_name = (f'''{self.tj.model.package.app_name}_{app_version}''').replace('_', ' ') status = True for feed in self.ij.model.feeds: if feed.job_file in self.invalid_json_files: # no need to check if schema if json is invalid continue jj = JobJson(filename=feed.job_file) # validate the job file exists if not jj.fqfn.is_file(): self.validation_data['errors'].append( f'''Schema validation failed for {feed.job_file}. ''' f'''The job.json file could not be found.''' ) continue try: # validate the schema jj.model except ValidationError as ex: status = False for error in json.loads(ex.json()): location = [str(location) for location in error.get('loc')] self.validation_data['errors'].append( f'''Schema validation failed for {feed.job_file}. ''' f'''{error.get('msg')}: {' -> '.join(location)}''' ) # validate program name if status is True and jj.model.program_name != program_name: status = False self.validation_data['errors'].append( f'''Schema validation failed for {feed.job_file}. ''' f'''The job.json programName {jj.model.program_name} != {program_name}.''' ) # validate program version if status is True and jj.model.program_version != self.ij.model.program_version: status = False self.validation_data['errors'].append( f'''Schema validation failed for {feed.job_file}. The job.json program''' f'''Version {jj.model.program_version} != {self.ij.model.program_version}.''' ) self.validation_data['schema'].append({'filename': feed.job_file, 'status': status}) def check_layout_json(self) -> None: if not self.lj.has_layout or 'layout.json' in self.invalid_json_files: return status = True try: self.lj.model except ValidationError as ex: self.invalid_json_files.append(self.ij.fqfn.name) status = False for error in json.loads(ex.json()): location = [str(location) for location in error.get('loc')] self.validation_data['errors'].append( f'''Schema validation failed for layout.json. ''' f'''{error.get('msg')}: {' -> '.join(location)}''' ) except ValueError: # any JSON decode error will be caught during syntax validation return self.validation_data['schema'].append({'filename': self.lj.fqfn.name, 'status': status}) if status is True: self.check_layout_params() def check_layout_params(self) -> None: # do not track hidden or serviceConfig inputs as they should not be in layouts.json ij_input_names = list(self.ij.model.filter_params(service_config=False, hidden=False)) ij_output_names = [o.name for o in self.ij.model.playbook.output_variables] # Check for duplicate inputs for name in self.ij.validate.validate_duplicate_input(): self.validation_data['errors'].append( f'Duplicate input name found in install.json ({name})' ) status = False # Check for duplicate sequence numbers for sequence in self.ij.validate.validate_duplicate_sequence(): self.validation_data['errors'].append( f'Duplicate sequence number found in install.json ({sequence})' ) status = False # Check for duplicate outputs variables for output in self.ij.validate.validate_duplicate_output(): self.validation_data['errors'].append( f'Duplicate output variable name found in install.json ({output})' ) status = False if 'sqlite3' in sys.modules: # create temporary inputs tables self.permutations.db_create_table(self.permutations._input_table, ij_input_names) # inputs status = True for i in self.lj.model.inputs: for p in i.parameters: if p.name not in ij_input_names: # update validation data errors self.validation_data['errors'].append( 'Layouts input.parameters[].name validations failed ' f'''("{p.get('name')}" is defined in layout.json, ''' 'but hidden or not found in install.json).' ) status = False else: # any item in list afterwards is a problem ij_input_names.remove(p.name) if 'sqlite3' in sys.modules: if p.display: display_query = ( f'''SELECT * FROM {self.permutations._input_table}''' # nosec f''' WHERE {p.display}''' ) try: self.permutations.db_conn.execute(display_query.replace('"', '')) except sqlite3.Error: self.validation_data['errors'].append( '''Layouts input.parameters[].display validations failed ''' f'''("{p.display}" query is an invalid statement).''' ) status = False # update validation data for module self.validation_data['layouts'].append({'params': 'inputs', 'status': status}) if ij_input_names: input_names = ','.join(ij_input_names) # update validation data errors self.validation_data['errors'].append( f'Layouts input.parameters[].name validations failed ("{input_names}" ' 'values from install.json were not included in layout.json.' ) status = False # outputs status = True for o in self.lj.model.outputs: if o.name not in ij_output_names: # update validation data errors self.validation_data['errors'].append( f'''Layouts output validations failed ({o.name} is defined ''' '''in layout.json, but not found in install.json).''' ) status = False if 'sqlite3' in sys.modules: if o.display: display_query = ( f'''SELECT * FROM {self.permutations._input_table} ''' # nosec f'''WHERE {o.display}''' ) try: self.permutations.db_conn.execute(display_query.replace('"', '')) except sqlite3.Error: self.validation_data['errors'].append( f"""Layouts outputs.display validations failed ("{o.display}" """ f"""query is an invalid statement).""" ) status = False # update validation data for module self.validation_data['layouts'].append({'params': 'outputs', 'status': status}) def check_syntax(self, app_path=None) -> None: fqpn = Path(app_path or os.getcwd()) for fqfn in sorted(fqpn.iterdir()): error = None status = True if fqfn.name.endswith('.py'): try: with fqfn.open(mode='rb') as fh: ast.parse(fh.read(), filename=fqfn.name) except SyntaxError: status = False # cleanup output e = [] for line in traceback.format_exc().split('\n')[-5:-2]: e.append(line.strip()) error = ' '.join(e) elif fqfn.name.endswith('.json'): try: with fqfn.open() as fh: json.load(fh) except ValueError as e: # update tracker for common files self.invalid_json_files.append(fqfn.name) status = False error = e else: # skip unsupported file types continue if error: # update validation data errors self.validation_data['errors'].append( f'Syntax validation failed for {fqfn.name} ({error}).' ) # store status for this file self.validation_data['fileSyntax'].append({'filename': fqfn.name, 'status': status}) def interactive(self) -> None: while True: line = sys.stdin.readline().strip() if line == 'quit': sys.exit() elif line == 'validate': self.check_syntax() self.check_imports() self.check_install_json() self.check_layout_json() self.check_job_json() self.print_json() # reset validation_data self.validation_data = self._validation_data def print_json(self) -> None: print(json.dumps({'validation_data': self.validation_data})) # TODO: [low] switch to typer echo? def _print_file_syntax_results(self) -> None: if self.validation_data.get('fileSyntax'): print(f'\n{c.Style.BRIGHT}{c.Fore.BLUE}Validated File Syntax:') print(f'''{c.Style.BRIGHT}{'File:'!s:<60}{'Status:'!s:<25}''') for f in self.validation_data.get('fileSyntax'): status_color = self.status_color(f.get('status')) status_value = self.status_value(f.get('status')) print(f"{f.get('filename')!s:<60}{status_color}{status_value!s:<25}") def _print_imports_results(self) -> None: if self.validation_data.get('moduleImports'): print(f'\n{c.Style.BRIGHT}{c.Fore.BLUE}Validated Imports:') print(f'''{c.Style.BRIGHT}{'File:'!s:<30}{'Module:'!s:<30}{'Status:'!s:<25}''') for f in self.validation_data.get('moduleImports'): status_color = self.status_color(f.get('status')) status_value = self.status_value(f.get('status')) print( f'''{f.get('filename')!s:<30}{c.Fore.WHITE}''' f'''{f.get('module')!s:<30}{status_color}{status_value!s:<25}''' ) def _print_schema_results(self) -> None: if self.validation_data.get('schema'): print(f'\n{c.Style.BRIGHT}{c.Fore.BLUE}Validated Schema:') print(f'''{c.Style.BRIGHT}{'File:'!s:<60}{'Status:'!s:<25}''') for f in self.validation_data.get('schema'): status_color = self.status_color(f.get('status')) status_value = self.status_value(f.get('status')) print(f'''{f.get('filename')!s:<60}{status_color}{status_value!s:<25}''') def _print_layouts_results(self) -> None: if self.validation_data.get('layouts'): print(f'\n{c.Style.BRIGHT}{c.Fore.BLUE}Validated Layouts:') print(f'''{c.Style.BRIGHT}{'Params:'!s:<60}{'Status:'!s:<25}''') for f in self.validation_data.get('layouts'): status_color = self.status_color(f.get('status')) status_value = self.status_value(f.get('status')) print(f"{f.get('params')!s:<60}{status_color}{status_value!s:<25}") def _print_feed_results(self) -> None: if self.validation_data.get('feeds'): print(f'\n{c.Style.BRIGHT}{c.Fore.BLUE}Validated Feed Jobs:') print(f'''{c.Style.BRIGHT}{'Feeds:'!s:<60}{'Status:'!s:<25}''') for f in self.validation_data.get('feeds'): status_color = self.status_color(f.get('status')) status_value = self.status_value(f.get('status')) print(f"{f.get('name')!s:<60}{status_color}{status_value!s:<25}") def _print_errors(self) -> None: if self.validation_data.get('errors'): print('\n') # separate errors from normal output for error in self.validation_data.get('errors'): # print all errors print(f'* {c.Fore.RED}{error}') # ignore exit code if not self.ignore_validation: self.exit_code = 1 def print_results(self) -> None: # Validating Syntax self._print_file_syntax_results() # Validating Imports self._print_imports_results() # Validating Schema self._print_schema_results() # Validating Layouts self._print_layouts_results() # Validating Feed Job Definition Files self._print_feed_results() self._print_errors() @staticmethod def status_color(status) -> str: return c.Fore.GREEN if status else c.Fore.RED @staticmethod def status_value(status) -> str: return 'passed' if status else 'failed'

true

7907f7bfa4aa7da93fcd44748d4064e05c159089

4,298

py

Python

tests/functional/test_objects_issues.py

AKhodus/adcm

98dbf22af3f1c6afa94505e9acaff0ac4088a602

[ "Apache-2.0" ]

null

tests/functional/test_objects_issues.py

AKhodus/adcm

98dbf22af3f1c6afa94505e9acaff0ac4088a602

[ "Apache-2.0" ]

null

tests/functional/test_objects_issues.py

AKhodus/adcm

98dbf22af3f1c6afa94505e9acaff0ac4088a602

[ "Apache-2.0" ]

null

# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import allure import coreapi import pytest from adcm_client.base import ActionHasIssues from adcm_client.objects import ADCMClient from adcm_pytest_plugin import utils from tests.library.errorcodes import UPGRADE_ERROR def test_action_should_not_be_run_while_cluster_has_an_issue(sdk_client_fs: ADCMClient): bundle_path = utils.get_data_dir(__file__, "cluster") bundle = sdk_client_fs.upload_from_fs(bundle_path) cluster = bundle.cluster_create(name=utils.random_string()) with allure.step(f"Run action with error for cluster {cluster.name}"): with pytest.raises(ActionHasIssues): cluster.action(name="install").run() def test_action_should_not_be_run_while_host_has_an_issue(sdk_client_fs: ADCMClient): bundle_path = utils.get_data_dir(__file__, "host") bundle = sdk_client_fs.upload_from_fs(bundle_path) provider = bundle.provider_create(name=utils.random_string()) host = provider.host_create(fqdn=utils.random_string()) with allure.step(f"Run action with error for host {host.fqdn}"): with pytest.raises(ActionHasIssues): host.action(name="install").run() def test_action_should_not_be_run_while_hostprovider_has_an_issue( sdk_client_fs: ADCMClient, ): bundle_path = utils.get_data_dir(__file__, "provider") bundle = sdk_client_fs.upload_from_fs(bundle_path) provider = bundle.provider_create(name=utils.random_string()) with allure.step(f"Run action with error for provider {provider.name}"): with pytest.raises(ActionHasIssues): provider.action(name="install").run() def test_when_cluster_has_issue_than_upgrade_locked(sdk_client_fs: ADCMClient): with allure.step("Create cluster and upload new one bundle"): old_bundle_path = utils.get_data_dir(__file__, "cluster") new_bundle_path = utils.get_data_dir(__file__, "upgrade", "cluster") old_bundle = sdk_client_fs.upload_from_fs(old_bundle_path) cluster = old_bundle.cluster_create(name=utils.random_string()) sdk_client_fs.upload_from_fs(new_bundle_path) with allure.step("Upgrade cluster"): with pytest.raises(coreapi.exceptions.ErrorMessage) as e: cluster.upgrade().do() with allure.step("Check if cluster has issues"): UPGRADE_ERROR.equal(e, "cluster ", " has issue: ") def test_when_hostprovider_has_issue_than_upgrade_locked(sdk_client_fs: ADCMClient): with allure.step("Create hostprovider"): old_bundle_path = utils.get_data_dir(__file__, "provider") new_bundle_path = utils.get_data_dir(__file__, "upgrade", "provider") old_bundle = sdk_client_fs.upload_from_fs(old_bundle_path) provider = old_bundle.provider_create(name=utils.random_string()) sdk_client_fs.upload_from_fs(new_bundle_path) with allure.step("Upgrade provider"): with pytest.raises(coreapi.exceptions.ErrorMessage) as e: provider.upgrade().do() with allure.step("Check if upgrade locked"): UPGRADE_ERROR.equal(e) @allure.link("https://jira.arenadata.io/browse/ADCM-487") def test_when_component_has_no_constraint_then_cluster_doesnt_have_issues( sdk_client_fs: ADCMClient, ): with allure.step("Create cluster (component has no constraint)"): bundle_path = utils.get_data_dir(__file__, "cluster_component_hasnt_constraint") bundle = sdk_client_fs.upload_from_fs(bundle_path) cluster = bundle.cluster_create(name=utils.random_string()) cluster.service_add() with allure.step("Run action: lock cluster"): cluster.action(name="lock-cluster").run().try_wait() with allure.step("Check if state is always-locked"): cluster.reread() assert cluster.state == "always-locked"

45.242105

88

0.746626

import allure import coreapi import pytest from adcm_client.base import ActionHasIssues from adcm_client.objects import ADCMClient from adcm_pytest_plugin import utils from tests.library.errorcodes import UPGRADE_ERROR def test_action_should_not_be_run_while_cluster_has_an_issue(sdk_client_fs: ADCMClient): bundle_path = utils.get_data_dir(__file__, "cluster") bundle = sdk_client_fs.upload_from_fs(bundle_path) cluster = bundle.cluster_create(name=utils.random_string()) with allure.step(f"Run action with error for cluster {cluster.name}"): with pytest.raises(ActionHasIssues): cluster.action(name="install").run() def test_action_should_not_be_run_while_host_has_an_issue(sdk_client_fs: ADCMClient): bundle_path = utils.get_data_dir(__file__, "host") bundle = sdk_client_fs.upload_from_fs(bundle_path) provider = bundle.provider_create(name=utils.random_string()) host = provider.host_create(fqdn=utils.random_string()) with allure.step(f"Run action with error for host {host.fqdn}"): with pytest.raises(ActionHasIssues): host.action(name="install").run() def test_action_should_not_be_run_while_hostprovider_has_an_issue( sdk_client_fs: ADCMClient, ): bundle_path = utils.get_data_dir(__file__, "provider") bundle = sdk_client_fs.upload_from_fs(bundle_path) provider = bundle.provider_create(name=utils.random_string()) with allure.step(f"Run action with error for provider {provider.name}"): with pytest.raises(ActionHasIssues): provider.action(name="install").run() def test_when_cluster_has_issue_than_upgrade_locked(sdk_client_fs: ADCMClient): with allure.step("Create cluster and upload new one bundle"): old_bundle_path = utils.get_data_dir(__file__, "cluster") new_bundle_path = utils.get_data_dir(__file__, "upgrade", "cluster") old_bundle = sdk_client_fs.upload_from_fs(old_bundle_path) cluster = old_bundle.cluster_create(name=utils.random_string()) sdk_client_fs.upload_from_fs(new_bundle_path) with allure.step("Upgrade cluster"): with pytest.raises(coreapi.exceptions.ErrorMessage) as e: cluster.upgrade().do() with allure.step("Check if cluster has issues"): UPGRADE_ERROR.equal(e, "cluster ", " has issue: ") def test_when_hostprovider_has_issue_than_upgrade_locked(sdk_client_fs: ADCMClient): with allure.step("Create hostprovider"): old_bundle_path = utils.get_data_dir(__file__, "provider") new_bundle_path = utils.get_data_dir(__file__, "upgrade", "provider") old_bundle = sdk_client_fs.upload_from_fs(old_bundle_path) provider = old_bundle.provider_create(name=utils.random_string()) sdk_client_fs.upload_from_fs(new_bundle_path) with allure.step("Upgrade provider"): with pytest.raises(coreapi.exceptions.ErrorMessage) as e: provider.upgrade().do() with allure.step("Check if upgrade locked"): UPGRADE_ERROR.equal(e) @allure.link("https://jira.arenadata.io/browse/ADCM-487") def test_when_component_has_no_constraint_then_cluster_doesnt_have_issues( sdk_client_fs: ADCMClient, ): with allure.step("Create cluster (component has no constraint)"): bundle_path = utils.get_data_dir(__file__, "cluster_component_hasnt_constraint") bundle = sdk_client_fs.upload_from_fs(bundle_path) cluster = bundle.cluster_create(name=utils.random_string()) cluster.service_add() with allure.step("Run action: lock cluster"): cluster.action(name="lock-cluster").run().try_wait() with allure.step("Check if state is always-locked"): cluster.reread() assert cluster.state == "always-locked"

true

7907f80ed26ea8ab1b224f9116d262ea3518a9ec

6,875

py

Python

exampledoc/docs/Extractor.py

sofiapasquini/Code-Astro-Group-23-Project

97dcbaf1b04822d56582e51332666dc5045e1154

[ "MIT" ]

null

exampledoc/docs/Extractor.py

sofiapasquini/Code-Astro-Group-23-Project

97dcbaf1b04822d56582e51332666dc5045e1154

[ "MIT" ]

null

exampledoc/docs/Extractor.py

sofiapasquini/Code-Astro-Group-23-Project

97dcbaf1b04822d56582e51332666dc5045e1154

[ "MIT" ]

null

#define functions that will extract the data from SDSS based on an input RA/DEC from astroquery.sdss import SDSS from astropy import coordinates as coords import pandas as pd from astroquery.ned import Ned import matplotlib.pyplot as plt from astropy.convolution import convolve, Box1DKernel import numpy as np from astropy import units as u def ra_dec_format(val): """ Ra/Dec string formatting Converts the input string format of a right ascension/ declination coordinate to one recognizable by astroquery Args: val (str): string; an ra/dec expression formatted as "005313.81 +130955.0". Returns: string: the ra/dec coordinates re-formatted as "00h53m13.81s +13d09m55.0s" """ #ra hour = val[0:2] min_ = val[2:4] sec = val[4:9] ra = hour+'h'+min_+'m'+sec+'s' #dec deg = val[9:13] min_d = val[13:15] sec_d = val[15:] dec = deg+'d'+min_d+'m'+sec_d+'s' return ra+" "+dec def extractor(position): """ This function extracts the information from the SDSS database and returns a pandas dataframe with the query region. Please ensure that the 'position' input is formatted as '005313.81 +130955.0 extractor(str) --> pd.DataFrame """ # convert the input position argument to the format recognized by astroquery.SDSS # position=ra_dec_format(position) # query the region and get the data position = ra_dec_format(position) pos = coords.SkyCoord(position, frame='icrs') data = SDSS.query_region(pos, spectro=True) return data.to_pandas() def downloader(data): """ This function uses extracted information in order to dwonaload spectra, separating the data from th SDSS and BOSS. downloader(pd.Dataframe) --> [list(fits)] """ #create a empty list spec_list=[] # iteration over the pandas for i in range(len(data)): results = SDSS.query_specobj(plate = data['plate'][i], mjd = data['mjd'][i], fiberID = data['fiberID'][i]) # try if it can download the data (SDSS) try: spec = SDSS.get_spectra(matches=results)[0] spec_list.append(spec) # if it cant download, is because is from (BOSS) except: results.remove_column("instrument") results.add_column(name="instrument", col="eboss") # replace the instrument column spec = SDSS.get_spectra(matches=results)[0] spec_list.append(spec) return spec_list # test=downloader(result) # print(test) # define a function which grabs the object's redshift from the Ned database (better calibration)- needed for plotting in the object's rest-frame def redshift(position): # make sure to format the input position argument such that it is recognizable by astroquery.Ned # position=ra_dec_format(position) position = ra_dec_format(position) pos=coords.SkyCoord(position, frame='icrs') # create a position object ned_results=Ned.query_region(pos,equinox="J2000", radius=2*u.arcsecond) # query the database z=ned_results[0][6] # grab the redshift value from the query results return z # define a function that transforms an objects wavelength array into the object's rest-frame def redshift_correct(z, wavelengths): # takes as input the redshift and the array of wavelengths wavelengths_corrected = wavelengths/(z+1) return wavelengths_corrected # define a function that transforms the results of downloader() into an array of data which will be plotted def transform_data(spec_list, z): # takes as input a list of (I think?) fits files results and the redshift of the object # iterate over each file and grab the important data #fluxes={} # containers for each of the data arrays to be plotted ( will be lists of lists/arrays) #wavelengths={} #inverse_variances={} # <- dictionaries! dict={} for spec in spec_list: flux_array=[] wavelength_array=[] sigma_array=[] data=spec[1].data # this is the data part of the file #print(data.shape[0]) #print(data) # store the appropriate columns in the designated containers- each row is a single spectrum? # SOFIA- try a nested dictionary?!?! for j in range(data.shape[0]): #print(data[j][0]) #smoothedFlux=convolve(data[0],Box1DKernel(9)) # smooth the fluxes using a boxcar #print(smoothedFlux) flux_data = data[j][0] flux_array.append(flux_data) wavelengths_uncorrected=10**data[j][1] # the wavelengths (transformed from the log scale) #print(wavelengths_uncorrected) wavelengths_corrected=redshift_correct(z, wavelengths_uncorrected) # save the wavelengths after they have been scaled to the rest-frame #print(wavelengths_corrected) wavelength_array.append(wavelengths_corrected) inverse_variance=data[j][2] # the inverse variance of the flux one_over_sigma=inverse_variance**0.5 sigma=1/one_over_sigma # the one-sigma uncertainty associated with the flux array sigma_array.append(sigma) smoothedFlux = convolve(flux_array,Box1DKernel(9)) if 'flux' in dict: dict['flux'].append([smoothedFlux]) else: dict['flux'] = [smoothedFlux] if 'wavelength' in dict: dict['wavelength'].append([wavelength_array]) else: dict['wavelength'] = [wavelength_array] if '1sigma' in dict: dict['1sigma'].append([sigma_array]) else: dict['1sigma'] = [sigma_array] # now return the nested dictionary with three keys:(flux, wavelength and sigma) # each key should have data.shape[0] number of arrays with all fluxes, wavelength and sigmas for every spec in spec_list return dict def plot_spec(dict, radec, z): # takes as input the dictionary holding the data, the radec, and the redshift for i in range(len(dict['wavelength'])): #extract data wavelength = dict['wavelength'][i] sigma = dict['1sigma'][i] flux = dict['flux'][i] # instantiate a figure object fig=plt.figure() plt.title(str(radec)+str('; ')+'z={}'.format(z)) plt.xlabel("Rest-frame Wavelength [$\AA$]") plt.ylabel("Flux [$10^{-17}$ erg$^{-1}$s$^{-1}$cm$^{-2}$$\AA^{-1}$]") plt.plot(wavelength, flux) # plot the actual data # now create upper and lower bounds on the uncertainty regions sigmaUpper=np.add(flux,sigma) sigmaLower=np.subtract(flux,sigma) plt.fill_between(wavelength, sigmaLower, sigmaUpper, color='grey', alpha=0.5) plt.show() #TEST radec='223812.39 +213203.4' z=redshift(radec) data=extractor(radec) spec_list=downloader(data) dic = transform_data(spec_list,z) plot_spec(dic, radec, z)

34.722222

147

0.666764

from astroquery.sdss import SDSS from astropy import coordinates as coords import pandas as pd from astroquery.ned import Ned import matplotlib.pyplot as plt from astropy.convolution import convolve, Box1DKernel import numpy as np from astropy import units as u def ra_dec_format(val): hour = val[0:2] min_ = val[2:4] sec = val[4:9] ra = hour+'h'+min_+'m'+sec+'s' deg = val[9:13] min_d = val[13:15] sec_d = val[15:] dec = deg+'d'+min_d+'m'+sec_d+'s' return ra+" "+dec def extractor(position): position = ra_dec_format(position) pos = coords.SkyCoord(position, frame='icrs') data = SDSS.query_region(pos, spectro=True) return data.to_pandas() def downloader(data): spec_list=[] for i in range(len(data)): results = SDSS.query_specobj(plate = data['plate'][i], mjd = data['mjd'][i], fiberID = data['fiberID'][i]) try: spec = SDSS.get_spectra(matches=results)[0] spec_list.append(spec) except: results.remove_column("instrument") results.add_column(name="instrument", col="eboss") spec = SDSS.get_spectra(matches=results)[0] spec_list.append(spec) return spec_list def redshift(position): position = ra_dec_format(position) pos=coords.SkyCoord(position, frame='icrs') ned_results=Ned.query_region(pos,equinox="J2000", radius=2*u.arcsecond) z=ned_results[0][6] return z def redshift_correct(z, wavelengths): # takes as input the redshift and the array of wavelengths wavelengths_corrected = wavelengths/(z+1) return wavelengths_corrected # define a function that transforms the results of downloader() into an array of data which will be plotted def transform_data(spec_list, z): # takes as input a list of (I think?) fits files results and the redshift of the object # iterate over each file and grab the important data #fluxes={} # containers for each of the data arrays to be plotted ( will be lists of lists/arrays) #wavelengths={} #inverse_variances={} # <- dictionaries! dict={} for spec in spec_list: flux_array=[] wavelength_array=[] sigma_array=[] data=spec[1].data # this is the data part of the file #print(data.shape[0]) #print(data) # store the appropriate columns in the designated containers- each row is a single spectrum? # SOFIA- try a nested dictionary?!?! for j in range(data.shape[0]): #print(data[j][0]) #smoothedFlux=convolve(data[0],Box1DKernel(9)) # smooth the fluxes using a boxcar #print(smoothedFlux) flux_data = data[j][0] flux_array.append(flux_data) wavelengths_uncorrected=10**data[j][1] # the wavelengths (transformed from the log scale) #print(wavelengths_uncorrected) wavelengths_corrected=redshift_correct(z, wavelengths_uncorrected) # save the wavelengths after they have been scaled to the rest-frame #print(wavelengths_corrected) wavelength_array.append(wavelengths_corrected) inverse_variance=data[j][2] # the inverse variance of the flux one_over_sigma=inverse_variance**0.5 sigma=1/one_over_sigma # the one-sigma uncertainty associated with the flux array sigma_array.append(sigma) smoothedFlux = convolve(flux_array,Box1DKernel(9)) if 'flux' in dict: dict['flux'].append([smoothedFlux]) else: dict['flux'] = [smoothedFlux] if 'wavelength' in dict: dict['wavelength'].append([wavelength_array]) else: dict['wavelength'] = [wavelength_array] if '1sigma' in dict: dict['1sigma'].append([sigma_array]) else: dict['1sigma'] = [sigma_array] # now return the nested dictionary with three keys:(flux, wavelength and sigma) # each key should have data.shape[0] number of arrays with all fluxes, wavelength and sigmas for every spec in spec_list return dict def plot_spec(dict, radec, z): # takes as input the dictionary holding the data, the radec, and the redshift for i in range(len(dict['wavelength'])): #extract data wavelength = dict['wavelength'][i] sigma = dict['1sigma'][i] flux = dict['flux'][i] # instantiate a figure object fig=plt.figure() plt.title(str(radec)+str('; ')+'z={}'.format(z)) plt.xlabel("Rest-frame Wavelength [$\AA$]") plt.ylabel("Flux [$10^{-17}$ erg$^{-1}$s$^{-1}$cm$^{-2}$$\AA^{-1}$]") plt.plot(wavelength, flux) # plot the actual data # now create upper and lower bounds on the uncertainty regions sigmaUpper=np.add(flux,sigma) sigmaLower=np.subtract(flux,sigma) plt.fill_between(wavelength, sigmaLower, sigmaUpper, color='grey', alpha=0.5) plt.show() #TEST radec='223812.39 +213203.4' z=redshift(radec) data=extractor(radec) spec_list=downloader(data) dic = transform_data(spec_list,z) plot_spec(dic, radec, z)

true

7907f819f2647b99f87d9ca4a74578c3d905cc76

26,149

py

Python

jsonpickle/pickler.py

cclauss/jsonpickle

18c353f7581698a1056a1ad234b1486ffd51758c

[ "BSD-3-Clause" ]

null

jsonpickle/pickler.py

cclauss/jsonpickle

18c353f7581698a1056a1ad234b1486ffd51758c

[ "BSD-3-Clause" ]

null

jsonpickle/pickler.py

cclauss/jsonpickle

18c353f7581698a1056a1ad234b1486ffd51758c

[ "BSD-3-Clause" ]

null

# Copyright (C) 2008 John Paulett (john -at- paulett.org) # Copyright (C) 2009-2018 David Aguilar (davvid -at- gmail.com) # All rights reserved. # # This software is licensed as described in the file COPYING, which # you should have received as part of this distribution. from __future__ import absolute_import, division, unicode_literals import decimal import warnings import sys import types from itertools import chain, islice from . import compat from . import util from . import tags from . import handlers from .backend import json from .compat import numeric_types, string_types, PY3, PY2 def encode( value, unpicklable=True, make_refs=True, keys=False, max_depth=None, reset=True, backend=None, warn=False, context=None, max_iter=None, use_decimal=False, numeric_keys=False, use_base85=False, fail_safe=None, indent=None, separators=None, ): """Return a JSON formatted representation of value, a Python object. :param unpicklable: If set to False then the output will not contain the information necessary to turn the JSON data back into Python objects, but a simpler JSON stream is produced. :param max_depth: If set to a non-negative integer then jsonpickle will not recurse deeper than 'max_depth' steps into the object. Anything deeper than 'max_depth' is represented using a Python repr() of the object. :param make_refs: If set to False jsonpickle's referencing support is disabled. Objects that are id()-identical won't be preserved across encode()/decode(), but the resulting JSON stream will be conceptually simpler. jsonpickle detects cyclical objects and will break the cycle by calling repr() instead of recursing when make_refs is set False. :param keys: If set to True then jsonpickle will encode non-string dictionary keys instead of coercing them into strings via `repr()`. This is typically what you want if you need to support Integer or objects as dictionary keys. :param numeric_keys: Only use this option if the backend supports integer dict keys natively. This flag tells jsonpickle to leave numeric keys as-is rather than conforming them to json-friendly strings. Using ``keys=True`` is the typical solution for integer keys, so only use this if you have a specific use case where you want to allow the backend to handle serialization of numeric dict keys. :param warn: If set to True then jsonpickle will warn when it returns None for an object which it cannot pickle (e.g. file descriptors). :param max_iter: If set to a non-negative integer then jsonpickle will consume at most `max_iter` items when pickling iterators. :param use_decimal: If set to True jsonpickle will allow Decimal instances to pass-through, with the assumption that the simplejson backend will be used in `use_decimal` mode. In order to use this mode you will need to configure simplejson:: jsonpickle.set_encoder_options('simplejson', use_decimal=True, sort_keys=True) jsonpickle.set_decoder_options('simplejson', use_decimal=True) jsonpickle.set_preferred_backend('simplejson') NOTE: A side-effect of the above settings is that float values will be converted to Decimal when converting to json. :param use_base85: If possible, use base85 to encode binary data. Base85 bloats binary data by 1/4 as opposed to base64, which expands it by 1/3. This argument is ignored on Python 2 because it doesn't support it. :param fail_safe: If set to a function exceptions are ignored when pickling and if a exception happens the function is called and the return value is used as the value for the object that caused the error :param indent: When `indent` is a non-negative integer, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0 will only insert newlines. ``None`` is the most compact representation. Since the default item separator is ``(', ', ': ')``, the output might include trailing whitespace when ``indent`` is specified. You can use ``separators=(',', ': ')`` to avoid this. This value is passed directly to the active JSON backend library and not used by jsonpickle directly. :param separators: If ``separators`` is an ``(item_separator, dict_separator)`` tuple then it will be used instead of the default ``(', ', ': ')`` separators. ``(',', ':')`` is the most compact JSON representation. This value is passed directly to the active JSON backend library and not used by jsonpickle directly. >>> encode('my string') == '"my string"' True >>> encode(36) == '36' True >>> encode({'foo': True}) == '{"foo": true}' True >>> encode({'foo': [1, 2, [3, 4]]}, max_depth=1) '{"foo": "[1, 2, [3, 4]]"}' """ backend = backend or json context = context or Pickler( unpicklable=unpicklable, make_refs=make_refs, keys=keys, backend=backend, max_depth=max_depth, warn=warn, max_iter=max_iter, numeric_keys=numeric_keys, use_decimal=use_decimal, use_base85=use_base85, fail_safe=fail_safe, ) return backend.encode( context.flatten(value, reset=reset), indent=indent, separators=separators ) class Pickler(object): def __init__( self, unpicklable=True, make_refs=True, max_depth=None, backend=None, keys=False, warn=False, max_iter=None, numeric_keys=False, use_decimal=False, use_base85=False, fail_safe=None, ): self.unpicklable = unpicklable self.make_refs = make_refs self.backend = backend or json self.keys = keys self.warn = warn self.numeric_keys = numeric_keys self.use_base85 = use_base85 and (not PY2) # The current recursion depth self._depth = -1 # The maximal recursion depth self._max_depth = max_depth # Maps id(obj) to reference IDs self._objs = {} # Avoids garbage collection self._seen = [] # maximum amount of items to take from a pickled iterator self._max_iter = max_iter # Whether to allow decimals to pass-through self._use_decimal = use_decimal if self.use_base85: self._bytes_tag = tags.B85 self._bytes_encoder = util.b85encode else: self._bytes_tag = tags.B64 self._bytes_encoder = util.b64encode # ignore exceptions self.fail_safe = fail_safe def reset(self): self._objs = {} self._depth = -1 self._seen = [] def _push(self): """Steps down one level in the namespace.""" self._depth += 1 def _pop(self, value): """Step up one level in the namespace and return the value. If we're at the root, reset the pickler's state. """ self._depth -= 1 if self._depth == -1: self.reset() return value def _log_ref(self, obj): """ Log a reference to an in-memory object. Return True if this object is new and was assigned a new ID. Otherwise return False. """ objid = id(obj) is_new = objid not in self._objs if is_new: new_id = len(self._objs) self._objs[objid] = new_id return is_new def _mkref(self, obj): """ Log a reference to an in-memory object, and return if that object should be considered newly logged. """ is_new = self._log_ref(obj) # Pretend the object is new pretend_new = not self.unpicklable or not self.make_refs return pretend_new or is_new def _getref(self, obj): return {tags.ID: self._objs.get(id(obj))} def flatten(self, obj, reset=True): """Takes an object and returns a JSON-safe representation of it. Simply returns any of the basic builtin datatypes >>> p = Pickler() >>> p.flatten('hello world') == 'hello world' True >>> p.flatten(49) 49 >>> p.flatten(350.0) 350.0 >>> p.flatten(True) True >>> p.flatten(False) False >>> r = p.flatten(None) >>> r is None True >>> p.flatten(False) False >>> p.flatten([1, 2, 3, 4]) [1, 2, 3, 4] >>> p.flatten((1,2,))[tags.TUPLE] [1, 2] >>> p.flatten({'key': 'value'}) == {'key': 'value'} True """ if reset: self.reset() return self._flatten(obj) def _flatten(self, obj): ######################################### # if obj is nonrecursive return immediately # for performance reasons we don't want to do recursive checks if PY2 and isinstance(obj, types.FileType): return self._flatten_file(obj) if util.is_bytes(obj): return self._flatten_bytestring(obj) if util.is_primitive(obj): return obj # Decimal is a primitive when use_decimal is True if self._use_decimal and isinstance(obj, decimal.Decimal): return obj ######################################### self._push() return self._pop(self._flatten_obj(obj)) def _max_reached(self): return self._depth == self._max_depth def _flatten_obj(self, obj): self._seen.append(obj) max_reached = self._max_reached() try: in_cycle = _in_cycle(obj, self._objs, max_reached, self.make_refs) if in_cycle: # break the cycle flatten_func = repr else: flatten_func = self._get_flattener(obj) if flatten_func is None: self._pickle_warning(obj) return None return flatten_func(obj) except (KeyboardInterrupt, SystemExit) as e: raise e except Exception as e: if self.fail_safe is None: raise e else: return self.fail_safe(e) def _list_recurse(self, obj): return [self._flatten(v) for v in obj] def _get_flattener(self, obj): list_recurse = self._list_recurse if util.is_list(obj): if self._mkref(obj): return list_recurse else: self._push() return self._getref # We handle tuples and sets by encoding them in a "(tuple|set)dict" if util.is_tuple(obj): if not self.unpicklable: return list_recurse return lambda obj: {tags.TUPLE: [self._flatten(v) for v in obj]} if util.is_set(obj): if not self.unpicklable: return list_recurse return lambda obj: {tags.SET: [self._flatten(v) for v in obj]} if util.is_dictionary(obj): return self._flatten_dict_obj if util.is_type(obj): return _mktyperef if util.is_object(obj): return self._ref_obj_instance if util.is_module_function(obj): return self._flatten_function # instance methods, lambdas, old style classes... self._pickle_warning(obj) return None def _ref_obj_instance(self, obj): """Reference an existing object or flatten if new""" if self.unpicklable: if self._mkref(obj): # We've never seen this object so return its # json representation. return self._flatten_obj_instance(obj) # We've seen this object before so place an object # reference tag in the data. This avoids infinite recursion # when processing cyclical objects. return self._getref(obj) else: max_reached = self._max_reached() in_cycle = _in_cycle(obj, self._objs, max_reached, False) if in_cycle: # A circular becomes None. return None self._mkref(obj) return self._flatten_obj_instance(obj) def _flatten_file(self, obj): """ Special case file objects """ assert not PY3 and isinstance(obj, types.FileType) return None def _flatten_bytestring(self, obj): if PY2: try: return obj.decode('utf-8') except UnicodeDecodeError: pass return {self._bytes_tag: self._bytes_encoder(obj)} def _flatten_obj_instance(self, obj): """Recursively flatten an instance and return a json-friendly dict""" data = {} has_class = hasattr(obj, '__class__') has_dict = hasattr(obj, '__dict__') has_slots = not has_dict and hasattr(obj, '__slots__') has_getnewargs = util.has_method(obj, '__getnewargs__') has_getnewargs_ex = util.has_method(obj, '__getnewargs_ex__') has_getinitargs = util.has_method(obj, '__getinitargs__') has_reduce, has_reduce_ex = util.has_reduce(obj) # Support objects with __getstate__(); this ensures that # both __setstate__() and __getstate__() are implemented has_getstate = hasattr(obj, '__getstate__') # not using has_method since __getstate__() is handled separately below if has_class: cls = obj.__class__ else: cls = type(obj) # Check for a custom handler class_name = util.importable_name(cls) handler = handlers.get(cls, handlers.get(class_name)) if handler is not None: if self.unpicklable: data[tags.OBJECT] = class_name return handler(self).flatten(obj, data) reduce_val = None if self.unpicklable: if has_reduce and not has_reduce_ex: try: reduce_val = obj.__reduce__() except TypeError: # A lot of builtin types have a reduce which # just raises a TypeError # we ignore those pass # test for a reduce implementation, and redirect before # doing anything else if that is what reduce requests elif has_reduce_ex: try: # we're implementing protocol 2 reduce_val = obj.__reduce_ex__(2) except TypeError: # A lot of builtin types have a reduce which # just raises a TypeError # we ignore those pass if reduce_val and isinstance(reduce_val, string_types): try: varpath = iter(reduce_val.split('.')) # curmod will be transformed by the # loop into the value to pickle curmod = sys.modules[next(varpath)] for modname in varpath: curmod = getattr(curmod, modname) # replace obj with value retrieved return self._flatten(curmod) except KeyError: # well, we can't do anything with that, so we ignore it pass elif reduce_val: # at this point, reduce_val should be some kind of iterable # pad out to len 5 rv_as_list = list(reduce_val) insufficiency = 5 - len(rv_as_list) if insufficiency: rv_as_list += [None] * insufficiency if getattr(rv_as_list[0], '__name__', '') == '__newobj__': rv_as_list[0] = tags.NEWOBJ f, args, state, listitems, dictitems = rv_as_list # check that getstate/setstate is sane if not ( state and hasattr(obj, '__getstate__') and not hasattr(obj, '__setstate__') and not isinstance(obj, dict) ): # turn iterators to iterables for convenient serialization if rv_as_list[3]: rv_as_list[3] = tuple(rv_as_list[3]) if rv_as_list[4]: rv_as_list[4] = tuple(rv_as_list[4]) reduce_args = list(map(self._flatten, rv_as_list)) last_index = len(reduce_args) - 1 while last_index >= 2 and reduce_args[last_index] is None: last_index -= 1 data[tags.REDUCE] = reduce_args[: last_index + 1] return data if has_class and not util.is_module(obj): if self.unpicklable: data[tags.OBJECT] = class_name if has_getnewargs_ex: data[tags.NEWARGSEX] = list(map(self._flatten, obj.__getnewargs_ex__())) if has_getnewargs and not has_getnewargs_ex: data[tags.NEWARGS] = self._flatten(obj.__getnewargs__()) if has_getinitargs: data[tags.INITARGS] = self._flatten(obj.__getinitargs__()) if has_getstate: try: state = obj.__getstate__() except TypeError: # Has getstate but it cannot be called, e.g. file descriptors # in Python3 self._pickle_warning(obj) return None else: return self._getstate(state, data) if util.is_module(obj): if self.unpicklable: data[tags.REPR] = '{name}/{name}'.format(name=obj.__name__) else: data = compat.ustr(obj) return data if util.is_dictionary_subclass(obj): self._flatten_dict_obj(obj, data) return data if util.is_sequence_subclass(obj): return self._flatten_sequence_obj(obj, data) if util.is_iterator(obj): # force list in python 3 data[tags.ITERATOR] = list(map(self._flatten, islice(obj, self._max_iter))) return data if has_dict: # Support objects that subclasses list and set if util.is_sequence_subclass(obj): return self._flatten_sequence_obj(obj, data) # hack for zope persistent objects; this unghostifies the object getattr(obj, '_', None) return self._flatten_dict_obj(obj.__dict__, data) if has_slots: return self._flatten_newstyle_with_slots(obj, data) # catchall return for data created above without a return # (e.g. __getnewargs__ is not supposed to be the end of the story) if data: return data self._pickle_warning(obj) return None def _flatten_function(self, obj): if self.unpicklable: data = {tags.FUNCTION: util.importable_name(obj)} else: data = None return data def _flatten_dict_obj(self, obj, data=None): """Recursively call flatten() and return json-friendly dict""" if data is None: data = obj.__class__() # If we allow non-string keys then we have to do a two-phase # encoding to ensure that the reference IDs are deterministic. if self.keys: # Phase 1: serialize regular objects, ignore fancy keys. flatten = self._flatten_string_key_value_pair for k, v in util.items(obj): flatten(k, v, data) # Phase 2: serialize non-string keys. flatten = self._flatten_non_string_key_value_pair for k, v in util.items(obj): flatten(k, v, data) else: # If we have string keys only then we only need a single pass. flatten = self._flatten_key_value_pair for k, v in util.items(obj): flatten(k, v, data) # the collections.defaultdict protocol if hasattr(obj, 'default_factory') and callable(obj.default_factory): factory = obj.default_factory if util.is_type(factory): # Reference the class/type value = _mktyperef(factory) else: # The factory is not a type and could reference e.g. functions # or even the object instance itself, which creates a cycle. if self._mkref(factory): # We've never seen this object before so pickle it in-place. # Create an instance from the factory and assume that the # resulting instance is a suitable examplar. value = self._flatten_obj_instance(handlers.CloneFactory(factory())) else: # We've seen this object before. # Break the cycle by emitting a reference. value = self._getref(factory) data['default_factory'] = value # Sub-classes of dict if hasattr(obj, '__dict__') and self.unpicklable: dict_data = {} self._flatten_dict_obj(obj.__dict__, dict_data) data['__dict__'] = dict_data return data def _flatten_obj_attrs(self, obj, attrs, data): flatten = self._flatten_key_value_pair ok = False for k in attrs: try: value = getattr(obj, k) flatten(k, value, data) except AttributeError: # The attribute may have been deleted continue ok = True return ok def _flatten_newstyle_with_slots(self, obj, data): """Return a json-friendly dict for new-style objects with __slots__.""" allslots = [ _wrap_string_slot(getattr(cls, '__slots__', tuple())) for cls in obj.__class__.mro() ] if not self._flatten_obj_attrs(obj, chain(*allslots), data): attrs = [ x for x in dir(obj) if not x.startswith('__') and not x.endswith('__') ] self._flatten_obj_attrs(obj, attrs, data) return data def _flatten_key_value_pair(self, k, v, data): """Flatten a key/value pair into the passed-in dictionary.""" if not util.is_picklable(k, v): return data if k is None: k = 'null' # for compatibility with common json encoders if self.numeric_keys and isinstance(k, numeric_types): pass elif not isinstance(k, string_types): try: k = repr(k) except Exception: k = compat.ustr(k) data[k] = self._flatten(v) return data def _flatten_non_string_key_value_pair(self, k, v, data): """Flatten only non-string key/value pairs""" if not util.is_picklable(k, v): return data if self.keys and not isinstance(k, string_types): k = self._escape_key(k) data[k] = self._flatten(v) return data def _flatten_string_key_value_pair(self, k, v, data): """Flatten string key/value pairs only.""" if not util.is_picklable(k, v): return data if self.keys: if not isinstance(k, string_types): return data elif k.startswith(tags.JSON_KEY): k = self._escape_key(k) else: if k is None: k = 'null' # for compatibility with common json encoders if self.numeric_keys and isinstance(k, numeric_types): pass elif not isinstance(k, string_types): try: k = repr(k) except Exception: k = compat.ustr(k) data[k] = self._flatten(v) return data def _flatten_sequence_obj(self, obj, data): """Return a json-friendly dict for a sequence subclass.""" if hasattr(obj, '__dict__'): self._flatten_dict_obj(obj.__dict__, data) value = [self._flatten(v) for v in obj] if self.unpicklable: data[tags.SEQ] = value else: return value return data def _escape_key(self, k): return tags.JSON_KEY + encode( k, reset=False, keys=True, context=self, backend=self.backend, make_refs=self.make_refs, ) def _getstate(self, obj, data): state = self._flatten(obj) if self.unpicklable: data[tags.STATE] = state else: data = state return data def _pickle_warning(self, obj): if self.warn: msg = 'jsonpickle cannot pickle %r: replaced with None' % obj warnings.warn(msg) def _in_cycle(obj, objs, max_reached, make_refs): """Detect cyclic structures that would lead to infinite recursion""" return ( (max_reached or (not make_refs and id(obj) in objs)) and not util.is_primitive(obj) and not util.is_enum(obj) ) def _mktyperef(obj): """Return a typeref dictionary >>> _mktyperef(AssertionError) == {'py/type': 'builtins.AssertionError'} True """ return {tags.TYPE: util.importable_name(obj)} def _wrap_string_slot(string): """Converts __slots__ = 'a' into __slots__ = ('a',)""" if isinstance(string, string_types): return (string,) return string

35.005355

88

0.575548

from __future__ import absolute_import, division, unicode_literals import decimal import warnings import sys import types from itertools import chain, islice from . import compat from . import util from . import tags from . import handlers from .backend import json from .compat import numeric_types, string_types, PY3, PY2 def encode( value, unpicklable=True, make_refs=True, keys=False, max_depth=None, reset=True, backend=None, warn=False, context=None, max_iter=None, use_decimal=False, numeric_keys=False, use_base85=False, fail_safe=None, indent=None, separators=None, ): backend = backend or json context = context or Pickler( unpicklable=unpicklable, make_refs=make_refs, keys=keys, backend=backend, max_depth=max_depth, warn=warn, max_iter=max_iter, numeric_keys=numeric_keys, use_decimal=use_decimal, use_base85=use_base85, fail_safe=fail_safe, ) return backend.encode( context.flatten(value, reset=reset), indent=indent, separators=separators ) class Pickler(object): def __init__( self, unpicklable=True, make_refs=True, max_depth=None, backend=None, keys=False, warn=False, max_iter=None, numeric_keys=False, use_decimal=False, use_base85=False, fail_safe=None, ): self.unpicklable = unpicklable self.make_refs = make_refs self.backend = backend or json self.keys = keys self.warn = warn self.numeric_keys = numeric_keys self.use_base85 = use_base85 and (not PY2) self._depth = -1 self._max_depth = max_depth self._objs = {} self._seen = [] self._max_iter = max_iter self._use_decimal = use_decimal if self.use_base85: self._bytes_tag = tags.B85 self._bytes_encoder = util.b85encode else: self._bytes_tag = tags.B64 self._bytes_encoder = util.b64encode self.fail_safe = fail_safe def reset(self): self._objs = {} self._depth = -1 self._seen = [] def _push(self): self._depth += 1 def _pop(self, value): self._depth -= 1 if self._depth == -1: self.reset() return value def _log_ref(self, obj): objid = id(obj) is_new = objid not in self._objs if is_new: new_id = len(self._objs) self._objs[objid] = new_id return is_new def _mkref(self, obj): is_new = self._log_ref(obj) pretend_new = not self.unpicklable or not self.make_refs return pretend_new or is_new def _getref(self, obj): return {tags.ID: self._objs.get(id(obj))} def flatten(self, obj, reset=True): if reset: self.reset() return self._flatten(obj) def _flatten(self, obj): ycle: # break the cycle flatten_func = repr else: flatten_func = self._get_flattener(obj) if flatten_func is None: self._pickle_warning(obj) return None return flatten_func(obj) except (KeyboardInterrupt, SystemExit) as e: raise e except Exception as e: if self.fail_safe is None: raise e else: return self.fail_safe(e) def _list_recurse(self, obj): return [self._flatten(v) for v in obj] def _get_flattener(self, obj): list_recurse = self._list_recurse if util.is_list(obj): if self._mkref(obj): return list_recurse else: self._push() return self._getref # We handle tuples and sets by encoding them in a "(tuple|set)dict" if util.is_tuple(obj): if not self.unpicklable: return list_recurse return lambda obj: {tags.TUPLE: [self._flatten(v) for v in obj]} if util.is_set(obj): if not self.unpicklable: return list_recurse return lambda obj: {tags.SET: [self._flatten(v) for v in obj]} if util.is_dictionary(obj): return self._flatten_dict_obj if util.is_type(obj): return _mktyperef if util.is_object(obj): return self._ref_obj_instance if util.is_module_function(obj): return self._flatten_function # instance methods, lambdas, old style classes... self._pickle_warning(obj) return None def _ref_obj_instance(self, obj): if self.unpicklable: if self._mkref(obj): # We've never seen this object so return its return self._flatten_obj_instance(obj) # reference tag in the data. This avoids infinite recursion # when processing cyclical objects. return self._getref(obj) else: max_reached = self._max_reached() in_cycle = _in_cycle(obj, self._objs, max_reached, False) if in_cycle: # A circular becomes None. return None self._mkref(obj) return self._flatten_obj_instance(obj) def _flatten_file(self, obj): assert not PY3 and isinstance(obj, types.FileType) return None def _flatten_bytestring(self, obj): if PY2: try: return obj.decode('utf-8') except UnicodeDecodeError: pass return {self._bytes_tag: self._bytes_encoder(obj)} def _flatten_obj_instance(self, obj): data = {} has_class = hasattr(obj, '__class__') has_dict = hasattr(obj, '__dict__') has_slots = not has_dict and hasattr(obj, '__slots__') has_getnewargs = util.has_method(obj, '__getnewargs__') has_getnewargs_ex = util.has_method(obj, '__getnewargs_ex__') has_getinitargs = util.has_method(obj, '__getinitargs__') has_reduce, has_reduce_ex = util.has_reduce(obj) # Support objects with __getstate__(); this ensures that # both __setstate__() and __getstate__() are implemented has_getstate = hasattr(obj, '__getstate__') # not using has_method since __getstate__() is handled separately below if has_class: cls = obj.__class__ else: cls = type(obj) # Check for a custom handler class_name = util.importable_name(cls) handler = handlers.get(cls, handlers.get(class_name)) if handler is not None: if self.unpicklable: data[tags.OBJECT] = class_name return handler(self).flatten(obj, data) reduce_val = None if self.unpicklable: if has_reduce and not has_reduce_ex: try: reduce_val = obj.__reduce__() except TypeError: # A lot of builtin types have a reduce which # just raises a TypeError # we ignore those pass # test for a reduce implementation, and redirect before # doing anything else if that is what reduce requests elif has_reduce_ex: try: # we're implementing protocol 2 reduce_val = obj.__reduce_ex__(2) except TypeError: pass if reduce_val and isinstance(reduce_val, string_types): try: varpath = iter(reduce_val.split('.')) curmod = sys.modules[next(varpath)] for modname in varpath: curmod = getattr(curmod, modname) return self._flatten(curmod) except KeyError: pass elif reduce_val: # at this point, reduce_val should be some kind of iterable # pad out to len 5 rv_as_list = list(reduce_val) insufficiency = 5 - len(rv_as_list) if insufficiency: rv_as_list += [None] * insufficiency if getattr(rv_as_list[0], '__name__', '') == '__newobj__': rv_as_list[0] = tags.NEWOBJ f, args, state, listitems, dictitems = rv_as_list # check that getstate/setstate is sane if not ( state and hasattr(obj, '__getstate__') and not hasattr(obj, '__setstate__') and not isinstance(obj, dict) ): # turn iterators to iterables for convenient serialization if rv_as_list[3]: rv_as_list[3] = tuple(rv_as_list[3]) if rv_as_list[4]: rv_as_list[4] = tuple(rv_as_list[4]) reduce_args = list(map(self._flatten, rv_as_list)) last_index = len(reduce_args) - 1 while last_index >= 2 and reduce_args[last_index] is None: last_index -= 1 data[tags.REDUCE] = reduce_args[: last_index + 1] return data if has_class and not util.is_module(obj): if self.unpicklable: data[tags.OBJECT] = class_name if has_getnewargs_ex: data[tags.NEWARGSEX] = list(map(self._flatten, obj.__getnewargs_ex__())) if has_getnewargs and not has_getnewargs_ex: data[tags.NEWARGS] = self._flatten(obj.__getnewargs__()) if has_getinitargs: data[tags.INITARGS] = self._flatten(obj.__getinitargs__()) if has_getstate: try: state = obj.__getstate__() except TypeError: # Has getstate but it cannot be called, e.g. file descriptors # in Python3 self._pickle_warning(obj) return None else: return self._getstate(state, data) if util.is_module(obj): if self.unpicklable: data[tags.REPR] = '{name}/{name}'.format(name=obj.__name__) else: data = compat.ustr(obj) return data if util.is_dictionary_subclass(obj): self._flatten_dict_obj(obj, data) return data if util.is_sequence_subclass(obj): return self._flatten_sequence_obj(obj, data) if util.is_iterator(obj): # force list in python 3 data[tags.ITERATOR] = list(map(self._flatten, islice(obj, self._max_iter))) return data if has_dict: # Support objects that subclasses list and set if util.is_sequence_subclass(obj): return self._flatten_sequence_obj(obj, data) # hack for zope persistent objects; this unghostifies the object getattr(obj, '_', None) return self._flatten_dict_obj(obj.__dict__, data) if has_slots: return self._flatten_newstyle_with_slots(obj, data) # catchall return for data created above without a return # (e.g. __getnewargs__ is not supposed to be the end of the story) if data: return data self._pickle_warning(obj) return None def _flatten_function(self, obj): if self.unpicklable: data = {tags.FUNCTION: util.importable_name(obj)} else: data = None return data def _flatten_dict_obj(self, obj, data=None): if data is None: data = obj.__class__() # If we allow non-string keys then we have to do a two-phase # encoding to ensure that the reference IDs are deterministic. if self.keys: # Phase 1: serialize regular objects, ignore fancy keys. flatten = self._flatten_string_key_value_pair for k, v in util.items(obj): flatten(k, v, data) # Phase 2: serialize non-string keys. flatten = self._flatten_non_string_key_value_pair for k, v in util.items(obj): flatten(k, v, data) else: # If we have string keys only then we only need a single pass. flatten = self._flatten_key_value_pair for k, v in util.items(obj): flatten(k, v, data) # the collections.defaultdict protocol if hasattr(obj, 'default_factory') and callable(obj.default_factory): factory = obj.default_factory if util.is_type(factory): # Reference the class/type value = _mktyperef(factory) else: # The factory is not a type and could reference e.g. functions # or even the object instance itself, which creates a cycle. if self._mkref(factory): # We've never seen this object before so pickle it in-place. value = self._flatten_obj_instance(handlers.CloneFactory(factory())) else: # Break the cycle by emitting a reference. value = self._getref(factory) data['default_factory'] = value # Sub-classes of dict if hasattr(obj, '__dict__') and self.unpicklable: dict_data = {} self._flatten_dict_obj(obj.__dict__, dict_data) data['__dict__'] = dict_data return data def _flatten_obj_attrs(self, obj, attrs, data): flatten = self._flatten_key_value_pair ok = False for k in attrs: try: value = getattr(obj, k) flatten(k, value, data) except AttributeError: # The attribute may have been deleted continue ok = True return ok def _flatten_newstyle_with_slots(self, obj, data): allslots = [ _wrap_string_slot(getattr(cls, '__slots__', tuple())) for cls in obj.__class__.mro() ] if not self._flatten_obj_attrs(obj, chain(*allslots), data): attrs = [ x for x in dir(obj) if not x.startswith('__') and not x.endswith('__') ] self._flatten_obj_attrs(obj, attrs, data) return data def _flatten_key_value_pair(self, k, v, data): if not util.is_picklable(k, v): return data if k is None: k = 'null' # for compatibility with common json encoders if self.numeric_keys and isinstance(k, numeric_types): pass elif not isinstance(k, string_types): try: k = repr(k) except Exception: k = compat.ustr(k) data[k] = self._flatten(v) return data def _flatten_non_string_key_value_pair(self, k, v, data): if not util.is_picklable(k, v): return data if self.keys and not isinstance(k, string_types): k = self._escape_key(k) data[k] = self._flatten(v) return data def _flatten_string_key_value_pair(self, k, v, data): if not util.is_picklable(k, v): return data if self.keys: if not isinstance(k, string_types): return data elif k.startswith(tags.JSON_KEY): k = self._escape_key(k) else: if k is None: k = 'null' # for compatibility with common json encoders if self.numeric_keys and isinstance(k, numeric_types): pass elif not isinstance(k, string_types): try: k = repr(k) except Exception: k = compat.ustr(k) data[k] = self._flatten(v) return data def _flatten_sequence_obj(self, obj, data): if hasattr(obj, '__dict__'): self._flatten_dict_obj(obj.__dict__, data) value = [self._flatten(v) for v in obj] if self.unpicklable: data[tags.SEQ] = value else: return value return data def _escape_key(self, k): return tags.JSON_KEY + encode( k, reset=False, keys=True, context=self, backend=self.backend, make_refs=self.make_refs, ) def _getstate(self, obj, data): state = self._flatten(obj) if self.unpicklable: data[tags.STATE] = state else: data = state return data def _pickle_warning(self, obj): if self.warn: msg = 'jsonpickle cannot pickle %r: replaced with None' % obj warnings.warn(msg) def _in_cycle(obj, objs, max_reached, make_refs): return ( (max_reached or (not make_refs and id(obj) in objs)) and not util.is_primitive(obj) and not util.is_enum(obj) ) def _mktyperef(obj): return {tags.TYPE: util.importable_name(obj)} def _wrap_string_slot(string): if isinstance(string, string_types): return (string,) return string

true

7907f92d955db9bf68a50b78eaf8174c038b395e

514

py

Python

tools/LevelCreator/ezTypes.py

Kronifer/cj8-repo

6d0f4a45b16ea184bc429f7e7b10b752595ea65e

[ "MIT" ]

1

2021-07-09T17:23:34.000Z

tools/LevelCreator/ezTypes.py

Kronifer/cj8-repo

6d0f4a45b16ea184bc429f7e7b10b752595ea65e

[ "MIT" ]

3

2021-07-18T15:03:49.000Z

2021-07-18T15:04:11.000Z

tools/LevelCreator/ezTypes.py

Kronifer/cj8-repo

6d0f4a45b16ea184bc429f7e7b10b752595ea65e

[ "MIT" ]

null

# List the type colors for the editor AIR = (0, 0, 0) GRASS = (100, 200, 40) ROCK = (106, 106, 106) LAVA = (252, 144, 3) WATER = (0, 0, 255) PLAYER = (155, 191, 250) PLAYER_END = (40, 30, 100) SPIKE_UP = (204, 24, 24) SPIKE_DOWN = (166, 8, 8) # List all the used types types = ['GRASS', 'ROCK', 'LAVA', 'WATER', 'PLAYER', 'SPIKE_UP', 'SPIKE_DOWN', 'PLAYER_END', 'AIR'] colorTypes = [GRASS, ROCK, LAVA, WATER, PLAYER, SPIKE_UP, SPIKE_DOWN, PLAYER_END, AIR] # Set default type select = 'GRASS' colorSelect = GRASS

27.052632

99

0.640078

AIR = (0, 0, 0) GRASS = (100, 200, 40) ROCK = (106, 106, 106) LAVA = (252, 144, 3) WATER = (0, 0, 255) PLAYER = (155, 191, 250) PLAYER_END = (40, 30, 100) SPIKE_UP = (204, 24, 24) SPIKE_DOWN = (166, 8, 8) types = ['GRASS', 'ROCK', 'LAVA', 'WATER', 'PLAYER', 'SPIKE_UP', 'SPIKE_DOWN', 'PLAYER_END', 'AIR'] colorTypes = [GRASS, ROCK, LAVA, WATER, PLAYER, SPIKE_UP, SPIKE_DOWN, PLAYER_END, AIR] select = 'GRASS' colorSelect = GRASS

true